http://www.ics.uci.edu/ Donald Bren School of Information and Computer Sciences @ University of California, Irvine

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Latest News

  • Dean Stern to speak at AAAS symposium on stronger science behind forensics
  • ICS alum Steve Trimberger Elected to National Academy of Engineering
  • Gary Olson, 2 ICS Alums receive SIGCHI honors
  • UCI News: “Private practices” (Mehrotra quoted)
  • Los Angeles Times: “Hi, I'm a digital junkie, and I suffer from infomania” (Mark quoted)

Fall 2015 Annual Report

The 2014-15 academic year was a momentous time for ICS--from celebrating UCI's 50th Anniversary to launching a new B.S. in Data Science. This year's Annual Report details all of these past achievements, along with some of the most noteworthy accomplishments of our faculty, students and alumni. Download a PDF of the 2015 Annual Report.

2015 Annual Report cover thumbnail

Upcoming Events

22 Feb 2016 (Mon)
1:00 PM-2:00 PM
AI/ML Seminar Series: Divijotham Krishnamurthy, Center for Mathematics of Information, Caltech
23 Feb 2016 (Tue)
11:00 AM-12:00 PM
INFORMATICS SEMINAR - Assistant Professor Sarita Schoenebeck, School of Information, University of Michigan
25 Feb 2016 (Thu)
4:00 PM-5:00 PM
Statistics Seminar, Speaker: Guang Cheng
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Bren school home > Undergraduate > Academic advising
Academic Advising

It is the student's responsibility to make decisions about his/her educational and career goals.

Academic counselors are here to help students work towards those goals, and academic advising is an intentional partnership between the student and his/her advisor(s) in which the shared goal is the student’s academic success.

Below is information on when, why, and how often to see academic advising, whether you come during walk-in hours or for an appointment.

 


 

» Meet with an Academic Advisor

  • Whenever you have a question about your academic standing, progress, or goals
  • Once a quarter, or at least once a year, to make sure you are on track
  • Three quarters before your intended graduation date

 

» Some Benefits of Consistent Academic Advising Meetings

  • Be confident that you are on track for graduation
  • Receive assistance with short- and long-term goal setting
  • Get help with planning and selecting courses to meet your academic and career goals
  • Obtain referrals to and advice about campus resources and opportunities
  • Gain clarification on University and School policies and procedures

 

» While Meeting with an Advisor

  • Arrive on time (if meeting during a scheduled appointment)
  • Come prepared with questions and concerns
  • Honestly discuss your goals, interests, and priorities
  • Keep an open mind to consider the options presented to you
  • Don’t be afraid to ask questions if you don't understand something

 

» What it is a Study Plan, and Why Should You Have One?

A Study Plan maps out of the courses and/or units you would need to take in the upcoming academic year(s) in order to make progress on your degree requirements.

  • It presents a bird's eye view of your course load and potential academic commitments over the year
  • It allows you to make quick adjustments to your schedule and plan ahead
  • It can help reduce stress when enrolling for classes

 

» Tips for Building a Study Plan

  • Know what classes you need to take. These include courses that satisfy University and School Requirements, as well as any core courses required for your major.
  • Make special note of any required classes that are only offered once a year or once every other year, or must be taken in a specific order.
  • Pay attention to course prerequisites, and know which ones you have cleared. If you need to submit a prerequisite clearing request, take care of it in advance.
  • Use the tentative academic year course plan when mapping out your year. But keep in mind that this course plan is tentative, so some of the elective classes you were hoping to take may be cancelled or conflict with each other.
  • Be prepared to be flexible!
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Add, Drop, and Change Options

The Bren School strictly follows the campus policy for adding, dropping, and changing grade option or variable units for its courses at both the undergraduate and graduate levels.

However, instructors will sometimes set earlier add or drop deadlines, so it is important to read the syllabus for each class carefully at the start of the quarter. It is the student's responsibility to be aware of deadlines and make the modifications to his or her schedule before those deadlines pass.

 


» Add, Drop, and Change Deadlines

Always check the course syllabus carefully for course specific add and drop information. If earlier add/drop deadliness are required by the instructor, enrollment will be controlled by the instructor with authorization codes.

The deadline to add or drop courses and to change grade option or variable units is 5:00pm on Friday of Week 2.

Once these deadlines have passed, an Enrollment Exception request must be submitted through Student Access.

 

Timeline of Enrollment Changes
Week Enrollment Actions Notes
1 Use WebReg to add, drop, and change grade option or variable units. No fee applies.
2 Use WebReg to add, drop, and change grade option or variable units. The deadline to add, drop or to change grade option or variable units is 5:00pm on Friday.
3-6 An enrollment exception request must be submitted for adds, drops and changes to grade option or variable units. Supporting documentation is required for all exception requests. A $3 transaction fee will apply if the request is approved.
7-10 An enrollment exception request must be submitted for adds, drops and changes to grade option or variable units. Supporting documentation is required for all exception requests. A $3 transaction fee will apply if the request is approved. The "W" notation on transcript is applied for dropped courses.

 

» Submitting Enrollment Exception Requests

Requests to add or drop are reviewed by the academic advising office of the school offering the course, as well as the school of the student's major (if different). Requests to change grade option or variable units require the approval of the academic advising office of the student's major.

Enrollment exceptions for adds, drops, and change of grade option or variable units in ICS are not guaranteed. Exception requests will only be approved for extenuating and documented circumstances outside of the student's control. It is therefore important to continue attending class meetings and keep up with the assignments until your request is fully processed.

Requests will be denied if submitted for the following reasons:

  • You did notknow the deadlines
  • You did not understand the Add/Drop process
  • You did not know the grade requirements for your degree
  • The course is not required to meet Major, School, or University requirements
  • You are doing poorly in the course because of difficulties with the course material
  • You are doing poorly in the course because of a heavy course/work load
  • You are doing poorly in the course because of failure to attend

If your drop request is approved, please let your professor know as a courtesy once you have formally dropped the course.

 

» Course Conflicts

Time conflicts between courses may be approved on a case-by-case basis. Note that a lecture to lecture conflict will not be approved under any circumstances. If a course's lab or discussion time conflicts with another course's lecture, lab, or discussion, AND there are open spaces in both conflicting sections, follow these steps:

  1. Enroll in one of the conflicting courses (if the conflict involves a lecture, be sure to enroll in the lecture).
  2. Obtain written approval from both instructors to allow the conflict.
  3. Forward that approval to ucounsel@uci.edu. Be sure to provide the 5-digit course codes for the conflicting courses.
  4. Allow ICS Student Affairs 2-3 business days to process the request. You will be notified by email when it has been reviewed.
  5. If approved, use WebReg to enroll in the conflicting section.
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Bren school home > Undergraduate
ICS Petitions

 

Petition Purpose
Undergraduate Student Petition
  • Course substitution or waiver
  • Request credit for major or general education courses taken at a college for which there is no articulation agreement
  • Waive residence requirement
  • Request exception to an administrative decision, policy or deadline
Note: Supporting documentation may be required
Online Prerequisite Clearing Request

Course prerequisite(s) were satisfied via any of the following:

  • AP exam credit
  • Transfer school credit for a course prerequisite
  • Access UCI credit for a course prerequisite
  • Course credit by student petition for a course prerequisite

Part-Time Study

(paper form available from Registrar's Office or ICS Student Affairs)

If you meet one of these conditions: 

  • Work 20 or more hours a week*
  • Have health problems
  • Have significant family responsibilities
*Documentation required

Request Excess Units

(Download the Undergraduate Student Petition)

  • Open to continuing students with the most recent quarterly and cumulative GPA of 3.0 or higher
  • Excess units are not intended to increase waitlist options. Only submit requests if you plan on taking more than four classes
  • Requests for over 20 units must detail which courses you intend to take and why
  • Petitions are subject to review and final approval by the Associate Dean

 

» The "Dean's Signature"

The Dean's Signature may be obtained at the Bren ICS Student Affairs Office.

Counselors are authorized to provide Dean's review and signature in all instances involving LATE add/drop/change of grading option requests done via the on-line Enrollment Exceptions system, and on other student-related forms issued by the Registrar's Office or other administrative offices.

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http://www.ics.uci.edu/about/about_deanmsg.php Welcome from the Dean @ Bren School of Information and Computer Sciences
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Bren school home > About
Welcome from Dean Stern
Hal S. Stern

Welcome to the Donald Bren School of Information and Computer Sciences (ICS) at UC Irvine. As the only computing-focused school in the University of California system, the Bren School is a unique place for study, research and collaboration. Our three departments — Computer Science, Informatics and Statistics — incorporate a broad range of curricular and research interests across the informational and computational sciences.

The school offers a range of undergraduate majors (six in all), including recently developed majors in Computer Game Science and Business Information Management. The diverse selection of majors enables students to gain in-depth knowledge of the information and computer sciences while developing an understanding of the broad reach of the discipline. Our goal is to give students the real-world tools necessary to succeed in the ever-changing technology industry.

Our faculty and graduate students are leaders in their research areas which span information and computer sciences; they obtain corporate and federal extramural grants, they write leading textbooks in their fields, publish influential journal articles, and participate in key conferences and workshops.  Research in the information and computer sciences is applicable across many scholarly and scientific fields.  Indeed, applications to scientific and policy questions in the biological/health, engineering, and social science disciplines motivate much of our research.

I invite you to explore our website and learn more about the Bren School. Please feel free to contact me at icsdean@ics.uci.edu if you have questions or comments.

Thanks,

Hal Stern

Hal Stern
Ted and Janice Smith Family Foundation Dean

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http://www.ics.uci.edu/ugrad/courses/index undergraduate course listing @ the bren school of information and computer sciences

This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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Bren school home > Undergraduate > Courses
Undergraduate course listing

This is a tentative schedule of CompSci, CSE, ICS, Informatics and Statistics courses that the Bren School is planning to offer.

Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change. Please note that some of the upper division core courses will NOT be offered every quarter.

If you need help with your course planning, please schedule an appointment with an academic counselor. They can also provide information about proposed course offerings for summer sessions, which are not included in this list.

NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


Year
Level
Department
Core Classes for

Please select from the search criteria above.

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Bren school home > About > Search the Bren school
Graduate student list
Mahdi Abbaspour Tehrani
Ashwin Srinivas Achar
Rohan Achar
Christian Medeiros Adriano | Website
Nitin Agarwal
Prakul Agarwal
Laleh Aghababaie Beni
Forest Agostinelli
Hedyeh Ahmadi | Website
Sepehr Akhavan Masoulleh | Website
Murtadha Makki Al Hubail
Sanazsadat Alamian
Nailah Saleh Alhassoun
Sanna Jamila Ali
Emmanouil Alimpertis
Abulrahman Abdulhamid Alsaudi
Anil Altinay
Moin Aminnaseri
Maral Amir
Harun Rashid Anver
Dmitri Ivanovich Arkhipov | Website
Danny Armenta
Reza Asadi
Kevin Michael Bache
Brian Michael Bachman Okihiro | Website
Lu Bai
Yi Bai
Sabur Hassan Baidya
Shonali Balakrishna
Prasannah Balasubramanian
Mark S. Baldwin
Indona Vinita Barua
Prateek Basavapur Swamy | Website
Brian Jon Belleville
Kyle Edward Benson
Brandon Berman | Website
Juan Jose Besa Vial
Michael Beyeler | Website
Arjun Deepak Bhadra
Hiten Ram Bhakta
Varun Bharill
Prashanth Reddy Billa
Vinayak Ravindra Borkar | Website
Gerald Bortis | Website
Yash Girishkumar Botadra
Louanne Erin Boyd
Tatiana E. Bradley
John Hendershott Brock
Filjor Broka
Jamie Kumar Brown
Igor Burago
Zachary Butler | Website
Cheng Cai
Douglas Sean Campbell
Nicholas Joseph Ceglia
Wai Man Chan
Andrew H. Chang
Charu Sanjeev Chaudhari
Biying Chen
Chien-Lin Chen
Huan Chen
Jeffrey Chen
Jia Chen
Jian-Zhang Chen
Liang Yu Chen
Lu Chen
Siwei Chen
Te-Yu Chen
Tian Chen
Wei-Han Chen
Xi Chen
Yixian Chen
Zhi Chen
Fangfei Cheng
Yen-Feng Cheng
Jaehwan Choi
Fletcher Christensen | Website
Blerim Cici | Website
Dylan Taylor Cockerham
Julian Collado Umana
Nicole Kathryn Crenshaw
Maricela Francis Cruz
Van Erick Custodio | Website
Andrea Renika D'souza
Thomas Debeauvais | Website
Jason Morris Desrosiers
Zachary Destefano | Website
William Eric Devanny | Website
Archit Dey
Ajey Dheen Rathan Dheenrajappa
Yue Dong
Zhangfan Dong
Bryan Donyanavard | Website
Michael Doostdar | Website
Maxim Dorofiyenko
Stylianos Doudalis | Website
Mohammad Raafat Ali Eletriby
Timothy Weil Elfenbein
Jamshid Esmaelnezhad | Website
Sky Faber
Lu Fang
Heather Ashley Faucett
Yang Feng
David Reese Fooshee | Website
Elmirasadat Forouzmand | Website
Joel Fuentes
Eugenia Gabrielov | Website
Minghao Gai
Christopher Michael Galbraith
Nick Gallo | Website
Aravind Ganesan
Xiucen Gao
Xu Gao
Daniel Lowell Gardner
Zana Ghaderi | Website
Cesar Ghali
Golnaz Ghiasi | Website
Dhrubajyoti Ghosh
Scott Godfrey | Website
Ravi Kiran Godugu
Sarah Alex Gonsalves
Michael Gorlick | Website
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Bren school home > Undergraduate
Undergraduate Programs

As the only computing-focused school in the University of California system, the Bren School offers a broad array of undergraduate majors and minors in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics.

We invite you to explore the links on this page, which feature our courses, majors, and policies, as well as opportunities for student life in ICS. If you need assistance with specific questions about the undergraduate student experience, please contact our Office of Student Affairs, whose information is listed below.

 

CONTACT INFORMATION
Telephone (949) 824-5156
Fax (949) 824-4163
Mailing Address Bren School Student Affairs
ICS Building, Suite 352
Irvine, CA 92697-3430
Office Hours Monday thru Friday
9:00am-12:00pm and 1:00pm-4:00pm
E-mail ucounsel@uci.edu

 
Questions submitted through e-mail will be reviewed by a peer academic advisor or undergraduate counselor.

  • Due to the volume of e-mails received, expect to wait a minimum of 2-3 business days for a response.
  • If your question is complex or time sensitive, we recommend that you come to walk-in hours or schedule an appointment.



UNDERGRADUATE STUDENT AFFAIRS STAFF
Kristine Bolcer

Director of Student Affairs

Neha Rawal

Associate Director of Student Affairs

Jessica Shanahan

Undergraduate Counselor

Michelle V. Nguyen

Undergraduate Counselor

Mare Stasik

Office Manager

Lumen Hwang

Instructional Support Manager


PEER ACADEMIC ADVISORS
Ariel Xiao

Peer Academic Advisor

Benjamin You

Peer Academic Advisor

Carrie Shen

Peer Academic Advisor


Most walk-ins during the Fall, Winter and Spring quarters will be with a Peer Academic Advisor. For more information on when appointments and walk-ins are available and how the types of meeting differ, please see the Appointments and Walk-In Advising page.

Peer counselors have completed intensive training from the Division of Undergraduate Education and the Bren School's SAO counselors. They are available for walk-in advising in the ICS Student Affairs Office (CS Building 1, Ste. 352) at the following times:

Winter 2016
Ariel Benjamin Carrie
Monday 2:30-4 - 1-4
Tuesday - 11-12
1-2
-
Wednesday 10:30-12 - 1-4
Thursday - 11-12
1-2
9-12
Friday 9:30-12
1-4
10-12
1-4
-




Career counseling is also available in the ICS Student Affairs office. Students can meet with Mark Carolino on a walk-in basis during the hours listed below:

Thursday: 1:00pm - 2:30pm*
Friday:

1:00pm - 2:30pm

* Except on scheduled Career Fair dates

 


» Associate Dean for Student Affairs, Tony Givargis

Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the Bren School's undergraduate program.

Associate Dean Givargis holds weekly office hours in the Bren School Student Affairs Office (SAO), ICS, Suite 352. Please call the SAO's front desk at (949)824-5156 to find out his hours of availability.

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Equity & Diversity

Some of the 30+ students who earned their Ph.D. from the Bren School in AY 2010-11.

Message from the ICS equity advisor

The UC Irvine campus is committed to supporting gender equity and diversity. There has never been a more important time to consider diversity in the STEM (science, technology, engineering and math) fields. The field of computer science is changing rapidly, and an academic body with a diverse background can contribute unique ideas and perspectives to the creative development and study of new technologies for a global world. One of the ways we can achieve a more inclusive climate is to educate ourselves on the importance of diversity in our field. To this end, I will use this site to assemble articles that relate to issues concerning women and underrepresented minorities regarding educational achievement, advancement, pay inequity and other topics. If you have any questions regarding our equity program, or if you'd like to suggest articles for posting on this site, please send a message to advance@ics.uci.edu.

I hope you enjoy learning more about this important topic as I have.

Your ICS equity advisor,
Gloria Mark

 


How ICS compares to the rest of the nation

Here’s how ICS compares with the rest of the country on gender equity in terms of degrees awarded and faculty gender ratio. Data is taken from the 2013-2014 CRA Taulbee Survey.

 

Degree recipients: nationwide (2013-2014)

 

Male

Female

Undergraduate

83.9%

16.1%

Masters

67.7%

32.3.0%

Ph.D.

79.9%

20.1%

Degree recipients: Bren School (2009-2010)

 

Male

Female

Total

Undergraduate

90.4%

9.6%

167

Masters

76.9%

23.1%

78

Ph.D.

69.7%

30.3%

33

 

Gender of current faculty nationwide

 

Male

Female

Assistant

74.2%

25.8%

Associate

84.1%

15.9%

Full

87.4%

12.6%

 

Gender of Bren School faculty

ICS-total

 

Male

Female

Total

Computer Science

82.5%

17.5%

40

Informatics

50%

50%

20

Statistics

75%

25%

8

  

ICS-total

 

Male

Female

Total

Assistant

61.5%

38.5%

13

Associate

76.9%

23.1%

13

Full

73.8%

26.2%

42

 

Computer Science

 

Male

Female

Total

Assistant

100%

0%

5

Associate

75%

25%

8

Full

81.5%

18.5%

27

 

Informatics

 

Male

Female

Total

Assistant

20%

80%

5

Associate

66.7%

33.3%

3

Full

58.3%

41.7%

12

 

Statistics

 

Male

Female

Total

Assistant

66.7%

33.3%

3

Associate

100%

0

2

Full

66.7%

33.3%

3

Additional resources »
  • UCI ADVANCE Program
  • National Center for Women & Information Technology
  • Grace Hopper Celebration of Women in Computing
  • Anita Borg Institute for Women and Technology
  • Taulbee Survey
  • CRA's Committee on the Status of Women in Computing Research
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Taking Courses Outside of UCI 

Continuing Bren School students may petition to take up to four approved, UC-transferable, lower-division courses—excepting writing classes—at a community college or other university.

 


 

» Finding Transferable Courses

Consult www.assist.org to find courses that are UC-transferable.

It is extremely important to consult with a Bren School academic counselor prior to taking off-campus courses, and especially if you are near or in your last year prior to graduation.

A counselor will review your list of proposed courses to:

  • ensure that they will count towards your general education or major requirements
  • ensure that students do not compromise your ability to meet the UCI residence requirement.

 

» Transferring Credit

Students must submit an official transcript in order to have any course taken outside of UCI applied to their degree audit. 

For courses taken at another 2- or 4-year institution, send official transcripts to the Office of Admissions and Relations with Schools.

For courses taken through UCI Extension or Access UCI, send transcripts to the Office of the Registrar.

Note: If you are a graduating senior, it is strongly recommended that you submit your transcripts as soon as grades are posted, and inform the appropriate the appropriate office(s) of that submission.

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http://www.ics.uci.edu/community/events/competition/ Butterworth Product Development and the Beall Design Competitions @ the bren school of information and computer sciences
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Bren school home > Community > Events >
Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

HOW TO ENTER:

  • Review competition requirements
  • Complete the Intent to Enter form by Monday, February 29, 2016 at midnight

Questions? Please contact Kristin Huerth at khuerth@ics.uci.edu.

Prizes for each competition are:
1st Place: $7,500
2nd Place: $5,000
3rd Place: $2,500

Butterworth Product Development Competition at the Donald Bren School of Information and Computer Sciences
Is a product development competition designed to encourage the creation of new technologies with potential for commercialization. Ideas and products are evaluated on their technological merits and potential to impact the marketplace. Students are encouraged to submit new products that involve the development of software and systems. Products that entail integration are acceptable as long as there is a substantial development effort.

The competition is open to all UCI students. Teams must be composed of at least two (2) students, one (1) of which must be enrolled at the Donald Bren School of Information and Computer Sciences.

Beall Student Design Competition at The Henry Samueli School of Engineering
Offered to encourage the creation of new technologies, or solutions to current design problems that have the potential for commercialization. Ideas and products are evaluated on their technological merits as well as potential to impact the marketplace. Students are encouraged to submit new product ideas that involve the development of hardware and devices. Products that entail integration are acceptable as long as there is a substantial development effort.

The competition is open to all UCI students. Teams must be composed of at least two (2) students, one (1) of which must be enrolled at The Henry Samueli School of Engineering.

2014 Competitors
Last year's winners of the Butterworth Competition were composed of Business, Engineering, Medicine and ICS students. For more information on finalist projects, please visit http://www.ics.uci.edu/community/news/features/view_feature?id=2.

Competition details »
  • Homepage
  • Teams
  • Judges
  • Sponsors
  • Rules and Guidelines
  • Schedule, Workshops & Deadlines
  • Past Participants and Results:
    2015
    2014
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http://www.ics.uci.edu/involved/leadership_council Dean's Leadership Council @ The Bren School of Information and Computer Sciences
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Dean's Leadership Council

BREN SCHOOL VISION:

The Bren School of ICS at UC Irvine will establish a new vision for the field of information and computer sciences in the 21st century.

We will emphasize a broad and diverse view of the field including the what, why and how of information and computer science; we will advance interdisciplinary collaborations at UC Irvine and beyond; and we will produce relevant, cutting edge research that addresses key challenges facing our nation and the world.

LC MISSION:

The Bren School of ICS Dean’s Leadership Council has been established to raise community awareness of the Bren School students and faculty, to raise funds to support the Dean’s vision and fundraising priorities and to lend strategic advice to the Dean when called upon.

EXPECTATIONS OF MEMBERSHIP:

  • Attend three Council meetings per year
  • Be available to participate in a Strategic Priority meeting(s) around a particular topic (as appropriate)
  • Financially support Dean’s Discretionary Fund at a minimum $3,000 level
  • Participate in the Strategic Direction for the School
  • Serve as an Ambassador for the School in both personal and business interactions
  • Use your connections aggressively for ICS fundraising and relationship building
  • Partner your organization with ICS students in regards to internships, recruitment and company projects
  • Secure tables for the UCI Medal Awards held in Oct./Nov.
  • Make recommendations for incoming board members

BENEFITS OF MEMBERSHIP:

  • Members will play a critical role in expanding the visibility of the Donald Bren School of Information and Computer Sciences throughout Orange County, California and nationwide
  • Members will have access to the Dean, faculty and student research
  • Members will be invited to share expertise, serve as speakers and participate in events
  • Members will be given networking opportunities through Council meetings and mixers
  • Members will have early access in recruiting high quality interns and graduating students

PARTICIPATION OPPORTUNITIES:

  • Participate as a speaker, mentor or panelist
  • Provide a project in your company for Project ICS
  • Hire a graduate student as an intern
  • Recruit ICS students as employees
  • Host an LC meeting, retreat or committee meeting at your company

Contact Ed Hand at elhand@uci.edu or at 949-824-6563

Opportunities for Engagement »
  • Tech Talks
  • Information Sessions
  • Sponsor a Project Class
  • Dean’s Leadership Council
  • Corporate Partners
  • Sponsored Internship Program
  • Tech Jobs
  • Butterworth Product Development Competition
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Bren school home > About > Donald Bren Hall
Donald Bren Hall

artist rendering of bren hall

The six-story Donald Bren Hall expanded the existing Bren School campus and increased general assignment classroom space by more than 90,800 square feet.

The design of this facility is intended to enhance interaction between faculty and students and to create a progressive learning environment.

Designed with flexibility in mind, the building accommodates the Bren School's growing faculty, staff and student populations.

The first classes in Donald Bren Hall were held Jan. 5, 2007; the Dean's office moved in on Jan. 8, 2007.

On June 20, Donald Bren Hall was offically dedicated during a morning ceremony which was attended by Donald Bren, former Dean Debra J. Richardson, Chancellor Michael Drake and about 400 campus and community guests.

An open house followed the ceremony, allowing the on-campus and off-campus community a rare sneak peek into the school's various research projects and their global impact on everyday lives.

Donald Bren Hall accommodates:

  • More than 125 faculty offices
  • 90 research labs, wet labs and/or offices
  • 10 classrooms (from 30-seat to 65-seat)
  • 250-seat lecture hall
  • 125-seat lecture hall
  • 2 50-seat lecture hall
  • Dean's office/administrative support
  • Departmental offices
  • Bren School staff offices/facilities

Ceremonial groundbreaking on Donald Bren Hall took place on June 9, 2004. Visitors can view the groundbreaking press release as well as photos from the groundbreaking ceremony, attended by the building's namesake, philanthropist and chairman of The Irvine Company, Donald Bren.

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School highlights »
  • Bren School students traditionally lead UCI with the highest average SAT and GRE scores
  • The Bren School offers six undergraduate degrees
  • The only independent computer science school in the UC system and one of the first in the U.S.
  • Bren gift included most endowments created by single gift to UC (10)
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Laptop Requirement and Computer Use

Beginning in Fall 2014, students enrolling in ICS 31/CSE 41, ICS 32/CSE 42, and ICS 33/CSE 43 are required to own a personal laptop for coursework.

» If You're Planning to Get a New Laptop

Students receiving financial aid in the fall can request an additional one-time budget increase of up to $2,000 for a new computer. The additional funding available is limited to student and/or parent loans, depending on eligibility and borrowing limits. This allowance covers hardware, software, monitor, printer, and extended warranty.

Students are encouraged to visit or contact the Office of Financial Aid and Scholarships, 102 Aldrich Hall, by email at finaid@uci.edu, or phone at (949) 824-8262, for details on the budget increase process and limitations.

» Recommended Computer Specifications

  • RAM: 4 GB or higher
  • Hard drive: 250 GB or more
  • Processor: Intel Core i5 or i7, or similar AMD
  • Weight: Students are expected to bring their laptops to class so please consider weight if this is a factor for you.
  • Screen: When writing/debugging programs, more screen space tends to be very useful.
  • Warranty: Extended hardware warranty is recommended.

*If you have a laptop that was purchased within the last 4 years, the system should be adequate for lower division work. Your computer should be running an operating system which will receive security updates and should be minimum 2 GB of RAM and have sufficient hard drive space to install required programs.

**Recommended, but not required:

  • Laptop cable and lock
  • External monitor, keyboard and mouse
  • USB drive or external hard drive for data back-ups
  • Small black & white laser printer (Printers for use are available throughout campus; nominal fee to print)

If you need to connect from off-campus to UCI's network, check out the UCI Libraries' page on this topic to get set up.

 


 

» Computer Use Policies

All ICS students should make themselves familiar with the Bren school's policies governing computer use. Be sure to review the information provided in the below pages, as well as on the pages maintained by ICS Computing Support.

  • Account allocation and Backups
  • Copyright Infringement
  • Ethical Use of Computing
  • Remote Access
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Bren school home > About > Visit the bren school
Visit ICS

Information and computer science buildings at UCI

We can offer virtual tours, pictorials and viewbooks but nothing will substitute for an in-person visit.

The Donald Bren School of Information and Computer Sciences (ICS) is the first and only computer science school in the University of California, offering visitors opportunities to attend academic lectures, interact with leading computer science researchers and recruit intelligent and out of the box thinking computer science students.

If you're interested in attending ICS and want to learn more about a particular degree program - even meet with faculty doing research that piques your interest - please call the Student Affairs Office at (949) 824-5156 and ask to speak to an undergraduate or graduate counselor.

Campus walking tours are offered almost every Monday through Friday. Please check the Tour Calendar for available times and to ensure that there is a tour offered on the day you wish to visit.

Directions to the Bren School
» From LAX (42 miles from UCI)
» From John Wayne (5 miles from UCI)
» From major freeways (405, 73, 55, 5)

Campus Map
» Campus map of UCI (PDF)
The Bren School Student Affairs Office has relocated to: Information and Computer Science, Suite 352 (building #302 on the campus map).

Parking
» UCI guest parking information

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Bren school home > Community > Alumni
ICS Alumni

ICS alumni at Homecoming

You're off campus and on your career path. But you can still make a difference in the Bren School community, whether you've moved down the block, across the country or around the world. For information on involvement opportunites, view the programs below or contact us at khuerth@ics.uci.edu or (949) 824-3074.


Hall of Fame

This fall, UCI will celebrate its 50th anniversary as one of the nation’s leading computer science schools. To honor those alumni who have made a significant impact in their profession or, in other ways, have brought distinction to the School, we are creating the Donald Bren School of Information and Computer Sciences Hall of Fame. If you know of ICS alumni who should be honored in this esteemed group, please fill out the online nomination form. For questions or additional information, please contact Kristin Huerth at khuerth@ics.uci.edu.


Class Notes

We're looking for your Class Notes! Whether you have a new job, started a new company or have any other exciting professional or personal news you want to share with other ICS alumni, we want to know. Please submit all of your Class Notes online so that we can consider them for the next ICS Annual Report, as well as on social media and our website.


Stay Connected

Do we have your most current email and mailing addresses? To get the latest news about the Bren School, as well as exclusive alumni event invitations, please click here to update your contact information. 


ICS Alumni Chapter

The Donald Bren School Alumni Chapter welcomes Anteaters interested in leadership and volunteer roles. In addition to networking and alumni outreach, the chapter offers opportunities to get involved with Bren School student groups. To get involved, please contact Kristin Huerth at khuerth@ics.uci.edu. 


Butterworth Product Development Competition

The Butterworth Product Development Competition encourages the development of new, technically innovative products by ICS graduate and undergraduate students. Formely known as hITEC, this program offers alumni a chance to mentor a team of students as they develop a new product and attempt to enter it into the marketplace. Learn more »


Undergraduate Mentorship Program

The program, comprised of alumni, community members and industry professionals, forms meaningful relationships with female and other traditionally underrepresented students in the fields of engineering and computer science where they explore career interests, academic guidance and personal development. Learn how to get involved as a mentor here.

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Bren school home > Departments
Departments
The Donald Bren School of Information and Computer Sciences is comprised of three departments that teach the broad foundation of each discipline but allow students the flexibility to explore many areas of computer science with carefully crafted curriculums that appeal to students’ diverse interests.

Computer Science Department
The Department of Computer Science engages in the full spectrum of intellectual activity in computer science, ranging from fundamental theoretical principles, to design and analysis of computer systems and networks, to new and exciting applications in areas as diverse as bioinformatics and computer security.

Informatics Department
The Department of Informatics focuses on the interdisciplinary study of the design, application, use and impacts of information technology.

Statistics Department
The new Department of Statistics, which officially joined ICS in July 2003, has core strength in statistical theory with a focus on statistical methods for solving interdisciplinary problems.

Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: March 12 2012
http://www.ics.uci.edu/faculty/ faculty @ the bren school of information and computer sciences
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Faculty

Faculty members in the Bren School are of national and international renown, including ACM and IEEE Fellows, AAAI Career Fellow and many respected authors, leaders and directors of preeminent research and academic endeavors.

Despite an exhaustive list of accolades, the most notable trait of each faculty member is the unparalleled commitment to teaching and instruction as demonstrated in the classroom.

The Bren School is recruiting faculty at all levels of tenure. For information on faculty opportunities, please visit the employment page.


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Computer Science | Informatics | Statistics | Complete list

photo::Shannon Alfaro Shannon Alfaro
Lecturer
Department: Computer Science
Email: alfaro@uci.edu
Phone: (949) 824-9544
Office: DBH 4208
Learn more
photo::Ardalan Amiri Sani Ardalan Amiri Sani
Assistant Professor
Research Area: Embedded Systems
Networks and Distributed Systems

Department: Computer Science
Email: ardalan@uci.edu
Phone: (949) 824-6753
Office: DBH 3062
Learn more
photo::Pierre Baldi Pierre Baldi
Chancellor's Professor, Director Institute for Genomics and Bioinformatics
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Databases and Data Mining
Environmental Informatics
Statistics and Statistical Theory

Department: Computer Science
Email: pfbaldi@ics.uci.edu
Phone: (949) 824-5809
Office: DBH 4038
Learn more
photo::Brigitte Baldi Brigitte Baldi
Lecturer
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: baldib@uci.edu
Phone: (949) 824-1912
Office: 2208 Bren Hall
Learn more
photo::Lubomir Bic Lubomir Bic
Professor
Research Area: Networks and Distributed Systems
Department: Computer Science
Email: bic@ics.uci.edu
Phone: (949) 824-5248
Office: DBH 3224
Learn more
photo::Geoffrey Bowker Geoffrey C Bowker
Professor
Department: Informatics
Email: gbowker@uci.edu
Phone: (949) 824-4558
Office: DBH 5091
Learn more
photo::Elaheh (Eli) Bozorgzadeh Elaheh (Eli) Bozorgzadeh
Associate Professor
Research Area: Computer Architecture and Design
Department: Computer Science
Email: eli@ics.uci.edu
Phone: (949) 824-8860
Office: DBH 3092
Learn more
photo::Michael Carey Michael J Carey
Bren Professor
Research Area: Databases and Data Mining
Department: Computer Science
Email: mjcarey@ics.uci.edu
Phone: (949) 824-2302
Office: DBH 2091
Learn more
photo::Yunan Chen Yunan Chen
Associate Professor
Research Area: Human Computer Interaction
Medical Informatics

Department: Informatics
Email: yunanc@uci.edu
Phone: (949) 824-0959
Office: DBH 5066
Learn more
photo::Rina Dechter Rina Dechter
Professor; Vice Chair Computing Division, Computer Science
Research Area: Artificial Intelligence and Machine Learning
Department: Computer Science
Email: dechter@ics.uci.edu
Phone: (949) 824-6556
Office: DBH 4232
Learn more
photo::Michael Dillencourt Michael Dillencourt
Professor
Research Area: Algorithms and Complexity
Networks and Distributed Systems

Department: Computer Science
Email: dillenco@ics.uci.edu
Phone: (949) 824-7556
Office: DBH 4086
Learn more
photo::Paul Dourish Paul Dourish
Professor
Research Area: Computer-Supported Cooperative Work
Environmental Informatics
Human Computer Interaction
Medical Informatics
Social Informatics
Ubiquitous Computing

Department: Informatics
Email: jpd@ics.uci.edu
Phone: (949) 824-8127
Office: DBH 5086
Learn more
photo::Nikil Dutt Nikil Dutt
Chancellor's Professor
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: dutt@ics.uci.edu
Phone: (949) 824-7219
Office: DBH 3091
Learn more
photo::Magda El Zarki Magda El Zarki
Professor
Research Area: Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: elzarki@uci.edu
Phone: (949) 228-8584
Office: DBH 3216
Learn more
photo::David Eppstein David Eppstein
Chancellor's Professor
Research Area: Algorithms and Complexity
Computer Graphics and Visualization

Department: Computer Science
Email: eppstein@ics.uci.edu
Phone: (949) 824-6384
Office: DBH 4082
Learn more
photo::Julian Feldman Julian Feldman
Professor Emeritus
Department: Informatics
Email: feldman@ics.uci.edu
Phone: (949) 824-2901
Office: DBH 5029
Learn more
photo::Charless Fowlkes Charless Fowlkes
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Computer Vision

Department: Computer Science
Email: fowlkes@ics.uci.edu
Phone: (949) 824-6945
Office: DBH 4076
Learn more
photo::Michael Franz Michael Franz
Professor, Director: Secure Systems and Languages Laboratory
Research Area: Programming Languages and Systems
Security
Software Engineering

Department: Computer Science
Email: franz@uci.edu
Phone: (949) 824-0016
Office: ICS 444
Learn more
photo::Dan Frost Dan Frost
Senior Lecturer SOE
Research Area: Artificial Intelligence and Machine Learning
Department: Informatics
Email: frost@ics.uci.edu
Phone: (949) 824-1588
Office: DBH 5058
Learn more
photo::Daniel Gillen Daniel Gillen
Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: dgillen@uci.edu
Phone: (949) 824-9862
Office: DBH 2226
Learn more
photo::Tony Givargis Tony Givargis
Professor, Assoc. Dean for Student Affairs
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: givargis@uci.edu
Phone: (949) 824-9357
Office: DBH 3076
Learn more
photo::Michael Goodrich Michael T. Goodrich
Chancellor's Professor
Research Area: Algorithms and Complexity
Computer Graphics and Visualization

Department: Computer Science
Email: goodrich@ics.uci.edu
Phone: (949) 824-9366
Office: DBH 4091
Learn more
photo::Judith Gregory Judith Gregory
Associate Adjunct Professor
Department: Informatics
Email: judithgr@uci.edu
Phone: (312) 315-3371
Office: DBH 5064
Learn more
photo::Stacey Hancock Stacey Hancock
Lecturer PSOE
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: staceyah@uci.edu
Phone: (949) 824-9795
Office: 2204 DBH
Learn more
photo::Ian Harris Ian G. Harris
Associate Professor; Vice Chair of Undergraduate Studies, Computer Science
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: harris@ics.uci.edu
Phone: (949) 824-8842
Office: DBH 3088
Learn more
photo::Wayne Hayes Wayne Hayes
Associate Professor
Research Area: Biomedical Informatics and Computational Biology
Scientific and Numerical Computing

Department: Computer Science
Email: wayne@ics.uci.edu
Phone: (949) 824-1753
Office: DBH 4092
Learn more
photo::Gillian Hayes Gillian R Hayes
Associate Professor, Vice Chair of Graduate Affairs, Informatics
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Social Informatics
Ubiquitous Computing

Department: Informatics
Email: gillianrh@ics.uci.edu
Phone: (949) 824-1483
Office: DBH 5084
Learn more
photo::Daniel Hirschberg Daniel S. Hirschberg
Professor, Irvine Senate Parliamentarian
Research Area: Algorithms and Complexity
Department: Computer Science
Email: dan@ics.uci.edu
Phone: (949) 824-6480
Office: DBH 4226
Learn more
photo::Alexander Ihler Alexander Ihler
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Department: Computer Science
Email: ihler@ics.uci.edu
Phone: (949) 824-3645
Office: DBH 4066
Learn more
photo::Sandy Irani Sandy Irani
Professor
Research Area: Algorithms and Complexity
Department: Computer Science
Email: irani@ics.uci.edu
Phone: (949) 824-6346
Office: DBH 4042
Learn more
photo::Mimi Ito Mimi Ito
Professor in Residence
Department: Informatics
Email: mizukoi@uci.edu
Phone: (949) 824-9011
Office: DBH 5224
Learn more
photo::Ramesh Jain Ramesh Jain
Bren Professor
Research Area: Computer Vision
Multimedia Computing

Department: Computer Science
Email: jain@ics.uci.edu
Phone: (949) 824-0133
Office: DBH 3222
Learn more
photo::Stanislaw Jarecki Stanislaw Jarecki
Professor
Research Area: Algorithms and Complexity
Department: Computer Science
Email: stasio@ics.uci.edu
Phone: (949) 824-8878
Office: DBH 4026
Learn more
photo::Wesley Johnson Wesley O. Johnson
Professor; Vice Chair of Graduate Affairs, Statistics
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: wjohnson@ics.uci.edu
Phone: (949) 824-0147
Office: DBH 2232
Learn more
photo::James Jones James A. Jones
Associate Professor
Research Area: Software Engineering
Department: Informatics
Email: jajones@uci.edu
Phone: (949) 824-0942
Office: DBH 5214
Learn more
photo::Scott Jordan Scott Jordan
Professor
Research Area: Networks and Distributed Systems
Department: Computer Science
Email: sjordan@uci.edu
Phone: (949) 824-2177
Office: DBH 3214
Learn more
photo::David Kay David G. Kay
Senior Lecturer SOE; Vice Chair for Undergraduate Affairs, Informatics
Department: Informatics
Email: kay@uci.edu
Phone: (949) 824-5072
Office: DBH 5056
Learn more
photo::Dennis Kibler Dennis Kibler
Professor Emeritus
Department: Computer Science
Email: kibler@ics.uci.edu
Phone: (949) 824-0016
Office: DBH 4072
Learn more
photo::Ray Klefstad Ray Klefstad
Lecturer
Research Area: Embedded Systems
Networks and Distributed Systems
Programming Languages and Systems

Department: Computer Science
Email: klefstad@uci.edu
Phone: (949) 824-6753
Office: ICS 424
Learn more
photo::Alfred Kobsa Alfred Kobsa
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Security
Privacy and Cryptography
Ubiquitous Computing

Department: Informatics
Email: kobsa@uci.edu
Phone: (949) 485-5020
Office: DBH 5092
Learn more
photo::Richard Lathrop Richard Lathrop
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology

Department: Computer Science
Email: rickl@ics.uci.edu
Phone: (949) 824-4021
Office: DBH 4224
Learn more
photo::Marco  Levorato Marco Levorato
Assistant Professor
Research Area: Artificial Intelligence and Machine Learning
Networks and Distributed Systems
Statistics and Statistical Theory

Department: Computer Science
Email: levorato@uci.edu
Phone: (949) 824-2175
Office: DBH 3206
Learn more
photo::Chen Li Chen Li
Professor
Research Area: Databases and Data Mining
Department: Computer Science
Email: chenli@ics.uci.edu
Phone: (949) 824-9470
Office: DBH 2092
Learn more
photo::Cristina Lopes Cristina V. Lopes
Professor
Research Area: Programming Languages and Systems
Software Engineering
Ubiquitous Computing

Department: Informatics
Email: lopes@ics.uci.edu
Phone: (949) 824-1525
Office: DBH 5076
Learn more
photo::George Lueker George S. Lueker
Professor Emeritus
Research Area: Algorithms and Complexity
Department: Computer Science
Email: lueker@ics.uci.edu
Phone: (949) 824-5866
Office: DBH 4206
Learn more
photo::Aditi Majumder Aditi Majumder
Professor
Research Area: Computer Graphics and Visualization
Computer Vision

Department: Computer Science
Email: majumder@ics.uci.edu
Phone: (949) 824-8877
Office: DBH 4056
Learn more
photo::Sam Malek Sam Malek
Professor
Research Area: Software Engineering
Department: Informatics
Email: malek@uci.edu
Phone: (949) 824-0639
Office: DBH 5226
Learn more
photo::Gloria Mark Gloria Mark
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Social Informatics

Department: Informatics
Email: gmark@ics.uci.edu
Phone: (949) 824-5955
Office: DBH 5212
Learn more
photo::Melissa Mazmanian Melissa Mazmanian
Associate Professor
Department: Informatics
Email: mmazmani@ics.uci.edu
Phone: (949) 824-9284
Office: DBH 5074
Learn more
photo::Gopi Meenakshisundaram Gopi Meenakshisundaram
Professor
Research Area: Computer Graphics and Visualization
Computer Vision

Department: Computer Science
Email: gopi@ics.uci.edu
Phone: (949) 824-9498
Office: DBH 4212
Learn more
photo::Sharad Mehrotra Sharad Mehrotra
Professor; Vice Chair of Graduate Studies, Computer Science
Research Area: Databases and Data Mining
Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: sharad@ics.uci.edu
Phone: (949) 824-5975
Office: DBH 2082
Learn more
photo::Eric Mjolsness Eric Mjolsness
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Computer Vision
Scientific and Numerical Computing

Department: Computer Science
Email: emj@uci.edu
Phone: (949) 824-3533
Office: DBH 6082
Learn more
photo::Bonnie Nardi Bonnie Nardi
Professor
Research Area: Computer-Supported Cooperative Work
Social Informatics

Department: Informatics
Email: nardi@ics.uci.edu
Phone: (949) 824-6534
Office: DBH 5088
Learn more
photo::Alexandru Nicolau Alexandru Nicolau
Professor, Department Chair
Research Area: Computer Architecture and Design
Embedded Systems
Programming Languages and Systems

Department: Computer Science
Email: nicolau@ics.uci.edu
Phone: (949) 824-4079
Office: DBH 3082
Learn more
photo::Judy Olson Judy Olson
Bren Professor of Information & Computer Sciences
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction

Department: Informatics
Email: jsolson@uci.edu
Phone: (949) 824-0080
Office: DBH 5206
Learn more
photo::Gary Olson Gary M. Olson
Bren Professor of Information & Computer Sciences
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Social Informatics

Department: Informatics
Email: golson@uci.edu
Phone: (949) 824-0077
Office: DBH 5202
Learn more
photo::Hernando Ombao Hernando Ombao
Professor
Research Area: Artificial Intelligence and Machine Learning
Statistics and Statistical Theory

Department: Statistics
Email: hombao@uci.edu
Phone: (949) 824-5679
Office: DBH 2206
Learn more
photo::Donald Patterson Donald J Patterson
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Ubiquitous Computing

Department: Informatics
Email: djp3@ics.uci.edu
Phone: (206) 355-5863
Office: DBH 5084
Learn more
photo::Richard Pattis Richard E Pattis
Senior Lecturer SOE
Department: Computer Science
Email: pattis@ics.uci.edu
Phone: (949) 824-2704
Office: DBH 4062
Learn more
photo::David Redmiles David Redmiles
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Software Engineering

Department: Informatics
Email: redmiles@ics.uci.edu
Phone: (949) 824-3823
Office: DBH 5232
Learn more
photo::Amelia Regan Amelia C. Regan
Professor
Research Area: Algorithms and Complexity
Networks and Distributed Systems

Department: Computer Science
Email: aregan@uci.edu
Phone: (949) 824-2611
Office: DBH 4068
Learn more
photo::Debra Richardson Debra J. Richardson
Professor Emeritus
Research Area: Software Engineering
Department: Informatics
Email: djr@ics.uci.edu
Phone: (949) 824-7353
Office: DBH 5241
Learn more
photo::Walt Scacchi Walt Scacchi
Sr. Research Scientist, Institute for Software Research
Research Area: Computer-Supported Cooperative Work
Software Engineering

Department: Lecturer
Email: wscacchi@ics.uci.edu
Phone: (949) 824-4130
Office: ICS2 202
Learn more
photo::Isaac Scherson Isaac D. Scherson
Professor
Research Area: Computer Architecture and Design
Embedded Systems
Networks and Distributed Systems

Department: Computer Science
Email: isaac@ics.uci.edu
Phone: (949) 824-8144
Office: ICS 464C
Learn more
photo::Babak Shahbaba Babak Shahbaba
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Statistics and Statistical Theory

Department: Statistics
Email: babaks@uci.edu
Phone: (949) 824-0623
Office: DBH 2224
Learn more
photo::Weining Shen Weining Shen
Assistant Professor
Research Area: Statistics and biostatistics
Department: Statistics
Email: weinings@uci.edu
Phone: (949) 824-5968
Office: DBH 2241
Learn more
photo::Padhraic Smyth Padhraic Smyth
Professor, Director, UCI's Data Science Initiative
Research Area: Artificial Intelligence and Machine Learning
Databases and Data Mining
Scientific and Numerical Computing
Statistics and Statistical Theory

Department: Computer Science
Email: smyth@ics.uci.edu
Phone: (949) 824-2558
Office: DBH 4216
Learn more
photo::Thomas Standish Thomas A. Standish
Professor Emeritus
Department: Informatics
Email: standish@uci.edu
Phone: (949) 497-3064
Office: DBH 5048
Learn more
photo::Hal Stern Hal Stern
Professor and Dean
Research Area: Artificial Intelligence and Machine Learning
Statistics and Statistical Theory

Department: Statistics
Email: sternh@uci.edu
Phone: (949) 824-7405
Office: DBH 6215
Learn more
photo::Joshua Tanenbaum Joshua G. Tanenbaum
Acting Assistant Professor
Department: Informatics
Email: tanenbaj@uci.edu
Phone: 949-824-7078
Office: DBH 5052
Learn more
photo::Richard Taylor Richard Taylor
Professor Emeritus, Director, Institute for Software Research
Research Area: Computer-Supported Cooperative Work
Networks and Distributed Systems
Software Engineering

Department: Informatics
Email: taylor@ics.uci.edu
Phone: (949) 824-6429
Office: DBH 5216
Learn more
photo::Bill Tomlinson Bill Tomlinson
Professor
Research Area: Computer Graphics and Visualization
Environmental Informatics
Human Computer Interaction
Ubiquitous Computing

Department: Informatics
Email: wmt@uci.edu
Phone: (949) 824-9804
Office: DBH 5068
Learn more
photo::Gene Tsudik Gene Tsudik
Chancellor's Professor
Research Area: Security
Privacy and Cryptography

Department: Computer Science
Email: gts@ics.uci.edu
Phone: (949) 824-3410
Office: ICS 458E
Learn more
photo::Jessica Utts Jessica Utts
Professor and Chair
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: jutts@uci.edu
Phone: (949) 824-0649
Office: DBH 2212
Learn more
photo::Andre van der Hoek Andre van der Hoek
Professor and Chair, Department of Informatics
Research Area: Software Engineering
Department: Informatics
Email: ichair@ics.uci.edu
Phone: (949) 824-6326
Office: DBH 5038
Learn more
photo::Alexander Veidenbaum Alexander Veidenbaum
Professor
Research Area: Computer Architecture and Design
Databases and Data Mining
Programming Languages and Systems

Department: Computer Science
Email: alexv AT ics.uci.edu
Phone: (949) 824-6188
Office: DBH 3056
Learn more
photo::Nalini Venkatasubramanian Nalini Venkatasubramanian
Professor
Research Area: Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: nalini@ics.uci.edu
Phone: (949) 824-5898
Office: DBH 2086
Learn more
photo::Richert Wang Richert Wang
Lecturer
Research Area: Networks and Distributed Systems
Programming Languages and Systems
Software Engineering

Department: Computer Science
Email: rkwang@uci.edu
Phone: (949) 824-6753
Office: ICS 424
Learn more
photo::Xiaohui Xie Xiaohui Xie
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Medical Informatics

Department: Computer Science
Email: xhx@ics.uci.edu
Phone: (949) 824-9289
Office: DBH 4058
Learn more
photo::Harry Xu Harry Xu
Assistant Professor
Research Area: Programming Languages and Systems
Software Engineering

Department: Computer Science
Email: harry.g.xu@uci.edu
Phone: (949) 824-8870
Office: DBH 3212
Learn more
photo::Yaming Yu Yaming Yu
Associate Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: yamingy@uci.edu
Phone: (949) 824-7361
Office: DBH 2228
Learn more
photo::Zhaoxia Yu Zhaoxia Yu
Associate Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: yu.zhaoxia@uci.edu
Phone: (949) 824-0491
Office: DBH 2214
Learn more
photo::Shuang Zhao Shuang Zhao
Assistant Professor
Research Area: Computer Graphics and Visualization
Department: Computer Science
Email: shz@ics.uci.edu
Phone: (949) 824-4942
Office: DBH 4214
Learn more
photo::Kai Zheng Kai Zheng
Associate Professor
Research Area: Human Computer Interaction
Medical Informatics

Department: Informatics
Email: kai.zheng@uci.edu
Phone: (949) 824-6920
Office: DBH 5228
Learn more
photo::Hadar Ziv Hadar Ziv
Lecturer/ Asst. Project Scientist
Department: Informatics
Email: ziv@ics.uci.edu
Phone: (949) 824-2901
Office: DBH 5062
Learn more

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http://www.ics.uci.edu/grad/admissions/Prospective_ApplicationProcess.php graduate application process @ the bren school of information and computer sciences
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Bren school home > Graduate > Admissions
Graduate Application Process

Online applications for graduate admissions are accepted from September 1st to December 15th. All materials as outlined below should be submitted before the December 15th deadline. (Additional information for those re-applying to the Bren School can be found here.)

You can still apply to ICS if your undergraduate degree is not in the same area as the graduate degree in which you are interested. However, it is helpful if you have taken courses in computer science and math, and/or have some related work experience.

ICS accepts applications for admission beginning in the fall quarter only.

» Online Application

Complete the online application. Pay particular attention to the following:

  • Enter the appropriate codes for ICS. Note that you must choose either M.S. or Ph.D. as ICS does not have a M.S./Ph.D. program.
  • Computer Science, Ph.D.
  • Computer Science, M.S.
  • Informatics, Ph.D.
  • Networked Systems, Ph.D.
  • Networked Systems, M.S.
  • Software Engineering, Ph.D.
  • Software Engineering, M.S.
  • Statistics, Ph.D.
  • Statistics, M.S.
  • Information and Computer Science, M.S.
  • Enter a specific area of interest. More information about research areas can be found here.
  • Save your application ID and student ID numbers; they will be asked of you when filling out other documents.
  • Applications are processed after the application fee ($90 for domestic applicants, $110 for foreign applicants) has been received. The application fee canot be waived.
  • You can apply for more than one program simultaneously, but you must fill out a separate application and pay a separate fee for each program.
  • If you have questions regarding your electronic application or your payment, contact UCI Graduate Division at (949) 824-4611 or ogs@uci.edu. 

» GRE and TOEFL Scores

Arrange to have your official test scores sent from the testing agency to UC Irvine. Photocopies or scans will not be accepted.

GRE

  • The General GRE is required of ALL applicants.
  • For official GRE General test scores, use Institutional code: 4859
  • There is no minimum GRE score.
  • The Computer Science Subject GRE is recommended for applicants who do not have a degree in Computer Science or a related field.
  • GRE scores are good for five years, after which time the exam must be retaken.
  • We cannot accept GMAT scores in lieu of GRE scores.

TOEFL

  • The TOEFL (Test of English as a Foreign Language) is required of international applicants.
  • For official TOEFL test scores, use Institutional code: 4859
  • The minimum TOEFL score is 80. For more information, please see Graduate Division's English Proficiency site.
  • UCI will only waive the TOEFL requirement for applicants who have completed ALL requirements for a B.S., M.S. or Ph.D. degree in the U.S. prior to submitting their application.

» Letters of Recommendation

Three letters of recommendation are required, but you may submit as many letters as you wish.

For paper submission

Provide the Letter of Recommendation and Waiver of Access forms to at least three people you have identified who can evaluate your academic and/or professional achievements, describe your strengths and weaknesses and comment on your character, integrity and motivation. Your recommenders should mail his/her letter directly to:

ICS Graduate Office
Donald Bren School of Information and Computer Sciences
352 Information & Computer Science Bldg.
University of California, Irvine
Irvine, CA 92697-3430

For online submission

Applicants who have completed and submitted their online application should send their recommenders a link to that page, along with their application ID number.

» Personal Statement

The Personal Statement (same as Statement of Purpose) should be submitted via the online application.

» Official Transcripts

Arrange to have one copy of your official transcripts from the colleges you attended sent directly to the ICS Graduate Office.

An official English translation is required from international students.

Photocopies, scans, or unofficial copies of transcripts will not be accepted.

» Checking Your Application Status

Applicants may login to the online GATS TRACKER to check for application materials received and their current application status.

Ph.D. and M.S. admissions decisions are ongoing from approximately March through May. Acceptance and denial letters will be sent electronically.

We cannot provide you with the final decision over the phone.

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http://www.ics.uci.edu/grad/funding/index Graduate Funding & Housing - UCI's Donald Bren School of Information and Computer Sciences
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Bren school home > Graduate > Funding
Graduate Funding & Housing

Fellowships for ICS Graduate Students

Fellowship and award opportunities available to ICS Graduate Students are outlined in this PDF document. This is a combined list of those offered by Graduate Division and external sources. Please see the links provided in the document for more information on each fellowship or award.

*Many fellowships have internal department deadlines 7-10 days prior to the official due date.

Teaching Assistantships

Students can receive funding through appointments as a Teaching Assistant (TA) or Reader. Information on responsibilities, requirements, and benefits can be found on the Graduate Division website here.

Interested ICS graduate students must submit an application for each quarter they wish to search as a TA or Reader. The application requirement applies to ALL students, including those on a TA/Reader fellowship.

Students are notified of their appointments via email as soon as they are assigned. Any questions about TA and Reader assignments should be directed to the Department Managers.

Graduate Housing

On-campus housing is not guaranteed.

All students looking for on-campus housing options must apply in order to be considered. Continuing students may apply to the housing waiting list at any time during the year. Admitted students may apply for housing after they submit their Statement of Intent to Register (SIR), and must do so by May 1st.

Continuing students can find more information on the Student Housing website.

Information for new graduate students may be found here.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Grading Policies

All Bren School and Major requirements must be taken for a letter grade unless the required course is designated as “P/NP Only”.

 


 

» Calculating your GPA

The Official UC GPA is calculated by dividing the total number of Grade Points by the total number of Attempted UC Units.

Letter Grade Grade Points
A 4 points per unit
B 3 points per unit
C 2 points per unit
D 1 points per unit
F and I (incomplete) 0 points per unit

A+ grade is assigned 4 points per unit.

Plus or minus suffixes modify the above by plus or minus 0.3 grade point per  unit. E.g., B+ is 3.3 points per unit, D- is 0.7 points per unit.

Non-letter grades (P, NP, I, etc.) are excluded in calculating GPA.


 

 

 

 

 

 

 

 

 

 

Example: A student takes the following courses and receives the grades:

Course Units Grade Received Grade Points Total Grade Points

Math

4

A

4.0

16.0

Writing

4

B-

2.7

10.8

Science

4

C+

2.3

9.2

Art

4

P

0.0

0

 

 

 

 

 

 

 

             Total Grade Points (36.0)                           = 3.0 GPA

Total Units Passed with Letter Grade (12)

 

» Pass/No Pass

The P/NP grading option may be used for courses that count toward the unit requirement for the B.A. degree, and toward the General Education Requirement.

No more than two P/NP courses may be applied to any minor on campus.

  • An average of 4 units may be taken P/NP per quarter
  • No more than 12 units TOTAL may be applied towards graduation requirements during your undergraduate career.
  • “Pass” is equal to grade of C or better.
  •  “Not Pass” is equal to a grade of C- or below.

Detailed information can be found in the General Catalogue.

 

» Illegal Duplication of Credit

Undergraduate and graduate students may not receive unit credit or earn grade points for college courses in which the content duplicates material of a previously completed course or examination for which the student has been granted college credit.

 

» Repeating Deficient Grades

A course may be repeated only when grades of C-, D+, D, D-, F, or NP were received. Unit credit for courses so repeated will be given only once.

The grade assigned at each enrollment shall be permanently recorded.

Only the most recently received grades and grade points shall be used for the first 16 units repeated when calculating GPA. (The grade point average is based on all additional grades assigned in cases of further course repetitions.)

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Sponsor a Project Class

The Informatics Department in the Bren School of Information and Computer Sciences at UC Irvine runs project courses in two sizes: one-quarter and three-quarters. We are looking for project sponsors.

Informatics projects involve using good software engineering processes interlaced with human centered methods to build and install something useful for you. Projects include different types of sources and sponsors ranging from corporate projects to university based, community-driven, non-profit or entrepreneurial projects. Project implementations range from web applications and portals to online services, cloud computing, ipad, iphone and mobile applications.

One-Quarter Project Class
Currently our one-quarter class is held during different times of the year, and is determined by enrollment. If you are interested, please contact us so that we can put you on the wait list for the class. The sponsorship amount for this project class is $4,500.

Three-Quarter Project Class
Each year we offer our three-quarter class starting in spring quarter and ending in winter quarter of the following year. If you are interested, project proposal deadlines are due six weeks prior to the starting of class. The sponsorship amount for this project class is $12,500.

 

For more information contact:

Informatics 117 – Software System Design (One-Quarter Project)
Professor Hadar Ziv at ziv@ics.uci.edu

Informatics 132 – HCI & User Interface (One-Quarter Project)
Professor Alfred Kobsa at kobsa@uci.edu

Informatics 191 – Senior Design Project: Usability Engineering & Software Development (Three-Quarter Project)
Professor Judith Olson at jsolson@uci.edu
Professor Hadar Ziv at ziv@ics.uci.edu

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Bren school home > Community > Friends
Friends of the Donald Bren School

ICS students with their corporate sponsor

As public funding for our state's universities declines and the costs and risks of corporate R&D escalate, there is increasing motivation for forging bonds between academia and industry.

The Donald Bren School of Information and Computer Sciences is research oriented -- even at the undergraduate level -- with formal projects addressing hardware, software, algorithm design, artificial intelligence and the societal impacts of computing.

Research-orientated education means ICS students arrive at companies already comfortable on the leading edge and poised to make an immediate impact.

In addition to students, our world-renowed faculty work with outside companies and frequently collaborate with professionals in other academic areas to create even greater synergy.

This results in a combination of corporate insight, faculty guidance and student energy that has proven time and again to be the spark that ignites tomorrow’s products and services.

The ICS programs listed below offer you an opportunity to interface with our students and faculty.


Butterworth Product Development Competition
This competition offers an opportunity for students to develop their business skills and earn cash prizes. Learn more »

Tech Jobs
Whether you are looking for computer science interns, part-time or full-time employees, Tech Jobs is the place for you. Post your job opening and know that you are reaching ICS students and alumni each and every time! Learn more » 

Scholarships & Fellowships
Support a student by sponsoring a scholarship or fellowship. In forging relationships with the students they support, current donors have discovered that ICS students are more than scholars, they also are community volunteers, dedicated youth leaders, responsible young adults and promising future professionals. Learn more »

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http://www.ics.uci.edu/grad/admissions/index Prospective Graduate Students @ the bren school of information and computer sciences
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Bren school home > Graduate > Admissions
Prospective Graduate Students

Students studyingYour Success Starts at ICS

Join more than 400 graduate students studying virtually every principle area within the fields of Computer Science, Informatics, Networked Systems, Software Engineering and Statistics, as well as many new interdisciplinary topics. With more than 75 faculty members spanning three departments—Computer Science, Informatics and Statistics—a state-of-the-art 90,000 sq. ft. building and a $20 million gift from philanthropist Donald Bren, this is a very exciting time to be a part of the Donald Bren School.

Almost all of our Ph.D. students receive financial aid in the form of teaching assistantships or research assistantships during their time at UC Irvine. Many of them also receive fellowships from the Bren School's corporate partners and private supporters.

You can still apply to ICS if your undergraduate degree is not in the same area as the graduate degree in which you are interested. However, it is helpful if you have taken courses in computer science and math, and/or have some related work experience.

A complete list of our graduate programs is below so you can explore which one is right for you. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

ICS Graduate Programs of Study

» Computer Science — Ph.D.  l  M.S.

» Informatics — Ph.D.  l  M.S.

» Networked Systems — Ph.D.  l  M.S.

» Software Engineering — Ph.D.  l  M.S.

» Statistics — Ph.D.  l  M.S.

» Information & Computer Science
(Informatics Concentration) — 
M.S.

» Information & Computer Science
(Embedded Systems Concentration)  — 
M.S.


Apply Now

For information on how to apply to one or multiple graduate programs, click here. 


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http://www.ics.uci.edu/involved/ Opportunities for Engagement @ The Bren School of Information and Computer Sciences
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Bren school home > Get involved
Opportunities for Engagement

Want to get involved with the Bren School community? The programs below offer corporations and industry representatives a range of opportunities for engagement with our students and faculty.

Sponsor a Project Class
The Department of Informatics seeks sponsors for one- and three-quarter project courses. more
Dean’s Leadership Council
Members of the Leadership Council raise community awareness of Bren School students and faculty, raise funds to support the Dean’s vision and provide strategic advice to the Dean. more
Corporate Partners
Our Corporate Partners Program promotes the exchange of the most advanced information in the field between university researchers and their corporate counterparts. It also provides access to exclusive recruiting and networking opportunities. more
ZotLink Jobs & Internships
Post jobs or find the right student for your company. more
Product Development Competition
We’re seeking sponsors, judges and mentors for this annual competition, which encourages the development of new, technically innovative products by graduate and undergraduate students. more
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Academic Standing

Students' academic status is reviewed on a quarterly basis. Academic standing is determined by GPA and degree progress. To stay in good standing:

  • Maintain a quarterly and cumulative GPA above 2.0, and
  • Make progress towards your chosen degree

The Student Affairs Office will notify the student of their academic probation status via their UCI email account.  The letter will include an academic probation contract.  The contract will outline specific conditions that must be met in order to return to good academic standing.

It is the student’s responsibility to:

  • manage their UCI account
  • to read and respond to official messages from their academic counselors in a timely manner.
  • monitor academic progress and maintain good academic standing.

Failure to follow through on the steps outlined in your academic probation letter will result in an administrative hold on the student’s record.

Student should contact their academic counselor soon after receiving the academic probation notification to ensure their upcoming course load is reasonable and to learn about helpful campus resources.

 


 

» Academic Probation

Students are subject to probation any time their quarterly or cumulative GPA falls below 2.0, or if they have violated one or more terms of the academic probation contract they are currently on.

**If a student’s term or cumulative GPA falls below a 1.5, the student will be dismissed from the Donald Bren School of ICS effective immediately.  This policy applies to full-time and part-time students.

» Academic Probation Stages

Academic Probation 1

-AP 1 results if the student’s term or cumulative GPA falls below a 2.0.

-Student will be placed on an academic probation contract for the following quarter.

Academic Probation 2 / Subject to Disqualification

-AP 2 results if the student is on two consecutive quarters of academic probation, or has not met one or more terms of the academic probation 1 contract. 

-Student is subject to disqualification

-Student is given the opportunity to appeal the possible / pending disqualification status

*Please note that being subject to disqualification is NOT the same as being formally disqualified

» Submitting a Letter of Appeal

A letter of appeal should explain any unusual or extenuating circumstances that contributed to the student's academic difficulties.  It must also detail the student's plans and actions for effectively addressing these circumstances (including the use of campus resources) so student can return to good academic standing.

The Associate Dean will carefully review the student's appeal letter, transcript and academic file with the academic counselors before making a decision.  Note that the Associate Dean for Student Affairs is the chief academic officer for the Bren School of ICS, and his decisions regarding disqualification appeals are final.

If the appeal is granted, the student will be held to an academic contract that outlines the expectations and timeframe for returning to good academic standing.

If the appeal is denied, the student will be formally disqualified from the major, the Bren School of ICS, and UC Irvine.  

*Instructions on how to appeal, including deadline and where to send the letter will be indicated in the student’s academic probation letter.

» Disqualification

If a student is formally disqualified, he or she will be restricted from registering for courses or using university services after a stated date. The Registrar's Office will also be instructed to note the effective date of the disqualification on the student's official transcript.  An official letter will be sent to the student's permanent address on file.

It is recommended for the student to see an academic counselor to learn about the readmission process and policy.

» Readmission

If a student has been formally disqualified, the student is encouraged to make an appointment to speak with an academic counselor to learn about the readmission policy and to strategize future plans and goals. Please visit the page on Withdrawal and Readmission for additional information.

If / when the student readmits to ICS after having been academically disqualified, the student must for the first quarter of return earn a 2.0 term GPA and Cs or higher in each course.  Failure to meet these terms may result in academic probation or being subject to disqualification.

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Bren school home > Graduate > policies
Graduate student policies

» Academic Honesty

» Advancement to Candidacy (M.S.)

» Advancement to Candidacy (Ph.D.)

» California Residency

» Candidacy Committee's Duties and Responsibilities

» Candidacy Committee Membership

» Comprehensive Exam/Phase II

» Computer and Network Use

» Copyright Infringement

» Curricular Practical Training (CPT)

» Ethical Use of Computing Resources

» Defense, Final

» Defense, Topic

» Filing Fee

» Grading Standards

» Graduate Student Review

» In-Absentia Registration

» Leave of Absence

» M.S. Thesis Option

» Optional Practical Training (OPT)

» Part-Time Enrollment

» Previously Earned M.S. Degree

» Residency Requirement

» Summer Enrollment

» Teaching Requirement

» Transfer of Academic Credit

» UC Policy on Sexual Harassment

* Other policies important for students to know include the Non-Discrimination Policy Statements, Americans with Disabilities Act, and Jeanne Clery Act. It is recommended that students be familiar with the rules and regulations that govern students at UCI as outlined in the UCI General Catalogue.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Withdrawal and Readmission

Students who are planning to withdraw from the University are encouraged to seek advice from an academic counselor to discuss the withdrawal and readmission process.

 


 

» Withdrawing from UCI

If the decision to withdraw is made after tuition and fees for an academic quarter are paid, a Withdrawal form is required. The form can be picked up in any academic advising office (including the ICS Student Affairs Office), as well as from the Registrar's Office. Bren Students must obtain the signature of the Associate Dean for Student Affairs before submitting the form to the Registrar's Office for processing.  Please note that an ICS academic counselor may sign on the Associate Dean for Student Affairs' behalf.

Information on the schedule of refunds can be found on the University Registrar website.

If the decision to withdraw is made before tuitions and fees for the next quarter are paid, a formal notice of withdrawal or withdrawal form is not necessary, although recommended.

 

» Readmitting to UCI

Readmission to UCI and the Bren School of ICS is NOT automatic.  There are different policies and processes for readmission based on the student's reasons for and time of withdrawal.

If a student is seeking to readmit after voluntary withdrawal from UCI while in good standing, readmission will be granted if the student re-enrolls within one year. After more than one year, it will be considred on a case by case basis. The Associate Dean for Student Affairs will make the decision to readmit a student based on additional documentation, including the following:

  • number of completed units while at UCI
  • number of units remaining for completion of general education and degree requirements
  • grades for coursework taken at other institutions or through ACCESS UCI
  • demonstrated readiness for the academic challenge of UCI coursework.

All materials required for readmission petition must be submitted as a complete package. An academic counselor will attach a summary of previous course work and/or contract conditions for the Associate Dean’s consideration.

There are additional readmission requirements if a student withdrew while on academic probation or after academic disqualification:

For Readmission After Voluntary Withdrawal from UCI While on Academic Probation

For Readmission After Academic Disqualification

 

» Other Readmission Info

Students must meet with an ICS academic counselor well in advance of the quarter in which the student plans on readmitting. Readmission applications are processed by ICS academic counselors.

For information about fees, procedures and deadlines, visit the Registrar’s website on readmission.

Note that only UC-transferable courses will be considered when determining a student's eligibility for readmission to one of the Bren School majors.

A readmitted student who has not been enrolled at UCI for three or more consecutive quarters must adhere to the graduation requirements in effect for the quarter in which the student is readmitted, OR those subsequently established.

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Bren school home > Graduate > Degrees
ICS Graduate Programs

Students and faculty collaboratingAs the only computing-focused school in the University of California system, ICS offers an array of graduate degree programs in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics. Click on a related link below to learn more about a specifc degree program. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

» Computer Science — Ph.D.  l  M.S.

» Informatics — Ph.D.  l  M.S.

» Networked Systems — Ph.D.  l  M.S.

» Software Engineering — Ph.D.  l  M.S.

» Statistics — Ph.D.  l  M.S.

» Information & Computer Science
(Informatics Concentration) — 
M.S.

» Information & Computer Science
(Embedded Systems Concentration)  — 
M.S.


Apply Now

For information on how to apply to one or multiple graduate programs, click here. 


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Bren school home > Get involved
Corporate Partners Membership Program

MISSION

The Bren ICS Corporate Partners Program promotes the exchange of the most advanced information in the field between university researchers and their corporate counterparts, and provides access to the abilities and interests of UC Irvine’s students through exclusive recruiting and networking opportunities.

OBJECTIVES

  1. Excellent customer service/individualized attention
  2. Streamlined communication
  3. Priority access to students and faculty within your specific area(s) of interest

BASIC MEMBERSHIP

Basic members receive the following benefits:
  • Recruiting – Customized attention to your recruiting needs including:
    • ICS Jobs - Access to students through student website featuring resumes, and ability to sort by G.P.A, major, specific skills & more
    • Early Career Fair featuring first access to top students, on-campus interviews, company info sessions
    • Facilitation of interns
  • Networking – Discuss leading research and innovations with faculty and other Corporate Partners through our quarterly Distinguished Speaker Series. Attend VIP receptions (4 tickets) prior to each Distinguished Speaker event.
  • Student Showcase – Attend the annual Student Showcase event in spring featuring top Bren School student projects
  • Host Career Nights - Direct and individualized access to students through company hosted career nights
The fee for basic membership is $10,000.

PREMIER MEMBERSHIP

Premier members receive the following additional benefits:
  • Center Access
    • Direct access to a specified research center including regular exchanges of research and information
    • Access to office space, computer facilities, research labs, library
  • Visiting Scholars – Partners may send a company researcher to join a faculty member’s research group for a period of time (up to 12 months). Includes auditing courses, attending research seminars.
  • Meetings & Workshops – Attend topic specific annual meeting where faculty, grad students and industry leaders present latest research findings.
The fee for premium membership is $25,000.

Contact Nancy Kim Yun at nancy.kim.yun@uci.edu or at 949-824-3088

Opportunities for Engagement »
  • Tech Talks
  • Information Sessions
  • Sponsor a Project Class
  • Dean’s Leadership Council
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  • Sponsored Internship Program
  • Tech Jobs
  • Butterworth Product Development Competition
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Bren school home > About > Visit the bren school
Visit ICS

Information and computer science buildings at UCI

We can offer virtual tours, pictorials and viewbooks but nothing will substitute for an in-person visit.

The Donald Bren School of Information and Computer Sciences (ICS) is the first and only computer science school in the University of California, offering visitors opportunities to attend academic lectures, interact with leading computer science researchers and recruit intelligent and out of the box thinking computer science students.

If you're interested in attending ICS and want to learn more about a particular degree program - even meet with faculty doing research that piques your interest - please call the Student Affairs Office at (949) 824-5156 and ask to speak to an undergraduate or graduate counselor.

Campus walking tours are offered almost every Monday through Friday. Please check the Tour Calendar for available times and to ensure that there is a tour offered on the day you wish to visit.

Directions to the Bren School
» From LAX (42 miles from UCI)
» From John Wayne (5 miles from UCI)
» From major freeways (405, 73, 55, 5)

Campus Map
» Campus map of UCI (PDF)
The Bren School Student Affairs Office has relocated to: Information and Computer Science, Suite 352 (building #302 on the campus map).

Parking
» UCI guest parking information

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http://www.ics.uci.edu/grad/courses/index graduate course listing @ the bren school of information and computer sciences

This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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Bren school home > Graduate > Courses
Graduate course listing

This is a tentative schedule of CompSci, Informatics, Networked Systems and Statistics courses that the Bren School is planning to offer.

Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change.

NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


Year
Level
Department
Core Classes for

Please select from the search criteria above.

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http://www.ics.uci.edu/grad/index Donald Bren School of Information and Computer Sciences - Office of Student Affairs
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Bren school home > Graduate
ICS Student Affairs Office

The primary focus of the Student Affairs Office is to assist students and faculty with University policies, procedures and requirements related to ICS academic programs. The graduate staff coordinates the graduate admissions process, fellowships and the graduate student review. It also handles the various forms and administrative functions relating to graduate students. 

Contact Information

Phone: (949) 824-5156

Fax: (949) 824-4163

Mailing Address: Donald Bren School Student Affairs, ICS Building, Suite 352, Irvine, CA 92697-3430

Office Hours:  Monday-Friday from 9 a.m.-12 p.m. and 1 p.m.-4 p.m.

Walk-In Hours: Monday-Friday from 1-4 p.m.

General Office E-mail: gcounsel@ics.uci.edu


Graduate Student Affairs Staff

Kristine Bolcer – Director of Student Affairs

Neha Rawal – Associate Director of Student Affairs

Andrea O'Donnell – Graduate Counselor

Julie Kennedy – Graduate Counselor

Karina Bocanegra – Graduate Counselor

Mare Stasik – Office Manager

Lumen Hwang – Instructional Support Manager

Tony Givargis – Associate Dean for Student Affairs
Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the ICS graduate program. Associate Dean Givargis holds weekly office hours in the Donald Bren School Student Affairs Office, ICS, Suite 352. Please call the SAO's front desk at (949) 824-5156 to find out his hours of availability.

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Bren school home > About
Facts and figures
BREN SCHOOL FACTS
  • Only computing-focused school in UC system
  • Founded in 1968 as Department of Information and Computer Science
  • Established in 2002 as an independent school, the Donald Bren School of Information and Computer Sciences (ICS)
RANKINGS
  • US News & World Report
    Bren School rated 29th among computer science graduate programs and 14th among public university programs (2014)
  • Microsoft Academic Search website ranking of authors and organizations by citations (as of March 11, 2014)
    UC Irvine 20th among U.S. universities in computer science. Some top subareas:
    - Human-computer interaction: 4th
    - Software engineering: 9th
    - Databases: 8th
BY THE NUMBERS
  • 2,831 students
  • 78 Faculty (full-time and lecturers)
  • 48 Staff
  • 8,000+ Alumni
  • Three departments: Computer Science, Informatics, Statistics
UNDERGRADUATE PROGRAM

2,366 undergraduate students in seven majors
  • Computer Science
  • Data Science (established 2015)
  • Informatics
  • Software Engineering (established 2012)
  • Computer Game Science (established 2010)
  • Business Information Management (joint with the Paul Merage School of Business)
  • Computer Science & Engineering (joint with the Henry Samueli School of Engineering)
GRADUATE PROGRAM

465 graduate students enrolled in Fall 2014-2015

  • Computer Science: Ph.D. 130 • M.S. 136
  • Informatics: Ph.D. 42 • M.S. 21
  • Software Engineering: Ph.D. 16 • M.S. 24
  • Statistics: Ph.D. 29 • M.S. 30
  • Networked Systems: Ph.D. 9 • M.S. 28

INFLUENTIAL FACULTY AND ALUMNI

Our faculty

  • 1 member of the National Academy of Engineering
  • 3 Fellows of the American Association for the Advancement of Science (AAAS)
  • 12 Fellows of the Association for Computing Machinery (ACM)
  • 6 Fellows of the Institute of Electrical and Electronics Engineers (IEEE)
  • 3 Fellows of the American Statistical Association (ASA)

Our alumni

  • Roy Fielding (B.S. '88, M.S. '93, Ph.D. '00) primary architect of HTTP/1.1
  • Paul Mockapetris (Ph.D. '82) inventor of the Domain Name System (DNS)
  • Larry Rowe (B.S. '70, PhD '76) professor emeritus, UC Berkeley
  • Barbara Kew (B.S. '75) CIO of Hill-Rom (medical technology)
  • Tim Kashani (B.S. '86) CEO of IT Mentors and Tony Award-winning
RESEARCH CENTERS
  • Intel Science and Technology Center for Social Computing
  • Institute for Genomics and Bioinformatics
  • Institute for Software Research
  • Institute for Virtual Environments and Computer Games
  • Center for Algorithms and Theory of Computation
  • Center for Emergency Response Technologies
  • Center for Machine Learning and Intelligent Systems
  • Laboratory for Ubiquitous Computing and Interaction 
  • Secure Computing and Networking Center
STUDENT ORGANIZATIONS
  • ACM (Association for Computing Machinery) - UC Irvine Chapter
  • Design Art and Technology Makerspace (DAT space)
  • Graduate Women in Computer Science (GWICS)
  • ICS Student Council (ICSSC)
  • Management Information Student Society (MAISS)
  • Women in Information and Computer Sciences (WICS)
  • Video Game Development Club (VGDC)
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Bren school home > Undergraduate > Degrees
ICS Majors

Degree Programs
Business Information Management

Catalogue

Website

Computer Game Science

Catalogue

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Computer Science

Catalogue

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Computer Science and Engineering

Catalogue

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Data Science

Catalogue

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Informatics

Catalogue

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Software Engineering

Catalogue

Website

Students must also complete University and General Education Requirements

 

» Changing Majors

Changing from one ICS major to another ICS major

  • Please watch this short video: How to Change Majors
  • Meeting with an academic counselor or peer academic advisor is recommended. They will help you determine the most efficient way for you to meet the degree requirements of your new intended major.
  • If you are eligible to change, submit a Change of Major Application online through your Student Access page.

Changing from a Major in another school to ICS

  • Please watch this short video: How to Change Majors
  • Visit the UCI Change of Major Criteria website for a listing of specific change of major guidelines for your intended major.
  • Meet with an ICS academic counselor or peer advisor as soon as possible to determine whether you are eligible to switch majors and to map out an academic plan.
  • Once you have met the Bren School of ICS change of major requirements:
  • Submit the Change of Major Application online
  • If you have over 120 units, an academic plan with intended graduate quarter and year must also be submitted.

Changing from ICS to a Major in another school

  • Visit the UCI Change of Major Criteria website for a listing of specific change of major guidelines for your intended major.
  • Meet with an academic counselor or peer advisor in the school of your intended major; they can help you create an academic plan that will help you meet your goals.


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ICS faculty member

Organized research programs provide a mechanism and organizational structure within which collective research activities can take place that are fundamentally different from those that occur normally within the schools and departments. They are intended to foster the development of short- and long-term research programs that span disciplines and academic units, thereby making it possible for faculty to acquire extramural resources for which they might not otherwise qualify. The following is a list of the research centers that are part of ICS:

ABRC - Ada Byron Research Center
ABRC studies and promotes diverse access to and participation in computer science, engineering, digital media and related information technology fields.

Calit2 - California Institute for Telecommunications and Information Technology
A multidisciplinary research institute in collaboration with UCSD, Calit2@UCI integrates academic research with industry experience to seek innovative IT approaches that will benefit society and ignite economic development.

CDT - Center for Digital Transformation
One of the world's leading think tanks on the impact of information technology on organizations and society and on the management of information technology.

Center for Algorithms and Theory of Computation
The goal of research in theoretical computer science is to produce results, supported by rigorous proof, about problems dealing with computers and their applications.

Center for Embedded Computer Systems
Conducting leading-edge interdisciplinary research in embedded systems, the center develops innovative design methodologies, and promote technology and knowledge transfer for the benefit of the individual and society.

Center for Machine Learning and Intelligent Systems
Addresses the challenges of the modern data-driven world, using computer algorithms to discover useful information from vast data archives.

Center for Social Computing
Conducts foundational research into the relationship between information technology and society. Based at UCI, the center brings together an interdisciplinary group of researchers from leading U.S. universities.

CERT - Center for Emergency Response Technologies
Works to radically transform the ability of responding organizations to gather, manage, use, and disseminate information within emergency response networks and to the general public. By using more robust information systems, response can focus on activities that have the highest potential to save lives and property.

COR - Center for Organizational Research
COR contributes to the development of organization theory by connecting scholars from many disciplines who bring their knowledge and methods to a common understanding of these issues.

CPCC - Center for Pervasive Communications and Computing
Dedicated to serving the vision of wearable computers with wireless connections that enable anyone to have continuous voice, video, and data connectivity.

Computational Vision Lab
The Computational Vision Lab focuses on understanding the information processing capabilities of biological visual systems and on developing computational systems for processing visual media.

IGB - Institute for Genomics and BioInformatics
Fostering innovative basic and applied research in genomics and BioInformatics, IGB works with established companies, start-ups, government agencies and standards bodies to develop and transition these technologies to widespread and practical application.

ISR - Institute for Software Research
Works toward advancing software and information technology through research partnerships and educating the next generation of software researchers and practitioners in advanced software technologies.

IVECG - Institute for Virtual Environments and Computer Games
Brings together researchers for the common goal of understanding and creating technology and applications that transform how we: see the world through immersive visualization and virtual tours; interact and socialize with global communities; communicate and collaborate with colleagues in virtual collaborative space; provide medical care and training to remote corners of the world; and educate all ages and populations using virtual environments.

LUCI - Laboratory for Ubiquitous Computing and Interaction
Addresses the entire range of research problems that arise from the ubiquitous computing vision: the design of novel devices, the structure of software systems, techniques for designing and building systems, patterns of interaction, and the cultural implications of ubiquitous computing.

Secure Systems and Software Laboratory
The Secure Systems and Software Laboratory at the University of California, Irvine.

SCONCE - Secure Computing and Networking Center
SCONCE focuses on research for protecting information and computing infrastructure with an emphasis in areas like applied cryptography, network security and information assurance.

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Bren school home > Faculty > Research areas
ICS Research Areas

student conducting researchCuriosity about the world and a commitment to solving problems are the passions that drive ICS faculty. Their research in the information and computer sciences are applicable to many scholarly and scientific fields. But our faculty don't do it alone, students work side-by-side with nationally renowned professors to advance knowledge and improve lives. Below is a list of ICS research areas:

Algorithms and Complexity
Bren School faculty members have made significant contributions to many topics in this field, including graph algorithms and graph drawing (computing with systems of pairwise interactions between objects such as web page links, protein interactions, or social networks) and computational geometry (computing with planar or spatial data).

Artificial Intelligence and Machine Learning
Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include computer vision, bioinformatics, constraint-based problem solving, text understanding, data mining and smart sensor networks.

Biomedical Informatics and Computational Biology
Involves the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Current areas of research at the Bren School include medical information access and knowledge representation for health-care guidelines.

Computer Architecture and Design
Develops methods and tools for ensuring the reliability and quality of complex, large-scale software systems. These methods and tools support the development, deployment, maintenance and evolution of complex software systems.

Computer Graphics and Visualization
Focuses on the field of visual computing that deals with generating/capturing, representing, rendering and interacting with synthetic and real-world images and video. We work on end-to-end solutions from capturing of images and geometry; representing large geometric, image, and video data sets; geometry and image processing; interactive access and rendering of large visual data sets; algorithms for building large area immersive displays for the presentation of visual content; and interation techniques in both small personal displays and in large displays for collaborative environments.

Computer-Supported Cooperative Work
Information technologies bring people together -- through social networking, through collaborative systems, through digital media, and through communications. Informatics has been a long-term leader in the study of social engagement through information systems. Topics include distance collaboration, workflow and process-based systems, multi-user gaming, and cultural engagements.

Computer Vision
Computer vision at UCI focuses on understanding the information processing capabilities of biological visual systems and on developing computational systems for processing visual media. Research spans both theoretical questions of perception and object representation as well as practical applications ranging from automated surveillance to biological image analysis. 

Databases and Data Mining
Focuses on research related to architectures, index structures, algorithms, models, and performance evaluation of a variety of next-generation databases and information systems and technologies for data mining.

Embedded Systems
Focuses on issues relating to embedded systems, a special-purpose system in which software and hardware computing elements are completely encapsulated by the device or environment it controls. Unlike a general-purpose computer, such as a personal computer, an embedded system performs pre-defined tasks, usually under very specific constraints (e.g, low power) and requirements (e.g., reliability). 

Environmental Informatics
Humanity is currently facing a range of significant environmental challenges such as global warming, species extinction, pollution, and overpopulation. Informatics tools and techniques can help facilitate responses to these challenges, and assist with planning for future environmental issues. 

Human Computer Interaction
HCI research at UCI stretches from the architecture of novel interactive systems to the social and cultural considerations of information technology adoption and use. We employ laboratory, ethnographic, and prototyping techniques to understand how people adopt, adapt, and respond to information systems. Recent research has investigated privacy issues in mobile systems, tangible interfaces for group awareness, interactive animation, and visualization of location information. 

Medical Informatics
This topic concerns the development and application of information systems to healthcare. Information systems have a critical role to play in contemporary health and wellness programs. This includes technology in hospital settings but also persuasive technologies for healthy living, health care in the home and in the community, and in the interactions between partners in the health care system. 

Multimedia Computing
Multimedia computing started receiving attention more than a decade ago.  Naturally, early systems dealt with very limited aspect of multimedia.  With progress in technology, several computing addresses important issues in creation, communication, storage, access, and presentation of information and experiences.  In our department, we are addressing research issues in fundamentals of multimedia systems and their advanced applications. 

Networks and Distributed Systems
Researchers investigate various issues in the design and analysis of high-speed networks for multimedia applications. They are actively involved in research on computer networks and distributed systems, with the goal of designing, analyzing and implementing communication systems that allow high-speed transport of multimedia information between end-users. 

Operating Systems 
The operating systems area at UCI embraces a wide range of topics related to theory and practice of computer systems software. Researchers here are building systems for reliable and efficient big data processing, mobile I/O virtualization, program analyses and various other applications.

Programming Languages and Systems
Systems software research at UCI has expanded to include topics such as program restructuring and transformation techniques for parallelization and distribution, compiler-assisted memory management, component-oriented languages and dynamic code optimization. 

Scientific and Numerical Computing
Refers to the application of computers to scientific problems, from astrophysics to zoology. The mode of application can be system modelling, data analysis and mining, or visualization. The focus can be on developing new computational techniques, such as parallel algorithms or new data mining ideas, or on the novel application of existing techniques to new scientific problems.

Security, Privacy and Cryptography
Bren School research in this area includes anonymity and authentication in network security, key agreement and digital signatures in cryptography, and security issues in electronic commerce.

Social Informatics
UC Irvine is an acknowledged center for the study of social informatics, which incorporates the social and cultural aspects of information technology development and use. Social informatics employs techniques and theories from social sciences and cultural studies to understand the shaping and applications of digital media and their organizational, political, historical, and economic contexts. This topic links information system analysis with design.

Software Engineering
Software research at UCI is aimed at creating new software technology and solutions, furthering the information revolution. The central goal of this research is improvement in software development, evolution, deployment, quality, understandability and cost-effectiveness.

Statistics and Statistical Theory
Researchers at UCI are concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistical principles and methods are important for addressing questions in public policy, medicine, industry and virtually every branch of science.

Ubiquitous Computing
Ubiquitous computing builds upon and unites virtually all of thfe current research strengths in the Bren School. Researchers are addressing issues such as context-aware computing, whereby mobile computing responds to one's current context.

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Bren school home > Undergraduate > Degrees
ICS Minors

A minor is an established set of courses which together take a student beyond the introductory level in an academic field, subject, and/or discipline. They are a way to pursue a focused study in an additional subject that complements your major, career goals, or is an area of personal interest.

A minor in the computer science field, for instance, can help prepare students for a profession, career, or academic pursuit in which computer science is an integral part but is not the primary focus.

 



ICS Minors
Bioinformatics Catalogue
Digital Information Systems Catalogue
Health Informatics Catalogue
Informatics Catalogue
Information and Computer Science Catalogue
Statistics Catalogue


» Adding/Declaring a Minor

Most minors, including all minors within Bren ICS, do not need to be officially declared. However, a few minors at UCI do have specific declaration procedures. Students should speak with a counselor or peer advisor in the School of their intended minor to clarify any questions about the process and requirements.

Some major/minor combinations are not allowed. This applies to some ICS programs, and is outlined in this Majors and Minors Restriction Chart.

To have the minor added to your degree audit, notify your academic counselor in the School of your major. To officially receive a minor, you must successfully pass all the requirements and you must list it on your Application for Graduation.

Important details to note:

Pass/Not Pass and Minor Requirements

  • No more than two courses may be taken P/NP and applied to any minor on campus.
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Bren school home > About
About ICS

Bren School group of faculty and students

By establishing the University of California's first computer science school in 2002, UC Irvine made an investment in the future that reflects its historical commitment to raising the bar of excellence.

From pioneering computer science courses more than three decades ago to the creation of the Donald Bren School of Information and Computer Sciences, UCI continues to be an institute that leads information technology education and research across the globe.

The Bren School began as a department in 1968. More than 35 years later, it was formally recognized as a school — and in June 2004, the School adopted benefactor Donald Bren's name in recognition of his generous contribution and visionary leadership.

As an independent school focused solely on the computer and information sciences, the Bren School has a unique perspective on the information technology disciplines that allows us a broad foundation from which to build educational programs and research initiatives that explore the many applications of the computing discipline — from circuits and systems to software engineering and human aspects of computing.

Building on our strong foundation of computer science fundamentals, the Bren School conducts cutting-edge research in strategic areas ideal for collaborative work.

By blending research with education in multiple disciplines, the Bren School is leading interdisciplinary efforts in order to meet the challenges of the future.

Our Mission

The Donald Bren School of Information and Computer Sciences aims for excellence in research and education.

Our mission is to lead the innovation of new information and computing technology by fundamental research in the core areas of information and computer sciences and cultivating authentic, cutting-edge research collaborations across the broad range of computing and information application domains as well as studying their economic, commercial and social significance. 

The diversity of our collaborations serves to reshape domains as far reaching as education, art and entertainment, business and law, the environment and biological systems, health care and medicine. 

Consistent with our mission, we are committed to ensuring excellence through inclusion, producing a diverse, educated workforce for advancing technology, stimulating the economy and transferring new technology into the public realm to greatly advance quality of life.

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  • Bren School students traditionally lead UCI with the highest average SAT and GRE scores
  • The Bren School offers six undergraduate degrees
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  • Bren gift included most endowments created by single gift to UC (10)
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Bren school home > Community > News > Noteworthy achievements
Noteworthy achievements

Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


WINTER 2016

Gary Olson, 2 ICS Alums receive SIGCHI honors

photo: Gary Olson

Gary
Olson

Informatics professor Gary Olson has received a Lifetime Service Award from the ACM Special Interest Group on Computer–Human Interaction (SIGCHI), as part of the group’s annual effort to recognize and honor leaders and shapers within the field of human-computer interaction.

According to the award website, recipients of the Lifetime Service Award are individuals who have contributed to the growth and success of SIGCHI in a variety of capacities over a number of years. Olson has worked in the human-computer interaction (HCI) field since 1983, when he and colleagues Judy Olson (a fellow informatics professor), Paul Green and Marilyn Mantei taught the first graduate course on the subject at the University of Michigan.

Olson’s contribution to HCI has largely revolved around the concept of distance. From the mid-1980s he and Judy Olson began researching the role technology plays in collaboration. The pair published their highly cited paper, “Distance Matters,” on the subject in 2000 and later authored “Working Together Apart.” Olson has long played an active role in SIGCHI, co-chairing and chairing numerous conferences, as well as award and steering committees. SIGCHI previously elected Olson to the CHI academy and, along with Judy Olson, awarded him a Lifetime Achievement Award in 2006.

In addition to Olson, two ICS alumni were also honored in this year’s round of awards. Leysia Palen, professor and founding chair of the newly established Department of Information Science at the University of Colorado Boulder, was elected to the CHI academy. She earned her Ph.D. in information and computer science in 1998. Daniel Russell, a senior research scientist at Google, was also elected to the CHI academy. He earned his B.S. in information and computer science in 1977 and has been recognized as a UC Irvine Lauds & Laurels Distinguished Alumnus.

 


Kobsa receives Mercator Fellowship

photo: Alfred Kobsa

Alfred
Kobsa

Informatics professor Alfred Kobsa has received a Mercator Fellowship from the German Research Foundation (DFG), the largest research funding organization in Germany. The Mercator fellowship will enable Kobsa—whose research focuses on the areas of user modeling and personalized systems, privacy, support for personal health maintenance, and information visualization—to participate in “intensive, long-term project-based collaboration between researchers from both domestic and foreign institutions,” according to the DFG. Throughout the duration of the fellowship, Kobsa will work both on-site at a German institution and continue his project collaboration here in Irvine. “Foreign Mercator Fellowship holders are awarded the title of Mercator Fellows in recognition of their dedication,” the DFG notes.

As the largest independent research funding organization in Germany, the DFG “promotes the advancement of science and the humanities by funding research projects, research [centers] and networks, and facilitating cooperation among researchers,” according to its website. It also joins major international funding counterparts like the National Science Foundation and the Royal Society as a member of the International Council for Science (ICSU).

 


FALL 2015

Franz named 2016 IEEE Fellow

photo: Michael Franz

Michael
Franz

The Institute of Electrical and Electronics Engineers (IEEE) has named Computer Science Professor Michael Franz a 2016 IEEE Fellow. Franz is being recognized by IEEE for his contributions to just-in-time compilation as well as his contributions to computer security through compiler-generated software diversity.

The IEEE Grade of Fellow is conferred by the IEEE Board of Directors upon a person with an outstanding record of accomplishments in any of the IEEE fields of interest. It is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement. The total number of fellows selected in any one year cannot exceed one-tenth of 1 percent of the total voting membership. “It is a great achievement receiving recognition from one's peers and being included among such a distinguished group of IEEE members," says Franz.

The IEEE is the world’s leading professional association for advancing technology for humanity with 400,000 members in 160 countries. Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards.

 


Tsudik elected to The Academy of Europe

photo: Gene Tsudik

Gene
Tsudik

Chancellor's Professor of Computer Science Gene Tsudik has been elected a member of The Academy of Europe (Academia Europaea), the organization dedicated to the “advancement and propagation of excellence in scholarship in the humanities, law, the economic, social, and political sciences, mathematics, medicine, and all branches of natural and technological sciences anywhere in the world for the public benefit and for the advancement of the education of the public of all ages in the aforesaid subjects in Europe,” according to the organization’s website.

Tsudik was elected to the computational and information science-focused section—dubbed the Informatics section—of the academy. Membership is by invitation only, with invitations made only after a peer group nomination and rigorous scrutiny of the eminence and scholarship of the potential member. Tsudik is the only United States-based member elected to the Informatics section in 2015, joining a total of 11 U.S. members in the 237 member total section.

The Academy of Europe endeavors to encourage the highest possible standards in scholarship, identifying topics of trans-European importance to science and scholarship, as well as making recommendations to national governments and international agencies concerning matters affecting science, scholarship and academic life in Europe. It counts among its members some of the foremost scholars in the world. Tsudik joins in the Informatics section eminent U.S.-based scholars like Victor Vianu, computer scientist and editor-in-chief of the Journal of the ACM, and Mihalis Yannakakis, professor of computer science at Columbia University and winner of the 2015 Donald E. Knuth prize—awarded to those who have made outstanding contributions to the foundations of computer science.

 


NSA grants Tsudik $286K for cybersecurity research

photo: Gene Tsudik

Gene
Tsudik

Chancellor's Professor of computer science Gene Tsudik has received a $286,000 grant from the National Security Agency (NSA) for his project “ERADS: Efficient Remote Attestation of Dynamic Swarms.”

As embedded devices—including automotive sensors or controllers, drones, household appliances, and factory automation components—proliferate into many aspects of everyday life, they also become targets for attacks. This project aims to develop techniques for detecting and mitigating malware infestations of networks consisting of a myriad of such embedded devices.

The NSA designates UC Irvine as a National Center of Academic Excellence (CAE), with a focus in Information Assurance Research. Institutions with CAE designations promote higher education in information assurance—the management of risks related to the use, processing, storage, and transmission of data—and cyber defense, while helping to meet the need to reduce vulnerabilities in the Nation’s networks. The grant comes out of the CAE cybersecurity research program.

 


Informatics Ph.D. student to present at ACM-DEV 2015

photo: Ankita Raturi

Ankita
Raturi

Informatics Ph.D. student Ankita Raturi received an ACM Women in Computing (ACM-W) scholarship to attend the ACM Symposium on Computing for Development (ACM DEV), held at the Queen Mary University of London in December. ACM-W provides scholarships to enable women in computer science to attend research conferences around the world.

At ACM DEV, Raturi will present on a paper she co-authored with current and former UC Irvine faculty Bill Tomlinson, Bonnie Nardi, Donald J. Patterson, Debra Richardson, Jean-Daniel Saphores and Dan Stokols. The paper, “Toward Alternative Decentralized Infrastructures,” looks at how we can build interfaces between infrastructures to improve robustness, reliability and resilience. “Enabling communities to transition to a more resilient configuration of infrastructures is crucial for establishing a distributed portfolio of processes and systems by which human needs may be met,” Raturi says.

This will be Raturi’s first time at the conference, “an ideal venue for this work to be presented,” Raturi says. The conference is a platform for “original and innovative work on the applications, technologies, architectures and protocols for computing in developing regions,” according to the ACM DEV website.

“Having the opportunity to present my work, engage with the community and learn from leading researchers in my field is a major part of my professional growth," Raturi says. "Discussing our work with experts who have been working on computing for development will be incredibly valuable.”

 


Ziv collaborates in groundbreaking NSF-funded privacy research

photo: Hadar Ziv

Hadar
Ziv

Informatics lecturer Hadar Ziv will be a research collaborator in a groundbreaking NSF-funded project titled “Privacy Compliance by Design: Ideation Techniques to Facilitate System Design Compliant with Privacy Laws and Regulations.”

The project attempts to bring privacy protection to the forefront of software developer’s minds in the wake of the explosion of big data. “Software professionals typically have no formal training or education on sociotechnical aspects of privacy. As a result, addressing privacy issues raised by a system is frequently an afterthought and/or a matter of compliance-check during the late phases of the system development lifecycle,” the project’s abstract explains. To tackle this challenge, the project’s research team will develop “privacy ideation cards” based on relevant U.S. laws and regulations, which “can potentially transform how privacy-relevant aspects are handled in real-world software solutions built by industry and inform how students are taught these issues in undergraduate software curricula.” The team includes Principal Investigator Sameer Patil from New York University, who received a $175,000 Early-concept Grant for Exploratory Research (EAGER) for the project, Ziv, Janice Tsai of Microsoft and Jonathan Fox of Intel.

In addition to the deck of privacy ideation cards, the project will promote privacy by design, making privacy protection a built-in framework for all software development. Ziv will connect the research team with students in his senior Capstone Informatics project course, “as a test-bed for ideas and presentations related to privacy,” Ziv says. “Their engagement will affect change in the students' projects. I will likely participate in collecting and analyzing data about those changes.”

 


Professor Tsudik Keynoting Two Conferences in November

photo: Gene Tsudik

Gene
Tsudik

Chancellor’s Professor of Computer Science Gene Tsudik is delivering two keynote addresses on “Secure and Private Proximity-Based Discovery of Common Factors in Social Networks” at conferences in November. First, on November 4, he will be speaking at the 9th International Conference on Network and System Security in New York City, before traveling to Sydney, Australia to speak at the 25th International Telecommunication Networks and Applications Conference on November 20.

 


Study reveals ICS degree-friendly jobs have the best work-life balance

photo: Glassdoor logo

Careers in data science, user experience design, web development and software engineering promote excellent work-life balance, according to a survey from Glassdoor, a job rankings website.

Glassdoor notes that, across the board, employee satisfaction with work-life balance has been declining in the past few years, but there are a number of careers that won’t leave employees working 24/7—many of these careers bolstered by skills learned at the Donald Bren School of Information and Computer Sciences (ICS).

Glassdoor analyzed feedback from around 60,000 company reviews to determine the top 25 careers where employees report balance between their personal lives and the workplace. Among the 25, 10 were careers in tech, including data scientist (#1), user experience (UX) designer (#7), web developer (#10), instructional designer (#14), software quality assurance (QA) engineer (#16), web designer (#17), data analyst (#20), solutions engineer (#22), software developer (#24), and front-end developer (#25).

ICS is well-placed to foster future careers in tech. As the only school focused on computer and information sciences in the University of California system, ICS offers undergraduate programs of study in business information management, computer game science, computer science, computer science and engineering, informatics, and software engineering. The newly established data science major is unique at the undergraduate level, equipping budding data scientists—Glassdoor’s career with the highest work-life balance—with the necessary combined skills in computing and statistics. The major is part of UC Irvine’s Data Science Initiative, a coordinated effort to bring together researchers and students across campus involved in various aspects of data science.

At the graduate level, students at ICS can pursue deeper educational opportunities in computer science, informatics, embedded systems, networked systems, software engineering, and statistics.

 


Tsudik part of panel at UCI-Nossaman Cybersecurity Symposium

photo: Gene Tsudik

Gene
Tsudik

Chancellor’s Professor of Computer Science Gene Tsudik took part in a panel at the 2015 UCI-Nossaman Cybersecurity Symposium at the City Club Los Angeles on Oct. 12. The symposium, titled “Cybersecurity, Data Breach and Privacy: A Dialogue on the Rising Risks and Evolving Legal Landscape,” was a joint effort by the UC Irvine School of Law and Nossaman LLP, a nationwide law firm that has made privacy and security one of its focus areas. The emphasis of the panel that Tsudik spoke on was “Not If, But When — Hack Offensives, Investigating Breaches, and Closing the Gaps on Data Leaks.”

 


Postdoctoral scholar Per Larsen recognized as “DARPA Riser”

photo: Per Larsen

Defense Advanced Research Projects Agency (DARPA) has recognized assistant project scientist in computer science Per Larsen as a “DARPA Riser.” The early-career honor is conferred to “up-and-coming standouts in their fields, capable of discovering and leveraging innovative opportunities for technological surprise—the heart of DARPA’s national security mission,” DARPA says.

Larsen, along with 54 other honorees from around the country, attended “Wait, What? A Future Technology Forum,” in September with special guest U.S. Secretary of Defense Ashton Carter (the gentleman on the left in the photo). The forum, which drew more than 1,200 participants from around the world, explored future technologies “on their potential to radically change how we live and work, and on the opportunities and challenges these technologies will raise within the broadly defined domain of national security,” according to the event website. Larsen was among a small subset of honorees who were treated to lunch with the U.S. Secretary of Defense.

“DARPA organized Wait, What? to bring together forward-looking thinkers across a host of fields that are abundant with possibilities,” DARPA Director Arati Prabhakar said in the event press release. “In particular, our DARPA Rising effort aimed to identify and inspire some of the nation’s emerging leaders in research and technology—so we at DARPA can learn from them, and to make them aware of opportunities to apply their expertise in the important domain of national security.”

Larsen works as a postdoctoral scholar with Computer Science Professor Michael Franz. His research interests include information security, including software diversity and exploits and mitigations; compilers, including profiling, randomization and control-flow integrity; and systems software, including interpreters and virtual machines.

 


Van der Hoek to speak at SCSIM Fall Event

photo: André van der Hoek

André
van der Hoek

Department of Informatics Chair André van der Hoek will be speaking at the Southern California Society for Information Management (SCSIM) Fall Event: “The Southern California Disruptors—How Startups and the New Innovation Culture in Southern California are affecting IT” on Sept. 30 at the Long Beach Marriott. As the head of the UCI Software Design and Collaboration Lab, van der Hoek is part of a three-person panel that will relate their applicable experiences crucial to participating in the new business environment developing around us.

 


SUMMER 2015

Franz amasses $3.9 million in research funding

photo: Michael Franz

Michael
Franz

This year alone, Computer Science Professor Michael Franz has accumulated over $3.9 million in research funding from prestigious organizations such as the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), Qualcomm, Oracle and Mozilla. This follows his trend of more than $1 million per year on average in research expenditures.

Franz currently runs two projects funded by DARPA’s Cyber Fault-Tolerant Attack Recovery (CFAR) Program, for which he received nearly $2 million and roughly $700,000 in May, respectively. The CFAR Program aims to “produce revolutionary breakthroughs in defensive cyber techniques that can be deployed to protect existing and planned software systems in both military and civilian contexts without requiring changes to the concept of operations of these systems,” according to a statement by program manager John Everett.

Franz also runs a project funded by DARPA’s Vetting Commodity IT Software and Firmware Program (VET), which addresses “the threat of hidden malicious functionality in COTS (Commercial Off-the-Shelf) IT devices ... including mobile phones, printers, computer workstations and many other everyday items,” according to a statement by program manager Timothy Fraser. He received nearly $65,000 for this project.

Finally, in July, Franz received nearly $620,000 from the NSF for a collaborative project titled “ENCORE—ENhanced program protection through COmpiler-REwriter cooperation.” According to the abstract, the project will produce “a prototype implementation consisting of a producer-side metadata derivation engine, and a consumer-side binary rewriting engine using this metadata to safely perform binary code manipulation.” In the past year, Franz has also received unrestricted gifts from Qualcomm, Oracle and Mozilla totaling $263,000.

 


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General information for ICS:

Donald Bren School of Information and Computer Sciences
University of California, Irvine
6210 Donald Bren Hall
Irvine, CA 92697-3425

Dean's Office phone:
(949) 824-7427
Dean's Office fax: (949) 824-3976

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Department Contacts:

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Undergraduate Programs

As the only computing-focused school in the University of California system, the Bren School offers a broad array of undergraduate majors and minors in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics.

We invite you to explore the links on this page, which feature our courses, majors, and policies, as well as opportunities for student life in ICS. If you need assistance with specific questions about the undergraduate student experience, please contact our Office of Student Affairs, whose information is listed below.

 

CONTACT INFORMATION
Telephone (949) 824-5156
Fax (949) 824-4163
Mailing Address Bren School Student Affairs
ICS Building, Suite 352
Irvine, CA 92697-3430
Office Hours Monday thru Friday
9:00am-12:00pm and 1:00pm-4:00pm
E-mail ucounsel@uci.edu

 
Questions submitted through e-mail will be reviewed by a peer academic advisor or undergraduate counselor.

  • Due to the volume of e-mails received, expect to wait a minimum of 2-3 business days for a response.
  • If your question is complex or time sensitive, we recommend that you come to walk-in hours or schedule an appointment.



UNDERGRADUATE STUDENT AFFAIRS STAFF
Kristine Bolcer

Director of Student Affairs

Neha Rawal

Associate Director of Student Affairs

Jessica Shanahan

Undergraduate Counselor

Michelle V. Nguyen

Undergraduate Counselor

Mare Stasik

Office Manager

Lumen Hwang

Instructional Support Manager


PEER ACADEMIC ADVISORS
Ariel Xiao

Peer Academic Advisor

Benjamin You

Peer Academic Advisor

Carrie Shen

Peer Academic Advisor


Most walk-ins during the Fall, Winter and Spring quarters will be with a Peer Academic Advisor. For more information on when appointments and walk-ins are available and how the types of meeting differ, please see the Appointments and Walk-In Advising page.

Peer counselors have completed intensive training from the Division of Undergraduate Education and the Bren School's SAO counselors. They are available for walk-in advising in the ICS Student Affairs Office (CS Building 1, Ste. 352) at the following times:

Winter 2016
Ariel Benjamin Carrie
Monday 2:30-4 - 1-4
Tuesday - 11-12
1-2
-
Wednesday 10:30-12 - 1-4
Thursday - 11-12
1-2
9-12
Friday 9:30-12
1-4
10-12
1-4
-




Career counseling is also available in the ICS Student Affairs office. Students can meet with Mark Carolino on a walk-in basis during the hours listed below:

Thursday: 1:00pm - 2:30pm*
Friday:

1:00pm - 2:30pm

* Except on scheduled Career Fair dates

 


» Associate Dean for Student Affairs, Tony Givargis

Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the Bren School's undergraduate program.

Associate Dean Givargis holds weekly office hours in the Bren School Student Affairs Office (SAO), ICS, Suite 352. Please call the SAO's front desk at (949)824-5156 to find out his hours of availability.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Academic Integrity

Every student should be familiar with the UCI Academic Senate Policies on Academic Honesty. This text is also duplicated in the appendix of the UCI General Catalogue. The policies outlined for the campus also apply within the Bren School.

(And if you are not familiar with UCI's Code of Student Conduct, which is another aspect of academic integrity, we would encourage you to also take the time to explore the website of the Office of Student Conduct!)

 



» Academically Honest Conduct

To be academically integrous means holding to values such as honesty, fairness, respect, and accountability in your scholastic pursuits. Students are expected to follow the rules and guidelines established by instructors for assignments and exams, and to accept responsibility for his or her own work. Examples of academically honest conduct are:

  • Turning in work done alone or with the help of the course's staff (instructor, teaching assistant, or reader).
  • Submission of one assignment for a group of students if group work is explicitly permitted (or required).
  • Getting or giving help on how to operate the computer or terminal.
  • Getting or giving help on how to eliminate minor syntax errors.
  • High-level discussion of course material for better comprehension.
  • Discussion of assignments with the instructor or TA to better understand what is being asked for.
  • Seeking help from course staff and/or other campus resources if you do not understand the material or are feeling overwhelmed by your courseload.

 

» Academically Dishonest Conduct

Actions associated with academic dishonesty include cheating, lying, plagiarizing, forging, and stealing. Examples of such behavior in the classroom are:

  • Turning in someone else's work as your own (with or without his or her knowledge). Submitting a completely duplicated assignment is a flagrant offense, but even copying only a portion of the assignment and turning it in as your own is considered cheating.
  • Allowing someone else to turn in your work as his or her own.
  • Several people writing one program and turning in multiple copies, all represented (implicitly or explicitly) as individual work.
  • Using any part of some else's work without proper acknowledgement. This is plagiarism.
  • Stealing an examination or a solution from the instructor. This is an extremely flagrant offense.

For instance, an example of program plagiarism would be if an assignment that calls for independent development and implementation of a program (assignment intent and specified ground rules) results in two or more solutions so similar that one can be converted to another by a mechanical transformation. Or, cheating might be suspected if a student who was to complete an assignment independently cannot explain both the intricacies of his or her solution and the techniques used to generate that solution.

Any case in which academic dishonesty is suspected is given careful, individual scrutiny. The intent of an assignment, the ground rules specified by the instructor, and the behavior of the student are all factors considered before a decision is made.

In the event that an instructor writes a letter accusing a student of academic dishonesty, the student may prepare a statement giving his or her side of the case for inclusion in the student's file.

 

» Penalties of Academic Dishonesty

  • A recorded incident of academic dishonesty may disqualify you for consideration for honors at graduation.
  • A first incident of academic dishonesty (if egregious) may be sufficient to cause suspension or dismissal from the University; a second incident likely will result in suspension or dismissal.
  • An incident of academic dishonesty is sufficient to cause denial of a petition to change major into the Bren School.
  • An incident of academic dishonesty may be sufficient to cause denial of admission into the Bren School Honors Program.
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Bren school home > Undergraduate
Graduation and Beyond

Congratulations to all 2014-15 ICS Honors and Awards recipients!

 

» Graduation Requirements

You must meet the following requirements:

  • University: completion of all University Requirements as outlined in the General Catalogue.
  • School and Major: completion of all courses required for your School and Major
  • Units: credit for a minimum of 180 quarter units.
  • GPA: 
    • 2.0 GPA cumulative
    • 2.0 GPA in your major
    • 2.0 GPA in the upper-division courses in your major.
  • Senior Residency: 36 of the final 45 units completed for the bachelor’s degree must be earned in residence at the UCI campus  (exceptions include courses taken through the Education Abroad Program, the UCDC Academic Internship Program, or the UC Center Sacramento Scholar Intern Program).

If you have any questions about requirements or your degree audit, please meet with a peer advisor or an academic counselor as soon as possible. You should be getting graduation checks at least twice a year as a senior.

Please be aware, however, that students are ultimately responsible for making sure they are on track and indeed meet all graduate requirements.

 

» Applying for Graduation

Graduation is not automatic! You must submit the online Application for Graduation, which is found on Student Access.

Review your degree audit carefully to make sure you will meet all University, Breadth, School and Major requirements. If there is a discrepancy or error, meet with a peer advisor immediately. Do not send an e-mail.

When you fill out the application, be sure to indicate minors, concentrations and specializations that you may be completing. Please also ensure that your diploma notification address and telephone number are correct.

Once the online Application has been submitted, only a Bren School academic counselor may make changes to it.

The Bren School's graduate application deadlines for 2014-15 are as follows. ALL courses must be completed by the end of the quarter in which you intend/apply to graduate.

Deadline Quarter Notes
September 21, 2015 Fall  
December 11, 2015 Winter If you intend to participate in the Commencement Ceremony
December 11, 2015 Winter If you DO NOT intend to participate in the Commencement Ceremony
December 11, 2015 Spring If you intend to participate in the Commencement Ceremony
March 11, 2016 Spring If you DO NOT intend to participate in the Commencement Ceremony
December 11, 2015 Summer If you intend to participate in the Commencement Ceremony
March 11, 2016 Summer If you DO NOT intend to participate in the Commencement Ceremony

NOTE: If you submit an application for graduation past the deadline listed for the quarter above, it will be moved to the following quarter.

 

» Commencement and Honors

Commencement: Visit the official website of the UCI Commencement Office for full information

Latin Honors: No more than 12% of  seniors graduating during an academic year (summer to spring) will receive Latin honors:

  • 1 percent summa cum laude
  • 3 percent magna cum laude
  • 8 percent cum laude
Selection is based on the School’s winter quarter, rank-ordered grade point averages. To be eligible for honors at graduation:
  • You must have an application to graduate on file by the first week of February of the academic year you intend to graduate (e.g. if you are graduating Spring 2010, the application must be submitted by February of that same year)
  • You must be able to verify completion of all course work by the end of spring of the academic year you intend to graduate
  • You must be officially declared as a Bren School of ICS major
  • Seventy-two (72) quarter units in residence at a UC campus must be completed by the end of winter quarter of the academic year you intend to graduate. 
  • Any corrections to your academic record must be processed by the Registrar's Office by March of the intended graduation year.

Note that latin honors designations are finalized after Spring grades are determined. Students who are eligible for latin honors based on Winter grades may see their designation go up, down, or off the latin honors scale.

Summer graduates participating in the June commencement ceremony prior to their date of graduation are not eligible for honors for that academic year; they will instead be considered for the following academic year.

 

» Transcript and Diploma

Degrees are posted to official transcripts approximately six to seven weeks after the end of the quarter in which they are conferred. 

Diplomas will be available approximately four months after the quarter in which the degree was awarded.  The Registrar’s Office will notify you by mail when your diploma is available. More information is available on the Registrar's website.

Upon graduation, academic records may not be changed. It is your responsibility to check the accuracy of your academic record, and to resolve all I and NR grades, before you graduate.


And After Graduation...
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Bren school home > Undergraduate > Student resources
Undergraduate Student Resources

Opportunities for ICS Students

As members of the UCI community, all ICS students have access to a wide range of campus resources (a partial list of which can be viewed here). In addition to these, there are also opportunities for engagement and enrichment geared specifically towards ICS students.

ICS students are encouraged to complement their academics with extra-curricular activities such as research or joining a student organization.  Benefits of this include, but are not limited to: networking with other students, faculty and companies; participating in social activities; forming study groups and friendships; attending various workshops and presentations; gaining deeper experience and knowledge of your chosen field; and more.

 



»
Campus Engagement
  • ICS student organizations
  • Other student organizations

Stay up to date with upcoming events organized by ICS student organizations on the official ICS Clubs Events calendar.

 

» ICS Theme Housing

The Arroyo Vista theme house provides informal, academic and social functions for students interested in the field of information and computer science. The goals of this house are to expose residents to faculty, alumni and businesspersons in the information and computer sciences field.

Activities include:

  • visits to corporations
  • opportunities to participate in symposiums and workshops centering on academic success
  • community and career opportunities
  • preparation for graduate/professional schools and learning skills
  • social activities

The house is open to all students with an interest in information and computer sciences.

 

» Honors Opportunities

Students may apply to be in the ICS Honors Program during Spring quarter if they meet certain academic criteria and have the support of faculty.  Benefits of being in the ICS Honors Program include networking with other ICS Honors students, conducting research with a faculty mentor, and working on a thesis.

  • Bren School of ICS Honors Program
  • Campuswide Honors Program

 

» Research Opportunities

All Bren ICS majors are encouraged to take advantage of this valuable experience. Faculty advertise many research opportunities every year.

  • Independent Study: Enroll in an independent study (199 course number) under a faculty advisor unit credit
  • Summer Undergraduate Research Internship in Computer Science:This program provides an opportunity for select students to spend 8-10 weeks in the summer working with faculty on a research project.
  • UC LEADS: This program offers educationally or economically disadvantaged sophomore students (or juniors planning on staying a complete fifth year) in science, technology, engineering or mathematics programs an opportunity to begin research training at the very beginning of their junior year
  • UROP: The Undergraduate Research Opportunities Program encourages and facilitates research and creative activities by undergraduates from all schools and academic disciplines at UCI. Programs include SURP, SURF-IT, MDP, and others.

 

» ICS Scholarships

To search for some available scholarships, students should visit the resources below.

  • ICS Scholarships
  • To search for more scholarships, visit the Financial Aid and Scholarships website.

 

» Jobs for Students

To search for available technical jobs and internships, students can visit the resources below.

  • Zotlink

Non-technical internships and opportunities are also available and listed here.

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Bren school home > Community > Scholarships and fellowships
Scholarships

The Bren School offers undergraduate students the following competitive scholarships and student awards.

The application period for scholarships has closed. Please check back in the Spring for more opportunities.

These awards are possible through the generous support of our community, industry friends, and ICS endowments.

These individuals and companies, through their commitment to higher education, play an active role in the future of information technology by helping deserving and highly competent students afford a quality education.

Learn more about sponsoring a scholarship or fellowship to support an ICS student's educational goal.


BOB & BARBARA KLEIST ENDOWED STUDENT AWARD IN ICS

OVERVIEW: The Bob and Barbara Kleist Endowment was established through the generous donation from Bob and Barbara Kleist themselves. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

AWARDS: 2 awards of $2,500 each

SELECTION CRITERIA:

  • Transfer students only
  • Academic excellence
  • Essay required

STEVE & JENNY MIZUSAWA ENDOWED STUDENT AWARD IN ICS

OVERVIEW: The Steve and Jenny Mizusawa Student Award Endowment was established in 2005 through the generous donation from Steve and Jenny themselves. This award is designated to support undergraduate juniors and seniors preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

AWARD: 1 award of $1,500

SELECTION CRITERIA:

  • Juniors and Seniors
  • Minimum ICS GPA of 3.0
  • Self nomination and/or faculty recommendation
  • Essay required

ACCENTURE ENDOWED OUTSTANDING JUNIOR AWARD IN ICS

OVERVIEW: The Accenture Endowed Outstanding Junior Award was established through and endowment in 1992 by Accenture to recognize and financially assist selected student recipients during their final year at UC Irvine.

AWARD: 1 award of $1,500

SELECTION CRITERIA:

  • 3.0 ICS GPA minimum
  • Graduate in the following academic year
  • 40% academic standing
  • 30% demonstrated leadership abilities
  • 30% civic and/or charitable involvement (eg. volunteerism with various charity or civic minded organizations or individuals efforts taken by the applicants)
  • Essay required

ESSIE LEV ENDOWED MEMORIAL STUDENT AWARD IN ICS

OVERVIEW:
The Essie Lev Endowed Memorial Student Award was established by Sara Sandel to honor her sister and former UC Irvine academic counselor Essie Lev. The award is designated for transfer or re-entering undergraduate students with demonstrated financial need. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

AWARDS: 2 awards of $1,250 each

SELECTION CRITERIA:

  • Transfer or re-entry students only
  • ICS majors
  • Demonstrated campus or community service
  • Essay required

JULIAN FELDMAN ENDOWED SCHOLARSHIP IN ICS

OVERVIEW:
The Julian Feldman Scholarship was established in 1998 by Jim Hobbs ’73 in honor of ICS Professor Emeritus Julian Feldman. The scholarship used to be funded through an annual gift made by Jim and Trinidad Hobbs. In 2006, the scholarship was turned into an Endowed Scholarship, now called, The Julian Feldman Endowed Scholarship in ICS, funded through Jim Hobbs’ annual gifts as well as gifts from ICS’ Annual Fund campaign.

AWARDS: 2 awards of $1,250 each

SELECTION CRITERIA:

  • High academic standing
  • Possess demonstrated leadership abilities
  • No essay required

KENNETH SIMMS ENDOWED MEMORIAL SCHOLARSHIP IN ICS

OVERVIEW: The Kenneth Simms Memorial Scholarship endowment was established in 1989 by Laguna Software to honor UCI alumnus and one of the most significant contributors to the development of the PICK Operating System, Kenneth Simms ’70.

AWARDS: 2 awards of $2,000 each

SELECTION CRITERIA:

  • Academic excellence
  • Recipient must be a U.S. citizen or a permanent resident
  • The recipient should be preparing for a profession in the field of computer science
  • The recipient is selected by the selection committe according to the guidelines
  • Demonstrated financial need
  • No essay required

SUMALEE JOHNSON TRANSFER STUDENT AWARD IN ICS

OVERVIEW: The Sumalee Johnson Transfer Student Award was established through the generous donation from ICS alumnus, Sumlalee Johnson '82. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

AWARD: 1 award of $2,000

SELECTION CRITERIA:

  • Transfer students only
  • 3.2 ICS GPA minimum
  • Essay required

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Bren school home > Community > News
ICS News

Welcome to the ICS news page! Below you'll find links to press releases, articles from national and local media featuring the school, as well as noteworthy achievements of our faculty, students and staff. Media interested in interviewing ICS faculty, students or alumni should contact Matt Miller at (949) 824-1562 or via email: communications@ics.uci.edu.

ICS News

Press Releases
Current listing and archive of press releases and media advisories. 

Videos
Visit our YouTube page to view a wide range of videos, from student life, to innovative research and alumni profiles.

Features
Stories about our faculty, students, alumni, staff and programs.

Noteworthy Achievements
Collection of news briefs about faculty or student awards, accolades and recognition.

In the News
Articles in global, national, local and campus media that feature or mention the Bren School, our students or faculty members.

Annual Reports
Check out our current and past issues of the ICS Annual Report.

BrenBits
A quarterly e-newsletter with news and event updates. 

Stay Connected
Subscribe to the ICS RSS feed to get news delivered directly to your desktop. 

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Bren school home > Faculty > Research highlights
Research highlights

Every day, our faculty and students make important research contributions that bring real and positive change to people worldwide. Below are examples of the life-changing work conducted at the Bren School of ICS.

Adaptive Software and Hardware for Distributed, Networked Embedded SystemsAlgorithmic LivingApplying Machine Learning and Advanced Computation to CancerAsterixDB: Big Data Management 2.0CloudProtectCollaboration Success WizardFault ComprehensionGene Regulation Network (GRN) InferenceMachine Learning for Text and Social Network Data over TimeModeling Languages for Computational Biology and AIModeling and Simulation of Biologically-Realistic Brain NetworksMulti-tasking in the WorkplaceRobust and Flexible Statistical Models for Environmental Epidemiology StudiesSatwareSherlockSimilarity-based Program AnalysisSoftware Engineering for SustainabilityStatistical Models for Brain ConnectivityTechnologies for Autism: Activity CoachThe I-Sensorium ProjectTrust in Software Development TeamsValues in DesignWebRTC Benchmarking and Optimization

Adaptive Software and Hardware for Distributed, Networked Embedded Systems

The project investigates adaptive strategies to deal with dynamic application environments for distributed, networked embedded systems, covering diverse applications such as the Internet-of-Things low-power mobile systems.

Department of Computer Science

Algorithmic Living

All of the data collected about us is used to make decisions about what sort of products and financial offers we receive. How does this manifest in our self-image, and what are the historical and legal implications?

Department of Informatics

Applying Machine Learning and Advanced Computation to Cancer

In this project, researchers have identified an elusive pocket on the surface of the p53 protein that can be targeted by cancer-fighting drugs.

Department of Computer Science

AsterixDB: Big Data Management 2.0

AsterixDB is a full-function, open-source, next-generation Big Data Management System that is designed to scale to very large shared clusters.

Department of Computer Science

CloudProtect

CloudProtect seeks to develop a middleware so users can employ encryption methods to control risk of data exposure in Cloud-based applications.

Department of Computer Science

Collaboration Success Wizard

This online diagnostic survey looks at how to improve collaborations on projects where workers are spread out geographically. The survey probes factors that may strengthen or weaken the collaboration and provides reports that help build productive collaborations.

Department of Informatics

Fault Comprehension

When software fails it must be "debugged," a largely manual process that determines why the program failed. This project is creating tools and visualizations that give an automated diagnosis to help software developers create effective and efficient software.

Department of Informatics

Gene Regulation Network (GRN) Inference

In collaboration with UCI biologists, researchers will use their pioneering GRN mathematical models along with machine learning-based inference methods to computationally understand the gene-regulation network that determines early endoderm patterning in vertebrates.

Department of Computer Science

Machine Learning for Text and Social Network Data over Time

Researchers are developing new statistical machine learning algorithms that can automatically extract useful information from both large bodies of and large social network data sets.

Department of Computer Science

Modeling Languages for Computational Biology and AI

This project seeks to exploit useful overlap between formally defined scientific modeling languages researchers have developed for computational biology, including machine-learning techniques for model reduction.

Department of Computer Science

Modeling and Simulation of Biologically-Realistic Brain Networks

In collaboration with neuroscientists, this project develops modeling and simulation frameworks for exploring large spiking neural networks, with a specific focus on visually guided motion perception, learning, tracking and autonomous navigation.

Department of Computer Science

Multi-tasking in the Workplace

Modern technology has helped create workplaces filled with interruptions, influencing work flow and productivity.  This project studies information workers to figure out ways of mitigating the impact of constant interruptions.

Department of Informatics

Robust and Flexible Statistical Models for Environmental Epidemiology Studies

Researchers are developing new statistical methods to flexibly estimate the association between spatially and/or temporally correlated environmental exposures and the risk of clinical outcomes in humans.

Department of Statistics

Satware

This scalable data collection, querying and analysis technology allows for the creation of situational awareness applications from across diverse, multimodal sensors and data sources.

Department of Computer Science

Sherlock

A pay-as-you-go data cleaning framework, Sherlock can improve data quality in (near) real-time application contexts.

Department of Computer Science

Similarity-based Program Analysis

This project explores the idea of using "program similarity" to find better heuristics that require a lot less time and effort to find a solution. Program similarity can be defined in a number of ways and part of this research is seeking to find intrinsic program characteristics that can be used for this purpose.

Department of Computer Science

Software Engineering for Sustainability

There is growing awareness that living within the Earth's means is crucial to humanity. This sustainability has been focused on engineering advances to reduce waste and energy use. But information and computing technology could also play a key role. This project seeks to understand the potential of information technology in helping people make smarter decisions and ways in which to make IT software more sustainable.

Department of Informatics

Statistical Models for Brain Connectivity

Under the context of decision making, this project seeks to develop novel statistical approaches for identifying brain connectivity features from high dimensional multimodal imaging spatio-temporal data that can predict human behavior.

Department of Statistics

Technologies for Autism: Activity Coach

For many people living with autism spectrum disorder, remembering detailed schedules can be a challenge. A well planned day can be disrupted by a late bus or cancelled work. ActivityCoach, a mobile application, provides support that adapts to changing schedules.

Department of Informatics

The I-Sensorium Project

Parts of the UCI campus are equipped with a variety of experimental sensing, networking, storage and computing technologies to convert the campus into a "living laboratory." Data from I-Sensorium infrastructure is driving experimental research on pervasive systems, mobile computing, situation awareness, big data management, data streams, multimodal event detection and privacy.

Department of Computer Science

Trust in Software Development Teams

Teams that are productive tend to have a great amount of trust among the team members. In virtual teams, trust is difficult to establish. This project explores how trust is formed among team members, and it's creating tools to help teams that are spread out geographically develop trust.

Department of Informatics

Values in Design

This project seeks to understand the personal and cultural values we design into our technologies, and how these technologies allow us to express our values. For example, how do we balance values of efficiency and security with a need for friendliness and fun when we design a new electronic marketplace?

Department of Informatics

WebRTC Benchmarking and Optimization

WebRTC is an exciting new standard being developed to provide real-time communication capabilities between browsers. It is a major component of the HTML5, the newest version of HTML protocol under development today. This project develops methodology and software for performing multi-platform, browser-independent performance benchmarking. The benchmarking results will be used to optimize and improve WebRTC performance on mobile devices.

Department of Computer Science

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Bren school home > Undergraduate > Courses
Undergraduate course listing

This is a tentative schedule of CompSci, CSE, ICS, Informatics and Statistics courses that the Bren School is planning to offer.

Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change. Please note that some of the upper division core courses will NOT be offered every quarter.

If you need help with your course planning, please schedule an appointment with an academic counselor. They can also provide information about proposed course offerings for summer sessions, which are not included in this list.

NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


Year
Level
Department
Core Classes for

Please select from the search criteria above.

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Bren school home > Undergraduate
Undergraduate Programs

As the only computing-focused school in the University of California system, the Bren School offers a broad array of undergraduate majors and minors in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics.

We invite you to explore the links on this page, which feature our courses, majors, and policies, as well as opportunities for student life in ICS. If you need assistance with specific questions about the undergraduate student experience, please contact our Office of Student Affairs, whose information is listed below.

 

CONTACT INFORMATION
Telephone (949) 824-5156
Fax (949) 824-4163
Mailing Address Bren School Student Affairs
ICS Building, Suite 352
Irvine, CA 92697-3430
Office Hours Monday thru Friday
9:00am-12:00pm and 1:00pm-4:00pm
E-mail ucounsel@uci.edu

 
Questions submitted through e-mail will be reviewed by a peer academic advisor or undergraduate counselor.

  • Due to the volume of e-mails received, expect to wait a minimum of 2-3 business days for a response.
  • If your question is complex or time sensitive, we recommend that you come to walk-in hours or schedule an appointment.



UNDERGRADUATE STUDENT AFFAIRS STAFF
Kristine Bolcer

Director of Student Affairs

Neha Rawal

Associate Director of Student Affairs

Jessica Shanahan

Undergraduate Counselor

Michelle V. Nguyen

Undergraduate Counselor

Mare Stasik

Office Manager

Lumen Hwang

Instructional Support Manager


PEER ACADEMIC ADVISORS
Ariel Xiao

Peer Academic Advisor

Benjamin You

Peer Academic Advisor

Carrie Shen

Peer Academic Advisor


Most walk-ins during the Fall, Winter and Spring quarters will be with a Peer Academic Advisor. For more information on when appointments and walk-ins are available and how the types of meeting differ, please see the Appointments and Walk-In Advising page.

Peer counselors have completed intensive training from the Division of Undergraduate Education and the Bren School's SAO counselors. They are available for walk-in advising in the ICS Student Affairs Office (CS Building 1, Ste. 352) at the following times:

Winter 2016
Ariel Benjamin Carrie
Monday 2:30-4 - 1-4
Tuesday - 11-12
1-2
-
Wednesday 10:30-12 - 1-4
Thursday - 11-12
1-2
9-12
Friday 9:30-12
1-4
10-12
1-4
-




Career counseling is also available in the ICS Student Affairs office. Students can meet with Mark Carolino on a walk-in basis during the hours listed below:

Thursday: 1:00pm - 2:30pm*
Friday:

1:00pm - 2:30pm

* Except on scheduled Career Fair dates

 


» Associate Dean for Student Affairs, Tony Givargis

Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the Bren School's undergraduate program.

Associate Dean Givargis holds weekly office hours in the Bren School Student Affairs Office (SAO), ICS, Suite 352. Please call the SAO's front desk at (949)824-5156 to find out his hours of availability.

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Bren school home > Undergraduate > Academic advising
Appointments and Walk-In Advising

Academic advising is available during regular office hours through appointments scheduled in advance, or same day walk-in meetings when counseling staff is available. A comparsion of the two types of meetings, as well as a timeline of when they are available, is below.


Appointments Walk-Ins
Appointments are with an Academic Counselor Can be with an Academic Counselor or a Peer Academic Counselor
Scheduled at least 24 hours in advance in person or by calling the ICS Student Affairs front desk at (949) 824-5156 Students sign in and are seen on the same day
Generally lasts 30 minutes Generally 15 minutes or shorter
Recommended for complicated concerns regarding probation, withdrawal, readmission, etc. Ideal for schedule planning, degree checks, questions about policies, petitions, etc.
May require a waiting period of a week or more as appointment slots can fill far in advance Meetings are available on short notice (wait times will vary)

 

 

 

 

 

 

 

 

 

 

 

 

Appointment and Walk-In Availability Reference

Appointments Walk-ins
Week 1 NO Yes
Week 2 NO Yes
Week 3 Yes Yes
Week 4 Yes Yes
Week 5 Yes Yes
Week 6 Yes Yes
Week 7 Yes Yes
Week 8 Yes Yes
Week 9 Yes Yes
Week 10 NO Yes
Finals Week NO Yes
Winter/Spring Break Yes NO
Summer Yes

Limited*

* Walk-in availability during the summer is limited and may vary from week to week. If you are driving long distance, please call in advance for more information about the walk-in schedule for the day or week of your planned visit.

  • Be aware that there is usually a wait to do a walk-in, especially during Weeks 1-2, and Weeks 8-10.
  • Walk-ins are not available during the Winter and Spring breaks.
  • Walk-ins can be with a Staff Counselor or a Peer Academic Counselor.
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Bren school home > Undergraduate > Degrees
Second Baccalaureate Degrees

For students whose first degree was from another institution other than UC Irvine:

Applications will be accepted for the Fall quarter only. Applications must be submitted during the filing period in order to be considered for Fall admission. Detailed information on the application procedure can be found on the Office of Admissions and Relations with Schools website.

For students whose first degree was completed at UC Irvine:

Applications will be accepted for any quarter. Former UC Irvine students must meet with an academic counselor to discuss admission for the second baccalaureate. Former UCI students will be admitted via the campus' readmission policy. Students must meet with an ICS academic counselor to process the readmission application. All interested individuals must adhere to the readmission deadlines that are set by the Registrar's Office.

 


 

» ICS Second Baccalaureate Degrees

  • Computer Science
  • Informatics
  • Computer Science and Engineering
  • Software Engineering

» Tips for Second Baccalaureate Applicants

Applications cannot be considered or activated until all required coursework is completed and relevant transcripts are received.

Required coursework may be completed at UCI or with equivalent courses taken at an accredited institution. Courses taken at a four-year or two-year institution can be examined by UC Irvine faculty through a petition process to determine equivalency. In order to determine equivalency, a course syllabus/outline with a course description, weekly topics, and a textbook (if necessary) must be submitted to syllabi@ics.uci.edu

Courses may be taken at UCI through ACCESS UCI via University Extension. For more information about ACCESS and the process, contact an ICS academic counselor.

Courses taken at universities other than local community colleges should be evaluated for equivalency before submitting your application to ensure you have completed all relevant coursework. Articulation agreements between California community colleges and the Bren School of ICS can be found at www.assist.org.

Grade point averages as stated in the policy are required. If you complete several math and computer science courses and find you are not earning high enough grades, you may need to reconsider your decision to apply for a second baccalaureate degree in ICS. You may decide not to finish the required course work if you will not be able to get into ICS. The academic counselors in ICS are available to discuss your chances of being admitted to the program and to help you to identify other options.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Academic Honesty

Every student should be familiar with the UCI Academic Senate Policies on Academic Honesty. This text is also duplicated in the appendix of the UCI General Catalogue. The policies outlined for the campus also apply within the Bren School.

(And if you are not familiar with UCI's Code of Student Conduct, which is another aspect of academic integrity, we would encourage you to also take the time to explore the website of the Office of Student Conduct!)

 



» Academically Honest Conduct

To be academically integrous means holding to values such as honesty, fairness, respect, and accountability in your scholastic pursuits. Students are expected to follow the rules and guidelines established by instructors for assignments and exams, and to accept responsibility for his or her own work. Examples of academically honest conduct are:

  • Turning in work done alone or with the help of the course's staff (instructor, teaching assistant, or reader).
  • Submission of one assignment for a group of students if group work is explicitly permitted (or required).
  • Getting or giving help on how to operate the computer or terminal.
  • Getting or giving help on how to eliminate minor syntax errors.
  • High-level discussion of course material for better comprehension.
  • Discussion of assignments with the instructor or TA to better understand what is being asked for.
  • Seeking help from course staff and/or other campus resources if you do not understand the material or are feeling overwhelmed by your courseload.

 

» Academically Dishonest Conduct

Actions associated with academic dishonesty include cheating, lying, plagiarizing, forging, and stealing. Examples of such behavior in the classroom are:

  • Turning in someone else's work as your own (with or without his or her knowledge). Submitting a completely duplicated assignment is a flagrant offense, but even copying only a portion of the assignment and turning it in as your own is considered cheating.
  • Allowing someone else to turn in your work as his or her own.
  • Several people writing one program and turning in multiple copies, all represented (implicitly or explicitly) as individual work.
  • Using any part of some else's work without proper acknowledgement. This is plagiarism.
  • Stealing an examination or a solution from the instructor. This is an extremely flagrant offense.

For instance, an example of program plagiarism would be if an assignment that calls for independent development and implementation of a program (assignment intent and specified ground rules) results in two or more solutions so similar that one can be converted to another by a mechanical transformation. Or, cheating might be suspected if a student who was to complete an assignment independently cannot explain both the intricacies of his or her solution and the techniques used to generate that solution.

Any case in which academic dishonesty is suspected is given careful, individual scrutiny. The intent of an assignment, the ground rules specified by the instructor, and the behavior of the student are all factors considered before a decision is made.

In the event that an instructor writes a letter accusing a student of academic dishonesty, the student may prepare a statement giving his or her side of the case for inclusion in the student's file.

 

» Penalties of Academic Dishonesty

    • A recorded incident of academic dishonesty may disqualify you for consideration for honors at graduation.
    • A first incident of academic dishonesty (if egregious) may be sufficient to cause suspension or dismissal from the University; a second incident likely will result in suspension or dismissal.
    • An incident of academic dishonesty is sufficient to cause denial of a petition to change major into the Bren School.
    • An incident of academic dishonesty may be sufficient to cause denial of admission into the Bren School Honors Program.
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Academic Standing

Students' academic status is reviewed on a quarterly basis. Academic standing is determined by GPA and degree progress. To stay in good standing:

  • Maintain a quarterly and cumulative GPA above 2.0, and
  • Make progress towards your chosen degree

The Student Affairs Office will notify the student of their academic probation status via their UCI email account.  The letter will include an academic probation contract.  The contract will outline specific conditions that must be met in order to return to good academic standing.

It is the student’s responsibility to:

  • manage their UCI account
  • to read and respond to official messages from their academic counselors in a timely manner.
  • monitor academic progress and maintain good academic standing.

Failure to follow through on the steps outlined in your academic probation letter will result in an administrative hold on the student’s record.

Student should contact their academic counselor soon after receiving the academic probation notification to ensure their upcoming course load is reasonable and to learn about helpful campus resources.

 


 

» Academic Probation

Students are subject to probation any time their quarterly or cumulative GPA falls below 2.0, or if they have violated one or more terms of the academic probation contract they are currently on.

**If a student’s term or cumulative GPA falls below a 1.5, the student will be dismissed from the Donald Bren School of ICS effective immediately.  This policy applies to full-time and part-time students.

» Academic Probation Stages

Academic Probation 1

-AP 1 results if the student’s term or cumulative GPA falls below a 2.0.

-Student will be placed on an academic probation contract for the following quarter.

Academic Probation 2 / Subject to Disqualification

-AP 2 results if the student is on two consecutive quarters of academic probation, or has not met one or more terms of the academic probation 1 contract. 

-Student is subject to disqualification

-Student is given the opportunity to appeal the possible / pending disqualification status

*Please note that being subject to disqualification is NOT the same as being formally disqualified

» Submitting a Letter of Appeal

A letter of appeal should explain any unusual or extenuating circumstances that contributed to the student's academic difficulties.  It must also detail the student's plans and actions for effectively addressing these circumstances (including the use of campus resources) so student can return to good academic standing.

The Associate Dean will carefully review the student's appeal letter, transcript and academic file with the academic counselors before making a decision.  Note that the Associate Dean for Student Affairs is the chief academic officer for the Bren School of ICS, and his decisions regarding disqualification appeals are final.

If the appeal is granted, the student will be held to an academic contract that outlines the expectations and timeframe for returning to good academic standing.

If the appeal is denied, the student will be formally disqualified from the major, the Bren School of ICS, and UC Irvine.  

*Instructions on how to appeal, including deadline and where to send the letter will be indicated in the student’s academic probation letter.

» Disqualification

If a student is formally disqualified, he or she will be restricted from registering for courses or using university services after a stated date. The Registrar's Office will also be instructed to note the effective date of the disqualification on the student's official transcript.  An official letter will be sent to the student's permanent address on file.

It is recommended for the student to see an academic counselor to learn about the readmission process and policy.

» Readmission

If a student has been formally disqualified, the student is encouraged to make an appointment to speak with an academic counselor to learn about the readmission policy and to strategize future plans and goals. Please visit the page on Withdrawal and Readmission for additional information.

If / when the student readmits to ICS after having been academically disqualified, the student must for the first quarter of return earn a 2.0 term GPA and Cs or higher in each course.  Failure to meet these terms may result in academic probation or being subject to disqualification.

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Bren school home > Undergraduate > Academic advising
Academic Advising

It is the student's responsibility to make decisions about his/her educational and career goals.

Academic counselors are here to help students work towards those goals, and academic advising is an intentional partnership between the student and his/her advisor(s) in which the shared goal is the student’s academic success.

Below is information on when, why, and how often to see academic advising, whether you come during walk-in hours or for an appointment.

 


 

» Meet with an Academic Advisor

  • Whenever you have a question about your academic standing, progress, or goals
  • Once a quarter, or at least once a year, to make sure you are on track
  • Three quarters before your intended graduation date

 

» Some Benefits of Consistent Academic Advising Meetings

  • Be confident that you are on track for graduation
  • Receive assistance with short- and long-term goal setting
  • Get help with planning and selecting courses to meet your academic and career goals
  • Obtain referrals to and advice about campus resources and opportunities
  • Gain clarification on University and School policies and procedures

 

» While Meeting with an Advisor

  • Arrive on time (if meeting during a scheduled appointment)
  • Come prepared with questions and concerns
  • Honestly discuss your goals, interests, and priorities
  • Keep an open mind to consider the options presented to you
  • Don’t be afraid to ask questions if you don't understand something

 

» What it is a Study Plan, and Why Should You Have One?

A Study Plan maps out of the courses and/or units you would need to take in the upcoming academic year(s) in order to make progress on your degree requirements.

  • It presents a bird's eye view of your course load and potential academic commitments over the year
  • It allows you to make quick adjustments to your schedule and plan ahead
  • It can help reduce stress when enrolling for classes

 

» Tips for Building a Study Plan

  • Know what classes you need to take. These include courses that satisfy University and School Requirements, as well as any core courses required for your major.
  • Make special note of any required classes that are only offered once a year or once every other year, or must be taken in a specific order.
  • Pay attention to course prerequisites, and know which ones you have cleared. If you need to submit a prerequisite clearing request, take care of it in advance.
  • Use the tentative academic year course plan when mapping out your year. But keep in mind that this course plan is tentative, so some of the elective classes you were hoping to take may be cancelled or conflict with each other.
  • Be prepared to be flexible!
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Bren school home > Undergraduate > Academic advising
Academic Advising

It is the student's responsibility to make decisions about his/her educational and career goals.

Academic counselors are here to help students work towards those goals, and academic advising is an intentional partnership between the student and his/her advisor(s) in which the shared goal is the student’s academic success.

Below is information on when, why, and how often to see academic advising, whether you come during walk-in hours or for an appointment.

 


 

» Meet with an Academic Advisor

  • Whenever you have a question about your academic standing, progress, or goals
  • Once a quarter, or at least once a year, to make sure you are on track
  • Three quarters before your intended graduation date

 

» Some Benefits of Consistent Academic Advising Meetings

  • Be confident that you are on track for graduation
  • Receive assistance with short- and long-term goal setting
  • Get help with planning and selecting courses to meet your academic and career goals
  • Obtain referrals to and advice about campus resources and opportunities
  • Gain clarification on University and School policies and procedures

 

» While Meeting with an Advisor

  • Arrive on time (if meeting during a scheduled appointment)
  • Come prepared with questions and concerns
  • Honestly discuss your goals, interests, and priorities
  • Keep an open mind to consider the options presented to you
  • Don’t be afraid to ask questions if you don't understand something

 

» What it is a Study Plan, and Why Should You Have One?

A Study Plan maps out of the courses and/or units you would need to take in the upcoming academic year(s) in order to make progress on your degree requirements.

  • It presents a bird's eye view of your course load and potential academic commitments over the year
  • It allows you to make quick adjustments to your schedule and plan ahead
  • It can help reduce stress when enrolling for classes

 

» Tips for Building a Study Plan

  • Know what classes you need to take. These include courses that satisfy University and School Requirements, as well as any core courses required for your major.
  • Make special note of any required classes that are only offered once a year or once every other year, or must be taken in a specific order.
  • Pay attention to course prerequisites, and know which ones you have cleared. If you need to submit a prerequisite clearing request, take care of it in advance.
  • Use the tentative academic year course plan when mapping out your year. But keep in mind that this course plan is tentative, so some of the elective classes you were hoping to take may be cancelled or conflict with each other.
  • Be prepared to be flexible!
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Bren school home > Undergraduate > Student resources
Undergraduate Student Resources

Opportunities for ICS Students

As members of the UCI community, all ICS students have access to a wide range of campus resources (a partial list of which can be viewed here). In addition to these, there are also opportunities for engagement and enrichment geared specifically towards ICS students.

ICS students are encouraged to complement their academics with extra-curricular activities such as research or joining a student organization.  Benefits of this include, but are not limited to: networking with other students, faculty and companies; participating in social activities; forming study groups and friendships; attending various workshops and presentations; gaining deeper experience and knowledge of your chosen field; and more.

 



»
Campus Engagement
  • ICS student organizations
  • Other student organizations

Stay up to date with upcoming events organized by ICS student organizations on the official ICS Clubs Events calendar.

 

» ICS Theme Housing

The Arroyo Vista theme house provides informal, academic and social functions for students interested in the field of information and computer science. The goals of this house are to expose residents to faculty, alumni and businesspersons in the information and computer sciences field.

Activities include:

  • visits to corporations
  • opportunities to participate in symposiums and workshops centering on academic success
  • community and career opportunities
  • preparation for graduate/professional schools and learning skills
  • social activities

The house is open to all students with an interest in information and computer sciences.

 

» Honors Opportunities

Students may apply to be in the ICS Honors Program during Spring quarter if they meet certain academic criteria and have the support of faculty.  Benefits of being in the ICS Honors Program include networking with other ICS Honors students, conducting research with a faculty mentor, and working on a thesis.

  • Bren School of ICS Honors Program
  • Campuswide Honors Program

 

» Research Opportunities

All Bren ICS majors are encouraged to take advantage of this valuable experience. Faculty advertise many research opportunities every year.

  • Independent Study: Enroll in an independent study (199 course number) under a faculty advisor unit credit
  • Summer Undergraduate Research Internship in Computer Science:This program provides an opportunity for select students to spend 8-10 weeks in the summer working with faculty on a research project.
  • UC LEADS: This program offers educationally or economically disadvantaged sophomore students (or juniors planning on staying a complete fifth year) in science, technology, engineering or mathematics programs an opportunity to begin research training at the very beginning of their junior year
  • UROP: The Undergraduate Research Opportunities Program encourages and facilitates research and creative activities by undergraduates from all schools and academic disciplines at UCI. Programs include SURP, SURF-IT, MDP, and others.

 

» ICS Scholarships

To search for some available scholarships, students should visit the resources below.

  • ICS Scholarships
  • To search for more scholarships, visit the Financial Aid and Scholarships website.

 

» Jobs for Students

To search for available technical jobs and internships, students can visit the resources below.

  • Zotlink

Non-technical internships and opportunities are also available and listed here.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Grading Policies

All Bren School and Major requirements must be taken for a letter grade unless the required course is designated as “P/NP Only”.

 


 

» Calculating your GPA

The Official UC GPA is calculated by dividing the total number of Grade Points by the total number of Attempted UC Units.

Letter Grade Grade Points
A 4 points per unit
B 3 points per unit
C 2 points per unit
D 1 points per unit
F and I (incomplete) 0 points per unit

A+ grade is assigned 4 points per unit.

Plus or minus suffixes modify the above by plus or minus 0.3 grade point per  unit. E.g., B+ is 3.3 points per unit, D- is 0.7 points per unit.

Non-letter grades (P, NP, I, etc.) are excluded in calculating GPA.


 

 

 

 

 

 

 

 

 

 

Example: A student takes the following courses and receives the grades:

Course Units Grade Received Grade Points Total Grade Points

Math

4

A

4.0

16.0

Writing

4

B-

2.7

10.8

Science

4

C+

2.3

9.2

Art

4

P

0.0

0

 

 

 

 

 

 

 

             Total Grade Points (36.0)                           = 3.0 GPA

Total Units Passed with Letter Grade (12)

 

» Pass/No Pass

The P/NP grading option may be used for courses that count toward the unit requirement for the B.A. degree, and toward the General Education Requirement.

No more than two P/NP courses may be applied to any minor on campus.

  • An average of 4 units may be taken P/NP per quarter
  • No more than 12 units TOTAL may be applied towards graduation requirements during your undergraduate career.
  • “Pass” is equal to grade of C or better.
  •  “Not Pass” is equal to a grade of C- or below.

Detailed information can be found in the General Catalogue.

 

» Illegal Duplication of Credit

Undergraduate and graduate students may not receive unit credit or earn grade points for college courses in which the content duplicates material of a previously completed course or examination for which the student has been granted college credit.

 

» Repeating Deficient Grades

A course may be repeated only when grades of C-, D+, D, D-, F, or NP were received. Unit credit for courses so repeated will be given only once.

The grade assigned at each enrollment shall be permanently recorded.

Only the most recently received grades and grade points shall be used for the first 16 units repeated when calculating GPA. (The grade point average is based on all additional grades assigned in cases of further course repetitions.)

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Laptop Requirement and Computer Use

Beginning in Fall 2014, students enrolling in ICS 31/CSE 41, ICS 32/CSE 42, and ICS 33/CSE 43 are required to own a personal laptop for coursework.

» If You're Planning to Get a New Laptop

Students receiving financial aid in the fall can request an additional one-time budget increase of up to $2,000 for a new computer. The additional funding available is limited to student and/or parent loans, depending on eligibility and borrowing limits. This allowance covers hardware, software, monitor, printer, and extended warranty.

Students are encouraged to visit or contact the Office of Financial Aid and Scholarships, 102 Aldrich Hall, by email at finaid@uci.edu, or phone at (949) 824-8262, for details on the budget increase process and limitations.

» Recommended Computer Specifications

  • RAM: 4 GB or higher
  • Hard drive: 250 GB or more
  • Processor: Intel Core i5 or i7, or similar AMD
  • Weight: Students are expected to bring their laptops to class so please consider weight if this is a factor for you.
  • Screen: When writing/debugging programs, more screen space tends to be very useful.
  • Warranty: Extended hardware warranty is recommended.

*If you have a laptop that was purchased within the last 4 years, the system should be adequate for lower division work. Your computer should be running an operating system which will receive security updates and should be minimum 2 GB of RAM and have sufficient hard drive space to install required programs.

**Recommended, but not required:

  • Laptop cable and lock
  • External monitor, keyboard and mouse
  • USB drive or external hard drive for data back-ups
  • Small black & white laser printer (Printers for use are available throughout campus; nominal fee to print)

If you need to connect from off-campus to UCI's network, check out the UCI Libraries' page on this topic to get set up.

 


 

» Computer Use Policies

All ICS students should make themselves familiar with the Bren school's policies governing computer use. Be sure to review the information provided in the below pages, as well as on the pages maintained by ICS Computing Support.

  • Account allocation and Backups
  • Copyright Infringement
  • Ethical Use of Computing
  • Remote Access
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  • » Account
    • » New User Guide
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New User Guide

Account Information

  • Account activation
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Accessing Your Account

  • Mapping your home directory
    • Windows
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  • Logging onto Unix/Linux host

ICS E-mail

  • ICS Gmail (grad, faculty, staff)
  • Change e-mail settings (fowarding, spam settings, etc)
  • Reading e-mail via Thunderbird

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Withdrawal and Readmission

Students who are planning to withdraw from the University are encouraged to seek advice from an academic counselor to discuss the withdrawal and readmission process.

 


 

» Withdrawing from UCI

If the decision to withdraw is made after tuition and fees for an academic quarter are paid, a Withdrawal form is required. The form can be picked up in any academic advising office (including the ICS Student Affairs Office), as well as from the Registrar's Office. Bren Students must obtain the signature of the Associate Dean for Student Affairs before submitting the form to the Registrar's Office for processing.  Please note that an ICS academic counselor may sign on the Associate Dean for Student Affairs' behalf.

Information on the schedule of refunds can be found on the University Registrar website.

If the decision to withdraw is made before tuitions and fees for the next quarter are paid, a formal notice of withdrawal or withdrawal form is not necessary, although recommended.

 

» Readmitting to UCI

Readmission to UCI and the Bren School of ICS is NOT automatic.  There are different policies and processes for readmission based on the student's reasons for and time of withdrawal.

If a student is seeking to readmit after voluntary withdrawal from UCI while in good standing, readmission will be granted if the student re-enrolls within one year. After more than one year, it will be considred on a case by case basis. The Associate Dean for Student Affairs will make the decision to readmit a student based on additional documentation, including the following:

  • number of completed units while at UCI
  • number of units remaining for completion of general education and degree requirements
  • grades for coursework taken at other institutions or through ACCESS UCI
  • demonstrated readiness for the academic challenge of UCI coursework.

All materials required for readmission petition must be submitted as a complete package. An academic counselor will attach a summary of previous course work and/or contract conditions for the Associate Dean’s consideration.

There are additional readmission requirements if a student withdrew while on academic probation or after academic disqualification:

For Readmission After Voluntary Withdrawal from UCI While on Academic Probation

For Readmission After Academic Disqualification

 

» Other Readmission Info

Students must meet with an ICS academic counselor well in advance of the quarter in which the student plans on readmitting. Readmission applications are processed by ICS academic counselors.

For information about fees, procedures and deadlines, visit the Registrar’s website on readmission.

Note that only UC-transferable courses will be considered when determining a student's eligibility for readmission to one of the Bren School majors.

A readmitted student who has not been enrolled at UCI for three or more consecutive quarters must adhere to the graduation requirements in effect for the quarter in which the student is readmitted, OR those subsequently established.

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Bren school home > Undergraduate
Graduation and Beyond

Congratulations to all 2014-15 ICS Honors and Awards recipients!

 

» Graduation Requirements

You must meet the following requirements:

  • University: completion of all University Requirements as outlined in the General Catalogue.
  • School and Major: completion of all courses required for your School and Major
  • Units: credit for a minimum of 180 quarter units.
  • GPA: 
    • 2.0 GPA cumulative
    • 2.0 GPA in your major
    • 2.0 GPA in the upper-division courses in your major.
  • Senior Residency: 36 of the final 45 units completed for the bachelor’s degree must be earned in residence at the UCI campus  (exceptions include courses taken through the Education Abroad Program, the UCDC Academic Internship Program, or the UC Center Sacramento Scholar Intern Program).

If you have any questions about requirements or your degree audit, please meet with a peer advisor or an academic counselor as soon as possible. You should be getting graduation checks at least twice a year as a senior.

Please be aware, however, that students are ultimately responsible for making sure they are on track and indeed meet all graduate requirements.

 

» Applying for Graduation

Graduation is not automatic! You must submit the online Application for Graduation, which is found on Student Access.

Review your degree audit carefully to make sure you will meet all University, Breadth, School and Major requirements. If there is a discrepancy or error, meet with a peer advisor immediately. Do not send an e-mail.

When you fill out the application, be sure to indicate minors, concentrations and specializations that you may be completing. Please also ensure that your diploma notification address and telephone number are correct.

Once the online Application has been submitted, only a Bren School academic counselor may make changes to it.

The Bren School's graduate application deadlines for 2014-15 are as follows. ALL courses must be completed by the end of the quarter in which you intend/apply to graduate.

Deadline Quarter Notes
September 21, 2015 Fall  
December 11, 2015 Winter If you intend to participate in the Commencement Ceremony
December 11, 2015 Winter If you DO NOT intend to participate in the Commencement Ceremony
December 11, 2015 Spring If you intend to participate in the Commencement Ceremony
March 11, 2016 Spring If you DO NOT intend to participate in the Commencement Ceremony
December 11, 2015 Summer If you intend to participate in the Commencement Ceremony
March 11, 2016 Summer If you DO NOT intend to participate in the Commencement Ceremony

NOTE: If you submit an application for graduation past the deadline listed for the quarter above, it will be moved to the following quarter.

 

» Commencement and Honors

Commencement: Visit the official website of the UCI Commencement Office for full information

Latin Honors: No more than 12% of  seniors graduating during an academic year (summer to spring) will receive Latin honors:

  • 1 percent summa cum laude
  • 3 percent magna cum laude
  • 8 percent cum laude
Selection is based on the School’s winter quarter, rank-ordered grade point averages. To be eligible for honors at graduation:
  • You must have an application to graduate on file by the first week of February of the academic year you intend to graduate (e.g. if you are graduating Spring 2010, the application must be submitted by February of that same year)
  • You must be able to verify completion of all course work by the end of spring of the academic year you intend to graduate
  • You must be officially declared as a Bren School of ICS major
  • Seventy-two (72) quarter units in residence at a UC campus must be completed by the end of winter quarter of the academic year you intend to graduate. 
  • Any corrections to your academic record must be processed by the Registrar's Office by March of the intended graduation year.

Note that latin honors designations are finalized after Spring grades are determined. Students who are eligible for latin honors based on Winter grades may see their designation go up, down, or off the latin honors scale.

Summer graduates participating in the June commencement ceremony prior to their date of graduation are not eligible for honors for that academic year; they will instead be considered for the following academic year.

 

» Transcript and Diploma

Degrees are posted to official transcripts approximately six to seven weeks after the end of the quarter in which they are conferred. 

Diplomas will be available approximately four months after the quarter in which the degree was awarded.  The Registrar’s Office will notify you by mail when your diploma is available. More information is available on the Registrar's website.

Upon graduation, academic records may not be changed. It is your responsibility to check the accuracy of your academic record, and to resolve all I and NR grades, before you graduate.


And After Graduation...
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Bren school home > Undergraduate
ICS Petitions

 

Petition Purpose
Undergraduate Student Petition
  • Course substitution or waiver
  • Request credit for major or general education courses taken at a college for which there is no articulation agreement
  • Waive residence requirement
  • Request exception to an administrative decision, policy or deadline
Note: Supporting documentation may be required
Online Prerequisite Clearing Request

Course prerequisite(s) were satisfied via any of the following:

  • AP exam credit
  • Transfer school credit for a course prerequisite
  • Access UCI credit for a course prerequisite
  • Course credit by student petition for a course prerequisite

Part-Time Study

(paper form available from Registrar's Office or ICS Student Affairs)

If you meet one of these conditions: 

  • Work 20 or more hours a week*
  • Have health problems
  • Have significant family responsibilities
*Documentation required

Request Excess Units

(Download the Undergraduate Student Petition)

  • Open to continuing students with the most recent quarterly and cumulative GPA of 3.0 or higher
  • Excess units are not intended to increase waitlist options. Only submit requests if you plan on taking more than four classes
  • Requests for over 20 units must detail which courses you intend to take and why
  • Petitions are subject to review and final approval by the Associate Dean

 

» The "Dean's Signature"

The Dean's Signature may be obtained at the Bren ICS Student Affairs Office.

Counselors are authorized to provide Dean's review and signature in all instances involving LATE add/drop/change of grading option requests done via the on-line Enrollment Exceptions system, and on other student-related forms issued by the Registrar's Office or other administrative offices.

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Bren school home > Undergraduate > Degrees
Double Majoring
» Steps for adding another major
  1. REVIEW the degree requirements for the major you want to add.
  2. DRAFT a four- or five-year study plan of the courses you will need to complete for both degrees. Expect this plan to evolve as class offerings will change from year to year! Even with changes, you will still have a better idea of what classes you will be taking than if you had just taken them without any forethought. Note that a study plan is mandatory if you are: over 90 units (not including units acquired outside of UCI), a transfer student, OR entering your third quarter.
  3. MEET with a Peer Academic Advisor or academic counselor to discuss how to most efficiently meet the additional degree requirements— Don't forget to bring your study plan! If you are going to double major in two different Schools, it is advised you meet with a counselor in each School.
  4. COMPLETE the Change of Major criteria for the major you want to add
  5. SUBMIT the application through Student Access (under Applications > Change of Major)

 

Also keep in mind:

  • some ICS major combinations are not allowed; check the Double Major Restrictions Chart to see what degree programs are eligible for double majoring.
  • ICS students may not add a double major during his or her senior year at UCI. Students will be directed to consider a Second Baccalaureate program in Computer Science, CSE, Informatics or Software Engineering.
  • some majors (e.g., Biology) require that you graduate within four years regardless of single or double majors. If you are hoping to double major with degrees from another School and ICS, you may wish to do an ICS Second Baccalaureate instead.
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Bren school home > Undergraduate > Degrees
ICS Minors

A minor is an established set of courses which together take a student beyond the introductory level in an academic field, subject, and/or discipline. They are a way to pursue a focused study in an additional subject that complements your major, career goals, or is an area of personal interest.

A minor in the computer science field, for instance, can help prepare students for a profession, career, or academic pursuit in which computer science is an integral part but is not the primary focus.

 



ICS Minors
Bioinformatics Catalogue
Digital Information Systems Catalogue
Health Informatics Catalogue
Informatics Catalogue
Information and Computer Science Catalogue
Statistics Catalogue


» Adding/Declaring a Minor

Most minors, including all minors within Bren ICS, do not need to be officially declared. However, a few minors at UCI do have specific declaration procedures. Students should speak with a counselor or peer advisor in the School of their intended minor to clarify any questions about the process and requirements.

Some major/minor combinations are not allowed. This applies to some ICS programs, and is outlined in this Majors and Minors Restriction Chart.

To have the minor added to your degree audit, notify your academic counselor in the School of your major. To officially receive a minor, you must successfully pass all the requirements and you must list it on your Application for Graduation.

Important details to note:

Pass/Not Pass and Minor Requirements

  • No more than two courses may be taken P/NP and applied to any minor on campus.
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Add, Drop, and Change Options

The Bren School strictly follows the campus policy for adding, dropping, and changing grade option or variable units for its courses at both the undergraduate and graduate levels.

However, instructors will sometimes set earlier add or drop deadlines, so it is important to read the syllabus for each class carefully at the start of the quarter. It is the student's responsibility to be aware of deadlines and make the modifications to his or her schedule before those deadlines pass.

 


» Add, Drop, and Change Deadlines

Always check the course syllabus carefully for course specific add and drop information. If earlier add/drop deadliness are required by the instructor, enrollment will be controlled by the instructor with authorization codes.

The deadline to add or drop courses and to change grade option or variable units is 5:00pm on Friday of Week 2.

Once these deadlines have passed, an Enrollment Exception request must be submitted through Student Access.

 

Timeline of Enrollment Changes
Week Enrollment Actions Notes
1 Use WebReg to add, drop, and change grade option or variable units. No fee applies.
2 Use WebReg to add, drop, and change grade option or variable units. The deadline to add, drop or to change grade option or variable units is 5:00pm on Friday.
3-6 An enrollment exception request must be submitted for adds, drops and changes to grade option or variable units. Supporting documentation is required for all exception requests. A $3 transaction fee will apply if the request is approved.
7-10 An enrollment exception request must be submitted for adds, drops and changes to grade option or variable units. Supporting documentation is required for all exception requests. A $3 transaction fee will apply if the request is approved. The "W" notation on transcript is applied for dropped courses.

 

» Submitting Enrollment Exception Requests

Requests to add or drop are reviewed by the academic advising office of the school offering the course, as well as the school of the student's major (if different). Requests to change grade option or variable units require the approval of the academic advising office of the student's major.

Enrollment exceptions for adds, drops, and change of grade option or variable units in ICS are not guaranteed. Exception requests will only be approved for extenuating and documented circumstances outside of the student's control. It is therefore important to continue attending class meetings and keep up with the assignments until your request is fully processed.

Requests will be denied if submitted for the following reasons:

  • You did notknow the deadlines
  • You did not understand the Add/Drop process
  • You did not know the grade requirements for your degree
  • The course is not required to meet Major, School, or University requirements
  • You are doing poorly in the course because of difficulties with the course material
  • You are doing poorly in the course because of a heavy course/work load
  • You are doing poorly in the course because of failure to attend

If your drop request is approved, please let your professor know as a courtesy once you have formally dropped the course.

 

» Course Conflicts

Time conflicts between courses may be approved on a case-by-case basis. Note that a lecture to lecture conflict will not be approved under any circumstances. If a course's lab or discussion time conflicts with another course's lecture, lab, or discussion, AND there are open spaces in both conflicting sections, follow these steps:

  1. Enroll in one of the conflicting courses (if the conflict involves a lecture, be sure to enroll in the lecture).
  2. Obtain written approval from both instructors to allow the conflict.
  3. Forward that approval to ucounsel@uci.edu. Be sure to provide the 5-digit course codes for the conflicting courses.
  4. Allow ICS Student Affairs 2-3 business days to process the request. You will be notified by email when it has been reviewed.
  5. If approved, use WebReg to enroll in the conflicting section.
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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Taking Courses Outside of UCI 

Continuing Bren School students may petition to take up to four approved, UC-transferable, lower-division courses—excepting writing classes—at a community college or other university.

 


 

» Finding Transferable Courses

Consult www.assist.org to find courses that are UC-transferable.

It is extremely important to consult with a Bren School academic counselor prior to taking off-campus courses, and especially if you are near or in your last year prior to graduation.

A counselor will review your list of proposed courses to:

  • ensure that they will count towards your general education or major requirements
  • ensure that students do not compromise your ability to meet the UCI residence requirement.

 

» Transferring Credit

Students must submit an official transcript in order to have any course taken outside of UCI applied to their degree audit. 

For courses taken at another 2- or 4-year institution, send official transcripts to the Office of Admissions and Relations with Schools.

For courses taken through UCI Extension or Access UCI, send transcripts to the Office of the Registrar.

Note: If you are a graduating senior, it is strongly recommended that you submit your transcripts as soon as grades are posted, and inform the appropriate the appropriate office(s) of that submission.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies
Undergraduate Student Policies
Academic Honesty
Academic Standing
Adds, drops, & change of grade option
Computer Account Allocation and Backups
Copyright Infringement
Coursework outside UCI
Ethical Use of Computing
Grade Policy
Laptop and computer use
Remote Computer Access
Withdrawal/Readmission

* Other policies important for students to know include the Non-Discrimination Policy Statements, Americans with Disabilities Act, and Jeanne Clery Act. It is recommended that students be familiar with the rules and regulations that govern students at UCI as outlined in the UCI General Catalogue.

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http://www.ics.uci.edu/about/bren/index.php about the bren gift @ the bren school of information and computer sciences
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Bren school home > About > Bren gift
About the Bren gift

The Donald Bren School of Information and Computer Sciences was made possible by the generous $20 million gift from Orange County business leader and philanthropist Donald Bren, chairman of The Irvine Company.

Mr. Bren's gift equals the largest single gift ever to UC Irvine and has created the most endowed faculty positions at one time on any University of California campus.

In recognition of this transformational gift, the school, renowned worldwide for leading the innovation of new information and computing technologies and producing an educated work force to fuel the economic engine, was renamed the Donald Bren School of Information and Computer Sciences at UC Irvine.

In conjunction with the June 9, 2004, naming announcement, the school also broke ground on its new home, Donald Bren Hall; the first classes were held there in January 2007.

Competitively positioned among other top-ranked schools, this gift allows the school of information and computer sciences to continue in its upward trajectory of recruiting and retaining the best and the brightest students, faculty and researchers.

The school of information and computer sciences has long been a program that combines vision and determination with wisdom. Those attributes, in concert with Mr. Bren’s support, will continue to allow its students, alumni and faculty to impact the world.

Elevated to school status in December 2002, the nationally acclaimed school is the first independent computer science school within the UC system and one of the fastest-growing programs of its kind in the nation.

Please explore these special pages for details of the gift and an up-close look at the Donald Bren School of Information and Computer Sciences. 

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Bren school home > About > iTunes U
iTunes U

link to the bren school on itunes uiTunes U is a free service provided by the Bren School and Apple that provides easy access to Bren School educational content, including lectures and interviews from such distinguished individuals as Marvin Minsky, John Seely Brown and Larry Rowe.

Through iTunes U, users can download content to their Macs or PCs regardless of their location. They can then listen to and view content on their Mac or PC or transfer that content to iPod for listening or viewing on the go.

In order to access the Bren School iTunes U content, you will need to have iTunes installed on your computer. Visit the apple website for the free download of iTunes.

Access the Bren School on iTunes U »


Turn us on @ YouTube
We also encourage you to visit our visit our YouTube page page and watch videos focusing on the life changing research, cutting edge computer science education and the history of the the University of California's first and only information and computer science school. more »

 

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http://www.ics.uci.edu/grad/index.php Donald Bren School of Information and Computer Sciences - Office of Student Affairs
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Bren school home > Graduate
ICS Student Affairs Office

The primary focus of the Student Affairs Office is to assist students and faculty with University policies, procedures and requirements related to ICS academic programs. The graduate staff coordinates the graduate admissions process, fellowships and the graduate student review. It also handles the various forms and administrative functions relating to graduate students. 

Contact Information

Phone: (949) 824-5156

Fax: (949) 824-4163

Mailing Address: Donald Bren School Student Affairs, ICS Building, Suite 352, Irvine, CA 92697-3430

Office Hours:  Monday-Friday from 9 a.m.-12 p.m. and 1 p.m.-4 p.m.

Walk-In Hours: Monday-Friday from 1-4 p.m.

General Office E-mail: gcounsel@ics.uci.edu


Graduate Student Affairs Staff

Kristine Bolcer – Director of Student Affairs

Neha Rawal – Associate Director of Student Affairs

Andrea O'Donnell – Graduate Counselor

Julie Kennedy – Graduate Counselor

Karina Bocanegra – Graduate Counselor

Mare Stasik – Office Manager

Lumen Hwang – Instructional Support Manager

Tony Givargis – Associate Dean for Student Affairs
Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the ICS graduate program. Associate Dean Givargis holds weekly office hours in the Donald Bren School Student Affairs Office, ICS, Suite 352. Please call the SAO's front desk at (949) 824-5156 to find out his hours of availability.

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http://www.ics.uci.edu/about/about_meet_the_dean.php Meet the Dean @ Donald Bren School of Information and Computer Sciences
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Bren school home > About
Meet the Dean
Hal S. Stern
Hal S. Stern, Ph.D.
Ted and Janice Smith Family Foundation Dean
Professor of Statistics

Hal Stern, professor of statistics and dean of the Donald Bren School of Information and Computer Sciences, leads the school in its mission of providing computer science and information technology leadership for the 21st century through research and development of emerging technologies; collaborations that address societal concerns; and innovative and broad curricula.

Stern came to UC Irvine in 2002 as the founding chair of the Department of Statistics. That department has since grown to include nine faculty and about 40 graduate students who are enrolled in its M.S./Ph.D. programs. In 2010, Stern was named the Ted and Janice Smith Family Foundation Dean of the Donald Bren School. Prior to joining UCI, he was a professor of statistics and the Laurence H. Baker Chair in Biological Statistics at Iowa State University’s Department of Statistics. He also previously served on the faculty at Harvard University.

Within the field of statistics, Stern is known for his research work in Bayesian statistical methodology and model assessment techniques. He has authored more than 100 publications (over 80 of which were refereed) and is a co-author of the highly regarded graduate-level statistics text Bayesian Data Analysis. The hallmark of his work is interdisciplinary research collaboration wherein modern statistical methodology is developed to address needs that arise from ongoing scientific research in a variety of fields. Over the years, this has included applications in the social sciences, biological/health sciences, physical sciences and sports. Stern is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is extremely active in the statistics community, having recently served as editor of the American Statistical Association’s premier journal and previously serving as editor of Chance magazine. He has also served on several expert committees for the U.S. National Academies.

Stern received a B.S. degree in mathematics from the Massachusetts Institute of Technology in 1981, and his M.S. and Ph.D. degrees in statistics from Stanford University in 1985 and 1987, respectively.

Contact Dean Stern at icsdean@ics.uci.edu


Download a copy of Dean Stern’s Bio here.

Download high-resolution photos of Dean Stern:
Hal S. Stern - Bren Hall Hal S. Stern

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http://www.ics.uci.edu/about/about_mission.php bren ics mission @ the bren school of information and computer sciences
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Bren school home > About
Bren ICS mission

photo:: students collaboratingThe Donald Bren School of Information and Computer Sciences aims for excellence in research and education.

Our mission is to lead the innovation of new information and computing technology by fundamental research in the core areas of information and computer sciences and cultivating authentic, cutting-edge research collaborations across the broad range of computing and information application domains as well as studying their economic, commercial and social significance.

The diversity of our collaborations serves to reshape domains as far reaching as education, art and entertainment, business and law, the environment and biological systems, health care and medicine.

Consistent with our mission, we are committed to ensuring excellence through inclusion, producing a diverse, educated workforce for advancing technology, stimulating the economy and transferring new technology into the public realm to greatly advance quality of life.

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Safety procedures

UCI Preparedness & Emergency Response

University of California, Irvine Emergency Management

Safety at UC Irvine

  1. Emergency Preparedness
    1. Emergency Preparedness (1.46 MB, PDF)
    2. Assembly Areas
    3. Zone 3 Safety Response Team
    4. Areas of responsibilities/duties
      • Zone Captains
      • Building Coordinators
      • Floor Wardens

  2. General Safety
    1. General Safety at Home (1.73 MB, PDF)
    2. What to do in an Emergency
    3. Emergency Phone Numbers
    4. Campus Emergency Phone Numbers
    5. What to do in a Medical Emergency (PDF)
    6. Obtaining Medical Care for work related Injuries
    7. Reporting Unsafe Conditions
    8. Hazardous Materials (1 MB, PDF)
    9. Emergency Procedures: Fires, Chemical Spills, Releases, Accidents (56 KB, PDF)
    10. Ergonomics
      • Computer Workstation Ergonomic Assessment Tools
      • EHS general information and responsibilities
      • Request an Ergonomic Evaluation

  3. Laboratory Safety at UCI
    1. Laboratory Safety
    2. Hazardous Waste Disposal
    3. Chemical Hygiene Plan (PDF)
    4. Laboratory Safety Guidelines
    5. Material Safety Data Sheets (MSDS)
      • UC Irvine EHS information about MSDS
      • MSDS Search Tool
    6. Safety Training Programs
    7. Faculty Laboratory Safety Handbook
    8. Laboratory Relocation Guidelines

Terrorism—Preparing for the Unexpected Guidelines from the American Red Cross

EHS Safety Newsletter


This page was adapted from The Henry Samueli School of Engineering.
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http://www.ics.uci.edu/ugrad/courses/ undergraduate course listing @ the bren school of information and computer sciences

This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

  • ABOUT
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This is a tentative schedule of CompSci, CSE, ICS, Informatics and Statistics courses that the Bren School is planning to offer.

Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change. Please note that some of the upper division core courses will NOT be offered every quarter.

If you need help with your course planning, please schedule an appointment with an academic counselor. They can also provide information about proposed course offerings for summer sessions, which are not included in this list.

NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


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http://www.ics.uci.edu/~numanare/ U.N.Niranjan
Niranjan
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Niranjan Uma Naresh

niranjan 

Uma Naresh, Niranjan
PhD Candidate, Computer Science
University of California, Irvine
Email: un <dot> niranjan <at> uci <dot> edu

Research

For more about my research, see my publications.

News

I co-organized the NIPS 2015 Workshop on Non-convex Optimization for Machine Learning.

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Bren school home > Community > Events >
Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

MEET THE JUDGES

Beall Student Design Competition in Engineering
Brian Delahaut: Vice President & General Manager, MK Diamond Products, Inc.
Angela Gribble: President, Small Seeds, Inc. (EMBA ’99, School of Business, UCI)
Jon Gribble: President, Taylor-Dunn Manufacturing Co. (BS ’83, School of Engineering, EMBA ’01, School of Business, UCI)
Harvey Kaplan: President & Founder, Automated Retail Shops
Marshall Parsons, PMP: Manager, Product Development, Southern California Edison
Jojo Seva: Chief Information Officer, CoastHills Federal Credit Union
Butterworth Product Development Competition in ICS
Tony Crisp: Chief Executive Officer & Brand Strategist, CRISP^YHKG Brand Agency (BS ’92, School of Biological Sciences, UCI)
Kevin Kinsey: Chief Executive Officer & Co-Founder, Netreo
Roger Lloyd: Chief Operating Officer, Amada Senior Care
David Ochi: Co-Founder, Alpha Sprouts, and Executive Director, UCI Blackstone Launchpad (BS ’97, Schools of Biological Sciences and Social Sciences, MBA ’99, School of Business, UCI)
Neil Sahota: Ecosystem Engagement Manager, IBM – Watson Group (BS ’97, Schools of Social Sciences and Physical Sciences, BS ’00, School of Information and Computer Sciences, MBA ’03, School of Business, UCI)
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Bren school home > Community > Events >
Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

COMPETITION RULES AND GUIDELINES

Participants
Both competitions are open to all UC Irvine students. Teams must be composed of at least two (2) students, of which at least one (1) must be an ICS student (for the Butterworth Product Development Competition) and one (1) must be an Engineering student (for the Beall Design Competition). For example, a team member from the Merage School of Business could contribute business planning knowledge that would help the team or students from schools of science or engineering could contribute knowledge of a particular application or technology. Students can be graduate or undergraduate, but must be registered at UCI for the duration of the academic year of the competition.

Guidelines
To participate in these competitions, your team must fill out the Web-based Intent to Enter form indicating participation interest. Upon completion of this form, teams will be assigned industry/faculty mentors according to the criteria delineated in the registration form. Participants can enter both the Butterworth Product Development Competition and also the Beall Student Design Competition, but can only win one (1) prize. Entering both competitions increase the chance of winning, but must qualify with the appropriate student team members (1 ICS and 1 Engineering student to qualify for both competitions).

Obtaining Additional Team Members: Potential participants are encouraged to meet students with similar interests who may be willing to collaborate and form a team at one of the school based workshops, information sessions or the Merage School’s Matrix Mixers that are offered. Students from across campus are encouraged to attend even if they do not have a concept developed.

Team Integrity: Once the Concept Paper has been submitted, 50% of the team must remain intact for the rest of the competition. 100% of the team must remain intact after submitting the Product Specification/Proof of Concept.

Product Plan/Idea: Teams must present an original idea or current design problem, and be in a "pre-incubator" form. They must not be backed by incubators, existing companies, venture capitalists or other investors. Teams or team members that have received any form of venture capital financing for their competition entry plan or a likeness thereof may not participate in the competition. Any team receiving venture capital funding while participating in the competition will be disqualified.

Workshops: Workshops will be offered throughout every phase of the competition. Topics will include,

  • “Working with Your Mentor/How to Define a Product”
  • “How to Incorporate a Business Case into your Proposed Product”
  • “How to Demo Your Product”
  • “Who Owns What… Intellectual Property at UCI”

Documentation, Presentations and Demos: Documents should be single spaced with a font size between 10 and 12 points. One (1) oral presentation will be due mid-way through the program that will offer proof of concept, feasibility and specifications. A demo and presentation are required for the final phase of the competition. Both oral presentation and final demo will be presented before the judges of the competition. Working prototypes are suggested for the final demo.

Awards: Final awards will be presented by the Dean of the School and the Judge Chair following the final demo at an Awards Ceremony.

  • Cash awards will be given to the top three (3) teams for each competition in amounts of $7,500, $5,000 and $2,500.
  • The top three (3) teams for each competition will be offered a Meet & Greet with UCI’s Office of Technology Alliances to discuss product, patents, copyrights and trademarks and to answer any questions they may have.
  • All teams receiving an award will qualify to participate as a sponsor in a project course in order to help develop their products. Project submissions must occur within the academic year immediately following the competition (certain restrictions apply).
  • First place winners are recognized by presenting their product in the ICS and Engineering school’s premiere student showcase event, INGENUITY.
  • First place winners will have the opportunity to meet with local investors.
  • All teams will have access to UCI’s Antrepreneur Center to further develop their concepts/ideas and can be directed to other avenues to explore entrepreneurism.

Judging Guidelines: The judges are professionals who come from various backgrounds. Judges are experts in the process of starting a business including business plan developments, or in the technologies the students are developing. The judges consider many different criteria when evaluating concept papers and the business case. Some of the elements that are considered are as follows:

  • Does the technological/design innovation provide a sustainable, competitive advantage?
  • Does a competitive advantage exist over other products in this application?
  • Does the functionality satisfy the application?
  • Is there a significant return on investment to user?
  • Is the product description clear, detailed and descriptive?
  • Is there a market need for this product?
  • Has the user been realistically identified?
  • Are the benefits to the user clear and sufficient to result in purchase?
  • Have specific user interfaces been provided?
  • Is the user interface well described?
  • Are the requirements thorough, realistic and accurate?
  • Is there a block diagram?
  • Have all of the major modules been described in detail?
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Bren school home > Community > Events >
Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

COMING SOON - MEET THE TEAMS

Beall Student Design Competition in Engineering (Hardware) +
Butterworth Product Development Competition in ICS (Software)
Beall Student Design Competition in Engineering (Hardware)
Butterworth Product Development Competition in ICS (Software)
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http://www.ics.uci.edu/community/events/competition/intent/index.php Butterworth Product Development and Beall Design Competitions Intent to Enter @ the bren school of information and computer sciences
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Bren school home > Community > Events > > Intent form
Butterworth Product Development and Beall Design Competitions Intent to Enter

Deadline: Monday, February 29, 2016 by Midnight

By submitting this form, I certify that I have read and agreed to the competition rules and guidelines set forth for the Butterworth Product Development Competition at the Donald Bren School of ICS, and the Beall Design Competition at The Henry Samueli School of Engineering and I understand that any actions that do not follow those rules/guidelines can result in the immediate disqualification of my team.

To register please provide the following information.

Only ONE entry per team.

Intent to Enter form (all fields required)
Please indicate which competition you will be entering
Butterworth Product Development Competition (Software)
Beall Design Competition (Hardware)
Both Competitions (Software + Hardware)
Do you have an ICS or Engineering student on your team to qualify?
(ICS for Butterworth, Engineering for Beall)

yes no
Comments:
Proposed Team Name
Team Leader
Team Members (Complete up to 6)
  Name E-Mail Major Student ID Class
1)
2)
3)
4)
5)
6)
Briefly describe your product
If you already have a mentor, please add their name and e-mail address here
If you would like to request a specific individual as your mentor, please indicate the desired research area here
Check here if you are not on a team and would like to be placed on one:
Comments

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http://www.ics.uci.edu/community/events/competition/past2015.php Butterworth Product Development and the Beall Design Competitions - 2015 Participants and Results
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Bren school home > Community > Events >

2015 Participants and Results

WINNERS

Beall Student Design Competition at the Samueli School
  • 1st Place ($7,500):
    MuTech Scientific

    Project Description: DECAY-Z is a device that will help promote the ease of access to HIV diagnostics by decreasing the cost of production through microfluidics. Microfluidics allow greater reagent efficiencies with increased reaction surface area to fluid ratios. This enables a reduced cost by minimizing the most expensive regents in current diagnostics platforms.

    Students: Ya Kevin Thao, Abraham Phung, Diego Sandoval, Eric Wilde, Zaw Mai, Cory Schoenborn, Michael Bulotano

  • 2nd Place ($5,000):
    Sixth Sense

    Project Description: Energy Conservation Observer (ECO) is a context-aware device that accumulates a user’s energy consumption data, senses personal energy waste and provides eco-feedback to promote sustainability in the following ways: It will understand the user’s energy needs and learn her usage habits to alert her (and potentially the public utility company) when energy wastage is detected. It will prompt the user to take action by correlating utility bills with ECO’s recommendations. It will illuminate the context in which the user can contribute to save resources by giving timely alerts.

    Students: Shriti Raj, Neeraj Kumar, LouAnne Boyd, Ali Shahbaz

  • 3rd Place ($2,500):
    OneSeed

    Project Description: Similar to nurturing a child’s development, experiencing the plant lifecycle from seed, to seedling, to mature plant, provides people with intrinsic joy and happiness. Rather than an enjoyable hobby, gardening is perceived by most as intimidating and difficult. OneSeed will change this perception by simplifying and demystifying gardening. OneSeed is a “smart” gardening “system” that will remove the guesswork from gardening, making it easy and even FUN! By integrating multiple sensors and wireless technologies into a portable OneSeed Smart Unit, we’re able to collect and monitor vital parameters (e.g. soil moisture; UV exposure; ambient temperature; pH) to determine the condition and exact needs of plants without consumer intervention or the need for specific gardening knowledge/experience.

    Students: Stella Liu, Vouy Yeng, Sandy Pham, Aaron Rosario, Briana O'Hern, Erik Henriquez, Ryan Kuhs

Butterworth Product Development Competition at the Donald Bren School of Information and Computer Sciences
  • 1st Place ($7,500):
    Roco

    Project Description: Roco is an intelligent mobile application that aims to use machine learning methods to create an automated private photosharing system. Using the application, users can capture and share pictures in an effortless way. A distinctive feature is that users can select from their list of friends the people they want to share their pictures with, set a timer specific to each person and take a set of pictures that will be automatically shared with these friends in realtime. During registration, Roco has a unique facescanning feature that uses a oneshot learning method combined with geofiltering to allow for facial recognition at higher accuracy. Geofiltering is an efficient filtering method by only queryingfaces with individuals that are close in location. If a picture contains people, the faces in it are automatically recognized and the picture is shared with everyone who appears in the picture. Roco also enables users to share their pictures with people who do not have the application by allowing the users to add nonusersor send out a link of photos they want to share to people by email or phone number.

    Students: Jenny Hua, Bing Hui Feng, Naren Sathiya, Tu Nguyen, Kasean Herrera

  • 2nd Place ($5,000):
    NoteCycle

    Project Description: Quicknotes is a Crowdsourced classroom note platform. Students that feel that they take good notes can upload their class notes to Quicknotes and be rewarded. Other students in the class can then see that notes have been posted for this lecture section. Students who wish to view the notes are given a short survey (whenever available) or if not available are asked to pay a micro-transaction on the order of 50 cents per set of notes. After they download the notes, they can rate them so that other students in the class will know which note have found most useful. In order to incentivize students into uploading notes they receive some of the profits for each download of their notes. This also encourages students to post the best possible notes so that they get positive ratings, resulting in more downloads, giving them a larger income.

    Students: Arvind Sontha, Ofri Harlev, Treston Mendonsa

  • 3rd Place ($2,500):
    FanFeed!

    Project Description: FanFeed is a free mobile application, available on iOs and Android, that aims to enhance the fan in-stadium experience by helping users avoid long concession stand lines at sporting events by providing in-stadium food delivery and in-seat food purchase. Forget the idea of leaving your seat at that crucial moment that you don’t want to miss for a beer, some food, or beverages. Fans will now have the opportunity of being able to order food to their seats, no matter where they are located in the stadium. With our efficient delivery system and easy-to-use mobile application, attending a game will never be the same again.

    Students: Candace Wu, Ravi Patel, Brandon Troung

MEET THE TEAMS

Beall Student Design Competition in Engineering (Hardware) +
Butterworth Product Development Competition in ICS (Software)
ARIS
Bonnie Gonzalez, Francisco Mendoza, Humberto Gaeta, Miguel Blanco, Derek Omuro
MuTech Scientific
Ya Kevin Thao, Abraham Phung, Diego Sandoval, Eric Wilde, Zaw Mai, Cory Schoenborn, Michael Bulotano
OneSeed
Stella Liu, Vouy Yeng, Sandy Pham, Aaron Rosario, Briana O'Hern, Erik Henriquez, Ryan Kuhs
Primitives
Cameron Samak, Richmond Chang, John Ader, Cory Mortimer, Sahand Nayebaziz
Sixth Sense
Shriti Raj, Neeraj Kumar, LouAnne Boyd, Ali Shahbaz
The Ngineers
Efren Aguilar, Sabrina Ng
URA
Elena Sy Su, Shirley Zhu
Waytrade
Roger Lloret-Batlle, Amine Mahmassani, Si-Yuan Kong, Felipe De Souza, Vaibhav Saini
Beall Student Design Competition in Engineering (Hardware)
HelioTeq
Michael Bryant, Naomi Thomson, Stuart Foster, Laszlo Kurta, Alvin Ma, Wee Lim, Jared Lau, Austin Chow
S.S.
Jie Shen, Qi Shi
Butterworth Product Development Competition in ICS (Software)
Big Data Raiders
Neil Jaramillo, Carrie Zhao, Lauren Stasiak, Mirka Murillo, Chen Lu (Leo)
FanFeed!
Candace Wu, Ravi Patel, Brandon Troung
Femforce
Angela Li, Stephanie Eng, Sijia Liu, Gurveen Sekhon
GIDMaPS
Farshad Momtaz, Alireza Farahmand, Omid Mazdiyasni
Roco
Jenny Hua, Kasean Herrera, Bing Hui Feng, Naren Sathiya, Tu Nguyen
NoteCycle
Arvind Sontha, Ofri Harlev, Treston Mendonsa
UBO
Tristan Biles, Mark Archer, Sofanah Alrobayan
Competition details »
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    2014
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Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

HOW TO ENTER:

  • Review competition requirements
  • Complete the Intent to Enter form by Monday, February 29, 2016 at midnight

Questions? Please contact Kristin Huerth at khuerth@ics.uci.edu.

Prizes for each competition are:
1st Place: $7,500
2nd Place: $5,000
3rd Place: $2,500

Butterworth Product Development Competition at the Donald Bren School of Information and Computer Sciences
Is a product development competition designed to encourage the creation of new technologies with potential for commercialization. Ideas and products are evaluated on their technological merits and potential to impact the marketplace. Students are encouraged to submit new products that involve the development of software and systems. Products that entail integration are acceptable as long as there is a substantial development effort.

The competition is open to all UCI students. Teams must be composed of at least two (2) students, one (1) of which must be enrolled at the Donald Bren School of Information and Computer Sciences.

Beall Student Design Competition at The Henry Samueli School of Engineering
Offered to encourage the creation of new technologies, or solutions to current design problems that have the potential for commercialization. Ideas and products are evaluated on their technological merits as well as potential to impact the marketplace. Students are encouraged to submit new product ideas that involve the development of hardware and devices. Products that entail integration are acceptable as long as there is a substantial development effort.

The competition is open to all UCI students. Teams must be composed of at least two (2) students, one (1) of which must be enrolled at The Henry Samueli School of Engineering.

2014 Competitors
Last year's winners of the Butterworth Competition were composed of Business, Engineering, Medicine and ICS students. For more information on finalist projects, please visit http://www.ics.uci.edu/community/news/features/view_feature?id=2.

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Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

ABOUT THE SPONSORS

Butterworth Product Development Competition at the Donald Bren School of Information and Computer Sciences

Paul Butterworth Paul Butterworth '74, M.S. '81
Chief Technology Officer, Appconomy Inc.

Paul is currently CTO at Appconomy Inc. Past roles include Co-founder and CTO of Emotive, where he conceived and designed the Emotive Cloud Platform for enterprise mobile computing. Before that, Paul was an Architect at Oracle and a founder & CTO at AmberPoint where directed the technical strategy for the AmberPoint SOA governance products. Prior to AmberPoint, Paul was a Distinguished Engineer and Chief Technologist for the Developer Tools Group at Sun Microsystems and a founder, Chief Architect and Senior Vice President of Forte Software. Paul also served as Chief Systems Architect at Ingres. Paul holds undergraduate and graduate degrees in Computer Science from UC Irvine.

Beall Student Design Competition at The Henry Samueli School of Engineering

Donald Beall Donald R. Beall
Chairman Emeritus
Retired Chairman & Chief Executive Officer, Rockwell
Partner, Dartbrook Partners, LLC (a family partnership)

Don retired as Chairman/CEO in 1998 after a 30-year career with Rockwell. When he stepped down, he was named Chairman Emeritus and continues to serve as a director on the board of Rockwell Collins. He also currently serves on the board of CT Realty and is a former director of Conexant Systems, Mindspeed Technologies, Skyworks Solutions, Proctor & Gamble, Amoco, Rockwell, Times Mirror and ArvinMeritor. He is the founding partner in Dartbrook Partners (a family partnership) and the chairman of the Beall Family Foundation.

Don serves on several advisory boards and executive committees. At UC Irvine, his involvement includes: the UCI Paul Merage School of Business Dean’s Advisory Board, Executive Committee, and the Don Beall Center for Innovation and Entrepreneurship Advisory Board; UCI Henry Samueli School of Engineering Advisory Board; UCI Beall Center for Art + Technology Advisory Board; UCI Medical Center CEO Advisory Board and the UCI Chief Executive Roundtable. He is an advisor to the San Jose State University School of Engineering and member of the Engineering Leadership Council; and a trustee and President’s Circle member of the Naval Postgraduate School Foundation. He served as an Overseer of the Hoover Institution at Stanford from 1997 until 2011, and remains active on the Hoover Council today. He is involved in numerous professional, educational, public service and philanthropic endeavors and is an investor, director, and/or advisor with several venture capital groups, individual companies and investment partnerships.

Don earned an engineering degree from San Jose State University and an MBA from the University of Pittsburgh. He has been married for more than 50 years to Joan Beall. They have two married sons, two granddaughters and three grandsons. Don and Joan reside in Corona del Mar and Pebble Beach, California.

Competition details »
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    2015
    2014
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Butterworth Product Development
Competition in ICS
and
Beall Student Design Competition in Engineering

COMPETITION SCHEDULE & DEADLINES

Intent to Enter Due February 29, 2016
Concept Paper Due (2 Pages) March 8, 2016
Product Specification - Summary Due (1 to 2 Page Summary) April 5, 2016
Product Specification - Presentation Slides Due (3 to 5 Slides) April 12, 2016
Product Specification - Student Oral Presentation and Judge’s Feedback April 18 (week of), 2016 TBD
Final Product & Business Case Due (7 to 10 text pages including graphics) May 10, 2016
Blackstone Pitch Deck Prep May 11-18, 2016
Student Demos and Judging and Awards Ceremony May 19, 2016
Ingenuity 2016, A Student Showcase Event – First Place Team from each competition to Present June 1 or 2, 2016 TBD

 

SEMINARS

The Entrepreneurship for Scientists and Engineers Seminar Series provides a real-world introduction to the theory and practice of entrepreneurship. Through a series of presentations by prestigious entrepreneurs and industry leaders, participants will explore the various organizational, strategic and financial challenges facing successful and unsuccessful entrepreneurs. Topics include start-up strategies, business idea evaluation, business plan writing, and introduction to venture capital.

(All seminars featured here are free and open to the public. Seating is limited)

Date: February 11, 2016
Speaker: Neil Sahota, ’97, ’00, MBA ’03, IBM Certified Consultant and Senior Project Manager, IBM - Global Business Services
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: February 16, 2016
Speaker: Judy Greenspon, President, NPI Services, Inc.
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: February 18, 2015
Speaker: Jim Mazzo, CEO, Acufocus
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: February 23, 2016
Speaker: William “Bill” Link, PhD, Managing Director, Versant Ventures
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: February 25, 2016
Speaker: Feyzi Gatehi, CEO, Corent Technology
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: March 1, 2016
Speaker: Sydney Edwards, PhD, former Senior Director, Portfolio Management & Strategic Planning, Allegran
Time: 5:00 - 6:00 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Date: March 3, 2016
Speaker: Henry Samueli, Ph.D., Co-Founder and CTO, Broadcom Corporation
Time: 5:00-6:30 p.m.
Location: McDonnell Douglas Engineering Auditorium (MDEA) 200 Rockwell Center, in The Henry Samueli School of Engineering

Competition details »
  • Homepage
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  • Past Participants and Results:
    2015
    2014
Quick Links
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Bren school home > Community > Events >

2014 Participants and Results

WINNERS

Beall Student Design Competition at the Samueli School
  • 1st Place Prize Winners ($7,500):
    Spero Diagnostics

    Project Description: This product addresses the dangerous, even deadly prevalence of sepsis in hospital settings. Today, a full sepsis diagnostic process takes up to four days, requires multiple tests, advanced trained personnel, and costs several hundred dollars. Spero Diagnostics’ goal was to reduce that time frame to less than three hours and bring the cost down to under $100. Their design is a software package that automates a device that performs complicated diagnostic tests and reduces the volumes of reagents and samples. The design also includes pre-filled blister packs that both store and dispense all the appropriate reagents and volumes needed to perform tests. The automated device and blister packs will allow a minimally trained user to perform complicated sepsis diagnostic tests at the point of care.

    Students: Jessica Motherwell, Jose Gallegos, Ernesto Sosa, Ting-Yi Chu, Maka Pennell, Zachary Campagna

  • 2nd Place Prize Winners ($5,000):
    A Hundred Tiny Hands

    Project Description: A Hundred Tiny Hands developed three “Inventor’s Toolboxes” designed to teach children core concepts in engineering and science in a fun and interactive way. The products take the latest discoveries in science and use current engineering methodologies to create an educational toolbox, tailored to children of all ages. The Inventor’s Toolboxes encourage children to explore, create, and invent by allowing them to build their own structures and models. The simple design of these three Inventor’s Toolboxes makes even the most cutting edge scientific technologies easy enough for any child or parent to learn. The team’s goal and vision is to create and foster an online community of young inventors able to interact and share with one another, similar to scientists in real life.

    Students: Sophia Lin, Nicole Mendoza, Jolie McLane, Eugene Lee, Danny Greene

  • 3rd Place Prize Winners ($2,500):
    HydroTrack

    Project Description: Capitalizing on the rapidly growing digital fitness market and the rapidly falling costs of electronics, HydroTrack is a smart hydration tool that helps make keeping track of one’s hydration easier than it has ever been. Using Bluetooth low-energy and a novel flow sensor, HydroTrack transmits how much water one is consuming to one’s smartphone or favorite digital fitness device. The accompanying smartphone application sends gentle reminders when hydration goals are not met, and coaches the user to drink the appropriate amount of water based on activity level.

    Students: Michael Bryant, Aurelia Darling, Anni Liu

Butterworth Product Development Competition at the Donald Bren School of Information and Computer Sciences
  • 1st Place Prize Winners ($7,500):
    Blueberry

    Project Description: The app its creators now call “Sparky” is a networking tool. Turning on the app on a smartphone creates a 10-foot Bluetooth bubble around the person. Whenever one “Sparky” bubble encounters another, a silent connection is created. Throughout the day, the app monitors interactions collecting metadata like time, location, and duration. The profile pages and interaction metadata the app offers are so comprehensive that the need to take notes is all but eliminated. Once professionals are ready to follow up, “Sparky” presents them with an intelligently sorted list of everybody they interacted with at an event.

    Students: Karan Sekhri, Adrien Deguzman, Sheng Xia, Tai Cao, Derek Omuro

  • 2nd Place Prize Winners ($5,000):
    Sprout

    Project Description: “BeatPool” allows multiple music-player users to send songs from their music libraries to one speaker – creating a kind of crowdsourced playlist. Every device that has a “BeatPool” app on it can send songs from its own library into collaborative pool. The system is structured in a way that allows it to function either in crowded environments such as parties, or even on camping trips.

    Students: Ekin Oguz, Sky Faber, Cesar Ghali, Fulya Ozcan

  • 3rd Place Prize Winners ($2,500):
    Ant mApp

    Project Description: “Ant-mApp” is a mobile and website application that creates a central hub of event information for on-campus events at UCI. It serves as a platform where advertisers of events, such as student organizations and school departments, can meet the seekers of event information, the audience. By allowing users to filter through events and subscribe to groups and categories of interest, “Ant-mApp” will enhance the community involvement and learning experience of UCI students. “Ant-mApp” is a sustainable alternative to flyering intended to grow and strengthen the bonds of members of the UCI community.

    Students: Alexsandra Guerra, Esosa Agbonwaneten, Yerlan Turekeshov, Avedis Simonian, Maharshi Patel

MEET THE TEAMS

Beall Student Design Competition in Engineering (Hardware) +
Butterworth Product Development Competition in ICS (Software)
Ant Tech
Tim Kelly
Thomas Gheorghe
Dalen Ellis
Aptus
Yu-Jye Tung
Moses Choi
Anchit Roy
Sambodhi Chakrabarty
DocBot
Evan Schein
Michael Tran
HindSight
Scott Godfrey
David Carrillo
Kemal Davaslioglu
HydroTrackTMda
Aurelia Darling
Michael Bryant
Anni Liu
Mattina
Fady Barsoum
Anderson Nguyen
Irvin Huang
Charles Lam
Hong San Wong
Spero Diagnostics
Jessica Motherwell
Jose Gallegos
Ernesto Sosa
Ting-Yi Chu
Maka Pennell
Zachary Campagna
Beall Student Design Competition in Engineering (Hardware)
A Hundred Tiny Hands
Sophia Lin
Nicole Mendoza
Jolie McLane
Eugene Lee
Danny Greene
BioGuideUCI
Hana Yamate-Morgan
Nazneen Pashutanizadeh
Joycelin Luc
Sharon Kuruvilla
Aleksandar Metulev
Xiaoxuan Zhang
Grip Trainer Team
Scott Nguyen
Kevin Wang
Michelle Freret
Jessica Trieu
Naren Sathiya
Helico Optics
Chris Campbell
Kunal Dave
Elliott Kwan
Alex Matlock
Leanne Young
Magnetos
Diana Wu
Chun Wu
Joey Pazzi
Brian Huynh
Juan Contreras
Melissa Ali-Santosa
Racket Buddy
Kenneth Gutierrez
Alecxandr Andres
Anthony Christensen
Sanometric Skin TM
Michael Minihan
Adam Dunn
Nitish Nag
Joe Tesoriero
Surya Krishnan
Nika Nikbakht
Seams
Maunika Gosike
Sonia Kaushal
Everardo Camacho
Ariel Beroukhim
Shalom Stevenson
Team Iron Fist
Nate Directo
Joseph Cuevas
Kenneth Gutierrez
Team Wiggles
Jason Tsang
Charith Samarasena
Andrew Towstopiat
Patricia Chen
Kaleigh Halford
Eric Poon
Monika Faruque
Vertai
Amanda Nozaki
Isabella Samra
Kevin Lam
Jesus Ramos
Butterworth Product Development Competition in ICS (Software)
Ant mApp
Alexsandra Guerra
Esosa Agbonwaneten
Yerlan Turekeshov
Avedis Simonian
Maharshi Patel
App Builders
Sholeh Forouzan
Sonali Madireddi
Blueberry
Karan Sekhri
Adrien Deguzman
Sheng Xia
Tai Cao
Derek Omuro
Team Bru
Govind Rai
Gio Carlo Cielo Borje
Digital Afterlife
Grace Pai
Nafiri Kusumakaulika
Anita Marie Gilbert
Fruit Mill
Nathaniel Major
Sivabalan Thirunavukkarasu
Rohan Achar
MediaStream
Jason Parsons
Joseph Yu
Kevin Lee
Victor Lee
Van Erick Custodio
Sohrob Raja
Cherrie Yu Cheng
Nimbl
Candace Borders
Ron Sahyouni
Chris Orchid
Jasper Chou
Thomas Truong
Robhy Bustami
Personicle
Laleh Jalali
Siripen Pongpaichet
Da Huo
Hyungik Oh
Sprout
Ekin Oguz
Sky Faber
Cesar Ghali
Fulya Ozcan
Turtle Writing
Suhang Jiang
Leonard Bejosano
Yuan Ning
Youngmin Park
Jiayi Shen
Debbie Le Yu
Melissa Niiya
Competition details »
  • Homepage
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  • Sponsors
  • Rules and Guidelines
  • Schedule, Workshops & Deadlines
  • Past Participants and Results:
    2015
    2014
Quick Links
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http://www.ics.uci.edu/community/events/ Untitled Document http://www.ics.uci.edu/involved/tech_talk.php Tech Talks @ The Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
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    • Donald Bren Hall
    • Visit the Bren School
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  • DEPARTMENTS
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    • Undergraduate ▸
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Tech Talks

On a quarterly basis, the Bren School invites members of the corporate community to Bren Hall to make a technical presentation to computing and engineering faculty and graduate students. Each talk spotlights one company.

Companies benefit from participation in a number of ways:

  • Your company receives heightened exposure among Bren School faculty and graduate students.
  • Your company representatives have the opportunity to meet faculty and graduate students in a relaxed and informal setting based upon interests in certain research areas.

Tech Talks are usually scheduled from 12:00 – 1:30 p.m. throughout each quarter. During this time, a senior technical representative or technology VP from your company presents on research topics and items of interests, such as software engineering, user interfaces, security and privacy to name a few.

Contact Kristin Huerth at khuerth@uci.edu or at 949-824-3074

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http://www.ics.uci.edu/community/events/butterworth/index.php butterworth product development competition @ the bren school of information and computer sciences
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Butterworth Product Development Competition
Information about this year's Butterworth Product Development Competition can be found here.
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http://www.ics.uci.edu/involved/project_class.php Sponsor a Project Class @ The Bren School of Information and Computer Sciences
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Sponsor a Project Class

The Informatics Department in the Bren School of Information and Computer Sciences at UC Irvine runs project courses in two sizes: one-quarter and three-quarters. We are looking for project sponsors.

Informatics projects involve using good software engineering processes interlaced with human centered methods to build and install something useful for you. Projects include different types of sources and sponsors ranging from corporate projects to university based, community-driven, non-profit or entrepreneurial projects. Project implementations range from web applications and portals to online services, cloud computing, ipad, iphone and mobile applications.

One-Quarter Project Class
Currently our one-quarter class is held during different times of the year, and is determined by enrollment. If you are interested, please contact us so that we can put you on the wait list for the class. The sponsorship amount for this project class is $4,500.

Three-Quarter Project Class
Each year we offer our three-quarter class starting in spring quarter and ending in winter quarter of the following year. If you are interested, project proposal deadlines are due six weeks prior to the starting of class. The sponsorship amount for this project class is $12,500.

 

For more information contact:

Informatics 117 – Software System Design (One-Quarter Project)
Professor Hadar Ziv at ziv@ics.uci.edu

Informatics 132 – HCI & User Interface (One-Quarter Project)
Professor Alfred Kobsa at kobsa@uci.edu

Informatics 191 – Senior Design Project: Usability Engineering & Software Development (Three-Quarter Project)
Professor Judith Olson at jsolson@uci.edu
Professor Hadar Ziv at ziv@ics.uci.edu

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Sponsored Internship Program

What it is:
The Sponsored Internship Program offers companies a unique opportunity to enhance their workforce by hand-selecting undergraduate seniors or graduate-level students from the Bren School of Information and Computer Sciences to complete internships at their companies. This very popular and successful program also serves as a cost effective recruitment tool for highly qualified IT professionals.

How it works:

  1. Participating companies submit criteria for desired internship. They then receive background information on Bren School students who have expressed interest in company internships.
  2. Companies rank the students whose credentials best match their employment needs, while students rank the sponsoring companies in a similar fashion.
  3. Bren School personnel review these two sets of rankings and make matches that best reflect the interests and needs of both parties.
  4. Companies interview the candidate/s and, should the match be mutually agreeable, offer a paid internship during the summer and/or academic year, as well pay for the student’s fees.
  5. The financial terms of the internship are negotiated directly between the student and the sponsoring company.
Students are expected to maintain a minimum GPA of 2.8 (undergraduate) and 3.3 (graduate) during their involvement in the sponsored internship program.

How to participate:
In order to participate in this program, each company pays a tax-deductible donation to the Bren School, which covers the cost of the student’s annual fees.

  • Undergraduate Resident Fees donation: $15,500*
  • Graduate Resident Fees donation: $16,500*
*Amounts may vary based on fee increases and/or if the desired student is a non-resident.

 

For additional information, please contact Kristin Huerth, Associate Director of External Relations, at (949) 824-3074 or khuerth@uci.edu.

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http://www.ics.uci.edu/involved/corporate_partner.php Corporate Partners Membership Program @ The Bren School of Information and Computer Sciences
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Corporate Partners Membership Program

MISSION

The Bren ICS Corporate Partners Program promotes the exchange of the most advanced information in the field between university researchers and their corporate counterparts, and provides access to the abilities and interests of UC Irvine’s students through exclusive recruiting and networking opportunities.

OBJECTIVES

  1. Excellent customer service/individualized attention
  2. Streamlined communication
  3. Priority access to students and faculty within your specific area(s) of interest

BASIC MEMBERSHIP

Basic members receive the following benefits:
  • Recruiting – Customized attention to your recruiting needs including:
    • ICS Jobs - Access to students through student website featuring resumes, and ability to sort by G.P.A, major, specific skills & more
    • Early Career Fair featuring first access to top students, on-campus interviews, company info sessions
    • Facilitation of interns
  • Networking – Discuss leading research and innovations with faculty and other Corporate Partners through our quarterly Distinguished Speaker Series. Attend VIP receptions (4 tickets) prior to each Distinguished Speaker event.
  • Student Showcase – Attend the annual Student Showcase event in spring featuring top Bren School student projects
  • Host Career Nights - Direct and individualized access to students through company hosted career nights
The fee for basic membership is $10,000.

PREMIER MEMBERSHIP

Premier members receive the following additional benefits:
  • Center Access
    • Direct access to a specified research center including regular exchanges of research and information
    • Access to office space, computer facilities, research labs, library
  • Visiting Scholars – Partners may send a company researcher to join a faculty member’s research group for a period of time (up to 12 months). Includes auditing courses, attending research seminars.
  • Meetings & Workshops – Attend topic specific annual meeting where faculty, grad students and industry leaders present latest research findings.
The fee for premium membership is $25,000.

Contact Nancy Kim Yun at nancy.kim.yun@uci.edu or at 949-824-3088

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http://www.ics.uci.edu/involved/information_session.php Information Sessions @ The Bren School of Information and Computer Sciences
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Information Sessions

On a quarterly basis, the Bren School invites members of the corporate community to Bren Hall to make a recruiting presentation to computing and engineering students. Each information session spotlights one company.

Companies benefit from participation in a number of ways:

  • Your company receives heightened exposure among Bren School students.
  • Your human resources personnel have the opportunity to meet potential candidates in a relaxed and informal setting.
  • Students receive a chance to educate themselves about your company.

Information sessions are usually scheduled from 12:00 – 1:00 p.m. or 5:00-7:00 p.m. throughout each quarter. During the first 20–30 minutes, company representatives give a general overview of the company, highlighting aspects such as company culture and employment opportunities. The next 15-20 minutes are used as a formal question and answer period. The remaining time is dedicated to informal conversation and resume collection. During this time, students often speak one-on-one with company representatives to ask further questions.

CURRENTLY These sessions are free of charge; however, most companies choose to provide pizza and sodas to encourage student participation.

 

For more details or to set up an Information Session, please contact Brandon Hastings, the ICS Student Council President, at bhasting@uci.edu.

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Dean's Leadership Council

BREN SCHOOL VISION:

The Bren School of ICS at UC Irvine will establish a new vision for the field of information and computer sciences in the 21st century.

We will emphasize a broad and diverse view of the field including the what, why and how of information and computer science; we will advance interdisciplinary collaborations at UC Irvine and beyond; and we will produce relevant, cutting edge research that addresses key challenges facing our nation and the world.

LC MISSION:

The Bren School of ICS Dean’s Leadership Council has been established to raise community awareness of the Bren School students and faculty, to raise funds to support the Dean’s vision and fundraising priorities and to lend strategic advice to the Dean when called upon.

EXPECTATIONS OF MEMBERSHIP:

  • Attend three Council meetings per year
  • Be available to participate in a Strategic Priority meeting(s) around a particular topic (as appropriate)
  • Financially support Dean’s Discretionary Fund at a minimum $3,000 level
  • Participate in the Strategic Direction for the School
  • Serve as an Ambassador for the School in both personal and business interactions
  • Use your connections aggressively for ICS fundraising and relationship building
  • Partner your organization with ICS students in regards to internships, recruitment and company projects
  • Secure tables for the UCI Medal Awards held in Oct./Nov.
  • Make recommendations for incoming board members

BENEFITS OF MEMBERSHIP:

  • Members will play a critical role in expanding the visibility of the Donald Bren School of Information and Computer Sciences throughout Orange County, California and nationwide
  • Members will have access to the Dean, faculty and student research
  • Members will be invited to share expertise, serve as speakers and participate in events
  • Members will be given networking opportunities through Council meetings and mixers
  • Members will have early access in recruiting high quality interns and graduating students

PARTICIPATION OPPORTUNITIES:

  • Participate as a speaker, mentor or panelist
  • Provide a project in your company for Project ICS
  • Hire a graduate student as an intern
  • Recruit ICS students as employees
  • Host an LC meeting, retreat or committee meeting at your company

Contact Ed Hand at elhand@uci.edu or at 949-824-6563

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http://www.ics.uci.edu/about/bren/bren_press.php renaming press room @ the bren school of information and computer sciences
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Press Releases

»June 09 , 2004
Business leader and philanthropist Donald Bren awarded UC Presidential Medal

» June 02 , 2004
UCI School of ICS named in honor of business leader and philanthropist Donald Bren

 


Photo Gallery

Click on a photo for a larger version.

photo::ICS Dean Debra J. Richardson addresses nearly 300 guests in attendance at the June 9 ceremonies. - link to full size version of photo
photo::Donald Bren, wearing the UC Medal, meets with media after the June 9 event - link to full size version of photo
photo::From (l) to (r), Dean Richardson, Chancellor Cicerone, Mr. Bren, and Regent Kozberg break ground on the site of Bren Hall. The building is to be completed in late 2006. - link to full size version of photo
ICS Dean
Debra J. Richardson
Chairman of
The Irvine Company
Donald Bren
Breaking ground
on Bren Hall
photo::From (l) to (r), ICS Dean Debra J. Richardson looks on as Mr. Bren receives a memento from ICS students Shawn Shah, Nhu Vuoung, Jose Romero, Tempe Kraus and Albert Udompanyvit - link to full size version of photo
photo::From (l) to (r), ICS Professor Emeritus Tim Standish, inventor of DNS Paul Mockapetris and father of CAD Pat Hanratty, share a conversation over lunch - link to full size version of photo
photo::ICS Dean Debra J. Richardson presents Donald Bren with the UC Medal. The medal is the university's highest honor - link to full size version of photo
Donald Bren receives a gift from ICS students
ICS community members enjoy the festivities
Mr. Bren receives the UC Medal
photo::Roy Fielding (BS '88, MS '93, Ph.D. '00), architect of the Internet's Hypertext Transfer Protocol (HTTP), addresses guests during the event - link to full size version of photo
photo::Domain Name Server creator Paul Mockapetris (Ph.D. '81) discusses his experiences as an ICS student - link to full size version of photo
photo::Adam Bonner (BS '00), co-founder of Network Synthesis in Irvine, tells how ICS prepared him and partner Victor Liu (BS '00) to start their own company right out of school - link to full size version of photo
ICS graduate
Roy Fielding
ICS graduate
Paul Mockapetris
ICS graduate
Adam Bonner
photo::2004 graduate Sepideh Gazeri tells how ICS has helped her start on the road to success. Gazeri is the first ICS student ever admitted into UCI's 3-2 program that offers students an opportunity to complete an undergraduate degree and MBA within 5 years - link to full size version of photo
photo::UCI Foundation chairman Ted Smith thanks Mr. Bren for his continued support of UC Irvine and education - link to full size version of photo
photo::Father of CADD Patrick Hanratty (Ph.D. '77) recalls advice given to him by his faculty mentors while a graduate student in ICS - link to full size version of photo
ICS graduate
Sepideh Gazeri
UCI Foundation chairman Ted Smith
ICS graduate
Patrick Hanratty
photo::Chancellor Ralph J. Cicerone formally announces the renaming of ICS in honor of philanthropist Donald Bren - link to full size version of photo
photo::Provost M.R.C. Greenwood presents Donald Bren with UC's highest honor, the Presidential Medal - link to full size version of photo
photo::UC Regent Joanne Kozberg offers her thanks and congratulations to Mr. Bren and ICS - link to full size version of photo
Chancellor
Ralph J. Cicerone
Provost M.R.C. Greenwood and Donald Bren
UC Regent
Joanne Kozberg


News Articles

June 10 , 2004
Making a name for itself
The Orange County Register



June 10 , 2004

Ground broken Wednesday for Bren Hall
Irvine World News



June 10 , 2004

UCI benefactor Bren breaks new ground
Daily Pilot



June 03 , 2004

Big Donor Is Donald Bren, UC Irvine Says
Los Angeles Times



June 03 , 2004

A concrete thank you
The Orange County Register



June 03 , 2004

Buildings filling to the Bren at UC Irvine
Daily Pilot



June 03 , 2004

UCI School of Information and Computer Science named in honor of Donald Bren
Irvine World News
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Bren school home > About > Donald Bren Hall
Donald Bren Hall

artist rendering of bren hall

The six-story Donald Bren Hall expanded the existing Bren School campus and increased general assignment classroom space by more than 90,800 square feet.

The design of this facility is intended to enhance interaction between faculty and students and to create a progressive learning environment.

Designed with flexibility in mind, the building accommodates the Bren School's growing faculty, staff and student populations.

The first classes in Donald Bren Hall were held Jan. 5, 2007; the Dean's office moved in on Jan. 8, 2007.

On June 20, Donald Bren Hall was offically dedicated during a morning ceremony which was attended by Donald Bren, former Dean Debra J. Richardson, Chancellor Michael Drake and about 400 campus and community guests.

An open house followed the ceremony, allowing the on-campus and off-campus community a rare sneak peek into the school's various research projects and their global impact on everyday lives.

Donald Bren Hall accommodates:

  • More than 125 faculty offices
  • 90 research labs, wet labs and/or offices
  • 10 classrooms (from 30-seat to 65-seat)
  • 250-seat lecture hall
  • 125-seat lecture hall
  • 2 50-seat lecture hall
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  • Departmental offices
  • Bren School staff offices/facilities

Ceremonial groundbreaking on Donald Bren Hall took place on June 9, 2004. Visitors can view the groundbreaking press release as well as photos from the groundbreaking ceremony, attended by the building's namesake, philanthropist and chairman of The Irvine Company, Donald Bren.

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Dedication Information »


photo:: donald bren cuts the ribbon

Donald Bren cuts the ribbon during a June 20th dedication ceremony for Donald Bren Hall
as Dean Debra J. Richardson looks on. view more photos »






Donald Bren, Dean Debra J. Richardson, Chancellor Michael Drake and about 400 campus and community guests celebrated the opening of Donald Bren Hall June 20.

Bren participated in a ceremonial ribbon cutting, toured the building’s laboratories and saw faculty research displays. Joining Bren at the event were Chancellor Drake; his wife, Brenda; Assemblyman Todd Spitzer; Orange County Supervisor Bill Campbell; and Irvine Mayor Beth Krom.

Both the school and building were named in honor of Irvine Company Chairman Donald Bren, whose $20 million gift bolstered the school's efforts.

Bren Hall is the new home of the Donald Bren School of Information and Computer Sciences, a nationally ranked program that promotes cutting-edge research and interdisciplinary learning.

An open house followed the dedication ceremony, allowing the on campus and off-campus community a rare sneak peak into the school's various research projects and their global impact on everyday lives.

To learn more about some of these innovative and collaborative projects, please view these informational videos.

A photo gallery from the dedication can be viewed below and a video recording of the dedication ceremony and ribbon cutting is also available for viewing.

A collection of media articles about the event is also available.






Multimedia Showcase »


Dedication and Ribbon Cutting Ceremony
The dedication and ribbon cutting ceremony for Donald Bren Hall took place on Wednesday, June 20, 2007 at 11:30 (PST). view the video »

History of the Bren School video
Video highlighting the Bren School and its successful achievements since its founding in 1968. view the video »

Donald Bren Hall of Fame video
A commemorative video highlighting Mr. Bren's gifts to UC Irvine and the Bren School and his pioneering real estate work. view the video »

These commemorative videos were produced by Merit/Andrew.






Dedication Photo Gallery »


Click on a photo for a larger version. (Photos by Paul R. Kennedy )

photo:: the ceremony room bh1100
photo:: donald bren checking out a demo
photo:: mr. bren, dean richardson and chancellor drake
photo:: donald bren hall lobby
The ceremony
about to begin
Dean Richardson
and Mr. Bren
view a demo
Dean Richardson,
Mr. Bren and
Chancellor Drake
Multi-screen wall in the
Donald Bren Hall
lobby
photo:: mr. bren wears a uci baseball cap
photo:: dean debra j. richardson
photo:: chancellor drake views a demo
Mr. Bren shows
his support for the
UCI baseball team
Bren School Dean
Debra J. Richardson
UC Irvine Chancellor
Michael V. Drake, M.D.
Chancellor Drake views
a Second Life demo
photo:: mr. bren gets flowers from ics students
photo:: dean richardson presents mr. bren with a gift
photo:: bren professor ramesh jain gives a demo
Mr. Bren accepts a
bouquet of flowers
Dean Richardson presents
Mr. Bren with a gift
in honor of his support
Bren Professor
Ramesh Jain
gives a demo
City of Irvine Mayor
Beth Krom tries
a demo
photo:: demo on improving crisis response
photo:: donald bren cuts the ribbon
photo:: bren school technology alliance wall
photo:: enjoying a toast
Demo on improving
crisis response
Mr. Bren cuts the ribbon
in front of Donald Bren Hall
Bren School
Technology Alliance
wall
Enjoying a toast





News Articles »


June 21, 2007
UCI building dedicated to Bren
Daily Pilot


June 20, 2007

Bren Dedicates UCI Computer School Building
Orange County Business Journal


 

© 2007 The Donald Bren School of Information and Computer Sciences

University of California, Irvine

6210 Donald Bren Hall

Irvine, CA 92697-3425

info@ics.uci.edu

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Remote Access

Computer systems maintained by the both the UCI Office of Information Technology and Bren School computing support accept connections only from systems which have ther names correctly registered in Domain Name System (DNS). See OIT's Limitation of Services to non-registered hosts for more details on this policy.

For security reasons, Bren School systems always reject connections from remote hosts which do not have matching forward and reverse DNS entries. If you are having trouble telnet'ing or ftp'ing to a Bren School system while away from UCI this may be the cause of the problem.

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Bren school home > Undergraduate > policies
Undergraduate Student Policies

Ethical Use of Computing

Introduction

This document describes some of the ethical responsibilities of computer use and explains Bren School Department policy on student use of computing resources. Some of these policies might be different from what you would expect, so please read over and understand this document.

The Bren School provides for you, the student, a wide range of computing resources from X-Terminals to PC's to large multiuser UNIX systems. These machines are expensive to buy and maintain, but it is our goal to provide you with the very best computing environment that we can. Many users depend on these computers for class assignments, research, and communications. We are a community of computer users, and we can all make the best use of our resources if we establish guidelines for how we can use them responsibly.

Some computing facilities, such as those which hold classified data, may establish expensive and complex security systems. We do not do this; we have some security mechanisms which greatly decrease the risk that one user will accidentally interfere with another, but it does not require great ingenuity to get around these mechanisms. As a result, we need to trust the people who use our machines.

The fundamental principle behind our policies is this: While using the computers, you should never do anything that harms another user or prevents him or her from getting work done.

If you have any questions about these rules, or if you suspect that an account (your or someone else's) has been broken into, please contact the Bren School Computing Support Group. To do this, send mail to the address helpdesk@ics.uci.edu, or go to Support's office, CS346, and explain the problem.

Computer Accounts

The Bren School has a wide range of computers available, located in several different labs. Some of these labs are open only to people enrolled in certain classes, some are available for general drop-in use. Each Bren School major is given both a Windows account, and a UNIX account on the Sun workstations. Different courses will require different platforms to be used. Non-majors will be given accounts only if required for a course in which they are enrolled.

All Bren School computers are to be used only by Bren School students, faculty, and staff. People outside the Bren School who wish to use computers should go to the Office of Information Technology (Multipurpose Science and Technology Building (Building 415 - 2nd Floor)). They provide computing for non-Bren School people.

Any computer account created for you remains the property of the Regents of the University of California. You are responsible for this account, and you may not allow any other person to use it. The primary purpose of your account is to allow you to carry out your computing assignments and other instructional activities. You may also make modest use of these resources for other purposes, such as sending electronic mail to friends on campus, reading the electronic bulletin boards, and playing games, provided that this usage does not significantly interfere with instructional use of the machines.

An example of how one might "significantly interfere" would be to tie up a computer for game-playing when no other computers are free and someone else is waiting to use the computer to do an assignment. If you have a game or other program you would like to make available to other users, please give it to the system administrator for public installation (You can contact the system administrator by sending mail to helpdesk@ics.uci.edu). You may not use the machines for commercial purposes, such as preparing bills for your company or advertising products, or for work related to non-UCI organizations, such as an off-campus political or religious group. More details about this are given below. If you are in doubt about whether some use of the machines is allowed, ask the Support Group.

Ethical Behavior

Here is a list of some examples of activities that the department does not allow. If a student makes such unethical use of Bren School computers, he/she will be subject to the penalties described in the section on Disciplinary Procedures.

  • You may not introduce viruses, worms, Trojan horses, password cracking or login spoofing programs on any University computer or network. In fact, because of the serious damage such programs can cause, the Bren School faculty have adopted a policy which forbids students even to have these types of programs in their accounts or to place them onto any Bren School computer; you may not store such a program on a Bren School computer even if you only wish to study it.

  • You may not try to use equipment or accounts that have not been assigned to you.

  • You may not interfere with others' ability to make use of the resources. For example, it might be reasonable to lock a workstation if you need to leave the room for two or three minutes, but it is not reasonable to lock it while you leave to buy lunch. Another example would be doing something that ties up all or a significant fraction of the machine, thus preventing others from receiving their fair share.

  • You may not destroy other people's work.

  • You may not "spy" on people, that is, you may not attempt to gain information from their accounts or from their external drives when there is good reason to believe that they do not wish you to obtain that information. This includes both attempting to violate the protection facilities provided by the system and also taking deliberate advantage of someone else's failure to protect sensitive information on their account. This works both ways; faculty, staff and members of Computing Support also have the responsibility to respect the privacy of the student. For example, it would be unethical for a faculty member or Support Group member to browse through your personal messages just out of curiosity, even if they have a security level that allows them to do so; we agree to respect your privacy. We do, however retain the right to inspect material on your account when this is necessary to investigate a suspected violation of university rules, such as a cheating incident or a violation of the rules in this document.

  • You may not send mail that appears to come from someone else.

  • You may not advertise any commercial products or use your account to earn money. Non-commercial things like posting your used car advertisement on ics.market are permitted, though. If for some reason you need an account that can be used for commercial purposes, see the Office of Academic Computing.

  • You may not display offensive material in any publicly accessible area. There are materials available on the Internet and elsewhere that some members of the Bren School community will find offensive. (One example is sexually explicit graphics; another is political argument on such issues as abortion.) Bren School and the University are committed to maintaining the free and open exchange of ideas as well as a non-offensive working environment. Thus, Bren School does not restrict the availability of potentially offensive material, but Bren School does regard as unethical conduct the display of such material in any publicly accessible area, including on workstation screens in public rooms and in computer labs.

  • You may not use the computers' printers as copying machines. For example, you may not print out one hundred copies of a report; instead, print out one copy of the report and use a copying machine to obtain the other 99.

  • You may not use Bren School resources to illegally distribute copyrighted material.

Good Citizenship

Your cooperation in the following areas will help us make efficient use of the computing resources and will avoid unnecessary impositions on the time of faculty, staff, and other students. These are not the sort of things which we can expect to enforce rigidly; rather, we are asking your cooperation for the benefit of the whole departmental community Violations of these guidelines would not ordinarily result in any of the penalties listed above beyond number one, unless they were especially flagrant or persist after faculty or staff have asked you to stop.

1. Please be careful not to use the computer to annoy people, for example by sending them messages which they do not wish to receive. (The mail system makes it rather easy to send a message to a very large group of people; please be responsible in your use of this capability. In particular, when you reply to a message sent to a large group, avoid cc'ing your reply to the entire group unless it is a matter of interest to them.)

2. Please do not waste anything (i.e., paper, disk space, CPU time, people time, etc.). Please put your old printouts in the recycling bins.

Acknowledgments

Some of these polices are adapted from those used by the UCLA CS Department. They adapted some of their polices from Columbia University and the California Institute of Technology.

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Account Allocation

GENERAL ACCOUNT INFORMATION

Most computing in the Bren School is done on either UNIX based Sun workstations or on Windows based PCs. These machines have various uses -- research, departmental administration, undergraduate instruction, graduate instruction -- but all are networked together and share a common file space.

In particular, no matter which departmental computer you use, either Windows or Sun, you will use the same account with the same home directory.

All Bren School majors receive both UNIX and Windows accounts. Non-major students receive accounts only if the Bren School course they are enrolled in requires an account on a particular platform.

There are directions for activating instructional accounts on-line at Activating Instructional Accounts and in the CS 364 lab.

Bren School majors and other students enrolled in certain Bren School courses have access to a variety of computers in several labs.

These machines allow you to: read and send e-mail, read announcements for your classes through newsgroups, and complete classwork programming assignments.

Unless you are involved in research, these computers in the instructional labs are the only machines you will be able to use.

If you do become involved in research with a faculty member she may decide to give you access to her machines.

You can check out the Lab Hour and Lab Equipment for more information.

  • Expiration - Expiration dates for accounts are taken from enrollment data provided by the Registrar. When an account is about to expire, an automated email notice is sent.

    In the normal course of events, if you are a Bren School major your account will be automatically extended and you will never see such messages.

    Non-Bren School majors will receive these messages as their accounts are opened and closed each quarter depending on course enrollment. If you continue to enroll in a Bren School course, your account will reactivate automatically.

    If the Registrar does not list you, or you fail to enroll on time, you will be sent one of these notices. If you receive a notice in error, contact helpdesk@ics.uci.edu.
  • Quotas - Your quota is the amount of disk space you are allowed to use. Undergraduate students get a quota of 100 MB. For information on how to check your quota, please see Account Quota.
  • Please Note - When contacting support, please include your Bren School login and student ID number to facilitate the processing of your request. If you change your name or student ID number, you need to notify helpdesk@ics.uci.edu. Otherwise, your account may be automatically locked or your quota removed because of the way the registrar's system works.

For general information about what support offers undergraduate students see Student Information.




Backups

Any data on the Bren School network filer TRON have the snapshot ability available for self restore. Snapshots retain data for 30 days.

To request something be restored from backups, please contact computing support.

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Copyright Infringement

It is illegal to distribute copyrighted materials -- such as software, movies, music, pictures -- without proper authorization.

If a complaint of copyright infringement is reported involving a computer assigned to a Bren School student, the student's account will be locked out until Bren School support personnel have a chance to investigate the allegations.

If copyrighted materials are found, they will be removed immediately.

For the initial complaint (first violation), the Bren School Student Affairs Office (SAO) staff and the appropriate Associate Dean (as well as the academic advisor, for grad students) will be notified.

The student will be required to meet in person with appropriate SAO staff to review applicable campus policies, and to sign a statement verifying receipt and notice of such policies.

This signed statement will become part of the student's file.

For any subsequent complaints, the appropriate Associate Dean, academic advisor (for graduate students) and SAO staff will be notified.

The student will then be referred to the campus Director of Student Judicial Affairs, for academic suspension (upon 2nd violation) or expulsion from the university(upon 3rd violation).

The above statement is based upon:
UNIVERSITY OF CALIFORNIA POLICIES APPLYING TO CAMPUS ACTIVITIES, ORGANIZATIONS AND STUDENTS, UCI CAMPUS IMPLEMENTATION
August 1996, APPENDIX K:
Computer Use Policy (Reference to Section 102.05), which includes "Violating the terms of applicable software licensing agreements or copyright laws."

Please see the most current UCI Copyright Information.


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      • Policies: Add, drop, & change options
      • Policies: Coursework outside UCI
      • Policies: Grade policy
      • Policies: Laptop & computer use
      • Policies: Withdrawal/Readmission
      • Graduation
      • ICS Student Life
      • Computing Support
    • Prospective Undergrad
    • Scholarships & Fellowships
  • PEOPLE
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  • COMMUNITY
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Bren school home > About > Visit the bren school
Directions from LAX
From LAX, go east on Century Blvd; turn right on La Cienega Blvd. and immediately get into the left lane; watch for the sign that says "405" and turn left into the on ramp for the 405 Freeway going South; the drive is forty five minutes to an hour and a half, in traffic.

map on how to get to ICS from LAX

From the 405 Fwy:

  • take the Jamboree exit,

  • turn right on Jamboree,

  • turn left on Campus Dr.,

  • turn right on E. Peltason Dr. to purchase a parking pass at the kiosk,

  • go past one traffic light and watch for Los Trancos (two stop signs),

  • at the first entrance past Los Trancos turn right into Parking Lot 12B

 

map on how to get to ICS from 405 Freeway

From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

More about the school »
  • Bren ICS Mission
  • About the Bren gift
  • Dean's welcome
  • Bren School Intranet
  • Donald Bren Hall
  • Undergraduate education
  • Graduate studies
  • Find faculty and staff
  • Contact us
  • iTunes U
  • Safety procedures
  • Visit the Bren School
Useful links »
  • Local traffic report
  • Local weather forecast
  • Local bus routes
  • AMTRAK
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http://www.ics.uci.edu/about/visit/ Visit Us @ the Donald Bren School of Information & Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
  • EDUCATION
    • Graduate ▸
      • Prospective Students
      • How to Apply
      • Programs of Study
      • Academic Year Plan
      • Forms
      • Policies
      • Funding & Housing
      • Computing Support
      • Campus Resources
      • Visit the Bren School
      • Graduate Student Handbook
      • Contact
    • Undergraduate ▸
      • Contact
      • Academic Advising
      • Academic Year Plan
      • Petitions
      • ICS Majors
      • ICS Minors
      • Policies: Academic Integrity
      • Policies: Academic Standing
      • Policies: Add, drop, & change options
      • Policies: Coursework outside UCI
      • Policies: Grade policy
      • Policies: Laptop & computer use
      • Policies: Withdrawal/Readmission
      • Graduation
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      • Computing Support
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Bren school home > About > Visit the bren school
Visit ICS

Information and computer science buildings at UCI

We can offer virtual tours, pictorials and viewbooks but nothing will substitute for an in-person visit.

The Donald Bren School of Information and Computer Sciences (ICS) is the first and only computer science school in the University of California, offering visitors opportunities to attend academic lectures, interact with leading computer science researchers and recruit intelligent and out of the box thinking computer science students.

If you're interested in attending ICS and want to learn more about a particular degree program - even meet with faculty doing research that piques your interest - please call the Student Affairs Office at (949) 824-5156 and ask to speak to an undergraduate or graduate counselor.

Campus walking tours are offered almost every Monday through Friday. Please check the Tour Calendar for available times and to ensure that there is a tour offered on the day you wish to visit.

Directions to the Bren School
» From LAX (42 miles from UCI)
» From John Wayne (5 miles from UCI)
» From major freeways (405, 73, 55, 5)

Campus Map
» Campus map of UCI (PDF)
The Bren School Student Affairs Office has relocated to: Information and Computer Science, Suite 352 (building #302 on the campus map).

Parking
» UCI guest parking information

More about the school »
  • Bren ICS Mission
  • About the Bren gift
  • Dean's welcome
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  • Graduate studies
  • Find faculty and staff
  • Contact us
  • iTunes U
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Useful links »
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Quick Links
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http://www.ics.uci.edu/about/visit/visit_fromjwa.php directions from john wayne airport @ the bren school of information and computer sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
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  • RESEARCH
    • Research Areas
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    • Undergraduate ▸
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Bren school home > About > Visit the bren school
Directions from John Wayne Airport
From John Wayne Airport:
  • exit the airport at MacArthur - turn right on MacArthur,

  • turn left on Campus Dr.,

  • turn right on E. Peltason Dr. to purchase a parking pass at the kiosk,

  • go past one traffic light and watch for Los Trancos (two stop signs),

  • at the first entrance past Los Trancos turn right into Parking Lot 12B

map on how to get to ICS from John Wayne Airport

From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

More about the school »
  • Bren ICS Mission
  • About the Bren gift
  • Dean's welcome
  • Bren School Intranet
  • Donald Bren Hall
  • Undergraduate education
  • Graduate studies
  • Find faculty and staff
  • Contact us
  • iTunes U
  • Safety procedures
  • Visit the Bren School
Useful links »
  • Local traffic report
  • Local weather forecast
  • Local bus routes
  • AMTRAK
  • Mapquest
Quick Links
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http://www.ics.uci.edu/about/visit/visit_fromfreeway.php Directions from major freeways @ the bren school of information and computer sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
  • EDUCATION
    • Graduate ▸
      • Prospective Students
      • How to Apply
      • Programs of Study
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      • Visit the Bren School
      • Graduate Student Handbook
      • Contact
    • Undergraduate ▸
      • Contact
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      • Petitions
      • ICS Majors
      • ICS Minors
      • Policies: Academic Integrity
      • Policies: Academic Standing
      • Policies: Add, drop, & change options
      • Policies: Coursework outside UCI
      • Policies: Grade policy
      • Policies: Laptop & computer use
      • Policies: Withdrawal/Readmission
      • Graduation
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Bren school home > About > Visit the bren school
Directions from major freeways
405 Freeway | 73 Freeway | 55 Freeway | 5 Freeway


From the 405 Freeway

from the north

  • Exit at Jamboree Road
  • Right on Jamboree Road
  • Left on Campus Drive
  • Right on West or East Peltason Drive into UCI

from the south

  • Exit at Culver Drive
  • Left on Culver Drive
  • Right on Campus Drive
  • Left on West or East Peltason Drive into UCI

From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

back to top



From the 73 Freeway/Toll Road

from the north

  • Exit at University Drive
  • Left on University Drive
  • Right on California Avenue into UCI

from the south

  • Exit at Bison Avenue
  • Right on Bison Avenue into UCI

From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

back to top


From the 55 Freeway

from the north

  • Take 405 south
  • Exit at Jamboree Road
  • Right on Jamboree Road
  • Left on Campus Drive
  • Right on West or East Peltason Drive into UCI

from the south

  • Take 405 south
  • Exit at Jamboree Road
  • Right on Jamboree Road
  • Left on Campus Drive
  • Right on West or East Peltason Drive into UCI

From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

back to top



From the 5 Freeway

from the north

  • Take 5 south to 55 south to 405 south
  • Exit at Jamboree Road
  • Right on Jamboree Road
  • Left on Campus Drive
  • Right on West or East Peltason Drive into UCI

from the south

  • Take 5 north to 405 north
  • Exit at Culver Drive
  • Left on Culver Drive
  • Right on Campus Drive
  • Left on East or West Peltason Drive into UCI


From the parking lot, take paths that go away from East Peltason Drive, and look for Donald Bren Hall.

The Bren School Front Desk is in room 6210 on the 6th floor of Donald Bren Hall. The office telephone number is (949) 824-7427 in case you need to call.

back to top

More about the school »
  • Bren ICS Mission
  • About the Bren gift
  • Dean's welcome
  • Bren School Intranet
  • Donald Bren Hall
  • Undergraduate education
  • Graduate studies
  • Find faculty and staff
  • Contact us
  • iTunes U
  • Safety procedures
  • Visit the Bren School
Useful links »
  • Local traffic report
  • Local weather forecast
  • Local bus routes
  • AMTRAK
  • Mapquest
Quick Links
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http://www.ics.uci.edu/community/news/index.php ICS News @ the Donald Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
  • EDUCATION
    • Graduate ▸
      • Prospective Students
      • How to Apply
      • Programs of Study
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      • Graduate Student Handbook
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      • Policies: Academic Standing
      • Policies: Add, drop, & change options
      • Policies: Coursework outside UCI
      • Policies: Grade policy
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    • Make a Gift
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Bren school home > Community > News
ICS News

Welcome to the ICS news page! Below you'll find links to press releases, articles from national and local media featuring the school, as well as noteworthy achievements of our faculty, students and staff. Media interested in interviewing ICS faculty, students or alumni should contact Matt Miller at (949) 824-1562 or via email: communications@ics.uci.edu.

ICS News

Press Releases
Current listing and archive of press releases and media advisories. 

Videos
Visit our YouTube page to view a wide range of videos, from student life, to innovative research and alumni profiles.

Features
Stories about our faculty, students, alumni, staff and programs.

Noteworthy Achievements
Collection of news briefs about faculty or student awards, accolades and recognition.

In the News
Articles in global, national, local and campus media that feature or mention the Bren School, our students or faculty members.

Annual Reports
Check out our current and past issues of the ICS Annual Report.

BrenBits
A quarterly e-newsletter with news and event updates. 

Stay Connected
Subscribe to the ICS RSS feed to get news delivered directly to your desktop. 

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http://www.ics.uci.edu/community/alumni/stayconnected/index.php Stay Connected contact information form @ The Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
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  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
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    • Graduate ▸
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Bren school home > Community > Alumni >
Stay Connected

Welcome, ALUMNI!

Want to get the latest news about the Bren School, as well as exclusive alumni event invitations? Please update your contact information by submitting the form below. We also invite you to follow us on:

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Please complete all applicable fields:

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Degrees

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B.S. M.S. Ph.D.
Graduation Year
Degree 2
B.S. M.S. Ph.D.
Graduation Year
Degree 3
B.S. M.S. Ph.D.
Graduation Year

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http://www.ics.uci.edu/community/friends/index.php Friends of the Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
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    • Visit the Bren School
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    • Contact Us
  • DEPARTMENTS
    • Computer Science
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Bren school home > Community > Friends
Friends of the Donald Bren School

ICS students with their corporate sponsor

As public funding for our state's universities declines and the costs and risks of corporate R&D escalate, there is increasing motivation for forging bonds between academia and industry.

The Donald Bren School of Information and Computer Sciences is research oriented -- even at the undergraduate level -- with formal projects addressing hardware, software, algorithm design, artificial intelligence and the societal impacts of computing.

Research-orientated education means ICS students arrive at companies already comfortable on the leading edge and poised to make an immediate impact.

In addition to students, our world-renowed faculty work with outside companies and frequently collaborate with professionals in other academic areas to create even greater synergy.

This results in a combination of corporate insight, faculty guidance and student energy that has proven time and again to be the spark that ignites tomorrow’s products and services.

The ICS programs listed below offer you an opportunity to interface with our students and faculty.


Butterworth Product Development Competition
This competition offers an opportunity for students to develop their business skills and earn cash prizes. Learn more »

Tech Jobs
Whether you are looking for computer science interns, part-time or full-time employees, Tech Jobs is the place for you. Post your job opening and know that you are reaching ICS students and alumni each and every time! Learn more » 

Scholarships & Fellowships
Support a student by sponsoring a scholarship or fellowship. In forging relationships with the students they support, current donors have discovered that ICS students are more than scholars, they also are community volunteers, dedicated youth leaders, responsible young adults and promising future professionals. Learn more »

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http://www.ics.uci.edu/community/friends/leadershipcouncil/index.php Leadership Council for UCI's Donald Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
  • EDUCATION
    • Graduate ▸
      • Prospective Students
      • How to Apply
      • Programs of Study
      • Academic Year Plan
      • Forms
      • Policies
      • Funding & Housing
      • Computing Support
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      • Visit the Bren School
      • Graduate Student Handbook
      • Contact
    • Undergraduate ▸
      • Contact
      • Academic Advising
      • Academic Year Plan
      • Petitions
      • ICS Majors
      • ICS Minors
      • Policies: Academic Integrity
      • Policies: Academic Standing
      • Policies: Add, drop, & change options
      • Policies: Coursework outside UCI
      • Policies: Grade policy
      • Policies: Laptop & computer use
      • Policies: Withdrawal/Readmission
      • Graduation
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Bren school home > Community > Friends > Leadership council
Leadership Council

The Dean's Leadership Council is an advisory board that helps advance the Donald Bren School of ICS' research, teaching and public service goals by strengthening the school's ties to industry and the community. Council members provide invaluable input to the dean of the school from their industry and community perspective on industry trends, the ICS curriculum, and so forth.

MISSION

The mission of the Dean's Leadership Council is to:

  • Serve as an advisory group to the dean of the Bren School of ICS;
  • Serve as advocates for the Bren School of ICS and promote the interests of the school in the community;
  • Assist in recruiting additional members to the council who fit the profile for membership and identify other support groups who can contribute to the advancement of the school; and
  • Assist in fund-raising campaigns to help increase the financial resources of the school.

The council meets three times per academic year.

If you are interested in getting involved in the Leadership Council, please contact Ed Hand, Director of Development, at elhand@uci.edu or at (949) 824-6563.

CURRENT COUNCIL MEMBERS

Dave Goff, Chair
Senior VP and CIO, Skilled Healthcare, LLC

Roger Andelin ’87
Senior VP, CTO Online Technology

Paul Butterworth ’74, M.S. ‘81
CTO, Emotive Communications, Inc.

David Cheng ‘91

Kevin Daly, Ph.D.
CEO, Maxxess Systems, Inc.

Rick Dutta
Chairman and CEO, Nexvisionix, Inc.

Jon Hahn ‘81
FFF Enterprises

Arthur Hitomi ‘96
CTO and Co-Founder, Numecent

Robert Kleist
Retired. Founder and Chairman, Printronix

Joel Manfredo
Managing Director, Acies Consulting

Kevin Mun
Vice President of Operations, Vangard Voice Systems, Inc.

Himanshu Palsule
Executive Vice President of Strategy, Sage

Daryl G. Pelc
VP Engineering & Technology, Phantom Works
Huntington Beach Site Engineering Leader

Dinesh Ramanathan M.S. ’95, Ph.D. ‘00

Robert Romney ‘83
Retired. Founder, Zenographics, Inc.

Larry Rowe ’70, Ph.D. ‘76
Retired

Ted Smith
Chairman and CEO, MIND Research Institute

Julie Sokol
VP, Information Technology Services
Irvine Company

Sandra Smart-Ashburn B.S. ‘87
Senior Director, DIRECTTV Group, Inc.

Binh Dang ‘97
Managing Partner, MerdidianLink

Hiq Lee
President, BIS, Experian

Steve M. Anderson B.S.‘86
Partner, Quinnemanuel

Carlos Oliveira PhD ’03
COO, Nextfort Ventures

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http://www.ics.uci.edu/community/alumni/index.php Alumni from the Donald Bren School of Information and Computer Sciences
  • ABOUT
    • About the School
    • Dean's Welcome
    • Facts and Figures
    • Donald Bren Hall
    • Visit the Bren School
    • Equity & Diversity
    • Contact Us
  • DEPARTMENTS
    • Computer Science
    • Informatics
    • Statistics
  • RESEARCH
    • Research Areas
    • Research Centers
    • Research Highlights
  • EDUCATION
    • Graduate ▸
      • Prospective Students
      • How to Apply
      • Programs of Study
      • Academic Year Plan
      • Forms
      • Policies
      • Funding & Housing
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      • Campus Resources
      • Visit the Bren School
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Bren school home > Community > Alumni
ICS Alumni

ICS alumni at Homecoming

You're off campus and on your career path. But you can still make a difference in the Bren School community, whether you've moved down the block, across the country or around the world. For information on involvement opportunites, view the programs below or contact us at khuerth@ics.uci.edu or (949) 824-3074.


Hall of Fame

This fall, UCI will celebrate its 50th anniversary as one of the nation’s leading computer science schools. To honor those alumni who have made a significant impact in their profession or, in other ways, have brought distinction to the School, we are creating the Donald Bren School of Information and Computer Sciences Hall of Fame. If you know of ICS alumni who should be honored in this esteemed group, please fill out the online nomination form. For questions or additional information, please contact Kristin Huerth at khuerth@ics.uci.edu.


Class Notes

We're looking for your Class Notes! Whether you have a new job, started a new company or have any other exciting professional or personal news you want to share with other ICS alumni, we want to know. Please submit all of your Class Notes online so that we can consider them for the next ICS Annual Report, as well as on social media and our website.


Stay Connected

Do we have your most current email and mailing addresses? To get the latest news about the Bren School, as well as exclusive alumni event invitations, please click here to update your contact information. 


ICS Alumni Chapter

The Donald Bren School Alumni Chapter welcomes Anteaters interested in leadership and volunteer roles. In addition to networking and alumni outreach, the chapter offers opportunities to get involved with Bren School student groups. To get involved, please contact Kristin Huerth at khuerth@ics.uci.edu. 


Butterworth Product Development Competition

The Butterworth Product Development Competition encourages the development of new, technically innovative products by ICS graduate and undergraduate students. Formely known as hITEC, this program offers alumni a chance to mentor a team of students as they develop a new product and attempt to enter it into the marketplace. Learn more »


Undergraduate Mentorship Program

The program, comprised of alumni, community members and industry professionals, forms meaningful relationships with female and other traditionally underrepresented students in the fields of engineering and computer science where they explore career interests, academic guidance and personal development. Learn how to get involved as a mentor here.

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Bren school home > Community > Alumni >
ICS Hall of Fame Nomination Form

ICS Hall of Fame

The inaugural Donald Bren School of Information and Computer Sciences Hall of Fame will honor ICS alumni who have made a significant impact in their profession or, in other ways, have brought distinction to ICS. If you know of ICS alumni who should be honored in this esteemed group, please complete the nomination form below; nominations will be reviewed on a rolling basis. For questions or additional information, please contact Ed Hand at elhand@uci.edu or Kristin Huerth at khuerth@ics.uci.edu.

 

NOMINEE

Name:

Class Year:

Address:

City:

State:

Zip:

Phone:

E-mail:

Nomination Statement (Reasons for nominating the Alumnus/a):

Supporting documentation:
Please email Ed Hand at elhand@uci.edu and Kristin Huerth at khuerth@ics.uci.edu additional documentation supporting this nomination such as a biographical sketch, papers showing college or professional projects or activities, or letters of support. The decision of the selection committee will be guided strictly by the materials submitted with the nomination. Please combine supporting documents into one PDF or DOC.

NOMINATOR

Name:

Address:

City:

State:

Zip:

Phone:

E-mail:

Please ensure that names and addresses are filled out above, that nomination statement has clear and concise reasons for nomination, and that all supporting documentation are emailed to Ed Hand and Kristin Huerth.
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http://www.ics.uci.edu/~eli/

 

Home

Students

Curriculum Vitae

Publications

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Contact Information
 

 

 

Elaheh (Eli) Bozorgzadeh

Associate Professor

Computer Science Department

University of California, Irvine

phone: 949-824-8860

 

Affiliated with:

UC Irvine Center for Embedded Computer Systems (CECS)

California Institute for Telecommunication and Information Technology (Calit2)

 

 Research Interests

  • System Synthesis for Self-adaptive Reconfigurable Embedded Systems
  • Energy Sustainability in Embedded systems through micro energy harvesting
  • Physically-aware Architectural Synthesis and layout planning for Embedded Systems
  • Early physical planning for rapid timing closure (Timing budget management)

Awards

  • NSF CAREER Award, 2008
  • Best paper award, IEEE International Conference in Field Programmable Logic and Applications (FPL), 2006.

Recent Activities

  • ACM SIGDA DAC Summer School 2013 (chair).
  • PerCom 2013, Demo Co-chair, San Diego, March, 2013.
  • TPC member of IEEE Field Programmable Logic and Applications (FPL), 2013.
  • TPC member of ACM/IEEE International Symposium in Low Power Electronic Design (ISLPED), 2013.
  • TPC member, ACM/IEEE CODES/ISSS (ESWEEK), 2012.

 

 

http://www.ics.uci.edu/faculty/index.php faculty @ the bren school of information and computer sciences
  • ABOUT
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  • DEPARTMENTS
    • Computer Science
    • Informatics
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  • RESEARCH
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Bren school home > Faculty
Faculty

Faculty members in the Bren School are of national and international renown, including ACM and IEEE Fellows, AAAI Career Fellow and many respected authors, leaders and directors of preeminent research and academic endeavors.

Despite an exhaustive list of accolades, the most notable trait of each faculty member is the unparalleled commitment to teaching and instruction as demonstrated in the classroom.

The Bren School is recruiting faculty at all levels of tenure. For information on faculty opportunities, please visit the employment page.


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Computer Science | Informatics | Statistics | Complete list

photo::Shannon Alfaro Shannon Alfaro
Lecturer
Department: Computer Science
Email: alfaro@uci.edu
Phone: (949) 824-9544
Office: DBH 4208
Learn more
photo::Ardalan Amiri Sani Ardalan Amiri Sani
Assistant Professor
Research Area: Embedded Systems
Networks and Distributed Systems

Department: Computer Science
Email: ardalan@uci.edu
Phone: (949) 824-6753
Office: DBH 3062
Learn more
photo::Pierre Baldi Pierre Baldi
Chancellor's Professor, Director Institute for Genomics and Bioinformatics
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Databases and Data Mining
Environmental Informatics
Statistics and Statistical Theory

Department: Computer Science
Email: pfbaldi@ics.uci.edu
Phone: (949) 824-5809
Office: DBH 4038
Learn more
photo::Brigitte Baldi Brigitte Baldi
Lecturer
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: baldib@uci.edu
Phone: (949) 824-1912
Office: 2208 Bren Hall
Learn more
photo::Lubomir Bic Lubomir Bic
Professor
Research Area: Networks and Distributed Systems
Department: Computer Science
Email: bic@ics.uci.edu
Phone: (949) 824-5248
Office: DBH 3224
Learn more
photo::Geoffrey Bowker Geoffrey C Bowker
Professor
Department: Informatics
Email: gbowker@uci.edu
Phone: (949) 824-4558
Office: DBH 5091
Learn more
photo::Elaheh (Eli) Bozorgzadeh Elaheh (Eli) Bozorgzadeh
Associate Professor
Research Area: Computer Architecture and Design
Department: Computer Science
Email: eli@ics.uci.edu
Phone: (949) 824-8860
Office: DBH 3092
Learn more
photo::Michael Carey Michael J Carey
Bren Professor
Research Area: Databases and Data Mining
Department: Computer Science
Email: mjcarey@ics.uci.edu
Phone: (949) 824-2302
Office: DBH 2091
Learn more
photo::Yunan Chen Yunan Chen
Associate Professor
Research Area: Human Computer Interaction
Medical Informatics

Department: Informatics
Email: yunanc@uci.edu
Phone: (949) 824-0959
Office: DBH 5066
Learn more
photo::Rina Dechter Rina Dechter
Professor; Vice Chair Computing Division, Computer Science
Research Area: Artificial Intelligence and Machine Learning
Department: Computer Science
Email: dechter@ics.uci.edu
Phone: (949) 824-6556
Office: DBH 4232
Learn more
photo::Michael Dillencourt Michael Dillencourt
Professor
Research Area: Algorithms and Complexity
Networks and Distributed Systems

Department: Computer Science
Email: dillenco@ics.uci.edu
Phone: (949) 824-7556
Office: DBH 4086
Learn more
photo::Paul Dourish Paul Dourish
Professor
Research Area: Computer-Supported Cooperative Work
Environmental Informatics
Human Computer Interaction
Medical Informatics
Social Informatics
Ubiquitous Computing

Department: Informatics
Email: jpd@ics.uci.edu
Phone: (949) 824-8127
Office: DBH 5086
Learn more
photo::Nikil Dutt Nikil Dutt
Chancellor's Professor
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: dutt@ics.uci.edu
Phone: (949) 824-7219
Office: DBH 3091
Learn more
photo::Magda El Zarki Magda El Zarki
Professor
Research Area: Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: elzarki@uci.edu
Phone: (949) 228-8584
Office: DBH 3216
Learn more
photo::David Eppstein David Eppstein
Chancellor's Professor
Research Area: Algorithms and Complexity
Computer Graphics and Visualization

Department: Computer Science
Email: eppstein@ics.uci.edu
Phone: (949) 824-6384
Office: DBH 4082
Learn more
photo::Julian Feldman Julian Feldman
Professor Emeritus
Department: Informatics
Email: feldman@ics.uci.edu
Phone: (949) 824-2901
Office: DBH 5029
Learn more
photo::Charless Fowlkes Charless Fowlkes
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Computer Vision

Department: Computer Science
Email: fowlkes@ics.uci.edu
Phone: (949) 824-6945
Office: DBH 4076
Learn more
photo::Michael Franz Michael Franz
Professor, Director: Secure Systems and Languages Laboratory
Research Area: Programming Languages and Systems
Security
Software Engineering

Department: Computer Science
Email: franz@uci.edu
Phone: (949) 824-0016
Office: ICS 444
Learn more
photo::Dan Frost Dan Frost
Senior Lecturer SOE
Research Area: Artificial Intelligence and Machine Learning
Department: Informatics
Email: frost@ics.uci.edu
Phone: (949) 824-1588
Office: DBH 5058
Learn more
photo::Daniel Gillen Daniel Gillen
Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: dgillen@uci.edu
Phone: (949) 824-9862
Office: DBH 2226
Learn more
photo::Tony Givargis Tony Givargis
Professor, Assoc. Dean for Student Affairs
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: givargis@uci.edu
Phone: (949) 824-9357
Office: DBH 3076
Learn more
photo::Michael Goodrich Michael T. Goodrich
Chancellor's Professor
Research Area: Algorithms and Complexity
Computer Graphics and Visualization

Department: Computer Science
Email: goodrich@ics.uci.edu
Phone: (949) 824-9366
Office: DBH 4091
Learn more
photo::Judith Gregory Judith Gregory
Associate Adjunct Professor
Department: Informatics
Email: judithgr@uci.edu
Phone: (312) 315-3371
Office: DBH 5064
Learn more
photo::Stacey Hancock Stacey Hancock
Lecturer PSOE
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: staceyah@uci.edu
Phone: (949) 824-9795
Office: 2204 DBH
Learn more
photo::Ian Harris Ian G. Harris
Associate Professor; Vice Chair of Undergraduate Studies, Computer Science
Research Area: Computer Architecture and Design
Embedded Systems

Department: Computer Science
Email: harris@ics.uci.edu
Phone: (949) 824-8842
Office: DBH 3088
Learn more
photo::Wayne Hayes Wayne Hayes
Associate Professor
Research Area: Biomedical Informatics and Computational Biology
Scientific and Numerical Computing

Department: Computer Science
Email: wayne@ics.uci.edu
Phone: (949) 824-1753
Office: DBH 4092
Learn more
photo::Gillian Hayes Gillian R Hayes
Associate Professor, Vice Chair of Graduate Affairs, Informatics
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Social Informatics
Ubiquitous Computing

Department: Informatics
Email: gillianrh@ics.uci.edu
Phone: (949) 824-1483
Office: DBH 5084
Learn more
photo::Daniel Hirschberg Daniel S. Hirschberg
Professor, Irvine Senate Parliamentarian
Research Area: Algorithms and Complexity
Department: Computer Science
Email: dan@ics.uci.edu
Phone: (949) 824-6480
Office: DBH 4226
Learn more
photo::Alexander Ihler Alexander Ihler
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Department: Computer Science
Email: ihler@ics.uci.edu
Phone: (949) 824-3645
Office: DBH 4066
Learn more
photo::Sandy Irani Sandy Irani
Professor
Research Area: Algorithms and Complexity
Department: Computer Science
Email: irani@ics.uci.edu
Phone: (949) 824-6346
Office: DBH 4042
Learn more
photo::Mimi Ito Mimi Ito
Professor in Residence
Department: Informatics
Email: mizukoi@uci.edu
Phone: (949) 824-9011
Office: DBH 5224
Learn more
photo::Ramesh Jain Ramesh Jain
Bren Professor
Research Area: Computer Vision
Multimedia Computing

Department: Computer Science
Email: jain@ics.uci.edu
Phone: (949) 824-0133
Office: DBH 3222
Learn more
photo::Stanislaw Jarecki Stanislaw Jarecki
Professor
Research Area: Algorithms and Complexity
Department: Computer Science
Email: stasio@ics.uci.edu
Phone: (949) 824-8878
Office: DBH 4026
Learn more
photo::Wesley Johnson Wesley O. Johnson
Professor; Vice Chair of Graduate Affairs, Statistics
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: wjohnson@ics.uci.edu
Phone: (949) 824-0147
Office: DBH 2232
Learn more
photo::James Jones James A. Jones
Associate Professor
Research Area: Software Engineering
Department: Informatics
Email: jajones@uci.edu
Phone: (949) 824-0942
Office: DBH 5214
Learn more
photo::Scott Jordan Scott Jordan
Professor
Research Area: Networks and Distributed Systems
Department: Computer Science
Email: sjordan@uci.edu
Phone: (949) 824-2177
Office: DBH 3214
Learn more
photo::David Kay David G. Kay
Senior Lecturer SOE; Vice Chair for Undergraduate Affairs, Informatics
Department: Informatics
Email: kay@uci.edu
Phone: (949) 824-5072
Office: DBH 5056
Learn more
photo::Dennis Kibler Dennis Kibler
Professor Emeritus
Department: Computer Science
Email: kibler@ics.uci.edu
Phone: (949) 824-0016
Office: DBH 4072
Learn more
photo::Ray Klefstad Ray Klefstad
Lecturer
Research Area: Embedded Systems
Networks and Distributed Systems
Programming Languages and Systems

Department: Computer Science
Email: klefstad@uci.edu
Phone: (949) 824-6753
Office: ICS 424
Learn more
photo::Alfred Kobsa Alfred Kobsa
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Security
Privacy and Cryptography
Ubiquitous Computing

Department: Informatics
Email: kobsa@uci.edu
Phone: (949) 485-5020
Office: DBH 5092
Learn more
photo::Richard Lathrop Richard Lathrop
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology

Department: Computer Science
Email: rickl@ics.uci.edu
Phone: (949) 824-4021
Office: DBH 4224
Learn more
photo::Marco  Levorato Marco Levorato
Assistant Professor
Research Area: Artificial Intelligence and Machine Learning
Networks and Distributed Systems
Statistics and Statistical Theory

Department: Computer Science
Email: levorato@uci.edu
Phone: (949) 824-2175
Office: DBH 3206
Learn more
photo::Chen Li Chen Li
Professor
Research Area: Databases and Data Mining
Department: Computer Science
Email: chenli@ics.uci.edu
Phone: (949) 824-9470
Office: DBH 2092
Learn more
photo::Cristina Lopes Cristina V. Lopes
Professor
Research Area: Programming Languages and Systems
Software Engineering
Ubiquitous Computing

Department: Informatics
Email: lopes@ics.uci.edu
Phone: (949) 824-1525
Office: DBH 5076
Learn more
photo::George Lueker George S. Lueker
Professor Emeritus
Research Area: Algorithms and Complexity
Department: Computer Science
Email: lueker@ics.uci.edu
Phone: (949) 824-5866
Office: DBH 4206
Learn more
photo::Aditi Majumder Aditi Majumder
Professor
Research Area: Computer Graphics and Visualization
Computer Vision

Department: Computer Science
Email: majumder@ics.uci.edu
Phone: (949) 824-8877
Office: DBH 4056
Learn more
photo::Sam Malek Sam Malek
Professor
Research Area: Software Engineering
Department: Informatics
Email: malek@uci.edu
Phone: (949) 824-0639
Office: DBH 5226
Learn more
photo::Gloria Mark Gloria Mark
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Social Informatics

Department: Informatics
Email: gmark@ics.uci.edu
Phone: (949) 824-5955
Office: DBH 5212
Learn more
photo::Melissa Mazmanian Melissa Mazmanian
Associate Professor
Department: Informatics
Email: mmazmani@ics.uci.edu
Phone: (949) 824-9284
Office: DBH 5074
Learn more
photo::Gopi Meenakshisundaram Gopi Meenakshisundaram
Professor
Research Area: Computer Graphics and Visualization
Computer Vision

Department: Computer Science
Email: gopi@ics.uci.edu
Phone: (949) 824-9498
Office: DBH 4212
Learn more
photo::Sharad Mehrotra Sharad Mehrotra
Professor; Vice Chair of Graduate Studies, Computer Science
Research Area: Databases and Data Mining
Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: sharad@ics.uci.edu
Phone: (949) 824-5975
Office: DBH 2082
Learn more
photo::Eric Mjolsness Eric Mjolsness
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Computer Vision
Scientific and Numerical Computing

Department: Computer Science
Email: emj@uci.edu
Phone: (949) 824-3533
Office: DBH 6082
Learn more
photo::Bonnie Nardi Bonnie Nardi
Professor
Research Area: Computer-Supported Cooperative Work
Social Informatics

Department: Informatics
Email: nardi@ics.uci.edu
Phone: (949) 824-6534
Office: DBH 5088
Learn more
photo::Alexandru Nicolau Alexandru Nicolau
Professor, Department Chair
Research Area: Computer Architecture and Design
Embedded Systems
Programming Languages and Systems

Department: Computer Science
Email: nicolau@ics.uci.edu
Phone: (949) 824-4079
Office: DBH 3082
Learn more
photo::Judy Olson Judy Olson
Bren Professor of Information & Computer Sciences
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction

Department: Informatics
Email: jsolson@uci.edu
Phone: (949) 824-0080
Office: DBH 5206
Learn more
photo::Gary Olson Gary M. Olson
Bren Professor of Information & Computer Sciences
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Social Informatics

Department: Informatics
Email: golson@uci.edu
Phone: (949) 824-0077
Office: DBH 5202
Learn more
photo::Hernando Ombao Hernando Ombao
Professor
Research Area: Artificial Intelligence and Machine Learning
Statistics and Statistical Theory

Department: Statistics
Email: hombao@uci.edu
Phone: (949) 824-5679
Office: DBH 2206
Learn more
photo::Donald Patterson Donald J Patterson
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Computer-Supported Cooperative Work
Human Computer Interaction
Medical Informatics
Ubiquitous Computing

Department: Informatics
Email: djp3@ics.uci.edu
Phone: (206) 355-5863
Office: DBH 5084
Learn more
photo::Richard Pattis Richard E Pattis
Senior Lecturer SOE
Department: Computer Science
Email: pattis@ics.uci.edu
Phone: (949) 824-2704
Office: DBH 4062
Learn more
photo::David Redmiles David Redmiles
Professor
Research Area: Computer-Supported Cooperative Work
Human Computer Interaction
Software Engineering

Department: Informatics
Email: redmiles@ics.uci.edu
Phone: (949) 824-3823
Office: DBH 5232
Learn more
photo::Amelia Regan Amelia C. Regan
Professor
Research Area: Algorithms and Complexity
Networks and Distributed Systems

Department: Computer Science
Email: aregan@uci.edu
Phone: (949) 824-2611
Office: DBH 4068
Learn more
photo::Debra Richardson Debra J. Richardson
Professor Emeritus
Research Area: Software Engineering
Department: Informatics
Email: djr@ics.uci.edu
Phone: (949) 824-7353
Office: DBH 5241
Learn more
photo::Walt Scacchi Walt Scacchi
Sr. Research Scientist, Institute for Software Research
Research Area: Computer-Supported Cooperative Work
Software Engineering

Department: Lecturer
Email: wscacchi@ics.uci.edu
Phone: (949) 824-4130
Office: ICS2 202
Learn more
photo::Isaac Scherson Isaac D. Scherson
Professor
Research Area: Computer Architecture and Design
Embedded Systems
Networks and Distributed Systems

Department: Computer Science
Email: isaac@ics.uci.edu
Phone: (949) 824-8144
Office: ICS 464C
Learn more
photo::Babak Shahbaba Babak Shahbaba
Associate Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Statistics and Statistical Theory

Department: Statistics
Email: babaks@uci.edu
Phone: (949) 824-0623
Office: DBH 2224
Learn more
photo::Weining Shen Weining Shen
Assistant Professor
Research Area: Statistics and biostatistics
Department: Statistics
Email: weinings@uci.edu
Phone: (949) 824-5968
Office: DBH 2241
Learn more
photo::Padhraic Smyth Padhraic Smyth
Professor, Director, UCI's Data Science Initiative
Research Area: Artificial Intelligence and Machine Learning
Databases and Data Mining
Scientific and Numerical Computing
Statistics and Statistical Theory

Department: Computer Science
Email: smyth@ics.uci.edu
Phone: (949) 824-2558
Office: DBH 4216
Learn more
photo::Thomas Standish Thomas A. Standish
Professor Emeritus
Department: Informatics
Email: standish@uci.edu
Phone: (949) 497-3064
Office: DBH 5048
Learn more
photo::Hal Stern Hal Stern
Professor and Dean
Research Area: Artificial Intelligence and Machine Learning
Statistics and Statistical Theory

Department: Statistics
Email: sternh@uci.edu
Phone: (949) 824-7405
Office: DBH 6215
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photo::Joshua Tanenbaum Joshua G. Tanenbaum
Acting Assistant Professor
Department: Informatics
Email: tanenbaj@uci.edu
Phone: 949-824-7078
Office: DBH 5052
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photo::Richard Taylor Richard Taylor
Professor Emeritus, Director, Institute for Software Research
Research Area: Computer-Supported Cooperative Work
Networks and Distributed Systems
Software Engineering

Department: Informatics
Email: taylor@ics.uci.edu
Phone: (949) 824-6429
Office: DBH 5216
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photo::Bill Tomlinson Bill Tomlinson
Professor
Research Area: Computer Graphics and Visualization
Environmental Informatics
Human Computer Interaction
Ubiquitous Computing

Department: Informatics
Email: wmt@uci.edu
Phone: (949) 824-9804
Office: DBH 5068
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photo::Gene Tsudik Gene Tsudik
Chancellor's Professor
Research Area: Security
Privacy and Cryptography

Department: Computer Science
Email: gts@ics.uci.edu
Phone: (949) 824-3410
Office: ICS 458E
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photo::Jessica Utts Jessica Utts
Professor and Chair
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: jutts@uci.edu
Phone: (949) 824-0649
Office: DBH 2212
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photo::Andre van der Hoek Andre van der Hoek
Professor and Chair, Department of Informatics
Research Area: Software Engineering
Department: Informatics
Email: ichair@ics.uci.edu
Phone: (949) 824-6326
Office: DBH 5038
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photo::Alexander Veidenbaum Alexander Veidenbaum
Professor
Research Area: Computer Architecture and Design
Databases and Data Mining
Programming Languages and Systems

Department: Computer Science
Email: alexv AT ics.uci.edu
Phone: (949) 824-6188
Office: DBH 3056
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photo::Nalini Venkatasubramanian Nalini Venkatasubramanian
Professor
Research Area: Multimedia Computing
Networks and Distributed Systems

Department: Computer Science
Email: nalini@ics.uci.edu
Phone: (949) 824-5898
Office: DBH 2086
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photo::Richert Wang Richert Wang
Lecturer
Research Area: Networks and Distributed Systems
Programming Languages and Systems
Software Engineering

Department: Computer Science
Email: rkwang@uci.edu
Phone: (949) 824-6753
Office: ICS 424
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photo::Xiaohui Xie Xiaohui Xie
Professor
Research Area: Artificial Intelligence and Machine Learning
Biomedical Informatics and Computational Biology
Medical Informatics

Department: Computer Science
Email: xhx@ics.uci.edu
Phone: (949) 824-9289
Office: DBH 4058
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photo::Harry Xu Harry Xu
Assistant Professor
Research Area: Programming Languages and Systems
Software Engineering

Department: Computer Science
Email: harry.g.xu@uci.edu
Phone: (949) 824-8870
Office: DBH 3212
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photo::Yaming Yu Yaming Yu
Associate Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: yamingy@uci.edu
Phone: (949) 824-7361
Office: DBH 2228
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photo::Zhaoxia Yu Zhaoxia Yu
Associate Professor
Research Area: Statistics and Statistical Theory
Department: Statistics
Email: yu.zhaoxia@uci.edu
Phone: (949) 824-0491
Office: DBH 2214
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photo::Shuang Zhao Shuang Zhao
Assistant Professor
Research Area: Computer Graphics and Visualization
Department: Computer Science
Email: shz@ics.uci.edu
Phone: (949) 824-4942
Office: DBH 4214
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photo::Kai Zheng Kai Zheng
Associate Professor
Research Area: Human Computer Interaction
Medical Informatics

Department: Informatics
Email: kai.zheng@uci.edu
Phone: (949) 824-6920
Office: DBH 5228
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photo::Hadar Ziv Hadar Ziv
Lecturer/ Asst. Project Scientist
Department: Informatics
Email: ziv@ics.uci.edu
Phone: (949) 824-2901
Office: DBH 5062
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http://www.ics.uci.edu/~djr/ http://www.ics.uci.edu/~taylor/ Richard Taylor

Richard Taylor

Richard N. Taylor
  • Chancellor's Professor Emeritus
  • Director of the Institute for Software Research (ISR)
  • Professor of Information and Computer Sciences
  • Department of Informatics
  • Research Area: Software Engineering
  • Electronic Mail:  taylor 'at sign goes here' uci 'dot goes here' edu
  • ISR Office: ICS2-203
  • Bren Hall Office: 5216 ( Campus map & directions
    School maps and directions )
  • Office Phone: (949) 824-6429
  • Fax: (949) 824-1715
  • Mailing Address:
    Information and Computer Sciences
    University of California, Irvine
    Irvine, California 92697-3440


  • Google Scholar Profile

Cover image of "Software Architecture: Foundations, Theory, and Practice"

The Taylor/Medvidovic/Dashofy textbook/reference on software architecture is available!

  • Software Architecture: Foundations, Theory, and Practice at Wiley.com, our publisher's site
  • Software Architecture: Foundations, Theory, and Practice at Amazon.com
  • Software Architecture: Foundations, Theory, and Practice at Barnes and Noble

New: Visit the book's website for discussions and additional resources.

 

Last update: November 27, 2015



Biography | Institute for Software Research | Design | Research Projects | Graduate Students and Ph.D. Graduates | Classes | More Important Stuff | Publications


Biography

Richard N. Taylor is a Professor Emeritus of Information and Computer Sciences at the University of California at Irvine and a member of the Department of Informatics (of which he was chair from its founding in January of 2003 through June, 2004). He received the Ph.D. degree in Computer Science from the University of Colorado at Boulder in 1980. His research interests are centered on design and software architectures, especially event-based and peer-to-peer systems and the way they scale across organizational boundaries. Professor Taylor is the Director of the Institute for Software Research, which is dedicated to fostering innovative basic and applied research in software and information technologies through partnerships with industry and government. He has served as chairman of ACM's Special Interest Group on Software Engineering, SIGSOFT, chairman of the steering committee for the International Conference on Software Engineering, and was general chair of the 1999 International Joint Conference on Work Activities, Coordination, and Collaboration and the 2004 International Symposium on the Foundations of Software Engineering. He was the General Chair for the 2011 International Conference on Software Engineering, held in Honolulu, Hawaii, May 2011.

Taylor was a 1985 recipient of a Presidential Young Investigator Award and in 1998 was recognized as an ACM Fellow. In 2005 he was awarded the ACM SIGSOFT Distinguished Service Award. In May 2008 he received ICSE's "Most Influential Paper Award", along with co-authors Peyman Oreizy and Nenad Medvidovic, for "Architecture-based runtime software evolution" from ICSE 1998. In May, 2009 he was recognized with the 2009 ACM SIGSOFT Outstanding Research Award. In February 2010 he was designated a University of California, Irvine Chancellor's Professor.

A PDF version of my April, 2015 resume.


Software Engineering

Events of Note

  • Keynote talk at ESEC/FSE 2009 (Amsterdam), for the 2009 ACM SIGSOFT Outstanding Research Award (slides 13.7Mb)

  • 2011 International Conference on Software Engineering: ICSE '11

    ICSE 2011ICSE 2011


  • The IMPACT project: "an ongoing initiative whose goal is to determine the impact of software engineering research upon software engineering practice."

Institute for Software Research (ISR)

Established in July 1999, the University of California's Institute for Software Research (ISR) is dedicated to

  • fostering innovative basic and applied research in software and information technologies;
  • working with established companies, startups, government agencies, and standards bodies to develop and transition the technologies to widespread and practical application;
  • educating the next generation of software researchers and practitioners in advanced software technologies;
  • supporting the public service mission of the University of California in developing the economic basis of the State of California.

The Institute's activities are focused on:

  • Direct support of research projects, including funding personnel
  • Research support services (administrative services)
  • Conference and seminar services
  • Technology transition
The Institute extends beyond the boundaries of UC Irvine and includes faculty from a variety of institutions. The Institute's web site details the faculty, staff, and students involved, the current projects, events, and news.

Research

My research is focused on design — the issues, techniques, and agents involved in creating and evolving software artifacts and processes. Specific emphases include:

  • software architecture: means for designing, organizing, and describing distributed and decentralized applications.
  • architecture-based software development environments: tools to support the conceptual approach, ranging from design-time tools to implementation to run-time dynamic adaptation.

The foundational work on architectures has centered on means for describing architectures in various styles and development of new architectural styles (notably the "C2" components-and-connectors style and, with Roy Fielding, the REST style for Internet applications such as the WWW). Current work in this domain is directed at developing, with Michael Gorlick and Justin Erenkrantz, the CREST style for computational exchange on the Internet.

The environment and tools work has emphases on environment architectures (see the ArchStudio site for details), an extensible software architecture description language and supporting toolkit (see the xADL web site for the details and the download), and architecture-driven dynamic adaptation of applications. Virtually all of this work utilizes event-based approaches. A variety of publications in this area can be found at http://www.isr.uci.edu/architecture/publications.html.

Sample Publications (See full list):

  • Richard N. Taylor, Nenad Medvidovic, Kenneth M. Anderson, E. James Whitehead, Jr., Jason E. Robbins, Kari A. Nies, Peyman Oreizy, and Deborah L. Dubrow. A component and message-based architectural style for GUI software. IEEE Transactions on Software Engineering, 22, 6, pp. 390-406 (June, 1996.)
  • Roy Fielding, E. James Whitehead, Jr., Kenneth Anderson, Peyman Oreizy, Gregory Bolcer, and Richard Taylor. Web-based Development of Complex Information Products. Communications of the ACM, 41,8, pp. 84-92. (August 1998.)
  • Nenad Medvidovic and Richard N. Taylor. A Classification and Comparison Framework for Software Architecture Description Languages. IEEE Transactions on Software Engineering, Vol. 26, No. 1, pp. 70-93 (January 2000).
  • Peyman Oreizy, Michael M. Gorlick, Richard N. Taylor, Dennis M. Heimbigner, Gregory F. Johnson, Nenad Medvidovic, Alex Quilici, David S. Rosenblum, Alexander L. Wolf. An Architecture-Based Approach to Self-Adaptive Software. IEEE Intelligent Systems,14, 3, pp. 54–62 (May/June 1999).
  • Kenneth M. Anderson, Richard N. Taylor, E. James Whitehead, Jr. Chimera: Hypermedia for Heterogeneous Environments. ACM Transactions on Office Information Systems, 18, 3, pp. 211-245 (July, 2000).
  • Roy Fielding and Richard N. Taylor. Principled design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2, 2, pp. 115-150 (May 2002).
  • Eric M. Dashofy, André van der Hoek, Richard N. Taylor. An Infrastructure for the Rapid Development of XML-based Architecture Description Languages. Proceedings of the 2002 International Conference on Software Engineering (ICSE 2002), Orlando, May 22-24, 2002.
  • Rohit Khare and Richard N. Taylor. “Extending the Representational State Transfer (REST) Architectural Style for Decentralized Systems.” Proceedings of the International Conference on Software Engineering (ICSE), May, 2004, Edinburgh, Scotland.
  • Justin R. Erenkrantz, Michael Gorlick, Girish Suryanarayana, Richard N. Taylor. From representations to computations: the evolution of web architectures. Proceedings of the 6th joint meeting of the European Software Engineering Conference and the 14th ACM SIGSOFT Symposium on Foundations of Software Engineering (ESEC/FSE 07), September 03 - 07, 2007, Pages: 255 - 264.
  • Georgas, J. C. and Taylor, R. N. 2008. Policy-based self-adaptive architectures: a feasibility study in the robotics domain. In Proceedings of the 2008 international Workshop on Software Engineering For Adaptive and Self-Managing Systems (Leipzig, Germany, May 12 - 13, 2008). SEAMS '08. ACM, New York, NY, 105-112

Design

(Not just software!)

The Proposal for a School of Design at the University of California, Irvine (dated November 2002; PDF format; 188 pages; 8.5M) recommends establishment of a research-based school having an interdisciplinary approach to design and a comprehensive array of degree programs at the bachelor's, master's, and doctoral levels. The School of Design proposal was transmitted to the Irvine Division of the Academic Senate on January 13, 2003. The proposal is no longer under active consideration, but remains a vision document for the design research community.

Software Design

Andre van der Hoek and I wrote an extended paper on the future of software design and architecture, as part of FOSE 2007: Future of Software Engineering.


Graduate Students and Ph.D. Graduates

Ph.D. Graduates

I've had the privilege of supervising the following Ph.D. graduates.

1986 Stephen Clarke-Willson Studio Technical Director, ArenaNet
1988 Ray Klefstad Lecturer, Electrical Engineering and Computer Science, UC Riverside
1989 Michal Young Associate Professor, Computer Science, University of Oregon
1992 M. Gregory James Lead Product Manager, American Express
1993 David Levine

Vice President of Research and Development, CombineNET (Formerly, Director, Center for Distributed Object Computing; Washington University, St. Louis)

1993 Dennis Troup Emerging Technologies Consultants, Inc.
1994 Patrick Young Lecturer, Computer Science Department, Stanford University
1996 Holly Hildreth Software Safety Engineer at General Atomics Aeronautical Systems
1996 John Self Senior Principal Engineer, BAE Systems, San Diego
1997 Kenneth Mark Anderson Associate Professor and Associate Chair, Computer Science Department, University of Colorado, Boulder
1998 Gregory Alan Bolcer CTO, Bitvore Corporation
1998 Neno Medvidovic

Professor and Associate Chair, Computer Science Department, University of Southern California. Director Emeritus, Center for Systems and Software Engineering.

2000 Peyman Oreizy Founder, Dynamic Variable LLC
2000 E. James Whitehead Professor and past Chair, Department of Computer Science, University of California, Santa Cruz
2000 Roy Thomas Fielding Senior Principal Scientist at Adobe Systems
Director at The Apache Software Foundation
Co-founder and member at Apache HTTP Server Project
2002 Robb Klashner Assistant Professor, New Jersey Institute of Technology
2003 Rohit Khare Founder at Ångströ.com; now Product Manager, Google, Inc.
2004 Peter Kammer Senior Software Engineer, Google, Inc.
2006 Jie Ren Senior Staff Software Engineer, Google, Inc.
2007 Girish Suryanarayana

Senior Research Scientist, Siemens Corporate Research & Technologies, India.

2007 Eric Dashofy

Principal Director of Development, Enterprise Information Services, The Aerospace Corporation

2008 John Georgas Associate Professor, Department of Computer Science, Northern Arizona University
2009 Hazel Asuncion Associate Professor, Computing and Software Systems, University of Washington, Bothell
2009 Justin Erenkrantz

Head of Compute Architecture, Bloomberg L.P.
Former President, The Apache Software Foundation

2010 Art Hitomi

CTO and Co-Founder at Numecent

2011 Scott Hendrickson Software Engineer, Google, Inc.
2012 Yongjie Zheng Assistant Professor, University of Missouri—Kansas City
2013 Leyna Zimdars Lecturer, Santa Clara University
2014 Alegria Baquero Software Engineer, ZocDoc

Current Ph.D. Students

Student Area
Michael Gorlick (Advanced to Candidacy/Passed topic defense) Computational State Transfer (COAST): Secure and Adaptive Decentralized Services

The Things that Really Matter

  • My Family! This picture is of my wife and myself halfway up the Aiguille du Midi, near Chamonix.
  • St. Andrew's Presbyterian is where you'll find me on Sunday. Look for me in the choir singing tenor II. Here's a photo of the St. Andrew's choirs, taken in April of 2002.
  • Music: anything baroque, sacred choral music, Rondo Veneziano.
  • The mezzotints of John Martin. See, for example, Adam and Eve,The Morning Hymn .
  • Most everything French, but especially food and St. Barts . Cannes is wonderful, except during the cheesy film festival...

References or pointers on these pages to non-University entities do not represent endorsement by the Regents of the University of California. But you knew that already....
School of Information and Computer Sciences,
University of California, Irvine CA 92697-3440
Copyright © 1996-2015
http://www.ics.uci.edu/~rkwang/ Richert Wang Homepage http://www.ics.uci.edu/~goodrich/ Michael T. Goodrich

Contact Information

Curriculum Vitae (PDF)

Book websites:

  • Algorithm Design and Applications (new)
  • Introduction to Computer Security
  • Data Structures and Algorithms in Java
  • Data Structures and Algorithms in C++
  • Data Structures and Algorithms in Python
  • Algorithm Design (2001)
Teaching and Seminars

Publications on DBLP

Publications on arXiv.org

Google Scholar Profile

Selected Archived Publications:

  • Information Security Algorithms
  • Parallel, Distributed, and External-Memory Algorithms
  • Graph and Network Algorithms
  • Data Structures and Algorithms
  • Geometric Algorithms
Research Colleagues

Research Projects

Michael T. Goodrich

Chancellor's Professor
Dept. of Computer Science
Bren School of Info. and Computer Sciences

Dept. of EECS (by courtesy)
Samueli School of Engineering

University of California, Irvine

Short Biography

Prof. Goodrich received his B.A. in Mathematics and Computer Science from Calvin College in 1983 and his PhD in Computer Sciences from Purdue University in 1987.

He is a Chancellor's Professor at the University of California, Irvine, where he has been a faculty member in the Department of Computer Science since 2001. In addition, he currently serves as Technical Director for the ICS Center for Algorithms and Theory of Computation. He was a professor in the Department of Computer Science at Johns Hopkins University from 1987-2001.

Dr. Goodrich's research is directed at the design of high performance algorithms and data structures with applications to information assurance and security, the Internet, machine learning, and geometric computing. He has pioneered and led research on efficient solutions to a number of fundamental problems, including sorting, convex hull construction, nearest-neighbor searching, linear programming, privacy-preserving data access, network traceback, and data authentication.

With over 300 publications, including several widely-adopted books, his recent work includes contributions to efficient and secure distributed data structures, information privacy, social networks, and cloud security. He has served as a consultant to AT&T, Walt Disney Animation Studios, and the National Science Foundation. He has experience as an expert witness in patent litigation involving algorithms, cryptography, machine learning, digital rights management (DRM), computer security, networking, and storage technologies. He is an ACM Distinguished Scientist, a Fellow of the American Association for the Advancement of Science (AAAS), a Fulbright Scholar, a Fellow of the IEEE, and a Fellow of the ACM. He is a recipient of the IEEE Computer Society Technical Achievement Award, the NSF Research Initiation Award, the DARPA Spirit of Technology Transfer Award, the Brown Univ. Award for Technological Innovation, the ACM Recognition of Service Award, and the Pond Award for Excellence in Undergraduate Teaching.

His Erdős number is three (3), here's why.


Recent and Upcoming Conference Committee Service:

  • 2016 Workshop on Algorithm Engineering and Experiments (ALENEX), PC co-chair.
  • 2015 ACM Cloud Computing Security Workshop (CCSW), PC member.
  • Algorithms and Data Structures Symposium (WADS) 2015, PC member.
  • 23rd International Symposium on Graph Drwaing (GD), 2015, PC member.
  • 21st ACM Conference on Computer and Communications Security (CCS), 2014, PC member.
  • 2013 IEEE Int. Conf. on Big Data (BigData), 2013, PC member.
  • 20th Annual European Symposium on Algorithms (ESA), Ljubljana, Slovenia, 2012, PC member.
  • 24rd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), Pittsburgh, PA, USA, June 25-27, 2012, PC member.
  • 19th International Symposium on Graph Drawing, 2011, PC member.
  • 8th Workshop on Algorithms and Models for the Web Graph (WAW), 2011, PC member.
  • Workshop on Analytic Algorithmics and Combinatorics (ANALCO), 2011, PC member.
  • 18th International Symposium on Graph Drawing, 2010, PC member.
  • The 31st IEEE Symposium on Security and Privacy, 2010, PC member.
  • The ACM International Symposium on Advances in Geographic Information Systems (ACM GIS), 2009, PC member.
  • The ACM International Symposium on Advances in Geographic Information Systems (ACM GIS), 2008, PC member.
  • Second International Frontiers of Algorithmics Workshop (FAW '08), PC member.
  • 7th International Workshop on Experimental Algorithms (WEA), 2008, PC member.
  • The 5th Workshop on Algorithms and Models for the Web-Graph (WAW2007), PC member.
  • 19th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), 2007, PC member.
  • 21st IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2007, PC member.
  • 5th International Conference on Applied Cryptography and Network Security (ACNS), 2007, PC member.
  • 15th Annual European Symposium on Algorithms (ESA), 2007, PC member.
  • 13th ACM Conference on Computer and Communications Security (CCS), 2006, PC member.
  • 32nd Int. Colloq. on Automata, Languages, and Programming (ICALP), 2005, PC member, Track C, Security and Cryptography Foundations.
  • ACM-SIAM Symp. on Discrete Algorithms (SODA) 2005, PC member
  • Graph Drawing 2003, PC member
  • Graph Drawing 2002, PC chair
  • ALENEX 2002, PC member
  • Symp. on Computational Geometry (SoCG), 2002, PC member
  • Symp. on Discrete Algorithms (SODA), 2002, PC member
  • Graph Drawing 2001, PC member
  • WADS 2001, PC member
  • Graph Drawing 2000, PC member
  • WAE 2000, PC member
  • IEEE Symp. on Foundations of Computer Science (FOCS), 2000, PC member
  • ALENEX 00, steering committee member
  • ALENEX 99, co-founder

Other Links:

  • Tiemersma's Simple Rules for Coherent Writing
  • Simple Tips for Giving Good Research Talks
http://www.ics.uci.edu/~wscacchi/ Walt Scacchi

Home Page for Walt Scacchi




Coordinates and Addresses

Positions: Sr. Research Scientist, Research Faculty, ISR and
Research Director, Institute for Virtual Environments and Computer Games

Areas: Software, Computer Games and Virtual Worlds
Office: Room ICS2-202, Bldg. 304

US mail:

Walt Scacchi
Institute for Software Research
Donald Bren School of Information and Computer Sciences
University of California, Irvine
Irvine, CA 92697-3455 USA

Other:

Tel: +1-949-824-4130
Fax: +1-949-824-1715
E-mail: wscacchi <at> ics <onedot> uci <onedot> edu
Web: http://www.ics.uci.edu/~wscacchi

Curriculum Vita (pdf)
Bio paragraph (pdf)

Research Interests

  • Computer Games and Virtual Worlds

  • Organizational Studies of Free/Open Source Software Development

  • Acquisition of Open Source Software Systems (including governance, procurement, licensing, software engineering, deployment, operations and field support)

  • Computer Supported Collaborative Work Environments

  • Organizational and Software Process Modeling and Simulation


Interested in applying for Graduate School in Software Engineering?
Interested in applying for
Graduate School in Human-Centered Computing?

Upcoming, Recent, or Legacy Events

  • Intern. Conf. Software Engineering: 5th Workshop on Games and Software Engineering, Austin, TX, May 2016.

  • Intern. Conf. Software Engineering: 4th Workshop on Games and Software Engineering, Florence, IT, 18 May 2015.

  • Intern. Conf. Software Engineering: 3rd. Workshop on Games and Software Engineering, San Francisco, CA, 17 May 2013.

  • Ninth International Conference on Open Source Systems, Koper-Capodistria, Slovenia., 25-28 June 2013.

  • Eighth International Conference on Open Source Systems, 10-13 September 2012, Hammamet, Tunisia

  • International Conference on Software Engineering: Workshop on Games and Software Engineering, 9 June 2012.

  • International Conference on Software Engineering: Software Engineering in Practice Track, 21-28 May 2011.

  • UCI NSF Workshop on the Future of Research in Computer Games and Virtual Worlds, 22-24 September 2010. (Final Report).

  • CCC Workshop on Future Research in Free/Open Source Software, Newport Beach, CA, 10-12 February 2010 (Final Report).

  • Sixth International Conference on Open Source Systems, Notre Dame, IN, June 2010.

  • New organizational research unit at UCI, the Institute for Virtual Environments and Computer Games (formerly the ICS Center for Computer Games and Virtual Worlds)

    • Podcast (or Web) on the future of smart games and computer game science at UCI, March 2010

    • New computer game science degree program announced at UCI, March 2010

  • IFIP Working Group 2.13 on Open Source Software

  • Web 3.0: The Game Web, Calit2, UC Irvine, 21 June 2007, and follow-up news story

  • MASSIVE: A Research Summit on Networked Multiplayer Games at UCI (20 April 2006).

  • Corporate Opportunities for Multiplayer Games, OCTANe@UCI and AeA, at UCI (21 April 2006)


Current, Recent, or Legacy Research Projects

  • Advancing Software Acquisition Research in Open Architecture and Open Source Software Systems (2007-2015+)

  • Developing an Informal Music Learning Game Environment, with The San Francisco Symphony, (2011-2013--released, March 2014!)

    • SFS-UCI Press Release, February 2014.

    • San Francisco Chronicle (SFGate.com), Symphony's SFSKids.org plays off savvy students' curiosity, April 2014.

    • San Francisco Classical Voice, S.F. Symphony's New Website for Kids: Let the Games Begin, April 2014.

    • UCI New University, Music Ed on the Web, April 2014.

  • Creating a Framework for Prototyping Science Mission Games, (2012-2013).

  • Workshop on Future Research and Challenges in Computer Games and Virtual Worlds, (2010-2012). Final Report now available (August 2012)

  • Naval Postgraduate School, Center for the Edge Power, Exploring the Potential of Computer Games and Virtual Worlds for Decentralized Command and Control, (2010-2011)

  • Workshop on Future Research in Free/Open Source Software, (2009-2010)

  • Decentralized Virtual Activities and Technologies: A Socio-Technical Approach, (2008-2012)

    • News story on one research effort spanning physical and virtual worlds as part of this project, March 2010.

    • News story on Getting Down to Business with Games, UCI Calit2 Interface, Spring 2009.

  • Naval Postgraduate School, Acquisition Research Program, Emerging Issues in the Acquisition of Open Source Software and Open Architectures, (2007-2015).

  • Workshop to Establish National and International Research Infrastructures for Multidisciplinary Empirical Science of Free/Open Source Software, (2007-2008)

  • Naval Postgraduate School, Center for the Edge, Governance Issues in Open Source Software Development, (2007-2008)

  • Story on research partnership investigating generation-beyond-next computer game culture and technology (2007-2009)

  • Discovering the Practices and Processes of Open Source Software Development (2006-2008)

  • Game Research Grid for Science Learning Games (2004-2006)

  • Another news story on game research at UCI Calit2 Interface, Summer 2007.

    • Story on game research at UCI Calit2 Interface, Spring 2007.

    • DinoQuest Online now up and running--start here (you will need to register to play)

    • NSF Presentation on DinoQuest, DinoQuest Online, and Cyberinfrastructure for Networked Science Museum Exhibits, September 2006.

    • Story on DinoQuest and DinoQuest Online science learning game environment, UCI Calit2 Interface, Spring 2006.

    • Early story on Dinosaur science learning game project, UCI Calit2, Summer 2005.

  • The Organizational Dynamics of Software Bugs, Errors, Issues, and Repairs (2002-2006).

  • An Integrated Social and Technical Approach to Development of Distributed Inter-Organizational Applications (2002-2006)

  • Understanding Open Source Software Development Processes, Practices and Communities (2000-2005)

    • Special Issue of Software Process--Improvement and Practice on Free/Open Source Software Development Processes (March/April, 2006)

    • CNet.com news story on Open Source under the Microscope, 5 January 2004

    • NSF Press Release, Faster, Better, Cheaper: Open-Source Practices May Help Improve Software Engineering, 3 December 2003.

  • Research Directions for Continuous (Re)Design of Open Source Software (Fall 2003-2005)

  • Dynamic Process Enactment, Discovery and Recovery (Spring 2003-2005)

  • Exploring Open Software Systems Acquisition Processes and Architectures (2000-2002, archived).


Past Projects and Research Legacy

  • The USC ATRIUM Laboratory Web site

  • Organizational Studies of Computing and Software Development

  • The USC System Factory Project and related Software Engineering Environments

  • Organizational and Software Process Engineering

  • Research Autobiography


Selected Research Publications and Working Papers

  • Social Interaction for Knowledge Transfer within Game-Based Virtual Worlds (to appear, 2016).

  • Virtual Meetings, in W.S. Bainbridge and M. Roco (Eds.),Handbook of Science and Technology Convergence, Springer, (revised version to appear).

  • Case Studies and Practices in Local Game Jam Software Development Organization: A Software Engineering Perspective,Foundations of Digital Games 2015 Workshop on Game Jams, Hackathons and Game Creation Events, Pacific Grove, CA, June 2015.

  • Research Challenges at the Intersection of Computer Games and Software Engineering, Proc. 2015 Conf. Foundations of Digital Games(FDG 2015), Pacific Grove, CA, June 2015.

  • Introduction to Computer Games and Software Engineering, (with K. Cooper), in K. Cooper and W. Scacchi,Computer Games and Software Engineering, CRC Press, Taylor & Francis Pubs. (2015).

  • Computer Games and Software Engineering, (with K. Cooper). CRC Press, Taylor & Francis Pubs., Boca Raton, FL, (2015).

  • Repurposing Game Play Mechanics as a Technique for Developing Game-Based Virtual Worlds, in K. Cooper and W. Scacchi,Computer Games and Software Engineering, CRC Press, Taylor & Francis Pubs. (2015).

  • Achieving Better Buying Power Through Acquisition of Open Architecture Software Systems for Web-Based and Mobile Devices, (with T. Alspaugh),Proc. 12th.Annual Acquisition Research Symposium,Monterey, CA, May 2015.

  • SFSKids.org: An Informal Music Learning Environment, Walkthrough Guide for Teachers and Parents, (with K. Fluor-Scacchi and A. Szeto), Fall 2014.

  • Making Learning Fun: An Analysis of Game Design in Science Learning Games, (with R. Lim and M. Yampolsky), ISR Technical Report, UCI-ISR-14-3, October 2014.

  • Achieving Better Buying Power Through Cost-Sensitive Acquisition of Open Architecture Software Systems (with T. Alspaugh), Proc. 11th. Annual Acquisition Research Symposium, Monterey, CA, NPS-AM-14-C11P07R01-036, May 2014.

  • Convergence Platforms: Human-Scale Convergence and the Quality of Life (with D. MacGregor, M. Baba, A. Oliva, et al.) in Convergence of Knowledge, Technology and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies, Science Policy Reports 2013, 53-93. Springer, New York.

  • Implications: Societal Collective Outcomes, Including Manufacturing, (with J. Cao, M.A. Meador, M.L. Baba, et al.) in Convergence of Knowledge, Technology and Society: Beyond Convergence of Nano-Bio-Info-Cognitive Technologies, Science Policy Reports 2013, 255-285. Springer, New York.

  • Ongoing Software Development Without Classical Requirements, (with T. Alspaugh), Proc. 21st. IEEE Intern. Conf. Requirements Engineering, Rio de Janeiro, Brazil, 165-174, 15-19 July 2013.

  • Challenges in the Development and Evolution of Secure Open Architecture Command and Control Systems, (with T. Alspaugh), Proc. 18th. Intern. Command and Control Research and Technology Symposium, Paper 098, Alexandria, VA, June 2013.

  • Streamlining the Process of Acquiring Secure Open Architecture Software Systems, (with T. Alspaugh), Proc. 10th. Annual Acquisition Research Symposium, Monterey, CA, 608-623, May 2013.

  • Processes in Securing Open Architecture Software Systems, (with T. Alspaugh), Proc. 2013 Intern. Conf. Software and System Processes, 126-135, May 2013, San Francisco, CA.

  • Advances in the Acquisition of Secure Systems Based on Open Architectures, (with T. Alspaugh), in Journal of Cybersecurity & Information Systems, 1(2), 2-16, February 2013.

  • Open Source Systems: Long-Term Sustainability, (with I. Hammouda, B. Lundell, and T. Mikkonen, (Eds.)), Proc. 8th. IFIP WG 2.13 International Conference, OSS 2012, IFIP Advances in Information and Communications Technology, Vol. 378, Hammamet, Tunisia, September 2012.

  • Security Licensing, (with T. Alspaugh), Proc. Fifth Intern. Workshop on Requirements Engineering and Law, 25-28, September 2012.

  • Software Licenses, Coverage, and Subsumption, (with T. Alspaugh and R. Kawai), Proc. Fifth Intern. Workshop on Requirements Engineering and Law, 17-24, September 2012.

  • The Challenge of Heterogeneously Licensed Systems in Open Architecture Software Ecosystems, (with T. Alspaugh and H. Asuncion), S. Jansen, S. Brinkkemper, and M. Cusumano (Eds.), Software Ecosystems: Analyzing and Managing Business Networks in the Software Industry, Edward Elgar Publishing, 103-120, Northmapton, MA, 2013.

  • Developing Secure Systems using Open Architectures with Open Source and Closed Source Components, (with T. Alspaugh), Proc. 8th. IFIP International Conf. Open Source Systems, 144-159, Hammamet, Tunisia, September 2012.

  • The Future of Research in Computer Games and Virtual Worlds: NSF Workshop Report, (August 2012).

  • Understanding the Role of Licenses and Evolution in Open Architecture Software Ecosystems, (with Thomas Alspaugh), Journal of Systems and Software, 85(7), 1479-1494, July 2012.

  • Exploring the Potential of Virtual Worlds for Decentralized Command and Control, (with C. Brown and K. Nies), 17th. Intern. Command and Control Research and Technology Symposium (ICCRTS), Paper-096, Fairfax, VA, June 2012.

  • Exploring the Potential of Computer Games for Decentralized Command and Control, (with C. Brown and K. Nies),17th. Intern. Command and Control Research and Technology Symposium (ICCRTS), Paper-104, Fairfax, VA, June 2012.

  • Software Licenses, Open Source Components, and Open Architectures, (with T. Alspaugh and H. Asuncion), in I. Mistrik, A. Tang, R. Bashon and J.A. Stafford (Eds.), Aligning Enterprise, System, and Software Architectures, IGI-Global Publishers, 58-79, October 2012.

  • Addressing the Challenges in the Acquisition of Secure Systems with Open Architectures, (with T. Alspaugh), Proc. 9th. Annual Acquisition Research Symposium, Monterey, CA May 2012.

  • License Update and Migration Processes in Open Source Software Projects, (with C. Jensen), in S. Hissam, B. Russo, M.G. de Mendonca Neto, and F. Kan (Eds.), Open Source Systems: Grounding Research, Proc. 7th. IFIP Intern. Conf. Open Source Systems, 177-195, IFIP ACIT 365, Salvador, Brazil, October 2011.

  • Modding as an Open Source Approach to Extending Computer Game Systems, in S. Hissam, B. Russo, M.G. de Mendonca Neto, and F. Kan (Eds.), Open Source Systems: Grounding Research, Proc. 7th. IFIP Intern. Conf. Open Source Systems, 62-74, IFIP ACIT 365, (Best Paper award), Salvador, Brazil, October 2011. Also in Intern. J. Open Source Software and Processes, 3(3), 36-47, July-September 2011. Reprinted in S. Koch (Ed.), Open Source Software Dynamics, Processes, and Applications, 177-188, Information Science Reference, IGI Global, 2013.

  • Presenting Software License Conflicts through Argumentation, (with T. Alspaugh and H. Asuncion), Proc. 22nd. Intern. Conf. Software Engineering and Knowledge Engineering (SEKE2011), Miami, FL, July 2011.

  • The Future of Research in Free/Open Source Software Development, in Proc. ACM Workshop on the Future of Software Engineering Research (FoSER), Santa Fe, NM, 315-319, November 2010.

  • Towards a Science of Free/Open Source Systems, (multiple authors), FOSS 2010 Workshop Final Report, Prepared for the Computing Community Consortium, Fall 2010.

  • Open Source Software Development, (with C. Jensen), in W.S. Bainbridge (Ed.), Leadership in Science and Technology: A Reference Handbook, Sage Publishers, 772-781,2012.

  • Open Source Software, Encyclopedia of Software Engineering, Taylor and Francis, New York, 614-626, 2011.

  • Software Licenses in Context: The Challenge of Heterogeneously Licensed Systems, (with T. Alspaugh and H. Asuncion), Journal of the Association for Information Systems, 11(11), 730-755, November 2010.

  • Computer Game Mods, Modders, Modding, and the Mod Scene, First Monday, 15(5), May 2010.

  • Governance in Open Source Software Development Projects: A Comparative Multi-Level Case Study Analysis, (with C. Jensen), Proc. 6th. Intern. Conf. Open Source Systems, Notre Dame, IN, 130-142, May 2010.

  • Collaboration Practices and Affordances in Free/Open Source Software Development, revised version in I. Mistrík, J. Grundy, A. van der Hoek, and J. Whitehead, (Eds.), Collaborative Software Engineering, Springer, New York, 307-328, 2010.

  • Game-Based Virtual Worlds as Decentralized Virtual Activity Systems, in W.S. Bainbridge (Ed.), Online Worlds: Convergence of the Real and the Virtual, Springer, New York, 225-236, 2010.

  • The Role of Software Licenses in Open Architecture Ecosystems, (with T. Alspaugh and H. Asuncion), Intern. Workshop on Software Ecosystems, Intern. Conf. Software Reuse, Falls Church, VA, September 2009.

  • Intellectual Property Rights Requirements for Heterogeneously Licensed Systems, (with T. Alspaugh and H. Asuncion), in Proc. 17th. Intern. Conf. Requirements Engineering (RE09), Atlanta, GA, 24-33, September 2009.

  • Envisioning National and International Research on the Multidisciplinary Empirical Science of Free/Open Source Software, (with K. Crowston, G. Madey, and M. Squire), working paper for the Computing Community Consortium, Spring 2009.

  • Analyzing Software Licenses in Open Architecture Software Systems, Proc. Workshop on Emerging Trends in FLOSS Research and Development, Intern. Conf. Software Engineering, Vancouver, Canada, May 2009.

  • Understanding Requirements for Open Source Software, in K, Lyytinen, P. Loucopoulos, J. Mylopoulos, and W. Robinson (eds.), Design Requirements Engineering: A Ten-Year Perspective, LNBIP 14, Springer-Verlag, 467-494, 2009.

  • Towards a Global Research Infrastructure for Multidisciplinary Study of Free/Open Source Software Development, (with L. Gasser), in IFIP Intern. Federation Info. Processing, Vol. 275; Open Source Development, Community and Quality; B. Russo, E. Damiani, S. Hissan, B. Lundell, and G. Succi (Eds.), Boston, Springer, 143-158, 2008.

  • A Collaborative Science Learning Game Environment for Informal Science Education: DinoQuest Online, (with R. Nideffer and J. Adams), revised version in IFIP International Federation for Information Processing, Volume 279; New Frontiers for Entertainment Computing; P. Ciancarini, R. Nakatsu, M. Rauterberg, M. Roccetti (Eds.); Boston: Springer, 71–82, 2008.

  • Collaborative Game Environments for Informal Science Education: DinoQuest and DinoQuest Online, (with R. Nideffer and J. Adams), IEEE Conf. Collaboration Technology and Systems,(CTS 2008), Irvine, CA 229-236, May 2008, (Extended version).

  • Governance in Open Source Software Development Projects: Towards a Model for Network-Centric Edge Organizations, (with C. Jensen), Proc. 13th. Intern. Command and Control Research and Technology Symposium, Bellevue, WA, July 2008.

  • Emerging Issues in the Acquisition of Open Source Software within the U.S. Department of Defense, (with T. Alspaugh), Proc. 5th. Annual Acquisition Research Symposium, Vol. 1, 230-244, NPS-AM-08-036, Naval Postgraduate School, Monterey, CA.

  • Mobilization of Software Developers: The Free Software Movement, (with Margaret Elliott), revised version in Information, Technology and People, 21(1), 4-33, 2008. Received 2009 Outstanding Paper Award of Excellence from Emerald Literati Network.

  • Emerging Patterns of Intersection and Segmentation when Computerization Movements Interact, revised version in M.S. Elliott and K.L. Kraemer (Eds.), Computerization Movements and Technology Diffusion: From Mainframes to Ubiquitous Computing, ASIST Monograph Series, Information Today, Inc. 381-404, 2008.

  • Guest Editorial—Open Source Software for Engineering Education: Pedagogical Strategies that Leverage Open Source Tools, (with M. Lytras), IEEE Transactions on Education, 50(4), November 2007.

  • Knowledge Work Artifacts: Kernel Cousins for Free/Open Source Software Development, (with M. Elliott and M. Ackerman), Proc. ACM Conf. Support Group Work (Group07), Sanibel Island, FL, 177-186, November 2007.

  • Free/Open Source Software Development: Recent Research Results and Emerging Opportunities, Proc. European Software Engineering Conference and ACM SIGSOFT Symposium on the Foundations of Software Engineering, Dubrovnik, Croatia, 459-468, September 2007.

  • Open Source Development, Adoption and Innovation, (edited by J. Feller, B. Fitzgerald, W. Scacchi, and A. Sillitti,), Springer, New York, June 2007.

  • Guiding the Discovery of Open Source Software Processes with a Reference Model, (with C. Jensen),in Proc. Third IFIP International Conference on Open Source Systems, Limerick, IR, 11-13 June 2007, 265-270.

  • Role Migration and Advancement Processes in OSSD Projects: A Comparative Case Study, (with C. Jensen), in Proc. 29th. Intern. Conf. Software Engineering, Minneapolis, MN, May 2007, 364-374.

  • Free/Open Source Software Development: Recent Research Results and Methods, in M.V. Zelkowitz (ed.), Advances in Computers, 69, 243-295, 2007.

  • Understanding the Development of Free E-Commerce/E-Business Software: A Resource-Based View, in S.K. Sowe, I. Stamelos, and I. Samoladas (eds.), Emerging Free and Open Source Software Practices, IGI Publishing, Hershey, PA, 170-190, 2007.

  • Open Source Systems, (edited by E. Damiani, B. Fitzgerald, W. Scacchi, M. Scotto, and G. Succi), Springer, New York, June 2006.

  • Understanding Free/Open Source Software Development Processes, (with Joseph Feller, Brian Fitzgerald, Scott Hissam, and Karim Lakhani), Software Process--Improvement and Practice, 11(2), 95-105, March/April 2006.

  • Experiences in Discovering, Modeling, and Reenacting Open Source Software Development Processes, (with Chris Jensen), in Mingshu Li, Barry Boehm, and Leon J. Osterweil (eds.), Unifying the Software Process Spectrum: Proc. Software Process Workshop, Beijing, China, May 2005, 442-469, Springer-Verlag, 2006.

  • Modeling Recruitment and Role Migration Processes in OSSD Projects, (with Chris Jensen), Proc. 6th. Intern. Workshop on Software Process Simulation and Modeling, St. Louis, MO, May 2005.

  • Multi-Modal Modeling, Analysis and Validation of Open Source Software Development Processes, (with Chris Jensen, John Noll, and Margaret Elliott), Intern. J. Internet Technology and Web Engineering, 1(3), 49-63, 2006. Previous version appeared in Proc. First Intern. Conf. Open Source Software, 1-8, Genova, Italy, July 2005. Received Best Paper Award. Reprinted in G. Alkhatib and D. Rine, (Eds.), Integrated Approaches in Information Technology and Web Engineering: Advancing Organizational Knowledge Sharing, Information Science Reference, Hershey, PA, 51-65, 2009.

  • Socio-Technical Interaction Networks in Free/Open Source Software Development Processes, in S.T. Acuña and N. Juristo (eds.), Software Process Modeling, 1-27, Springer Science+Business Media Inc., New York, 2005.

  • Collaboration, Leadership, Control, and Conflict Negotiation in the NetBeans.org Software Development Community, (with Chris Jensen), Proc. 38th. awaii Intern, Conf. Systems Science, Waikola Village, HI, January 2005.

  • Process Modeling Across the Web Information Infrastructure, (with Chris Jensen), Software Process--Improvement and Practice, 10(3), 255-272, July-September 2005. Earlier version appears in Proc. 5th. Intern. Workshop on Software Process Simulation and Modeling, Edinburgh, Scotland, May 2004.

  • Opportunities and Challenges for Modeling and Simulating Free/Open Source Software Processes, working paper, Institute for Software Research, October 2004, based on a Keynote Address presented at the Proc. 5th. Intern. Workshop on Software Process Simulation and Modeling, Edinburgh, Scotland, May 2004.

  • Data Mining for Software Process Discovery in Open Source Software Development Communities, (with Chris Jensen), Proc. Workshop on Mining Software Repositories, 96-100, Edinburgh, Scotland, May 2004.

  • Discovering, Modeling, and Reenacting Open Source Software Development Processes, (with Chris Jensen), revised version in S.T. Acuna and M.I. Sanchez-Segura (eds.), New Trends in Software Process Modeling, Series in Software Engineering and Knowledge Engineering, Vol. 18, 1-20, 2006.

  • Continuous Design of Free/Open Source Software: Workshop Report and Research Agenda, (with Les Gasser), UCI-UIUC Workshop on Continuous Design of Open Source Software, 15 October 2003.

  • Understanding Continuous Design in F/OSS Projects, (with Les Gasser, G. Ripoche, and B. Penne), 16th. Intern. Conf. Software & Systems Engineering and their Applications, Paris, December 2003.

  • Socio-Technical Design, in W. S. Bainbridge (ed.), The Encyclopedia of Human-Computer Interaction, 656-659, Berkshire Publishing Group, 2004.

  • Free/Open Source Software Development Practices in the Computer Game Community, IEEE Software, 21(1), 59-67, January/February 2004.

  • Understanding Free/Open Source Software Evolution, revised version in N.H. Madhavji, J.F. Ramil and D. Perry (eds.), Software Evolution and Feedback: Theory and Practice, 181-206, John Wiley and Sons Inc, New York, 2006.

  • Free Software Development: Cooperation and Conflict in A Virtual Organizational Culture, (with Margaret Elliott), revised version in S. Koch (ed.), Free/Open Source Software Development, 152-172, Idea Publishing, Pittsburgh, PA, 2005.

  • Free Software Developers as an Occupational Community: Resolving Conflicts and Fostering Collaboration, (with Margaret Elliott), Proc. ACM Intern. Conf. Supporting Group Work (Group'03), 21-30, Sanibel Island, FL, November 2003.

  • When is Free/Open Source Software Development Faster, Better, and Cheaper than Software Engineering? Working Paper, Institute for Software Research, UC Irvine, April 2003.

  • Simulating an Automated Approach to Discovery and Modeling of Open Source Software Development Processes, (with Chris Jensen), Proc. ProSim'03 Workshop on Software Process Simulation and Modeling, Portland, OR May 2003.

  • Issues and Experiences in Modeling Open Source Software Processes, Proc. 3rd. Workshop on Open Source Software Engineering, 25th. Intern. Conf. Software Engineering, Portland, OR, May 2003.

  • Automating the Discovery and Modeling of Open Source Software Processes, (with Chris Jensen), Proc. 3rd. Workshop on Open Source Software Engineering, 25th. Intern. Conf. Software Engineering, Portland, OR, May 2003.

  • Communicating and Mitigating Conflict in Open Source Software Development Projects, (with Margaret Elliott), Projects & Profits, IV(10), 25-41, October 2004.

  • Open EC/B: A Case Study in Electronic Commerce and Open Source Software Development, Working Paper, Institute for Software Research, UC Irvine, July 2002.

  • Open Acquisition: Combining Open Source Software Development with System Acquisition, Working Paper, Institute for Software Research, UC Irvine, July 2002.

  • Understanding the Social, Technological, and Policy Implications of Open Source Software Development, position paper presented at the NSF Workshop on Open Source Software, January 2002 (revised August 2002).

  • Understanding the Requirements for Developing Open Source Software Systems, IEE Proceedings--Software, 149(1), 24-39, February 2002.

  • Hypertext for Software Engineering, revised version appears in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, Wiley, 612-621, 2002.

  • Process Models in Software Engineering, revised version to appear in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, Wiley, 993-1005, 2002.

  • Modeling and Simulating Software Acquisition Process Architectures, (with James Choi) appears in Journal of Systems and Software, 59(3), 343-354, 15 December 2001.

  • Software Development Practices in Open Software Development Communities, presented at the 1st Workshop on Open Source Software Engineering, Toronto, Ontario, May 2001).

  • Specifying Process-Oriented Hypertext for Organizational Computing, (with John Noll) appears in J. Network and Computer Applications, 24(1):39-61, 2001 (in PDF or in Postscript).

  • Understanding Software Process Redesign using Modeling, Analysis and Simulation, revised version appears in Software Process--Improvement and Practice, 5(2/3), 183-195, 2000.

  • Redesigning Contracted Service Procurement for Internet-based Electronic Commerce: A Case Study, appears in J. Information Technology and Management, 2(3), 313-334, 2001.


List of abstracts and links to some older research papers that I have (co-)authored


Web accessible presentations (in HTML, PDF, or PPT)

  • Game-Based Virtual Worlds for an Internet of VR/AR Things (PDF)

  • Achieving Better Buying Power Through Acquisition of Open Architecture Software Systems for Web-Based and Mobile Devices (PDF)

  • Building Virtual Worlds at UCI: Past, Present, Future (PDF)

  • Open Source Software Ecosystems: Challenges and Opportunities (PDF)

  • Computer Games and Virtual Environments for Medical Education and Research (PDF)

  • Realizing a Multi-Disciplinary Center for Games Research at UCI (PDF)

  • Engineering Challenges in Developing an Informal Music Learning Game Environment (PDF)

  • Processes in Securing Open Architecture Software Systems (PDF)

  • Creating Opportunitities for Computer Game R&D Projects (PDF)

  • Center for Computer Games and Virtual Worlds (PDF)

  • Game Development Competitions: Software Engineering as a Team Sport (PDF)

  • Computer Games and Virtual Worlds: New Modalities for Rehabilitation and Therapy (PDF)

  • Modding as an Open Source Approach to Extending Computer Game Systems, and a related paper can be found here.

  • Free/Open Source Software Development as an Approach to Global Software Engineering (PDF)

  • Computer Games and Virtual Worlds for Health, Assistive Threapeutics and Performance Enhancement (PDF)

  • Advances and Challenges for Decentralized Command and Control Systems Based on Computer Games and Virtual World Technologies (PDF)

  • Game-Based Virtual Worlds as Decentralized Virtual Activity Systems (PDF)

  • Recent Advances in Virtual Worlds for Science and Technology Research and Development (PDF)

  • Open Source Software: Issues, Challenges, and Opportunities (PDF)

  • The Process of Innovation in Computing: A Personal 30 Year Perspective (PDF)

  • Free/Open Source Software Development: Recent Research Results and Emerging Opportunities (PDF), or the full paper can be found here

  • Recent Developments in Science Learning Games for Informal Science Education (PDF)

  • Web 3.0: Game Web Research at the UCI Game Lab (PDF) (RM video)

  • Research and Educational Innovations in Computer Games (PDF)

  • Enabling Exponential Innovation via Open Source Software Development (PDF)

  • Recent Advances in Science Learning Games (PDF)

  • Identifying New Market Opportunities through Process Discovery (PDF)

  • Computing Gaming as a Social Movement (PDF)

  • Innovations in Informal Science Education: DinoQuest and DinoQuest Online (PDF)

  • Patterns of Sustained Collaborative Creativity Across Open Computerization Movements (PDF)

  • Collaboration Infrastructure for a Virtual Residency in Game Culture and Technology (PDF)

  • Opportunities for Game Culture and Technology in Public Libraries (PDF)

  • Understanding and Improving Software Productivity (PDF), or a vintage paper can be found here.

  • Computer Games, Open Source Software, and other Socio-Technical Processes (PDF)

  • Discovering, Modeling, and Reenacting Free/Open Source Software Development Processes and Practices, or the paper can be found here.

  • Collaboration, Leadership, Control, and Conflict Negotiation in the NetBeans.org Community, or the paper can be found here

  • Modeling and Simulating Free/Open Source Software Development Processes, or the full paper can be found here.

  • Strategies for Developing and Deploying Free/Open Source Software

  • Understanding the Requirements for Developing and Designing Open Source Software

  • Software Process Simulation and Modeling: A Review

  • Simulating an Automated Approach to Discovery and Modeling of Open Source Software Development Processes, or the full paper can be found here

  • Understanding the Potential for Open Government: Open Source Processes for E-Government

  • The UCI Game Lab Research Program

  • Understanding the Requirements for Open Source Software System Development, or the full paper can be found here

  • Understanding and Visualizing Information Work Processes and Practices

  • Understanding and (Re)Designing Software Development Processes

  • Understanding, Communicating, and (Re)Designing Complex Organizational Processes

  • Process Life Cycle Engineering, or the full paper can be found here.

  • Experience with Software Process Simulation and Modeling,or the full paper can be found here.

  • Supporting Software Development in Virtual Enterprises,or the full paper can be found here.

  • Understanding Work Processes and Practices: A Computational Approach

  • Developing a Knowledge Web for Process Redesign, or the full paper can be found here.

  • Computational Business Processes as Software Components for Electronic Commerce, or a position paper can be found here.

  • (Re)Designing Software Production Architectures

  • Experience with Software Architectures and Configured Software Descriptions, or a position paper can be found here.


Current/Vintage Editorial Board Positions

  • Journal of Software: Evolution and Process (http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292047-7481),

    • formerly Journal of Software Maintenance and Evolution: Research and Practice

    • formerly Software Process -- Improvement and Practice (http://www.interscience.wiley.com/jpages/1077-4866/)

  • Intern. J. Open Source Software & Processes, (http://www.igi-global.com/journals/details.asp?ID=7978&v=callForPapers)

  • Intern. J. Social and Humanistic Computing(IJSHC), (http://www.inderscience.com/browse/index.php?journalID=225)

  • Journal of Computer Supported Cooperative Work (http://www.wkap.nl/journalhome.htm/0925-9724)

  • Encyclopedia of Software Engineering (2nd. Edition, 2002), John Wiley and Sons, Inc.


Other Interests

Having a good time!
Playing games and having fun ;-)


Institute for Software Research

Donald Bren School of Information and Computer Sciences

University of California, Irvine

Irvine, CA 92697-3455


Last modified: 8 December 2015



http://www.ics.uci.edu/~dan/ Dan Hirschberg
Dan Hirschberg
Professor of Computer Science & EECS
UC Irvine Senate Parliamentarian
 
 



  Courses
 ICS 6B
Boolean Algebra & Logic
 CompSci 161
Design and Analysis of Algorithms
 CompSci 165
Project in Algorithms & Data Structures
 CompSci 260
Fundamentals of the
    Design and Analysis of Algorithms
 CompSci 267
Data Compression
 CompSci 269S
Seminar on Theory of
    Algorithms & Data Structures
 Univ Studies 3
Puzzlers for Computer Scientists




  Curriculum Vitae ( pdf )


  Publications


Research Areas
  • Longest Common Subsequences
  • Data Compression
  • Search
  • Combinatorial Group Testing

Side Interests
  • genealogy, especially in Krakow
  • playing bridge, sometimes on-line
  • 60's classical rock
  • math/logic puzzles

Contact Information

   Dan Hirschberg
   Computer Science Department
   University of California, Irvine
   Irvine, CA 92697-3435

(949) 824-6480
dan@ics.uci.edu
 
http://www.ics.uci.edu/~jpd/ Redirecting... Directing to www.dourish.com in a few seconds; if you can't find what you need, try here. http://www.ics.uci.edu/~klefstad/ Raymond Klefstad, Ph.D.

Raymond Klefstad, Ph.D.


Department of Computer Science, Donald Bren School of Information & Computer Science, U.C. Irvine

  • Research Areas: Compilers, Operating Systems, Distributed Computing, Real-time Computing, Embedded Systems, Middleware, Object-Oriented (OO) Design, Design Patterns, OO Programming Languages
  • Electronic Mail: Klefstad(a)uci.edu
  • Office: ICS Building
  • Office Hours: Link
  • Short Biography Shortest Biography
  • Curriculum Vitae (Resume)
  • If you want a letter of recommendation from me...
Computers and communications technologies have changed the world. From command-and-control systems to control of our automobiles, these technologies play a vital role. This revolution is only a few decades old, but already futurists talk about the twenty-first century in terms of the ``information economy,'' the ``information society,'' and even the ``information age.'' UCI's Donald Bren School of Information & Computer Science is in the center of this revolution, with programs of instruction and research in a variety of areas within computer science technology.

|| Research || Educational Philosophy || Courses || Course Info || Success || Recreation || UCI Links ||

Research

Middleware

Educational Philosophy

I assume my students want to learn. True learning requires hard work. I motivate my students to learn with lots of course work including weekly homeworks, weekly quizzes, and a comprehensive final exam. My role is to define what I want students to learn then to help them understand and learn that material. I try to balance theory and practice so students can see the value of what they are learning. I convey enthusiasm for the material in lecture and I'm sure my students can sense that I love my job and that I really care about their futures. I've been very honored to receive fifteen UCI teaching awards over the past fifteen years of teaching.

Courses Taught at University of California

Course Title
UCI ICS 141 Programming Languages
UCI ICS 142A Compilers & Interpreters

General Course-related Information

Dr. Klefstad's Cheating Policy
C++ Coding Style Rules

Personal Success Information

Tips for success in Programming Courses
The importance of setting priorities
Life's principles
Good habits to develop

Recreational Interests

  • Freediving
  • Bowhunting
  • salt-water fishing
  • eating

UCI Related Links

  • Schedule of Classes
  • Academic Calendar
  • EEE at UCI
    Department of Computer Science, University of California, Irvine CA 92697-2625
    http://www.ics.uci.edu/~mlevorat/ Marco Levorato
    ML
    • Home
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    Marco Levorato

    Assistant professor

    Donald Bren School of Information and Computer Science
    Computer Science Department

    3206 Donald Bren Hall,
    University of California, Irvine
    Irvine, CA, 92697-2800
    @ levorato at uci dot edu
    Marco Levorato

    Research

    1. Cognitive networks
    2. Sparse methods and graphical modeling in wireless networks
    3. Energy efficient sensors for remote health-care applications
    4. Demand response and Smart energy grid

    Teaching

    1. Computer and Communication Networks Fall 2013 (COMPSCI232/EECS248A/NETSYS201)
    1. Computer and Communication Networks Fall 2014 (COMPSCI232/EECS248A/NETSYS201)
    1. Wireless Networks Winter 2015 (COMPSCI236/NETSYS230/COMPSCI190)
    1. Computer and Communication Networks Fall 2015 (COMPSCI232/EECS248A/NETSYS201)

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    © Marco Levorato 2013

    http://www.ics.uci.edu/~chenli/ Chen Li's homepage

    Chen Li


    Professor
    School of Information and Computer Sciences
    Bren Hall, Room 2092
    University of California, Irvine
    Irvine, CA 92697-3435
    Phone:  (949) 824-9470 (Office) 
    CHENLI AT ics DOT uci . E Du
    http://www.ics.uci.edu/~chenli
    Information Systems Group
    Founder of SRCH2
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    Recent News

    • Check our ISG Group Page for recent news about our group.
    • (1/2016) Teaching CS122B this quarter.
    • (9/2015) Teaching CS222 this quarter.
    • (9/2015) We had a great VLDB 2015 conference in Hawaii!
    • (1/2015) I came back to UCI after a 1.5-year leave at SRCH2.
    • (2/2014) Together with Prof. Volker Markl, I will be a Program Co-Chair (PVLDB Editor-in-Chief) for VLDB 2015, which will be in Hawaii :-)
    • (7/2013) Starting from July 2013, I am taking a leave of absence from UCI to work at my startup, SRCH2.
    • Archived news

    Research

    My research interests are in the fields of databases and information retrieval, including search, data-intensive computing, data integration and sharing, data warehouses, data cleansing, and Web information management. The following are several database projects I am working on.

    Current Projects

    • ASTERIX: This project is about Active, Scalable, Transactional Enterprise Repository for Information in XML. It's a new effort to develop a scalable semi-structured information management system, based on XML and XQuery technologies, targeting very large shared-nothing compute clusters.

    Past Projects

    • FLAMINGO: A project on data cleaning.
    • IPUBMED: Efficient instant search on large amounts of data. It started with the joint research project with Tsinghua University on efficient auto-complete and type-ahead search on large data sets.
    • Family Reunification. Help people find their loved ones during or after a disaster.
    • The Raccoon Project on Data Integration and Sharing. I started this project several years ago, and it's in its final stage. I still have some ongoing research related to this project. But compared to the first two projects, this one is less active.
    • Data sets of the history of data objects collected from 6 web sites in 1.5 years..

    Released Prototypes and Source Code Packages

    • Flamingo Packge: C++ package to do approximate string queries.
    • ASTERIX.
    • Fuzzy keyword search on maps
    • qSpell: Spelling Correction of Web Search Queries (won the 3rd Prize in Microsoft's speller challenge in 2011)
    • Lightweight In-Memory Implementation of R*-Tree (maintained by Sattam Alsubaiee).
    • iPubMed: Instant fuzzy search on more than 20 million medical publications from MEDLINE.
    • Instant fuzzy search for learning.
    • Location-based instant fuzzy search.
    • Location-based approximate keyword search.
    • CHIME: Error-tolerant Chinese input method.
    • PSearch: Instant fuzzy search on the UCI directory.
    • Efficient Parallel Set-Similarity Joins Using MapReduce.
    • Haiti family reunification: Instant fuzzy search on records about people affected by the Haiti earthquake.
    • DNAzip: DNA sequence compression using a reference genome.
    • Hobbes: genome sequence mapping.

    Students

    Current PhD students

    • Inci Cetindil
    • Jamshid Esmaelnezhad
    • Jianfeng Jia
    • Taewoo Kim
    • Young-Seok Kim

    Graduated PhD Students

    • Michal Shmueli-Scheuer: PhD 2009, first appointment: researcher at IBM Haifa Research Lab
    • Shengyue Ji, PhD 2011, first appointment: Google.
    • Rares Vernica: PhD 2011 (co-advised with Professor Michael Carey), first appointment, HP Labs.
    • Alex Behm: PhD 2013 (co-advised with Professor Michael Carey), first appointment, Cloudera.
    • Sattam Mubark Alsubaiee, PhD 2014 (co-advised with Professor Michael Carey), first appointment, Research Assistant Professor, KACST, Saudi Arabia

    Past MS students, Postdocs, and visitors

    • Athena Ahmadi, MS, 2012, Google
    • Manik Sikka, MS, 2011
    • Sandeep Paul Katumalla, MS
    • Nagesh Honnalli, MS, 2010 - 2011, Amazon
    • Andrea Zilio, Exchange student from Italy, 2010 - 2011, Google.
    • Yabin Zheng, 2009 - 2010, Visiting PhD student from Tsinghua University, China.
    • Prof. Heri Ramampiaro, 2009 - 2010, Visiting professor from Norwegian University of Science and Technology (NTNU).
    • Vijay Rajakumar, MS 2008-2010. Bimaple
    • Minh Doan, MS 2008-2010.
    • Jiaheng Lu, Postdoc 2006-2008, first appointment: faculty at Renmin University, China.
    • Yiming Lu, MS 2006-2008, first appointment: Microsoft.
    • Vassia Pavlaki, PhD student from NTUA, Greece. Visitor, Summer 2005 and April 2006.
    • Bin Wang (PhD candidate) and Xiaochun Yang (professor), Northeastern University, China. Visitors, summer 2006, summer 2007.
    • Houtan Shirani-Mehr, MS in 2006, became a PhD student at USC.
    • Chris Trezzo, Undergrad student (SURF-IT), summer 2006.
    • Jia Li, MS in 2005, first appointment: an IT company in the bay area.
    • Liang Jin, MS in 2005, first appointment: Microsoft.
    • Qi Zhong, MS in 2005, first appointment: Microsoft.

    Grants, fellowships, awards

    • 2013, 10-year Best Paper Award for the DASFAA 2003 paper titled "Efficient Record Linkage in Large Data Sets" by Liang Jin, Chen Li, and Sharad Mehrotra.
    • 2012, ACM SIGMOD 2012 Test-of-Time Award for the SIGMOD 2002 paper titled "Executing SQL over encrypted data in the database-service-provider model" by Hakan Hacigumus, Bala Iyer, Chen Li, and Sharad Mehrotra.
    • 2012, UCI ICS Dean's Award for Graduate Student Mentoring.
    • 2011, I advised a group of students to participate in the Microsoft Speller Challenge and won the third place.
    • 2010, Intel grant on compression of human genome data.
    • NSF research award IIS 1030002 on powerful keyword search in a cloud-computing infrastructure, 2010.
    • 2009 UCI ICS Dean's Award for excellence in Mid-Career Research.
    • 2009, NSF research award 0910989 titled "DC: Large: Collaborative Research: ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis."
    • 2008, Research Funding Award of the "Research Funds for Oversea Scholars" program of the National Natural Science Foundation of China.
    • NSF SGER grant No. IIS-0742960, 2007.
    • ICS Ted & Janice Smith Faculty Seed Fund (December 2006).
    • Google Research Award (September 2006).
    • Microsoft unrestricted research grant (April 2006).
    • UCI Faculty Career Development Award (2005).
    • Recipient of National Science Foundation CAREER Award (No. IIS-0238586, 2003 - 2008, single PI).
    • Senior investigator of National Science Foundation Award No. 0331707: RESCUE (2003 - 2008).
    • Stanford Graduate Fellowship, Stanford University, 1997 - 2001.
    • Entrance exams to Tsinghua University waived (undergrad 1989, MS 1994)
    • Recent committee services: PVLDB 2011, SIGMOD 2010 (Group Leader), VLDB 2010, ICDE 2010 (also Local Arrangements Chair), PVLDB 2009, PVLDB 2008, ICDE 2009 Demo Track, CIKM 2008, VLDB 2008, WWW 2008, ICDE 2008, KDD 2007, SIGMOD07, ICDT07, KDD06, CIKM06, CleanDB06 (Workshop cochair), SIGMOD06 Undergraduate Scholarship Program (chair), IQIS06, ICDE06 Committee for tutorial proposals, VLDB05 (IIS), SIGMOD05 Demo track, PODS05 Proceedings Chair, DASFAA05.
    http://www.ics.uci.edu/faculty/centers/index.php Research Centers at UCI's Donald Bren School of Information and Computer Sciences
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    Bren school home > Faculty > Research centers
    ICS Research Centers
    ICS faculty member

    Organized research programs provide a mechanism and organizational structure within which collective research activities can take place that are fundamentally different from those that occur normally within the schools and departments. They are intended to foster the development of short- and long-term research programs that span disciplines and academic units, thereby making it possible for faculty to acquire extramural resources for which they might not otherwise qualify. The following is a list of the research centers that are part of ICS:

    ABRC - Ada Byron Research Center
    ABRC studies and promotes diverse access to and participation in computer science, engineering, digital media and related information technology fields.

    Calit2 - California Institute for Telecommunications and Information Technology
    A multidisciplinary research institute in collaboration with UCSD, Calit2@UCI integrates academic research with industry experience to seek innovative IT approaches that will benefit society and ignite economic development.

    CDT - Center for Digital Transformation
    One of the world's leading think tanks on the impact of information technology on organizations and society and on the management of information technology.

    Center for Algorithms and Theory of Computation
    The goal of research in theoretical computer science is to produce results, supported by rigorous proof, about problems dealing with computers and their applications.

    Center for Embedded Computer Systems
    Conducting leading-edge interdisciplinary research in embedded systems, the center develops innovative design methodologies, and promote technology and knowledge transfer for the benefit of the individual and society.

    Center for Machine Learning and Intelligent Systems
    Addresses the challenges of the modern data-driven world, using computer algorithms to discover useful information from vast data archives.

    Center for Social Computing
    Conducts foundational research into the relationship between information technology and society. Based at UCI, the center brings together an interdisciplinary group of researchers from leading U.S. universities.

    CERT - Center for Emergency Response Technologies
    Works to radically transform the ability of responding organizations to gather, manage, use, and disseminate information within emergency response networks and to the general public. By using more robust information systems, response can focus on activities that have the highest potential to save lives and property.

    COR - Center for Organizational Research
    COR contributes to the development of organization theory by connecting scholars from many disciplines who bring their knowledge and methods to a common understanding of these issues.

    CPCC - Center for Pervasive Communications and Computing
    Dedicated to serving the vision of wearable computers with wireless connections that enable anyone to have continuous voice, video, and data connectivity.

    Computational Vision Lab
    The Computational Vision Lab focuses on understanding the information processing capabilities of biological visual systems and on developing computational systems for processing visual media.

    IGB - Institute for Genomics and BioInformatics
    Fostering innovative basic and applied research in genomics and BioInformatics, IGB works with established companies, start-ups, government agencies and standards bodies to develop and transition these technologies to widespread and practical application.

    ISR - Institute for Software Research
    Works toward advancing software and information technology through research partnerships and educating the next generation of software researchers and practitioners in advanced software technologies.

    IVECG - Institute for Virtual Environments and Computer Games
    Brings together researchers for the common goal of understanding and creating technology and applications that transform how we: see the world through immersive visualization and virtual tours; interact and socialize with global communities; communicate and collaborate with colleagues in virtual collaborative space; provide medical care and training to remote corners of the world; and educate all ages and populations using virtual environments.

    LUCI - Laboratory for Ubiquitous Computing and Interaction
    Addresses the entire range of research problems that arise from the ubiquitous computing vision: the design of novel devices, the structure of software systems, techniques for designing and building systems, patterns of interaction, and the cultural implications of ubiquitous computing.

    Secure Systems and Software Laboratory
    The Secure Systems and Software Laboratory at the University of California, Irvine.

    SCONCE - Secure Computing and Networking Center
    SCONCE focuses on research for protecting information and computing infrastructure with an emphasis in areas like applied cryptography, network security and information assurance.

    More faculty »
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    http://www.ics.uci.edu/~kibler/ Dennis F. Kibler

    Dennis F. Kibler

     

    Position:

    Professor Emeritus

    Area:

    Artificial Intelligence

    Office:

    Bren Hall 4044

    Email:

    dfkibler70@gmail.com

    Office Hours:

    By appointment

    • I have retired and am not accepting new students
    • Research
    • Publications
    • Students
    • Courses
    • CS174: Bioinformatics

    Genomic Analysis via Machine Learning Methods

    These research projects are being done in collaboration with a number of faculty from the School of Biological Science and the College of Medicine. For validation of the computational approach each project is focussed on a particular genome.

    • Promoter modelling. Collaborator: Ming Tan. Students: Hilda Yu and Johnny Ackers. We have developed a bimotif variable gap model that has been successful at predicting the sigma-28 promoter site in Chlamydia and E. coli Extensions of this model will be applied to search for dimer binding sites.
    • Hydra database Collaborators: Hans Bode, Robert Steele, and Steve Hampson. Analysis and data from Hydra ests, as they are being sequenced. Currently we have about 140,000 ests available and new sequences are added monthly. The main goals are to identify genes, their functions, and their evolutionary histories.
    • Discovery of Chlamydial Transcription factor Binding Sites Collaborator: Ming Tan. Student: Bob Chan. Chlamydia is an unusual bacterial that lives entirely within human cells. We are developing algorithms that combine various forms of evidence to identify the transcription binding sites that control the early and late genes.

    In general our goal is to develop programs that can use the available data to help determine biological significant substructures or patterns in genomes.


    Current Graduate Advisees and Projects

    �        Johnny Akers (Bio phd student): Advisor Ming Tan. Identifying regulatory elements in Chlamydia arising from dimerization.

    �        Martin Brandon�� Advisor Pierre Baldi�� MitoMap


    Graduate students completing with doctorate

      • Bob Chan (2007) Discovery of Local Patterns in DNA that Predict CRMs and Protein Structural Similarity
      • Hilda Yu (2006) co-advisor with Ming Tan
      • Catherine Blake (2003), Assistant Professor, University of North Carolina, Chapel Hill
      • Yuh-Jyh Hu (1999), Assistant Professor in Computer Science and Engineering Department, Tatung University, Taipei.
      • Piew Datta (1997), Researcher at GTE Research Laboratory.
      • Pedro Domingos (1997), Associate Professor, University of Washington.
      • Pat Murphy (1996), Reseacher Scientist at JPL.
      • David Ruby (1993), Independent Consultant.
      • David Aha (1990), Researcher at Naval Research Laboratory.
      • Etienne Wenger (1990), Research scientist at the Institute for Research on Learning, Xerox Park
      • Rogers Hall (1990), Associate Professor at Vanderbilt, Department of Education.
      • Douglas Fisher (1987), Associate Professor at Vanderbilt University.
      • Paul Morris (1984), Researcher at Intellicorp, Menlo Park, California.
      • Bruce W. Porter (1984), Professor at University of Texas.
      • Steven Hampson (1983), Research Scientist at UCI.
      • John Conery (1983), Professor at University of Oregon.
      • Stephen Fickas (1983), Professor at University of Oregon.

    Notable Undergraduates

      • Kimberly Ferguson(2003) Computing Research Award (honorable mention)

    Undergraduate Advisees and Projects

      • Frank Chen: Building a monad and dyad motif detector.
      • Timothy Uy: (High school student) Evaluating Genetic Algorithm on BSAT
      • Bach Ho: Extending Weka with improved Nearest Neighbor and k-means algorithms
      • Gary Suh: Diagnose Colon cancer using Protein expression data (Ciphergen)
      • Vishahk: Creating a web-interface and mysql database for HYDRA data.
      • David Hu: Building a dimer binding site detector for chlamydia trachomatis.

    Course Offerings:

    Cosmos: Data Mining

      • Course for Gifted High School Students
      • Geometric and intuitive approach to three problems in Data Mining
        • Classification
        • Regression
        • Clustering
      • Course will use the freely available Weka software
      • Prerequisites: Comfortable with algebra, geometry and computers
      • No programming experience required

    Cosmos (2005) BioInformatics: Understanding our Genome

    The goal of this course is to introduce students to the bioinformatics research paradigm by studying three significant problems in understanding the genome. The paradigm consists of starting with a biological problem, biological data and biological knowledge and then building an approximate computational model. The model is then applied to real data and its predictions are then validated by additional biological experiments. Ideally this cycle repeats. The course will begin with the problem of identifying genes from sequence data using the GENSCAN program. After genes are identified, their function will be hypothesized using the BLAST program. How genes are regulated will be answered using programs for finding surprising subsequences. In all of these studies students will work with real data using programs freely available on the web. The ideas underlying the algorithms, their limitations, and their connection to biology will be stressed.

    Student Projects with draft abstracts

      • Homosexuality: Determined by genetics or social environment by Mansi Shah and Wendy Kim
      • Applying Microarray Technology to predicting Adverse drug reaction in children with acute lymphoblastic leukemia by Ruwani Ekanayake, Amy Henry, and Brittany Horth
        Abstract: Pharmacogenomics, the study of how an individual's genetic inheritance affects the body's response to drugs, has expanded exponentially since the completion of the Human Genome Project in 2003. Now, thanks to emerging microarray technology, pharmacogenomics is being applied with great success to cancer research. Our project will explore the benefits of microarray technology in pharmacogenomics, specifically as applied to cancers.
      • Stem Cells by Micheal Jenkins, Uchechukwu Nnadi, and Tom Garrett
      • Predicting SNPs and Their Effects on Proteins using SIFT and Polyphen by Eve Shih and Kuo
      • Predicting Leukemia Classes Based on gene expression data using WEKA by Alex Doo, Stehpanie Lang . and George Quinonez
      • Similarity Sequencing and its use for Generation of Phylogenetic Trees by Ivan Cvitkovic and Daniel Kaufman
        Abstract: We plan to explore the concept of similarity sequencing. Through our research, we will discuss the different algorithms for similarity sequencing and a basic description of how they work. Additionally we will discuss the application of similarity sequencing for the generation of phylogenetic trees. During our project we will generate two separate phylogenetic trees, based on different proteins.
      • Pharmacogenomics and Bioinformatcis of Long QT Syndrome by Daryl Serrano and Carlos Palacios .
        Abstract: Long QT syndrome is a genetic disease that affects the heart. One's heart has electrical activity that is made by a flow of ions. This causes the heart to beat normally. People with LQTS have a slower QT interval (heartbeat) than normal. People can acquire LQTS through prescription/over the counter medications or inherit the disease from their parents at birth. LQTS can cause many symptoms in a person that can greatly affect their everyday lives. However, there are treatments for LQTS that help people live normal lives. Acquired LQTS is caused by medications that include antihistamines, antidepressants, mental illness medications, heart medications, etc. LQTS is also congenital, or caused by a mutation in the gene that forms ion channels. There are different classifications of LQTS, such as LQT1, LQT2, LQT3, LQT4, and LQT5. These different forms of LQTS affect different ion channels in the human heart. When LQTS slows one's heart beat, one is likely to faint, have a seizure, or die. One will most likely experience these symptoms when exercising or becomes emotionally excited. Although LQTS may be fatal, doctors have found treatments that help shorted the QT interval. and allow patients to live normal lives.
      • How Breast Cancer response to chemotherapy by Amanda Farrar and Rosalinda Ruiz
        Abstract:< Chemotherapy is very important in metastasis Breast Cancer. Doxorubicin, epirubicin, Paclitaxel, and docetaxle are the most active chemical drugs used in Breast Cancer treatment. Activating tumor-suppressor genes are emerging as important targets for therapy. Researchers test various models to see which combinations of drugs improve Breast Cancer treatment. By understanding the molecular processes underlying Breast Cancer, researchers hope to create specific drugs that will prevent the growth of Breast Cancer. The malfunction of genes allows cancer to spread to various parts of the bodies. Understanding these processes will help find a cure for this disease.
      • Title: What is DEB and how is it treated by Maritza Navarro and Alexandria Magallan
        Abstract: Dystrophic epidermolysis bullosa (DEB) is caused by a mutation in the collagen, type VII alpha 1 protein, which is found in chromosome 3. Type VII collagen, is a protein that helps keep your skin intact. Those who have dystrophic epidermolysis bullosa are usually diagnosed at birth. Patients with the disease have "butterfly skin" which is so thin that any minor trauma to the skin causes it to blister and scar. The disease is a rare genetic disease that can be both recessive and dominant. If a person has dominant DEB then he or she has no real cure except for the nurture from those who care for them. But those who have recessive DEB, may have a chance since there has recently been 4 cases of skin graphs, or cutaneous pinch grafts, that helps to heal the wounds of the patients.
      • Similarity Sequencing and its use for phylogenetic trees by Ivan Cvitkovic and Daniel Kaufman.
      • The genetic causes and physiological effect of sickle cell anemia by Alda Caan and Blanca Trujillo
      • How gene therapy may be applied to the curing genetic diseases by Michael Jenkins and Uchechukwu (Uli)Nnadi.

    Freshman Seminar: Artificial Intelligence: Is it for Real?

      • Discussion of historical and significant papers in Artificial Intelligence dealing with the creating and evaluation of computational artifacts that do or do not exhibit intelligence. Students will be encouraged to suggest their own approaches to the problems. Brainstorming and constructive evaluation will be encouraged.
      • Prerequisite: ICS21 with a grade of B.
      • Readings: Significant papers will be chosen, corresponding to student interest. If you miss class, then you can pick up the paper outside my office. Also if you miss class, you are required to write a one paragraph comment on the reading which you should send to me via email.
      • First Paper: the Turing test (1950)
      • Syllabus
      • Meetings Wednesdays: 3-3:50, CSE 310.
      • First Class: April 2; Last Class: June 4
      • Grading: Grades are based on class participation and written assignments. Attendance is not sufficient. Each week you are expected to hand in a one paragraph comment on the reading.

    H22 Honors Introduction to Computer Science II

      • Introduction to basic abstract data structures and associated algorithms, including their implementation, selection, and complexity. Data structures include lists, stacks, queues, tables, and trees.
      • Prerequisite: H21 or consent of instructor
      • Required Texts:
        • Data Structures and Algorithms with Object-Oriented Design Patterns in Java by Bruno Priess.
        • Core Java (2nd ed) by Horstmann and Cornell
      • Recommended: Any book on Java that works for you. Many students like Core Java 2. Java Texts .
      • Syllabus
      • Ethics


    Honors 23 Problem Solving and Data Structures

      • Further analysis of basic and non-basic data structures and associated algorithms. With respect to representation, covers arrays, lists, trees and graphs. With respect to algorithms covers recursion, divide-and-conquer, backtracking, and dynamic programming.
      • Prerequisite: H22 or consent of instructor
      • Required Text: Data Structures and Problem Solving in Java
      • Author: Mark Allen Weiss
      • Recommended for Swing: Up to Speed with Swing by Steven Gutz.
      • Syllabus
      • Homework Details
      • Ethics

    ICS 23 Problem Solving and Data Structures

      • Further analysis of basic and non-basic data structures and associated algorithms. With respect to representation, covers arrays, lists, trees and graphs. With respect to algorithms covers recursion, divide-and-conquer, and separate-and-conquer,
      • Prerequisite: ICS22 or consent of instructor
      • Required Text: Data Structures and Problem Solving in Java
      • Author: Mark Allen Weiss
      • Suggested for Gui's: Up to Speed with Swing by Steven Gutz.
      • Syllabus
      • Homework Details
      • Ethics

    171 Introduction to Artificial Intelligence (4)

      • The course is divide into four major topics and we will spend about two weeks on each topic. The major topics are: Problem Solving via search, Logical reasoning in propositional and first-order logic, Probabilistic Reasoning and Learning.
      • Prerequisites: ICS 52 and and Mathematics 2A-B and 67.
      • Text: Artificial Intelligence: A Modern Approach
      • Authors: Stuart Russell and Peter Norvig
      • Grading. There will be 4 quizzes,2 coding assignments, and 3 written homeworks. The quizzes will each count 15% of your grade. The lowest homework/coding score will be dropped. Scores on late homework will be reduced by 20% per day.
      • Class Lectures: MWF 2:00-2:50 CS174
      • Time: June 28 - Sept 3.
      • Teaching Assistant: Rajyashree Mukherjee mukherjr@uci.edu
      • Course email: 36490-M04@classes.uci.edu
      • Discussion Section: Wed 3:00-3:50 CS174
      • Syllabus and Lectures
      • Ethics
      • Homework Details Future homeworks will be collected in class and returned via the distribution center.

    172 Programming Techniques in Artificial Intelligence (4) W.

      • The study of the object-oriented design as applied to AI algorithms and representations. The goal is to create an in-depth understanding of some of the important AI approaches by coding various algorithms in an object-oriented language. On the coding side, we will examine graphical displays, user-interfaces, and code libraries. On the AI side, we will implement algorithms for problem-solving, optimization, decision-making, and learning. There will be three to five coding/design assigments. The code will be in Java or Python.
      • Prerequisites: ICS 171, knowledge of object-oriented programming
      • Texts: Any Java text you like plus any AI text you like.
      • Syllabus plus fuller description of course.
      • Programming Language: Java
      • Ethics

    CS 174 BioInformatics (4) S.

      • Meetings: M-W-F: 11-11:50 ELH 110
      • Office Hours: M-W: 9-10 and by appointment. Room 414D CS
      • First Class: Monday, April 3
      • Last Class: Friday, June 9
      • Final: Friday, June 16, 8am -10am
      • Questions: Do not hesitate to ask questions in class, in my office hours or by email. Do hesitate to ask questions on the weekends - that's family time for me. Untimely questions may not be answered.
      • Text: Fundamental Concepts of BioInformatics by Dan Krane and Michael Raymer
      • Course Mailing List: TBD
      • Teaching Assistant: Daniel Sanchez (valencid@ics.uci.edu)
      • Teaching Assistant Office hours: Wed- Fri, 10-11, TA office in the ICS Trailers
      • Grading: There will be eight assignments plus one quiz and a final. The lowest score on a assignment will be dropped. The final will count 20% of your grade, the quiz 10% and the best seven of your assignment scores will each count 10%. Homeworks can be turned in one day late for 1/2 credit. All homeworks are due a week after the assignment on Monday by 10 am. If it is turned in at 10:01, then you can only get at most 1/2 credit. Homeworks will be deposited using the checkmate program.
      • Course Goals: Bioinformatics is the study of biological problems via computational tools. The goal of the course is to provide students with sufficient biological knowledge and computational methods that they can use, understand, and possibly generate algorithms that are appropriate for available biological data. This course will concentrate on approaches that deal with the most voluminous and accurate data, namely DNA data, protein data, and gene-expression data. As part of the course students will use current tools on existing databases to a) locate genes and determine their function, b) build phylogenic trees, c) locate regulatory elements and d) predict protein structure.
      • Overview of Course: Problems in Biology and Computational Approaches
        Week 1: Basic structure of DNA, RNA, genes and proteins.
        Week 2&3: Methods: Dot Matrices, Local, Global and Multiple Alignment algorithms.
        Problems: Gene identification and gene function
        Week 4&5. Methods: Tree building: UPGMA, clustering, maximum likelihood
        Problems: Tree of Life: fitting all organism into an evolutionary history
        Week 6&7: Methods: Pattern discover by search (exhaustive and heuristic)
        Problems: Gene regulation in Prokaryotes and Eukaryotes
        Week 8&9: Methods: Dynamic Programming, machine learning
        Problems: Determine Secondary and Tertiary structure of RNA and Proteins.
        Week 10: Methods: Machine Learning
        Problem: Determining proteomic disease diagnoses.
      • Course Workload: Expect weekly assignments. You will have four assignments where you implement basic algorithms for a particular biological problem. These assignments will alternate with using existing, more sophisticated algorithms for related tasks. In particular you will write algorithms for finding genes, determining similarity between DNA and protein sequences, building evolutionary trees, and locating regulatory elements. I will provide a sketch or design for these algorithms so that they are all implementable in one afternoon plus, at least for me, another afternoon for debugging. Only the next homework assignment is guarantee to be correct - other assignments may change as the class progresses.
      • Quiz: Multiple choice and fill in the blank. Monday of the second week of classes. This will be based on chapter 1 of the text (pages 1-20) plus the lectures. For computer scientist learning the basic vocabulary of molecular biology is somewhat difficult. I recommend reading the chapter at least twice, making note of the important concepts.
      • First Assignment to hand in: Due the beginning of the 3rd week of classes. The assignment has two parts for submission: a coding part and question part. The assignment is due by 10am on Monday of the third week of classes. In general assignment are due by 10am on Monday of week after the assignment is made. Assignments are to be deposited in the appropriate folder using checkmate. Most of you have already used this software, but just in case: To set up for electronic submission, go to checkmate.ics.uci.edu, log in with your UCInet ID, choose "Course Listing" for "Spring 2006," click "Go" next to ICS 174, and then click "List me for this course." For the answers to questions, you will submit a word file hwkn.doc file. For the coding part, when they occur, submit a single hwkn.java. To do this you will need collect your java files into a single file. You may also do the homework in Python. I reserve the right to change assignments as the course progresses.
      • Assignment Details

    CS 175A Projects in Artificial Intelligence (4) S.

    �                                 Meetings: MWF 3:00-3:50 SE2 1304

    �                                 Office Hours: MW before and after class and by appointment.

    �                                 Course Mailing List: 36485-F05@classes.uci.edu

    �                                 Course Project web site TBD

    �                                 Final paper and code due TBD.

    �                                 Teaching Assistants: James Worcester (jworcest@uci.edu) Office hours: TBD

    �                                 Poster Presentations (Power-point) with demonstration; Date to be decided.

    �                                 First Class: September 23 Last Class: December 2

    �                                 Work Load: Several presentations, interim documents, design documents, website, power-point presentation, Java code, and a final paper. The final paper should include at least two references, either to papers or texts. The final presentation will be done in PowerPoint and should include a demonstration and evaluation of the program. Each project will have an associated WebSite

    �                                 Grading: All students in a group will assign credit to one another. The individual grade will be based on the total project grade (decided by me) distributed as group members decided.

    �                                 In this course students will build Java programs that demonstrate AI techniques. The potential topics include Expert Systems, Natural Language Processing, Problem Solving, Search, Game Playing, Learning, Reasoning, Perception, etc. A more specific but still incomplete list of potential projects is listed under AI projects. One common approach would be to implement some AI method and apply it to some domain. The AI text by Russell and Norvig provides many examples of possible projects.

    Each student will be involved in one project done in a group of three people. In the project each object will be identified with its author. All students are responsible for some of the code. The programs generated will be made public so that all can view, use, and evaluate. Students will be responsible for maintaining their programs. There will be an emphasis on design so every project will go through a design review in which the entire class participates. Each group will be responsible for several presentations: the basic goal, the history on the problem, the design, and the final demonstration with evaluation. Each group is also responsible for a report that corresponds to each presentation.

    �                                 Prerequisites: ICS 171, knowledge of Java

    �                                 AI Projects.

    �                                 Work Schedule.

    �                                 Ethics

    202 Seminar in Research in ICS (2) F.

    Graduate orientation program and colloquium series. Includes talks by ICS faculty in all areas about their current research. Satisfactory/Unsatisfactory Only. Formerly ICS 295.

    209 Seminar in Bioinformatics(2)

    Graduate seminar in which recent papers and research are discussed and analyzed. Papers will concentrate on analysis of genomic and gene expression data. Supporting papers on biology, machine learning, and statistics may also be included. It is likely that each enrolled person will present two papers. The first presentation will be informal and will likely include necessary background material. The second presentation will be a formal one, done either via PowerPoint or overheads. During the second presentation background material can be assumed.

    �                                 ICS 249 Mondays12-2pm (Note new place and time)

    �                                 Bioinformatic Papers



    CS 270A: Introduction to Artificial Intelligence

    �                                 Introduction to basic AI representations and algorithms, problem solving, planning, logical and probabilistic reasoning, natural language processing and learning.

    �                                 Text: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig 2nd Edition. I suggest ordering this from Amazon.

    �                                 Lecture Time: Tues/Thurs 9:30 - 10:50 Room: cs253

    �                                 Office hours: 11-12 tues/thurs and by appointment.

    �                                 First class: Sept 30 2003. Last class: Dec. 4 2003.

    �                                 Grading: Three coding assignments and a final: all weighted equally.

    �                                 Final: Thursday Dec 11 8am-10am

    �                                 Click here for lectures and assignments.


    CS273: Machine Learning (4).

    �                                 Computational approaches to learning, concentrating on classification and regression. Covers standard learning representations (rules, decision trees, instances, linear threshold units, neural nets, etc), their representation, limitations, and evaluation.

    �                                 Prerequisite: ICS 270A. Formerly ICS 275.

    �                                 Required Text: Introduction to Data Mining

    �                                 Authors: Pang-Ning Tan, Michael Steinbach, Vipin Kumar

    �                                 Publisher: Addison-Wesley ISBN 0-321-32136-7

    �                                 Recommended Text: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. 2nd Edition.

    �                                 Authors: Ian H. Witten and Eibe Frank. Publisher: Morgan Kaufman

    �                                 Recommended Text: Machine Learning by Tom Mitchell. Publisher: McGraw-Hill

    �                                 First Class: Jan 10. Last class: Thursday March 16

    �                                 Final: Thursday March 23 8am - 10am

    �                                 Lectures: tues-thurs 9:30-10:50

    �                                 Room: CS-213

    �                                 Office Hours: tuesday/thursday after class and by appointment.

    �                                 Syllabus

    �                                 Ethics

    277A Representations and Algorithms for Molecular Biology(4)

    o                                The primary goal of this class is to introduce molecular biologists to computer science and computer scientist to molecular biology. It is not expected that biologist will become programmers, but they will learn what might be accomplished with computational analysis. Nor is it expected that computer scientists will conduct biological experiments, but they will learn enough biology to understand the important problems in biology that are addressable by computational means.

    �                                 Required Text: Bioinformatics: Sequence and Genome Analysis by David W. Mount 2nd Edition

    �                                 Recommended Text: Introduction to Computation Molecular Biology by Sebutal and Meidanis

    �                                 Recommended (undergraduate text): Fundamental Concepts of Bioinformatics by Krane and Kramer

    �                                 First Class: Thursday Jan 8, 2006. Last class: Thursday March 16

    �                                 Final: Thurs Dec 12, 1:30-3:30pm

    �                                 Meetings: Tuesday/Thursday 9:30- 10:50 CS253

    �                                 Course Work: Weekly readings from the text and papers, a few homeworks, and a final project.

    �                                 Course Mailing List: 36774-F05@classes.uci.edu

    �                                 Project: A joint project and presentation teaming a biologist with a computer scientist.

    �                                 Syllabus


    280 Seminar in Computational Biology

    This is an advanced course that concentrates on recent computational methods that aid in the functional analysis of genomes. Papers will be drawn primarily from Bioinformatics, Proceedings of the Conference on Intelligent Systems for Molecular Biology, Journal of Molecular Biology, Proceedings of the National Academy of Science, Science, and the Journal of Computational Biology. The course will focus on algorithms that find patterns in DNA, particularly those that relate to gene regulation, such as finding regulatory elements, finding promoters, and clustering gene expression data. There is no text. We will all be reading, discussing, and presenting papers as well as any research-in-progress.


    Java Demos:

    o                                Traveling Salesman. Not available now.
    This Java (JDK1.0) program illustrates the use of hill-climbing to solve the traveling saleman program. The program is simple so that the general ideas can be understood. Many valuable extensions are possible. The program only uses one operator, that of uncrossing edges that intersect. Additional operators are useful. The general problem of defining useful operators for hill-climbing is unsolved. Simulated annealing, multiple restarts, better initialization would all be useful. If your browser executes JDK1.1, then you might prefer the following similar demo: Not available now.

    o                                Dynamic Programming. Dynamic Programming
    This program illustrate the use of dynamic programming to find the minimum edit distance between two strings. This distance depends on the cost one associates with various edit operations. It can be used for spell checking but major applications are in comparing amino acid and nucleotide sequences. If you look at the source,the main action is in the routine doSimMatch. This was one of my first Java programs, so the program can be greatly improved.

    o                                Kmean clustering. Not available now.
    You'll see that is doesn't always work. Requires JDK1.1.

    o                                Valdimir Vapnik's applet on Support Vector Machines . This is a beautiful applet that displays the probability density function associated with the generated decision surface.

    o                                N Queens Problem. Not available now.
    N Queens problem is solved by local improvement or repair search. This same method is most applied to scheduling problems, where it can generate anytime and approximate solutions. Coded in ics175 by Son Tran.


    Publications

    2006

    In silico prediction and functional validation of sigma-28-regulated genes in Chlamydia and E. coli. Yu, H.H.Y., Ming Tan, and Dennis Kibler. Journal of Bacteriology. Online at JB01082-06

    2005

    Using Hexamers to Predict Cis-Regulatory Modules in Drosophila. Bob Chan and Dennis Kibler, BMC Bioinformatics. http://www.biomedcentral.com/1471-2105/6/262 6: 262, October 2005.

    2004

    A horizontally transferred protist gene in the Hydra genome Figure 1 Current Biology. Robert E. Steele, Steven E. Hampson, Nicholas A. Stover, Dennis F. Kibler, Hans R. Bode. Volume 14, number 8.

    2003

    Using DNA MicroArrays to Identify SP1 as a Transcription Regulatory Element of Insulin-Like Growth Factor in Cardiac Muscle Cells. Circulation Research. Tao Li, Yung-Hsiang Chen, Tsun-Jui Lui, Jia Jia, Steven Hampson, Yue-Xin Shan, Dennis Kibler, Ping H. Wang. pp 1-35. 2003

    Evaluating Representations for the Shine-Dalgarno Site in Escherichia coli Steven Hampson and Dennis Kibler. TR#03-14. School of Information and Computer Science. University of California, Irvine.

    2002

    Characterizing the E. coli Shine-Dalgarno Site: Probability Matrices and Weight Matrices Dennis Kibler and Steven Hampson, International Conference on Mathematical and Engineering Techniques in Medicine and Biological Science ( METMBS-2002). pp. 358-364.

    Distribution Patterns of over-represented k-mers in non-coding yeast DNA Steven Hampson, Dennis Kibler, and Pierre Baldi, BioInformatics. vol. 18 no.4 pp. 513-528.

    2001

    Learning Weight Matrices for Identifying Regulatory Elements, Dennis Kibler and Steven Hampson, International Conference on Mathematical and Engineering Techniques in Medicine and Biological Science ( METMBS-2001). pp. 208-214.

    Bay, S. D., Kibler, D., Pazzani, M. J., and Smyth, P. (2001). The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. In Information Processing Society of Japan Magazine. Volume 42, Number 5, pages 462-466. English language version reprinted in SIGKDD Explorations. Volume 2, Issue 2, pp. 81-85, 2000.

    2000

    Analysis of Yeast's ORF Upstream Regions by Parallel Processing, Microarrays, and Computational Methods. Steve Hampson, Pierre Baldi, Dennis Kibler, and Suzanne Sandmeyer. Tenth International Conference on Intelligent Systems for Molecular Biology ( ISMB-2000). pp. 109-201.

    Combinatorial Motif Analysis and Hypothesis Generation on a Genomic Scale , with Yuh-Jyh Hu, Suzanne Sandmeyer, Calvin McLaughlin. BioInformatics, Vol 16, 222-232.

    1999

    Minimum Generalization via Reflection: A Fast Linear Threshold Learner , with Steven Hampson, Machine Learning 37 pp. 51-73. 1999.

    Detecting Motifs from Sequences with Yuh-Jyh Hu and Susan Sandmeyer, International Conference on Machine Learning 1999.

    1997
    Symbolic Nearest Mean Classifiers , with Piew Datta, AAAI-97.

    Learning Symbolic Prototypes , with Piew Datta, ICML-97.

    GalaII: Integrating Construction of Boolean and Prototypical Features , with Yuh-Jyh Hu, ECML-97 (in press).

    1996
    A Generative Approach to Constructive Induction , with Yuh-Jyh Hu, AAAI-96.

    1995
    ``Plateaus and Plateau Search in Boolean Satisfiability Problems: When to Give Up Searching and Start Again,'' with Steven Hampson. DIMACS Challenge, 1995.

    Learning Prototypical Concept Descriptions , with Piew Datta, Twelfth International Conference on Machine Learning.

    1993
    ``Learning recurring subplans'' with David Ruby. in Machine Learning Methods for Planning, Minton, S., 466--497, Morgan Kaufman, 1993.

    Concept Sharing: A Means to Improve Multi-Concept Learning , with Piew Datta, Machine Learning Conference.

    1992
    ``The Utility of Knowledge in Inductive Learning'', with Michael Pazzani, Machine Learning, 9, 57--94, 1992.

    Utilizing Prior Concepts , with Piew Datta, Machine Learning Workshop on Bias.

    1991
    ``Instance-Based Learning Algorithms'', with David Aha and Marc Albert, Machine Learning, 37--66, 1991.

    ``SteppingStone: An Empirical and Analytic Evaluation'', with David Ruby, Proceedings of the Ninth National Conference on Artificial Intelligence, 527--531, Morgan Kaufmann, 1991.

    1990
    ``Machine Learning as an Experimental Science'', with Pat Langley. Readings in Machine Learning, Dietterich, T., and Shavlik, J. (eds.), 38--43, Morgan Kaufmann, 1990.

    1989
    ``Instance-Based Prediction of Real-Valued Attributes'', with David Aha and Marc Albert, Computational Intelligence: an International Journal, Vol 6, 3, 51--57, 1989.

    ``Exploring the Episodic Structure of Algebra Story Problem Solving'', with Rogers Hall, Etienne Wenger, and Chris Truxaw, Cognition and Instruction, 1989.

    1986
    ``Experimental Goal Regression A Method for Learning Problem Solving Heuristics'', with Bruce Porter, Machine Learning 3, 245--289, 1986.

    1985
    ``Differing Methodological Perspectives in Artificial Intelligence Research'', with Rogers P. Hall, Artificial Intelligence Magazine, Volume 6, Number 3, pp. 166-178, August 1985.


    Professional Activities:

    Reviewer for Bioinformatics, Machine Learning, KDD, IEEE

    Scientific Advisor for Oncotech.

    CEP and UCEP member.


    Other Interests:

    Reading, bridge, hiking.


    Information and Computer Science
    University of California, Irvine CA 92717-3425

    Last modified: May 16, 2005

    http://www.ics.uci.edu/~pattis/ Richard Pattis Home Page
    Richard E. Pattis
    Senior Lecturer
    Department of Computer Science
    and
    Department of Informatics
    Donald Bren School of Information
      and Computer Sciences
    University of California, Irvine
    Irvine, CA 92697
    pattis@ics.uci.edu
    Office: 4062 Bren Hall
    Phone: (949) 824-2704
    Fax:     (949) 824-4056

    Teaching and learning are mirror
    images. This calligraphy appears in
    Inversions, a book by Scott Kim.

    Students accepted to CMU would wear
    this badge when visiting the campus; I
    sometimes wore it too, because I admit
    I'm still a student, learning new things.


    I have put my collection of Quotations for Learning and Programing on the web. I hope to continue expanding
    (and correcting) it. I always welcome feedback (e.g., corrections, misattributions, other quotations).


    I am starting to index, annotate, and put on the web various Education-Related Video Clips.


    Winter 2016 Teaching Schedule

    Information and Computer Sciences (ICS) 33: Intermediate Programming
    Final Exam: Friday, March 18th from 1:30pm - 3:30pm
    (closed book/closed notes/closed computer/closed calculator)

    Information and Computer Sciences (ICS) 46: Data Structure Implementation and Analysis
    Final Exam: Friday, March 18th from 8:00am - 10:00am
    (closed book/closed notes/closed computer/closed calculator)

    Information and Computer Sciences (ICS) 193: Tutoring in ICS
    No Final Exam

    I'll be in class (lecture/yellow) and/or will hold office hours (gray) most days. I have online office hours using AIM
    every weekday evening on a day before class meets (MTuWTh).

    Please note that my office hours are open. There is no need to schedule an appointment ahead of time. Just drop by.

    Time/DayMondayTuesdayWednesdayThursdayFriday
    11:00-11:30 Lecture: ICS 46
    SSLH 100
     
     
    Lecture: ICS 46
    SSLH 100
     
     
    Lecture: ICS 46
    SSLH 100
    11:30-12:00 Lecture: ICS 46
    SSLH 100
     
     
    Lecture: ICS 46
    SSLH 100
     
     
    Lecture: ICS 46
    SSLH 100
    12:00-12:30 Office Hours
    DBH 4062
    Office Hours
    DBH 4062
    Office Hours*
    DBH 4062
     
     
    Office Hours
    DBH 4062
    12:30-  1:00 Office Hours
    DBH 4062
    Office Hours
    DBH 4062
    Office Hours*
    DBH 4062
     
     
    Office Hours
    DBH 4062
      1:00-  1:30  
     
    Office Hours
    DBH 4062
     
     
     
     
     
     
      1:30-  2:00  
     
    Office Hours
    DBH 4062
     
     
     
     
     
     
      2:00-  2:30 Lecture: ICS 33
    ELH 100
    Lecture: ICS 193
    DBH 5011
    Lecture: ICS 33
    ELH 100
     
     
    Lecture: ICS 33
    ELH 100
      2:30-  3:00 Lecture: ICS 33
    ELH 100
    Lecture: ICS 193
    DBH 5011
    Lecture: ICS 33
    ELH 100
     
     
    Lecture: ICS 33
    ELH 100
      3:00-  3:30 Office Hours
    DBH 4062
    Lecture: ICS 193
    DBH 5011
    Office Hours
    DBH 4062
     
     
     
     
      3:30-  4:00 Office Hours
    DBH 4062
     
     
    Office Hours
    DBH 4062
     
     
     
     
    ...
    ...
    ...
    ...
    ...
    ...
      9:00pm-10:00pm Online Help: AIM
    richardepattis
    Online Help: AIM
    richardepattis
    Online Help: AIM
    richardepattis
    Online Help: AIM
    richardepattis
     
     

    For Office Hours* (with a *: W 12pm-1pm) I will sometimes have to cancel because
    of faculty meetings. I will send email that day if I cannot attend these office hours.


    Interesting Snippets

    While developing a manuscript for a textbook on the Ada programming language in the late 1980s, I wrote a chapter on EBNF and began teaching it on the "first" day of my CS-1 class: primarily as a microcosm of programming, but also as a practical tool for later describing the syntax of Ada. These 21 pages (less than 1/4 the size of the original Karel book) discuss the sequence, choice, option, repetition, and recursion control structures (along with "procedural" abstracton via named EBNF rules). They explore various methods of proving that tokens satisfy descriptions, that descriptions are equivalent (and how to simplify them), and the difference between syntax and semantics. I have continued to use this approach until this day in my CS-1 classes. In fact, I have rewritten this EBNF chapter for an introduction to Python course I am teaching.

    A short opinion piece on Plagiarism from the NY Times.

    A new cure for Short Bowel Syndrome (Brainstorm to Breakthrough: A Surgical Procedure is Born).

    An excerpt from the chapter "He Fixes Radios by Thinking!" from the book "Surely You're Joking, Mr. Feynman!": Adventures of a Curious Character (start at the bottom of page 18: "One day I got a telephone call..." and finish at the bottom of page 20: "...never thought that was possible.") Explains why debugging is best accomplished by thinking, not fiddling.

    Cartoons:

    • If Charles Schultz wrote Karel the Robot
    • Arlo and Janis: The hardest teacher
    • Doonesbury: Walden's Last B
    My Favorite Graph: I show this graph (and its associated article) in class after discussing general graph theory terminology (up to connected components). It is scary and compelling at the same time.

    De Millo, Lipton, and Perlis: Social Processes and Proofs of Theorems and Programs Communications of the ACM, May 1979; Volume 22, Number 5, Pages 271-280.

    Genius is a Thing that Happens from Jordan Ellenberg's How Not to Be Wrong: The Power of Mathematical Thinking. Why Mathematics (and Computer Science) students should not be discouraged by smarter classmates.


    Philosphical Musings

    Doubts are such tiny things. A mind with no room for doubts must have no room for thoughts either. -R. Pattis


    The following dialog is from the transcript of "Between Time and Timbuktu" (a synthesis of the writings of Kurt Vonnegut). For more on Bokononism, from which this passage is inspired, see The Books of Bokonon (from the novel "Cat's Cradle").

    Narrator: In the beginning, G-d created the Earth, and he said, "Let there be mud." And there was mud. And G-d said, "Let Us make living creatures out of mud, so the mud can see what We have done." And G-d created every living creature that now moveth, and one was man. Mud-as-man alone could speak. "What is the purpose of all this?" man asked politely. "Everything must have a purpose?" asked G-d. "Certainly," said man. Then I leave it up to you to think of one for all of this," said G-d. And he went away.

    Stony Stevenson: I feel very unimportant compared to you [G-d].

    Voice of Bokonon: The only way you can feel the least bit important is to think of all the mud that didn't even get to sit up and look around.

    Stony Stevenson: I got so much, and most mud got so little.

    http://www.ics.uci.edu/~bic/ Lubomir Bic

    Lubomir Bic

    • email: bic@ics.uci.edu
    • office: ICS3 (Bren Hall), Room 3224
    • phone/fax: 949-824-5248

    Teaching:

    • ICS 6B
    • ICS 51
    • CS 143A--Summer Session II
    • CS 143B

    Research:

    • My current research is in the areas of parallel and distributed computing.
    • Current Project: MESSENGERS

    Recent Book:

    Operating Systems Principles, Prentice-Hall, 2003

    A textbook for undergraduate-level college courses

     

     

     

    Education Abroad Program (EAP):

    • I am the ICS liaison for the EAP, which offers UC students the opportunity to spend some time at a foreign university or college. For more information, visit the Center for International Education home page.


     

    School of Information and Computer Sciences,


    University of California, Irvine CA 92717-3425
     

    http://www.ics.uci.edu/~kobsa/ Homepage Alfred Kobsa <BODY BGCOLOR="#FFFFFF" TEXT="#000000"> </BODY> http://www.ics.uci.edu/employment/index.php employment @ the bren school of information and computer sciences
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    http://www.ics.uci.edu/~givargis/ Tony Givargis

    Tony Givargis

    Professor of Computer Science (cv, linkedin)
    University of California, Irvine
    givargis@uci.edu

    Publication

    Books

    • B3. F. Vahid, T. Givargis. Programming Embedded Systems – An Introduction to Time-Oriented Programming. UniWorld Publishing, ISBN: 978-0-9829626-4-0, August 2012.

    • B2. A. Nacul, M. Lajolo, T. Givargis. Interface-Centric Abstraction level for Rapid Hardware/Software Integration, Book Chapter in Applications of Specification And Design Languages for SOCs. Springer, ISBN: 1-4020-4997-8, July 2006.

    • B1. F. Vahid, T. Givargis. Embedded System Design: A Unified Hardware/Software Introduction. John Wiley and Sons, ISBN: 0471386782, October 2001.

    Patents

    • P11. T. Givargis, R. Sadri. Methods for Optimizing Data Movement in Solid State Devices. United States Patent, 8,612,719, December 2013.

    • P10. T. Givargis. Systems and Methods for Managing Key-Value Stores. United States Patent, 8,612,402, December 2013.

    • P9. A. Nacul, T. Givargis. Phantom Serializing Compiler and Method of Operation of Same. United States Patent, 7,886,283, February 2011.

    • P8. J. Addink, S. Addink, T. Givargis. Methods and apparatus for using water use signatures and water pressure in improving water use efficiency. United States Patent 7,330,796, February 2008.

    • P7. J. Addink, S. Addink, T. Givargis. Methods and Apparatus for Using Water use Signatures in Improving Water use Efficiency. United States Patent 6,963,808, November 2005.

    • P6. J. Addink, T. Givargis. Interactive Irrigation System. United States Patent 6,950,728, September 2005.

    • P5. J. Addink, K. Buhler, T. Givargis. Modifying Irrigation Schedules of Existing Irrigation Controllers. United States Patent 6,892,114, May 2005.

    • P4. J. Henkel, T. Givargis, F. Vahid. Method for Core-Based System-Level Power Modeling using Object-Oriented Techniques. United States Patent 6,865,526, March 2005.

    • P3. K. Buhler, T. Givargis. Two Tire Irrigation Valve Controller. United States Patent 6,812,826, November 2004.

    • P2. J. Addink, T. Givargis. Detecting Weather Sensor Malfunctions. United States Patent 6,714,134, March 2004.

    • P1. J. Addink, K. Buhler, T. Givargis. Irrigation Accumulation Controller. United States Patent 6,298,285, October 2001.

    Peer-Reviewed/Archived Journal

    • J27. T. Springer, S. Peter and T. Givargis. Fuzzy Logic Based Adaptive Hierarchical Scheduling for Periodic Real-Time Tasks. ACM Special Interest Group on Embedded Systems (SIGBED) Review, to appear.

    • J26. S. Peter, T. Givargis. Component-Based Synthesis of Embedded Systems using Satisfiability Modulo Theories. ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 20, no. 4, pp. 49:1-49:27, September 2015. pdf

    • J25. T. Springer, S. Peter and T. Givargis. Adaptive Resource Synchronization In Hierarchical Real-Time Systems. ACM Special Interest Group on Embedded Systems (SIGBED) Review, vol. 11, no. 4, pp. 37-42, December 2014. pdf

    • J24. V. Gunes, S. Peter, T. Givargis, F. Vahid. A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems. KSII Transactions on Internet and Information Systems (TIIS), vol. 8, no. 12, pp. 4242-4268, December 2014. pdf

    • J23. B. Miller, F. Vahid, T. Givargis, P. Brisk. Graph-Based Approaches to Placement of Processing Element Networks on FPGAs for Physical Model Simulation. ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 7, no. 4, article 10, December 2014. pdf

    • J22. C. Huang, F. Vahid, T. Givargis. Automatic synthesis of physical system differential equation models to a custom network of general processing elements on FPGAs. ACM Transactions on Embedded Computing Systems (TECS), vol 13, no. 2, article 23, September 2013. pdf

    • J21. C. Huang, B. Miller, F. Vahid, T. Givargis. Synthesis of Networks of Custom Processing Elements for Real-Time Physical System Emulation. ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 18, no. 2, pp. 22-42, March 2013. pdf

    • J20. C. Huang, F. Vahid, and T. Givargis. A Custom FPGA Processor for Physical Model Ordinary Differential Equation Solving. IEEE Embedded Systems Letters, vol. 3, no. 4, pp. 113-116, September 2011. pdf

    • J19. S. Choudhuri, T. Givargis. Deterministic Service Guarantees for NAND Flash using Partial Block Cleaning. Academy Publisher Journal of Software (JSW), vol. 4, no. 7, pp. 728-737, September 2009. pdf

    • J18. M. Ghodrat, T. Givargis, A. Nicolau. Optimizing Control Flow in Loops using Interval and Dependence Analysis. Springer Journal on Design Automation of Embedded Systems (DAES), vol. 13, no. 3, pp. 193-221, September 2009. pdf

    • J17. S. Sirowy, D. Sheldon, T. Givargis, F. Vahid. Virtual Microcontrollers. ACM SIGBED Review, vol. 6, no. 1, January 2009. pdf

    • J16. A. Nacul, T. Givargis. Synthesis of Time-Constrained Multitasking Embedded Software. ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 11, no. 4, pp. 822-847, October 2006. pdf

    • J15. M. Ghodrat, T. Givargis, A. Nicolau. Expression Equivalence Checking using Interval Analysis. IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 14, no. 8, pp. 830-842, August 2006. pdf

    • J14. C. Lopes, A. Haghighat, A. Mandal, T. Givargis, P. Baldi. Localization of Off-the-Shelf Mobile Devices Using Audible Sound: Architectures, Protocols and Performance Assessment. ACM Mobile Computing and Communications Review (MC2R), vol. 10, no. 2, pp. 38-50, April 2006. pdf

    • J13. T. Givargis. Zero Cost Indexing for Improved Processor Cache Performance. ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 11, no. 1, pp. 3-25, January 2006. Received the 2006 TODAES Best Paper Award. pdf

    • J12. T. Givargis, David Eppstein. Memory Reference Caching for Activity Reduction on Address Buses. Elsevier Journal of Microprocessors and Microsystems (MICPRO), vol. 29, no. 4, pp. 145-153, May 2005. pdf

    • J11. A. Ghosh, T. Givargis. Cache Optimization for Embedded Processor Cores: An Analytical Approach. ACM Transactions on Design Automation of Electronic Systems (TODAES), vol. 9, no. 4, pp. 419-440, October 2004. pdf

    • J10. A. Nacul, T. Givargis. Adaptive Cache Management for Low Power Embedded Systems. Korea Multimedia Society, Key Technology of Next Generation IT, ISSN 1229-778X, pp. 30-39, December 2003. pdf

    • J9. T. Givargis, F. Vahid, J. Henkel. Instruction-Based System-Level Power Evaluation of System-on-a-Chip Peripheral Cores. IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 10, no. 6, pp. 856-863, December 2002. pdf

    • J8. T. Givargis, F. Vahid, J. Henkel. System-Level Exploration for Pareto-Optimal Configurations in Parameterized System-on-a-Chip. IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 10, no. 4, pp. 416-422, December 2002. pdf

    • J7. T. Givargis, F. Vahid. Platune: A Tuning Framework for System-on-a-Chip Platforms. IEEE Transactions on Computer Aided Design (TCAD), vol. 21, no. 11, pp. 1317-1327, November 2002. pdf

    • J6. F. Vahid, T. Givargis, S. Cotterell. Power Estimator Development for Embedded System Memory Tuning. Journal of Circuits, Systems, and Computers (JCSC), vol. 11, no. 5, pp. 459-476, October 2002. pdf

    • J5.. T. Givargis, F. Vahid. Tuning of Cache Ways and Voltage for Low-Energy Embedded System Platforms. Springer Journal on Design Automation of Embedded Systems, vol. 7, issue 1-2, pp. 35-51, September 2002. pdf

    • J4. T. Givargis, F. Vahid, J. Henkel. Evaluating Power Consumption of Parameterized Cache and Bus Architectures in System-on-a-Chip Designs. IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 9, no. 4, pp. 500-508, August 2001. pdf

    • J3. F. Vahid, T. Givargis. Platform Tuning for Embedded Systems Design. IEEE Computer, vol. 34, no. 3, pp. 112-114, March 2001. pdf

    • J2. J. Farrell, T. Givargis, M. Barth. Real-Time Differential Carrier Phase GPS-Aided INS. IEEE Transactions on Control Systems Technology (TCST), vol. 8, no. 4, pp. 709-721, July 2000. pdf

    • J1. J. Farrell, T. Givargis. Differential GPS Reference Station Algorithm – Design and Analysis. IEEE Transactions on Control Systems Technology (TCST), vol. 8, no. 3, pp. 519-531, May 2000. pdf

    Peer-Reviewed/Archived Conference

    • C61. S. Peter, F. Momtaz, T. Givargis. From the Browser to the Remote Physical Lab: Programming Cyber-Physical Systems. IEEE Frontiers in Education (FIE), pp. 1-7, El Paso, October 2015. pdf

    • C60. V. Gunes, S. Peter, T. Givargis. Improving Energy Efficiency and Thermal Comfort of Smart Buildings with HVAC Systems in the Presence of Sensor Faults. IEEE International Conference on Embedded Software and Systems (ICESS), pp. 945-950, New York, August 2015. pdf

    • C59. V. Gunes and T. Givargis. XGRID: A Scalable Many-Core Embedded Processor. IEEE International Conference on Embedded Software and Systems (ICESS), pp. 1143-1146, New York, August 2015. pdf

    • C58. H. Buini, S. Peter, T. Givargis. Including Variability of Physical Models into the Design Automation of Cyber-Physical Systems. Design Automation Conference (DAC), pp. 153:1-153:6, San Francisco, June 2015. pdf

    • C57. T. Springer, S. Peter, T. Givargis. Resource Synchronization in Hierarchically Scheduled Real-Time Systems using Preemptive Critical Sections. IEEE International Symposium on Object/Component-Oriented Real-Time Distributed Computing (ISORC), pp. 293-300, Reno, June 2014. pdf

    • C56. V. Gunes, S. Peter, T. Givargis. Modeling and Mitigation of Faults in Cyber-Physical Systems with Binary Sensors. IEEE International Conference on Computational Science and Engineering (CSE), pp. 515-522, Sydney, December 2013. pdf

    • C55. S. Peter, T. Givargis. Utilizing Intervals in Component-Based Design of Cyber Physical Systems. IEEE International Conference on Computational Science and Engineering (CSE), pp. 635-642, Sydney, December 2013. pdf

    • C54. B. Miller, F. Vahid, T. Givargis. Exploration with Upgradeable Models Using Statistical Methods for Physical Model Emulation. Design Automatic Conference (DAC), pp. 1-6, Austin, June 2013. pdf

    • C53. S. Peter, F. Vahid, T. Givargis. A Ball Goes to School – Our Experiences from a CPS Design Experiment. Workshop on Cyber-Physical Systems Education (CPS-Ed) at Cyber Physical Systems Week (CPSWeek), pp. 1-4, Philadelphia, April 2013. pdf

    • C52. B. Miller, F. Vahid, T. Givargis. Embedding-Based Placement of Processing Element Networks on FPGAs for Physical Model Simulation. International Symposium on Field-Programmable Gate Arrays (FPGA), pp. 181-190, Monterey, February 2013. pdf

    • C51. T. Chou, C. Huang, B. Miller, F. Vahid, T. Givargis. An Efficient Compression Scheme for Checkpointing of FPGA-Based Digital Mockups. IEEE/ACM Asian and South Pacific Design Automation Conference (ASP-DAC), pp. 632-637, Yokohama, January 2013. pdf

    • C50. B. Miller, F. Vahid, T. Givargis. RIOS: A Lightweight Task Scheduler for Embedded Systems. Workshop on Embedded Systems Education (WESE), pp. 1-7, Tampere, October 2012. pdf

    • C49. C. Huang, B. Miller, F. Vahid, T. Givargis. Synthesis of Custom Networks of Heterogeneous Processing Elements for Complex Physical System Emulation. International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 215-224, Tampere, October 2012. pdf

    • C48. B. Miller, F. Vahid, T. Givargis. MEDS: Mockup Electronic Data Sheets for Automated Testing of Cyber-Physical Systems Using Digital Mockups. Design Automation and Test in Europe (DATE), pp. 1417-1420, Grenoble, March 2012. pdf

    • C47. B. Miller, F. Vahid, T. Givargis. Digital Mockups for the Testing of a Medical Ventilator. ACM SIGHIT International Health Informatics Symposium (IHI), pp. 859-862, Miami, January 2012. pdf

    • C46. B. Miller, F. Vahid, T. Givargis. Application-Specific Codesign Platform Generation for Digital Mockups in Cyber-Physical Systems. Electronic System Level Synthesis Conference (ESLsyn), pp. 1-6, San Diego, June 2011. pdf

    • C45. M. Ghodrat, T. Givargis. Efficient Dynamic Voltage/Frequency Scaling through Algorithmic Loop Transformation. International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 203-209, Grenoble, October 2009. pdf

    • C44. S. Sirowy, F. Vahid, T. Givargis. Digitally-Bypassed Transducers: Interfacing Digital Mockups to Real-Time Medical Equipment. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 919-922, Minneapolis, September 2009. pdf

    • C43. A. Ghosh, T. Givargis. Source Routing made Practical in Embedded Networks. International Conference on Computer Communications and Networks (ICCCN), pp. 1-6, San Francisco, August 2009. pdf

    • C42. A. Ghosh, T. Givargis. QoS Routing in Wired Sensor Networks with Partial Updates. World Academy of Science, Engineering and Technology (WASED), pp. 389-393, Oslo, July 2009. pdf

    • C41. S. Mylavarapu, S. Choudhuri, A. Shrivastava, J. Lee, T. Givargis. FSAF: File System Aware Flash Translation Layer for NAND Flash Memories. Design Automation and Test in Europe (DATE), pp. 339-344, Dresden, April 2009. pdf

    • C40. S. Choudhuri, T. Givargis. FlashBox: A System for Logging Non-Deterministic Events in Deployed Embedded Systems. International ACM Symposium on Applied Computing (SAC), pp. 1676-1682, Honolulu, March 2009. pdf

    • C39. M. Ghodrat, T. Givargis, A. Nicolau. Control Flow Optimization in Loops using Interval Analysis. International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), pp. 157-166, Atlanta, October 2008. 2008 CASES Best Paper Award. pdf

    • C38. F. Vahid, T. Givargis. Timing is Everything – Embedded Systems Demand Teaching of Structured Time-Oriented Programming. Workshop on Embedded Systems Education (WESE), Atlanta, October 2008. pdf

    • C37. S. Sirowy, D. Sheldon, T. Givargis, F. Vahid. Virtual Microcontrollers. Workshop on Embedded Systems Education (WESE), Atlanta, October 2008. pdf

    • C36. F. Vahid, T. Givargis. Highly-Cited Ideas in System Codesign and Synthesis. International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 191-196, Atlanta, October 2008. pdf

    • C35. S. Choudhuri, T. Givargis. Deterministic Service Guarantees for NAND Flash using Partial Block Cleaning. International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 19-24, Atlanta, October 2008. pdf

    • C34. S. Choudhuri, T. Givargis. Real-Time Access Guarantees for NAND Flash using Partial Block Cleaning. IEEE Workshop on Software Technologies for Future Embedded & Ubiquitous Systems (SEUS), pp. 138-149, Italy, September 2008. pdf

    • C33. A. Ghosh, T. Givargis. A Software Architecture for Accessing Data in Sensor Networks. International Conference on Networked Sensing Systems (INSS), pp. 67-70, Japan, June 2008. pdf

    • C32. S. Choudhuri, T. Givargis. Performance Improvement of Block Based NAND Flash Translation Layer. International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 257-262, Salzburg, September 2007. pdf

    • C31. M. Ghodrat, T. Givargis. A. Nicolau. Short-Circuit Compiler Transformation: Optimizing Conditional Blocks. Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 504-510, Tokyo, January 2007. pdf

    • C30. S. Choudhuri, T. Givargis. System Architecture for Software Peripherals. Asia and South Pacific Design Automation Conference (ASP-DAC), pp. 56-61, Tokyo, January 2007. pdf

    • C29. A. Nacul, T. Givargis. Phantom: A Serializing Compiler for Multitasking Embedded Software. American Control Conference (ACC), Minneapolis, pp. 1918-1923, Minneapolis, June 2006. 2006 ACC Best Paper Award. pdf

    • C28. M. Ghodrat, T. Givargis, A. Nicolau. Equivalence Checking of Arithmetic Expressions using Fast Evaluation. International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), pp. 147-156, San Francisco, September 2005. pdf

    • C27. A. Nacul, T. Givargis. Lightweight Multitasking Support for Embedded Systems using the Phantom Serializing Compiler. Design Automation and Test in Europe (DATE), pp. 740-747, Munich, March 2005. pdf

    • C26. A. Ghosh, T. Givargis. LORD: A Localized, Reactive and Distributed Protocol for Node Scheduling in Wireless Sensor Networks. Design Automation and Test in Europe (DATE), pp. 190-195, Munich, March 2005. pdf

    • C25. A. Mandal, C. Lopes, T. Givargis, A. Haghighat, R. Jurdak, P. Baldi. Beep: 3D Indoor Positioning Using Audible Sound. IEEE Consumer Communications and Networking Conference (CCNC), pp. 348-353, Las Vegas, January 2005. pdf

    • C24. A. Nacul, T. Givargis. Code Partitioning for Synthesis of Embedded Applications with Phantom. International Conference on Computer-Aided Design (ICCAD), pp. 190-196, San Jose, November 2004. pdf

    • C23. A. Nacul, T. Givargis. Dynamic Voltage and Cache Reconfiguration for Low Power. Design Automation and Test in Europe (DATE), pp. 1376-1377, Paris, February 2004. pdf

    • C22. M. Buss, T. Givargis, N. Dutt. Exploring Efficient Operating Points for Voltage Scaled Embedded Processor Cores. Real-Time Systems Symposium (RTSS), pp. 275-281, Cancun, December 2003. pdf

    • C21. A. Ghosh, T. Givargis. Cache Optimization for Embedded Processor Cores: An Analytical Approach. International Conference on Computer-Aided Design (ICCAD), pp. 342-347, San Jose, November 2003. pdf

    • C20. T. Givargis. Improved Indexing for Cache Miss Reduction in Embedded Systems. Design Automation Conference (DAC), pp. 872-880, Anaheim, June 2003. pdf

    • C19. A. Ghosh, T. Givargis. Analytical Design Space Exploration of Caches for Embedded Systems. Design Automation and Test in Europe (DATE), pp. 650-655, Munich, March 2003. pdf

    • C18. T. Givargis, D. Eppstein. Reference Caching Using Unit Distance Redundant Codes for Activity Reduction on Address Buses. International Workshop on Embedded System Hardware/Software Codesign (ESCODES), San Jose, September 2002. pdf

    • C17. M. Palesi, T. Givargis. Multi-Objective Design Space Exploration Using Genetic Algorithms. International Workshop on Hardware/Software Codesign (CODES), Estes Park, May 2002. pdf

    • C16. T. Givargis, F. Vahid, J. Henkel. System-Level Exploration for Pareto-Optimal Configurations in Parameterized Systems-on-a-Chip. International Conference on Computer-Aided Design (ICCAD), San Jose, November 2001. pdf

    • C15. T. Givargis, F. Vahid. J. Henkel. Trace-Driven System-Level Power Evaluation of System-on-a-Chip Peripheral Cores. Asia and South Pacific Design Automation Conference (ASP-DAC), Yokohama, January 2001. pdf

    • C14. G. Stitt, F. Vahid, T. Givargis, R. Lysecky. A First-Step Towards an Architecture Tuning Methodology. International Conference on Compilers, Architecture, and Synthesis for Embedded Systems (CASES), San Jose, November 2000. pdf

    • C13. T. Givargis, F. Vahid, J. Henkel. Instruction-Based System-Level Power Evaluation of System-on-a-ChipPeripheral Cores. International Symposium on System Synthesis (ISSS), Madrid, September 2000. pdf

    • C12. R. Lysecky, F. Vahid, T. Givargis. Experiments with the Peripheral Virtual Component Interface. International Symposium on System Synthesis (ISSS), Madrid, September 2000. pdf

    • C11. T. Givargis, F. Vahid. Parameterized System Design. International Workshop on Hardware/Software Codesign (CODES), San Diego, May 2000. pdf

    • C10. T. Givargis, F. Vahid, J. Henkel. Fast Cache and Bus Power Estimation for Parameterized System-on-a-Chip Design. Design Automation and Test in Europe (DATE), Paris, March 2000. pdf

    • C9. R. Lysecky, F. Vahid, T. Givargis. Techniques for Reducing Read Latency of Core Bus Wrappers. Design Automation and Test in Europe (DATE), Paris, March 2000. 2000 DATE Best Paper Award. pdf

    • C8. T. Givargis, F. Vahid. J. Henkel. A Hybrid Approach for Core-Based System-Level Power Modeling. Asia and South Pacific Design Automation Conference (ASP-DAC), Yokohama, January 2000. pdf

    • C7. T. Givargis, J. Henkel, F. Vahid. Interface and Cache Power Exploration for Core-Based Embedded System Design. International Conference on Computer-Aided Design (ICCAD), San Jose, November 1999. pdf

    • C6. R. Lysecky, F. Vahid, T. Givargis, R. Patel. Pre-Fetching for Improved Core Interfacing. International Symposium on System Synthesis (ISSS), San Jose, November 1999. pdf

    • C5. J. Farrell, T. Givargis. Experimental Differential GPS Reference Station Evaluation. American Control Conference (ACC), San Diego, June 1999. pdf

    • C4. J. Farrell, T. Givargis. M. Barth. Differential Carrier Phase GPS-Aided INS for Automotive Applications. American Control Conference (ACC), San Diego, June 1999. pdf

    • C3. F. Vahid, T. Givargis. The Case for a Configure-and-Execute Paradigm. International Workshop on Hardware/Software Codesign (CODES), Rome, May 1999. pdf

    • C2. F. Vahid, T. Givargis. Incorporating Cores into System-Level Specification. International Symposium on System Synthesis (ISSS), Hsinchu, December 1998. pdf

    • C1. T. Givargis, F. Vahid. Interface Exploration for Reduced Power in Core-Based Systems. International Symposium on System Synthesis (ISSS), Hsinchu, December 1998. pdf

    Workshop

    • W2. A. Nacul, M. Lajolo, T. Givargis. Interface-Centric Abstraction Level for Rapid Hardware/Software Integration. Forum on Specification and Design Languages (FDL), Lausanne, September 2005. pdf

    • W1. A. Haghighat, C. Lopes, T. Givargis, and A. Mandal. Location-Aware Web System. Workshop on Building Software for Pervasive Computing at the Object-Oriented Programming, Systems, Languages and Applications (OOPSLA) Conference, Vancouver, October 2004. pdf

    Miscellaneous

    • M1. U. Brinkschulte, M. Cinque, T. Givargis, S. Russo. Guest Editorial. Journal of Software, vol. 4, no. 7, pp. 631-633, September 2009.

    http://www.ics.uci.edu/~dechter/ Dr. Rina Dechter @ UCI :: Home
    Dr. Rina Dechter
    Prof. Rina Dechter, Ph.D
    Artificial Intelligence
    Office: DBH 4232
    Phone: 1.949.824.6556
    Email: dechter_at_ics.uci.edu
    Highlights and News
    BOOK (2013)
    Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
    AWARD
    2013 ACM Fellow (link 1 / link 2)
    PASCAL CHALLENGE (2012)
    Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
    UAI COMPETITION (2010)
    Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
    CS 275
    Constraint Networks Course Page
    MINI-SCHOOL
    UCI Lifted Algorithms Mini-School (November 3-6)
    BOOK (2010)
    'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
    IJCAI 2013 TUTORIAL
    Constraint Processing and Probabilistic Reasoning
    More
    Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

    Research in my group: Automated reasoning in Artificial Intelligence

    My research is in the field of Automated Reasoning in Artificial Intelligence and focused on Graphical Models. Graph based models (e.g., Bayesian and constraint networks, influence diagrams and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both Artificial Intelligence and Computer Science in general. These models are used to accomplish many science and engineering tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification and bioinformatics. These reasoning problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization and probabilistic inference. It is well known that these tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques for significantly advancing the state of the art. Our approach is to devise methods through the understanding and exploitation of tractable reasoning tasks and use those islands of tractability in the design of general anytime algorithms. As their name implies, anytime methods provide a solution anytime during the processing, with the added provision that the quality of the solution improves if more time is available.

    To summarize, my research interests are in the areas of Automated Reasoning, Knowledge-Representation, Planning and Learning.
    School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
    http://www.ics.uci.edu/~magda/ Magda El Zarki

    Magda El Zarki

    • Position: Faculty Member
    • Title: Professor
    • Area: Telecommunications and Networking
    • Office: 3216 Bren Hall
    • Office Tel: +1(949) 824-8584
    • Office Fax: +1(949) 824-4056
    • E-mail: elzarki@uci.edu

    Research Lab - Pointer to my lab and research group

    A Sampling of my Publications

    Overview of Current Research Projects

    • Video over IP (VIP)
    • Real Time Services over Wireless Networks
    • Inter Vehicular Communication Systems
    • Ghana Slave Trade
    • Online Games
    Presentations - Some of my recent presentations

    Courses - Information on my current course offerings

    Networked Systems Program - Read more about the aims and objectives of this MS/PhD degree offered jointly by ICS and ECE.


    Sabbatical

    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425
    Last modified:  3 Oct 2007
    http://www.ics.uci.edu/~ziv/ Hadar Ziv

    Hadar Ziv

    • Position: Ph.D. Researcher (Info. and Computer Science), Instructor (UCI Extension)
    • Area: Software Eng., OO Analysis/Design, Hypertext, Uncertainty, Bayesian nets
    • Advisor: Debra J. Richardson
    • Office: ICS2 259
    • Office Tel: +1(714)824-4047
    • Office Fax: +1(714)824-4056
    • E-mail: ziv@ics.uci.edu

    My Ph.D. Dissertation

    I recently turned in my dissertation manuscript to the UCI library.
    Here, I make available the following dissertation chapters:
    Abstract
    Introduction
    Conclusion
    Your feedback on form and content of these chapters is welcome!

    Course Information

    I teach two courses for UCI Extension, one on object-oriented analysis and design, the other on Windows NT/95 Programming using Visual C++ and MFC.
    Textbooks and syllabus for my Object-Oriented Analysis and Design course are likely to change soon, in anticipation of new books on the latest offering of OO methodologies, including
    The OPEN Consortium's OML and Rational's UML.
    For Tysh, I enclose HTML versions of weeks 1 and 3 of my OOAD course, Week 1,
    and Week3.

    Research Projects

    I develop a hypertext browser, called IVAN, that affords better traceability of software systems by explicit modeling of software uncertainties.
    This work is done as part of the Arcadia research project at UC Irvine.

    Research Papers

    Recently completed three papers on Bayesian-network modeling of software engineering uncertainties.
    The first is submitted to ICSM'97 conference, and is titled Constructing Bayesian-network models of software testing and maintenance uncertainties, also in Adobe PDF in
    The second is submitted to
    International Workshop on Incorporating Hypertext Functionality into Software Systems, to be held in conjunction with the conference on Hypertext'97. This paper is available here in postscript form, under the title Adding Uncertainty to Hypertext Models of Software Systems.
    The second paper is submitted to ESEC/FSE 97 conference. The paper is available in postscript form, under the title "Bayesian-network Confirmation of Software Testing Uncertainties."
    The same paper in Adobe PDF format (for Acrobat 3.0) is in "Bayesian-network Confirmation of Software Testing Uncertainties."
    Please treat all papers as draft submissions, since notification of acceptance is not due until April 1997.
    A previous incarnation of this paper was submitted to ICSE 97. The paper in its original, 10-point font, two column style as required by ICSE, is entitled
    "The Uncertainty Principle in Software Engineering."
    Links to even earlier versions of this paper can also be found in the collection of papers on
    Workflow and Process Automation.

    Professional Interests

    I know a few things about software engineering, software testing, process modeling, and object-oriented analysis and design. I am also interested in Bayesian networks and uncertainty modeling, particularly as they apply to software development.

    Object-Oriented Techniques

    I am familiar with leading methods for OO analysis and design, including the latest work on the Unified Modeling Language (UML) and the OPEN/MOSES effort.
    I am in fact a member of the OPEN Consortium.
    A good source of information on OO CASE Tools on the Web can be found Here.
    In addition, a collection of more than 2000 links about object-orientation is available at these U.S. mirror sites:
    Chicago, Illinois and Provo, Utah.

    Bayesian Networks

    I am interested in Bayesian networks. There are a few quality pages on this topic, including:
    AFIT AI Laboratory
    Lonnie Chrisman's Roadmap to Bayesian Research
    You can find a Bayesian Network Editor, implemented in Java, in the Bayesian Editor page
    and the accompanying implementation of Bayesian updating, courtesy of Fabio Cozman of CMU, at JavaBayes.

    Other Interests

    Besides the aforementioned professional interests, I like to play soccer and tennis, hike, jog, swim, and play rollerhockey. My favorite rollerhockey arena is the Irvine Hockey Club.
    My favorite sport, however, is soccer. A good place to look for the latest soccer information is the ESPN Sports Zone Soccer web page. For additional sports links, check with my friend Neno.
    To learn more about me, take a look at my Resume.

    Getting started with HTML

    UCI's Office of Academic Computing has put together some pointers on how to learn HTML.

    Information and Computer Science
    University of California, Irvine CA 92697-3425
    Last modified: 26 February 1997 http://www.ics.uci.edu/~zhaoxia/ Zhaoxia Yu @ UCI

    Zhaoxia Yu, Ph.D.
    Associate Professor


    Department of Statistics
    University of California
    Irvine, CA 92697-1250

     

     

    Curriculum Vitae

     

    Research Interests

    Statistical modeling. In particular, statistical genetics, imaging genetics, and bioinformatics

     

    Current Teaching

    None.

    I will be teaching STAT120C and STAT200C in the Spring.

    Courses that I taught before: STAT8, STAT120ABC STAT120C, STAT240, STAT257, STAT262, STAT200ABC

     

    Selected peer-reviewed articles

    (a complete list can be found here)

    o   Yu Z, Demetriou M, Gillen D. (2015) Genome-wide analysis of gene-gene and gene-environment interactions using closed-form Wald tests. Genetic Epidemiology, 39: 446-455. (software: GG_Wald)

    o   Yu Z, Li CF, Mkhikian H, Zhou RW, Newton BL, Demetriou M. (2014) Family studies of Type 1 diabetes reveal additive and epistatic effects between MGAT1 and three other polymorphisms. Genes and Immunity, 15:218-223.

    o   Yu Z, Gillen D, Li CF, Demetriou M. (2013) Incorporating parental information into family-based association tests. Biostatistics, 14: 556-572.supplementary material

    o   Yu Z (2012). Family-based association tests using genotype data with uncertainty. Biostatistics, 13:228-240.

    o   Shahbaba B, Shachaf CM, Yu Z. (2012) A pathway analysis method for genome-wide association studies. Statistics in Medicine, 31:988-1000.

    o   Yu Z, Deng L. (2011) Pseudosibship methods in the case-parents design. Statistics in Medicine, 30:3236-3251.

    o   Yu Z, Wang S (2011). Contrasting linkage-disequilibrium as a multi-locus family-based association test. Genetic Epidemiology, 35:487-498.

    o   Yu Z (2011). Testing gene-gene interactions in the case-parents design. Human Heredity, 71:171-179.

    o   Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z, Xie X. (2011) SNP-based pathway enrichment analysis for genome-wide association studies. BMC Bioinformatics, 12:99.

    o   Browning B, Yu Z. (2009). Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false positive associations for genome-wide association studies. American Journal of Human Genetics, 85:847-861.

    o   Yu Z, Garner C, Ziogas A, Anton-Culver H, Schaid D. (2009). Genotype determination for polymorphisms in linkage disequilibrium. BMC Bioinformatics, 10:63.

    o   Yu Z, Wang L, Hildebrandt MAT, Schaid D. (2008). Testing whether genetic variation explains correlation of quantitative measures of gene expression, and application to genetic network analysis. Statistics in Medicine, 27:3847 - 3867.

    o   Yu Z, Schaid D. (2007). Methods to impute missing genotypes for population data. Human Genetics, 122:495-504.

    o   Yu Z, Schaid D. (2007). Sequential haplotype scan methods for association analysis. Genetic Epidemiology, 31: 553-564.

     

    My son and daughter.

    http://www.ics.uci.edu/~gbowker/ Geoffrey C. Bowker | Department of Informatics, ICS | University of California, Irvine

    Geoffrey C. Bowker

    Professor

    Director, Values in Design Laboratory

    Department of Informatics

    E-mail: gbowker@uci.edu

    Current CV | Bio Page


    Here at the Bren School of Information and Computer Sciences, I am delighted to serve as Professor and Director of our Values in Design Laboratory. Prior to finding a place in the sun, I was Professor of Cyberscholarship at the iSchool, University of Pittsburgh, and before that Executive Director and the Regis and Dianne McKenna Professor at the Center for Science, Technology and Society at Santa Clara University (CA).

    I research on the use of web and other digital resources across a set of disciplines. I work with scholars to uncover ways in which new forms of knowledge are being (or could be) generated by creative use of  these digital resources.  For example: how did a complete database of classical Greek literature transform work in the classics; or how could intensive, long term monitoring of ecosystems feed into a new policy framework for sustainability? 

    The Values in Design Laboratory has a mission to train researchers in the broad range of disciplines necessary (including Informatics, Computer Science, Design and Science and Technology Studies) to produce new forms of information systems and technology which express/perform strong social and ethical values.

    I earned my PhD at the University of Melbourne, Australia, in History and Philosophy of Science and followed up with an extended post doctoral position at the Ecole des Mines in Paris.

    My work on information infrastructure involves looking at shifting classification systems in medicine, distributed collaborative work practices in environmental science, data sharing practices and biodiversity informatics. My central analytic question here is how scientists in the various sciences contributing to the subject of biodiversity communicate both with each other and with policymakers - and in particular how do the data structures and practices in use affect this communication. Here is an interview with me about classification and infrastructure. Here is a paper written with Marc Berg on medical records; and here is one written with Leigh Star on classification, standards and actor-network theory. Here is a more complete set of papers.

    My book on information management and industrial geophysics at Schlumberger, Science on the Run, is to be found in quality bookshops in airports everywhere; my book with Susan Leigh Star, Sorting Things Out: Classification and its Consequences was published by MIT Press in October, 1999 and is available at your neighbourhood online bookseller. A paperback version came out in September 2000. I am working right now (even as you are reading this) on distributed scientific work, with an emphasis on social and organizational features of emerging scientific cyberinfrastructures. My most recent publication, All Knowledge Is Local, was recently published in Learning Communities: An International Journal of Learning in Social Contexts in 2010. I am on the editorial board of The Information Society, Information and Organization, Metascience and Social Studies of Science.

    And here is my cat, or at least one of them - and that was then, you should see her now... .



    http://www.ics.uci.edu/~lopes/ Crista Lopes Home Page at UCI
         Cristina Videira Lopes
    Professor

    Department of Informatics 

    Donald Bren School of Information and Computer Sciences
    University of California
    Irvine, CA 92697

    Tel: (949) 824-1525
    lopes at ics dot uci dot edu

    Highlights:

    Research Group
      Mondego

    Other Projects
      Digital Voices
      Research Assistant
      ICSERGen
      OpenSimulator
    Companies
      MetaverseInk
      Encitra
    Affiliations
        ISR
        Calit2
     
    Publications
     
    Patents
     
        

     

    Check out my blog for the latest news and activities.

    I joined ICS in the Fall of 2002. Prior to being in ICS, I was a Research Scientist at the Xerox Palo Alto Research Center. While at PARC, I am most known as a founder of the group that developed Aspect-Oriented Programming (AOP) and started aspectj.org. My interests have expanded considerably since then. To find out about my current projects and students, follow the links on the left or visit the mondego pages.

    I have B.S. and M.S. degrees in Electrical and Computer Engineering from Instituto Superior Técnico, in Lisbon, and a Ph.D. in Computer Science from Northeastern University, in Boston. I also studied piano and voice, and have sung in choirs such as the San Francisco Symphony Chorus (1999-2002) and the Gulbenkian Choir (1989-1992). I received a National Science Foundation's CAREER Award, 2004-2009. I'm Erdos number 3 (Erdos -> Specker -> Lieberherr -> me).

     

    Teaching

    Fall 14 Winter 15 Spring 15
      INF 225/ CS 221 Information Retrieval
    INF 212 / CS 253 Analysis of PLs
    INF 124 Internet Applications Engineering
    Fall 13 Winter 14 Spring 14
      INF 225/ CS 221 Information Retrieval
    INF 212 / CS 253 Analysis of PLs
    ICS 168 Multiplayer Game Project
    INF 102 Programming Languages II
    Fall 12 Winter 13 Spring 13
      CS 221 Information Retrieval
    CS 121 / INF 141 Intro to Information Retrieval
    ICS 168 Multiplayer Game Project
    Fall 11 Winter 12 Spring 12
      CS 221 Information Retrieval
    CS 121 / INF 141 Intro to Information Retrieval
    INF 212 Analysis of Prog. Languages
    INF 295 (Special) Network Games
         
    http://www.ics.uci.edu/~ardalan/ Ardalan Amiri Sani

    Ardalan Amiri Sani

    Assistant Professor
    Computer Science Department
    University of California, Irvine
    Email: ardalan at uci dot edu
    Office: Donald Bren Hall #3062

    • Home
    • Students
    • Publications
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    • Services
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    I joined the CS department at UC Irvine in July 2015. I received my M.Sc. and Ph.D. from the ECE department at Rice University, and my B.Sc. from Sharif University of Technology.

    My research involves building efficient, high performance, and reliable systems. I enjoy solving operating system problems related to both mobile devices and datacenter servers.

    Prospective Students

    I'm looking for motivated graduate and undergraduate students who enjoy building systems. If you're interested, send me an email or drop by my office.

    Selected Publications

    • Ardalan Amiri Sani, Kevin Boos, Min Hong Yun, Lin Zhong, "Rio: A System Solution for Sharing I/O between Mobile Systems," in Proc. ACM Int. Conf. Mobile Systems, Applications and Services (MobiSys), June 2014.
      Best Paper Award. (PDF) (video demo) (source code)
    • Ardalan Amiri Sani, Kevin Boos, Shaopu Qin, Lin Zhong, "I/O Paravirtualization at the Device File Boundary," in Proc. ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2014. (PDF) (source code)
    • Ardalan Amiri Sani, Lin Zhong, Ashutosh Sabharwal, "Directional Antenna Diversity for Mobile Devices: Characterizations and Solutions," in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), September 2010. (PDF) (data traces)

    http://www.ics.uci.edu/~sjordan/ Scott Jordan
    Scott Jordan
    Department of Computer Science University of California, Irvine
      Scott Jordan

    Office:
    3214 Bren Hall
    Department of Computer Science
    University of California, Irvine

    Mailing address:
    3019 Bren Hall
    University of California, Irvine
    Irvine, CA 92697-3435

    sjordan at uci dot edu

    During 2014-2016, I am on leave from UCI, and serving as the Chief Technology Officer of the Federal Communications Comission

      Courses

    I will not be teaching during 2015-2016. Here are archived webpages from past courses:


    ICS 11 The Internet and Public Policy

    CS 132 Computer Networks

    CS 232 Computer and Communication Networks

     

      Information about our Graduate Programs

    Networked Systems Graduate Program

    Computer Science Graduate Program

    Information for Prospective Graduate Students

      Research and Publications

    ISP Service Tiers and Data Caps

    Device Attachment

    Net Neutrality

    Pricing of Internet Resources

    Dynamic Resource Allocation for Wireless Multimedia DS-CDMA

    Universal Service

    Network QoS

    Connection Access Control in Multimedia Networks

    Dynamic Channel Allocation in Cellular Networks

    Web Server Performance under User Impatience

     

     
    Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
    http://www.ics.uci.edu/faculty/highlights/index.php research highlights @ the bren school of information and computer sciences

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    Bren school home > Faculty > Research highlights
    Research highlights

    Every day, our faculty and students make important research contributions that bring real and positive change to people worldwide. Below are examples of the life-changing work conducted at the Bren School of ICS.

    Adaptive Software and Hardware for Distributed, Networked Embedded SystemsAlgorithmic LivingApplying Machine Learning and Advanced Computation to CancerAsterixDB: Big Data Management 2.0CloudProtectCollaboration Success WizardFault ComprehensionGene Regulation Network (GRN) InferenceMachine Learning for Text and Social Network Data over TimeModeling Languages for Computational Biology and AIModeling and Simulation of Biologically-Realistic Brain NetworksMulti-tasking in the WorkplaceRobust and Flexible Statistical Models for Environmental Epidemiology StudiesSatwareSherlockSimilarity-based Program AnalysisSoftware Engineering for SustainabilityStatistical Models for Brain ConnectivityTechnologies for Autism: Activity CoachThe I-Sensorium ProjectTrust in Software Development TeamsValues in DesignWebRTC Benchmarking and Optimization

    Adaptive Software and Hardware for Distributed, Networked Embedded Systems

    The project investigates adaptive strategies to deal with dynamic application environments for distributed, networked embedded systems, covering diverse applications such as the Internet-of-Things low-power mobile systems.

    Department of Computer Science

    Algorithmic Living

    All of the data collected about us is used to make decisions about what sort of products and financial offers we receive. How does this manifest in our self-image, and what are the historical and legal implications?

    Department of Informatics

    Applying Machine Learning and Advanced Computation to Cancer

    In this project, researchers have identified an elusive pocket on the surface of the p53 protein that can be targeted by cancer-fighting drugs.

    Department of Computer Science

    AsterixDB: Big Data Management 2.0

    AsterixDB is a full-function, open-source, next-generation Big Data Management System that is designed to scale to very large shared clusters.

    Department of Computer Science

    CloudProtect

    CloudProtect seeks to develop a middleware so users can employ encryption methods to control risk of data exposure in Cloud-based applications.

    Department of Computer Science

    Collaboration Success Wizard

    This online diagnostic survey looks at how to improve collaborations on projects where workers are spread out geographically. The survey probes factors that may strengthen or weaken the collaboration and provides reports that help build productive collaborations.

    Department of Informatics

    Fault Comprehension

    When software fails it must be "debugged," a largely manual process that determines why the program failed. This project is creating tools and visualizations that give an automated diagnosis to help software developers create effective and efficient software.

    Department of Informatics

    Gene Regulation Network (GRN) Inference

    In collaboration with UCI biologists, researchers will use their pioneering GRN mathematical models along with machine learning-based inference methods to computationally understand the gene-regulation network that determines early endoderm patterning in vertebrates.

    Department of Computer Science

    Machine Learning for Text and Social Network Data over Time

    Researchers are developing new statistical machine learning algorithms that can automatically extract useful information from both large bodies of and large social network data sets.

    Department of Computer Science

    Modeling Languages for Computational Biology and AI

    This project seeks to exploit useful overlap between formally defined scientific modeling languages researchers have developed for computational biology, including machine-learning techniques for model reduction.

    Department of Computer Science

    Modeling and Simulation of Biologically-Realistic Brain Networks

    In collaboration with neuroscientists, this project develops modeling and simulation frameworks for exploring large spiking neural networks, with a specific focus on visually guided motion perception, learning, tracking and autonomous navigation.

    Department of Computer Science

    Multi-tasking in the Workplace

    Modern technology has helped create workplaces filled with interruptions, influencing work flow and productivity.  This project studies information workers to figure out ways of mitigating the impact of constant interruptions.

    Department of Informatics

    Robust and Flexible Statistical Models for Environmental Epidemiology Studies

    Researchers are developing new statistical methods to flexibly estimate the association between spatially and/or temporally correlated environmental exposures and the risk of clinical outcomes in humans.

    Department of Statistics

    Satware

    This scalable data collection, querying and analysis technology allows for the creation of situational awareness applications from across diverse, multimodal sensors and data sources.

    Department of Computer Science

    Sherlock

    A pay-as-you-go data cleaning framework, Sherlock can improve data quality in (near) real-time application contexts.

    Department of Computer Science

    Similarity-based Program Analysis

    This project explores the idea of using "program similarity" to find better heuristics that require a lot less time and effort to find a solution. Program similarity can be defined in a number of ways and part of this research is seeking to find intrinsic program characteristics that can be used for this purpose.

    Department of Computer Science

    Software Engineering for Sustainability

    There is growing awareness that living within the Earth's means is crucial to humanity. This sustainability has been focused on engineering advances to reduce waste and energy use. But information and computing technology could also play a key role. This project seeks to understand the potential of information technology in helping people make smarter decisions and ways in which to make IT software more sustainable.

    Department of Informatics

    Statistical Models for Brain Connectivity

    Under the context of decision making, this project seeks to develop novel statistical approaches for identifying brain connectivity features from high dimensional multimodal imaging spatio-temporal data that can predict human behavior.

    Department of Statistics

    Technologies for Autism: Activity Coach

    For many people living with autism spectrum disorder, remembering detailed schedules can be a challenge. A well planned day can be disrupted by a late bus or cancelled work. ActivityCoach, a mobile application, provides support that adapts to changing schedules.

    Department of Informatics

    The I-Sensorium Project

    Parts of the UCI campus are equipped with a variety of experimental sensing, networking, storage and computing technologies to convert the campus into a "living laboratory." Data from I-Sensorium infrastructure is driving experimental research on pervasive systems, mobile computing, situation awareness, big data management, data streams, multimodal event detection and privacy.

    Department of Computer Science

    Trust in Software Development Teams

    Teams that are productive tend to have a great amount of trust among the team members. In virtual teams, trust is difficult to establish. This project explores how trust is formed among team members, and it's creating tools to help teams that are spread out geographically develop trust.

    Department of Informatics

    Values in Design

    This project seeks to understand the personal and cultural values we design into our technologies, and how these technologies allow us to express our values. For example, how do we balance values of efficiency and security with a need for friendliness and fun when we design a new electronic marketplace?

    Department of Informatics

    WebRTC Benchmarking and Optimization

    WebRTC is an exciting new standard being developed to provide real-time communication capabilities between browsers. It is a major component of the HTML5, the newest version of HTML protocol under development today. This project develops methodology and software for performing multi-platform, browser-independent performance benchmarking. The benchmarking results will be used to optimize and improve WebRTC performance on mobile devices.

    Department of Computer Science

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    http://www.ics.uci.edu/~mjcarey/ Home : index

    Michael J. Carey

    Department of Computer Science

    University of California, Irvine

    • PROFESSIONAL INTERESTS

    • EDUCATION

    • PROFESSIONAL EXPERIENCE

    • PUBLICATIONS

    • PROFESSIONAL ACTIVITIES

    • PhD STUDENTS SUPERVISED

    • RESEARCH FUNDING

    • HONORS and AWARDS

    • PATENTS

    • INVITED LECTURES and PANELS

    • CONTACT



    Professional Interests

    Database management systems, data-intensive computing, information integration, middleware, distributed systems, and computer system performance evaluation.

    Education

    Ph.D. in Computer Science, December 1983.
    University of California at Berkeley

    M.S. in Electrical Engineering (Computer Engineering), May 1981.
    Carnegie-Mellon University

    B.S. (University Honors) in Electrical Engineering and Mathematics, May 1979.
    Carnegie-Mellon University

    Home

    http://www.ics.uci.edu/~andre/ André van der Hoek
    home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
    projects
    Calico
    CodeExchange
    Crowd Development
    PorchLight
    Ph.D. students
    Christian Adriano
    Gerald Bortis
    Lee Martie
    André van der Hoek is a professor in and serves as chair of the Department of Informatics at the University of California, Irvine. He holds a joint B.S. and M.S. degree in Business-Oriented Computer Science from Erasmus University Rotterdam, the Netherlands, and a Ph.D. degree in Computer Science from the University of Colorado at Boulder.

    André heads the Software Design and Collaboration Laboratory, which focuses on understanding and advancing the role of design, coordination, and education in software development. His graduate work addressed distributed configuration management and versioned software architecture from a strictly technical perspective, but since his arrival at UC Irvine he has been positively corrupted by his colleagues in the Department of Informatics to address a broader research agenda that integrates a strong focus on people and how they work.

    Education is a key interest of André. He was the principal designer of the new B.S. in Informatics at UC Irvine, and is responsible for delivering several courses in this innovative curriculum. His research bridges into the educational realm by developing and critically evaluating new approaches to teaching software engineering, particularly for those topics that traditionally are difficult to address in the classroom.

    Andre's picture
    contact
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    andre@ics.uci.edu

    skype
    awvanderhoek

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    AW van der Hoek
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    http://www.ics.uci.edu/~dillenco/ Michael Dillencourt

    Michael Dillencourt

    • Position: Professor, Department of Computer Science at the University of California, Irvine
    • Areas:
      • Theory - Algorithms and Data Structures
      • Networked and Distributed Systems
    • Research interests: Distributed computing; Analysis of algorithms; Data structures; Computational geometry; Graph algorithms.
    • Office: DBH 4086
    • Office Hours: Click here
    • E-mail: dillenco AT ics dot uci dot edu

    Current Courses

    • Summer, 2015
      • ICS 6D - Discrete Mathematics for Computer Science
      • ICS 33 - Intermediate Programming

    Current Projects (Partial List)

    • MESSENGERS: a programming paradigm for distributed systems based on the principle of autonomous messages (mobile agents).

    Publications



    Michael B. Dillencourt
    Computer Science Department
    University of California, Irvine
    Irvine, CA 92697-3435 USA
    dillenco AT ics dot uci dot edu

    Last modified: June 18, 2015 http://www.ics.uci.edu/~dgillen/ http://www.ics.uci.edu/~ihler/ Alexander Ihler

    Alexander Ihler

    Associate Professor

    Information & Computer Science, UC Irvine


    Bren Hall 4066
    ph: 949-824-3645
    fx: 949-824-4056
    ihler (at) ics.uci.edu /
    ihler (at) alum.mit.edu


    Home
    Publications
    Code
    Recent Classes
      CS179, Graphical Models
      ... archive of older offerings
    Group Wiki

    Bio and CV

    Personal
    Photos


    Piano
     
     
    I work in artificial intelligence and machine learning, focusing on statistical methods for learning from data and on approximate inference techniques for graphical models. Applications of my work include data mining and information fusion in sensor networks, computer vision and image processing, and computational biology.
     
     

     
    Research themes
     

    Graphical models are used to organize and structure probability distributions over large systems, and enable efficient approximate or exact reasoning. My group balances developing theoretical and algorithmic advances with applications to the real-world systems of our collaborators.

    Algorithms. One of our main focuses is on finding maxima or computing probabilities using variational methods, including the family of belief propagation (BP) message-passing algorithms. Our contributions include analyzing the convergence and accuracy properties of BP, developing new BP-like bounds, extending BP techniques to continuous valued systems, improving the efficiency of "adaptive" or incremental inference, and extending variational algorithms to ``mixed'' inference tasks such as marginal MAP and decision making problems, including influence diagrams (or decision networks) and distributed team decision problems.

    Applications. We have applied our algorithms to a wide variety of problems, including tracking and understanding data from sensor networks, efficient representations for large text corpora, computer vision and image processing, and gene expression data in biology.

     
     

     
    News
     
    Our solver ("ai") won first place in five categories of UAI's 2014 Approximate Inference Challenge. Congratulations also to Rina Dechter's group ("daoopt"), which won several other categories.

    We co-organized the NIPS'13 workshop, "Crowdsourcing: Theory, Algorithms and Applications".

    I received an NSF CAREER award, "Estimation and Decisions in Graphical Models" (IIS-1254071)

    I was awarded the 2013 Chancellor's Award for Excellence in Fostering Undergraduate Research, and my student Michael Vorobyov the Award for Excellence in Undergraduate Research for his honors thesis work.

    Qiang Liu co-organized the workshop, "Machine Learning Meets Crowdsourcing" at ICML 2013 in Atlanta.

    Our collaboration with Rina Dechter's group won the MAP task components of the 2011 Probabilistic Inference Challenge, and our group's entry was runner-up in the marginalization tasks.

    Qiang Liu has received a 2011 Microsoft Research Fellowship award.
     
     

     
    Students (group page)
     
    Current:
    • David Keator
    • Sholeh Forouzan
    • Wei Ping
    • Nick Gallo
    • Qi Lou
    Graduated:
    • Qiang Liu (PhD, 2014)
    • Andrew Frank (PhD, 2013)
    • Ozgur Sumer (PhD, 2012)
    • Jonathan Hutchins (PhD, 2010)
    • Sidharth Shekhar (MS, 2009)
    • Priya Venkateshan (MS, 2011)
     
     

     
    Other links
     
    • UCI's Center for Machine Learning and our AI/ML seminar series
    • UCI Machine Learning Dataset Repository
     
     

     
    Funding Acknowledgements
     
    We gratefully acknowledge support for our current and recent research from the National Science Foundation, DARPA, Microsoft Research, the National Institute of Health, NIAMS, and UCI's Center for Complex Biological Systems.
     
     
    http://www.ics.uci.edu/~lueker/ George S. Lueker
    George S. Lueker
    Position: Professor Emeritus
    Area: Theory
    Office: Bren Hall 4206
    Office
    Phone:
    (949) 824-5866


    Research | Selected Publications


    Electronic mail:

      To construct my electronic mail address, concatenate my last name and "@ics.uci.edu".


    Research Interests

      George Lueker's interests are in the area of the design and analysis of algorithms for concrete problems. Lueker is especially interested in applications of probability to problems in computer science, in particular the analysis of the behavior of algorithms and optimization problems when some assumption is made about the probability distribution of inputs. For example, he has investigated the behavior of the optimum solution to the bin-packing problem and the partition problem. With graduate student Mariko Molodowitch he developed a remarkably simple proof of the result that the probabilistic behavior of double hashing is asymptotically equivalent to that of the theoretically ideal uniform hashing, for fixed load factors arbitrarily close to one. He has also investigated improved bounds on the expected length of the longest common subsequence when the input strings are random.


    Selected Publications:

    ``Bin Packing Can Be Solved within 1+ε in Linear Time,'' with W. Fernandez de la Vega, Combinatorica 1,4, pp. 349-355, 1981.

    ``Probabilistic Analysis of Optimum Partitioning,'' with N. Karmarkar, R. M. Karp, and A. M. Odlyzko, Journal of Applied Probability 23,3 (1986), pp. 626-645.

    Probabilistic Analysis of Packing and Partitioning Algorithms, with Ed Coffman, Jr., Wiley Interscience Series in Discrete Mathematics and Optimization, John Wiley & Sons, 1991, 192 pages.

    ``More Analysis of Double Hashing,'' with M. Molodowitch, Combinatorica 13,1 (1993), pp. 83-96.

    ``Exponentially Small Bounds on the Expected Optimum of the Partition and Subset Sum Problems,'' Random Structures and Algorithms 12 (1998), pp. 51-62.

    ``Average-Case Analysis of Off-Line and On-Line Knapsack Problems,'' Journal of Algorithms 29 (1998), pp. 277-305.

    ``Packing Random Rectangles,'' with E. G. Coffman, Jr., Joel Spencer, and Peter M. Winkler, Probability Theory and Related Fields 120 (2001), pp. 585-599. An earlier version appeared as DIMACS TR 99-44.

    ``The Minimum Expectation Selection Problem,'' with David Eppstein. Presented at Random Structures and Algorithms 2001 held in Poznan, Poland. ACM Computing Research Repository, cs.DS/0110011. Random Structures & Algorithms, 21 (2002), pp. 278-292.

    ``C-Planarity of Extrovert Clustered Graphs,'' with Michael T. Goodrich and Jonathan Z. Sun, Lecture Notes in Computer Science, Vol. 3843, 2006, pp. 211-222. (Graph Drawing, 13th International Symposium, Patrick Healy and Nikola S. Nikolov, eds., Limerick, Ireland, September 12-14, 2005.)

    ``Approximation Algorithms for Extensible Bin Packing,'' with E. G. Coffman, Journal of Scheduling 9,1 (2006). An earlier version appeared in Proc. Twelfth Annual ACM/SIAM Symposium on Discrete Algorithms, 2001, pp. 586-588.

    ``On the Convergence of Upper Bound Techniques for the Average Length of Longest Common Subsequences," Proceedings of the Tenth Workshop on Algorithm Engineering and Experiments (ALENEX) and the Fifth Workshop on Analytic Algorithmics and Combinatorics (ANALCO), (January 19, 2008), pp. 169-182.

    ``On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem," with Wenliang Du, David Eppstein, and Michael T. Goodrich, Algorithms and Data Structures Symposium (WADS 2009).

    ``Improved Bounds on the Average Length of Longest Common Subsequences," Journal of the ACM 56,3 (May 2009), Article 17, 38 pages. http://doi.acm.org/10.1145/1516512.1516519. This is based on the paper of the same title in the Fourteenth Annual ACM/SIAM Symposium on Discrete Algorithms, 2003, pp. 130-131, and the ANALCO paper "On the Convergence of Upper Bound Techniques for the Average Length of Longest Common Subsequences" listed above.


    Research | Publications

    Department of Computer Science
    School of Information and Computer Science
    University of California, Irvine
    Irvine, CA  92697-3435

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<style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1030"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor=ivory lang=EN-US link=blue vlink=blue style='tab-interval:.5in'> <div class=WordSection1> <p class=MsoNormal><span style='font-size:11.0pt;mso-bidi-font-size:12.0pt'><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='margin-left:2.0in'><span style='font-size:11.0pt; mso-bidi-font-size:12.0pt;mso-no-proof:yes'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="Picture_x0020_2" o:spid="_x0000_i1025" type="#_x0000_t75" style='width:519pt;height:54pt;visibility:visible;mso-wrap-style:square'> <v:imagedata src="index_files/image001.jpg" o:title=""/> </v:shape><![endif]--><![if !vml]><img width=521 height=56 src="index_files/image002.png" v:shapes="Picture_x0020_2"><![endif]></span><span style='font-size:11.0pt;mso-bidi-font-size:12.0pt'><o:p></o:p></span></p> <div align=center> <table class=MsoNormalTable border=0 cellspacing=8 cellpadding=0 width="100%" style='width:100.0%;mso-cellspacing:7.5pt;mso-yfti-tbllook:1184;mso-padding-alt: 0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-row-margin-right:3.25pt'> <td width=557 colspan=2 style='width:557.25pt;background:#CCCCFF;padding: .75pt .75pt .75pt .75pt'> <p class=MsoNormal align=center style='text-align:center'><strong><span style='font-size:24.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: minor-bidi;color:#003300'>Nikil Dutt</span></strong><span style='mso-bidi-font-family: "Times New Roman"'><o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:1;mso-row-margin-right:3.25pt'> <td width=119 rowspan=11 style='width:119.4pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'><!--[if gte vml 1]><v:shape id="_x0000_i1071" type="#_x0000_t75" style='width:118pt;height:150pt'> <v:imagedata src="index_files/image003.jpg" o:title="dutt-portrait"/> </v:shape><![endif]--><![if !vml]><img width=120 height=152 src="index_files/image004.png" alt="AppleMark&#10;" v:shapes="_x0000_i1071"><![endif]><o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> </td> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><span style='font-size:10.0pt;mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:2;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>Position:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'> <a href="http://www.evc.uci.edu/DistProf/chan_prof.asp">Chancellor s Professor</a><o:p></o:p></span></p> <p class=MsoNormal style='margin-left:.5in'><span style='mso-bidi-font-size: 14.0pt;mso-bidi-font-family:"Times New Roman"'><span style="mso-spacerun:yes">������ </span><ns0:PlaceType><span>University</ns0:PlaceType></span> of <ns0:PlaceName><span>California</ns0:PlaceName></span>, <ns0:place><span><ns0:City><span>Irvine</ns0:City></span></ns0:place></span> <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:3;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>Academic Department Affiliations:</span></strong><span style='font-size:14.0pt; mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"'> <o:p></o:p></span></p> <ul style='margin-top:0in' type=square> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'><a href="http://www.ics.uci.edu/computerscience/">Department of Computer Science</a> (primary)<o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'><a href="http://www.eng.uci.edu/dept/eecs">Department of EECS</a> <o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'><a href="http://www.cogsci.uci.edu/">Department of Cognitive Sciences</a> <o:p></o:p></span></li> </ul> <p class=MsoNormal style='margin-left:.5in'><strong><span style='font-size: 11.0pt;mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;font-weight:normal'><o:p>&nbsp;</o:p></span></strong></p> <p class=MsoNormal><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>Research Center Affiliations:</span></strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman"'> </span><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt'><o:p></o:p></span></p> <ul style='margin-top:0in' type=square> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>Center for Cognitive Neuroscience and Engineering (<a href="http://cence.ss.uci.edu//">CENCE</a>), Associate Director<o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>Center for Embedded Computer Systems <a href="http://www.cecs.uci.edu/">(CECS)</a><o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>California Institute for Telecommunications and Information Technology <a href="http://www.calit2.net/">(Cal<span class=GramE>-(</span>IT)2)</a><o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>Center for Pervasive Communications and Computing <a href="http://www.eng.uci.edu/cpcc/">(CPCC) </a><o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>Laboratory for Ubiquitous Computing and Interaction <a href="http://luci.ics.uci.edu/">(LUCI)</a><o:p></o:p></span></li> <li class=MsoNormal style='mso-list:l1 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family:"Times New Roman"'>Center for Computer Games and Virtual Worlds (<a href="http://cgvw.ics.uci.edu/">CGVW</a>)<o:p></o:p></span></li> </ul> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:4;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>Laboratory: </span></strong><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi; font-weight:normal;mso-bidi-font-weight:bold'><a href="http://duttgroup.ics.uci.edu/doku.php"><b style='mso-bidi-font-weight: normal'>Dutt Research Group</b></a></span></strong><strong><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi'><o:p></o:p></span></strong></p> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>Old lab page (before 2007): <a href="http://www.ics.uci.edu/~aces">ACES: Architectures and Compilers for Embedded Systems</a> <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:5;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi'>Office:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'> Bren Hall 3086 <a href="uci-directions-dutt-office.htm">Office Map and Directions</a> <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:6;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi'>Phone:</span></strong><span style='mso-bidi-font-family: "Times New Roman"'> +1 (949) 824-7219 <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:7;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi'>Personal Fax:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'> +1 (949) 824-7219 <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:8;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><strong><span style='mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi'>Alt. Fax:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'> +1 (949) 824-4056<o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:9;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><span class=SpellE><strong><span style='mso-bidi-font-family: "Times New Roman";mso-bidi-theme-font:minor-bidi'>EMail</span></strong></span><strong><span style='mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'> <a href="mailto:dutt@uci.edu">dutt@uci.edu</a> <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:10;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt'></td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:11;height:82.35pt;mso-row-margin-right:3.25pt'> <td width=430 style='width:430.35pt;padding:.75pt .75pt .75pt .75pt; height:82.35pt'> <p class=MsoNormal><strong><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:minor-bidi'>Mail Address:</span></strong><span style='mso-bidi-font-family:"Times New Roman"'><br> </span><span style='mso-bidi-font-size:14.0pt;mso-bidi-font-family:"Times New Roman"'>Nikil Dutt<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-size:14.0pt;mso-bidi-font-family: "Times New Roman"'>Chancellor s Professor<o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-size:14.0pt;mso-bidi-font-family: "Times New Roman"'>Department of Computer Science, <span class=SpellE>Zot</span> Code 3435<br> <ns0:place><span><ns0:PlaceName><span>Donald</ns0:PlaceName></span> <ns0:PlaceName><span>Bren</ns0:PlaceName></span> <ns0:PlaceName><span>School</ns0:PlaceName></span></ns0:place></span> of Information and Computer Sciences <o:p></o:p></span></p> <p class=MsoNormal><span style='mso-bidi-font-size:14.0pt;mso-bidi-font-family: "Times New Roman"'><ns0:PlaceType><span>University</ns0:PlaceType></span> of <ns0:PlaceName><span>California</ns0:PlaceName></span>, <ns0:City><span>Irvine</ns0:City></span><br> <ns0:place><span><ns0:City><span>Irvine</ns0:City></span>, <ns0:State><span>CA</ns0:State></span> <ns0:PostalCode><span>92697-3435</ns0:PostalCode></span>, <ns0:country-region><span>USA</ns0:country-region></span></ns0:place></span></span><span style='mso-bidi-font-family:"Times New Roman"'> <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:12;mso-row-margin-right:3.25pt'> <td width=557 colspan=2 style='width:557.25pt;padding:.75pt .75pt .75pt .75pt'> <p class=MsoNormal><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Dutt's</span></span><span style='mso-bidi-font-family:"Times New Roman"'> research is in the area of embedded systems and computer-aided design, with a specific focus on the exploration, evaluation and design of domain-specific embedded systems spanning both software and hardware. His group has developed a novel architectural description language that facilitates rapid exploration of programmable embedded systems, as well as automatic generation of software toolkits supporting embedded systems development (including optimizing compilers and simulators). Other projects within his group include cross-layer design and optimization of reliable, distributed embedded systems, memory architecture exploration for embedded systems, and brain-inspired architectures and computing. <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:13;mso-row-margin-right:3.25pt'> <td width=557 colspan=2 style='width:557.25pt;padding:.75pt .75pt .75pt .75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='mso-bidi-font-family:"Times New Roman"'> <hr size=2 width="100%" align=center> </span></div> <p class=MsoNormal align=center style='text-align:center'><span style='mso-bidi-font-family:"Times New Roman"'>[<a href="biography.htm">Biography</a>] [<a href="research.htm">Research </a>] [Publications (<a href="http://www.informatik.uni-trier.de/~ley/db/indices/a-tree/d/Dutt:Nikil_D=.html"><span class=SpellE>dblp</span></a>) (<a href="http://scholar.google.com/citations?user=CpBXPVoAAAAJ&amp;hl=en&amp;oi=ao">Google scholar</a>)] [<a href="teaching.htm"> Teaching] </a>] [<a href="services.htm">Professional Activities</a>] [<a href="students-visitors.htm">Students &amp; Visitors </a>] [<a href="http://www.ics.uci.edu/~aces/sponsors.htm">Sponsors </a>] <o:p></o:p></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:14;mso-row-margin-right:3.25pt'> <td width=557 colspan=2 style='width:557.25pt;padding:.75pt .75pt .75pt .75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='mso-bidi-font-family:"Times New Roman"'> <hr size=2 width="100%" align=center> </span></div> <address><span style='font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/computerscience/">Department of Computer Science</a><o:p></o:p></span></address> <address><span style='font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/">Donald Bren School of Information and Computer Sciences</a> <br> <a href="http://www.uci.edu/">University of California, Irvine</a> <br> <ns0:State><span>CA</ns0:State></span> <ns0:PostalCode><span>92697-3435</ns0:PostalCode></span> <o:p></o:p></span></address> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=3><p class='MsoNormal'>&nbsp;</td> </tr> <tr style='mso-yfti-irow:15;mso-yfti-lastrow:yes'> <td width=568 colspan=3 style='width:568.0pt;padding:.75pt .75pt .75pt .75pt'> <address><span style='font-family:"Times New Roman";mso-fareast-font-family: "Times New Roman"'>&nbsp;<o:p></o:p></span></address> </td> </tr> </table> </div> <p class=MsoNormal><span style='mso-bidi-font-family:"Times New Roman"'>&nbsp;<o:p></o:p></span></p> </div> </body> </html> http://www.ics.uci.edu/~gts/ Gene Tsudik's Info Page
    Gene ("The older I is, the better I was") Tsudik

    Chancellor's Professor of Computer Science
    formerly known as
    "Lois and Peter Griffin Professor" and "The Simpson Family Professor"
    Quagmire Computer Science Department
    Pewterschmidt School of Information and Computer Sciences

    University of Caliphoneya, Irvine
    An urgent message to all humorless barbarians inhabiting this planet:
    • As a free human being, I reserve the right to disrespect, mock, parody, laugh at, and disbelieve any and all of your deities, saints, prophets and idols.
    Favorite Venues:
    • ACM Cloud Computing Security Workshop (CCSW) (Steering Committee Chair, 2015-2017)
    • ACM Transactions on Information and System Security (TISSEC) (Editor-in-Chief, 2009-2015)
    • ACM Conference on Wireless Network Security (WiSEC) (Steering Committee Chair, 2011-2015)
    • ACM Conference on Computer and Communications Security (CCS) (Steering Committee Member, 2007-2011)
    Venues and Publishers to avoid:
    • IARIA
    • ICST
    • HINDAWI
    • MDPI
    • EAI
    • CREATE-NET
    • WSEAS
    Research Interests:
    • Privacy
    • Computer & Network Security
    • Applied Cryptography
    Pointers:
    • UCI Networked Systems (NetSys) Graduate Program
    • Secure Computing and Networking Center (SCONCE)
    • Security and Privacy Research Outfit (SPROUT)
    • Contact  
    • Publications
    • Google Scholar Publications
    • DBLP Publications
    • Patents
    • Current and Former Students
    • Assorted Facts
    • Favorite Expressions
    • Invented Words
    • Are you a prospective graduate student looking for an advisor?
      If so, here is an easy and painless way to see if you and I can work together.
      Just click here and then here.
    • Here's a presentation I gave several times over the last few years with some platitudinal advice for graduating PhD students who are interested in academic research careers: academia.pdf
    Brief Bio: Gene Tsudik is a "Lois and Peter Griffin" Professor of Computer Science at the University of California, Irvine (UCI). He obtained his PhD in Computer Science from USC in 1991. Before coming to UCI in 2000, he was at IBM Zurich Research Laboratory (1991-1996) and USC/ISI (1996-2000). Over the years, his research interests included many topics in security and applied cryptography. He currently serves as Director of Secure Computing and Networking Center (SCONCE) at UCI. Gene Tsudik is a former Fulbright Scholar and a fellow of ACM and IEEE. From 2009 to 2015 he served as Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC). He suffers from a debilitating academic condition known as "Research ADHD".
    http://www.ics.uci.edu/~gmark/ http://www.ics.uci.edu/~babaks/ http://www.ics.uci.edu/~harris/ Ian G. Harris, Associate Professor

    Ian G. Harris

    Associate Professor, Department of Computer Science

    University of California Irvine

    • Home
    • Publications
    • Courses
    • Projects
    • Contact

    Short Bio

    Ian G. Harris is currently Vice Chair of Undergraduate Education in the Computer Science Department at the University of California Irvine. He received his BS degree in Computer Science from Massachusetts Institute of Technology in 1990. He received his MS and PhD degrees in Computer Science from the University of California San Diego in 1992 and 1997 respectively. He was a member of the faculty in the Electrical and Computer Engineering Department at the University of Massachusetts Amherst from 1997 until June 2003.

    Research Areas

    • Functional Verification
    • Electronic Design Automation from Natural Language
    • Embedded Systems Security
    • Social Engineering Attack Detection

    Research projects in Professor Harris' group is related to testing of hardware and software systems. His field of interest includes validation of hardware systems to ensure that the behavior of the system matches the intentions of the designer. He also investigates the application of testing for computer security. Natural Language Processing (NLP) is a prominent theme in Professor Harris' work in both security and verification. NLP techniques are used to extract information from hardware specifications, and NLP techniques are used to identify social engineering attacks in a dialog between two speakers.

    Service

    Professor Harris serves on the program committees of several leading conferences in verification and security including IEEE Hardware Oriented Security and Trust (HOST), Haifa Verification Conference (HVC), and the IEEE Conference on Technologies for Homeland Security.

    Professor Harris is also co-organizer of the Workshop for Design Automation for Understanding of Hardware Designs (DUHDe) held in conjunction with IEEE/ACM Design Automation and Test in Europe 2016. The aim of the workshop is to consolidate the research community for problems related to design understanding across multiple levels of abstraction. Problems of interest include Automatic Feature Extraction, Reverse Engineering, and Synthesis/Verification from Natural Language.



    http://www.ics.uci.edu/~wmt/ Bill Tomlinson

    Bill Tomlinson

    Professor
    Informatics Department
    Bren School of ICS
    UC Irvine

    Director, Social Code Group

    Researcher, Calit2
















       News
    • We're ramping up a new online course, ICS 5: Global Disruption and Information Technology.
       
    • Center for Research in Sustainability, Collapse-preparedness, and Information Technology (RiSCIT) launched!
       
    • Named to the EPA's Board of Scientific Counselors, Sustainable and Healthy Communities subcommittee
    Links
    Curriculum Vitae (pdf)
    Projects
    Publications
    Office Hours
    Biography
    Students
    Courses








     
     





    Email
    wmt then at-sign then uci dot edu.
    Why the funny format?



    http://frost.ics.uci.edu/ Dan Frost's Home Page

    Dan Frost


    Information about the UC Irvine major in Computer Game Science. Also, a related Facebook page about Games at UC Irvine.

    Want to write computer games with Java? Check out Ucigame, a Java game programming library.

    Thinking of applying to a Ph. D. program in a few years? I recommend reading Applying to Ph.D. Programs in Computer Science.


    In Winter, 2016, I am teaching:

    • ICS 161 - Game Engine Lab
    • ICS 169B - Computer Game Science Capstone Game Project


    In Fall, 2015, I am taught:

    • CS 113 / Inf 125 - Computer Game Development
    • ICS 169A - Computer Game Science Capstone Game Project


    http://www.ics.uci.edu/~sternh/ Hal S. Stern's Home Page

    Hal S. Stern

    Ted and Janice Smith Family Foundation Dean and Professor of Statistics Donald Bren School of Information and Computer Sciences University of California, Irvine


    mailing address:
            University of California, Irvine
            6215 Bren Hall
            Irvine, CA 92697-3425

    phone: 949-824-7405        (asst: 949-824-7427)
    fax: 949-824-3976
    email: sternh@uci .edu

    Curriculum vitae (June 2015)

    Office hours:   By appointment


    Research interests:
    Bayesian methods, model diagnostics, forensic statistics, and statistical applications in biology/health, social sciences, and sports.



    Bayesian Data Analysis: The third edition of Bayesian Data Analysis by A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin was published in 2014. The new edition includes additional chapters on computation and Bayesian nonparametric modeling tools. It also includes illustrations of computation using STAN. For additional information about this book including how to purchase please visit the book web site.
    CHANCE magazine: Chance is a magazine about statistics and the use of statistics in society. Chance features articles that showcase the use of statistical methods and ideas in the social, biological, physical, and medical sciences. I'm a former editor and a big fan! Please visit the web site for more information.

    To top of page


    Last updated: August 24, 2015 http://www.ics.uci.edu/~rickl/ Richard H. Lathrop home page

     

    Richard H. Lathrop


    Description: Description: Description: Description: Description: Description: Description: Description: Orcas Island: click for enlargement
    Orcas Island, Puget Sound, state of Washington, USA

     


    Current Teaching (WQ'2016):

     

    Current CS-171 class (WQ'2016, Introduction to Artificial Intelligence).

     

     


    Short Bio | Summary | Curriculum Vitae | Publications | Students | Teaching | Community Service | Industrial Experience | Other

     


    Research Areas: Biomedical Informatics and Computational Biology | Artificial Intelligence and Machine Learning

    Programs: ICS Honors Program (Director) | Computer Science & Engineering (Steering Committee) | Institute for Genomics and Bioinformatics (Program Leader)

    Institutional: Computer Science Department (home) | Biomedical Engineering Department (courtesy) | Bren School of Information and Computer Sciences (ICS) | University of California, Irvine (UCI)

    External: Intl. Soc. for Computational Biology (Board of Directors) | ISMB 2012 Conference (Co-Chair) | Verdezyne, Inc. (Co-Founder)



     

    "Blessed are they who can laugh at themselves, for they shall never cease to be amused."


    Computer ScienceDepartment
    Donald Bren School of Information and Computer Sciences
    University of California, Irvine
    Irvine, CA 92697-3435 USA
    phone: (949) 824-4021
    fax: (949) 824-4056
    email: rickl@uci.edu


    Last modified: November 19, 2010

    http://www.ics.uci.edu/faculty/area/index.php Research Areas at UCI's Donald Bren School of Information and Computer Sciences
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    Bren school home > Faculty > Research areas
    ICS Research Areas

    student conducting researchCuriosity about the world and a commitment to solving problems are the passions that drive ICS faculty. Their research in the information and computer sciences are applicable to many scholarly and scientific fields. But our faculty don't do it alone, students work side-by-side with nationally renowned professors to advance knowledge and improve lives. Below is a list of ICS research areas:

    Algorithms and Complexity
    Bren School faculty members have made significant contributions to many topics in this field, including graph algorithms and graph drawing (computing with systems of pairwise interactions between objects such as web page links, protein interactions, or social networks) and computational geometry (computing with planar or spatial data).

    Artificial Intelligence and Machine Learning
    Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include computer vision, bioinformatics, constraint-based problem solving, text understanding, data mining and smart sensor networks.

    Biomedical Informatics and Computational Biology
    Involves the use of techniques from applied mathematics, informatics, statistics, and computer science to solve biological problems. Current areas of research at the Bren School include medical information access and knowledge representation for health-care guidelines.

    Computer Architecture and Design
    Develops methods and tools for ensuring the reliability and quality of complex, large-scale software systems. These methods and tools support the development, deployment, maintenance and evolution of complex software systems.

    Computer Graphics and Visualization
    Focuses on the field of visual computing that deals with generating/capturing, representing, rendering and interacting with synthetic and real-world images and video. We work on end-to-end solutions from capturing of images and geometry; representing large geometric, image, and video data sets; geometry and image processing; interactive access and rendering of large visual data sets; algorithms for building large area immersive displays for the presentation of visual content; and interation techniques in both small personal displays and in large displays for collaborative environments.

    Computer-Supported Cooperative Work
    Information technologies bring people together -- through social networking, through collaborative systems, through digital media, and through communications. Informatics has been a long-term leader in the study of social engagement through information systems. Topics include distance collaboration, workflow and process-based systems, multi-user gaming, and cultural engagements.

    Computer Vision
    Computer vision at UCI focuses on understanding the information processing capabilities of biological visual systems and on developing computational systems for processing visual media. Research spans both theoretical questions of perception and object representation as well as practical applications ranging from automated surveillance to biological image analysis. 

    Databases and Data Mining
    Focuses on research related to architectures, index structures, algorithms, models, and performance evaluation of a variety of next-generation databases and information systems and technologies for data mining.

    Embedded Systems
    Focuses on issues relating to embedded systems, a special-purpose system in which software and hardware computing elements are completely encapsulated by the device or environment it controls. Unlike a general-purpose computer, such as a personal computer, an embedded system performs pre-defined tasks, usually under very specific constraints (e.g, low power) and requirements (e.g., reliability). 

    Environmental Informatics
    Humanity is currently facing a range of significant environmental challenges such as global warming, species extinction, pollution, and overpopulation. Informatics tools and techniques can help facilitate responses to these challenges, and assist with planning for future environmental issues. 

    Human Computer Interaction
    HCI research at UCI stretches from the architecture of novel interactive systems to the social and cultural considerations of information technology adoption and use. We employ laboratory, ethnographic, and prototyping techniques to understand how people adopt, adapt, and respond to information systems. Recent research has investigated privacy issues in mobile systems, tangible interfaces for group awareness, interactive animation, and visualization of location information. 

    Medical Informatics
    This topic concerns the development and application of information systems to healthcare. Information systems have a critical role to play in contemporary health and wellness programs. This includes technology in hospital settings but also persuasive technologies for healthy living, health care in the home and in the community, and in the interactions between partners in the health care system. 

    Multimedia Computing
    Multimedia computing started receiving attention more than a decade ago.  Naturally, early systems dealt with very limited aspect of multimedia.  With progress in technology, several computing addresses important issues in creation, communication, storage, access, and presentation of information and experiences.  In our department, we are addressing research issues in fundamentals of multimedia systems and their advanced applications. 

    Networks and Distributed Systems
    Researchers investigate various issues in the design and analysis of high-speed networks for multimedia applications. They are actively involved in research on computer networks and distributed systems, with the goal of designing, analyzing and implementing communication systems that allow high-speed transport of multimedia information between end-users. 

    Operating Systems 
    The operating systems area at UCI embraces a wide range of topics related to theory and practice of computer systems software. Researchers here are building systems for reliable and efficient big data processing, mobile I/O virtualization, program analyses and various other applications.

    Programming Languages and Systems
    Systems software research at UCI has expanded to include topics such as program restructuring and transformation techniques for parallelization and distribution, compiler-assisted memory management, component-oriented languages and dynamic code optimization. 

    Scientific and Numerical Computing
    Refers to the application of computers to scientific problems, from astrophysics to zoology. The mode of application can be system modelling, data analysis and mining, or visualization. The focus can be on developing new computational techniques, such as parallel algorithms or new data mining ideas, or on the novel application of existing techniques to new scientific problems.

    Security, Privacy and Cryptography
    Bren School research in this area includes anonymity and authentication in network security, key agreement and digital signatures in cryptography, and security issues in electronic commerce.

    Social Informatics
    UC Irvine is an acknowledged center for the study of social informatics, which incorporates the social and cultural aspects of information technology development and use. Social informatics employs techniques and theories from social sciences and cultural studies to understand the shaping and applications of digital media and their organizational, political, historical, and economic contexts. This topic links information system analysis with design.

    Software Engineering
    Software research at UCI is aimed at creating new software technology and solutions, furthering the information revolution. The central goal of this research is improvement in software development, evolution, deployment, quality, understandability and cost-effectiveness.

    Statistics and Statistical Theory
    Researchers at UCI are concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistical principles and methods are important for addressing questions in public policy, medicine, industry and virtually every branch of science.

    Ubiquitous Computing
    Ubiquitous computing builds upon and unites virtually all of thfe current research strengths in the Bren School. Researchers are addressing issues such as context-aware computing, whereby mobile computing responds to one's current context.

    More faculty »
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    http://www.ics.uci.edu/~kay/ David G. Kay
    Photo of David G. Kay   David G. Kay   UCI Logo
     

    • Informatics Department

     
      • Computer Science Department  
    Donald Bren School of Information and Computer Sciences
  • Office: 5056 Donald Bren Hall
  • Electronic mail: kay@uci.edu
  • Postal: ICS, UC Irvine, Irvine, CA 92697-3440
  • Phone: (949) 824-5072; Fax: (949) 824-4056

  • Teaching at UCI

  • Winter 2016: ICS 31 (Introduction to Programming)
  • Winter 2016: University Studies 3: Freshman Seminar (Linguistics for Fun and Profit)
  • Winter 2016: ICS 193 (Tutoring in ICS)
  • Winter 2016: University Studies 197B (UTeach Theory and Practice)
  • Fall 2015: ICS 31 (Introduction to Programming)
  • Fall 2015: ICS 90 (New Student Seminar)
  • Fall 2015: ICS 193 (Tutoring in ICS)
  • Fall 2015: ICS 398A (Teaching Assistant Seminar)
  • Previous quarters' courses
  • For students

  • Sign up to be an ICS Lab Tutor! Read more about teaching in ICS (as a TA, grader, or tutor)
  • To get the most out of college, understand why it's different from high school and read some additional tips.
  • Thinking about choosing courses? Thinking about grad school?
  • Need a letter of recommendation?
  • Outside of ICS

  • UCI UTeach program, where undergrads become seminar instructors (Faculty Director, 2015–present)
  • The systemwide University Committee on Academic Computing and Communications (UCACC) (Chair, 2015–16)
  • The systemwide University Committee on Educational Policy (UCEP) (Chair, 2010–11)
  • The UCI Council on Educational Policy (CEP) (Chair, 2006–07)
  • Publications, presentations, conferences, workshops
  • Consulting in the area of computer law and "forensic computer science," working with other attorneys on computer-related legal matters or as an arbitrator, mediator, or expert witness
  • Academic interests

  • Computer law, including intellectual property protection for software and in cyberspace
  • Computer science education, including introductory curricula and pedagogy, evaluation of student software, and training and advising of instructors.
  • Extracurricular interests

  • Domestic and international travel
  • Contemporary and Japanese art
  • Architectural and nature photography
  • Preparing and consuming diverse cuisines
  • Reading crime fiction and much else
  • http://www.ics.uci.edu/~feldman/ Julian Feldman

    Julian Feldman

    • Position: Faculty Member
    • Area: Computing, Organizations, Policy, and Society (CORPS)
    • Office: 430 CS
    • Office Tel: 949-824-7078
    • Office Fax: +1(949)824-4056
    • E-mail: feldman@ics.uci.edu

    Projects

    ICS 131 Fall 2000
    ICS 131 Win 2000

    Other Interests

    Here are some pointers on how to learn HTML.


    Information and Computer Science

    University of California, Irvine
    Irvine, CA 92697-3425

    Last modified: 14 Dec 1999 http://www.ics.uci.edu/~eppstein/ David Eppstein

    David Eppstein

    • About
    • Contact
    • Research
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    • Math Fun
    self-portrait in mirror
    I am a Chancellor's Professor in the Computer Science Department of the University of California, Irvine.

    self-portrait in mirror

    My research (see selected publications) has covered many topics in computational geometry and graph algorithms, including:

    • Graph drawing and information visualization
    • Dynamic graph algorithms and dynamic closest pair data structures
    • Mesh generation and optimal triangulation
    • K-shortest paths and related combinatorial enumeration algorithms
    • Subgraph isomorphism and network statistics
    • Data depth and robust statistics
    • Exponential-time algorithms for NP-hard problems
    • Distance-preserving embeddings of graphs and metric spaces

    I am also an avid photographer and have many photos in my web photo gallery.

    This site is quite static; if you want more frequent updates (or to find out what's changed here) go to my livejournal or Google+ accounts.

    My name is not uncommon (although the spelling is atypical); see my page of Eppsteins on the net if you think you've reached the wrong me.

    About | Contact | Research | Students | Classes | Software | Math Fun
    http://www.ics.uci.edu/~irani/ Sandy Irani http://www.ics.uci.edu/grad/degrees/index.php Graduate Programs of Study @ the bren school of information and computer sciences
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    Bren school home > Graduate > Degrees
    ICS Graduate Programs

    Students and faculty collaboratingAs the only computing-focused school in the University of California system, ICS offers an array of graduate degree programs in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics. Click on a related link below to learn more about a specifc degree program. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

    » Computer Science — Ph.D.  l  M.S.

    » Informatics — Ph.D.  l  M.S.

    » Networked Systems — Ph.D.  l  M.S.

    » Software Engineering — Ph.D.  l  M.S.

    » Statistics — Ph.D.  l  M.S.

    » Information & Computer Science
    (Informatics Concentration) — 
    M.S.

    » Information & Computer Science
    (Embedded Systems Concentration)  — 
    M.S.


    Apply Now

    For information on how to apply to one or multiple graduate programs, click here. 


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    http://www.ics.uci.edu/grad/courses/index.php graduate course listing @ the bren school of information and computer sciences

    This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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    Bren school home > Graduate > Courses
    Graduate course listing

    This is a tentative schedule of CompSci, Informatics, Networked Systems and Statistics courses that the Bren School is planning to offer.

    Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change.

    NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


    Year
    Level
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    Core Classes for

    Please select from the search criteria above.

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    Bren school home > Graduate > policies
    Graduate student policies

    » Academic Honesty

    » Advancement to Candidacy (M.S.)

    » Advancement to Candidacy (Ph.D.)

    » California Residency

    » Candidacy Committee's Duties and Responsibilities

    » Candidacy Committee Membership

    » Comprehensive Exam/Phase II

    » Computer and Network Use

    » Copyright Infringement

    » Curricular Practical Training (CPT)

    » Ethical Use of Computing Resources

    » Defense, Final

    » Defense, Topic

    » Filing Fee

    » Grading Standards

    » Graduate Student Review

    » In-Absentia Registration

    » Leave of Absence

    » M.S. Thesis Option

    » Optional Practical Training (OPT)

    » Part-Time Enrollment

    » Previously Earned M.S. Degree

    » Residency Requirement

    » Summer Enrollment

    » Teaching Requirement

    » Transfer of Academic Credit

    » UC Policy on Sexual Harassment

    * Other policies important for students to know include the Non-Discrimination Policy Statements, Americans with Disabilities Act, and Jeanne Clery Act. It is recommended that students be familiar with the rules and regulations that govern students at UCI as outlined in the UCI General Catalogue.

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    http://www.ics.uci.edu/grad/resources.php graduate resources @ UC Irvine, Bren:ICS
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    http://www.ics.uci.edu/grad/forms/index.php graduate student forms @ the bren school of information and computer sciences
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    Bren school home > Graduate >
    Graduate Student Forms

    ICS Forms

    • Conference Presentation Grant Application
    • Research Verification Form
    • Topic Defense Form

    Graduate Division Forms

    Below is a partial list of Graduate Division forms. To view the complete list and download these forms, visit the Graduate Division website.

    • Advancement to Candidacy/Final Degree Report - to M.S. Degree Comp Exam
    • Leave of Absence (LOA) Petition
    • M.S. Thesis Signature Page/Report on Final Examination
    • Ph.D. Form I - Advancement to Candidacy
    • Ph.D. Form II - Signature Page/Report on Final Examination

    International Center Forms

    Below is a partial list of International Center forms. To view the complete list and download these forms, visit the International Center website, or go directly the F-1 or J-1 student pages.

    • Address Update
    • CPT Application
    • OPT Application
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    • Student Visa Document Request
     
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    http://www.ics.uci.edu/grad/admissions/Prospective_Re-applying.php graduate application process @ the bren school of information and computer sciences
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    Bren school home > Graduate > Admissions
    Re-Applying to ICS

    How to re-apply to an ICS gradute program

    You will need to submit an online application and pay the application fee in order to re-apply to the program. If you are re-applying within a one-year period, you may re-use the original documents; however, we encourage you to update your file with a new statement of purpose and letters of recommendation. If you are re-applying after one year, you will need to re-submit all new documents. GRE and TOEFL scores must be current at the time of re-application. Please send an email to gcounsel@ics.uci.edu letting us know which documents you would like to re-use.

     

     

     

     

     

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    http://www.ics.uci.edu/grad/admissions/ Prospective Graduate Students @ the bren school of information and computer sciences
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    Bren school home > Graduate > Admissions
    Prospective Graduate Students

    Students studyingYour Success Starts at ICS

    Join more than 400 graduate students studying virtually every principle area within the fields of Computer Science, Informatics, Networked Systems, Software Engineering and Statistics, as well as many new interdisciplinary topics. With more than 75 faculty members spanning three departments—Computer Science, Informatics and Statistics—a state-of-the-art 90,000 sq. ft. building and a $20 million gift from philanthropist Donald Bren, this is a very exciting time to be a part of the Donald Bren School.

    Almost all of our Ph.D. students receive financial aid in the form of teaching assistantships or research assistantships during their time at UC Irvine. Many of them also receive fellowships from the Bren School's corporate partners and private supporters.

    You can still apply to ICS if your undergraduate degree is not in the same area as the graduate degree in which you are interested. However, it is helpful if you have taken courses in computer science and math, and/or have some related work experience.

    A complete list of our graduate programs is below so you can explore which one is right for you. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

    ICS Graduate Programs of Study

    » Computer Science — Ph.D.  l  M.S.

    » Informatics — Ph.D.  l  M.S.

    » Networked Systems — Ph.D.  l  M.S.

    » Software Engineering — Ph.D.  l  M.S.

    » Statistics — Ph.D.  l  M.S.

    » Information & Computer Science
    (Informatics Concentration) — 
    M.S.

    » Information & Computer Science
    (Embedded Systems Concentration)  — 
    M.S.


    Apply Now

    For information on how to apply to one or multiple graduate programs, click here. 


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    http://www.ics.uci.edu/grad/funding/index.php Graduate Funding & Housing - UCI's Donald Bren School of Information and Computer Sciences
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    Bren school home > Graduate > Funding
    Graduate Funding & Housing

    Fellowships for ICS Graduate Students

    Fellowship and award opportunities available to ICS Graduate Students are outlined in this PDF document. This is a combined list of those offered by Graduate Division and external sources. Please see the links provided in the document for more information on each fellowship or award.

    *Many fellowships have internal department deadlines 7-10 days prior to the official due date.

    Teaching Assistantships

    Students can receive funding through appointments as a Teaching Assistant (TA) or Reader. Information on responsibilities, requirements, and benefits can be found on the Graduate Division website here.

    Interested ICS graduate students must submit an application for each quarter they wish to search as a TA or Reader. The application requirement applies to ALL students, including those on a TA/Reader fellowship.

    Students are notified of their appointments via email as soon as they are assigned. Any questions about TA and Reader assignments should be directed to the Department Managers.

    Graduate Housing

    On-campus housing is not guaranteed.

    All students looking for on-campus housing options must apply in order to be considered. Continuing students may apply to the housing waiting list at any time during the year. Admitted students may apply for housing after they submit their Statement of Intent to Register (SIR), and must do so by May 1st.

    Continuing students can find more information on the Student Housing website.

    Information for new graduate students may be found here.

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    http://www.ics.uci.edu/grad/ Donald Bren School of Information and Computer Sciences - Office of Student Affairs
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    ICS Student Affairs Office

    The primary focus of the Student Affairs Office is to assist students and faculty with University policies, procedures and requirements related to ICS academic programs. The graduate staff coordinates the graduate admissions process, fellowships and the graduate student review. It also handles the various forms and administrative functions relating to graduate students. 

    Contact Information

    Phone: (949) 824-5156

    Fax: (949) 824-4163

    Mailing Address: Donald Bren School Student Affairs, ICS Building, Suite 352, Irvine, CA 92697-3430

    Office Hours:  Monday-Friday from 9 a.m.-12 p.m. and 1 p.m.-4 p.m.

    Walk-In Hours: Monday-Friday from 1-4 p.m.

    General Office E-mail: gcounsel@ics.uci.edu


    Graduate Student Affairs Staff

    Kristine Bolcer – Director of Student Affairs

    Neha Rawal – Associate Director of Student Affairs

    Andrea O'Donnell – Graduate Counselor

    Julie Kennedy – Graduate Counselor

    Karina Bocanegra – Graduate Counselor

    Mare Stasik – Office Manager

    Lumen Hwang – Instructional Support Manager

    Tony Givargis – Associate Dean for Student Affairs
    Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the ICS graduate program. Associate Dean Givargis holds weekly office hours in the Donald Bren School Student Affairs Office, ICS, Suite 352. Please call the SAO's front desk at (949) 824-5156 to find out his hours of availability.

    More Graduate »
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    http://www.ics.uci.edu/grad/admissions/index.php Prospective Graduate Students @ the bren school of information and computer sciences
    • ABOUT
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    Bren school home > Graduate > Admissions
    Prospective Graduate Students

    Students studyingYour Success Starts at ICS

    Join more than 400 graduate students studying virtually every principle area within the fields of Computer Science, Informatics, Networked Systems, Software Engineering and Statistics, as well as many new interdisciplinary topics. With more than 75 faculty members spanning three departments—Computer Science, Informatics and Statistics—a state-of-the-art 90,000 sq. ft. building and a $20 million gift from philanthropist Donald Bren, this is a very exciting time to be a part of the Donald Bren School.

    Almost all of our Ph.D. students receive financial aid in the form of teaching assistantships or research assistantships during their time at UC Irvine. Many of them also receive fellowships from the Bren School's corporate partners and private supporters.

    You can still apply to ICS if your undergraduate degree is not in the same area as the graduate degree in which you are interested. However, it is helpful if you have taken courses in computer science and math, and/or have some related work experience.

    A complete list of our graduate programs is below so you can explore which one is right for you. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

    ICS Graduate Programs of Study

    » Computer Science — Ph.D.  l  M.S.

    » Informatics — Ph.D.  l  M.S.

    » Networked Systems — Ph.D.  l  M.S.

    » Software Engineering — Ph.D.  l  M.S.

    » Statistics — Ph.D.  l  M.S.

    » Information & Computer Science
    (Informatics Concentration) — 
    M.S.

    » Information & Computer Science
    (Embedded Systems Concentration)  — 
    M.S.


    Apply Now

    For information on how to apply to one or multiple graduate programs, click here. 


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    http://www.ics.uci.edu/grad/funding/ Graduate Funding & Housing - UCI's Donald Bren School of Information and Computer Sciences
    • ABOUT
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    Bren school home > Graduate > Funding
    Graduate Funding & Housing

    Fellowships for ICS Graduate Students

    Fellowship and award opportunities available to ICS Graduate Students are outlined in this PDF document. This is a combined list of those offered by Graduate Division and external sources. Please see the links provided in the document for more information on each fellowship or award.

    *Many fellowships have internal department deadlines 7-10 days prior to the official due date.

    Teaching Assistantships

    Students can receive funding through appointments as a Teaching Assistant (TA) or Reader. Information on responsibilities, requirements, and benefits can be found on the Graduate Division website here.

    Interested ICS graduate students must submit an application for each quarter they wish to search as a TA or Reader. The application requirement applies to ALL students, including those on a TA/Reader fellowship.

    Students are notified of their appointments via email as soon as they are assigned. Any questions about TA and Reader assignments should be directed to the Department Managers.

    Graduate Housing

    On-campus housing is not guaranteed.

    All students looking for on-campus housing options must apply in order to be considered. Continuing students may apply to the housing waiting list at any time during the year. Admitted students may apply for housing after they submit their Statement of Intent to Register (SIR), and must do so by May 1st.

    Continuing students can find more information on the Student Housing website.

    Information for new graduate students may be found here.

    More Graduate »
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    Copyright Inquiries | UCI Directory | Intranet | icswebmaster
    http://www.ics.uci.edu/computing/email/group_email.php group account email @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
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        • » Windows
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    Group Account Email via Thunderbird

    Please make sure that you are already part of both the group account and the group.

    1. Select Tools from the menu (use the Alt key to display it in case it is hidden), select Account Settings...
    2. Under Server Settings, click on the Advanced... button to the bottom right
    3. In the space Public (shared), enter: 
      • "Shared/groupname/"
      • Please note that the quotation marks (") are required.  Replace groupname with your specific group name. Do not forget the / at the end.

      group email
    4. Click OK
    5. Restart Thunderbird
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: July 16 2014
    http://www.ics.uci.edu/computing/account/renewal.php account renewal @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
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      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
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        • » Self-restore snapshot
        • » Restore request
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      • » Ethics Summary
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    Account Renewal

    Student

    • Major
      • Your ICS account will be automatically renewed as long as you remain under a major in the Bren School of ICS.
    • Non-major
      • Your account is only active for the quarter in which you are enrolled in a course under the Bren School of ICS. The account will automatically renew the next time you are enrolled in an appropriate course. If it has been two years since you have had an active account, you will have to create the account again.
    • Extension
      • Please bring in a receipt to CS 346L as proof that you have paid for your course/s. The lab manager will then extend your account for the quarter. You will have to do this at the beginning of each quarter.

    Staff

    • Your account will be renewed by your supervisor annually.

    Faculty

    • Your account will be automatically renewed annually.
    • You may extend accounts for your students/visitors annually.
      • Login to el nino system.
      • Select Extend My Users from the toolbar on the left.
      • If an account is not listed or if you are no longer the advisor of a listed account, please contact helpdesk.
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: November 04 2013
    http://www.ics.uci.edu/computing/services/snapshot.php snapshot @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
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        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
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      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
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      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
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      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
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    Snapshot

    Snapshot is only available for accounts on the TRON server.  If you cannot find a .snapshot folder in your home directory (see below for steps), please contact helpdesk to do a file restore.

    ** Snapshots are taken daily for only up to 30 days **

    Windows

    Your home directory (generally H:) drive is automatically mapped if you are logged into a machine on the UCI-ICS domain.  If you need to map your home directory, please refer to Mapping Home/Group Directory.

    1. Navigate to where your deleted/corrupted file is on the H: drive (or whichever letter you assigned to your ICS mapped drive).
      • If your file is corrupted, you will need to rename it with a .old extension (e.g. filename.old).
    2. Open another window and navigate to the .snapshot directory.
      • From the Start menu, go to Run and type in H:\~snapshot and hit OK.
      • You should now see folders named in yyyy-mm-dd_time.daily format.  Find the most recent nightly folder where you file is intact.
      • Continue until you find the file that you want to restore.
    3. Copy and paste the file from Step 2 into the window from Step 1.
    Unix/Linux
    1. Login to one of the ICS hosts.
    2. From the root of your home directory, cd .snapshot (you will not see the folder listed, but you will be able to cd into it).
      • If you have a corrupted file, rename it first (e.g. filename.old).
    3. You should now see folders named in yyyy-mm-dd_time.daily format.  Find the most recent daily folder where you file is intact.
    4. Continue until you find the file that you want to restore.
    5. Copy the file over to the original location by using:
      • user@ics ~/.snapshot/2010-12-01_0600-0700.daily
        $ cp backupfile ~/original/folder/backupfile
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/contact/helpdesk.php helpdesk @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
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        • » Windows
        • » Mac
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      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
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      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
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      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
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        • » Windows
        • » Mac
      • » Microsoft DreamSpark
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        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Helpdesk

    What is the ICS Helpdesk?

    The Helpdesk is a service of the ICS Computing Support Group. The purpose of the Helpdesk is to provide a single point of contact and record for technical support requests from ICS faculty, staff, and students.

     

    Why a Helpdesk?

    • To improve the communications between the people who need services and the people who provide services.
    • To organize service requests to ensure prompt and complete service.
    • To simplify service request history through the database underneath RT.
    • To increase services by allowing other groups within the School to use RT.

     

    How does one get service?

    by e-mail: helpdesk@ics.uci.edu
    by telephone:

    (949) 824-4222

    Monday-Friday: 8AM-11:30AM and 1:00PM-5PM

    Closed for University holidays; messages left activate page notification

    by visiting CS 346:

    Monday- Friday 8AM-11:30AM and 12:30PM-5PM

    Closed for University holidays

     

    How does the ICS Helpdesk work?

    The ICS Helpdesk uses a program called Request Tracker (RT).  The RT system assigns a unique number to each ticket, which helps keep track of each help request.  Critical to the success of the Helpdesk, every request must be input to RT to be assigned a ticket number, whether the request originates as an e-mail message, phone message, or walk-in visit.  A general outline of the process follows.

    • E-MAIL MESSAGE
      • E-mail messages sent to helpdesk@ics.uci.edu are automatically input to RT and assigned a ticket number (#3 for purposes of this explanation).
      • The Helpdesk analyst on duty will triage the ticket and assign it to the appropriate technician.
      • Once the ticket is assigned to a technician, the sender of the original request will be notified of the ticket number by e-mail.
      • Adding new information to the ticket is as simple as sending another e-mail to helpdesk@ics.uci.edu with this contained in the subject line of the message: [ics.uci.edu #3]
        Please include the square brackets [ ] or simply reply to the original message leaving the subject line intact.
      • If the technician cannot resolve the request within one working day, the requestor will be notified of the estimated time of completion and an brief explanation of the delay.
      • Once the request is resolved, the ticket will be closed and the requestor will be notified by e-mail.
      • If the ticket is not resolved to the requestor's satisfaction, the ticket can re-opened by responding to the closed ticket message and the ticket, with its original number, will be re-entered into RTs 'open' queue.
    • TELEPHONE AND WALK IN
      • Helpdesk analysts will be responsible for creating tickets within RT for all telephoned and walk-in requests.
      • Once the ticket is created by the Helpdesk analyst, the process follows the same path as outlined above.

     

    What about e-mail messages sent to support@ics.uci.edu?

    Messages sent to support@ics.uci.edu are still monitored.  If appropriate, messages to 'support' will be forwarded to Helpdesk and assigned a ticket number.

     

    RT sounds great, can I use it?

    Contact helpdesk for details.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 04 2013
    http://www.ics.uci.edu/computing/linux/gsu.php gsu group home directory @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Accessing group home directory using GSU module

    To access a group account from a linux host, you must first login to one of the ICS hosts.  Once logged in, you will then utilize the gsu module.  If you would like to be part of an existing group account or to create a new group account, please send a request to helpdesk.

    1. At the prompt, type:
      gsu groupAccountName
      • If you get a "command not found" error, type:
        module load gsu
      • If you get an error that says "you won't be doing that today," please check that your group account name is entered correctly and try again.
      • If you still get an error, please send an email message to helpdesk.
    2. You will then be prompted for a password.  Please type your ICS password.
    3. You are now the groupAccountName user.  However, you may still be in your own home directory. 
      • To see if you are in the right place, at the prompt enter:
        pwd
      • If you see /home/yourUsername then you need move to the group account home directory:
        cd /home/groupAccountName
    4. Done!
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: December 03 2013
    http://www.ics.uci.edu/computing/contact/who.php who to contact @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Who to contact
    Here is a listing of who to contact for different issues.

    Please send mails to helpdesk@ics.uci.edu for questions/problems regarding:

    • Unix/Linux system and software
    • Email, email forwarding
    • Windows system and software
    • Print queues and printer access
    • MSDNAA software
    • ICS account requests and issues
    • Quota information and requests
    • File restoration from H drive (please also refer to How to restore file)

    Please send mails to webmaster@ics.uci.edu for:

    • WWW questions/problems (please also refer to ICS Web Resources)

    Please send mails to lab@ics.uci.edu for:

    • ICS instructional lab questions (please also refer to Lab Home Page or visit the lab at CS 364)

    Please send mails to oit@uci.edu for:

    • Wireless/remote access to the UCI network
    • General UNIX questions
    • UCI account requests and issues
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 04 2013
    http://www.ics.uci.edu/computing/account/mapdrive_win.php mapping home/group directory on windows @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
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    Mapping Home/Group Directory - Windows

    *** Important ***

    If you are using the UCInet Mobile Access (UCI wireless) or connecting from off-campus (including on-campus housing),

    please make sure that you are connected via the UCI VPN first.

     

    * The instructions below are for Windows 7 operating systems. If you are using Windows XP, the settings are the same, but the name of those settings will differ slightly (e.g. My Computer instead of Computer).

    • Quick instructions
    • Instructions with pictures
    • Common errors

     


    Quick Instructions

    1. If you are on the campus wireless network or at an off-campus location (which includes on-campus housing), you need to connect to the campus VPN first.  Please install the VPN client, as WebVPN will not work for the purposes of this discussion.  Once you have connected to the VPN using your UCI credentials, you may continue.
    2. Right click on Computer and select Map Network Drive...
    3. Fill out the Drive and Folder information.
      • Drive: Choose any drive letter that is available (you can choose H: to match the setup on the ICS machines if preferred).
      • Folder: Fill in the path of where your home directory or group account is located.
        • Home directory: \\tron.ics.uci.edu\your_username
          • If you are running Windows 8, please enter: \\samba.ics.uci.edu\your_username
        • Group directory: \\samba.ics.uci.edu\group_acct_name
      • Please keep in mind that the server name may be different for some users. If you cannot map with the above server names, please contact helpdesk to find out which server you should be mapping to.
      • If you would like to always have your network drive available, check the box next to Reconnect at Logon. This will cause the network connection to be re-established at every login so you do not have to repeat this process every time. *** You will still need to establish a connection to the VPN each time.
    4. If you are connecting from a machine which is not connected to the ICS domain (e.g. a laptop using wireless or your home desktop machine), please place a checkmark on Connect using a different credentials.

      • Username: your_username
      • Password: your_ics_password
    5. * If Domain does not say UCI-ICS or ics.uci.edu, please enter your username in the form UCI-ICS\your_username

      ** For XP users: Please enter your username in the form UCI-ICS\your_username

    6. Click OK and then Finish. You are now done.

     


    Instructions with pictures

    1. If you are on the campus wireless network or at an off-campus location (which includes on-campus housing), you need to connect to the campus VPN first.  Please install the VPN client, as WebVPN will not work for the purposes of this discussion.  Once you have connected to the VPN using your UCI credentials, you may continue.
    2. Right click on Computer and select Map Network Drive...
    3. Fill out the Drive and Folder information.
      • Drive: Choose any drive letter that is available (you can choose H: to match the setup on the ICS machines if preferred).
      • Folder: Fill in the path of where your home directory or group account is located.
        • Home directory: \\tron.ics.uci.edu\your_username
        • Group directory: \\samba.ics.uci.edu\group_acct_name
      • Please keep in mind that the server name may be different for some users. If you cannot map with the above server names, please contact helpdesk to find out which server you should be mapping to.
      • If you would like to always have your network drive available, check the box next to Reconnect at Logon. This will cause the network connection to be re-established at every login so you do not have to repeat this process everytime. *** You will still need to establish a connection to the VPN each time.

        Map drive

    4. If you are connecting from a machine which is not connected to the ICS domain (e.g. a laptop using wireless or your home desktop machine), please place a checkmark on Connect using a different credentials.
      • Username: your_username
      • Password: your_ics_password

        * If Domain does not say UCI-ICS or ics.uci.edu, please enter Username in the form UCI-ICS\your_username

        ** For XP users: Please enter Username in the form UCI-ICS\your_username

        connect as

    5. Click OK and then Finish. You are now done.

     


    Common Errors

    • If you are on the UCI Mobile Access or are connecting from off-campus locations, please make sure that you are connected to the UCI VPN before trying to map the network drive.
    • Please make sure that you are entering in your ICS password and NOT your UCInetID password.
    • If you get an error about the path not found, please try replacing the server name with its corresponding IP address:
      • samba.ics.uci.edu - 128.195.1.18
      • tron.ics.uci.edu - 128.195.1.5
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: November 06 2013
    http://www.ics.uci.edu/computing/services/msoffice.php how to activate ms office @ the bren school of information and computer sciences

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    Microsoft Windows and Office

     

    Microsoft Windows OS and Microsoft Office that are provided by Computing Support require periodic registration.  These products need to contact the UCI-KMS license server at least every 6 months to keep them activated.  To re-activate, you will need to be on the UCI network and start one of the Office programs.  If you are off campus or on the UCI wireless, this can be accomplished by starting the UCI VPN client and then opening one of the Office programs.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/quarterlyAnnouncement/index.php quarterly accouncements @ the bren school of information and computer sciences

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    Quarterly Announcements
    • Winter 2016
    • Fall 2015
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    • Winter 2014
    • Fall 2013
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 06 2016
    http://www.ics.uci.edu/computing/linux/shell.php changing your shell @ the bren school of information and computer sciences

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    Changing your shell

    ICS accounts are given bash shell by default, but there are other shells available that users may change to. These other shells offer improvements such as tab completion. To change your shell, please go to the below website (if connecting from off-campus, please start up VPN first).

    1. Go to Password page and scroll down to the bottom of the page to the Change Unix (Solaris and Linux) Shell section.
    2. Enter in your ICS username/password and select the shell that you want.
    3. Using PuTTY or other SSH client, login to openlab.ics.uci.edu using your ICS credentials.
    4. Rename your old shell login file for backup. For example, if you already have .cshrc then do

      cp ~/.cshrc ~/.cshrc.old

    5. Copy over a new copy of the login file for the shell that you have chosen:
      • csh

        cp /opt/local/etc/skel/example.cshrc ~/.cshrc

      • tcsh

        cp /opt/local/etc/skel/example.tcshrc ~/.tcshrc

      • bash

        cp /opt/local/etc/skel/example.bashrc ~/.bashrc
        cp /opt/local/etc/skel/example.profile ~/.bash_profile

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 20 2013
    http://www.ics.uci.edu/computing/policy/index.php > computer usage policies @ the bren school of information and computer sciences

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    Computer Usage Policies

    The University of California, Irvine (UCI) provides computing resources and worldwide network access to members of the UCI electronic community for legitimate academic and administrative pursuits to communicate, access knowledge, and retrieve and disseminate information. As members sharing these resources, we also share the rights and responsibilities of their use. The following computer use policy documents describe the shared rights and responsibilities as well as the consequences of misuse. YOU ARE RESPONSIBLE FOR KNOWING AND FOLLOWING THESE POLICIES. We welcome your use of campus computing resources and your cooperation.

     


     

    Computer Use Policies

    • UCI Computer Use Policy
    • ICS Ethical Use of Computing Policy   //   Summary
    • ICS Instructional Computing Use Policy

    Electronic Mail Policies

    • UC Electronic Mail Policy   //   Summary
    • UCI Electronic Mail Policy

    Other Policies

    • Account Allocation Policy
    • Backups Policy
    • Remote Access Policy
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 06 2013
    http://www.ics.uci.edu/computing/linux/modules.php Using modules at the Bren:School of Information and Computer Science

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    Module information

    In order to handle the many different software packages that are available here at ICS, we use a system called Modules. The Modules package is a system that will allow you to easily set your shell environment by selecting from different software packages.

    Introduction

    Modules is a system for configuring and maintaining the shell and environment variables needed to use various software packages. The Modules system is easy to use, has a built-in help facility, and runs on many platforms under many operating system versions.

    Most importantly, Modules insulates you from the petty details of a package's installation. To use a software package, you usually have to add to your PATH and MANPATH variables, and you often have to set other application-specific or system variables as well. The Modules system frees you from having to know the precise pathnames to the various pieces of the package; from knowing what variables need to be set for the package to work; and from having to spend time tracking changes to all of this information.

    The Basics of the Modules System

    If you are using the bash shell, the following line contains the minimum code required in your shell configuration files:

    source /opt/Modules/init/bash

    You can see the different shell configuration files in our changing your shell page. Note that these lines should be the very first statements executed in the configuration files. This assures a well-configured environment for commands run later in your shell configuration files, as well as for later interactive sessions. Complete examples of a shell configuration files can be found on any ICS Linux or UNIX server in:

     /pkg/ics/etc/skel

    Modules commands come first, to guarantee a well-configured environment for other commands. Every time you login, these commands are automatically executed and your environment is set up.

    Useful Module Commands

    module avail

    Lists all of the modules available at ICS (Note: Some of these may not be available on your particular system).

    module display modulename

    Prints out a short description of modulename, including the changes to your environment.

    module showmodulename

    Shows information about modulename. It is similar to the module display command.

    module help modulename

    Shows you where you can get help and report bugs with the software in the module.

    module list

    Displays the list of modules that you have currently loaded.

    module load modulename

    Loads the current default version for the software package modulename. If you need to use a different version, then run module load modulename/version.

    module unload modulename

    Removes the specified module package from your shell environment.

    More Information about Modules

    The ics-default module loads several commonly-used modules. You can add software packages to your environment by using the module load command, either interactively at the shell prompt or by adding the command to your shell configuration files. If you run the command interactively, it will affect your environment in that window for the current session only. If you wish the change to be permanent, you will need to add it to your shell configuration files. Following a few simple rules will let you personalize your environment safely: Modules commands should come before any direct setting of shell or environment variables. If you must assign a value to a variable that a module modifies (e.g. PATH and MANPATH), make sure you include the current value of that variable.

    Using Modules Well

    It is possible to make every command you will ever want to run available all the time, by loading all of the modules. This may seem desirable at first, but it has some drawbacks. Your environment will be needlessly bloated with packages you will rarely use. The negative effects of this are particularly noticeable when the man command has to search a long MANPATH. The time that it takes to login is increased since the system has to configure everything for each module you load. Some packages may be mutually incompatible or have undesireable interactions. Every shell you start (or window you open), executes your shell configuration files in order to load the PATH, MANPATH, and other variables needed. Running the module load command in a shell window only affects that shell.

    Additional Information

    Running the command

    module help

    lists the options of the Modules system. There are UNIX man pages for Modules (man module) and for modulerun. There is also a man page (man modulefile) with information on writing your own module file. The author of Modules is John L. Furlani of SunSoft. He presented a paper, "Modules: Providing a Flexible User Environment," at the USENIX LISA V conference held in San Diego, October 1991. The paper is available in the proceedings from that conference.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 04 2013
    http://www.ics.uci.edu/computing/web/personalpage.php creating personal webpage @ the bren school of information and computer sciences

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    Creating Personal Webpage

    This document is a modified version of the one written for UIUC by Ed Kubaitis. Send comments or questions to webmaster@ics.uci.edu.

    What is a Personal Web?

    A personal webpage is a section of webspace in which you (as opposed to the webmaster) have control over the contents.  Our server allows a special subdirectory of your UNIX home directory to act as an attachment to the server's document namespace (the part of the URL which follows http://www.ics.uci.edu/).  Since this area is in your home directory, it get counted as part of your quota.  This is where your homepage resides.

    Learn about HTML and URLs

    HTML is the HyperText Markup Language used for documents on the World Wide Web.  A URL is a Uniform Resource Locator, the WWW address of a document or other network resources.  Although other document types (e.g. PostScript) can be accessed through the Web, HTML is by far the best format for documents which are intended to be read online.  There are many good tutorials available online if you would like to find out more information about this topic.

    Setting Up Your ~/public_html Directory

    There are two ways to setup your publich_html directory.

    1. Create the directory manually, then set the proper permissions.
      • Using your ICS account, login to one of the ICS servers via PuTTY or your preferred SSH client. 
      • Once you sre logged in, make your home directory world searchable (711) or world readable (755).  Use the chmod command to change file permissions.
        • chmod 711 /home/you_username

        • run the command ls -la to check that you have set it up correctly.  The first line of the output should disply something like:
          • drwx--x--x   62 your_username ugrad 18K Oct 16 10:00 ./
      • Create a subdirectory public_html in your home directory.  This directory must also be at least world readable (755).
        • mkdir public_html
        • chmod 755 public_html
      • Place the documents you want to serve in this directory or in further subdirectories.  You can simply go to your home directory (H-drive on lab machines) to copy/paste files/folders into the public_html folder.  For more instructions on how to map to your home directory, please see instructions for Windows or Mac.
      • All documents under ~/public_html should be world readable (644) and all subdirectories should be world readable (755).  Do not leave any auxiliary, temp, scratch, backup, or otherwise undesirable files under ~/public_html.
    2. Run a script and answer the prompted questions.
      • Using your ICS account, log in to one of the ICS servers.
      • Run the command /pkg/ics/bin/make_my_web

    All filenames must end with an appropriate extension (the part after the last dot).  For example, all HTML documents should have filenames ending in .html, all text documents should have filenames ending in .txt, and all PostScript documents should have filenames ending in .ps.  Compressed documents should have filenames ending in .extension.Z or .extension.gz (for compress or gzip -9, respectively).

    Also, please avoid using file/pathnames which are excessively long.  Long filenames make for long URLs which are difficult for users to type and problematic for many WWW clients.

    The URL for files in this directory is http://www.ics.uci.edu/~username/filename

    For example, for login name peteranteater, the URL is http://www.ics.uci.edu/~peteranteater/myfile.html and will access ~peteranteater/public_html/myfile.html

    The URL http://www.ics.uci.edu/~peteranteater/ (i.e. no filename) is the same as http://www.ics.uci.edu/~peteranteater/index.html

    So if you save your personal home page as the file ~/public_html/index.html, people can access it with the short URL.  You should always include the trailing slash in URLs when referring to a directory --some servers can get confused otherwise.  If there is no file called index.html, the short URL will generate a menu of all files if the directory is world readable (755) or an error message if it is only world searchable (711).

    For security reasons, never put symbolic links under ~/public_html which point to directories outside of ~/public_html.  Symbolic links to other files are okay provided that both the link and its destination are owned by the same user.  Symbolic links from outside directories pointing to destinations under ~/public_html are always okay.

    Other Tips and Notes

    You can test if things are working the way you want by checking them with your favorite browser.  For example, using Firefox, you can use the File, Open file menu item or by copy pasting your website's URL into your browser's address bar.

    You can also inspect the HTML used for any document you find on the Web using your favorite browser.  With Firefox, use the View, Page source menu item to look at a page's source code. 

    Always remember that the whole world is watching what you do.  Do not put anything in your personal webspace which is not intended to be public information.  Furthermore, you must always obey the Ethical Use of Computing Resources guidelines to which you agreed before receiving an ICS account.  Please review those guidelines before making any questionable material available to the World Wide Web.  If you have any questions, feel free to email helpdesk.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 08 2013
    http://www.ics.uci.edu/computing/services/microsoft_dreamspark.php Microsoft DreamSpark FAQ @ the Donald Bren School of Information and Computer Sciences

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    Microsoft DreamSpark (formerly known as MSDNAA) FAQ
    • What is the Microsoft DreamSpark Program?
    • Am I eligible to download Microsoft DreamSpark software?
    • What software is available for download?
    • What if I want a software that has not been made available?
    • How much does it cost to get the software?
    • What can the software being provided be used for?
    • Do I have to uninstall the software once I graduate or stop taking ICS courses?
    • How can I obtain the software?
    • Why is the size of the file I downloaded so small or unexecutable?
    • I am getting an error in the middle of my download, what does it mean?
    • How do I receive my login/password?
    • What is an SDC file and why can't I open or unpack it?
    • How much hard disk space do I need?
    • How can I reuse my product keys if I need to reinstall my computer?

    Q: Am I eligible to download Microsoft DreamSpark software?

    If you are enrolled in any course(s) under the Donald Bren School of Information and Computer Sciences for credit, you are eligible to download Microsoft DreamSpark software.  Faculty, instructors, and TAs are eligible to receive the software if you have an active ICS account.

    Q: What software is available for download?

    Please login to the Microsoft DreamSpark page and look under the Software section for an updated listing.

    Q: What if I want a software that has not been made available?

    If you need a particular software for a class or research project, please email helpdesk@ics.uci.edu with an explanation of why you need it and we will look into its availability.  *NOTE: Microsoft Office is not included in the Microsoft DreamSpark program, but may be available via the Microsoft Home Use Program (HUP) at http://www.microsofthup.com or the Office 365 Pro Plus Personal Use Program at http://www.oit.uci.edu/microsoft-office-365-pro-plus

    Q: How much do I need to pay for the software?

    In an agreement with Microsoft, the Microsoft DreamSpark program provides this software free of charge.  There is a small charge if you choose to have the installation CD mailed to you.

    Q: What can the software being provided be used for?

    The software is provided for instructional and non-profit research purposes.  It cannot be used for infrastructure or profit.

    Q: Do I have to uninstall the software once I graduate or once I am no longer a student of the Donald Bren School of ICS?

    The license is perpetual.  This means that you do not need to uninstall the software.  The software and product keys you receive are yours to keep, but you will lose the ability to download Microsoft DreamSpark software once your ICS account expires. All free updates to the software, such as security updates and service packs, that are available to everyone through Windows Update, continue for the life-cycle of each product.

    Q: How can I obtain the software?

    You may download the software from the Microsoft DreamSpark website.

    Alternatively, the CD/DVD creation system in CS 364 lab has a copy of each software (usually as an ISO).  You can burn your own CDs/DVDs to take home or bring a USB flash drive and transfer the ISO files you might need.  However, you will still need to login into the Microsoft DreamSpark site and "checkout/purchase" the software from their website to get the necessary product keys.

    Q: Why is the size of the file I downloaded so small or unexecutable?

    If you are downloading Microsoft DreamSpark software, the file that first gets downloaded may not have the .exe file extension.  This has occurred when Mozilla Firefox is used as the browser.  Add the .exe extension if necessary and double-click the file.  It is a downloader file of a very small size that is used to download an SDC file.  After the SDC file is downloaded, the downloader is required to extract the actual software from it.

    Q: I am getting an error in the middle of my download, what does it mean?

    In many cases, you should be able to figure out what is wrong by the content of the error code.  If it is not immediately obvious what you should do, check the Download Error Help Page.  If you still cannot complete your download, please send an email message to helpdesk@ics.uci.edu.

    Q: How do I receive my login/password?

    Please use your ICS account username and password to login to the Microsoft DreamSpark website.

    Q: What is an SDC file and why can't I open or unpack it?

    When you double click the executable file that you downloaded from Microsoft DreamSpark, it will ask you where to unpack the file.  There will be an SDC file inside the folder you choose to store the downloaded file.  This is a zip file that the Downloader executable will extract for you when the download is completed.  You need to use the Downloader to extract the install files from the SDC file.  The SDC file is safe to delete after the setup files are extracted from it.

    Q: How much hard disk space do I need?

    Some files require at least 2GB of space for the SDC file that is downloaded plus the files that are extracted from it.  If you do not have enough disk space for the SDC and extracted files, you will need to download the file again in order to unzip the SDC file.  Make sure you have enough disk space before proceeding to download.

    Q: How can I reuse product keys issued to me if I need to reinstall my computer?

    If you made a hardware change or replace your old computer and need to reinstall software that needs to be activated, such as Windows 7, 8.1, 10, Visio, or Project, the first step is to try and re-enter the same key that was originally obtained from the Microsoft DreamSpark website. Alternatively, if the software activation prompt provides a telephone number or other means to contact Microsoft, try contacting a representative regarding the situation with the Microsoft DreamSpark key you are attempting to use. 

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 29 2015
    http://www.ics.uci.edu/computing/services/printing.php printing @ the bren school of information and computer sciences

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    Printing

    CS364 Lab Printing

    There is one black-and-white laser printer in the CS 364 lab.  Pleas follow the above link for further details.

    Bren Hall

    • Grad students: Contact your department manager for setup instructions and location of the printers available to you.
    • Faculty/Staff: Login to the Intranet and find instructions under the Computing Support section.  If you cannot login, please contact helpdesk.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/account/ account information @ the bren school of information and computer sciences

    • » Account
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    Account Information

    Setting Up an Account

    • Students (Undergrad and Grad)
    • Extension: Please bring proof of enrollment to CS 346L.
    • Faculty/Staff/Visitors: An email request is required from the department personnel.

    Password Change/Reset

    Quota

    Account Renewal

    Group Account

    • New Account: Please send an email request to helpdesk with the ICS usernames of those in the group.
    • Existing Account: To be added to an existing account, please have the account PI send a request to helpdesk with your ICS username.

    Accessing Your Account

    • Windows
    • Mac
    • Linux: Please login to the one of the ICS hosts.
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: November 13 2013
    http://www.ics.uci.edu/computing/services/sophos_mac.php sophos anti-virus on mac @ the bren school of information and computer sciences

    • » Account
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    Sophos Anti-Virus on Mac

    * Before installing Sophos, please make sure that you do not have any other anti-virus programs already installed.

    ** If you already have Sophos installed and has problem updating, please uninstall it following the instructions here. Then do a re-install using the instructions below.

    *** If you are connecting from off campus or if you are on the UCI wireless network, please make sure that you are connected via the UCI VPN first before proceeding.

    1. From the Finder menu, click on Go and select Connect to Server.
    2. For the server address, enter:

      smb://sophos-server.ics.uci.edu/sophos_mac

    3. Enter your ICS username and password.
    4. Navigate to the folder that matches your OS X install, and double click on the .dmg file to mount the drive.
    5. Once the drive is mounted, double click on the Sophos Anti-Virus.pkg file to start the install.  NOTE: Do not check "Install Sophos Client Firewall" as this might cause issues with UCI's VPN. Leave "Remove third-party security software" checked.
    6. Once the installation completes, click on the Sophos shield on the top right corner and select Open Sophos Anti-Virus Preferences.
    7. Click on Auto Update, then click on the lock in the lower left corner to allow the following changes to be made.
      • Update from Primary Location: Network Volume
      • Address: smb://sophos-server.ics.uci.edu/SophosUpdate/CIDs/S000/ESCOSX
             NOTE: The address is case-sensitive; S000 is capital S followd by 3 zeroes.
      • Username: your ICS username
      • Password: your ICS password
    8. Click on the lock to prevent further changes and close the window.
    9. Click on the Sophos shield up at the top again and select Update Now.
    10. Updates need to be performed manually.  It is recommended that you do this weekly to obtain the latest virus definitions.  Sophos has a new version released each month so, once a month, the update will take longer as a new version needs to be downloaded and installed.
      *** If you are connecting from off campus or if you are on the UCI wireless network, you will need to be connected via the VPN in order to do the update.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/policy/ethics_summary.php summary of ethical computing policy @ the bren school of information and computer sciences

    • » Account
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    • » Other Services
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    Summary of Ethical Computing Policy

    This document is intended to remind you of the key points covered in the document Ethical Use of Computing Resources which you read in order to get an account. If you have any questions about the information in this document, you can reread the ethics document by visting Ethical Use of Computing Resources, you can send an email message to helpdesk, or you can visit CS 346.


    Ethical Behavior
    The two most important points in the ethics document are that:

    • The user must behave ethically at all times.
    • Each user is solely responsible for the actions of anyone using his/her account. Please see Ethical Use of Computing Resources.
    Unethical behavior includes, but is not limited to the following:
    • Introducing viruses, worms, trojan horses, password cracking, or login/e-mail spoofing programs to any University computer
    • Gaining unauthorized access to equipment or accounts
    • Wasting resources
    • Destroying electronic information belonging to others
    • Spying on others
    • Making commercial or political use of computing resources
    • Misuse of licensed software
    • Sending mass mailings of messages to random people
    • Sending chain letters through e-mail
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 06 2013
    http://www.ics.uci.edu/computing/email/delivery_point.php Email Delivery Point @ the bren school of information and computer sciences

    • » Account
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      • » Specify Delivery Point
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      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
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      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
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    Email Delivery Point Setup

     

    If you are an existing ICS Faculty, Staff, or Graduate Student, then you will need to login to https://elnino.ics.uci.edu to specify your email delivery point.  Note: from off-campus locations, you must use the UCI VPN client.

     

    Under Forwarding Address, specify your delivery point.   For ICS GMail and UCI, leave the textbox empty.  
    For Other, you will need to specify the email address where your ICS e-mail will be delivered in the textbox.

    Elnino Password and Forwarding Address Setup

     

    Note:  Faculty may be presented with the following information when logging into elnino.com.  Click on the My Account button, as indicated below, in order to bring up the above page.

     

    ICS Faculty Elnino Presentation

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: April 16 2014
    http://www.ics.uci.edu/computing/linux/hosts.php cycle servers and grid engine @ the bren school of information and computer sciences

    • » Account
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      • » Specify Delivery Point
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      • » Thunderbird
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      • » Email Servers Information
      • » Checking Group Account Email
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      • » UCInet Mobile
      • » VPN
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      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
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      • » Activate MS Office
      • » Sophos
        • » Windows
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        • » Self-restore snapshot
        • » Restore request
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    • » Contact
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    Cycle Servers and Grid Engine

    Accessing your home directory from a Unix/Linux host is as simple as logging in. You may log in remotely from any host using a secure protocol such as SSH2. For Windows users, PuTTY is recommended and can be downloaded for free. For Mac users, you can use the terminal (found under Applications -> Utilities). 

    • Students: openlab.ics.uci.edu (also known as archer.ics.uci.edu)
    • Faculty: emp.ics.uci.edu

    There is an instance of the Sun Grid Engine software that is used to help faciliate running multiple jobs on the openlab. Please see our SGE page for more information. 

    New to Linux or UNIX? See this excellent introduction by Princeton for getting started with Linux command line.

    To get more information on accessing and viewing available software on the servers, see our Modules page.

    openlab.ics.uci.edu

    • Quad 2.4GHz 16-core AMD Opteron 6378 CPUs
    • 512GB RAM
    • 64-bit CentOS Linux
    • 1TB scratch space
    • 1 machine
    • Dual 2.2GHz Single Core AMD Opteron 252 CPUs
    • 16GB RAM
    • 64-bit CentOS Linux
    • 30GB scratch space
    • 13 machines
    • Dual dual-core AMD Opteron 2212HE
    • 16GB RAM
    • 64-bit CentOS Linux
    • 400GB scratch space
    • 2 machines
    • Dual Intel Pentium 3.0GHz
    • 4GB RAM
    • 64-bit CentOS Linux
    • 400GB scrach space
    • 3 machines

    andromeda-#.ics.uci.edu where # is 1-40

    • 2x Quad-core Intel Xeon 3.0GHz CPU E5450 CPUs
    • 32GB RAM
    • 64-bit CentOS Linux 6.6
    • 500GB scratch space
    • 40 machines
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: October 30 2015
    http://www.ics.uci.edu/computing/web/faqs.php web faqs @ the bren school of information and computer sciences

    • » Account
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        • » Windows
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    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
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      • » Email Servers Information
      • » Checking Group Account Email
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      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
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    Web FAQs

    This is a collection of common questions and answers.  Please check this list before contacting helpdesk.

     

    1. Can I have a webpage on this server?
    2. Will you help me set up a cool webpage?
    3. Will you make a link to my webpage?
    4. How do I add a counter to my home page?
    5. How do I run cgi-bin programs?
    6. What sort of webpages are allowed on this server?
    7. Is there a server where I can run PHP and cgi scripts?
    8. How does this affect my quota?
    9. How do I secure my pages?

    1. Can I have a webpage on this server?

      If you have an ICS UNIX account, you can create your own web page.  See Creating Your Own Web Space for more information.

    2. Will you help me set up a cool webpage?

      No.  If this is for a class, contact your instructor or TA.  Otherwise, there many good HTML resources online.  Use your favorite search engine to search for information on WWW programming.

    3. Will you make a link to my webpage?

      If you are a graduate student or visiting researcher or faculty, please send an email message to webmaster@ics.uci.edu.  We do not link home pages to servers outside the campus.  Please do not waste your time and our time by making such a request.

    4. How do I add a counter to my home page?

      We do not support the usage of counters.  They are very wasteful of bandwidth and system resources.  We appreciate your consideration on this matter.

    5. How do I run cgi-bin programs?

      We do not permit users to have or use cgi-bin scripts other than those already on the server: calendar, date, finger, fortune, imagemap and uptime.

    6. What sort of webpages are allowed on this server?

      Only static html webpages are allowed on the main webserver.  PHP webpages are not allowed.

    7. Is there a server where I can run PHP and cgi scripts?

      You may login to the UNIX server students.ics.uci.edu and create a webspace for yourself.  Login and create a public_html folder.   If you want to run cgi scripts, create a .htaccess file inside the public_html folder with the following options:

      Options -Indexes +ExecCGI
      AddHandler cgi-script .cgi .pl .py

      The URL would be http://students.ics.uci.edu/~loginname.  For security reasons these systems are stand-alone and do not mount any NFS based directories, such as /home or /extra.  You will need to SSH and download applications directly to the system or use SCP software like WinSCP.  The servers are not backed up, so you need to do it manually.  A great tool for doing such would be using the rsync command.  You will be responsible for maintaining the security of your website.  If you download an application, make sure you check for updates regularly or sign up for the application's mailing list.

    8. How does this affect my quota?

      Since your personal web space is contained in your home directory, it is included in your quota.  For more information on quotas, see ICS Instructional Quotas.

    9. How do I secure my pages?
      There are ways to limit who can see you ICS web page.  For more information, please refer to the following Apache documents.
      http://httpd.apache.org/docs/2.4/howto/htaccess.html
      http://httpd.apache.org/docs/2.4/howto/auth.html
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 08 2013
    http://www.ics.uci.edu/computing/email/ email @ the bren school of information and computer sciences

    • » Account
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      • » Security
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        • » Windows
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        • » Self-restore snapshot
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    Email

    ICS Email Accounts

    Each member of the ICS community receives an ICS email address of the following form:

      username@ics.uci.edu

    Your ICS email is different and separate from your UCI email.  If you have questions regarding your UCInetID or UCI email account, please contact OIT.

     

    Graduate Students, Staff, and Faculty can use either:

    ICS Gmail (ICS Google Apps)
    ICS email acccounts can be accessed at ICS Google Apps.

    ICS IMAP/POP (We will stop delivering in the near future.)

     

    Email Forwarding

    Graduate Students/Staff/Faculty

    • Please follow the set up instructions here.

    Undergraduate Students

    • Email messages are forwarded to your @uci.edu email address automatically.

     

    Vacation/Spam Filtering 

    ICS Gmail (ICS Google Apps)
    For users who have already migrated to ICS Gmail, to set automatic out-of-office and vacation replies, please see the instructions here. Spam filtering is provided by Gmail.  You may set your mail forwarding at elnino.ics.uci.edu.  Please follow the set up instructions here.

    ICS IMAP/POP users
    Spam filtering, automatic out-of-office and vacation replies, mail forwarding, and white listing are available on all ICS accounts.  The easiest way to configure your email account is using mailboss.ics.uci.edu.  

     

    Email Access 

    Using a Client

    • Thunderbird: It is the recommended and the only fully supported client at ICS.

            Setting up Thunderbird for ICS Gmail (new)

            Setting up Your Mail Client for ICS Gmail by Google

    Setting up Thunderbird for ICS Email

    Checking Group Account Email Using Thunderbird

    Via the Web

    • Gmail:  ICS Gmail can be accessed via https://mail.google.com/a/ics.uci.edu.
    • Webmail:  ICS email acccounts can be accessed at https://webmail.ics.uci.edu.

     

    Mailing Lists

    • To request a mailing list, please use OIT's Mailman.
    • Public ICS mailing lists are managed via the ICS Mailman.

     

    ICS Email Servers Information

    ICS runs IMAPS and POPS email servers.  Any email client that supports these protocols can be used.

    • Incoming 
      • imap.ics.uci.edu (recommended)
      • pop.ics.uci.edu
    • Outgoing
      • smtp.ics.uci.edu (accessible from on-campus connection only or via VPN)
      • smtp.uci.edu (off-campus accessible; please follow instructions 13-16 from OIT)
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: February 28 2014
    http://www.ics.uci.edu/computing/email/thunderbird_Gmail.php thunderbird setup for ICS Gmail @ the bren school of information and computer sciences

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    Thunderbird Setup for ICS Gmail

    This tutorial is based on Thunderbird 24.3.0. If you're using a different version, the naming convention may differ, but the settings will remain the same.

     

    This guide shows how to setup and access your ICS Gmail account via a web browser and/or Thunderbird.

    Activating your ICS Gmail Account

    1. If you have not changed your ICS password recently (since summer 2013), go to My Account at elnino.ics.uci.edu to change it.  You may skip Steps 1 and 2 if you already completed them as illustrated here. 
    2. Set your email forwarding to ICS Gmail by selecting ICS Gmail at elnino.ics.uci.edu, too.  Save your new settings.  The changes can take up to two hours to propagate.
    3. Activate your ICS Gmail by visiting www.gmail.com and entering your ICS credentials (username@ics.uci.edu and your ICS password).  If you already have a personal Gmail account, you will need to click Add an Account to proceed.  If your password was successfully changed, you should be able to log in and will receive a welcome agreement/screen.  Please accept and continue to UCI Gmail's inbox.  If you wish to read your ICS mail only via a web browser, you can stop here. Otherwise, continue.  
    4. If you will be using Thunderbird (or any other mail client) to read your ICS Gmail mail, you need to enable Gmail's IMAP feature at www.gmail.com.  Select the Gear symbol on your top right corner, Settings, Forwarding and POP/IMAP, Enable Map, and Save Changes.
    5. Now, choose which folders you want to display on your email client.  Select the Gear symbol on your top right corner, Settings, Labels, and click on Show and Show in IMAP as needed. 
    6. Next, you will add your newly-created ICS Gmail account to Thunderbird.  

    Configuring Thunderbird for ICS Gmail

    1. Start Thunderbird for the first time or go to Tools, Account Settings, down to Account Actions, select, and Add Mail Account if you have existing accounts configured on Thunderbird.
    2. In the Mail Account Setup window, enter your name and email address.  Enter your password here if you want to have it remembered. Click Continue.
    3. Thunderbird will now attempt to look for the IMAP information. Please make sure that you have IMAP selected.
    4. Click on Manual config in the lower left corner.  Enter the information as follows:
      • Incoming
        • IMAP
        • Server hostname: imap.gmail.com
        • Port: 993
        • SSL: SSL/TLS
        • Authentication: Normal password
      • Outgoing
        • Server hostname: smtp.gmail.com
        • Port: 587
        • SSL: STARTTLS
        • Authentication: Normal password
        • Username: username@ics.uci.edu
      •  

        server configuration

         

      • Click Re-test and then Done.
      • If you did not choose to have your password saved, you will now be prompted to enter your email password.
    5. Go to Tools and select Account Settings. Under your new account's Server Settings, click on the Advanced button to your right.
      • Make sure that IMAP server directory is empty. 
      • Uncheck Show only subscribed folders if checked. 
      • Uncheck Allow server to override these namespaces if checked.
    6.  

      Additional steps

       

    7. If you have multiple accounts, you might want to display your ICS Gmail account first.  To do so, on Account Settings, select your ICS Gmail account from the left pane, Account Actions at the bottom, Set as Default.  Please restart to have the changes applied.
    8. If you want multiple accounts and want to use ICS Gmail to send emails, on Account Settings, select Outgoing Server (SMTP) at the bottom of the left pane.  Select smtp.gmail.com and click on Set Default.  Make sure that your SMTP username is listed as username@ics.uci.edu.  Click OK.  
    9. You may copy signatures between accounts.  On Account Settings, select the account you want to copy the signature from on the left pane, go to Signature text on the right and copy whatever text/file path you have set there.  Now select the account you want to copy the signature to and paste whatever text/file path you copied.
    10. This last step is optional. If you have many email messages, you may want to disable Message Synchronizing so that your email loads more quickly. 
      • On Account Settings, select Synchronization & Storage for your new account.  
      • Uncheck Keep messages for this account on this computer.
    Now you may start drag and dropping messages and folders within your accounts.  Please note that if your folders contain thousands of messages, it will take a long time to complete the transfer.  Plan accordingly.  If you have any questions, email helpdesk@ics.uci.edu.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 12 2014
    http://www.ics.uci.edu/computing/policy/ethics.php ethical computing policy @ the bren school of information and computer sciences

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    Ethical Computing Policy

    Introduction
    Computer Accounts
    Ethical Behavior
    Disciplinary Procedures
    Good Citizenship
    Acknowledgements


    Introduction

    As a student you are probably aware of certain ethical responsibilities you have, such as honesty in doing classwork. Another area in which you have important ethical responsibilities is in your use of computing resources. This document describes some of these responsibilities and explains Bren School of ICS policy on student use of computing resources. Some of these policies might be different from what you would expect, so please read over and understand this document. As a student, you are responsible for being aware of these policies and abiding by them.

    The ICS department provides for you, the student, a wide range of computing resources from X-Terminals to PC's to large multiuser UNIX systems. These machines are expensive to buy and expensive to maintain, but it is our goal to provide you with the very best computing environment that we can. Many users depend on these computers for doing class assignments, research, and for communications. We are a community of computer users, and like any community we can all make the best use of our resources if we establish some guidelines for how we can use them responsibly.

    Some computing facilities, such as those which hold classified data, may establish expensive and complex security systems. In our department we do not do this; we have some security mechanisms which greatly decrease the risk that one user will accidentally interfere with another, but it does not require great ingenuity to get around these mechanisms. As a result, we need to trust the people who use our machines.

    The fundamental principle behind our policies is this: Do No Harm. While using the computers, you should never do anything that harms another user or prevents him or her from getting work done.

    If you have any questions about these rules, or if you suspect that an account (yours or someone else's) has been broken into, please contact the ICS Computing Support Group. To do this, send an email message to the address helpdesk@ics.uci.edu, or go to Support's office, CS 346 and explain the problem.

     

    Computer Accounts

    The Bren School of ICS has a wide range of computers available, located in several different labs. Some of these labs are open only to people enrolled in certain classes while some are available for general drop-in use. Each ICS student is given both a Windows account and a UNIX account to use on the lab workstations. Different courses will require different platforms to be used. Non ICS-majors will be given accounts only if required for an ICS course in which they are enrolled.

    All ICS computers are to be used only by ICS students, faculty, and staff. People outside ICS who wish to use computers should see the friendly people in Office of Information Technology. They provide computing services for non-ICS affiliated students. You can find a list of computer labs available to all UCI students here.

    Any computer account created for you remains the property of the Regents of the University of California. You are responsible for this account, and you may not allow any other person to use it. The primary purpose of your account is to allow you to carry out your computing assignments and other instructional activities. You may also make modest use of these resources for other purposes, such as sending electronic mail to friends on campus, reading the electronic bulletin boards, and playing games, provided that this usage does not significantly interfere with instructional use of the machines.

    An example of how one might "significantly interfere" would be to tie up a computer for game-playing when no other computers are free and someone else is waiting to use the computer to do an assignment. If you have a game or other program you would like to make available to other users, please give it to the system administrator for public installation. You may contact the system administrator via email. You may not use the machines for commercial purposes, such as preparing bills for your company or advertising products, or for work related to non-UCI organizations, such as an off-campus political or religious groups. More details about this are given below. If you are in doubt about whether some use of the machines is allowed, ask helpdesk.

     

    Ethical Behavior

    Here is a list of some examples of activities that the department does not allow. If a student makes such unethical use of ICS computers, he or she will be subjected to the penalties described in the Disciplinary Procedures section.

    • You may not introduce viruses, worms, Trojan horses, password cracking, keystroke logging, or login spoofing programs on any University computer or network. In fact, because of the serious damage such programs can cause, the Bren School of ICS faculty have adopted a policy which forbids students even to have these types of programs in their accounts or to place them onto any ICS computer. You may not store such a program on a departmental computer even if you only wish to study it.
    • You may not try to use equipment or accounts that you are not permitted to use.
    • You may not interfere with others' ability to make use of the resources. For example, it might be reasonable to lock a workstation if you need to leave the room for two or three minutes, but it is not reasonable to lock it while you leave to buy lunch. Another example would be logging on to a significant fraction of the available machines at once, thus preventing others from fair use of the lab machines.
    • You may not destroy other people's work either in electronic or physical form.
    • You may not "spy" on people, that is, you may not attempt to gain information from their accounts or from their external drives when there is good reason to believe that they do not wish you to obtain that information. This includes both attempting to violate the protection facilities provided by the system and also taking deliberate advantage of someone else's failure to protect sensitive information on their account. This works both ways; faculty, staff, and members of Computing Support also have the responsibility to respect the privacy of the student. For example, it would be unethical for a faculty member to browse through your personal messages just out of curiosity, even if they have a security level that allows them to do so. We do, however retain the right to inspect material on your account when this is necessary to investigate a suspected violation of university rules, such as a cheating incident or a violation of the rules in this document.
    • You may not send mail that appears to come from someone else.
    • You may not advertise any commercial products or use your account to earn money.

     

    Disciplinary Procedures

    What happens if you violate any of these rules? It depends on the seriousness of the offense, but it could be one or more of the following. Disciplinary procedures and sanctions will be consistent with those outlined in the UCI Implementation of "Interim Policies and Procedures Applying to Campus Activities, Organizations, and Students, Part A.''

    1. You may be required to meet with the chair of the Computing Resources Committee (CRC), the Dean of the Bren School, or the manager of the Bren School Support Group to discuss abuse of computing resources.
    2. Your account may be locked. (Again, we recognize an obligation to respect your rights as well. No student account will be locked without discussion and approval of the Dean of the Bren School, or the chair of the CRC, except in the case of security violations. It would not be ethical for us to lock your account capriciously; for example, we agree not to lock it simply because you send a message to a board expressing disagreement with some Department policy or action).
    3. For minor infractions, some form of departmental services (e.g., cleaning a lab) may be requested in exchange for unlocking the account.
    4. For offenses involving abusing computing resources, cheating on course-related work, or preventing others from working on assignments, your grade may be lowered in the class or you may receive a failing grade.
    5. For severe offenses, or repeating minor offenses, you may lose access to all Bren School computing facilities for a period of time. Access to computing resources can be denied for a limited time (e.g., one week, the remainder of the quarter, an entire quarter) or permanently.
    6. You may be suspended or dismissed from the University.
    7. In serious cases, your name and a description of the violation may be reported to the police. California Penal Code Section 502 makes certain computer abuses a crime, and the associated penalties can range up to a $10,000 fine and up to three years in prison.

     

    Good Citizenship

    Your cooperation in the following areas will help us make efficient use of the computing resources and will avoid unnecessary impositions on the time of faculty, staff, and other students. These are not the sort of things which we can expect to enforce rigidly; rather, we are asking your cooperation for the benefit of the whole departmental community. Violations of these guidelines would not ordinarily result in any of the penalties listed above beyond number one, unless they were especially flagrant or persist after faculty or staff have asked you to stop.

    1. Please be careful not to use the computer to annoy people, for example by sending them messages which they do not wish to receive. (The mail system makes it rather easy to send a message to a very large group of people; please be responsible in your use of this capability. In particular, when you reply to a message sent to a large group, avoid cc'ing your reply to the entire group unless it is a matter of interest to them.)
    2. Please be careful not to annoy students in the lab for any reason. The lab is not a library, but we ask that each student do his/her part to help to maintain a pleasant working environment for all. Activities that are not conducive to a pleasant work environment include, but are not limited to: listening to music at a volume that is distracting to others, carrying on loud or inappropriate conversations, excessive or distracting cell phone use, failure to leave your workstation clean and ready for the next student, etc.
    3. Please do not waste anything (i.e., paper, disk space, CPU time, personnel time, etc.). Please throw away all trash and put your old printouts in the recycling bins.

     

    Acknowledgments

    Some of these polices are adapted from those used by the UCLA CS Department. They adapted some of their polices from Columbia University and the California Institute of Technology.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 06 2013
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    Contacting ICS Computing Support

    The ICS Computing Support group serves the Bren School of ICS Community. Before contacting us, please refer to our website as most issues have been addressed there already. If you still cannot find the answer that you are looking for, please refer to the following on how to contact us.

    • A list of who to contact depending on the issue that you are experiencing
    • What is Helpdesk?
    • Computing Support contact list
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    Network Setup

    UCInet Mobile Access

    • Wireless on campus (including in Bren Hall, Computer Science, and Computer Science II) is managed by OIT, not ICS.
    • You may setup temporary wireless access for a guest.
    • Please contact OIT for any of these issues.

    VPN

    • You will need to first be connected via the Virtual Private Network (VPN) when accessing campus resources such as:
      • Mapping your ICS home directory
      • Updating Sophos antivirus
      • Accessing the ICS software repository on masterhit
    • You must install the VPN client. The WebVPN will not work for these purposes.
    • Please note that you will still need to be connected via VPN even if you are on the UCInet Mobile Access network or on the on-campus housing network.

    NetReg

    • Report a new machine that will be added to the ICS network in the Computer Science/Computer Science II buildings. 
    • New machines in Bren Hall will only need to be registered if a static IP address is needed.
    • Report a machine which will be moved to a new location.

    UCI Weather Report

    Open Port Request

    • All ports are blocked from off-campus access at the campus level.
    • To request a port to be open, please send an email to helpdesk with the following information:
      • name and/or IP address of the computer
      • port number(s) to be open
      • an approval from your advisor/owner of then machine if applicable
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    Bren school home > labs home >
    Lab hours »

    Winter Quarter:

    Finals Week Schedule

    Holidays: All labs closed

    • Martin Luther King Jr. Holiday (Monday, January 18th)
    • Presidents' Day Holiday (Monday, February 15)

    CS 364b Open Lab and 364a Laptop Instructional Lab

    Located on the third floor of the Computer Science Building.

    Monday - Thursday 8:00AM to 10:00PM
    Friday 8:00AM to 8:00PM
    Saturday - Sunday 12:00PM to 6:00PM

    CS183, CS189, CS192

    Located on the first floor of the Computer Science Building.

    Please click on the room number above to view the reservation schedule for the current quarter. Lab reservation schedules are also posted outside each lab door.

    Monday - Friday 8:00AM to 8:00PM
    Saturday - SundayClosed

    CS193 Reserved for Project Class in Software System Design

    Located on the first floor of the Computer Science Building. To get access to this lab, you must be enrolled in Informatics 191. Please read the class policy.

     

    More labs»
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    Mapping Home/Group Directory - Mac

    *** Important ***

    If you are using the UCInet Mobile Access (UCI wireless) or connecting from off-campus (including on-campus housing),

    please make sure that you are connected via the UCI VPN first.

     

    • Quick instructions
    • Instructions with pictures
    • Common errors

     


    Quick Instructions

    1. From the Finder, click Go, then select Connect to Server.
    2. In the Server Address, enter one of the following and click Connect.
      • Home directory: smb://tron.ics.uci.edu/your_username
        • If you are running Mavericks, please enter: smb://samba.ics.uci.edu/your_username
      • Group directory: smb://samba.ics.uci.edu/group_acct_name
    3. For Name, enter uci-ics\yourusername then enter your ICS password and click Connect.
    4. Your account is now mapped.
    5. If you would like to save the mapped drive as a shortcut, drag the newly mapped drive over to DEVICES on the left.
    6. When you map the drive next time, it will automatically show up under DEVICES.

     


    Instructions with pictures

    1. From the Finder, click on Go, then select Connect to Server.

      Connect to server

    2. In the Server Address, enter one of the following and click Connect.
      • Home directory: smb://tron.ics.uci.edu/your_username
        • If you are running Mavericks, please enter: smb://samba.ics.uci.edu/your_username
      • Group directory: smb://samba.ics.uci.edu/group_acct_name

      server address

    3. For Name, enter uci-ics\yourusername then enter your ICS password and click Connect.

      Enter username and password

    4. Your account is now mapped.

      mapped

    5. If you would like to save to save the mapped drive as a shortcut, drag the drive over to DEVICES on the left.

      short cut

    6. When you map the drive next time, it will automatically show up under DEVICES

      short cut

       


    Common Errors

    • If you are on the UCI Mobile Access or are connecting from off-campus locations, please make sure that you are connected to the UCI VPN before trying to map the network drive.
    • Please make sure that your entering in your ICS password and NOT your UCInetID password.
    • If you get an error about the path not found, please try replacing the server name with its corresponding IP address:
      • samba.ics.uci.edu - 128.195.1.18
      • tron.ics.uci.edu - 128.195.1.5
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: July 24 2014
    http://www.ics.uci.edu/computing/services/sophos_win.php sophos anti-virus on windows @ the bren school of information and computer sciences

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    Sophos Anti-Virus on Windows

    * Before installing Sophos, please make sure that you do not have any other anti-virus programs already installed.

    ** If you are connecting from off campus or if you are on the UCI wireless network, please make sure that you are connected via the UCI VPN first before proceeding.

        1. Right-click on My Computer and select Map Network Drive.
        2. Select any drive letter for the drive and enter the following path for the Folder:

          \\sophos-server.ics.uci.edu\sophos

        3. Click on Connect using a different user name and enter your ICS username and password.
          • Username: <your_username>@ics.uci.edu
          • Password: ICS password
        4. Double click on sav_103_sa_sfx.exe.
          • Note: You can also copy the entire content of the folder on to another folder on your computer and then run sav_103_sa_sfx.exe.
        5. Continue through the setup by clicking on Install, Next, etc until you get to Update Source. For the Address, enter:

          \\sophos-server.ics.uci.edu\SophosUpdate\CIDs\S000\SAVSCFXP\

          and enter your ICS username and password.  NOTE: Do not check "Install Sophos Client Firewall" as this might cause issues with UCI's VPN. Leave "Remove third-party security software" checked.Sophos - update source
        6. Proceed through the remaining setup windows to begin the install.  When the process has completed, you should now see a blue shield icon in your system tray.  Right click on it and select Open Sophos Endpoint Security and Control.


          Sophos - system tray

        7. Click on Configure updating and verify the information that you previously entered during setup.
        8. Go under the Schedule tab and modify the time interval between updates to your liking.  This will enable automatic updating but will only work if you are on campus or connected to the VPN when the program tries to update itself.
        9. Right click on the blue shield icon again and click on Update Now.
          • If you get an error "Could not contact server" and you are off campus, make sure you are connected to the VPN. If you are, then right click on the blue shield icon in the system tray and select Open Sophos Endpoint Security and Control and click on Configure updating.
          • Click on Primary Server and change the address you entered in Step 5. by replacing the portion sophos-server.ics.uci.edu with 128.195.1.249 to get: \\128.195.1.249\SophosUpdate\CIDs\S000\SAVSCFXP\
          • Select Update Now again.
        10. Updates usually need to be performed manually.  It is recommended that you do this weekly to obtain the latest virus definitions.  Sophos has a new version released each month so, once a month, the update will take longer as a new version needs to be downloaded and installed.
          *** If you are connecting from off campus or if you are on the UCI wireless network, you will need to be connected via the VPN in order to do the update.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/contact/staff.php ics support staff @ the bren school of information and computer sciences

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    ICS Support Staff

    The ICS Computing Support Group supports research, administrative, and instructional computing within ICS. For information about ICS computer lab facilities, see ICS Lab Support.

    For a list of email contacts for specific issues, please see Who do I contact...


    Computing Support Staff Directory

    • William Cohen
    • Vipada DeLeon
    • Lucia Diaz
    • Peera Nimsombun
    • Janet Salk
    • Du Tran
    • Hans Wunsch

     

    William Cohen

    Title: Director of ICS Computing Support
    Office: CS 346D
    Phone: 949-824-1478

    • Manage Computing Support Group
    • Liaison to Campus Computing Organizations
    • Oversee Instructional Computing Assignments

     

    Vipada DeLeon

    Title: Systems Administrator
    Office: CS 346H
    Phone: 949-824-6686

     

    Lucia Diaz

    Title: System Administrator
    Office: CS 346L
    Phone: 949-824-7039

     

    Peera Nimsombun

    Title: Systems Administrator
    Office: CS 346K
    Phone:949-824-7834

     

    Janet Salk

    Title: Staff Support
    Office: CS 346J
    Phone: 949-824-1366

     

    Du Tran

    Title: Windows Systems Administrator
    Office: CS 346B
    Phone: 949-824-1667

     

    Hans Wunsch

    Title: Assistant Director
    Office: CS 346F
    Phone: 949-824-4035

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: December 18 2015
    http://www.ics.uci.edu/computing/linux/sge.php ics grid computing @ the bren school of information and computer sciences

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    ICS Grid Computing

    What is the ICS grid?

    The ICS grid contains groups of both public and research distributed computing clusters. These clusters are unified by the Sun Grid Engine (SGE) where jobs are submitted, scheduled, and dispatched according to available resources. Jobs may embed requests for certain types of resources or these can be requested at time of submission. Your job will be queued and dispatched when the appropriate resources are available.

    Research clusters are owned by various faculty members and have dedicated nodes to run their computing jobs. Trying to submit jobs directly to these clusters will result in jobs being rejected or access being denied unless appropriate permissions are previously granted. The ICS grid also has public clusters for general use. These have wall clock limits so that the general public may have a turn on the grid. Below is a listing of the public queues.

    Public Queue OS Runtime Limit Slots
    12hour.q CentOS 6 (RHEL 6) x86_64 12 hours 48
    15day.q CentOS 6 (RHEL 6) x86_64 15 days 48

    For a list of all queues you can query SGE:

    $ qconf -sql

    To get details on a particular queue, such as time, nodes, access, etc.:

    $ qconf -sq 12hour.q

     

    How do I request an account?

    All ICS accounts can access the public queues. Contact helpdesk in order to get access to research queues.

     

    How do I access my account?

    Linux/Solaris/MacOS:

    To access the grid simply login via ssh since these systems have ssh installed by default.

    $ ssh -Y icsusername@openlab.ics.uci.edu

    Windows:

    On Windows, use the free PuTTY application or one of these alternatives. Telnet access is NOT available. If you use ssh without the -Y or -X option, you will not be able to view X11 graphics.

     

    I am logged in, now what?

    Once you are logged in, you have access to a shell for which you can access ICS computing resources such as our software stack along with the grid. In order to access these, you must know a few commands before you begin. 

    $ module load sge

    Let's go over the command above. The module command allows you to load specific software applications out of the software stack. In this case, the application is SGE. This will setup essential environment variables in order to run the commands provided by sge. You may edit your shell rc files to automatically load sge if that is your primary use or even if you find it convenient so you do not have to run it at every login. There are no additional resources taken up if you decide not to use it even with the module loaded as the command will only modify your environment variables.

    For more on modules and our software stack, please visit our module page.

     

    I loaded SGE, what commands can I run?

    For starters, the first command to get accustomed to is qstat. If you run qstat alone, you will see nothing for the first time because by default it displays the status for submitted jobs. To get a full listing run:

    $ qstat -f

    Once you submit a job, you will be given a job id to track the detail of job completion.

    $ qstat -j «job_id»

    This command will give you better detail as to why your job is sitting in the queue waiting to be dispatched. Resources may be busy, so you will have to wait and sit tight. If your job sits in the queue for too long, especially in our 12-hour or 15-day-long public queues, please email helpdesk to assist you in looking at the queues more thoroughly.

    You can also delete a job using its id number.

    $ qdel «job_id»

    Now, let's submit your first job! The simple method is to not request any particular resource constraints, such as memory, platform, or SGE queues.

    $ qsub /auto/sge-6.2u5/examples/jobs/sleeper.sh

    This simple script, once dispatched, will run on an SGE selected host and sleep for a given number of seconds. This exciting script is used to test that the basic SGE scheduling is working. It is a good idea to test and run it at times when your custom scripts and/or applications refuse to run. You can test particular SGE queues by specifying a queue request during submission, as follows:

    $ qsub -q 12hour.q /auto/sge-6.2u5/examples/jobs/sleeper.sh

    There are various requests that can be made. You can read about them in the man pages. The usage page is a valuable resource for your routine tasks of using the grid more effectively. For example, you may use job arrays for scenarios that require running several identical jobs which will work on different datasets. You would submit a job array of an appropriate size to do that. Otherwise, we would bombard the scheduler with hundreds of thousands of jobs.  This could result in bringing down the scheduler and you explaining to your colleagues that you single-handedly brought SGE down as they are on a deadline.

     

    How do I get files/scripts to and from my account?

    For systems managed by Computing Support, you will find that your UNIX home directory has already been mounted via NFS. All of our nodes on the grid automount your home directories as well as our software stack mentioned above via the use of modules. If this is your case, then you are already off on the right foot and have it easy when it comes to transporting files to and from your ICS account for use on the grid.

    For systems not managed by ICS, the easiest way of transporting files is using secure copy, i.e. scp. Besides the command line scp utility bundled with all Linux, Solaris, and Mac hosts, there are GUI clients for MacOS and Windows, and of course, Linux. If you have large collections of files or large individual files that change only partially, you might be interested in using rsync as well.

    For Windows users, we recommend the free WinSCP application, which gives you a graphical interface for SCP, SFTP, and FTP. Machines within the Windows UCI-ICS domain will already have their H: drive mounted, so you can just drag and drop directly to your home directory.

    For Mac OS X users, we recommend the free, though oddly named, Cyberduck application, which provides graphical file browsing via FTP, SCP, SFTP, WebDAV, and even Amazon S3(!). Macs also have access to your UNIX home directory, so you can easily drag and drop files.

    For Linux and Solaris users, we recommend utilizing the built-in capabilities of KDE's Swiss-army-knife browser Konqueror or twin-panel file manager Krusader. Both support the secure file browser kio-plugin called fish. Advanced users should read the document How to Move Data, which discusses in detail how to transfer large amounts of data over the network. Again if you have a Linux or Solaris install from ICS, you will have your home directory mounted and accessible from your desktop.

     

    How do I use the grid?

    There are basically two good uses for SGE. The Sun Grid Engine scheduler controls all the access to the grid's computing nodes. All jobs must use the qsub or qrsh commands, which submit jobs to the grid in an orderly fashion. There are policies in place to prevent a single user from dominating the machine by flooding the queue with jobs, particularly a 12-hour and 14-day wallclock limit for longer jobs on our public queues. THIS SYSTEM IS NOT FOOLPROOF. Please be courteous and run as few simultaneous jobs as possible, particularly if you notice that there is a lot of usage (with the qstat command).

    You can check the status of the batch queue backlog using the qstat and qhost commands. For more information see the section on Monitoring and Controlling Jobs from the wiki.

    Send email to helpdesk@ics.uci.edu if you have complaints about your job turnaround or if you need special scheduling considerations to meet a project deadline.

    Batch submission

    Use the qsub command to submit a job script to the grid. A job script consists of UNIX directives, comments, and executable statements. It is important to remember that all the commands in the job script execute serially on the node that runs your script.

    TIP: When your script begins execution the working directory is your home directory. Use the -cwd option with qsub to use the current working directory (wherever you currently are). Output and error will be directed wherever you happen to be, allowing for a cleaner environment. Otherwise stdout and stderr go to your home directory.

    SGE provides an abundant number of examples for you to start out with. You can find them located at $SGE_ROOT/examples, or just Google "SGE examples" and you will have plenty of references.

    There are several things to look out for when using batch submissions.

    • Submitting a large set of jobs. This has the affect of saturating the scheduler to a crawl and at times making it inaccessible to you and everyone else on the grid. The best solution is to use job arrays.
    • If you chain jobs, remember to check the status of the previous job before spawning another qsub. This will prevent the system from flooding the scheduler with failed jobs.
    • Remember that for your convenience we automounted your home directory along with our software stack. Please be aware that if you flood the scheduler with jobs that mount a software stack or access data from your home directory, your scripts will fail trying to look up a file or directory. If you encounter this, please make sure to make create more robust scripts by adding random timers or give the lookup some time to return your mounts.

    Interactive

    The submission of interactive jobs instead of batch jobs is useful in situations where a job requires your direct input to influence the job results. Such situations are typical for X Windows applications or for tasks in which your interpretation of immediate results is required to steer further processing.

    You can create interactive jobs in three ways:

    1. qlogin – An rlogin-like session that is started on a host selected by the Grid Engine software.
    2. qrsh – The equivalent of the standard UNIX rsh facility. A command is executed remotely on a host selected by the Grid Engine system. If no command is specified, a remote rlogin session is started on a remote host.
    3. qsh – An xterm that is displayed from the machine that is running the job. The display is set corresponding to your specification or to the setting of the DISPLAY environment variable. If the DISPLAY variable is not set, and if no display destination is defined, the Grid Engine system directs the xterm to the 0.0 screen of the X server on the host from which the job was submitted.

    The default handling of interactive jobs differs from the handling of batch jobs. Interactive jobs are not queued if the jobs cannot be executed when they are submitted. When a job is not queued immediately, the user is notified that the cluster is currently too busy.

    You can change this default behavior with the -now no option to qsh, qlogin, and qrsh. If you use this option, interactive jobs are queued like batch jobs. When you use the -now yes option, batch jobs that are submitted with qsub can also be handled like interactive jobs. Such batch jobs are either dispatched for running immediately, or they are rejected.

     

    Where can I get more information on grid computing?

    The best resource is always the user documentation. Your peers are the next best source since they may already have experience using the grid for what may be the very purpose you might need.

     

    Where do I report problems?

    Please contact helpdesk if you have problems accessing the grid or if there is a failure beyond normal script logic failures. We are here to make sure that the grid is operational and that you have access so that you can make full use of the grid. When emailing please articulate what problem(s) you are experiencing, what scripts you are trying to run, and how you are submitting them to the grid. Please copy/paste any relevant error messages. This will help us troubleshoot the problem in a timely fashion.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: April 14 2014
    http://www.ics.uci.edu/computing/email/google_apps.php group account email @ the bren school of information and computer sciences

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    ICS Google Apps

    Google icon

    Learn

         All ICS faculty, staff, and graduate students are encouraged to use ICS Google Apps, a suite of applications which includes:       
    • ICS Gmail - Send and receive email with powerful search options, spam filtering, and chat
    • ICS Google Docs - Publish and collaborate in real-time on documents, spreadsheets, and presentations
    The suite of ICS Google Apps is hosted online, and the applications are accessible via web browser from any computer and most mobile devices.

     


    google logo

    Set up

       

    Set up your ICS Google Apps Account (ICS faculty, staff, and grads)

    Set up Thunderbird for ICS Gmail



    Gmail logo

    Use

        ICS Gmail
        ICS Gmail Vacation Message
    ICS Google Drive
       

    Help icon

    Help

        Frequently Asked Questions    
    Google Help Documentation
    Reset or Change Your ICS Google Apps Password

     

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 26 2015
    http://www.ics.uci.edu/computing/account/activate.php account activation @ the bren school of information and computer sciences

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    Account Activation

    Students (Ugrad & Grad)

    Visitor/PhD Student

    • Please make sure that you first activate your UCInetID.
    • Once you have your UCInetID, please have your department manager/personnel or professor send us an email for the account request.
    • In the email, please include:
      • your UCInetID
      • any special group account that you might need to be added to

    Staff/Faculty

    • Please make sure that you first activate your UCInetID.
    • Once you have your UCInetID, please have your department manager send us an email for the account request.
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: October 31 2013
    http://www.ics.uci.edu/computing/account/password.php password change/reset @ the bren school of information and computer sciences

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    Password Change/Reset

    Resetting Password

    If you know your current password and would like to change it, please follow the link above. Use your current password to authenticate.

    Changing Password

    If you have forgotten your password and need it reset, please do one of the following:

    • Student (Undergrad and Grad): Please bring your student ID to CS 364 lab and ask the lab attendant for further assistance.
      • If you cannot make it to campus but need to change your password, please contact helpdesk.
    • Staff/Faculty: Please contact Helpdesk via email (helpdesk@ics.uci.edu) or telephone (949-824-4222).

    Furthermore, you may reset your password at https://support.ics.uci.edu/ltb if you:

    • know your current password, or
    • have set and can answer your security question, or
    • have access to your alternate email address, which will be used to send a password reset message to
    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page
    http://www.ics.uci.edu/computing/email/mailboss.php mailboss @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
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      • » Email Servers Information
      • » Checking Group Account Email
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      • » UCInet Mobile
      • » VPN
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      • » Open Port Request
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      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
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      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
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      • » Ethics Summary
    • » Contact
      • » Helpdesk
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      • » Who To Contact
    Mailboss

    Please login into Mailboss using your ICS account information.


    Spam Filter

    If you choose to, you can have Mailboss run a spam filter on your mail. Your email client can also do this. When scanned, each email message is assigned a spam score. The higher the score, the more likely that it is spam. You can then choose what to do depending on the level of the score.

    Vacation

    This will be dependent on your particular email provider.  If you are using ICS Gmail, these are the directions.

    Quota Check

    You can have the system send you a reminder when you are close to hitting the limit on your quota. Please specify the minimum amount of free space left before you get a notice.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 26 2015
    http://www.ics.uci.edu/computing/account/quota.php account quota @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
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      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
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      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Account Quota

    Each account type is assigned a different amount of quota as listed below. If your advisor has his/her own space, you may request for your account to be moved there.  This may result in a higher quota.  Please check with your advisor for details.

    If you need more space, please try using the Student Extra Space (not backed up).  To access this storage, ssh into openlab.ics.uci.edu, navigate to /extra/ugrad_space, and create a folder with your username, for example: /extra/ugrad_space/peter.  Please be aware that anything that you store here will not be backed up, so plan accordingly.  

    Quota Allocation

    Checking Your Quota

    Quota Reminder


    Quota Allocation

    • Students
      • Undergrad - 500MB
      • Grad - 10GB
    • Staff
      • 8GB
    • Faculty
      • 10GB
    • Visitor
      • 300MB

     

    Checking Your Quota

    • Login to one of the ICS hosts
    • At the prompt, type in the command quota -v
    • You will then get a return of something like this:
      • An account that is under quota.
        suzieanteater% /usr/sbin/quota -v
        Disk quotas for santeater (uid 1234):
        Filesystem usage quota limit timeleft files quota limit timeleft
        /home/santeater 1409 2000 4000 209 0 0
        In this example, Suzie Anteater has a quota of 2 MB, and a hard limit of 4 MB. She has used 1.4 MB worth of space and has 209 files in her account.
      • An account that is over quota.
        peteranteater% /usr/sbin/quota -v
        Disk quotas for panteater (uid 4567):
        Filesystem usage quota limit timeleft files quota limit timeleft
        /home/panteater 2404 4000 4000 3.0 days 209 0 0
        In this example, Peter's usage is above the quota limit, and unless his quota is brought down below the limit within 3 days, he will be locked out of his account, or he may lose files.

     

    Quota Reminder

    Would you like to receive a reminder when you are almost maxing out your quota? Please login to Mailboss and choose when to receive such notifications.

    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: January 03 2014
    http://www.ics.uci.edu/computing/email/thunderbird_setup.php thunderbird setup @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
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      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
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      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
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    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Thunderbird Setup

    This tutorial is based on Thunderbird 24.1.0. If you're using a different version, the naming convention may differ, but the settings will remain the same.

    1. Start up Thunderbird for the first time (or go to Tools, Account Settings, Account Actions, Add Mail Account)
    2. In the Mail Account Setup window, enter your name and email address. Enter your password here if you want to have it remembered. Click Continue.
    3. Thunderbird will now attempt to look for the IMAP information. Please make sure that you have IMAP selected.
    4. Click on Manual config in the lower left corner to make sure that the information is as follow:
      • Incoming
        • IMAP
        • Server hostname: imap.ics.uci.edu
        • Port: 993
        • SSL: SSL/TLS
        • Authentication: Normal password
      • Outgoing
        • UCI SMTP. If you're connecting from a laptop or from off campus, please use the UCI SMTP server settings or your ISP server settings.
          • For UCI SMTP, see settings from step number 7 in the link provided.
        • ICS SMTP. If your machine is in on the UCI-ICS domain, please use the following settings. 
          • Server hostname: smtp.ics.uci.edu
          • Port: 587
          • SSL: STARTTLS
          • Authentication: Normal password
          • Username: <your username>

      advanced window
    5. Click Re-test and then Done
    6. If you did not choose to have your password saved, you will now be prompted to enter your email password.
    7. Go to Tools and select Account Settings
    8. Under Server Settings, click on the Advanced button
      • Make sure that IMAP server directory is empty
      • Uncheck Show only subscribed folders
      • Uncheck Allow server to override these namespaces

      advanced window
    9. This last step is optional. If you have many email messages, you may want to disable Message Synchronizing so that your email loads more quickly.
      • While still on Account Settings window, select Synchronization & Storage
      • Uncheck Keep messages for this account on this computer
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: October 31 2013
    http://www.ics.uci.edu/computing/services/restore.php file restore request @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
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      • » Forwarding/Vacation/Spam Settings
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      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
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    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
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    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    File Restore Request

    Purpose:
    Accidentally deleted files or files that are discovered to be important after deletion can be recovered.

    Performed by:
    Vipada Deleon, Support Group

    Procedure:
    If your home directory is on the server named TRON, you can retrieve the data yourself from a snapshot.

    Otherwise, send an email message to helpdesk@ics.uci.edu containing the following information:

    1. Your ICS login name
    2. A full path from your ICS home directory to the missing files/directories.
    3. A complete list of what needs to be restored (if directories, there is no need to list directory contents).
    4. The last date the files existed on or were valid on.

    Lead time:
    Allow 2 working days for a file restore request.

    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
    http://www.ics.uci.edu/computing/contact/index.php contacting ics computing support @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Contacting ICS Computing Support

    The ICS Computing Support group serves the Bren School of ICS Community. Before contacting us, please refer to our website as most issues have been addressed there already. If you still cannot find the answer that you are looking for, please refer to the following on how to contact us.

    • A list of who to contact depending on the issue that you are experiencing
    • What is Helpdesk?
    • Computing Support contact list
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 04 2013
    http://www.ics.uci.edu/computing/account/faqs.php account FAQs @ the Bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Account FAQs
    • What is an ICS account?
    • Who can get an account?
    • How do I get my ICS account?
    • I am enrolled in an ICS course, why can't I get an account?
    • How do I change/reset my password?
    • Where do I go to log in?
    • How do I access my account from off-campus?
    • When does my account expire?
    • Why does my classmate have a larger quota than me?
    • What are my responsibilities as a fellow user of a multiuser system?
    • I entered my username and password, but the system won't log me on. What's wrong?

     



    What is an ICS account?

    Your ICS account is used to access ICS resources, such as the computers in the ICS labs, your ICS email account, the computers on the UCI-ICS domain, and any ICS servers. Your ICS account is distinct and separate from your UCInetID.

    Who can get an account?

    • Students (Undergrads & Grads): must be under one of the majors under the Bren School of ICS or be enrolled in a course under the Bren School of ICS. Courses under another school but equivalent to one under our school also qualify.
    • If you are a non-ICS student/faculty working with a professor under our school, you may also qualify for an account.
    • Any staff or faculty members who are under Bren School of ICS.

    How do I get an ICS account?

    • Students (Undergrads & Grads) - Please bring your student ID card and see the lab attendant in CS 364.
    • Extension students - Please bring your student ID card and proof of enrollment to the lab manager in CS 346L.
    • Staff/visitor/faculty - Please have your department personnel send in an account request to helpdesk.

    I am enrolled in an ICS course, why can't I get an account?

    Our system is linked to the UCI registrar's database, and we receive updates once a day. If your classes were dropped because you have not paid your fees, you will not be eligible for an account until your fees are paid. If you are adding a class, please wait overnight to ensure that we get the updated information.

    Where do I go to log in?

    Students taking ICS classes may access the ICS labs located on the 1st and 3rd floors of the Computer Science building. Check the Lab Information page for information on lab locations, operating hours, and hardware and software specifications. Note that these labs are not "public" labs in the sense that they are only for students taking ICS courses.

    How do I access my account from off-campus?

    • Windows
    • Mac
    • Unix/Linux

    Now that I have an ICS account, what are my responsibilities as a fellow user of a multiuser system?

    All UCI and ICS students are expected to follow a system of ethical academic and computing policies designated by the University of California and the Donald Bren School of ICS. You will be required to read these policies as part of your account activation process, and you are responsible for knowing what is and is not acceptable behavior regarding your ICS account and the use of school equipment and facilities.

    For details, please refer to the Computing Policy section.

    I entered my username and password, but the system won't log me on. What's wrong?

    If this is the very first time you have tried logging on:

    1. Make sure you have activated your account. If not, check out the Account Activation page.
    2. Wait at least 2 hours after activating before you attempt to log on. 

    ...if you still can't log on, check out the tips below.

    If you have logged on successfully during this quarter:

    1. Make sure you typed your username correctly.
    2. Make sure you typed your password correctly.
    3. Make sure you have selected "UCI-ICS" as the log on domain (UCI-ICS\username).

    ...if you still can't log on, contact the Lab Assistant in CS 364.

    UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: November 01 2013
    http://www.ics.uci.edu/computing/linux/security.php linux security @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Linux Security

    With the power and flexibility of UNIX and UNIX variants like Linux come many potential security holes.  To limit the vulnerability of your workstation as well as that of the system, please read the following suggestions and apply the patches appropriate to your particular version of Linux.

    • General Advice
    • Unnecessary Services
    • Allowing Access to Your Workstation
    • Useful Tools
    • Understanding and Setting File Permission

    General Advice

    • Use a current version of your version of Linux. For example, Red Hat 5.1 is known to be particularly favored by hackers.
    • Apply security patches as soon as they become available. As soon as a vulnerability becomes known, attack kits are posted on the Internet to take advantage of it.
    • Turn off services you do not need. Most versions of Linux come with preconfigured options enabled for things that most people never use. Many of these are popular targets for attack.
    • Look into available tools to prevent break-ins to your workstation.
    • Keep track of your logs for repeated access attempt or other unusual activity. These log files are usually located in the /var/log directory.
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 19 2013
    http://www.ics.uci.edu/computing/policy/ > computer usage policies @ the bren school of information and computer sciences

    • » Account
      • » New User Guide
      • » Activation
      • » Password Change/Reset
      • » Quota
      • » Renewal
      • » Mapping Network Drive
        • » Windows
        • » Mac
      • » FAQs
    • » E-mail
      • » ICS Google Mail
      • » Specify Delivery Point
      • » Webmail
      • » Thunderbird for ICS Gmail
      • » Thunderbird
      • » Mailing Lists
      • » Forwarding/Vacation/Spam Settings
      • » Email Servers Information
      • » Checking Group Account Email
    • » Network
      • » UCInet Mobile
      • » VPN
      • » ICS Netreg
      • » UCI Weather Report
      • » Open Port Request
    • » Linux
      • » ICS hosts
      • » Changing shell
      • » Using modules
      • » Security
      • » Group account access (gsu)
      • » Sun Grid Engine
    • » Other Services
      • » Labs
      • » Printing
      • » Activate MS Office
      • » Sophos
        • » Windows
        • » Mac
      • » Microsoft DreamSpark
      • » File Restore
        • » Self-restore snapshot
        • » Restore request
      • » Quarterly announcements
    • » Web
      • » Personal Webpage
      • » General Information
    • » Policies
      • » Ethics
      • » Ethics Summary
    • » Contact
      • » Helpdesk
      • » Support Staff
      • » Who To Contact
    Computer Usage Policies

    The University of California, Irvine (UCI) provides computing resources and worldwide network access to members of the UCI electronic community for legitimate academic and administrative pursuits to communicate, access knowledge, and retrieve and disseminate information. As members sharing these resources, we also share the rights and responsibilities of their use. The following computer use policy documents describe the shared rights and responsibilities as well as the consequences of misuse. YOU ARE RESPONSIBLE FOR KNOWING AND FOLLOWING THESE POLICIES. We welcome your use of campus computing resources and your cooperation.

     


     

    Computer Use Policies

    • UCI Computer Use Policy
    • ICS Ethical Use of Computing Policy   //   Summary
    • ICS Instructional Computing Use Policy

    Electronic Mail Policies

    • UC Electronic Mail Policy   //   Summary
    • UCI Electronic Mail Policy

    Other Policies

    • Account Allocation Policy
    • Backups Policy
    • Remote Access Policy
    Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 06 2013
    http://www.ics.uci.edu/community/scholarships/scholar_corporate.php sponsor a scholarship/fellowship @ the bren school of information and computer sciences
    • ABOUT
      • About the School
      • Dean's Welcome
      • Facts and Figures
      • Donald Bren Hall
      • Visit the Bren School
      • Equity & Diversity
      • Contact Us
    • DEPARTMENTS
      • Computer Science
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    • RESEARCH
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    • GET INVOLVED
      • Make a Gift
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    Bren school home > Community > Scholarships and fellowships
    Sponsor a scholarship/fellowship

    Thank you for your interest in supporting our students via ICS scholarship programs.

    In forging relationships with the students they support, current donors have discovered that ICS students are more than scholars, they also are community volunteers, dedicated youth leaders, responsible young adults and promising future professionals. A list of current donor sponsored scholarships and fellowships is available.

    The Bren School of ICS offers individuals, corporations and organizations flexibility in designing tailored support programs. Some of the more popular opportunities for supporting students are listed below.

    • Contributions can be named in memory of friends and loved ones, after corporations or other organizations
    • Programs can be designed as awards, annual scholarships or undergraduate endowments
    • Donors can help set the criteria and be involved in the selection process
    • Donors also are given opportunities to meet the supported student(s)

    For further information on assisting ICS students, please contact the program administrator.

    More community »
    • Alumni
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    • Give a gift
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    Copyright Inquiries | UCI Directory | Intranet | icswebmaster
    http://www.ics.uci.edu/grad/degrees/degree_cs.php computer science degree @ the bren school of information and computer sciences
    • ABOUT
      • About the School
      • Dean's Welcome
      • Facts and Figures
      • Donald Bren Hall
      • Visit the Bren School
      • Equity & Diversity
      • Contact Us
    • DEPARTMENTS
      • Computer Science
      • Informatics
      • Statistics
    • RESEARCH
      • Research Areas
      • Research Centers
      • Research Highlights
    • EDUCATION
      • Graduate ▸
        • Prospective Students
        • How to Apply
        • Programs of Study
        • Academic Year Plan
        • Forms
        • Policies
        • Funding & Housing
        • Computing Support
        • Campus Resources
        • Visit the Bren School
        • Graduate Student Handbook
        • Contact
      • Undergraduate ▸
        • Contact
        • Academic Advising
        • Academic Year Plan
        • Petitions
        • ICS Majors
        • ICS Minors
        • Policies: Academic Integrity
        • Policies: Academic Standing
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    Bren school home > Graduate > Degrees
    Computer Science Degree

    The UCI General Catalogue is the official guide to all degree and graduation requirements; the information below is intended for general planning purposes only. For previous calendar years, please click here.


    Computer Science (CS) (M.S. and Ph.D.)

    The Computer Science degree is a broad and flexible program which offers students opportunities for graduate study in the full spectrum of intellectual activity in computer science. 

    • CURRENT DEGREE REQUIREMENTS
    Additional Information

    The set of core and elective courses chosen by a student must be approved by the student's research advisor before advancement to candidacy. Faculty associated with each research area will provide suggested curricula for that area to guide students in their selection of courses. These curricula will also help Ph.D. students to prepare for their candidacy examination (see below) which must be taken in a specific research area.

    Similarly, students may not take and count undergraduate courses in areas where they have already moved into graduate-level coursework. (The purpose of these undergraduate elective options are to help students to remedy undergraduate background deficiencies).

    Ph.D. Candidacy Exam

    The objective of the candidacy exam is to demonstrate in-depth knowledge of an area of computer science and readiness to carry out independent research at the doctoral level in that area. The student must complete all course requirements and the research project prior to advancing to candidacy.

    All requirements for candidacy including the candidacy exam must be completed by the end of the third year (or, for students with a previous MS in computer science or a related field, by the end of the second year). If the student does not pass on the first trial, they will be allowed until the end of the first quarter of the next year to advance to candidacy.

    The candidacy committee will consist of five faculty members, the majority of whom must be members of the student’s program, to administer the exam according to UCI Senate Policy. Please see the ICS Graduate Office for policies regarding the advancement committee membership.

    The student takes an oral exam, administered by the five-member committee, during which s/he is tested on knowledge relevant to the chosen area of specialization. Each area is defined by a set of topics and reading list, which are available in the documents below:

    • Artificial Intelligence and Machine Learning Syllabus (PDF)
    • Computer Architecture and Embedded Systems Syllabus (PDF)
    • Computer Graphics and Visualization Syllabus (PDF)
    • Computational Neuroscience (PDF)
    • Computer Networks Syllabus (PDF)
    • Cryptograph and Computer Security Syllabus (PDF)
    • Data Management (PDF)
    • Informatics in Biology and Medicine (PDF)
    • Parallel and Distributed Systems Syllabus (PDF)
    • Scientific Computing Syllabus (PDF)
    • Systems Software Syllabus (PDF)
    • Theory Syllabus (PDF)
    The field of computer science is concerned with the design, analysis and implementation of computer systems as well as the use of computation as it is applied to virtually every field of study and use in the everyday world.

    Computer systems can range in scope from tiny embedded systems to the internet as a whole. Research in computer science involves mathematical analysis, empirical experimentation and the implementation of proto-type systems.

    Core research areas include artificial intelligence and machine learning, bio-informatics, computer architecture, embedded systems, graphics and visual computing, databases and information management, multimedia, networked and distributed systems, programming languages and compilers, security and cryptography, design and analysis of algorithms, scientific computing, and ubiquitous computing.

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    Bren school home > Graduate > Degrees
    Information and Computer Science Degree

    The UCI General Catalogue is the official guide to all degree and graduation requirements; the information below is intended for general planning purposes only. For previous calendar years, please see here.


    Concentration in Informatics-General Track (INF-GEN), (M.S. and Ph.D.)

    In addition to the Informatics-General concentration, there are also two other Informatics concentrations listed below. Click on a track to learn more about it.

    • Interactive & Collaborative Technology
    • Ubiquitous Computing

    The general track of the Informatics concentration covers aspects from both the ICT track and the to broadly cover the topics of Informatics.

    • CURRENT DEGREE REQUIREMENTS
    Informatics is the interdisciplinary study of the design, application, use, and impact of information technology. It goes beyond technical design to focus on the relationship between information system design and use in real-world settings.

    These investigations lead to new forms of system architecture, new approaches to system design and development, new means of information system implementation and deployment, and new models of interaction between technology and social, cultural, and organizational settings.

    In the Bren School, Informatics is concerned with software architecture, software development, design and analysis, programming languages, ubiquitous computing, information retrieval and management, human-computer interaction, computer-supported cooperative work, and other topics that lie at the relationship between information technology design and use in social and organizational settings.

    Effective design requires an ability to analyze things from many different perspectives, including computer science, information science, organizational science, social science, and cognitive science.

    Relevant courses in those disciplines are therefore an integral part of the program and give this concentration a unique interdisciplinary flavor, which is imperative as the computing and information technology fields play such a pervasive role in our daily lives.

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    Bren school home > Graduate > Degrees
    Graduate Degrees and Concentrations

    The UCI General Catalogue is the official guide to all degree and graduation requirements; the information below is intended for general planning purposes only. For previous calendar years, please click here.


    ICS Concentration in Embedded Systems (M.S. only)

    The goal of this program is to prepare students for challenges in developing future embedded systems. These future systems will further integrate communications, multimedia, and advanced processors with complex embedded and real-time software for automotive, medical, telecommunications, and many other application domains.

    The goal of this program is to prepare students for challenges in developing future embedded systems. These future systems will further integrate communications, multimedia, and advanced processors with complex embedded and real-time software for automotive, medical, telecommunications, and many other application domains.

    Furthermore, embedded systems are becoming parallel, deploying multiprocessor systems-on-a-chip and parallel application software. An in-depth knowledge of the underlying scientific and engineering principles is required to understand these advances and to productively contribute to development of such systems.

    This program helps students master embedded system fundamentals, advanced computer architecture and compilers, networking, security, embedded, parallel and distributed software, and computer graphics in a sequence of courses and labs. Students also complete a large embedded systems project and may choose to write a Master's thesis.

    • CURRENT DEGREE REQUIREMENTS
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    Bren school home > Graduate > policies
    Graduate Student Policies: Teaching Requirement

    All Ph.D. students are required to participate in teaching activities for two quarters.

    Summer teaching or teaching at another university may be accepted in fulfillment of this requirement.

    Students who fulfill this requirement with a teaching assistantship at UCI are required to take the following:

    • an orientation course, ICS 398A
    • enroll in four units of ICS 399 each quarter they serve as a TA
    • attend the school's TA training seminar during Welcome Week of fall quarter

    For each quarter assigned as a TA/Reader, the student will receive four units of academic credit by signing up for ICS 399. Note: ICS 399 does not count toward degree requirements

     

     

     

     

     

     

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    Graduate Student Policies: CPT and OPT

    Curricular Practical Training (CPT)

    International students may be eligible for CPT, a type of employment authorization that allows students to complete an internship/training off campus during the summer. CPT is not available after a student completes a degree program.

    Consult the International Center for CPT policies and application requirements. Students will need the approval of their advisor on the CPT application.

     

     

    Optional Practical Training (OPT)

    International students may be eligible for OPT after graduation. OPT provides the opportunity to gain employment experience in the student’s major/field of study.

    Consult the International Center for OPT policies and application requirements. The ICS Graduate Counselors can verify your graduation date and sign the Graduation Confirmation/OPT Recommendation Form that is required as part of the OPT application package.

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    Graduate Student Policies: Transfers of Academic Credit

    Previously taken graduate-level courses may count toward ICS degree requirements if they meet the following criteria:

    • Must have been passed with a grade of B or better.
    • Must not have been applied toward a bachelor’s degree or M.S. degree from another university.
    • Cannot have been taken while the student was on a leave of absence from the ICS program.
    • Must be approved by the ICS Associate Dean for Student Affairs and the Dean of Graduate Division

    In addition, no more than one-fifth of the total units required for a Bren School M.S. or Ph.D. degree may have been transferred from another institution, from UCI Extension, or from Summer Sessions at any other UC campus. Note that students may only waive two courses of their Bren School degree requirements.

    To obtain a petition to transfer course work from another university, contact the ICS Graduate Counselor. Keep in mind that, for courses taken at another university, students should petition to WAIVE the course. If the course in question is a UCI course, they should petition to SUBSTITUTE the course.

    Waive: If a course taken at another university is identical to a Bren School course, contact the professor who usually teaches the course. They can determine whether or not the course is equivalent. If it is, they can sign the petition form, which must also be approved by the Associate Dean for Student Affairs. Students must provide a syllabus of the course and their transcript from the other university along with their petition form.

    Substitute: If the course to be substituted is a UCI course, students should speak to the professor who teaches the course, or to their faculty advisor. If they agree with the substitution, they can sign the petition form.

    Once the form is completed and signed by the professor or advisor, it should be returned to the ICS Graduate Office. It will be forwarded to Graduate Division for consideration, and once a decision is made, the ICS Graduate Office will notify the student via email.

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    Bren school home > Graduate > policies
    Graduate Student Policies: Committee Membership

    Complete policy and procedural guidelines regarding advancement and doctoral committee membership can be found on the instructions attached to the Ph.D Advancement to Candidacy form.


    The Advancement Committee

    The advancement committee is comprised of five members: one chair, three general members and one outside member.

    Chair: must hold either a primary or joint appointment in the student’s department, and must be a voting member of the UC Academic Senate. Senate faculty have the following titles: Assistant Professor, Associate Professor, Professor, and Lecturer/Senior Lecturer with Security of Employment (SOE).

    General Member: at least two of the general members must hold a primary or joint appointment in the student’s department. Non-voting Senate members or faculty holding professorial titles at other Universities will be considered on an exception-only basis.

    Outside Member: represents the faculty at large and must be from the Irvine Division and may not hold either a primary or joint appointment in the student’s department.

    For students whose committee chair or advisor may have a financial interest in their work, an Oversight Member is also required.

    The Doctoral Committee

    The three members of the dissertation committee is selected during the advancement phase. It is comprised of three voting members of the University of California Academic Senate (not necessarily the Irvine Division) or equivalent. A majority of the committee must be affiliated with the student’s program.

    The Chair of the Doctoral Committee is the faculty responsible for providing primary guidance for the student’s dissertation. He or she must hold either a primary or joint appointment in the student’s department, and must be a voting member of the UC Academic Senate.

    For students whose committee chair or advisor may have a financial interest in their work, an Oversight Member is also required in addition to the two general members.
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    Bren school home > Graduate > policies
    Graduate student policies

    » Academic Honesty

    » Advancement to Candidacy (M.S.)

    » Advancement to Candidacy (Ph.D.)

    » California Residency

    » Candidacy Committee's Duties and Responsibilities

    » Candidacy Committee Membership

    » Comprehensive Exam/Phase II

    » Computer and Network Use

    » Copyright Infringement

    » Curricular Practical Training (CPT)

    » Ethical Use of Computing Resources

    » Defense, Final

    » Defense, Topic

    » Filing Fee

    » Grading Standards

    » Graduate Student Review

    » In-Absentia Registration

    » Leave of Absence

    » M.S. Thesis Option

    » Optional Practical Training (OPT)

    » Part-Time Enrollment

    » Previously Earned M.S. Degree

    » Residency Requirement

    » Summer Enrollment

    » Teaching Requirement

    » Transfer of Academic Credit

    » UC Policy on Sexual Harassment

    * Other policies important for students to know include the Non-Discrimination Policy Statements, Americans with Disabilities Act, and Jeanne Clery Act. It is recommended that students be familiar with the rules and regulations that govern students at UCI as outlined in the UCI General Catalogue.

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    Bren school home > Graduate > policies
    Graduate Student Policies: Candidacy Committees

    Candidacy Committee Duties and Responsibilities

    The candidacy committee is charged with determining the fitness of the student to proceed with the doctoral dissertation through a formal candidacy examination. This examination should evaluate both general preparedness in the discipline, and specific competence to pursue the proposed dissertation topic.

    In its deliberation, the committee ordinarily will review the student's academic record, preliminary examinations, evaluations by other faculty, and may conduct any other examination it thinks appropriate. The candidacy committee will also review an outline of the proposed dissertation project, and will determine by oral examination the student's competence in that area. When, by unanimous vote, the committee decides the student is qualified for the dissertation phase, it should recommend advancement to candidacy to the Graduate council via the Graduate Dean.

    Following its formal appointment, the committee is free to adopt whatever procedures it thinks appropriate to conduct the qualifying examination for candidacy, subject to the rules of the program and those specified below. Administration of the qualifying examination must conform to the policies established by the Graduate Council.

    • The student must be given adequate notice of the content, form and time of the examination. The committee must meet to decide upon the procedures to be followed, and the student must be given an opportunity to comment upon the selected procedures.
    • Before voting upon its recommendation for or against candidacy, the committee as a whole shall meet with the student, and any member of the committee will have the right to pose appropriate questions to the student.
    • If it decides to do so, the committee may conduct part of the examination on an individual basis, e.g. the student may meet with each in turn. However, the committee must conclude its examination when convened with the student present.
    • Although the formal candidacy examination ordinarily is conducted in a single day, the committee may meet intermittently over a longer period, and may decide to reexamine the student on one or more topics after a specified interval.
    • When the committee meets to conduct the oral candidacy examination, it must report to the Graduate Council via the Graduate Dean within 30 days.
    • If the committee decides to reexamine the student at a later date or does not pass the student for any reason, this must be reported. The final vote and recommendation of the committee must be unanimous and unequivocal.

    Upon completion of the candidacy examination, the results should be submitted to the UCI Graduate Division on the Report of the Ph.D. Candidacy Committee, Ph.D. Form I. This form must be signed by all committee members at the time the candidacy examination is concluded, and submitted even if the examination was failed.

    If the unanimous recommendation of the committee is favorable, the $90 advancement to candidacy fee must be paid by the student to the campus Cashier's Office. This will validate the Ph.D. Form I, after which it should be submitted to the UCI Graduate Division. The signed and validated Ph.D. Form I serves as the application for advancement to candidacy.

    The candidate and graduate counselor will be notified of formal advancement and the appointment of a doctoral committee.

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    Bren school home > Graduate > policies
    Graduate Student Policies: Advancing to Candidacy

    Advancing to Candidacy for the M.S. Degree

    Advancement to candidacy implies that a student is almost finished with his/her degree requirements. This is not an automatic process. The students is responsible for making sure his or her form is completed and gets filed. The Advancement to Candidacy form should be submitted at least one quarter before the student plans on graduating.

     

    Advancing to Candidacy for the Ph.D. degree

    Prior to advance to candidacy for the Ph.D. degree, students must complete the following steps:

    1. Complete coursework
    2. Pass the Phase II exam at the Ph.D. level (if applicable to the student's program)
    3. Complete a research or survey paper of publishable quality (if applicable to the student's program)

    The advancement exam is an oral presentation to a five-member committee of senate faculty. Note that you may combine your Ph.D. advancement with your topic defense with the consent of your advisor. The committee must be composed of the Committee Chair, three general members, and one outside member. The Chair of the candidacy commitee must hold either a primary or joint appointment in the student's department, and the majority of the committee must be from the student's department. The outside member must be a UC Irvine faculty member and may not be affiliated with the student's department.

    Each committee member must agree on the research you would like to complete for your doctoral dissertation.

    » Preparing for the Advancement Exam

    • Arrange a date when all involved parties can meet
    • Reserve a conference room with your department
    • The Ph.D. Form I can be downloaded from Graduate Division here

    » After the Advancement Exam is complete

    • Get appropriate signatures on the Ph.D. Form I (including your own!)
    • The ICS Graduate Office can provide the Department Chair and Associate Dean signatures.
    • Pay $90 advancement fee at the Cashier's office
    • Turn form into Graduate Division (120 Aldrich Hall)

     

     


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    Bren school home > Graduate > policies
    Graduate Student Policies: Comprehensive Exam

    Written Comprehensive/Phase II Exam

    M.S. students who elect for the Comprehensive Exam option must pass a comprehensive examination offered by the department or by a representative of the specific research area. Students should speak to the ICS Graduate Counselor or research area representative regarding test dates, preparation, and reading lists. The faculty within the particular area develops the exam; this may result in the content and structure varying from year to year.

    Ph.D. students must also pass a written comprehensive examination offered by the student’s department (see specific degree requirements for more information as this exam may vary by degree/concentration). Students should speak to their advisor, research area representative or the ICS graduate counselors regarding test dates, preparation, and reading lists. Faculty from the particular research area develop the exam, so the content and structure may vary from year to year.

    The exam is graded on three levels: M.S. Pass, Ph.D. Pass, and Fail.

    Students may re-take the exam once. Ph.D. students who receive an M.S. Pass can qualify for the M.S. degree, but must re-take the exam and pass at the Ph.D. level to continue in the Ph.D. program.

    To take the exam for a third time, students must petition both the ICS Associate Dean for Student Affairs and the Dean of Graduate Division for permission. If the petition is denied, the student may be dismissed from the program.

    Once the exam is passed at the desired level, please make sure the appropriate paperwork is filed with the Bren School graduate counselor.
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    Bren school home > Graduate > policies
    Graduate Student Policies: Enrollment Status

    Filing Fee

    Graduate students are eligible for one filing fee quarter during their graduate career. During the filing fee quarter, students pay one-half the registration fee and do not enroll in any classes.

    The Filing Fee option applies only to students who have completed ALL requirements for a M.S. or Ph.D. degree except for official submission of a thesis or dissertation to the University Archives, or completion of the final formal examination (e.g., the comprehensive examination).This means that prior to the beginning of the "filing fee" quarter ALL other requirements for the degree must have been met by the student, including advancement to candidacy and the resolution of any NR or I grades. Consult the ICS Graduate Counselors for other policies and limitations related to the filing fee.

    International visa restrictions may preclude filing fee status for students who are not citizens or permanent residents of the United States. International students should verify their eligibility for filing fee status via the International Center well in advance of need.

    Students should note that there is a separate form when submitting a summer filing fee petition. Both the Filing Fee Petition and the Summer Filing Fee Petition can be found on the Graduate Division website here.

    In-Absentia Registration

    In absentia registration applies to full-time, regularly enrolled and registered students who have an academic need to conduct coursework or research related to their degree program outside of California.

    More information can be found on the In-Absentia Registration Form on the Graduate Division website here.

    Leave of Absence

    All graduate students can petition for a leave of absence from the program for a period of up to, but no more than, three quarters total.

    The LOA petition is available for download from the Graduate Division website here. Students should fill out the form well in advance to make sure it gets approved. The campus deadline is Friday of the third week of classes. However, ICS recommends that students submit the Leave of Absence petition by the registration deadline each quarter.

    The leave must be approved by both the ICS Graduate Office and the Dean of Graduate Division, as well as the International Center for all international students. The Graduate Division will notify students via email once the leave has been approved or denied.

    Part-Time Enrollment

    Unless enrolled in an approved part-time master's degree program (as described in UCI’s General Catalogue), approval of part-time enrollment status may be granted only for reasons of occupation, family responsibilities or health. Eligibility for part-time study also depends on other factors, such as citizenship or residency, and fellowships. ICS Ph.D. are typically not eligible to apply.

    The full guidelines to determine part-time eligibility, as well as instructions on how to submit the request, can be found on the Reduced Fee Part-Time Study Program petition, which can be downloaded from the Graduate Division website here.

    The campus deadline to submit a Part-Time Study petition is noon on Wednesday of the third week of classes. However, ICS recommends that students submit the Part-Time petition by the registration deadline each quarter.

    Summer Enrollment

    Continuing graduate students generally do not need to enroll in Summer Session (international students on CPT may be required to enroll). However, students who will graduate during summer quarter MUST either be enrolled or on Filing Fee (if eligible). During the summer, graduating students will generally enroll in the minimum allowable units (two) of Individual Study or Thesis Supervision with their advisor. Graduating students who do not have an advisor should consult the ICS Graduate Counselors for assistance with summer enrollment.

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    Graduate Student Policies: M.S. Thesis Option

    Master’s Thesis Option

    The thesis option is available to M.S. students who do not plan to take the comprehensive exam and who are in good academic standing with the School (note this option not available to students in the Statistics program). Students pursuing this option must enroll in at least two quarters of Thesis Supervision (CS 298 or Inf 298), which will substitute for two required courses as specified under the concentration area or track of choice.

    A committee of three faculty members, the majority of whom must be from the student’s department, will guide the student and give final approval of the thesis. The committee consists of an advisor, who must be from the student’s department and who is willing to supervise the thesis project, and two other faculty members who are willing to serve on the committee as readers. An oral presentation of the thesis to the committee is required.M.S. Thesis Option Policy

    A thesis option is available to M.S. students who do not plan to take the comprehensive exam and who are in good academic standing with the School. Students pursuing this option must enroll in at least two quarters of Thesis Supervision (CS 298 or INF 298) which will substitute for two required courses as specified under the concentration area or specialization of choice (not available to students in the Statistics program).

    A committee of three faculty members will guide the student and give final approval of the thesis. The committee consists of an advisor (from the student's department/program), who is willing to supervise the thesis project, and two other faculty members (one of whom must be from the student's department/program), who are willing to serve on the committee as readers. An oral presentation of the thesis to the committee is required.

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    Graduate Student Policies: Topic and Final Defense

    Topic Defense

    Once a dissertation topic is determined, it must presented it in a defense to your doctoral committee. Each area differs as to when students present this defense, so please confirm with your advisor as to when this requirement should be completed. Note that you may combine your Ph.D. advancement with your topic defense with the consent of your advisor.

    A “Topic Defense ” form must be brought to the committee at the time of the defense, to be signed after they have approved your defense. These forms are available for download from the ICS Graduate Student Affairs website page for Forms. The signed form should be returned to the ICS Graduate Office.

     

    Final Defense

    The Ph.D. final defense committee is a three member committee of senate faculty drawn from your advancement committee. The majority of the committee MUST be from your department. Consult with the ICS graduate counselors if changes to the composition of your defense committee occur.

    For the final defense of your dissertation, e-mail the ICS Graduate Office with the date, time and location of the defense, the names of your committee members, and the title and abstract of your dissertation. This information will be made available to all Bren School faculty and graduate students. In addition to your committee members, anyone else who wishes to attend your defense may do so.

    Also, be sure to obtain a "Ph.D. Form II: Report on Final Examination for the Ph.D. Degree" from Graduate Division, available for download here. At the end of the defense, have each of your committee members sign it.

    When you have finished your dissertation, submit it and the Ph.D. Form II to the UCI Library Archives (Langson Library, Room 525). Alternatively, the dissertation may be submitted electronically. Guidelines for thesis submission can be found on the UCI Library website here. A Thesis Submission Checklist can also be downloaded from the Graduate Division website here.

    The Library will notify the UCI Graduate Division that your dissertation has been turned in and your degree is ready for conferral. Graduate Division will notify you once your degree has been conferred.


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    Graduate Student Policies: Residency Requirements

    Residency Requirement

    A minimum of six quarters for Ph.D. students or three quarters for M.S. students in academic residence is required prior to the award of the degree. Typically, a longer period of study (two to six years) is required for completion of all degree requirements.

    After a Ph.D. student has been registered for over 21 quarters (seven years), and a M.S. student has been registered for over six quarters (two years), they are considered to be beyond normative time for completing the degree.

    A letter regarding this status is sent to the student and to the department requesting a deadline be implemented for the student to complete all requirements and submit their dissertation. If this deadline is missed the student is subject to academic disqualification.

     

     

     

     

     

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    Bren school home > Graduate > policies
    Graduate Student Policies: Previously Earned M.S. Degree

    Bren School Ph.D. students with an M.S. degree in computer science or a related field from another university may qualify to receive credit for some or all required courses.

    Ph.D. students waived from the M.S. portion of their degree may not, in future, request that they be granted an M.S. degree in Computer Science or a closely related field from UC Irvine.

    Be aware that advisors can require that students take additional courses when appropriate.

    Course equivalency will be determined by the ICS Associate Dean for Student Affairs following a written recommendation from the student’s advisor. Advisors may require that students take additional courses when appropriate. Final approval will be determined by the Dean of Graduate Division. Students will be notified of the decision by email.

     

     

     

     

     

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    Graduate Student Policies: UC Policy on Sexual Harassment
    Effective December 14, 2004, the Office of the President issued a revised University of California Policy on Sexual Harassment and associated Procedures for Responding to Reports of Sexual Harassment.

    These policies cover all members of the University community, including faculty and other academic personnel, staff employees, students, and non-student or non-employee participants in University programs.

    Revisions to the policy include an updated definition of sexual harassment, clarification of the University's obligation to respond promptly and effectively to reports of sexual harassment, provisions for training employees and educating the University community regarding sexual harassment, and a statement that the policy shall be implemented in a manner that recognizes principles of free speech and academic freedom.

    The Office of Equal Opportunity and Diversity has developed local policy and guidelines that implement the system wide procedures and address consensual relationships.

    These supersede the previous UCI Policy on Sexual Harassment:

    Section 700-17: Guidelines for Sexual Harassment Complaint Resolution

    Section 700-16: Policy on Conflicts of Interest Created by Consensual Relationships

    The University of California Policy on Sexual Harassment can be found here.

    All documents can be accessed through the Sexual Harassment Officer website at www.sho.uci.edu

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    Bren school home > Graduate > policies
    Graduate Student Policies: Copyright Infringement

    Bren School Copyright Infringement Policy

    It is illegal to distribute copyrighted materials—such as software, movies, music, pictures—without proper authorization.

    If a complaint of copyright infringement is reported involving a computer assigned to a Bren School student, the student's account will be locked out until Bren School support personnel have a chance to investigate the allegations. If copyrighted materials are found, they will be removed immediately.

    For the initial complaint (first violation), the Bren School Student Affairs Office (SAO) staff and the appropriate Associate Dean (as well as the academic advisor, for grad students) will be notified.

    The student will be required to meet in person with appropriate SAO staff to review applicable campus policies, and to sign a statement verifying receipt and notice of such policies. This signed statement will be placed in the student's file.

    For any subsequent complaints, the appropriate Associate Dean, academic advisor (for grad students) and SAO staff will be notified.

    The student will then be referred to the campus Director of Student Judicial Affairs for academic suspension (upon second violation) or expulsion from the university(upon third violation).

    The above statement is based upon:
    IRVINE CAMPUS POLICIES , APPENDIX K: COMPUTER USE POLICY
    Computer Use Policy (Reference to Section 102.05), which includes "Violating the terms of applicable software licensing agreements or copyright laws."

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    Bren school home > Graduate > policies
    Graduate Student Policies: Graduate Student Review

    The ICS Graduate Student Affairs Office, in consultation with the department Vice Chairs and the Associate Dean, conducts an annual, formal review of all graduate students enrolled in an ICS graduate program. The Department of Informatics conducts a review in Fall and Spring quarters. The Bren School Graduate Office, Associate Dean for Student Affairs, and the student’s advisor review each student’s progress.

    In order to maintain satisfactory academic progress, all graduate students must:

    • retain a cumulative grade point average of 3.0 or higher
    • pass all required and elective courses with a grade of B or better
    • not have more than two (2) “I” grades (Incompletes) on your transcript
    • pass all oral exams in a timely manner
    • meet published timelines for completion of the degree
    • meet deadlines and requirements imposed on them as individual students

    Ph.D. students, in addition to the above, must also:

    • affiliate with a primary research advisor by the end of the first year and remain affiliated with an advisor throughout your tenure as a student (Ph.D. students onl
    • meet the requirements to qualify as a teaching assistant, including demonstrating English proficiency, by the specific date mentioned in your funding letter
    • make adequate progress in research and/or creative work as determined by the department faculty
    • not have two consecutive cautionary letters resulting from formal departmental reviews
    If unsatisfactory progress is being made, a letter of probation is sent to the student and placed in their file. The probation period varies, depending on the individual’s situation. If the problem is not cleared during the probation period, the Bren School Graduate Office may recommend to the UCI Graduate Division that the student be dismissed from the program.

    It is the student’s responsibility to make sure the Bren School Graduate Office is aware that a problem has been cleared during the probation period.

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    Bren school home > Graduate > policies
    Graduate Student Policies: Residency Requirements

    California Residency

    To establish California residency for tuition/fee purposes students must:

    1. File a Petition for Residence Classification with the Registrar’s Office
    2. Be a U.S. citizen or permanent resident
    3. Be physically present in California for more than one calendar year
    4. Have come to California with the intent to make California their permanent home
    5. Show that they intend California to become their home

    Examples of (#5) include:

    • Registration as a voter in California
    • Designation of California as their permanent residence on all University documents
    • Obtaining a California Driver’s License or ID Card
    • Registration of their car with the State of California
    • Payment of California income taxes as a resident
    • Maintenance of a home in California

    More information about residence classification can be found on the Registrar's website here.

     

     

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    http://www.ics.uci.edu/grad/policies/GradPolicies_Grading.php graduate student policies @ the bren school of information and computer sciences
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    Bren school home > Graduate > policies
    Graduate Student Policies: Grading Standards

    Grades are available from the Registrar’s Office (fees apply for copies of official transcripts; unofficial transcripts are free of charge) or through Student Access. It is the student’s responsibility to check grades at the end of each quarter.

    Traditional grades (A, B, C, etc…)

    Students are expected to maintain a B average (this is an absolute requirement if on a fellowship—GSR, TA, GAANN, etc.). In order to receive graduation credit, students must earn a grade of B or better in all required courses, core courses and breadth courses. A grade of B- or below does not have to be improved and can remain on the transcript if that grade is not going to be counted toward graduation requirements.

    Pass/No Pass

    P/NP is for undergraduate students only. Graduate students must receive a letter grade to pass a course. Graduate students should not sign up for a course P/NP.

    Satisfactory/Unsatisfactory

    S/U is for graduate courses only and is considered to be a letter grade. Faculty can assign an S/U instead of an A, B, C, etc. Students are responsible for asking the instructor if they can receive the S/U option instead of an A, B, C, etc. at the beginning of the quarter. Please keep in mind that the S grade is equal to a B or better and the U grade is equal to a B- or below. A student who receives a U grade will have to repeat the course if it is going to count towards degree requirements.

    Incomplete

    The “I” grade is reserved for occasions when a student’s work is satisfactory but is incomplete because of circumstances beyond the student’s control, and when the student has been excused in advance from completing the quarter’s work. Students may ask the instructor to assign an “I,” but it may or may not be granted. If granted, a student has up to three quarters to complete the work. If the work is not completed and a grade has not been assigned after three quarters, the “I” will turn to an F. Once the work has been completed, the instructor must turn in a grade change report to change the “I” into a letter grade. The student should check Student Access often to confirm that the grade has been changed.

    Graduate Division will not continue to approve employment for a student who has more than two Incompletes on their transcript.

    No Report

    NR means no grade was reported. This can be the result of a variety of reasons:

    • The faculty member did not turn the grades in on time
    • The faculty member does not recognize the student’s name on the class roster
    • The faculty member turned in the wrong grade
    • The course’s grade roster was unreadable

    An NR will turn into an F after one quarter. Students who receive an NR should talk to their instructor immediately. The professor will need to complete a grade change report.

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    http://www.ics.uci.edu/ugrad/policies/Readmission_DQ.php readmission after disqualification @ the bren school of information and computer sciences
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    Undergraduate Student Policies

    Readmission after Disqualification

    Readmission is possible for students who have been academically disqualified! It is highly recommended that a student meet with an academic counselor to prepare for readmission application and requirements.  Student should visit the ICS acadmic counselor as soon as he/she has been notified of formal disqualification.

    In general, a student will need to demonstrate that he/she can finish remaining coursework within the scope of university policies regarding credit hour unit limits.

    A student readmitted after academic disqualification will be held to the conditions of a two quarter contract specifying a course plan, unit maximum per quarter, GPA expectations, and mandatory advising sessions. Failure to meet the contract conditions for any of two quarters may lead to automatic disqualification.

    The UCI readmission application must be accompanied by:

    1. A written statement no longer than 2 pages that details the following:

    • reason(s) for leaving,
    • reason(s) for returning (including new or renewed interest/focus on Bren School major),
    • ways that previous or existing obstacles to academic progress have been addressed and will be addressed in the future.

    2. An official transcript showing completion of a minimum of one full-time academic year of courses taken through UCI Extension (ACCESS), a community college or another comparable institution.  Please note that the full time course load for the quarters / semesters must be taken consecutively; breaks in academic study between a quarter or semester are not allowed.

    • At least six of these courses must meet major requirements (includes math courses).
    • Must earn Cs or higher in each course.
    • Minimum of 2.5 cumulative GPA for this year of work.
    • If there is considerable evidence that a student is fully prepared to return to UCI full time, the requirement that a full year of coursework be completed elsewhere may be waived.  Student should speak to an academic counselor for more information.

    3. If the student intends to change into or out of the Bren School upon readmission, the submission of a Change of Major application is also required.

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    Undergraduate Student Policies

    Readmission and Academic Probation

    The Bren School may approve a student's time-limited withdrawal if it becomes evident that doing so will prevent further damage to the student's academic record. Conditions for readmission in this case are discussed at the time of withdrawal; any academic contract conditions in place at the time of withdrawal remain in effect upon the student's return.

    The UCI readmission application must be accompanied by:

    1. A written statement no longer than 2 pages that details the following:

    • reason(s) for leaving,
    • reason(s) for returning (including new or renewed interest/focus on Bren School major),
    • ways that previous or existing obstacles to academic progress have been addressed and will be addressed in the future.

    2. An official transcript showing completion of at least 1 full-time quarter or semester of coursework in Computer Science or math with a GPA satisfying the conditions specified in the student's academic contract. At least 1 course must be a transferable Computer Science course.

     

     

     

     

     

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    Bren school home > Graduate > Degrees
    ICS Graduate Programs

    Students and faculty collaboratingAs the only computing-focused school in the University of California system, ICS offers an array of graduate degree programs in virtually every principal area within its three departments — Computer Science, Informatics and Statistics — as well as many interdisciplinary topics. Click on a related link below to learn more about a specifc degree program. You can also visit the UCI Course Catalog for more in-depth information about each ICS graduate program.

    » Computer Science — Ph.D.  l  M.S.

    » Informatics — Ph.D.  l  M.S.

    » Networked Systems — Ph.D.  l  M.S.

    » Software Engineering — Ph.D.  l  M.S.

    » Statistics — Ph.D.  l  M.S.

    » Information & Computer Science
    (Informatics Concentration) — 
    M.S.

    » Information & Computer Science
    (Embedded Systems Concentration)  — 
    M.S.


    Apply Now

    For information on how to apply to one or multiple graduate programs, click here. 


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    http://www.ics.uci.edu/grad/courses/ graduate course listing @ the bren school of information and computer sciences

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    Bren school home > Graduate > Courses
    Graduate course listing

    This is a tentative schedule of CompSci, Informatics, Networked Systems and Statistics courses that the Bren School is planning to offer.

    Please note that this proposed course schedule, while NOT guaranteed, is intended to help with your general academic planning. You are encouraged to consider back-up options that align with your intended date of graduation, since course offerings and faculty assignments may change.

    NOTE: The course listings shown here are neither guaranteed, nor considered “final”. Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.


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    http://www.ics.uci.edu/ugrad/degrees/degree_se.php undergraduate degree in software engineering @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Undergraduate degree in Software Engineering

    Do you like to work in teams to develop cool new software? Are you curious to learn how the architectures of Twitter, Snapchat or Google are designed? Do you care about the quality of the software you develop? Are you not afraid of talking to people in order to learn what their needs are so you can develop better software for them? Are you a builder who might foresee having your own software company at some point?

    If you answered yes to one or more of these questions, UC Irvine’s software engineering major just might be the choice for you.

    What will I learn?

    The B.S. in software engineering is designed around a set of core courses that introduce the fundamentals of software engineering (requirements analysis, design and testing), programming (data structures, libraries and languages), and relevant computer science concepts (algorithms, databases, networking and operating systems). From there, nearly two dozen electives offer students the chance to specialize, focusing anywhere from systems-level engineering to the human side of developing software.

    Throughout the major, students gain hands-on experience in creating a variety of software systems, giving you the opportunity to use different programming languages, apply your skills to different domains and work in different teams. This culminates in the three-quarter capstone course, in which you will be part of a team that develops a real system for a real client, typically from a company or organization outside the university.

    Overall, the major strongly emphasizes the design and implementation of software systems, as well as learning how to adapt to what are the continuous new circumstances of the profession — whether it is a new client and their habits, a new programming language or technology to be used or a new team and its development practices.

    Careers

    Software engineers are in demand everywhere. Large Internet companies, automotive and aerospace corporations, medical and health software providers, enterprise software vendors and startups are all in need of software engineers to program, design, architect and lead the development of their software projects. Business Insider, glassdoor, CNN (#3, #7, and #8), U.S. News & World Report all highlight the excellent career prospects for software engineers in terms of job satisfaction, job prospects and salary.

    Of course, graduate school in software engineering, computer science, informatics, or related field is a career path that a portion of our students also choose to take after they complete the major.

    Qualifications

    We welcome students with a variety of backgrounds. We have had many students join who already knew how to program, and many students who did not yet know how to program. Both groups are successful in the major.

    If you have an interest in solving problems, aspire to creative thinking, and have an affinity with design, software engineering can be for you.

    Why software engineering at UC Irvine?

    • Excellence. You will be part of a world-class group of faculty and staff, who have an outstanding track record of delivering innovative educational experiences in — and beyond — the classroom.
    • Depth. With no fewer than ten courses dedicated to software engineering, and dozens more on topics closely surrounding it, you receive an education that prepares you very well for the many challenges that will arise in your future career.
    • Connections. Our alumni have gone on to study in some of the most prestigious Ph.D. programs, work for well-known, innovative corporations and found successful startups. We stay in touch with them, and can help connect you with for internships that complement your studies.
    • Location. Orange County has a very vibrant and diverse tech industry, and is just a mere hop away from Silicon Valley.

    Detailed requirements

    Please see the catalogue for a detailed description of the requirements of the Software Engineering major.

    More information

    Prospective and current UCI students interested in learning more about the Bren School’s degree options are encouraged to meet with the school’s associate dean of student affairs, counselors and student ambassadors. They will help you determine which of our majors and minors best support your academic strengths and interests. Call our Student Affairs Office at 949-824-5156 to make an appointment or to inquire about campus visit opportunities.

    (Note: Appointments are made by phone only, not by email request.)

    CONTACT:
    Bren School Student Affairs Office
    Information and Computer Science Building I, Suite 352
    Irvine, CA 92697-3430
    949-824-5156
    ucounsel@uci.edu

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    http://www.ics.uci.edu/ugrad/degrees/degree_cgs.php undergraduate degree in computer game science @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Computer Game Science undergraduate degree

    Are you curious how computer games work on the inside? Do you want to learn how you can use computer games for social good? Do you have creative ideas for fun new mobile games? Can you foresee yourself programming intelligent avatars for a major title? Have thoughts about the visual appearance of new virtual worlds?

    If you answered yes to one or more of these questions, UC Irvine’s computer game science major just might be the choice for you.

    What will I learn?

    The B.S. in computer game science is designed around a set of core courses that introduce the fundamentals of computer science (programming, data structures, graphics and artificial intelligence), math (statistics, linear algebra and logic), and games (games and society, game design, game engines and multiplayer games). From there, nearly thirty electives offer students the chance to specialize, focusing anywhere from typical game topics such as modeling, world building and mobile games to more peripheral topics such as software design and social impacts.

    Throughout the major, students gain hands-on experience in creating a variety of digital games, for entertainment purposes, but also for education, training and engendering social change. Working in teams, you will employ a variety of different programming languages, game platforms and hardware. This culminates in the two-quarter capstone course, in which you will be part of a team that designs and implements a new game from scratch under the supervision of game designers from the local industry.

    Overall, the major strongly emphasizes the technical aspects of creating games, as well as working in teams to design and implement them. You will be prepared to adapt to what are the always-changing circumstances of the profession — whether it is a new game platform, newly emerging game mechanics, or new ways of earning revenue.

    Careers

    Because of the strong technical underpinnings of the degree program, demand for our computer game science majors is strong. The majority find employment in the industry, whether at a major publisher, smaller studio or as self-employed freelancers. Many squarely focus on entertainment, others succeed in bringing their skills to the design and development of serious games in a variety of domains, including healthcare and education.

    Of course, graduate school in game design, interactive media, computer science, informatics or related field is a career path that a portion of our students also choose to take after they complete the major.

    Qualifications

    We welcome students with a variety of backgrounds. We have had students join who were avid gamers and already knew how to program, and students who were merely curious about games and did not yet know how to program. Both groups are successful in the major.

    If you have an interest in solving problems, aspire to creative thinking, and have an affinity with design, computer game science can be for you.

    Why computer game science at UC Irvine?

    • Excellence. You will be part of a world-class group of faculty and staff, who have an outstanding track record of delivering innovative educational experiences in — and beyond — the classroom.
    • Depth. With no fewer than ten courses dedicated to games, and dozens more on topics closely surrounding it, you receive an education that prepares you very well for the many challenges that will arise in your future career.
    • Connections. Our alumni have gone on to study in some of the most prestigious Ph.D. programs, work for well-known, innovative corporations and found successful startups. We stay in touch with them, and can help connect you with for internships that complement your studies.
    • Location. Orange County has a very vibrant and diverse tech industry, and is just a mere hop away from Silicon Valley.

    Detailed requirements

    Please see the catalogue for a detailed description of the requirements of the computer game science major.

    More information

    Prospective and current UCI students interested in learning more about the Bren School’s degree options are encouraged to meet with the school’s associate dean of student affairs, counselors and student ambassadors. They will help you determine which of our majors and minors best support your academic strengths and interests. Call our Student Affairs Office at 949-824-5156 to make an appointment or to inquire about campus visit opportunities.

    (Note: Appointments are made by phone only, not by email request.)

    CONTACT:
    Bren School Student Affairs Office
    Information and Computer Science Building I, Suite 352
    Irvine, CA 92697-3430
    949-824-5156
    ucounsel@uci.edu

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    http://www.ics.uci.edu/ugrad/degrees/degree_cs.php undergraduate degree in computer science @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Undergraduate degree in Computer Science

    DEGREES AVAILABLE: Major

    photo:: computer science student

    Computer science is the catalyst for every evolutionary – and revolutionary – step in computer development.

    From mathematical theories, data structures and algorithms to the operating systems and programs that employ them, an understanding of computer science is essential if you wish to develop the next advances in computer technology and applications.

    The Computer Science program at UC Irvine is internationally recognized for its unique group of faculty and researchers, outstanding students and cutting edge educational programs.

    › Is Computer Science for me?
    › What do I take?
    › How do I choose a CS Specialization?
    › What can I do with this degree?
    › Still not sure?

    Is Computer Science for me?

    The Computer Science major emphasizes the principles of computing that underlie our modern world, and provides a strong foundational education to prepare students for the broad spectrum of careers in computing. This major can serve as preparation for either graduate study or a career in industry. Students receive a solid background in low-level architecture and systems; middle-level infrastructure, algorithms, and mathematical foundations. This is a highly flexible degree that allows students to explore a broad range of topics in modern computing. In order to achieve some focus in their upper-division studies, students are required to satisfy the requirements for one of the eight specializations described below. Additional electives can be used to satisfy a second specialization or obtain a broader understanding of the field.

     

    Algorithms. This specialization focuses on fundamental computational techniques, including their analysis and applications to topics in computer vision, computer games, graphics, artificial intelligence, and information retrieval. Topics include data structures, graph and network algorithms, computational geometry, probabilistic algorithms, complexity theory, and cryptography.

    Architecture and Embedded Systems. This specialization integrates principles of embedded systems, software, hardware, computer architecture, distributed systems and networks, and prepares students to design and create efficient hardware/software architectures for emerging application areas. Students in this specialization will build upon a strong foundation in software and hardware and learn how to design networked embedded systems, and efficient computer architectures for a diverse set of application domains such as gaming, visualization, search, databases, transaction processing, data mining, and high-performance and scientific computing.

    Bioinformatics. This specialization introduces students to the interdisciplinary intersection of biology and medicine with computer science and information technology. Students who complete the specialization will understand biomedical computing problems from the computer science perspectives, and be able to design and develop software that solves computational problems in biology and medicine.

    General Computer Science. This specialization allows students to acquire a well-rounded knowledge of computer science that may be tailored to their individual interests. Students choose 11 upper-division computer science courses, including two project courses. This specialization will appeal to those who are interested in a broad education in computer science, or who wish to create their own unique specialization not found in the current list of (other) specializations under this major.

    Information. This specialization is intended to prepare students for working with and developing a wide variety of modern data and information systems. Topics covered by this concentration include database management, information retrieval, Web search, data mining, and data-intensive computing.

    Intelligent Systems. This specialization will introduce students to the principles underlying intelligent systems, including topics such as representing human knowledge, building automated reasoning systems, developing intelligent search techniques, and designing algorithms that adapt and learn from data. Students in this specialization will use these principles to solve problems across a variety of applications such as computer vision, information retrieval, data mining, automated recommender systems, bioinformatics, as well as individually designed projects.

    Networked Systems. This specialization focuses on Internet architecture, Internet applications, and network security. It also encourages students to learn about operating systems, databases, search, programming, embedded systems, and performance.

    Systems and Software. This specialization deals with principles and design of systems and software. It emphasizes the interaction between software and the computing infrastructure on which it runs and the performance impact of design decisions. Core topics include the hardware/software interface, languages and compilers, operating systems, parallel and distributed computing. Elective topics include networking, security, graphics, and databases.

    Visual Computing. This specialization encompasses the digital capture, processing, synthesis and display of visual data such as images and video. This specialization includes computer vision, image processing, and graphics, and covers such topics as the representation of 3D objects, visual recognition of objects and people, interactive and photo-realistic image rendering, and physics and perception of light and color.

    ↑ Back to top

    What do I take?

    For a full listing of courses required for the major, see the General Catalogue.

    Students are encouraged to consult an academic advisor in the Bren School of ICS to determine the coursework designed to meet their educational goals.

    ↑ Back to top

    How do I choose a CS Specialization?

    Your specialization should by driven by your individual interests. Please use the following resources to guide you:

    • Watch our CS specialization Q & A with ICS Student Affairs and Prof. Ian Harris. [transcript]
    • View the CS specialization slideshow discussed in the Q & A.
    • Explore CS research areas to identify and connect with faculty.
    • Check out the syllabi of specialization courses and find out which overlap.
    • For a detailed list of what courses are required for each specialization, consult the General Catalogue.
    • When you're ready to declare, submit a CS specialization request form online.

     

    ↑ Back to top

    What can I do with this degree?

    photo:: computer science studentGraduates of the Computer Science program will be in a position to pursue a variety of careers that involve the design and development of embedded systems, programming languages, compilers, networks and operating systems.

    They can be principal designers or involved in implementation, typically at companies that design, implement and sell these products. They may find themselves in charge of large-scale deployments and/or customizations at the organizations that use them.

    Finally, the strong scientific preparation allows students to become involved in such areas as artificial intelligence and computational biology – whether in graduate school or industry.

    Many students also go on to graduate school, continuing their studies, conducting research, and earning graduate degrees in software engineering, computer science, information science, management and law.

    ↑Back to top

    Still not sure?

    ↑Back to top

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    http://www.ics.uci.edu/ugrad/degrees/degree_cse.php undergraduate degree in computer science and engineering @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Undergraduate degree in Computer Science and Engineering

    DEGREES AVAILABLE: Major
    Offered jointly with The Henry Samueli School of Engineering

    photo:: computer science student

    The Computer Science and Engineering major gives students access to multidisciplinary problems with a focus on total systems engineering.

    Students will learn the computer science principles that are critical to development of software, hardware and networking of computer systems.

    From that background, engineering concepts and methods are added to give students exposure to circuit design, network design and digital signal processing.

    Elements of engineering practice include the systems view, manufacturing and economic issues, and multi-disciplinary engineering applications.

    This program is designed to provide students with the fundamentals of computer science, both hardware and software, and the application of engineering concepts, techniques and methods to both computer systems engineering and software system design.

    › Is Computer Science and Engineering for me?
    › What do I take?
    › What can I do with this degree?
    › Still not sure?

    Is Computer Science and Engineering for me?

    Graduates of the program will:

    1. establish a productive Computer Science and Engineering career in industry, government, or academia;
    2. engage in professional practice of computer systems engineering and software systems engineering;
    3. promote the development of innovative systems and solutions using hardware and software integration;
    4. promote design, research, and implementation of products and services in the field of Computer Science and Engineering through strong communication, leadership, and entrepreneurial skills.

    For more information on CSE goals and objectives see http://plaza.eng.uci.edu/degree-program/cse/mission.

    Annual student enrollment and graduation data can be found at: http://www.oir.uci.edu/student-data.html. Please note that annual student enrollment and graduation data for CSE is the sum of the data under both Engineering and Information and Computer Science.

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    What do I take?

    For a full listing of the degree requirements, see the General Catalogue.

    To better help students plan their upcoming quarters, a list of approved science elective courses* for satisfying the CSE major's science elective requirement has been made available.

    *The science elective must be taken at UCI if the student is currently enrolled at UCI.

    Students are encouraged to consult an academic advisor in the Bren School of ICS to determine the coursework designed to meet their educational goals.

    ↑ Back to top

    What can I do with this degree?

    photo:: computer science studentComputer Science and Engineering majors are involved in building the hardware infrastructure – computers, networks, embedded devices, as well as operating systems, compilers and networking software.

    The focus is on cooperation between hardware and software to yield the highest performance.

    Examples of such problem areas would be in traffic management, flight control, earthquake monitoring, automotive control and smart homes.

    Many students also go on to graduate school, continuing their studies, conducting research, and earning graduate degrees in computer engineering, computer science, information science, management or law.

    ↑Back to top

    Still not sure?

    Prospective and current UCI students interested in learning more about the Bren School’s degree options are encouraged to meet with the school’s associate dean of student affairs, counselors and student ambassadors. They will help you determine which of our majors and minors best support your academic strengths and interests. Call our Student Affairs Office at 949-824-5156 to make an appointment or to inquire about campus visit opportunities.

    (Note: Appointments are made by phone only, not by email request.)

    CONTACT:
    Bren School Student Affairs Office
    Information and Computer Science Building I, Suite 352
    Irvine, CA 92697-3430
    949-824-5156
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    http://www.ics.uci.edu/ugrad/degrees/ ics majors @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    ICS Majors

    Degree Programs
    Business Information Management

    Catalogue

    Website

    Computer Game Science

    Catalogue

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    Computer Science

    Catalogue

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    Computer Science and Engineering

    Catalogue

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    Data Science

    Catalogue

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    Students must also complete University and General Education Requirements

     

    » Changing Majors

    Changing from one ICS major to another ICS major

    • Please watch this short video: How to Change Majors
    • Meeting with an academic counselor or peer academic advisor is recommended. They will help you determine the most efficient way for you to meet the degree requirements of your new intended major.
    • If you are eligible to change, submit a Change of Major Application online through your Student Access page.

    Changing from a Major in another school to ICS

    • Please watch this short video: How to Change Majors
    • Visit the UCI Change of Major Criteria website for a listing of specific change of major guidelines for your intended major.
    • Meet with an ICS academic counselor or peer advisor as soon as possible to determine whether you are eligible to switch majors and to map out an academic plan.
    • Once you have met the Bren School of ICS change of major requirements:
    • Submit the Change of Major Application online
    • If you have over 120 units, an academic plan with intended graduate quarter and year must also be submitted.

    Changing from ICS to a Major in another school

    • Visit the UCI Change of Major Criteria website for a listing of specific change of major guidelines for your intended major.
    • Meet with an academic counselor or peer advisor in the school of your intended major; they can help you create an academic plan that will help you meet your goals.


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    http://www.ics.uci.edu/ugrad/degrees/degree_datascience.php undergraduate degree in data science @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Undergraduate degree in Data Science

    DEGREES AVAILABLE: Major

    photo:: data science student

    Data science can be thought of as the “science of data analysis”: developing mathematical models and algorithms for extracting useful information from data, combining statistical thinking with computational methods. We are living in an increasingly data-driven world, with rich and complex data available from sensors in our mobile phones, from our social networks, from our cars, our homes, from the Web, from new scientific instruments, and more. One of the key questions of our age is how to use of all of this data to make effective predictions and decisions. How can our smart devices learn our preferences and make recommendations? Can we develop more intelligent Web search engines that learn from data? How can statistical models take advantage of modern genomics to develop personalized medicine? How can climate scientists harness the wealth of satellite data to better understand the planet? Can we predict the stock market? These are just a fraction of the questions that drive data science. This degree program teaches students the underlying principles of how to organize, model, and analyze data in a systematic fashion and how to apply these principles to a wide variety of real-world problems.

    › Is Data Science for me?
    › What Courses will I take?
    › Career Paths: What can I do with This Degree
    › Why study Data Science at UC Irvine?
    › How can I enroll in the Major?
    › Sample Program of Study for the Data Science Major
    › Additional Information related to Data Science

    

    Is Data Science for me?

    If you enjoy the combination of working with data, understanding basic mathematical principles, and implementing your ideas in algorithms and software, then the Data Science major is well worth considering. The Data Science major has a dual emphasis on the principles of both statistics and computation. As a student you will learn the principles underlying mathematical and statistical aspects of data analysis as well as a broad range of foundational skills in computing. The program will build on these ideas to teach you how to utilize your knowledge to analyze and solve a variety of data analysis problems.

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    What Courses will I take?

    In the first two years of the program, students take core courses in both the Statistics and Computer Science Departments, providing a strong foundation in the principles of each field. In the third and fourth years of the program, students take more specialized courses on topics such as design of algorithms, machine learning, statistical modeling, information visualization and Bayesian statistics. A major component of this degree is the final year capstone project course, which is a two-quarter course that teaches students how to apply statistical and computational principles to solve large-scale, real-world data analysis problems. For a full list of required and elective courses see the description of the Data Science Major in the UCI General Catalogue.

    Students are encouraged to consult an academic advisor in the Bren School of ICS to determine the coursework designed to meet their educational goals.

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    Career Paths: What can I do with this Degree?

    Demand for graduates with skills in both statistics and computer science currently outpaces supply - the McKinsey Global Institute forecasts that by 2018 the United States may face a shortage of 1.5 million individuals with the skills to analyze and use data effectively. Thus, students with data science skills typically find employment quickly, across a wide variety of sectors, including software and internet companies (e.g., Google, Facebook, Apple, Microsoft), finance (banking, financial investment and insurance), engineering, and more. Data Science graduates are well-qualified for job titles such as data scientist, data analyst or statistician, both in the public and private sectors. Graduate school in areas such as Computer Science or Statistics is also a possible career path leading to advanced research careers in industry and in government, in addition to being qualified for university faculty positions.

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    Why study Data Science at UC Irvine?

    UC Irvine is the only university in the UC system, and one of the few in the world, where statistics and computer science are co-located in the same school and building. The Data Science major takes full advantage of this synergy, providing an integrated experience for our students at the interface of computing and statistics. You will be taught by faculty who are world-class researchers in areas such as machine learning, Bayesian statistics, database management, graph algorithms, and more. You will have opportunities to participate in undergraduate research projects with our faculty and to take part in summer internships in Southern California or Silicon Valley. And when you graduate you will be able to take advantage of our many connections to startups, to major corporations, and to graduate schools, who are well aware of UC Irvine’s strengths in Data Science.

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    How can I enroll in the Major?

    In addition to material below, you are also encouraged to visit ICS Majors Webpage for further information. Note that the earliest that you will be able to complete this degree is Spring 2018.

    Prospective Freshmen: If you are a prospective freshman or transfer student looking for more information on applying to the Data Science undergraduate program, please visit the Undergraduate Admissions website.

    Transfer Students: If you are a prospective transfer student please visit the UCI Bren School admissions page for transfer students and please also consult the section on Transfer Applicants for the Data Science Major in the UCI General Catalogue.

    Enrolled UCI Students: If you are a currently-enrolled UCI student you can transfer into this major if you satisfy the following requirements for a Change of Major:
    • Cumulative UC GPA of 2.7 or higher
    • 3.0 or higher average GPA and no grade lower than a C for ICS 31, ICS 32, and one of the following: Math 2A, Math 2B, Math 2D, ICS 6B, or ICS 6D.
    • As of Academic Year 2015-2016 we are accepting students at either the freshman or sophomore levels.

    If you are interested in a change of major, meeting with an ICS academic counsellor is strongly recommended. For general University information on a change of major see the UC Irvine Change of Major Criteria.

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    Additional Information related to Data Science

    • Majoring in Data Science: a video discussion by UC Irvine faculty
    • The UCI Data Science Initiative: all about research in data science at UC Irvine
    • The UCI Statistics Department
    • The UCI Center for Machine Learning and Statistics
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    http://www.ics.uci.edu/ugrad/degrees/degree_bim.php undergraduate degree in business information management @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Undergraduate degree in Business Information Management

    Are you interested in business as much as technology? Do you want to learn how to build a data-driven sales and marketing plan? Would you love a career at one of the major consulting firms? Do you like to solve analytical problems? Do you want to be part of a highly interdisciplinary team setting corporate strategy? If you answered yes to one or more of these questions, UC Irvine’s business information management major just might be the choice for you.

    What will I learn?

    The B.S. in business information management is designed around a set of core courses that introduce the fundamentals of computing (programming, requirements analysis, information retrieval and visualization, databases), business (accounting, finance, marketing, management), and analytical methods (statistics, economics, business intelligence, decision analysis). From there, more than two dozen electives offer students the chance to specialize in topics as diverse as global strategy, branding and design, social and organizational aspects of computing, project management and more.

    Throughout the major, you study a variety of business cases and gain hands-on experience in a variety of forums. Projects range from learning to understand and use technology that gathers data, to practicing methods and techniques for distilling that data into meaningful information, to analyzing and proposing strategic directions. Many projects are inspired by past and current real-world situations, providing you with practice directly relevant to your future career.

    Overall, the major strongly emphasizes how raw business data is gathered, stored, analyzed and distilled into valuable strategic information, all with a proper understanding of the technical, social, ethical and organizational challenges that are involved. Additionally, students learn how to adapt to ever-changing business conditions — whether it is new disruptive technologies, trends, rules and regulations or corporate strategies.

    Careers

    Because technology drives nearly all aspects of business and innovation today, demand for business information management majors is strong. The majority finds employment at consulting companies, though many find a home in specialized industries as well. Many squarely focus on joining for-profit organizations; others succeed in bringing their skills to and excelling at nonprofits. Of course, graduate school in business, information systems, informatics, law or related field is a career path that some of our students also choose to take after they complete the major.

    Qualifications

    We welcome students with a variety of backgrounds. We have had many students join who were more interested in the computing aspects of the major, others who were more interested in the business and analytical aspects. Both groups are successful in the major. If you have an interest in solving problems, aspire to creative thinking and have an affinity with business and organizational concerns, business information management can be for you.

    Why business information management at UC Irvine?

    • Excellence. You will be part of a world-class group of faculty and staff who have an outstanding track record of delivering innovative educational experiences in — and beyond — the classroom.
    • Depth. With no fewer than ten courses dedicated to business information management, and dozens more on topics closely surrounding it, you receive an education that prepares you very well for the many challenges that will arise in your future career.
    • Connections. Our alumni have gone on to study in some of the most prestigious Ph.D. programs; work for well-known, innovative corporations; and found successful startups. We stay in touch with them, and can help connect you for internships that complement your studies.
    • Location. Orange County has a vibrant business community, with, as just a few examples, numerous financial, biomedical, consulting, entertainment and real-estate companies around.

    Detailed requirements

    Please see the catalogue for a detailed description of the requirements of the Business Information Management major.

    More information

    Prospective and current UCI students interested in learning more about the Bren School’s degree options are encouraged to meet with the school’s associate dean of student affairs, counselors and student ambassadors. They will help you determine which of our majors and minors best support your academic strengths and interests. Call our Student Affairs Office at 949-824-5156 to make an appointment or to inquire about campus visit opportunities.

    (Note: Appointments are made by phone only, not by email request.)

    CONTACT:
    Bren School Student Affairs Office
    Information and Computer Science Building I, Suite 352
    Irvine, CA 92697-3430
    949-824-5156
    ucounsel@uci.edu

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    http://sconce.ics.uci.edu/ Welcome to SCONCE
    Sconce
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    ©SCONCE 2014
    http://www.ics.uci.edu/%7etheory/ Center for Algorithms and Theory of Computation Center for Algorithms and Theory of Computation

    Faculty

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    Former students

    Faculty research interests

    Weekly seminar (CompSci 269)

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    CS theory student wiki (access limited to UCI campus)



    Department of Computer Science
    University of California, Irvine, CA 92697-3425
    http://vision.ics.uci.edu/ Computational Vision | ICS | UC Irvine

    Computational Vision at UC Irvine  small eye

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    Welcome to the vision group in ICS at UC Irvine.

    Our lab studies computational vision, seeking to understand both the information processing capabilities of biological visual systems and develop computer vision systems. We are interested theoretical questions as well as practical applications ranging from motion capture to biological image analysis.



    Prospective Students:

    We are actively recruiting qualified undergradute and graduate students who are interested in pursuing research in computer vision. Current students are encouraged to sit in on our weekly meetings. Applicants interested in pursuing a graduate degree should apply via ICS Graduate Admissions and clearly indicate their research interests. The yearly deadline is December 15th.




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    Golnaz and Yi presenting at CVPR 2014


    Yi and Sam presenting at CVPR 2010


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    Face Detection


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    Face Recognition + Augmented Reality?

    Computational Vision | School of Information and Computer Sciences | UC Irvine
    © 2007-2015 UC Irvine
    http://luci.ics.uci.edu/ LUCI: The Laboratory for Ubiquitous Computing and Interaction

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    Alternatively, alternatively you can bypass the flash detection if you wish.

    http://www.ics.uci.edu/faculty/area/area_cscw.php computer-supported cooperative work @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Computer-Supported Cooperative Work

    Research Faculty

    »Paul Dourish
    »Gillian R Hayes
    »Alfred Kobsa
    »Gloria Mark
    »Bonnie Nardi
    »Judy Olson
    »Gary M. Olson
    »Donald J Patterson
    »David Redmiles
    »Walt Scacchi
    »Richard Taylor

    Information technologies bring people together -- through social networking, through collaborative systems, through digital media, and through communications. Informatics has been a long-term leader in the study of social engagement through information systems. Topics include distance collaboration, workflow and process-based systems, multi-user gaming, and cultural engagements.

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    http://www.ics.uci.edu/faculty/area/area_medical.php medical informatics @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Medical Informatics

    Research Faculty

    »Pierre Baldi
    »Yunan Chen
    »Paul Dourish
    »Charless Fowlkes
    »Wayne Hayes
    »Gillian R Hayes
    »Alfred Kobsa
    »Richard Lathrop
    »Eric Mjolsness
    »Donald J Patterson
    »Babak Shahbaba
    »Xiaohui Xie
    »Kai Zheng

    This topic concerns the development and application of information systems to healthcare. Information systems have a critical role to play in contemporary health and wellness programs. This includes technology in hospital settings but also persuasive technologies for healthy living, health care in the home and in the community, and in the interactions between partners in the health care system.

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    http://www.ics.uci.edu/faculty/area/area_security.php security, privacy, and cryptography @ the bren school of information and computer sciences
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    Security, Privacy, and Cryptography

    Research Faculty

    »Alfred Kobsa
    »Gene Tsudik

    The development of secure communication protocols is a critical issue in today's age of pervasive communication.

    Bren School research in this area includes anonymity and authentication in network security, key agreement and digital signatures in cryptography, and security issues in electronic commerce.

    Researchers also are designing high-performance algorithms and data structures for solving large-scale problems motivated from information assurance and security, the Internet, information visualization and geometric computing.

    More information: http://sconce.ics.uci.edu/

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    http://www.ics.uci.edu/faculty/area/area_systems.php programming languages and systems @ the bren school of information and computer sciences
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    Programming Languages and Systems

    Research Faculty

    »Michael Franz
    »Ray Klefstad
    »Cristina V. Lopes
    »Alexandru Nicolau
    »Alexander Veidenbaum
    »Richert Wang
    »Harry Xu

    This research area focuses on "Systems Software," the pervasive software infrastructure that makes the development and use of applications possible, and on the programming languages that are used for creating software. Among the topics being studied are programming languages, compilers, operating systems and networking software.



    Research in the Programming Languages and Systems area frequently emphasizes a practical element in addition to theoretical rigor. New ideas are not just studied on paper but implemented as research prototypes, which can then be measured under realistic conditions.



    Some of the specific research interests of Bren School faculty in this area include:



    • Program restructuring and transformation techniques for parallelization and distribution

    • Compilers for high-performance computing

    • Web 2.0+ technologies such as AJAX, JavaScript, Python and Ruby
    • 
Just-in-time compilation and optimization

    • Mobile code representations, distribution and optimization

    • Cloud computing software infrastructure

    • Memory management
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    http://www.ics.uci.edu/faculty/area/area_software.php software engineering @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Software Engineering

    Research Faculty

    »Michael Franz
    »James A. Jones
    »Cristina V. Lopes
    »Sam Malek
    »David Redmiles
    »Debra J. Richardson
    »Walt Scacchi
    »Richard Taylor
    »Andre van der Hoek
    »Richert Wang
    »Harry Xu

    Software has transformed society in dramatic and powerful ways; software systems inform, control and enhance daily activities.

    Software research at the Bren School is aimed at creating new software technology and solutions, furthering the information revolution.

    The central goal of this research is improvement in software development, evolution, deployment, quality, understandability and cost-effectiveness.

    A unifying theme of the software engineering research group is software architecture.

    Specific research emphases within this area include:

    • Hypermedia
    • Analysis and testing
    • Software understanding
    • Environments and user-interface software
    • Process/workflow
    • Distributed component-based systems
    • Extensible systems
    • Mobile code
    • Software architecture
    • WWW technology and protocols

    More information: http://se.ics.uci.edu/

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    http://www.ics.uci.edu/faculty/area/area_network.php networked and distributed systems @ the bren school of information and computer sciences
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    Networked and Distributed Systems

    Research Faculty

    »Ardalan Amiri Sani
    »Lubomir Bic
    »Michael Dillencourt
    »Magda El Zarki
    »Scott Jordan
    »Ray Klefstad
    »Marco Levorato
    »Sharad Mehrotra
    »Amelia C. Regan
    »Isaac D. Scherson
    »Richard Taylor
    »Nalini Venkatasubramanian
    »Richert Wang

    Applications requiring voice and data, as well as video, are rapidly expanding and future communication services must be able to facilitate a wide variety of diversified services in a practical and easily expandable fashion.

    Bren School researchers investigate various issues in the design and analysis of high-speed networks for multimedia applications. They are actively involved in research on computer networks and distributed systems, with the goal of designing, analyzing and implementing communication systems that allow high-speed transport of multimedia information between end-users.

    Research collectively addresses a complete view of the communications network, from the network substrate (high-speed networks, ATM networks, wireless networks), to end-to-end protocols support and communication subsystem architectures (Object-Oriented frameworks), to distributed applications (multimedia distance learning applications).

    In the area of distributed systems, researchers are developing new programming paradigms for distributed systems based on the principle of autonomous messages, which achieves speed-up through parallel processing, and permits interactive open-ended applications through dynamic functional composition.

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    http://www.ics.uci.edu/faculty/area/area_biomed.php biomedical informatics and computational biology @ the bren school of information and computer sciences
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    Biomedical Informatics and Computational Biology

    Research Faculty

    »Pierre Baldi
    »Charless Fowlkes
    »Wayne Hayes
    »Richard Lathrop
    »Eric Mjolsness
    »Babak Shahbaba
    »Xiaohui Xie

    There are a number of significant problems in biology and medicine for which computational approaches can yield important results.

    The worldwide efforts to construct databases of protein and small molecule structures, DNA sequences, metabolic pathways, regulatory mechanisms, pharmaceutical structures and activities, patient response data, etc., have created many opportunities for intelligent systems.

    At UCI, current areas of research in bio/medical informatics include:

    • Medical Information Access
    • Knowledge Representation for Health-Care Guidelines
    • Structure in Biomedical Data
    • Biomedical Simulations
    • Discovery of Gene Expression Control
    • Knowledge Discovery in Clinical Databases
    • Computational Biology
    • Bioinformatics and Probabilistic Modeling.
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    http://www.ics.uci.edu/faculty/area/area_multimedia.php multimedia computing @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Multimedia Computing

    Research Faculty

    »Magda El Zarki
    »Ramesh Jain
    »Sharad Mehrotra
    »Nalini Venkatasubramanian

    Multimedia computing started receiving attention more than a decade ago. Naturally, early systems dealt with very limited aspect of multimedia. With progress in technology, several computing addresses important issues in creation, communication, storage, access, and presentation of information and experiences. In our department, we are addressing research issues in fundamentals of multimedia systems and their advanced applications.

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    http://www.ics.uci.edu/faculty/area/area_scientific.php scientific and numerical computing @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Scientific and Numerical Computing

    Research Faculty

    »Wayne Hayes
    »Eric Mjolsness
    »Padhraic Smyth

    Scientific Computing refers to the application of computers to scientific problems, from astrophysics to zoology.

    The mode of application can be system modeling, data analysis and mining, or visualization.

    The focus can be on developing new computational techniques, such as parallel algorithms or new data mining ideas, or on the novel application of existing techniques to new scientific problems.

    A number of our faculty are focussed primarily on bio-informatics, so in addition to the links below, more information can be found about bio-informatics at UCI by checking out IBAM and IGB.

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    http://www.ics.uci.edu/faculty/area/area_social.php social informatics @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Social Informatics

    Research Faculty

    »Paul Dourish
    »Gillian R Hayes
    »Gloria Mark
    »Bonnie Nardi
    »Gary M. Olson

    UC Irvine is an acknowledged center for the study of social informatics, which incorporates the social and cultural aspects of information technology development and use.

    Social informatics employs techniques and theories from social sciences and cultural studies to understand the shaping and applications of digital media and their organizational, political, historical, and economic contexts.

    This topic links information system analysis with design. 

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    http://www.ics.uci.edu/faculty/area/area_vision.php computational vision research @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Computational Vision research

    Research Faculty

    »Charless Fowlkes
    »Ramesh Jain
    »Aditi Majumder
    »Gopi Meenakshisundaram
    »Eric Mjolsness

    Research in computer vision lies at the intersection of vision science, artificial intelligence, computer graphics, and image processing. The focus here at UCI includes both theory and applications.

    One focus is on the understanding of biological visual systems, including low, mid, and high-level processing. We examine fundamental issues in perception including include boundary detection, perceptual grouping, appearance and shape modeling, and object representation.

    We are ultimately interested in the computational mechanisms and principles that govern human visual perception.

    We are also interested in computer vision from a system perspective.

    Digital media content is widespread in today's society; digital cameras and camcorders are now everywhere. Medical and biological experimentation also produce immense quantities of visual data that cannot be manually processed.

    A major challenge that lies ahead is the creation of systems that process, search and manipulate this visual data.

    Applications include image search, video surveillance, and biological image analysis.

    For more information please visit the Computational Vision Lab.

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    http://www.ics.uci.edu/faculty/area/area_ubi.php ubiquitous computing @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Ubiquitous Computing

    Research Faculty

    »Paul Dourish
    »Gillian R Hayes
    »Alfred Kobsa
    »Cristina V. Lopes
    »Donald J Patterson
    »Bill Tomlinson

    Ubiquitous computing refers to the creation and deployment of computing technology in such a way that it becomes an invisible part of the fabric of everyday life and commerce.

    In the ubiquitous computing era, computers in the traditional sense gradually fade from view. Information and communication mediated by computers is available anywhere and anytime through devices that are embedded in our environment, completely inter-connected, intuitive, effortlessly portable and constantly available.

    Users share information and communicate continuously throughout the day. In short, ubiquitous computing provides computing for all - access to anything, by anyone, at anytime, anywhere.

    Advances in wireless networking and the Internet, embedded systems and human-computer interaction are the forerunners for ubiquitous computing. It is the convergence of these and other technologies that brings about the trend towards ubiquitous computing.

    Ubiquitous computing places considerable requirements on both hardware and software development and support, increasing needs for information and data analysis, and also leads to new applications that improve society.

    Ubiquitous computing builds upon and unites virtually all of the current research strengths in the Bren School. With particular emphasis on ubiquitous computing, the Bren School faculty are addressing issues such as context-aware computing, whereby mobile computing responds to one's current context.

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    http://www.ics.uci.edu/faculty/area/area_compdesign.php computer architecture and design @ the bren school of information and computer sciences
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    Computer Architecture and Design

    Research Faculty

    »Elaheh (Eli) Bozorgzadeh
    »Nikil Dutt
    »Tony Givargis
    »Ian G. Harris
    »Alexandru Nicolau
    »Isaac D. Scherson
    »Alexander Veidenbaum

    The CS faculty in this area conduct research to develop new general-purpose computer architectures and computer design tools, investigate software-architecture interaction and co-design, design application- and domain-specific architectures, and many other topics.

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    http://www.ics.uci.edu/faculty/area/area_compgraphics.php computer graphics and visualization @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Computer Graphics and Visualization

    Research Faculty

    »David Eppstein
    »Michael T. Goodrich
    »Aditi Majumder
    »Gopi Meenakshisundaram
    »Bill Tomlinson
    »Shuang Zhao

    Focuses on the field of visual computing that deals with generating/capturing, representing, rendering and interacting with synthetic and real-world images and video. We work on end-to-end solutions from capturing of images and geometry; representing large geometric, image, and video data sets; geometry and image processing; interactive access and rendering of large visual data sets; algorithms for building large area immersive displays for the presentation of visual content; and interaction techniques in both small personal displays and in large displays for collaborative environments.

    Current and future research areas include, but are not limited to:

    • Designing new cameras with advanced capabilities for capturing real-world scenes.
    • Image processing for enhanced visual content quality
    • Computational geometry and graph algorithms for geometry processing
    • Interactive rendering of very large geometric data sets.
    • Ubiquitous displays -- Reconfigurable multi-projector display systems.
    • Interaction techniques for large area displays.
    • Sketch based modeling and interaction for personal displays.

    Applications are Internet-based visualization of remotely located complex objects, volume manipulation and visualization, geographic information systems, and simplified 3-D rendering based on 2-D images.

    More information: http://graphics.ics.uci.edu/

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    http://www.ics.uci.edu/faculty/area/area_stats.php statistics and statistical theory @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Statistics and Statistical Theory

    Research Faculty

    »Pierre Baldi
    »Brigitte Baldi
    »Daniel Gillen
    »Stacey Hancock
    »Wesley O. Johnson
    »Marco Levorato
    »Hernando Ombao
    »Babak Shahbaba
    »Padhraic Smyth
    »Hal Stern
    »Jessica Utts
    »Yaming Yu
    »Zhaoxia Yu

    In recognition of the interdisciplinary nature of statistics on the computational field, UCI created an independent Department of Statistics that joined the Bren School of ICS in July 2003.

    An emerging field of study in information and computer sciences, Statistics faculty members bring with them a strength in statistical theory and a focus on the development of statistical methods for solving problems.

    Researchers at UCI are concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Statistical principles and methods are important for addressing questions in public policy, medicine, industry and virtually every branch of science.

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    http://www.ics.uci.edu/faculty/area/area_ai.php artificial intelligence and machine learning @ the bren school of information and computer sciences
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    Artificial Intelligence and Machine Learning

    Research Faculty

    »Pierre Baldi
    »Rina Dechter
    »Charless Fowlkes
    »Dan Frost
    »Alexander Ihler
    »Richard Lathrop
    »Marco Levorato
    »Eric Mjolsness
    »Hernando Ombao
    »Donald J Patterson
    »Babak Shahbaba
    »Padhraic Smyth
    »Hal Stern
    »Xiaohui Xie

    Research in artificial intelligence and machine learning addresses some of the most exciting and challenging problems in computer science today.

    How can computers interpret large sets of images without any human supervision? How can algorithms intelligently search the Web and make sense of millions of text documents? What are the fundamental principles that govern systems that perform automated reasoning?

    UCI researchers are at the forefront of building the next generation of intelligent systems. Their projects span a broad range of open research problems, combining basic theoretical principles from algorithms, probability, and applied mathematics, with highly visible and interdisciplinary applications in areas such as computer vision, bioinformatics, constraint-based problem solving, text understanding, data mining, smart sensor networks.

    For more information please visit the Center for Machine Learning and Intelligent Systems.

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    http://www.ics.uci.edu/faculty/area/area_environmental.php environmental informatics @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Environmental Informatics

    Research Faculty

    »Pierre Baldi
    »Paul Dourish
    »Bill Tomlinson

    Humanity is currently facing a range of significant environmental challenges such as global warming, species extinction, pollution, and overpopulation. Informatics tools and techniques can help facilitate responses to these challenges, and assist with planning for future environmental issues.

    Researchers in the Bren School are working on projects relating to scientific collaboration, environmental monitoring, interactive education, social networking, and a wide range of other environmental topics.

    These efforts involve collaborations with faculty from departments around campus including Ecology & Evolutionary Biology, Chemical Engineering & Materials Science, Civil & Environmental Engineering, Education, and Operations & Decision Technologies.

    By connecting research on computing and information science with work in these and other fields, environmental informatics can help human civilizations move toward more sustainable ways of living.

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    http://www.ics.uci.edu/faculty/area/area_embedded.php embedded systems @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Embedded Systems

    Research Faculty

    »Ardalan Amiri Sani
    »Nikil Dutt
    »Tony Givargis
    »Ian G. Harris
    »Ray Klefstad
    »Alexandru Nicolau
    »Isaac D. Scherson

    Embedded Systems are ubiquitous and range from small devices (e.g., motion sensors or smart phones) to large systems-of-systems (e.g., airplanes or hybrid cars). The convergence of multiple trends -- including rapid miniaturization of computer electronics and advances in software development and connectivity -- enable embedded software and hardware to monitor, compute and control a wide array of physically engineered systems. Numerous examples of such products exist in the automotive, telecom, bio-medical, avionics, consumer electronics, and other industries.

    Embedded systems must often deliver functionality within tight constraints (e.g., real-time, power, and reliability). Additionally, safety-critical embedded systems must deliver provably correct behavior when interfacing with physical sensors and actuators that interact with the environment. Thus research in embedded systems is highly interdisciplinary in nature, requiring a tight integration of core CS principles with domain specific knowledge of the end application/environment.

    Researchers at the Bren School cover a broad range of embedded systems research topics, including topics such as embedded software, architectures for embedded computer systems, design automation, embedded systems methodologies, validation and verification, formal methods, and cyber-physical systems.

    For more information please visit the Center for Embedded Computer Systems.

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    http://www.ics.uci.edu/faculty/area/area_algorithms.php algorithms and complexity @ the bren school of information and computer sciences
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    Algorithms and Complexity

    Research Faculty

    »Michael Dillencourt
    »David Eppstein
    »Michael T. Goodrich
    »Daniel S. Hirschberg
    »Sandy Irani
    »Stanislaw Jarecki
    »George S. Lueker
    »Amelia C. Regan

    Theoretical computer science aims at understanding the mathematical structure of computational problems in order to derive efficient and scalable solutions to those problems.

    Bren School faculty members have made significant contributions to many topics in this field, including:

    • Graph algorithms and graph drawing (computing with systems of pairwise interactions between objects such as web page links, protein interactions, or social networks)
    • Computational geometry (computing with planar or spatial data)
    • String and tree algorithms (for indexing and processing large textual and XML documents, and DNA sequence information)
    • Data compression (computationally efficient methods for removing redundancy from stored information)
    • Online algorithms (automated decision making in the face of uncertainty)

    For more information visit the ICS Theory Group page.

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    http://www.ics.uci.edu/faculty/area/area_hci.php human-computer interaction @ the bren school of information and computer sciences
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    Bren school home > Faculty > Research areas
    Human-Computer Interaction

    Research Faculty

    »Yunan Chen
    »Paul Dourish
    »Gillian R Hayes
    »Alfred Kobsa
    »Gloria Mark
    »Judy Olson
    »Gary M. Olson
    »Donald J Patterson
    »David Redmiles
    »Bill Tomlinson
    »Kai Zheng

    HCI research at UCI stretches from the architecture of novel interactive systems to the social and cultural considerations of information technology adoption and use. We employ laboratory, ethnographic, and prototyping techniques to understand how people adopt, adapt, and respond to information systems. Recent research has investigated privacy issues in mobile systems, tangible interfaces for group awareness, interactive animation, and visualization of location information.

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    http://www.ics.uci.edu/faculty/area/area_os.php Operating Systems @ Donald Bren School of Information and Computer Sciences
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    Bren school home > Faculty > Research areas
    Operating Systems

    Research Faculty

    »Ardalan Amiri Sani
    »Harry Xu

    The operating systems area at UCI embraces a wide range of topics related to theory and practice of computer systems software. Researchers here are building systems for reliable and efficient big data processing, mobile I/O virtualization, program analyses and various other applications.

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    http://www.ics.uci.edu/faculty/area/area_data.php databases and data mining @ the bren school of information and computer sciences
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    Databases and Data Mining

    Research Faculty

    »Pierre Baldi
    »Michael J Carey
    »Chen Li
    »Sharad Mehrotra
    »Padhraic Smyth
    »Alexander Veidenbaum

    Research in databases and data mining addresses exciting research challenges related to architectures, index structures, algorithms, models, and performance evaluation of a variety of next-generation databases and information systems and technologies for data mining. Current topics of interest include database systems, data analysis and data cleansing, data warehousing systems, information integration tools, search techniques, scalable data-intensive computing, data sharing, data dissemination, and statistical data mining. Current projects are exploring challenges in the realization of such systems stemming from data volume, complexity, diversity, and heterogeneity, and emerging application needs including privacy and security, mobility, quality of data, quality of service, and reliability and robustness in extreme situations.

    More information is available from http://isg.ics.uci.edu (for databases and next-generation information systems) and http://cml.ics.uci.edu (for data mining).

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    http://www.ics.uci.edu/community/news/notes/ noteworthy achievements @ the bren school of information and computer sciences
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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


    WINTER 2016

    Gary Olson, 2 ICS Alums receive SIGCHI honors

    photo: Gary Olson

    Gary
    Olson

    Informatics professor Gary Olson has received a Lifetime Service Award from the ACM Special Interest Group on Computer–Human Interaction (SIGCHI), as part of the group’s annual effort to recognize and honor leaders and shapers within the field of human-computer interaction.

    According to the award website, recipients of the Lifetime Service Award are individuals who have contributed to the growth and success of SIGCHI in a variety of capacities over a number of years. Olson has worked in the human-computer interaction (HCI) field since 1983, when he and colleagues Judy Olson (a fellow informatics professor), Paul Green and Marilyn Mantei taught the first graduate course on the subject at the University of Michigan.

    Olson’s contribution to HCI has largely revolved around the concept of distance. From the mid-1980s he and Judy Olson began researching the role technology plays in collaboration. The pair published their highly cited paper, “Distance Matters,” on the subject in 2000 and later authored “Working Together Apart.” Olson has long played an active role in SIGCHI, co-chairing and chairing numerous conferences, as well as award and steering committees. SIGCHI previously elected Olson to the CHI academy and, along with Judy Olson, awarded him a Lifetime Achievement Award in 2006.

    In addition to Olson, two ICS alumni were also honored in this year’s round of awards. Leysia Palen, professor and founding chair of the newly established Department of Information Science at the University of Colorado Boulder, was elected to the CHI academy. She earned her Ph.D. in information and computer science in 1998. Daniel Russell, a senior research scientist at Google, was also elected to the CHI academy. He earned his B.S. in information and computer science in 1977 and has been recognized as a UC Irvine Lauds & Laurels Distinguished Alumnus.

     


    Kobsa receives Mercator Fellowship

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Informatics professor Alfred Kobsa has received a Mercator Fellowship from the German Research Foundation (DFG), the largest research funding organization in Germany. The Mercator fellowship will enable Kobsa—whose research focuses on the areas of user modeling and personalized systems, privacy, support for personal health maintenance, and information visualization—to participate in “intensive, long-term project-based collaboration between researchers from both domestic and foreign institutions,” according to the DFG. Throughout the duration of the fellowship, Kobsa will work both on-site at a German institution and continue his project collaboration here in Irvine. “Foreign Mercator Fellowship holders are awarded the title of Mercator Fellows in recognition of their dedication,” the DFG notes.

    As the largest independent research funding organization in Germany, the DFG “promotes the advancement of science and the humanities by funding research projects, research [centers] and networks, and facilitating cooperation among researchers,” according to its website. It also joins major international funding counterparts like the National Science Foundation and the Royal Society as a member of the International Council for Science (ICSU).

     


    FALL 2015

    Franz named 2016 IEEE Fellow

    photo: Michael Franz

    Michael
    Franz

    The Institute of Electrical and Electronics Engineers (IEEE) has named Computer Science Professor Michael Franz a 2016 IEEE Fellow. Franz is being recognized by IEEE for his contributions to just-in-time compilation as well as his contributions to computer security through compiler-generated software diversity.

    The IEEE Grade of Fellow is conferred by the IEEE Board of Directors upon a person with an outstanding record of accomplishments in any of the IEEE fields of interest. It is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement. The total number of fellows selected in any one year cannot exceed one-tenth of 1 percent of the total voting membership. “It is a great achievement receiving recognition from one's peers and being included among such a distinguished group of IEEE members," says Franz.

    The IEEE is the world’s leading professional association for advancing technology for humanity with 400,000 members in 160 countries. Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards.

     


    Tsudik elected to The Academy of Europe

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor's Professor of Computer Science Gene Tsudik has been elected a member of The Academy of Europe (Academia Europaea), the organization dedicated to the “advancement and propagation of excellence in scholarship in the humanities, law, the economic, social, and political sciences, mathematics, medicine, and all branches of natural and technological sciences anywhere in the world for the public benefit and for the advancement of the education of the public of all ages in the aforesaid subjects in Europe,” according to the organization’s website.

    Tsudik was elected to the computational and information science-focused section—dubbed the Informatics section—of the academy. Membership is by invitation only, with invitations made only after a peer group nomination and rigorous scrutiny of the eminence and scholarship of the potential member. Tsudik is the only United States-based member elected to the Informatics section in 2015, joining a total of 11 U.S. members in the 237 member total section.

    The Academy of Europe endeavors to encourage the highest possible standards in scholarship, identifying topics of trans-European importance to science and scholarship, as well as making recommendations to national governments and international agencies concerning matters affecting science, scholarship and academic life in Europe. It counts among its members some of the foremost scholars in the world. Tsudik joins in the Informatics section eminent U.S.-based scholars like Victor Vianu, computer scientist and editor-in-chief of the Journal of the ACM, and Mihalis Yannakakis, professor of computer science at Columbia University and winner of the 2015 Donald E. Knuth prize—awarded to those who have made outstanding contributions to the foundations of computer science.

     


    NSA grants Tsudik $286K for cybersecurity research

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor's Professor of computer science Gene Tsudik has received a $286,000 grant from the National Security Agency (NSA) for his project “ERADS: Efficient Remote Attestation of Dynamic Swarms.”

    As embedded devices—including automotive sensors or controllers, drones, household appliances, and factory automation components—proliferate into many aspects of everyday life, they also become targets for attacks. This project aims to develop techniques for detecting and mitigating malware infestations of networks consisting of a myriad of such embedded devices.

    The NSA designates UC Irvine as a National Center of Academic Excellence (CAE), with a focus in Information Assurance Research. Institutions with CAE designations promote higher education in information assurance—the management of risks related to the use, processing, storage, and transmission of data—and cyber defense, while helping to meet the need to reduce vulnerabilities in the Nation’s networks. The grant comes out of the CAE cybersecurity research program.

     


    Informatics Ph.D. student to present at ACM-DEV 2015

    photo: Ankita Raturi

    Ankita
    Raturi

    Informatics Ph.D. student Ankita Raturi received an ACM Women in Computing (ACM-W) scholarship to attend the ACM Symposium on Computing for Development (ACM DEV), held at the Queen Mary University of London in December. ACM-W provides scholarships to enable women in computer science to attend research conferences around the world.

    At ACM DEV, Raturi will present on a paper she co-authored with current and former UC Irvine faculty Bill Tomlinson, Bonnie Nardi, Donald J. Patterson, Debra Richardson, Jean-Daniel Saphores and Dan Stokols. The paper, “Toward Alternative Decentralized Infrastructures,” looks at how we can build interfaces between infrastructures to improve robustness, reliability and resilience. “Enabling communities to transition to a more resilient configuration of infrastructures is crucial for establishing a distributed portfolio of processes and systems by which human needs may be met,” Raturi says.

    This will be Raturi’s first time at the conference, “an ideal venue for this work to be presented,” Raturi says. The conference is a platform for “original and innovative work on the applications, technologies, architectures and protocols for computing in developing regions,” according to the ACM DEV website.

    “Having the opportunity to present my work, engage with the community and learn from leading researchers in my field is a major part of my professional growth," Raturi says. "Discussing our work with experts who have been working on computing for development will be incredibly valuable.”

     


    Ziv collaborates in groundbreaking NSF-funded privacy research

    photo: Hadar Ziv

    Hadar
    Ziv

    Informatics lecturer Hadar Ziv will be a research collaborator in a groundbreaking NSF-funded project titled “Privacy Compliance by Design: Ideation Techniques to Facilitate System Design Compliant with Privacy Laws and Regulations.”

    The project attempts to bring privacy protection to the forefront of software developer’s minds in the wake of the explosion of big data. “Software professionals typically have no formal training or education on sociotechnical aspects of privacy. As a result, addressing privacy issues raised by a system is frequently an afterthought and/or a matter of compliance-check during the late phases of the system development lifecycle,” the project’s abstract explains. To tackle this challenge, the project’s research team will develop “privacy ideation cards” based on relevant U.S. laws and regulations, which “can potentially transform how privacy-relevant aspects are handled in real-world software solutions built by industry and inform how students are taught these issues in undergraduate software curricula.” The team includes Principal Investigator Sameer Patil from New York University, who received a $175,000 Early-concept Grant for Exploratory Research (EAGER) for the project, Ziv, Janice Tsai of Microsoft and Jonathan Fox of Intel.

    In addition to the deck of privacy ideation cards, the project will promote privacy by design, making privacy protection a built-in framework for all software development. Ziv will connect the research team with students in his senior Capstone Informatics project course, “as a test-bed for ideas and presentations related to privacy,” Ziv says. “Their engagement will affect change in the students' projects. I will likely participate in collecting and analyzing data about those changes.”

     


    Professor Tsudik Keynoting Two Conferences in November

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor’s Professor of Computer Science Gene Tsudik is delivering two keynote addresses on “Secure and Private Proximity-Based Discovery of Common Factors in Social Networks” at conferences in November. First, on November 4, he will be speaking at the 9th International Conference on Network and System Security in New York City, before traveling to Sydney, Australia to speak at the 25th International Telecommunication Networks and Applications Conference on November 20.

     


    Study reveals ICS degree-friendly jobs have the best work-life balance

    photo: Glassdoor logo

    Careers in data science, user experience design, web development and software engineering promote excellent work-life balance, according to a survey from Glassdoor, a job rankings website.

    Glassdoor notes that, across the board, employee satisfaction with work-life balance has been declining in the past few years, but there are a number of careers that won’t leave employees working 24/7—many of these careers bolstered by skills learned at the Donald Bren School of Information and Computer Sciences (ICS).

    Glassdoor analyzed feedback from around 60,000 company reviews to determine the top 25 careers where employees report balance between their personal lives and the workplace. Among the 25, 10 were careers in tech, including data scientist (#1), user experience (UX) designer (#7), web developer (#10), instructional designer (#14), software quality assurance (QA) engineer (#16), web designer (#17), data analyst (#20), solutions engineer (#22), software developer (#24), and front-end developer (#25).

    ICS is well-placed to foster future careers in tech. As the only school focused on computer and information sciences in the University of California system, ICS offers undergraduate programs of study in business information management, computer game science, computer science, computer science and engineering, informatics, and software engineering. The newly established data science major is unique at the undergraduate level, equipping budding data scientists—Glassdoor’s career with the highest work-life balance—with the necessary combined skills in computing and statistics. The major is part of UC Irvine’s Data Science Initiative, a coordinated effort to bring together researchers and students across campus involved in various aspects of data science.

    At the graduate level, students at ICS can pursue deeper educational opportunities in computer science, informatics, embedded systems, networked systems, software engineering, and statistics.

     


    Tsudik part of panel at UCI-Nossaman Cybersecurity Symposium

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor’s Professor of Computer Science Gene Tsudik took part in a panel at the 2015 UCI-Nossaman Cybersecurity Symposium at the City Club Los Angeles on Oct. 12. The symposium, titled “Cybersecurity, Data Breach and Privacy: A Dialogue on the Rising Risks and Evolving Legal Landscape,” was a joint effort by the UC Irvine School of Law and Nossaman LLP, a nationwide law firm that has made privacy and security one of its focus areas. The emphasis of the panel that Tsudik spoke on was “Not If, But When — Hack Offensives, Investigating Breaches, and Closing the Gaps on Data Leaks.”

     


    Postdoctoral scholar Per Larsen recognized as “DARPA Riser”

    photo: Per Larsen

    Defense Advanced Research Projects Agency (DARPA) has recognized assistant project scientist in computer science Per Larsen as a “DARPA Riser.” The early-career honor is conferred to “up-and-coming standouts in their fields, capable of discovering and leveraging innovative opportunities for technological surprise—the heart of DARPA’s national security mission,” DARPA says.

    Larsen, along with 54 other honorees from around the country, attended “Wait, What? A Future Technology Forum,” in September with special guest U.S. Secretary of Defense Ashton Carter (the gentleman on the left in the photo). The forum, which drew more than 1,200 participants from around the world, explored future technologies “on their potential to radically change how we live and work, and on the opportunities and challenges these technologies will raise within the broadly defined domain of national security,” according to the event website. Larsen was among a small subset of honorees who were treated to lunch with the U.S. Secretary of Defense.

    “DARPA organized Wait, What? to bring together forward-looking thinkers across a host of fields that are abundant with possibilities,” DARPA Director Arati Prabhakar said in the event press release. “In particular, our DARPA Rising effort aimed to identify and inspire some of the nation’s emerging leaders in research and technology—so we at DARPA can learn from them, and to make them aware of opportunities to apply their expertise in the important domain of national security.”

    Larsen works as a postdoctoral scholar with Computer Science Professor Michael Franz. His research interests include information security, including software diversity and exploits and mitigations; compilers, including profiling, randomization and control-flow integrity; and systems software, including interpreters and virtual machines.

     


    Van der Hoek to speak at SCSIM Fall Event

    photo: André van der Hoek

    André
    van der Hoek

    Department of Informatics Chair André van der Hoek will be speaking at the Southern California Society for Information Management (SCSIM) Fall Event: “The Southern California Disruptors—How Startups and the New Innovation Culture in Southern California are affecting IT” on Sept. 30 at the Long Beach Marriott. As the head of the UCI Software Design and Collaboration Lab, van der Hoek is part of a three-person panel that will relate their applicable experiences crucial to participating in the new business environment developing around us.

     


    SUMMER 2015

    Franz amasses $3.9 million in research funding

    photo: Michael Franz

    Michael
    Franz

    This year alone, Computer Science Professor Michael Franz has accumulated over $3.9 million in research funding from prestigious organizations such as the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), Qualcomm, Oracle and Mozilla. This follows his trend of more than $1 million per year on average in research expenditures.

    Franz currently runs two projects funded by DARPA’s Cyber Fault-Tolerant Attack Recovery (CFAR) Program, for which he received nearly $2 million and roughly $700,000 in May, respectively. The CFAR Program aims to “produce revolutionary breakthroughs in defensive cyber techniques that can be deployed to protect existing and planned software systems in both military and civilian contexts without requiring changes to the concept of operations of these systems,” according to a statement by program manager John Everett.

    Franz also runs a project funded by DARPA’s Vetting Commodity IT Software and Firmware Program (VET), which addresses “the threat of hidden malicious functionality in COTS (Commercial Off-the-Shelf) IT devices ... including mobile phones, printers, computer workstations and many other everyday items,” according to a statement by program manager Timothy Fraser. He received nearly $65,000 for this project.

    Finally, in July, Franz received nearly $620,000 from the NSF for a collaborative project titled “ENCORE—ENhanced program protection through COmpiler-REwriter cooperation.” According to the abstract, the project will produce “a prototype implementation consisting of a producer-side metadata derivation engine, and a consumer-side binary rewriting engine using this metadata to safely perform binary code manipulation.” In the past year, Franz has also received unrestricted gifts from Qualcomm, Oracle and Mozilla totaling $263,000.

     


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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements 2012-2013

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


    SUMMER 2013

    Papers by LUCI lab faculty, students lauded at UbiComp 2013

    photo: Don Patterson

    Don
    Patterson

    Associate professor Don Patterson and fellow authors of the research paper “Inferring High-Level Behavior from Low-Level Sensors” were presented the 10 Year Impact Award at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2013), held September 8-12 in Zurich, Switzerland.

    According to the award committee, the paper, first presented at UbiComp 2003, “is an excellent example of how one can learn very useful context information from simple GPS traces, and it formed the basis for today's thriving smart cities/smart transportation work. It is a nice example of how higher order information can be gleaned from everyday sensing — which is an important thread of work at Ubicomp and one of the enduring methods.”

    A UbiComp 2013 best paper award also was given to graduate students Lynn Dombrowski, Jed Brubaker and Sen Hirano, and faculty members Melissa Mazmanian and Gillian Hayes — authors of “It takes a network to get dinner: Designing location-based systems to address local food needs.”

     


    Tsudik delivers keynote at IEEE TrustCom-13

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor’s Professor Gene Tsudik gave the keynote talk “Secure and Minimal Architecture for Remote Attestation of Embedded Devices” at the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13), held July 16-18 in Melbourne, Australia.

    The annual conference brings together researchers and practitioners working on trusted computing and communications, and provides a forum to present and discuss emerging ideas and trends in this highly challenging research field.

    Tsudik’s research interests include many topics in security, privacy and applied cryptography. He serves as the Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC).

     


    Baldi selected as ISCB Fellow

    photo:  Pierre Baldi

    Pierre
    Baldi

    The International Society for Computational Biology has selected Chancellor’s Professor Pierre Baldi as an ISCB Fellow.

    The ISCB Fellows program honors members who have distinguished themselves through outstanding contributions to the fields of computational biology and bioinformatics. The 2013 fellows were recognized at the Intelligent Systems for Molecular Biology conference, held July 21-23 in Berlin.

    Baldi, who directs the Institute for Genomics and Bioinformatics at UC Irvine, also is a fellow of the American Association for the Advancement of Science, the Institute of Electrical and Electronics Engineers, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.

    His research focuses on understanding biological and artificial intelligence, through the development of machine learning and data mining approaches to study fundamental problems in chemo- and bio-informatics, systems biology, and computational neuroscience. His group has developed databases and software applications for use in numerous biology and chemistry settings, including comparing genomes, predicting protein properties, understanding gene regulation, and screening and designing new drugs.

     


    Ihler receives NSF CAREER Award

    photo:  Alexander Ihler

    Alexander
    Ihler

    Alexander Ihler, associate professor of computer science, has been awarded the National Science Foundation’s (NSF) Faculty Early Career Development (CAREER) Award for his project, “Estimation and Decisions in Graphical Models.” Ihler will receive $442,000 over five years for his CAREER project, which seeks to develop a new framework for exact and approximate methods for advanced computational reasoning problems. It extends the abilities of intelligent systems to reasoning and decision-making under uncertainty, and it applies and tests these methods on a variety of application domains, including sensor networks and computer vision.

    The CAREER program is the NSF’s most prestigious award for junior faculty members. Awardees are chosen because they exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organizations.

    Ihler joined the UC Irvine faculty in 2007. His research focuses on artificial intelligence and machine learning, specifically on statistical methods for learning from data and on approximate inference techniques for graphical models. Applications of his work include data mining and information fusion in sensor networks, computer vision and image processing, and computational biology.

     


    Smyth serves as program chair for UAI 2013

    photo:  Padhraic Smyth

    Padhraic
    Smyth

    Computer science professor Padhraic Smyth served as program chair for the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), held July 11-15 in Bellevue, Wash. Sponsored by Microsoft Research, Google, Facebook, Amazon, Toyota and IBM, UAI is the leading international conference on the use of probabilistic models and algorithms in artificial intelligence and machine learning. More than 240 papers were submitted to the conference; 73 were accepted for presentation at the meeting, after extensive peer review by a program committee of over 200 researchers in the area. Topics included approximate inference algorithms, machine learning methods, causal models, Markov decision processes, and applications in medical diagnosis, biology and text analysis.

     


    Tomlinson’s work contributes to UCI’s STARS gold rating

    photo: Bill Tomlinson

    Bill
    Tomlinson

    UC Irvine has received the prestigious STARS gold rating from the Association for the Advancement of Sustainability in Higher Education, in part due to informatics professor Bill Tomlinson’s efforts. The Sustainability Tracking, Assessment & Rating System measures and encourages responsible stewardship of Earth's resources in all aspects of higher education.

    Over the past several years Tomlinson, who heads the Social Code Group, and his team have undertaken a series of information technology projects — e.g., Software Engineering for Sustainability; Games, Education, and Sustainability; and Resource Sharing — that have contributed to the STARS gold rating.

    Tomlinson’s 2010 MIT Press book, “Greening through IT,” explores the ideas behind these projects, which have been presented at the National Science Foundation’s Workshop on the Role of Information Sciences and Engineering in Sustainability and at the National Academies’ Workshop on Innovation in Computing and Information Technology for Sustainability.

     


    Brock awarded Data Science for Social Good fellowship

    photo: John Brock

    John
    Brock

    Computer science Ph.D. student John Brock is among three dozen fellows taking part in the Eric and Wendy Schmidt Data Science for Social Good program this summer.

    The highly selective program — founded by Google chairman Eric Schmidt and the University of Chicago’s Rayid Ghani — allows fellows to work closely with governments and nonprofits to take on real-world challenges in such areas as education, health, energy and transportation. From June through August, they will apply their coding, machine learning and quantitative skills, collaborate in teams, and learn from mentors in industry, academia and the Obama campaign.

    The 36 fellows — a mix of graduate and undergraduate students from all over the world — were selected from a pool of 550 applicants.

    Brock, who last fall received the inaugural Butterworth Recruitment Fellowship from the Bren School, is primarily interested in applied machine learning, computing in developing countries and crisis informatics.

     


    Mazmanian receives early career faculty award from Intel

    photo: Melissa Mazmanian

    Melissa
    Mazmanian

    Assistant professor of informatics Melissa Mazmanian has been awarded $40,000 as part of the 2013 Intel Early Career Faculty Honor Program (ECFHP). The ECFHP was created to help Intel connect with the best and brightest early career faculty members at top universities around the world. The award supports recipients’ academic research and allows them to travel to Intel and/or Intel-sponsored events in order to network and collaborate with the company’s researchers.

    Mazmanian’s research interests revolve around the experience of communication technologies as used in-practice within organizational and personal contexts. She has conducted a variety of ethnographic and qualitative research projects on the individual experience and social dynamics that emerge when people adapt to using wireless modes of communication.

     


    Gillen joins NIH Center for Scientific Review study section

    photo: Dan Gillen

    Dan
    Gillen

    Associate professor of statistics Dan Gillen has been appointed to a six-year term as a member of the Biostatistical Methods and Research Design (BMRD) Study Section, a division of the National Institutes of Health’s Center for Scientific Review.

    Study sections review grant applications submitted to the NIH, make recommendations on these applications to the appropriate NIH national advisory council or board, and survey the status of research in their fields of science. Members are selected on the basis of their demonstrated competence and achievement in their scientific discipline as evidenced by the quality of research accomplishments, publications in scientific journals, and other significant scientific activities, achievements and honors. The BMRD Study Section reviews applications focused on the development and application of statistical methodology for biomedical studies.

    Gillen’s research focuses primarily on the development of statistical methodology for censored survival data and group sequential methods for the design and analysis of clinical trials. He serves as associate editor of the Journal of the American Statistical Association and the Journal of Statistical Software, and is past president of the Western North American Region of the International Biometrics Society.

     


    SPRING 2013

    Tsudik awarded Astor Visiting Lecturership

    photo: Gene Tsudik

    Gene
    Tsudik

    Computer science professor Gene Tsudik in May gave a talk on “Security and Privacy in Named-Data Networking” at the University of Oxford as an Astor Visiting Lecturer. Astor Visiting Lecturerships provide funding for weeklong visits by distinguished academics from the United States to Oxford. Only one per year is granted in the area of computer science.

    Tsudik currently serves as Director of Secure Computing and Networking Center (SCONCE) and Director of the Networked Systems (NetSys) Graduate Program at UC Irvine. Since 2009, he has been the Editor-in-Chief of ACM Transactions on Information and Systems Security (TISSEC).

     


    CS team receives ACM ICMR Best Paper Award

    photo: Dmitri Kalashnikov

    Dmitri
    Kalashnikov

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    photo: Liyan Zhang

    Liyan
    Zhang

    Associate adjunct professor of computer science Dmitri Kalashnikov, computer science professor Sharad Mehrotra and Ph.D. student Liyan Zhang received a Best Paper Award at the 2013 ACM International Conference on Multimedia Retrieval. Their paper, “A Unified Framework for Context Assisted Face Clustering,” develops a novel context-based methodology for improving the quality of automatic face clusters — a key component for face tagging and image management. The proposed framework is capable of fusing heterogeneous contextual features and self-tunes to a given image album by leveraging bootstrapping ideas. ACM ICMR 2013, held April 16-19 in Dallas, is a top international conference on multimedia retrieval.

     


    Smyth presents talk at SIAM International Conference on Data Mining

    photo: Padhraic Smyth

    Padhraic
    Smyth

    Computer science professor Padhraic Smyth gave an invited plenary talk on “Modeling Individual-Level Data in the 21st Century” at the Society for Industrial and Applied Mathematics (SIAM) International Conference on Data Mining. Considered one of the major international conferences in the field of data mining, the May 2-4 event in Austin, Texas, drew leading academic and industry researchers from North America, Europe and Asia. Smyth's talk described how we, in recent decades, have progressed from collecting relatively simple data about individuals (such as age and address) to much more detailed behavioral data from both the digital world (web browsing and searches, social media data, email), as well as the physical world (fitness, activity and sleep monitoring devices). In his talk, Smyth outlined what new algorithmic techniques will be required to analyze such data and how this type of analysis can benefit individuals in a variety of ways, including health monitoring and personal information management.

     


    NSF fellowship awarded to 1st-year Ph.D. student

    photo: Kristin Roher

    Kristin
    Roher

    Ph.D. student Kristin Roher has been awarded a 2013 National Science Foundation Graduate Research Fellowship (GRFP). She is among 32 UC Irvine students to have received the fellowship this year.

    The highly prestigious NSF Graduate Research Fellowship Program is designed to help ensure the vitality and diversity of the scientific and engineering workforce in the United States. It provides outstanding graduate students with three years of support, including a $30,000 annual stipend.

    Roher’s research goal is to contribute to the effort of aiding developing societies in becoming more environmentally sustainable through IT interventions — “greening through IT” — in hopes that it will help reduce the looming threats of global climate change and other environmental concerns. She is interested in designing technologies that will help software developers build systems that meet stakeholder needs while reducing the environmental impacts brought about by those systems — “Software Engineering for Sustainability.”

     


    Microsoft Research, Google Research and UC Mexus recognize Hayes

    photo: Gillian Hayes

    Gillian
    Hayes

    Assistant professor of informatics Gillian Hayes has been awarded three grants totaling $110,000 in support of her research on children with autism and on families of high-risk infants.

    Most recently Hayes received a 2013 Software Engineering Innovation Foundation (SEIF) award, presented by Microsoft Research, for her project “Empowering Interactive Surfaces with Body-Based Interactions to Provide Step-by-Step Guidance to Children with ASD.” SEIF supports academic research in software engineering technologies, tools, practices and teaching methods. Hayes’ proposal was one of 16 worldwide this year to receive a $25,000 SEIF award.

    In 2012 Hayes received a $60,000 Google Faculty Research Award for her proposal “Providing Privacy-Sensitive Social Support for Families of High-Risk Infants Using Mobile Computing.” Google Research Awards support the work of world-class full-time faculty members at top universities around the world. Additionally, Hayes and co-PI Monica Tentori of CICESE (The Ensenada Center for Scientific Research and Higher Education) received $25,000 in seed funding from UC Mexus (the University of California Institute for Mexico and the United States) for their project “Enriching Interactive Visual Supports with Video Modeling for Children with Autism.”

    Hayes is director of the Social & Technological Action Research (STAR) group and director of technology research at the Center for Autism and Neurodevelopmental Disorders of Southern California. Her research focuses on vulnerable populations in their efforts to understand their own data.

     


    Harris, student receive Best Paper Cyber Security

    photo: Ian Harris

    Ian
    Harris

    Associate professor of computer science Ian Harris and his student Zi-Shun Huang received the Best Paper Cyber Security award at the 13th annual IEEE Conference on Technologies for Homeland Security (IEEE HST) for “Return-Oriented Vulnerabilities in ARM Executables.” The paper explores detecting security vulnerabilities in ARM processors, the predominant type of processor used in mobile and embedded applications. IEEE HST is the leading international conference addressing the challenges of homeland security technology innovation gaps. It brings together innovators from leading academic, industry, business, Homeland Security Centers of Excellence, and government programs to provide a forum to discuss ideas, concepts, and experimental results. Harris' research interests include functional verification, natural language processing for design automation, and embedded systems security. Huang’s research has a strong focus on security issues and attack/defense on ARM processors.

     


    WINTER 2013

    Richardson receives Retrospective Paper Award

    photo: Debra Richardson

    Debra
    Richardson

    Informatics professor Debra Richardson and co-authors Stephanie Leif Aha and T. Owen O'Malley have received a 2013 ACM SIGSOFT Retrospective Paper Award for “Specification-based Test Oracles for Reactive Systems,” which appeared in ICSE ’92: Proceedings of the 14th International Conference on Software Engineering.

    A leader in software engineering research, Richardson inspired much of the work in “specification-based testing,” beginning with her early development of the Partition Analysis Method, which proposed incorporating information from both specification and implementation in an integrated application of verification and testing techniques.

     


    Fowlkes receives NSF CAREER Award

    photo: Charless Fowlkes

    Charless
    Fowlkes

    Charless Fowlkes, assistant professor of computer science, has been awarded the National Science Foundation’s (NSF) Faculty Early Career Development (CAREER) Award for his project, “Combinatorial Inference and Learning for Fusing Recognition and Perceptual Grouping.” The CAREER program is NSF’s most prestigious award for junior faculty members. Awardees are chosen because they exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organizations.

    Fowlkes will receive more than $500,000 over five years for his CAREER project. His research focuses on computational vision, both in understanding the information processing capabilities of the human visual system and in developing machine vision systems. He also works on developing tools for biological image analysis in order to measure morphology and spatial patterns of gene expression in developing animals.

     


    Assembling complete individual genomes: Xie, Li awarded $662K NIH grant

    photo: Xiaohui Xie

    Xiaohui
    Xie

    photo: Chen Li

    Chen
    Li

    Computer science faculty Xiaohui Xie and Chen Li have been awarded a three-year grant of nearly $662,000 from the National Institutes of Health (NIH) to develop new computational tools essential for future advances in sequencing human genomes.

    DNA sequencing has become an indispensable tool for basic biomedical research, understanding disease mechanisms and the development of new, increasingly personalized treatments. Current sequencing of an individual’s genome is done by extracting short segments of DNA, randomly sampling those segments and then assembling the hundreds of millions, or even hundreds of billions, of pieces into a whole genome, which remains a costly undertaking. Next-generation sequencing (NGS) techniques, which enable the rapid generation of billions of bases of genes at relatively low cost, pose a significant computational challenge on how to analyze the large amount of sequence data efficiently and accurately.

    “Although a number of computational tools have been developed to address this problem, they are insufficient in mapping and studying genome features located within repeat, duplicated and other so-called unmappable regions of genomes,” says Xie.

    The primary goal of his NIH-funded research is to develop computational algorithms and open-source software to improve both the efficiency and accuracy of NGS analysis tools and expand the accessibility of those tools to previously understudied regions. This will create a new way of mapping the sequencing to the reference genome, identifying all mapping locations instead of one or only a few. That will be followed by a machine-learning method to resolve ambiguously mapped reads by pooling information from the entire collection of reads.

    UCI computer scientists on the project are collaborating with biologists developing NGS assays to study biomedical problems, including Timothy Osborne of Sanford-Burnham Medical Research Institute, Kyoko Yokomori of the UCI School of Medicine and Ken Cho of UCI’s School of Biological Sciences.

    The UCI team’s work to reduce the cost and improve the accuracy of sequencing ultimately may make it possible to create an individual patient’s genome as part of a routine diagnostic procedure.

     


    Meenakshisundaram, Mukherjee receive best paper award at ICVGIP 2012

    photo: Gopi Meenakshisundaram

    Gopi
    Meenakshisundaram

    photo: Uddipan Mukherjee

    Uddipan
    Mukherjee

    “Tweening Boundary Curves of Non-Simple Immersions of a Disk,” by associate professor of computer science Gopi Meenakshisundaram and Ph.D. student Uddipan Mukherjee, won the sole best paper award at the eighth Indian Conference of Computer Vision, Graphics and Image Processing (ICVGIP) held in Mumbai, India.

    Tweening, also known as shape morphing, is an important concept in keyframe animation wherein an initial shape is transformed smoothly into a final shape. The paper introduces a robust tweening algorithm capable of creating smooth transformations between non-simple polygonal shapes that are immersions of a disk. All the intermediate shapes that are generated by the authors’ algorithm are guaranteed to be disk immersions.

    Both Meenakshisundaram and Mukherjee are members of UC Irvine’s Interactive Graphics and Visualization lab, also known as iGravi. Meenakshisundaram’s research focuses primarily on topics related to geometry and topology motivated by problems in computer graphics and interactive rendering, medical and biological image processing and visualization. Mukherjee’s research interests include computer graphics, geometry processing, image processing and multimedia.

     


    Enhancing privacy in embedded systems: Tsudik receives grant from Bosch

    photo: Gene Tsudik

    Gene
    Tsudik

    Bosch Research and Technology Center has awarded computer science professor Gene Tsudik $50,000 to support his efforts to enhance privacy in embedded systems. This grant represents the first partnership between Bosch and UC Irvine.

    The term “embedded systems” refers to systems that control specific functions within a larger structure. Embedded systems help control everything from pacemakers, security systems and cell phones to planes, trains and automobiles. Used by the billions in countless applications, these systems have become essential to daily life. As long as they operate safely and effectively, most of us never even think about them.

    Tsudik, who also directs the Secure Computing and Networking Center (SCONCE) at UCI, continuously explores ways to increase the security of such systems. Bosch Research and Technology Center — the research arm of one of the world’s leading manufacturers of appliances, automotive components, security systems, medical equipment and many other devices dependent on embedded systems — sought Tsudik’s collaboration in research on embedded systems security.

    As more devices use embedded systems, and as these systems increasingly communicate with other equipment, privacy becomes even more challenging and essential. “Communication between embedded systems is like opening a new door,” Tsudik observes. “Once that door is open, you never know who — or what — might come in.”

    Through his collaboration with Bosch and his ongoing research, Tsudik seeks to improve security and to maintain integrity of embedded devices in the most efficient manner.

     


    Li, Mehrotra receive DASFAA 10-year Best Paper Award

    photo: Chen Li

    Chen
    Li

    Sharad Mehrotra

    Sharad
    Mehrotra

    Computer science professors Chen Li and Sharad Mehrotra have won the 2013 DASFAA 10-year Best Paper Award, to be presented by the Database Systems for Advanced Applications at its 18th international conference. The award recognizes the best paper from DASFAA proceedings 10 years prior, based on the criterion that the paper has had the biggest impact (research, products, methodology) over the last decade.

    Li and Mehrotra’s 2003 paper, “Efficient Record Linkage in Large Data Sets,” co-authored with Liang Jin (M.S. ’03) describes an efficient and accurate approach to record linkage.

    The annual DASFAA international database conference provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. DASFAA 2013 will be held April in Wuhan, China.

    Mehrotra’s current research focuses on building sentient spaces using multimodal sensors, data privacy and data quality. Li’s research interests are in the fields of databases and information retrieval, including search, data-intensive computing, data integration and sharing, data warehouses, data cleansing, and Web information management.

    In 2012, Li and Mehrotra won the SIGMOD Test-of-Time Award for their paper, “Executing SQL over Encrypted Data in the Database-Service-Provider Model.” The paper was co-authored by Hakan Hacigumus (M.S. ’02, Ph.D. ’04) and IBM collaborator Bala Iyer.

     


    NSF awards $500K grant to Welling, Shahbaba

    Max Welling

    Max
    Welling

    Babak Shahbaba

    Babak
    Shahbaba

    The National Science Foundation has awarded computer science professor Max Welling and assistant professor of statistics Babak Shahbaba $500,000 to fund the project "Efficient Bayesian Learning from Stochastic Gradients."

    Markov Chain Monte Carlo (MCMC) is a technique that allows one to draw representative samples from almost any probability distribution. While the MCMC technology has revolutionized the usefulness of Bayesian statistics over the last few decades, it has not been able to scale well to today’s very large data problems. Welling and Shahbaba will examine a new family of MCMC procedures that requires only a few hundred data-cases per update.

    "We believe this new class of methods will for the first time unlock the full strength of Bayesian methods for very large datasets," stated Welling and Shahbaba in their NSF proposal. "Due to their highly practical nature, the techniques developed under this grant are likely to gain widespread acceptance across a broad spectrum of academic disciplines as well as in industry."

    The grant will enable UC Irvine students to collaborate with students and postdocs from the University of Oxford and the University of Bristol. Research results will be integrated into artificial intelligence and machine learning courses at UCI through class projects.

     


    Johnson elected to IBS executive board

    Wesley Johnson

    Wesley
    Johnson

    Statistics professor Wesley Johnson has been elected to the International Biometric Society (IBS) executive board. IBS promotes the development and application of statistical and mathematical theory and methods in the biosciences. Members include statisticians, mathematicians, biological scientists and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences.

    Johnson’s eclectic research interests include developing Bayesian statistical methods for biostatistical and epidemiologic applications. He is currently involved with collaborative efforts to develop Bayesian nonparametric and semi-parametric methods in survival analysis, longitudinal analysis, analysis of diagnostic outcome data, and joint modeling of survival and longitudinal data.

     


    Nardi, alumna elected to 2013 CHI Academy

    Bonnie Nardi

    Bonnie
    Nardi

    Beki Grinter

    Beki
    Grinter

    SIGCHI (Special Interest Group on Computer-Human Interaction) has announced that informatics professor Bonnie Nardi and Bren School alumna Beki Grinter (MS ’94 and Ph.D. ’96) have been elected to the 2013 CHI Academy — an honorary group of individuals who have made substantial contributions to the field of human-computer interaction.

    SIGCHI is the premier international society for professionals, academics and students who are interested in human-technology and human-computer interaction. CHI Academy members are considered principal leaders whose efforts have shaped the discipline and/or industry, and led the research and/or innovation in HCI. The criteria for election are:

    • cumulative contributions to the field
    • impact on the field through development of new research directions and/or innovations
    • influence on the work of others

    Nardi is the fourth Bren School professor elected to the CHI Academy, joining fellow informatics faculty Paul Dourish (2008), Gary Olson (2003) and Judy Olson (2001). The Olsons also were recognized in 2006 with a Lifetime Achievement Award, the most prestigious honor given by SIGCHI. A complete list of the 2013 SIGCHI awardees is available here.

    Nardi’s research interests include activity theory, games and social media, interaction design, and society and technology. Her recent books include: “Ethnography and Virtual Worlds: A Handbook of Method,” written with Tom Boellstorff, Celia Pearce and T.L. Taylor, published in September 2012 by Princeton University Press, and “Materiality and Organizing: Social Interaction in a Technological World,” co-edited with Paul M. Leonardi and Jannis Kallinikos, published in January 2013 by Oxford University Press. Grinter is a professor in the School of Interactive Computing at the Georgia Institute of Technology.

     


    FALL 2012

    CS professors, students receive SmartGridComm Best Paper Award

    photo: Lubomir Bic

    Lubomir
    Bic

    photo: Michael Dillencourt

    Michael
    Dillencourt

    Graduate students Kiyoshi Nakayama and Kyle Benson, along with computer science professors Lubomir Bic and Michael Dillencourt, have received the 2012 IEEE International Conference on Smart Grid Communications Best Paper Award for “Complete Automation of Future Grid for Optimal Real-Time Distribution of Renewables.”

    According to the abstract: “In this paper, a novel distributed control technique, which integrates tie-set graph theory with an intelligent agent system, is presented to distribute renewable energy resources to consumers in a future large-scale power grid connecting with huge amounts of real-time end-use devices on its demand side automatically and perfectly.”

    The IEEE International Conference on Smart Grid Communications (SmartGridComm) is the premier conference aimed at developing the Smart Grid, which has become an urgent global priority — promising economic, environmental and societal benefits.

     


    Eppstein receives GD2012 best paper award

    46-vertex Halin graph formed from a complete ternary free tree

    “Planar Lombardi Drawings for Subcubic Graphs,” authored by computer science professor David Eppstein, has recieved the best paper award in the combinatorial and algorithmic aspects track of the 20th International Symposium on Graph Drawing.

    Lombardi drawing is a style of information visualization inspired by the art of Mark Lombardi, who drew social networks in which the nodes and edges represent the players and financial connections in international conspiracies — using a style characterized by curved edges and even node spacing. Eppstein’s paper uses circle packings and hyperbolic geometry to show how to construct Lombardi drawings for every planar graph that has, at most, three edges per node.

    Eppstein’s research focuses on many topics in computational geometry and graph algorithms, including: graph drawing and information visualization; dynamic graph algorithms and dynamic closest pair data structures; and mesh generation and optimal triangulation.

     


    Securing the Internet’s next generation: Tsudik receives $95K from Cisco

    photo: Gene Tsudik

    Gene
    Tsudik

    Cisco Systems has awarded $95,000 to help support the work of computer science professor Gene Tsudik in making the next-generation Internet more secure. An expert in computer security and privacy, Tsudik leads a team that is exploring security advantages and strengthening potential weaknesses of Named-Data Networking (NDN) in preventing denial-of-service (DoS) attacks. Such attacks disable their victims by overloading them with communications from “zombies” or “bots” — that is, computers that have already been compromised by hackers for malicious purposes.

    NDN aims to replace the current Internet Protocol (IP). Rather than the IP method of assigning names to computers and addresses to their network interfaces, NDN names the content that travels through the network. To provide trust in communicated information, each piece of named content must be signed by its producer.

    The current IP-based Internet allows anyone to “talk” to any entity, such as a computer or a router. This opens the door for DoS and attack types. And attacks are not the only problem with today’s Internet. Tsudik says the Internet’s decades-old architecture “was not designed for the kind of communication that takes place over it today: web traffic and rich multimedia communication. It was primarily designed for email and remote terminal access.”

    In other words, the Internet is not just threatened by hackers and other miscreants; it is also a victim of its own popularity. Billions of people use it intensively, and cracks are starting to show.

    Tsudik is one of a number of experts from dozens of institutions who answered the call from the National Science Foundation to construct a more resilient future Internet architecture. To make that next incarnation safer from attacks (such as DoS), Tsudik and his group at UCI are collaborating with colleagues at the Palo Alto Research Center — where the security team is lead by Ersin Uzun, a former Ph.D. student of Tsudik’s — and UCLA. They are also jointly exploring privacy protection techniques for NDN.

     


    Patterson receives AIJ Prominent Paper Award

    photo: Donald Patterson

    Donald
    Patterson

    Informatics associate professor Donald Patterson, with co-authors Lin Liao, Dieter Fox and Henry Kautz, have been recognized with the inaugural AIJ Prominent Paper Award. Their paper, “Learning and inferring transportation routines,” was published in volume 171 (April 2007) of the Artificial Intelligence journal. It introduces a hierarchical Markov model that can learn and infer a user’s daily movements through an urban community, and applies it in an application that helps cognitively-impaired people use public transportation safely.

    The recently instituted AIJ Prominent Paper Award recognizes outstanding papers published not more than five years ago in the AI Journal that are exceptional in their significance and impact. Factors considered for the award include: the paper’s influence on a new line of research, whether the paper has made any major theoretical advances, and whether the paper has influenced applications.

    Patterson said he and his co-authors have decided to donate their 500 Euro prize — which Google will match 1:1 — to the Alzheimer’s Association, “which is in line with the ideas in the paper.” Patterson’s current research interests focus on context-aware computing and how to make a computer operate appropriately when it leaves the office and moves into the greater world. He is a faculty affiliate of LUCI (Laboratory for Ubiquitous Computing and Interaction).

     

     

     

     


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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements 2009

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements for 2009.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to icsnews@ics.uci.edu to be considered for publication.


    DECEMBER 2009

    Tsudik and Uzun awarded Google Research Award

    photo: gene tsudik

    Gene
    Tsudik

    Gene Tsudik and Ersin Uzun (PhD Candidate, Netsys Program) have been awarded a Google Research Award for $50,000 for a research effort entitled "Secure and Usable Group Association of Personal Wireless Devices."

    The goal of this research is to come up, and experiment with, scalable, and usable techniques for spontaneous *secure* association among a group of wireless devices and gadgets. The group device association problem arises whenever a set of individuals, each "armed" with a personal wireless device, want to set up a common secure communication channel for the purpose of a short-term meeting or a longer-term collaboration. It also figures prominently in emergency response and law enforcement settings.

    This project dovetails from a larger NSF-funded project (jointly with Professor Alfred Kobsa) on secure and usable association of wireless devices.

    Tsudik's research interests are mainly in computer/network security, privacy and applied cryptography. His recent work focuses on privacy in Internet services, RFID systems and mobile ad hoc networks, as well as security in sensor networks and storage systems. His research also covers secure group communication, in particular, group key agreement, group signatures and group access control.


    Tsudik appointed Cor Wit Chair

    photo: gene tsudik

    Gene
    Tsudik

    Gene Tsudik, professor of computer science, has been appointed Cor Wit Chair at Delft University of Technology in the Netherlands.

    Established in 2003 by the Cor Wit Foundation, the Cor Wit chair is awarded annually to international researchers in the field of telecommunications and computer systems whose research focuses on the interface of technology and society. Gene Tsudik's core research is in Network Security & Privacy with emphasis on cryptographic protocols in the context of wireless, sensor and mobile ad hoc networks.

    Recipients are invited to work in the Telecommunications Department of the Faculty Electrical Engineering, Mathematics and Computer Science (EEMCS) at Delft University of Technology.

    Associate Dean Magda El Zarki was previously appointed Cor Wit chair in 2006.

    Tsudik's research interests are mainly in computer/network security, privacy and applied cryptography. His recent work focuses on privacy in Internet services, RFID systems and mobile ad hoc networks, as well as security in sensor networks and storage systems. His research also covers secure group communication, in particular, group key agreement, group signatures and group access control.


    NOVEMBER 2009

    Lopes speaks at Intel CTO keynote address

    photo: Prof. Cristina Lopes (UCI, Informatics), Justin Rattner  (CTO Intel)

    Prof. Cristina Lopes (UCI, Informatics), Justin Rattner (CTO Intel)

    Cristina Lopes, Associate Professor of Informatics participated as a guest speaker in the keynote address at SuperComputing '09 in Portland Oregon. The Keynote was delivered by Justin Rattner, CTO of Intel. Lopes was a guest at Rattner's keynote, in her role as one of the main architects of OpenSim.

    Justin Rattner's opening keynote address at SuperComputing'09 message addressed how the super-computing industry has stagnated, and the only thing that will save it from collapse is a drastic change on what people think of as "super-computing." Other guests were Aaron Duffy, a biology researcher at Utah State University, and Shenlei Winkler, CEO of the Fashion Design Institute.

    Lopes' research is related to languages and communication systems. The ultimate goal of her research is to deepen the knowledge about communication, in particular in systems that involve humans and machines. With this utopic goal in mind, she has done work in a variety of fields such as programming languages, security and applications of audio signal processing.


    Navarro, Van der Hoek win Premier Award for SimSe

    Project scientist Emily Navarro and Associate Dean André van der Hoek have been recognized with the 2009 Premier Award for SimSE, a game-based educational software engineering simulation environment that allows students to practice "virtual" software engineering processes in a graphical, interactive and fun setting.

    SimSE's direct, graphical feedback enables students to learn the complex cause and effect relationships underlying software engineering processes. During the game, the student takes on the role of the project manager and directs engineers to perform typical process tasks.

    SimSE helps bridge the gap between the conceptual knowledge about software engineering that is presented in lecture but that often times is not fully explored or practiced in assignments or projects.

    SimSE includes a customizable modeling environment that allows instructors to create new scenarios, application domains, organizations and cultures. SimSE has been used worldwide and has been found to be an educationally effective tool that increases students' understanding of software engineering process concepts.

    Van der Hoek's research focuses on understanding and advancing the role of design, coordination, and education in software engineering. He has authored and co-authored over 80 peer-reviewed journal and conference publications, and in 2006 was a recipient of an ACM SIGSOFT Distinguished Paper Award. He is a co-author of the 2005 Configuration Management Impact Report as well as the 2007 Futures of Software Engineering Report on Software Design and Architecture.


    ICS Alum and UT Faculty Arthur Reyes Passes

    photo:

    Arthur
    Reyes

    Arthur Reyes, Ph.D. '99 died unexpectedly this week in Arlington, Texas. He was a faculty member in the Department of Computer Science and Engineering at the University of Texas Arlington.

    Reyes was a software engineering student under Dean Debra J. Richardson. Details regarding his funeral services will be made available at the UTA website.

     


    OCTOBER 2009

    Southern California Computer Vision Meet-Up brings over 80 local researchers to UC Irvine

    On October 30, 2009, the Computational Vision group at UC Irvine hosted the second annual Southern California Computer Vision Meetup, a day of talks and discussion on the latest results in computer vision. Topics of discussion ranged from surveillance and automotive safety, to differentiating white blood cells by qualitatively analyzing a single image.

    Over 80 researchers attended the day-long event, including faculty and students from UC Irvine, USC, UC Riverside, UCLA, UC San Diego, and Caltech, as well as researchers from JPL, Google and Evolution Robotics.

    The Computational Vision Lab studies computational vision, seeking to understand both the information processing capabilities of biological visual systems and develop computer vision systems. Researchers are interested in theoretical questions as well as practical applications ranging from motion capture to biological image analysis.

    For more information about Computer Vision at UCI, visit: http://vision.ics.uci.edu/.


    Majumder, Sajadi receive paper award at IEEE Visualization

    photo: Aditi Majumder

    Aditi
    Majumder

    A paper entitled, "Markerless View-Independent Registration of Multiple Distorted Projectors on Extruded Surfaces Using an Uncalibrated Camera" by Computer Science professor Aditi Majumder and graduate student Behzad Sajadi has won the runner up in the Best Paper Award at the IEEE Visualization 2009 conference held in Atlantic City this month.

    The paper presents the first algorithm to geometrically register multiple projectors in a view-independent manner on a common type of curved surface, vertically extruded surface, using an uncalibrated camera without attaching any obtrusive markers to the display screen. This simple markerless registration has the potential to have a large impact on easy set-up and maintenance of large curved multi-projector displays, common for visualization, edutainment, training and simulation applications.

    IEEE Visualization is the premier annual forum for visualization advances for academia, government, and industry. This event brings together researchers and practitioners with a shared interest in visualization tools, techniques, and technology.

    Majumder's research addresses how to produce a seamless image on a large-scale tiled display - an important problem to both the scientific and entertainment fields. She has developed a suite of mathematical models, methods and software to correct the geometric and color variations that arise when tiling multiple projection displays.


    Kobsa named Associate Editor of new ACM journal

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Informatics Professor Alfred Kobsa has been appointed Associate Editor of the new "ACM Transactions on Intelligent Interactive Systems" (TIIS). The journal will publish research articles concerning the design, realization, and/or evaluation of interactive systems that exhibit some form of "intelligent" behavior.

    The Journal's focus will be on sensing and perception, knowledge representation and reasoning, learning, creativity, planning, autonomous motion and manipulation, natural language processing, and social interaction.

    Kobsa's research lies in the areas of user modeling and personalized systems (with applications in the areas of information environments, expert finders, and user interfaces for disabled and elderly people), privacy, and in information visualization.

    He is also the editor of "User Modeling and User-Adapted Interaction: The Journal of Personalization Research", edited several books and authored numerous publications in the areas of user-adaptive systems, human-computer interaction and knowledge representation.


    Fowlkes, Ramanan and Desai awarded Marr Prize in Kyoto, Japan

    photo: Charless Fowlkes

    Charless
    Fowlkes


    photo: Deva Ramanan

    Deva
    Ramanan

    A paper entitled "Discriminative models for multi-class object layout" by PhD student Chaitanya Desai and Assistant Professors Deva Ramanan and Charless Fowlkes received the Marr Prize at the International Conference on Computer Vision (ICCV) held the first week of October in Kyoto, Japan.

    The prize is awarded to the best paper at ICCV and is considered one of the top honors in computer vision. The award is named after David Marr, a theoretical neuroscientist who made profound contributions to the theory of both human and machine vision in the 1970's.

    The paper describes research on a new approach to modeling contextual relations between objects in an image (e.g. bottles are often seen resting on top of tables but not the other way around). The system automatcially learns these relations from example images and uses this information to outperform existing approaches to object detection.

    Fowlkes' research is in computational vision, both in understanding the information processing capabilities of the human visual system and in developing machine vision systems. He is also interested in applying computer vision techniques to automating the analysis of biological data and developing algorithmic tools for understanding morphology and spatial aspects of gene expression.

    Ramanan's research interests span computer vision, machine learning, and computer graphics. His past work focused on the analysis of human movement from video, including tracking people and recognizing their actions. Current interests include object recognition, large-scale image/video processing, structured-prediction approaches to learning, and activity recognition.


    SEPTEMBER 2009

    Dourish receives $500,000 grant for social networking study in Kazakhstan, Russia

    photo:  paul dourish

    Paul
    Dourish

    Paul Dourish, Professor of Informatics, has received a $500,000 National Foundation Grant for his research entitle "From Local Ties to Transnational Connections: The Role of Computer-mediated Communication in Relational Maintenance." The research is in collaboration with Irina Shklovski, an assistant professor at the IT University in Copenhagen, Denmark, and formerly a post-doc at the Bren School.

    The focus of the grant is a study of the use of social networking software and web sites in Kazakhstan and Russia, looking at the cultural specificities of information technology use.

    The research investigates the ways culturally embedded expectations of personal networks shape the use of social network sites and other Internet-based social applications for relational maintenance. Dourish and Shklovski will also explore the way people reconnect with contacts that relocate to other cities and, more often, other countries and how such connections may define new transnational forms of belonging.

    Dourish's primary research interests are in the areas of Computer-Supported Cooperative Work, Human-Computer Interaction, and Ubiquitous Computing. He is especially interested in the foundational relationships between social scientific analysis and technological design.


    Eppstein wins best paper award at WADS

    photo: andre van der hoek

    David
    Eppstein

    A paper by computer science professor David Eppstein and former ICS student Kevin A. Wortman, "Optimal Embedding into Star Metrics," has received the best paper award from the 11th Algorithms and Data Structures Symposium (WADS), held this August in Banff, Canada.

    Their research concerns algorithms for choosing the location of a central facility such as an airline hub in order to minimize the increase in distance caused by traveling through the hub instead of taking a direct route. The WADS best paper award was announced on the conference web site after the conference was held, and comes with a prize of 500 Euro in books from Springer-Verlag.

    Eppstein's research interests are varied and include geometric optimization, finite element mesh generation and mesh improvement, information visualization and graph drawing, robust statistics and estimation of web-graph properties, graph theory and graph algorithms, exponential-time algorithms for NP-hard problems, and cellular automata and combinatorial games.


    SCONCE students Uzun and Wang named UCI Graduate Dean's Dissertation Fellows

    Secure Computing & Networking Center (SCONCE) Ph.D. candidates Ersin Uzun and Yang Wang have been awarded the UCI Graduate Dean's Dissertation Fellowship for Fall 2009.

    They are among only 25 recipients campus-wide. This fellowship frees students from non-research related employment, allowing them focus on completion of their dissertations.

    Uzun's research focuses on usable security and Wang's research revolves around balancing privacy and personalization.


    Franz receives $600,000 NSF CyberTrust Grant

    photo: andre van der hoek

    Michael
    Franz

    Michael Franz, Professor of Computer Science, has received a $600,000 grant from the National Science Foundation's CyberTrust program. His project with co-PI Cormac Flanagan from UC Santa Cruz will investigate next-generation infrastructure for trustworthy web applications.

    Many services traditionally performed by stand-alone programs running on desktop computers are being migrated to "Web 2.0" applications, remote services that reside "in the cloud" and are that accessed through a browser. This migration process offers a unique opportunity to re-engineer the way that software is constructed, adding some extra capabilities that reduce the vulnerability of the global information infrastructure to problems such as viruses, cyber-attacks, loss of privacy, and integrity violations.

    With this goal in mind, this project designs and implements a next-generation infrastructure for trustworthy web applications. It evolves the existing Web 2.0 technologies into a more trustworthy "Web 2.Sec" version by introducing information-labeling and strong information-flow controls pervasively at the service provider, at the user's end, and on all paths in between.

    A key feature of the new Web 2.Sec architecture is that all application programs are executed on top of a virtual machine (VM) rather than directly on physical hardware. Hence the VM retains full control over the data at all times, allowing it to enforce information-þow policies that guarantee conÞdentiality and integrity. Even a malicious or faulty program running on top of the Web 2.Sec VM cannot cause any action that would violate these policies.

    Franz is an expert on virtual machines, mobile-code security, and dynamic compilation. He is the co-inventor (with a graduate student) of the Trace Compilation method that now drives JavaScript performance in Mozilla's Firefox browser. He has graduated 13 Ph.D. students, been awarded more than $7 Million in competitive Federal research funding as Principal Investigator, and has published more than 90 refereed papers.


    Van der Hoek receives NSF grant for the improvement of software design education

    photo: andre van der hoek

    André van
    der Hoek

    Associate Dean André van der Hoek has received a $500,000 National Science Foundation grant to research software design education, specifically to understand and innovate tool support, design exercises and course modules for sketch-based design practice and reflection.

    Van der Hoek's research will take an introspective look at the process by which a student or group of students arrive at a final software design. He hopes to delve into the alternatives students consider, as well as the thought processes students go through before arriving at a final product. His project will observe actual software designers in
    action and implement a tool for creative, sketch-based software design, and migrate to a studio-based approach to software design education.

    Through this research, van der Hoek hopes to enable students to gain a broader and deeper understanding of software design, and provide
    instructors with an enhanced portfolio of teaching methods.

    Van der Hoek's research focuses on understanding and advancing the role of design, coordination, and education in software engineering. He has authored and co-authored over 80 peer-reviewed journal and conference publications, and in 2006 was a recipient of an ACM SIGSOFT Distinguished Paper Award. He is a co-author of the 2005 Configuration Management Impact Report as well as the 2007 Futures of Software Engineering Report on Software Design and Architecture.

    Van der Hoek was honored, in 2005, as UC Irvine Professor of the Year for his outstanding and innovative educational contributions.


    Goodrich, Kobsa and Tsudik awarded $300,000 NSF grant to study Usable Location Privacy

    photo: michael goodrich

    Michael
    Goodrich


    photo: alfred kobsa

    Alfred
    Kobsa


    photo: gene tsudik

    Gene
    Tsudik

    Computer Science Professors Michael Goodrich, Alfred Kobsa and Gene Tsudik have been awarded $300,000 by the National Science Foundation for research on Usable Location Privacy in Geo-Social Networks.

    The project will explore the usability, feasibility, and scalability of preserving privacy and securing location-aware geo-social networking platforms on mobile devices, such as Google Latitude. The research group is basing their project on a belief that security and privacy can best be incorporated with usability at the beginning of its design.

    The research will focus on the usability of privacy-agile secure location-based communication and supporting protocols that scale to large numbers of users and accommodate various privacy levels suitable for different application domains.

    This project envisions a wide range of future applications, with three unifying factors: (1) geo-social undertone, i.e., applications that combine social groups and locality, (2) lack of, or need to avoid using, fixed infrastructure facilities, and (3) need for both security and privacy.

    Goodrich's research is directed at the design of high-performance algorithms and data structures for solving large-scale problems surrounding the increased demands of computer graphics, information visualization, scientific data analysis, information assurance and security, and the Internet. He also is interested in computer science education, specifically ways of more effectively teaching data structures and algorithms.

    Kobsa's research lies in the areas of user modeling and personalized systems (with applications in the areas of information environments, expert finders, and user interfaces for disabled and elderly people), privacy, and in information visualization.

    Tsudik's research interests are mainly in computer/network security, privacy and applied cryptography. His recent work focuses on privacy in Internet services, RFID systems and mobile ad hoc networks, as well as security in sensor networks and storage systems. His research also covers secure group communication, in particular, group key agreement, group signatures and group access control.

     


    AUGUST 2009

    Professor Jordan receives $500,000 NSF grant for Internet architecture and public policy integration

    photo: jscott jordan

    Scott
    Jordan

    Professor of Computer Science Scott Jordan has received a $500,000 National Science Foundation grant to incorporate telecommunications policy and economics into the Internet architecture.

    “As a result of technical, economic and public policy forces, the Internet's original design principles – layering and end-to-end – are increasingly violated,” says Jordan.

    Currently, Internet Service Providers (ISPs) are deploying quality of service mechanisms, but only allowing their use for certain applications sold to their own subscribers. Some ISPs have used deep packet inspection techniques to implement traffic management practices that throttle or block peer-to-peer applications.

    Professor Jordan hopes to counteract this deterioration by proposing an interdisciplinary approach that updates the Internet architectural principles to account for telecommunications policy and economics.

    The project will identify the flaws of the end-to-end and layering models that are not withstanding the technical, economic, and legal forces upon networking. The project aims to modify these models so that they promote good technical design, respond appropriately to economic pressures, and encourage societally beneficial outcomes.

    To validate these new models, Professor Jordan plans to illustrate their potential use by applying them to three case studies – net neutrality, traffic management, and Quality of Service.

    Professor Jordan is also developing an undergraduate course on “The Internet and Public Policy”. This research will help bridge the gulf that exists between communication lawmakers and networking researchers by informing staff members in the United States Congress about the technical aspects of telecommunication issues, and by developing an architectural framework for the networking research community to help them consider impacts of network economics and law.

    Professor Jordan’s research interests currently include pricing and differentiated services in the Internet, resource allocation in wireless multimedia networks, and telecommunications policy.


    Franz, Tsudik take part in National Cyber Leap Year Summit

    photo: michael franz

    Michael
    Franz


    photo: chen li

    Gene
    Tsudik

    Professors of Computer Science Michael Franz and Gene Tsudik took part in the National Cyber Leap Year Summit (NCLY), organized by the Networking and Information Technology Research and Development (NITRD) Program. Tsudik served as the co-chair of the Digital Provenance group, which addressed the issue of basing trust decisions on verified assertions. The summit was held August 17-19 in Washington, D.C.

    NITRD is the Nation's primary source of Federally funded revolutionary breakthroughs in advanced information technologies such as computing, networking, and software.

    The National Cyber Leap Year initiative is the result of the call from The White House Office of Science and Technology to secure our nation's cyber infrastructure. NCLY take a complementary approach to the traditional methodology of solving cybersecurity problems, which researches better solutions to current issues. Instead, NCLY attempts to change the cybsersecurity game to shift focus onto new problems that are on the horizon.

    Franz is an expert on virtual machines, mobile-code security, and dynamic compilation. He is the co-inventor (with a graduate student) of the Trace Compilation method that now drives JavaScript performance in Mozilla's Firefox browser. He has graduated 13 Ph.D. students, been awarded more than $7 Million in competitive Federal research funding as Principal Investigator, and has published more than 90 refereed papers.

    Tsudik's research interests are mainly in computer/network security, privacy and applied cryptography. His recent work focuses on privacy in Internet services, RFID systems and mobile ad hoc networks, as well as security in sensor networks and storage systems. His research also covers secure group communication, in particular, group key agreement, group signatures and group access control.


    Utts receives the American Statistical Association Founder's Award

    photo: jessica utts

    Jessica
    Utt
    s

    Professor of statistics, Jessica Utts, received the American Statistical Association (ASA) Founder Award, the organization's highest honor, at the 2009 Joint Statistical Meetings held August 1 - 6 in Washington, DC.

    Utts, along with four other recipients, were selected based on their service over an extended period of time and in a variety of leadership roles, including chapter, section, committee, officer or editorial activities, in which effective service or leadership was provided within ASA or on behalf of ASA to other organizations.

    Utts was chosen based on her leadership on many ASA committees and the Statistical Education and Bayesian Statistical Science Sections; for extraordinary service in the development of the ASA’s strategic plan; for editorial service to the American Statistician and to the Journal of the American Statistical Association; and for her outstanding commitment to the profession through leaderships roles in AAAS, CAUSE, COPSS, NISS and WNAR.

    Utts’ research interests include statistics education and applications of statistics to a variety of areas, most notably parapsychology, medicine, and transportation. She is the recipient of two distinguished teaching awards, the author of three statistics textbooks with an emphasis on statistical literacy, and the editor-in-chief of an online statistics course.


    JULY 2009

    Dutt, Nicolau and Veidenbaum Receive Best Paper Award at IJCNN 2009

    photo: nikil dutt

    Nikil
    Dutt


    photo: alexandru nicolau

    Alexandru
    Nicolau


    photo: alexander veidenbaum

    Alexander
    Veidenbaum

    Computer Science Professors Nikil Dutt, Alexandru Nicolau, and Alexander Veidenbaum have been recognized with a Best Paper Award at the 2009 International Joint Conference on Neural Networks (IJCNN).

    An interdisciplinary collaboration, the paper entitled “Efficient Simulation of Large-Scale Spiking Neural Networks Using CUDA Graphics Processors” was also co-authored by Computer Science Ph.D. students Jayram Moornikara and Assistant Professor of Cognitive Science Jeffrey Krichmar.

    The paper describes new techniques for parallelization of spiking neural network models of the brain and their efficient realization on emerging graphics processor platforms. This will enable close-to-real-time simulation of realistic networks of nerve cells and could have many practical applications.

    IJCNN is the premier international conference in the area of neural networks theory, analysis and applications.

    Dutt is a Chancellor’s Professor whose research interests lie in the area of embedded systems and computer-aided design, with a specific focus on the exploration, evaluation and design of domain-specific embedded systems spanning both software and hardware. Other projects within his group include low-power/low-energy compilation and synthesis, validation and verification of embedded systems, software/hardware interfaces for distributed embedded systems, memory architecture exploration for embedded systems, and brain-inspired architectures and computing.

    Nicolau's work is in the design and implementations of a system of program transformations that support the semi-automatic (and eventually fully-automatic) exploitation of substantially all the parallelism available in a given program. Nicolau is also interested in developing a tool for the rigorous study and development of parallelizing compilers.

    Veidenbaum's research is in the areas of computer architecture, embedded systems, and compilers. He investigates new ways to build faster processors and systems and to reduce their power consumption and cost.


    Tomlinson awarded NSF EAGER Grant to study interactive media for childhood environmental awareness

    photo: bill tomlinson

    Bill
    Tomlinson

    Bill Tomlinson, professor of informatics, has been awarded a $280,371 Early-Concept Grant for Exploratory Research (EAGER) for his project which will utilize narrative-centered computing (NCC) to allow children to see how their own behavior can cause positive or negative changes in a story ecosystem.

    Unlike traditional computational storytelling that utilizes a linear “filmstrip”, NCC framework begins with a new mechanism for computational storytelling called spatiotemporal anchoring. Spatiotemporally anchored stories consist of a network of story nodes in which each node depicts a small element of the overall plot, and is anchored to a specific location in space and time.

    To advance the story users explore a rich geographical representation of the relevant spatiotemporal locale, discovering story nodes and the interconnections between them. Because nodes can be anchored at variable levels of spatiotemporal resolution and interlinked in non-linear ways, exploring these narratives will help children to develop more nuanced abilities for reasoning about distributed causation and variable scale. These abilities, in turn, will translate into more effective engagement with environmental issues.

    The EAGER program is intended to support exploratory work in its early stages on untested, but potentially transformative, research ideas or approaches, therefore the grant will go toward funding the creation of a testbed interactive narrative, to be deployed online and as a temporary science museum exhibit. This narrative will use spatiotemporal anchoring along with video and traditional cinematographic techniques to dramatize the interactions that take place within a representative California ecosystem, for example, a marine environment in which sea otters, kelp forests, and sea urchins all interact.

    Tomlinson's research deals with the social impacts of information technologies, in particular regarding environmental issues and interactive education systems. His previous contributions to informatics and computer science are significant in human-computer interaction, interactive animation, autonomous agents, and multi-device systems.


    JUNE 2009

    Jarecki awarded Distinguished Assistant Professor Award for Research

    photo: stanislaw jarecki

    Stanislaw
    Jarecki

    Stanislaw Jarecki, assistant professor of computer science, has been awarded the annual UC Irvine Distinguished Assistant Professor Award for Research. It is the first time a Bren School professor has received the award.

    The recipient of the award must be nominated by his or her peers and have made significant contributions through research and/or other creative activity that has had a major impact on their field, either through a career-long record of contributions, or as a result of a major contribution.

    The award includes a $3,000 prize and an invitation to give a campuswide lecture on his research topic in the fall quarter.

    Jarecki’s research in cryptography and computer security has attracted funding from a variety of sources including the very selective National Science Foundation (NSF) Cybertrust Program as well as from the Intelligence Advance Research Funding Agency (IARPA).

    In 2008, Jarecki received a $450,000 Faculty Early Career Development (CAREER) award from the NSF for his proposed research, Secure Multi-Party Protocols, from Feasibility to Practice, which has a goal of designing cryptographic algorithms for a variety of secure tasks. The CAREER award is the NSF's most prestigious award for newer faculty. The program supports early career development of teacher-scholars who are most likely to become the academic leaders of the future.


    Congratulations 2008-09 Bren School Honors recipients!

    photo:: award

    Students on this list received awards for latin honors, Phi Beta Kappa, Campuswide Honors, Outstanding Contribution to Research and others. Final latin honors recipients will be determined once Spring quarter grades are processed in to the final g.p.a. calculation.

    Note for students on this list: please pick up your cord and/or stole from Neha at the ICS Student Affairs Office.


    MAY 2009

    Tomlinson received Environment Institute Grant, Support from Urban Water Research Center

    photo: Bill Tomlinson

    Bill
    Tomlinson

    Bill Tomlinson, Professor of Informatics, along with Professors Brett Sanders and Robin Keller have received a $38,000 grant from the Environment Institute at UC Irvine to support a new project entitled “Using IT to Compress Perceived Time and Space in How People Think About Global Change: A Step Towards Behavioral Change”.

    This interdisciplinary research collaboration is also being supported in part from a $10,000 contribution from the Urban Water Research Center at UC Irvine. Sanders and Keller are respectively professors at The Henry Samueli School of Engineering and The Paul Merage School of Business.

    The research collaboration will study the difficulties people have in engaging with environmental issues, in part because global change occurs on scales of time and space that are relatively large compared to the usual scope of human decision making. People respond enthusiastically to fast-acting disasters such as fires and earthquakes, but less so to issues that occur more gradually over many years, even when the consequences are far greater.

    To date, there has been little research on how to connect long-term global environmental change to human scales of time and space in a systematic way, thereby enabling behavioral change. Tomlinson et al’s research will focus on the science and public perception of sea level rise.

    Tomlinson's research deals with the social impacts of information technologies, in particular regarding environmental issues and interactive education systems. His previous contributions to informatics and computer science are significant in human-computer interaction, interactive animation, autonomous agents, and multi-device systems.

    More about the Environment Institute at UC Irvine: http://environment.uci.edu/

    More about the Urban Water Research Center at UC Irvine: http://www.uwrc.uci.edu/


    Olson gives keynote talks on Scientific Collaboration and Science Collaboratories

    photo: Gary Olson

    Gary
    Olson

    Gary Olson, Bren Professor of Information and Computer Sciences will give two keynote talks at the University of Siegen in Germany, and the 2009 International Symposium on Collaborative Technologies and Systems.

    Earlier this month, Olson gave a keynote entitled "Scientific Collaboration on the Internet", at the conference on Enhancing Humanities: Potentials of Media and ICT in the Humanities, held at the University of Siegen in Germany.

    In 1989 a small group of pioneering thinkers, led by Joshua Lederberg and William Wulf, sketched out a vision of what has come to be known by various names, such as collaboratories, eScience, and cyberscience.

    In the several decades since then many such projects have emerged in almost all areas of science. But there has been a complex pattern of success and failure in such efforts. Olson and his colleagues have spent nearly a decade studying these projects, trying to figure out what accounts for the pattern of success and failure in them. In his talk he reviewed lessons learned, and where he hopes to go with further research in the area.

    The current understanding from this investigation was recently summarized in a book entitled "Scientific Collaboration on the Internet" (MIT Press, 2008).

    This week in Baltimore, Olson will give a keynote entited "The Next Generation of Science Collaboratories". Olson will also talk about how the changing technical scene opens new opportunities for the next generation of collaboratories, as well as the sociotechnical factors that distinguish successful from unsuccessful collaboratories.

    Collaboratories to support scientific research have been around for at least two decades, and have emerged as an important form of cyberinfrastructure to enable ever more ambitious geographically distributed research projects. A broadened view of what a collaboratory is suggests there are a variety of kinds of functions they could support.

    Early collaboratories were often rather narrow in focus, but some have broadened to mimic fully-functional laboratories. Furthermore, will almost all early collaboratories were in the physical and biological sciences, by now they have emerged as serious research infrastructure in most domains, including the social sciences and humanities.

    Olson’s latest research focuses on how to support small groups of people working on difficult intellectual tasks, particularly when the members of the group are geographically distributed. This research has involved both field studies of groups attempting to do such work and lab studies that evaluate specific technologies. He is one of four Donald Bren Professors of Information and Computer Sciences at the Bren School.


    Dourish in Taiwan for Service Science Workshop on Qualitative Field Research in Organizations

    photo: Prof. Fu-Ren Lin, director of the Institute for Service Science at National Tsing Hua University, Calvin Morrill (UCI, Sociology), Martha Feldman (UCI, Planing Policy and Design),  Prof Chintay Shih, Dean of the College of Technology Management, National Sting Hua University, and Paul Dourish (UCI, Informatics).

    Prof. Fu-Ren Lin, director of the Institute for Service Science at National Tsing Hua University, Calvin Morrill (UCI, Sociology), Martha Feldman (UCI, Planing Policy and Design), Prof Chintay Shih, Dean of the College of Technology Management, National Sting Hua University, and Paul Dourish (UCI, Informatics).

    Paul Dourish, Professor of Informatics, along with UCI Professors Calvin Morrill (Sociology) and Martha Feldman (Planning, Policy, and Design) were recently in Taiwan presenting workshops at National Tsing Hau University in Hsinchu, and at National Sun Yat-Sen University in Kaohsiung.

    The three-day workshop presentsed exemplars and strategies for doing qualitative field research in organizations. The activities of the workshop were divided into three areas: 1) introducing the background and roles of qualitative methods in the research of workshop leaders and participants; 2) introducing methodological issues and exercises in qualitative field research; and 3) discussion and feedback on workshop participants’ qualitative research experiences and goals.

    Dourish's primary research interests are in the areas of Computer-Supported Cooperative Work, Human-Computer Interaction, and Ubiquitous Computing. He is especially interested in the foundational relationships between social scientific analysis and technological design. More about Dourish can be found on the Web.


    APRIL 2009

    Judy Olson gives talk on social ergonomics at CHI 2009

    photo: Judy Olson

    Judy
    Olson

    Judy Olson, Bren Professor of Information and Computer Sciences, gave the plenary opening at Computer-Human Interaction (CHI) 2009. Olson's talk, Even Small Distance Matters: Social Ergonomics in Collocated and Remote Teams, focused on the study of social ergonomics, the design of workplaces and systems that fit the natural social capabilities and inclinations of workers and users.

    Olson reviewed some of the highlights of what is known about natural social capabilities and inclinations, showed how they play out in both “radically collocated” teamwork and remote teamwork before finishing with a set of guidelines for everyone to use when having to work either collocated or remotely.

    CHI is the premier worldwide forum for exchanging information on all aspects of how people interact with computers. CHI 2009 ran from April 4-9, in Boston, MA offering two days of pre-conference workshops and four days of dynamic sessions that explored the future of computer-human interaction with researchers, practitioners, educators and students.

    More than 2000 professionals from over 40 countries attended this year's conference, which marked 27 years of research, innovation and development of the Computer-Human Interaction community.

    Olson has published about 110 peer-reviewed research articles and is best known for her work on distance collaborations and has achieved international acclaim for her studies that compared office workers in geographically distributed organizations to those working in the same location.


    Tsudik to give keynote on secure device pairing at IPSEC 2009

    photo: gene tsudik

    Gene
    Tsudik

    Professor of Computer Science Gene Tsudik will be giving an invited keynote talk entitled "Secure and Usable Device Pairing" at the 5th Information Security Practice and Experience Conference (ISPEC 2009) to be held April 13-15, 2009, in Xi’an, China.

    “Secure Device Pairing” is the process of bootstrapping a secure channel between two or more previously unassociated personal devices over a (usually wireless) human-imperceptible communication channel. Lack of prior security context and absence of common trust infrastructure open the door for so-called "Man-in-the-Middle" (or "Evil Twin") attacks. Mitigation of these attacks requires user involvement in the device pairing process.

    Tsudik’s talk will summarize notable secure device pairing techniques, comparing and contrasting their advantages, shortcomings and limitations, followed by the first comprehensive and comparative evaluation of these methods.

    ISPEC is an annual conference that brings together researchers and practitioners to provide a confluence of new information security technologies, their applications and their integration with IT systems in various vertical sectors. More information about ISPEC can be found at the Conference Web site: http://www.ispec2009.net/.

    Tsudik's research interests are mainly in computer/network security, privacy and applied cryptography. His recent work focuses on privacy in Internet services, RFID systems and mobile ad hoc networks, as well as security in sensor networks and storage systems.

    His research also covers secure group communication, in particular, group key agreement, group signatures and group access control. He also is interested in database security and public key cryptography.


    DuBois awarded National Defense Science and Engineering Fellowship

    photo: christopher dubois

    Christopher
    DuBois

    Christopher DuBois, a first-year PhD student in the Department of Statistics, has been awarded a 3-year National Defense Science and Engineering (NDSEG) Graduate Fellowship.

    NDSEG Fellowships are awarded based on a national competition, with approximately 200 fellowships awarded each year in the United States to graduate students across a broad range of fields of study in the sciences and engineering.

    DuBois will use his fellowship funding to pursue his Ph.D. research on statistical modeling of large dynamic social networks, such as email communication networks between individuals, working with professor Padhraic Smyth in the Departments of Computer Science and Statistics and professor Carter Butts in the Department of Sociology.


    Finalists of hITEC entrepreneurship competition announced

    photo: idea

    Three teams advanced to the finals of hITEC, the Bren School's Technology Entrepreneurship Competition. The finalists also earned a spot in the Stradling Yocca Carlson and Rauth Business Plan Competition sponsored by the Paul Merage School of Business.

    The final products, created by Bren School students, with the guidance of a faculty or corporate mentor, span a broad spectrum of uses.

    Clarity Labs
    Ron Villalon, Niraj Desai, Manjot Bhuller
    Mentors: Professor Chen Li, Arie Shen

    A product that leverages television viewers interest in the products on their favorite TV shows (clothing, props, etc.) and gives these viewers access to information through tag placement in interactive videos.

    Event Viz
    Pinaki Sinha, Hamed Pirsiavash, Mingyan Gao
    Mentors: Ramesh Jain, Janell So

    Web-based software that organizes isolated events and related information, such as documents, photos, audio and video, and creates an organized multimedia chronicle to visualize, access, search, and create customized stories.

    Olepta
    Nathan Esquenazi and Thomas Shafer
    Mentors: Professor Andre van der Hoek, TJ Thinakaran

    A relationship management product that provides end-to-end management for communication with patients, and allows doctors and patients to have a continuous relationship using modern communication technologies.

    The final competition placing and prizes will be awarded in early June at the ICS Awards Ceremony and Project ICS Showcase.

    hITEC is the cornerstone of the Bren School entrepreneurship program. This year, the program was sponsored with generous donations from: Northwind Ventures, Printronix and Ted and Janice Smith.

    The competition is designed to foster a spirit of entrepreneurship among Bren School and UC Irvine students, and fuel the development of new technologies that have the potential to positively impact the marketplace.


    MARCH 2009

    Jain to give talk at the final conference of the European CHORUS

    photo: ramesh jain

    Ramesh
    Jain

    Bren Professor Ramesh Jain will be the opening speaker at the final conference of the European CHORUS project on Multimedia Search Engines (MMSE). Jain will speak about state of the art multimedia search technology and its future during a session entitled, "Chorus Roadmap and International perspectives".

    CHORUS is a European Coordination Action which aims at creating the conditions of mutual information and cross fertilisation between the European projects dealing with Multimedia Content Search Engines. National and international initiatives are also included in CHORUS action.

    Jain is an active researcher in multimedia information systems, image databases, machine vision, and intelligent systems.

    CHORUS will be held May 26- 28, 2009 in Brussels, Belgium. More information can be found on the conference Web site.


    Carey and Li awarded seed money from UC Discovery Grant and eBay

    photo: michael carey

    Michael
    Carey


    photo: chen li

    Chen
    Li

    Computer Science professors Michael Carey and Chen Li have been awarded a total of $132,000 in seed funding in support of their research entitled ASTERIX: A Scalable Platform for XML Information Analysis. The funding is made in part from the UC Discovery Grant ($52,000) and eBay ($80,000).

    ASTERIX, which stands for “Active, Scalable, Transactional Enterprise Repository for Information in XML,” is a new effort to develop a scalable semistructured information management system, based on XML and XQuery technologies, targeting very large shared-nothing compute clusters.

    A Bren Professor in Information and Computer Sciences, Carey’s research interests are in database systems, information integration, service-oriented computing, middleware, distributed systems, and computer system performance evaluation.

    Li's research interests are in the fields of database and information systems, including data integration and sharing, data warehouses, data cleansing, data privacy, and information management on the Web.


    Suda named IEEE Distinguished Lecturer

    photo: tatsuya suda

    Tatsuya
    Suda

    Computer Science Professor Tatsuya Suda has been named IEEE Communications Society Distinguished Lecturer with a term effective through December 2010. Suda will be lecturing on:

    (1) Molecular Communication: New Paradigm for Communication Among Nano-Scale Biological Machines

    (2) The Bio-Networking Architecture: A Biologically Inspired Approach to the Design of Computer Networks and Network Applications

    (3) New Research Directions in Networks: from Sociology to Biology

    The IEEE Distinguished Lecturers are selected to provide a pool of technical experts for lectures by IEEE Chapters and sections. A complete list of Communications Society Distinguished Lecturers are available online.

    Suda's research is in computer networks and distributed computing systems; his interests span the entire spectrum from the design and performance evaluation of these systems to their actual implementation.

    His current research focuses on applications of biological principles and large complex system principles onto networks, high speed networks, next generation Internet, ATM (Asynchronous Transfer Model) networks, object-oriented distributed systems, and multimedia applications.


    Vaisenberg receives first prize at IEEE Percom'09 Ph.D. Forum

    photo: ronen vaisenberg

    Ronen
    Vaisenberg

    Third year computer science Ph.D. student, Ronen Vaisenberg has been awarded the first prize at the IEEE International Conference on Pervasive Computing and Communications' (IEEE Percom '09) Ph.D Forum.

    The Ph.D. Forum Committee evaluated research projects based on a submitted extended abstract, poster, and discussion at the poster session. Vaisenberg's research was also recognized as the most innovative and/or most promising interdisciplinary research project.

    Under the monitorship of Professor Sharad Mehrotra, Vaisenberg's Ph.D dissertation deals with the issues related to the data management support for sentient systems, motivated by real-world emergency-response application needs which are funded by NSF’s ITR-Rescue (RESponding to Crisis and Unexpected Events) and DHS’s Safire (Situational Awareness for Firefighters).

    More about Vaisenberg and his research can be found on his Web site.


    Chloe Azencott awarded IBM Ph.D. Fellowship

    photo: chloe azencott

    Chloe
    Azencott

    Chloe Azencott, fourth year Ph.D. student in computer science has been awarded an IBM Ph.D. Fellowship program for the 2009-2010 academic year. The award which covers tuition and mandatory fees, also comes with a $17,500 stipend.

    Azencott's, research interest lies in the areas of machine learning applied to the life sciences, more particularly chemistry and chemoinformatics.

    Under the mentorship of faculty advisor Pierre Baldi, Azencott's research topic is entitled Statistical Machine Learning and Data Mining for Chemoinformatics and Drug Discovery.

    The IBM Ph.D. Fellowship Awards is an intensely competitive program which honors exceptional Ph.D. students in many academic disciplines and areas of study.

    IBM pays special attention to an array of focus areas of interest to IBM and fundamental to innovation, including technology that creates new business value, innovative software, new types of computers and interdisciplinary projects.


    Lewis and Tomlinson featured in inaugural issue of International Journal of Learning and Media

    photo: bill tomlinson

    Bill
    Tomlinson

    Undergraduate student Lauren Lewis and ICS Professor Bill Tomlinson, along with Education professor Rebecca Black, are currently featured in the inaugural issues of the International Journal of Learning and Media, a MacArthur Foundation/MIT Press journal.

    “Let everyone play: An educational perspective on why fan fiction is, or should be, legal” makes a theoretical, legal, and moral proposition that fan fiction, a form of derivative writing based on existing media and popular culture, be considered fair use of copyrighted materials under U.S. copyright law.

    Lewis is a second year Computer Science major and helped co-author the publication in the summer of 2008 as a participant in Calit2's Summer Undergraduate Research Fellowship in Information Technology (SURF-IT) program.

    Tomlinson's research deals with the social impacts of information technologies, in particular regarding environmental issues and interactive education systems. His previous contributions to informatics and computer science are significant in human-computer interaction, interactive animation, autonomous agents, and multi-device systems.

    The complete publication can be found online.


    Baldi awarded the Grant in Chemical Sciences to support development of Reaction Explorer

    photo: pierre baldi

    Pierre
    Baldi

    Chancellor's Professor Pierre Baldi has been awarded a grant in the Special Grant Program in Chemical Sciences by the Camille and Henry Dreyfus Foundation.

    The grant is in support of the development of Reaction Explorer, a new interactive electronic tutorial system for teaching organic reactions, reaction mechanisms, organic synthesis and retrosynthesis at the undergraduate level.

    Reaction Explorer is an interactive tutorial system for organic chemistry reactions, which enables students to learn about reactions in ways previously unrealized.

    With the Reaction Explorer project, Baldi, together with MD/PhD graduate student Jonathan Chen, aim to provide a richer learning experience including: dynamic generation of customized multi-step synthesis design problems; context-specific feedback messages; and support for inquiry-based learning through experimentation and interactive dialogue with the system.

    Baldi’s research focuses in several areas of AI, data mining, machine learning, bioinformatics, and chemoinformatics. Projects in his group include understanding and predicting protein structures, analyzing and modeling gene expression data and regulatory networks, and building expert systems for chemistry and drug discovery.

    The purpose of the Camille and Henry Dreyfus Foundation, Inc., is to advance the science of chemistry, chemical engineering and related sciences as a means of improving human relations and circumstances.

    Established in 1946 by chemist, inventor and businessman Camille Dreyfus as a memorial to his brother Henry, the Foundation became a memorial to both men when Camille Dreyfus died in 1956. Throughout its history the Foundation has sought to take the lead in identifying and addressing needs and opportunities in the chemical sciences.


    FEBRUARY 2009

    Li receives NSF award to study large-scale data cleaning

    photo: chen li

    Chen
    Li

    Chen Li, associate professor of computer science, has
    received an award for $221,730 from the NSF CluE program to support his research on large-scale data cleaning
    using cloud computing.

    In addition, his team will also use software and services on a Google-IBM cluster to explore innovative research ideas in data-intensive computing.

    The project will study research challenges to support efficient data-cleaning queries on large text repositories using the MapReduce/Hadoop parallel computing paradigm.

    Supporting such queries is becoming increasingly more important in applications that need to deal with a variety of data inconsistencies in structures, representations, or semantics.

    The techniques developed in this project will have a broad impact on many information systems that need to support approximate query processing on large data sets, such as Web search, enterprise search, data integration, and query relaxation.

    Li's research interests are in the fields of database and information systems, including data integration and sharing, data warehouses, data cleansing, data privacy, and information management on the Web.


    Carey awarded Google Research Award

    photo: michael carey

    Michael
    Carey

    Michael Carey, Bren Professor of Information and Computer Sciences, has been awarded a Google Research Award for $70,000 for his research entitled, “A Declarative and Open Source Data Mapping Tool for OpenII.”

    Carey’s research is tackling the need for large organizations to effectively access and analyze data coming from disparate sources, including multiple databases, legacy information stores, applications and data stores published via Web services, XML files, and CSV files.

    This project aims to produce a declarative and open source data-mapping tool for incorporation into the OpenII initiative. The Google OpenII initiative aims to enable such repositories to be put together much more easily than they can be today, creating better quality data that can then be (re)surfaced on the Web and made available to everyone. Areas of potential impact include medical informatics, biology research, and access to many public data sets that are currently disparate.

    Carey's research interests are in database systems, information integration, service-oriented computing, middleware, distributed systems, and computer system performance evaluation.

    A National Academy of Engineering member, Carey is acknowledged as one of the 50 most influential computer scientists in the world. He is an ACM Fellow and in 2005, he received ACM’s SIGMOD Edgar F. Codd Innovations Award.

    He has also has earned two of the most esteemed research publication awards in the field: the Very Large Data Base (VLDB) Conference’s 10-Year Best Paper Award in 1996, and the 2004 Test of Time Paper Award at the ACM SIGMOD International Conference.


    Dutt appointed to the ACM Publications Board

    photo: nikil dutt

    Nikil
    Dutt

    Nikil Dutt, Chancellor's Professor of Computer Science, has been appointed to the Association of Computing Machinery (ACM) Publications Board for a 3-year term.

    ACM is the largest international professional computing society representing the educational and scientific computing community.

    ACM provides the computing field's premier Digital Library and serves its members and the computing profession with leading-edge publications, conferences, and career resources.

    The ACM Publications Board is responsible for setting publication policy, approving new publications and appointing the Editors-in-Chief of the premier ACM journals and transactions.

    Dutt's research interests are in embedded systems, with topics that are at the intersection of compilers, architectures and computer-aided design. His specific focus is on the exploration, evaluation and design of domain-specific embedded systems that span research issues in hardware, software, networked, and ubiquitous systems.

    Other projects in his group include brain-inspired computing platforms, low-power/low-energy compilation and synthesis, embedded system validation and verification, and memory architecture exploration for embedded systems.

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    Bren school home > Community > News > Press releases
    Press releases

    December 9, 2015
    Two UCI professors named 2015 ACM Fellows


    November 16, 2015
    36-Hour Hackathon takes over eSports Arena Nov. 20-22


    September 28, 2015
    40 ICS Students Attending 2015 Grace Hopper Celebration of Women in Computing Conference


    May 27, 2015
    UCI Launches First Undergraduate Data Science Program in the UC System


    April 9, 2015
    UC Irvine and the National Center for Women & Information Technology to recognize 52 high school women for computing achievements


    March 23, 2015
    International gathering of information schools and scholars at this week's iConference


    March 12, 2015
    SCSIM, UC Irvine partner to strengthen research and collaboration among IT industry


    February 27, 2015
    Innovative game developer Brianna Wu to discuss leading an all-women game studio Friday


    January 23, 2015
    Media Advisory: UC Irvine Holds Global Game Jam


    January 9, 2015
    Two ICS professors honored as ACM Fellows


     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

     

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    http://www.ics.uci.edu/community/news/notes/notes_2010.php noteworthy achievements 2010-2011 @ the bren school of information and computer sciences
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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements 2010-2011

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


    SUMMER 2011

    ISG members receive multiple NSF grants

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    photo: Nalini Venkatasubramanian

    Nalini
    Venkatasubramanian

    photo: Dmitri V. Kalashnikov

    Dmitri
    Kalashnikov

    Several members of the Information Systems Group (ISG) within the department of computer science received National Science Foundation grants to support a variety of research projects. Computer science professor Sharad Mehrotra received $500,000 from the NSF’s Division of Computer and Network Systems to explore query processing in mixed security environments wherein data migrates across different components, each of which may offer different levels of security guarantees and may be susceptible to different attacks. Mehrotra, along with assistant adjunct professor Dmitri V. Kalashnikov, also received $500,000 from the NSF’s Division of Information and Intelligent Systems, to build a query and goal-driven entity resolution framework. Professor Nalini Venkatasubramanian was awarded $205,000 to lead a research project on “GeoSocial Alerting Systems.”

    ISG consists of computer science faculty members, affiliated faculty, students, visitors and project staff. It aims to address today’s rapidly evolving information infrastructure by conducting research on all aspects of modern data and information systems.

     


    Smyth Serves as KDD-2011 Program Chair

    photo: Padhraic Smyth

    Padhraic
    Smyth

    Computer science professor Padhraic Smyth is the program chair for the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, currently underway in San Diego, Calif. Considered the premier annual international research conference on data mining, the event this year drew a record-setting 1,000+ attendees.

    Approximately 725 research papers were submitted (another record), of which 125 were accepted for oral or poster presentation at the meeting. The review process involved more than 350 reviewers and 35 senior program committee members. Keynote presenters include Peter Norvig (Google), Stephen Boyd (Stanford University), David Haussler (UC Santa Cruz) and Judea Pearl (UCLA).

    Smyth's research includes machine learning, pattern recognition, applied statistics, data mining, information theory and artificial intelligence. His work focuses on how to automatically extract information from large and complex data sets. His research group works on the basic theory of inference from data, as well as on a variety of applications of data analytic algorithms to problems in medicine, biology, climate modeling, astronomy, planetary science, and analysis of Web and text data.

     


    Paper by Tsudik and El Defrawy Selected as IEEE Spotlight

    photo: Gene Tsudik

    Gene
    Tsudik

    photo: Gene Tsudik

    Karim
    El Defrawy

    Computer science professor Gene Tsudik and Network Systems alumnus Karim El Defrawy, Ph.D. '10 co-authored "ALARM: Anonymous Location-Aided Routing in Suspicious MANETs," which has been selected as the Spotlight Paper for the September 2011 issue of IEEE Transactions of Mobile Computing. For a limited time it will be available to the public for free on the journal home page.

    The paper addresses mobile ad hoc networking (MANET) scenarios in hostile or suspicious settings by designing and analyzing a privacy-preserving and secure link-state based routing protocol (ALARM). The work, according to Tsudik and El Defrawy, represents the first comprehensive study of security, privacy and performance tradeoffs in link-state MANET routing.

     


    Students recognized by Yahoo! and IEEE, ACM

    photo: Ronen Vaisenberg

    Ronen
    Vaisenberg

    photo: Reza Rahimi

    Reza
    Rahimi

    Computer science graduate student Ronen Vaisenberg received the 2011-12 Yahoo! Best Dissertation Fellowship Award for his work on "Scalability in Event Detection Systems."

    Reza Rahimi, also a computer science graduate student, received the Best Student Poster Award for “Cloud Based Framework for Rich Content Mobile Applications” at CCGrid2011, the IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

     


    Top-Ranked Journal Edited by Kobsa Celebrates 20th Anniversary

    photo: Alfred Kobsaw

    Alfred
    Kobsa

    User Modeling and User-Adapted Interaction: The Journal of Personalization Research, founded by Informatics professor Alfred Kobsa and published by Springer Verlag, celebrates its 20th anniversary this year. UMUAI is ranked No. 20 among 445 computer science journals based on its ISI/Thompson impact factor, and No. 3 by Microsoft Academic Search among 26 human-computer interaction journals.

     


    Tsudik Co-Chairs Two International Conferences

    photo: Gene Tsudik

    Gene
    Tsudik

    Computer science professor Gene Tsudik served as program co-chair of two conferences this summer. The 9th International Conference on Applied Cryptography and Network Security (ACNS '11), an annual research conference focusing on cutting-edge results in applied cryptography and network and computer security, took place June 7-10 in Nerja (Malaga), Spain. The 4th ACM Conference on Wireless Network Security (WiSec'11), which aims to explore attacks on wireless networks and the techniques to thwart them, was held June 14-16 in Hamburg, Germany.

    Tsudik’s research interests include computer/network security and applied cryptography. He serves as director of UCI’s Secure Computing and Networking Center and as vice chair of the Bren School’s computer science department.

     


    SPRING 2011

    UCI team receives DARPA grant

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    photo: Nalini Venkatasubramanian

    Nalini
    Venkatasubramanian

    photo: Dmitri V. Kalashnikov

    Dmitri
    Kalashnikov

    Computer science professors Sharad Mehrotra and Nalini Venkatasubramanian, and assistant adjunct professor Dmitri V. Kalashnikov — in collaboration with the International Computer Science Institute, UC Berkeley and SRI International— have been awarded a $1.2 million grant by the Defense Advanced Research Projects Agency to explore speech-based situational awareness for event response.

     


    Givargis Wins ASEE’s Terman Award

    photo: Tony Givargis

    Tony
    Givargis

    Tony Givargis, Computer Science Associate Professor, was selected to receive the 2011 Frederick Emmons Terman Award of the American Society for Engineering Education’s Electrical and Computer Engineering Division.

    Sponsored by Hewlett-Packard, the award is bestowed annually upon an outstanding young electrical engineering educator in recognition of his/her contributions to the profession, including the publication of an electrical engineering textbook judged to be outstanding by peers.

    The award, which includes an honorarium, a gold-plated medal and bronze replica, and a presentation scroll, will be presented to Givargis at the Frontiers in Education Conference in October. Its namesake, Silicon Valley pioneer F.E. Terman, was an electrical engineering professor and Stanford University administrator known for mentoring students who went on to establish successful businesses, including William Hewlett and David Packard.

    At UC Irvine, Givargis conducts research in the area of software for embedded systems, investigating issues related to Realtime Operating System (RTOS) synthesis, serializing compilers, and code transformations techniques for efficient software to hardware migration.

     


    Stern, Johnson Named IMS Fellows

    photo: Hal Stern

    Hal
    Stern

    photo: Wesley Johnson

    Wesley
    Johnson

    Dean Hal Stern and Professor Wesley Johnson, both with the Bren School’s Department of Statistics, have been elected to fellowship in the Institute of Mathematical Statistics, in honor of their outstanding research and professional contributions.

    Stern’s research interests include statistical inference using Bayesian methods, assessing the fit of statistical models, applications of statistics in the social and biological sciences, and statistics in sports. Current collaborative projects include studies of climate systems with faculty at UC Irvine’s Department of Earth System Science and development of novel statistical models for genomewide association studies.

    Johnson is mainly interested in developing Bayesian statistical methods for biostatistical and epidemiologic applications. He also works on diagnostic screening protocols and methodology when no gold standard test is available. Johnson collaborates regularly with veterinary medicine researchers at UC Davis.

     


    Microsoft Research Recognizes van der Hoek

    photo: André van der Hoek

    André
    van der Hoek

    Informatics Professor André van der Hoek received a 2011 Software Engineering Innovation Foundation (SEIF) award, presented by Microsoft Research to support academic advances in software engineering technologies, tools, practices and teaching methods. van der Hoek’s proposal — “Calico: Software Design Sketching with a Cloud-based Software Whiteboard” — was one of 10 selected worldwide to receive the one-year award. Other recipients hail from such institutions as the University of Chile, Hong Kong University of Science and Technology, and Carnegie Mellon University.

    At UCI, van der Hoek's research focuses on understanding and advancing the role of design, coordination and education in software. His research bridges into the educational realm by seeking and evaluating new approaches to teaching software engineering.

     



    Franz Wins Award from Samsung

    photo: Michael Franz

    Michael
    Franz

    Michael Franz, UCI computer science professor, has teamed up with Samsung in a project to create better virtual machine architectures, especially for applications in mobile devices. For the year 2011, Samsung has awarded the sum of $350,000 to Franz as sole PI; at least two more years at the same funding level are planned.

    Franz leads the Secure Systems and Languages Laboratory at UC Irvine, one of the top research teams on dynamic compilation, virtual machines and language-based computer security. In collaboration with the Mozilla foundation, he transitioned the JavaScript compilation technology invented in his lab into the Firefox browser, where it is used every day by hundreds of millions of people.

     


    NSF awards $373k to CS professors

    img

    Computer science faculty and Information Systems Group (ISG) members Sharad Mehrotra, Mike Carey, Ramesh Jain, Nalini Venkatasubramanian and Chen Li received $373,000 from the National Science Foundation to develop I-sensorium, to serve as a "living laboratory" to support research in several related areas of cyber-physical systems including: theoretical foundations and underlying principles of building sentient systems; engineering, software and systems level challenges; and novel application contexts where such sentient systems can be used. Further information about this project can be found here.

    ISG consists of computer science faculty members, affiliated faculty, students, visitors and project staff. It aims to address today’s rapidly evolving information infrastructure by conducting research on all aspects of modern data and information systems.

     


    WINTER 2011

    Doctoral Student Named Microsoft Research Fellow

    photo: Qiang Liu

    Qiang
    Liu

    Qiang Liu, a third-year Ph.D. student in Computer Science, has been awarded a prestigious Microsoft Research Fellowship for 2011-2013. Exceptionally competitive, the award seeks to recognize “the best and the brightest from North America.” Liu, whose research focuses primarily on problems in artificial intelligence, statistics and computational biology, was one of 12 recipients this year; 174 applied from a select list of departments that were invited to participate. The fellowship provides two years of support, including tuition, living expenses and a conference travel allowance. Fellows also are offered a summer internship opportunity at Microsoft Research.

    Liu’s research, advised by Professor Alexander Ihler, works to develop efficient algorithms for learning and approximate reasoning in graphical models, which are used to describe large, complex systems in terms of smaller, more manageable local dependencies. Graphical models also have come to be used across a wide range of disciplines.

     


    Franz Wins Large DARPA Award

    photo: Michael Franz

    Michael
    Franz

    Michael Franz, UCI Computer Science Professor, has received a three-year $1.38 million award from DARPA’s Transformational Convergence Technology Office to investigate a new defense against software attacks. He is the sole PI of the project, which is inspired by biodiversity in nature and aims to create a similar diversity in the software that runs on the world’s computers. Currently, all users of a program download the exact same version of that program from a hosting site or an App Store. In Franz’s approach, the App Store will contain a diversification engine that will generate a unique version of every program for every user. This process will occur automatically inside of the AppStore, so that neither the software creators nor the users downloading it need to be aware of it.

    From the end-user’s perspective, the different versions are indistinguishable and they all behave in exactly the same way, but internally, subtle differences make it impossible for an attacker to use the same attack for all the versions.

    In order to make a large-scale attack successful, a perpetrator would have to launch many different attacks and would have no way of knowing which attack would succeed on which target. Equally important, this also makes it much more difficult to generate attack vectors by reverse-engineering security patches.

    Franz leads the Secure Systems and Languages Laboratory at UC Irvine, one of the top research teams on dynamic compilation, virtual machines and language-based computer security. In collaboration with the Mozilla foundation, he transitioned the JavaScript compilation technology invented in his lab into the Firefox browser, where it is used every day by hundreds of millions of people.

     


    Tomlinson Addresses Sustainability Conference

    photo: Bill Tomlinson

    Bill
    Tomlinson

    Bill Tomlinson, Informatics Associate Professor, delivered a plenary address last week in Washington, D.C. at a workshop that explored research challenges facing worldwide sustainability. Sponsored by the National Science Foundation and the Computing Community Consortium, “The Role of Information Sciences and Engineering in Sustainability” aimed to identify new research opportunities in the information sciences and engineering that address sustainability objectives.

    Tomlinson’s talk, “IT and (Un)sustainable Cultures,” addressed core societal principles that he believes are the root causes of our greatest environmental concerns. Specifically, he said, industrialized civilizations’ emphases on growth and consumption are deeply unsustainable. Profound transformations in personal, institutional and infrastructure goals will be necessary to support a more sustainable existence and IT can play an important role in this evolution.

    He discussed the concept explored in his book, “Greening through IT,” that environmental issues occur on broad scales of time, space and complexity, but humans work best at narrower scales. “IT can help bridge this gap, enabling us to understand the complex chains of causality that underlie global climatic disruption, biodiversity loss, sea level rise and a host of other environmental issues,” he said. IT is also implicated in the creation of the world’s current environmental problems, he said, calling it a “force multiplier” that allows people to accomplish more and more. One way to approach these problems is to explore the ways in which IT can help alter unsustainable cultural norms and provide acceptable alternatives.

    For example, he said, IT systems can help support local sustainable agriculture, public health, education and nutrition. They can provide social support and identity via social networking, virtualized workspaces and other support networks. These systems can also help people learn more about the world and how to live sustainably in it.

    “These are not new ideas,” he said. But in order to create necessary cultural change that can lead to increased sustainability, “we need more IT researchers to work on these projects.”

     



    Ramanan Collaborates on Next-Generation Visual Computing

    photo: Deva Ramanan

    Deva
    Ramanan

    Assistant professor of computer science Deva Ramanan will collaborate with Intel and Stanford University researchers to design innovative visual computing projects as part of a $100 million Intel initiative.

    The new Intel Science and Technology Center on the Stanford campus, announced this week, is the first of several such centers on university campuses planned by the semiconductor and microprocessor giant. It is supported by a five-year, $10 million grant, and will focus on improving visual computing experiences for consumers and professionals. The center includes researchers from UC Berkeley and UC Davis.

    Ramanan, whose research interests span computer vision, machine learning and computer graphics, will contribute to the center in the area of perceiving people and places, specifically “projects that use computer vision techniques to analyze people in images and recognize actions in videos,” he said.

    “I am excited by the opportunity to collaborate… with the center. Intel is providing generous support in resources and funds which I’m confident will spur progress on such problems in visual computing.”

    http://www.santacruzsentinel.com/nationalbreaking/ci_17204724

     


    Tsudik is Keynote Speaker at Top Australasian Computer Science Event

    photo: Gene Tsudik

    Gene
    Tsudik

    Computer science professor Gene Tsudik delivered one of three keynote addresses last week in Perth, Australia, at the 2011 Australasian Computer Science Week conference – the region’s premier annual computer science event.

    His talk, “Usable Security: The Case of Secure Pairing of Wireless Devices,” focused on the process of creating an initial secure channel between multiple wireless devices that were previously unassociated, for example: two cell phones, a cell phone/Bluetooth combination, or an MP3 player and a wireless headset. Lack of a prior security context and absence of a global security infrastructure opens the door for so-called "man-in-the-middle" or “evil twin” attacks.

    Tsudik summarized notable pairing techniques, comparing and contrasting their advantages and limitations. He evaluated these methods based on usability and security, and discussed methods best-suited for specific combinations of devices and human abilities.

    His talk also covered situations where more than two unfamiliar devices must be associated in order to ensure secure communications, and reported on a usability study that compares several such techniques. He concluded by highlighting still-unresolved issues and potential avenues for future research.

    Tsudik, who serves as director of UCI’s Secure Computing and Networking Center (SCONCE) and vice-chair of ICS’s Department of Computer Science, is also editor-in-chief of the ACM “Transactions on Information and Systems Security.” His research interests include computer/network security and privacy, and applied cryptography.

     


    DECEMBER 2010

    Majumder Delivers ISVC Keynote

    photo: Aditi Majumder

    Aditi
    Majumder

    Aditi Majumder, Computer Science Associate Professor, was a keynote speaker at the International Symposium on Visual Computing 2010, a prestigious graphics and vision conference that brings together renowned researchers from all over the world.

    Majumder’s talk, “Ubiquitous Displays: A Distributed Network of Active Displays,” presents her team’s work-in-progress on developing a new display paradigm in which displays are not mere carriers of information, but active members of the workspace — interacting with data, user, environment and other displays. The goal is to integrate such active displays seamlessly with the environment, making them ubiquitous to multiple users and data.

    Majumder’s research aims to make multi-projector displays truly commodity products and easily accessible to the common man. Her significant research contributions include photometric and color registration across multi-projector displays, enabling use of imperfect projectors in tiled displays, and more recently a distributed framework for tiled displays via a distributed network of projector-camera pairs. A 2009 recipient of the NSF CAREER award, she has played a key role in developing the first curved screen multi-projector display being marketed by NEC/Alienware and is an advisor at Disney Imagineering for advances in their projection-based theme park rides.

     


    NOVEMBER 2010

    Kobsa receives support from Disney for location-sharing research

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Informatics Professor Alfred Kobsa received a $25,000 gift from Disney Company to support his research on location-sharing applications on mobile devices. Together with Informatics PhD student Xinru Page, he will investigate novel interface designs for such applications that accommodate users' privacy and impression management desires. Special emphasis will be put on practical usage for car pooling purposes.

    Professor Kobsa's research interests lie in the areas of user modeling and personalized systems, privacy, and in information visualization.

     


    OCTOBER 2010

    UC Irvine Computer Scientists awarded an NIH grant to develop novel search methods for searching biomedical literature

    photo: Chen Li

    Chen
    Li

    photo: Xiaohui Xie

    Xiaohui
    Xie

    Computer science professors Chen Li and Xiaohui Xie have been awarded a $371,000 two-year grant from the National Institute of Health to develop novel search methods for mining biomedical literature.

    The MEDLINE database, complied by the United States National Library of Medicine, is a comprehensive bibliographic database of life sciences and biomedical information. The database contains more than 19 million records from approximately 5,000 selected publications, covering much of the literature in biomedicine and health. The database is also growing fast, with thousands of updates every day.

    Searching MEDLINE has become an indispensable component in the daily life of medical practitioners, biological researchers, and an increasing number of patients who prefer to seek medical information through their own hands.

    Currently, searching MEDLINE is primarily conducted through the PubMed web server, maintained by National Center for Biotechnology Information of NIH, which handles millions of searches per day. Given the high popularity and importance, it is critical to study how to make MEDLINE search more powerful and easier to use.

    Li and Xie, together with collaborators at Tsinghua University, China, have developed a system called iPubMed, to study how to support instant, error-tolerant search on MEDLINE publications.

    Their published paper in the journal Bioinformatics demonstrated that the experience of searching MEDLINE can be significantly improved by instant search.

    In the new search paradigm, a user can view the search results instantly as he or she types each letter of the query. Because the user can modify the query on the fly according to the instantly returned results, it can take much less time for them to locate the right items.

    This new search model is also gaining popularity in other domains. For instance, Google recently released a new web search tool called Google Instant, which implements the similar idea. The error-tolerant feature is especially important when the user does not remember the exact spelling of the keywords, such as a disease name or an author name.

    The NIH grant will support Professors Li and Xie’s research efforts to further improve the iPubMed system.

    Li's research interests are in the fields of database and information systems, including Web search, large-scale data management, data cleansing, and data integration.

    Xie's research focuses in machine learning, bioinformatics, computational biology and neural computation. He is interested in both developing novel machine learning theory and algorithms, and applying them to practical problems, such as biology and medical science.

     


    SEPTEMBER 2010

    Dechter et al place first in the 2010 UAI Challenge

    photo: Rina Dechter

    Rina
    Dechter

    Computer science professor Rina Dechter, with her students, has won first place in the 2010 Uncertainty in Artificial Intelligence (UAI) Approximate Inference Challenge. The UAI community is focused on developing algorithms for answering queries over knowledge-bases known as "graphical models" that represent both certain and uncertain information about the world.

    For example, the algorithms can express the information that if an individual is a smoker, their chances for developing cancer are higher than someonw who is  a non-smoker. Given such statements with numerous additional such rules with and without numerical probabilities, which involve numbers in a particular domain such as medicine, researchers want to answer a variety of questions.

    “When we get ‘evidence’ or observations, we want to know how they change our ‘belief’ about certain other facts,” says Dechter.  

    “The evidence in the example is ‘Peter is smoking’, and I want to know the probability that ‘Peter has cancer.’  Or, I can have evidence that ‘Peter has cancer’ and the question I am interested in is what is the probability that ‘Peter smokes.’”

    Overall, when there is a probabilistic graphical model describing some world – such as the medical domain in the example above – and evidence on specific individual conditions, computer scientist often attempt to compute such answers.

    The UAI challenge is third in a series of events that challenge the UAI community to provide their code for approximation algorithms so that  they   all work on  the same set of problems and run on the same machines. The results are then announced.

    In the UAI 2010 challenge Dechter and her team compared algorithms on each of three tasks, and for 3 different lengths of time, comparing the accuracy of their results against actual results.

    Of the nine competing teams, Dechter with  Vibhav Gogate (currently a postdoctoral student at university of Washington)   were placed first on the  two tasks and (with  Lars Otten) placed third place on the third task.

    Professor Dechter's research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning. The primary aim of her research is to devise efficient methods through the understanding and exploitation of tractable reasoning tasks.

    Professor Dechter is an author of the book "Constraint Processing" published by Morgan Kaufmann, 2003, and has authored over 100 research papers. 

     


    Olsons awarded $400,000 for study on distributed collaborations

    photo: Gary Olson

    Gary
    Olson

    photo: Judy Olson

    Judy
    Olson

    Gary and Judy Olson, Bren Professors in Information and Computer Sciences, have been awarded $400,000 for their research entitled “Next Steps in Articulating Success Factors for Distributed Collaborations.”

    The Olsons have spent the past decade studying work at a distance, both in science and engineering venues as well as in corporations. During this time, they have identified the major the factors that make for success in work at a distance. From these, the Olsons have developed a preliminary theory, called the Theory of Remote Scientific Collaboration (TORSC), published in 2008.

    Given this theory, the Olsons have developed an online assessment device called the Collaboration Success Wizard, which goes through each of the important factors in question form and either confirms that the collaboratory has a good chance of success or suggests a remedy for the factors that are not met.

    These assessments and data collected from the tool will be directed at deepening the understanding of what differentiates successful from unsuccessful distributed collaborations, and provide guidance to those who are developing such collaborations.

    Gary and Judy Olson expect that their research will have major implications for the emergence of successful virtual organizations to facilitate distance collaborations, in science and beyond.

    Gary Olson, author of more than 120 published research articles, has dedicated his work to understanding how technology can support remote collaboration. He also has made important contributions to the studies of management practice and the cultural aspects of collaboration, as well as the complex socio-technical issues surrounding technology design.

    Judy Olson has published about 110 published research articles and is best known for her work on distance collaborations and has achieved international acclaim for her studies that compared office workers in geographically distributed organizations to those working in the same location.

    They are both Fellows of the Association for Computing Machinery, and were jointly awarded the ACM SIGCHI Lifetime Achievement Award.

     


    Dourish, Mazmanian receive $201,000 grant for "Scaling Social Networks to Social Movements"

    photo: Paul Dourish

    Paul
    Dourish

    photo: Melissa Mazmanian

    Melissa
    Mazmanian

    Paul Dourish and Melissa Mazmanian, Professors of Informatics, have received a $201,000 grant for their work entitled "Scaling Social Networks to Social Movements."

    The research concerns the application of theories of social movement formation to digital media. Dourish and Mazmanian will explore questions such as how do people become involved in social or political movements through their participation online, and can digital systems help people to see themselves as part of a broader collective? The team will focus specifically on environmental sustainability.

    Dourish's primary research interests are in the areas of Computer-Supported Cooperative Work, Human-Computer Interaction, and Ubiquitous Computing. He is especially interested in the foundational relationships between social scientific analysis and technological design.

    Mazmanian's interests revolve around on the experience of communication technologies as used in-practice within organizational contexts, specifically in relation to identity projection and the nature of personal and professional time in the digital age. Her dissertation research explores the individual experiences and social dynamics that emerge when people adapt to using wireless email devices.

     


    Dourish, Mazmanian receive $400,000 grant for "Innovating Across Cultures in Virtual Organizations"

    photo: Paul Dourish

    Paul
    Dourish

    photo: Melissa Mazmanian

    Melissa
    Mazmanian

    Professors of Informatics, Paul Dourish and Melissa Mazmanian have received a $400,000 grant over four years for the research entitled, "Innovating Across Cultures in Virtual Organizations."

    The research team is looking at how design and creative work is managed in cross-cultural settings. In particular, how collaboration technologies and material practices shape the design process, and how cultural processes shape the production and interpretation of these practices.

    Ph.D. student Lilly Irani will be spending a year doing ethnographic fieldwork with a design firm in India where the team will look at these practices in detail.

    Dourish's primary research interests are in the areas of Computer-Supported Cooperative Work, Human-Computer Interaction, and Ubiquitous Computing. He is especially interested in the foundational relationships between social scientific analysis and technological design.

    Mazmanian's interests revolve around on the experience of communication technologies as used in-practice within organizational contexts, specifically in relation to identity projection and the nature of personal and professional time in the digital age. Her dissertation research explores the individual experiences and social dynamics that emerge when people adapt to using wireless email devices.

     


    Dourish, Hayes, receive $247,000 grant for "The Persistence of Digital Identity"

    photo: Paul Dourish

    Paul
    Dourish

    photo: Gillian Hayes

    Gillian
    Hayes

    Professors of Informatics, Paul Dourish and Gillian Hayes have been awarded a $247,000 grant over two years for their project entited "The Persistence of Digital Identity."

    In this work, the team will be looking at phenomena concerning social media and death -- how people make arrangements for the curation of their digital identity, how the Internet provides a site for post-mortem memorialization, and how we manage the different "lifetimes" of people and their information.

    Dourish's primary research interests are in the areas of Computer-Supported Cooperative Work, Human-Computer Interaction, and Ubiquitous Computing. He is especially interested in the foundational relationships between social scientific analysis and technological design.

    Hayes’ interests are in human-computer interaction and ubiquitous computing. She studies record keeping and surveillance technologies, particularly in natural, unplanned and/or public settings. She also focuses on the application and uses of ubiquitous computing and CSCW technologies in the areas of education and healthcare.

     


    Majumder receives Best Paper Award at IEEE CVPR Workshop on PROCAMS

    photo: Aditi Majumder

    Aditi
    Majumder

    Computer science professor Aditi Majumder has been awarded a Best Paper Award at the IEEE Workshop on Projector and Camera Systems (PROCAMS) held at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in San Francisco. Her paper entitled, "Display Gamut Reshaping for Color Emulation and Balancing" was coauthored by researchers at Ostendo Technologies Ltd.

    Majumder et al present a hardware-assisted 3D gamut reshaping method that handles a gamut expansion in LED based DLP displays in emerging mobile digital light projectors (known commonly as pico-projectors). These projectors use multiple LED/laser sources instead of a singular white lamp providing a larger color gamut. The full abstract and paper can be found here [PDF link].

    The IEEE CVPR Workshop on PROCAMS is one of the top international venues for projector-camera systems researchers and practitioners.

    Majumder's research addresses novel projection based displays and methodologies to register and interact with them - an important problem to both the scientific and entertainment fields. Majumder has developed a suite of mathematical models, methods and software to register and interact with large tiled projection based displays.

     


    Jain receives SIGMM Technical Achievement Award

    photo: Ramesh Jain

    Ramesh
    Jain

    Bren Professor of Information and Computer Sciences Ramesh Jain has received ACM’s Special Interest Group on Multimedia (SIGMM) Technical Achievement award.

    The award, given in recognition of outstanding contributions over a researcher’s career, cited Jain’s “pioneering research and inspiring leadership that transformed multimedia information processing to enhance the quality of life and visionary leadership of the multimedia community”.

    The SIGMM award will be presented at the ACM International Conference on Multimedia 2010 that will be held October 25-29, 2010 in Florence, Italy.

    Jain has made pioneering contributions in the areas of multimedia information systems, image databases, machine vision, and intelligent systems for more than three decades. His early work on accumulative difference pictures established the dynamic vision field that has directly influenced today’s digital video processing. Many techniques such as background subtraction, tracking, and event detection were first introduced in his pioneering papers in this field.

    In 1992, Jain proposed and organized the inaugural NSF workshop on visual information management systems that established a new research direction leading to the enormous advances that we see today in content-based analysis and search of images and video. Jain has pioneered techniques that analyze content and combine information from multiple sources.

    His emphasis in recent years on event-based representation and analysis of multimedia data has led the community’s focus to be on live dynamic data. In fact, he has recently pioneered the first comprehensive multimedia Event model which is increasingly being adopted.

    Professor Jain's research has also impacted society through his active entrepreneurship, having founded five companies producing software and services in the area of image processing, visual media retrieval and multimedia experience management on the mobile platform.

     


    Stern, Yu receive $620,000 NSF grant for study in stastistical methods for climate systems

    photo: Hal Stern

    Hal
    Stern

    photo: Yaming Yu

    Yaming
    Yu

    Statistics professors Hal Stern and Yaming Yu, along with professor of earth systems science, Gudrun Magnusdottir, received a $620,000 3-year grant from the National Science Foundation to develop novel statistical methods for the analysis for climate systems and their interactions.

    The award from the Collaboration in Mathematical Geosciences program will support the interdisciplinary work of a team that includes the faculty and graduate students from the two departments. The traditional approach to studying climate systems combines data from long periods of time assuming that the location of the systems is consistent over time. The proposed new approach would allow for the position of systems to vary smoothly over time.

    Hal Stern is Ted and Janice Smith Family Foundation Dean. His research interests include: statistical inference using Bayesian methods, assessing the fit of statistical models, applications of statistics in health sciences and in the physical sciences.

    Yu's research interests include statistical methods for missing data problems and statistical computing algorithms.

     


    Welling wins Koenderink Prize at European Conference on Computer Vision

    photo: Max Welling

    Max
    Welling

    Max Welling, professor of computer science, has been awarded European Conference on Computer Vision’s Koenderink Prize in recognition of his computer vision research paper that has “withstood the test of time.” Entitled “Unsupervised Learning of Models for Recognition,” the paper was originally published in 2000.

    The research paper presents a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. The authors focused on a particular type of model where objects are represented as flexible constellations of rigid parts (features):

    “The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the method automatically identifies distinctive parts in the training set by applying a clustering algorithm to patterns selected by an interest operator. It then learns the statistical shape model using expectation maximization. The method achieves very good classification results on human faces and rear views of cars.”

    Professor Welling's research lies in the areas of machine learning and machine vision with links to closely related areas such as pattern recognition, data mining, computational statistics, and large-scale data analysis.

     


    AUGUST 2010

    Lilly Irani wins Best Paper Award at ACM ICIC

    photo: Lilly Irani

    Lilly
    Irani

    Informatics Ph.D. Student Lilly Irani, along with Professors Paul Dourish and Melissa Mazmanian, have been awarded the Best Paper Award at the first ACM International Conference on Intercultural Collaboration.

    The paper, entitled “Shopping for Sharpies in Seattle: Mundane Infrastructures of Transnational Design,” describes the importance of mundane tools for design practitioners in India who are working with Euro-American clients. The published findings are based on an ongoing ethnographic study of a design firm based in Delhi, India.

    The research team analyzed everyday tools such as post-its as infrastructures with both practical and symbolic functions. These infrastructures are made meaningful in the shared practices of a transnational but primarily Euro- American design community. Material infrastructures shape the design processes and design communication as the teams work to established effective collaborations.

    Irani’s research interests lie in the areas of political economy, globalization issues in technoculture, development, and critical innovation studies.

    The full paper is available here (PDF).

     


    Tsudik awarded $600,000 in NSF effort to re-design the Internet

    photo: Gene Tsudik

    Gene
    Tsudik

    Gene Tsudik, professor of computer science, has been awarded $600,000 from the National Science Foundation (NSF)
as part of the Named Data Networking (NDN) project in the NSF Future Internet Architecture (FIA) program. The goal of 
FIA is to design a more effective, trustworthy and robust Internet protocols and techniques.

    Led by Professor Lixia Zhang of UCLA, the NDN project will focus on Internet architecture that moves the communication paradigm from today's focus on "where" (i.e., Internet addresses, servers, and hosts), to "what" (i.e., content that 
users and applications care about).

    Current Internet communication is based on a client-server model of interaction: communicating parties establish 
a relationship and then proceed to transfer information contained within IP packets transported along one or more paths. However, the most predominant use of the Internet is actually centered on content creation, dissemination and delivery, 
and this trend will continue for the foreseeable future. While the basic client-server model has enabled a wide range of services and applications, it does not offer adequate mechanisms to support secure content-oriented functionality, 
regardless of the specific physical location where the content resides.

    By naming data instead of its location (IP address), NDN transforms data into a first-class entity. 
While the current Internet mainly secures the channel between communication points,
NDN secures the content and provides essential context for security. NDN allows the decoupling of trust 
in data from trust in hosts and servers, enabling trustworthiness as well as several very scalable 
communication mechanisms, for example, automatic caching to optimize bandwidth and the 
potential to move content along multiple paths to the destination.

    This goal of this project is to address technical challenges in creating NDN, including: routing scalability, 
fast forwarding, trust models, network security, content protection and privacy, as well as new 
fundamental communication theory.

    UC Irvine will focus specifically on security and privacy aspects of the NDN architecture. Besides UCLA, other collaborating institutions include: PARC, Yale, Washington University and Colorado State.

    Gene Tsudik's research interests include computer/network security, privacy and applied cryptography. 
His recent work focuses on privacy in Internet services, RFID systems, mobile ad hoc networks, as well as 
security in sensor networks and storage systems.

    More about the Future Internet Architecture Awards is available here.

     


    JULY 2010

    Alumnus named Juan de la Cierva Fellow in Spain

    photo: Claudio Soriente

    Claudio
    Soriente

    Networked Systems alumnus, Claudio Soriente, Ph.D. '10 has been awarded the Juan de la Cierva Fellowship by the Spanish government.

    As part of this fellowship award, Claudio will be conducting research at the Polytechnic University of Madrid as a member of the Distributed Systems Laboratory.

    Soriente’s research focuses on security and privacy of large-scale distributed systems such as, sensor networks and parallel stream processing engines.

    Juan de la Cierva Fellowships are highly prestigious and very selective 3-year post-doctoral positions financed by the Spanish Ministry of Science and Education. The program is designed to promote the recruitment of young PhDs, with the aim of helping them to join and strengthen Spanish research teams. Juan de la Cierva fellowships are highly competitive and are offered to young (less than three years from obtaining a PhD) promising individuals with an outstanding record of research.

     


    Javanmardi places third in an international competition for detecting vandalism in Wikipedia

    photo: Sara Javanmardi

    Sara
    Javanmardi

    Sara Javanmardi, a Ph.D. student in Informatics, has placed third in an international competition for detecting vandalism in Wikipedia.

    Vandalism has always been one of Wikipedia's biggest problems, yet there are only few automatic countermeasures. Instead, volunteers spend their time in reverting vandalism edits — time, which is not spend on improving other parts of the Wikipedia. The goal of the evaluation campaign was to research and develop new, reliable ways to detect vandalism edits, which can be used to aid Wikipedia.

    Sponsored by Yahoo! Research, the competition is part of the fourth International Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse PAN-10 will be held as an evaluation lab in conjunction with the Conference on Multilingual and Multimodal Information Access Evaluation in Padua, Italy.

    Javanmardi's current research, in collaboration with Professor Cristina Lopes, focuses on social media content analysis. Her goal is to develop general quality-analysis and real-time search solutions that will be extendable to other Web 2.0 (social media) applications such as Twitter, Digg and blogs.


    JUNE 2010

    Irani receives 2010-11 Academic Senate’s Distinguished Award

    photo: Sandy Irani

    Sandy
    Irani

    Computer science professor Sandy Irani has been named recipient of the 2010-11 Academic Senate Distinguished Award for Service. Irani was nominated given her outstanding performance as the first Chair of the Computer Science department, which was created during the ICS' transition from an autonomous department to the Bren School.

    Part of the UC Irvine Academic Senate Distinguished Awards, the award recognizes faculty who have achieved excellence through their activities in research, teaching and service. The Academic Senate's Distinguished Faculty awards are selected by the Committee on Scholarly Honors and Awards.

    Professor Irani's principal research interests are in quantum computation and information. Her recent work has focused on understanding the computational complexity of computing fundamental properties of quantum system as well as characterizing which quantum systems can be used for quantum computation.


    Franz receives Academic Senate’s top research award

    photo: Michael Franz

    Michael
    Franz

    Computer science professor Michael Franz has been named the recipient of the 2010-11 Academic Senate Distinguished Award for Mid-Career Research — the Academic Senate’s highest honor for excellence in research.

    Professor Franz, along with former graduate student Andreas Gal, is the inventor of Trace Compilation, a radically new way of building just-in-time compilers. This technique is widely regarded as the single biggest invention in compilers in 20 years. His research impact currently extends to 500 million people globally using software that integrates his technique, such as Mozilla’s Firefox browser. In addition, Franz’s research has enabled third world countries to connect using no-cost cloud software such as Gmail and Google Docs, thereby closing the gap in the “digital divide”.

    Franz’s invention is a radical departure from the long-established convention of “control flow graph”, a technique used for over five decades to model a program’s control flow that a compiler builds and then traverses while generating code. Instead of constructing a control-flow graph of the program, Franz’s patent-pending technique called Trace Tree utilizes an intermediate representation that is constructed on-demand while the program is simultaneously executed, incrementally compiled and optimized.

    The advantage of the Trace Tree technique is that it utilizes one-seventh (1/7) of the memory footprint and one-thirtieth (1/30) of the compile time of traditional compilers. Working with the Mozilla foundation, Franz has incorporated this technique into the Firefox browser, beginning with version 3.5. By incorporating the Trace Tree, Mozilla was able to raise Firefox’s JavaScript performance 700%.

    Franz is currently in collaboration with Adobe to incorporate his just-in-time compiler into their next version of Flash. In addition, Google’s Android team has adopted Trace Compilation for the next generation smart phone operating system. These integrations coupled with the Firefox deployment will result in the impact of potentially several billion devices.

    Professor Franz is director at UC Irvine’s Secure Systems and Software Laboratory. A Distinguished Scientist of the Association for Computing Machinery and a Senior Member of The Institute of Electrical and Electronics Engineers, Franz has graduated 13 Ph.D. students, been awarded more than $7 Million in competitive Federal research funding as Principal Investigator, and has published more than 90 refereed papers.

    The Academic Senate's Distinguished Faculty awards are selected by the Committee on Scholarly Honors and Awards.


    Leon receives Excellence in Leadership Award

    Christine Leon, Director of Student Affairs, has been named one of three UC Irvine Excellence in Leadership Award winners.

    Leon was nominated by the Student Affairs Office staff for her excellent leadership of student affairs staff and was named an award winner by Executive Vice Chancellor-Provost Michael Gottfredson at last week's campuswide Staff Service Awards ceremony.

    The Excellence in Leadership Award Program recognizes select staff supervisors who – through outstanding leadership – enhance staff morale, build an enriching work environment, and serve as a mentor or otherwise support the career development of their staff, all to the benefit of our UCI community.

    EVC/Provost Gottfredson described Christine as a magnetic and engaging leader, who is selfless, honest and one who makes sound decisions for the good of her staff and the students of ICS. Christine has a great sense of fun and energy. She has inspired her staff to volunteer as a group annually for the Harvest Food Bank of Orange County. This has been not only a fun team-building and bonding experience, but one that allows the group to go beyond the everyday job and make a difference in the community.

    Annette Luckow and Mark Cartnal were also nominated by their staff. With three nominations, ICS had the most nomination from a single unit on campus.


    Tim Kashani '86 wins Best Musical Tony for Memphis

    Tim Kashani '86, an undergraduate ICS alumnus, was a part of the production team that was awarded the Best Musical Tony Award last night at Radio City Music Hall. Kashani's company Apple and Oranges Productions is a partner in the Broadway release of the musical.

    Memphis is loosely based on Memphis disc jockey Dewey Phillips, one of the first white DJs to play black music in the 1950s. It was staged during the 2003-04 season in Beverly, Massachusetts and Mountain View, California and opened on Broadway on October 19, 2009.

    Kashani is also the CEO of IT Mentors, a technology training company. View his complete alumni profile here:

     

     

    MAY 2010

    ICS undergrads named finalist in Google's Juicy Ideas Competition

    A Bren:ICS team of undergraduate students, PWNAGE, has been named one of six finalists in the Juicy Ideas Collegiate Competition. Computer Science and Engineering majors Jared Haren and Adrian Guzman, along with Informatics major Sabel Barganza are vying for an opportunity to win an all-expenses paid trip to Google headquarters in Mountain View and an Android-powered phone.

    PWNAGE developed a mobile application for UCI Dining that allows users to locate dining options based on menus, wi-fi availability, hours and other key information.

     

    Lopes named IEEE Senior Member

    photo: Cristina Lopes

    Cristina
    Lopes

    Cristina Lopes, associate professor in Informatics, has been elected a Senior Member of the Institute of Electrical and Electronic Engineers (IEEE), the world's leading professional association for the advancement of technology.

    Senior Member status is a rare honor attained by approximately eight percent of IEEE's 382,400 members. It is conferred only on those who have outstanding research achievements and who have performed great service to the scientific community.

    Dr. Lopes is best known as one of the co-inventors of Aspect-Oriented Programming. She conducts research in software engineering, specifically in large-scale source code search and analysis, and architectures for massive multi-user systems.


    Van Dyk elected IMS Fellow

    photo: David van Dyk

    David
    van Dyk

    Statistics professor David van Dyk has been named a Fellow of the Institute of Mathematical Statistics. The Fellowship honors the outstanding research and professional contributions of IMS members who are leaders in the field of statistics and probability.

    Professor van Dyk's scholarly work focuses on methodological and computational issues involved with Bayesian analysis of highly structured statistical models and emphasizes serious interdisciplinary research. He is particularly interested in improving the efficiency of computationally intensive methods involving data augmentation, such as EM-type algorithms and Markov chain Monte Carlo methods. A primary area of interdisciplinary work is Astro-statistics and focusing on constructing and fitting highly structured models for data obtained with the Chandra X-ray Observatory.

    Newly elected IMS Fellows will be recognized at the 73rd Annual IMS conference to be held in Gothenburg, Sweden, August 9-13.


     

    APRIL 2010

    Borkar named inaugural Facebook Fellow

    photo: Vinayak Borkar

    Vinayak
    Borkar

    Vinayak Borkar, a PhD student in computer science, has been named one of five inaugural Facebook Fellows.

    Borkar's research focuses on ways to improve distributed computing platforms for data analysis and applied for a fellowship because of the complex data processing challenges Facebook is tackling.

    "Large-scale data processing is undergoing a radical change. Innovation in these areas is happening at places like Facebook," says Borkar. "I look here for interesting data problems that will push the frontiers of research."

    The Facebook Fellowship Program supports Ph.D. students who show promise in solving some of the biggest challenges facing the social web and Internet technology. Each fellow receives paid tuition and fees, a $30,000 stipend, conference travel and other benefits.

    To see a complete list of winners, visit the Facebook announcement.


    Vernica named Yahoo! Key Scientific Challenges winner

    photo: Rares Vernica

    Rares
    Vernica

    Rares Vernica, a PhD student in computer science, has been named one of the winners of the 2010 Yahoo! Key Scientific Challenges Program, sponsored by Yahoo! Inc. for his work on "Efficient Similarity-Based Operators for Social Data." He was nominated by professors Michael J. Carey and Prof. Chen Li.

    Vernica will take a look at social networking sites to explore how an extremely large social graph can be efficiently analyzed for the purposes of information retrieval/recommendation and social trend detection.

    "The increasing popularity of social networking sites like Facebook and MySpace has led to the emergent trend of increasing integration between content and social sites," says Vernica.

    "In one direction, social sites are adding more and more content — such as photo, video, and news articles — to their sites to provide more practical utility to their users. Conversly, content sites like Amazon and Yahoo! Travel are incorporating social activities and connections to more deeply engage their users."

    With the establishment of the OpenSocial (opensocial.org) Foundation, the integration of social sites and content sites will likely be one of the major trends in the next few years.

    The Yahoo! Key Scientific Challenges Program encourages top graduate students globally to collaborate with Yahoo! and help invent the future of the Internet. The program focuses on a variety of scientific issues, from developing algorithms that turn raw information into personally relevant experiences, to discovering insights about online advertising and experimenting with new sociological models for how people engage with the Web. More about the program can be found here.


    Sambasivan, Nardi nominated for Best Paper at CHI 2010

    photo:

    Bonnie
    Nardi

    photo: nithya sambasivan

    Nithya
    Sambasivan

    Nithya Sambasivan, graduate student in informatics, and Professor Bonnie Nardi received a a Best Paper nomination at the CHI Conference 2010. Entitled, “Intermediated Technology Use in Developing Communities,” the paper is published in the Proceedings of the ACM Conference on Human Factors in Computer Systems.

    The paper describes a prevalent mode of information access in low-income communities of the developing world— intermediated interactions. The research takes an ethnographic look of two urban slums of Bangalore, India, studying how digitally skilled users can enable persons for whom technology is inaccessible due to non-literacy, lack of technology-operation skills, or financial constraints.

    The paper presents some requirements and challenges in interface design of these interactions and explains how they are different from direct interactions. They go on to explain the broader effects of these interactions on low-income communities, and present implications for design.

    Nithya Sambasivan is interested in human-centered technologies towards socio-economic betterment. She is also interested in mobile and ubiquitous computing as vehicles of social change.

    Professor Nardi's research interests include theory in human-computer interaction and computer-supported collaborative work and studies of social life on the Internet. Her current work concerns World of Warcraft and online crafting communities such as Ravelry.


    El Zarki, Lopes, Scacchi proposals win new initiative grants

    photo: magda el zarki

    Magda
    El Zarki

    photo: crista lopes

    Crista
    Lopes

    A program to train scientists to better manage vast, complex datasets, and a center that will transform human mobility through information technology and robotics, have been selected as the first recipients of the Large-Scale Interdisciplinary Research Ignition Initiative sponsored by Calit2, The Henry Samueli School of Engineering and the Donald Bren School of Information and Computer Sciences.

    The funding program, announced last December, aims to promote interdisciplinary research that will evolve into large-scale, agency-funded projects or centers in the areas of health, energy, environment, information, communications or digital technologies.

    The selection committee, comprised of deans Debra Richardson and Rafael Bras, and Calit2 Irvine director G.P. Li, chose the iScience project and iMove Center based on their multidisciplinary nature, likelihood of attracting significant funding from outside agencies, innovation, scientific value and long-term impact.

    iScience seeks to address the escalating volume of data collected by natural scientists, who increasingly are stymied by the sheer size of the datasets. Data-driven computing faces challenges in storing and managing these large, complex distributed and dynamic data collections, and needs radically new tool sets and visualization schemes to effectively explore, mine, understand and extract new knowledge from the information.

    Project PI Magda El Zarki (information and computer science) and co-PI Crista Lopes (informatics) propose a program to educate students, along with their faculty, across disciplines to create a new crop of scientists who are knowledgeable in the basic concepts of natural and computational sciences.

    The iMove Center, led by co-PIs David Reinkensmeyer (mechanical and aerospace engineering, and biomedical engineering), Steve Cramer (neurology), Mark Bachman (electrical engineering and computer science) and Walt Scacchi (information and computer science) will search for ways to use information technology, robotics and neuro-regenerative therapies, including dance, sport and computer games, to improve human mobility and challenge patients beyond what is possible with current rehabilitation models.

    Researchers hope to help shape new brain circuits and assess movement recovery with novel electrophysiological, functional imaging and behavioral outcome measures. The center also seeks to produce technologies useful to those without disabilities, including innovative, interactive training and performance technologies for sport and dance.

    Each winning project will receive grants totaling $40,000: $20,000 now and $20,000 when a proposal is submitted to an outside agency requesting a minimum of $500,000 per year for at least 3 years. Additional calls for proposals for the initiative will occur in October 2010 and February 2011.

    "We are pleased to be a part of this new multidisciplinary initiative that will ultimately benefit society," said Calit2's Li, "and we look forward to the long-term success of our first two recipient projects."


    Mehrotra, Hore receive NEC Research Award

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    Professor Sharad Mehrotra and postdoctoral student Bijit Hore have received a $60,000 NEC Research Award to study Risk Containment in Cloud Computing Services for Data centric Applications.

    Building upon the pioneering work by Mehrotra's group on privacy challenges in data outsourcing (also known as the database as a service model), the goal of this study is to understand information disclosure risks and design mechanisms to prevent disclosure in multi-tenant cloud environments and dynamic data integration applications such as mashups.

    As cloud computing becomes prevalent for enterprise data management, increasingly privacy sensitive user data will be managed in such systems.

    In addition, the project will address how one can reduce the risk of memory based leakage of critically sensitive information in the Mapreduce framework. The Mapreduce framework provides a highly scalable and robust approach for large data processing applications in a cloud-computing environment.

    The NEC Research Award is a gift from the Nippon Electric Corporation's R&D division.

    Mehrotra's current research focuses on next-generation information systems focusing on issues related to data quality, data dynamicity, and data privacy.


    Majumder and Sajadi win Best Paper award at IEEE Virtual Reality 2010

    photo: Aditi Majumder

    Aditi
    Majumder

    photo: Behzad Sajadi

    Behzad
    Sajadi

    Computer Science Professor Aditi Majumder and graduate student Behzad Sajadi have received the Best Paper Award at the IEEE Virtual Reality 2010 conference held in Boston.

    The paper, entitled "Auto-Calibration of Cylindrical Multi-Projector Systems," explores registering multiple projectors on vertically extruded a cylindrical display, which previously was only possible with a calibrated stereo camera.

    This papers shows that using some simple priors, one can achieve multiple projector registration on a cylindrical display using a single uncalibrated camera without any markers on the display. More importantly, the new method enables use of multiple overlapped projectors across corners of a vertically extruded surface with sharp edges. This is of tremendous benefit to virtual reality display systems like CAVEs, that avoided mounting projectors across the corners until today.

    The full paper can be accessed here: http://www.ics.uci.edu/~majumder/docs/VR10.pdf.

    IEEE Virtual Reality 2010 is the top international venue for virtual reality researchers and practitioners.

    Professor Majumder's research addresses how to produce a seamless image on a large-scale tiled display - an important problem to both the scientific and entertainment fields. Majumder has developed a suite of mathematical models, methods and software to correct the geometric, chromatic and luminescent variations that arise when tiling multiple projection displays.


    Lilly Irani speaks at the Commonwealth Club panel in San Francisco

    Informatics graduate student Lilly Irani spoke on March 3 at a Commonwealth Club panel in San Fancisco. The panel brought together people from business, NGOs, and research to discuss the ethics and possibilities of crowdsourcing platforms such as Mechanical Turk, Crowdflower, and Samasource.

     

     

    MARCH 2010

    Li receives NSF grant to support Family Reunification project

    photo: Chen Li

    Chen
    Li

    Professor of Computer Science Chen Li has received a $50,000 NSF grant for his project entitled "RAPID: Supporting Family Reunification for the Haiti Earthquake and Future Emergencies."

    Li has been leading an effort on a website for family reunification for the Haiti earthquake (http://fr.ics.uci.edu/haiti/). In addition to crawling and scraping data from Web pages for the Google-hosted repository at http://haiticrisis.appspot.com/, the team also built a powerful search interface on the data.

    During this effort, the team has identified several interesting technical challenges that Li will study in this proposal. The research will focus on how to support powerful search in a could-computing environment, such as Google App Engine. The techniques developed in this project will have a broad impact on many information systems that are moving to the cloud-computing paradigm. The team will use the Google Person Finder project as a real application to test the techniques, and could provide the techniques and source code to be used in family reunification during future disasters.

    Li's research interests are in the fields of database and information systems, including data integration, data cleaning, Web search, and large-scale information processing using parallel computing.

    More on the project is available at: http://fr.ics.uci.edu/


    Newman awarded NSF EAGER to analyze grant portfolios

    photo: David Newman

    David
    Newman

    Research Scientist David Newman has been awarded a $25,000 NSF EAGER Award for his research entitled "Analyzing Grant Portfolios through Topic Modeling." The goal of this research is to develop and apply topic models — Bayesian models for document collections — to model and analyze collections of grant proposals and their metadata (funding amount, NSF Directorate, NSF Program, etc.).

    The award will be used to develop and demonstrate tools to help NSF program officers better analyze, visualize and interact with large collections of both unfunded proposals and funded projects. This will both help streamline the grant proposal review and decision process, and help NSF understand what areas of research could use more funding, or are under-represented.

    Newman's current research focuses on theory and application of topic models and related text mining techniques. His research is marked by a commitment to combining theoretical advances with practical applications in ways that widen access and use for individuals and communities, and ultimately improve the way people find and discover information.


     

    FEBRUARY 2010

    Mehrotra awarded Google Research Award to improve data quality

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    Professor Sharad Mehrotra and Assistant Adjunct Professor Dmitri V. Kalashnikov have been awarded a Google Research Award for $50,000 for a research effort entitled "Exploiting Entity Resolution for Web People Search."

    The goal of this research is to explore a novel graphical domain-independent framework that exploits semantics of various forms to improve data quality. Data quality challenge is ubiquitous in various domains - it arises whenever we collect and create large repositories of information, especially when information is captured and assimilated automatically.

    The award will be used to develop an adaptive self-tuning entity resolution framework which will be used to build an online search engine that can disambiguate amongst namesakes on the Web. The system will exploit web queries and search engine statistics for disambiguation.

    Mehrotra's current research focuses on next generation database management systems that provide natural and efficient support for complex multidimensional data sets. Multidimensional data sets abound in numerous application domains in which database technology is currently being deployed. For example, medical information systems require databases to provide native support for X-rays, volumetric MRI scans, and time varying volumetric information.


    Vaisenberg awarded IBM Ph.D. Fellowship

    Doctoral student Ronen Vaisenberg has been awarded an IBM Ph.D. Fellowship.

    The IBM Ph.D. Fellowship Awards Program is an intensely competitive worldwide program, which honors exceptional Ph.D. students who have an interest in solving problems that are important to IBM and fundamental to innovation.

    Vaisenberg's research focus is on problems that relate to the management, extraction and fusion of information from multiple media sources. This area relates the fields of databases and data management, time series data management, statistical databases, model building and classification applied in the context of media (text,image,video) processing.

    His Ph.D dissertation deals with the issues related to the data management support for sentient systems, for various first responding and life preserving applications funded by NSF's ITR-Rescue (RESponding to Crisis and Unexpected Events) and the Department of Homeland Security's Safire (Situational Awareness for Firefighters). 

     

     


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    http://www.ics.uci.edu/community/news/notes/notes_2013.php noteworthy achievements 2013-2014 @ the bren school of information and computer sciences
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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements 2013-2014

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

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    SUMMER 2014

    Kobsa to co-chair recommender systems conference in Silicon Valley

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Informatics and computer science professor Alfred Kobsa will co-chair the 8th Association of Computing Machinery (ACM) Conference on Recommender Systems (RecSys 2014), held Oct. 6-10 in Silicon Valley’s Foster City. The conference is the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. Such systems, as part of broader information filtering system networks, attempt to predict user preferences and ratings for items by exploiting users’ past behaviors. Their use has surged in popularity in recent years.

    According to the website, the conference “will bring together researchers and practitioners from academia and industry to present their latest results and identify new trends and challenges in providing recommendation components in a range of innovative application contexts.” Netflix’s chief product officer Neil Hunt will deliver the keynote speech, titled “Quantifying the Value of Better Recommendations.”

    Kobsa praised Silicon Valley as an ideal location for RecSys 2014, saying that it “allows local high-tech industry to learn about the most recent research results in recommender technology.” Attracting local industry has proved successful: Tickets to the conference sold out before the early registration deadline.

     


    Nicolau and Veidenbaum published in computing retrospective

    photo: Alex Nicolau

    Alex
    Nicolau

    photo: Alex Veidenbaum

    Alex
    Veidenbaum

    Computer science chair Alex Nicolau and professor Alex Veidenbaum have each been published in the 25th anniversary volume of the International Conference on Supercomputing. The volume is a collection of author retrospectives for the best papers published in the first 25 years of the conference — the Association for Computing Machinery's flagship conference in high-performance computing. Thirty-five papers were selected for their impact on the field, out of the approximately 1000 papers published in the first 25 years of the conference.

    Nicolau’s paper “A Global Resource-Constrained Parallelization Technique,” co-authored with Kemal Ebcioğlu and originally published in 1989, helped to innovate the obstacle-ridden process of automatic parallelization of arbitrary sequential code. In their retrospective on the paper, Nicolau and Ebcioğlu remark, “Automatic parallelization of arbitrary sequential code remains an exciting and active area as of today, and promising research and industry efforts continue.”

    Veidenbaum’s paper “Compiler-Directed Data Prefetching in Multiprocessors with Memory Hierarchies,” co-authored with Edward H. Gornish and Elana D. Granston, was the first paper on compiler-managed data prefetching for multiprocessor systems with caches. Prefetching is used to reduce large memory access times. The group explains in their retrospective that compiler-prefetching algorithms used in memory management have not changed much since the paper’s original publishing in 1990. They ask: “Are there better ways to do it for today's hardware?”

     


    Baldi, Kobsa, Mark receive Google Faculty Research Awards

    photo: Pierre Baldi

    Pierre
    Baldi

    photo: Alfred Kobsa

    Alfred
    Kobsa

    photo: Gloria Mark

    Gloria
    Mark

    Three ICS professors have received a Google Faculty Research Award as part of Google’s biannual open call for proposals in computer science, engineering and related fields. Computer science Chancellor’s Professor Pierre Baldi, informatics and computer science professor Alfred Kobsa and informatics professor Gloria Mark join several other ICS faculty who have received the award in recent years.

    As part of Google’s mission to “organize the world's information and make it universally accessible and useful,” Faculty Research Awards identify and support faculty pursuing research of mutual interest at internationally renowned universities. The award is an unrestricted gift, and is typically funded at the amount needed to support one graduate student for one year.

    Baldi received $65,000 for his proposal, “Deep Learning and Dropout.” The proposal studies the properties of dropout — a recently introduced deep learning approach with applications in computer vision, speech recognition and natural language processing — and seeks to develop optimized randomization strategies and algorithms for deep learning, to be applied to challenging problems in the natural sciences and other areas.

    Kobsa received $60,000 for his proposal, “Predicting People’s Privacy Preferences for Ubiquitous Personal Data Disclosure,” which will investigate the predictability of user privacy preferences in a ubiquitous personal data disclosure scenario. In a world where apps and websites increasingly ask users to make privacy decisions, predicted privacy preferences may allow for a balance between desired privacy and personal data disclosure.

    Mark’s proposal, “In Situ Precision-tracking of Online Behavior: A Comprehensive View of Focus, Mood and Context,” garnered $64,000. Her work seeks to understand how people's focus, mood and stress change while using digital media in a real-world context, through precision tracking using wearable sensors and other methods.

     


    ICS team earns honorable mention at programming contest in Russia

    UCI Constructors

    A team of ICS undergrads garnered an honorable mention at the Association for Computing Machinery’s International Collegiate Programming Contest World Finals held in Ekaterinburg, Russia in late June. The team known as UCI Constructors — consisting of Nick Alajat, Alan Castro and Michael Cappe — finished 88th out of 122 teams, beating out fellow North American competitors UC Berkeley and Carnegie Mellon University, among others.

    Computer science senior lecturer Richard Pattis, who sponsored the team, pointed out that UCI Constructors was the first UCI team in over a decade to qualify for the world finals. “These students resurrected competitive programming at UCI,” Pattis declared.

    During a five-hour span, international teams tackled 12 difficult programming questions. The UCI team was able to solve one problem, while the winning team from Russia’s St. Petersburg State University solved seven.

    “The World Finals were just as difficult as we expected them to be,” explained Gio Borje, coach of UCI Constructors and chair of UC Irvine’s ACM chapter. “Relative to other West Coast teams, UC Irvine had a great start at the beginning of the competition by solving the first problem faster than the others, but eventually the other teams caught up.”

    Apart from competing, the team was able to squeeze in some sightseeing and tour the Iset River in Ekaterinburg.

    UCI Constructors had taken second place in ICPC’s Southern California Regional last year. This year’s tournament starts up again soon, with the Southern California Regional taking place at Riverside Community College Nov. 8.

     


    Jain co-authors textbook on multimedia computing

    photo: Ramesh Jain

    Ramesh
    Jain

    book cover

    Professor Ramesh Jain has recently published a new textbook on multimedia computing. Co-written with Gerald Friedland, director of audio and multimedia research at the International Computer Science Institute (a private research lab affiliated with UC Berkeley), the book is simply titled Multimedia Computing.

    The textbook presents emerging techniques in multimedia computing, treating each medium — audio, images, text, etc. — as a strong component of the complete exchange of information.

    “The multimedia field needed a textbook,” states Mubarak Shah, a professor at the University of Central Florida, in his back-cover blurb for the book, “and it is finally here.”

    Jain and Friedland introduce the fundamentals of multimedia computing, describing the properties of perceptually encoded information; presenting common algorithms and concepts for handling it; and outlining the typical requirements for emerging applications that use diverse information sources. Designed for advanced undergraduate and beginning graduate courses, the book also serves as an introduction for engineers and researchers interested in understanding the elements of multimedia and their role in building specific applications.

     


    Smyth gives keynote talk at top artificial intelligence conference

    photo: Padhraic Smyth

    Padhraic
    Smyth

    Professor Padhraic Smyth gave an invited keynote talk at the recent AAAI Conference on Artificial Intelligence (AAAI-14). Held July 27-31 in Québec City, Canada, the conference promotes research in artificial intelligence and scientific exchange among AI researchers, practitioners, scientists and engineers in affiliated disciplines. More than 400 papers were presented at the conference, which attracted more than 1000 attendees from North and South America, Europe, Asia, and Australia.

    Smyth's talk, titled “30 Years of Probability in AI and Machine Learning,” discussed the historical development of AI and machine learning algorithms in the context of probabilistic and statistical foundations, as well as potential future directions for the field. Smyth described how probabilistic and statistical thinking is a key aspect of developing machine learning solutions for many diverse data-driven problems in areas like computer vision, computational advertising, text mining, bioinformatics and climate science.

    Founded in 1979, the nonprofit Association for the Advancement of Artificial Intelligence (formerly the American Association for Artificial Intelligence) seeks to further “research in, and responsible use of, artificial intelligence,” per the group’s website.

     


    Van der Hoek and LaToza receive $1.4 million from NSF to study “crowdprogramming”

    photo: André van der Hoek

    André
    van der Hoek

    photo: Thomas LaToza

    Thomas
    LaToza

    Informatics professor André van der Hoek and postdoctoral scholar Thomas LaToza have received a $1.4 million grant over four years from the National Science Foundation for their research into what they have termed “crowdprogramming.”

    Where crowdsourcing leverages the power of mass individual input to complete tasks that were previously too labor-intensive to be feasible or even possible, van der Hoek and LaToza propose applying those same principles to software development. Their research will address the fundamental question of how the nature of software affects what may and may not be possible in terms of crowdsourcing.

    In their NSF-funded project, the pair will develop theoretical understandings of crowd programming in terms of whether it can be achieved, in which form, under what conditions and with which benefits and drawbacks; and also create a publicly available crowdsourcing platform, CrowdCode, that will offers a tool set specifically designed to address the intricacies of crowdprogramming.

     


    Statistics graduate student places second in prestigious biometrics competition

    photo: Sepehr Akhavan

    Sepehr
    Akhavan

    Sepehr Akhavan, a statistics Ph.D. student, has placed second in the student paper competition at the Western North America Region (WNAR) conference of the International Biometric Society. The twenty submissions for this prestigious award included papers from statistics and biostatistics departments at Stanford, UC Berkeley, UCLA and the University of Washington.

    Akhavan’s paper, titled “A Flexible Joint Longitudinal-Survival Model For Quantifying The Association Between Within-Subject Volatility In Serum Biomarkers and Mortality,” proposes a statistical method for tracking albumin, an important biological marker, in patients undergoing dialysis treatment for kidney disease. By identifying potentially dangerous fluctuations in albumin levels, Akhavan’s model seeks to reduce the mortality rate among this critically ill population. The project is being co-supervised by professor Dan Gillen and associate professor Babak Shahbaba.

    Founded in 1948, WNAR is devoted to sharing the applications of statistics and mathematics to biology. Gillen points out that the Bren School’s statistics department has had a student place first or second in this competition for three of the past four years.

     


    SPRING 2014

    RWJF grants $1.9 million to project co-led by Bietz

    photo: Matthew Bietz

    Matthew
    Bietz

    The Health Data Exploration Project—co-led by informatics assistant project scientist Matthew Bietz—has received a $1.9 million grant from The Robert Wood Johnson Foundation (RWJF) to create a network of researchers, scientists, companies and others to catalyze the use of personal health data for the public good.

    The Health Data Exploration Project, a multi-campus collaboration led by the California Institute for Telecommunications and Information Technology (Calit2), seeks to establish a network that will “bring together companies that collect and store personal health data, captured through the use of wearable devices, smartphone apps and social media, with researchers who mine these data for patterns and trends and other strategic partners,” according to a press release about the grant.

    The network is inspired by findings about health-related data sharing practices, which were published in The Health Data Exploration project’s recent report “Personal Data for the Public Good.” The report was also funded by RWJF, the nation’s largest health-focused philanthropic organization.

     


    Kobsa receives $70,000 grant from Intel Labs for research on users’ privacy decision-making

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Intel Labs has awarded informatics and computer science professor Alfred Kobsa a $70,000 grant for his research on users’ privacy decision-making in the context of mobile and ubiquitous computing.

    Computer users are increasingly asked to make privacy decisions, but research has shown that such decisions are cumbersome and difficult. Kobsa’s research proposes a “privacy adaptation procedure” that predicts users’ decisions through machine learning, and sets personal default privacy preferences accordingly.

    The research is in the vein of Intel’s “context-aware” computing—in which devices anticipate user needs and desires and help fulfill them before users ask.

    Kobsa’s proposed “privacy adaptation procedure” could allow Intel to act as an information broker between the user and the app developer. This information broker could provide privacy decision support in two ways: by informing the app about the users’ privacy preferences, which the app can then use to determine permissible requests; and by providing a safe subset of the users’ personal information to the app without user intervention, based on its predictions about the users’ disclosure preferences. The research maintains that in either case, the user should choose the level of automation.

     


    Tomlinson, Nardi and Patterson to design UC-wide online course

    photo: Bill Tomlinson

    Bill
    Tomlinson

    photo: Bonnie Nardi

    Bonnie
    Nardi

    photo: Don Patterson

    Don
    Patterson

    The UC Office of the President’s Innovative Learning Technology Initiative (ILTI) has granted professor Bill Tomlinson, professor Bonnie Nardi and associate professor Don Patterson $110,000 to design an online course to be offered across all nine undergraduate University of California campuses. According to the instructors, the course, titled “ICS 5: Global Disruption and Information Technology,” will explore “how sociotechnical systems — collections of people and information technologies — may support a transition to a sustainable civilization.” The course is to be first offered in Winter 2015 and will feature online content from faculty with backgrounds in computing, anthropology and sustainability; recorded guest lectures with world-class academics, practitioners and domain experts; and a multidisciplinary group assignment to conceive and deploy real-world projects focused on human wellbeing in California.

    The course will be part of the first group of online courses to be offered across all nine undergraduate UC campuses as part of the ILTI. Established in January 2013 by then-UC President Mark G. Yudof, the ILTI “is designed to meet UC campus needs for high-quality online or hybrid course offerings. The Initiative will enhance the educational opportunities and achievements of UC students by helping them get access to high-demand courses, satisfy degree requirements and graduate on time,” according to the ILTI website.

     


    Khademi receives Dean’s Prize for Best Presentation at AGS Symposium

    photo: Maryam Khademi

    Maryam
    Khademi

    Informatics Ph.D. student Maryam Khademi has received the Dean’s Prize for Best Presentation in Information and Computer Sciences at the first ever Associated Graduate Students (AGS) Symposium. Khademi presented her research abstract, titled “Utility of Augmented Reality in Relation to Virtual Reality in Stroke Rehabilitation,” to an audience of UCI faculty, students, industry representatives and the public.

    The AGS Symposium “provides a venue for UCI to showcase outstanding graduate and professional student research to the Irvine community,” according to the AGS website. The symposium, which took place on April 18, consisted of a day of TED-style research talks focused on how UCI students are using collaborative or innovative methods to tackle important problems in their field.

    Khademi’s research interests include natural user interfaces, computer vision and gesture recognition.

     


    Utts elected president of American Statistical Association

    photo: Jessica Utts

    Jessica
    Utts

    Jessica Utts, chair of the Department of Statistics, has been elected president of the American Statistical Association (ASA). She will serve a one-year term as president-elect starting on Jan. 1, 2015, and will become president for one year starting on Jan. 1, 2016.

    “I’m excited about the opportunity to serve as president of the ASA,” Utts said in an announcement released by the association. “With the increasing demand for statisticians, one of my primary goals will be to educate high school and college students about the diverse and exciting career opportunities in statistics. Another goal will be to continue my career’s work to promote the importance of statistical literacy to the public.”

    Utts, who has long been a highly active member of the ASA, joined UCI as a professor of statistics in 2008 and was named department chair in 2011. Before coming to ICS, Utts was a faculty member in the department of statistics at UC Davis from 1978 to 2008. She earned her doctorate in statistics from Pennsylvania State University.

     


    Computer science undergraduates win Facebook Southern California Regional Hackathon

    photo: Olivier Truong

    Olivier
    Truong

    photo: Lucas Ou-Yang

    Lucas
    Ou-Yang

    photo: Kevin Jonaitis

    Kevin
    Jonaitis

    Undergraduate team “Facebook Context,” comprising computer science majors Olivier Truong, Lucas Ou-Yang and computer science/electrical engineering major Kevin Jonaitis, took first place at the Facebook Southern California Regional Hackathon. Held April 4-5 in Santa Monica, the hackathon challenged student teams from all over Southern California to bring a product from idea to prototype in 24 hours. Team “Facebook Context” built a feature that seamlessly adds relevant images to Facebook posts using natural language processing and a Google Chrome extension, for which they won three Google Nexus 7 tablets and an all-expenses-paid trip to the Menlo Park, Calif. headquarters of Facebook to compete at the Global Hackathon Final in November.

    The Facebook Southern California Regional Hackathon is one of 17 regional Facebook hackathons hosted around the world, culminating in the Global Hackathon Final judged by Facebook executives. Winners of the Global Hackathon Final have been known to meet CEO Mark Zuckerberg.

    Pictures from the Southern California hackathon are available here.

     


    Lopes publishes book on programming styles

    photo: Crista Lopes

    Crista
    Lopes

    Exercises in Programming Style - book cover
    Informatics professor Crista Lopes has published a book titled Exercises in Programming Style, available in June 2014. Published by Chapman and Hall and CRC Press, the book helps readers understand the various ways of writing programs and designing systems, using a simple computational task — term frequency — to illustrate different programming styles. Written for anyone who regularly practices programming, the book is designed for use in conjunction with code provided on an online repository.

    The book contains 33 different styles for writing the term-frequency task, grouped into nine categories: historical, basic, function composition, objects and object interactions, reflection and metaprogramming, adversity, data-centric, concurrency and interactivity.

    Computer scientist James Noble of Victoria University of Wellington, New Zealand, has hailed the book as “the most important book on programming in the last 20 years.” Industry veteran and IBM Fellow Grady Booch calls it “an instant classic.”

    Exercises in Programming Style is available for pre-order here.

     


    Tsudik receives research grant from Verisign

    photo: Gene Tsudik

    Gene
    Tsudik

    The network infrastructure company Verisign has awarded Chancellor’s Professor Gene Tsudik a $50,000 grant for research on privacy-enhancing techniques for the Internet’s network/transport layers, including the Transmission Control Protocol and Internet protocol suite (TCP/IP) and the Secure Sockets Layer and Transport Layer Security (SSL/TLS) — cryptographic protocols designed to provide communication security over the Internet.

    The grant is part of the Verisign infrastructure grant program, designed to support the Internet’s robust growth and development. The program fosters “new research which advances security and stability, encourages Internet deployment and improves the Internet infrastructure overall,” according to the grant program website. Research proposals are judged on the criteria of relevance, innovation, feasibility and overall quality.

     


    CHI 2014 Conference honors several papers by ICS faculty and researchers

    photo: Silvia Lindtner

    Silvia
    Lindtner

    photo: Paul Dourish

    Paul
    Dourish

    photo: Garnet Hertz

    Garnet
    Hertz

    photo: Gloria Mark

    Gloria
    Mark

    Numerous ICS faculty will be honored at this year’s ACM Conference of Human Factors in Computing Systems (CHI), the premier international conference in the field of human-computer interaction. Hosted by ACM SIGCHI, ACM’s special-interest group on human-computer interaction, the annual conference attracts thousands of attendees each year.

    A Laboratory for Ubiquitous Computing and Interaction (LUCI) research team including postdoctoral scholar Silvia Lindtner, professor Paul Dourish and assistant project scientist Garnet Hertz, received a Best Paper Award for “Emerging Sites of HCI Innovation: Hackerspaces, Hardware Startups & Incubators.” The paper discusses how a flourishing scene of DIY makers is turning visions of tangible and ubiquitous computing into products.

    Informatics professor Gloria Mark also earned a Best Paper Award. Her paper “'Narco'” Emotions: Affect and Desensitization in Social Media during the Mexican Drug War," co-authored with Andres Monroy-Hernandes and Munmun de Choudhury, both of Microsoft Research, found that tweets can reveal the desensitized emotions of a society that is experiencing violence during a war — in this case, the Mexican drug war.

    Mark also received two Honorable Mention Awards for her papers, “Bored Mondays & Focused Afternoons: The Rhythm of Attention & Online Activity in the Workplace,” and “Stress and Multitasking in Everyday College Life: An Empirical Study of Online Activity.” “Bored Mondays & Focused Afternoons,” co-authored with Microsoft researchers Shamsi T. Iqbal, Mary P. Czerwinski and Paul R. Johns, explores engagement in workplace activities by presenting a framework of how engagement and challenge in work relate to focus, boredom and rote work. “Stress and Multitasking in Everyday College Life,” co-authored with ICS graduate student Yiran Wang and education graduate student Melissa Niiya, reports on multitasking among Millennials who grew up with digital media, with a focus on college students.

    A research team comprising associate professor Gillian Hayes, assistant professor Melissa Mazmanian, graduate student Lynn Dombrowski and former postdoctoral scholar Amy Voida, received an Honorable Mention Award for their paper “Shared Values/Conflicting Logics: Working Around E-Government Systems,” which provides an analytic framework for exploring value tensions as values are enacted in practice — the result of fieldwork conducted at a social services site where the workers evaluate citizens’ applications for food and medical assistance submitted via an e-government system.

    CHI 2014, held in Toronto April 26-May 1, “is a celebration of the conference's one of a kind diversity; from the broad range of backgrounds of its attendees, to the diverse spectrum of communities and fields which the conference and its research have an impact on,” according to the CHI 2014 website.

     


    Devanny, Quang and Wood awarded NSF Graduate Research Fellowships

    photo: William Eric Devanny

    William Eric
    Devanny

    photo: Daniel Xin Quang

    Daniel Xin
    Quang

    photo: Christopher Wood

    Christopher
    Wood

    Ph.D. students William Eric Devanny, Daniel Xin Quang and Christopher Wood have been awarded 2014 National Science Foundation Graduate Research Fellowships.

    The highly selective fellowship “helps ensure the vitality of the human resource base of science and engineering in the United States and reinforces its diversity,” according to the fellowship website. The fellowship provides multi-year support, including a $32,000 annual stipend, a $12,000 cost-of-education allowance, opportunities for international research and professional development, and access to the Extreme Science and Engineering Discovery Environment (XSEDE) Supercomputer — a robust virtual system that scientists can use to interactively share computing resources, data and expertise.

    Devanny is part of the Center for Algorithms and Theory of Computation, and is advised by professor David Eppstein. His research concerns universal point sets and superpatterns; he plans to explore the connection between these two structures and look for other applications of superpatterns.

    Quang's research interests lie in systems biology. Advised by associate professor Xiaohui Xie, Quang applies machine learning techniques to integrate and analyze large sets of genomics data.

    Wood’s multidisciplinary research interests are rooted in applied cryptography; computer and network security; and heterogeneous computing. He is co-advised by Chancellor’s Professor Gene Tsudik and associate professor Stanislaw Jarecki.

     


    Gabrielova receives Palantir Scholarship for Women in Technology

    photo: Eugenia Gabrielova

    Eugenia
    Gabrielova

    ICS Ph.D. student Eugenia Gabrielova has received the Palantir Scholarship for Women in Technology. The scholarship supports women in the STEM (science, technology, engineering and mathematics) disciplines by aiding them in their academic careers and providing them with opportunities to learn from other women making a difference through technological innovation. Initially established in 2012 to serve underrepresented populations in computer science, the scholarship was expanded this year to all women in the STEM disciplines who may rely on Palantir’s technology in their work, according to a recent news brief publicizing the scholarship finalists.

    Gabrielova’s research interests include virtual worlds, large-scale scientific data exploration and self-managing software systems. She works under the guidance of professor Crista Lopes, and is affiliated with the Mondego Lab, which focuses on research in large systems and large data.

    Palantir is a mission-focused software and services company whose data fusion platforms are used in “the most difficult problems facing the world’s most critical institutions: finding missing and exploited children, combatting terrorism, enabling hundreds of thousands of homeowners to avoid foreclosure, preventing the spread of foodborne illness,” among other issues, according to the Palantir Scholarship for Women in Technology website.

     


    WINTER 2014

    Journal features Baldi and team’s research on diet and circadian rhythm

    photo: Pierre Baldi

    Pierre
    Baldi

    Chancellor's Professor Pierre Baldi was part of a team whose study was featured in the December 2013 issue of the life science journal Cell. Baldi, director of the Institute for Genomics and Bioinformatics, played a key role in the study, collecting and analyzing highly complex genetic data.

    Titled “Reprogramming of the Circadian Clock by Nutritional Challenge,” the paper reveals a surprising connection between diets and gene oscillation, finding that a high-fat diet (HFD) generates a profound reorganization of specific metabolic pathways, leading to disruption of the normal circadian cycle regulated by the liver clock.

    The research also demonstrates that the nutritional challenge specifically, and not the development of obesity, causes reprogramming of the liver clock. This indicates that the reprogramming takes place independent of the state of obesity and that the effects of diet on the liver clock are reversible.

     


    ICS research team published in RWJF report on personal health data

    photo:  Judith Gregory

    Judith
    Gregory

    The Robert Wood Johnson Foundation (RWJF) has published a report titled “Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health,” to which ICS associate adjunct professor Judith Gregory and assistant project scientists Matthew Bietz and Scout Calvert contributed. The report examines attitudes toward personal health data from the individuals who self-track personal data; the companies designing self-tracking devices, apps, or services; and the researchers who might use the data.

    The report is a culmination of research that comes out of the Health Data Exploration project, a multi-campus collaboration led by the California Institute for Telecommunications and Information Technology (Calit2) with support from RWJF — the nation’s largest health-focused philanthropic organization. Key findings include: high willingness of individuals to share self-tracked data for research with assured privacy; how current methods of informed consent are challenged by the use and reuse of personal health data in research; and researcher enthusiasm for personal health data tempered by concern for its validity and the lack of standardization of devices.

     


    SPROUT team receives Distinguished Paper Award at NDSS 2014

    photo:  Gene Tsudik

    Gene
    Tsudik

    A team from ICS’s Security and Privacy Research Outfit (SPROUT) — including Chancellor’s Professor Gene Tsudik and former postdoctoral scholars Ivan Martinovic and Kasper B. Rasmussen, as well as a visiting graduate student Marc Roeschlin — has received a Distinguished Paper Award at the 2014 Network & Distributed System Security Symposium (NDSS). The paper, “Authentication Using Pulse-Response Biometrics,” proposes and evaluates a new biometric based on the human body’s response to an electric square pulse signal. It explores how this biometric can enhance security as an additional authentication mechanism in PIN entry systems, and as a continuous authentication mechanism on a secure terminal.

    NDSS “brings together innovative and forward-thinking members of the Internet community, including leading-edge security researchers and implementers, globally-recognized security-technology experts, and users from both the private and public sectors who design, develop, exploit, and deploy the technologies that define network and distributed system security,” according to the NDSS 2014 website. Hosted by The Internet Society, a global cause-driven organization dedicated to ensuring that the Internet stays open and transparent, NDSS 2014 took place in February in San Diego.

    SPROUT, part of the Secure Computing and Network Center, specializes in applied cryptography, computer and network security, and privacy. Research directions and projects range from cryptographic protocols to human-focused usable security and privacy techniques.

     


    Hayes receives Google Glass Accessibility Research Award

    photo:  Gillian Hayes

    Gillian
    Hayes

    Professor Gillian Hayes has received a Google Glass Accessibility Research Award for her proposal “Wearable Visual Supports for People with Autism Spectrum and other Neurodevelopmental Disorders.” Collaborators in the project include professor Don Patterson and former postdoctoral scholar Monica Tentori, now a professor at CICESE in Ensenada, Mexico.

    Google will provide Hayes with five Google Glass devices and $15,000 to support the project, which seeks to establish the feasibility of using the Glass platform for visual supports to help individuals with autism and related disorders, demonstrating how to design such assistive technologies in light of substantial background research and related design efforts.

    Google has previously funded Hayes with Google Faculty Research Awards in 2011 and 2012 for her work with technology and premature infants.

     


    Smyth receives a Google Faculty Research Award

    photo:  Padhraic Smyth

    Padhraic
    Smyth

    Professor Padhraic Smyth has received a Google Faculty Research Award as part of Google’s biannual open call for proposals in computer science, engineering and related fields. Smyth will receive $60,000 for his proposal, “Analyzing Individual Event Data over Time,” which will develop new statistical machine learning techniques for extracting useful information from time-stamped email histories. The project will also look more broadly at developing statistical models for other types of individual communication such as texting, microblogging and social media interactions

    Google Faculty Research awards are one-year awards “structured as unrestricted gifts to universities to support the work of world-class full-time faculty members at top universities around the world,” according to the Google Research website.

    Smyth is one of three recipients of the award at UCI this round, with Google also honoring two professors from the School of Education: Joshua Lawrence and Associate Dean Mark Warschauer.

     


    Mark to participate in SXSW panel on focus in the workplace

    photo: Gloria Mark

    Gloria
    Mark

    South by Southwest (SXSW) has invited professor Gloria Mark to participate in a panel discussion at this year's Music, Film and Interactivity conference on March 8 in Austin, Texas. Titled “Workplace Distractions: A New Focus on Focus,” the panel will “explore the latest research to understand the importance of focus in the workplace, the cost of workplace distraction, how to stay focused in the midst of a chaotic workplace, and despite recent research to the contrary, why focus perhaps is not all it is cracked up to be,” according to the SXSW website.

    Joining Mark will be Gensler workplace architect Janet Pogue and Sociometric Solutions CEO Ben Waber, with Wall Street Journal reporter Rachel Silverman moderating. Mark will discuss her research using sensors to track how mood and stress is related to digital activity in the workplace.

     


    Mehrotra and team receive DASFAA 10-year Best Paper Award

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    Computer science professor Sharad Mehrotra has received the 10-year Best Paper Award from the International Conference on Database Systems for Advanced Applications (DASFAA) for his paper titled “Efficient Execution of Aggregation Queries over Encrypted Relational Databases.” The paper, co-authored with researchers Hakan Hacigümüs and Bala Iyer, was originally published in the DASFAA 2004 proceedings.

    DASFAA is an annual database conference located in the Asia-Pacific region that showcases state-of-the-art research and development activities in database systems and their applications. This award recognizes the best paper from the DASFAA proceedings 10 years prior based on the criterion that the paper has had the biggest impact—in research, products, or methodology—over the last decade. Mehrotra was also a winner of the DASFAA 10-year best paper award in 2013.

    DASFAA serves as a forum for academic exchanges and technical discussions among researchers, developers and users of databases from academia, business and industry. DASFAA 2014 will be held in Bali, Indonesia, in April.

     


    Gary and Judith Olson receive NSF grant for distributed-work study

    photo: Gary Olson

    Gary
    Olson

    photo: Judith Olson

    Judith
    Olson

    The National Science Foundation has awarded a grant to Bren Professors Gary Olson and Judith Olson for their project “Micro-Analytics of Collaboration in Distributed Work: What Makes Collaboration Work.”

    The study is a four-phase examination of the micro-processes of collaborative document creation to identify what processes and tool features ensure a high-quality end product — and to determine what new features might contribute to more successful collaborations. Distributed work has become ubiquitous in industry as well as in the sciences, but, as the project abstract notes, current theories of collaborative work were generated when technology-supported distributed work was less common. With the help of nearly $397,000 in NSF funding, the Olsons will expand these theories to include the collaboration patterns when people use new tools and cope with the challenges of being distant.

     


    Carey and Li receive $1.1 million in funding for AsterixDB research

    photo: Michael Carey

    Michael
    Carey

    photo: Chen Li

    Chen
    Li

    Professors Michael Carey and Chen Li have received $750,000 from the National Science Foundation and nearly $400,000 from corporations — including Google, Oracle and HTC — to continue the development of their Big Data system, AsterixDB.

    Carey, Li and UCI project scientist Vinayak Borkar developed the system in collaboration with Vassilis J. Tsotras, a professor at the University of California, Riverside. AsterixDB promises to be the most versatile among platforms aimed at managing Big Data; the open source software is now available for free download at http://asterix.ics.uci.edu.

    The AsterixDB engine operates on a “shared nothing” architecture, in which each computer node is independent and self-sufficient. Its distinct advantages come by adding management of semi-structured data (data not organized in the traditional tabular form), supporting a variety of data types (e.g., spatial and temporal as well as textual and numeric data), and borrowing techniques from parallel databases that increase the speed and scale at which it can operate.

     


    ICS grad students take second place in sbv IMPROVER competition

    photo: Peter Sadowski

    Peter
    Sadowski

    photo: Michael Zeller

    Michael
    Zeller

    Peter Sadowski and Michael Zeller, both Ph.D. students with the Department of Computer Science, earned a second-place finish in an international data-mining competition. The honor was given by sbv IMPROVER, a collaborative project designed to enable scientists to learn about and contribute to the development of a new crowdsourcing method for verification of scientific data and results.

    Sadowski and Zeller, students in professor Pierre Baldi’s group and researchers at the Institute for Genomics and Bioinformatics, developed a pipeline for translating protein phosphorylation status from rat primary lung cells to human primary lung cells after subjecting cells to various stimuli consisting of known drugs and chemicals. Their pipeline consisted of two parts: an artificial neural network; and a statistical analysis that aggregated evidence from the replicated measurements. They placed second out of 13 teams and were awarded travel-cost reimbursement to the 2013 sbv IMPROVER Symposium in Athens, Greece, where they presented their findings.

     


    Judith and Gary Olson release new book on distributed collaborations

    photo: Judith Olson

    Judith
    Olson

    photo: Gary Olson

    Gary
    Olson

    Professors Judith Olson and Gary Olson have published a new book, titled Working Together Apart: Collaboration over the Internet. Published by Morgan & Claypool Publishers, the Olsons’ new offering reviews the latest insights into how teams work together when they are not in the same location.

    Guided by a framework they developed during two decades of research on this topic, the professors organize a series of factors they have found to differentiate between successful and unsuccessful distributed collaborations. They then review the kinds of technology options that are available today, focusing more on types of technologies rather than specific instances. They describe a database of geographically distributed projects they have studied and introduce the Collaboration Success Wizard, an online tool for assessing past, present, or planned distributed collaborations. The book closes with a set of recommendations for individuals, managers, and those higher in the organizations who wish to support distance work.

     


    FALL 2013

    ACCESS features Regan’s article on vehicular communication networks

    photo: Amelia Regan

    Amelia
    Regan

    In the latest issue of ACCESS Magazine, professor Amelia Regan explores the implications of vehicular communication networks. Such systems allow vehicles to sense not only traffic patterns, but also dangers outside a driver’s line of sight. These abilities improve driving efficiency and safety and are the first steps towards an automated driving network. But what happens if the communication network fails? Or worse, is hacked into?

    Regan’s article, “Vehicular ad hoc Networks: Storms on the Horizon,” describes the advantages and challenges these networks present. While the latest technology turns both cars and even pedestrians into nodes on the network, issues of security, privacy and liability continue to be major barriers to broad implementation.

    ACCESS Magazine highlights research funded by the University of California Transportation Center, presenting academic work to policymakers, practitioners and the public.

     


    Lindtner and Hertz get NSF grant to research maker movement

    photo: Silvia Lindtner

    Silvia
    Lindtner

    photo: Garnet Hertz

    Garnet
    Hertz

    Two members of the Department of Informatics have received a National Science Foundation grant for their inquiry into how the maker movement is changing material culture, production and creativity. Postdoctoral scholar Silvia Lindtner and assistant project scientist Garnet Hertz have been awarded $500,000 for their work studying makerspaces in China, New York City and San Francisco.

    Their project uses ethnographic investigation to examine how DIY (Do-It-Yourself) making as a practice, and makerspaces as physical sites, contribute to the development of technical, economic and social innovation.

     


    Bowker and team receive EarthCube award from NSF

    photo: Geoffrey Bowker

    Geoffrey
    Bowker

    Professor Geoffrey C. Bowker is part of a team that has earned a $1.5 million grant from the National Science Foundation’s EarthCube awards program. A partnership between the NSF’s Directorate for Geosciences and Office of Cyberinfrastructure, EarthCube awards seek “to greatly increase the productivity and capability of researchers and educators working at the frontiers of Earth system science,” according to the NSF website.

    The grant awarded to Bowker’s team was given under EarthCube’s Building Blocks category. Titled “A Broker Framework for Next Generation Geoscience,” the project, sponsored by the University of Colorado, Boulder, brings together an accomplished team of geoscientists, social scientists, cyberinfrastructure experts and educators to explore how expert systems can improve access between scientific fields.

    Such interdisciplinary initiatives and academic community building have long been a focus of Bowker’s work. As scientific director of the EVOKE Lab at UCI, he brings together a community of scholars, makers and designers that builds new technology and digital experiences with social concerns as the starting point.

     


    Bannister Wins Best Presentation Award

    photo: Michael Bannister

    Michael
    Bannister

    Michael Bannister, a computer science Ph.D. candidate associated with the Center for Algorithms and Theory of Computing, has won the best presentation award at the 21st International Symposium on Graph Drawing, held Sept. 23-25 in Bordeaux, France. The award, which was offered for the first time, was determined by the votes of conference participants.

    The presentation for which Bannister won the award, “Superpatterns and Universal Point Sets,” concerned his research with fellow graduate students Zhanpeng Cheng and Will Devanny, as well as Professor David Eppstein, about new connections between information visualization and the mathematics of permutation patterns.

     


    Tsudik to co-chair seminar, keynote two conferences

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor’s Professor Gene Tsudik opens the fall 2013 quarter by playing key roles in three international events. From Oct. 6-9, he will be co-chairing a Dagstuhl Seminar on the compelling and timely topic of Genomic Privacy. Held at Germany’s Schloss Dagstuhl, these seminars are a well-known venue in computer science for bringing to light cutting-edge issues and emerging topics — and require submitting a comprehensive proposal for competitive selection.

    On Oct. 14, Tsudik will give a keynote talk, titled “Secure Fragmentation for Content-Centric Networks,” at the inaugural IEEE Conference on Communications and Network Security in Washington, D.C. At the 12th International Conference on Cryptology and Network Security in Paraty, Brazil, from Nov. 20-22, Tsudik will give another keynote address, “Security and Privacy in Named-Data Networking.”

     


    van der Hoek co-edits book on software designers

    photo: André van der Hoek

    André
    van der Hoek

    Professor André van der Hoek, chair of the Department of Informatics and head of the Software Design and Collaboration Laboratory at UCI, has co-edited a new book released Sept. 10. Software Designers in Action: A Human-Centric Look at Design Work, part of the Chapman & Hall/CRC Innovations in Software Engineering and Software Development Series, takes a look at how developers design their software. Co-edited by Marian Petre, the book features an interdisciplinary selection of writings that provide a comprehensive exploration of early software design, as well as an examination of how human interaction influences software design.

     

     


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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements 2011-2012

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


    SUMMER 2012

    El Zarki receives Fulbright fellowship

    photo: Magda El Zarki

    Magda
    El Zarki

    Professor of computer science Magda El Zarki has received a nine-month Fulbright-Nehru Teaching/Research Fellowship to pursue her research on networked games and virtual worlds at the Indian Institute of Technology, Bombay.

    With the advent of online gaming and the tremendous success of this new application area, El Zarki’s research thrust has shifted to studying the networking requirements of online game technologies, in particular Massively Multiuser Virtual Environments (MMVEs). With the anticipated growth in this application area, more and more stress is being placed on the underlying transport system to provide the kind of quality of experience (QoE) that these applications and their users expect. The quality of an end user’s experience is the true litmus test of a proper online game deployment. El Zarki’s Fulbright research project, “Networked Games and Virtual Worlds,” will further explore how understanding both the application and network facilities for QoE can ensure the highest quality user experience.

    El Zarki is director of the Center for Computer Games and Virtual Worlds and heads the computer game science degree program at UC Irvine. She is an editor for several journals in the telecommunications field and is actively involved in many international conferences.

    The Fulbright Program is the flagship international educational exchange program sponsored by the U.S. government and is designed to increase mutual understanding between the people of the United States and the people of other countries.

     


    Newman receives $120K from NSF

    photo: David Newman

    David
    Newman

    Associate research scientist David Newman has been awarded a $120,000 grant by the National Science Foundation to develop topic models to help better understand and manage research conducted in NSF's Chemical, Bioengineering, Environmental and Transport Systems Division (CBET).

    The CBET Division supports research and education in the rapidly evolving fields of bioengineering and environmental engineering.

    “Managing a portfolio of research projects is a complex business,” says Newman. “We will develop topic models that accurately characterize CBET-funded research — as described by the investigators. These topic models will help program managers better understand the unique role of CBET programs, and be in a better position to set research strategy.”

     


    Franz wins a U.S. Patent on software diversity and over $900,000 in new grant funding to investigate software diversity

    photo: Michael Franz

    Michael
    Franz

    Professor Michael Franz and two of his former Ph.D. students were just awarded a patent by the U.S. Patent Office for a fundamental invention in the field of software security. The patent, on which Prof. Franz is the first named inventor and which is assigned to the Regents of the University of California, protects the idea of using program diversity in conjunction with parallel programming to protect a computer against malicious hacker attacks.

    Like many major inventions, the idea is quite simple at the core: instead of generating a single binary from a source program, a special compiler generates several slightly different program versions that implement the program's functionality in subtly different ways. These different versions are then executed in lockstep on a multicore processor.

    The key idea is to generate the versions in such a way that all "in specification" behavior is identical across the versions, but "out of specification" behavior differs significantly. As a result, the versions will execute in lockstep as long as the program is behaving as designed, but will typically diverge as soon as an attacker exploits a programming bug, causing "out of specification" behavior. This can be detected almost in real time.

    In addition to this U.S. Patent, Prof. Franz won over $900,000 in additional funding for his work on software diversity in the past month. First, he won an additional year of funding from DARPA for his project "Defending Mobile Apps Through Automated Software Diversity." The existing project, on which Dr. Franz is the sole PI, has been extended through February 2015 along with an additional award of $467,442, bringing the total to $1,847,602. Second, he received a further award of $456,809 from the Navy on a subcontract from Johns Hopkins University for his project "Meta-Circular Software Diversity for Intrusion Tolerant Clouds."

     


    Newman awarded $300K from NSF

    photo: David Newman

    David
    Newman

    Associate Research Scientist David Newman has been awarded a $600,000 grant by the National Science Foundation, with funds split equally with co-Investigator Meg Blume-Kohout, an Economist at University of New Mexico.

    Newman and Blume-Kohout's interdisciplinary research will combine machine learning and econometrics to create models that assess effectiveness and efficiency of biomedical funding, focusing on the National Institutes of Health.

    "In this current age of scarcity, two things are required: fiscal restraint, and an accurate assessment of research effectiveness and efficiency" says Newman. "Matching publications and grant funding levels by machine learned topics will allow us to evaluate the contribution of publicly-funded research to the body of published research. Our models will be able to measure whether changes in funding for a topic affect the quality and quantity of publications on that topic."

     


    ICML 2012 best paper award goes to Welling and students

    photo: Sungjin Ahn

    Sungjin
    Ahn

    photo: Anoop Korattikara

    Anoop
    Korattikara

    photo: Max Welling

    Max
    Welling

    photo: ICML award

    Co-authored by Ph.D. students Sungjin Ahn and Anoop Korattikara, and computer science professor Max Welling, “Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring” won the best paper award at the 29th International Conference on Machine Learning (ICML 2012) held in Edinburgh, Scotland.

    Markov Chain Monte Carlo (MCMC) is a technique that allows one to draw representative samples from almost any probability distribution. According to the paper, while the MCMC technology has revolutionized the usefulness of Bayesian statistics over the last few decades, it has not been able to scale well to today’s very large data problems. The authors’ new method borrows ideas from Stochastic Approximation Theory to improve the efficiency of MCMC samplers and make them relevant to big data challenges.

    Ahn and Korattikara on June 27 presented the paper in an ICML 2012 plenary session.

     


    SPRING 2012

    ACM publication features open virtual 3-D world developed by Baldi, Lopes

    photo: Pierre Baldi

    Pierre
    Baldi

    photo: Crista Lopes

    Crista
    Lopes

    “The Universal Campus: An open virtual 3-D world infrastructure for research and education,” written by Chancellor’s Professor Pierre Baldi and Associate Professor Crista Lopes, was featured in the April issue of eLearn Magazine, an Association for Computing Machinery publication. According to the paper: “One should not discount the power of technology to provide increasingly realistic, almost haptic, interactions in real time, or the additional creative inspiration that could emerge for users by simply being immersed in a completely novel and unusual environment.”

    Developed by Baldi and Lopes, the Universal Campus allows users in academia and research settings to interact and collaborate in a 3-D virtual world that includes multiple buildings with fully furnished laboratories, classrooms, meeting rooms and lecture halls. Deployed using Second Life and OpenSimulator, the Universal Campus can host multiple scale gatherings, from lab meetings to classes, lectures, symposia and conferences. Its infrastructure provides a default set of 12 avatars that are freely customizable by users. A local chat feature allows avatars to converse with other avatars within the same virtual room, while a voice conference feature allows avatars to speak with multiple other avatars simultaneously.

    All the content and source code of the Universal Campus is downloadable under Creative Commons or similar licenses. The Universal Campus provides a complete open access and open source infrastructure that can be replicated and used for a variety of research or educational purposes.

     


    Bren School hosts annual AP Stats project competition

    AP Stats graph

    Dozens of students from five high schools in Orange, Los Angeles and Riverside counties presented their work May 19 in Donald Bren Hall as part of the 7th Annual AP Statistics Project Competition of the American Statistical Association’s Southern California chapter. Sponsored by the Bren School Department of Statistics and the City of Hope National Medical Center, the event featured 28 poster presentations by student teams and a breakout session for instructors on teaching AP Statistics. Judges came from academia — including Bren School professors and graduate students — as well as business and industry. Click here for competition results and photos from the event. 


    Faculty, alumnus win SIGMOD Test-of-Time Award

    photo: Sharad Mehrotra

    Sharad
    Mehrotra

    photo: Chen Li

    Chen
    Li

    The 2012 ACM SIGMOD Test-of-Time Award, given annually by the Association for Computing Machinery’s Special Interest Group on Management of Data to recognize the most impactful paper from a decade prior, was presented in May to Hakan Hacigumus (M.S. '02, Ph.D. '04), IBM collaborator Bala Iyer, and Bren School faculty Sharad Mehrotra and Chen Li for “Executing SQL over Encrypted Data in the Database-Service-Provider Model.”

    According to the award citation: This paper from the SIGMOD 2002 Conference remarkably anticipated the world of “Database as Service” which did come about and continues to grow in importance. To get a sense of how visionary the work was, consider that this paper was published in June 2002 (and thus accepted in Jan 2002), even a couple of months before Amazon EC2 and S3 services were launched (of course, Amazon RDS and SQL Azure came much later). The core of the paper focuses on the challenges of how to leverage cloud services while keeping some of the information (at the discretion of the enterprise/user) hidden from the service provider. Beyond the specific algorithmic details, the key contribution is the framework: (i) introduction of a mapping function, and (ii) query splitting logic to ensure how the work can be distributed across cloud and client when some information is encrypted. Is this framework used by enterprises today? As best as we can tell, the answer is perhaps no. But, is the framework interesting and has real possibilities of adoption and further impact and more follow-on by research community? Absolutely. In summary, this paper is one of the early papers to foresee the world of Database as Service (before any one of us were working on that problem). The specific technical focus was dealt with reasonable depth. The impact of the technical focus has not yet been seen by the industry but this paper has the possibility of inspiring much more follow-on work/thinking (beyond 140+ citations it already has in ACM DL).

     


    UCI team earns prize for best educational game at intercollegiate showcase

    Q-Bitz robot

    The top two finishers in the latest Game Jam “build a video game in a week” Tournament at UCI were recognized at the first-ever IEEE GameSig Intercollegiate Computer Game Showcase, held April 28 at Chapman University. Q-Bitz won the MIND Research Institute Prize for Best Educational Game, while Massteroid received honorable mention. A third UCI team — the creators of Godfighter — also competed in the final round, along with seven others from Chapman, Cal State Fullerton, Cal Poly Pomona, Cal State San Bernardino and Westwood College. “I hope [the showcase] means we can groom the local talent pool so the next Blizzard can exist here in Orange County,” Brian Fargo, CEO of InXile Entertainment, told the Orange County Register.

     


    Doctoral student receives award from Yahoo! Labs Key Scientific Challenges Program

    photo: Raman Grover

    Raman
    Grover

    Computer science Ph.D. student Raman Grover has been awarded $5,000 in unrestricted research seed funding from the Yahoo! Labs Key Scientific Challenges (KSC) Program. The program supports a limited number of outstanding Ph.D. students who are conducting research in scientifically challenging areas, including web information management, machine learning and search experiences.

    Grover, whose research interests include large scale data-intensive computing, databases, parallel processing and data feeds, was one of 30 researchers selected from a pool of 208 proposals. Along with the funds, the program’s award benefits include exclusive access to select Yahoo Datasets, opportunities to collaborate with Yahoo’s industry-leading scientists, and an invitation to the upcoming KSC Graduate Student Summit, where recipients join top minds in academia and industry to present work, discuss research trends and jointly develop revolutionary approaches to fundamental problems.

    Grover’s research projects include ASTERIX, which focuses on developing new technologies for ingesting, storing, managing, indexing, querying, analyzing and subscribing to vast quantities of semi-structured information; and Hyracks, a new partitioned-parallel software platform designed to run data-intensive computations on large shared-nothing clusters of computers. Grover is advised by Bren Professor Michael Carey and is a member of the Information Systems Group at UCI.

     


    Olson delivers 2012 Athena Lecture

    photo: Judy Olson

    Judy
    Olson

    As the 2011-12 Athena Lecturer, Bren Professor Judy Olson presented “Broader Impacts: Research You Can Use” at the Association for Computing Machinery Conference on Computer-Supported Cooperative Work.

    According to Olson’s abstract: A number of threads of thought have come together recently having to do with how we make our research useable and useful to the world. One thread is inspired by a movement in medicine called Clinical Translational Science in which funding is given to researchers to translate basic research into guidelines, treatments, and regimens that clinicians can use. A second thread arose in reflecting about our own recent work in which we translated a theory about what makes for good distance collaboration into an online assessment tool and administered it to hundreds of people involved in remote collaboration. Upon completion of the assessment, each participant immediately gets a personalized report on the strengths of their collaboration, the challenges, and what to do about it. We get the data, and they get the help. These two threads point to making a difference, having broader impact. In this talk I will review some ways we can have an impact, both directly to people, through design practice (our clinicians), and via a myriad of other tools while doing good research. I encourage us all to spend more energy on having more direct effects on the world in which we live.

    A video of her lecture and other background materials are available through the ACM Digital Library.

     


    WINTER 2012

    Nardi serves as ICIC 2012 program co-chair

    photo: Bonnie Nardi

    Bonnie
    Nardi

    Informatics professor Bonnie Nardi is the program co-chair for the 4th ACM International Conference on Intercultural Collaboration (ICIC 2012), which takes place March 21-23 in Bangalore, India.

    The main theme of this year’s conference is intercultural collaboration, from both technical and socio-cultural perspectives. Topics include collaboration support (e.g., natural language processing, Web and Internet technologies), social psychological analyses of intercultural interaction, and case studies from activists working to increase mutual understanding in a multicultural world.

    Nardi's research interests include theory in human-computer interaction and computer-supported collaborative work and studies of social life on the Internet.

     


    iGraVi lab highlighted in IEEE, ACM conferences

    photo: Gopi Meenakshisundaram

    Gopi
    Meenakshisundaram

    photo: Aditi Majumder

    Aditi
    Majumder

    Two Bren School computer science associate professors served as general chairs for recently held IEEE and ACM conferences sponsored by UC Irvine and co-located in Costa Mesa, Calif.

    Aditi Majumder co-chaired IEEE Virtual Reality 2012 (VR 2012), the top international conference and exhibition involving the fields of virtual environments, augmented reality and 3D user interfaces, while Gopi Meenakshisundaram co-chaired the ACM Interactive 3D Graphics and Games 2012 (i3D 2012), a leading conference for interactive realistic rendering, hardware acceleration, animation, and geometry processing.

    As part of the conferences, Majumder and Meenakshisundaram hosted open houses to showcase 10 research projects conducted by members of the Interactive Graphics and Visualization (iGraVi) lab at UCI. Click here for photos taken at the March 6 open house.

    Led by the two professors, the iGraVi lab features expertise in various areas of graphics and visualization including interactive rendering systems, optical systems for future cameras and displays, geometric processing for rendering, user interfaces and applications, and building multi-projector display environments for visualization, simulation and training. The lab includes 10 graduate students and many undergraduates.

    iGraVi is supported by funding from NSF and equipment donations from nVidia, Epson and Canon. Collaborators include faculty within the UCI campus, such as stem cell researchers in the School of Medicine, as well as colleagues in other parts of the country, such as Purdue University and MIT, and the world, including researchers in Switzerland and Brazil.

     


    Kobsa receives $80K from Samsung

    photo: Alfred Kobsa

    Alfred
    Kobsa

    Informatics professor Alfred Kobsa received a gift in the amount of $80,000 from Samsung Information Systems America. The gift will support his research in the area of user privacy preferences and international privacy legislation in cloud services. A recent gift from Ericsson Research also supports his work in this area.

     

     


    FALL 2011

    UCI team receives ‘Energy to Educate’ grant

    photo: Bill Tomlinson

    Bill
    Tomlinson

    Informatics associate professor Bill Tomlinson and his group, in collaboration with assistant professor of education Rebecca Black, received a $50,000 grant from Constellation Energy in support of their work on the “Causality Project.”

    Through a novel online system currently in the works, the Causality Project collects information about the cause-and-effect relationships across a wide range of topics, to help people understand how everyday decisions can affect the environment.

    “By illustrating chains of causality, which can often be indirect and complex, we hope to encourage people to recognize the ripples they make, big or small,” Tomlinson says. “For example, people may not think about where their power comes from or the effects brought about by the generation, distribution and use of that power. The Causality Project aims to address this type of disconnect.”

    Constellation Energy awarded 14 E(2) Energy to Educate grants in 2011 to support hands-on projects that enhance student understanding of the science and technology needed to address energy issues. Tomlinson’s grant will enable the team to launch a contest next year among UC Irvine undergraduate students to contribute to the Causality Project and create online videos about indirect causal chains related to energy issues in their own lives. Participants will work in interdisciplinary teams to learn about energy technologies, the environmental impacts of various energy systems, and how these systems relate to their world. Students will then use this new knowledge to create an online repository of causal linkages among energy issues and other topics of global importance.

     


    Four faculty recognized by ACM

    The Association for Computing Machinery (ACM), the world’s largest educational and scientific computing society, has announced that four faculty members from the Donald Bren School of Information and Computer Sciences at UC Irvine have been selected for the following honors:

    ACM FELLOW

    photo: David Eppstein
    Computer science professor David Eppstein has been named a 2011 ACM Fellow for his achievements in graph algorithms and computational geometry. Established in 1993, the ACM Fellows Program recognizes and celebrates the exceptional contributions of leaders in the computing field. According to ACM President Alain Chesnais, this year’s fellows are “some of the leading thinkers and practitioners in computer science and engineering… These international luminaries are responsible for solutions that are transforming our society for the better.” Eppstein's research areas include algorithms and complexity, and computer graphics and visualization.

    ACM DISTINGUISHED MEMBERS

    The ACM Distinguished Member Recognition Program recognizes ACM members with at least 15 years of professional experience and five years of continuous professional membership who have achieved significant accomplishments or have made a significant impact on the computing field.

    photo: Cristina Lopes Named a 2011 ACM Distinguished Scientist, informatics associate professor Cristina Lopes is one of the co-inventors of aspect-oriented programming and one of the original designers of the AspectJ programming language. She is also a core developer and one of the main architects of OpenSimulator, a platform for massive online 3D virtual environments.

    photo: David Redmiles Named a 2011 ACM Distinguished Scientist, Informatics professor David Redmiles is the author of more than 100 research publications integrating the areas of software engineering, human-computer interaction and computer-supported cooperative work. His research focuses on the processes and technologies needed to develop and support useful and usable interactive software.

    photo: Richard Pattis Named a 2011 ACM Distinguished Educator, computer science senior lecturer Richard Pattis is the author of the Karel programming language and published Karel the Robot, an introductory computer science textbook used in high schools and colleges for nearly 30 years. He serves as the computer science vice chair for undergraduate studies at the Bren School.

     


    Baldi named IEEE Fellow

    photo: Pierre Baldi

    Pierre
    Baldi

    Pierre Baldi, Chancellor’s Professor in the department of computer science and director of the Institute for Genomics and Bioinformatics, has been named an IEEE Fellow for his contributions to machine learning and its applications in the life sciences.

    Current projects in his laboratory include mining high-throughput genomic data and developing expert systems for chemistry and systems biology, to better understand the computations carried by metabolic, signaling and gene regulatory networks, and identify new diagnostic and therapeutic strategies. Baldi also is a Fellow of the Association for the Advancement of Artificial Intelligence and of the American Association for the Advancement of Science.

    The IEEE is the world’s leading professional association for advancing technology for humanity and publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields. IEEE Fellow is the highest grade of membership; the total number selected in any one year does not exceed one-tenth of 1 percent of the total voting membership. IEEE boasts 385,000 members in 160 countries. This year, 312 individuals were elevated to IEEE Fellow.

     


    Tsudik reappointed editor-in-chief of ACM TISSEC

    photo: Gene Tsudik

    Gene
    Tsudik

    The Association for Computing Machinery Publications Board has unanimously approved Gene Tsudik's reappointment for a second three-year term as editor-in-chief of the ACM Transactions on Information and System Security (TISSEC), a top scholarly, scientific journal covering all aspects of computer/network/information security and privacy. Tsudik's new term ends on Dec. 31, 2014.

    Tsudik’s research interests include computer/network security and applied cryptography. He serves as director of UCI’s Secure Computing and Networking Center and as director of the networked systems graduate program.

     


    NSF awards $500K to Franz

    photo: Michael Franz

    Michael
    Franz

    Computer science professor Michael Franz, as sole PI, has been awarded a three-year $500,000 grant by the National Science Foundation to create better virtual machines (VMs).

    A virtual machine is an “ideal computer” built out of software. “Most people use multiple VMs every day,” Franz explains. “For example, web applications such as Gmail or Google Maps run on a JavaScript VM inside of your web browser, animations run on Adobe’s Flash VM, and, if you are using an Android mobile phone, most of the apps on that phone run on a VM called Dalvik. Running software on virtual machines rather than directly on hardware creates cross-platform portability and provides some insulation against malicious or faulty programs.”

    Franz’s research project aims to simplify the development of virtual machines and improve their architecture, especially when used on mobile devices. The project has garnered additional support from Samsung, which awarded a supplemental $350,000 to Franz this spring.

    Franz continues: “It doesn’t make sense to have three separate VMs (JavaScript, Flash and Dalvik) on a cell phone, where space is at a premium, when these VMs are actually quite similar to each other under the hood and have lots of overlapping functionality.” Franz’s research aims to create a modular VM that can support multiple languages while using only a fraction of the space. Rather than compete for processor resources and memory, the modules of the VM supporting the various languages would collaborate with each other.

    Franz leads the Secure Systems and Languages Laboratory at UC Irvine, one of the top research teams on dynamic compilation, virtual machines and language-based computer security. In collaboration with the Mozilla foundation, he transitioned the JavaScript compilation technology invented in his lab into the Firefox browser, where it is used every day by hundreds of millions of people.

     


    Baldi, Tsudik and team present at CSS 2011

    photo: Pierre Baldi

    Pierre
    Baldi

    photo: Gene Tsudik

    Gene
    Tsudik

    A paper on efficient handling of fully-sequenced human genomes — co-authored by computer science professors Pierre Baldi and Gene Tsudik, recent alumnus Emiliano De Cristofaro (Ph.D. ’11), postdoctoral researcher Paolo Gasti, and Institute for Genomics and Bioinformatics researcher Roberta Baronio — will be presented at the 18th ACM Conference on Computer and Communications Security in Chicago. Titled “Countering GATTACA: Efficient and Secure Testing of Fully-Sequenced Human Genomes,” this paper addresses the issue of privacy in the emerging field of digital genome sequencing and already has been featured on the MIT Technology Review home page and as a feature article in the journal NewScientist. The authors have devised methods for implementing privacy-preserving operations over digital genome sequences. These genome sequences could be stored by users on their computers or smartphones and queried in secure and private ways in several applications, ranging from medical, to authentication, to social interactions. This work is supported in part by a grant from the National Institutes of Health.

     


    Padhraic Smyth awarded $2.9M by Office of the Director of National Intelligence

    photo: Padhraic Smyth

    Padhraic
    Smyth

    Padhraic Smyth, professor of computer science and director of the Center for Machine Learning and Intelligent Systems at UC Irvine, has been awarded two grants worth $2.9 million by the Intelligence Advanced Research Projects Activity (IARPA), a center housed within the Office of the Director of National Intelligence, for research on statistical text mining.

    The first project — with co-investigators David Newman, assistant researcher in computer science at UCI, and Mark Steyvers, professor of cognitive sciences at UCI —will focus on developing new statistical topic modeling algorithms to help users automatically search and understand large amounts of unstructured text. Funded by a $1.3 million IARPA award over four years, the project is a collaborative effort with Cornell University, the University of Maryland, UC Santa Barbara, the University of Massgachusetts Amherst and Purdue University.

    Supported by a $1.6 million five-year award from IARPA, the second project focuses on developing models and algorithms for automatically detecting and quantifying trends and changes in scientific literature. In a recent news release, IARPA announced that the Foresight and Understanding from Scientific Exposition (FUSE) Program seeks to produce a new capability to accelerate the process of identifying and prioritizing emerging technologies across the globe. Smyth and co-investigator Newman will develop algorithms that can detect statistically significant changes in language usage and citation patterns to measure how scientific disciplines are evolving over time. FUSE will be carried out in collaboration with researchers at the Georgia Institute of Technology, the University of Michigan and the University of Illinois.

    Both projects will build on research by Smyth, Newman, Steyvers and their students, who for the past few years have been developing statistical models and algorithms for automatically extracting information from text. These techniques have broad applications in such areas as Web search, digital libraries and biomedical text mining.

     


    Jarecki, Tsudik receive $750,000 IARPA grant

    photo: Stanislaw Jarecki

    Stanislaw
    Jarecki

    photo: Gene Tsudik

    Gene
    Tsudik

    Stanislaw Jarecki and Gene Tsudik have been awarded a $750,000 three-year grant as part of a subcontract from IBM Research, by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The research project is titled “ESPADA: Efficient Security and Privacy Assurance for Database Access.” The goal of ESPADA is to efficiently and securely support a wide range of database queries between mutually mistrustful parties, while minimizing the amount of information learned by either party.

     


    Utts receives NISS Distinguished Service Award

    photo: Jessica Utts

    Jessica
    Utts

    Statistics professor Jessica Utts received a 2011 Distinguished Service Award from the National Institute of Statistical Sciences in recognition of her multiple terms on the NISS board of trustees and the executive committee, and for serving as chair of the awards committee. The NISS Distinguished Service Awards were established in 2005 to recognize individuals who have given extraordinary service that significantly advances NISS and its mission. Utts joined the NISS board in 1997, served as vice chair from 2008-11, and is now one of its longest-serving members.

     

     

     

     


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    Bren school home > Community > News > Features
    Features archive
    • Unlocking the potential of deep learning
      February 18, 2016

      Unlocking the potential of deep learning

      Pierre Baldi leads ICS’s machine learning research, which assists particle physics experiments at CERN.

    • Applying data to real-world challenges
      January 27, 2016

      Applying data to real-world challenges

      Computer Science Ph.D. student Dimitrios Kotzias wins Yelp Dataset Challenge with novel machine learning algorithm.

    • Hayes receives Jacobs Foundation Advanced Research Fellowship
      January 22, 2016

      Hayes receives Jacobs Foundation Advanced Research Fellowship

      The $400,000 award will allow the informatics professor to continue her work on “Inclusive and Evidence-Based Technologies for Child and Youth Development.”

    • Banking on data
      January 8, 2016

      Banking on data

      ICS alumnus Pedro Domingos, Ph.D. 1997, discusses the push toward a master algorithm in machine learning.

    • Building the ‘perfect’ app
      December 16, 2015

      Building the ‘perfect’ app

      UCI Emergency Services Manager Anne Widney collaborates with Hadar Ziv’s capstone project class to expand the successful, cost-effective ZOTFinder app to Android users.

    • Mehrotra set to lead two NSF-funded research projects
      December 14, 2015

      Mehrotra set to lead two NSF-funded research projects

      Computer Science Professor Sharad Mehrotra receives $725,000 in NSF funding to head research projects in big data and disaster response cyber physical systems.

    • Two ICS professors named 2015 ACM Fellows
      December 9, 2015

      Two ICS professors named 2015 ACM Fellows

      Informatics Professor Paul Dourish and Computer Science Professor Michael Franz honored for their outstanding accomplishments in computing and IT

    • Research for the public good
      December 4, 2015

      Research for the public good

      Informatics Professor Crista Lopes’ research project evolves into a UC-wide library data-sharing portal, the DASH system.

    • Harnessing the power of new technology
      December 3, 2015

      Harnessing the power of new technology

      Informatics alumnus Nick Jonas '09 is reinventing everyday objects, starting with a smart umbrella stand called Raincheck.

    • zyBooks Touts 'Less Text, More Action'
      November 25, 2015

      zyBooks Touts 'Less Text, More Action'

      ICS alumni Smita Bakshi and Frank Vahid have found success with their interactive online textbook replacement platform zyBooks.

    • Connected learning through Minecraft
      November 13, 2015

      Connected learning through Minecraft

      Mimi Ito’s Connected Camps uses Minecraft as an educational platform to connect youth engagement with learning.

    • Creating a global social system inspired by UCI students
      October 27, 2015

      Creating a global social system inspired by UCI students

      ICS alumnus Sajjad Mustehsan '04 is hoping to tap a diverse mix of talented UCI grads to help him expand his location-based networking app Locye.

    • ICS, Engineering Alumni Celebrate 50th; Kickoff Hall of Fame
      October 16, 2015

      ICS, Engineering Alumni Celebrate 50th; Kickoff Hall of Fame

      ICS honors 20 alumni who have made a significant impact in their profession.

    • Student Spotlight: Homer Strong
      October 16, 2015

      Student Spotlight: Homer Strong

      Q&A with graduate statistics student Howard Strong who has received the Robert L. Newcomb Memorial Endowed Graduate Student Award.

    • A game changer
      October 9, 2015

      A game changer

      ICS research scientist Walt Scacchi testifies before California Assembly on the future and potential of games in education and the workforce.

    • Inquiry and equity in computer science education
      September 10, 2015

      Inquiry and equity in computer science education

      ICS hosts innovative Code.org professional development workshops during the summer in an effort to help facilitate more robust computer science instruction in K-12 schools.

    • Planting the seeds for women in technology
      August 24, 2015

      Planting the seeds for women in technology

      With grants from Google and NCWIT, ICS women's group facilitated computer science workshops for 30 young women from Westminster’s La Quinta High School.

    • Building an e-commerce powerhouse
      July 27, 2015

      Building an e-commerce powerhouse

      As CEO of EYEMAGINE, alumnus Andy Etemadi continues to utilize what he learned as an ICS student in the ’90s to remain at the forefront of the e-commerce industry.

    • Teens tackle world dilemmas in UCI’s summer APPcamp
      July 27, 2015

      Teens tackle world dilemmas in UCI’s summer APPcamp

      Middle school students develop mobile apps to address challenges posed by National Academy of Engineering

    • UCI to host CSULA students as part of $1.25 million NASA grant
      July 10, 2015

      UCI to host CSULA students as part of $1.25 million NASA grant

      The grant will directly support 60 undergrad and grad CSULA students in visiting UCI, and indirectly support another 120 students who will participate in the program. more

    • Autism AppJam highlights academia’s growing impact on the autism community
      July 2, 2015

      Autism AppJam highlights academia’s growing impact on the autism community

      The third annual competition expands collaboration while continuing to facilitate the discussion about technological interventions to aid those affected by autism.

    • ICS Day 2015 keeps students involved and connected
      June 8, 2015

      ICS Day 2015 keeps students involved and connected

      This year’s annual student-run tech carnival was filled with swag and selfies.

    • Developing an ANTrepreneurial Spirit
      May 19, 2015

      Developing an ANTrepreneurial Spirit

      Computer science freshman Alec Kriebel created and released an iOS app development course with the help of UCI’s Blackstone LaunchPad.

    • ICSSC presents Google-themed hackathon
      April 14, 2015

      ICSSC presents Google-themed hackathon

      Google Web Hacks provided nearly 50 students with 24 hours to build any web application using Google technologies.

    • Taking on the telecom industry
      April 3, 2015

      Taking on the telecom industry

      Alumnus Bayan Towfiq, CEO of Flowroute Inc., discusses bootstrapping a successful startup with a team of ICS alumni.

    • ICS grad students to present Racial Violence Archive at iConference
      March 6, 2015

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      Microsoft Research awarded the team of seven students $3,000 to present their collaborative class project at iConference 2015 in March.

    • Global Game Jam Spurs Inspiration and Connectivity
      February 26, 2015

      Global Game Jam Spurs Inspiration and Connectivity

      Informatics professor Joshua Tanenbaum brings the world’s largest game- creation event to UCI, producing seven games in 48 hours.

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    http://www.ics.uci.edu/~perl/ Per Larsen's Web Page

    Per Larsen

    Download full CV

    Email:perl at uci dot edu

    LinkedIn: linkedin.com/in/perlarsen

    GitHub: github.com/thedataking

    Picture of me.

    I am a Project Scientist working with Professor Michael Franz at the Donald Bren School of Information and Computer Science at UC Irvine. I'm also a co-founder of Immunant, Inc.

    Research Interests

    Information Security

    Software diversity. Exploits and Mitigations.

    Compilers

    Profiling, optimization, and rewriting.

    Systems Software

    Interpreters. Virtual Machines and hypervisors.

    Publications

    K. Braden, S. Crane, L. Davi, M. Franz, P. Larsen, Ch. Liebchen, and AR Sadeghi.

    Leakage-Resilient Layout Randomization for Mobile Devices

    To appear at 23rd Annual Network & Distributed System Security Symposium (NDSS), 2016. pdf.

     

    P. Larsen, S. Brunthaler, L. Davi, AR Sadeghi, and M. Franz.

    Automatic Software Diversity.

    Synthesis Lectures on Information Security, Privacy, and Trust. Morgan & Claypool, December 2015. 10.2200/S00686ED1V01Y201512SPT014.

     

    S. Crane, S. Volckaert, F. Schuster, Ch. Liebchen, P. Larsen, L. Davi, AR Sadeghi, T. Holz, B. De Sutter, and M. Franz.

    It's a TRAP: Table Randomization and Protection against Function-Reuse Attacks

    In 22nd ACM Conference on Computer and Communications Security, 2015, (CCS'15). pdf.

     

    M. Conti, S. Crane, L. Davi, M. Franz, P. Larsen, C. Liebchen, M. Negro, M. Qunaibit, AR Sadeghi.

    Losing Control: On the Effectiveness of Control-Flow Integrity under Stack Attacks

    In 22nd ACM Conference on Computer and Communications Security, 2015, (CCS'15). pdf.

     

    G. Savrun-Yeniceri, M. L. Van de Vanter, P. Larsen, S. Brunthaler, and M. Franz.

    Efficient and Generic Event-based Profiler Framework for Dynamic Languages

    In International Conference on Principles and Practices of Programming on the Java platform: Virtual machines, Languages, and Tools, 2015, (PPPJ'15). 10.1145/2807426.2807435.

     

    C. Stancu, Ch. Wimmer, S. Brunthaler, P. Larsen, and M. Franz.

    Safe and Efficient Hybrid Memory Management for Java

    In 2015 ACM SIGPLAN International Symposium on Memory Management, 2015, (ISMM'15). 2754169.2754185.

     

    S. Crane, Ch. Liebchen, A. Homescu, L. Davi, P. Larsen, AR Sadeghi, S. Brunthaler, and M. Franz.

    Readactor: Practical Code Randomization Resilient to Memory Disclosure

    In 36th IEEE Symposium on Security and Privacy, 2015, (S&P'15).
    IEEE version, BlackHat 2015 extended version, Demo Video.

     

    A. Homescu, T. Jackson, S. Crane, S. Brunthaler, P. Larsen, and M. Franz.

    Large-scale Automated Software Diversity-Program Evolution Redux

    In IEEE Transactions on Dependable and Secure Computing, 2015, (TDSC). 10.1109/TDSC.2015.2433252.

     

    AR Sadeghi, L Davi, and P. Larsen.

    Securing Legacy Software against Real-World Code-Reuse Exploits: Utopia, Alchemy, or Possible Future?

    Keynote at 10th ACM Symposium on Information, Computer and Communications Security, 2015, (AsiaCCS). pdf.

     

    V. Mohan, P. Larsen, S. Brunthaler, M. Franz, and K. Hamlen.

    Opaque Control-Flow Integrity

    In 22nd Annual Network & Distributed System Security Symposium (NDSS), 2015. pdf.

     

    S. Crane, A. Homescu, S. Brunthaler, P. Larsen, and M. Franz.

    Thwarting Cache Side-Channel Attacks Through Dynamic Software Diversity

    In 22nd Annual Network & Distributed System Security Symposium (NDSS), 2015. pdf.

     

    P. Larsen, S. Brunthaler, and M. Franz.

    Automated Software Diversity

    In IEEE S&P Magazine, vol. 13, no. 2, 2015. 10.1109/MSP.2015.23

     

    M. Murphy, P. Larsen, S. Brunthaler, and M. Franz.

    Software Profiling Options and Their Effects on Security Based Code Diversification

    First ACM Workshop on Moving Target Defense, 2014, (MTD 2014). 10.1145/2663474.2663485

     

    W. Zhang, P. Larsen, S. Brunthaler, and Michael Franz.

    Accelerating Iterators in Optimizing AST Interpreters

    ACM Research Conference on Object-Oriented Programming, 2014, (OOPSLA'14). 10.1145/2660193.2660223

     

    C. Stancu, Ch. Wimmer, S. Brunthaler, P. Larsen, and M. Franz.

    Comparing Points-to Static Analysis with Runtime Recorded Profiling

    11th International Conference on the Principles and Practice of Programming in Java, 2014, (PPPJ'14). 10.1145/2647508.2647524

     

    P. Larsen, A. Homescu, S. Brunthaler, and Michael Franz.

    SoK: Automated Software Diversity

    35th IEEE Symposium on Security and Privacy, 2014, (S&P'14). pdf. Expanded into a book on diversity.

     

    G. Wagner, P. Larsen, S. Brunthaler, and M. Franz.

    Thinking Inside the Box: Compartmentalized Garbage Collection

    To appear in ACM Transactions on Programming Languages and Systems, 2013, (TOPLAS).

     

    C. Kerchbaumer, E. Hennigan, P. Larsen, S. Brunthaler, and M. Franz.

    Information flow tracking meets just-in-time compilation

    In ACM Transactions on Architecture and Code Optimization, vol. 10, no. 4, 2013, (TACO). 10.1145/2541228.2555295

     

    G. Savrun-Yeniceri, W. Zhang, H. Zhang, E. Seckler, C. Li, S. Brunthaler, P. Larsen, and M. Franz.

    Efficient Hosted Interpreters on the JVM

    In ACM Transactions on Architecture and Code Optimization , vol. 11, no. 9, 2014 (TACO). 10.1145/2532642.

     

    C. Kerchbaumer, E. Hennigan, P. Larsen, S. Brunthaler, and M. Franz.

    CrowdFlow: Efficient Information Flow Security

    In Proceedings of the 16th Information Security Conference, Dallas, TX, USA 2013 (ISC'13). 10.1007/978-3-319-27659-5_23.

     

    P. Larsen, S. Brunthaler, and M. Franz.

    Security Through Diversity: Are We There Yet?

    In IEEE S&P Magazine, vol. 12, no. 2, 2014. 10.1109/MSP.2013.129.

     

    A. Homescu, S. Brunthaler, P. Larsen, and M. Franz.

    librando: Transparent Code Randomization for Just-in-Time Compilers.

    In Proceedings of the 20th ACM Conference on Computer and Communications Security, Berlin, Germany, 2013 (CCS'13). 10.1145/2508859.2516675

     

    S. Crane, P. Larsen, S. Brunthaler, and M. Franz.

    Booby Trapping Software.

    In Proceedings of the 2013 New Security Paradigms Workshop, Banff, Canada, 2013 (NSPW'13). 10.1145/2535813.2535824

     

    G. Savrun-Yeniceri, W. Zhang, H. Zhang, C. Li, S. Brunthaler, Per Larsen, and M. Franz.

    Efficient Interpreter Optimizations for the JVM.

    In Proceedings of the 2013 International Conference on Principles and Practices of Programming on the Java Platform, Stuttgart, Germany, 2013 (PPPJ'13). 10.1145/2500828.2500839.

     

    C. Kerschbaumer, E. Hennigan, P. Larsen, S. Brunthaler, and M. Franz.

    Towards Precise and Efficient Information Flow Control in Web Browsers.

    In Proceedings of the 6th International Conference on Trust & Trustworthy Computing, London, United Kingdom, 2013 (TRUST'13). 10.1007/978-3-642-38908-5_12.

     

    E. Hennigan, C. Kerschbaumer, S. Brunthaler, P. Larsen, and M. Franz.

    First-Class Labels: Using Information Flow to Debug Security Holes.

    In Proceedings of the 6th International Conference on Trust & Trustworthy Computing, London, United Kingdom, 2013 (TRUST'13). 10.1007/978-3-642-38908-5_12.

     

    A. Homescu, S. Neisius, P. Larsen, S. Brunthaler, and Michael Franz.

    Profile-guided Automated Software Diversity.

    In Proceedings of the 2013 International Symposium on Code Generation and Optimization, Shenzhen, China, 2013 (CGO'13). 10.1109/CGO.2013.6494997.

     

    P. Larsen, R. Ladelsky, J. Lidman, S. A. McKee, S. Karlsson and A. Zaks.

    Parallelizing More Loops with Compiler Guided Refactoring.

    In Proceedings of the 41th International Conference on Parallel Processing, Pittsburg, PA, September 10-13, 2012 (ICPP'12). 10.1109/ICPP.2012.48.

     

    N. Jensen, P. Larsen, R. Ladelsky, A. Zaks, and S. Karlsson

    Guiding Programmers to Higher Memory Performance.

    In Proceedings of the 5th Workshop on Programmability Issues for Multi-Core Computers, Paris, France, January 23rd, 2012 (MULTIPROG'12). pdf.

     

    T. Jackson, A. Homescu, S. Crane, P. Larsen, S. Brunthaler, and M. Franz

    Diversifying the Software Stack using Randomized NOP Insertion.

    In Jajodia, Sushil and Ghosh, Anup K. and Subrahmanian, V.S. and Swarup, Vipin and Wang, Cliff and Wang, X. Sean (Eds.) Moving Target Defense II: Applications of Game Theory and Adversarial Modeling, Springer Advances in Information Security, Vol. 100, ISBN 978-1-4614-5415-1, pp. 151-174, 2013. 10.1007/978-1-4614-5416-8_8.

     

    A. Homescu, S. Neisius, P. Larsen, S. Brunthaler, and Michael Franz.

    Microgadgets: Size Does Matter in Turing-Complete Return Oriented Programming.

    In In Proceedings of the 6th USENIX Workshop on Offensive Technologies, Bellevue, WA, USA, 2012 (WOOT'12). pdf.

     

    C. Wimmer, S. Brunthaler, P. Larsen, and Michael Franz.

    Fine-Grained Modularity and Reuse of Virtual Machine Components.

    In Proceedings of the 11th Annual International Conference on Aspect-Oriented Software Development, Potsdam, Germany, 2012 (AOSD'12). 10.1145/2162049.2162073.

     

    P. Larsen, S. Karlsson, and Jan Madsen.

    Expressing Coarse-Grain Dependencies Among Tasks in Shared Memory Programs.

    Special Issue of IEEE Transactions on Industrial Informatics, Vol. 7, Issue 4. 2011. 10.1109/TII.2011.2166769..

     

    P. Larsen.

    Feedback Driven Annotation and Refactoring of Parallel Programs.

    PhD. thesis. Series: IMM-PHD-2011. August 2011. pdf.

     

    P. Larsen, Razya Ladelsky, Sven Karlsson, and Ayal Zaks.

    Compiler Driven Code Comments and Refactoring.

    In Proceedings of the 4th Workshop on Programmability Issues for Multi-Core Computers, Heraklion, Crete, 2011 (MULTIPROG'11). Awarded Best Paper. pdf.

     

    P. Larsen, Sven Karlsson, and Jan Madsen.

    Expressing Inter-task Dependencies between Parallel Stencil Operations.

    In Proceedings of the 3rd Workshop on Programmability Issues for Multi-Core Computers, Pisa Italy, 2010 (MULTIPROG'10). pdf.

     

    P. Larsen, Sven Karlsson, and Jan Madsen.

    Identifying Inter-task Communication in Shared Memory Programming Models.

    In Proceedings of the 5th International Workshop on OpenMP, Dresden, Germany, 2009 (IWOMP'09). 10.1007/978-3-642-02303-3

     

    Work Experience

    University of California, Irvine

    Postdoctoral Scholar

    2011-Present

    I perform the day to day activities necessary to run the research lab and advice an average of 10 graduate students in cooperation with another post-doc and the professor. The most important task is the generation of cool, new research ideas. Other activities include grant proposal writing, attending conferences and performing independent research within the areas of compilation, optimization and cyber-security.

    IBM Haifa Research Labs

    Summer Intern

    July 2010-October 2010

    Worked as a part of the compiler team at the IBM Haifa Research labs. The internship was hosted by Ayal Zaks. Focus was on compiler driven suggestions for source code improvements. Such improvements allow auto-parallelization, auto-vectorization and locality enhancing transformations to succeed.

    IHPostal A/S

    Lead Software Engineer

    2004-2005

    Lead the planning and subsequent development of a major new revenue protection system for the Norwegian Postal Services. A later version of the system was also adopted by the Danish Postal Services. It is used in production today as the primary means of collecting fees for missing postage on business and private letters of parcels in both Norway and Denmark.

    Corena Denmark

    Software Engineer

    2004-2005

    Partook in the design and prototyping of an innovative information delivery system to be used in the cockpits of commercial airline companies as a replacement of prior paper-based solutions.

    Aston Business Solutions (Mobilized Workforce from '02)

    Software Engineer

    2000-2004

    Participated in the development of a web-based administration systems for a large and well known Danish pension fund, ATP. Solely designed and implemented an electronic self-service system for delivery of take-off data to regional and European airlines with Scandinavian Airline Services as the client.

    Education

    Doctor of Philosophy, Computer Science, 2007-2011

    Technical University of Denmark - Lyngby, Denmark

    My thesis is about communicating programmer insights about software being developed to the compiler. This leads to better use of compiler optimizations, particularly loop parallelization and vectorization. It also exposes areas for improvement to compiler developers.

    Master of Science in Engineering Informatics, 1999-2005

    Technical University of Denmark - Lyngby, Denmark

    The thesis was co-authored by Dr. Peter Verner Bojsen Sørensen with Professor Jan Madsen as the advisor. We implemented, optimized, and tested a system modeling hardware design language–Gezel–on the Microsoft .NET platform.

    Per Larsen — perl at uci dot edu — University of California, Irvine — 2011-2014

    http://www.ics.uci.edu/community/news/articles/index.php news articles @ the bren school of information and computer sciences
    • ABOUT
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    Bren school home > Community > News > Articles
    News articles

    February 11, 2016
    Iowa State statistician organizes symposium to discuss stronger science behind forensics
    EurekAlert
    Re: Hal Stern


    February 11, 2016
    Dean Stern to speak at AAAS symposium on stronger science behind forensics


    February 11, 2016
    What Your Facebook Habit Means For Your Sleep
    Time
    Quoted: Gloria Mark


    February 10, 2016
    Check Facebook a lot? You might be sleep deprived
    KPCC Southern California Public Radio
    Quoted: Gloria Mark


    February 10, 2016
    2016 TLT Symposium announces keynote speakers
    Penn State News
    Re: Mimi Ito


    February 9, 2016
    ICS alum Steve Trimberger Elected to National Academy of Engineering


    February 4, 2016
    UCI researchers link compulsive Facebook checking to lack of sleep
    UCI News
    Quoted: Gloria Mark


    January 25, 2016
    Get A Grip On Your Information Overload With 'Infomagical'
    NPR
    Re: Gloria Mark


    January 19, 2016
    Hi, I'm a digital junkie, and I suffer from infomania
    Los Angeles Times
    Quoted: Gloria Mark


    January 14, 2016
    Private practices
    UCI News
    Quoted: Sharad Mehrotra


    January 6, 2016
    The triumph of email
    The Atlantic
    Re: Gloria Mark


    January 6, 2016
    From computer consultant to comic
    UCI News
    Quoted: Sanjay Manaktala '05


    January 5, 2016
    Put the cellphone away! Fragmented baby care can affect brain development
    UCI News
    Re: Dean Hal Stern mentioned


    December 23, 2015
    IEEE awards Fowlkes the Helmholtz Prize for paper with enduring impact
    ICS News
    Re: Charless Fowlkes


    December 21, 2015
    Statistics Ph.D. student receives ENAR Distinguished Student Paper Award
    ICS News
    Re: Statistics Ph.D. student Duy Ngo


    December 14, 2015
    University of California pressured to count computer science toward high school math requirement
    Contra Costa Times
    Quoted: Debra Richardson


    December 2, 2015
    Death by Flaming Water Ski, and Other Misfortunes
    The New Yorker
    Quoted: Geoffrey Bowker


    December 1, 2015
    Artificial intelligence called in to tackle LHC data deluge
    Nature
    Re: Pierre Baldi


    November 25, 2015
    Code.org partners with Microsoft in attempt to make coding fun
    The Student Newspaper
    Quoted: Mimi Ito


    November 25, 2015
    UCI hackathon has tech junkies working overtime
    Daily Pilot
    Re: HackUCI


    November 12, 2015
    Company Bans Email for 1 Week, Employee Stress Levels Plummet
    Time
    Quoted: Gloria Mark


    November 12, 2015
    Is Email Evil?
    The Atlantic
    Quoted: Gloria Mark


    October 29, 2015
    The next Silicon Beach? Orange County wants to build its tech community
    Los Angeles Times


    October 29, 2015
    Social Media Quizzes Could Give Hackers Access
    NBC Los Angeles
    Quoted: Gene Tsudik


    October 20, 2015
    Researchers aim to make privacy second nature for software developers
    EurekAlert
    Re: Hadar Ziv


    October 7, 2015
    Want to lose weight? Freeze the fat off with an ice vest, UCI researcher says
    Orange County Register
    Quoted: Wayne Hayes


    October 5, 2015
    Statisticians wanted: The lesser-known, but also hot tech field
    Seattle Times
    Quoted: Jessica Utts


    September 30, 2015
    Facebook seeks to conquer the workplace
    San Jose Mercury News
    Quoted: Gloria Mark


    September 29, 2015
    UCI to celebrate 50th with Festival of Discovery
    Orange County Register
    Re: exhibit exploring human-powered aircraft


    September 15, 2015
    Political campaigns fav donations via Twitter
    Marketplace Public Radio
    Quoted: Mimi Ito


    September 5, 2015
    After-School STEM Camps Emphasize Minecraft, Coding Skills
    Benzinga
    Quoted: Mimi Ito


    September 1, 2015
    Digital Entrepreneurs Over 50 In the App World
    AARP
    Quoted: Ramesh Jain


    August 21, 2015
    UCI grad named a Microsoft YouthSpark Challenge for Change winner
    Orange County Register
    Quoted: Nithin Jilla


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    http://www.ics.uci.edu/community/news/notes/notes_2014.php noteworthy achievements @ the bren school of information and computer sciences
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    Bren school home > Community > News > Noteworthy achievements
    Noteworthy achievements

    Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

    Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

    Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


    SUMMER 2015

    Franz amasses $3.9 million in research funding

    photo: Michael Franz

    Michael
    Franz

    This year alone, Computer Science Professor Michael Franz has accumulated over $3.9 million in research funding from prestigious organizations such as the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), Qualcomm, Oracle and Mozilla. This follows his trend of more than $1 million per year on average in research expenditures.

    Franz currently runs two projects funded by DARPA’s Cyber Fault-Tolerant Attack Recovery (CFAR) Program, for which he received nearly $2 million and roughly $700,000 in May, respectively. The CFAR Program aims to “produce revolutionary breakthroughs in defensive cyber techniques that can be deployed to protect existing and planned software systems in both military and civilian contexts without requiring changes to the concept of operations of these systems,” according to a statement by program manager John Everett.

    Franz also runs a project funded by DARPA’s Vetting Commodity IT Software and Firmware Program (VET), which addresses “the threat of hidden malicious functionality in COTS (Commercial Off-the-Shelf) IT devices ... including mobile phones, printers, computer workstations and many other everyday items,” according to a statement by program manager Timothy Fraser. He received nearly $65,000 for this project.

    Finally, in July, Franz received nearly $620,000 from the NSF for a collaborative project titled “ENCORE—ENhanced program protection through COmpiler-REwriter cooperation.” According to the abstract, the project will produce “a prototype implementation consisting of a producer-side metadata derivation engine, and a consumer-side binary rewriting engine using this metadata to safely perform binary code manipulation.” In the past year, Franz has also received unrestricted gifts from Qualcomm, Oracle and Mozilla totaling $263,000.

     


    Jones receives ACM SIGSOFT Impact Paper Award

    photo: James Jones

    James
    Jones

    Associate professor of Informatics James Jones, along with co-authors Mary Jean Harrold and John Stasko, received an Impact Paper Award from ACM’s Special Interest Group on Software Engineering (SIGSOFT). Their paper, “Visualization of Test Information to Assist Fault Localization,” presents a color-coded visualization technique to help locate errors and faults in software. The paper originally appeared in the 2002 Proceedings of the 24th International Conference on Software Engineering (ICSE).

    ACM SIGSOFT Impact Paper Awards are presented annually to authors of significantly influential papers published in SIGSOFT sponsored or co-sponsored conference proceedings at least a decade prior to the award year. Awardees receive a $1,000 honorarium to split among themselves, an award plaque for each author, an invitation for the authors to present a keynote talk at the SIGSOFT joint Foundations of Software Engineering (FSE) symposium and European Software Engineering Conference (ESEC), and inclusion of a full-length paper in the SIGSOFT conference proceedings. Jones accepted the award at the joint conference in Bergamo, Italy this September.

    According to the website, “The ACM Special Interest Group on Software Engineering provides a forum for computing professionals from industry, government and academia to examine principles, practices, and new research results in software engineering.” The group holds several software engineering conferences and symposiums annually.

    Jones’ research focuses on software testing, software analysis (run-time and compile-time) and debugging. He is particularly interested in supporting the creative and intellectual process of developing and maintaining software.

     


    CS team to present award-winning paper at 2015 IEEE NCA Conference

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor’s Professor of Computer Science Gene Tsudik will be presenting a paper with computer science Ph.D. students Cesar Ghali and Christopher Wood (also a software engineer at PARC), Ashok Narayanan from Google, and David Oran from CISCO at the at the 2015 IEEE Network Computing and Applications Conference (IEEE NCA 2015) in early October in Massachusetts. Their paper, titled “Secure Fragmentation for Content-Centric Networks," received the Best Paper Award from the conference. Check out the tentative conference program here.

     


    UCI ranked among top 30 computer science programs in the world by ARWU

    ARWU logo

    UC Irvine’s Donald Bren School of Information and Computer Sciences (ICS) once again found itself ranked among the top 30 best computer science programs in the world. The most recent ranking came in August from the 2015 Academic Ranking of World Universities (ARWU) by the Center for World-Class Universities at Shanghai Jiao Tong University. Out of the top 200 computer science programs in the world recognized by ARWU, the ICS computer science program was ranked 29th. “This is great news because it’s consistent with the U.S. News and World Report global ranking that had our computer science program as No. 23 in the world,” says Hal Stern, Ted and Janice Smith Family Foundation Dean of ISC. Since 2003, ARWU has presented the world top 500 universities annually based on a set of objective indicators and third-party data. For more information and to view the full rankings, visit the ARWU website.


    Lopes’ Book Acknowledged as Best Programming Book of the Decade

    photo: Crista Lopes

    Crista
    Lopes

    Informatics Professor Crista Lopes’ book, Exercises in Programming Style, has been named best programming book of the decade in a Software Development (SD) Times review. A compilation of 33 different styles for writing programs and designing systems, the book, released last summer, can be used in conjunction with a GitHub-hosted repository of code. In the review, SD Times columnist Larry O’Brien calls the book “the best programming book to come along in many years.”

    As previously reported by the Donald Bren School of Information and Computer Sciences, Lopes wrote her book as a response to a recurring programming student problem. While students could write code that worked, they often had no idea how to structure it, leaving their work difficult to decipher and vulnerable to bugs. Lopes' book resolves this issue through a series of constraints: It takes a simple computational task and demonstrates 33 different ways to possibly structure it. “Every programmer will find some styles that trigger a happy recognition and others that cause an intriguing confusion. Every chapter is a delight,” O’Brien writes.

    “I made an effort to write a book that is both informative and fun,” Lopes says in response to the review. “It's very rewarding to see that people are getting my message!”

    At the review’s end, O’Brien commends the book further: “I honestly cannot think of a more enlightening and more valuable text that’s been published since the turn of the century,” he wrote. “The hardest part about reviewing it is coming up with a way to say ‘Every developer should read this book’ in a way that doesn’t come across as clichéd and rote. Seriously. Every developer should read this book.”

     


    Informatics Ph.D. student speaks about consumer privacy at TEDxUCIrvine

    photo: Bart Knijnenburg

    Bart
    Knijnenburg

    Former informatics Ph.D. student Bart Knijnenburg, who is joining the Human-Centered Computing division at Clemson University as an assistant professor in the fall, was a featured speaker at this year’s TEDxUCIrvine. Pulling from his expertise in privacy decision-making and user-centric evaluation of adaptive systems, Knijnenburg delivered a talk titled “How Come They Know So Much About Me?” According to Knijnenburg, “Addressing the privacy decision problem is crucial because an increasingly important part of our social and financial lives happens online, and if we constantly feel that we’re being monitored, and hacked and tracked, then how can we freely express ourselves.” Watch Knijnenburg’s full TEDxUCIrvine talk here.

     


    Kobsa-edited UMUAI journal celebrates silver anniversary

    photo: Alfred Kobsa

    Alfred
    Kobsa

    User Modeling and User-Adapted Interaction: The Journal of Personalization Research (UMUAI), an interdisciplinary journal edited by Informatics Professor Alfred Kobsa, celebrates its 25th anniversary with the August 2015 volume. The annual journal has long been a forum for research into the adaptability and personalization of interactive computer systems.

    According to Kobsa, the journal has come a long way since its launch in 1991. “When the journal was launched 25 years ago the idea that computers should adapt to each individual user was virtually unheard of,” he says. “Today, however, personalization can be found everywhere on the Web.”

    As an interdisciplinary forum, the journal serves audiences in several fields, including human-computer interaction, artificial intelligence, the instructional sciences, information systems, linguistics, and the information sciences. It publishes papers on applications in office machines and consumer electronics, applications in ubiquitous and mobile computing, privacy and security of information for personalization, and cultural adaptation, among many other topics.

    UMUAI consistently ranks high in field-specific metrics. According to 2014 Source-Normalized Impact per Paper (SNIP) statistics, the journal is ranked:

    • No. 5 among 84 journals in human-computer interaction
    • No. 4 among 918 journals in education
    • No. 5 among 507 journals in computer science applications
    • No. 12 among more than 1,000 journals in computer science
    • No. 5 among Microsoft Academic Search’s 26 human-computer interaction journals.

    Since 2002, the journal has also been known for awarding the James Chen Annual Award for Best UMUAI Article, a $1,000 cash prize commemorating James R. Chen, a creative researcher in the area of user modeling and information retrieval, and twice a UMUAI author. Members of the editorial board form an award committee each year to evaluate the nominees

     


    UCI ranked one of the top universities to land a job in Silicon Valley

    Business Insider logo

    In a July 2015 article from Business Insider, UC Irvine is ranked one of the top 20 universities in the nation most likely to land students jobs in Silicon Valley. The data came from recruiting platform Jobvite after it analyzed 7 million job applications and 40,000 hires to determine which universities had the most number of students hired by the best companies in and around Silicon Valley, which has become home to several of the world’s largest high-tech corporations and a hotbed for tech startup companies. With Silicon Valley being a prime job market for ICS graduates, this is great news because, as the article states, “If your degree comes from one of these schools, you’re in demand.” Read the full story online here.

     


    NSF Awards $240,000 grant to UCI trio for researching distraction in security

    photo: Alfred Kobsa

    Alfred
    Kobsa

    photo: Gene Tsudik

    Gene
    Tsudik

    The National Science Foundation (NSF) has awarded a $240,000 Early-Concept Grant for Exploratory Research (EAGER) to three UCI professors who are researching distraction in security. The co-principal investigators for the project include Informatics Professor Alfred Kobsa, Chancellor’s Professor of Computer Science Gene Tsudik, and Associate Professor of Cognitive Science Bruce Berg.

    Today’s technology allows, and sometimes requires, people to engage in security-critical tasks in often distracting public spaces. For example, a user may need to enter a PIN, enter a password, or solve a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) on his smartphone in distracting settings. According to the group’s proposal summary, “User errors or delays while performing security-critical tasks can lead to undesirable or even disastrous consequences.” The impact of such errors or delays has yet to be investigated. However, using a fully automated experimental setup, this project will study whether and how sensory stimuli influence users’ behavior and trigger mistakes.

    The project is in its preliminary stages, but, according to the professors, it entails two potentially transformational research ideas: “If sensory stimuli have a negative effect on users performing security-relevant tasks, its better understanding can lead to awareness and eventual countermeasures,” they say. “If, however, certain auditory stimuli actually improve user performance, new opportunities would arise for applying audio stimuli as a means of aiding users.” The work also “further explores the feasibility of conducting security-related user studies in a fully automated manner, which makes large-scale studies feasible and also avoids any potential experimenter bias.”

    According to the NSF website, funding from the NSF EAGER program “may be used to support exploratory work in its early stages on untested, but potentially transformative, research ideas or approaches.” NSF EAGER-funded projects often entail radically different and experimental approaches to scientific research.

     


    ICS Professors make Computing Reviews’ Best of Computing notable books list

    Computing Reviews’ Best of Computing notable book

    Computing Reviews (CR), an Association for Computing Machinery/ThinkLoud publication, has released its 19th Annual Best of Computing list, a compilation of the most interesting books and articles published in 2014. Among the 87 books recognized on this year’s list are two from ICS faculty: Professor Cristina Videira Lopes’ Exercises in Programming Style and Professor Judith Olson’s Ways of Knowing in HCI. The Notable Books and Articles for 2014 list brings together influential items published in computing last year based on nominations from CR reviewers, category editors, editors-in-chief of journals, and others in the computing community.

    If you’re an ICS faculty member and we missed your name, please contact matt.miller@uci.edu.

     


    Grad students participate in global UCSB iCTF hacking competition; place third among U.S. teams

    photo: UCSB iCTF ICS team

    Graduate students from the SPROUT (Security & Privacy Research OUTfit) and SSLLAB (Secure Systems & Software Laboratory) research groups at UCI collaborated as a team, called KHCZUU Labs, to participate in this year’s UC Santa Barbara International Capture The Flag (iCTF) competition. The iCTF contest is an annual competition where academic teams pit their skills at cyber attacks and defenses against each other. In security capture-the-flag events, the teams compete to try to find vulnerabilities in programs provided by the organizers and hack other teams to steal “flags,” while defending their own flags. CTF events provide a realistic but safe environment to learn and practice how real-world computer security works.

    Even though most of the UCI team members were new to the competition, KHCZUU still managed to place third out of the U.S. teams and 31st overall, out of nearly 100 participating teams. "The whole team is looking forward to next year’s competition and plans to practice the necessary computer security skills in the meantime," says UCI Ph.D. candidate Stephen Crane. “For most on the team, this was their first experience with capture-the-flag-style competitions, and the competition was an amazing learning experience for everyone who participated.”

    Congratulations to everyone who competed on this year's KHCZUU team: Brian Belleville, Stephen Crane, Sky Faber, Cesar Ghali, Julian Lettner, Ekin Oguz, Mohaned Qunaibit, Marc Schlosberg, and Stijn Volckaert (visiting scholar from Ghent University).

    Watch a time-lapse video of the competition here.

     


    Tsudik Co-Authors Award-Winning Student Paper on ‘Violating Consumer Anonymity’

    photo: Gene Tsudik

    Chancellor's Professor of Computer Science Gene Tsudik and Alberto Compagno, a visiting Ph.D. student from University of Rome (Italy), co-authored a paper titled “Violating Consumer Anonymity: Geo-locating Nodes in Named Data Networking” that received the Best Student Paper Award at the 13th International Conference on Applied Cryptography and Network Security 2015 (ACNS 2015), which took place in New York in June. The paper's other authors included Mauro Conti (University of Padua), Paolo Gasti (New York Institute of Technology) and Luigi V. Mancini (Sapienza University of Rome). View a PDF of the paper here.

     


    SPRING 2015

    From Google to Germany, ICS students and postdocs succeed at securing jobs and internships worldwide

    As the 2014-2015 academic year winds down, we’d like to congratulate the following ICS students and postdocs on the jobs they’ve accepted as they leave UCI:

    • Lynn Dombrowski accepted a tenure track position at Indiana University-Purdue University Indianapolis
    • Julia Haines accepted a position at Google
    • Bart Knijnenburg accepted a tenure track position at Clemson University
    • Thomas LaToza accepted a tenure track position at George Mason University
    • Lilly Nguyen accepted a tenure track position at UNC in Chapel Hill
    • Sunyoung Park accepted a tenure track position at the University of Michigan
    • Paco Servant accepted a tenure track position at Virginia Tech
    • Six Silberman accepted a position at IG Metall in Germany

    We’re also excited to see that a number of our ICS students have secured some amazing internships that will provide them with hands-on industry experience. Here’s a list of the students and where they’ll be interning:

    • ​Lee Martie – TJ Watson
    • Eugenia Grabrielova – SPAWAR Systems Pacific in San Diego
    • Maryam Kadhemi – Intel in Santa Clara
    • Martin Shelton – Google
    • Di Yang – GrammaTech in Ithaca, NY
    • Arthur Valadares – Broadcom in Irvine
    • Mengyao Zhao – Xerox in El Segundo

    Congratulations to all of our ICS students and soon-to-be alumni. Keep up the good work and let us know us about all of your career achievements by emailing communications@ics.uci.edu

     


    Report shows ICS grad degrees lead to high-paying, low-stress jobs

    Fortune / PayScale

    Those graduating this spring with advanced degrees in statistics, informatics, computer science and other information sciences can rest easy, according to a graduate degree and job prospect report released by Fortune magazine. Time Inc.’s business magazine—best known for its business revenue ranking list the “Fortune 500”—commissioned career website PayScale to identify the top 15 graduate degrees that lead to lucrative careers, and those that lead to high stress and low pay. The top four graduate degrees included doctorates in statistics and computer science, and master’s degrees in biostatistics and human-computer interaction. Several information science-related degrees rounded out the top 15, including master’s degrees in statistics, computer science, software engineering, and information science.

    The Donald Bren School of Information and Computer Sciences (ICS) offers graduate degrees in nearly all of these fields, with master’s and doctoral degrees offered in statistics, computer science, software engineering, and an innovative approach to human-computer-interaction with the informatics program. ICS was ranked 23rd worldwide among computer science graduate programs by U.S. News and World Report in 2014.

    PayScale evaluated graduate degrees based on long-term outlook for job growth, mid-career median salaries, job satisfaction scores and work stress levels. Specific breakdowns of these figures can be found in Fortune’s complete report.

     


    UCI ranked top school for game design and development, according to College Magazine and ACR

    College Magazine / ACR

    College Magazine ranked UC Irvine first in its list “10 Best Schools for Gamers,” while the university placed in the top 20% nationally on the Animation Career Review’s (ACR) 2015 Game Design and Development School Rankings, both rankings came out in May 2015. ACR ranked UC Irvine’s game development cachet 25th nationally, eighth among public institutions and the sixth best on the West Coast. Among the reasons for the No. 1 ranking, College Magazine commended UCI’s consistently strong showing at the annual IvyLoL National Championship, a collegiate League of Legends tournament, observing: “With a gaming community half a thousand students strong, UC Irvine proves that games are only fun if you have people to share them with.”

    Both lists recognize UCI’s computer game science major, offered by ICS, which combines a solid foundation in computer science with a focus on designing, building, and understanding computer games and other forms of interactive media. The fundamentals of information and computer science — along with coursework in mathematics, statistics, physics, and film and media studies — provide students with the concepts and tools to study a wide scope of computer game technologies.

    "I'm excited to see that UC Irvine's computer game science major is moving up in the rankings,” says Informatics Lecturer Dan Frost. “The world is discovering what our students already know — that we provide a top-notch education, an enthusiastic student body and faculty, and an excellent track record of placing students in game-industry jobs."

     


    ICS dean will help lead national effort to improve criminal evidence analysis, cut wrongful convictions

    photo: Hal Stern

    Hal
    Stern

    Hal Stern, dean of the Donald Bren School of Information & Computer Sciences and professor of statistics, will help lead a new national Forensic Science Center of Excellence. Aimed at improving criminal evidence analysis and reducing wrongful convictions, it will be funded by a five-year, $20 million grant from the National Institute of Standards & Technology. The campus will receive about $4 million, to be used by ICS and social ecology faculty and students. “UC Irvine is honored to be a part of this. There is a critical need to advance the scientific underpinnings for the analysis of forensic evidence – including fingerprints, firearms, marks left by tools, and documents – and to ensure that participants in the law enforcement process have a strong understanding of proper analyses and interpretation,” said Stern, who is principal investigator for UCI. The center, headquartered at Iowa State University, also will partner with Carnegie Mellon University and the University of Virginia. It will incorporate both a research agenda – developing new probabilistic methods and statistical tools – and education to ensure that judges, lawyers and investigators can effectively utilize the results of forensic analyses. For more information, visit here.

     


    UCI Data Science Initiative awarded $1.25 million from NASA for minority research opportunities

    photo: Padhraic Smyth

    Padhraic
    Smyth

    UCI has been awarded $1.25 million from NASA as part of a new $5-million 5-year award to establish a new center at Cal State LA for STEM education. This new center will collaborate with UCI’s Data Science Initiative and the Jet Propulsion Laboratory (JPL)’s Center for Data Science and Technology to train undergraduate and masters students in areas such as climate change, hydrology, computational physics, and data science with an emphasis on minority and low-income students.

    Professor Padhraic Smyth, UCI Data Science Initiative Director, will be UCI’s PI on the project and will be working with five other UCI faculty members from engineering, physical science and computer science to host Cal State LA students for short summer workshops and lab visits. The funding will support a project coordinator and provide some summer support for participating faculty and Ph.D. students.

    For more information, click here.

     


    Stats professor receives $160,000 NSF grant for neuroscience-focused research

    photo: Hernando Ombao

    Hernando
    Ombao

    Statistics professor Hernando Ombao has received a $160,000 grant from the National Science Foundation (NSF) for his collaborative research project, “Bayesian State-Space Models for Behavioral Time Series Data.” With a focus on neuroscience and its analysis and integration of behavioral and neural-derived data, the project will develop novel statistical models and inferential methods for the analysis of multi-domain behavioral data and time series with complex temporal and dependence structures.

    “This NSF proposal will be useful as we build our group of students at UCI who are working on statistical methods that will push forward boundaries of neuroscience research,” Ombao says.

    Such research has broad-reaching impact, with the potential to advance knowledge on the neural underpinnings of human and animal behavior; integrate data from different domains to be used by behavioral scientists to test directly for associations between decision making and brain response; and advance knowledge in other fields that collect temporal data with complex structure, such as sociology (network modeling), environmental sciences, linguistics and signal processing.

    The grant comes from the NSF’s Division of Social and Economic Sciences (SES). According to the SES website, the division “seeks to enhance our understanding of human, social and organizational behavior by building social science infrastructure, [and] by developing social disciplinary and interdisciplinary research projects that advance knowledge in the social and economic sciences.”

    Read more information about Ombao's research project here.

     


    Informatics Ph.D. student receives Google Anita Borg Memorial Scholarship

    photo: Katherine Lo

    Katherine
    Lo

    Informatics Ph.D. student Katherine Lo has received the Google Anita Borg Memorial Scholarship. The scholarship supports women in technology with a $10,000 financial award for the academic year as well as an invitation to the annual Google Scholars' Retreat, which offers unique professional development and community outreach opportunities, at the Googleplex in Mountain View, Calif. In addition to her Ph.D. studies, Lo is an adviser to the student organization Women in Information and Computer Sciences (WICS) at UC Irvine. She has previously received an honorable mention from the prestigious NSF Graduate Research Fellowship Program (GRFP).

    The scholarship is named for the late Dr. Anita Borg, who was committed to dismantling barriers for women and minorities in technology. Google memorializes Dr. Borg through this scholarship in “hopes to encourage women to excel in computing and technology and become active role models and leaders in the field,” according to the scholarship website.

     


    Professor Olson Recognized by Google Co-founder

    photo: Judith Olson

    Judith
    Olson

    Informatics Professor Judith Olson has won many accolades and been widely published over the years, but it’s not often that you are recognized by the likes of Google Co-founder and CEO Larry Page. In a book recently released titled The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution, Page acknowledges the impact Olson had on him as a student: "The college course that made the greatest impression on me was one on human-computer interaction taught by Judith Olson. The goal was to understand how to design interfaces that were easy and intuitive."

     


    ICS graduate students receive prestigious NSF fellowship

    NSF GRFP logo

    Three graduate students from the Donald Bren School of Information and Computer Sciences (ICS) have just received the prestigious National Science Foundation Graduate Research Fellowship (NSF GRFP). Informatics students Oliver Haimson and Van Erick Custodio and statistics student Maricela Cruz join 2,000 students nationwide to be awarded the fellowship in 2015. Informatics student Katherine Lo also received an honorable mention in the highly competitive program. Haimson, Custodio and Cruz contribute to UC Irvine’s 35 total awardees in 2015, while Lo joins an additional 37 honorable mentions across campus.

    “I'm very humbled by this award to be included in such great company both present and those that came before,” says Custodio. “My research will greatly benefit from this award in that it gives me the freedom to explore the far reaches of my imagination. Receiving the NSF GRFP award allows me to broadly share the story I have to tell through my research. I am excited about the difference in the world my research can make because of this fellowship. This, I hope, is the first of many signs that my work has potential to contribute to the research community and society at large.”

    Founded in 1952, the NSF GRFP provides graduate students in the early stages of their research with three years of support, including a $34,000 annual stipend and a $12,000 cost-of-education allowance to their institution. Fellows enjoy supercomputer access, as well as the opportunity to participate in the Graduate Research Intern Program (GRIP) and the Graduate Research Opportunities Worldwide Program (GROW). Past GRFP recipients have gone on to make significant scientific and engineering breakthroughs, with some even becoming Nobel laureates.

     


    Ph.D. Computer Science Students Win iDASH Competition

    iDASH Competition Winners

    At the recent 2015 iDash Privacy & Security Workshop at UC San Diego, two of Chancellor’s Professor Gene Tsudik's lab members, Sky Faber (a fourth year Ph.D. student) and Luca Ferretti (a visiting Ph.D. student from University of Modena, Italy), were winners of the iDASH Genome Privacy Protection Competition for their solution to "Task 2: Secure comparison between genomic data." As part of winning this competition, they received a $300 prize.

     


    Ito presents on 'connected learning' at SXSWedu Conference

    photo: Mimi Ito

    Mimi
    Ito

    South by Southwest’s education conference (SXSWedu) held in Austin, Texas, invited research scientist Mimi Ito to present at its newly formed closing program in March. Ito discussed “connected learning,” the concept of harnessing information and social connectivity for education. She presented alongside GRAMMY Museum Executive Director Bob Santelli, actress and founder of Hawn Foundation Goldie Hawn, and Khan Academy founder and Executive Director Sal Khan.

    The single-stage closing program highlighted unique educational convergences by exploring education through the lens of music, film and interactive technologies. “The closing session is a new format, and intended to bridge the education and interactive programs,” Ito says. “I think it is an important signal that we need to find ways to unite the social agenda of educational reform to innovative technology approaches.”

    Ito’s discussion of connected learning advocated for using today's tools to advance the longstanding goals of progressive education. “Too often, new educational technologies are used to reinforce traditional forms of education which we know are not effective or empowering to the learner. We have the opportunity to change that,” she says.

     


    Tsudik to Discuss Trust Management Architecture for NDN at University of Calgary

    photo: Gene Tsudik

    Gene
    Tsudik

    Chancellor's Professor of Computer Science Gene Tsudik is speaking as part of the Institute for Security, Privacy and Information Assurance (ISPIA) Distinguished Lecture Series at the University of Calgary on Thursday, March 26. Tsudik​' lecture, titled "Elements of Trust in Named-Data Network," will provide a brief overview of NDN and a summary of various security and privacy issues, while focusing on network-layer trust management. Motivated by the need to mitigate so-called "content poisoning" attacks, Tsudik will also explore the design of a trust management architecture for NDN. For more information, visit the ISPIA site.

     


    Tomlinson appointed to EPA BOSC Subcommittee

    photo: Bill Tomlinson

    Bill
    Tomlinson

    Informatics professor Bill Tomlinson was appointed to the U.S. Environmental Protection Agency’s (EPA) Board of Scientific Counselors (BOSC) Sustainable and Healthy Communities Subcommittee earlier this year. The EPA BOSC provides counsel and information to the Office of Research and Development (ORD), the scientific research arm of the EPA. Per the BOSC mission statement, BOSC members evaluate and advise research programs and practices at the EPA, evaluate the use of and provide peer-review at ORD, review ORD’s program development progress, and advise human resources planning, such as scientist career development and rotational assignment programs.

    As a member of the Sustainable and Healthy Communities Subcommittee, Tomlinson is involved with the subcommittee’s research program. This program works to provide the knowledge and tools to answer the question: “How do we meet today’s needs without compromising the ability of future generations to meet their needs in ways that are economically viable, beneficial to human health and well-being, and socially just?”

    According to the EPA, “Members of the BOSC subcommittees constitute a distinguished body of scientists and engineers who are recognized experts in their respective fields.” BOSC subcommittee members report to the executive committee. All BOSC members are drawn from academia, industry, governments, non-governmental and environmental organization, as well as research laboratories, among other relevant entities.

    The subcommittee aligns with Tomlinson’s research interests, which involve the social impacts of information technologies, environmental issues and interactive education systems.

     


    WINTER 2015

    ICS Ph.D. Grad Receives Best Doctoral Dissertation Award from iSchools

    photo: Xinru Page

    Xinru
    Page

    Information Schools (iSchools) has awarded recent ICS Ph.D. graduate Xinru Page a Best Doctoral Dissertation Award for her dissertation “Factors that Influence Adoption and Use of Location-Sharing Social Media,” which seeks to understand real-world factors shaping behaviors and attitudes toward location-sharing social networks (LSSN), especially as to why people avoid or abandon this technology, or limit their usage. Page, now an assistant professor in computer information systems at Bentley University, was advised by Informatics Professor Alfred Kobsa. Her current research interests include privacy, technology adoption, interpersonal communication, social media and human computer interaction. During her time with ICS, she received a Dean’s Fellowship and Yahoo! Best Dissertation Fellowship Award. She will receive her latest $2,500 prize at this week’s iConference, iSchools’ international gathering of scholars and researchers concerned with critical information issues in contemporary society. The iSchools selection committee, drawn from leading international schools, noted that Page's dissertation is timely and important, with one reviewer calling it “a multi-method tour de force which masterfully integrates qualitative and quantitative research.”

    Read more about Page and the award here.

     


    NSA, DHS again dub UCI a National Center of Academic Excellence in cybersecurity research

    photo: Gene Tsudik

    Gene
    Tsudik

    UC Irvine has been redesignated a National Center of Academic Excellence in Cyber Defense Research for academic years 2015-2019 by the National Security Agency and the Department of Homeland Security. “This acknowledges UCI’s prominence and growing impact, as well as expertise, in information assurance,” said Chancellor’s Professor of computer science Gene Tsudik, who heads the Secure Computing & Networking Center at the Donald Bren School of Information & Computer Sciences. “It also highlights UCI’s ability to help the nation in educating and training IA specialists.” The program is intended to reduce vulnerabilities in — and threats to — the national information infrastructure by facilitating graduate education and research and training both researchers and practitioners. In addition, the redesignation allows UCI to compete for targeted grants from the National Science Foundation, the DHS and the NSA. For more information, visit the NSA National Centers of Academic Excellence website.

     


    Informatics Ph.D. student receives DoD SMART Fellowship

    photo: Eugenia Gabrielova

    Eugenia
    Gabrielova

    Informatics Ph.D. student Eugenia Gabrielova has received the Science, Mathematics, and Research for Transformation (SMART) Fellowship. Established by the U.S. Department of Defense (DoD), the fellowship funds and supports STEM undergraduate and graduate students’ academic endeavors, places them in DoD lab summer internships, and promises post-graduate employment upon degree completion. The program aims to increase the number of civilian scientists and engineers working at DoD laboratories.

    Gabrielova will be joining the Space and Naval Warfare Systems Command (SPAWAR) Systems Center Pacific, a Navy research and development lab in San Diego. Her research interests include virtual worlds, large-scale scientific data exploration, and self-managing software systems. She currently works under the guidance of professor Crista Lopes as part of the ICS Mondego Group, which focuses on research in large systems and data.

     


    FALL 2014

    Informatics graduate student awarded Global Food Initiative fellowship

    photo: Ankita Raturi

    Ankita
    Raturi

    Ankita Raturi, a graduate student in informatics, is one of five UC Irvine students who have been awarded $2,500 fellowships to fund projects that focus on food issues. They’re among 54 University of California students receiving support from UC President Janet Napolitano’s Global Food Initiative Student Fellowship Program for efforts addressing such subjects as community gardens, food pantries, urban agriculture and food waste.

    Raturi will work to develop a software program that models the environmental impact of agricultural systems. Called the Open Resource Tracker, the program will be designed to help understand how resources flow both within and between food systems. The software could theoretically be used with any resource-consuming system (e.g., farms, restaurants, factories). While the primary audience for this tool will be those directly involved in the environmental assessment of food systems —farmers, analysts and government workers — it will also have a consumer-facing interface.

    The Global Food Initiative was launched in July to align the UC’s research, outreach and operations in a sustained effort to develop, demonstrate and export solutions — throughout California, the United States and the world — for food security, health and sustainability.

     


    UCI ranks in top 20 nationally for theoretical computer science

    photo: Michael Goodrich

    Michael
    Goodrich

    photo: David Eppstein

    David
    Eppstein

    photo: Sandy Irani

    Sandy
    Irani

    Theoretical computer science at ICS scores in the top 20 computer science departments nationally, according to a ranking list developed collaboratively by the Massachusetts Institute of Technology (MIT) and the University of Maryland.

    The list, titled “Ranking of CS Departments based on the Number of Papers in Theoretical Computer Science,” was developed in response to a perceived lack of transparency in the criteria for U.S. News and World Report’s Best Graduate Schools for theoretical computer science list. Theoretical computer science includes a focus on logic and mathematics. The list considers the quality of computer science department faculty at respective schools and publication rates in major computer science conferences as key ranking measures. It is a part of a larger MIT and University of Maryland collaborative project, titled “BigDND: Big Dynamic Network Data.”

    There are two ranking lists, each developed with the same dataset but using a different equation to organize the data. Depending on the list, computer science at UC Irvine comes in at 18th or 19th place, beating out Ivy League schools like Harvard and Yale.

    Chancellor's Professor Michael Goodrich attributes UC Irvine’s placement on the list to the work coming out of the Center for Algorithms and Theory of Computation, of which he is technical director and Chancellor’s Professor David Eppstein is director. The center’s research seeks to produce rigorously tested results about problems dealing with computers and their applications. In a recent example, professor Sandy Irani, in collaboration with professor Shahram Ghandeharizadeh at USC, has developed several novel algorithms for managing key-value caches for database management systems that greatly reduce processing overhead and enhance system throughput.

    “I think this ranking data is an excellent recognition of impact the faculty in the center are having on the area,” Goodrich says.

    The list is currently a Beta version; the developers welcome feedback at csdepartmentranking@gmail.com.

     


    Kobsa receives $666,000 from NSF to research user privacy decision support

    photo: Alfred Kobsa

    Alfred
    Kobsa

    The National Science Foundation (NSF) has awarded informatics and computer science professor Alfred Kobsa $666,000 to research user privacy decision support.

    Even though consumers want to be in charge of their privacy and current privacy norms and research recommend transparency and user control, expecting all users to make all privacy decisions themselves turns out to be unrealistic. Kobsa’s proposal, “A User-Tailored Approach to Privacy Decision Support,” seeks to realistically empower users for privacy choices, through personalized default settings and through rationales for disclosure that best suit users’ predicted decision-making. Throughout his research, he will work with industry to deploy solutions for privacy decision support.

    The research is funded by the NSF Social & Economic Sciences (SES) division, which “seeks to enhance our understanding of human, social and organizational behavior by building social science infrastructure, by developing social disciplinary and interdisciplinary research projects that advance knowledge in the social and economic sciences,” according to the website.

     


    Tomlinson, Patterson receive $400,000 NSF grant for crowdsourcing and food security project

    photo: Bill Tomlinson

    Bill
    Tomlinson

    photo: Don Patterson

    Don
    Patterson

    The National Science Foundation (NSF) has awarded informatics professor Bill Tomlinson $400,000 for his project “Fostering Non-Expert Creation of Sustainable Polycultures through Crowdsourced Data Synthesis.” Associate professor Donald Patterson and Assistant Professor of Crop Sciences at the University of Illinois Sarah Taylor Lovell serve as co-principal investigators.

    The project integrates research in computing and sustainability science with the goal of enabling a new approach to sustainable food security. By combining cyber-human systems and crowdsourcing research with the science of agroecology, the project seeks to develop an understanding of how online design tools may contribute to sustainability through enhanced local food production; to use the process of populating a plant species database as an instance of a class of problems amenable to intelligent crowdsourcing; and to pioneer new knowledge in crowdsourcing optimization.

    According to the project abstract, “The work will contribute to long-term food security and offer lessons, concepts, methods, and software tools that may be transferable to other sustainability challenges.”

    The award is part of the Cyber-Innovation for Sustainability Science and Engineering (CyberSEES) program at NSF, and is funded through the Division of Computing and Communication Foundations (CCF), which supports research and education projects that explore the foundations of computing and communication devices and their usage. According to the CCF website, “CCF-supported projects also investigate revolutionary computing models and technologies based on emerging scientific ideas and integrate research and education activities to prepare future generations of computer science and engineering workers.”

     


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    http://www.ics.uci.edu/about/search/search_dept_in4matx.php department of informatics @ the bren school of information and computer sciences
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    Department of Informatics

    photo::Adriana Avina Adriana Avina
    atavina@uci.edu
    Faculty Assistant
    (949) 824-0247
    DBH 5026
    photo::Susan Barrows Susan K. Barrows
    sbarrows@ics.uci.edu
    Faculty Assistant
    (949) 824-9661
    DBH 5028
    photo::Martin Beach Martin Beach
    mbeach@ics.uci.edu
    Department Manager
    (949) 824-2901
    DBH 5042
    photo::Geoffrey Bowker Geoffrey C Bowker | Web site
    gbowker@uci.edu
    Professor
    (949) 824-4558
    DBH 5091
    photo::Yunan Chen Yunan Chen | Web site
    yunanc@uci.edu
    Associate Professor
    (949) 824-0959
    DBH 5066
    photo::Paul Dourish Paul Dourish | Web site
    jpd@ics.uci.edu
    Professor
    (949) 824-8127
    DBH 5086
    photo::Julian Feldman Julian Feldman | Web site
    feldman@ics.uci.edu
    Professor Emeritus
    (949) 824-2901
    DBH 5029
    photo::Dan Frost Dan Frost | Web site
    frost@ics.uci.edu
    Senior Lecturer SOE
    (949) 824-1588
    DBH 5058
    photo::Judith Gregory Judith Gregory | Web site
    judithgr@uci.edu
    Associate Adjunct Professor
    (312) 315-3371
    DBH 5064
    photo::Gillian Hayes Gillian R Hayes | Web site
    gillianrh@ics.uci.edu
    Associate Professor, Vice Chair of Graduate Affairs, Informatics
    (949) 824-1483
    DBH 5084
    photo::Mimi Ito Mimi Ito | Web site
    mizukoi@uci.edu
    Professor in Residence
    (949) 824-9011
    DBH 5224
    photo::James Jones James A. Jones | Web site
    jajones@uci.edu
    Associate Professor
    (949) 824-0942
    DBH 5214
    photo::David Kay David G. Kay | Web site
    kay@uci.edu
    Senior Lecturer SOE; Vice Chair for Undergraduate Affairs, Informatics
    (949) 824-5072
    DBH 5056
    photo::Alfred Kobsa Alfred Kobsa | Web site
    kobsa@uci.edu
    Professor
    (949) 485-5020
    DBH 5092
    photo::Cristina Lopes Cristina V. Lopes | Web site
    lopes@ics.uci.edu
    Professor
    (949) 824-1525
    DBH 5076
      Vinh Luong
    vinh.luong@uci.edu
    ECEP Coordinator
    (949) 824-7353
    DBH 6091
    photo::Sam Malek Sam Malek | Web site
    malek@uci.edu
    Professor
    (949) 824-0639
    DBH 5226
    photo::Gloria Mark Gloria Mark | Web site
    gmark@ics.uci.edu
    Professor
    (949) 824-5955
    DBH 5212
    photo::Melissa Mazmanian Melissa Mazmanian | Web site
    mmazmani@ics.uci.edu
    Associate Professor
    (949) 824-9284
    DBH 5074
    photo::Bonnie Nardi Bonnie Nardi | Web site
    nardi@ics.uci.edu
    Professor
    (949) 824-6534
    DBH 5088
    photo::Judy Olson Judy Olson | Web site
    jsolson@uci.edu
    Bren Professor of Information & Computer Sciences
    (949) 824-0080
    DBH 5206
    photo::Gary Olson Gary M. Olson | Web site
    golson@uci.edu
    Bren Professor of Information & Computer Sciences
    (949) 824-0077
    DBH 5202
    photo::Donald Patterson Donald J Patterson | Web site
    djp3@ics.uci.edu
    Associate Professor
    (206) 355-5863
    DBH 5084
    photo::David Redmiles David Redmiles | Web site
    redmiles@ics.uci.edu
    Professor
    (949) 824-3823
    DBH 5232
    photo::Debra Richardson Debra J. Richardson | Web site
    djr@ics.uci.edu
    Professor Emeritus
    (949) 824-7353
    DBH 5241
    photo::Thomas Standish Thomas A. Standish | Web site
    standish@uci.edu
    Professor Emeritus
    (949) 497-3064
    DBH 5048
    photo::Joshua Tanenbaum Joshua G. Tanenbaum | Web site
    tanenbaj@uci.edu
    Acting Assistant Professor
    949-824-7078
    DBH 5052
    photo::Richard Taylor Richard Taylor | Web site
    taylor@ics.uci.edu
    Professor Emeritus, Director, Institute for Software Research
    (949) 824-6429
    DBH 5216
    photo::Bill Tomlinson Bill Tomlinson | Web site
    wmt@uci.edu
    Professor
    (949) 824-9804
    DBH 5068
    photo::Andre van der Hoek Andre van der Hoek | Web site
    ichair@ics.uci.edu
    Professor and Chair, Department of Informatics
    (949) 824-6326
    DBH 5038
    photo::Kai Zheng Kai Zheng | Web site
    kai.zheng@uci.edu
    Associate Professor
    (949) 824-6920
    DBH 5228
    photo::Hadar Ziv Hadar Ziv | Web site
    ziv@ics.uci.edu
    Lecturer/ Asst. Project Scientist
    (949) 824-2901
    DBH 5062
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    Personnel office

    photo::Peggy Munhall Peggy Munhall
    mmunhall@ics.uci.edu
    Director of Human Resources
    (949) 824-1963
    DBH 6026
    photo::Wendy Wehofer Wendy Wehofer
    wendyw@ics.uci.edu
    Visitor Coordinator
    (949) 824-8543
    DBH 6024
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    http://www.ics.uci.edu/about/search/search_dean.php dean's office @ the bren school of information and computer sciences
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    Dean's office

      Sharnnia Artis
    sartis@uci.edu
    Director of Access & Inclusion
    (949) 824-4301
    314A Rockwell Engineering Center
      Susan Drost
    smdrost@uci.edu
    Academic Personnel Administrator
    (949) 824-4025
    DBH 6219
    photo::Tony Givargis Tony Givargis | Web site
    givargis@uci.edu
    Professor, Assoc. Dean for Student Affairs
    (949) 824-9357
    DBH 3076
      Nancy Kim Yun
    nancy.kim.yun@uci.edu
    Director of Research Collaborations
    (949) 824-3088
    DBH 6048
    photo::Jim McKenzie Jim McKenzie
    jpmckenz@uci.edu
    Assistant Dean
    (949) 824-4036
    DBH 6212
    photo::Beth Mersky Beth Mersky
    bmersky@uci.edu
    Dean's Office Assistant
    (949) 824-7427
    DBH 6210
    photo::Hal Stern Hal Stern | Web site
    sternh@uci.edu
    Professor and Dean
    (949) 824-7405
    DBH 6215
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    http://www.ics.uci.edu/about/search/search_business.php business and finance office @ the bren school of information and computer sciences
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    Business and finance office

      Cassandra Jue Low
    cljuelow@uci.edu
    Director of Finance
    (949) 824-9004
    DBH 6036
      Jodi MacGregor
    jmacgreg@uci.edu
    Accounts Payable Manager
    (949) 824-8276
    DBH 6032
      Karen Reiser
    ksreiser@uci.edu
    Senior Financial Analyst
    (949) 824-5834
    DBH 6034
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    http://www.ics.uci.edu/about/search/search_tec.php TEC Business Center @ the bren school of information and computer sciences
    • ABOUT
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    TEC Business Center

      Olga Dunaevsky
    odunaevs@uci.edu
    Sr. Contract & Grant Analyst
    (949) 824-1915
    5272 Engineering Hall
      Mark Gardiner
    mgardine@uci.edu
    Sr. Contract & Grant Analyst
    (949) 824-5960
    EH 5276
      Emily Jen
    emilyjen@uci.edu
    Sr. Contract & Grant Analyst
    (949) 824-2473
    5244 Engineering Hall
      Dylan Le
    dylanle@uci.edu
    TEC Team Lead
    (949) 824-4223
    5284 Engineering Hall
      Barbara Marr
    bemarr@uci.edu
    TEC Team Lead
    (949) 824-0214
    5256 Engineering Hall
      Beatrice Mei
    bmei@uci.edu
    Sr. Contract & Grant Analyst
    (949) 824-9250
    5248 Engineering Hall
      Cristian Millang
    cmillang@uci.edu
    Sr. Contracts and Grants Analyst
    (949) 824-9701
    EH 5268
      Gloria Muringayi
    gmuringa@uci.edu
    Sr. Contract & Grant Analyst
    (949) 824-3787
    5200 Engineering Hall
      Lauren Shim
    shiml@uci.edu
    Sr. Contracts & Grants Analyst
    (949) 824-6067
    5252 Engineering Hall
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    http://www.ics.uci.edu/about/search/search_external.php external relations office @ the bren school of informtion and computer sciences
    • ABOUT
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    External Relations Office

      Alan Arredondo
    arredon@uci.edu
    Development Coordinator
    (949) 824-3923
    5200 Engineering Hall
    photo::Antigone Blackwell Antigone Blackwell
    ablackwe@uci.edu
    Director of Development
    (949) 824-4588
    DBH 6218
      Shana Chance
    schance@uci.edu
    Director of Corporate Relations
    (949) 824-3977
    EH 5236
      Ed Hand
    elhand@uci.edu
    Executive Director of Development and External Relations
    (949) 824-6563
    EH 5220
    photo::Kristin Huerth Kristin Huerth
    khuerth@ics.uci.edu
    Associate Director of External Relations
    (949) 824-3074
    DBH 6074
      Catherine Rupp
    crupp@uci.edu
    Development Specialist
    (949) 824-5094
    EH 5200
    More about the school »
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    http://www.ics.uci.edu/about/search/search_facilities.php facilities office @ the bren school of information and computer sciences
    • ABOUT
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    Below is a partial list of Graduate Division forms. To view the complete list and download these forms, visit the Graduate Division website.

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    Below is a partial list of International Center forms. To view the complete list and download these forms, visit the International Center website, or go directly the F-1 or J-1 student pages.

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    Bren school home > Undergraduate > policies
    Undergraduate Student Policies

    Academic Integrity

    Every student should be familiar with the UCI Academic Senate Policies on Academic Honesty. This text is also duplicated in the appendix of the UCI General Catalogue. The policies outlined for the campus also apply within the Bren School.

    (And if you are not familiar with UCI's Code of Student Conduct, which is another aspect of academic integrity, we would encourage you to also take the time to explore the website of the Office of Student Conduct!)

     



    » Academically Honest Conduct

    To be academically integrous means holding to values such as honesty, fairness, respect, and accountability in your scholastic pursuits. Students are expected to follow the rules and guidelines established by instructors for assignments and exams, and to accept responsibility for his or her own work. Examples of academically honest conduct are:

    • Turning in work done alone or with the help of the course's staff (instructor, teaching assistant, or reader).
    • Submission of one assignment for a group of students if group work is explicitly permitted (or required).
    • Getting or giving help on how to operate the computer or terminal.
    • Getting or giving help on how to eliminate minor syntax errors.
    • High-level discussion of course material for better comprehension.
    • Discussion of assignments with the instructor or TA to better understand what is being asked for.
    • Seeking help from course staff and/or other campus resources if you do not understand the material or are feeling overwhelmed by your courseload.

     

    » Academically Dishonest Conduct

    Actions associated with academic dishonesty include cheating, lying, plagiarizing, forging, and stealing. Examples of such behavior in the classroom are:

    • Turning in someone else's work as your own (with or without his or her knowledge). Submitting a completely duplicated assignment is a flagrant offense, but even copying only a portion of the assignment and turning it in as your own is considered cheating.
    • Allowing someone else to turn in your work as his or her own.
    • Several people writing one program and turning in multiple copies, all represented (implicitly or explicitly) as individual work.
    • Using any part of some else's work without proper acknowledgement. This is plagiarism.
    • Stealing an examination or a solution from the instructor. This is an extremely flagrant offense.

    For instance, an example of program plagiarism would be if an assignment that calls for independent development and implementation of a program (assignment intent and specified ground rules) results in two or more solutions so similar that one can be converted to another by a mechanical transformation. Or, cheating might be suspected if a student who was to complete an assignment independently cannot explain both the intricacies of his or her solution and the techniques used to generate that solution.

    Any case in which academic dishonesty is suspected is given careful, individual scrutiny. The intent of an assignment, the ground rules specified by the instructor, and the behavior of the student are all factors considered before a decision is made.

    In the event that an instructor writes a letter accusing a student of academic dishonesty, the student may prepare a statement giving his or her side of the case for inclusion in the student's file.

     

    » Penalties of Academic Dishonesty

    • A recorded incident of academic dishonesty may disqualify you for consideration for honors at graduation.
    • A first incident of academic dishonesty (if egregious) may be sufficient to cause suspension or dismissal from the University; a second incident likely will result in suspension or dismissal.
    • An incident of academic dishonesty is sufficient to cause denial of a petition to change major into the Bren School.
    • An incident of academic dishonesty may be sufficient to cause denial of admission into the Bren School Honors Program.
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    Beyond Graduation

    » If You're Considering Graduate School

    Speak with academic counselors, faculty mentors, and/or current graduate students about your long term academic and career goals. Consider these questions:

    • Why do you want to go to pursue an advanced degree?
    • Do you have the financial resources? Do you want to apply for financial aid?
    • Do you have the qualities necessary to succeed in graduate school?
    • Is an advanced degree necessary to achieve your career goals?

      If you are certain graduate school is your next step:

        • Research universities and graduate programs to find the ones most suited to your goals and interests.
        • The UCI Career Center offers a range of services for those considering graduate school, including:
          • resource materials for graduate school research
          • tips for preparation and application
          • an on-line Letter of Recommendation service
        • Once you have narrowed down your choices, be sure to contact those programs directly for information. Every school is different! If possible, try to speak with staff, faculty, and even current students in those programs to learn more about what graduate life is like there.
        • You may make an appointment to speak with an ICS graduate counselor for additional advice and guidance.  Call 949-824-5156 to set up an appointment.
          Watch this helpful video where ICS faculty talk about the grad school application process, and ICS grad students share their experiences https://www.youtube.com/watch?v=1IY8c4w5D6E

          » Applying for Jobs and Internships

          The UCI Career Center offers a range of services for those looking for jobs, including:

            • tips building a strong resume and cover letter
            • preparing for interviews
            • job search strategies

              Also check out UCIrvine Tech Jobs for current opportunities.

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    Bren school home > Community > Scholarships and fellowships
    Scholarships

    The Bren School offers undergraduate students the following competitive scholarships and student awards.

    The application period for scholarships has closed. Please check back in the Spring for more opportunities.

    These awards are possible through the generous support of our community, industry friends, and ICS endowments.

    These individuals and companies, through their commitment to higher education, play an active role in the future of information technology by helping deserving and highly competent students afford a quality education.

    Learn more about sponsoring a scholarship or fellowship to support an ICS student's educational goal.


    BOB & BARBARA KLEIST ENDOWED STUDENT AWARD IN ICS

    OVERVIEW: The Bob and Barbara Kleist Endowment was established through the generous donation from Bob and Barbara Kleist themselves. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARDS: 2 awards of $2,500 each

    SELECTION CRITERIA:

    • Transfer students only
    • Academic excellence
    • Essay required

    STEVE & JENNY MIZUSAWA ENDOWED STUDENT AWARD IN ICS

    OVERVIEW: The Steve and Jenny Mizusawa Student Award Endowment was established in 2005 through the generous donation from Steve and Jenny themselves. This award is designated to support undergraduate juniors and seniors preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARD: 1 award of $1,500

    SELECTION CRITERIA:

    • Juniors and Seniors
    • Minimum ICS GPA of 3.0
    • Self nomination and/or faculty recommendation
    • Essay required

    ACCENTURE ENDOWED OUTSTANDING JUNIOR AWARD IN ICS

    OVERVIEW: The Accenture Endowed Outstanding Junior Award was established through and endowment in 1992 by Accenture to recognize and financially assist selected student recipients during their final year at UC Irvine.

    AWARD: 1 award of $1,500

    SELECTION CRITERIA:

    • 3.0 ICS GPA minimum
    • Graduate in the following academic year
    • 40% academic standing
    • 30% demonstrated leadership abilities
    • 30% civic and/or charitable involvement (eg. volunteerism with various charity or civic minded organizations or individuals efforts taken by the applicants)
    • Essay required

    ESSIE LEV ENDOWED MEMORIAL STUDENT AWARD IN ICS

    OVERVIEW:
    The Essie Lev Endowed Memorial Student Award was established by Sara Sandel to honor her sister and former UC Irvine academic counselor Essie Lev. The award is designated for transfer or re-entering undergraduate students with demonstrated financial need. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARDS: 2 awards of $1,250 each

    SELECTION CRITERIA:

    • Transfer or re-entry students only
    • ICS majors
    • Demonstrated campus or community service
    • Essay required

    JULIAN FELDMAN ENDOWED SCHOLARSHIP IN ICS

    OVERVIEW:
    The Julian Feldman Scholarship was established in 1998 by Jim Hobbs ’73 in honor of ICS Professor Emeritus Julian Feldman. The scholarship used to be funded through an annual gift made by Jim and Trinidad Hobbs. In 2006, the scholarship was turned into an Endowed Scholarship, now called, The Julian Feldman Endowed Scholarship in ICS, funded through Jim Hobbs’ annual gifts as well as gifts from ICS’ Annual Fund campaign.

    AWARDS: 2 awards of $1,250 each

    SELECTION CRITERIA:

    • High academic standing
    • Possess demonstrated leadership abilities
    • No essay required

    KENNETH SIMMS ENDOWED MEMORIAL SCHOLARSHIP IN ICS

    OVERVIEW: The Kenneth Simms Memorial Scholarship endowment was established in 1989 by Laguna Software to honor UCI alumnus and one of the most significant contributors to the development of the PICK Operating System, Kenneth Simms ’70.

    AWARDS: 2 awards of $2,000 each

    SELECTION CRITERIA:

    • Academic excellence
    • Recipient must be a U.S. citizen or a permanent resident
    • The recipient should be preparing for a profession in the field of computer science
    • The recipient is selected by the selection committe according to the guidelines
    • Demonstrated financial need
    • No essay required

    SUMALEE JOHNSON TRANSFER STUDENT AWARD IN ICS

    OVERVIEW: The Sumalee Johnson Transfer Student Award was established through the generous donation from ICS alumnus, Sumlalee Johnson '82. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARD: 1 award of $2,000

    SELECTION CRITERIA:

    • Transfer students only
    • 3.2 ICS GPA minimum
    • Essay required

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    Non-Tech Internships and Jobs

     

    UCI Administrative Intern Program: Under the guidance of carefully chosen UCI mentors, Administrative Interns work 10 hours per week in campus departments where they assume responsibility for projects and enhance their leadership skills. Students selected for this year-long program also enroll in a three-quarter course through the Paul Merage School of Business where they receive 12 units of Pass/No Pass credit for the academic year.

    UCI SAGE: SAGE Scholars is a competitive program that seeks out the most talented students at UC Irvine, targetting particularly students who demonstrate great financial need, and provides them with training and coursework that help develop and refine their socio-professional skills and their abilities to be successful in the work world.

    UCI California Teach Initiative: UCI Cal Teach is a unique academic program that recruits talented undergraduate science and math majors to prepare for a teaching career. It is jointly sponsored by the School of Biological Sciences, School of Physical Sciences, and School of Education.

    UCI Washington Academic Internship Program: The UC Irvine Washington DC Academic Internship Program, located in the exciting environment of Washington DC, enables UCI students from ALL majors to pursue internships, seminar and elective courses, and research in fall, winter, or spring quarters.

    UC/DC Internship Program: UCDC is an internship program in Washington, D.C., sponsored by UCI's Career Center. It is an ideal vantage point for students to examine behind- the-scenes activities that shape and implement our nation's future course.

    UCI Sacramento Internship Program: This program is designed to immerse students into the workings of state government while showing the important role that policy makers play in decisions that will affect all of California.

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    Opportunities for ICS Students

    As members of the UCI community, all ICS students have access to a wide range of campus resources (a partial list of which can be viewed here). In addition to these, there are also opportunities for engagement and enrichment geared specifically towards ICS students.

    ICS students are encouraged to complement their academics with extra-curricular activities such as research or joining a student organization.  Benefits of this include, but are not limited to: networking with other students, faculty and companies; participating in social activities; forming study groups and friendships; attending various workshops and presentations; gaining deeper experience and knowledge of your chosen field; and more.

     



    »
    Campus Engagement
    • ICS student organizations
    • Other student organizations

    Stay up to date with upcoming events organized by ICS student organizations on the official ICS Clubs Events calendar.

     

    » ICS Theme Housing

    The Arroyo Vista theme house provides informal, academic and social functions for students interested in the field of information and computer science. The goals of this house are to expose residents to faculty, alumni and businesspersons in the information and computer sciences field.

    Activities include:

    • visits to corporations
    • opportunities to participate in symposiums and workshops centering on academic success
    • community and career opportunities
    • preparation for graduate/professional schools and learning skills
    • social activities

    The house is open to all students with an interest in information and computer sciences.

     

    » Honors Opportunities

    Students may apply to be in the ICS Honors Program during Spring quarter if they meet certain academic criteria and have the support of faculty.  Benefits of being in the ICS Honors Program include networking with other ICS Honors students, conducting research with a faculty mentor, and working on a thesis.

    • Bren School of ICS Honors Program
    • Campuswide Honors Program

     

    » Research Opportunities

    All Bren ICS majors are encouraged to take advantage of this valuable experience. Faculty advertise many research opportunities every year.

    • Independent Study: Enroll in an independent study (199 course number) under a faculty advisor unit credit
    • Summer Undergraduate Research Internship in Computer Science:This program provides an opportunity for select students to spend 8-10 weeks in the summer working with faculty on a research project.
    • UC LEADS: This program offers educationally or economically disadvantaged sophomore students (or juniors planning on staying a complete fifth year) in science, technology, engineering or mathematics programs an opportunity to begin research training at the very beginning of their junior year
    • UROP: The Undergraduate Research Opportunities Program encourages and facilitates research and creative activities by undergraduates from all schools and academic disciplines at UCI. Programs include SURP, SURF-IT, MDP, and others.

     

    » ICS Scholarships

    To search for some available scholarships, students should visit the resources below.

    • ICS Scholarships
    • To search for more scholarships, visit the Financial Aid and Scholarships website.

     

    » Jobs for Students

    To search for available technical jobs and internships, students can visit the resources below.

    • Zotlink

    Non-technical internships and opportunities are also available and listed here.

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    Bren school home > Undergraduate > Student resources
    Undergraduate Student Resources

    ICS Student Organizations

    ICS students are encouraged to participate in student organizations to complement their academics.  Below is a list of student organizations within the Donald Bren School of ICS. 

    ICS Student Council

    The ICS Student Council is the official student government for the Donald Bren School of Information and Computer Sciences. Its aim is to improve the lives of students academically, professionally, and socially. The ICS Student Council hosts career information nights with companies such as Google, Blizzard, Accenture and numerous others; organizes training sessions in which students teach each other about new and upcoming technologies; and performs resume-writing and interview-training sessions. In addition, it is responsible for a series of very popular social events, including ICS Day (featuring games, coding competitions, dance crew performances and the infamous dunk tank and inflatable jousting), reverse career fairs (where students demo their work to prospective employers) and app jams (themed competitions to design and implement new apps).

    iCAMP

    Interdisciplinary Computation and Applied Mathematics Program provides an opportunity for undergrads to learn about a topic in computational applied mathematics. Opportunities are also available for paid summer research.

    WICS

    The mission of Women in Computer Sciences is to encourage women to pursue a college degree and a successful career in the computer science field by offering the resources they need to excel in their degrees from freshman to senior year. WICS provides professional and technical workshops, academic tutoring, leadership and team building projects, as well as numerous opportunities for women to connect with companies and alumni. It hosts peer-networking and mentorship events to foster community and to inspire collaboration on extracurricular projects.

    ACM UC Irvine Student Chapter

    The Association for Computing Machinery is the world’s largest society of information technology students and professionals. The UC Irvine student chapter represents the needs of the UCI computing community, and hosts regular workshops to share knowledge and hosts presentations by distinguished speakers about new ideas. The chapter also is actively engaged in preparing teams for various programming competitions, such as the ACM Intercollegiate Programming Competition and IEEExtreme.

    INSA

    Informatics Student Association works to unite the Informatics community at UCI and to promote the Informatics major to students and companies alike. We offer workshops, corporate visits and tours, individual tutoring and mentoring, tournaments and competitions and networking/recruiting events with technology companies.

    UCF

    Undergraduate Computing Federation promotes interaction between and provide resources to students with an interest in Unix, Linux, FreeBSD, Network Security, Cryptography, or Ethical Hacking. Open to all UCI undergraduate or graduate students.

    VGDC

    The Video Game Development Club is dedicated to anyone interested in video games and game development, with the particular objective of supporting students in developing portfolios, enhancing skills and connecting to industry professionals. VDGC always has an active set of projects on which anyone can choose to work to learn new game design techniques, hone artistic skills or simply learn about new game development platforms. The club holds weekly workshops on topics such as 3D art, design, programming and project management. It also organizes the annual Game Developers Week, which is UCI’s very own implementation of the Game Developers Conference (GDC), featuring speakers from game companies in the area (e.g., Obsidian, Blizzard).

    MAISS

    Management Information Student Society is tailored to the interests of business information management majors at UCI as well as those interested in business and technology. MAISS aims to provide professional opportunity, an academic community and a strong social network. It offers training sessions, holds beach bonfire, and organizes a popular annual spring banquet. MAISS regularly brings in professionals to talk about such topics as consulting, startups and entrepreneurship.

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    http://www.ics.uci.edu/ugrad/internship/index.php Summer Undergraduate Research Internship in Computer Science @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Summer internship
    Internship program
    Summer Undergraduate Research Internship
    in Computer Science
    @ the University of California, Irvine

    We welcome applications for a newly launched summer undergraduate research internship program at the UC Irvine Donald Bren School of Information and Computer Sciences. Selected students will get an opportunity to visit UCI during the summer of 2015 and spend roughly 8-10 weeks working with faculty on exciting research projects. Projects are expected to cover a wide variety of topics including artificial intelligence, data management, embedded systems and architecture, machine learning, networking, systems and software, secure computing, and theoretical computer science.

    Who should apply?
    The internships are open to both domestic and international students enrolled in an undergraduate Computer Science program (or related field) who are currently in their junior or senior year. (In exceptional cases, recent graduates also will be considered). The internships are suitable for highly motivated undergraduate students, potentially interested in exploring doctoral studies, who wish to gain valuable research experience. It is especially well suited for students considering UCI as a possible destination for their doctoral study. The internship provides an opportunity for students to visit UCI, discover the charm of Southern California, get to know our faculty, and participate in research projects that are changing the computing landscape.

    How many internships are available?
    We expect to have 8-10 internship positions. The program will cover travel expenses to/from Irvine, and interns will have the opportunity of earning up to $450 a week to cover living expenses.

    What types of research projects will I work on?
    Click here for examples of research projects available through this internship program.

    • Offered by Bren Professor Michael Carey:

      AsterixDB is an open source BDMS (Big Data Management System) developed over a 4-year period at UC Irvine and UC Riverside. It has a rich feature set, managing as well as querying/analyzing data, that distinguishes it from the other Big Data platforms available in the open source world today. Its feature set makes it well-suited to modern uses such as web data warehousing or social data storage and analysis. AsterixDB has a semistructured NoSQL style data model (ADM), a declarative query language (AQL), and is designed to run on large "shared nothing" clusters like those powering Google, Facebook, and other big web companies. It has a novel storage architecture that supports fast data ingestion as well as efficient parallel queries, and has a number of other interesting features as well. The AsterixDB project has 1-2 positions for interns wishing to work with open source Big Data technologies. Possible projects include the development of flashy graphical administration and query monitoring tools (for which browser-based application development experience is needed), development of showcase applications (where the intern could perhaps provide their own idea for an application that utilize AsterixDB's spatial, temporal, and textual data support), or the development of a new component or policy manager for the system in cooperation with one of the team's graduate students (for which comfort with reading open source Java code, in a large code base, would be important). Interested interns are encouraged to download and test drive the system on their favorite Mac or Linux laptop in order to get a taste of what AsterixDB is all about.

    • Offered by Professor Michael Franz:

      We are developing a new way to automatically harden computer systems against cyber-attacks. Our techniques fundamentally improve the odds of defenders by equipping all computing systems with unique software. Like biological diversity curbs the spread of diseases, our artificial software diversity makes cyber-attacks costly and ineffective. Concretely, we are building a new compiler (on top of the industry-leading LLVM infrastructure) that does not just try to produce the fastest binary; instead it randomly transforms each program as it is produced. The resulting set of programs isn’t vulnerable to any one exploit. Our work has many implications at the OS and hardware level and is not just theoretical. You can go ahead and protect open source software simply by recompiling it.

      We don't expect you to have a lot of prior experience. You'll be working in team of half a dozen graduate students and post docs who offer lots of advice---all we ask is that you are motivated, disciplined, and know how to code.

      Project 1: Next-generation Cyber Defenses
      We are working to bring our compiler to new platforms, contribute our work back to the LLVM project, and develop new ways to randomize code. For example, you could help measure the security and performance on ARM systems or implement randomized function parameter shuffling. We are also working on new Control Flow Integrity (CFI) techniques that prevent attackers' code from executing. Specific projects are determined by the interns particular interests and skill sets.

      Project 2: Thinking Like an Attacker

      If you like to learn how to hack, reverse engineer or exploit vulnerabilities, this project is for you. We cannot evaluate the security of our research without trying to attack it---so we do exactly that. We build same kinds of attacks that are used in the wild today including return-oriented programming, JIT-spraying and side-channel attacks. Don't worry if you haven't developed exploits before, we'll help you find a project that matches your experience.

    • Offered by Chancellor's Professor Michael Goodrich:

      Algorithms are central to computing, and the Center for Algorithms and Theory of Computation is sponsoring summer internships to study paradigms and principles for the design and implementation of correct and efficient data structures and algorithms. Specific topics of interest include graph algorithms, randomized algorithms, geometric algorithms, and algorithms for computer security and privacy. We are looking for students with experience in algorithm design and analysis, ideally including proficiency with probability theory and combinatorics.

    • Offered by Professor Wayne Hayes:

      Professor Wayne Hayes works in the area of Computational Science, which means using computers to do science in the physical, biological, mathematical, and social sciences. Currently his projects include: the analysis of images of spiral galaxies; graph theory applied to biological networks; prediction of sea level rise due to global warming (in collaboration with NASA/JPL); applied numerical mathematics; all of which use parallel computation. Candidates will need to pass a test before being considered. More details can be found here.

    • Offered by Professor Sharad Mehrotra:

      With the proliferation of cloud computing, organizations and individuals are increasingly outsourcing their data management needs, leading to the problem of “loss of control” over one’s data. The Radicle project at UCI is taking a novel risk-based approach to secure data management in the cloud. In particular, we are developing revolutionary middleware solutions that selectively expose data based on balancing the benefit of such data exposure with the associated risks. An example of such a middleware is the CloudProtect system that sits between a user and web applications such as Picasa, DropBox, Google Drive, etc. The middleware intercepts the user’s interactions with the service, appropriately encrypts data when possible to support user’s security policies, and strikes a balance between service usability and security. The Radicle team is looking for 1 to 2 summer interns who are interested in contributing to the development of solutions for secure data management. This work could include building core component of CloudProtect, extending the nature of policies supported, hardening the software for broader release, incorporating new cloud-based services, conducting user studies using the software, etc.

    Will the entire internship program focus on research?
    While the program emphasizes research experience, it will include social activities so interns get to know one another and experience some of what Southern California has to offer.

    How do I apply?
    You may apply online at https://recruit.ap.uci.edu/apply/JPF02710; you must create an account to access the application and submit the required information, which includes your transcript(s), two letters of recommendation and a short personal statement. We will begin looking at applicants December 15th. Depending upon the level of interest and the number of qualified students we can find, we may have a second round selection on February 15th. If you miss the first deadline, we still encourage you to apply for the second deadline.

    Will housing be provided?
    Students must arrange their own housing. Information about off-campus housing options is available here.

    Can you tell me more about UCI?
    Located in coastal Orange County, near a thriving employment hub in one of the nation’s safest cities, UC Irvine was founded in 1965. One of only 62 members of the Association of American Universities, it’s ranked first among U.S. universities under 50 years old by the London-based Times Higher Education. The campus has produced three Nobel laureates and is known for its academic achievement, premier research, innovation and anteater mascot. UC Irvine has more than 28,000 students and 1,100 faculty and offers 192 degree programs. It’s Orange County’s second-largest employer, contributing $4.3 billion annually to the local economy. The university is about 5 miles from the Pacific Ocean, 45 miles from Los Angeles and 80 miles from San Diego. The beach cities of Orange County that neighbor Irvine are among the top tourist destinations in the United States.

    Can you tell me more about UCI’s computer science program?
    The Donald Bren School of Information and Computer Sciences is the only computing-focused school in the University of California system. UCI holds the No. 28 spot in the most recent U.S. News and World Report ranking of computer science programs, and Microsoft’s Academic Search website ranks the Bren School faculty as the 21st most influential group in the United States. Students graduating with a Ph.D. from the Bren School at UCI have flourished in positions in industry, industrial research labs, government and academia. For more information about our school and alumni, visit: http://www.ics.uci.edu/about/about_factsfigures.php

    Who do I contact for more information?
    For questions about the UCI summer undergraduate internship program in computer science, please email summerinternships@ics.uci.edu.

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    Bren school home > Community > Scholarships and fellowships
    Scholarships

    The Bren School offers undergraduate students the following competitive scholarships and student awards.

    The application period for scholarships has closed. Please check back in the Spring for more opportunities.

    These awards are possible through the generous support of our community, industry friends, and ICS endowments.

    These individuals and companies, through their commitment to higher education, play an active role in the future of information technology by helping deserving and highly competent students afford a quality education.

    Learn more about sponsoring a scholarship or fellowship to support an ICS student's educational goal.


    BOB & BARBARA KLEIST ENDOWED STUDENT AWARD IN ICS

    OVERVIEW: The Bob and Barbara Kleist Endowment was established through the generous donation from Bob and Barbara Kleist themselves. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARDS: 2 awards of $2,500 each

    SELECTION CRITERIA:

    • Transfer students only
    • Academic excellence
    • Essay required

    STEVE & JENNY MIZUSAWA ENDOWED STUDENT AWARD IN ICS

    OVERVIEW: The Steve and Jenny Mizusawa Student Award Endowment was established in 2005 through the generous donation from Steve and Jenny themselves. This award is designated to support undergraduate juniors and seniors preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARD: 1 award of $1,500

    SELECTION CRITERIA:

    • Juniors and Seniors
    • Minimum ICS GPA of 3.0
    • Self nomination and/or faculty recommendation
    • Essay required

    ACCENTURE ENDOWED OUTSTANDING JUNIOR AWARD IN ICS

    OVERVIEW: The Accenture Endowed Outstanding Junior Award was established through and endowment in 1992 by Accenture to recognize and financially assist selected student recipients during their final year at UC Irvine.

    AWARD: 1 award of $1,500

    SELECTION CRITERIA:

    • 3.0 ICS GPA minimum
    • Graduate in the following academic year
    • 40% academic standing
    • 30% demonstrated leadership abilities
    • 30% civic and/or charitable involvement (eg. volunteerism with various charity or civic minded organizations or individuals efforts taken by the applicants)
    • Essay required

    ESSIE LEV ENDOWED MEMORIAL STUDENT AWARD IN ICS

    OVERVIEW:
    The Essie Lev Endowed Memorial Student Award was established by Sara Sandel to honor her sister and former UC Irvine academic counselor Essie Lev. The award is designated for transfer or re-entering undergraduate students with demonstrated financial need. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARDS: 2 awards of $1,250 each

    SELECTION CRITERIA:

    • Transfer or re-entry students only
    • ICS majors
    • Demonstrated campus or community service
    • Essay required

    JULIAN FELDMAN ENDOWED SCHOLARSHIP IN ICS

    OVERVIEW:
    The Julian Feldman Scholarship was established in 1998 by Jim Hobbs ’73 in honor of ICS Professor Emeritus Julian Feldman. The scholarship used to be funded through an annual gift made by Jim and Trinidad Hobbs. In 2006, the scholarship was turned into an Endowed Scholarship, now called, The Julian Feldman Endowed Scholarship in ICS, funded through Jim Hobbs’ annual gifts as well as gifts from ICS’ Annual Fund campaign.

    AWARDS: 2 awards of $1,250 each

    SELECTION CRITERIA:

    • High academic standing
    • Possess demonstrated leadership abilities
    • No essay required

    KENNETH SIMMS ENDOWED MEMORIAL SCHOLARSHIP IN ICS

    OVERVIEW: The Kenneth Simms Memorial Scholarship endowment was established in 1989 by Laguna Software to honor UCI alumnus and one of the most significant contributors to the development of the PICK Operating System, Kenneth Simms ’70.

    AWARDS: 2 awards of $2,000 each

    SELECTION CRITERIA:

    • Academic excellence
    • Recipient must be a U.S. citizen or a permanent resident
    • The recipient should be preparing for a profession in the field of computer science
    • The recipient is selected by the selection committe according to the guidelines
    • Demonstrated financial need
    • No essay required

    SUMALEE JOHNSON TRANSFER STUDENT AWARD IN ICS

    OVERVIEW: The Sumalee Johnson Transfer Student Award was established through the generous donation from ICS alumnus, Sumlalee Johnson '82. This award is designated to support undergraduate students who have transferred to UCI from a community college and who are preparing for a career in the computer science field. Students must have a declared major in the Donald Bren School of Information and Computer Sciences.

    AWARD: 1 award of $2,000

    SELECTION CRITERIA:

    • Transfer students only
    • 3.2 ICS GPA minimum
    • Essay required

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    http://www.ics.uci.edu/about/annualreport/index.php ICS Annual Reports @ the Donald Bren School of Information and Computer Sciences
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    Bren school home > Community > News >
    Brenbits Winter 2016
    Donald Bren School of Information < Computer Sciences
    Bren Hall Winter Scene

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    Spotlight

    Hall of Fame: ICS honors 20 alumni who have made a significant impact in their profession

    Hall of Fame inductees


    More than 400 alumni, faculty, donors and industry partners turned out for the 50th Anniversary and Hall of Fame Celebration on Oct. 3, as 54 alumni were inducted into the 2015 Hall of Fame for the UC Irvine Donald Bren School of Information and Computer Sciences and the Samueli School of Engineering. Read more >

    Features

    Hayes receives Jacobs Foundation Fellowship

    Gillian Hayes

    The $400,000 award will allow the informatics professor to continue her work on “Inclusive and Evidence-Based Technologies for Child and Youth Development.” Read more >

    2015 ACM Fellows: Profs. Dourish & Franz

    2015 ACM Fellows

    Professors Paul Dourish and Michael Franz have been named 2015 ACM Fellows by the Association for Computing Machinery. Read more >

    Private practices

    computer science professor Sharad Mehrotra

    UCI researchers seek to balance electronic surveillance with privacy of people being monitored. Read more >

    Banking on data

    ICS alumnus Pedro Domingos

    ICS alumnus Pedro Domingos, Ph.D. 1997, discusses the push toward a master algorithm in machine learning. Read more >

    Stats Ph.D. student receives Distinguished Paper Award

    Statistics Ph.D. student Duy Ngo

    Duy Ngo receives ENAR Distinguished Student Paper Award for his work, "An Exploratory Coherence Analysis of Electroencephalograms Using the Functional Boxplots Approach.” Read more >

    Graduate Student Spotlight: Homer Strong

    Statistic student Homer Strong

    Q&A with graduate statistics student Homer Strong who received the Robert L. Newcomb Memorial Endowed Graduate Student Award. Read more >

    Informatics Seminar Series 2015-16 Videos

    Informatics Seminar Videos

    Browse the collection of videos that made up the fall Informatics Seminar Series. Watch now >

    Faculty Profile Video: Ardalan Amiri Sani

    Ardalan Amiri Sani

    Meet new Assistant Professor of Computer Science Ardalan Amiri Sani and learn about his research. Watch now >

    "Wayne's World of Science" Video Series

    Wayne Hayes

    Professor Wayne Hayes discusses biological network alignment and spiral galaxies in these first two episodes of his new series. Watch now > 

    Undergraduate Profile: Angela Liu

    Angela Liu

    Senior Angela Liu shares her experience as a software development intern at Pacific Dental in Irvine. Watch now >

    ICS Annual Report

    ICS Annual Report

    The 2014-2015 academic year was a momentous time for ICS -- from celebrating UCI's 50th Anniversary to launching a new B.S. in Data Science. This year's Annual Report details all of these past achievements, along with some of the most noteworthy accomplishments of our faculty, students and alumni. Download the recently released Fall 2015 Annual Report here >

    Upcoming Events

    Homecoming 2015

    UCI Homecoming
    Saturday, Jan. 30, 2016
    More information >

     

    Stay tuned for upcoming ICS alumni events in Los Angeles, the Bay Area and New York.

    News

    UCI study shows maternal infant-rearing link to adolescent depression (Dean Hal Stern) >

     

    Franz named IEEE Fellow >

     

    Kobsa receives Mercator Fellowship >

     

    NSA grants Tsudik $286K for cybersecurity research >

     

    Building the 'perfect' app >

     

    Postdoctoral scholar Per Larsen recognized as “DARPA Riser” >

     

    From computer consultant to comic (Profile on alumnus Sanjay Manaktala ’05) >

    Giving

    Maryam Khademi

    Informatics Ph.D. candidate Maryam Khademi (above right) is the first recipient of the new Fred M. Tonge Endowed Graduate Award, which was established during UCI’s 50th Anniversary Celebration in October 2015. The award, named in honor of founding ICS and UCI faculty member Fred M. Tonge, gives recipients $1,000 toward their education. The award fund was made possible with a generous donation from ICS alumna Marsha Drapkin Hopwood, Ph.D. 1974. "The ICS experience opened a world of opportunity for me," says Hopwood. "I believe it is important to contribute to providing similar opportunities for current and future students."

    Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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    http://radicle.ics.uci.edu/ Project RADICLE Website

    RADICLE

    Risk Aware Data Management in Cloud Environments

    Donald Bren School of Computer Science

    University of California, Irvine

     

    Motivation

    As data management systems become more distributed, risk of data loss arises whenever data migrates from more secure environments to those that are less secure. One way to mitigate such risk is to identify components that are vulnerable, and fortify them with security solutions. Nonetheless, such actions cannot prevent attacks carried out by insiders. For example, a malicious database administrator would still have access to the sensitive data within the databases he or she manages. In other instances, performance overheads involved might prevent usage of strong security practices, such as encrypting all sensitive data in motion or at rest. Hence, new security solutions need to be developed as data management systems become more distributed and migrate to the cloud.

     

    Overview

    In this project, we take an alternate/complementary approach towards securing query processing for database workloads. We acknowledge the fact that different system components/environments through which data moves during query processing offer different security guarantees. Instead of attempting to prevent or thwart attacks, we design risk aware query processing techniques that control how data flows through different components (in particular from more secure to less secure ones) such that the risk of loss of data, while not entirely eliminated, is substantially reduced. The major intuition behind our approaches is that migration/motion of data during query processing is primarily guided by efficiency concerns (such as, how much data to cache, how to access the data, via index or table scan etc.). Therefore, varying the characteristics of data movement through these components exposes a tradeoff between risk and performance.

    For instance, query processing on a typical database server requires that data be brought into memory and be kept in memory (cached) for as long as possible to prevent expensive disk I/Os. Indeed, given the large amounts of data involved, often the primary optimization that database servers support is minimizing the number of disk I/Os. However, longer the data resides in memory, larger the risk that it can be stolen through a variety of memory scraping attacks. Instead, an alternate strategy like toss-immediately after use may incur performance degradation, but significantly reduce risk of data loss. Likewise, consider an public-private hybrid cloud setup, where and organization utilizes the public cloud resources during peak demands to offload some of the work. Again, limiting the queries and data offloaded to the (less-trusted) public cloud can limit exposure risks.

    Given the potential tradeoffs between performance and risks of data loss, we postulate the problem of risk aware query processing wherein the goal changes from purely attempting to minimize costs (and hence maximize performance) to that of achieving a balance between performance and exposure risks. Given the above postulation, multi-criteria optimization techniques can be employed to achieve a balance between performance and exposure risk. Two specific settings could be: (a) optimize for performance while ensuring that exposure risks are constrained, or alternatively, (b) constrain the additional overhead of query processing, while minimizing the risk of data loss.

    The mechanisms we will design will address risk management at both the query optimization level, as well as, at the level of redesign of individual relational operators (such as joins, selections, etc.) so as to limit exposure risks. Additionally, we will explore data and workload partitioning methods for the hybrid cloud models that minimize exposure risks while ensuring performance goals.

     

    Project Funding

    This project is partially supported by grants from NSF and NEC Labs, USA.

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    © RADICLE | Design by Cirkuit Networks, Inc. Modified by Kerim Yasin Oktay, ICS

    http://sherlock.ics.uci.edu/ Project SHERLOCK @ UCI: Entity Resolution, Data Cleaning, and Data Quality Project at UC Irvine.
    http://www.ics.uci.edu/%7esysarch/ SysArch Group Home Page

      Parallel Architectures and Systems Lab

    The Parallel Architectures and Systems group in the Department of Computer Science at UCI conducts research on processor architecture and software for parallel, high-performance and distributed systems (including embedded systems). The best way to learn about our work is to look at our projects and publications.

    People (2013)



    Left to right: Ro, Alex V, Nam, Laleh, Dali, Taesu, and our summer intern Vivek
    Prof. Nicolau was travelling at the time this picture was taken...

    Projects

    WebRTC and WebRTCBench

    "WebRTC is perhaps the most disruptive of all HTML5 APIs. By making the audio and video communications programmable in the browser language, it is expected to have a profound impact on the way we communicate with each other. WebRTC will enable exciting new experiences on computing devices in individual communications, group meetings, e-commerce, customer support, remote collaborations, media, and online education among others. WebRTC has the potential of transforming the web to the main real-time interaction medium.

    Kudos to the UC Irvine research team of Prof. Nicolau and Prof. Veidenbaum for developing WebRTCBench, a ground breaking benchmarking suite for quantitative measurement of the system characteristics of fundamental operations of WebRTC. With the rapidly increasing importance of WebRTC in computing and communication industries, I have growing hope that WebRTCBench will play a major role in helping optimize WebRTC implementations and shape this exceptionally important emerging technology."

    Dr. Mohammad Reza Haghighat
    Senior Principal Engineer and leader of Intel HTML5 technical strategy

    Compiler and performance optimization using similarity analysis

    Improving single core performance via compiler-assisted out-of-order commit

    Some prior projects


    Publications


    http://hombao.ics.uci.edu/ Space-Time Modeling
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    WHAT WE DO

  • We develop novel statistical methods and models for analyzing massive spatio-temporal data with complex dependence structures.
  • We collaborate with scientists on study design, modeling and analysis of space-time data arising from various fields such as neuroscience, neurology, psychiatry, sociology and epidemiology.
  • Through collaborative projects, we engage undergraduate and graduate students in all phases of inter-disciplinary research including model formulation, implementation and presentation of results.
  • ANNOUCEMENT

  • NSF-Funded Workshop on Developing Novel Statistical Methods for NeuroImaging

  • For pre-prints of papers and computer codes contact

    Hernando Ombao, Ph.D.

    Department of Statistics

    University of California at Irvine

    Irvine, CA 92697

    EMAIL: hombao AT uci DOT edu

    PHONE: (949) 824-5679

    http://www.ics.uci.edu/~djr/DebraJRichardson/SE4S.html SE4S: Software Engineering for Sustainability
     
     
     
     

    SE4S: Software Engineering for Sustainability

     

    The use of information and communications technology (ICT) has led to profound advances in human civilizations, but in doing so has also contributed significantly to the exploitation of our planet’s resources. New forms of ICT, on the other hand, also generate considerable potential for “greening through ICT” – that is, making the world more sustainable by means of ICT interventions. This project seeks to understand how software engineering can help in this endeavor.

    Software-intensive systems are deeply engaged with many different aspects of life in the industrialized world; as such, they provide a powerful leverage point for enabling sustainability concerns to be brought to bear across a wide range of domains. Yet, developers of these systems lack a comprehensive understanding of how to integrate sustainability into their software development processes.  We are creating a methodology for developing software-intensive systems that meet the functional needs of users while reducing the environmental impacts brought about by those systems. This project, software engineering for sustainability (SE4S), therefore, is focused on how software engineering can support environmental sustainability in the wide variety of domains in which software is deployed, rather than just how software engineering can contribute to improving the sustainability of either software or ICT itself.

    One of our primary emphases is on requirements engineering for sustainable software-intensive systems. Specifically, how can sustainability be considered as a first class quality in describing constraints on the design and implementation of a software-intensive system so that it mitigates negative impact on its environment and thereby supports sustainability? We are also exploring how different aspects of sustainability (e.g., reducing carbon emissions, increasing sustainable energy sources, managing water, supporting biodiversity, organizing food production) can be represented and implemented in common ways across various domains.  We are designing adaptable, reusable sustainability “widgets” that will reduce the costs associated with developing software that addresses sustainability.  We view these efforts as one way to incentivize stakeholder buy-in, which will be critical to adopting this new approach. Considering sustainability as a first-class requirement is likely to incur additional costs at some point in the production process and/or deployment of the software. Whether overall costs go up or down, incentivizing developers and/or consumers to expend greater local costs to address sustainability adequately will be an important part of this effort's success.

    This interdisciplinary project links the fields of software engineering and environmental informatics, and also reaches beyond these disciplines to other environmental sciences and industries to integrate the most current understanding of sustainability issues.

    This work is in progress with Professor Bill Tomlinson and German postdoc Birgit Penzenstadler of TU Munich.

     
     
    http://asterix.ics.uci.edu/ AsterixDB
    University of California
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    SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

    Overview

    Welcome to the homepage of Sherlock@UCI: a UC Irvine project on Entity Resolution and Data Quality!

    The significance of data quality research is motivated by the observation that the effectiveness of data-driven technologies such as decision support tools, data exploration, analysis, and scientific discovery tools is closely tied to the quality of data on which such techniques are applied. It is well recognized that the outcome of the analysis is only as good as the data on which the analysis is performed. That is why today organizations spend a tangible percent of their budgets on cleaning tasks such as removing duplicates, correcting errors, filling missing values, to improve data quality prior to pushing data through the analysis pipeline.

    Given the critical importance of the problem, many efforts, in both industry and academia, have explored systematic approaches to addressing the cleaning challenges. The work of our group focuses primarily on the entity resolution challenge that arises because objects in the real world are referred to using references or descriptions that are not always unique identifiers of the objects, leading to ambiguity.

    The traditional approach for entity resolution uses features associated with a reference (or a record) to find references that co-refer. In our project we are exploring which other sources and types of information could be used, in addition to features, to better disambiguate among references. This information could be present in that data being cleaned itself or can be obtained from external data sources, including ontologies, encyclopedias, and the Web. We are also looking into ways to guide and fine-tune the data cleaning process based on the type of analysis that will be done on the data being cleaned for it to reach higher disambiagution quality as well as efficiency.

    Past Work

    As part of our project, in the past we have pioneered a novel entity resolution methodology which we refer to as Relationship-Based Data Cleaning (RelDC). RelDC relies upon the observation that many real-world datasets are relational in nature and contain not only information about entities but also relationships among them, knowledge of which can be used to disambiguate among representations more effectively. RelDC is a principled, domain-independent framework that exploits the entity-relationship graph of the dataset, and specifically relationships, for high-quality entity resolution that is self-tuning and requires minimal intervention by analysts.

    Keywords

    Entity Resolution, Data Cleaning, Data Cleansing, Information Quality, Data Quality.

    Acknowledgement

    This material is based upon work supported by the National Science Foundation under Grant No. 1118114. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


    © 2013 SHERLOCK @ UCI. All Rights Reserved.
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    Overview

    Welcome to the new home of the AsterixDB Big Data Management System (BDMS). The AsterixDB BDMS is the result of about 3.5 years of R&D involving researchers at UC Irvine, UC Riverside, and UC San Diego. The AsterixDB code base now consists of roughly 250K lines of Java code that has been co-developed at UC Irvine and UC Riverside.

    Initiated in 2009, the NSF-sponsored ASTERIX project has been developing new technologies for ingesting, storing, managing, indexing, querying, and analyzing vast quantities of semi-structured information. The project has been combining ideas from three distinct areas—semi-structured data, parallel databases, and data-intensive computing (a.k.a. today’s Big Data platforms)—in order to create a next-generation, open-source software platform that scales by running on large, shared-nothing commodity computing clusters.

    The ASTERIX effort has been targeting a wide range of semi-structured information, ranging from “data” use cases—where information is well-typed and highly regular—to “content” use cases—where data tends to be irregular, much of each datum may be textual, and the ultimate schema for the various data types involved may be hard to anticipate up front. The ASTERIX project has been addressing technical issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging time-tested parallel database techniques with modern data-intensive computing techniques to support performant yet declarative solutions to the problem of storing and analyzing semi-structured information effectively.

    The first fruits of this labor have been captured in the AsterixDB system that is now being released in preliminary or “Beta” release form. We are hoping that the arrival of AsterixDB will mark the beginning of the “BDMS era”, and we hope that both the Big Data community and the database community will find the AsterixDB system to be interesting and useful for a much broader class of problems than can be addressed with any one of today’s current Big Data platforms and related technologies (e.g., Hadoop, Pig, Hive, HBase, MongoDB, and so on). One of our project mottos has been “one size fits a bunch”—at least that has been our aim.

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    http://www.ics.uci.edu/~spongpai/ Siripen Pongpaichet
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    Siripen Pongpaichet

    Ph.D. Candidate, Researcher

    About

    Siripen Pongpaichet is a PhD candidate in Computer Science at University of California, Irvine. She is working with Professor Ramesh Jain, in Social Life Networks (SLN) lab. The fundamental goal of SLNs is to identify people’s needs in a given situation, locate appropriate resources to meet those needs, and connect them efficiently, effectively and promptly. She has been developing a macro situation-modeling platform for SLNs called EventShop. This framework allows for ingestion of different data streams and converts them to a unifying space, time, and thematic (STT) representation. Her current research interests are in event information systems, database management systems, and situation recognition. Her PhD research topic is focused on multi-resolution spatial-temporal analysis across multimedia streams.

    She obtained her B.Sc in Computer Science with first class honors from Mahidol University, Thailand in 2008. Her research project was E-Advisory System - an expert system providing academic advices liked a real human counselor. After graduating, she was a Lecturer and Software Engineering at the same university. She then came to United States in 2009, and obtained her M.S. in Computer Science from University of California, Irvine in 2011. She worked with Professor Michael Carey on her master thesis which is Monitoring Console for Hyracks Platform - a real time web based console to monitor a data parallel platform running data-intensive jobs on a shared-noting cluster.

    • spongpai@uci.edu
    • Curriculum Vitae
    • Continue to LinkedIn
    • SLN Lab Website

    EventShop

    Dabuntu EventShop Open Source

    • View on GitHub

    Visual Analytics on 100M Yahoo Flickr Photos: Live Demo!

    Publications

    Journal Articles

    • Ramesh Jain, Laleh Jalali, Siripen Pongpaichet, Amarnath Gupta: Building Social Life Networks. IEEE Data Eng. Bull. 36(3): 91-98 (2013)

    Conference and Workshop Papers

    • Minh-Son Dao, Koji Zettsu, Siripen Pongpaichet, Laleh Jalali, Ramesh Jain: Exploring spatio-temporal-theme correlation between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors. Big Data 2015: 2690-2699
    • Mengfan Tang, Pranav Agrawal, Siripen Pongpaichet, Ramesh Jain: Geospatial interpolation analytics for data streams in eventshop. ICME 2015: 1-6
    • Minh-Son Dao, Siripen Pongpaichet, Laleh Jalali, Kyoung-Sook Kim, Ramesh Jain, Koiji Zettsu: A Real-time Complex Event Discovery Platform for Cyber-Physical-Social Systems. ICMR 2014: 201
    • Siripen Pongpaichet, Vivek K. Singh, Mingyan Gao, Ramesh Jain: EventShop: recognizing situations in web data streams. WWW (Companion Volume) 2013: 1359-1368

    • Continue to Google Scholar

    Presentations

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    Others

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    http://www.ics.uci.edu/~wping/ Wei Ping's Home Page
    Wei Ping

    About me

    I am a PhD student (5th year) in department of computer science at UC Irvine, working with Prof. Alexander Ihler. Before that, I received my B.S. in computer science from Harbin Institute of Technology, and M.E. in software engineering from Tsinghua University.

    Email: weiping dot thu at gmail dot com


    Research interests      Publications      Professional experience      Professional activities     

    Research interests

    I am broadly interested in machine learning and data mining. Currently, I am focusing on statistical learning and inference in probabilistic graphical models.

    Publications

    Decomposition Bounds for Marginal MAP
    Wei Ping, Qiang Liu, Alexander Ihler
    Neural Information Processing Systems (NIPS) 2015.
    [ Paper full-length ]
    Marginal Structured SVM with Hidden Variables
    Wei Ping, Qiang Liu, Alexander Ihler
    International Conference on Machine Learning (ICML) 2014.
    [ Paper full-length ] [ Slides ]
    Multi-instance Metric Learning
    Ye Xu, Wei Ping, Andrew T Campbell
    IEEE International Conference on Data Mining (ICDM) 2011.
    [ Paper ]
    FAMER: Making Multi-Instance Learning Better and Faster
    Wei Ping, Ye Xu, Jianyong Wang, Xian-Sheng Hua
    SIAM International Conference on Data Mining (SDM) 2011.
    [ Paper ] [ Slides ]
    TAKES: a fast method to select features in the kernel space
    Ye Xu, Furao Shen, Wei Ping, Jinxi Zhao
    ACM international conference on Information and knowledge management(CIKM) 2011.
    [ Paper ]
    Million-scale Near-duplicate Video Retrieval System
    Yang Cai, Linjun Yang, Wei Ping, Fei Wang, Tao Mei, Xian-Sheng Hua, Shipeng Li
    ACM international conference on Multimedia(MM) 2011.
    [ Paper ]
    Non-I.I.D. Multi-Instance Dimensionality Reduction by Learning a Maximum Bag Margin Subspace
    Wei Ping, Ye Xu, Kexin Ren, Chi-Hung Chi, Furao Shen
    AAAI Conference on Artificial Intelligence (AAAI), 2010.
    [ Paper ] [ Slides ]

    Professional experience

    • Microsoft Research Asia, Research Intern
      • Media Computing Group, 02/2010 ~ 02/2011
      • Media Communication Group, 08/2009 ~ 02/2010

    Professional activities

    • Journal Refereeing
      • IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
    • Program committee member
      • AAAI 2015
    Copyright 2015 Wei Ping | Last updated: 10/31/2015
    http://www.ics.uci.edu/~sajjadt/ Sajjad Taheri, PhD Student at UCI

    Sajjad Taheri

    Phd Student
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    Sajjad Taheri

    I am a PhD student in Computer Science at UC Irvine, working with Professor Alex Nicolau.

    Get My CV!

    Research

    .

    WebRTC Benchmark

    Computer Vision for the Web platform

    Biometric security using RGB-D signals

    Contact

    Drop me a message.

    Send Message

    © 2015 Sajjad Taheri. All rights reserved.

    http://www.ics.uci.edu/~mirzaeib/ Index of /~mirzaeib

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    http://www.ics.uci.edu/~lmartie/

    Lee Martie

    PhD Candidate in Software Engineering - CV
    Advisor: André van der Hoek - Lab: SDCL - Office: ICS 414 (Room A)
    Publications
    c: L. Martie, T. D. LaToza, and A. van der Hoek, “CodeExchange: Supporting Reformulation of Internet-Scale Code Queries in Context", Proceedings of the 30th International Conference on Automated Software Engineering (ASE), 2015, pages 24-35.
    c: L. Martie and A. van der Hoek, “Sameness: An Experiment in Code Search” , Proceedings of the 12th Working Conference on Mining Software Repositories (MSR), 2015, pages 76-87.
    w: L. Martie and A. van der Hoek, “Context in Code Search”, Proceedings of the 1st International Workshop on Context in Software Development Workshop (CSD), 2014 (4 pages).
    w: L. Martie and A. van der Hoek, “Toward Social-Technical Code Search”, in Proceedings of the 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), 2013, pages 101-104.
    c: S. Rugaber, A. Goel, and L. Martie, “GAIA: A CAD Environment for Model-Based Adaptation of Game-Playing Software Agents” ,11th Annual Conference on Systems Engineering Research (CSER), 2013, pages 29–38.
    c: L. Martie, V. Palepu Krishna, H. Sajnani, C. Lopes , “Trendy bugs: Topic trends in the Android bug reports”, 9th IEEE Working Conference on Mining Software Repositories (MSR), 2012, pages 120-123.
    Current Projects
    Past Projects
    Beowulf: A 45 Raspberry Pi loosely coupled cluster to mine code off the Internet. (picture here)
    Graduate Class Project: Space Debugs: Real time debugging game (video here)
    Professional Work:GAIA: Adaptive Game Playing Agents (video here) at the Design Intelligence Lab at Georgia Tech
    Undergraduate Work: Curve Fitting educational applet for the Systems Realization Laboratory
    REU Work: Papers with Professor Johan G. F. Belinfante and his theorem prover GOEDEL: clock.nb.pdf unary.nb.pdf unop-pow.nb.pdf uocpsdup.nb.pdf iterclok.nb.pdf fu-im.nb.pdf
    Educational Code
    TA Work: Processing code tutorials
    Tutorial Videos
    Eclipse Modeling Framework (EMF)
    Graphical Modeling Framework (GMF)
    http://www.ics.uci.edu/~yubok/ Yubo Kou

    Yubo Kou

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    I'm a third year Ph.D. student in Department of Informatics, UCIrvine. I'm interested in regulation and governance in online communities, particularly in online games. I'm currently working with Prof. Bonnie Nardi. I use ethnographic methods to study people's interaction with technologies. My email address is yubok(at)uci.edu.

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    http://www.ics.uci.edu/prospective/en/opportunities/applied-learning/  Applied Learning « Opportunities « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    A Bren School education provides you with more than just course work

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    Applied Learning

    Standing room only at project presentations

    Our majors are structured so you actually apply what you learn as part of the educational experience. Every major includes a number of capstone project courses throughout the years.

    Project courses have several lecture courses as prerequisites, so you enter knowing the basics of the subject matter and can concentrate on advanced issues under the guidance of an experienced instructor.

    By virtue of often addressing real world problems, Bren School capstone project courses touch upon a wide spectrum of possible domains. Past students have had the opportunity to learn to specify, design, and develop computer-based systems comprised of software and/or hardware in such domains as:

    • aerospace
    • artificial intelligence
    • biomedical
    • business finance
    • education
    • entertainment
    • environment
    • games
    • health care
    • internet
    • law
    • management
    • manufacturing
    • non-profit
    • pharmacology

    Many of our extensive network of affiliated companies participate as host sites for student teams in project courses. These school-industry partnerships often provide rich internship and career opportunities for graduates, who frequently report that capstone courses were the highlights of their time at UCI and made the critical difference in preparing them with a skill set that got them their first jobs.

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    http://www.ics.uci.edu/prospective/en/careers/breadth/  Breadth « Careers « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    What alumni accomplish in life with a Bren School degree continues to surprise even us

    • Breadth
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    • Graduate School
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    Breadth

    The Bren School offers over one hundred courses covering a broad range of topics. You are sure to find multiple courses in your area(s) of interest, courses that not only introduce you to fundamentals but also explore topics in depth. This means undergraduates have the opportunity to study advanced computer science topics usually only offered in graduate programs elsewhere.

    Why are breadth and depth of the curriculum important for you to consider? Some students start college with only a vague idea of what they would like to study – they should be able to sample the offerings and find their niche. Others enter with a stronger sense of interest in a major – they should be able to delve in and continue to be challenged. Yet others find that their interests change or become more defined as they move through their major-required courses – they should not be prohibited from following their dreams.

    Our broad portfolio of courses ensures that regardless of interest, aptitude and talent, or desired career path, you will find what you need.

    Here is a sample of areas in which we have significant threads of undergraduate courses:

    • Networking
    • Distributed Computing
    • Advanced Computer Networks
    • Computer and Network Security
    • Software Architectures, Interoperability, and Distributed Systems
    • Social Impacts of Computing
    • Human Computer Interaction
    • Social Analysis of Computerization
    • Organizational Information Systems
    • Technology and Literacy
    • Software Engineering
    • Software Design I and II
    • Requirements Analysis and Engineering
    • Project in Software System Design
    • Software Tools and Methods
    • Theory of Computer Science
    • Fundamental Data Structures
    • Design and Analysis of Algorithms
    • Formal Languages and Automatas
    • Graph Algorithms
    • Computer Graphics
    • Digital Image Processing
    • Project in Advanced 3D Computer Graphics
    • Computational Geometry
    • Information Visualization
    • Computer Game Science
    • Computer Games and Society
    • Game Technologies and Interactive Media
    • Mobile and Ubiquitous Games
    • Multiplayer Game Systems
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    http://www.ics.uci.edu/prospective/en/opportunities/entrepreneurship/  Entrepreneurship « Opportunities « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    Entrepreneurship

    Adball.com – hITEC competition winners

    Bren School students are not just students. They want to shape their careers and society. Their entrepreneurial spirit shows up in many ways. Every year students form teams to compete in hITEC, our product development competition that involves industrial mentors and over $9,000 in prizes. The past two years' winning product ideas have gone on to form actual startups:

    • Clarity Labs leverages TV audiences' consumer behavior through a tag placement application that allows viewers to click on their favorite products in any given TV show for convenient online shopping.
    • Olepta revolutionizes relationship management for medical practitioners by modernizing the start-to-finish practice workflow. Its software opens new channels between the medical provider, the patient, insurance provider, family members, and all other parties involved in a patient's medical history.

    The top three product development teams in hITEC automatically qualify for The Paul Merage School of Business's annual Stradling Yocca Carlson & Rauth Business Plan Competition with cash awards totaling $30,000.

    As exemplified by Clarity Labs and Olepta, every year some of our of students launch new start-up companies to apply their innovative spirit to networking, gaming, medical information systems, internet technology, or some other area. A student who is interested in participating in one of these product development competitions may enroll in the Entrepreneurship course taught by Bren Professor Ramesh Jain, who has founded three highly successful companies himself.

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    http://www.ics.uci.edu/prospective/en/degrees/computer-game-science/  Computer Game Science « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    Our broad selection of majors lets you be as specialized or general as you like

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    Computer Game Science

    The Computer Game Science (CGS) major combines a solid foundation in computer science with a focus on designing, building, and understanding computer games and other forms of interactive media. The fundamentals of information and computer science — along with coursework in mathematics, statistics, physics, and film and media studies — provide students with the concepts and tools to study a wide scope of computer game technologies.

    CGS emphasizes design, teamwork, and the understanding of computer games and related technologies and media in a social and cultural context. The term “computer game” includes games that run on cell phones, mobile devices, PCs, consoles, Macs, web pages and even inside automobiles. CGS majors design and create games for entertainment, and also for education, training and social change.

    The study of computer games is an emerging field driven by advancing computer hardware and software technology, the widespread popularity of video games as an entertainment medium, and by the interest of artists, economists, educators, scientists and many others to use game technologies for communication, visualization, computation and learning.

    IS COMPUTER GAME SCIENCE FOR ME?

    CGS is ideal for anyone interested in learning the technical components of creating games — computer programming, graphics, network design, database management, artificial intelligence and much more — and working in teams to design and implement exciting new games. If you are primarily interested in the art or management sides of creating games, the CGS major may not be the best fit for you.

    Students who major in Computer Game Science will:

    • acquire a solid foundation in computer science and software development;
    • learn how to create interactive and human-centered computer game designs;
    • employ an interdisciplinary approach to computer game design and development, drawing on coursework in modeling and design, graphics, software engineering, hardware architectures, AI, algorithms, distributed systems, human interfaces and aesthetics;
    • be able to analyze and discuss computer game systems as communication, teaching and entertainment media that can be a force for education, social change and activism;
    • graduate with an extensive portfolio of implemented games.

    WHY STUDY AT UC IRVINE?

    Several factors contribute to the strength of UC Irvine’s Computer Game Science program, including:

    • Overall excellence. Ranked 28th nationally by U.S. News and World Report, computer science education at UCI is broad, deep and cutting-edge.
    • Location. Irvine and Orange County are home to a remarkable concentration of game development studios large and small, including industry giant Blizzard Entertainment. We consulted with these companies (many of which employ or were founded by UCI alumni) while planning the CGS major, and they look forward to offering internships and jobs to our students.
    • Collaboration. We partner with nearby Laguna College of Art and Design, which offers a Game Art major. Their student artists work with our CGS students to develop innovative, visually engaging games.

    WHAT COURSES DO I TAKE?

    The CGS major combines the fundamentals of computer science with about a dozen game-focused courses. Current requirements for this major can be found in the General Catalogue.

    WHAT CAN I DO WITH A DEGREE IN CGS?

    A wide variety of careers and graduate programs are open to Computer Game Science graduates. The video game industry is comparable in size to the film and music industries, and job growth projections are excellent for people with strong technical backgrounds. Many other fields, including mobile software development, interactive entertainment, and training and education software, have demand for similar skill sets and knowledge. CGS graduates are well trained in computer science, and can thus pursue graduate programs or any career that involves designing, implementing, evaluating or interacting with computer-based systems.

    Computer Game Science Open House Associate Professor Crista Lopes at the Computer Game Science Open House demonstrates her 3D simulation of a podcar system.

    In the News

    UCI students build games in a week
    The Orange County Register features Game Jam, a popular competition sponsored by the UCI Video Game Development Club. Visit the Bren School YouTube channel to view a wrap-up of the spring 2011 contest.
    UC Irvine’s new computer games major gets its game on
    The OC Weekly publishes a six-page spread about the Bren School’s newest undergraduate major.
    UC Irvine takes video games to the next level
    The Los Angeles Times previews CGS before its launch in Fall 2010.
    Meet the CGS mascot (mentioned in the LA Times article above), a character from the game Colossal Crisis, developed by UCI undergraduates James Dalby, Fritzie Mercado, Edward Fleischman and Quin Kennedy. The city is under attack by Godzilla, and the professor assigns multiple clones of our hero to collect equipment needed to fight back. Catch the demo video on YouTube.
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    http://www.ics.uci.edu/prospective/en/degrees/software-engineering/  Software Engineering (SE) « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    Software Engineering (SE)

    The Software Engineering major prepares students to be productive members of software engineering teams in a variety of application domains, with focus on the domains of major importance within each decade. It combines a solid foundation in computer science with knowledge of how to engineer modern software systems, and how to function within teams.

    Coursework in mathematics and statistics provide students the basis for rigorous thinking; coursework in the foundations of computer science provide students the basis for computational thinking; coursework in topics of software engineering prepares students for the production of software; project courses prepare students for the practice of software development. The major emphasizes the design and implementation of large software systems.

    IS SOFTWARE ENGINEERING FOR ME?

    Students who major in Software Engineering will:

    • Acquire a strong foundation in software engineering as well as a solid basis in computer science
    • Have the ability to become a productive member of software engineering teams in a variety of application domains including, but not restricted to, Web and mobile applications
    • Be inspired by technical knowledge and have an appreciation for life-long learning
    • Be capable of placing software in the social context in which is it developed and create novel applications that have the potential to bring social change

    WHAT COURSES DO I TAKE?

    Coursework involves mathematics and statistics, foundations of computer science, topics of software engineering and project courses. Current requirements for this major can be found in the General Catalogue.

    WHAT CAN I DO WITH A DEGREE IN SE?

    A wide variety of careers and graduate programs are open to Software Engineering graduates. The Web and mobile applications industry is a multi-billion dollar industry, and job growth projections are the strongest for people with strong technical backgrounds. Many other application domains, including interactive entertainment, medical information systems, and training and education software have demand for similar skill sets and knowledge. Graduate school in either computer science or software engineering or a related IT field is also a possible career path.

    Of interest

    Software Engineering Careers Continue to Boom
    According to IEEE publication Today's Engineer, software engineering is a hot industry, "with more demand for talented professionals than ever." In fact, the U.S. Department of Labor’s Bureau of Labor Statistics predicts a 30 percent growth rate for software engineering jobs through 2020 — much higher than the 14 percent average growth rate for all other occupations.
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    • ICS Undeclared Pre-Major
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    Overview

    The field of computer science continues to rapidly change as new technical possibilities and new application areas constantly arise. Your education must keep pace and position you for an exciting and relevant career. A single degree program can no longer do this in the face of how broad and all-encompassing the field has become. The Bren School is known worldwide for its long history of innovative educational programs, and continues this tradition today with six streamlined majors for you to choose from.

    All majors share a strong foundation, each branching from there to provide you with a tailored, modern curriculum that builds upon our ever-expanding portfolio of courses; courses that we carefully and continuously update to stay current.

    Our counselors can assist you in choosing the right major. Because our majors share a core philosophy, should your interests change after arrival you will be able to change majors in your freshman or early in your sophomore year without having to take additional coursework.

    For student perspectives on each of the majors, download the fliers below:


    Business
    Information
    Management

    Computer
    Game
    Science

    Computer
    Science

    Computer
    Science &
    Engineering

    Data
    Science

    Informatics

    Software
    Engineering

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    • Minors

    Minors

    Students interested in supplementing another Bren School degree or any UCI bachelor’s degree, whether it be in the arts or biological sciences or any other discipline, will find these minors complement their undergraduate education.

    • Bioinformatics: The minor provides a focused study of bioinformatics to supplement a student’s major program of study and prepares students for a profession, career, or academic pursuit in which biomedical computing is an integral part but not the primary focus.
    • Digital Information Systems: The minor is designed for students who want to learn about information systems, computation, and digital communication without preparing to be computer programmers.
    • Health Informatics: The minor in Health Informatics prepares students to understand the expanding role of information technology (IT) in health care and to participate in creating IT solutions to health care issues.
    • Informatics: The minor particularly centers on understanding the relationships among computers and people, and how these relationships must be addressed in information and software design.
    • Information and Computer Science: The minor contributes to students' competence in computing technology and programming proficiency, and in addition, exposes them to the fundamentals of computer science.
    • Statistics: The minor is designed to provide students with exposure to both statistical theory and practice.
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    Business Information Management

    As the business environment becomes increasingly global and information-centric, the need has increased for graduates who understand and can use technology that gathers and provides information, who are able to distill and recognize patterns in that information, and who can apply those analyses to achieve business objectives.

    Administered by the Donald Bren School of Information and Computer Sciences, Business Information Management (BIM) is a collaborative, interdisciplinary degree program between the Bren School and The Paul Merage School of Business.

    BIM majors receive a firm grounding in mathematics, statistics, software engineering, databases, economics and business accounting, management science and information technology. Students interested in learning how to apply computational methods and tools for achieving strategic business analysis and decision-making goals are encouraged to explore this degree program.

    IS BUSINESS INFORMATION MANAGEMENT FOR ME?

    Graduates of the program will:

    • learn the fundamentals of information and computer science, including the rudiments of software design and construction with an emphasis on data management;
    • grasp business fundamentals, covering all the functional areas in the business school;
    • understand the background and context in which information and its analysis will be applied.

    WHAT COURSES DO I TAKE?

    The curriculum is presented across three general academic areas:

    • Computing (computer science, informatics and software)
    • Business Foundations (accounting, finance, marketing, strategy and operations)
    • Analytical Methods (mathematics, statistics, economics, management science and decision analysis)

    Current requirements for the BIM major can be found in the General Catalogue .

    WHAT CAN I DO WITH A DEGREE IN BIM?

    This degree program prepares students for a wide variety of careers and life experiences. Business Information Management majors can pursue careers in the for-profit and not-for-profit sectors or can proceed to graduate school in several disciplines, including information systems, computing, economics, business and law.

    Potential careers for BIM majors include:

    • working at a consulting firm, auditing other companies’ technology policies for business efficiency.
    • becoming a business risk analyst, identifying ways to reduce a client’s dependency on seasonal e-commerce traffic.
    • serving as a program manager, leading a team in creating incentive and loyalty programs, so companies can get better business data.
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    Computer Science & Engineering

    Offered jointly with The Henry Samueli School of Engineering, the Computer Science and Engineering (CSE) major provides a unique educational opportunity for students interested in learning about both the hardware and software aspects of computers, and the application of computers to real-world problems. CSE includes methods of organizing and manipulating information (computer science), as well as the design of computers and their components (computer engineering). As such, CSE integrates interesting topics in computing devices, networks, software and systems engineering.

    CSE graduates are well-positioned for career opportunities in both academia and industry. Our alumni are employed at leading companies or continue their studies toward M.S. or Ph.D. degrees at prestigious universities.

    IS COMPUTER SCIENCE AND ENGINEERING FOR ME?

    Embedded devices increasingly drive innovation in consumer goods and products, and often are used in large quantities to form sensor networks that monitor the weather, environment, or physical structures. Students interested in learning how such devices are designed and constructed are encouraged to explore the CSE major.

    Graduates of the program will:

    • demonstrate broad knowledge of computer science and engineering;
    • design, describe and use state-of-the-art hardware/software systems;
    • maintain awareness of contemporary issues in computer science and engineering in a global and societal context, and an understanding of the professional and ethical responsibilities of their profession;
    • demonstrate effective oral and written communication.

    For more information on CSE goals and objectives see http://plaza.eng.uci.edu/degree-program/cse/mission.

    Annual student enrollment and graduation data can be found at: http://www.oir.uci.edu/student-data.html. Please note that annual student enrollment and graduation data for CSE is the sum of the data under both Engineering and Information and Computer Science.

    WHAT COURSES DO I TAKE?

    Current requirements for the CSE major can be found in the General Catalogue.

    WHAT CAN I DO WITH A DEGREE IN CSE?

    Computer Science and Engineering majors are involved in building hardware infrastructure — computers, networks and embedded devices — as well as operating systems, compilers and networking software. The focus is on cooperation between hardware and software to yield the highest performance.

    Graduates of the CSE program at UCI can pursue careers that involve traffic management, flight control, earthquake monitoring, automotive control and building smart homes. Many students also go on to graduate school, continuing their studies, conducting research and earning advanced degrees in computer engineering, computer science, information science, management or law.

    CSE senior design project students CSE students Niraj Desai, Patrick Murtha, Christopher Escobedo and Michael Sevilla with their senior design project, the Automated Labyrinth.

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    Data Science

    As the only undergraduate Data Science degree in the UC system, the major will prepare students for careers in data analysis by combining foundational statistical concepts with computational principles from computer science. UCI’s new Data Science major has a dual emphasis on the principles of both statistics and computer science, with foundational training in statistical and mathematical aspects of data analysis, as well as in the broad principles of computer science (including algorithms, data structures, data management and machine learning). The program teaches students how to utilize their knowledge of statistical and computing principles to analyze and solve real-world data analysis problems. The major is suitable for students interested in either a career in industry or who wish to pursue more specialized graduate study.

    WHAT WILL YOU LEARN?

    As a Data Science major, you have the unique opportunity to take an interdisciplinary course of study that includes classes from all three ICS departments: statistics, computer science and informatics. Classes you will have the opportunity to take as a Data Science student include:

    Programming in C/C++ as a Second Language (ICS)
    An introduction to the lexical, syntactic, semantic and pragmatic characteristics of the C/C++ languages, with an emphasis on object-oriented programming, using standard libraries and programming with manual garbage collection.

    Machine Learning and Data-Mining (Computer Science)
    Learn the principles of machine learning and data mining applied to real-world data sets, with typical applications including spam filtering, object recognition and credit scoring.

    Statistical Methods for Data Analysis (Statistics)
    An exploration of statistical methods for analyzing data from surveys, experiments and cohort studies.

    Information Visualization (Informatics)
    An introduction to interactive visual interfaces for large data sets, including a foundation on the principles of human visual perception and human computer interaction that inform their design.

    POSSIBLE CAREERS

    • With a degree in Data Science from UCI, you might get a job working in the web and technology industries for companies such as Google, Facebook, Twitter, Microsoft or Samsung.
    • You might find a career in finance, working on Wall Street or for a banking or insurance company.
    • You might delve into engineering, working for a company like Boeing.
    • Or you might use your degree to get a job in the medical or public health field.

    DEMAND FOR DATA SCIENCE

    The demand for graduates with skills in both statistics and computer science currently outpaces supply. According to a 2011 McKinsey Global Institute study on big data, demand for individuals with data analysis skills will grow to almost 500,000 individuals by 2018, with a projected shortfall of about 200,000 individuals with deep analytical skills, as well as a shortage of 1.5 million managers and analysts to analyze big data and make decisions.

    UCI DATA SCIENCE INITIATIVE

    UC Irvine’s Data Science Initiative is a broad initiative with a focus on coordinating and linking the activities of researchers and students across campus involved in various aspects of data science. This initiative was founded in 2014 by the Office of the Provost and Executive Vice Chancellor and is supported through the Office of Academic Initiatives. One of the goals of the Data Science Initiative is to support the formation of the new Data Science major. For more information, visit datascience.uci.edu

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    • Application Tips

    Freshmen

    The campus offers a wealth of information for prospective freshmen, as well as details on all aspects of the admissions process. We particularly encourage you to explore the following:

    Admissions: http://www.admissions.uci.edu/

    Online publications: http://www.admissions.uci.edu/publications/online_publications.html

    and do not forget our mascot, Peter the Anteater: http://www.uci.edu/peter/

    Currently, undergraduate enrollment in the Bren School is about 2,000 students, out of a total of almost 23,500 for UC Irvine as a whole.

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    http://www.ics.uci.edu/prospective/en/degrees/computer-science/  Computer Science « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
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    Degrees

    Our broad selection of majors lets you be as specialized or general as you like

    • Overview
    • Business Information Management
    • Computer Game Science
    • Computer Science
    • Computer Science & Engineering
    • Data Science
    • Informatics
    • Software Engineering
    • ICS Undeclared Pre-Major
    • Minors

    Computer Science

    The Computer Science major emphasizes the principles of computing that underlie our modern world and provides a strong foundational education to prepare students for a broad spectrum of possible careers in computing.

    Students receive a balance of knowledge in low-level computer architecture and systems, middle-level infrastructure like programming languages, databases and networks, and high-level topics such as artificial intelligence, computer graphics and network security. Students also learn about the mathematical and algorithmic foundations that form the basis of modern computing. Subsequent courses allow students to concentrate in one or more such directions.

    IS COMPUTER SCIENCE FOR ME?

    The CS major balances theoretical underpinnings with a strong emphasis on designing, programming and implementing large-scale systems. The curriculum covers the infrastructure of computers and networks, as well as the software that runs them. Students receive a broad view of the field through a comprehensive set of core classes and, through upper-division elective courses, are given the opportunity to specialize in topics such as machine learning, data mining, distributed and embedded systems, and security. The major places a heavy emphasis on “learning by doing” through a wide selection of classes in which students design and build in-depth projects over the course of a 10-week quarter. In addition, students receive a solid background in computer algorithms, which form the basis of modern computing. The major provides many practical skills that can be immediately put to use in the workplace while also giving students a conceptual foundation that will serve them well for the long term in a rapidly evolving field.

    WHAT COURSES DO I TAKE?

    Current Requirements for the CS major can be found in the General Catalogue.

    WHAT CAN I DO WITH A DEGREE IN CS?

    Graduates of the Computer Science program at UCI will be in a position to pursue a variety of careers in high-demand areas such as embedded systems, cloud infrastructure, web services, computer security, networking and data mining. They can be principal designers or involved in implementation, typically at companies that design, implement and sell or manage products or services in such areas. They may find themselves in charge of large-scale deployments and/or customizations at the organizations that use them.

    The strong scientific preparation also allows students to become involved in such areas as high-performance computing, computational biology and neuroscience — whether in industry or graduate school. In fact, many students choose to continue their studies, conducting research and earning graduate degrees in fields such as computer science, software engineering or information science. A background in computer science is also excellent preparation for careers in management, law, finance or consulting.

    Chen Li, associate professor of computer science, and his students created a Web search tool to reunite people after the quake that devastated Haiti. Chen Li, associate professor of computer science, and his students created a Web search tool to reunite people after the quake that devastated Haiti.

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    • Degrees
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    Contact

    Feel free to contact us with any questions you may have

    • Student Affairs

    Student Affairs

    Bren School academic advisors assist all Bren School majors, from start to finish of their academic career. School-based academic advising begins with summer orientation, during which counselors help new students map out their academic plan and guide them through on-line registration for fall quarter classes. Through the remainder of a student’s career, Bren School advisors are available to help define and support academic and career goal-setting, identify the most efficient routes for meeting degree requirements, and check for timely progress toward graduation. The counseling staff also sponsor workshops for Bren School students about such topics as career exploration, internships, finding a faculty mentor, and graduate school preparation. The Bren School academic advising staff offers assistance via individual appointments, drop-in counseling, and email .

    ICS 1, Suite 352 (find building # 302 on campus map here: http://www.uci.edu/campusmap/
    (949) 824-5156

    ucounsel@uci.edu

    Hours: Monday through Friday, 9 a.m. – 12:00 p.m. and 1:00 – 4:00 p.m.

    Prospective applicants are welcome to call (949) 824-5156 to schedule an appointment with an academic advisor to learn more about the various degree options and which might best fit their academic and career interests.

    Student Affairs Student Affairs

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    http://www.ics.uci.edu/prospective/en/student-life/diversity/  Diversity « Student Life « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
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    Student Life

    Your life!

    • Diversity
    • Campus Involvement
    • Bren School Organizations

    Diversity

    UC Irvine’s student body is very diverse, and offers a wealth of opportunities to learn with and from students whose backgrounds and experiences support, complement, challenge, enrich, or expand your understanding of how to learn, live, and work in a multicultural society.

    Even within the smaller Bren School student community, our students’ backgrounds and interests are richly diverse – as represented by their participation in extracurricular activities that include dance troupes, athletics, theater, community service, ROTC, greek-letter organizations, ethnic/cultural group affiliations, and leadership in student clubs and organizations.

    Girls Inc. visiting UC Irvine Girls Inc. visiting UC Irvine

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    • Degrees
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    Contact

    Feel free to contact us with any questions you may have

    • Student Affairs

    Student Affairs

    Bren School academic advisors assist all Bren School majors, from start to finish of their academic career. School-based academic advising begins with summer orientation, during which counselors help new students map out their academic plan and guide them through on-line registration for fall quarter classes. Through the remainder of a student’s career, Bren School advisors are available to help define and support academic and career goal-setting, identify the most efficient routes for meeting degree requirements, and check for timely progress toward graduation. The counseling staff also sponsor workshops for Bren School students about such topics as career exploration, internships, finding a faculty mentor, and graduate school preparation. The Bren School academic advising staff offers assistance via individual appointments, drop-in counseling, and email .

    ICS 1, Suite 352 (find building # 302 on campus map here: http://www.uci.edu/campusmap/
    (949) 824-5156

    ucounsel@uci.edu

    Hours: Monday through Friday, 9 a.m. – 12:00 p.m. and 1:00 – 4:00 p.m.

    Prospective applicants are welcome to call (949) 824-5156 to schedule an appointment with an academic advisor to learn more about the various degree options and which might best fit their academic and career interests.

    Student Affairs Student Affairs

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    http://www.ics.uci.edu/prospective/en/degrees/ics-undeclared-pre-major/  Undeclared Pre-Major « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
    • Opportunities
    • Careers
    • Student Life
    • Admissions
    • Contact

    Degrees

    Our broad selection of majors lets you be as specialized or general as you like

    • Overview
    • Business Information Management
    • Computer Game Science
    • Computer Science
    • Computer Science & Engineering
    • Data Science
    • Informatics
    • Software Engineering
    • ICS Undeclared Pre-Major
    • Minors

    Undeclared Pre-Major

    New freshmen who want to explore their interests in computer science and information technology before committing to a specific major may apply as ICS Undeclared.

    This option gives freshmen all the benefits of being a Bren School student. An academic counselor will help you structure a first-year course plan that meets your interests and includes a core set of lower-division computer science and math courses common to all the majors. You'll learn what the Bren School majors are like and will transfer into a specific major before the start of your second year.

    Note that students interested in the Computer Science and Engineering degree program are strongly encouraged to start as a CSE major at the time they enter UCI, as the major is highly structured.

    Quick Facts

    Did you know?
    According to CNBC, of the top 10 highest paying bachelor’s degrees in 2010, the Bren School offers three of them? See the complete list here.

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    http://www.ics.uci.edu/prospective/en/opportunities/research/  Research « Opportunities « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
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    Opportunities

    A Bren School education provides you with more than just course work

    • Applied Learning
    • Honors
    • Research
    • Entrepreneurship

    Research

    Every quarter in the Bren School, scores of undergraduate students (including first- and second-year students) work on research projects with faculty. There is no prerequisite. If you and a professor agree to work together, you can, for as long as you want to.

    By working directly with professors, you not only get a taste of what cutting-edge research is like, you also get a preview of what graduate school might be like. Many undergraduates work not only with the faculty member, but also closely with the Ph.D. and M.S. students that the faculty member may employ.

    You become part of a team. As a result, it is not uncommon for undergraduate researchers to be co-authors on scientific publications. Some, indeed, have led these efforts and are first authors.

    As an undergraduate researcher, you will sharpen your skills on complex problems that are still in the process of being defined, and understand what contributes to a research project’s success. Participation in undergraduate research also positions you well to attend graduate or professional (MBA, Law, etc.) schools. Our alumni succeed at Stanford, UCLA, UC Berkeley, UC San Diego, Carnegie Mellon University, Georgia Tech, Columbia, Harvard, MIT, and others.

    UCI’s HIPerWall can display 25600x8000 pixels

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    http://www.ics.uci.edu/prospective/ko/  BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인
    • 학위
    • 기회
    • 진로
    • 대학 생활
    • 입학
    • 문의
    전공 다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다. 비즈니스 정보 관리 | 컴퓨터 게임 과학 | 컴퓨터 과학 | 컴퓨터 과학 및 공학 | Data Science | 정보학 | Software Engineering | ICS 전공미정 교양과정
    부전공 부전공 선택으로 UCI 전공을 심화할 수 있습니다. 정보학 | 정보와 컴퓨터 과학 | 디지털 정보 시스템 | 통계학
    UCI UC 어바인은 풍부하고 다양한 공동체의 고향입니다.
    대학 생활 내가 누리는 생활!
    진로 Bren:ICS의 동문들이 자신들의 영역에서 놀라운 일들을 해내고 있는 모습을 살펴 보십시오.
    기회 Bren:ICS의 교육은 단순한 교육과정 그 이상을 선사합니다. 응용 학습 | 우등생 | 연구 | 창업
    문의 궁금한 점이 있으면 부담없이 질문해 주십시오.
    입학 자세한 지원 요건, 지원 마감일, 좋은 지원서를 만드는 요령
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    http://www.ics.uci.edu/prospective/en/uci/campus/  Campus « UC Irvine « Bren School of Information and Computer Sciences « University of California Irvine ?>
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    UC Irvine

    UC Irvine is home to a rich and diverse community

    • Bren School
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    • Student Services

    Campus

    The University of California, Irvine is at the forefront of research, discovery, and scholarly efforts that improve lives throughout California and benefit communities in every corner of the globe. Our graduates include leaders in the arts, sciences, business, and education – all walks of life. Among them are three Pulitzer Prize winners and the architect of the “HTTP/1.1” Internet protocol used worldwide.

    A top choice for undergraduate education, students who attend UCI discover stellar faculty who are easily accessible for research and mentoring; excellent professional schools in the fields of medicine, law, business, education, and the arts; a beautiful suburban campus; award-winning student housing; exciting campus events throughout the year; and unparalleled academic and leadership preparation to succeed in today’s interdependent world.

    Learn more about UCI: http://www.uci.edu/prospective.php.

    Graduation at UCI Graduation at UCI

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    http://www.ics.uci.edu/prospective/vi/  BREN: Khoa học Thông tin và Điện toán « Đại học California Irvine
    • BẰNG CẤP
    • CƠ HỘI
    • NGHỀ NGHIỆP
    • ĐỜI SỐNG SINH VIÊN
    • TUYỂN SINH
    • LIÊN HỆ
    CHUYÊN NGÀNH Sự đa dạng của các chuyên ngành giúp bạn tìm được cho mình chương trình học chuyên sâu hay đại cương đúng như mong muốn Quản trị Thông tin Kinh doanh | Khoa học Trò chơi Điện toán | Khoa học Điện toán | Khoa học & Kỹ thuật Điện toán | Data Science | Tin học | Software Engineering | Ngành học tiền chính thức ICS
    Chuyên ngành phụ Nâng cao chuyên ngành UCI của bạn bằng một chuyên ngành phụ ưa thích của bạn Tin học | Khoa học Thông tin & Điện toán | Hệ thống Thông tin Kỹ thuật số | Thống kê
    UCI UC Irvine là nơi hội tụ của một cộng đồng phong phú và đa dạng
    ĐỜI SỐNG SINH VIÊN Cuộc sống của chính bạn!
    NGHỀ NGHIỆP Những việc mà các cựu sinh viên hoàn thành với tấm bằng từ trường Bren:ICS vẫn tiếp tục làm ngay cả chúng tôi không hết bất ngờ
    CƠ HỘI Chương trình đào tạo của Bren:ICS đem đến cho bạn không chỉ những kiến thức trên giảng đường Nghiên cứu Ứng dụng | Chương trình danh dự | Nghiên cứu | Quản trị doanh nghiệp
    LIÊN HỆ Nếu bạn có bất cứ thắc mắc nào, đừng ngần ngại hãy liên hệ với chúng tôi
    TUYỂN SINH Thông tin chi tiết về yêu cầu tuyển sinh, hạn chót nộp đơn và chỉ dẫn làm cho đơn xin nhập học phong phú
    Language: English | Chinese (Traditional) | Korean | Vietnamese
    Chia sẻ

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    Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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    http://www.ics.uci.edu/prospective/en/degrees/informatics/  Informatics « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
    • Opportunities
    • Careers
    • Student Life
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    • Contact

    Degrees

    Our broad selection of majors lets you be as specialized or general as you like

    • Overview
    • Business Information Management
    • Computer Game Science
    • Computer Science
    • Computer Science & Engineering
    • Data Science
    • Informatics
    • Software Engineering
    • ICS Undeclared Pre-Major
    • Minors

    Informatics

    Informatics at UC Irvine brings together software engineering, human-computer interaction and the study of information technology in organizations into a single degree program.

    Traditional computer science programs concentrate on analyzing and designing computers and computer systems, but the success of those systems depends not only on their intrinsic features but also on human users and their requirements, characteristics and organizations. Informatics focuses on understanding the effect of information technology on people, studying computer systems in their real-world context, and determining how those systems can work effectively.

    Students with an affinity for design and an interest in learning how to develop effective and usable software systems are encouraged to explore this major.

    IS INFORMATICS FOR ME?

    The Informatics degree program offers a contemporary curriculum with an emphasis on group work (starting with the first course), studio-oriented design courses and a yearlong senior project.

    You may want to consider this major if you:

    • enjoy solving problems using all the tools you have available;
    • can work not only with technical details but also with “big-picture” issues;
    • have strong reading and writing skills and can think freely, creatively and systematically.

    Previous experience in computer programming is not required to start the Informatics major. Programming is just one aspect of Informatics, and the major introduces all the necessary skills at a manageable pace. Students who already have some programming experience will also find new concepts, even in the very first course.

    The first year of the program provides students with a hands-on introduction to the broad field of Informatics, centering on the Informatics Core Course. This yearlong course develops students’ basic understanding of software: how to design and construct programs, and how the programs operate as part of information technology systems.

    The second year builds up a portfolio of foundational concepts and techniques that establish the discipline of Informatics; these contribute to the “toolbox” students will use in subsequent years to solve large-scale information and software design problems. As sophomores, students begin to take more advanced courses that support their specialization in either software engineering, human-computer interaction, or the study of organizations and information technology. These may involve courses in Management, Psychology, Computer Science or Engineering.

    In the third year, all students study the design process, project management and the impacts of technology on the real world. Students continue to take electives in their specialized area of study.

    The fourth year is built around a yearlong capstone project in which groups of students tackle a significant assignment, typically from an outside client.

    WHAT COURSES DO I TAKE?

    Current requirements for this major can be found in the General Catalogue.

    WHAT CAN I DO WITH A DEGREE IN INFORMATICS?

    A degree in Informatics provides excellent preparation for a career at the forefront of the computing industry.

    Our graduates work in many industrial settings — ranging from start-up companies and small software houses to consulting firms and multinational corporations — in various roles, including:

    • Software Engineer
    • Human-Computer Interface Designer
    • Information Architect
    • Game Designer
    • Usability Engineer
    • Mobile Computing Systems Designer

    Many also go on to graduate school to pursue an advanced degree in computer engineering, computer science, information science, management or law.

    Informatics students demonstrate a prototype of their senior project — an interactive comic book app that teaches English to Japanese school-age children. Informatics students demonstrate a prototype of their senior project — an interactive comic book app that teaches English to Japanese school-age children.

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    http://www.ics.uci.edu/prospective/en/opportunities/honors/  Honors « Opportunities « Bren School of Information and Computer Sciences « University of California Irvine ?>
    • Degrees
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    Opportunities

    A Bren School education provides you with more than just course work

    • Applied Learning
    • Honors
    • Research
    • Entrepreneurship

    Honors

    UCI offers a vibrant honors program (CHP, the Campuswide Honors Program), for which Bren School students are eligible. This program offers select events, peer mentorship, a campus “hang out” where you and your fellow honors students convene, work, and socialize, honors housing, and special research opportunities. The Bren School traditionally has a strong representation in the CHP program.

    The Bren School also offers its own research honor sequence. Juniors and seniors have the opportunity to learn about the research process and engage in advanced work with a faculty advisor. Students admitted to the program participate in an honors seminar, conduct independent research under the guidance of a faculty advisor (for a minimum of two quarters), and write a research paper for review by their faculty advisor and the Honors Program Faculty Director.

    Campuswide Honors Program

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    http://www.ics.uci.edu/~jianfenj/ Jianfeng's website
    About Recent News Current Projects Education Experience Past Projects Contact

    Jianfeng Jia (贾剑峰)

    Avatar

    Department of Computer Science
    Donald Bren Hall, Room 2066
    University of California, Irvine
    jianfeng.jia@uci.edu


    About Me

    I am a Ph.D. Candidate at Computer Science of University of California, Irvine.
    I have wide interests in Parallel Computing, Database, Large scale data processing, and Natural Language Processing.

    Currently I am doing research in ISG, my advisor is Professor Chen Li.

    Recent News

    • (07,2015) Summer Intern at CTO office of Sumo Logic
    • (04,2015) This quarter(Spring,2015), I am the TA of CS122B:Projects in Databases and Web Applications and CS222:Principles of Data Management. Yes, two courses!
    • (01,2015) This quarter(Winter,2015), I am the TA of ICS 33:Introduction to Programming
    • (12,2014) Advanced to Candidate Exam passed. Now I'm a Ph.D. Candidate!
    • (06,2014) Jun 2014 - Sept 2014, I was doing my internship at SRCH2
    • (02,2014) My daughter was born! I became a father!!
    • (01,2014) This quarter(Winter,2014), I am the TA of ICS 46:Data Structure Implementation
    • (09,2013) This quarter(Fall 2013), I am the TA of ICS 45C:Programming in C++ as a Second Language
    • (09,2013) Jun 2013 - Sept 2013, I was doing my internship at SRCH2

    Current Projects

    • AsterixDB is now an Apache incubator project!
    • Checkout our Big Graph Analytics System : Pregelix !
    • Checkout our Big Data Management System : Asterix !
    • Checkout our Parallel Computing System : Hyracks !
    • Checkout our gene assembling project: Genomix !

    Education

    • M.S. of Computer Science, Xiamen University, China
    • B.S. of Computer Science, Xiamen University, China

    Industry Experience

    • Researcher Associate at IME Department of Sogou.com

    Past Projects

    • Feedback Data flow System of IME using HBase and Pig at Sogou
    • Large-scale Language Model for Cloud IME at Sogou
    • Automatic New Word Detection at Sogou
    • Dependency Treelet Based Chinese-to-English SMT System at XMU
    • Shift-Reduced Dependency Parser at XMU

    Contact

    Welcome to check my social accounts:

    GitHub Linkedin Facebook Weibo

    Last updated: Sun Sep 6 23:34:32 PDT 2015
    http://www.ics.uci.edu/~eward/ Elizabeth Ben Ward

    Elizabeth Ben Ward

    My Picture
    • Position: Graduate Student
    • Area: Statistics
    • Office: DBH 2246
    • E-mail:eward AT ics.uci.edu

    Stat 67 Files

    Intro To Graphics For Class 10_10_14

    Brady Gun Data For Project 1



    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425
    Last modified: 08 Oct 2014 http://www.ics.uci.edu/~gbortis/ Gerald Bortis
    Gerald Bortis
    • Projects
    • Publications

    Gerald Bortis

    Ph.D. Candidate under André van der Hoek
    Software Design and Collaboration Laboratory
    Department of Informatics
    Donald Bren School of Information & Computer Sciences
    University of California, Irvine

    I also am the VP of Platform at Mirth and was a founding developer on Mirth Connect, a popular open source health care integration engine. You can contact me gbortis@ics.uci.edu.

    Projects

    My research is focused on supporting software developers in managing bug reports. More specifically, I am interested the in the process of bug triaging and how it can be improved, especially within the context of large open source projects.

    I am currently developing PorchLight, a tool for exploring, organizing, and triaging large collections of bug reports. The ultimate goal is to help developers take on the task of sorting through and assigning bug reports by providing insights through patterns and relationships between bugs.

    Publications

    Conference Papers

    • Gerald Bortis and André van der Hoek. 2013. PorchLight: a tag-based approach to bug triaging. In Proceedings of the 2013 International Conference on Software Engineering (ICSE '13). IEEE Press, Piscataway, NJ, USA, 342-351.
    • Gerald Bortis, Experiences with Mirth: an open source health care integration engine, ICSE '08 Proceedings of the 30th international conference on Software engineering, 2008
    • Anita Sarma, Gerald Bortis, Andre van der Hoek, Towards supporting awareness of indirect conflicts across software configuration management workspaces, ASE '07 Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering, 2007

    Workshop Papers

    • Gerald Bortis, André van der Hoek, Teambugs: a collaborative bug tracking tool, CHASE '11 Proceedings of the 4th International Workshop on Cooperative and Human Aspects of Software Engineering, 2011
    • Gerald Bortis, Informal software design knowledge reuse, ICSE '10 Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2, 2010
    • Gerald Bortis, André van der Hoek, Software pre-patterns as architectural knowledge, SHARK '08 Proceedings of the 3rd international workshop on Sharing and reusing architectural knowledge, 2008
    • Gerald Bortis, André van der Hoek, DesignMinders: A Design Knowledge Collaboration Approach, Third International Workshop on Knowledge Collaboration in Software Development, 2009
    • Gerald Bortis, André van der Hoek, DesignMinders: Preserving and Sharing Informal Software Design Knowledge, 2009 ICSR Second Workshop on Knowledge Reuse, 2009

    Adapted from a template by Andreas Viklund.

    http://www.ics.uci.edu/~bdonyana/ Bryan Donyanavard

    Bryan Donyanavard

    • Position: Graduate Student
    • Area: Embedded Systems
    • Advisor: Nikil Dutt
    • Office: 3069 embedded Systems Lab, Bren Hall
    • E-mail: bdonyana@uci.edu

    Projects

    Other Interests



    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425
    Last modified: 19 May 2014 http://www.ics.uci.edu/~jurngyup/ Jurngyu Park

    Jurngyu Park

    About Me

    In his heart a man plans his course, but the LORD determines his steps - Proverbs 16:9

    Hi, my name is JurnGyu Park. I'm a Ph.D student in the ICS department at UCI and a member of the Dutt's Research Group. My advisior is Prof. Nikil D. Dutt. "

    • Position: Graduate Student
    • Area: Embedded Systems
    • Advisor: Nikil D. Dutt
    • Office: DBH 3069
    • E-mail: jurngyup@ics.uci.edu

    Research Interests

    • Energy-efficient DVFS design for mobile graphics GPUs
    • Embedded Virtualization
    • Parallel Programming using OpenCL/CUDA and heterogeneous (GPGPU/CPU) architectures.
    • Low-power high-performance embedded processors, especially for mobile systems.
    • Embedded VLIW GPGPU architectures.
    • Thesis Title : Performance and Power Consumption of Embedded GPGPUs considering CUDA/OpenCL: An Analytical Model for Embedded VLIW GPGPU Architecture

    Industry Interests

    • MDS Technology (Sep. 2002 ~ Feb. 2011)
    • Android Porting and Android Software Architecture (HAL, Libs, NDK, Application Framework)
    • ARM-based Embedded Linux Kernel Porting and Device Driver Development.
    • ARM CPU architectures and startup code analysis.

    Projects

    • Global Research
    • Variability Project - VarEMU

    Publications

    • God will be with me...!!!

      [C1] J. Park, C. Hsieh, N. Dutt, and S. Lim, "Quality-aware Mobile Graphics Workload Characterization for Energy-efficient DVFS Design", proceedings of the 12th International Symposium on Embedded Systems for Real Time Multimedia (ESTIMEDIA 2014), New Delhi, India, October 2014.

    Education

    • PhD, Computer Science, University of California, Irvine, USA, 2012 ~ 2017 (According to my plan)
    • MS, Computer Engineering, Yonsei University, South Korea, 2009.09 ~ 2012.02
    • BS, Mechanical and Automotive Engineering, Kookmin University, South Korea, 1993 ~ 2001

    Other Interests

    • My Church: Disciple Community Church
    Here are some pointers on how to learn HTML.

    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425
    Last modified: 28 Mar 2013 http://www.ics.uci.edu/~khanhtn1/ Khanh Nguyen
    School of ICS

      Khanh Nguyen


    I am a Ph.D. candidate in Computer Science Department at UCI, working under my advisor, Prof. Guoqing (Harry) Xu.
    My research aims to develop techniques and analyses to solve scalability issue in Big Data applications.
    I earned my M.S. and B.S. in Computer Science from UCI in Spring 2015 and Spring 2012, respectively. Prior to this, I earned my A.A. in Mathematics and A.S. in Computer Science from Fullerton College in Spring 2010.

    I mentored several summer interns in our research group:

    • Yoonseung Choi and Soyeong Park (2015, UCI I-SURF Fellows from Kookmin University, South Korea)
    • Ankur Gupta (2014, from University high school)
    • Wendy Wei (2013, from University high school, now at MIT)
    • Allen Min and Jonathon Tsai (2012, from Gretchen A. Whitney high school, both are now at UCI)


    My current CV can be found here


    Publications
    • Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, and Guoqing Xu. "Understanding and Combating Memory Bloat in Managed Data-Intensive Systems", ACM Transactions on Computer Systems (TOCS) To appear
    • Lu Fang, Khanh Nguyen, Guoqing Xu, Brian Demsky, and Shan Lu. "Interruptible Tasks: Treating Memory Pressure As Interrupts for Highly Scalable Data-Parallel Programs", The 25th ACM Symposium on Operating Systems Principles (SOSP), Monterey, CA, October 4-7, 2015. (Acceptance rate: 30/186, 16%) [PDF] [1-column PDF] [Slides]
    • Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Xu. "Facade: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications", The 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Istanbul, Turkey, March 14-18, 2015. (Acceptance rate: 48/287, 17%) [PDF] [Slides]
    • Khanh Nguyen, and Guoqing Xu. "Cachetor: Detecting Cacheable Data to Remove Bloat", Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), Saint Petersburg, Russia, August 18-26, 2013. (Acceptance rate: 51/251, 20%) [PDF] [Slides]
    Research Projects
    • Interruptible Tasks: Treating memory pressure as interrupts
      • Abstract: Real-world data-parallel tasks developed in managed languages such as Java and C# commonly suffer from great memory pressure. This leads to excessive GC effort and out-of-memory errors, significantly hurting system performance and scalability. Interruptible task is a new type of data-parallel task that can be interrupted upon memory pressure—with part or all of their used memory reclaimed—and resumed when the pressure goes away. Experiments on two state-of-the-art platforms Hadoop and Hyracks show the effectiveness of the technique. All 13 reproduced real-world out-of-memory problems reported on Hadoop are solved using our system. A second set of experiments with 5 already well-tuned programs in Hyracks on datasets of different sizes shows that the our versions are 1.5–3× faster and scale to 3–24×+ larger datasets.
      • The paper was accepted in SOSP'15. [PDF] [1-column PDF]

    • Facade: a compiler and runtime support for (almost) object-bounded Big Data applications
      • Abstract: Popular Big Data platforms use managed object-oriented programming language such as Java due to its quick development cycle and rich community resources. However, when object-orientation meets Big Data, the cost of the managed runtime system is significantly magnified and becomes a scalability-prohibiting bottleneck. This project aims to remove this bottleneck in Big Data applications by introducing the novel compiler framework called Facade. Facade can automatically generate highly efficient data manipulation code by transforming the data path of an existing Big Data application. The key to efficiency is that in the generated code, the number of heap objects created for data types in each thread is statically bounded regardless of how much data an application has to process. Using Facade, one can obtain significantly reduced memory management costs and improved scalability.
      • The paper was accepted in ASPLOS'15. [PDF]
      • Using this project, I competed and won the third prize (bronze medal) in the ACM Student Research Competition in PLDI'14.
      • This work was presented at the SoCalPLS Workshop in Spring 2014 in Harvey Mudd College.

    • Cachetor: Detecting cacheable data to remove bloat
      • Abstract: Modern object-oriented software commonly suffers from runtime bloat that significantly affects its performance and scalability. One pattern of bloat is the work repeatedly done to compute the same data values. Cachetor is a novel runtime profiling tool that is effective in exposing caching opportunities and substantial performance gains can be achieved by modifying the program to cache the reported data.
      • The paper was accepted in ESEC/FSE'13. [PDF]
      • This work was presented at the SoCalPLS Workshop in Fall 2012 in UC Riverside.
    Service
    • PLDI 2015 - Artifact Evaluation Committee
    • SOSP 2015 - Student Volunteer
    • Sub-reviewer for ECOOP 2016, PLDI 2015, ECOOP 2015, JTOC, TOSEM
    Others

    Asides from my research and programming, I am very interested in individual taxation. I volunteered in the IRS-sponsored VITA program 2011-2014.

    I was the UCI liaison and project manager for the Orange County United Way's Free Tax Preparation Campaign. I managed the development of the online scheduling and volunteer management platform, working with several UCI undergraduates.

    I am the research group's coordinator, working with prospective summer interns. I'm proud to say that in the school of ICS, our group is one of the few groups that have been accepting high school students and undergraduates for summer internship for a number of years. The experience has been very positive for the interns and the group.

    Acknowledgements

    I greatly appreciate the Donald Bren School of Information and Computer Sciences for awarding me the Dean's Fellowship with four years of support for my study. I also appreciate NSF, ACM, ACM SIGSOFT, ACM SIGPLAN (PAC), and ACM SIGOPS for their travel grants.


    UCI
    http://www.ics.uci.edu/~edwinv/ http://www.ics.uci.edu/~shirano/ Sen H. Hirano http://www.ics.uci.edu/~mengfant/
    Mengfan Tang

    Mengfan Tang

    Office: 3211, Donald Bren Hall

    Department of Computer Science
    School of Information and Computer Sciences
    University of California, Irvine
    email: mengfant at uci dot edu


    News:
    • 5/20: Our ICME paper was nominated as both Best student paper candidate and Best paper candidate.
    • 5/15: Our ACM WebSci paper was accepted as oral.
    • 3/15: Our ICME paper was accepted as oral.
    • 11/14: I will be doing my internship in the Data Science team at Intuit, Mountain View Office, in summer 2015.

    About me:

    I am a Ph.D. candidate at Computer Science Department, working with Prof. Ramesh Jain. Before coming to UCI, I received my B.S. in mathematics from School for the Gifted Young, University of Science and Technology of China in 2010.


    Publications:
    • M. Tang, F. Nie, R Jain, A graph regularized dimension reduction method for out-of-sample data. (submitted)

    • S. Pongpaichet, M. Tang, L. Jalali, R Jain,  Using Photos as Micro-Reports of Events. Submitted to The 2016 ACM International Conference on Multimedia Retrieval (ICMR 2016)

    • M. Tang, P. Agrwal, F. Nie, S. Pongpaichet, R Jain, A Graph Based Multimodal Geospatial Interpolation Framework

    • M. Tang, P. Agrwal, R Jain, Habits vs Environment: What Really Causes Asthma?, ACM Web Science 2015

    • M. Tang, P. Agrwal, S. Pongpaichet, R. Jain, Geospatial interpolation Analytics for Data Streams in EventShop, 2015 IEEE International Conference on Multimedia & Expo. (Best Paper Candidate and Best Student Paper Candidate)

    • L. Jalali, D. Huo, H. Oh, M. Tang, S. Pongpaichet, R. Jain, Personicle: Personal Chronicle of Life Events, Workshop on Personal Data Analytics in the Internet of Things, VLDB 2014.

    • S. Pongpaichet, M. Tang, L. Jalali, R. Jain, Observing Real-World Phenomena through EventWeb over Space, Time and Theme, 2nd International Workshop on Building Web Observatories, ACM Web Science 2014.

    • G. Ye, M. Tang, J. Cai, Q. Nie, X. Xie, Low-Rank Regularization for Learning Gene Expression Programs, PLOS ONE, 2013.


    professional service:
    • Journal Refereeing: IEEE Transactions on Neural Networks and Learning Systems, Neurocomputing, Cognitive Computation

    • Program Committee: ICMR 2016








    http://www.ics.uci.edu/~kroher/ http://www.ics.uci.edu/~dfooshee/ dfooshee

    Under construction

    http://www.ics.uci.edu/~adrianoc/  Christian Medeiros Adriano
     

    Christian M. Adriano

      

    Home
    Blog
     

    PhD Student
    Software Design and Collaboration Lab

    adrianoc [at] uci.edu

    Office: ICS-01 414

    Mailing Address:
    5208 Bren Hall
    Department of Informatics
    University of California, Irvine
    Irvine, CA 92697-3440


    CV
        Linked-in     Google Scholar

    Professional Service
    SEWORLD Moderator April/2014 - Now
    SEWORLD
     

    I am PhD student in Software engineering at the University of California Irvine in the Donald Bren School of Information and Computer Science. My advisor is professor André van der Hoek.

    My research focus is on enabling more people to contribute to software development. I am particularly curious about the sheer amount of knowledge and context people gather to accomplish complex tasks (e.g., refactoring or fixing an existing system). This poses several interesting challenges. For instance, how size of tasks affect performance? How tasks differ in terms of context and information needs? Which tasks can be performed in parallel and which ones demand explicit coordination? Are there compromises impacting quality, time, and cost? I am currently exploring these questions in the realm of software debugging. For that I am developing tools to help people divide work , perform smaller tasks, and make sense of aggregated results.

    As I study and do research, my interests usually span the topics of program comprehension, program analysis, software architecture, and software design. Education is my cross-cutting interest, especially teaching through case studies and applying alternative approaches for evaluation.

    If you are an undergrad or master student interested in developing an individual project, please contact me! I am actively collaborating with a set of brilliant people.

    Talking about that, I've been fortunate to work with a number of great people:

    Current
    Thomas La Toza - Development of CrowdCode
    Danilo Cardoso - Development of a tool to crowdsource debugging questions

    Past
    Rafael Leano - Experimental Design for the Bug Exchange Project
    Ivan L. M. Ricarte - My Master Thesis Advisor
    Matthias R. Brust - Studies on Complex Systems and Digital Artifacts

    http://www.ics.uci.edu/~ohaimson/

    OLIVER L. HAIMSON

    My website can be found HERE

    http://www.ics.uci.edu/~hsajnani/ Hitesh Sajnani's homepage

    Hitesh Sajnani Follow Hitesh
on
LinkedIn

    PhD Candidate
    School of Information and Computer Science
    University of California Irvine
    Irvine, CA 92697

    hsajnani[at]uci[dot]edu

    Recent News

    I am on the Program Committee for ICSME 2016 and Mining Software Repositories Challenge 2016. Both are excellent venues to show case your research. Please consider submitting.
    I successfully defended my Topic Proposal on "Large Scale Code Clone Detection" on September 9, 2016. I am honoured to have Prof. Cristina Lopes (chair), Prof. Andre van der Hoek and Prof. James Jones
    in my dissertation committee.

    Brief Bio

    I am currently a PhD candidate in the School of Information and Computer Science, University of California Irvine. My advisor is Prof. Cristina Lopes, Department of Informatics, University of California Irvine.
    I was awarded Master of Science in Information and Computer Science at University of California, Irvine in 2012. My disseration topic was Enhancing Component Identification Using Machine Learning.
    I was fortunate to have Prof. Cristina Lopes, Prof. Andre van der Hoek, Prof. James Jones, Prof. Nenad Medvidovic, and Prof. Alexander Ihler in my dissertation committee.

    Before joining University of California Irvine, I worked at Tata Research Development and Design Centre, India. I have had the privilege of doing internships from industrial research labs to product groups to startups.
    I spent my summer'11 at SRCH2, a startup specialized in improving user experiences in the domain of search; summer'12 in a product group (Interactive Entertainment Business) at Microsoft Silicon Valley;
    summer'13 in Human Interactions in Programming and Empirical Software Engineering groups at Microsoft Research in Redmond.

    Research Interests

    My primary research interest has been in the area of Software Engineering with an emphasis on Software Evolution. I am interested in developing theoretical and practical techniques and tools for helping people to
    understand, and modify software systems. My research activities to achieve this goal so far span the spectrum from quantitative measures for software properties, to analysis of software changes, to code modularization, to
    architecture recovery.

    In order to ultimately overcome the essential difficulties, it has been recognized that both the processes and products of software development should be formalized and automated. I believe that AI/ML techniques can
    play an important role in this effort since the field of software engineering turns out to be one such fertile domain where many software development and maintenance tasks could be formulated as learning problems
    and ML techniques could be used to obtain solutions.

    Publications

    1. SourcererCC: Scaling Code Clone Detection to Big-Code
    Hitesh Sajnani, Vaibhav Saini, Jeffrey Svajlenko, Chanchal Roy, and Cristina Lopes. Submitted to ICSE 2016

    2. Can the Use of Types and Query Expansion Help Improve Large-Scale Code Search?
    Otavio Lemos, Adriano de Paula, Hitesh Sajnani and Cristina Lopes. In the proceedings of Source Code Analysis and Manipulation, Sept. 2015

    3. A Parallel and Efficient Approach to Large Scale Code Clone Detection
    Hitesh Sajnani, Vaibhav Saini, and Cristina Lopes. In proceedings of Journal of Software: Evolution and Process

    4. A Comparative Study of Bug Patterns in Java Cloned and Non-cloned Code
    Hitesh Sajnani, Vaibhav Saini, and Cristina Lopes. In the proceedings of Source Code Analysis and Manipulation, Sept. 2014

    5. Is Popularity a Measure of Quality? An Analysis of Maven Components
    Hitesh Sajnani, Vaibhav Saini, Joel Ossher, and Cristina Lopes. In the proceedings of International Conference on Software Maintenance and Evolution, Victoria, Oct 2014

    6. Active Files as a Measure of Software Maintainability
    Lukas Schulte, Hitesh Sajnani, and Jacek Czerwonka. In the proceedings of International Conference on Software Engineering (SEIP track), Hyderabad, June 2014

    7. A Dataset for Maven Artifacts and Bug Patterns Found in Them
    Vaibhav Saini, Hitesh Sajnani, Joel Ossher, and Cristina Lopes. In the proceedings of conference on Mining Software Repositories, Hyderabad, May 2014

    8. Probabilistic Component Identification
    Hitesh Sajnani and Cristina Lopes. In the proceedings of India Software Engineering Conference, Chennai, India, Feb 2014

    9. A Parallel and Efficient Approach to Large Scale Code Clone Detection
    Hitesh Sajnani and Cristina Lopes. International Workshop on Software Clones IWSC-2013, Co-located with ICSE 2013 in San Francisco, May 2013

    10. ASTRA: Bottom-up Construction of Structured Artifact Repositories
    Joel Ossher, Hitesh Sajnani, and Cristina Lopes. In the proceedings of Working Conference on Reverse Engineering, WCRE-2012, Kingston, ON, October 2012

    11. Automatic Software Architecture Recovery: A Machine Learning Approach
    Hitesh Sajnani. In the Proceedings of International Conference on Program Comprehension, Passau, Germany, June 2012

    12. Parallel Code Clone Detection Using MapReduce
    Hitesh Sajnani, Joel Ossher, and Cristina Lopes. In the Proceedings of International Conference on Program Comprehension, Passau, Germany, June 2012

    13. Trendy Bugs: Topic Trends in the Android Bug Reports - PDF
    Lee Martie, Vijay Krishna Palepu, Hitesh Sajnani, and Cristina Lopes. In the Proceedings of Mining Software Repositories, Zurich, Switzerland, June 2012

    14. Multi-Label Classification of Short Text: A Casestudy on Wikipedia Barnstars - PDF
    Hitesh Sajnani, Sarah Javanmardi, David McDonald, and Cristina Lopes. In the Proceedings of Workshop on Analyzing Microtext, AAAI-2011, San Francisco, CA, August 2011

    15. Application Architecture Discovery: Towards Domain-driven, Easily Extensible Code Structure - (Best paper award in the industrial track, Most outstanding paper award at TCS Innovation Summit) - PDF
    Hitesh Sajnani, Ravindra Naik, and Cristina Lopes. In the Proceedings of 18th Working Conference on Reverse Engineering, Limerick, Ireland, October 2011

    16. Easing Software Evolution: A Change-data and Domain-driven Approach - PDF
    Hitesh Sajnani, Ravindra Naik, and Cristina Lopes. India Software Engineering Conference, Kanpur, India, February 2012

    17. Clone Detection in Open Source Java Projects: The Good, The Bad, and The Ugly - PDF
    Joel Ossher, Hitesh Sajnani, and Cristina Lopes. In the Proceedings of International Conference on Software Maintenance, Williamsburg, VA, September 2011

    18. Using Change History of Software To Improve Software Evolvability
    Ravindra Naik and Hitesh Sajnani. In the Proceedings of India Software Engineering Conferecene, Mysore, India, February 2010

    Projects/Tools

    1. Using Big Sky for Empirical Software Engineering Studies. My work at MSR in Summer 2014 (tool screen-shots)
    2. Classifying Yelp reviews into relevant categories. Our entry in Yelp Dataset Challenge (project homepage)
    3. SourcererCC: Scaling Code Clone Detection to Big-Code (project homepage)

    Services

    Co-chair, Local arrangements, International Conference on Program Comprehension, ICPC'14 Co-located with ICSE'14, Hyderabad, India
    Member of Artifact Evaluation Committee, OOPSLA/SPLASH 2013
    Student Volunteer, Program Committee meeting held for OOPSLA/SPLASH 2013 in Irvine, CA, USA
    Student Volunteer, OOPSLA/SPLASH 2011, Portland, Orgeon, USA
    Reviewer for ICSE 2012, Journal for Software: Evolution and Processes

    Teaching

    I've had the opportunity to assist in teaching of the following courses at University of California, Irvine
    Informatics 133: User Interaction Software, Fall 2010
    Informatics 43: Introduction to Software Engineering, Winter 2011
    Informatics 191A: Senior Design Project, Spring 2011

    Collaborators

    University of California, Irvine: Prof. Cristina Lopes, Prof. Chen Li, Joel Ossher, Vaibhav Saini, Sarah Javanmardi, Vijay Krishna Palepu, Lee Martie
    Microsoft Research, Redmond: Dr. Rob DeLine, Dr. Michael Barnett, Jacek Czerwonka
    University of Washington, Seattle: Prof. David McDonald
    University of Saskatchewan, Canada: Prof. Chanchal Roy, Jeffrey Svajlenko
    Tata Research Design and Development Center, Pune, India: Ravindra Naik http://www.ics.uci.edu/~anamakis/ Majid Namaki Shoushtari, PhD Candidate, UC Irvine

    Majid Namaki Shoushtari
    PhD Candidate


    Department of Computer Science
    Donald Bren School of Information and Computer Sciences
    University of California, Irvine


    Current inspirational quote:
    "Kids, you tried your best and you failed miserably. The lesson is, never try."
    - Homer Simpson

     

    I am a 3rd year PhD candidate in Computer Science at the University of California, Irvine, working with Professor Nikil Dutt in DRG Lab on the Variability Expedition. I received my B.Sc. (Hons) and M.Sc. in Computer Engineering from School of Electrical and Computer Engineering at University of Tehran in 2009 and 2012 respectively, where I worked with Professor Zain Navabi in CAD Research Group. My current research is in the area of "Embedded Systems" with a specific focus on "Dynamic Variability and Reliability Management". Over the last years, I have researched in the areas of "Power Management in VLSI System" and "Design for Testability". My CV can be found here.

     

     

     

     

     

     


    Contact Info:
    3069 Embedded Systems Lab, Bren Hall
    University of California, Irvine
    Irvine, CA 92697-3435
    E-mail: anamakis AT uci DOT edu


     

    Last updated on March 2014

    http://www.ics.uci.edu/~arajpuro/ Anmol Rajpurohit

    Anmol Rajpurohit

    University of California, Irvine

    • About Me
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    • Publications
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    • Request CV
    Anmol Rajpurohit

    Anmol Rajpurohit

    • Graduate Student, Data Science
    • University of California
    • Irvine

    Bio

    Anmol Rajpurohit is a graduate student (MS, Computer Science) at UC, Irvine. His areas of interest are data science, machine learning and information retrieval. His novel analytics solution for online education was the runner-up at UCLA Developer's Contest 2014. His research work on "Big Data for Business Managers: Bridging the gap between potential and value" was selected for IEEE BigData 2013 conference.

    His strong technical background and zeal to research novel solutions has led to him to a number of international research projects and internships. In collaboration with UCLA, he developed components for the popular future internet architecture known as Named-Data Networking(NDN).

    Besides research and development, he loves blogging on data science news and trends at kdnuggets.com. Anmol completed his Bachelor of Technology in Computer Science & Engineering from LNM Institute for Information Technology (LNMIIT), Jaipur, India in 2013.

    Tweets by @hey_anmol

    Work Experience

    • Sept'14 June'14

      Data Science Intern

      KDnuggets.com, USA

    • May'14 Jun'13

      Visiting Researcher and Developer

      University Of California, Los Angeles, USA

    • Sep'13 May'13

      Research Intern

      Information Cryptology & Construction Lab(ICCL), Taiwan

    • Dec'11 Aug'11

      Teaching Assistant

      LNM Institute of Information Technology, India

    • Jul'11 May'11

      Developer Intern

      Communication Empowerment Lab, IIT Kharagpur, India

    • Feb'12 Apr'11

      Head Co-ordinator, Counselling Cell

      LNM Institute of Information Technology, India

    Education

    • B.Tech. 2013

      B.Tech. Computer Science and Engineering

      LNM Institute Of Information Technology, India

    • M.S 2016

      M.S. Computer Science

      University of California, Irvine

    Awards and Scholarship

    • 2014
      Runner-up at UCLA Developer's Contest 2014
      UCLA Developer's Contest Logo
      Developed a mobile-first application that ranks online courses provided by various Massive Open Online Course(MOOC) providers. The app provide overall rating alongwith rating for specific attributes such as usefulness, content, etc. Reviews were limited to 140 characters in order to keep them short and concise. Plugin for course providers would stick by the end the of video so that user can rate/review while or after watching course video.
    • 2014
      Finalist at Google Code for India Global Hackathon 2014
      Code for India Logo
      Created a web application to predict the quantity of food required in each operational region based on the historical data for past one year. Applied following three predictive models: Moving Averages, Logistic Regression, ARIMA (autoregressive integrated moving average) and found logistic regressing to be working best on data provided. The solution was developed for Akshaya Patra, a non-profit organization in India that runs school lunch programme across India serving more than 1.2 million meals daily through their network in 9 states in India. Code
    • 2013
      Yahoo Hack USA Invitee
      Yahoo Hack Logo
      The two-day hackathon celebrated collaboration, learning and innovation using the world’s top technologies from both YDN and tech partners. Developers were invited across USA to come together and build hacks, learn from experts and play with tech and interactive games.
    • 2013
      Google Student Ambassador
      Google Student Ambassador
      Liaison between Google and my university, The LNM Institute of Information Technology, Jaipur Planned and hosted events, introducing students to new Google products and features.
      Worked closely with local Google teams. Attended Google events as a Google representative.
      Helped Google to better understand LNMIIT culture.
    • 2012
      Microsoft Student Partner
      Microsoft Student Partner
      Enhanced the employability of LNMIIT students by offering training in skills not usually taught in academia. Led the efforts of a team of volunteers to organize knowledge sharing events for academic community, such as arranging courses, giving presentations and initiating projects.
    • 2012
      Firefox Student Ambassador
      Firefox Student Ambassador
      Being passionate about Mozilla, lead campaigns and projects at LNMIIT to encourage others to contribute to Mozilla (and utilize Mozilla products). Played a significant role in helping to improve the global experience of a large community on the Web.
    • 2011
      Regional Finalist in ACM International Collegiate Programming Contest
      ACM ICPC
      Secured Rank 36 in ACM International Collegiate Programming Contest 2011 Asia Amritapuri Region.

    Research Summary

    My research and development efforts are primarily inspired towards developing the next-generation of internet and internet based applications. Within that universe, my focus is particularly on three aspects of future internet – Data Science (understanding the information flow over internet), Network Architecture (designing protocols for future internet), and Video Coding (improving the performance of the most popular data type used over internet).


    Data Science
    My work on Big Data for Business Managers was published in IEEE BigData 2013. Besides research work, I blog on Big Data trends at KDnuggets.com.
    Paper(abstract & full-copy): http://bit.ly/1fiohcT
    Blog/articles: http://bit.ly/NcDilS
    In case you do not have IEEE subscription and need my paper, email me with the subject "Request for IEEE paper: Big Data - Potential to Value".


    Network Architecture
    Developed modules to facilitate the use and assessment of NDN (Named Data Networking) framework for real-life innovative applications such as server-less multi-user chat and environmental sensing.
    Technical Report (which I contributed to): http://named-data.net/wp-content/uploads/2014/09/CCLTechReport.pdf
    UCLA page: http://bit.ly/1finWHf


    Video Coding
    My research on video coding is currently on hold due to other time commitments. I am working on high performance low complexity alternatives to H.264/AVC, which would enable transmission of higher resolution videos to mobile devices(having low computational power) while also creating opportunities to boost security and protect IP rights.
    Lab link: http://bit.ly/1bNvmDi

    Interests

    • Data Science
    • Machine Learning
    • Information Retrieval

    Academic Research Projects

    • Relation Manager

      October 2014 – November 2014

      The realtion manager administers the database tables. It handles the creation/deletion of tables and other basic operations performed on top of a table. Relation manager (RM) also creates and initializes catalog to store all information about database.

      Key concerns with the existing protocols were identified and listed as opportunities for future research.

      Simulation tool: NS2

    • Paged File System and Record-Based File Manager

      September 2014 – October 2014

      The page file manager facilities for higher-level client components to perform file I/O in terms of pages. The record-based file manager built on top of the basic paged file system handles record-based operations such as inserting, updating, deleting, and reading records.

      Key concerns with the existing protocols were identified and listed as opportunities for future research.

      Simulation tool: NS2

    • A Survey on Geography Based Routing in Mobile Ad Hoc Networks

      September 2011 – December 2011

      Researched the current popular approaches to enhance MANet (Mobile Ad hoc NETworks) performance through location services. A comprehensive qualitative comparison was performed and documented in the research paper.

      Key concerns with the existing protocols were identified and listed as opportunities for future research.

      Simulation tool: NS2

    • URL Ranking Algorithm for Web Crawler Optimization

      October 2011 – November 2011

      Designed an efficient algorithm to assist a web crawler through prioritization and optimization of the input URL set. The algorithm was tested against URL Reputation Data Set (a set of 16,000 URLs with over 3 million attributes). The design included data selection, preprocessing, entropy calculations and finally, information gain comparisons to deliver the list of selected URLs, sorted in order of importance.

      Development platform: C/C++, Matlab

    • Smart Document Management System

      March 2011 – May 2011

      Developed a document management system that personalizes the document search results to the user’s activities, making the search results much more relevant. The software reads the PDF files, extracts keywords, prepares an index, records user activity and delivers search results through a smart integration of all the information.

      Development platform: Java (Using DynamicPDF class library)

    • Online Goods Tracking for Shipping Corporation

      February 2011 – March 2011

      Built the E-R model, data schema and database application for tracking goods at shipping ports and during transportation. The application enabled real-time monitoring and management of goods across various stages of transportation – right from pick-up to the ultimate delivery. Different user roles were created with varying access to data and management tools.

      Development platform: MySQL, PHP, HTML, CSS

    • Simulator for CPU scheduling algorithms

      February 2011 – March 2011

      Designed and developed a simulator program that runs different CPU scheduling algorithms and produces utilization matrices including CPU utilization, waiting time of each process and average waiting time, response time of each process and average response time, turn-around time of each process and turn-around waiting time.

      Development platform: C++

    Publications

    I investigated why Big Data Return over Investment (ROI) lagged far behind its potential despite using the best technology and the best people. I did an extensive analysis of Big Data processes across industries from business as well as technology perspective. To bridge the gaps in my analysis, I interviewed a few business managers and academic researchers. It was amazing to observe how dismal ROI results are linked to very basic set of common errors prevalent across the various steps of data mining. Based on this research I developed a generic ROI focused framework for leveraging Big Data, which could be easily customized to particular industry needs. My research work was selected for IEEE Big Data 2013. Based on the industry response at the conference, I am now working on developing a toolkit comprising of templates, checklists and industry benchmarks that can be conveniently used by Big Data business leaders with little background in technology.

    Big Data for Business Managers: Bridging the gap between potential and value

    Anmol Rajpurohit
    Conference PapersBig Data, 2013 IEEE International Conference on 6-9 Oct. 2013

    Abstract

    Given the surge of interest in research, publication and application on Big Data over the last few years, the potential of Big Data seems to be well-established now across businesses. However, in most of the business implementations Big Data still seem to be struggling to deliver the promised value (ROI). Such results despite using the market leading Big Data solutions and talented deployment team are forcing the business managers to think what needs to be done differently. This paper lays down the framework for business managers to understand Big Data processes. Besides providing a business overview of Big Data core components, the paper presents several questions that the managers must ask to assess the effectiveness of their Big Data processes. This paper is based on the analysis of several Big Data projects that never delivered and comparison against successful ones. The hypothesis is developed based on public information and is proposed as the first step for business managers keen on effectively leveraging Big Data.

    Work Experience

    I have significant work experience on research and development projects. Since my second year of UG degree program, I have contributed to several multi-disciplinary research projects as highlighted below.

    Past

    • Sep'14 June'14

      Data Science Intern at KDnuggets

      In collaboration with Dr. Gregory Piatetsky-Shapiro, I researched Big Data trends and publish articles on KDnuggets.com. I also do integration, mining and analysis of data from multiple sources such as social media (Twitter and LinkedIn) and web analytics for customer insights. Besides, I have developed interactive data visualization to assist quick progress from observing issue to root cause identification.

    • May'14 June'13

      Named Data Networking(NDN) at UCLA

      Since May 2012, I have been actively involved in the research and development of a new, better architecture for internet – Named Data Networking (NDN) at UCLA REMAP (Center for Research in Engineering, Media and Performance). The fundamental changes introduced by NDN to the internet communication paradigm call for extensive and multi-dimensional evaluations of various aspects of the NDN design, which comprised of majority of my work at UCLA.
      During my research, I built several modules that facilitate the use and assessment of NDN framework for real-life innovative applications such as server-less multi-user chat and environmental sensing. I used these modules to do performance assessment and benchmarking of key components of the NDN architecture. The insights provided by my analysis led to the identification of architecture gaps, thus, providing direction to future research. My research and consequently developed applications clearly demonstrated how NDN’s embedding of application names in the routing system promotes efficient authoring of sophisticated distributed applications, reducing complexity and thus opportunities for error, as well as time and expense of design and deployment.

    • Sep'13 May'13

      Side-match prediction based intra-coding method for H.264/AVC

      Implemented and integrateda side-matchprediction scheme into the state-of-the-art H.264/AVC framework to provide a new predictive approach. The proposed method generates a side-match predicted image from original image through the process of side match prediction. Side match prediction uses the neighboring known pixels to predict an unknownpixel.The prediction error sub-image generated by subtracting the side-match prediction image from original image is then encoded with intra prediction modes, quantization parameters and, scanning patterns.

    • Sep'13 May'13

      Video encoding seminar and workshops

      Delivered lectures on a range of video encoding related (specifically H.264/AVC) topics to graduate students , research scholars and faculty. The lectures covered the latest research in the arena of efficient entropy coding scheme for H.264/AVC lossless video coding, reversible data hiding using side-match prediction on stegnographic images and reversible watermarking based on invariant image classification and dynamic histogram shifting.

    • July'11 May'10

      File Browser for Sahaj Linux at IIT Kharagpur, India

      Improved and expanded the functionalities of file browser for Sahaj Linux – a simple, minimalist and user-friendly OS for rural India. Developed and optimized the new file browser capabilities as a wrapper to the gnome interface of Linux using GTK+ library for windowing. The Sahaj Linux file browser lets users navigate multiple folders and applications in an intuitive and convenient way reducing browsing time significantly.

    • July'11 May'10

      Computer aided learning tools for children and rural people

      Designed and developed a mathematical visual learning tool supporting floating-point numbers and better error-handling capabilities. The tool visually explains how a complex mathematical expression is evaluated as per BODMAS rule, and lets children play with it to enhance their learning.

    Blog

    The blog is currently under construction. Until then you can access my following publications on KDnuggets, a leading site on Analytics, Big Data, Data Mining, and Data Science

    Filter by type:

    Big Data and Hadoop, Boot Camp LA

    Anmol Rajpurohit
    Conference CoverageKDnuggets - October 17, 2014.

    Excerpt

    Big Data Boot Camp LA provided attendees a comprehensive understanding of Big Data and Hadoop technologies. Sujee Maniyam provided a good technical overview of Hadoop and current trends. We provide key takeaways.

    Sports Analytics Innovation Summit 2014 San Francisco: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - October 11, 2014.

    Excerpt

    Highlights from the presentations by Analytics leaders from San Francisco Giants, New York University and LA Dodgers on day 2 of Sports Analytics Innovation Summit 2014 in San Francisco.

    Sports Analytics Innovation Summit 2014 San Francisco: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - October 10, 2014.

    Excerpt

    Highlights from the presentations by Analytics leaders from San Francisco 49ers, United States Olympic Committee, and Chelsea FC on day 1 of Sports Analytics Innovation Summit 2014 in San Francisco.

    Big Data & Analytics for Retail Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - October 9, 2014.

    Excerpt

    Highlights from the presentations by Big Data leaders from The Hershey Company, Gongos, Clarks, and Mediacom on day 2 of Big Data & Analytics for Retail Summit 2014 in Chicago.

    Big Data & Analytics for Retail Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - October 8, 2014.

    Excerpt

    Highlights from the presentations by Big Data leaders from Sony Pictures Entertainment, Macy's and Nuevora on day 1 of Big Data & Analytics for Retail Summit 2014 in Chicago.

    SPOTLIGHT: Can Data Science Save Humanity from Mosquitoes and other Deadly Insects?

    Anmol Rajpurohit
    ArticleKDnuggets - October 8, 2014.

    Excerpt

    KDnuggets launches Spotlight initiative to bring attention to academic research. The journey begins with Prof. Eamonn Keogh and his student, Yanping Chen, who are applying data mining to save us all from insect-vectored diseases.

    Interview with Toni Jones, U-Haul on Deriving Business Insights from Social Media

    Anmol Rajpurohit
    InterviewKDnuggets - October 5, 2014.

    Excerpt

    We discuss social media strategy at U-Haul, the key drivers of a social media campaign, identifying what data to focus on, important metrics, career advice and more.

    INFORMS The Business of Big Data 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - Sptember 15, 2014.

    Excerpt

    Highlights from the presentations by Big Data leaders from Accenture, Analytics Media Group, SAS and Intel on day 2 of INFORMS The Business of Big Data.

    Customer Analytics Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - Sptember 12, 2014.

    Excerpt

    Highlights from the presentations by Big Data & Analytics experts from Microsoft, Sears Holdings and Obama for America on day 2 of Customer Analytics Summit 2014.

    Interview with Imran Siddiqi, SAP on the Strategic Value Proposition of Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - September 11, 2014.

    Excerpt

    We discuss the impact of Big Data advancements on business strategy, value proposition of Big Data, importance of partnerships, key risks and mitigation strategy, how to win sustained patronage for Big Data projects and more.

    Customer Analytics Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - Sptember 11, 2014.

    Excerpt

    Highlights from the presentations by Big Data & Analytics experts from ShareThis, Netflix and Ancestry on day 1 of Customer Analytics Summit 2014.

    Interview with Joe Otto, Alpine on Why Big Data is all about Empowerment & Collaboration

    Anmol Rajpurohit
    InterviewKDnuggets - September 9, 2014.

    Excerpt

    We discuss the story of Alpine Data Labs, the recent recognition of Alpine, effect of YARN, major customer use cases, and challenges in consumerizing Big Data.

    Founders of Yahoo! and MySQL Invest in Import.io

    Anmol Rajpurohit
    ArticleKDnuggets - September 8, 2014.

    Excerpt

    Import.io raises $3M round from Jerry Yang and David Axmark. Released a streamlined version of web data extraction tool with exciting new features.

    Interview with Ajay Bhargava, TCS shares the Big Data Mantra: Harness Data and Harvest Value

    Anmol Rajpurohit
    InterviewKDnuggets - September 4, 2014.

    Excerpt

    We discuss how to tame Big Data through harnessing data and harvesting value, the top Big Data priorities in Insurance sector, short-term and long-term needs of Healthcare Analytics, and more.

    Interview with Debora Donato, StumbleUpon on the Secret Sauce of Impressive Content Curation

    Anmol Rajpurohit
    InterviewKDnuggets - August 29, 2014.

    Excerpt

    We discuss the role of data science at StumbleUpon, the shift from search to discovery, metrics for user engagement, the art of collaborative filtering, how native ads improve user experience, major trends, advice and more.

    Interview with Arpit Gupta, CEO, Actionable Analytics on Enterprise Challenges in Big Data and Cloud

    Anmol Rajpurohit
    InterviewKDnuggets - August 24, 2014.

    Excerpt

    We discuss Actionable Analytics start-up, enterprise challenges in Big Data, relationship with cloud computing, metrics vs. insights, Big Data expectations and more.

    INFORMS The Business of Big Data 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 21, 2014.

    Excerpt

    Highlights from the presentations by Big Data technology practitioners from Teradata, Booz Allen Hamilton, Databricks and ProbabilityManagement.org during INFORMS The Business of Big Data in San Jose.

    Interview with Saikat Mukherjee, ShareThis on Why Marketers can no longer Ignore Social TV?

    Anmol Rajpurohit
    InterviewKDnuggets - August 20, 2014.

    Excerpt

    We discuss the role of Analytics at ShareThis, the emergence of Social TV, better user behavior insights through Social TV, major challenges with Social TV analytics, interesting insights, future trends, recommendation and more.

    Interview with John Funge, CTO, Knack on Why Gaming is the Next Big Thing for Hiring

    Anmol Rajpurohit
    InterviewKDnuggets - August 18, 2014.

    Excerpt

    We discuss the gamification of hiring, founding story of Knack, applications of Predictive Human Analytics, challenges, Big Data tools and technology used, key qualities sought in data scientists, career advice and more.

    Interview with Pallas Horwitz, Blue Shell Games on Why Data Science is So Critical for Gaming Studios

    Anmol Rajpurohit
    InterviewKDnuggets - August 14, 2014.

    Excerpt

    We discuss the role of data science at Blue Shell Games, the importance of "Lean Data", key metrics for online games, cross-product projects and optimizing meeting the data needs across an organization.

    ASE International Conference on Big Data Science 2014: Day 4 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 8, 2014.

    Excerpt

    Highlights from the presentations by Data Science leaders from UC Berkeley, Clark Atlanta Univ, Florida Institute of Technology, Rober Bosh LLC and HP on day 4 of ASE Conference on Big Data Science 2014, Stanford.

    Big Data Innovation Summit 2014 Toronto: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 7, 2014.

    Excerpt

    Highlights from the presentations by Big Data leaders from Aviva, Canadian Imperial Bank, Royal College of Physicians and Surgeons of Canada, and University Health Network on day 2 of Big Data Innovation Summit 2014.

    Interview with Sujee Maniyam, Elephant Scale on the Best Free Online Resources to Learn Hadoop

    Anmol Rajpurohit
    InterviewKDnuggets - August 7, 2014.

    Excerpt

    We discuss the startup - Elephant Scale, DIY Hadoop learning, best free online resources for learning Hadoop, getting a good job in Big Data, and the experience of authoring a book - Hadoop Illuminated (available for free).

    Big Data Innovation Summit 2014 Toronto: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 6, 2014.

    Excerpt

    Highlights from the presentations by Big Data leaders from TD Bank, Public Health Ontario and First Nations Education Steering Committee on day 1 of Big Data Innovation Summit 2014 in Toronto, Canada.

    ASE International Conference on Big Data Science 2014: Day 3 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 5, 2014.

    Excerpt

    Highlights from the presentations by Data Science leaders from UC Davis, UT Dallas, Northrop Grumman Corp and NIST on day 3 of ASE Conference on Big Data Science 2014 held in Stanford University.

    ASE International Conference on Big Data Science 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 4, 2014.

    Excerpt

    Highlights from the presentations by Data Science leaders from USC, YarcData and Revolution Analytics on day 2 of ASE Conference on Big Data Science 2014 held in Stanford University.

    Interview with Vita Markman, LinkedIn on Practical Solutions for Sentiment Mining Challenges

    Anmol Rajpurohit
    InterviewKDnuggets - August 4, 2014.

    Excerpt

    We discuss sentiment data models, significance of linguistic features, handling the noise in social conversations, industry challenges, important use cases and the appropriateness of over-simplified binary classification.

    Interview with Christophe Toum, Talend on Why Big Data Needs Big Governance

    Anmol Rajpurohit
    InterviewKDnuggets - August 2, 2014.

    Excerpt

    We discuss the priority order of data governance for Big Data initiatives, impact of increasing shift towards Hadoop and NoSQL, data quality, current trends, talent crunch, advice and more.

    ASE International Conference on Big Data Science 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - August 1, 2014.

    Excerpt

    Highlights from the presentations by Data Science leaders from Pivotal, IBM Research, George Washington University, IARPA at ASE Conference on Big Data Science 2014 held in Stanford University.

    Interview with Taylor Phillips, Square on Why Finance Needs Machine Learning and Data Science

    Anmol Rajpurohit
    InterviewKDnuggets - August 1, 2014.

    Excerpt

    We discuss the role of data science at Square, common machine learning use cases, transition to real-time architecture, major challenges, expectations from data science, key qualities for data scientists, and more.

    ASE International Conference on Big Data Science 2014: Highlights from Workshops

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 31, 2014.

    Excerpt

    Highlights from the presentations by Data Science leaders from MIT, Georgia Tech, Microsoft Research and CUHK during workshops at ASE Conference on Big Data Science 2014 held in Stanford University.

    Predictive Analytics Innovation Summit 2014 London: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 31, 2014.

    Excerpt

    Highlights from the presentations by Predictive Analytics leaders from Spotify, ING, Quintiles, and Riot Games on day 2 of Predictive Analytics Innovation Summit 2014 in London, UK.

    Future of Consumer Intelligence 2014: Day 3 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 30, 2014.

    Excerpt

    Highlights from the presentations by Market Research leaders on day 3 of Future of Consumer Intelligence 2014 in Los Angeles.

    Predictive Analytics Innovation Summit 2014 London: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 29, 2014.

    Excerpt

    Highlights from the presentations by Predictive Analytics leaders from eBay, Skype, Yahoo and AbsolutData on day 1 of Predictive Analytics Innovation Summit 2014 in London, UK.

    Interview with Thomas Levi, POF on How Online Dating is Improving Matching through Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - July 29, 2014.

    Excerpt

    We discuss Big Data use cases at Plenty of Fish, insights from text mining of user profiles, using topic modeling for developing user archetypes, challenges and more.

    Future of Consumer Intelligence 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 28, 2014.

    Excerpt

    Previous postNext post Highlights from the presentations by Market Research leaders on day 2 of Future of Consumer Intelligence 2014 in Los Angeles.

    Interview with Sastry Malladi, StubHub on Designing Big Data Architecture for the Unknown Future

    Anmol Rajpurohit
    InterviewKDnuggets - July 28, 2014.

    Excerpt

    We discuss the Big Data architecture at StubHub, important factors in architecture design, hybrid approach of using Big Data along with traditional data warehouses, challenges, importance of meta-data and more.

    Interview with Dr. Kavita Ganesan, FindiLike on Building Decision Support Systems based on User Opinions

    Anmol Rajpurohit
    InterviewKDnuggets - July 27, 2014.

    Excerpt

    We discuss the founding story of FindiLike, Opinion-driven Decision Support Systems (ODSS), challenges in analyzing user opinions, future of Sentiment Analysis, favorite books and more.

    Interview with Aparna Pujar, eBay on Evolution of Behavior Analytics for User Engagement

    Anmol Rajpurohit
    InterviewKDnuggets - July 25, 2014.

    Excerpt

    We discuss Behavior Analytics vs. Web Analytics, important metrics for user engagement, challenges of behavior insights domain, future of multi-screen analytics, key soft skill and more.

    Big Data & Analytics in Healthcare Summit 2014 Philadelphia: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 25, 2014.

    Excerpt

    Highlights from the presentations by Healthcare Analytics leaders from Cigna, National Parkinson Foundation, Quintiles and NYU Langone Medical Center on day 2 of Big Data & Analytics in Healthcare Summit 2014 in Philadelphia.

    Interview with Leo Meyerovich, Graphistry on Browser-based Interactive Big Data Visualization

    Anmol Rajpurohit
    InterviewKDnuggets - July 24, 2014.

    Excerpt

    We discuss the merits of Superconductor architecture, comparison with current JavaScript visualization library, use cases, future plans, launch of Graphistry, visualization trends, and more.

    Big Data & Analytics in Healthcare Summit 2014 Philadelphia: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 24, 2014.

    Excerpt

    Highlights from the presentations by Healthcare Analytics leaders from GlaxoSmithKline, Excellus BlueCross BlueShield, Adventist Health and Mayo Clinic on day 1 of Big Data & Analytics in Healthcare Summit 2014 in Philadelphia.

    Business Intelligence Innovation Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 23, 2014.

    Excerpt

    Highlights from the presentations by Business Intelligence leaders from Netflix, Hyatt, GE Capital and University of Texas on day 2 of Business Intelligence Innovation Summit 2014 in Chicago.

    Interview with Amy Gaskins, AVP, MetLife on Smarter Analytics through Qualitative Research

    Anmol Rajpurohit
    InterviewKDnuggets - July 22, 2014.

    Excerpt

    We discuss the relevance of qualitative research for customer intelligence, MetLife Infinity, and the increasing trend of behavior-based customer segmentation.

    Business Intelligence Innovation Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 22, 2014.

    Excerpt

    Highlights from the presentations by Business Intelligence leaders from Boeing, Salesforce, Wells Fargo, and Citibank on day 1 of Business Intelligence Innovation Summit 2014 in Chicago.

    Future of Consumer Intelligence 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 19, 2014.

    Excerpt

    Highlights from the presentations by Market Research leaders from H(app)athon Project, Socratic Technologies, TNS, PepsiCo on day 1 of Future of Consumer Intelligence 2014 in Los Angeles.

    Interview with Cliff Lyon, Stubhub on Mastering the Art of Recommendation and Personalization Analytics

    Anmol Rajpurohit
    InterviewKDnuggets - July 18, 2014.

    Excerpt

    We discuss challenges in designing recommendation and personalization systems, how to select the right metrics, and learning regarding presentation of recommendation on different channels.

    Manufacturing Analytics Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 17, 2014.

    Excerpt

    Highlights from the presentations by Analytics leaders from World Fuel Services, Vigilent Corporation, Caterpillar and SunEdison on day 2 of Manufacturing Analytics Summit 2014 in Chicago.

    Interview with Piero Ferrante, BCBS on Why Healthcare is Rich in Data but Poor in Information

    Anmol Rajpurohit
    InterviewKDnuggets - July 17, 2014.

    Excerpt

    We discuss role of analytics in healthcare payer firms, major challenges in leveraging healthcare data, shift to value-based payments, personal motivation towards analytics, career advice and more.

    Interview with Marc Smith, Chief Social Scientist, Connected Action, on Why We Need Open Tools for Social Networks

    Anmol Rajpurohit
    InterviewKDnuggets - July 14, 2014.

    Excerpt

    We discuss NodeXL impact stories, upcoming NodeXL features, importance of an open environment, future of social media analytics, advice for novice researchers and more.

    Manufacturing Analytics Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 14, 2014.

    Excerpt

    Highlights from the presentations by Analytics leaders from McCormick, HP, Patheon and Boeing on day 1 of Manufacturing Analytics Summit 2014 in Chicago.

    Interview with Amy Gershkoff, Director of Customer Analytics & Insights, eBay on How to Design Custom In-House BI Tools

    Anmol Rajpurohit
    InterviewKDnuggets - July 11, 2014.

    Excerpt

    We discuss key principles for designing business intelligence tools, exploring causation based on correlation insights, attributes of future Analytics leaders, interesting Big Data trends, important qualities in data scientists and more.

    Business Analytics Innovation Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 11, 2014.

    Excerpt

    Highlights from the presentations by Business Analytics leaders from State of Illinois, Navistar, BMO Harris Bank and McGraw Hill Education on day 2 of Business Analytics Innovation Summit 2014 in Chicago.

    Chief Data Officer Summit 2014 – Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 11, 2014.

    Excerpt

    Highlights from the presentations by Data Governance experts from Visa, Bing, San Francisco County, and RS Investments at Chief Data Officer Summit 2014 in San Francisco, CA.

    Business Analytics Innovation Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - July 9, 2014.

    Excerpt

    Highlights from the presentations by Business Analytics leaders from Bank of America, Northern Trust, AOL and Liberty Mutual on day 1 of Business Analytics Innovation Summit 2014 in Chicago.

    Interview with Dave Marvit, Innovation Strategy Consultant, Fujitsu on Modern Sentiment Analysis using Ubiquitous Continuous Sensing

    Anmol Rajpurohit
    InterviewKDnuggets - June 30, 2014.

    Excerpt

    We discuss traditional sentiment analysis vs. modern sentiment analysis, role of data science in Human Centric Intelligent Society, mainstream adoption of bio sensors and opportunities created by Big Data from ubiquitous continuous sensing.

    Interview with Samaneh Moghaddam, Applied Researcher, eBay on Opinion Mining

    Anmol Rajpurohit
    InterviewKDnuggets - June 27, 2014.

    Excerpt

    We discuss typical sentiment analysis problems at eBay, underrated challenges, career motivation, important soft skills and more.

    Interview with Samaneh Moghaddam, Applied Researcher, eBay on Aspect-based Opinion Mining

    Anmol Rajpurohit
    InterviewKDnuggets - June 26, 2014.

    Excerpt

    We discuss aspect-based opinion mining, major challenges, cold start items, the need for accurate opinion mining models for cold start items and how factorized LDA can be leveraged.

    The Chief Data Officer Summit

    Anmol Rajpurohit
    Conference CoverageKDnuggets - June 24, 2014.

    Excerpt

    Highlights from the presentations by Data Governance experts from State of Colorado, IBM, Informatica and Sony Pictures Entertainment on day 1 of Chief Data Officer Summit 2014 in San Francisco, CA.

    Interview with Conal Sathi, Data Scientist, Slice on Creating Value from Mining Shoppers’ e-Receipts

    Anmol Rajpurohit
    InterviewKDnuggets - June 16, 2014.

    Excerpt

    We discuss the relevance of "Purchase Graph", Slice platform, analytical insights from mining all activity around a customer's purchase, experimentation strategy, experience of working as a data scientist and more.

    INFORMS Conference The Business of BIG DATA

    Anmol Rajpurohit
    Conference CoverageKDnuggets - June 10, 2014.

    Excerpt

    We interview the co-chairs of INFORMS Conference The Business of Big Data 2014 (June 22-24, 2014) on Big Data maturity, opportunities assessment, analytics for operations research, conference agenda and more.

    Interview with Lloyd Tabb, Chairman & CTO, Looker on Front-line Analytics and Data Democratization

    Anmol Rajpurohit
    InterviewKDnuggets - June 9, 2014.

    Excerpt

    We discuss the capabilities of Looker, data democratization across organization, change in the tools being used by analytics-savvy business managers, front-line analytics, competitive landscape and more.

    Interview with Don Zereski, VP, Local Search & Discovery, HERE (Nokia) on Location Analytics and Architecture Evolution

    Anmol Rajpurohit
    InterviewKDnuggets - June 8, 2014.

    Excerpt

    We discuss trends in location analytics, evolution of HERE's analytics architecture, infrastructure challenges, data governance and more.

    Interview with Santhosh Adayikkoth, CEO, BigInfo Labs on Data Relevance and Intel Partnership

    Anmol Rajpurohit
    InterviewKDnuggets - June 6, 2014.

    Excerpt

    We discuss BigInfo Labs, the concept of "Data Relevance" in Big Data, experience of partnership with Intel, and BigInfo Labs' strategy for competitive differentiation.

    HR & Workforce Analytics Innovation Summit 2014 Chicago: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - June 5, 2014.

    Excerpt

    Highlights from the presentations by HR leaders from Caterpillar, Coca-Cola, Pfizer, and Marriott International on day 2 of HR & Workforce Analytics Innovation Summit 2014 in Chicago.

    Big Data Assessment – Key Business Drivers, Expected Benefits and Common Challenges

    Anmol Rajpurohit
    ArticleKDnuggets - Jun 5, 2014.

    Excerpt

    Recent survey on Big Data outlook reports increasing interest in Big Data for more accurate and timely decision-making; and concerns about project costs and ability to scale.

    Interview with Lynn Goldstein, Chief Data Officer, NYU on the Need for Data Governance

    Anmol Rajpurohit
    InterviewKDnuggets - June 3, 2014.

    Excerpt

    We discuss the role of Data Governance, establishing Big Data accountability, impact of Data Governance on Data Quality, and assessing the education available for Data Governance.

    HR & Workforce Analytics Innovation Summit 2014 Chicago: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - June 2, 2014.

    Excerpt

    Highlights from the presentations by HR leaders from Wells Fargo, Sears Holdings, Johnson Controls, Trulia on day 1 of HR & Workforce Analytics Innovation Summit 2014 in Chicago.

    Interview with Tom Kern, Risk Modeling Manager, Paychex on Risk Analytics and Sales Anticipation Model

    Anmol Rajpurohit
    InterviewKDnuggets - June 2, 2014.

    Excerpt

    We discuss the role of Risk Analytics at Paychex, strategic importance of Sales Anticipation Model, optimizing business processes by leveraging Big Data, and advice for companies thinking about Big Data as well as aspiring students.

    Big Data Innovation Summit 2014 London: Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 31, 2014.

    Excerpt

    Highlights from the presentations by Big Data technology practitioners from Sears Holdings, Microsoft, Ticketmaster during Big Data Innovation Summit 2014 in London.

    US Open Data Action Plan and Datasets

    Anmol Rajpurohit
    ArticleKDnuggets - May 31, 2014.

    Excerpt

    We summarize the key findings in the recently released US Open Data Action Plan, highlighting the principles, commitments, datasets released and future outlook.

    Gaming Analytics Innovation Summit: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 30, 2014.

    Excerpt

    Highlights from the presentations by Gaming Analytics experts from Ubisoft, Electronic Arts, Sega on Day 2 of Gaming Analytics Summit 2014.

    Interview with Kirk Borne, Data Scientist, GMU on Big Data in Astrophysics and Correlation vs. Causality

    Anmol Rajpurohit
    InterviewKDnuggets - May 30, 2014.

    Excerpt

    We discuss how to build the best data models, significance of correlation and causality in Predictive Analytics, and impact of Big Data on Astrophysics.

    Big Data for Executives 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 23, 2014.

    Excerpt

    Highlights from the presentations by Big Data experts from McKinsey Solutions, SAP, Techfetch, Weather Analytics on Day 2 of Big Data for Executives 2014.

    Gaming Analytics Summit 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 29, 2014.

    Excerpt

    Highlights from the presentations by Gaming Analytics experts from Activision, Valve, Microsoft and Broken Bulb Studios on Day 1 of Gaming Analytics Summit 2014.

    Interview with Walter Maguire, Chief Field Technologist on HP Big Data Strategy and HAVEn

    Anmol Rajpurohit
    InterviewKDnuggets - May 28, 2014.

    Excerpt

    We discuss how HP views Big Data, capabilities of HP HAVEn, leveraging Big Data for improving customer experience, Analytics challenges, outsourcing criteria and current trends.

    Interview with Martin Hack, CEO, Skytree on Industrializing Machine Learning for Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - May 26, 2014.

    Excerpt

    We discuss the mission of Skytree, product strategy, complimentary consulting programs, recent trends, and current expectations from Machine Learning.

    Big Data for Executives 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 23, 2014.

    Excerpt

    Highlights from the presentations by Big Data experts from Sears Holdings, PWC, Oracle, Altamira, Tesora on Day 1 of Big Data for Executives 2014.

    Interview with Richard Wendell, VP, Data Science, TE Connectivity on Strategy for Analytics Projects

    Anmol Rajpurohit
    InterviewKDnuggets - March 19, 2014.

    Excerpt

    We discuss the last mile of the execution path of Analytics projects, five critical pillars of success and data-driven decision making through advanced analytics.

    Interview with Dale Russell, CTO, Talksum on Winning the IE Big Data Startup Award

    Anmol Rajpurohit
    InterviewKDnuggets - May 20, 2014.

    Excerpt

    We discuss Talksum data stream router and cross-domain networking with real-time data management using data streams.

    Code for India 2014 Global Hack-a-thon – Building a Better India through Innovative Solutions

    Anmol Rajpurohit
    ArticleKDnuggets - May 19, 2014.

    Excerpt

    Non-stop 24 hours of coding at the Code for India 2014 hackathon leads to creative solutions for major social problems of India through interesting software applications.

    Interview with Michael O’Connell, Chief Data Scientist, TIBCO on How to Lead in Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - May 19, 2014.

    Excerpt

    We discuss Big Data vs. Fast Data, Data Visualization trends, Jaspersoft acquisition, factors differentiating future leaders of Big Data and more.

    Sentiment Analysis Innovation Summit 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 14, 2014.

    Excerpt

    Highlights from the presentations by opinion mining experts from Fujitsu, FindiLike and Stanford University on Day 2 of Sentiment Analysis Innovation Summit 2014 in San Francisco.

    Has Predictive Analytics Crossed The Chasm?

    Anmol Rajpurohit
    ArticleKDnuggets - May 15, 2014.

    Excerpt

    Recent study highlights the increasing market perception that Predictive Analytics leads to competitive advantage. The report also outlines current trends and challenges for Predictive Analytics.

    Interview with Gary Shorter, Director of Data Science, Quintiles on Big Data for Healthcare

    Anmol Rajpurohit
    InterviewKDnuggets - May 15, 2014.

    Excerpt

    We discuss the rising medical costs, how can Big Data help, key features of Quintiles Inforsario and Topological Data Analysis.

    Social Media & Web Analytics Innovation Summit 2014: Day 2 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 15, 2014.

    Excerpt

    Highlights from the presentations by analytics experts from Youtube, Evernote and Wikia on day 2 of Social Media & Web Analytics Innovation Summit 2014 in San Francisco.

    Sentiment Analysis Innovation Summit 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 14, 2014.

    Excerpt

    Highlights from the presentations by opinion mining experts from Twitter, eBay and Samsung on Day 1 of Sentiment Analysis Innovation Summit 2014 in San Francisco.

    Social Media & Web Analytics Innovation Summit 2014: Day 1 Highlights

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 14, 2014.

    Excerpt

    Highlights from the presentations by experts from Google, CapitalOne, StubHub and Social Media Research Foundation on day 1 of Social Media & Web Analytics Innovation Summit 2014 in San Francisco.

    Interview with Prateek Jain, Director of Engineering, eHarmony on Fast Search and Sharding

    Anmol Rajpurohit
    InterviewKDnuggets - May 14, 2014.

    Excerpt

    We discuss Big Data architecture, fast multi-attribute searches, database sharding and scaling challenges at eHarmony.

    Media Industry Embracing Analytics for Innovation and Competitive Edge

    Anmol Rajpurohit
    ArticleKDnuggets - May 13, 2014.

    Excerpt

    Survey results highlight the importance of Analytics capability in media industry and the consumer beliefs on privacy vs. personalization benefits.

    Big Data BootCamp: Highlights of talks on Day 3

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 12, 2014.

    Excerpt

    Highlights from the presentations by big data technology practitioners from Hortonworks, Intel, Rackspace, SciSpike, and Yahoo at Big Data Bootcamp 2014 in Santa Clara.

    Interview with George Corugedo, CTO, RedPoint on YARN and Customer Analytics

    Anmol Rajpurohit
    InterviewKDnuggets - May 12, 2014.

    Excerpt

    We discuss significance of YARN for Hadoop 2.0 platform, unique benefits of RedPoint Convergent Marketing Platform and Master Key Management for Customer Analytics.

    Healthcare Analytics: Identifying Leaders and Key Trends

    Anmol Rajpurohit
    ArticleKDnuggets - May 12, 2014.

    Excerpt

    We review recently released report on Healthcare perceptions towards BI/Analytics and share key insights into who is leading healthcare analytics in different categories and what are the key dominant trends.

    White House Report on Big Data: Opportunities and Values

    Anmol Rajpurohit
    ArticleKDnuggets - May 8, 2014.

    Excerpt

    We summarize the key findings in the recently released White House report on Big Data, highlight the key opportunities and concerns, and list the recommendations made to the President.

    Big Data BootCamp Santa Clara: Highlights of talks on Days 1-2

    Anmol Rajpurohit
    Conference CoverageKDnuggets - May 9, 2014.

    Excerpt

    Highlights from the presentations by big data technology practitioners from Caspida, Datastax, ElephantScale, Hortonworks, MapR and Qubole at Big Data Bootcamp 2014 in Santa Clara.

    Interview with Arijit Sengupta, CEO, BeyondCore on Advanced Analytics and Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - May 9, 2014.

    Excerpt

    We discuss traditional analytics vs. modern analytics, avoiding over-simplification, human-technology interaction for Big Data, challenges in democratizing analytics and more.

    Interview with Xinghua Lou (Microsoft) on Mining Clinical Notes and Big Data in Healthcare

    Anmol Rajpurohit
    InterviewKDnuggets - May 7, 2014.

    Excerpt

    We discuss data mining of cancer clinical data, LDA topic model, challenges in mining clinical notes, big data in healthcare and more.

    Interview with Todd Holloway, Data Science Lead, Trulia

    Anmol Rajpurohit
    InterviewKDnuggets - May 5, 2014.

    Excerpt

    We discuss the responsibilities of Data Science and Analytics teams, significance of programming knowledge for data scientists, important soft skills, talent landscape in data science and more.

    Data Scientists Not Required: Promises the recently launched Alteryx Analytics 9.0

    Anmol Rajpurohit
    ArticleKDnuggets - May 3, 2014.

    Excerpt

    Alteryx Analytics 9.0 blends new sources of customer Insight such as Social Media, Google Analytics, and Marketo with data from legacy environments such as SAS Analytics.

    Interview with Vasanth Kumar, Principal Data Scientist, Live Nation

    Anmol Rajpurohit
    InterviewKDnuggets - May 2, 2014.

    Excerpt

    We discuss challenges in analyzing bursty data, real-time classification, relevance of statistics and advice for newcomers to Data Science.

    Massachusetts releases Big Data Report 2014

    Anmol Rajpurohit
    ArticleKDnuggets - April 29, 2014.

    Excerpt

    Massachusetts Big Data Report 2014 (free download) highlights state successes, including almost 500 Big Data companies, $2.5B invested, 5600 students graduating from 14 data science-related programs, and identifies key priorities and growth opportunities.

    Interview with Juan Miguel Lavista, Principal Data Scientist, Microsoft Data Science

    Anmol Rajpurohit
    InterviewKDnuggets - April 30, 2014.

    Excerpt

    We discuss Randomized Controlled Experiments, common errors during A/B testing, Correlation vs. Causality, Big Data Myths and setting up realistic expectations from Big Data and more...

    Big Data Leads Top Paying Skills

    Anmol Rajpurohit
    ArticleKDnuggets - April 29, 2014.

    Excerpt

    Big Data related skills led the list of top paying technical skills (six-figure salaries) in 2013. Several other useful insights are available in the Dice Tech Survey Report, available for free download.

    Big Data Innovation Summit 2014 Santa Clara: Highlights Day 2

    Anmol Rajpurohit
    Conference CoverageKDnuggets - April 29, 2014.

    Excerpt

    Highlights from the presentations by big data technology practitioners from NYSE, Glassdoor, Slice and Paychex on day 2 of Big Data Innovation Summit 2014 in Santa Clara.

    Interview with David Stringfellow, Chief Economist, State Utah Auditor

    Anmol Rajpurohit
    InterviewKDnuggets - April 25, 2014.

    Excerpt

    We discuss Analytics for Public Policy decisions, responsibilities of Utah Chief Data Officer, crowdsourcing analytics for resolving Government problems and most important skills for data science practitioners.

    Big Data Innovation Summit 2014 Santa Clara: Highlights Day 1

    Anmol Rajpurohit
    Conference CoverageKDnuggets - April 23, 2014.

    Excerpt

    Highlights from the presentations by big data technology practitioners from eBay, YarcData, LinkedIn, Trulia, and other leading companies on day 1 of Big Data Innovation Summit 2014 in Santa Clara.

    Big Data Innovation Summit 2014: Highlights of Keynote Speeches on Day 2

    Anmol Rajpurohit
    Conference CoverageKDnuggets - April 23, 2014.

    Excerpt

    Highlights from keynote speeches by big data experts from Facebook, RedPoint Global, Quintiles, Samsung, GMU, PayPal, and others on Day 2 of Big Data Innovation Summit 2014 in Santa Clara.

    Big Data Innovation Summit 2014: Highlights of Keynote Speeches on Day 1

    Anmol Rajpurohit
    Conference CoverageKDnuggets - April 23, 2014.

    Excerpt

    Highlights from keynote speeches by big data technology leaders from industry and academia on first day of Big Data Innovation Summit 2014 in Santa Clara.

    Is Data Scientist the right career path for you? Candid advice

    Anmol Rajpurohit
    ArticleKDnuggets - March 27, 2014.

    Excerpt

    Candid advice from an industry veteran, Paco Nathan reveals the true picture behind the much-talked-about Data Scientist "glamour" and helps people have the right expectations for a Data Science career.

    Interview with Sriram Sankar,Principal Staff Engineer at LinkedIn

    Anmol Rajpurohit
    InterviewKDnuggets - April 2, 2014.

    Excerpt

    Anmol talks with Sriram Sankar, Principal Staff Engineer at LinkedIn about LinkedIn’s “Economic Graph”, Entity-Oriented Search, and the biggest challenges towards delivering relevant, personalized search results.

    Interview with Anjul Bhambhri, VP of Big Data Products at IBM

    Anmol Rajpurohit
    InterviewKDnuggets - March 24, 2014.

    Excerpt

    Anmol talks with Anjul Bhambhri, IBM’s Vice President of Big Data Products about Big Data Trends, developing the Big Data capabilities in-house vs. outsourcing, five crucial steps to adopting a success big data strategy and advice for beginners.

    Interview with Daniel Tunkelang, Head of Query Understanding at LinkedIn

    Anmol Rajpurohit
    InterviewKDnuggets - March 19, 2014.

    Excerpt

    Anmol talks with Daniel Tunkelang, Head of Query Understanding at LinkedIn about search quality, IR, query understanding, and advice for data science enthusiasts. Don't miss: 4 steps to get your LinkedIn profile show up on top of search results.

    Interview with Geoffrey Moore: Crossing the Chasm and Big Data

    Anmol Rajpurohit
    InterviewKDnuggets - March 6, 2014.

    Excerpt

    Anmol talks with Geoffrey Moore, an author, speaker and advisor who splits his consulting time between start-up companies in the Mohr Davidow portfolio and established high-tech enterprises, most recently including Salesforce, Microsoft, Intel, Box, Aruba, Cognizant, and Rackspace. In the interview, we discuss his "Crossing the Chasm" book, his vision for Big Data analytics, when Big Data will cross the chasm, and advice for entrepreneurs.

    Interview with Paco Nathan, Chief Scientist at Mesosphere

    Anmol Rajpurohit
    InterviewKDnuggets - March 6, 2014.

    Excerpt

    Anmol talks with Paco Nathan, Chief Scientist at Mesosphere. In the interview, we discuss about Apache Mesos, Cascading, his books and Big Data trends.

    Interview with Quentin Clark, CVP, Microsoft Data Platform Group

    Anmol Rajpurohit
    InterviewKDnuggets - March 6, 2014.

    Excerpt

    Anmol talks with Quentin Clark, Corporate Vice President, Microsoft Data Platform Group. In the interview, we discuss Power BI for Office 365, Big Data trends and Microsoft’s strategic decisions.

    Strata 2014 Santa Clara: Highlights of Day 3

    Anmol Rajpurohit
    Conference CoverageKDnuggets - February 28, 2014.

    Excerpt

    Strata 2014 was a great conference, and here are key insights from some of the best sessions on day 3: Data Journalism, Analytics over Real-time Streaming Data, Facebook Graph Analysis with One Trillion Edges, Socializing Search by LinkedIn.

    Strata 2014 Santa Clara: Highlights of Day 2

    Anmol Rajpurohit
    Conference CoverageKDnuggets - February 27, 2014.

    Excerpt

    Strata 2014 was a great conference, and here are key insights from some of the best sessions on day 2: Big Data Vendor Landscape, Machine Learning for Social Change, Secrets of Gertrude Stein, and Facebook Exascale Analytics.

    Qualitative Analytics: Why numbers do not tell the complete story?

    Anmol Rajpurohit
    ArticleKDnuggets - February 21, 2014.

    Excerpt

    Data scientists love numbers, yet not all data is numerical. Qualitative analytics should not be ignored, especially given the unique value it provides.

    Strata 2014: Highlights from Keynote Speeches

    Anmol Rajpurohit
    Conference CoverageKDnuggets - February 16, 2014.

    Excerpt

    Highlights from keynote speeches delivered by various eminent big data technology leaders from industry and academia at Strata 2014 Conference held in Santa Clara recently.

    Is data mining the new tool for gamers seeking pre-launch secrets?

    Anmol Rajpurohit
    ArticleKDnuggets - February 20, 2014.

    Excerpt

    Despite great data analytics capabilities, gaming companies are facing an interesting data mining challenge from an unexpected end – their audience.

    Big Data for Business Managers

    Anmol Rajpurohit
    ArticleKDnuggets - January 22, 2014.

    Excerpt

    Why do Big Data projects fail to deliver the promised value, that too despite the “clearly” established potential? What should business managers do to avoid the media hype and focus on achieving sustainable benefits from big data investments?

    Review of Analytics Marketplaces: The Next Big Thing for Big Data

    Anmol Rajpurohit and Gregory Piatetsky
    ReviewKDnuggets - November 18, 2013.

    Excerpt

    We reviewed Analytics App Marketplaces from Alteryx, Amazon (AWS), BigML, Datameer, RapidMiner, and Windows Azure. Who will create the next iTunes for Analytics?

    Gallery

    • Anmol Rajpurohit UCLA
    • Los Angeles
    • Anmol playing pool
    • Anmol with Marissa Mayer
    • University of California, Los Angeles
    • Anmol at Beach in Pondicherry
    • Anmol at UCLA REMAP
    • Anmol at Google Student Ambassador Summit
    • Anmol Rajpurohit Closeup
    • Anmol in UCLA
    • Anmol at Wall Street
    • Anmol at GSA Summit

    Contact

    I strongly prefer emails for all communication, which I would normally respond to within a day. I am trying best to keep my social accounts updated with the latest news.
    For any communication related to KDnuggets.com, please use the following email id: anmol AT kdnuggets.com

    •    arajpuro AT uci.edu
    •    anmol.rajpurohit AT gmail.com
    •    anmol.rajpurohit
    •    hey_anmol
    •    linkedin.com/in/arajpurohit
    http://www.ics.uci.edu/~enalisni/ Eric T. Nalisnick | UC Irvine

    eric t. nalisnick

    PhD Student
    Computer Science
    University of California, Irvine

    Smyth DataLab
    Room 4228
    Donald Bren Hall
    Irvine, CA 92617

    enalisni (at) uci (dot) edu

    about     resume     code     blog    

    • Our survey and evaluation of hand datasets and pose estimation methods will be a poster at ICCV 2015 .
    • We present our paper on everyday hands in action.
    • We present our paper on hand pose recognition in egocentric workspaces.
    1. Our survey and evaluation of hand datasets and pose estimation methods is on arXiv
    2. J. Supancic, G. Rogez, Y. Yang, J. Shotton, D. Ramanan. "Depth-based hand pose estimation: methods, data, and challenges " arXiv preprint arXiv:1504.06378 2015.
      • Deep Convolutional Network w/ Prior Bottleneck source code and a pre-trained network
      • Other methods, specifically including Dual-Tree accelerated 1-NN
      • Modified LibHand w/ Inverse-Kinematic Annotation
      • Dataset Link
    3. Links to datasets mentioned in the paper: KTH , LISA , ASTAR , MSR , NYU , ICL , FORTH , Dexter , ETH-Z , Max-Planck-Gesellschaft ( Synthetic , Real ), HandNet , FingerPaint

    HANDS-2015 Workshop

    July 30th, 2014Posted by James

     
    I'm organizing a workshop and challange associated with CVPR-2015. More information can be hound here .
    <

    3D Hand Pose in Egocentric RGB+D

    Jan 5th, 2015Posted by James

     
    Hand pose estimation from an egocentric view using random cascades with synthetic depth data
    • G. Rogez, M. Khademi, J. Supancic, J. Montiel, D. Ramanan. "3D Hand Pose Detection in Egocentric RGB-D Images" Workshop on Consumer Depth Cameras for Computer Vision, European Conference on Computer Vision (ECCV), Zurich, Switzerland, Sept. 2014. PDF

    Self Paced Long Term Tracking

    April, 2013Posted by James

     
    Our paper, "Self Paced Learning for Long-Term Tracking" was accepted for publication at CVPR 2013. The paper presents a novel technique for adapting an appearance model for long term tracking.
    • Project Source Code (Release Version, WIP)
      1. See it's README.txt for instructions.
      2. Feel free to email me if you have problems.
    • Self-paced learning for long-term tracking (Paper in PDF form)
    • Videos and Ground Truth (Data Set)
    • Output Tracks (CSV: x1, y1, x2, y2, cost). If x1 == NaN then the tracker said the target was occluded or out of frame.
      1. Offline Output Tracks (CSV format)
      2. Online Output Tracks (CSV format)

    Dec 18th, 2012Posted by James

     
    The above figure contains the inverse HOG visualization of the HOG features in a picture of me. I am a first year PhD student at UC Irvine. I work in computer vision. I'm interested in how we might exploit temporal data (tracking) and depth data (e.g. Kinect) in semi-supervised learning to create effective detectors.
    • James Steven Supančič III




      4209 Donald Bren Hall (Office)

      jsupanci at uci.edu

      PGP Key

      github profile

      Links


      My Adviser, Deva Ramanan

      My Supervisor/Collaborator, Gregory Rogez

      Computational Vision Group

      Department of Computer Science

      University of California at Irvine

     

    Copyright (c) 2012 James Supancic III. All rights reserved. Design by FCT.

    http://www.ics.uci.edu/~tdebeauv/ Thomas Debeauvais
    Thomas Debeauvais
    • About
    • Games User Research
    • Scaling
    Portrait

    About Me

    My name is Thomas Debeauvais. I'm a PhD student in the Informatics department of UC Irvine, in the group of Crista Lopes. Expected graduation date: June 2016.

    My research focuses on player behavior in games, particularly real-money transactions, skill, content consumption, and retention. I apply statistical methods such as regressions or clustering algorithms on datasets with millions of player data. I can also use qualitative methods such as participant observation and semi-structured interviews.

    Have a look at my resume, research projects below, github page, or drop me an email at: tdebeauv (then @uci.edu).

    Games User Research

    The Impact of Gating on Retention in Jelly Splash

    In the mobile game Jelly Splash, three mechanics limit player progression: level difficulty, friend gates, and life regeneration. We found that the difficulty and gate mechanics increase revenues, but also cause churn. Published at FDG 2015.

    Screenshot of Jelly Splash

    An Empirical Study of Driving Skill in Forza Motorsport 4

    In this paper, we looked at patterns of play, skill, and progression in a racing game. We also predicted when a player is ready to permanently increase the game's difficulty with precision and recall reaching up to 90%. Published at FDG 2014.

    Screenshot of Forza 4

    Retention and Progression: Seven Months in World of Warcraft

    We correlate demographic variables with in-game activity and churn. For 100 World of Warcraft players, 10 drop out every month, but 5 come back to play again. Published at FDG 2014.

    Evolution of WoW player base

    Business Intelligence in World of Warcraft

    2012. PhD advancement paper about retention and buying gold in World of Warcraft.

    Correlation network

    10,000 gold for $20: an exploratory study of World of Warcraft gold buyers

    We looked at demographic, social, and game features related to buying virtual gold in World of Warcraft. Achievement-oriented men with full-time jobs and little time on their hands were more likely to buy gold. Published at FDG 2012.

    Screenshot of World of Warcraft

    If you build it, they might stay: retention mechanisms in World of Warcraft

    This paper looked at demographic, social, and game features related to player commitment in World of Warcraft. A quarter of players keep paying $13/month for 6 months or more without even playing the game. Published at FDG 2011.

    Histogram of break durations.

    A qualitative study of Ragnarok Online private servers: in-game social issues

    Players of Ragnarok Online prefer private servers because they provide more customization, tighter communities, and less repetitive gameplay. Published at FDG 2010.

    Screenshot of Ragnarok Online

    Scaling

    RCAT: A Scalable Architecture for Massively Multiuser Online Environments

    Technical Report from 2013. Performance analysis of a multiplayer jigsaw game server.

    Screenshot of a Jigsaw puzzle app relying on RCAT

    RESTful Massively Multi-User Virtual Environments: A Feasibility Study

    Preliminary numbers and microbenchmarks for a REST-based game server. Published at IGIC 2012.

    Performance curves

    RCAT: a RESTful Client-server ArchiTecture

    RCAT is a scalable RESTful back-end architecture aimed at supporting thousands of concurrent and interacting users. Published at Netgames 2011.

    RCAT Architecture

    Distributed Tuning of Machine Learning Algorithms using MapReduce Clusters

    Map-Reduce scales up parameter tuning, but can harm accuracy. I used Weka, Amazon EC2, and random forests. Published at LDMTA 2011.

    Random Forest results
    http://www.ics.uci.edu/~akhavans/ http://www.ics.uci.edu/~wangk7/ Kai Wang's Homepage



    Kai Wang's Homepage



    I am a 3rd year PhD candidate in the Computer Science Department at the University of California, Irvine. Padhraic Smyth and Anima Anandkumar are my co-advisors. My research interests reside in both the theory and application of neural networks and other latent variable models. I've done research internships at Amazon, building Deep Learning systems for product recommendations, and at Microsoft Research / Bing (under Rich Caruana and Nick Craswell), investigating word embeddings for information retrieval.
    preprints / working papers
    Eric T. Nalisnick and Sachin Ravi. Infinite Dimensional Word Embeddings.

    Eric T. Nalisnick, Anima Anandkumar, and Padhraic Smyth. A Scale Mixture Perspective of Multiplicative Noise in Neural Networks.
    conference publications
    Eric Nalisnick, Bhaskar Mitra, Nick Craswell, and Rich Caruana. Improving Document Ranking with Dual Word Embeddings. In Proceedings of the 25th World Wide Web Conference (WWW), Short Paper, Montreal, Canada, April 11-15 2016.

    Eric T. Nalisnick and Henry S. Baird. Character-to-Character Sentiment Analysis in Shakespeare's Plays. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), Short Paper, pages 479-83, Sofia, Bulgaria, August 4-9 2013. [Shakespeare Sentiment Explorer]

    Eric T. Nalisnick and Henry S. Baird. Extracting Sentiment Networks from Shakespeare's Plays. In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR), pages 758-762, Washington, DC, August 25-28, 2013.
    http://www.ics.uci.edu/~hyungiko/ Hyungik Oh

    Hyungik Oh


    Hyungik Oh
    PhD Student

    Information and Computer Science
    University of California, Irvine
    Social Life Networks Lab
    Office: DBH 3209
    Email: hyungiko@uci.edu

    Curriculum Vitae

    About Me

    I am a PhD student in Donald Bren Schoold of Information and Computer Science at University of California, Irvine. My advisor is Bren Professor Ramesh Jain.

    My research focuses on building a context awareness system which analyzes contexts and then detects human’s high level daily activities called life events. It turns myriad data collected from mobile devices or wearable devices into chronological data stream and then builds a human event model on a real time basis. I am building a complete infrastructure for Objective Self and hope to demonstrate its applicability in a few application soon. 


    My research interest is intelligent system, context-awareness, machine learning, data mining and information retrieval. I do not want to do just pure theoretical approaches, but applying my research into the real-world application. I am a mobile person, as well. I enjoy utilizing the mobile device in my research.

    Research Project

    Life Event Detection
    Pixel Averages for Auxological Assessments:An Innovative Method for Obtaining Frequent and Precise Measures of Height and Length (Supported by Gates Foundation)
    SmartNoti-Intelligent Notification System

    Publication

    Hyungik Oh, Laleh Jalali, and Ramesh Jain. "An intelligent notification system using context from real-time personal activity monitoring." Multimedia and Expo (ICME), 2015 IEEE International Conference on. IEEE, 2015.

    Laleh Jalali, Da Huo, Hyungik Oh, Mingfan Tang, Siripen Pongpaichet, Ramesh Jain. Personicle: Personal Chronicle of Life Events. Workshop on Personal Data Analytics in the Internet of Things, VLDB 2014
    http://www.ics.uci.edu/~dakuow1/ Dakuo Wang

    Dakuo Wang

    A HCI Researcher and Engineer

    • Home
    • CV
    • Projects
    • Publications
    • About Me
    • Contact Me

    Welcome.

    Hello there. I am Dakuo Wang. I am a Ph.D Candidate in the Department of Informatics at the University of California, Irvine.

    I study Human-Computer Interaction and Computer Supported Cooperative Work. I also design useful data visualizations, user interfaces, analytic tools and other digital stuff.

    Learn more about me or get in touch if you want to collaborate.

    Back to Top

    Recent Projects.

    • How People Write Together Now, and DocuViz Project

    • Chinese Internet User Experience Research

    • Sentiment Analysis on Twitter and Blog Following the U.S. 2012 Presidential Election

    Back to Top

    Publications

    • Wang, D., Olson, J. S., Zhang, J., Nguyen, T., & Olson, G. M. (2015). How Students Collaboratively Write using Google Docs. iConference 2015 Proceedings.

    • Wang, D., Olson, J. S., Zhang, J., Nguyen, T., & Olson, G. M. (2015). DocuViz: Visualizing Collaborative Writing. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM, New York, NY, USA, 1865-1874.

    • Wang, D., Mark, G. (2015). Internet Censorship in China: Examining User Awareness and Attitudes. ACM Trans. Computer-Human Interaction. under review.

    Back to Top

    About me.

    Education

    • University of California Irvine, California, U.S.A.
      From Sep 2012

      - Ph.D. in Informatics, Interested in Social Computing and Computer Support Cooperative Work

    • University of California Irvine, California, U.S.A.
      Sep 2011 -- Sep 2012

      - Master in Electrical Engineering and Computer Science (EECS) concentration of Computer Network and Distributed Computing

    • Ecole Centrale Electronique, Paris, France
      Sep 2008 -- Oct 2010

      - M.E. in Information System Engineering, Specialization in Enterprise Information System

    • Beijing University of Technology, Beijing, China
      Sep 2005 -- Sep 2008

      - B.S. Computer Science

    Employment

    • Research assistant, University of California Irvine, California, U.S.A.
      From Apr 2012

      - Working on Google Doc Visualization Project with Judith Olson
      - Working on Chinese Microblog Users’ Perception Towards Information Regulation Project with Gary Olson and Gloria Mark
      - Working on Sentiment Analysis of Twitter & Blogsphere during 2012 Presidential Election Project with Gloria Mark

    • Information System Design Engineer, France Telecom, Paris, France
      Apr 2009 – Sep 2010

      - Served as Information System Design Engineer in xDSL/FTTH PEMS Paris
      - Designed and implemented a knowledge base for DSLAM, using Linux/Apache/MYSQL/PHP

    • Information System Technician, Ecole Centrale Electronique, Paris, France
      Aug 2010 – Sep 2010

      - Designed and implemented a student internship information system for ECE Paris, using PHP Yii Framework for server side and JAVA Android for Android tablet app side
      - Worked with 3 other teammates following SCRUM develop methodology

    • Vice President of Student Union, Beijing University of Technology, Beijing, China
      Oct 2005 -- Oct 2006

      - Led 40 members, organize social practice activities (including internship, part-time job, social research and social survey) for whole university 30,000 students,
      - Took charge for the 2006 Summer Social Practice (more than 1000 students joined in and the final financial support achieved 30K euro).

    Awards

    • University of California Irvine Graduate Research/Teaching Fellowship
      Sep 2012 – Sep 2014

    Professional Associations

    • Member of Institute of Electrical and Electronics Engineers (IEEE)

    • Member of Association for Computing Machinery (ACM), ACM special interest group SIGCHI

    My Advisors

    • thumbnail

      Judith S. Olson

      the Bren Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine
      & with courtesy appointments in the School of Social Ecology and the Merage School of Business

    • thumbnail

      Gary Olson

      Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine
      & Professor Emeritus at the University of Michigan

    • thumbnail

      Gloria Mark

      Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine

    Skills

    • Proficient in C, Java, J2EE, JSP and familiar with C++, C#, .NET, ARM assembly

    • Proficient in MySQL Database and familiar with Oracle (PL/SQL)

    • Proficient in HTML, CSS, JavaScript (ExtJS), PHP (Zend and Yii), CMS (Drupal, Symphony and Magento)

    • Comfortable with Linux, Windows and Mac OS

    Languages

    • Proficient in Chinese – native speaker

    • Proficient in English

    • Conversational in French

    Interest

    • Sport : Swimming, Table Tennis, Chess

    • Hobbies : Traveling, Reading

    More about me

    Download Curriculum vitae
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    Get in touch.

    Leave your comment.

    Send me a message



    Contact Information

    Department of Informatics
    5211 Donald Bren Hall
    University of California, Irvine
    Irvine, CA, 92697

    Phone: +1 949 864 9778
    Email: dakuo.wang [@] uci.edu

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    Copyright 2013 Dakuo Wang. - Last Updated: January, 2013     Designed by Styleshout

    http://www.ics.uci.edu/~pjsadows/ Peter J Sadowski

    Peter J Sadowski

    UC Irvine, Department of Computer Science

    UCI Seal
    • Personal
    • Research
    • Home
    • PhD Student
      Department of Computer Science
      Institute for Genomics and Bioinformatics
      Advisor: Pierre Baldi

      I study machine learning, particularly deep learning with artificial neural networks. I am interested in the development of novel algorithms and model architectures for learning useful data representations.

      Artificial neural networks can learn a broad set of functions from data. At UCI, I am working on a number of applications to the natural sciences, including the prediction of chemical reactions and the detection of exotic particles in high-energy physics.

    • Peter Sadowski
    • Contact



    © 2015 • CE
    http://www.ics.uci.edu/~jesmaeln/ Jamshid Esmaelnezhad

    Jamshid Esmaelnezhad

    Ph.D. Candidate
    University of California - Irvine

    • Home
    • Academic
    • Publication
    • Research
    • Teaching

    Welcome to My HomePage

    My name is Jamshid Esmaelnezhad which is written as جمشید اسماعیل نژاد in persian.

    I was born on July 16th,1989 in Shiraz, Iran.

    I'm doing a PhD and I'm working on databases and data management at Donald Bren School of Information and Computer Sciences of University of California - Irvine.

    My advisor is Prof. Chen Li

    And here is my CV.

     
    • Research Interest

      • Information Retrieval
      • Databases and Data Management
      • Machine Learning: Data Mining
      • Social Networks
      • Image Processing
    • Contact

      • Emails
      • jesmaeln[at]uci.edu
     

    Last Update January 2014

    http://www.ics.uci.edu/~darkhipo/ Dmitri Ivanovich Arkhipov

    Dmitri I. Arkhipov Dmitri Arkhipov

    4081 Bren Hall

    1(505)234-6275

    darkhipo AT uci DOT edu

    Advisor:
    Amelia Regan


    Valid XHTML 1.0 Transitional



    A collection of my work from different courses, notes, and other similar sources

    My public github

    Education/Distinctions


    • University of California Irvine (2004 - 2009):
      B.S in Information and Computer Science (Cum Laude).
      Specializations: Computer Systems, Distributed Syste
      ms.
      Minor in Mathematics.
      Member Phi Beta Kappa.
    • University of California Irvine (2010 - 2012):
      M.S in Computer Science

    Software Experience


    • Programming Languages/Frameworks: Java, Scala, JDBC, JSP, C, C++, C#, SQL, x86 assembler, Perl, Python, Matlab, VHDL, Java Script, HTML, CSS, MySQL, PostgreSQL. Apache Tomcat, Apache Hadoop, Apache Hive, Cloudera, Horton Works.
    • Development Software: PostgeSQL, OpenGL, MS Visual Studio 2005, Model-Sim (VHDL), Eclipse IDE, PyDev, Scala IDE, Matlab, Vim, LaTex, Unix, Open/Libre Office.
    • Software: MS Windows XP, Linux (Redhat/Ubuntu), MS Office.
    • Language: English, Russian.

    Employment


    • Graduate Student Researcher and Teacher Assistant @ UCI Bren School of C.S (09/2011 - Current)
      Working on several ongoing optimization and parallel execution themed papers. Assisting in introductory computer programming, and discrete mathematics courses. Current research projects include:
      • Novel concurrency and inter-thread/inter-agent communication and synchronization methods.
      • Agent based navigational programming approaches for heuristic scheduling optimization in a distributed environment.
      • Dynamic approaches to a new varient of the probabilistic traveling salesman problem.
      • Congestion and load adaptive first hop selection in SDN networks.
      • Novel methods for parallelization and batch selection in stochastic gradient descent.

    • Software Engineering Intern (Java/Scala/Python)@ Adaptive Medias Inc (04/2014 - 11/2014):
      I worked on three primary projects. Firstly, a heuristic optimization solution to an ad-ordering problem written in Java. Secondly, I wrote a REST-ful web application in Python flask to accept web-domain urls and classify the semantic content into Internet Advertising Bureau tier 1 categories. Finally, I worked in Scala with the cloudera package of Hadoop to write Spark code to perform log aggregation.

    • Teacher Assistant @ UCI Bren School C.S (Java)(11/2010 - 1/2011):
      Discussed introductory to intermediate Java and Python concepts with students. Taught basic programming skills and principals to undergraduate students. Graded student Python and Java programs.

    • Web Application Developer (Perl/JS) @ Intellisurvey Inc (04/2010 - 10/2010):
      Designed and implemented new features and enhancements to Intellisurvey software. Found and resolved software defects. Assisted in developing Intellisurvey infrastructure by creating tools to aid in development, testing, and systems administration. Provided technical support to internal software users and to clients who users licensing Intellisurvey software tools.

    • Research Assistant @ Institute of Transportation Studies at University of California Irvine (05/2009 - 09/2009):
      Installed, configured and populated a Postgress database with heterogeneous data sources. Developed a front end for the data using JSP. Linked the sources together into larger databases on the basis of common fields and meta information.

    • Research Assistant @ Donald Bren School of Information and Computer Sciences (01/2009 - 05/2009):
      Optimization of MatLab algorithms for dynamic network flow optimization. Writing C functions called from the mex interface of Matlab, parsing work with python and re-implementing several algorithms from Matlab in Python. Developed a genetic algorithm solution.

    Publications


    • D.I. Arkhipov, Wu, Di, and A.C. Regan (2015), A Simple Genetic Algorithm Parallelization Toolkit (SGAPTk) for Transportation Planners and Logistics Managers. Proceedings of the 2015 meeting of the Transportation Research Board, 45(8) 765-778.

    • Wu, D., D I. Arkhipov, Y. Zhang, C.H. Liu and A.C. Regan (2015), Online Wardriving by Compressive Sensing. Wu, D., D I. Arkhipov, Y. Zhang, C.H. Liu and A.C. Regan (2015), IEEE Transactions on Mobile Computing, in press.


    • Chow, J.Y.J., A.C. Regan, F. Ranaiefar and D. Arkhipov (2011), A Network Option Portfolio Management Framework for Adaptive Transportation Planning. Transportation Research, Part A: Policy and Practice, 45(8) 765-778.

    • Tok, Y.C.A, M. Zhao, Chow, J.Y.J., S. G. Ritchie and D. Arkhipov (2011), An on-line data repository for statewide freight planning and analysis. Proceedings of the 2010 meeting of the Transportation Research Board.


    • Chow, J.Y.J., A.C. Regan and D.I. Arkhipov (2010). Fast converging global heuristic for continuous network design problem using radial basis functions. Proceedings of the 2010 meeting of the Transportation Research Board.

    • Chow, J.Y.J., A.C. Regan and D.I. Arkhipov (2010). Faster converging global heuristic for continuous network design problem using radial basis functions. Transportation Research Record: Journal of the Transportation Research Board, 2196, 210-110.


    http://www.ics.uci.edu/~jnnorton/ Index of /~jnnorton

    Index of /~jnnorton

    [ICO]NameLast modifiedSizeDescription

    [DIR]Parent Directory  -  
    [DIR]homepage/24-Jan-2014 16:56 -  
    [DIR]polyculturewebsite/11-Feb-2016 19:48 -  

    Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
    http://www.ics.uci.edu/~tmuck/ Tiago Muck

    Tiago Muck

    • Position: Graduate Student
    • Area: Computer Systems Design (CSD)
    • Advisor: Nikil Dutt
    • Office: DBH3069
    • Office Fax: +1(949)824-4056
    • E-mail: tmuck@uci.edu


    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425
    Last modified: 29 Oct 2015 http://www.ics.uci.edu/~wdevanny/ William E. Devanny
    Photo of Will Devanny

    William E. Devanny

    Graduate student

    I am a Computer Science PhD student in the Center for Algorithms and Theory of Computation at University of California, Irvine. I am advised by David Eppstein.

    • Email: wdevanny@uci.edu
    • Office: DBH 4032
    • CV: cv.pdf

    Teaching

    Summer 2015 Teaching: ICS/CSE 46

    Publications

    The Galois Complexity of Graph Drawing: Why Numerical Solutions are Ubiquitous for Force-Directed, Spectral, and Circle Packing Drawings
    M J Bannister, W E Devanny, D Eppstein, M T Goodrich
    Presented at Graph Drawing 2014
    Journal version in JGAA
    Preprint available at arXiv

    Windows into Geometric Events: Data Structures for Time-Windowed Querying of Temporal Point Sets
    M J Bannister, W E Devanny, M T Goodrich, J A Simons, L Trott
    Presented at CCCG2014
    Paper available at CCCG2014 Proceedings

    Small superpatterns for dominance drawing
    M J Bannister, W E Devanny, D Eppstein
    Presented at ANALCO14
    Preprint available at arXiv

    Superpatterns and universal point sets
    M J Bannister, Z Cheng, W E Devanny, D Eppstein
    Presented at Graph Drawing 2013
    Journal version in JGAA
    Preprint available at arXiv

    DREM 2.0: Improved reconstruction of dynamic regulatory networks from time-series expression data
    M H Schulz, W E Devanny, A Gitter, S Zhong, J Ernst, Z Bar-Joseph
    Published in BMC Systems Biology - Highly Accessed
    Software available here

    Last modified: Jan, 2015

    http://www.ics.uci.edu/~lalehj/ Laleh Jalali
    Laleh Jalali
    • Projects
    • Publications
    • Presentations
    • Education
    • CV
    lalehj {@} ics.uci.edu

    Laleh Jalali

    I am a PhD candidate in Computer Science at University of California, Irvine working with Prof. Ramesh Jain. My research is focused on knowledge representation, qualitative reasoning, and multimodal information processing in various applications such as healthcare and user modeling. This involves sensor data fusion, data management, event recognition, and knowledge discovery.

    My Ph.D. research is focused on qualitative causal modeling in multimedia data streams, where I designed and implemented an interactive causal modeling framework that builds on data-driven techniques while emphasizing and including the appropriate human knowledge in causal inference from observational data.

    Publications

      Conferences

    • Laleh Jalali , Ramesh Jain, "Estimating Qualitative Causal Models from Observational Data", Submitted to International Joint Conference on Artificial Intelligence (IJCAI'16).
    • Laleh Jalali , Ramesh Jain, Ramin Moazeni, "A Framework for Behavioral Trend Analysis from Multimodal Data Streams", Submitted to IEEE International Conference on Multimedia and Expo (ICME'16).
    • Huyong Oh, Laleh Jalali , Ramesh Jain , "Building a Personal Chronicle Using Semantic Fusion on Smartphones", Submitted to ACM International Conference in Multimedia Retrieval (ICMR'16).
    • Laleh Jalali , Ramesh Jain, "Bringing Deep Causality to Multimedia Data Streams", In Proceedings of the 23rd Annual ACM Conference on Multimedia (MM'15), Brave New Ideas, 2015. [PDF][long version PDF][BibTeX]
    • Huyong Oh, Laleh Jalali , Ramesh Jain, "An Intelligent Notification System Using Context from Real-time Personal Activity Monitoring", In Proceedings of International Conference on Multimedia and Expo (ICME'15), 2015. [PDF][BibTeX]
    • Minh-Son Dao, Koji Zettsu, Siripen Pongpaichet, Laleh Jalali , Ramesh Jain, "Exploring Spatio-Temporal-Theme Correlation between Physical and Social Streaming Data for Event Detection and Pattern Interpretation from Heterogeneous Sensors", IEEE International Conference on Big Data, 2015. [PDF]
    • Minhson Dao, Siripen Pongpaichet, Laleh Jalali, Kyoungsook Kim, Ramesh Jain, and Koji Zettsu, "A Real-time Complex Event Discovery Platform for Cyber-Physical-Social Systems", International Conference on Multimedia Retrieval (ICMR'14), 2014. [PDF][BibTeX]
    • Ramesh Jain, Laleh Jalali, and Mingming Fan, "From Health-Persona to Societal Health", In Proceedings of the 22nd international conference on World Wide Web (WWW'13), 2013. [PDF][BibTeX]
    • Laleh Jalali, and Hossein Ghafarian, "Maintenance of Robot’s Equilibrium in a Noisy Environment with Fuzzy Controller", IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS'09), 2009.
    • Laleh Jalali, Mahdi Nasiri, and Behrooz Minaei, "A Hybrid Feature Selection Method Based on Fuzzy Feature Selection and Consistency Measures", IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS'09) , 2009.
    • Magazine Articles

    • Ramesh Jain, Laleh Jalali , "Objective Self", Visions and Views in IEEE MultiMedia, 2014. [PDF][BibTeX]
    • Ramesh Jain, Laleh Jalali, Siripen Pongpaichet, and Amarnath Gupta. "Building Social Life Networks", IEEE Data Engineering Bulletin, 2013. [PDF][BibTeX]
    • Workshops

    • Laleh Jalali , Minh-Son Dao, Ramesh Jain, Koji Zettsu, "Complex Asthma Risk Factor Recognition from Heterogeneous Data Streams", Workshop on Multimedia Services and Technologies for E-health at ICME'15, 2015. [PDF][BibTeX]
    • Laleh Jalali , and Ramesh Jain. "Personicle: Personal Chronicle of Life Events", Workshop on Personal Data Analytics in the Internet of Things (PDA@IoT), Hangzhou, China, 2014. [PDF]
    • Siripen Pongpaichet, Mingfan Tang, Laleh Jalali , Ramesh Jain. "Observing Real-World Phenomena through EventWeb over Space, Time and Theme", 2nd International Workshop on Building Web Observatories (B-WOW'14), 2014. [PDF]
    • Laleh Jalali , and Ramesh Jain. "Building Health Persona from Personal Data Streams." In Proceedings of the 1st ACM Workshop on Personal Data Meets Distributed Multimedia (PDM'13), 2013. [PDF]
    • Technical Reports

    • Laleh Jalali , Ramesh Jain, "A Framework for Event Co-occurrence Detection in Event Streams",[PDF]

    Presentations

    • Bringing Deep Causality to Multimedia Data Streams
      23rd CM Multimedia conference, Brisbane, Australia , October 2015. [Slides]
    • Social Life Networks: Addressing Fundamental Problems
      Invited talk at University of Western Sydney, Australia, October 2015. [Slides]
    • Event-Driven Causal Analysis in Multimedia Data Streams
      Topic Defense, University of California Irvine, July 2015.[Slides]
      The 5th International workshop on Cyber-Physical Cloud Computing, Washington, July 2015.
    • Combining Heterogeneous Data Streams for Detecting Situations
      Web Science and Data Analytics summer school at National University of Singapore (NUS), December 2014.
    • Objective Self
      Invited talk at National University of Singapore (NUS), October 2014.
      Broadening Participation Workshop at UbiComp'14 , Seattle, Washington, September 2014. [Poster]
    • Mobile Phone Sensing
      Guest lecturer for the mobile development course at California State University Long Beach (CSULB), September 2014. [Slides]
    • UCIAsthma product
      Butterworth product development competition at University of California, Irvine, May 2014. [Demo]
    • Social Life Networks: EventShop and Personal EventShop
      The 3rd International workshop on Cyber-Physical Cloud Computing at University of California, Irvine. [Slides]

    Education

    • Ph.D. Computer Science, University of California, Irvine, California, USA, 2016.
    • M.S.Computer Science, University of California, Irvine, California, USA, 2015.
    • M.S. Computer Engineering, Iran University of Science and Technology, Tehran, Iran, 2010.
    • B.S. Computer Engineering, Isfahan University of Technology , Esfahan, Iran, 2006,
    http://www.ics.uci.edu/~msadri/ http://www.ics.uci.edu/~taow4/ Tao Wang
    Tao Wang
    About Me

    I am a PhD candidate at the Department of Informatics, Donald Bren School of Information and Computer Science, the University of California, Irvine. My advisor is Professor Donald J. Patterson. My research interests are human computer interaction (HCI) and ubiquitous computing. I am particularly interested in technologies and issues that improve the quality of life for people living with impaired abilities due to illness or aging. If there is anything you want to discuss or share, please feel free to reach me through one of the means listed in the "Contact" section.

    Before coming to UC Irvine, I was a web developer in New Zealand.

    Education

    I received my Bachelor of Science in Computer Science with Honors at the Department of Computer Science, the University of Auckland. My Honor dissertation supervisor was Dr. Beryl Plimmer. At UC Irvine, I have been working with Professor Alfred Kobsa and Professor Yunan Chen on diabetes patient personal management tools. I am also working with Professor Donald J. Patterson on another project where we explore the role of context in information sharing.

    Project

    • Using Geofencing to Improve the Performance of Location-based Services
      Geofencing is a functionatlity that has become available in all popular mobile platforms. In this project, we took advantage of its proactive nature and created location-based services that reduce power consumption of mobile user agents.
    • Context-awareness in Information Sharing
      With the progress in information technology, we are facing the challenge of information overflow. However, people's information needs are often context specific. In this project, we aim to improve the understanding of roles that context-awareness plays in information management.
    • Personal Rules in Diabetes Self-management
      Website: HealthWatch
      One important part of diabetes self-management is to see the patterns between diet, exercise and glucose levels. Experienced patients apply this knowledge to control their sugar levels and minimize the use of insulin injection. In this project, we aim to create systems that assist such a practise. This ongoing project is supervised by Professor Alfred Kobsa, Professor Yunan Chen (both from Informatics, UC Irvine), and Professor Dara Sorkin (School of Medicine, UC Irinve). I am actively involved in designing the study, developing the software prototypes, data collection, and data analysis.
    • Clinical Information on a Timeline
      Accumulated patient information from various sources over time can be hard to decode. Clinicians need to see the relation among events to hypothesize or verify theories. Temporal relations are useful clues for such an analysis. In this project, we conducted user studies and participatory design to explore requirements for a web system that visualizes temporal events. This ongoing project is supervised by Professor Wendy Chapman and Professor Robert El-Kareh (both from iDash, UC San Diego). As a 2012 summer intern, I was involved in the design and development of a prototype web system used in the study.

    Teaching

    As TA

    • IN4MATX 191: Senior Design Project
    • IN4MATX 62: Game Technologies and Interactive Media
    • IN4MATX 141: Information Retrieval
    • IN4MATX 131: Human-Computer Interaction
    • IN4MATX 45: Patterns of Software Construction
    • IN4MATX 123: Software Architecture
    • IN4MATX 117: Project in Software System Design
    • ICS 10: How Computers Work

    Working Experience

    • 2008 - 2011 Datacom (www.datacom.co.nz), Auckland, New Zealand
    • 2007 - 2008 Hyro (www.hyro.com), Auckland, New Zealand
    • 2005 - 2007 Intergen (www.intergen.co.nz), Auckland, New Zealand

    Other Interests

    Love travelling (who doesn't!). I also find myself spending a lot of my time on music, film. In terms of getting sweaty, I normally do my share by running, weight training, and playing badminton.

    Contact

    Office: 5059 Bren Hall (Bldg 314)
    E-mail: [FirstName].[LastName] @ uci.edu (Please replace the placeholders by my name)


    Last modified: Mar 26th 2015
    http://www.ics.uci.edu/~avaladar/ Arthur Valadares

    Arthur Valadares Personal Webpage

    PhD Student, Software Engineer and Developer

    Quick Biography

    I'm currently a PhD student in Donald Bren School of Information and Computer Science (also know as ICS) in University of California, Irvine. I was admitted to UCI in Fall 2010 and currently pursuing my degree.


    I graduated from University of Campinas, Brazil, as a Computer Engineer, and worked for 2 years as a Software Engineer in the Linux Technology Center in IBM - Brazil. I was involved with bug fixing of Linux installers (Red Hat, SUSE), developing for the OpenSimulator project in IBM's Lotus 3D endeavour, and finally developing and bug fixing KVM for Red Hat's RHEV application.


    My interests are in Software Engineering, Virtual Worlds, Free and Open Source Development, Distributed Systems and Networking.


    I can be reached on my email (dt=dot): avaladar at ics dt uci dt edu, and my github repository is in https://github.com/arthur00/

    Papers

    "Enabling Fine-Grained Load Balancing for Virtual Worlds with Distributed Simulation Engines, Winter Simulation Conference 2014"
    "Framework for Designing and Evaluating Distributed Real-Time Applications, DSRT 2014"
    "RCAT : A Scalable Architecture for Massively Multiuser Online Environments", Technical Report - ISR
    "Evolution of Scalability with Synchronized State in Virtual Environments", MMVE 2012
    "RESTful Massively Multi-User Virtual Environments: A Feasibility Study", IGIC 2012
    "RCAT: a RESTful Client-scalable ArchiTecture", NetGames 2011
    "Virtually Centralized , Globally Dispersed : A Sametime 3D Analysis", LAMDA 2011

    RCAT Demo:

    Multiplayer Jigsaw : My main demo application for driving the RCAT middleware is the multiplayer jigsaw puzzle. I try to keep the latest version deployed for user testing. (Runs in UCI only, send me an e-mail for a demo.)

    Links:

    • Resume
    Website Templates by Free CSS Templates
    http://www.ics.uci.edu/~guix/ Xinning Gui

    Xinning Gui

    I'm a first year PH.D. student with concentration on the Informatics General Track, in Department of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine.

    • Name: Xinning Gui
    • Research area: Collapse Informatics, Social Media
    • Office: 5099@DBH
    • Adivisor: Bonnie Nardi
    • Email: guix@ics.uci.edu


    My Hobbies: Handcraft, Badminton, Music, Detective novels
  • < http://www.ics.uci.edu/~vpsaini/ The Yelp dataset challenge - Multilabel Classification of Yelp reviews into relevant categories

    Yelp Dataset Challenge

    The Team
    Hitesh Sajnani, Vaibhav Saini, Kusum Kumar , Eugenia Gabrielova , Pramit Choudary, Cristina Lopes

    Classifying Yelp reviews into relevant categories

    Yelp users give ratings and write reviews about businesses and services on Yelp. These reviews and ratings help other Yelp users to evaluate a business or a service and make a choice. While ratings are useful to convey the overall experience, they do not convey the context which led a reviewer to that experience. For example, consider a yelp review about a restaurant which has 4 stars:
    "They have the best happy hours, the food is good, and service is even better. When it is winter we become regulars".

    If we look at only the rating, it is difficult to guess why the user rated the restaurant as 4 stars. However, after reading the review, it is not difficult to identify that the review talks about good "food", "service" and "deals/discounts" (happy hours).

    A quick inspection of few hundred reviews helped us to decide important categories that are frequent in the reviews. We found 5 categories which include “Food”, “Service”, “Ambience”, “Deals/Discounts”, and “Worthiness”. "Food" and "Service" categories are easy to interpret. "Ambience" category relates to the décor, and look and feel of the place. "Deals and Discounts" category correspond to offers during happy hours, or specials run by the venue. “Worthiness” category can be summarized as value for money. Users often express the sentiment whether the overall experience was worth the money. It is important to note that "Worthiness" category is different from the “Price” attribute already provided by Yelp. "Price" captures whether the venue is “inexpensive”, “expensive” or “very expensive”.

    This high level categorization of reviews into relevant categories can help user to understand why the reviewer rated the restaurant as “high” or “low”. This information can help other yelpers to make a personalized choice, especially when one does not have much time to spend on reading the reviews. Moreover, such categorization can also be used to rank restaurants according to these categories.

    We formulted the task of classifying a review into relevant categories as a learning problem. However, since a review is inexclusively associated with multiple categories at the same time, it is not a simple binary classification or a multi-class classification. It is rather a multi-label classification problem.
    Here is a short video describing how (and why?) Yelp can build some cool features using this categorization:



    Corpus

    The Yelp dataset released for the academic challenge contains information for 11,537 businesses. This dataset has 8,282 check-in sets, 43,873 users, 229,907 reviews for these businesses. For our study, since we are only interested in the restaurant data, we have considered out only those business that are categorized as food or restaurants. This reduced the number of business to around 5,000.

    We selected all the reviews for these restaurants that had atleast one useful vote. From this pool of useful reviews, we randomly chose 10,000 reviews. A labeling codebook describing what categories to include was developed through an initial open coding of a random sample of 400 reviews. The codebook was validated and refined based on a second random sample of 200 reviews. This exercise helped us to fix 5 categories which include food, ambience, service, deals, and worthiness. Once we identified these 5 categories, the 10,000 reviews were divided into 5 bins with repitition in each bin. 6 Graduate student researchers from our group then read and annotated each of these reviews in the identified categories. It took us approx. 225 man hours to annotate all the reviews. We identified the conflicts in the annotation of reviews among different annotators. We removed all the reviews from the analysis where there were discrepancies among the annotators. This left us with 9019 reviews. We split these annotated reviews into 80% train and 20% test data.

    The review annotation process was very challenging and time consuming. We believe that it is one of the major contributions of this work. We plan to release the annotated data for researchers to extend the work.

    Feature Extraction and Normalization

    We extracted two types of features: (i) star ratings and (ii) textual features consisting of unigrams, bigrams and trigrams.
    For star ratings we created three binary features representing rating 1-2 stars, 3 stars, and 4-5 stars respectively.
    For extracting textual features, we first normalized the review text by converting it to lower–case and removing the special characters. We did not remove the stop words as they play important role to understand user sentiments. The cleaned text is then tokenized to collect unigrams (individual words) and calculate their frequencies across the entire corpus. This results in 54,121 unique unigrams. We condense this feature set by only considering unigrams with a frequency greater than 300, which results in 375 unigram features. Similarly we extract 208 bigrams and 120 trigrams.

    The arff files for the the features extracted for Train and Test data can be downloaded from here: Train.arff and Test.arff

    Here is a short video describing the corpus and the feature engineering



    Classification

    In this section, we will describe the various approaches we took to build a classifier. We will reason about our choices based on the advantage and disadvantage of each approach. You can also have a look at the video presentation, to get a quick overall idea


    The problem of classifying a review into multiple categories is a not a simple binary classification problem. Since a reviewer can talk about various things in his or her review, each review can be classified into multiple categories.

    One of the most popular and perhaps the simplest way to deal with multiple categories is to create a binary classifier for each category. So in our case, we create 5 binary classifiers for food, service, ambience, deals, and worthiness category. In order to do this, we need to transform the dataset into 5 different datasets where each dataset has information only about one category.

    To understand consider a scaled down version of our dataset which has only 4 categories (food, ambience, service, and deals) As shown in Figure 1., we create four different dataset from this original dataset such that each dataset is only associated with a specific category. For example, The new dataset created for "food" category will have only one label. The label will be '1' for all the datapoints which had '1' for "food" in the original dataset. Similarly the dataset created for "service" will have label as '1' for all the datapoints that had service as '1' in the original dataset. This is done for all the categories present in the dataset. Given a new review, binary classifier for each category predicts if the review belongs to a category. The final prediction is the union of all the binary predictors.

    Figure 1.

    Once the dataset is transformed in 4 different datasets, any binary classifier like nearest neighbour, SVM, decision trees, etc. can be used with this approach. Although this approach is pretty simple and treats each category independently. However, as a consequence, it ignores correlation among categories. This assumption may not hold true, especially if the categories share some aspects with each other. For example, in our case, mostly when people get deals, they feel the restaurant is worthy to visit. This means that "deals" and "worthiness" categories are correlated. Similarly we saw correlation between "service" and "ambience" category. Sometimes the correlation may be high, and sometimes it may be very low. However, in our case, we thought it was worth accounting for.

    In order to account for correlation among categories, we considered each different subset of L as a single category. Here L is the set of all the categories, a.k.a targets. So L = {Food, Service, Ambience, Deals}. For example, as shown in Figure 2, we transform the target for the Review 1, {Food, Deals} as a single target with value "1001". Similary for Review 2, {Ambience, Deals}, is tranformed into "0011". Here the pattern is formed by creating a vector which has fixed indices for each category. We then learn a multi-class classifier h : X --> P(L), where X is the review, P(L) is the powerset of L, containing all possible category subsets. This approach takes into account correlations between categories, but also suffers from the large number of category subsets. E.g., if we have 5 categories, this approach will generate 25 possible targets to predict; most of which will have only few datapoints to learn. This approach might work well if there is a large training dataset which covers all (at least most of) the possible targets to predict.

    Figure 2.

    We wanted to get best of both worlds i.e., consider correlation among categories and at the same time not get hit by the large number of subsets generated by the previous approach. Hence, we decided to use ensemble of classifiers where each classifier is trained using a different small subset (k) of categories. For example, let's say there are 4 categories {Food, Service, Ambience, Deals}. We choose subset size = 2. Hence, we build a total of 4C2 = 6 classifiers for the following combination of categories: {(Food,Service), (Food,Ambience), (Food, Deals), (Service, Ambience), (Service, Deals), (Ambience, Deals)}. See first part of Figure 3. For prediction, as shown in second part of Figure 3, we consider prediction of all the six classifiers and then take a majority vote.
    This approach considers correlations among categories and at the same time does not generate very large number of targets by considering only a small subset of categories for each classifier.

    Figure 3.

    Experiments

    Evaluation metrics

    We use Precision and Recall to measure the performance of a classifier.
    To understand, what precision and recall means in our context, consider (x,Y) to be a datapoint where x is the review text and Y is the set of true categories. Y ⊆ L, where L = {Food, Service, Ambience, Deals, Worthiness}.
    Let h be a classifier
    Let Z = h(x) be the set of categories predicted by h for the datapoint(x, Y). Then,
    Precision = |Y ∩ Z|/|Z| (Out of the categories predicted, how many of the them are true categories)
    Recall = |Y ∩ Z|/|Y| (Out of the total true categories, how many of them were predicted)

    Results

    We experimented with all the three approaches discussed above. We used Precision and Recall as our evaluation metrics. For each review, dThe comprehensive set of experiment configurations (different approaches, different classifiers, different feature sets, paramter settings) can be found in this result sheet.

    In the first approach of using L binary classifiers, where L is the total number of categories, we used Naive Bayes, k-Nearest Neighbour, Support Vector Machines (SMO implementation), decision trees, and Neural Networks. In the figure below we report results for Naive Bayes and K-NN for this approach as only they were competitive.
    In the second approach, where we consider label correlations and predict the powerset of labels. Decision trees performed the best in this category. We also experimented with ensemble of classifiers approach using decision trees that gave us the best results overall.


    Conclusion

    Yelp reviews and ratings are important source of information to make informed decisions about a venue. We conjecture that further classification of yelp reviews into relevant categories can help users to make an informed decision based on their personal preferences for categories. Moreover, this aspect is especially useful when users do not have time to read many reviews to infer the popularity of venues across these categories. In this paper, we demonstrated how reviews for restaurants can be automatically classified into five relevant categories with precision and recall of 0.72 and 0.71 respectively. We found that an ensemble of two multi-label classification technique (Binary Relevance and Label Powerset) performed better than the techniques individually. Moreover, there is no significant difference in performance when using a combination of bigrams, unigrams and trigrams instead of only unigrams. We also showed how the results of this study can be incorporated into Yelp’s existing website.

    Technical Report

    Multilabel Calssification of reviews in Yelp data

    Download Presentation

    Multilabel Calssification of reviews in Yelp data

    comments powered by Disqus http://www.ics.uci.edu/~hstrong/ Homer Strong · Statistics PhD Student

    Homer Strong

    Statistics PhD student at University of California, Irvine

    Home About GitHub Currently v0.0.2

    © 2015. All rights reserved.

    Good day

    03 Apr 2015

    I'm pretty busy at the moment but if you'd like to get in touch then you can reach me via hstrong at uci.edu

    Currently I'm affiliated with the following groups and projects.

    • Professor Padhraic Smyth's DataLab
    • Cosmic RAys Found In Smartphones, CRAYFIS
    • The UCI Data Science Initiative
    • Data Team at Yieldbot, Inc
    Older Newer
    http://www.ics.uci.edu/~jsupanci/ James Steven Supančič III

    James Steven Supančič III

    Computer Vision PhD Student at UC Irvine

    Depth-based hand pose estimation

    October 2015Posted by James

     
    splash for paper

    J. Supancic, G. Rogez, Y. Yang, D. Ramanan, J. Shotton. "Depth-based hand pose estimation: data, methods, and challenges" International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015.

  • Dataset and code here
  • Everyday Hands in Action

    October 2015Posted by James

     
    splash for paper

    G. Rogez, J. Supancic, D. Ramanan. "Understanding Everyday Hands in Action from RGB-D Images" International Conference on Computer Vision (ICCV), Santiago, Chile, December 2015.

    Pose in Egocentric Workspaces

    June 28th, 2015Posted by James

     
    splash for paper

    G. Rogez, J. Supancic, D. Ramanan. "First-Person Pose Recognition using Egocentric Workspaces" Computer Vision and Pattern Recognition (CVPR), Boston, MA, June 2015.

  • The title was formerly "Egocentric Pose Recognition in Four Lines of Code".
  • Hand Datasets and Methods

    September 29th, 2015Posted by James

     
    data and eval splash
    headshot egocentric splash figure learning_splash_figure

    J. Supancic, D. Ramanan. "Self-Paced Learning for Long-Term Tracking" Computer Vision and Pattern Recognition (CVPR), Portland, OR, June 2013.

    Displaying Real Valued Data in OpenCV

    Dec 19th, 2012Posted by James

     
    The imageeq function, which follows, equalizes a floating point image before displaying it. It is much better than MATLAB's imagesc for visualizing depth data.
    void imageeq(const char* winName, cv::Mat_< float > im)
    {
        // compute the order statistics
        vector< float > values;
        for(int rIter = 0; rIter < im.rows; rIter++)
            for(int cIter = 0; cIter < im.cols; cIter++)
                    values.push_back(im.at< float >(rIter,cIter));
        std::sort(values.begin(),values.end());
        auto newEnd = std::unique(values.begin(),values.end());
        values.erase(newEnd,values.end());
        
        // compute an equalized image
        Mat showMe(im.rows,im.cols,DataType< uchar >::type);
        float oldQuant = 0;
        for(int qIter = 1; qIter <= 256; qIter++)
        {
            float quantile = static_cast< float >(qIter)/256;
          
            float thresh_low = values[oldQuant*(values.size() - 1)];
            float thresh_high = values[quantile*(values.size() - 1)];
            //printf("q = %f low = %f high = %f\n",quantile,thresh_low,thresh_high);
            for(int rIter = 0; rIter < im.rows; rIter++)
                for(int cIter = 0; cIter < im.cols; cIter++)
                {
                    float curValue = im.at< float >(rIter,cIter);
                    if(curValue <= thresh_high && curValue >= thresh_low)
                        showMe.at< uchar >(rIter,cIter) = qIter-1;
                }
            
            oldQuant = quantile;
        }
        
        imshow(winName,showMe);
    }
    

    Welcome to my academic homepage

    headshot School of ICS



    Ph.D. Student

    Computer Science Department
    Donald Bren School of Computer and Information Sciences
    University of California, Irvine


    Email: wangk7 at uci.edu
    Office: Donald Bren Hall 3243





    About me                                                                          

    I am currently a second-year Ph.D. student at University of California, Irvine. My advisor is Prof. Guoqing(Harry) Xu. Before coming to UC Irvine, I completed my Master's degree in CS at Institute of Computing Technology, Chinese Academy of Sciences I completed my undergraduate degree in CS at Huazhong Unversity of Science&Technology.
    It's really a brand-new journey. Enjoy! :)

    Research Interests                                                          

    Programming Language, Program Analysis, Software Engineering

    Current Projects                                                              

    1 Facade: A compiler and runtime system for (almost) object-bounded Big Data applications

    A managed Big Data application often suffers from large space overhead and GC cost due to extremely large numbers of objects and references in the heap. A key observation is that, in a scalable system, the number of heap objects representing data items cannot grow proportionally with the dataset cardinality. We develop Facade, a Java-based compiler and runtime, that can statically bound the number of heap objects that represent data items. Facade advocates to store data items in native memory and create objects as facades to represent data items. It uses a new execution model that dynamically establishes a many-to-one mapping between an unbounded set of data items in native memory and a statically bounded set of objects in the heap, thereby reducing significantly the number of objects, their associated space overhead (i.e., pointers and headers), as well as the GC cost.

    The paper is accepted by ASPLOS'15.

    2 GraphQ: Graph query processing with abstraction refinement

    GraphQ is a scalable querying framework for very large graphs. GraphQ is built on a key insight that many interesting graph properties --- such as finding cliques of a certain size, or finding vertices with a certain page rank --- can be effectively computed by exploring only a small fraction of the graph, and traversing the complete graph is an overkill. The centerpiece of our framework is the novel idea of abstraction refinement, where the very large graph is represented as multiple levels of abstractions, and a query is processed through iterative refinement across graph abstraction levels. As a result, GraphQ enjoys several distinctive traits unseen in existing graph processing systems: query processing is naturally budget-aware, friendly for out-of-core processing where ``Big Graphs'' can not entirely fit into memory, and endowed with strong correctness properties on query answers. With GraphQ, a wide range of complex analytical queries over very large graphs can be answered with resources affordable to a single PC, which is in compliant with the recent trend that advocates single-machine-based Big Data processing.

    The paper is accepted by USENIX ATC'15.

    Publications                                                                     

  • 1 Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Xu. "Facade: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications", 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Istanbul, Turkey, March 14-18, 2015. (Acceptance rate: 48/278, 17%)

  • 2 Kai Wang, Guoqing Xu, Zhendong Su, and Yu David Liu. "GraphQ: Graph Query Processing with Abstraction Refinement -- Programmable and Budget-Aware Analytical Queries over Very Large Graphs on a Single PC", 2015 USENIX Annual Technical Conference, Santa Clara, CA, July 2015. (Acceptance rate: 35/221, 15.8%)

    Personal Interests                                                           

    Music, Movies, Skiing.

    http://www.ics.uci.edu/~jseberge/ John S. Seberger CV Redirect
    The page you are looking for has moved. You will be redirected to the new location automatically in 5 seconds.

    http://www.ics.uci.edu/~gghiasi/ Golnaz Ghiasi

    Golnaz Ghiasi

    I am a Computer Science PhD student at the University of California Irvine and a member of Computational Vision Group where I am advised by Prof. Charless Fowlkes.

    My research interest is Computer Vision and Machine Learning. My current research is focused on Pose Estimation in the Presence of Occlusion.



    gghiasi [at] ics.uci.edu
    4209 Donald Bren Hall





    Publications


    G. Ghiasi, C. Fowlkes, "Using Segmentation to Predict the Absence of Occluded Parts" , Proc. of British Machine Vision Conference (BMVC) 2015
    paper COFW train data masks



    G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces" , Technical Report, 2015
    paper detection code UCI-OFD dataset Occluded HELEN



    G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model" , Proc. of Computer Vision and Pattern Recognition (CVPR) 2014
    paper poster



    G. Ghiasi, Y. Yang, D. Ramanan, C. Fowlkes, "Parsing Occluded People" , Proc. of Computer Vision and Pattern Recognition (CVPR) 2014
    paper poster



    http://www.ics.uci.edu/~aniketsh/ Aniket Shivam http://www.ics.uci.edu/~santanus/

    The page is under construction. Thanks for your patience.

    http://www.ics.uci.edu/~yuxiaow1/ Yuxiao

    Yuxiao Wang

    About me

    • Current Position: PhD student at Department of Statistics, University of California, Irvine
    • Research Interest: Spatio-temporal data modeling, Time Series.
    • Contact: yuxiaow1[at]uci.edu

    Education

    • PhD (In Progress), Department of Statistics, UC Irvine (Sept. 2012 - present)
    • B.S. in Physics (2010), University of Science and Technology of China (USTC)

    Project

    • Cortical source reconstruction and brain connectivity study uisng EEG data


    http://www.ics.uci.edu/~araturi/ PICARD
    picard's face

    Jean Luc Picard

    Captain Jean-Luc Picard (/ʒɑːnˌluːk pɨˌkɑrd/) is a fictional character who appears in fiction related to Star Trek. He appears in the television series Star Trek: The Next Generation and the feature films Star Trek Generations, Star Trek: First Contact, Star Trek: Insurrection and Star Trek: Nemesis. He also made a short appearance in the pilot episode of Star Trek: Deep Space Nine. He is portrayed by actor Patrick Stewart.

    • Favourite Tea: Earl Grey
    • Famous Quote: Make it so
    Year Employment History Description
    303030 Captain of the starship enterprise hustled
    50505005 First officer on star gazer hustled
    http://www.ics.uci.edu/~willmlam/ William Lam, UC Irvine
    • Home
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    William Lam

    Information

    PhD Candidate
    Computer Science
    UC Irvine
    Office: DBH 4099
    will...@ics.uci.edu
    PGP Key

    About Me


    I am currently a PhD candidate in Prof. Rina Dechter’s Automated Reasoning group.

    Graphical models are a widely used framework for reasoning tasks. I am particularly interested in search-based approaches to inference and developing/improving heuristics for it. I am also interested in exploring different search strategies.

    I have worked on exploring the AND/OR Decision Diagram framework for performing inference and evaluating its quality on solving high-treewidth problems with problem specific structures such as determinism and context-specific independence.

    I am currently working on different types of dynamic heuristics for AND/OR Branch and Bound. This work aims to find a good tradeoff between heuristic computation and node expansion in AOBB.



    Before UC Irvine, I was an undergraduate research assistant in R-LAIR (Riverside Lab for Artificial Intelligence Research) with Prof. Christian Shelton.

    I worked on the CTBN-RLE code base. Specifically, I implemented the structure learning component, which learns a Bayesian network and continuous time Bayesian network structure from trajectory data. It seamlessly incorporates any of the inference methods available in CTBN-RLE to handle the case of partially observed trajectories. I also implemented mean field variational inference algorithm inside CTBN-RLE.

    Education
    PhD Candidate in Computer Science (September 2010 - now)
    Bren School of Information and Computer Sciences, UC Irvine.
    MS in Computer Science (September 2010 - December 2012)
    Bren School of Information and Computer Sciences, UC Irvine.
    BS in Computer Science (September 2006 - June 2010)
    Bourns College of Engineering, UC Riverside.
    Work Experience
    Software Engineering Intern (June 2015 - September 2015)
    Google, New York, NY. Search infrastructure.
    Software Engineering Intern (June 2014 - September 2014)
    Google, Los Angeles, CA. YouTube.
    Publications
    Rina Dechter, Kalev Kask, William Lam, and Javier Larrosa
    Look-ahead with Mini-Bucket Heuristics for MPE.
    In Proceedings of AAAI 2016, Phoenix, AZ, USA, February 2016.
    [pdf]
    William Lam, Kalev Kask, and Rina Dechter
    Empowering Mini-Bucket in Anytime Heuristic Search with Look-Ahead: Preliminary Evaluation.
    In Proceedings of SoCS 2015, Ein Gedi, the Dead Sea, Israel, June 2015.
    [pdf]
    William Lam, Kalev Kask, Rina Dechter, and Alexander Ihler
    Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics.
    In Proceedings of SoCS 2014 , Prague, Czech Republic, August 2014.
    [pdf]
    Junkyu Lee, William Lam, and Rina Dechter.
    Benchmark on DAOOPT and GUROBI with the PASCAL2 Inference Challenge Problems.
    In DISCML 2013 (a workshop of NIPS 2013), Lake Tahoe, NV, USA, December 2013.
    [pdf]
    William Lam and Rina Dechter.
    Empirical Evaluation of AND/OR Multivalued Decision Diagrams for Inference.
    In Doctoral Programme of CP 2012, Québec City, QC, Canada, October 2012.
    [pdf | extended version]
    E. Busra Celikkaya, Christian R. Shelton, and William Lam.
    Factored Filtering of Continuous-Time Systems.
    In Proceedings of UAI 2011, Barcelona, Spain, July 2011.
    [pdf]
    Christian R. Shelton, Yu Fan, William Lam, Joon Lee, and Jing Xu.
    Continuous Time Bayesian Network Reasoning and Learning Engine.
    In Journal of Machine Learning Research, 11, 1137-1140.
    [link]

    © 2015 William Lam

    http://www.ics.uci.edu/~zbutler/ This is a stub, that may eventually become my legit homepage. #2lazy4webdesign #YOLO http://www.ics.uci.edu/~mlichman/ Index of /~mlichman

    Index of /~mlichman

    [ICO]NameLast modifiedSizeDescription

    [DIR]Parent Directory  -  
    [   ]data_sceince.zip01-Jun-2015 11:42 81M 
    [   ]review.pages22-Jan-2016 17:44 336K 

    Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
    http://www.ics.uci.edu/~vpalepu/ Vijay Krishna Palepu
    Research Projects News Notes CV
    Vijay Krishna Palepu • vpalepu [at] uci [dot] edu • 5243 Bren Hall, Spider Lab, University of California, Irvine, CA 92697-3440
    Vijay Krishna

    I am a Ph.D. Candidate in Software Engineering, at the Department of Informatics, University of California, Irvine. I graduated, my undergrad Bachelor of Engineering (B.E.) program, with a First Class with Distinction in Computer Engineering from the University of Pune, India.

    As a student of Software Engineering Research, I am interested in building, visualizing and studying reusable models of the runtime behavior of software components that can be leveraged for a number of software development, maintenance, and testing tasks. My current focus, however, is to facilitate software debugging which is a complex and ubiquitous activity within software engineering.

    My advisor is Prof. James A. Jones at the Spider Lab. James' research is focused towards software testing, analysis (compile-time and run-time) and debugging.

    @bitbucket.com • @Linkedin • @Instagram • @medium • @stackoverflow • @gist.github.com
    Background Photo: Mt. Ranier, © Vijay Krishna Palepu, 2015
    Latest
    • 01 Oct 2015 » Attended VISSOFT 2015 VISSOFT15 conference Software
    • 15 Sep 2015 » Summer Internship at Microsoft Microsoft internship engineering
    • 11 Jan 2015 » Part of Winning Team @ UCI Data Science Hackathon hackathon data science
    CEREBRO
    Hosted at: http://spideruci.github.io/cerebro
    Cerebro, formerly known as "The Brain", reveals clusters of source code that co-execute to produce behavioral features of the program throughout and within executions. We created a clustered visualization of source-code that is informed by dynamic control flow of multiple executions; each cluster represents commonly interacting logic that composes software features. In addition, we render individual executions atop the clustered multiple-execution visualization as user-controlled animations to reveal characteristics of specific executions—these animations may provide exemplars for the clustered features and provide chronology for those behavioral features, or they may reveal anomalous behaviors that do not fit with the overall operational profile of most executions. Both the clustered multiple-execution view and the animated individual-execution view provide insights for the constituent behaviors within executions that compose behaviors of whole executions. Inspired by neural imaging of human brains of people who were subjected to various external stimuli, we designed and implemented Cerebro to reveal program activity during execution. The result has revealed the principal behaviors of execution. Those behaviors were revealed to be (in some cases) cohesive, modular source-code structures and (in other cases) cross-cutting, emergent behaviors involving multiple modules.
    Research Publications ↵
    • Reddy, Nishaanth H.; Kim, Junghun; Palepu, Vijay Krishna and Jones, James, "Spider SENSE: Software-Engineering, Networked, System Evaluation," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-5, 27-28 September 2015. [paper] [website]
    • Palepu, Vijay Krishna and Jones, James, "Revealing Runtime Features and Constituent Behaviors within Software," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-10, 27-28 September 2015. [paper] [website]
    • Palepu, Vijay Krishna and Jones, James, "Discriminating Influences among Instructions in a Dynamic Slice," , 2014 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), to appear, 15-19 September 2014. [paper] [slides]
    • Palepu, Vijay Krishna; Xu, Guoqing and Jones, James, "Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries," , 2013 28th IEEE International Conference on Automated Software Engineering (ASE), pp.59-69, 11-15 November 2013. [paper] [slides]
    • Palepu, Vijay Krishna and Jones, James, "Visualizing Constituent Behaviors within Executions," , 2013 1st IEEE International Working Conference on Software Visualization (VISSOFT), pp.1-4, 27-28 September 2013. [paper] [vimeo]
    • Martie, Lee; Palepu, Vijay Krishna; Sajnani, Hitesh and Lopes, Cristina, "Trendy bugs: Topic trends in the Android bug reports," , 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp.120-123, 2-3 June 2012. [paper] [slides]
    Teaching
    • Reader, Senior Design Project (UCI, Spring 2012)
    • Reader, Concepts in Programming Languages II (UCI, Spring 2012)
    • Teaching Assistant, Senior Design Project (UCI, Fall 2012, Winter 2013, Spring 2013)
    • Guest Speaker on Testing Software Behavior, Graduate course on Software Testing and Analysis (UCI, Spring 2014)
    • Guest Speaker on QA and Testing, Introduction to Software Engineering (UCI, Summer 2013, Summer 2014)
    • Guest Speaker on Software Testing, Project in Software Engineering (UCI, Winter 2015)
    Talks
    • "Testing Software", Guest Lecture, Undergraduate course on Project in Software Engineering (UCI, Winter 2015) [slides]
    • "Discriminating Influences among Instructions in a Dynamic Slice", New Ideas Talk, Automated Software Engineering (Sweden, Summer 2014) [slides] [vimeo]
    • "QA and Software Testing", Guest Lecture, Undergraduate course on Introduction to Software Engineering (UCI, Summer 2014)
    • "Testing and Verifying Software Behavior", Guest Lecture, Graduate course on Software Testing and Analysis (UCI, Spring 2014) [notes]
    home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
    http://www.ics.uci.edu/~majumder/ Aditi Majumder

     

     

     

    Home

     

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    (Interactive Graphics

    & Visualization Lab)

     

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    Description: Y:\public_html\docs\aditi.jpg

    Aditi Majumder

    Professor
    University of California, Irvine
    Department of Computer Science
    4056 Bren Hall

    Irvine, CA 92697-3435

    (949) 824-8877 - voice
    (949) 824-4056 - fax
    majumder @ ics.uci.edu

     

    Affiliated with:

    California Institute of Telecommunication and Information Technology (Calit2)

    Research Interests

    • Novel Display Technologies
    • Computational Cameras and Projectors
    • Image or Video Processing
    • Computer Graphics and Visualization
    • Computer Vision

    A Peek at My Research

              Our Multi-Projector Display Research

    Awards

    • Deans Mid-Career Research Award, School of Information and Computer Science, UC-Irvine, 2011
    • NSF CAREER Award, 2009 on Ubiquitous Displays Via a Distributed Framework
    • Best Paper Award, IEEE Virtual Reality, 2010
    • Runner-up for Best Paper Award, IEEE Visualization 2009
    • Best Paper Award at IEEE Workshop on Projector Camera Systems, 2010
    • Faculty Research Incentive Award, 2009

    Recent Academic Activities

    • Program Co-Chair, ACM Virtual Reality Software and Technology (VRST), Edinburgh, UK, 2014
    • General Chair, IEEE Virtual Reality (VR), Orange County, March 2012.
    • Program Chair, IEEE Virtual Reality (VR), Singapore, March, 2011.
    • Program Chair, IEEE/ACM Workshop on Projector-Camera Systems (PROCAMS), Miami, June 2009.
    • General Co-Chair, ACM Virtual Reality Software and Technology (VRST),  Newport Beach, November, 2007.
    • Program Co-Chair, IEEE CVPR Workshop on Projector-Camera Systems (PROCAMS) , San Diego, June 2005.

    Recent Industrial Activities

    • Advisory Board, Allosphere, University of California, Santa Barbara.
    • Advising Consultant, Disney Imagineering, June 2009-June 2010
    • Advisory Board Member and Consultant, Ostendo Technologies, Carlsbad, 2006-Present [Developed the first curved screen multi-projector desktop]

    Recent Pedagogical Activities

    • E-Tech Booth at Siggraph 2015

     

      

    • Book

     

    bookpic

     

     

     

    Aditi Majumder and Michael S. Brown, Practical Multi-Projector Display Design, A.K. Peters, 2007

     

     

    • Book Chapter

     

    Distributed Video Sensor Networks

     

     

     

    Chapter 15 on Ubiquitous Displays by Aditi Majumder, Distributed Video Sensor Networks, Edited by Bir Bhanu, Chinya V. Ravishankar, Amit K. Roy-Chowdhury, Hamid Aghajan and Demetri Terzopoulos, Springer, 2011

     

     

    • Summer Course
      • Visual Perception for Visualization and Multimedia Group, Department of Informatics, University of Zurich, Switzerland, August 2010.

     

    • Invited Presentations
      • Keynote Speaker, BayArea Multi-media Forum, San Jose, April, 2015
      • Keynote Speaker, Brazilian Symposium on Virtual Reality, Rio, May, 2012
      • Keynote Speaker, International Symposium on Visual Computing, Las Vegas, December, 2010
      • Invited Speaker, Ubiquitous Displays Via a Distributed Framework, Johannes Kepler University, Linz, Austria, May, 2010.
      • Invited Speaker, Course on Projector in Graphics, Organized by Ramesh Raskar and Oliver Bimber, SIGGRAPH 2009 (pdf).
      • Invited Panelist, Future of the Projector-Camera Systems, IEEE Workshop on Projector-Camera Systems, 2008.

     Released Software

    • Contrast Enhancement of Images

     

    Site Meter

    http://www.ics.uci.edu/~yamingy/ Yaming Yu's Home Page

    Yaming Yu's Home Page


    Yaming Yu is an associate professor in the Department of Statistics at UC Irvine. He received his Ph.D. from the Department of Statistics at Harvard University and his B.S. in Mathematics from Beijing University. A picture of Yaming can be found here

    Research Interests

    Statistical computing; information theory; Bayesian analysis; applied probability.

    On Statistical Computing and Optimal Design:

    • To Center or Not to Center, That is Not the Question: An Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Efficiency (with Xiao-Li Meng) Journal of Computational and Graphical Statistics 20 (2011) 531-570 Rejoinder 603-615
    • Monotonically Overrelaxed EM Algorithms Accepted, Journal of Computational and Graphical Statistics
    • D-optimal Designs via a Cocktail Algorithm Statistics and Computing 21 (2011) 475-481 User's guide R code R demo1 R demo2 R demo3 R demo4 matlab code matlab demo
    • Monotonic Convergence of a General Algorithm for Computing Optimal Designs Annals of Statistics 38 (2010) 1593-1606
    • On a Multiplicative Algorithm for Computing Bayesian D-optimal Designs Preprint, 2010 R code demo 1 demo 2 demo 3
    • Improved EM for Mixture Proportions with Applications to Nonparametric ML Estimation for Censored Data Preprint, 2010 User's guide C code R wrapper
    • Squeezing the Arimoto-Blahut Algorithm for Faster Convergence IEEE Transactions on Information Theory 56 (2010) 3149-3157

    On Information Theory, Inequalities, and Limit Theorems:

    • On the Inclusion Probabilities in Some Unequal Probability Sampling Plans Without Replacement Accepted, Bernoulli
    • Some Stochastic Inequalities for Weighted Sums Bernoulli 17 (2011) 1044-1053
    • Monotonicity, Thinning and Discrete Versions of the Entropy Power Inequality (with Oliver Johnson) IEEE Transactions on Information Theory 56 (2010) 5387-5395
    • Sharp Bounds on the Entropy of the Poisson Law and Related Quantities (with Jose A. Adell and Alberto Lekuona) IEEE Transactions on Information Theory 56 (2010) 2299-2306
    • Monotonic Convergence in an Information-Theoretic Law of Small Numbers IEEE Transactions on Information Theory 55 (2009) 5412-5422
    • On the Entropy of Compound Distributions on Nonnegative Integers IEEE Transactions on Information Theory 55 (2009) 3645-3650

    More Papers

    Recent Teaching:

    • Statistics 230 (Spring 2013): Statistical Computing
    • Statistics 270: Stochastic Processes (Spring 2013)

    Other Interests

    He likes Russian literature.

    http://www.ics.uci.edu/~stasio/ Stanislaw Jarecki

    Stanislaw Jarecki

    • Associate Professor, School of Information and Computer Sciences, University of California at Irvine
    • Office: Bren Hall, room 4026
    • Office Tel: 949-824-8878
    • Office Fax: +1(949)824-4056
    • Electronic address:  concatenate my user name "stasio", the "@" sign, and a string "ics.uci.edu"
    • Mailing address:  School of Information and Computer Science,
                                444 Computer Science Bldg,
                                University of California, Irvine,
                                Irvine, CA 92697-3425

    Research Interests:

    • Cryptography, Security, Fault-Tolerant Distributed Computing

    Professional Activities:

    • Program committees:  SCN 2006 ,  Crypto 2005 ,  ACNS 2005 ,  CT-RSA 2005 ,   Eurocrypt 2003

    Current Teaching:

    • Spring’2010: ICS.6B, Intro to Discrete Math (Logic and Computation)
    • Spring’2010: ICS.167, Intro to Cryptography (Undergraduate)

    Some Past Courses:

    • ICS 280, Cryptographic Protocols (graduate): Spring'06
    • ICS 268, Cryptography and Communication Security (graduate): Fall'05, Fall'04, Winter'04 (ICS 280)
    • ICS 263, Computational Complexity (graduate): Sping'05
    • ICS 180, Intro to Cryptography (undergraduate): Spring'04
    • ICS 22H, Honors Introduction to Computer Science (undergraduate): Winter'06, Fall'05, Winter'05

    Students I have supervised:

    • Xiaomin Liu, Nitesh Saxena

    Publications (Conferences):

    • Group Secret Handshakes with Reusable Credentials, or Affiliation-Hiding Group Key Agreement, Cryptographic Track of RSA Security [CT-RSA] '07
      Stanislaw Jarecki, Jihye Kim, and Gene Tsudik
      (final version coming soon)
    • Dandelion: Secure Cooperative Content Distribution with Robust Incentives , First Workshop on the Economics of Networked Systems [NetEcon] '06
      Michael Sirivianos, Xiaowei Yang, and Stanislaw Jarecki
      (.pdf)
    • Authentication for Paranoids: Multi-Party Secret Handshakes, ACNS'06
      Stanislaw Jarecki, Jihye Kim, and Gene Tsudik
      (.pdf)
    • Further Simplifications in Proactive RSA Signatures,  Theory of Cryptography Conference'05
      Stanislaw Jarecki and Nitesh Saxena
      (.pdf)
    • Probabilistic Escrow of Financial Transactions with Cummulative Threshold Disclosure, Financial Cryptography'05
      Stanislaw Jarecki and Vitaly Shmatikov
      (.pdf)
    • Secret Handshakes from CA-oblivious Encryption,  Asiacrypt '04
      Claude Castelluccia, Stanislaw Jarecki, and Gene Tsudik
      abstract.html (.pdf)
    • An Attack on the Proactive RSA Signature Scheme in the URSA Ad Hoc Network Access Control Protocol, SASN '04
      Stanislaw Jarecki, Nitesh Saxena, and Jeong Hyun Yi
      abstract.html (.pdf)
    • Versatile Padding Schemes for Joint Signature and Encryption,  CCS '04
      Yevgeni Dodis, Michael J. Freedman, Stanislaw Jarecki, and Shabsi Walfish
      abstract.html (.pdf)
    • A Robust Multisignature Scheme with Applications to Multicast Acknowledgement Aggregation, SCN '04
      Claude Castelluccia, Stanislaw Jarecki, Jihye Kim, and Gene Tsudik
      abstract.html [This paper is superseded by an article in the special issue of the ComNet journal (see below).]
    • Handcuffing Big Brother: An Abuse-Resilient Transaction Escrow Scheme, Eurocrypt '04
      Stanislaw Jarecki and Vitaly Shmatikov
      abstract.html (.pdf)
    • A Signature Scheme as Secure as the Diffie Hellman Problem,  Eurocrypt '03
      Eu-Jin Goh and Stanislaw Jarecki
      abstract.html (.pdf) (.ps)
    • Revisiting the Distributed Key Generation for Discrete-Log Based Cryptosystems ,  Cryptographic Track of RSA Security [CT-RSA] '03
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      abstract.html (.pdf) (.ps) [This paper is superseded by a journal article in JoC'06 (see below).]
    • Negotiated Privacy, International Symposium on Software Security '02,
      Stanislaw Jarecki, Pat Lincoln and Vitaly Shmatikov,
      (.pdf)
    • Cryptographic Primitives Enforcing Communication and Storage Complexity, Financial Cryptography'02
      Philippe Golle, Stanislaw Jarecki, and Ilia Mironov
      (.pdf)
    • Adaptively Secure Threshold Cryptosystems without Erasures, manuscrypt, 1999.
      Stanislaw Jarecki and Anna Lysyanskaya
      (.ps) (.ps.gz)
      This work appeared as "Adaptively secure threshold cryptography: Introducing concurrency, removing erasures" in Eurocrypt '00, as a joint publication with another work of Anna Lysyanskaya,
      (.ps) (.ps.gz) (.pdf )
    • Adaptive Security for Threshold Cryptosystems,  Crypto '99
      Ran Canetti, Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      extended version:  (.ps) (.ps.gz)
    • Secure Distributed Key Generation for Discrete-Log Based Cryptosystems, Eurocrypt'99
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      extended version:  (.ps) (.ps.gz) [This paper, togher with the CT-RSA'03 one listed above, is superseded by a journal article in JoC'06 (see below).]
    • An efficient micropayment system based on probabilistic polling,  Financial Cryptography '97
      Stanislaw Jarecki and Andrew Odlyzko
      (.ps) (.ps.gz)
    • Proactive Public Key and Signature Systems, ACM Security '97
      Amir Herzberg, Stanislaw Jarecki, Hugo Krawczyk, Markus Jakobsson, and Moti Yung
      (.ps) (.ps.gz)
    • Robust and Efficient Sharing of RSA Functions, Crypto '96]
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      [This paper is superseded by its journal version in JoC'00 (see below).]
    • Robust Threshold DSS Signature,  Eurocrypt '96
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      [This paper is superseded by its journal version in I&C'01 (see below).]
    • Proactive Secret Sharing, or How to Cope with Perpetual Leakage, Crypto '95
      Amir Herzberg, Stanislaw Jarecki, Hugo Krawczyk, and Moti Yung
      an extended version: abstract.html (.ps) (.ps.gz)

    Publications (Journals):

    • Secure Acknowledgment Aggregation and Multisignatures with Limited Robustness, to appear in Computer Networks Journal (ComNet), special issue on Network Algorithms (R. De Prisco and S. Rajsbaum, editors),
      Claude Castelluccia, Stanislaw Jarecki, Gene Tsudik
      (.pdf) [The preliminary version of this paper, listed above, appeared in SCN'04.]
    • Secure Distributed Key Generation for Discrete-Log Based Cryptosystems, Journal of Cryptology, May 2006
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, Tal Rabin
      (Springer Link) [This article supersedes two conference publications listed above: ``Secure Distributed Key Generation for Discrete-Log Based Cryptosystems'' in Eurocrypt'99, and ``Revisiting the Distributed Key Generation for Discrete-Log Based Cryptosystems'' in CT-RSA'03.]
    • Robust Threshold DSS Signature,  Information and Computation , vol. 164 (1): 54-84, 2001
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      abstract.html (.ps) (.ps.gz) [The preliminary version of this article, listed above, appeared in Eurocrypt '96.]
    • Robust and Efficient Sharing of RSA Functions,  Journal of Cryptology , vol. 13 (2): 273-300, 2000
      Rosario Gennaro, Stanislaw Jarecki, Hugo Krawczyk, and Tal Rabin
      (.ps) (.ps.gz) [The preliminary version of this article, listed above, appeared in Crypto '96.]

    Short Bio:

    I joined UCI as an assistant professor in the School of Information and Computer Sciences in July 2003.  In June 2001 I graduated from the MIT Computer Science PhD program, where I studied cryptography under the guidance of Prof. Shafi Goldwasser.  Between MIT and UCI, I first worked at Intertrust's "StarLab", a small research lab in the Silicon Valley company which was developing Digital Rights Management systems, and then I spent a year as a postdoc at the applied cryptography group led by prof. Dan Boneh  at Stanford.


    Information and Computer Science
    University of California, Irvine
    Irvine, CA 92697-3425

    Last modified: 29 Oct 2004

    (Here are some pointers on how to learn HTML.)

    http://www.ics.uci.edu/~xhx/ Xiaohui Xie - Professor of Computer Science @ UC Irvine

    Xiaohui Xie

    Professor
    Department of Computer Science
    School of Information and Computer Science
    University of California
    Irvine, CA 92697
    Phone: 949-824-9289
    Fax: 949-824-4056
    Email: xhx AT ics.uci.edu

    office pic


    Research Interests

    AI/Machine Learning, Bioinformatics, and Applied Math
    Search for my publications at Google Scholar

    Publications


    Short Bio
    Xiaohui Xie is a profesor of Computer Science at UC Irvine, where he has been since 2007. He received his PhD from MIT, and completed his postdoctoral training at the Broad Institute of MIT and Harvard. He is interested in machine learning, bioinformatics, and neural computation. He lives in Irvine, California.

    Teaching

    • CS206 Principles of Scientific Computing, Spring, 2014
    • Math 227C/CS 285 Stochastic differential equations, Spring, 2014
    • CS 206 Principles of Scientific Computing , Spring, 2013
    • CS190/295: Programmng in Python for Life Sciences , Winter, 2012
    • CS295: Stochastic Differential Equations in Systems Biology and Engineering, Spring, 2011
    • CS 174 Bioinformatics Spring, 2011
    • CS295: Convex Optimization, Winter, 2011
    • CS284A: Representations & Algorithms for Molecular Biology , Fall, 2010
    • CS284A: Representations & Algorithms for Molecular Biology , Fall, 2009
    • CS295: Current Topics in Computational and Systems Biology, Winter, 2009
    • CS174: Bioinformatics, Spring, 2009

    My Research Group: CBCL Lab @UCI

     

    http://www.ics.uci.edu/~djp3/ Donald J. Patterson's UCI Home Page

    Transferring to LUCI!

    http://www.ics.uci.edu/~isaac/ Isaac D. Scherson

    University of California, Irvine. 

    Department of Computer Science - Systems

    The Bren School of Information and Computer Sciences

    Professor Isaac D. Scherson

    (a.k.a.The Schark)



     
     

    Prof. Isaac D. Scherson

     

     

    Professor

     

    The Donald Bren School of Information and Computer Sciences

     

    Electrical Engineering and Computer Science

     

    Research Areas: 

    Computer Systems Architecture

    Networked and Distributed Systems

    High Performance Computing

    Parallel Computing

    Office: 

    DBH 3226 � Lab: ICS Building, Room 464C

    Phone: 

    +1 (949) 824-8144

    Fax:

    +1 (949) 824-4056

    E-Mail: 

    isaac at ics.uci.edu  or  isaac at uci.edu

    Academic Web:

    www.ics.uci.edu/~isaac

    www.ics.uci.edu/~schark

    Other Professional Activities:

    www.scherson.com

    www.scherson.net


    Research Interests

    • Computer Architecture
    • Parallel and Distributed Computing Architectures
    • Operating Systems for Parallel and Distributed Computers
    • Architectures and Systems for Cluster Computing
    • Internet Server Architectures
    • Parallel and Distributed Algorithms
    • Performance Evaluation
    • The Internet
    • Interconnection Networks
    • Operating Systems (Windows, Linux, FreeBSD, MacOS)
    • Discrete Event Simulation

    Research Group and Publications

    Please follow the link to the SCHARK's home page: www.ics.uci.edu/~schark
     


    Teaching

    CompSci 152: Computer Systems Architectures

    CompSci 230: Distributed Computer Systems

    CompSci 242: Parallel Computing


    Other Links of Interest

    Personal Information: Resume in Acrobat Reader Format (.pdf)

    The Donald Bren School of Information and Computer Sciences

    The University of California, Irvine

    The Private Page, (Password Protected)


    Last modified: 02-21-2007


    http://www.ics.uci.edu/~gopi/

     

    Gopi-1

    Gopi Meenakshisundaram(M. Gopi)

    Professor of Computer Science

    University of California, Irvine

     

    949 824 9498

    gopi at ics.uci.edu

    http://www.ics.uci.edu/~gopi

    http://www.graphics.ics.uci.edu/

    Calendar

    Resume [pdf]

     

    My research work focuses mainly on topics related to geometry and topology motivated by

    problems in computer graphics and interactive rendering. Currently, I am also working on

    medical and biological image processing and visualization.

     

    All Publications

     

    Recent (Sample) Publications:

     

    Surface Sampling:

    Pablo Diaz-Gutierrez, Jonas Bosch, Renato Pajarola, M. Gopi

    Streaming Surface Sampling Using Gaussian e-nets.
    The Visual Computer (Computer Graphics International Conference), 25(5-7), pp 411-421, 2009 [Paper]

     

    Topology Processing:

    Pablo Diaz-Gutierrez, David Eppstein, M. Gopi
    Curvature Aware Fundamental Cycles 

    Computer Graphics Forum (Pacific Graphics) 2009 [Paper]

     

    Disk Layouts for Interactive Rendering:

    Behzad Sajadi, Shan Jiang, Jae-Pil Heo, Sung-Eui Yoon, M. Gopi.
    Data Management for SSDs for Large-Scale Interactive Graphics Applications
    ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D), 2011 [Paper]

     

    Large Area Displays (Color Seamlessness):

    Behzad Sajadi, Maxim Lazarov, Aditi Majumder, M. Gopi

    Color Seamlessness in Multi-Projector Displays Using Constrained Gamut Morphing
    IEEE Visualization (TVCG), 2009 [ Paper]

     

    Novel Projector Design:

    Behzad Sajadi, M. Gopi, Aditi Majumder

    Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing
    ACM Transactions on Graphics (SIGGRAPH), 2012 [Paper]

     

    Sketching

    Uddipan Mukherjee , M. Gopi , Jarek Rossignac,

    Immersion and Embedding of Self-Crossing Loops
    EUROGRAPHICS Symp. on Sketch-Based Interfaces and Modeling, 2011. [ Paper]

     

    Morphing:

    Uddipan Mukherjee , M. Gopi

    Tweening Boundary Curves of Non-Simple Immersions of a Disk,
    ICVGIP, 2012. [Paper]

     

    Tensor Decomposition (Volume Visualization):

    Susanne K. Suter, J. A. I. Guitian, F. Marton, M. Agus, A. Elsener, C. P. E. Zollikofer, M. Gopi, E. Gobbetti, R. Pajarola

    Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization,
    IEEE Visualization, 2011. [Paper]

     

    Biological Image Processing and Visualization:

    K. Mkrtchyan, D. Singh, M. Liu, V. Reddy, A. Roy-Chowdhury, M. Gopi

    Efficient cell segmentation and tracking of developing plant meristem,
    IEEE International Conference on Image Processing, 2011. [Paper]

     

    Medical Image Processing and Visualization:

    Koel Das, Monica Siegenthaler, Aditi Majumder, Hans Keirstead, M. Gopi

    Automated Cell Classification and Visualization for Analyzing Remyelination Therapy
    The Visual Computer, 2011. [Paper]

     


    TEACHING / RESEARCH AWARDS

    Best Paper Award, ICVGIP 2012.

    Second Best Paper Award, EUROGRAPHICS, Dublin, Ireland, 2005.

    Second Best Paper Award, EUROGRAPHICS, Grenoble, France, 2004.

    Excellence in Teaching Award, Division of Undergradaute Education, UCI, 2004

    Link Foundation Fellow, 1999-2000

    Gold Medalist, Thiagarajar College of Engineering, 1992.

    http://www.ics.uci.edu/~smyth/ Padhraic Smyth

    Padhraic Smyth

    Professor, Department of Computer Science
    Director, UCI Data Science Initiative
    University of California, Irvine
    CA 92697-3435
    [additional contact information]

    Publications

    • Links to papers

    News

    • (January 2015) We are currently hiring faculty in AI/machine learning as well as more broadly in computer science.
    • (Dec 2014) The new Data Science Initiative is now up and running - check out our Web site for the latest news on what's happening.
    • (July 2014) UCI kicked off a new 3-year Data Science Initiative on July 1st - looks like it will be interesting, watch this space!
    • (July 2014) Presented an invited talk at AAAI in July on "30 years of Probability in AI and Machine Learning", which was fun to put together. And also attended a summer school in Heidelberg with psychologists interested in using machine learning and statistics to better understand dialog - a fun week.
    • (June 2014) Congrats to various machine learning students who graduated with their PhDs recently including Jimmy Foulds from my group (headed to a postdoc at UCSC), Levi Boyles from Max Welling's group (headed to a postdoc at Oxford), and Qiang Liu from Alex Ihler's group (headed to a postdoc at MIT and then a faculty position at Dartmouth).

    Biographical Information, etc

    • Short biography and a rather long full curriculum vitae
    • Honors, joint faculty appointments, center affiliations, journal, conferences, etc
    • Information about the name "Padhraic"

    Smyth Research Group

    • Lis of students and postdocs in our research group (unfortunately this is almost always a bit well out of date!)
    • Information for students interested in joining our group .

    Centers, Seminars, Research Projects, and More

    • UCI Data Science Initiative
    • Center for Machine Learning and Intelligent Systems
    • Weekly AI/Machine Learning Seminar Series
    • MURI project on analysis of network data

    Teaching

    • CS 274A: Probabilistic Learning: Theory and Algorithms
    • CS 277: Data Mining
    • CS 175: Project in Artificial Intelligence.
    • Introduction to Artificial Intelligence
      • CS Undergraduate Course, CS 171
      • CS Graduate Course, CS 271.
    • CS 177: Applications of Probability in Computer Science.
    http://www.ics.uci.edu/~nalini/ Nalini Venkatasubramanian <body lang=EN-US link=blue vlink=purple style='tab-interval:.5in'> <div class=WordSection1> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal>This web site is designed with frames. Your browser cannot read frames. You can use the following <a href="home.html">address</a> to see much of the content. You will have to repeatedly follow the links at the bottom of the page. </p> </div> </body> http://www.ics.uci.edu/~redmiles/ David Redmiles Home Page at UCI

     

     

     

     

     

     

     

     

     

     

    David Redmiles

     

    Professor

    Department of Informatics

    Donald Bren School of Information and Computer Sciences

    University of California, Irvine

     

    Email: redmiles [at] ics [dot] uci [dot] edu

     

    Detailed Contact Information (including Courier)

     

     

    Vita

    Interests

     

    Research

    Students

    Publications

     

    Teaching

     

    Schedule

     

    Links

     

    → Home

    David Redmiles is a Professor in the Department of Informatics at the University of California, Irvine (UCI). He is the author of over 100 research publications integrating the areas of software engineering, human-computer interaction, and computer-supported cooperative work. He has graduated 8 PhD students and served on the dissertation committees of over 30 other PhD students. For many years, he has been involved in the IEEE/ACM Conference on Automated Software Engineering, serving on the steering committee and organizing the 2005 conference as General Chair. That research community designated him Fellow of Automated Software Engineering in 2009 and in 2010 awarded him and his co-authors the first Most Influential Paper Award for their 1996 paper on software design environments. The Argo/UML system described in that paper continues to evolve as a widely adopted design tool supported by a lively open-source community. His current research focuses on distributed and collaborative software engineering, especially the aspects of awareness and trust among collaborators. From 2004 to 2011, he chaired the Department of Informatics at UCI. During this period there was a great expansion of the faculty, facilities, and degree programs.

     

     

     

     

     

    http://www.ics.uci.edu/~staceyah/ Stacey Hancock

    Stacey Hancock

    Department of Statistics

    University of California, Irvine

    Donald Bren Hall 2204, Irvine, CA 92697-1250
    stacey.hancock@uci.edu | tel: 949/824-9795

    • Home
    • Teaching
    • Research
    • Curriculum Vitae
    • Department of Statistics

    Stacey Hancock

    "Reality is like a face reflected in the blade of a knife; its properties depend on the angle from which we view it." - Master Hsing Yun, Describing the Indescribable

    http://www.ics.uci.edu/~wjohnson/ Wesley Johnson's HOME PAGE

    Home Page for Professor Wesley O. Johnson

    Department of Statistics

    University of California, Irvine

    My bachelor's degree was in Mathematics from the University of Washington, Seattle in 1972, I received a Master's Degree in Statistics from the California State University as Hayward in 1974, and a Ph.D. in Statistics with a minor in Mathematics from the University of Minnesota, Minneapolis in 1979. I joined the Department of Statistics at UC Davis in 1979 and was chair of the graduate group in Epidemiology at UCD from 1997 to 2002. I joined the Department of Statistics at UC Irvine in 2005.

    My research interests are eclectic. I am mainly interested in developing Bayesian statistical methods for biostatistical and epidemiologic applications. I am currently involved with collaborative efforts to develop Bayesian nonparametric and semi-parametric methods in survival analysis, longitudinal analysis and joint modeling of survival and longitudinal data.  I also work on diagnostic screening protocols and methodology when no gold standard test is available.  This includes ROC curve estimation, development of models and methods for longitudinal screening data including change-point models that account for disease occurrence during the study, and general methods for combining information to improve diagnosis.  I have also been involved with the development of risk analysis models, sample size determinations in the context of risk assessment and models, longitudinal and spatial methods for predicting global foot and mouth disease, and for the analysis of hormone profile data.  I also have a general interest and expertise in the areas of regression diagnostics, prediction, multivariate analysis, models for correlated binary data, asymptotics and Markov chain Monte Carlo methods. I am a fellow of IMS, ASA and RSS.� My curriculum vitae gives more details.

    At UCI, I have taught courses on longitudinal data analysis (STA 212),  undergraduate probability (STA 120A), masters level probability and statistical theory  (STA 200ABC), Multivariate Analysis (STA 240), Introductory Bayesian Ideas and Data Analysis (STA205), Bayesian Theory, Methods and Data analsysis (STA 225), Bayesian Nonparametrics (STA 226) and Ph.D. level Probability and Mathematical Statistics (220AB).

    I have written a Bayesian book with my friends Adam Branscum, Ron Christensen and Tim Hanson. The book is intended for a broad audience of graduate students in all areas of science, including Statistics. You can find information about the book at http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html.

    Winters PalsMitch Watnik and Julie Yee
    My permanent home in Winters and my pals, the Taj Mahal, and Crete,
     Greece  

    and Santorini (Thira)
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
     
    Wesley O. Johnson / wjohnson@uci.edu












    http://www.ics.uci.edu/~mmazmani/ http://evoke.ics.uci.edu/ EVOKE Lab and Studio » • Theorize • Design • Make • Provoke • Evoke • Inspire •

    EVOKE Lab and Studio

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    EVOKE Lab and Studio


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    What We Are

    About

    What do we do at EVOKE Lab & Studio? 

    We start with an eye toward social issues and concerns, and integrate social theory, art, computer science, and design to create new technologies and artifacts that evoke thought, debate, discussion, and consideration of a more harmonious mediated world. Our projects range from small experiments with new innovations to large-scale studies on major issues like “big data”, privacy, social justice, and self-exploration through technology. We are an open community of interdisciplinary scholars and designers who have fun, learn, and grow together, and are always looking for more fellow travelers!

    Where do we work?

    We are located at the UC-Irvine campus in the Calit2 Building. While our research work takes us everywhere, and our field sites are varied and far-flung, much of our work takes place in the EVOKE Lab & Studio itself. Our Design Lab is modeled after the workflow of the User Experience (UX) design process, taking ideas from inception to formal design specs. When it’s time to start building, projects shift into the larger, adjacent Studio space where we move from idea to physical reality. See below for more details on what we have in the Lab & Studio.

     

    The EVOKE Lab

    Where the creative work happens, the Lab is set up to support UX design workflow from blue-sky sessions to conceptual prototyping.
    Find out more about what’s in the EVOKE Lab.

    Values in Design

    How do technologies reflect our human values? How do we design social values into our technologies and systems? Our Values in Design research track explores these questions.
    Learn more about our VID research.

    Data/Self

    Devices and sensors are making it easier to track ourselves and turn our bodies, activities, and choices into analytics. It’s changing what it means to know ourselves.
    Learn more about our work on the Data and the Self.

    The EVOKE Studio

    Projects move out of the Lab and to the Studio to take on material forms. Our “big black box” space is perfect for exhibits, performances, large construction, and anything the imagination can conjure.
    Find out more about the EVOKE Studio.

    Knowledge Infrastructures

    Scientific infrastructures are well understood to carry data and information, but how do they foster the creation of useful knowledge? Can we generative better systems and associated policy frameworks that cultivate knowledge-centered communities and initiatives?
    Learn more about our work on Knowledge Infrastructures.

    Emerging Configurations of Knowledge

    Although the book and journal article are the standard forms of scholarly communication, we give form to knowledge through a much broader range of expression. Our research explores emerging forms of communication through new technologies.
    See more about our work on Emerging Configurations of Knowledge.

    Recent

    News

    • Health Data Exploration report published (3/13/2014) - The final report from the Health Data Exploration project has been published by the Robert Wood Johnson Foundation! Three EVOKE members – Dr. Matthew Bietz, Dr. Judith Gregory, and Dr. Scout Calvert – collaborated with researchers at the California Institute for Telecommunications and Information Technology (Calit2) at UCSD (Kevin Patrick, PI) to conduct the research […]
    • Cory Knobel receives iConference 2014 best paper award (3/7/2014) - Dr. Cory Knobel, executive director of the EVOKE Lab & Studio, received the Lee Dirks Memorial Award for best paper at the 2014 iConference, held in Berlin, Germany on March 4-7, along with co-authors Dr. Leanne Bowler and Eleanor Mattern of the University of Pittsburgh iSchool. The paper, Developing Design Interventions for Cyberbullying: A Narrative-Based […]
    • Bowker co-organizing White House OSTP Conference on Big Data and Privacy (3/6/2014) - Dr. Geoffrey C. Bowker, scientific director of the EVOKE Lab & Studio and UCI professor of informatics, will be co-organizing a national panel sponsored by the White House Office of Science & Technology Policy and the Data & Society Research Institute.on March 17 at NYU with collaborators Helen Nissenbaum (NYU), danah boyd (Microsoft Research/Data & […]
    • Evoke members @ Western Humanities Alliance 2013 (10/21/2013) - Geoffrey C. Bowker, Cory Knobel, and John S. Seberger will be presenting on ‘Emerging Modes of Knowledge Expression’ at the annual meeting of the Western Humanities Alliance at University of California, San Diego on November 1, 2013. The conference is hosted by the UCSD Center for the Humanities. The theme of the conference is New […]
    • Bietz and Gregory speak at “Moving Genomics from Bench to Bedside” workshop (5/31/2013) - EVOKE members Matthew Bietz and Judith Gregory were recently invited to speak at the Moving Genomics Research from Bench to Bedside: Science, Technology & Society Perspectives workshop on May 24, 2013. The workshop was hosted by Ellen Balka in the School of Communication at Simon Fraser University, with additional support from the Michael Smith Foundation […]
    • Values in Design for Collaborative Systems at CTS 2013 (5/31/2013) - Lab members Matthew Bietz, Cory Knobel, and Katie Pine presented a tutorial on Values in Design for Collaborative Systems at the 2013 International Conference on Collaboration Technologies and Systems in San Diego. In the tutorial we provided an introduction to human-centered computing, explored the Values in Design concept, and then worked through a series of […]
    • Geof Bowker Speaks at NYU (4/9/2013) - Along with Sara Hendren (Harvard), Geof will be a speaker in the PROGRAM series at NYU organized by the departments of Media, Culture and Communication, English, and Comparative Literature. More event details can be found here. Values in Technological Design Abstract: The goal for many designers of technology is to produce objects that are useful, […]
    • Geof Bowker Speaks at Michigan (4/9/2013) - Geof will be speaking as part of the Yahoo! Lecture Series at the University of Michigan’s School of Information on April 10, 2013. The End of the Article Abstract: In this talk, I analyze the development of the scientific article as the coin of the realm for the transmission of knowledge. I argue that the […]
    • Values in Design Fellows honored at iConference 2013 (3/3/2013) - The 2013 iConference, held in Fort Worth, TX in mid-February, saw a number of entries from the Values in Design community. In the conference awards ceremony, two entries from VID-related work garnered top awards. 2012 VID Fellow Jaime Synder of Syracuse University shared the award for best dissertation, entitled “Image-Enabled Discourse; Investigating the Creation of […]
    Come Join Us

    Events

    Workshops & Special Events

    In the EVOKE Lab & Studio, we frequently put on workshops related to design, computing, social theory, etc. Sometimes it’ll be a skills workshop, other times a gaming session, reading group, or interesting speaker series. Often, the event will be in the EVOKE Lab, but sometimes we also have events at other locations. Keep an eye on our upcoming events and join us!

    Day-to-Day Happenings

    The Lab & Studio are in constant activity with faculty and students working on projects, meeting, studying, and doing what we all do. We’re not a secret society, so if the door is open, come on in. Drop in anytime to see what’s going on!

     

    Design in the Anthropocene
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    Design in the Anthropocene

    Join us at Calit2 on December 4, 2015 for the Design in the Anthropocene conference, ...

    Values in Design 2012
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    Values in Design 2012

    Held in August 2012, 36 doctoral students from North America and Europe converged to ...

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    Projects

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    • Current Projects
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    • BCube
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      BCube

      Studying policy formation and implementation in global ...

    • Social Analysis of PRISM
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      Social Analysis of PRISM

      Social, organizational, and policy dimensions of resear...

    • Health Data Exploration
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      Health Data Exploration

      To better understand the barriers to using personal hea...

    • Personal Genomics & QS
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      Personal Genomics & QS

      This project is investigating the similarities and rela...

    • Critical Quantification
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      Critical Quantification

      The proliferation of inexpensive, small, and connected ...

    • Feverbook
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      Feverbook

      Facebook as the ultimate social archive? A Derridean ex...

    • Music4Labs
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      Music4Labs

      Turning daily lab activities into information-rich musi...

    • Boy 2.0
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      Boy 2.0

      Modern toys for mannered boys. Traditional toys with a ...

    • AmpDamp
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      AmpDamp

      Social media is too noisy? Dial your feeds up or down w...

    • Parresia
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      Parresia

      Mindfulness. Reflection. Patience. Bringing focus back ...

    • String
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      String

      Ambient awareness of loved ones through tactile computi...

    • Social Capital
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      Social Capital

      Haven’t kept up with your friends’ social m...

    • Stickets
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      Stickets

      Want to fix something in your neighborhood? Put a stick...

    • Leveling the Stage
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      Leveling the Stage

      What knowledge is contained in gestures, body language ...

    Meet

    EVOKE

    • Geoffrey C. Bowker Director
      Email
    • Judith Gregory Associate Director
      Email
    • Matthew Bietz Associate Director
      Email
    • Steve Slota Doctoral Student
      Email
    • John Seberger Doctoral Student
      Email
    • Caitie Lustig Doctoral Student
      Email
    • Andy Echenique Doctoral Student
      Email
    • Aubrey Slaughter Doctoral Student
      Email
    • Eric Acampora Undergraduate Student
      Email
    • Tristan Biles Undergraduate Student
      Email
    • Cory Knobel Founding Executive Director
      Email
    • Garnet Hertz Former Associate Director
      Email
    • Norma Möllers Former Visiting Scholar
    • Ali Alkhatib Former Undergraduate Student
      Email
    And Our

    PARTNERS

    • Intel Partner
    • ISTC•Social Partner
    • Qualcomm Institute Partner
    • Re:Enlightenment Project Partner
    • Robert Wood Johnson Foundation Partner
    EVOKE

    Blog

    [QS] – Personal Data Landscapes Poster

    • June 3, 2014
    • By Steve Slota
    • In Blog, Data/Self
    • 2 Comments

    As written and presented by lab faculty Matthew Bietz, Judith Gregory, and Geof Bowker. PDF LINK.  

    Read More »

    My Day as a Data Dealer

    • March 19, 2014
    • By Norma Möllers
    • In Knowledge Infrastructures, Values in Design
    • 14 Comments
    My Day as a Data Dealer
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    A while back I stumbled upon a browser game which in some ways could be considered an attempt to gamify privacy literacy. Gamification in the context of surveillance usually means the exploitation of the pleasures of play in order to collect and sell data, i.e. in fitness or productivity apps. In a somewhat similar vein, a group of […]

    Read More »

    Viewing the World through Technology’s Eyes

    • March 19, 2014
    • By Andy Echenique
    • In Values in Design
    • No Comments

    Overlooking a recent technology article on BBC news (http://www.bbc.co.uk/news/technology-24332358), I read about how a Japanese research lab is developing a system to allow users (via augemnted reality) to be able to see the translation of writing in the environment. Naturally, its intended application is to allowing users to see the their foreign surroundings in a […]

    Read More »

    Hollowing ‘I’ in the authorship of letters: A note on Flusser and surveillance

    • March 19, 2014
    • By John Seberger
    • In Emerging Configurations
    • No Comments

    Although the 24-hour news cycle has recently moved on from Snowden and  surveillance to cover the latest set of unfortunate circumstances on Capitol Hill, there are some points I would like to make regarding the role of surveillance in the further dissolution of any sense of a human author that might underlie the use of […]

    Read More »
    Recent Posts
    • [QS] – Personal Data Landscapes Poster
    • My Day as a Data Dealer
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    Contact Info
    • Dr. Geoffrey C. Bowker
    • (949) 824-4558
    • 2100 Calit2 Building
      Irvine, CA 92697-2800
      (Campus Building #325)
    • evoke@ics.uci.edu
    EVOKE Lab & Studio @ University of California-Irvine © 2016^ Top
    http://www.ics.uci.edu/~nicolau/ Alex Nicolau's Home Page
    CECS logo
    Center for Embedded Computer Systems
    University of California, Irvine
    UCI logo

    Alex Nicolau

    Alex Nicolau
    Contact Info
    • Office: CECS (IERF) 204 (directions)
    • Phone: +1 (949) 824-4079
    • Personal Fax: +1 (949) 824-4079
    • Alt. Fax: +1 (949) 824-8019
    • Email: nicolau@ics.uci.edu
    • Assistant: Melanie Sanders (email)
    • Mail Address:
      Professor Alex Nicolau
      Center for Embedded Computer Systems
      444 CS, University of California
      Irvine, CA 92697-3425, USA
    Research Interests

            Parallelizing Compilers, High-Performance Java, Power-aware Computing, Reconfigurable Computing.

    Current Projects
    • Julius C: Compiler Optimizations for Divide and Conquer Applications

      Julius C is a C compiler that determines a model for divide and conquer algorithms. The model is an extension of the call graph and it conceives information about the dynamic behavior of the algorithm. The use of the model is twofold: at compile time, the model drives code generation and code optimizations; at run time, the model offers a support driving code execution, code adaptation and hardware adaptation.

    • EXPRESS Retargetable Compiler

      EXPRESS is an optimizing, memory-aware, Instruction Level Parallelizing (ILP) compiler. EXPRESS uses the EXPRESSION ADL to retarget itself to a wide class of processor architectures and memory systems. The inputs to EXPRESS are the application specified in C, and the processor architecture specified in EXPRESSION. The front-end is GCC-based and performs some of conventional optimizations. The core transformations in EXPRESS include RDLP -- a loop pipelining technique, TiPS : Trailblazing Percolation Scheduling -- a speculative code motion technique, Instruction Selection, Register Allocation and If-Conversion -- a technique for architectures with predicated Instruction Sets. The back-end generates assembly code for the processor ISA.

    • FORGE: A Framework for Optimization of Distributed Embedded Systems Software

      The FORGE project is a framework for optimization of distributed embedded systems software, which integrates the middleware abstraction layer with the hardware/OS abstraction layer and studies mechanisms for capturing resources/architectures at these two levels and allowing interactions between the levels. A resource description language (RDL) specifying the composition of the system as well as resource constraints can be used by a compiler for automatically generating the necessary services and middleware configuration and their deployment across the platform.

    • SPARK: High-Level Synthesis using Parallelizing Compiler Techniques

      SPARK is a C-to-VHDL high-level synthesis framework that employs a set of innovative compiler, parallelizing compiler, and synthesis transformations to improve the quality of high-level synthesis results. The compiler transformations have been re-instrumented for synthesis by incorporating ideas of mutual exclusivity of operations, resource sharing and hardware cost models.

    • CoReComp: A Compiler Framework for Mapping Applications on Mesh-Based Coarse-Grain Reconfigurable Architectures

      Coarse-grain reconfigurable architectures trade-off some of the configuration flexibility of fine-grain FPGAs in return for smaller delay, area and configuration time. They provide massive parallelism, high computational capability and their behavior can be configured dynamically, thus making them a better alternative to ASICs and fine-grain FPGAs in many aspects. Mapping applications to such architectures is a complex task that is a combination of the traditional operation scheduling, operation to PE binding (or mapping), and routing problems. The focus of this research is to build a compiler framework which should be capable of mapping applications on reconfigurable architectures by taking into account different architecture and application parameters.

    Past Projects
    • Annotation-Aware Java Virtual Machines
    • AMRM: Adaptive Memory Reconfiguration and Management
    • COPPER: Compiler-Controlled Continuous Power-Performance Management
    • Cache Power Optimization via Run-time Hardware Prediction
    • EVE Mutation Scheduling Compiler
    Teaching
    • ICS 144 - High Performance Compilers and Program Optimizations (Spring '04)
    • ICS 249 - Seminar in Parallel Distributed and Network Systems (Winter '04)
    • ICS 245 - High Performance Architectures and Their Compilers (Winter '03)
    • ICS 51 - Introductory Computer Organization (Winter '02)
    Affiliated Ph.D. Students
    • Weiyu Tang
    • Paolo Nicola D'Alberto
    • Radu Cornea
    • Arun Kejariwal
    • Nikhil Bansal
    • Carmen Badea
    Previous Ph.D. Students
    • Alexander Aiken (Graduated, 1988)
    • Laurie Hendren (Graduated, 1990)
    • Roni Potasman (Graduated)
    • Ki Kim (Graduated)
    • Haigeng Wang (Graduated)
    • Steve Novack (Graduated, 1992)
    • Srinivas Mantripagada (Graduated)
    • Joseph Hummel (Graduated, 1998)
    • David Kolson (Graduated, 1996)
    • Ana Azevedo Pazos (Graduated, 2002)
    Other Ph.D. Degrees Supervised
    • Prabhat Mishra (Graduated, 2004)
    • Sumit Gupta (Graduated, 2003)
    • Peter Grun (Graduated, 2001)
    • Preeti Ranjan Panda (Graduated, 1997)
    Selected Publications (most recent first)
    • Proxy-based Task Partitioning of Watermarking Algorithms for Reducing Energy Consumption in Mobile Devices
      Arun Kejariwal, Sumit Gupta, Alexandru Nicolau, Nikil Dutt, Rajesh Gupta,
      Design Automation Conference (DAC), June 2004.
    • Optimized Performance of Applets and Services in an Annotation-aware JVM for Smart Cards
      Ana Azevedo, Arun Kejariwal, Alex Veidenbaum and Alex Nicolau,
      International Workshop Construction and Analysis of Safe, Secure and Interoperable Smart Devices (CASSIS), March 2004.
    • Network Topology Exploration of Mesh-Based Reconfigurable Architectures
      Nikhil Bansal, Sumit Gupta, Rajesh Gupta, Nikil Dutt and Alex Nicolau,
      Design Automation and Test in Europe (DATE), February 2004.
    • Analysis of the Performance of Coarse-Grain Reconfigurable Architectures with Different Processing Element Configurations
      Nikhil Bansal, Sumit Gupta, Nikil Dutt and Alex Nicolau,
      Workshop on Architecture Specific Processors (WASP), December 2003.
    • Managing Cross-Layer Constraints for Interactive Mobile Multimedia
      Radu Cornea, Shivajit Mohapatra, Nikil Dutt, Alex Nicolau and Nalini Venkatasubramanian,
      Workshop on Constraint-Aware Embedded Software (WCAS), IEEE Real-Time Systems Symposium, December 2003.
    • Integrated Power Management for Video Streaming to Mobile Handheld Devices
      Shivajit Mohapatra, Radu Cornea, Nikil Dutt, Alex Nicolau and Nalini Venkatasubramanian,
      ACM Multimedia (ACMMM), November 2003.
    • A Data Cache with Dynamic Mapping
      Paolo D'Alberto, Alex Nicolau and Alex Veidenbaum,
      Workshop on Languages and Compilers for Parallel Computing (WLCPC), 2003.
    Professional Services (abridged)
    • Editor in Chief, International Journal of Parallel Programming
    • ICS 2004 - Program Committee member
    • LCTES 2001 and 2002 - Program Committee member
    • SCOPES 2001 and 2002 - Program Committee member
    • IWACT 2001 - Program Co-Chair
    • NATO Advanced Workshop on Distributed Computing for Clusters, Grids and Embedded Systems 2003 - Co-Director

    Last modified: Wed Mar 17 15:55:59 PST 2004
    XHTML 1.1 Checked  
    http://www.ics.uci.edu/~sharad/ Sharad Mehrotra Group Page

     

     

    Contact Information

    2082 Bren Hall
    University of California at Irvine
    Irvine, CA 92697-3425
    USA

    Office: ICS 424
    Office Tel: +1(949)824-5975
    Office Fax: +1(949)824-4056
    E-mail: sharad@ ics dot uci dot edu

    Office Hours

    Thurs: 11-12 or by appointment via email.

     

    Administrative Assistant

    Mary Carrillo

    mlcarril@ ics dot uci dot edu
    Office Tel: +1(949)824-3289
    Office Fax: +1(949)824-4056

    Links

    Past Projects

    Personal Home Page

    Publication List

    Students (Past & Present)

     

    Sharad Mehrotra, Professor of Computer Science

    Research Areas

    Data Management Systems, Distributed Systems, Sensor-based Pervasive Systems, Situational Awareness, Data Quality, Data Privacy, Emergency Response Technologies

    Research Centers

    Center for Emergency Response Technologies (CERT)

    California Institute Telecommunication & Information Technology (Calit2)

    Information Systems Research Group (ISG)

     

    Biography

    Sharad Mehrotra is a Professor in the School of Information and Computer Science at University of California, Irvine and Director of the  Center for Emergency Response Technologies (CERT) at UCI. He also serves as the Director and PI of the RESCUE project (Responding to Crisis and Unexpected Events) which, funded by NSF through its large ITR program, spans 7 schools and consists of 60 members. He is associated with the Cal-IT2 institute -- a multidisciplinary research facility spanning University of California, Irvine and University of California, San Diego. He is the recipient of Outstanding Graduate Student Mentor Award in 2005. Prior to joining UCI, me was a member of the faculty at University of Illinois, Urbana Champaign in the Department of Computer Science where he was the recipient of the C. W. Gear Outstanding Junior Faculty Award. Mehrotra has also served as a Scientist at Matsushita Information Technology Laboratory immediately after graduating with a Ph.D. from University of Texas at Austin(1988-1993).

     

    Mehrotra's research expertise is in data management and distributed systems areas in which he has made many pioneering contributions. Two such contributions include the concept of "database as a service"  and "use of information retrieval techniques, particularly relevance feedback, in multimedia search". Mehrotra is a recipient of numerous best paper nominations and awards including  SIGMOD Best Paper award in 2001 for a paper entitled  "Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases", Best of VLDB 1994 submissions for a paper entitled "Concurrency Control in Hierarchical Multidatabase System", best paper award in DASFAA 2004 for the paper entitled "Efficient Execution of Aggregation Queries over Encrypted Databases", best paper award nomination for a paper entitled Neighborhoods: A framework for enabling web-based synchronous collaboration and hierarchical Navigation at HIICS, 1996, and a best student paper award at IEEE Workshop on Multimodal Sentient Computing: Sensors, Algorithms and Systems (WMSC 2007) held in conjunction with IEEE CVPR (Computer Vision and Pattern Recognition) 2007.

     

    Mehrotra's current research focuses on building sentient spaces using multimodal sensors, data privacy, and data quality. Mehrotra's recent research, particularly, in the context of RESCUE & CERT has focused on situational awareness from multimodal input including conversational speech data. Many of his research contributions have been incorporated into software artifacts which are now in use at various first responder partner sites.

    Selected Publications

    1.     Y. Rui, T. Huang, M. Ortgega and S. Mehrotra, "Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval". IEEE Trans. on Circuits and Systems for Video Technology, Vol. 8, No. 5, Pages 644-655, September, 1998. An early paper on relevance feedback in image retrieval.

    2.     K. Chakrabarti, E. Keogh, M. Pazzani and S. Mehrotra, "Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases", ACM Transactions on Database Systems. TODS 27(2): 188-228 (2002). Winner of IGMOD best paper

    3.     H. Hacigumus, B. Iyer, C. Li and S. Mehrotra, "Executing SQL over Encrypted Data in Database- Service-Provider-Models ", ACM SIGMOD 2002 conference. Winner of SIGMOD Test of Time award in 2012

    4.     Hakan Hacigumus, Bala Iyer, Sharad Mehrotra. Secure Computation over Outsourced Data: A 10-Year Retrospective. DASFAA 2014. Invited Paper accompanying the 10 Year Best Paper Award in DASFAA 2014.

    5.     Chen Li, Sharad Mehrotra, Liang Jin. Record Linkage: A 10-Year Retrospective. DASFAA (1) 2013: 3-12. Invited Paper Accompanying the 10 Year Best Paper Award in DASFAA 2013.

    6.     S. Mehrotra, C. Butt , D. Kalashnikov, N. Venkatasubramanian, R. Rao, G. Chockalingam, R. Eguchi, B. Adams C. Huyck, Project RESCUE: Challenges in Responding to the Unexpected, SPIE Internet Imaging Conference, 2004 ( an early paper addressing the role of IT innovation in bringing transformation advances to� crisis response )

    7.     Liyan Zhang, Dmitri Kalashnikov, Sharad Mehrotra. A Unified Framework for Context Assisted Face Clustering. 2013 ACM International Conference on Multimedia Retrieval. Winner of est paper award.

    8.     Dmitri Kalashnikov; Sharad Mehrotra. Domain-independent data cleaning via analysis of entity-relationship graph, In ACM Transactions on Database Systems (ACM TODS) 2006. ( an early paper on relational approach to entity resolution)

    9.     Lazaridis, S. Mehrotra, Optimization of Multi-version Expensive Predicates, ACM SIGMOD 2007 (a paper with �unrealized� potential).

     

    Current /Ongoing Projects

     

    Sherlock Project on novel directions of improving data quality particularly focused on interactive and progressive data cleaning in the context of big data analysis and applications.. Sherlock is funded by NSF Grant 1118114.

     

    Radicle Project on a risk-based approach to data processing in mixed security environments with the focus on cloud computing. Radicle is funded by NSF Grant 1118127 and 1212943

     

    I-Sensorium Project on creating a pervasive computing testbed at UCI. I-sensorium is funded through NSF Grant 1059436

     

    Cypress Project on cyber physical system resilience and sustainability . Cypress is funded through NSF Grant 1063596

     

     

     

    http://www.ics.uci.edu/~fowlkes/ charless c. fowlkes - uc irvine - computer vision

    mugshot

    charless c. fowlkes

    associate professor
    computer science
    uc irvine

    fowlkes@ics.uci.edu
    4076 dbh
    949.824.6945

    uci : cs : vision group

    home
    publications
    presentations
    software


    My research is in computational vision, in particular how to integrate mechanisms for visual recognition and perceptual organization. I'm interested in how measuring the predictive power of different visual cues can provide general information-theoretic constraints on human visual processing. I also work on developing tools for biological image and shape analysis in order to measure morphology and spatial patterns of gene expression in developing animals.



    recent research and news:



    • D. Tcheng, A. Nayak, C. Fowlkes, S. Punyasena, "Visual recognition software for binary classification and its application to spruce pollen identification" PLoS ONE, to appear

    • R. Diaz, M. Lee, J. Schubert, C. Fowlkes, "Lifting GIS Maps into Strong Geometric Context" WACV 2016 arXiv:1507.03698 [pdf]

    • We received a Helmholtz Prize in 2015 for having a significant impact on the field of computer vision with our ICCV 2001 paper: D. Martin, C. Fowlkes, D. Tal, J. Malik. "A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics" [pdf]

    • M. Chiang, S. Hallman, A. Cinquin, N. Reyes de Mochel, A. Paz, S. Kawauchi, A. Calof, K. Cho, C. Fowlkes, O. Cinquin, "Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images", BMC Bioinformatics 2015, 16:397 doi:10.1186/s12859-015-0814-7. [pdf]

    • J. Yarkony, C. Fowlkes, "Planar Ultrametrics for Image Segmentation", Proc. of NIPS, (Dec. 2015). arXiv:1507.02407 [pdf]

    • J. B. Treweek, K. Chan, N. Flytzanis, B. Yang, B. Deverman, A. Greenbaum, A. Lignell, C. Xiao, L. Cai, M. Ladinsky, P. Bjorkman, C. Fowlkes, V. Gradinaru "Whole-Body Tissue Stabilization and Selective Extractions via Tissue-Hydrogel Hybrids for High Resolution Intact Circuit Mapping and Phenotyping", Nature Protocols, 10 (11), 1860-1896, (2015) DOI 10.1038/nprot.2015.122

    • S. Wang, C. Fowlkes, "Learning Optimal Parameters for Multi-target Tracking", BMVC 2015 [pdf]

    • G. Ghiasi, C. Fowlkes, "Using segmentation to predict the absence of occluded parts", BMVC 2015. [pdf] [data]

    • G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces", Technical Report, June 2015 arXiv:1506.08347 [pdf] [code] [dataset]

    • S. Hallman, C. Fowlkes, "Oriented Edge Forests for Boundary Detection", CVPR, Boston, MA, June 2015. arXiv:1412.4181 [pdf] [code]

    • X. Zhu, C. Vondrick, C. Fowlkes, D. Ramanan, "Do we need more training data?", IJCV, March 2015 arXiv:1503.01508 [pdf]

    • M. Staller, C. Fowlkes, M. Bragdon, J. Estrada, Z. Wunderlich, A. DePace, "A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate", Development 142, p 587-596, (2015) [pdf]



      more recent pubs here...




    teaching:


    cs 117 : project in computer vision [w09,f13,s15]
    ics 162 : modeling and world building [f12,s14,f15]
    cs 116 : computational photography and vision [w10,w11,w13,w14,w16]
    cs 216 : image understanding [f08,f10,f11,f12,s14,w16]
    cs 217 : light and geometry in vision [s10,w12,s15]
    cs 295 : research topics in vision [s09,w10,w11]
    cs 177 : applications of probability in computer science [s08]


    students:


    advisees
    • Raúl Díaz (PhD)
    • Golnaz Ghiasi (PhD)
    • Minhaeng Lee (PhD)
    • Bailey Kong (PhD)
    • Shu Kong (PhD)

    past advisees and collaborators

    • Sam Hallman (PhD, 2015) now at Amazon
    • Michael Chiang (PhD, 2015)
    • Hyeoungho Bae (PhD, 2013) now at Microsoft
    • Shaofei Wang (MS, 2013)
    • Julian Yarkony (PhD,2012) now at Experian Data Lab
    • Matthew Nease (BS, 2012) now at Edmunds
    • Shu-Chi Hsu (MS, 2012)
    • Yihang Bo (visiting PhD, 2010-2011) now at Beijing Film Academy
    • Ragib Morshed (MS, 2011) now at Adconion
    • Sangeeta Jha (MS, 2010) now at Intel
    • Tony Tran (MS, 2009) now at Bizo
    • Dennis Park (MS, 2009) now at Vicarious




    funding:


    My research is currently supported by:
    • NSF ( IIS-1253538, DBI-1262547 )
    • NIH (NIBIB 1R25EB022366)
    • NIST CSAFE: Center for Statistics and Applications in Forensic Evidence
    • Research Gift from Adobe
    previously:
    • National Science Foundation DBI-1053036
    • Google Research Award
    • UC Labs Research Program


    meetings organized:

    • USA-Sino Summer School in Vision, Learning, Pattern Recognition: VLPR 2012 Fudan Unviersity, Shanghai, China July 2012.

    • The Eighth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision (POCV 2012), CVPR 2012, Providence, RI.

    • Annual Southern California Computer Vision Meetup

    http://www.ics.uci.edu/~jajones/ http://www.ics.uci.edu/~jutts/ JESSICA UTTS' HOME PAGE

    Home Page for Professor Jessica Utts

    Department of Statistics

    University of California, Irvine

    Professor Jessica Utts
    Chair, Department of Statistics
    University of California, Irvine.

     

    Photo courtesy of Anna Karin Malm

    Contact Information

    Professor Jessica Utts
    Department of Statistics
    Donald Bren Hall
    University of California
    Irvine, CA 92697-1250

    Phone: 949-824-0649
    Fax: 949-824-9863
    Department Office: 949-824-5392
    Email (not clickable to reduce spamming): jutts at uci dot edu
    Office location: 2038 Donald Bren Hall, UC Irvine
    Department office: 2042 Donald Bren Hall, UC Irvine

    Major Administrative and Service Roles (Current and Recent Past, with links)

    • 2015 President-Elect and 2016 President, American Statistical Association
    • Chair, Department of Statistics, University of California, Irvine
    • Vice President, International Association for Statistical Education, 2011-2013
    • Member, Board of Directors, American Statistical Association, 2010-2012
    • Chair, Committee of Presidents of Statistical Societies (COPSS), Jan 2007 - Dec 2009
    • Chair of the Board, Consortium for the Advancement of Undergraduate Statistics Education (CAUSE)
    • Vice-Chair of the Board, National Institute of Statistical Sciences (NISS), 2008-2011

    Representative Presentations

    • AP Annual Conference 2015: Results and Teaching Tips from the 2015 AP Statistics Exam and Compact version with 4 slides per page
    • AP Annual Conference 2014: Results and Teaching Tips from the 2014 AP Statistics Exam
    • The Importance of Statistics Education, Santiago, Chile, January 2013 and Round Table Talk
    • Statistics Workshop, 56th Annual Conference of the Parapsychological Association, Viterbo, Italy, August 2013
    • AP Statistics Professional Night Talk: What Do Future Senators, Scientists, Social Workers and Sales Clerks Need to Learn from Your Statistics Class? (pdf version)
    • From Seances to Science: Investigating Psychic Phenomena with Statistics and pdf version
    • California State University, Fullerton, October 6, 2011: Are we all Bayesians? The strength of evidence vs the power of belief
    • California Mathematics Council, Community Colleges (CMC3), December 13, 2008, Monterey, CA
    • Analysis of Milton Wiseman Meta-analysis, July 15, 2007, Vancouver, British Columbia, Canada
    • WNAR New Researchers' Lunch, June 26, 2007, Irvine, CA
    • Japanese Behaviormetrics Society, September 12, 2006, Tokyo, Japan
    • Japan Joint Statistics Meeting, September 7, 2006, Sendai, Japan
    • Stor Konferens om Parapsykologisk Forskning, Stockholm, Sweden, August 3, 2006
    • GAISE Workshop/AMATYC, Nov 8&9, 2005
    • CMCCC, Dec 2, 2005

    Current Classes (Fall 2015)

    • Statistics 110, Statistical Methods for Data Analysis I (Undergraduate)

    Past Classes at UC Irvine

    • Statistics 7, Basic Statistics
    • Statistics 110, Statistical Methods for Data Analysis I (Undergraduate)
    • Statistics 201, Statistical Methods for Data Analysis I (Graduate)
    • Statistics 110 and 201, Statistical Methods for Data Analysis I (combined)
    • Statistics 210,Statistical Methods I: Linear Models
    • Statistics 8, Introduction to Biostatistics

    Past Classes at UC Davis

    • Statistics 108, Regression Analysis
    • Statistics 390, Methods of Teaching Statistics
    • Statistics 13V, Web-based Statistics
    • Integrated Studies 8C, Testing Psychic Claims

    My Curriculum Vitae

    • Updated CV (when I remember to do it!)
    • Employment History
    • Administrative Experience at UC Davis
    • Awards, Activities and Grants
    • Publications (Scroll down to "My Research Interests" on this page for links to some publications.)
      • Word
      • PDF

    Where I Received my Degrees

    • Bachelor of Arts in Math and Psychology at the State University of New York at Binghamton in 1973.
    • Master of Arts (1975) and Ph.D. (1978) from the Department of Statistics at Penn State University

    Places I Have Had Visiting Appointments

    • Department of Statistics, Stanford University
    • Koestler Chair of Parapsychology, University of Edinburgh
    • Institut f�r Grenzgebiete der Psychologie und Psychohygiene (IGPP)
    • SRI International, project now moved to Cognitive Sciences Laboratory

    My Research Interests

    I am interested in applied statistics, and have published most extensively on the use of statistics in parapsychology.
    I am also interested in statistics education and literacy.
    I have provided some of the most commonly requested papers here.

    In the Fall of 1995 Professor Ray Hyman (University of Oregon)and I prepared a report assessing the statistical evidence for psychic functioning in US government sponsored research. The report was part of a review done by the American Institutes of Research (AIR) at the request of Congress and the CIA. It received wide-spread media coverage.
    My report and related reports:

    • An Assessment Of The Evidence For Psychic Functioning
    • Professor Hyman's report.
    • Utts' reply to Hyman's report
    • Response by Dr. Edwin May, the lead investigator for the government-sponsored work and Director, Cognitive Sciences Laboratory

    "Replication and Meta-Analysis in Parapsychology."  (Statistical Science, with commentary by others) - summarizes some of the statistical evidence for psi phenomena that was available at the time the paper was written (1991).

    Utts, Jessica (1999). The Significance of Statistics in Mind-Matter Research, Journal of Scientific Exploration, 13(4), 615-638.

    "The Paranormal: the Evidence and its Implications for Consciousness"  published in the [London] Times Higher Education Supplement, Apr. 5th. 1996, page (v), with Nobel Laureate Brian Josephson

    Utts, Jessica (2003). What Educated Citizens Should Know about Statistics and Probability, American Statistician, 57(2), 74-79.

    My Books

    Seeing Through Statistics, 4th edition (2015), ISBN 9781285050881

    Mind On Statistics, 5th edition (2015) by Jessica Utts and Robert Heckard, ISBN-13: 9781285463186

    Statistical Ideas and Methods, 1st edition (2006) by Jessica Utts and Robert Heckard, ISBN 0-049-512250-5

    Last updated July 2015 (with some links updated in the more distant past)

     

    http://www.ics.uci.edu/~wayne/

    This is Wayne's new web page, which is really just a redirect to his old web page, http://www.cs.toronto.edu/~wayne

    You should have been redirected to the new address immediately.

    If you see this message, please click on the link above!

    Stop the Hacker! http://www.ics.uci.edu/~alexv/ Alexander V. Veidenbaum

    Alexander V. Veidenbaum

    Professor of Computer Science

     

    Education

    Ph.D. Computer Science, University of Illinois at Urbana-Champaign, 1985

    Research Interests

    • Computer Architecture: high-performance processors, memory hierarchy, low-power processors, multiprocessor systems

    • Compilers: optimization and restructuring techniques, compiler-assisted memory management, Java for embedded systems

    • Embedded Systems: system architecture, software, and low-power design

    Lab

    Parallel Architectures and Systems

    Selected Publications

    Press here for a list and to download

    Current Research

    • WebRTC and WebRTCBench
    • Cache-Aware Synchronization and Scheduling of Data-Parallel Programs for Multi-Core Processors
    • Low-power processors

    PhD Students

    Nam Duong (PhD 2014)
    Rosario Cammarota (PhD 2013)
    Dali Zhao
    Tae Su Kim
    Houman Homayoun (PhD 2010)
    Carmen Badea (PhD 2010)
    Jelena Trajkovic (PhD 2009)
    Dan Nicolaescu (PhD 2006)
    Weiyu Tang (PhD 2004)
    Ana Azevedo (PhD 2003)

    Program Committees

    International Parallel & Distributed Processing Symposium (IPDPS'14)

    Other conferences I am involved with

    International Conference on Supercomputing
    International Workshop on Innovative Architecture

    Computer science courses I teach

    CS250A Computer Systems Architecture
    CS250B Modern Microprocessors
    CS131 Parallel and Distributed Systems
    CS152 Computer Systems Architecture
    CS154 Computer Design Lab

    Contact Info

    Prof. A. Veidenbaum
    3056 Bren Hall
    Dept. of Computer Science
    University of California
    Irvine, CA 92697-3435
    Phone: (949)-824-6188, FAX: (949)-824-4056
    Mail: a l e x v AT i c s DOT u c i DOT e d u,
    Web: http://www ics uci edu/~alexv
    http://www.ics.uci.edu/~yunanc/ http://www.ics.uci.edu/~guoqingx/ Harry Xu's Homepage

      

    Guoqing (Harry) Xu

       Assistant Professor
       Office: 3212 Donald Bren Hall
       Computer Science Department
       Donald Bren School of Computer and Information Sciences
       University of California, Irvine
       harry.g.xu at uci dot edu

     

                            


    News

    A paper accepted to TOCS with minor revision.

    PerfBlower was released! If you work on dynamic analysis but do not want to hack JVM, try our instrumentation specification language (ISL) in PerfBlower. Read our ECOOP'15 paper for details.

    One idea paper was accepted to PLOS'15 (colocated with SOSP'15). Congratulations to Khanh and Lu!

    The ''interruptible task'' paper was accepted to SOSP'15. Congratulations to Lu and Khanh!

    GraphQ is publicly available! Try it if you have a very large graph but only want to know information about a small number of vertices and edges.

    The GraphQ paper was accepted to USENIX ATC'15. Congratulations to Kai!

    The PerfBlower paper was accepted to ECOOP'15. Congratulations to Lu!

    The Facade paper was accepted to ASPLOS'15. Congratulations to Khanh and everyone else in the group!

    Congratulations to Khanh for winning the third prize (bronze medal) in the ACM Student Research Competition at PLDI'14.

    A new collaborative grant awarded from NSF CCF to support my research on performance debugging and testing.

    A new (sole-PI) grant awarded from ONR to support my research on automated data structure replacement.

    A paper accepted to TOSEM.

    A paper accepted to CGO'14.

    A paper accepted to ASE'13.

    A new (sole-PI) grant awarded from NSF CNS.

    We have released Resurrector, a new object lifetime profiling tool! Please read our OOPSLA'13 paper for details.

    We have released our demand-driven context-sensitive alias analysis! This analysis gives you better performance as it does not need to perform a points-to analysis first.

    Our Big Data design paper got a Chinese transaltion!

    One paper accepted to ESEC/FSE'13. Congratulations to Khanh, who has a top conference paper before finishing the first year!

    One paper accepted to OOPSLA'13.

    All the talk slides for the pre-OOPSLA-PC-meeting workshop can be found here. Thanks to all the contributing PC members!

    One paper accepted to ISSTA'13.

    One paper accepted to ISMM'13.

    One paper accepted to ECOOP'13.

    About Me

    I am an assistant professor in the computer science department of University of California, Irvine.

    My research interests range from software engineering, through programming languages and compilers, to runtime/operating/distributed systems. I am recently interested in building scalable and low-latency Big Data systems.

    Professional

    o    Publications

    o    Curriculum Vitae

    Current Projects

    o    Systems support for highly-scalable program analyses

    o    Language, compiler, and runtime systems support for highly efficient, scalable, and adaptive Big Data systems

    o    Runtime bloat detection and optimizations

    Research Group

    o    Dr. Zhiqiang Zuo (PostDoc starting April 2015)

    o    Khanh Nguyen (Ph.D. student starting Fall 2012)

    o    Lu Fang (Ph.D. student starting Fall 2012)

    o    Kai Wang (Ph.D. student starting Fall 2013)

    o    Sanaz Alamian (Ph.D. student starting Fall 2015)

    o    Aftab Hussain (Ph.D. student starting Fall 2015)

    o    Cheng Cai (Ph.D. student starting Fall 2015)

    o    Keval Vora (Visiting Ph.D. student from UC Riverside, co-advised with Rajiv Gupta)

    o    Matthew Hartz (Undergrad)

    Affiliated Students

    o    Vijay Palepu (Software engineering group)

    o    Yingyi Bu (Information system group)

    Alumni

    o    Jianfei Hu (M.S. 2015, now at Google)

    o    Louis Zhang (2014 summer intern from Troy High, now at Berkeley CS)

    o    Ankur Gupta (2014 summer intern from University High)

    o    Wendy Wei (2013 summer intern from University High, now at MIT CS)

    o    Allen Min (2012 summer intern from Whitney High, now at UCI CS)

    o    Jonathon Tsai (2012 summer intern from Whitney High, now at UCI CS)

    Teaching

    o   CS 142: Compilers and Interpreters (Spring 2014, Winter 2015, Winter 2016)

    o   CS 141/CSE 141/INF 101: Concepts of Programming Languages (Winter 2014)

    o   CS 295 PL research seminar: memory consistency models (Fall 2013)

    o   CS 253/INF 212: Principles of program analysis (Spring 2013)

    o   CS 142 (b) compiler construction project (Winter 2013)

    o   CS 295 dynamic analysis research seminar (Winter 2012)

    o   UCI PL reading group (Fall 2011)

    Service

    o  ISMM'16 PC (Deadline: Feburary 7, 2016)

    o  ECOOP'16 PC (Deadline: December 8, 2015)

    o  WODA'15 PC co-chair (Colocated with OOPSLA'15, Deadline: August 7, 2015)

    o  OOPSLA'15 Doctoral Symposium (Deadline: June 30, 2015)

    o  ISMM'15 PC (Deadline: February 12, 2015)

    o  ISSTA'15 PC (Deadline: January 23, 2015)

    o  PLDI'15 PC (Deadline: November 13, 2014)

    o  ISEC'15 PC (Deadline: September 22, 2014)

    o  ECOOP'15 PC (Deadline: December 21, 2014)

    o  PERTEA'14 co-organizer (Deadline: April 10, 2014)

    o  WODA'14 PC (Deadline: Feb 21, 2014)

    o   PLDI'14 SRC (Deadline: March 10, 2014)

    o   ISMM'14 PC (Deadline: February 12, 2014)

    o   PLDI'14 ERC (Deadline: November 15, 2013)

    o   FSE'14 Research Demos (Deadline: June 30, 2014)

    o   ICSE'14 Poster (Deadline: January 14, 2014)

    o   COSMIC'13 Workshop PC (Deadline: January 10, 2013)

    o   OOPSLA'13 PC (Deadline: March 28, 2013)

    o   OOPSLA'12 SRC and Poster (Deadline: July 09, 2012)

    o   PPoPP'13 Workshop and Tutorial Chair (Deadline: September 1, 2012)

    Released Software

    • PerfBlower: A performance problem amplification framework and an instrumentation specification language
    • GraphQ: Analytical query processing based on abstraction refinement over very large graphs
    • Resurrector: A new object lifetime profiler
    • Demand-driven context-sensitive alias analysis for Java
    • LeakChaser: A Java memory leak detector
    • AJANA: A framework for source-code-level analysis of AspectJ software

    Contact Information

    guoqingx at ics dot uci dot edu
    http://www.ics.uci.edu/~guoqingx

    Office:
    3212 Donald Bren Hall
    Bren School of Computer and Information Sciences
    UC Irvine
    Irvine, CA, 92697-3435
    Work: 949-824-8870
     

    Last updated: June 29, 2015
    Maintained by guoqingx at ics dot uci dot edu (replace dot with .)

    http://cert.ics.uci.edu/ CERT - Center for Emergency Response Technologies
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    During a crisis event, bringing the right information, at the right time, to the right person, can significantly impact the quality of decision making for First Responders as well as the general public. Effective decision making during a crisis can be directly attributed to saving lives and property. The research team assembled by the Center for Emergency Response Technologies (CERT) believes that information technologies can enhance decision making abilities.

    The goal of the center is new and innovative technology research. The focus will be on how technology will improve emergency response. At the system level: robust systems, resiliency in extreme situations. At the Information level: convert large-scale, multi-modal information into actionable information upon which decisions can be made. A layer of social science research provides the application context. Engineering research (e.g., transportation systems and alert systems) provide a specific lifeline for which the value of IT can be illustrated.

    CERT will radically transform the ability of responding organizations to gather, manage, use, and disseminate information within emergency response networks and to the general public. Depending upon the severity of the crisis, response may involve numerous organizations including multiple layers of government, public authorities, commercial entities, volunteer organizations, media organizations, and the public. These entities work together to save lives, preserve infrastructure and community resources, and to reestablish normalcy within the population. The efficacy of response is determined by the ability of decision-makers to understand the crisis at hand and the state of the available resources to make vital decisions. The quality of these decisions in turn depends upon the timeliness and accuracy of the information available to the responders.

    CERT will be an interdisciplinary effort that brings computer scientists, engineers, social scientists, and disaster science experts together to explore technological innovations in order to deliver the right information to the right people at the right time during crisis response. Joint research collaboration with Calit2 and the UCI School of Social Sciences is being sought in order to maximize the impact of the proposed ICS Center.

    Latest News: Cert News

    Upcoming Event: Southern California UICDS Consortium. In conjunction with DHS, First Responders, and other stakeholders, the CERT Team is planning a "kick-off" meeting to establish a UICDS Consortium and UICDS Core at UCI. Date and time TBA.

    In March (2010), Prof. Chen Li received an NSF award to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake .

    Distingushed Lecture (10 NOV 09): Yueting Zhuang Professor and Dean of the College of Computer Science, Zhejiang University: Digital Libraries and its potential in-depth applications. http://isg.ics.uci.edu/events.html

    Workshop November 5th and 6th: DHS S&T / UCI CERT: Workshop on
    Emergency Management: Incident, Resource, and Supply Chain Management
    (EMWS09).

    Distinguished Lecture (October 8, 2009: 11AM, Bren Hall: Room 4011): Nanda Kambhatla, Ph.D, Manager, Data Analytics, IBM India Research Lab, Bangalore India: Abstract of Talk and Nanda's Bio


    WPI Precision Personnel Locator Workshop: August 3-4, 2009. Dr. Chris Davison presented: SAFIRE – Technological Research and Solutions Impacting Situational Awareness for Firefighters

    Distinguished Lecturer: Ron Eguchi, CEO ImageCat Inc., May 15, 2009. Topic: Earthquakes, Hurricanes and other Disasters: A View from Space.

    Firefighter Forum: May 15, 2009. Special Topic: Wildland Fires. Location: Bren Hall, Room 4001, UCI

    Disaster Pictures

    This page was last updated on: April 14, 2010 8:28 AM
    http://www.ics.uci.edu/~projects/satware/ Please update your bookmarks to: http://www.ics.uci.edu/~projects/SATware http://www.ics.uci.edu/ugrad/degrees/Second_BA_SE.php se second baccalaureate @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > Degrees
    Second Baccalaureate Degrees

    » Second Baccalaureate in Software Engineering

    The following minimum requirements must be complete prior to applying for a SE Second Baccalaureate:

    1. A GPA of at least 2.5 for the first bachelor's degree.
    2. Completion of 2 courses from ICS 21-22, ICS 31, ICS 32, ICS 33, ICS 45J, INF 43; completion of 2 additional courses from the list of ICS 6B or Stats 67.
    3. Satisfactory completion of the lower-division writing requirement.
    4. A combined GPA of at least 2.5 and no grade lower than a "C" on all courses taken above.

    *Please note that AP exam scores do not count toward the above criteria for admission.

    Informatics 43: Concepts, methods, and current practice of software engineering. Large-scale software production, software life cycle models, principles and techniques for each stage of development. Laboratory project applying these concepts.

    ICS 6B: Relations and their properties; Boolean algebras, formal languages; finite automata.

     

     

     

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    Bren school home > Undergraduate > Degrees
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    » Second Baccalaureate in Informatics

     

    The following minimum requirements must be complete prior to applying for a Informatics Second Baccalaureate:

    1. A GPA of at least 2.5 for the first bachelor’s degree.
    2. Completion of 2 courses from ICS 21-22, ICS 31, ICS 32, ICS 33, ICS 45J, INF 43; completion of 2 additional courses from the list of ICS 6B, Stats 7 or Stats 67.
    3. Satisfactory completion of the lower-division writing requirement.
    4. A combined GPA of at least 2.5 and no grade lower than a "C" on all courses taken above.

    *Please note that AP exam scores do not count toward the above criteria for admission.

    Informatics 43: Concepts, methods, and current practice of software engineering. Large-scale software production, software life cycle models, principles and techniques for each stage of development. Laboratory project applying these concepts.

    ICS 6B: Relations and their properties; Boolean algebras, formal languages; finite automata.

     

     

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    Bren school home > Undergraduate > Degrees
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    » Second Baccalaureate in Computer Science

    The following minimum requirements must be complete prior to applying for a CS Second Baccalaureate:

    1. A GPA of at least 2.5 in first bachelor's degree from a four year institution.
    2. Choose 2 courses from the following: ICS 21-22, ICS 31, ICS 32, ICS 33, ICS 45C, ICS 46, ICS 51, INF 43, or equivalents.
    3. Choose an additional 2 courses from: Math 2A, Math 2B, ICS 6B, ICS 6D, ICS 6N, Stats 67, or equivalents
    4. A combined GPA of at least 2.5 and no grade lower than a "C" on all courses taken above.

    *Please note that AP exam scores do not count toward the above criteria for admission. 

    ICS 6B-D-N: This is a yearlong sequence in discrete mathematics. It is a sequence designed for computer science majors and covers combinatorics and graph theory, logic and boolean algebra, and linear algebra.

    Math 2A-B and Stats 67: This is a year of calculus and computer science statistics. A standard, yearlong sequence in calculus at another institution is normally considered equivalent to Math 2A-B. Stats 67 must be completed at UCI through ACCESS UCI via University Extension as there is currently no equivalent available at a community college.

    ICS 31, 32, 32, 45C, 46 and 51: This is the UCI introductory sequence in computer science. It covers history, computer system fundamentals, productivity tools, an introduction to programming and problem solving, literacy topics in computer science, data structures and algorithms, and an introduction to system architecture.

    Informatics 43: Concepts, methods, and current practice of software engineering. Large-scale software production, software life cycle models, principles and techniques for each stage of development. Laboratory project applying these concepts.


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    Second Baccalaureate Degrees

    » Second Baccalaureate in Computer Science and Engineering 

    The following minimum requirements must be complete prior to applying for a CSE Second Baccalaureate:

    1. A GPA of at least 2.5 for the first bachelor’s degree.
    2. Completion of CSE 41 and 42 or its equivalents.
    3. Completion of Math 2A-B-D or its equivalents.
    4. Completion of Physics 7C and 7D with labs or its equivalents.
    5. A combined GPA of at least 2.5 and no grade lower than a "C" on all courses taken above.

    *Please note that AP exam scores do not count toward the above criteria for admission.

    Math 2A-B: This is a year of calculus and Computer Science Statistics. A standard, yearlong sequence in calculus at another institution is normally considered equivalent to Math 2A-B.

    Physics 7C-D and 7LC-LD: This is a two quarter sequence in classical physics. It is a sequence designed for engineering majors and covers force, energy, momentum, roation, and gravity, and electricity and magnetism.

    CSE 41 and 42: This is the UCI introductory sequence in computer science and engineering. It covers history, computer system fundamentals, productivity tools, an introduction to programming and problem solving, and literacy topics in computer science.

     

     

     

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    Bren school home > About > Bren gift
    Advancing the school

    chancellor ralph j. cicerone quote::

    This historic gift will have a transformational impact on students, faculty, alumni and community friends of the Bren School.

    The school, strengthened with new, unprecedented resources by this gift, is aggressively moving toward its overarching goal of creating a top ten computer science program within five years.

    But moving forward in this new era requires partnership with the community that we serve. We are looking to engage you as a champion in the Bren School's future through the support of our key priorities:

    » Acceleration of scholarship and fellowship awards to attract the most promising students
    » Funding endowed chairs for high-profile, mid-career faculty to compliment the existing endowment of ten distinguished scholars
    » Comprehensive expansion of industrial collaborations to transition emerging university technologies
    » Capital support for equipping and enhancing our research and educational spaces with cutting-edge technologies

    We believe that this is the right place, right time, right plan - all we need is you.

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    http://www.ics.uci.edu/about/bren/ about the bren gift @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift
    About the Bren gift

    The Donald Bren School of Information and Computer Sciences was made possible by the generous $20 million gift from Orange County business leader and philanthropist Donald Bren, chairman of The Irvine Company.

    Mr. Bren's gift equals the largest single gift ever to UC Irvine and has created the most endowed faculty positions at one time on any University of California campus.

    In recognition of this transformational gift, the school, renowned worldwide for leading the innovation of new information and computing technologies and producing an educated work force to fuel the economic engine, was renamed the Donald Bren School of Information and Computer Sciences at UC Irvine.

    In conjunction with the June 9, 2004, naming announcement, the school also broke ground on its new home, Donald Bren Hall; the first classes were held there in January 2007.

    Competitively positioned among other top-ranked schools, this gift allows the school of information and computer sciences to continue in its upward trajectory of recruiting and retaining the best and the brightest students, faculty and researchers.

    The school of information and computer sciences has long been a program that combines vision and determination with wisdom. Those attributes, in concert with Mr. Bren’s support, will continue to allow its students, alumni and faculty to impact the world.

    Elevated to school status in December 2002, the nationally acclaimed school is the first independent computer science school within the UC system and one of the fastest-growing programs of its kind in the nation.

    Please explore these special pages for details of the gift and an up-close look at the Donald Bren School of Information and Computer Sciences. 

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    Bren school home > About > Bren gift
    About Donald Bren
    photo::donald bren

    Donald Bren

    Donald Bren is Chairman of the Irvine Company, one of the most respected and diversified privately held real estate companies in America.

    Founded more than 140 years ago, the company is known for its best-of-class portfolio of premier California real estate including office, apartment, retail, resort and new housing properties in Orange County, San Diego, Los Angeles and Silicon Valley.

    Bren is a leader and innovator within the real estate industry, one of the first to combine well-designed homes with such amenities as parks and substantial open space, schools, employment centers and shopping centers to create balanced master-planned communities.

    Through his accomplishments, he has achieved a reputation as a national expert in the interrelated fields of planning, design, construction, marketing and finance.

    Bren is a nationally prominent philanthropist, with special focus on education, conservation and research, and has directed more than $200 million to support K-12 public schools in Southern California and other institutions such as University of California at Irvine and UC Santa Barbara.

    He is consistently ranked high on BusinessWeek magazine’s list of “The 50 Most Generous Philanthropists” in the nation. In 2008, the magazine placed Bren at No. 9, citing the more than $907 million he has given or pledged since 2003. His lifetime giving exceeds $1.3 billion, according to the magazine’s estimates.

    “Bren’s commitment to education runs the gamut from students to principals to school districts to university scholars on (The) Irvine Ranch,” BusinessWeek noted.

    Bren’s extensive educational and research philanthropy includes funding endowed distinguished professor chairs at Caltech, the University of California, Chapman University and Marine Corps University.

    At the University of California, his support has created more endowed chairs than any other single donor in UC history. In total, he has endowed 50 chairs for distinguished faculty and researchers at universities and research institutions.

    In recognition of his wide-ranging support for the UC system, Bren was awarded in 2004 its highest honor, the University of California Presidential Medal.

    “For well over two decades, Mr. Bren’s focus on the creation of endowment funds to support faculty chairs has helped bring to the University of California some of the best researchers and scholars in the nation and the world,” former UC President Robert Dynes said when Bren was awarded the UC Presidential Medal. “His passionate philanthropy and commitment to educational excellence have helped strengthen the university.”

    Bren’s contributions to public schools on The Irvine Ranch and elsewhere in Orange County and Southern California comprise major gifts that are benefiting tens of thousands of children.

    For example, in a gift that underscored Bren’s passion for public education and desire to help children succeed, in 2008 he gave $8.5 million to THINK Together, one of the largest gifts ever to an after-school program in California history.

    The gift brought to $10 million Bren’s contributions to the innovative and successful program, which helps at-risk children in Eastern Los Angeles County and Santa Ana and other Orange County schools.

    Meanwhile, Bren gave $20 million to local schools to protect and expand enrichment programs in music, art and science—programs he believes are vital to a child’s well-rounded academic and personal development.

    Bren for many years has been at the forefront of efforts to preserve environmentally sensitive land in Southern California, a commitment that was recognized in 2006 with the designation of much of The Irvine Ranch’s open space and parks as a National Natural Landmark by the U.S. Department of the Interior. In 2008, the land was designated as the first California Natural Landmark by the State of California.

    Bren has protected more than half of the 93,000-acre ranch—more than 50,000 acres—as permanent open space, parks and trails.

    His conservation efforts have protected a wide range of endangered plant and animal species, while expanding the public’s access to and enjoyment of the land.

    Bren also has committed $50 million for the long-term management, preservation and restoration of the natural resources on The Irvine Ranch, efforts now being carried out by the Irvine Ranch Conservancy, which Bren created.

    Its mission also is to protect and enhance the lands while increasing the public’s opportunity to enjoy them. These beautiful, permanently protected lands represent one of the nation’s largest natural and recreational resources located within minutes of millions of urban residents.

    Bren is on the board of trustees of the University of California, Irvine Foundation, the Los Angeles County Museum of Art, the Orange County Museum of Art and California Institute of Technology.

    In 2007, he was elected a Fellow of the prestigious American Academy of Arts & Sciences in the category of Business, Corporate and Philanthropic Leadership—Private Sector.

    Bren holds a bachelor's degree in business administration and economics from the University of Washington.

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    http://www.ics.uci.edu/about/bren/bren_vision.php vision for the gift @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift
    Vision for the gift

    donald bren quote::

    The vision for the Donald Bren School of Information and Computer Sciences, established by Dean Debra J. Richardson in 2002 as part of an overall strategic initiative to advance the school’s global reputation, is both focused and far-reaching in its aim to create a top ten computer science program within five years.

    The $20 million gift will accelerate initiatives aimed at advancing the school's vision, further reinforcing the impact of its academic and research approach within the information technology discipline.

    The gift by Donald Bren provides a transformational opportunity to anticipate and respond to the ever-growing demands of a technology-centric world.

    The vision for investing the unprecedented endowment builds upon Mr. Bren’s long belief that quality education begins and ends with quality educators.

    As some of the most honored faculty at UC Irvine, Bren School professors are trailblazers recognized with countless award and accolades in information and computer science education and research.

    Building upon that heritage, the Bren School is now uniquely positioned to realize its vision through the accelerated addition of ten highly distinguished scholars alongside its annual recruitment of shining, young stars.

    The hallmarks of a Bren School education are evident in the unparalleled UC Irvine culture, which emphasize cross-campus collaboration in learning and research; integrated, interdisciplinary coursework; innovation of cutting-edge technology and utilization of technology-enhanced learning.

    Our alumni successes are proof that we are forging the best path for tomorrow's students. For generations to come, Bren School students will be the beneficiaries of Mr. Bren’s selfless support and the school’s strategic planning.

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    http://www.ics.uci.edu/about/videos/ itunes u @ the bren school of information and computer sciences
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    Bren school home > About > iTunes U
    iTunes U

    link to the bren school on itunes uiTunes U is a free service provided by the Bren School and Apple that provides easy access to Bren School educational content, including lectures and interviews from such distinguished individuals as Marvin Minsky, John Seely Brown and Larry Rowe.

    Through iTunes U, users can download content to their Macs or PCs regardless of their location. They can then listen to and view content on their Mac or PC or transfer that content to iPod for listening or viewing on the go.

    In order to access the Bren School iTunes U content, you will need to have iTunes installed on your computer. Visit the apple website for the free download of iTunes.

    Access the Bren School on iTunes U »


    Turn us on @ YouTube
    We also encourage you to visit our visit our YouTube page page and watch videos focusing on the life changing research, cutting edge computer science education and the history of the the University of California's first and only information and computer science school. more »

     

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    http://www.ics.uci.edu/~numanare/miscellaneous.html U.N.Niranjan
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    Niranjan Uma Naresh

    Talks

    1. Online Tensor Method for Community Detection

    2. Support Vector Machines

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    Non-convex Robust PCA (Code)

    Sample results on the task of foreground-background separation in the restaurant video dataset:


    From left to right: Original video, non-convex low-rank, non-convex sparse



    From left to right: Vanilla PCA, convex low-rank, convex sparse

    Note that our non-convex method is superior in the quality of the foreground-background separation (for example, notice that artifacts are not present in our method when compared a gainst convex method), for a given accuracy, and has much faster running times (refer to the paper for more details).

    Visualization of algorithm convergence for foreground-background separation in the restaurant video dataset:


    From left to right: Non-convex low-rank, non-convex sparse


    From left to right: Convex low-rank, convex sparse
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    Publications

    1. Tensor Contractions with Extended BLAS Kernels on CPU and GPU
      Submitted

    2. Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
      A. Anandkumar, P. Jain, Y. Shi, U. N. Niranjan
      AISTATS 2016

    3. FEAST: Feature ExtrAction using Score function Tensors
      M. Janzamin, H. Sedghi, U. N. Niranjan, A. Anandkumar
      NIPS 2015 Workshop on Non-convex Optimization

    4. Non-convex Robust PCA
      P. Netrapalli, U. N. Niranjan, S. Sanghavi, A. Anandkumar, P. Jain
      NIPS 2014 (Code)

    5. Scalable Latent Tree Model and its Application to Health Analytics
      F. Huang, U. N. Niranjan, I. Perros, R. Chen, J. Sun, A. Anandkumar
      Submitted

    6. Online Tensor Methods for Learning Latent Variable Models
      F. Huang, U. N. Niranjan, M. U. Hakeem, A. Anandkumar
      JMLR 2014

    7. Understanding Machine-learned Density Functionals
      L. Li, J. Snyder, I. Pelaschier, J. Huang, U. N. Niranjan, P. Duncan, M. Rupp, K. Muller, K. Burke
      International Journal of Quantum Chemistry, 2015

    8. Puzzhull: Cavity and protrusion hierarchy to fit conformal polygons
      S. Y. Mistry, U. N. Niranjan, M. Gopi
      GD/SPM 2013, CAD 2014

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    Niranjan Uma Naresh

    niranjan 

    Uma Naresh, Niranjan
    PhD Candidate, Computer Science
    University of California, Irvine
    Email: un <dot> niranjan <at> uci <dot> edu

    Research

    For more about my research, see my publications.

    News

    I co-organized the NIPS 2015 Workshop on Non-convex Optimization for Machine Learning.

    http://www.ics.uci.edu/~numanare/about.html U.N.Niranjan
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    Niranjan Uma Naresh

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    CV (pdf).

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    un <dot> niranjan <at> uci <dot> edu

    http://www.ics.uci.edu/community/news/spotlight/spotlight_lamb.php david lamb spotlight @ the bren school of information and computer sciences
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    Bren school home > Community > News > Spotlights
    David Lamb spotlight

    The Second Time Around

    photo:: david lamb

    David Lamb

    At 45, David Lamb, a senior ICS major sometimes mistaken for a professor, is only months away from graduating; reaching a goal he never dreamed he was capable of achieving and beginning his second career as a computer scientist.

    Lamb used to be an electrician, installing traffic signals and street lighting, a physically demanding job he enjoyed until he suffered an injury and nerve damage from a subsequent surgery. Forced to pursue another career, Lamb decided to go back to school and pursue a degree in computer science.

    “Because of my earlier work with motor control, trouble-shooting, and some exposure to programming Programmable Logic Controllers (PLC), I felt that ICS would offer the same mental challenges of electrical work without the physical labor,” Lamb said. “I haven’t been disappointed yet.”

    But the computer science bug had bitten Lamb long before, when he was working for a poultry company in the San Joaquin Valley.

    “More and more, devices with embedded software were being used as part of process control systems,” Lamb said. “I was amazed that an entire wall of relays and timers could be replaced by a little 10” x 12” box. I wanted to know more about them. Those devices were my first introduction to programming.”

    Check out our spotlight page to read more profiles of Bren School students, faculty and alumni.

    His interest peaked, Lamb took a basic electronics course at Modesto Junior College, but as work became more time consuming, he found little time to take structured courses. Only after his hernia surgery would he find the time to feed his interest in computer science once again.

    “While recuperating from my surgery, I bought an algebra book and went through the problems. I treated them like crossword puzzles. After finishing it, I bought a trigonometry book at a second hand store,” Lamb said. “I was halfway through that when my wife suggested that I might as well get a real degree, so I enrolled at Yuba Community College.”

    TRANSITION

    While attending Yuba College, Lamb was invited to a Society for Advancement of Chicanos and Native Americans in Science (SACNAS) conference in Phoenix, where he meet Kika Friend from UCI’s Center for Educational Partnerships. Friend, who Lamb recalls as a ball of fire and enthusiasm, encouraged him to check out UCI.

    “I came out the following December, and liked what I saw and heard,” Lamb said. “The school seemed a little more business oriented than some of the other universities I was interested in. There also seems to be more opportunity for employment in ICS in this area.”

    Lamb found the transition to life at UCI difficult at first, mainly because he had to leave his wife behind and endure a six month wait from the time he started classes to the time he received an apartment in Verano Place. After three months, his wife’s company transferred her to the Irvine area, but the couple was still forced to live apart for a while longer.

    “We knew that Verano was going to have an opening soon and no one was going to rent us an apartment for just three months, so we rented a room in a different neighborhood for her,” Lamb said. “That was really odd.”

    There was also a transition to life inside the classroom.

    “I think I was a little spoiled by the professors at Yuba Community College,” Lamb said. “The classes were much smaller than at UCI so I was able to get more personal attention. Here it just takes a little more effort to get one-on-one time with professors. That is only because the classes are so much larger here.

    I find that the TA’s are a very important resource to take advantage of. If you can’t reach a professor, the TA’s are your next best source for assistance. Also, e-mail helps to fill in gaps. The professors and TA’s are usually very responsive to questions asked by e-mail.”

    TRANSFER TRIALS

    Lamb offers the following advice to help fellow transfer students and those who are looking to change careers.

    “Take advantage of Intersegmental General Education Transfer Curriculum (IGETC),” Lamb said. “If you are like me and maybe your high school transcripts aren’t exactly stellar, go to a community college and make sure that their curriculum is approved for credit transfer to a university.”

    Lamb learned the hard way that UCI does not recognize computer science courses from some community colleges and had to be prepared to repeat ICS courses taken there.

    “If I had known that from the beginning of my college experience, I would have focused more on general education at the community college level and just taken courses in the ICS major at UCI,” Lamb said.

    To avoid having to repeat ICS courses already taken, local community college students thinking of transferring to the Bren School should ask if their school is aware of the SMART-ICS program. The program encourages partnership with area community colleges in an effort to provide student transfers to ICS subject credit for all lower division math and ICS courses required for the ICS degree.

    Once on campus, Lamb suggests students find a study buddy to help in the more difficult classes as two heads are better than one. Students should also live on or near campus to save their sanity and time by skipping the grinding daily commute.

    “Get a bicycle,” Lamb said. “Parking lots are expensive, bike racks are free.”

    HANDS ON LEARNING

    Despite the early transition adjustments, Lamb has enjoyed his experience at UCI, especially his classes. Two in particular were especially memorable, Introduction to Artificial Intelligence and Mobile Gaming.

    “I was expecting a philosophy class that pondered the existence of HAL9000, Pinocchio and Star Trek’s Data,” Lamb said. “I was relieved when I found it covered legitimate algorithms and methods for mimicking intelligent behavior. It took a subject that I felt was unrealistic or magic and revealed it for what it really is: algorithms, data structures, probability and logic used to generate computationally intelligent decisions or behavior in computers.”

    Alarmed at the violence perpetuated in videos games, Lamb was initially apprehensive about taking a course that could take a violence promoting direction. But his fears were allayed on the first day of his Mobile Gaming class.

    “The professor laid down some ground rules for the programs we would be writing,” Lamb said. “The most prominent was that there was to be no killing in any games we wrote. It made the class much more creative.”

    Lamb and his team went on to create a music program with a Dance Revolution theme. The twist being that the person playing would learn how to read music. It was a very challenging project and even though the game was playable on an emulator, the team ran into trouble getting it on a mobile device.

    The programming language the team was using to create the program did not offer the detail of control they wanted. Had it been available, the program would have been much more compact. The mobile device specifications dictated that the device could handle only one Musical Instrument Digital Interface (MIDI) and one sound file at the same time.

    Once a MIDI was initiated, it would run until it stopped, which is it could not be interrupted. The program required the simulation of two sound files playing simultaneously. Since MIDI operates with 16 channels (each one representing a different instrument), that is more than enough instruments to play a song and make it sound full.

    What the team needed was the ability to control one MIDI channel that would take inputs from the user and feed it directly to the MIDI player. Unfortunately, the programming language did not give the team that detail of control and the problem couldn’t be solved in the short ten weeks of the course. But Lamb looks forward to getting back to this project in the future.

    WITH AGE COMES WISDOM

    Lamb’s past experience as an electrician has also helped him in his classes as he is able to understand the importance of some teachings that other students may see as trivial.

    “One of the lessons I learned in electrical work was to document my work. That meant label every wire, and make a schematic of any thing I wired up,” Lamb said. “That way when I came back to fix something a year later, I had a reference to work with. I learned that lesson the hard way.

    So when it comes to class work, professors are usually sticklers on project documentation. A comment that seems trivial five minutes after a piece of code is written could make a huge difference a year or two later when troubleshooting what you did when you knew everything. So when I have to run new cables or label machinery, I really don’t the mind the documentation; it’s just as important as the rest of the job.”

    Juggling class, married life and a job as a network assistant in the Computer Science Department, has left Lamb little time for hobbies, though he still manages to sneak in some time to play his guitar.

    EYES ON THE FUTURE

    With graduation on the horizon, Lamb is looking forward to reaching the road mark he never dreamed he was capable of achieving. But graduation may not be the last stop on Lamb’s educational road, he is pondering pursing a Master’s or even a Ph.D., but he is also putting out resumes to companies in the area.

    “I feel that work is not what defines me. Don’t get me wrong, I enjoy my major, but that is just one facet,” Lamb said. “But if you asked me what would be my dream job, I would have to say, one that combines my new skills with what I learned in the electrical field. I like to see things happen. I see myself creating software for automatic machinery.”

    Lamb is also looking forward to returning to the leisure activities he has given up in pursuit of his goal; photography, cycling, kite building, working on cars, small engines, Scrabble, fishing (actually he is more into the eating of fish than the act of fishing itself), camping and hiking.

    - Eric Kowalik

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    http://www.ics.uci.edu/community/events/competition/rules Butterworth Product Development and the Beall Design Competitions @ the bren school of information and computer sciences
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    Bren school home > Community > Events >
    Butterworth Product Development
    Competition in ICS
    and
    Beall Student Design Competition in Engineering

    COMPETITION RULES AND GUIDELINES

    Participants
    Both competitions are open to all UC Irvine students. Teams must be composed of at least two (2) students, of which at least one (1) must be an ICS student (for the Butterworth Product Development Competition) and one (1) must be an Engineering student (for the Beall Design Competition). For example, a team member from the Merage School of Business could contribute business planning knowledge that would help the team or students from schools of science or engineering could contribute knowledge of a particular application or technology. Students can be graduate or undergraduate, but must be registered at UCI for the duration of the academic year of the competition.

    Guidelines
    To participate in these competitions, your team must fill out the Web-based Intent to Enter form indicating participation interest. Upon completion of this form, teams will be assigned industry/faculty mentors according to the criteria delineated in the registration form. Participants can enter both the Butterworth Product Development Competition and also the Beall Student Design Competition, but can only win one (1) prize. Entering both competitions increase the chance of winning, but must qualify with the appropriate student team members (1 ICS and 1 Engineering student to qualify for both competitions).

    Obtaining Additional Team Members: Potential participants are encouraged to meet students with similar interests who may be willing to collaborate and form a team at one of the school based workshops, information sessions or the Merage School’s Matrix Mixers that are offered. Students from across campus are encouraged to attend even if they do not have a concept developed.

    Team Integrity: Once the Concept Paper has been submitted, 50% of the team must remain intact for the rest of the competition. 100% of the team must remain intact after submitting the Product Specification/Proof of Concept.

    Product Plan/Idea: Teams must present an original idea or current design problem, and be in a "pre-incubator" form. They must not be backed by incubators, existing companies, venture capitalists or other investors. Teams or team members that have received any form of venture capital financing for their competition entry plan or a likeness thereof may not participate in the competition. Any team receiving venture capital funding while participating in the competition will be disqualified.

    Workshops: Workshops will be offered throughout every phase of the competition. Topics will include,

    • “Working with Your Mentor/How to Define a Product”
    • “How to Incorporate a Business Case into your Proposed Product”
    • “How to Demo Your Product”
    • “Who Owns What… Intellectual Property at UCI”

    Documentation, Presentations and Demos: Documents should be single spaced with a font size between 10 and 12 points. One (1) oral presentation will be due mid-way through the program that will offer proof of concept, feasibility and specifications. A demo and presentation are required for the final phase of the competition. Both oral presentation and final demo will be presented before the judges of the competition. Working prototypes are suggested for the final demo.

    Awards: Final awards will be presented by the Dean of the School and the Judge Chair following the final demo at an Awards Ceremony.

    • Cash awards will be given to the top three (3) teams for each competition in amounts of $7,500, $5,000 and $2,500.
    • The top three (3) teams for each competition will be offered a Meet & Greet with UCI’s Office of Technology Alliances to discuss product, patents, copyrights and trademarks and to answer any questions they may have.
    • All teams receiving an award will qualify to participate as a sponsor in a project course in order to help develop their products. Project submissions must occur within the academic year immediately following the competition (certain restrictions apply).
    • First place winners are recognized by presenting their product in the ICS and Engineering school’s premiere student showcase event, INGENUITY.
    • First place winners will have the opportunity to meet with local investors.
    • All teams will have access to UCI’s Antrepreneur Center to further develop their concepts/ideas and can be directed to other avenues to explore entrepreneurism.

    Judging Guidelines: The judges are professionals who come from various backgrounds. Judges are experts in the process of starting a business including business plan developments, or in the technologies the students are developing. The judges consider many different criteria when evaluating concept papers and the business case. Some of the elements that are considered are as follows:

    • Does the technological/design innovation provide a sustainable, competitive advantage?
    • Does a competitive advantage exist over other products in this application?
    • Does the functionality satisfy the application?
    • Is there a significant return on investment to user?
    • Is the product description clear, detailed and descriptive?
    • Is there a market need for this product?
    • Has the user been realistically identified?
    • Are the benefits to the user clear and sufficient to result in purchase?
    • Have specific user interfaces been provided?
    • Is the user interface well described?
    • Are the requirements thorough, realistic and accurate?
    • Is there a block diagram?
    • Have all of the major modules been described in detail?
    Competition details »
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    http://www.ics.uci.edu/community/events/competition/intent/ Butterworth Product Development and Beall Design Competitions Intent to Enter @ the bren school of information and computer sciences
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    Bren school home > Community > Events > > Intent form
    Butterworth Product Development and Beall Design Competitions Intent to Enter

    Deadline: Monday, February 29, 2016 by Midnight

    By submitting this form, I certify that I have read and agreed to the competition rules and guidelines set forth for the Butterworth Product Development Competition at the Donald Bren School of ICS, and the Beall Design Competition at The Henry Samueli School of Engineering and I understand that any actions that do not follow those rules/guidelines can result in the immediate disqualification of my team.

    To register please provide the following information.

    Only ONE entry per team.

    Intent to Enter form (all fields required)
    Please indicate which competition you will be entering
    Butterworth Product Development Competition (Software)
    Beall Design Competition (Hardware)
    Both Competitions (Software + Hardware)
    Do you have an ICS or Engineering student on your team to qualify?
    (ICS for Butterworth, Engineering for Beall)

    yes no
    Comments:
    Proposed Team Name
    Team Leader
    Team Members (Complete up to 6)
      Name E-Mail Major Student ID Class
    1)
    2)
    3)
    4)
    5)
    6)
    Briefly describe your product
    If you already have a mentor, please add their name and e-mail address here
    If you would like to request a specific individual as your mentor, please indicate the desired research area here
    Check here if you are not on a team and would like to be placed on one:
    Comments

    Competition details »
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    http://www.ics.uci.edu/community/events/butterworth/ butterworth product development competition @ the bren school of information and computer sciences
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    Bren school home > Community > Events >
    Butterworth Product Development Competition
    Information about this year's Butterworth Product Development Competition can be found here.
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    http://www.ics.uci.edu/about/bren/gallery/gallery_07_jpg.php roy fielding (bs '88, ms '93, ph.d. '00) photo @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift > Renaming photo gallery
    Renaming photo gallery
    photo::Roy Fielding (BS '88, MS '93, Ph.D. '00), architect of the Internet's Hypertext Transfer Protocol (HTTP), addresses guests during the event.
    Roy Fielding (BS '88, MS '93, Ph.D. '00), architect of the Internet's Hypertext Transfer Protocol (HTTP), addresses guests during the event.
     
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    photo::ICS Donald Bren, wearing the UC Medal, meets with media after the June 9 event.
    Donald Bren, wearing the UC Medal, meets with media after the June 9 event.
     
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    Bren school home > About > Bren gift > Renaming photo gallery
    Renaming photo gallery
    photo::From (l) to (r), ICS Dean Debra J. Richardson looks on as Mr. Bren receives a memento from ICS students Shawn Shah, Nhu Vuoung, Jose Romero, Tempe Kraus and Albert Udompanyvit.
    From (l) to (r), ICS Dean Debra J. Richardson looks on as Mr. Bren receives a memento from ICS students Shawn Shah, Nhu Vuoung, Jose Romero, Tempe Kraus and Albert Udompanyvit.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_11_jpg.php ted smith photo @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::UCI Foundation chairman Ted Smith thanks Mr. Bren for his continued support of UC Irvine and education.
    UCI Foundation chairman Ted Smith thanks Mr. Bren for his continued support of UC Irvine and education.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_14_jpg.php photo of donald bren accepting the uc medal @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::Provost M.R.C. Greenwood presents Donald Bren with UC's highest honor, the Presidential Medal.
    Provost M.R.C. Greenwood presents Donald Bren with UC's highest honor, the Presidential Medal.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_05_jpg.php photo of community members @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::From (l) to (r), ICS Professor Emeritus Tim Standish, inventor of DNS Paul Mockapetris and father of CAD Pat Hanratty, share a conversation over lunch.
    From (l) to (r), ICS Professor Emeritus Tim Standish, inventor of DNS Paul Mockapetris and father of CAD Pat Hanratty, share a conversation over lunch.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_12_jpg.php father of cadd patrick hanratty (ph.d. '77) photo @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::Father of CADD Patrick Hanratty (Ph.D. '77) recalls advice given to him by his faculty mentors while a graduate student in ICS.
    Father of CADD Patrick Hanratty (Ph.D. '77) recalls advice given to him by his faculty mentors while a graduate student in ICS.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_15_jpg.php uc regent joanne kozberg photo @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::UC Regent Joanne Kozberg offers her thanks and congratulations to Mr. Bren and ICS.
    UC Regent Joanne Kozberg offers her thanks and congratulations to Mr. Bren and ICS.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_03_jpg.php ground breaking of bren hall photo @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift > Renaming photo gallery
    Renaming photo gallery
    photo::From (l) to (r), Dean Richardson, Chancellor Cicerone, Mr. Bren, and Regent Kozberg break ground on the site of Bren Hall. The building is to be completed in late 2006.
    From (l) to (r), Dean Richardson, Chancellor Cicerone, Mr. Bren, and Regent Kozberg break ground on the site of Bren Hall. The building is to be completed in late 2006.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_01_jpg.php photo of dean debra j. richardson @ the bren school of information and computer sciences
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    Renaming photo gallery
    photo::ICS Dean Debra J. Richardson addresses nearly 300 guests in attendance at the June 9 ceremonies.
    ICS Dean Debra J. Richardson addresses nearly 300 guests in attendance at the June 9 ceremonies.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_13_jpg.php photo of chancellor ralph j. cicerone announcing the renaming @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift > Renaming photo gallery
    Renaming photo gallery
    photo::Chancellor Ralph J. Cicerone formally announces the renaming of ICS in honor of philanthropist Donald Bren.
    Chancellor Ralph J. Cicerone formally announces the renaming of ICS in honor of philanthropist Donald Bren.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_06_jpg.php photo of dean richardson and donald bren @ the bren school of information and computer sciences
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    Bren school home > About > Bren gift > Renaming photo gallery
    Renaming photo gallery
    photo::ICS Dean Debra J. Richardson presents Donald Bren with the UC Medal. The medal is the university’s highest honor.
    ICS Dean Debra J. Richardson presents Donald Bren with the UC Medal. The medal is the university’s highest honor.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_10_jpg.php photo of 2004 graduate sepideh gazeri @ the bren school of information and computer sciences
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    photo::2004 graduate Sepideh Gazeri tells how ICS has helped her start on the road to success. Gazeri is the first ICS student ever admitted into UCI's 3-2 program that offers students an opportunity to complete an undergraduate degree and MBA within 5 years.
    2004 graduate Sepideh Gazeri tells how ICS has helped her start on the road to success. Gazeri is the first ICS student ever admitted into UCI's 3-2 program that offers students an opportunity to complete an undergraduate degree and MBA within 5 years.
     
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    http://www.ics.uci.edu/about/bren/gallery/gallery_09_jpg.php adam bonner (bs '00) photo @ the bren school of information and computer sciences
    • ABOUT
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    photo::Adam Bonner (BS '00), co-founder of Network Synthesis in Irvine, tells how ICS prepared him and partner Victor Liu (BS '00) to start their own company right out of school.
    Adam Bonner (BS '00), co-founder of Network Synthesis in Irvine, tells how ICS prepared him and partner Victor Liu (BS '00) to start their own company right out of school.
     
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    • ABOUT
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      • Visit the Bren School
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        • Visit the Bren School
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    photo::Domain Name Server creator Paul Mockapetris (Ph.D. '81) discusses his experiences as an ICS student.
    Domain Name Server creator Paul Mockapetris (Ph.D. '81) discusses his experiences as an ICS student.
     
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    http://www.ics.uci.edu/community/events/openhouse/gallery_06.html Dedication Photo Gallery

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    photo:: bren school dean debra j. richardson
    Bren School Dean Debra J. Richardson.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    http://www.ics.uci.edu/community/events/openhouse/video_dedication.html Video of Donald Bren Hall Dedication and Ribbon Cutting Ceremony

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    Video of Donald Bren Hall Dedication and Ribbon Cutting Ceremony »




       

    Donald Bren, Dean Debra J. Richardson, Chancellor Michael Drake and about 400 campus and community guests celebrated the opening of Donald Bren Hall during an 11:30 PST ceremony on June 20, 2007.

    An open house followed the ribbon cutting, allowing the on campus and off-campus community a rare sneak peak into the school's various research projects and their global impact on everyday lives.

    To learn more about some of these innovative and collaborative projects, please view these informational videos.

    More information about the dedication and open house can be found here.

     

     

     

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/gallery_02.html Dedication Photo Gallery

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    photo:: mr. bren and dean richardson
    Mr. Bren (r) and Dean Debra J. Richardson view a demo
    on Second Life presented by professor Crista Lopes (foreground)

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/school.html About the Bren School

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    About the School »


    photo:: bren school buildings

    The Bren School campus, Donald Bren Hall (top left), ICS 2 (middle), ICS 1 (bottom right)


    Founded as a department in 1968, information and computer science became the first computer science school in the University of California System in December 2002.

    As an independent school focused solely on the computer and information sciences, the Bren School has a unique perspective on the information technology disciplines that allows it a broad foundation from which to build educational programs and research initiatives that explore the many applications of the computing discipline; from circuits and systems to software engineering and human aspects of computing.

    By the Numbers »


    STUDENT ENROLLMENT

        Enrollment Average Test Scores
      Undergraduates    
        Bachelor of Science 960 598 Verbal
        Minor 40 670 Math
      Total 1,000 1268
           
      Graduates    
        Master of Science 60 534 GRE Verbal
        Doctor of Philosophy 221 760 GRE Quantitative
      Total 281 1294


    ALUMNI BY THE NUMBERS

      Bachelors of Science 5,370  
      Masters of Science 951  
      Doctors of Philosophy 345  


    FACULTY AND STAFF

      Professors 60  
      Emeriti Professors 3  
      Technical Staff 12  
      Professional Staff 26  
      Administrative Staff 10  
      Postdoctoral Scholars and Researchers 32  
      Lecturers 9  
      Visiting Scholars 10  
      Teaching Assistants 120  
      Graduate Student Researchers 306  
      Readers 76  


    FACILITIES

      ICS 1 24,416 (assignable square feet)  
      Computer Science 2 9,731  
      Donald Bren Hall 89,000  


    INSTRUCTIONAL COMPUTING

      » 500+ workstations available for instructional use    
      » 24-hour remote computing access for students    
      » 12 special-purpose labs    
      » Wireless access available throughout the Bren School complex    

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/gallery_11.html Dedication Photo Gallery

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    photo:: ramesh jain presents his demo
    Bren Professor of Information & Computer Sciences Ramesh Jain (l)
    presents his demo on Event Photos.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    http://www.ics.uci.edu/community/events/openhouse/gallery_13.html Dedication Photo Gallery

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    photo:: project rescue demo
    Project ResCUE demonstration on using
    technology to improve crisis response.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    http://www.ics.uci.edu/community/events/openhouse/video_bren.html Donald Bren Hall of Fame video

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    Donald Bren Hall of Fame video »




       

    This commemorative video highlights Mr. Bren's gifts to UC Irvine and the Bren School and his pioneering real estate work.

    Please view this video if you would like to watch the dedication ceremony and ribbon cutting held on June 20, 2007.

    This commemorative video was produced by Merit/Andrew.

     

     

     

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/demos.html Changing Lives

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    Changing Lives »


    Curiosity about the world and a commitment to solving problems are the passions that drive faculty at the Bren School. Their research in the information and computer sciences are applicable to many scholarly and scientific fields.

    But, our faculty don't do it alone. Students work side-by-side with nationally renowned professors to advance knowledge - and improve lives.

    Below are just some of the many Bren School research projects that have a global impact on everyday lives.

    To learn more about other projects please watch these informational videos on several Bren School research areas.

    Research Videos »


    You must have the Flash plug-in before you can view the videos. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

    Lean Software Development

    photo:: software developmentWhether your development team members are down the hall or in the land down under, Bren School researchers are working on ways to ensure bottlenecks are avoided and your project finishes on time and on budget. view video »
    Seamless Multi-screen Displays

    photo:: collageProfessor Aditi Majumder has created a way to display large images on multiple displays without seams, allowing users to view large picture files without scrolling.view video »
    Keeping Score

    photo:: baseball player Excerpted from a PBS special and narrated by Danny Glover, Statistics Chair Hal Stern talks about statistical analysis in baseball. Sports are full of small 's' statistics, which are the field of statistics that track a player's performance. view video »
    Testing Large-Scale Galaxy Simulations

    photo:: galaxy Professor Wayne Hayes collaborates across disciplines to test the reliability of large-scale physical simulations such as galaxy and cosmological n-body simulations as performed by astronomers. view video »
    Technology and Everyday Life

    photo:: phone at beach The design, use, and impact of technologies are determined not solely by technical factors but also by how those technologies are shaped by social pressures and demands.. view video »
    Embedded Systems

    photo:: embedded systems Professor Ian Harris' research group explores cost-critical and life-critical applications, including automotive design and sensor-based medical devices. view video »
    Cyber-Security and Privacy

    photo:: computer login screenThe Center for Cyber-Security and Privacy focuses on the importance of security and privacy in our increasingly computerized life. view video »
    Building the Genes of Tomorrow

    photo:: dna Professors Rick Lathrop and G. Wesley Hatfield, have developed a method for utilizing computation and biology to create synthetic genes which have helped in the development of better and safer drugs. view video »
    Creating Environmental Awareness

    photo:: earth Professor Bill Tomlinson and the students in his Social Code research group are utilizing technology to help educate people about their impact on the environment. view video »
    Improving Emergency Response

    photo:: structure fire UC Irvine and UC San Diego researchers are collaborating with a host of subcontractors and partners, to design reliable tools that will improve first responders abilities to save lives during an emergency or disaster. view video »
    Ubiquitous Computing

    photo:: ubiquitous computing Researchers at the Laboratory for Ubiquitous Computing and Interaction (LUCI) are interested in the challenges of designing, using and understanding the elements of a ubiquitous computing world. view video »
    Experiential Environments

    photo:: experiential enviroments In an experiential computing environment, users apply their senses directly, observing event-related data and information of interest. Users explore the data by following their own personal interests within the context of an event.



     

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/gallery_08.html Dedication Photo Gallery

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    photo:: drake views second life demo
    Chancellor Michael V. Drake, M.D. views a demo
    about Second Life.

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    © 2007 The Donald Bren School of Information and Computer Sciences

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    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    photo:: mr. bren with his gift of flowers
    Mr. Bren accepts a bouquet of flowers from Bren School of ICS students.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    photo:: mr. bren with uci baseball cap
    Mr. Bren wears a UC Irvine baseball cap to show his support
    for the UCI baseball team during its College World Series run.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    http://www.ics.uci.edu/community/events/openhouse/gallery_10.html Dedication Photo Gallery

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    photo:: mr. bren receives a gift from dean richardson
    Dean Debra J. Richardson (r) presents Mr. Bren with a gift in honor of
    his support for the Bren School of Information and Computer Sciences.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    http://www.ics.uci.edu/community/events/openhouse/rfid.php RFID Tag Lookup

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    Check your RFID tag »


    To see what time you visited a particular floor of Donald Bren Hall during the June 20th Open House just input your first and last name as it appears on the RFID tag located on the back of the passport you received the day of the event.

    On the day of the Bren Hall dedication there were 428 tags handed out and 2,567 readings were taken by the RFID readers located on Floors 1 - 5.

    RFID can have difficulty in transmitting clean radio signals when the tag is obstructed from the reader, i.e. by placing the tag in your pocket or through tag collision which can occur when many tags are present in a small area.

    Due to these difficulties, there is a chance your RFID tag may not have been read by any or all of the RFID readers.

    The Bren School would like to thank Project ResCUE for configuring the RFID readers and compiling the RFID tag data.


    First Name:
    Last Name:


     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    photo:: ribbon cutting
    Mr. Bren cuts the ribbon during a June 20th dedication ceremony
    for Donald Bren Hall, as Dean Debra J. Richardson looks on..

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    photo:: city of irvine mayor beth krom and interacts with a demo
    Professor Bill Tomlinson shows City of Irvine Mayor Beth Krom
    how to transfer plants and animals in the EcoRaft game.

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    © 2007 The Donald Bren School of Information and Computer Sciences

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    Irvine, CA 92697-3425

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    photo:: ribbon cutting party
    Vice Chancellor, University Advancement Thomas J. Mitchell (l), Brenda Drake,
    Chancellor Michael V. Drake, M.D., Mr. Bren and Dean Debra J. Richardson.

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    © 2007 The Donald Bren School of Information and Computer Sciences

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    Irvine, CA 92697-3425

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    photo:: bren school buildings
    The dedication ceremony about to start.

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    © 2007 The Donald Bren School of Information and Computer Sciences

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    photo:: donald bren hall lobby
    The multi-screen wall in the lobby of
    Donald Bren Hall.

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    © 2007 The Donald Bren School of Information and Computer Sciences

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    Irvine, CA 92697-3425

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    photo:: bren school technology alliance wall
    Technology Alliance wall representing
    the corporate partners of the Bren School.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/building.html About the Building

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    About the Building »


    The six-story Donald Bren Hall expands the existing Bren School campus and increase general assignment classroom space by more than 90,800 square feet.

    The design of this facility is intended to enhance interaction between faculty and students and to create a progressive learning environment.

    Designed with flexibility in mind, the building accommodates the Bren School's growing faculty, staff and student populations.

    The first classes in Donald Bren Hall were held January 5th, 2007 and the Dean's office moved in on January 8th, 2007.

    Join us at our virtual Donald Bren Hall created in Second Life by Crista Lopes' students to allow visitors to share in our open house no matter where in the physical world they are located.

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/gift.html About the Bren Gift

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    About the Gift »


    photo:: donald bren wearing the uc medal

    The Donald Bren School of Information and Computer Sciences was made possible by the generous $20 million gift from Orange County business leader and philanthropist Donald Bren, chairman of The Irvine Company.

    Mr. Bren's gift equaled the largest single gift ever to UC Irvine at the time and has created the most endowed faculty positions at one time on any University of California campus.

    In recognition of this transformational gift, the school, renowned worldwide for leading the innovation of new information and computing technologies and producing an educated work force to fuel the economic engine, was renamed the Donald Bren School of Information and Computer Sciences during a June 9, 2003 ceremony.

    At this event, Mr. Bren was also awarded the University of California Presidential Medal, recognizing his significant contributions to UC and higher education. The medal is the university's highest honor.

    This historic gift accelerated the school's plans for achieving its overarching goal of becoming a top ten computer science program within five years and has had a transformational impact on students, faculty, alumni and community friends of the Bren School.

    In 2005, Ramesh Jain, professor of embedded and experiential systems at Georgia Institute of Technology, was named the first Donald Bren Professor of Information and Computer Sciences at UC Irvine.

    Jain is a renowned pioneer in multimedia information systems, image databases, machine vision and intelligent systems, a collection of work that is classed as experiential computing - exploring information through the senses.

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/gallery_07.html Dedication Photo Gallery

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    UC Irvine Chancellor Michael V. Drake, M.D.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

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    photo:: dean richardson, mr. bren and chancellor drake
    Dean Debra J. Richardson (l), Mr. Bren and UC Irvine Chancellor
    Michael V. Drake, M.D. discuss the new Donald Bren Hall.

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    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/index.html Donald Bren Hall Dedication

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    Welcome to the Donald Bren Hall Dedication »


    Chancellor Michael V. Drake, M.D. and Dean Debra J. Richardson cordially invite you to the open house of Donald Bren Hall, Wednesday, June 20, 2007.

     

    photo:: donald bren cuts the ribbon

    Donald Bren cuts the ribbon during a June 20, 2007 dedication ceremony for Donald Bren Hall
    as Dean Debra J. Richardson looks on. view more photos »

    • See where you went in the building by checking the data collected from the RFID tag on your passport.

    • View a video recording of the dedication ceremony and ribbon cutting.

    • To learn more about the dedication day, please view these articles from the Daily Pilot and the Orange County Business Journal.

    • Curiosity about the world and a commitment to solving problems are the passions that drive faculty at the Bren School. Their research in the information and computer sciences are applicable to many scholarly and scientific fields. To learn more the Bren School's innovative projects, please view these informational videos.

    • Visit the virtual Donald Bren Hall in Second Life. Created by professor Crista Lopes' students, it allows you to explore our building no matter where in the world you are located.


     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/community/events/openhouse/video_wow.html History of the Bren School video

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    History of the Bren School video »




       

    This short videos highlights the Bren School and its successful achievements since its founding in 1968.

    Please view this video if you would like to watch the dedication ceremony and ribbon cutting held on June 20, 2007.

    This commemorative video was produced by Merit/Andrew.

     

     

     

     

    © 2007 The Donald Bren School of Information and Computer Sciences

    University of California, Irvine

    6210 Donald Bren Hall

    Irvine, CA 92697-3425

    info@ics.uci.edu

    http://www.ics.uci.edu/ugrad/policies/Computer_DisciplinaryProcedures.php computer use disciplinary procedures @ the bren school of information and computer sciences
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    Bren school home > Undergraduate > policies
    Undergraduate Student Policies

    Disciplinary Procedures

    What happens if you violate any of the rules on ethical computer use? It depends on the seriousness of the offense, but could be one or more of the following. Disciplinary procedures and sanctions will be consistent with those outlined in the UCI Implementation of "Interim Policies and Procedures Applying to Campus Activities, Organizations, and Students, Part A."

    1. You may have to meet with the chair of the Computing Resources Committee (CRC), the Dean of the Bren School, or the manager of the Bren School Support Group to discuss abuse of computing resources.

    2. Your account may be locked. (Again, we recognize an obligation to respect your rights as well. No student account will be locked without discussion and approval of the Dean of the Bren School, or the chair of the CRC, except in the case of security violations. It would not be ethical for us to lock your account capriciously; for example, we agree not to lock it simply because you send a message to a bboard expressing disagreement with some Department policy or action.)

    3. For minor infractions, some form of departmental services (e.g., cleaning a lab) may be requested in exchange for unlocking the account.

    4. For offenses involving abusing computing resources, cheating on course related work, or preventing others from working on assignments, your grade may be lowered in the class or you may receive a failing grade.

    5. For severe offenses, or repeating minor offenses, you may lose access to all Bren School computing facilities for a period of time. Access to computing can be denied for a limited time (e.g., one week, the remainder of the quarter, an entire quarter) or permanently.

    6. You may be suspended or dismissed from the University.

    7. In serious cases, your name and a description of the violation may be reported to the police. California Penal Code Section 502 makes certain computer abuses a crime, and penalties can range up to a $10,000 fine and up to three years in prison.

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    Bren school home > labs home > lab specifications
    Lab software »

    The ICS Computing Labs currently have over two hundred Windows and UNIX machines in five labs.

    Lab Hours

    Software Specifications

    Hardware Specifications

     

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    Bren school home > labs home > student information
    Student information »

    The ICS Computing Labs are provided for students who are enrolled in ICS classes. Before you can begin using the computers in the labs, you must first obtain an ICS account. This section will help you with everything pertaining to the lab.

    • Account Activation
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    For more information on troubleshooting, please visit the ICS Support page.


    MSDNAA Software

    The MSDN Academic Alliance helps provide software to students at no cost. For more information, please visit the MSDNAA FAQs page. If you already have an account, you can proceed to the login page.

    Password

    If you've forgotten your password, please see the Lab Assistant in CS364. You will be asked for your username and a form of picture ID.

    If you need to change your password, please go here. The page is only available from on-campus. If you're off-campus, please make sure that you're first connected via the UCI VPN.

    Turning in Assignments

    Assignments are turned in one of two ways: masterhit dropbox or checkmate. Please consult with your syllabus on which procedures you must follow for the specific class.

    Openlab Login Problems

    When you login to an ICS host, if you login and get sent back to the login screen, then you are either missing system files or you are over quota. To check, login to a Windows machine and then use SecureCRT to connect to openlab.ics.uci.edu.

    • If you can login, then you are probably over quota. Check your quota and remove files as necessary.
    • If you cannot login or if you can login but the prompt looks unusual, then you are probably missing important files. Please follow the instructions on how to Replace UNIX files.

     

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    Bren school home > labs home > student information
    Account Activation »

    All students with a major under the Bren School of Information and Computer Sciences can get an ICS account. Non-majors are allowed accounts only if enrolled in a course under the Bren School of ICS (ICS, CS, CSE, Statistics, or Informatics course).


    Take your UCI Photo Student ID Card and see the Lab Assistant in the ICS computer Lab (CS 364). Ask the Lab Assistant to verify your identity by handing them your student ID card so that you can obtain an ICS instructional account.

    * If you do not have an ID card, you may present other form of picture ID if you know your ID number.

    ** If you are enrolled through Extension please go to CS 346L and ask to see the Lab Manager regarding your account. You will need to show proof of enrollment, and some form of picture ID (such as a driver's license).

    After your card has been idverified by the lab assistant, you will be led to one of the Windows machine used for account activation. The lab assistant will login to the machine and start up an session for you.

    • You will be asked to type in your Student ID number. Type in your number without any punctuation characters and press Enter.
    • You will then have to enter your name. Type your name as it appears on your ID card, in the form Lastname,Firstname Middlename and press Enter.
    • A summary of your account status will appear.
    • A document on the Ethical Use of Computing will be displayed. It is important that you actually read the document since it contains information that you are required to know. Remember that at all times you are responsible and accountable for knowing what constitutes ethical behavior when using ICS computer systems.
    • You will be asked if you have read and understood the document. Press I agree to continue. If you do not agree with the policies or have anyquestions, please see the Lab Manager in CS346L.

    Once you finished, you will be asked to supply an initial password for your account. Please remember this password.

    • Do Not use your student ID number, your name, your birthday, your girl/boy-friend's name etc.
    • Do not use any part of your name or username (ie. if your name is Sam, you cannot have S and A next to each other in the password).
    • Do not use * in your password
    • Passwords must be at least 6 characters long
    • Passwords must contain characters from at least 3 of the following 4 categories:
      • English Upper Case Letters A, B, C, ..., Z
      • English Lower Case Letters a, b, c, ..., z
      • Westernized Arabic Numerals 0, 1, 2, ..., 9
      • Non-alphanumeric characters . , ; : & % ! #

    If all goes well, your account will be created within 2 hours. Be sure to write down your login name so you don't forget it.

    * If your username does not match your UCInetID, please let us know and we can make the change manually.
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    Bren school home > labs home >
    Lab hours »

    Winter Quarter:

    Finals Week Schedule

    Holidays: All labs closed

    • Martin Luther King Jr. Holiday (Monday, January 18th)
    • Presidents' Day Holiday (Monday, February 15)

    CS 364b Open Lab and 364a Laptop Instructional Lab

    Located on the third floor of the Computer Science Building.

    Monday - Thursday 8:00AM to 10:00PM
    Friday 8:00AM to 8:00PM
    Saturday - Sunday 12:00PM to 6:00PM

    CS183, CS189, CS192

    Located on the first floor of the Computer Science Building.

    Please click on the room number above to view the reservation schedule for the current quarter. Lab reservation schedules are also posted outside each lab door.

    Monday - Friday 8:00AM to 8:00PM
    Saturday - SundayClosed

    CS193 Reserved for Project Class in Software System Design

    Located on the first floor of the Computer Science Building. To get access to this lab, you must be enrolled in Informatics 191. Please read the class policy.

     

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    http://www.ics.uci.edu/community/alumni/stayconnected/ Stay Connected contact information form @ The Bren School of Information and Computer Sciences
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    Want to get the latest news about the Bren School, as well as exclusive alumni event invitations? Please update your contact information by submitting the form below. We also invite you to follow us on:

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    http://www.ics.uci.edu/community/friends/leadershipcouncil/ Leadership Council for UCI's Donald Bren School of Information and Computer Sciences
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    Bren school home > Community > Friends > Leadership council
    Leadership Council

    The Dean's Leadership Council is an advisory board that helps advance the Donald Bren School of ICS' research, teaching and public service goals by strengthening the school's ties to industry and the community. Council members provide invaluable input to the dean of the school from their industry and community perspective on industry trends, the ICS curriculum, and so forth.

    MISSION

    The mission of the Dean's Leadership Council is to:

    • Serve as an advisory group to the dean of the Bren School of ICS;
    • Serve as advocates for the Bren School of ICS and promote the interests of the school in the community;
    • Assist in recruiting additional members to the council who fit the profile for membership and identify other support groups who can contribute to the advancement of the school; and
    • Assist in fund-raising campaigns to help increase the financial resources of the school.

    The council meets three times per academic year.

    If you are interested in getting involved in the Leadership Council, please contact Ed Hand, Director of Development, at elhand@uci.edu or at (949) 824-6563.

    CURRENT COUNCIL MEMBERS

    Dave Goff, Chair
    Senior VP and CIO, Skilled Healthcare, LLC

    Roger Andelin ’87
    Senior VP, CTO Online Technology

    Paul Butterworth ’74, M.S. ‘81
    CTO, Emotive Communications, Inc.

    David Cheng ‘91

    Kevin Daly, Ph.D.
    CEO, Maxxess Systems, Inc.

    Rick Dutta
    Chairman and CEO, Nexvisionix, Inc.

    Jon Hahn ‘81
    FFF Enterprises

    Arthur Hitomi ‘96
    CTO and Co-Founder, Numecent

    Robert Kleist
    Retired. Founder and Chairman, Printronix

    Joel Manfredo
    Managing Director, Acies Consulting

    Kevin Mun
    Vice President of Operations, Vangard Voice Systems, Inc.

    Himanshu Palsule
    Executive Vice President of Strategy, Sage

    Daryl G. Pelc
    VP Engineering & Technology, Phantom Works
    Huntington Beach Site Engineering Leader

    Dinesh Ramanathan M.S. ’95, Ph.D. ‘00

    Robert Romney ‘83
    Retired. Founder, Zenographics, Inc.

    Larry Rowe ’70, Ph.D. ‘76
    Retired

    Ted Smith
    Chairman and CEO, MIND Research Institute

    Julie Sokol
    VP, Information Technology Services
    Irvine Company

    Sandra Smart-Ashburn B.S. ‘87
    Senior Director, DIRECTTV Group, Inc.

    Binh Dang ‘97
    Managing Partner, MerdidianLink

    Hiq Lee
    President, BIS, Experian

    Steve M. Anderson B.S.‘86
    Partner, Quinnemanuel

    Carlos Oliveira PhD ’03
    COO, Nextfort Ventures

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    Bren school home > Community > Alumni >
    ICS Hall of Fame Nomination Form

    ICS Hall of Fame

    The inaugural Donald Bren School of Information and Computer Sciences Hall of Fame will honor ICS alumni who have made a significant impact in their profession or, in other ways, have brought distinction to ICS. If you know of ICS alumni who should be honored in this esteemed group, please complete the nomination form below; nominations will be reviewed on a rolling basis. For questions or additional information, please contact Ed Hand at elhand@uci.edu or Kristin Huerth at khuerth@ics.uci.edu.

     

    NOMINEE

    Name:

    Class Year:

    Address:

    City:

    State:

    Zip:

    Phone:

    E-mail:

    Nomination Statement (Reasons for nominating the Alumnus/a):

    Supporting documentation:
    Please email Ed Hand at elhand@uci.edu and Kristin Huerth at khuerth@ics.uci.edu additional documentation supporting this nomination such as a biographical sketch, papers showing college or professional projects or activities, or letters of support. The decision of the selection committee will be guided strictly by the materials submitted with the nomination. Please combine supporting documents into one PDF or DOC.

    NOMINATOR

    Name:

    Address:

    City:

    State:

    Zip:

    Phone:

    E-mail:

    Please ensure that names and addresses are filled out above, that nomination statement has clear and concise reasons for nomination, and that all supporting documentation are emailed to Ed Hand and Kristin Huerth.
    Quick Links
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    http://www.ics.uci.edu/~eli/pub.htm

    Publications

    Book Chapters

    BC4- Nga Dang, Elaheh Bozorgzadeh, Nalini Venkatasubramanian , �Energy Harvesting for Sustainable Smart Spaces�, Book chapter in Green Computing, edited by Ali Hurson, Advance in Computing, volume 47, Elsevier, 2012.

    BC3- E. Bozorgzadeh, A. Kaplan, R. Kastner, S. Ogrenci Memik, and M. Sarrafzadeh, "Optimization for Reconfigurable Systems Using Hierarchical Abstraction", J. Cong and J. R. Shinnerl (Editors). Multilevel Optimization and VLSI CAD. Kluwer Academic Publishers, Boston, 2002.

    BC2- X. Yang, E. Bozorgzadeh, M. Sarrafzadeh, and M. Wang, "Modern Standard-cell Placement Techniques". Layout Optimization in VLSI Design, Kluwer Academic Publishers, 2002.

    BC1- E. Bozorgzadeh, R. Kastner, S. Ogrenci Memik, and M. Sarrafzadeh, "Strategically Programmable Systems ", The Computer Engineering Handbook, CRC Press, December 2001. 

    Journal Papers

    J21- M. Rahmatian, H. Kooti, Ian G. Harris and E. Bozorgzadeh, "Hardware-Assisted Detection of Malicious Software in Embedded Systems", IEEE Embedded Systems Letters (ESL), accepted for publication.

    J20- Nga Dang, Elaheh Bozorgzadeh, Nalini Venkatasubramanian , �QuARES:Quality-aware Renewable Energy-driven Sensing Framework�, in Elseiver Sustainable Computing: Informatics and Systems Journal, 2012.

    J19- Houman Homayoun, Shahin Golshan, Eli Bozorgzadeh, Alex Veidenbaum, Fadi Kurdahi, �On Leakage Power Optimization in Clock Tree Networks for ASICs and General-Purpose Processors�, Elsevier Journal of Sustainable Computing: Information and Sciences, Volume 1, Issue 1, March 2011, Pages 75-87.

    �J18-  Shahin Golshan, Hessam Kooti, Eli Bozorgzadeh , �SEU-aware High-level Data Path Synthesis and Layout Generation on SRAM-based FPGAs�, in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Vol.30 (No.6), pp.829-840, 2011.

    J17-� S. Banerjee, E. Bozorgzadeh, J Noguera, and N. Dutt, �Bandwidth Management in Application Mapping for Dynamically Reconfigurable Architectures�, in ACM Transactions on Reconfigurable Technology and Systems (TRETS),� pp. 1-30, Volume 3, No. 3, September 2010.

    J16- S. Banerjee, E. Bozorgzadeh, and N. Dutt, "Exploiting application data-parallelism on dynamically reconfigurable architectures: placement and architectural considerations ", in� IEEE Transactions on VLSI (TVLSI), vol.17, no.2, pp.234-247, Feb. 2009.

    J15- S. Oh, T. Kim, J. Cho and E. Bozorgzadeh, "Speculative Loop-Pipelining in Binary Translation for Hardware Acceleration", IEEE Transactions on CAD (TCAD), �pp. 409- 422, No. 3, Vol. 27, 2008.

    J14- L. Singhal and E. Bozorgzadeh, "Multi-layer Floorplanning for Reconfigurable Designs", in IET Computers & Digital Techniques, pp. 276-294, No. 1, Vol. 4, 2007.

     J13-L. Singhal, E. Bozorgzadeh, and D. Eppstein, "Interconnect Criticality Driven Delay Relaxation", in IEEE Transactions on CAD (TCAD),pp.1803-1817, ,No. 10,� Vol. 26, 2007.

    J12- S. Banerjee, E. Bozorgzadeh, N. Dutt, "Integrating physical constraints in HW-SW partitioning for architectures with partial dynamic reconfiguration", in IEEE Transactions on VLSI (TVLSI), Vol 14 (11), pp 1189-1202,  Nov 2006.

    J11- S. Ghiasi, E. Bozorgzadeh, P. Huang, R. Jafari, and M. Sarrafzadeh, "A Unified Theory of Timing Budget Management",� in  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Vol. 25, No. 11, pp. 2364-2375, November 2006.

    J10- S. Pasricha, N. Dutt, , E. Bozorgzadeh, M. Ben-Romdhane, " FABSYN: Floorplan-Aware Bus Architecture Synthesis", in IEEE Transactions on VLSI (TVLSI), pp. 241-253 ,Vol. 14, No. 3, 2006.

    J9- G. Wang, S. Sivaswamy, C.  Ababei,  K. Bazargan, R. Kastner, and E. Bozorgzadeh, "Statistical Analysis and Design of HARP FPGAs",� in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pp. 2088-2102, Vol. 25, No. 10, 2006.

    J8- S. Ghiasi, K. Nguyen, E. Bozorgzadeh, M. Sarrafzadeh, "Efficient Timing Budget Management for Accuracy Improvement in a Collaborative Object Tracking System",  in �Journal of VLSI Signal Processing for Signal Processing and Video Technology, 42(1), pp. 43-55. 2006. 

    J7- S. Ogrenci Memik, R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh, "A Scheduling Algorithm for Optimization and Early Planning in High level Synthesis",  ACM Transactions on Design Automation of Electronic Systems (TODAES), Vol. 10, No. 1, pp. 33�57, January 2005.

    J6- E. Bozorgzadeh, S. Ghiasi, A. Takahashi , and M. Sarrafzadeh, "Optimal Integer Delay Budget Assignment on Directed Acyclic Graphs", in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), Vol. 23, No. 7, �pp. 1184- 1199 , August 2004.

    J5- E. Bozrgzadeh, S. Ogrenci Memik, X. Yang, and M. Sarrafzadeh, "Routability-driven Packing : Metrics and Algorithms for Cluster-based FPGAs",  in  Journal of Circuits, Systems, and Computers (JCSC), Vol. 13, No. 1, pp. 77-100, Feb. 2004.

     J4- E. Bozorgzadeh, R. Kastner, and Majid Sarrafzadeh, "Creating and Exploiting Flexibility in Rectilinear Steiner Trees", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pp.605-615, Vol. 22, No. 5, May 2003.

    J3- R. Kastner, Adam Kaplan,  S. Ogrenci Memik, E. Bozorgzadeh, "Instruction Generation for Hybrid Reconfigurable Systems", ACM Transactions on Design Automation of Embedded Systems (TODAES), pp. 605-627, Vol.7, No. 4, October 2002.

    J2- C. Chen, E. Bozorgzadeh, A. Srivastava, and Majid Sarrafzadeh, "Budget Management with Applications",  Algorithmica, Vol. 34, No. 3,  pp. 261-275, July 2002.

    J1- R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh, "Pattern Routing: Use and Theory for Increasing Predictability and Avoiding Coupling", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), pp. 777-790, vol. 21, No. 7, July 2002

     

    Conference Papers

    C50- Mehryar Rahmatian, Hessam Kooti, Ian Harris and Elaheh Bozorgzadeh, �Minimization of Trojan Footprint by Reducing Delay and Area Impact �, in IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, October 2012.

    C49- M. Rahmatian, H. Kooti, I. Harris and E. Bozorgzadeh, " Adaptable Intrusion Detection Using Partial Runtime Reconfiguration�, in 30th IEEE International Conference on Computer Design (ICCD�12), October 2012.

    C48- H. Kooti, N. Dang, D. Mishra, E. Bozorgzadeh, "Energy Budget Management for Energy Harvesting Embedded Systems", in 18th IEEE International Conference on Embedded and Real-Time Computing System and Applications (RTCSA12), August 2012.

    C47- D. Mishra, Y. Samei, N. Dang, R. Doemer, E. Bozorgzadeh, ��Multi-layer Configuration Exploration of MPSoCs for Streaming Applications�, in Electronic System Level Synthesis Conference, San Francisco, California, June 2012.

    C46- L. Singhal, H. Kooti and E. Bozorgzadeh, " Process Variation-aware Task Replication for Throughput Optimization in Configurable MPSoCs�, in 2012 Electronic System Level Synthesis Conference (ESLsyn12), June 2012.

    C45- Shahin Golshan, Amin Khajeh, Houman Homayoun, Eli Bozorgzadeh, Ahmed M. Eltawil, Fadi J. Kurdahi: Reliability-aware placement in SRAM-based FPGA for voltage scaling realization in the presence of process variations. CODES+ISSS 2011: 257-266

    C44- Nga Dang, Elaheh Bozorgzadeh, Nalini Venkatasubramanian , �QuARES: Quality-aware Data Collection in Energy Harvesting Sensor Networks�, 2nd Green Computing Conference (IGCC'11), July 25-28, 2011, Florida, USA.

    C43- Shahin Golshan, Love Singhal, Eli Bozorgzadeh: Process variation aware system-level load assignment for total energy minimization using stochastic ordering. ISQED 2011: pp. 566-571.

    C42- H. Kooti, D. Mishra and E. Bozorgzadeh,� "Reconfiguration-aware real time Scheduling under QoS Constraint", in 16th Asia and South Pacific Design Automation Conference (ASP-DAC11), January 2011.

    C41- H. Kooti, E. Bozorgzadeh, "Unified Theory of Real-Time Task Scheduling and Dynamic Voltage/Frequency Scaling on MPSoCs", in ACM/IEEE  International Conference on Computer-Aided Design (ICCAD10), San Jose, November 2010.

    C40- S. Golshan, E Bozorgzadeh, B. Carri�n Sch�fer, K. Wakabayashi, H. Homayoun, A.. Veidenbaum, �Exploiting power budgeting in thermal-aware dynamic placement for reconfigurable systems�, in IEEE   International Symposium on Low Power Electronics and Design (ISLPED), pp. 49-54, 2010.

    C39- H. Homayoun, S. Golshan, E. Bozorgzadeh, Fadi Kurdahi, Alex Veidenbaum, �Post-Synthesis Sleep Transistor Insertion for Leakage Power Optimization in Clock Tree Networks�, in 11th  IEEE International Symposium on Quality Electronic Design. (ISQED), pp. 499-507, 2010.

    C38- H. Kooti, E. Bozorgzadeh, S. Liao and L. Bao, "Reconfiguration-aware Spectrum Sharing for FPGA based Software Defined Radio", in 17th Reconfigurable Architectures Workshop (RAW10), Atlanta, April 2010.

    C37- H. Kooti, E. Bozorgzadeh, S. Liao and L. Bao, "Transition-aware Real-Time Task Scheduling for Reconfigurable Embedded Systems", in IEEE Design, Automation and Test in Europe (DATE10), Germany, pp. 232-237, March 2010

    C36- L. Bao, S. Liao and E. Bozorgzadeh, "Spectrum Access Scheduling among Heterogeneous Wireless Systems", in Proc. of SDR Forum Technical Conference and Product Exposition (SDR), Washington, DC, 2009.

    C35- S. Golshan, and E. Bozorgzadeh, "SEU-Aware Resource Binding for Modular Redundancy Based Designs on FPGAs", to appear in ACM/IEEE International Conference on Design, Automation, and Test in Europe (DATE), pp. 1124-1129, April 2009.

    C34- L. Singhal and E. Bozorgzadeh, " Process Variation Aware System-level Task Allocation using Stochastic Ordering of Delay Distributions", in ACM/IEEE International Conference on Computer-Aided Design (ICCAD), pp. 570-574, November 2008.

    C33- L. Singhal, S. Oh, and E. Bozorgzadeh, " Yield Maximization for System-level Task Assignment and Configuration Selection of Configurable Multiprocessors", in ACM/IEEE IEEE/ACM international Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pp. 249-254, October 2008.

    C32. A. Gholamipour, E. Bozorgzadeh, and L. Bao, �Seamless Sequence of Software Defined Radio Designs through Hardware Reconfigurability of FPGAs�,� in IEEE International Conference on Computer Design (ICCD),pp. 260-265, October 2008.

    C31-  L. Singhal, S. Oh, and E. Bozorgzadeh, "Statistical Power Profile Correlation for Realistic Thermal Estimation", in ACM/IEEE Asia-South Pacific Design Automation Conference (ASPDAC), pp. 67-70, January 2008.

    C30- A. Gholamipour, E. Bozorgzadeh �and S. Banerjee, �Energy-aware Co-processor Selection for Embedded Processors on FPGAs�, in International Conference on Computer Design (ICCD), pp. 158-163, October 2007.

    C29- L. Singhal and E. Bozorgzadeh, "Novel multi-layer floorplanning for Heterogeneous FPGAs", in IEEE International Conference on Field Programmable Logic and Applications (FPL), pp. 613-616, �August 2007.

    C28- S. Golshan and E. Bozorgzadeh, "Single-Event-Upset Awareness in FPGA Routing", in Proc. of� ACM/IEEE Design Automation Conference (DAC), p. 330 � 333, June 2007.

    C27- S. Banerjee, E. Bozorgzadeh, J Noguera, and N. Dutt, "Selective bandwidth and resource management in scheduling for dynamically reconfigurable architectures",� in Proc. of ACM/IEEE Design Automation Conference (DAC), pp. 771 � 776, June 2007.

    C26- L. Singhal and E. Bozorgzadeh, "Heterogeneous Floorplanner for FPGA", in IEEE Field Programmable Custom Computing Machines (FCCM), pp. 311-312, April 2007.

    C25-S. Banerjee, E. Bozorgzadeh, J. Noguera, �N. Dutt, "Minimizing Peak Power For Application Chains on Architectures with Partial Dynamic Reconfiguration", in International Conference on Field Programmable Technology (FPT), pp. 273 � 276, �December 2006.

    C24- L. Singhal and E. Bozorgzadeh, "Multi-layer Floorplanning on a Sequence of Reconfigurable Designs", in IEEE International Conference on Field Programmable Logic and Applications (FPL), pp. 605-612, �2006. (received the best paper award)

    C23- S. Dai and E. Bozorgzadeh, �CAD Tool for FPGAs with Embedded Hard Cores for Design Space Exploration of Future Architectures�,� in Proceedings of IEEE Symposium on Field-Programmable Custom Computing Machines, pp. 329-330, April 2006.

    C22-L. Singhal, and E. Bozorgzadeh, "Physically-aware Exploitation of Component Reuse in Partially Reconfigurable Architectures�, in Proceedings of Parallel and Distributed Processing Symposium (IPDPS-RAW), Greece, April 2006.

     C21-S. Banerjee, E. Bozorgzadeh, and N. Dutt, "PARLGRAN: Parallelism Granularity Selection for Scheduling Task Chains on Dynamically Reconfigurable Architectures", in ACM/IEEE Asia-South Pacific Design Automation Conference (ASPDAC'01), pp.491-496 Japan, January 2006.

    C20- L. Singhal and E. Bozorgzadeh, �Fast Timing Closure through Interconnect Criticality Driven Delay Relaxation", in ACM/IEEE International Conference on Computer-Aided Design (ICCAD), pp. 791-796, Nov. 2005.

    C19- S. Banerjee, E. Bozorgzadeh, N. Dutt, "Physically-aware HW-SW Partitioning for reconfigurable architectures with partial dynamic reconfiguration", in ACM/IEEE Design Automation Conference (DAC), pp. 335 � 340, June 2005

    C18- S. Pasricha, N. Dutt, , E. Bozorgzadeh, M. Ben-Romdhane, "Floorplan-aware Automated Synthesis of Bus-based Communication Architectures",  in ACM/IEEE Design Automation Conference (DAC), pp. 565- 570, June 2005. (nominated for best paper award)

    C17-S. Banerjee, E. Bozorgzadeh, and N. Dutt, "Considering runtime reconfiguration overhead in Task Graph Transformations for dynamically reconfigurable architectures", in  IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM) , pp. 273- 274, Napa, April 2005

    C16- S. Sivaswamy, G. Wang, C. Ababei, K. Bazargan, R.Kastner, and E. Bozorgzadeh, �HARP:Hard-wired Routing Pattern FPGAs�, in Proceedings of  ACM International Symposium on Field-Programmable Gate Arrays (FPGA), pp. 21 � 29, Feb. 2005.

    C15- S. Ghiasi, E. Bozorgzadeh, S.  Choudhuri, M. Sarrafzadeh, "A Unified Theory for Timing Budget Management", ACM/IEEE International Conference on Computer-Aided Design, pp. 653 � 659, Nov. 2004.

    C14- E. Bozorgzadeh, S. Ghiasi, A. Takahashi, and M. Sarrafzadeh, "Incremental Timing Budget Management in Programmable Systems", International Conference on Embedded and Reconfigurable Systems and Architecture, pp. 240-246, July 2004.

    C13- S. Ghiasi, K. Nguyen, E Bozorgzadeh, and M Sarrafzadeh, "On Computation and Resource Management in Networked Embedded Systems", International Conference on Parallel and Distributed Computing and Systems, pp. 445-451, November 2003.

    C12- E. Bozorgzadeh, S. Ghiasi, A. Takahashi, and M. Sarrafzadeh, "Optimal Integer Delay Budgeting on Directed Acyclic Graphs",  ACM/IEEE Design Automation Conference (DAC'03) , pp. 920 - 925  ,2003.

    C11- E. Bozorgzadeh, S. Ogrenci Memik, R. Kastner, and M. Sarrafzadeh, "Pattern Selection: Customized Block Allocation for Domain-Specific Programmable Systems", International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA'02), pp. 190-196,� June 2002.

    C10- R. Kastner, S. Ogrenci Memik, E. Bozorgzadeh, and M. Sarrafzadeh, "Instruction Generation for Hybrid Reconfigurable Systems", ACM/IEEE International Conference on Computer-Aided Design (ICCAD'01), pp. 127-130, November, 2001.  

    C9- S. Ogrenci Memik, E. Bozorgzadeh, R. Kastner, and M. Sarrafzadeh, "A Super-Scheduler for Embedded Reconfigurable Systems ", ACM/IEEE International Conference on Computer-Aided Design (ICCAD'01), pp. 391-394, November, 2001.  

    C8- E. Bozorgzadeh, R.Kastner, and M. Sarrafzadeh, "Creating and Exploiting Flexibility in Steiner Trees", ACM/IEEE 38th Design Automation Conference (DAC'01), pp. 195 � 198, June 2001.

    C7- S. Ogrenci Memik, E. Bozorgzadeh, R. Kastner, and M. Sarrafzadeh, "SPS: A Strategically Programmable System", Reconfigurable Architecture Workshop (RAW'01), April 2001.

    C6- R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh, "An Exact Algorithm for Coupling-Free Routing", ACM/IEEE International Symposium on Physical Design (ISPD'01), pp. 10 � 15, April 2001.

    C5- M. Sarrafzadeh, E. Bozorgzadeh, R. Kastner and A. Srivastava, "Design and Analysis of Physical Design Algorithms ", ACM/IEEE International Symposium on Physical Design (ISPD'01), pp. 82 � 89, April 2001.

    C4- X.Yang, E. Bozorgzadeh, and M. Sarrafzadeh, "Wirelength and Rent exponents of Partitioning and Placement", ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP'01), pp. 25 � 31, April 2001.

    C3- E. Bozorgzadeh, S. Ogrenci Memik,  andM. Sarrafzadeh, "RPack: Routability-Driven Packing for Cluster-Based FPGAs", Asia-South Pacific Design Automation Conference (ASPDAC'01), pp. 629 - 634, January 2001.

    C2- R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh, "Predictable Routing", ACM/IEEE International Conference on Computer-Aided Design (ICCAD'00), pp. 110 � 114, November, 2000.

    C1- R. Kastner, E. Bozorgzadeh, and M. Sarrafzadeh, "Coupling Aware Routing", IEEE International ASIC/SOC Conference, pp. 392-396, September 2000.

    http://www.ics.uci.edu/~eli/home.htm Elaheh Bozorgzadeh

     

     

     

    Elaheh (Eli) Bozorgzadeh

    Associate Professor

    Computer Science Department

    University of California, Irvine

    phone: 949-824-8860

     

    Affiliated with:

    Center for Embedded Computer Systems (CECS)

    California Institute for Telecommunication and Information Technology (Calit2)

     

     Research Interests

    • System Synthesis for Self-adaptive Reconfigurable Embedded Systems
    • Energy Sustainability in Embedded systems through micro energy harvesting
    • Physically-aware Architectural Synthesis and layout planning for Embedded Systems
    • Early physical planning for rapid timing closure (Timing budget management)

    Awards

    • NSF CAREER Award, 2008
    • Best paper award, IEEE International Conference in Field Programmable Logic and Applications (FPL), 2006.

    Recent Activities

    • ACM SIGDA DAC Summer School 2013 (chair).
    • PerCom 2013, Demo Co-chair, San Diego, March, 2013.
    • TPC member of IEEE Field Programmable Logic and Applications (FPL), 2013.
    • TPC member of ACM/IEEE International Symposium in Low Power Electronic Design (ISLPED), 2013.
    • TPC member, ACM/IEEE CODES/ISSS (ESWEEK), 2012.

     

     

     

     

    Last updated on June 20, 2008

    http://www.ics.uci.edu/~eli/courses.html cv.html

    Teaching

    Spring 2010

    CS 259s: Research Seminar (topic: cyber physical systems)

    �          Past Courses: 

    �         CS 153: Logic Design Laboratory (Fall 2009, Fall 2008)

    �         CS252 (Fall 2008)- Introduction to Computer Design

    �        CS 252- Introduction to Computer Design (Fall 2007, Fall 2006, Fall 2005, Winter 2005, Winter 2004)

    �        CS 151-Introduction to Digital Design (Winter 2008, Fall 2007,Winter 2010, Winter 2009)

    �        ICS 151- Introduction to Logic Design (Winter 2007, Spring 2006, Spring 2005, Spring 2004)

    �        ICS 258: Combinatorial Algorithms in Design Synthesis (Spring 2006)

    �        CS258/CS280: Special Topics in CS (Combinatorial Algorithms in Design Synthesis) (Fall 2004)

     

    http://www.ics.uci.edu/~eli/contactInfo.html  
     

    Contact Information                           

     

    Mailing Address:  
    Office of Professor Elaheh Bozorgzadeh
     
    3092 Bren Hall
    Computer Science Department
    University of California at Irvine
    Irvine, CA 92697-3435
    USA

    Reconfigurable Computing Lab
    Director: Professor Eli Bozorgzadeh
    3065 Bren Hall
    Computer Science Department
    University of California at Irvine
    Irvine, CA 92697-3435
    USA

     

    Tel:

    Fax:

    949-824-8860

    949-824-4056

    Email:


    http://www.ics.uci.edu/~eli/students.html PhD Students

    PhD Students:

    • Shahin Golshan (PhD candidate / expected graduation: Fall 2010)
    • Hessam Kooti (PhD student/ expected graduation: Spring 2012)

    Past Members:

    • Love Singhal (PhD)- graduated January 2009
    • Thesis: System Level Design Planning for Parametric Yield Improvement
    • Current Emplyment: Synopsys, Sunnyvale, CA
    • Sudarshan Banerjee (PhD) (co-advised with Prof. Nik Dutt)- graduated in Winter 2007
    • Thesis: Application Mapping for Platform FPGAs with Partial Dynamic Reconfiguration
    • Current employment: Liga Systems, Sunnyvale, California
    • Simin Dai (MS) - graduated in Fall 2005
    • Thesis: H-FPGA: Heterogeneous FPGA Place and Route.
    • Current Employment: Analog Devices, Austin, TX
    • Visitors

      • Sejong Oh- Computer Science Dept, KAIST, Korea (September 2006-September 2007)

       

    • Undergraduate Researchers

      • Jin Hu (summer 2005)- CRA-DMP summer research fellowship (currently graduate student in Univ. of Michigan)
      • Richa Prasad (summer 2005)- CRA-DMP summer research fellowship (currently graduate student in Univ. of Washington)
      • Laura Beck (Summer 2004) - CRA-DMP summer research fellowship
      • Padmini Nagaraj (Summer 2004)- CRA-DMP summer research fellowship

     

     

    http://www.ics.uci.edu/~djr/DebraJRichardson/Home.html Debra J. Richardson
     
     
     
     

    Debra J. Richardson

     

    Update: back to the classroom!

     

    After having served the Bren School of ICS as founding dean and before that as chair, for a total of ten years, I’m headed back to the classroom!  I had a pleasant sabbatical, some of which was spent with NCWIT and the ATLAS Institute at the University of Colorado–Boulder, and a lot of time was spent on various Computer Science Education activities, such as chairing CSEdWeek and CCEAN.  Now I’m ready to see what’s next.  For now, that’s being,what some might call, a POP!

     
    Follow Me @DeanDebra
... once a dean
... always a dean!
    Contact Info:

Email: djr “at” uci “dot” edu
Office: 5241 Donald Bren Hall
Phone: 949-824-7353 
Address: Bldg 314
              University of California
              Irvine, CA 92697-3440
    
Vita & RESUME

download full CV

download administrative resume

http://www.ics.uci.edu/~djr/files/djrCV.pdfhttp://www.ics.uci.edu/~djr/files/djrAR.pdfshapeimage_4_link_0shapeimage_4_link_1
     
     
    Made on a Mac
    http://www.ics.uci.edu/~shendric/ Scott A. Hendrickson's Homepage

    Scott A. Hendrickson's Homepage

    I am a Ph.D. candidate in the Donald Bren School of Information & Computer Sciences at the University of California, Irvine. I'm pursuing a Ph.D. under the direction of Richard N. Taylor.


    Contact Information
    AOL IM: Prophet6379
    E-mail: shendric@uci.edu
    Office: Bren Hall 5209
    Mailing Address:
          Donald Bren School of Information and Computer Sciences
          University of California, Irvine
          Irvine, CA 92697-3440
    Research

    Modeling and Evolving Product Line Architectures

    My primary research focus is on modeling and evolving software product line architectures (PLAs) using change sets and relationships. In a nutshell, the approach stores sets of architectural modifications within selectable change sets, which when merged in to an architecture modify it by adding or removing features. Individual products are composed by merging together a selection of change sets. Dependencies, conflicts, and variant relationships between the change sets are modeled explicitly using relationships to guide an architect in creating only desired product compositions. This work was published as Modeling Product Line Architectures through Change Sets and Relationships and evolved from previous work on exploring design alternatives using a layer-based approach towards modeling designs, see Layered Class Diagrams: Supporting the Design Process and Towards Supporting the Architecture Design Process Through Evaluation of Design Alternatives.

    As an undergraduate and during the first few years of grad school, I focused on capturing and presenting causal relationships between events fired within asynchronous architecture, see An Approach for Tracing and Understanding Asynchronous Architectures. I've also done some work on architectural styles PACE: An Architectural Style for Trust Management in Decentralized Applications.

    Publications
    C.8 Hendrickson, S.A., Subramanian, S., and Hoek, A.v.d. Multi-Tiered Design Rationale for Change Set Based Product Line Architectures. In Proceedings of the Third Workshop on SHAring and Reusing architectural Knowledge (SHARK 2008). Leipzig, Germany, May 10-18, 2008.
    C.7 Hendrickson, S.A. and van der Hoek, A. Modeling Product Line Architectures through Change Sets and Relationships. In Proceedings of the 29th International Conference on Software Engineering (ICSE 2007). p. 189-198, Minneapolis, MN, May 20-26, 2007.
    C.6 Xu, L., Hendrickson, S.A., Hettwer, E., Ziv, H., van der Hoek, A., and Richardson, D.J. Towards Supporting the Architecture Design Process Through Evaluation of Design Alternatives. In Proceedings of the 2nd International Workshop on the Role of Software Architecture in Testing and Analysis (ROSATEA 2006). p. 38-44, July, 2006.
    C.5 Hendrickson, S.A., Jett, B., and van der Hoek, A. Layered Class Diagrams: Supporting the Design Process. In Proceedings of the 9th International Conference on Model Driven Engineering Languages and Systems (MoDELS 2006). p. 722-736, Genova, Italy, October 1-6, 2006.
    C.4 Nistor, E., Erenkrantz, J.R., Hendrickson, S.A., and van der Hoek, A. ArchEvol: Versioning Architectural-Implementation Relationships. In Proceedings of the 12th International Workshop on Software Configuration Management. p. 99-111, Lisbon, Portugal, September 5-6, 2005.
    C.3 Hendrickson, S.A., Dashofy, E.M., and Taylor, R.N. An (Architecture-Centric) Approach for Tracing, Organizing, and Understanding Events in Event-Based Software Architectures. In Proceedings of the 13th International Workshop on Program Comprehension, in conjunction with ICSE 2005. p. 227-236, St. Louis, MO, May 15-16, 2005.
    C.2 Suryanarayana, G., Erenkrantz, J.R., Hendrickson, S.A., and Taylor, R.N. PACE: An Architectural Style for Trust Management in Decentralized Applications. In Proceedings of the 4th Working IEEE/IFIP Conference on Software Architecture. p. 221-230, Oslo, Norway, June, 2004.
    C.1 Hendrickson, S.A., Dashofy, E.M., and Taylor, R.N. An Approach for Tracing and Understanding Asynchronous Architectures. Short paper. In Proceedings of the 18th IEEE International Conference on Automated Software Engineering (ASE 2003). p. 318-322, Montreal, Quebec, Canada, October 6-10, 2003.
    T.2 Suryanarayana, G., Erenkrantz, J.R., Hendrickson, S.A., and Taylor, R.N. PACE: An Architectural Style for Trust Management in Decentralized Applications. Institute for Software Research, University of California, Irvine, Technical Report UCI-ISR-03-9, September, 2003.
    T.1 Hendrickson, S.A., Dashofy, E.M., Bhor, A., Taylor, R.N., Li, S., and Nguyen, N. An Approach for Tracing and Understanding Asynchronous Systems. Institute for Software Research, University of California, Irvine, Technical Report UCI-ISR-02-7, December, 2002.
    O.1 Dashofy, E.M., Asuncion, H., Hendrickson, S.A., Suryanarayana, G., Georgas, J.C., and Taylor, R.N. ArchStudio 4: An Architecture-Based Meta-Modeling Environment. In Proceedings of the 29th International Conference on Software Engineering (ICSE 2007). Informal Research Demonstrations, Companion Volume, p. 67-68, Minneapolis, MN, May 20-26, 2007.
    Teaching

    ICS 121 / Informatics 111 - Software Tools and Methods (TA Fall 2005)
    ICS 125 - Project in Software System Design (TA Spring 2004)
    ICS 52 - Introduction to Software Engineering (TA Winter 2004)
    ICS 125 - Project in Software System Design (TA Fall 2003)
    ICS 102 - Requirements Engineering (TA Spring 2003)
    ICS 121 - Software Tools and Methods (TA Winter 2003)

    What's Important
    I'm pretty crazy about my family. There's nothing like playing with your kids after a stressful day!
    On Sundays, you'll find me here running sound, PowerPoint, or just hanging out.
    http://www.ics.uci.edu/~fielding/ Roy T. Fielding

    Roy T. Fielding

    Chief Scientist, Day Software
    Co-founder and member, The Apache Software Foundation
    Ph.D., Information and Computer Science, UC Irvine
    • E-Mail: fielding@gbiv.com, roy.fielding@day.com, fielding@apache.org
    • Office: Irvine, California, Fax: +1 (949) 679-2972
    • Meet me at ApacheCon
    • The personal website of Roy T. Fielding

    Research Projects

    I finished my doctorate within the Software Research Group here at UCI. Much of my work was done under the auspices of the Hyperware project and collaborations with industry as part of the Institute for Software Research. My research interests include global software engineering environments, software design, software architecture for network-based applications, and application-level protocol design. Richard N. Taylor was my advisor and dissertation committee chair.

    I have been actively involved in the World Wide Web project since 1993. I set up the original UCI-ICS WWW server and created several WWW software packages, and in early 1994 became involved in the effort to specify and improve the WWW infrastructure through the IETF working groups on URI, HTTP, and HTML (the set of protocols that were used to retrieve and view this document).

    I also co-founded the Apache HTTP Server Project and on the board of directors of The Apache Software Foundation. We created the Apache HTTP server that currently dominates the general-purpose server market with over 60% of the public Internet websites using our software. Apache is my favorite example of the power of global collaboration for the creation of software.

    My dissertation, Architectural Styles and the Design of Network-based Software Architectures, focused on the rationale behind the design of the modern Web architecture and how it differs from other architectural styles.

    Papers, Talks, and Specs

    • Curriculum Vitae and Publications
    • Presentations and Slides
    • Uniform Resource Identifiers (URI):
      Generic Syntax (RFC 2396) and Relative URL (RFC 1808)
    • Hypertext Transfer Protocol (HTTP):
      HTTP/1.1 (RFC 2616 [pdf, html], RFC 2145, and RFC 2068) and HTTP/1.0 (RFC 1945)
    • Internet Engineering Taskforce (IETF) Web-related Archives
      • URI Working Group
      • HTTP Working Group
      • HTML Working Group
      • WebDAV Working Group

    Open-Source Software

    Apache
    The best general-purpose HTTP server that money can't buy.
    libwww-ada95
    A library of Ada95 packages that provides a start for a simple programming interface to the World Wide Web. Unfortunately, we only had the time and resources to finish the first part: the Onions network streams library.
    libwww-perl
    A library of Perl4 packages which provides a simple and consistent programming interface to the World Wide Web. This library is being developed as a collaborative effort to assist the further development of useful WWW clients and tools.
    MOMspider
    A web robot for providing multi-owner maintenance of distributed hypertext infostructures.
    wwwstat
    A package for analyzing httpd server access logs and providing summary statistics in HTML format.

    Life Story

    What, you're still reading this? According to most hypertext research, you should have become bored by now and moved on to to another page. Well, this is for those who are extra curious (or just have nothing better to do).

    My background is a bit odd: I was conceived in New Zealand and born in South Laguna, California. Although most of my schooling has been in the U.S., I was taught how to read during a school term in Auckland. My father is an emeritus professor in Social Sciences here at UC Irvine, which is why I have been raised a Yank. I was born in September 1965, during the first week of classes of the year UCI was established, so you might say that the two of us grew up together (except for the three years I spent studying Physics and International Politics at Reed College). I am part Maori, Kiwi, Yank, Irish, Scottish, British, and California beach bum. Like I said, a bit odd.

    Most of the rest can be seen in my vita.

    I was a Visiting Scholar at MIT/LCS during the summer of 1995, working with Tim Berners-Lee and the World Wide Web Consortium (W3C). You can also see my W3C Home.

    I was named by MIT Technology Review as one of the TR100: The top 100 young innovators for 1999. "The 100 young visionaries who our editors and a distinguished Panel of Judges feel have the greatest potential for technological innovation in the 21st century." Gee, no pressure there.

    The Association of Computing Machinery recently awarded the 1999 ACM Software System Award to The Apache Group for the Apache HTTP Server. I talked a bit about that honor in an interview with LinuxWorld.

    On a more local note, I was named by the UCI Alumni Association as the Outstanding Graduate Student of 2000.

    Other Interests

    I like playing games -- especially non-betting card games (Bridge, Hearts, etc.) and obscure board games (british rails, naval war, etc.). I also like playing basketball, softball, football and going fishing. Mind you, I haven't had time to do any of these things since I started messing with the Web.

    Quotations

    What is life? It is the flash of a firefly in the night. It is the breath of a buffalo in the wintertime. It is the little shadow which runs across the grass and loses itself in the sunset.
    --- Crowfoot's last words (1890), Blackfoot warrior and orator.

    To most readers it will be easy, after reading this tale, to accept Rover's theory that Man is set up deliberately as the antithesis of everything the Dogs stand for, a sort of mythical straw-man, a sociological fable.

    This is underlined by the recurring evidence of Man's aimlessness, his constant running hither and yon, his grasping at a way of life which constantly eludes him, possibly because he never knows exactly what he wants.
    --- Clifford D. Simak, "City" [Notes on the Fifth Tale], 1952.


    Life is a distributed object system. However, communication among humans is a distributed hypermedia system, where the mind's intellect, voice+gestures, eyes+ears, and imagination are all components.
    --- Roy T. Fielding, 1998.

    Information and Computer Science,
    University of California, Irvine CA 92697-3425
    Last modified: 08 Dec 2003 http://www.ics.uci.edu/~sgirish/ Girish's Home Page

    Girish Suryanarayana's ICS Page
       
      Home     Research      Publications       Links      Contact     Curriculum Vitae    
     

    Page Last Updated - July 11th, 2007
     

     


    Welcome to Girish's home page

     

    About me

    I am a Post-doctoral researcher in the Institute for Software Research at the University of California, Irvine. I am working on trust and reputation management in decentralized peer-to-peer architectures and applications with Professor Richard Taylor. I have a PhD and Masters degree in Information and Computer Science from UC Irvine and completed my undergraduate studies in Electrical and Electronics Engineering from the Birla Institute of Technology and Science, Pilani, India.


    What's New?

    I am serving on the Technical Program Committee for the International Conference on Software Engineering Advances (ICSEA 2006) to be held from Oct 29 - Nov 1, 2006, in Tahiti, French Polynesia.

    I recently also served as a Program Committee (PC) member for the Trust, Recommendations, Evidence and other Collaboration Know-how (TRECK) track of the 21st Annual ACM Symposium on Applied Computing (SAC) held in Dijon, France from April 23-27, 2006.


    Research Interests

    My research interests lie in the area of decentralized peer-to-peer (P2P) architectures and applications. Decentralized P2P architectures are characterized by the absence of a central authority or infrastructure that controls and coordinates the behavior of peers in the system. Instead peers have to rely upon information received from other peers and execute local decisions autonomously in order to achieve their individual goals. In the absence of a single centralized authority that can help regulate and coordinate a decentralized system, each peer must take steps to safeguard itself against malicious attacks. This results in a host of challenges such as: how can these attacks be countered, what measures can be adopted to detect and neutralize these attacks, how can a peer be designed so as to facilitate the incorporation of these measures, etc.

    My current work has two primary focus: architecture-based approach for building trust-enabled decentralized systems, and examining various types of reputation-based systems. You can read more about my research in the Research section.

       
               
    http://www.ics.uci.edu/~taylor/Publications.htm Publications of Richard N. Taylor
    Publications
    Books | Journals | Book Chapters | Refereed Conferences and Workshops | Non-refereed and Other
    Books
    B-1 Richard N. Taylor, Nenad Medvidovic, Eric Dashofy, Software Architecture: Foundations, Theory, and Practice. ISBN: 978-0-470-16774-8. John Wiley & Sons, ©2009 736 pages.
     
    Book Chapters and Articles
    BC-9 Hazel U. Asuncion and Richard N. Taylor. Automated Techniques for Capturing Custom Traceability Links across Heterogeneous Artifacts. In: Software and Systems Traceability, ed. Jane Cleland-Huang, Andrea Zisman, and Olly Gotel. pp. 129-146, Springer, 2012.
    BC-8

    Richard N. Taylor. Software Architecture, (In)consistency, and Integration. In Engineering of Software: The Continuing Contributions of Leon J. Osterweil, ed. Peri L. Tarr and Alexander L. Wolf. Springer. 2011.

    BC-7 John Georgas and Richard N. Taylor. Policy-Based Architectural Adaptation Management: Robotics Domain Case Studies. In Self-Adaptive Systems, edited by Editors: Betty H.C. Cheng, Rogerio de Lemos, Holger Giese, Paola Inverardi, Jeff Magee. Lecture Notes in Computer Science 5525, pp. 89-108. Springer-Verlag Heidelberg 2009.
    BC-6 Oreizy, P., Medvidovic, N., and Taylor, R. N. 2008. Runtime software adaptation: framework, approaches, and styles.(Most Influential Paper of ICSE 1998 Invited Paper) In Companion of the 30th international Conference on Software Engineering (Leipzig, Germany, May 10 - 18, 2008). ICSE Companion '08. ACM, New York, NY, 899-910. DOI= http://doi.acm.org/10.1145/1370175.1370181
    BC-5 Richard N. Taylor and Andre van der Hoek. “Software Design and Architecture: The once and future focus of software engineering.” In Future of Software Engineering 2007. Edited by Lionel C. Briand and Alexander L. Wolf. pp. 226-243. IEEE Computer Society (2007). http://doi.ieeecomputersociety.org/10.1109/FOSE.2007.21
    BC-4 Coutaz, J. and R.N. Taylor.  Introduction to the workshop on software engineering and human-computer interaction:  Joint research issues.  In Proceedings of the Workshop on Software Engineering and Human Computer Interaction:  Joint Research Issues, published by Springer-Verlag as Lecture Notes in Computer Science, Vol. 896, pp. 1—3  (1995).
    BC-3 Taylor, R.N. User interfaces and software engineering environments. In Proceedings of the Workshop on Software Engineering and Human Computer Interaction: Joint Research Issues, published by Springer-Verlag as Lecture Notes in Computer Science, Vol. 896, pp. 113—126 (1995).
    BC-2 Taylor, R.N. Analysis of concurrent software by cooperative application of static and dynamic techniques. In Software Validation, ed. Hans-Ludwig Hausen, Elsevier Science Publishers B.V. (North Holland), pp. 127-137 (1984).
    BC-1 Osterweil, Leon J., L.D. Fosdick, and R.N. Taylor.  Error and anomaly diagnosis through dataflow analysis.  In Program Testing, ed. Chandrasekaran and Radicchi, North Holland, pp. 35-63 (1981).
     
    Refereed Journal Articles
    J-34

    Christoph Dorn, Richard N. Taylor, and Schahram Dustdar. Flexible Social Workflows: Collaborations as Human Architecture. Invited Paper. IEEE Internet Computing, 16, no. 2, pp. 72-77, Mar./Apr. 2012, doi:10.1109/MIC.2012.33

    J-33 John C. Georgas, André van der Hoek, Richard N. Taylor. Using Architectural Models at Runtime to Manage and Visualize Runtime Adaptation.IEEE Computer, 42, 10, pp. 52-60. (October 2009). Digital Object Identifier: 10.1109/MC.2009.335
    J-32 Nenad Medvidovic, Eric Dashofy, and Richard N. Taylor. Moving Architectural Description from Under the Technology Lamppost. Information and Software Technology. pp. 12-31. Vol. 49, No. 1, 2007. http://dx.doi.org/10.1016/j.infsof.2006.08.006
    J-31 John C. Georgas, Eric M. Dashofy, and Richard N. Taylor. Architecture-Centric Development: A Different Approach to Software Engineering. ACM Crossroads, issue on Software Engineering, Vol. 12, No. 4, Summer 2006.  Available online (only)at http://www.acm.org/crossroads/xrds12-4/arqcentric.html or http://doi.acm.org/10.1145/1144359.1144365
    J-30 Girish Suryanarayana, Mamadou H. Diallo, Justin R. Erenkrantz and Richard N. Taylor. Architecting Trust-enabled Peer-to-Peer File-sharing Applications.  ACM Crossroads, issue on Software Engineering, Vol. 12, No. 4, Summer 2006, pp. 11-19. http://doi.acm.org/10.1145/1144359.1144364
    J-29 Jie Ren and Richard N. Taylor. Automatic and Versatile Publications Ranking for Research Institutions and Scholars.  Communications of the ACM (CACM), Vol 50, No. 6 (June, 2007). Pages 81-85. http://doi.acm.org/10.1145/1247001.1247010
    J-28 Girish Suryanarayana, Justin Erenkrantz, and Richard Taylor. An Architectural Approach to Decentralized Trust Management.  IEEE Internet Computing, 9,  6, pp. 16-23, (November/December, 2005).  Special section on Security for P2P/Ad Hoc Networks. http://dx.doi.org/10.1109/MIC.2005.119
    J-27 Eric M. Dashofy, André van der Hoek, and Richard Taylor. A Comprehensive Approach for the Development of Modular Software Architecture Description Languages.   ACM Transactions on Software Engineering and Methodology (TOSEM) 14, 2, pp. 199-245 (April 2005). http://doi.acm.org/10.1145/1061254.1061258
    J-26 Medvidovic, N., E. M. Dashofy, Richard N. Taylor. The Role of Middleware in Architecture-Based Software Development. International Journal of Software Engineering and Knowledge Engineering, 13, 4, pp. 367-393 (August 2003) http://dx.doi.org/10.1142/S0218194003001330
    J-25 Roy Fielding and Richard N. Taylor.  Principled design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2, 2, pp. 115-150 (May 2002). http://doi.acm.org/10.1145/514183.514185
    J-24 Kenneth M. Anderson, Richard N. Taylor, E. James Whitehead, Jr.  Chimera: Hypermedia for Heterogeneous Environments.    ACM Transactions on Office Information Systems, 18, 3, pp. 211-245  (July, 2000). http://doi.acm.org/10.1145/352595.352596
    J-23 Peter Kammer, Gregory Alan Bolcer, Richard N. Taylor, Mark Bergman.  Techniques for Supporting Dynamic and Adaptive Workflow.  Computer Supported Cooperative Work (CSCW), 9, pp. 269-292 (2000). http://dx.doi.org/10.1023/A:1008747109146
    J-22 Peyman Oreizy, Michael M. Gorlick, Richard N. Taylor, Dennis M. Heimbigner, Gregory F. Johnson, Nenad Medvidovic, Alex Quilici, David S. Rosenblum, Alexander L. Wolf.  An Architecture-Based Approach to Self-Adaptive Software.    IEEE Intelligent Systems, 14, 3, pp. 54–62  (May/June 1999). http://dx.doi.org/10.1109/5254.769885
    J-21 Gregory Alan Bolcer and Richard N. Taylor.  Advanced Workflow Management Technologies.  Software Process – Improvement and Practice. Volume 4, Number 3, pp. 125-171 (September 1998 – but appeared physically October, 1999). http://dx.doi.org/10.1002/(SICI)1099-1670(199809)4:3<125::AID-SPIP100>3.0.CO;2-J
    J-20 Nenad Medvidovic and Richard N. Taylor.  A Classification and Comparison Framework for Software Architecture Description Languages.  IEEE Transactions on Software Engineering, Vol. 26, No. 1, pp. 70-93 (January 2000). http://dx.doi.org/10.1109/32.825767
    J-19 Peyman Oreizy and Richard N. Taylor. On the Role of Software Architectures in Runtime System Reconfiguration. IEE Proceedings Software Engineering. Vol. 145, No. 5, pp. 137-145 (October 1998). (Revision and expansion of RC-34 below.)
    J-18 Kenneth M. Anderson, Richard N. Taylor, and E. James Whitehead, Jr. A Critique of the Open Hypermedia Protocol.   Journal of Digital Information, 1,  Issue 2, December 1997. Supported by the British Computer Society and the Oxford University Press. http://journals.tdl.org/jodi/article/view/jodi-5/5
    J-17 Roy Fielding, E. James Whitehead, Jr., Kenneth Anderson, Peyman Oreizy, Gregory Bolcer, and Richard Taylor.  Web-based Development of Complex Information Products.  Communications of the ACM, 41,8, pp. 84-92.  (August 1998.) http://doi.acm.org/10.1145/280324.280337
    J-16 Nenad Medvidovic, Richard N. Taylor.  Exploiting Architectural Style to Develop a Family of Applications.   IEE Proceedings Software Engineering,  144.  Number 5-6, pp. 237-248 (October/December 1997).
    J-15 Richard N. Taylor, Nenad Medvidovic, Kenneth M. Anderson, E. James Whitehead, Jr., Jason E. Robbins, Kari A. Nies, Peyman Oreizy, and Deborah L. Dubrow. A component and message-based architectural style for GUI software. IEEE Transactions on Software Engineering, 22, 6, pp. 390-406 (June, 1996.) (Major revision and expansion of RC-23 below.``Best paper of ICSE-17” (one of three)) http://dx.doi.org/10.1109/32.508313
    J-14 Pezze, Mauro, Richard N. Taylor, and Michal Young.  Graph models for reachability analysis of concurrent programs.  ACM Transactions on Software Engineering and Methodology, 4, 2, pp. 171—213 (April 1995).  http://doi.acm.org/10.1145/210134.210180
    J-13 Richard N. Taylor, Kari A. Nies, Gregory Alan Bolcer, Craig A. MacFarlane, Gregory F. Johnson, and Kenneth M. Anderson. Supporting separations of concerns and concurrency in the Chiron-1 user interface system. ACM Transactions on Computer-Human Interaction, 2, 2, pp. 105—144 (June 1995). (Major revision and expansion of RC-16 below.) http://doi.acm.org/10.1145/210181.210182
    J-12 Michal Young, Richard N. Taylor, David L. Levine, Kari Forester, and Debra Brodbeck. A concurrency analysis tool suite: rationale, design, and preliminary experience. ACM Transactions of Software Engineering and Methodology, 4, 1 (January 1995), pp. 65—106. (Major revision and expansion of RC-10 below.) http://doi.acm.org/10.1145/201055.201080
    J-11 Young, M., D.L. Levine, and R.N. Taylor.  Comments on ``Temporal logic-based deadlock analysis for Ada”.  Correspondence item, IEEE Transactions on Software Engineering, 19, 2, pp. 198-199 (February, 1993). http://dx.doi.org/10.1109/32.214836
    J-10 Taylor, R., Cheryl D. Kelly, and David L. Levine. Structural testing of concurrent programs. IEEE Transactions on Software Engineering, 18, 3, pp. 206—215 (March, 1992). (Major revision of item RC-6 below). http://dx.doi.org/10.1109/32.126769
    J-09 Young, M. and R. Taylor.  Combining static concurrency analysis with symbolic execution.  IEEE Transactions on Software Engineering, SE-14, Number 10, 1499—1511 (October 1988).  An earlier version of this paper appeared as a refereed conference publication in Proceedings of the Workshop on Software Testing, Banff, Canada, pp. 170—178 (July 1986). http://doi.ieeecomputersociety.org/10.1109/32.6195
    J-08 Young, M., Taylor, R.N., and Troup, D.B. Software environment architectures and user interface facilities. IEEE Transactions on Software Engineering, SE-14, Number 6, 697—708 (June 1988). http://dx.doi.org/10.1109/32.6151
    J-07 Brindle, A., R. Taylor, and D. Martin. A debugger for Ada tasking. IEEE Transactions on Software Engineering, SE-15, Number 3, 293—304 (March 1989). http://dx.doi.org/10.1109/32.21757
    J-06 Taylor, R.N. and T.A. Standish. Steps to an advanced Ada programming environment. IEEE Transactions on Software Engineering, SE-11, Number 3, 302-310 (March 1985). This paper previously appeared as a refereed contribution in the Proceedings of the 7th International Conference on Software Engineering, Orlando, FL, pp. 116-125 (March 1984). http://ieeexplore.ieee.org/iel5/32/35873/01702006.pdf?isnumber=35873&arnumber=1702006
    J-05 Taylor, R.N. Debugging real-time software in a host-target environment. Technique et Science Informatiques (Technology and Science of Informatics), Vol. 3, 4, 281-288 (1984). This paper previously appeared as a refereed contribution in the Proceedings of the 2nd Software Engineering Conference, Nice, France, pp. 451-463 (June 1984).
    J-04 Taylor, R.N. An integrated verification and testing environment. Software-Practice and Experience, Vol. 13, pp. 697-713 (1983). http://www3.interscience.wiley.com/cgi-bin/abstract/113447270/ABSTRACT
    J-03 Taylor, R.N. Complexity of analyzing the synchronization structure of concurrent programs. Acta Informatica, Vol. 19, pp. 57-84 (1983). http://www.springerlink.com/content/m0022240x5221756/
    J-02 Taylor, R.N.  A general purpose algorithm for analyzing concurrent programs.  Communications of the ACM, Vol. 26, 5, pp. 362-376 (1983). Reprinted in Concurrent Programming, edited by Narain Gehani and Andrew McGettrick, Addison-Wesley (1988).  Reprinted in Tutorial: Distributed Software Engineering, edited by S.M. Schatz and Jia-Ping Wang, Computer Society Press of the IEEE (1989), pages 226—240. http://doi.acm.org/10.1145/69586.69587
    J-01 Taylor, R.N. and L.J. Osterweil.  Anomaly detection in concurrent software by static data flow analysis.  IEEE Transactions on Software Engineering, Vol. SE-6, No. 3, pp. 265-278 (May 1980).
     
    Selected Refereed Conference Proceedings
    RC-82 Alegria Baquero and Richard N. Taylor. "Computational Commerce: A Vision for the Future," Proceedings of the 13th International Conference on Electronic Commerce and Web Technologies. C. Huemer and P. Lops (Eds.): EC-Web 2012, LNBIP 123, pp. 124-136. Springer, Heidelberg (2012).
    RC-81 Michael M. Gorlick, Kyle Strasser, and Richard N. Taylor. "COAST: An Architectural Style for Decentralized On-Demand Tailored Services," Proceedings of Joint 10th Working IEEE/IFIP Conference on Software Architecture & 6th European Conference on Software Architecture (WICSA/ECSA 2012). Helsinki , August 20–24, 2012.
    RC-80

    Hazel Asuncion and Richard N. Taylor. "A Holistic Approach to Software Traceability" Proceedings of the 24th International Conference on Software Engineering and Knowledge Engineering (SEKE 2012), Redwood City, USA, July 2012

    RC-79

    Christoph Dorn and Richard N. Taylor. "Analyzing Runtime Adaptability of Collaboration Patterns" Proceedings of the 2012 International Conference on Collaboration Technologies and Systems (CTS 2012), May 21-25, 2012, Denver, Colorado, USA.

    RC-78

    Yongjie Zheng and Richard N. Taylor. "xMapper: An Architecture-Implementation Mapping Tool" (Informal Research Demonstration). Proceedings of the 2012 International Conference on Software Engineering (ICSE 2012), June 2012, Zurich, Switzerland.

    RC-77 Christoph Dorn and Richard N. Taylor."`Co-Adapting Human Collaborations and Software Architectures" Proceedings of the 2012 International Conference on Software Engineering -- New Ideas and Emerging Results (NIER) track (ICSE 2012), June 2012, Zurich, Switzerland.
    RC-76 Yongjie Zheng and Richard N. Taylor. ``Enhancing Architecture-Implementation Conformance with Change Management and Support for Behavioral Mapping" Proceedings of the 2012 International Conference on Software Engineering (ICSE 2012), June 2012, Zurich, Switzerland.
    RC-75 Yongjie Zheng and Richard N. Taylor. ``Taming Changes With 1.x-Way Architecture-Implementation Mapping" (Short Paper). Proceedings of ASE 2011: 26th IEEE/ACM International Conference On Automated Software Engineering, November 6–10, 2011, Lawrence, Kan.
    RC-74

    Michael Gorlick, Kyle Strasser, Alegria Baquero, and Richard N. Taylor. ``CREST: Principled Foundations for Decentralized Systems" (Poster and 2-page Abstract). Proceedings of SPLASH 2011, October 22-27, 2011, Portland, OR.

    ACM DL Author-ize serviceCREST: principled foundations for decentralized systems
    Michael Gorlick, Kyle Strasser, Alegria Baquero, Richard N. Taylor
    SPLASH '11 Proceedings of the ACM international conference companion on Object oriented programming systems languages and applications companion, 2011
    RC-73 Leyna C. Cotran and Richard N. Taylor. ``Developing Requirements in an Established Domain Using Tags and Metadata." (Short Paper) Proceedings of Requirements Engineering for Systems, Services, and Systems of Systems (Workshop at RE'11). August 30, 2011. Trento, Italy.
    RC-72 Richard N. Taylor. ``Enabling Innovation: A Choice for Software Engineering" Proceedings of the Foundations of Software Engineering (FSE) and NITR\&D/SPD Working Conference on the Future of Software Engineering Research. Santa Fe, New Mexico, September 2010.
    ACM DL Author-ize serviceEnabling innovation: a choice for software engineering
    Richard N. Taylor
    FoSER '10 Proceedings of the FSE/SDP workshop on Future of software engineering research, 2010
    RC-71

    Hazel Asuncion, Arthur Asuncion, Richard N. Taylor. ``Software Traceability with Topic Modeling''. Proceedings of the 2010 International Conference on Software Engineering (ICSE 2010). May, 2010, pp. 95-104. Cape Town, South Africa

    ACM DL Author-ize serviceSoftware traceability with topic modeling
    Hazeline U. Asuncion, Arthur U. Asuncion, Richard N. Taylor
    ICSE '10 Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, 2010
    RC-70 Richard N. Taylor, Peyman Oreizy, Nenad Medvidovic. "Architectural Styles for Runtime Software Adaptation." Proceedings of the 8th Joint Working IEEE/IFIP Conference on Software Architecture 2009 & the 3rd European Conference on Software Architecture 2009. Cambridge, England, September 2009. pp. 171- 180.
    RC-69 Yang Wang, Scott Hendrickson, André van der Hoek, Richard N. Taylor, and Alfred Kobsa. ``Modeling PLA Variation of Privacy-Enhancing Personalized Systems". Proceedings of the 13th International Software Product Line Conference. San Francisco, August, 2009.
    RC-68 Hazeline U. Asuncion and Richard N. Taylor. ``Capturing Custom Link Semantics among Heterogeneous Artifacts and Tools". Proceedings of the 5th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE). May, 2009, pp. 1-5. Vancouver, B.C. DOI= http://doi.acm.org/10.1109/TEFSE.2009.5069574
    RC-67 Georgas, J. C. and Taylor, R. N. 2008. Policy-based self-adaptive architectures: a feasibility study in the robotics domain. In Proceedings of the 2008 international Workshop on Software Engineering For Adaptive and Self-Managing Systems (Leipzig, Germany, May 12 - 13, 2008). SEAMS '08. ACM, New York, NY, 105-112. DOI= http://doi.acm.org/10.1145/1370018.13700
    RC-66 Justin R. Erenkrantz, Michael Gorlick, Girish Suryanarayana, Richard N. Taylor. From representations to computations: the evolution of web architectures. Proceedings of the 6th joint meeting of the European Software Engineering Conference and the 14th ACM SIGSOFT Symposium on Foundations of Software Engineering (ESEC/FSE 07), September 03 - 07, 2007, Pages: 255 - 264. http://doi.acm.org/10.1145/1287624.1287660
    RC-65 Hazeline U. Asuncion, Frederick Francois, Richard N. Taylor. An end-to-end industrial software traceability tool. Proceedings of the 6th joint meeting of the European Software Engineering Conference and the 14th ACM SIGSOFT Symposium on Foundations of Software Engineering (ESEC/FSE 07), September 03 - 07, 2007, Pages: 115 - 124. http://doi.acm.org/10.1145/1287624.1287642
    RC-64 John Georgas and Richard N. Taylor. “An Architectural Style Perspective on Robotic Architechtures.” 2nd International Workshop on Software Development and Integration into Robotics (part of the International Conference on Robotics and Automation). Rome, Italy, April 2007.
    RC-63 Girish Suryanarayana, Mamadou Diallo, Justin Erenkrantz and Richard N. Taylor. Architectural Support for Trust Models in Decentralized Applications.  Proceedings of  the 28th International Conference on Software Engineering (ICSE ‘06), Shanghai, China, May 2006. http://doi.acm.org/10.1145/1134285.1134295
    RC-62 Jie Ren and Richard N. Taylor. A Secure Software Architecture Description Language. Proceedings of the Workshop on Software Security Assurance Tools, Techniques, and Metrics. 7 and 8 November 2005 Long Beach, California, USA. Co-located with the 20th IEEE/ACM International Conference on Automated Software Engineering (ASE 2005). http://www.ics.uci.edu/~jie/ssattm05.pdf
    RC-61 Scott Hendrickson, Eric Dashofy, and Richard N. Taylor. “An (Architecture-centric) Approach for Tracing, Organizing, and Understanding Events in Event-based Software Architectures”. Proceedings of the 13th IEEE International Workshop on Program  Comprehension (IWPC) 2005.  May, 2005. http://doi.ieeecomputersociety.org/10.1109/WPC.2005.6
    RC-60 John C. Georgas, Michael M. Gorlick,  and Richard N. Taylor.  “Raging Incrementalism: Harnessing Change with OpenSource Software.”  Proceedings of the 5th Workshop on Open Source Software Engineering, held in conjunction with the 2005 International Conference on Software Engineering, May 2005. http://doi.acm.org/10.1145/1083258.1083263
    RC-59 Jie Ren and Richard N. Taylor. “Towards An Architectural Treatment of Software Security: A Connector-Centric Approach”.  Proceedings of the Workshop on Software Engineering for Secure Systems (SESS05), held in conjunction with the 2005 International Conference on Software Engineering, May 2005.http://doi.acm.org/10.1145/1083200.1083203
    RC-58 Peter Kammer and Richard N. Taylor. “An Architectural Style for Supporting Work Practice: Coping with the Complex Structure of Coordination”. Proceedings of the International Symposium on Collaborative Technologies and Systems (CTS'05),  May 15-19, 2005,  Saint Louis, Missouri, USA. http://dx.doi.org/10.1109/ISCST.2005.1553316
    RC-57 John Georgas, Andre van der Hoek, and Richard N. Taylor. “Architectural Runtime Configuration Management in Support of Dependable Self-Adaptive Software”. Proceedings of the ICSE 2005 Workshop on Architecting DependableSystems (WADS).  May 2005. http://doi.acm.org/10.1145/1083217.1083225
    RC-56 Jie Ren and Richard N. Taylor. “Utilizing Commercial Object Libraries within Loosely-coupled, Event-Based Systems.” Proceedings of IASTED International Conference on Software Engineering and Applications (SEA2004), November 09 - 11, 2004, Cambridge, USA. http://www.ics.uci.edu/~jie/sea04-proceeding.pdf
    RC-55 John Georgas and Richard N. Taylor. “Towards a Knowledge-Based Approach to Architectural Adaptation Management.” Proceedings of ACM SIGSOFT Workshop on Self-Managed Systems (WOSS ‘04). October 31- November 1, 2004, Newport Beach, CA. http://doi.acm.org/10.1145/1075405.1075417
    RC-54 Girish Suryanarayana, Justin R. Erenkrantz, Scott A. Hendrickson, and Richard N. Taylor. “PACE: An Architectural Style for Trust Management in Decentralized Applications.” Proceedings of the Fourth Working IEEE/IFIP Conference on Software Architecture (WICSA4), June 2004, Oslo, Norway pp. 221-230. Acceptance ratio: 25/82 (30%) http://doi.ieeecomputersociety.org/10.1109/WICSA.2004.1310705
    RC-53 Rohit Khare and Richard N. Taylor. “Extending the Representational State Transfer (REST) Architectural Style for Decentralized Systems.” Proceedings of the International Conference on Software Engineering (ICSE), May, 2004, Edinburgh, Scotland. pp. 428-437. Acceptance ratio: 59/436 (14%) Winner, Distinguished Paper Award. http://doi.ieeecomputersociety.org/10.1109/ICSE.2004.1317465
    RC-52 Justin R. Erenkrantz and Richard N. Taylor. “Supporting Distributed and Decentralized Projects: Drawing Lessons from the Open Source Community.” 1st Workshop on Open Source in an Industrial Context (OSIC'03), held in conjunction with OOPSLA 2003, October 2003, Anaheim. Complete workshop proceedings.
    RC-51 Scott Hendrickson, Eric Dashofy, Richard N. Taylor, “An Approach for Tracing and Understanding Asynchronous Systems”. Proceedings of the 18th IEEE International Conference on Automated Software Engineering (ASE) Montreal, pp. 318-322 (2003). Short paper. Acceptance ratio: 41/170 (24%). http://doi.ieeecomputersociety.org/10.1109/ASE.2003.1240329
    RC-50 Ren, J. and R. N. Taylor. “Incorporating Off-The-Shelf Components with Event-based Integration”. 12th International Conference on Intelligent and Adaptive Systems and Software Engineering (IASSE-2003), San Francisco, CA, pp. 188-191 (July 9-11, 2003)
    RC-49 Ren, J. and R. N. Taylor. “Visualizing Software Architecture with Off-The-Shelf Components. Fifteenth International Conference on Software Engineering and Knowledge Engineering”, San Francisco, CA., pp. 132-141 (July 1-3, 2003)
    RC-48 Dashofy, E., Hoek, A.v.d. and Taylor, R.N., Towards Architecture-Based Self-Healing Systems. in First ACM SIGSOFT Workshop on Self-Healing Systems, Charleston, South Carolina, ACM, pp. 21-26 (2002). http://doi.acm.org/10.1145/582128.582133
    RC-47 Girish Suryanarayana and Richard N. Taylor. “A Decentralized Algorithm for Coordinating Independent Peers: An Initial Examination”  Tenth International Conference on Cooperative Information Systems (CoopIS), Lecture Notes in Computer Science, Volume: 2519 / 2002.  Volume titled: “On the Move to Meaningful Internet Systems 2002: Confederated International Conferences CoopIS, DOA, and ODBASE 2002.”  Pages 213-229.   Springer-Verlag Heidelberg. 2002.
    RC-46 Eric M. Dashofy, André van der Hoek, Richard N. Taylor. An Infrastructure for the Rapid Development of XML-based Architecture Description Languages. Proceedings of the 2002 International Conference on Software Engineering (ICSE 2002), Orlando, May 22-24, 2002. http://doi.acm.org/10.1145/581339.581374
    RC-45 Taylor, R.N. Moving On:  Software Engineering Paradigms for the 21st Century.  Proceedings of the Working Conference on Complex and Dynamic Systems Architectures.  Brisbane, Australia, December 12-14, 2001. (See summary paper.)
    RC-44 Dashofy, E., Hoek, A.v.d., and Taylor, R.N. A Highly-Extensible, XML-Based Architecture Description Language. In Proceedings of the The Working IEEE/IFIP Conference on Software Architecture (WICSA 2001). Amsterdam, The Netherlands, August 28-31, 2001. http://doi.ieeecomputersociety.org/10.1109/WICSA.2001.948416
    RC-43 Rohit Khare, Michael Guntersdorfer, Peyman Oreizy, Nenad Medvidovic, and Richard N. Taylor.  xADL:  Enabling Architecture-Centric Tool Integration with XML. Hawaii International Conference on System Sciences (HICSS): Software minitrack, January 3-6, 2001. http://csdl.computer.org/comp/proceedings/hicss/2001/0981/09/09819053.pdf
    RC-42 Roy Fielding and Richard N. Taylor. Principled design of the Modern Web Architecture. Proceedings of the 2000 International Conference on Software Engineering (ICSE 2000). Limerick, Ireland, pp. 407-416, June 2000. http://doi.acm.org/10.1145/337180.337228
    RC-40 Nenad Medvidovic, David S. Rosenblum, and Richard N. Taylor. A Language and Environment for Architecture-Based Software Development and Evolution.   Proceedings of the 1999 International Conference on Software Engineering (ICSE 99). Los Angeles, pp. 44-53, May 1999. http://doi.ieeecomputersociety.org/10.1109/ICSE.1999.840994
    RC-39 Eric Dashofy, Nenad Medvidovic, and Richard N. Taylor. Using OTS Middleware to Implement Connectors in Distributed Software Architectures. Proceedings of the 1999 International Conference on Software Engineering (ICSE 99). Los Angeles, pp. 3–12, May 1999. http://doi.ieeecomputersociety.org/10.1109/ICSE.1999.840990
    RC-38 Peter Kammer, Gregory Alan Bolcer, Richard N. Taylor, and Arthur S. Hitomi. Supporting distributed workflow using HTTP.  Proceedings of the Fifth International Conference on the Software Process.  Lisle, IL, 14-17 June 1998, pp.83-94.
    RC-37 Peyman Oreizy and Richard N. Taylor.  On the Role of Software Architectures in Runtime System Reconfiguration.  Proceedings of the 4th International Conference on Configurable Distributed Systems.  Annapolis, Maryland, pp. 61-70, May 4-6, 1998.
    RC-36 Peyman Oreizy, Nenad Medvidovic, and Richard N. Taylor. Architecture-based runtime software evolution. Proceedings of the 1998 International Conference on Software Engineering, Kyoto, pp. 177-186, April 1998. DOI: 10.1109/ICSE.1998.671114 http://ieeexplore.ieee.org/iel4/5475/14745/00671114.pdf?tp=&isnumber=14745&arnumber=671114&punumber=5475
    RC-35 Peyman Oreizy, Nenad Medvidovic, Richard N. Taylor, David S. Rosenblum. “Software Architecture and Component Technologies: Bridging the Gap”. Proceedings of the OMG-DARPA Workshop on Compositional Software Architectures, Monterey, CA, January 6-8, 1998. <http://www.objs.com/workshops/ws9801/papers/paper007.pdf >
    RC-34 Nenad Medvidovic and Richard N. Taylor. A framework for classifying and comparing architecture description languages. Proceedings of the Joint Fifth ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE) and European Software Engineering Conference (ESEC). October, 1997, pp. 60—76. Acceptance ratio: 27/194.
    RC-33 Richard N. Taylor.  Dynamic, invisible, and on the Web.  Proceedings of the Workshop on Research Directions in Process Technology.  Nancy, France, July 7-9, 1997.
    RC-32 Nenad Medvidovic, Peyman Oreizy, and Richard N. Taylor. Reuse of off-the-shelf components in C2-style architectures. Proceedings of the 1997 Symposium on Software Reuse. Boston, Massachusetts, pp. 190—198, May 18-19, 1997. Also appeared in Proceedings of the 1997 International Conference on Software Engineering, pp. 692—700. Acceptance ratio: 21/63.
    RC-31 Gregory Alan Bolcer and Richard N. Taylor.  Endeavors:  A Process System Integration Infrastructure.  Proceedings of the 4th International Conference on the Software Process.  Brighton, England, pp. 76-85.  IEEE Computer Society Press. December 1996. http://doi.ieeecomputersociety.org/10.1109/ICSP.1996.565026
    RC-31 Peyman Oreizy and Richard N. Taylor.  Coping with Application Inconsistency in Decentralized Software Evolution.  Proceedings  of the Second International Conference on the Principles of  Software Evolution (IWPSE 2), pp. 74-78. Fukuoka, Japan. July 16-17, 1999.
    RC-30 Richard N. Taylor.  Generalization from domain experience:  The superior paradigm for software architecture research? Proceedings of the Second International Software Architecture Workshop (ISAW-2), pp. 12-14, October 1996.
    RC-29 Nenad Medvidovic, Peyman Oreizy, Jason E. Robbins, and Richard N. Taylor.  Using object-oriented typing to support architectural design in the C2 style.  Proceedings of the Fourth ACM SIGSOFT Symposium on the Foundations of Software Engineering, pp. 24-32, October 1996. http://doi.acm.org/10.1145/239098.239106
    RC-28 Nenad Medvidovic, Richard N. Taylor, and E. James Whitehead, Jr. Formal modeling of software architectures at multiple levels of abstraction. Proceedings of the 1996 California Software Symposium, pp. 28—40, April 17, 1996.
    RC-27 Patrick S. Young and Richard N. Taylor. Process programming languages: Issues and approaches. Proceedings of the Workshop on Research Issues in the Intersection of Software Engineering and Programming Languages, April 1995.
    RC-26 E. James Whitehead, Jr., Jason E. Robbins, Nenad Medvidovic, and Richard N. Taylor.  Software architecture: Foundations of a software component marketplace.  Proceedings of the First International Workshop on Architectures for Software Systems, pp. 276—282, April 24-25, 1995.
    RC-25 Richard N. Taylor, Nenad Medvidovic, Kenneth M. Anderson, E. James Whitehead, Jr., and Jason E. Robbins.  A component and message-based architectural style for GUI software.  Proceedings of the 17th International Conference on Software Engineering (ICSE 17), April 1995,  pp. 295—304. http://doi.acm.org/10.1145/225014.225042
    RC-24 Patrick S. Young and Richard N. Taylor.  Human-executed operations in the Teamware process programming system.  Proceedings of the 9th International Software Process Workshop.  October 1994.
    RC-23 E. James Whitehead, Jr., Kenneth M. Anderson, Richard N. Taylor.  A proposal for versioning support for the Chimera system.  Proceedings of the Workshop on Versioning in Hypertext Systems.  ACM/Siglink, pp. 51—60, Edinburgh Scotland, September 1994.
    RC-22 Kenneth Anderson, Richard N. Taylor, and E. James Whitehead, Jr.  Chimera:  Hypertext for heterogeneous software environments.  Proceedings of the ACM European Conference on Hypermedia Technology.  Edinburgh, September 18—23, 1994. http://doi.acm.org/10.1145/192757.192783
    RC-21 John Self and Richard N. Taylor.  A framework for debugging concurrent software.  Proceedings of the 1994 Irvine Software Symposium.  April 8, 1994.
    RC-20 Gregory F. Johnson and Richard N. Taylor.  An efficient constraint maintenance system for user interface development.  Proceedings of the 1993 Irvine Software Symposium, pp. 63—72.  April 30, 1993.
    RC-19 David L. Levine and Richard N. Taylor.  Metric-driven re-engineering for static concurrency analysis.  Proceedings of the International Symposium on Software Testing and Analysis, pp. 40-50.  Cambridge, Mass., June 28—30, 1993.  http://doi.acm.org/10.1145/154183.154196
    RC-18 Richard N. Taylor and Gregory F. Johnson. Separations of concerns in the Chiron-1 user interface development and management system. Proceedings of InterChi’93 (ACM SIGCHI’s CHI conference united with IFIP TC 13’s Interact conference), pp. 367—384. Amsterdam, April 1993. Acceptance ratio: 62/330. http://doi.acm.org/10.1145/169059.169294
    RC-17 R. Kadia (pen name for the authors involved). Issues encountered in building a flexible software development environment. Proceedings of the Fifth Symposium on Software Development Environments, SIGSOFT `92, pp. 169—180. Reston, Virginia, December 1992. Acceptance ratio: 18/78.http://doi.acm.org/10.1145/142868.143768
    RC-16 Keller, Rudolf K., Mary Cameron, Richard N. Taylor, and Dennis B. Troup. User interface development and software environments: The Chiron-1 system. Proceedings of the 13th International Conference on Software Engineering, pp. 208—218. Austin, Texas, May 1991. Acceptance ratio: 22/148.
    RC-15 Keller, Rudolf K., Mary Cameron, Richard Taylor, and Dennis Troup.  Chiron-1: A user interface development system tailored to software environments.  In Proceedings of the 24th Hawaii International Conference on System Sciences, Volume II.  Kailua-Kona, Hawaii, pp. 60—69, January 8—10, 1991.
    RC-14 Leon Osterweil and Richard Taylor. The architecture of the Arcadia-1 process centered software environment. In Proceedings of the 6th International Software Process Workshop, Sapporo, Japan, October 1990. Pages 155-158.
    RC-13 Shy, Izhar, Richard Taylor, and Leon Osterweil.  A metaphor and a conceptual architecture for software development environments.  In Software Engineering Environments: International Workshop on Environments, Chinon, September 1989.  Lecture Notes in Computer Science, Volume 467, November, 1990, pp. 77—97.
    RC-12 Young, M., R. Taylor, K. Forester, D. Brodbeck. Integrated concurrency analysis in a software development environment. Proceedings of the Third Symposium on Testing, Analysis, and Verification, Key West, pp. 200—209 (December 1989). Acceptance Ratio: 23/75.
    RC-11 Young, M. and R. Taylor. Rethinking the taxonomy of fault detection techniques. Proceedings of the 11th International Conference on Software Engineering, Pittsburgh, pp. 53—62 (May 1989). Acceptance Ratio: 36/220. http://doi.acm.org/10.1145/74587.74593
    RC-10 Taylor, R.N., F.C. Belz, L.A. Clarke, L.J. Osterweil, R.W. Selby, J.C. Wileden, A.L. Wolf, and M. Young. Foundations for the Arcadia environment architecture. Proceedings of the Third ACM SIGSOFT Symposium on Software Development Environments, Cambridge, pp. 1—13 (November 1988). Acceptance Ratio: 21/135. http://doi.acm.org/10.1145/64135.65004
    RC-09 Young, M., R. Taylor, D.B. Troup, and C. Kelly. Chiron: A user interface management system for software environments. Proceedings of the 10th International Conference on Software Engineering, Singapore, pp. 367—376 (April 1988). Acceptance Ratio: 42/220.
    RC-08 Taylor, R. and C. Kelly. Structural testing of concurrent programs. Proceedings of the Workshop on Software Testing, Banff, Canada, pp. 164—169 (July 1986). Acceptance Ratio: 18/43.
    RC-07 Taylor, R., L. Clarke, L. Osterweil, J. Wileden, M. Young. Arcadia: A Software development environment research project. Proceedings of the Second International Conference on Ada Applications and Environments, Miami, Florida, pp. 137—149 (April 1986).
    RC-06 Taylor, R.N. and L.J. Osterweil.  The use of sequencing information in software specifications for verification.  Proceedings of the 4th Jerusalem Conference on Information Technology, Jerusalem, Israel, IEEE 84CH2022-2, pp. 260-266 (May 1984). 
    RC-05 Taylor, R.N.  Software testing in an Ada programming environment (Invited Paper).  Proceedings of the 47th Symposium of the NATO-AGARD Avionics Panel on Design for Tactical Avionics Maintainability, Brussels, Belgium, AGARD-CP-361, pp. 20-1 to 20-12 (May 1984).
    RC-04 Standish, T.A. and R.N. Taylor. Arcturus: a prototype advanced Ada programming environment. Proceedings of the ACM SIGSOFT/SIGPLAN Symposium on Practical Software Development Environments, Pittsburgh, PA, pp. 57-64 (April 1984). Appeared as Software Engineering Notes, 9, 3 (May 1984) and SIGPLAN Notices, 19, 5 (May 1984).
    RC-03 Smith, M.K., L.L. Tripp, L.J. Osterweil, R.N. Taylor, and W.E. Howden. An approach to transferring verification and validation technology. AFIPS Conference Proceedings, Vol. 50, 1981 National Computer Conference. May 4-7, Chicago, Illinois, pp. 367-373.
    RC-02 Taylor, R.N. and L.J. Osterweil. Static analysis and dynamic testing techniques for concurrent process programs. Digest for the Workshop on Software Testing Test Documentation, Ft. Lauderdale, FL, pp.197-205 (1978).
    RC-01 Taylor, R.N. and L.J. Osterweil.  A facility for verification, testing and documentation of concurrent process software.  Proceedings, COMPSAC 78, Chicago, IL, pp. 36-41 (1978).
    Selected Non-refereed or Weakly-refereed Publications
    NR-40 Cheng, B. H., Lemos, R. d., Fickas, S., Garlan, D., Litoiu, M., Magee, J., Muller, H. A., and Taylor, R. 2007. SEAMS 2007: Software Engineering for Adaptive and Self-Managing Systems. In Companion To the Proceedings of the 29th international Conference on Software Engineering (May 20 - 26, 2007). International Conference on Software Engineering. IEEE Computer Society, Washington, DC, 152-153. DOI= http://dx.doi.org/10.1109/ICSECOMPANION.2007.64
    NR-39 Justin Erenkrantz, Michael Gorlick, and Richard N. Taylor. Rethinking Web Services from First Principles. 2nd International Conference on Design Science Research in Information Systems & Technology (DESRIST 2007). May 13-15, 2007, Pasadena, California, USA (pp. 60-64).
    NR-38 Dashofy, Eric; Asuncion, Hazel; Hendrickson, Scott; Suryanarayana, Girish; Georgas, John; Taylor, Richard; ArchStudio 4: An Architecture-Based Meta-Modeling Environment. Software Engineering - Companion, 2007. ICSE 2007 Companion. 29th International Conference on 20-26 May 2007 Page(s):67 - 68 Digital Object Identifier 10.1109/ICSECOMPANION.2007.21
    NR-37 Girish Suryanarayana and Richard N. Taylor.  TREF: A Threat-centric Comparison Framework for Decentralized Reputation Models -  -ISR Technical Report UCI-ISR-06-2, January 2006.
    NR-36 Peyman Oreizy and Richard N. Taylor, “Decentralized Software Evolution”, Technical Report, Institute for Software Research, No. UCI-ISR-03-10, September 2003
    NR-35 Justin Erenkrantz, Girish Suryanarayana, Scott Hendrickson, Richard Taylor, “PACE: An Architectural Style for Trust Management in Decentralized Applications”, Technical Report, Institute for Software Research, No. UCI-ISR-03-9, September 2003.
    NR-34 Rohit Khare and Richard N. Taylor, “Extending the REpresentational State Transfer (REST) Architectural Style for Decentralized Systems”, Technical Report, Institute for Software Research, No. UCI-ISR-03-8, September 2003.
    NR-33 Justin R. Erenkrantz, Richard N. Taylor, “Supporting Distributed and Decentralized Projects: Drawing Lessons from the Open Source Community”,  Technical Report, Institute for Software Research, No. UCI-ISR-03-4, June 2003.
    NR-32 Jie Ren, Richard N. Taylor, “Incorporating Off-The-Shelf Components with Event-based Integration”, Technical Report, Institute for Software Research, No. UCI-ISR-02-2, April 2003.
    NR-31 Scott Hendrickson, Eric Dashofy, Adrita Bhor, Richard N. Taylor, Santiago Li, Nghi Nguyen, “An Approach for Tracing and Understanding Asynchronous Systems”, Technical Report, Institute for Software Research, No. UCI-ISR-02-7, December 2002.
    NR-30 Proposal for a School of Design at the University of California, Irvine.  November 2002.  Accessible at http://www.evc.uci.edu/growth/design/SoD-proposal.pdf.  School of Design proposal committee (Chair:  Taylor).
    NR-29 Richard N. Taylor and Eric M. Dashofy. “Function Follows Form: Architecture and 21st Century Software Engineering,” Participant whitepaper for: Workshop of the Interagency Working Group for Information Technology Research and Development (ITRD) Software Design and Productivity (SDP) Coordinating Group December 13 - 14, 2001 Vanderbilt University, Nashville, TN. Participant White Papers at http://www.itrd.gov/iwg/pca/sdp/sdp-workshop/vanderbilt/
    NR-28 Nenad Medvidovic and Richard Taylor.  Separating Fact from Fiction in Software Architecture.  Proceedings of the Third International Software Architecture Workshop (ISAW-3).  Sponsored by ACM Sigsoft.  1-2 November, 1998, pp. 105-108.
    NR-27 Distributed Workflow using HTTP:  An Example using Software Pre-Requirements.  Arthur S. Hitomi, Peter J. Kammer, Gregory Alan Bolcer, and Richard N. Taylor.  Paper accompanying a formal research demo, Proceedings of the 1998 International Conference on Software Engineering, Kyoto, Volume II, pp. 40-44, April 1998.
    NR-26 Peyman Oreizy, David S. Rosenblum, Richard N. Taylor, “On the Role of Connectors in Modeling and Implementing Software Architectures”,  Technical Report UCI-ICS-98-04, Department of Information and Computer Science, University of California, Irvine, February 1998.
    NR-25 Richard N. Taylor, Nenad Medvidovic, and Peyman Oreizy.  Architectural implications of common operator interfaces. Ground System Architectures Workshop.  El Segundo, CA.  February, 1998.
    NR-24 Arthur S. Hitomi, Gregory Alan Bolcer, and Richard N. Taylor.  Endeavors: A Process System Infrastructure (Formal Research Demonstration).  Proceedings of the 1997 International Conference on Software Engineering.  Boston, Massachusetts (May 1997), pp. 598-599.
    NR-23 Gregory A. Bolcer and Richard N. Taylor.  Endeavors: An Execution Infrastructure for Maturing Processes.  Proceedings of the Conference on Software Process Improvement.  Irvine, California, January 23-24, 1997.
    NR-22 Nenad Medvidovic and Richard N. Taylor.  Reuse of Off-the-Shelf Constraint Solvers in C2-Style Architectures.  UCI Technical Report Number 96-28.  July 1996.
    NR-21 Richard N. Taylor, Will Tracz, and Lou Coglianese.  Software Development Using Domain-Specific Software Architectures.  Software Engineering Notes, 20, 5, pp. 27—38 (December 1995). http://doi.acm.org/10.1145/217030.217034
    NR-20 Rebecca E. Grinter and Richard N. Taylor.  Improvement of User Interface Development Methodologies through Rigorous Analysis.  UCI Technical Report Number 93-36.
    NR-19 Richard N. Taylor and Kari A. Forester.  A software engineering approach to user interface management systems.  CrossTalk—- The Software Engineering Report, pp. 7—10.  December 1993.
    NR-18 Anderson, Jennifer-Ann, Richard Taylor, and Michal Young.  Modularizing a concurrent artist-based UIMS for software environments.  Department of Information and Computer Science Technical Report Number UCI-92-80 (July, 1992).
    NR-17 R. Kadia (pen name for the eight authors who contributed).  Lessons from the Arcadia project.  Proceedings of the 1992 DARPA Software Technology Conference.  Los Angeles, CA (April 1992).  Pages 287—302.
    NR-16 Richard N. Taylor and Gregory F. Johnson.  The Chiron-1 user interface development system.  Proceedings of the 1992 DARPA Software Technology Conference.  Los Angeles, CA (April 1992).  Pages 303—309.
    NR-15 Patrick S. Young and Richard N. Taylor.  Team-oriented process programming.  Department of Information and Computer Science Technical Report Number UCI-91-68 (August, 1991).
    NR-14 J. Self and R. N. Taylor.  Using static concurrency analysis to instrument concurrent programs for dynamic debugging. (Abstract) Proceedings of the ACM/ONR Workshop on Parallel and Distributed Debugging, Santa Cruz, California (May 20-21, 1991). Pages 263—265.
    NR-13 Taylor, R.N.  Roundtable summary:  Event-based control/integration mechanisms.  Proceedings of the 1st Irvine Software Symposium (ISS ‘91).  Irvine, California (June 5, 1991).  Pages 85—87.
    NR-12 Taylor, R.N.  ``Letter from the Chairman”,  Software Engineering Notes Volume 14, number 5, page 1.  Volume 15, number 6, page 1.  Volume 16, number 1, page 1.  Volume 16, number 2, page 1.  Volume 17, number 1, page 1.  Volume 18, number 1, page 1.  Volume 18, number 2, pages 1-2.
    NR-11 Taylor, R. Diversity and object management in software development environments. Proceedings of the 1989 ACM SIGMOD Workshop on Software CAD Databases. Napa, California (February 27—28, 1989). Pages 145—148.
    NR-10 Taylor, R. Tool integration in Arcadia. Proceedings of TRI-Ada ‘88, pp. 218—223, October 24—27, 1988. Charleston, West Virginia.
    NR-09 Taylor, R., D. Baker, F. Belz, B. Boehm, L. Clarke, D. Fisher, L. Osterweil, R. Selby, J. Wileden, A. Wolf, M. Young.  Next generation software environments: Principles, problems, and research directions.  Technical Report Number 87—16, Department of Information and Computer Science, University of California, Irvine,  July 15, 1987.
    NR-08 Taylor, R.N.  Session Report: Process Programming.  Proceedings of the 3rd International Software Process Workshop, Breckenridge, Colorado.  pp. 75-77 (November 17—19 1986).
    NR-07 Martin, D., A. Brindle, R. Taylor, and L. Jansen.  Modifying a sequential Ada interpreter to support Ada tasking.  Aerospace Corporation Technical Report, Number ATR-86(8166)-1 (January 1986).
    NR-06 Brindle, A., D. Martin, R. Taylor, and L. Jansen.  A model for the run-time processing of Ada tasking.  Aerospace Corporation Technical Report, Number ATR-84(8233)-2 (August 1984).
    NR-05 Powell, Patricia B., editor.  Software Validation, Verification, and Testing Technique and Tool Reference Guide.  NBS Special Publication 500-93.  National Bureau of Standards, Washington, D.C.  (September 1982).  (R.N. Taylor assisted as a contributing author.)
    NR-04 Feiber, J.D., R.N. Taylor, and L.J. Osterweil.  Newton - A dynamic program analysis tool capabilities specification.  Department of Computer Science Technical Report CU-CS-200-81, University of Colorado (February 1981).
    NR-03

    Taylor, R.N., Assertions in programming languages.  SIGPLAN Notices, 15, No.1. pp. 105-114. (1980).

    ACM DL Author-ize serviceAssertions in programming languages
    Richard N. Taylor
    ACM SIGPLAN Notices, 1980

    NR-02 Taylor, R.N., R.L. Merilatt and L.J. Osterweil. Integrated testing and verification system for research flight software: Design document. NASA Contractor Report 159095, 238 pp., (available from NASA-STIF, Baltimore MD) (1979).
    NR-01 Taylor, R.N., L.J. Osterweil and L.G. Stucki.  An integrated verification and validation tool for flight software.  Tools for Embedded Computing Systems Software, NASA Conference Publication 2064, pp. 109-111 (1978).
    http://www.ics.uci.edu/~taylor/Classes.htm Richard Taylor's Classes

    Classes

    Year
    Fall Quarter
    Winter Quarter
    Spring Quarter
    2008-2009 Informatics 121 (Software Design I)

    Informatics 117 (Project in Software Systems Design)

    Informatics 221 (Software Architecture)

    No scheduled classes
    2007-2008 Informatics 211 (Software Engineering) Informatics 221 (Software Architecture) Informatics 123 (Software Architectures, Distributed Systems, and Interoperability)
    2006-2007 Informatics 211 (was ICS 221) Informatics 295 Topic: Software Patents, Litigation, and Intellectual Property Informatics 119 (was ICS 127) Advanced Project in Software Engineering.
    2005-2006
    On Sabbatical
    2004- 2005

    ICS 52

    ICS 125

    ICS 229 (Informatics Seminar)

    No scheduled classes No scheduled classes
    2003-2004 ICS 125

    ICS 52

    No scheduled classes
    2002-2003

    ICS 52

    ICS 221

    ICS 280, which is this year's disguise for the forthcoming ICS 223, "Software Architectures" No scheduled classes
    2001-2002

    ICS 52

    ICS 280 (Peer-to-Peer Architectures)

    ICS 228, Software Environments

    ICS 123 Software Architectures and Distributed Systems

    ICS 127: Project in System Design:

    2000-2001 ICS 229 On Sabbatical  
    1999-2000 ICS 125

    ICS 125 (second section)

    ICS 280E, Software Architectures and Internet Protocols

    No scheduled classes
    1998-1999  

    ICS 126A/125

    ICS 228, Software Environments

     
    1997-1998 ICS 126A

    ICS 126B

    ICS 227

     
    1996-1997

    ICS 221A

    ICS 229

    ICS 52 (co-taught with David Rosenblum)

    ICS 125B

     

     

    N.B. Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion.

     

    http://www.ics.uci.edu/~rkwang/navigation.html Navigation
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    http://www.ics.uci.edu/~rkwang/info.html General Information

    Richert Wang

    Lecturer

    School of Information and Computer Sciences
    Department of Computer Science
    University of California, Irvine

    Email: rkwang at uci dot edu
    Office: ICS 424D

    http://www.ics.uci.edu/~goodrich/writing.html Tiemersma's Simple Rules for Coherent Writing

    Tiemersma's Simple Rules for Coherent Writing

    Adding Transitions

    It is important in technical writing to add transitions between sections, paragraphs, and sentences. The following list gives some transitional phrases and their typical uses.

    1. and, or, nor, also, moreover, furthermore, indeed, in fact, first, second, third, in addition.
        Use to add another thought.
    2. for instance, for example, for one thing, similarly, likewise.
        Used for adding, illustrating, or expanding a point.
    3. therefore, thus, so, and so, hence, consequently, finally, on the whole, all in all, in other words, in short.
        Used for adding up consequences, summarizing minor points to emphasize a major point.
    4. frequently, occasionally, in particular, in general, specifically, especially, usually, often.
        Used for adding a qualifying point or illustration.
    5. of course, no doubt, doubtless, to be sure, granted, certainly.
        Used for conceding a point to the opposition, or recognizing a point just off the main line.
    6. but, however, yet, on the contrary, not at all, surely, no, until.
        Used for reversing or deflecting the line of thought, usually back to your own side.
    7. still, nevertheless, notwithstanding.
        Used for returning the thought to your side after a concession.
    8. although, though, whereas.
        Used for attaching a concession.
    9. because, since, for.
        Used to connect a reason to an assertion.
    10. if, provided, in case, unless, lest, when.
        Used for qualifying and restricting a more general idea.
    11. as if, as though, even if.
        Used for glancing at tentative or hypothetical conditions that strengthen and clarify your point.
    12. this, that, these, those, who, whom, he, she, it, they, all of them, few, many, most, several.
        Relative and demonstrative words, like adjectives and pronouns, tie things together, pointing back as they carry the reference ahead. But be sure there is no mistaking the specific word to which each refers.

    Gaining Coherence

    One can gain coherence in writing by using the following:

    1. filling in with thought,
    2. filling in with specific illustrative detail,
    3. using transitional tags (see above) that tie sentences together,
    4. repeating words or syntactical patterns.

    Sentence Starts

    As a rule of thumb, 60% of all sentences should start with the subject of that sentence. To avoid monotony the other 40% should start with one of the following:

    1. prepositional phrase,
    2. parenthetical element,
    3. an infinitive (e.g., To begin with, ...),
    4. adverbs, which modify a sentence (but don't use "hopefully" in this way),
    5. a subordinating conjunction,
    6. participial phrase (but make sure it modifies the right word),


    The above words are my recording of some of the advise from the late Dr. Tiemersma, who taught me how to write.

    Copyright © 1997, by Goodrich. http://www.ics.uci.edu/~goodrich/colleagues.html Research Colleagues of Michael T. Goodrich

    Research Colleagues of Michael T. Goodrich

    Recent Co-authors

    • Lars Arge, Aarhus University.
    • Mikhail J. Atallah, Purdue University.
    • Erin W. Chambers, Saint Louis University.
    • Matthew Dickerson, Middlebury College.
    • Mike Dillencourt, UC-Irvine.
    • Wenlieang (Kevin) Du, Syracuse University.
    • David Eppstein, UC-Irvine.
    • Michael Goldwasser, Saint Louis University.
    • Dan Hirschberg, UC-Irvine.
    • George Lueker, UC-Irvine.
    • Michael Mitzenmacher, Harvard University.
    • Roberto Tamassia, Brown University.
    • Nikos Triandopoulos, Boston University and RSA Labs.
    • Danfeng (Daphne) Yao, Virgina Tech.

    Postdoctoral Fellows

    • Maarten Loffler, UC-Irvine, 2010-2011, mentored jointly with David Eppstein. (Now at Utrecht University)
    • Martin Nollenburg, UC-Irvine, 2010, mentored jointly with David Eppstein. (Now at Karlsruhe Institute of Technology)
    • Amitabha Bagchi, UC-Irvine, 2002-2004, now at IIT-Dehli.
    • Amitabh Chaudhary, UC-Irvine, 2002-2004.
    • Pawel Gajer, Johns Hopkins, 2000. (Now at Univ. of Maryland)
    • Gill Barequet, Johns Hopkins, 1996-98. (Now at Technion)
    • Timothy Chan, Johns Hopkins, Postdoc, 1996. (Now at Univ. of Waterloo)

    PhD Students

    • Joseph A. Simons, current. Going to Google.
    • Pawel Pszona, current.
    • Lowell Trott, UC-Irvine, 2013, now at Google.
    • Darren Strash, UC-Irvine, 2011, now at Intel.
    • Nodari Sitchinava, UC-Irvine, 2009, now at University of Hawaii.
    • "Jeremy" Yu Meng, UC-Irvine, 2006, now at Microsoft.
    • "Jonathan" Zheng Sun, UC-Irvine, 2006, now at Univ. of Southern Mississippi.
    • Breno de Medeiros, co-advised with Giuseppe Ateniese. Johns Hopkins, 2004, now at Google.
    • Amitabha Bagchi, Johns Hopkins, 2002, now at IIT-Dehli.
    • Amitabh Chaudhary co-advised with Alex Szalay. Johns Hopkins, 2002.
    • Stephen Kobourov, Johns Hopkins, 2000, now at Univ. of Arizona.
    • Christian A. Duncan, Johns Hopkins, 1999, now at Quinnipiac University.
    • Christopher Wagner, co-advised with Lenore Cowen. Johns Hopkins, 1999.
    • Mark Orletsky, Johns Hopkins, 1996.
    • Kumar Ramaiyer, Johns Hopkins, 1996, now at Informix Software, Inc.
    • Paul Tanenbaum, Johns Hopkins, 1995, now at Army Research Lab.
    • Mujtaba Ghouse, Johns Hopkins, 1993, now at NetApp.


    Goodrich's Home Page. http://www.ics.uci.edu/~goodrich/pubs/ Selected Publications - Michael T. Goodrich

    Selected Publications - Michael T. Goodrich

    • Book chapters
    • Information Security Algorithms
    • Parallel, Distributed, and External-Memory Algorithms
    • Graph and Network Algorithms
    • Data Structures and Algorithms
    • Geometric Algorithms

    Book Chapters

    • Ch-4. M.T. Goodrich and K. Ramaiyer, Geometric data Structures, in J.-R. Sack and J. Urrutia, eds., Handbook of Computational Geometry, 463-489, Elsevier Science, 2000.
    • Ch-5. M.T. Goodrich and R. Tamassia, Simplified Analyses of Randomized Algorithms for Searching, Sorting, and Selection, in S. Rajasekaran, P.M. Pardalos, J.H. Reif, and J.D.P. Rolim, eds., Handbook of Randomization, Kluwer Academic Publishers, 2001, Vol. 1, 23-34.
    • Ch-6/Ch-3. M.T. Goodrich, Parallel Algorithms in Geometry, in J.E. Goodman and J. O'Rourke, eds., Handbook of Discrete and Computational Geometry, Second Edition, Chapman and Hall/CRC Press, 953-967, 2004.
    • Ch-7. C. Duncan and M.T. Goodrich, Approximate Geometric Query Structures, Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 26-1--26-17, 2005.
    • Ch-8. M.T. Goodrich, R. Tamassia, and L. Vismara, Data Structures in JDSL, Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 43-1--43-22, 2005.
    • Ch-9. Y. Cho, L. Bao and M.T. Goodrich, Secure Location-Based Access Control in WLAN Systems, From Problem Toward Solution: Wireless and Sensor Networks Security, Zhen Jiang and Yi Pan, eds., Nova Science Publishers, Inc., Chapter 17, 2007.
    • Ch-11. C.A. Duncan and M.T. Goodrich, Planar Orthogonal and Polyline Drawing Algorithms, Handbook of Graph Drawing and Visualization, Chapman & Hall/CRC Press, Inc., 2013.

    Information Security Algorithms

    • C-80. G. Ateniese, B. de Medeiros, and M.T. Goodrich, TRICERT: A Distributed Certified E-mail Scheme, Network and Distributed Systems Security Symposium, 2001, 47-56.
    • C-83. M.T. Goodrich, R. Tamassia, and A. Schwerin, Implementation of an Authenticated Dictionary with Skip Lists and Commutative Hashing, DARPA Information Survivability Conference & Exposition II (DISCEX II), IEEE Press, 2001, 68-82.
    • C-85. A. Anagnostopoulos, M. T. Goodrich, and R. Tamassia, Persistent Authenticated Dictionaries and Their Applications, Proc. Information Security Conference (ISC 2001), Lecture Notes in Computer Science, vol. 2200, Springer-Verlag, pp. 379-393, 2001.
    • C-86. M. T. Goodrich, and R. Tamassia and J. Hasic, An Efficient Dynamic and Distributed Cryptographic Accumulator, Proc. Information Security Conference (ISC 2002) Lecture Notes in Computer Science, vol. 2433, Springer-Verlag, pp. 372-388, 2002.
    • C-90. M. T. Goodrich, R. Tamassia, N. Triandopoulos and R. Cohen, Efficient Authenticated Data Structures for Graph Connectivity and Geometric Search Problems, Algorithmica, to appear. Earlier version appeared in RSA, Cryptographers' Track, 295-313, Springer, LNCS 2612, 2003.
    • C-91. M. T. Goodrich, M. Shin, R. Tamassia, W. H. Winsborough, Authenticated dictionaries for fresh attribute credentials, Proc. Trust Management Conference, 332-347, Springer, LNCS 2692, 2003.
    • C-94. A. Bagchi, A. Chaudhary, M.T. Goodrich, and S. Xu, Constructing disjoint paths for secure communication. 17th International Symposium on Distributed Computing (DISC), 181-195, 2003.
    • C-98. M. T. Goodrich, J. Z. Sun, and R. Tamassia, Efficient Tree-Based Revocation in Groups of Low-State Devices, CRYPTO 2004, Springer, LNCS 3152, 511-527, 2004.
    • C-100. M.J. Atallah, K.B. Frikken, M.T. Goodrich, and R. Tamassia, Secure Biometric Authentication for Weak Computational Devices, 9th Int. Conf. on Financial Cryptograpy and Data Security, 357-371, 2005.
    • C-101. M.T. Goodrich, Leap-Frog Packet Linking and Diverse Key Distributions for Improved Integrity in Network Broadcasts, IEEE Symposium on Security and Privacy (SSP), 196-207, 2005.
    • C-103. M.J. Atallah, M.T. Goodrich, and R. Tamassia, Indexing Information for Data Forensics, 3rd Applied Cryptography and Network Security Conference (ACNS), Lecture Notes in Computer Science 3531, Springer, 206-221, 2005.
    • C-104. W. Du and M.T. Goodrich, Searching for High-Value Rare Events with Uncheatable Grid Computing, 3rd Applied Cryptography and Network Security Conference (ACNS), Lecture Notes in Computer Science 3531, Springer, 122-137, 2005.
    • C-108. M.T. Goodrich, R. Tamassia, and D. Yao, Accredited DomainKeys: A Service Architecture for Improved Email Validation, Conference on Email and Anti-Spam (CEAS), 2005.
    • C-112. M.T. Goodrich, M. Sirivianos, J. Solis, G. Tsudik, E. Uzun, Loud And Clear: Human-Verifiable Authentication Based on Audio, 26th IEEE Int. Conference on Distributed Computing Systems (ICDCS), 2006.
    • C-113. M.T. Goodrich, R. Tamassia, and D. Yao, Notarized Federated Identity Management for Web Services, 20th IFIP WG Working Conference on Data and Application Secuirity (DBSec), Springer, LNCS 4127, 133-147, 2006.
    • J-61. M.T. Goodrich, Probabalistic Packet Marking for Large-Scale IP Traceback, IEEE/ACM Transactions on Networking, 16(1), 15--24, 2008. Preliminary version (C-89) appeared at 9th ACM Conf. on Computer and Communications Security (CCS), 2002, 117-126.
    • C-115. Y. Cho, L. Bao and M.T. Goodrich, LAAC: A Location-Aware Access Control Protocol, In Proc. of International Workshop on Ubiquitous Access Control (IWUAC), San Jose, CA, July 17, 2006.
    • J-64. M.T. Goodrich, Pipelined Algorithms to Detect Cheating in Long-Term Grid Computations, Theoretical Computer Science, vol. 408, 199-207, 2008.
    • C-131. M.T. Goodrich, The Mastermind Attack on Genomic Data, 30th IEEE Symp. on Security and Privacy, 2009.
    • C-133. M.T. Goodrich, R. Tamassia, and N. Triandopoulos, J.Z. Sun, Reliable Resource Searching in P2P Networks, 5th International ICST Conference on Security and Privacy in Communication Networks (SecureComm), Lecture Notes of ICST, Springer, 2009.
    • C-139. W. Du, M.T. Goodrich, T. Luo, and G. Wang, Bureaucratic Protocols for Secure Two-Party Sorting, Selection, and Permuting, 5th ACM Symposium on Information, Computer and Communications Security, 2010.
    • C-145. A.U. Asuncion and M.T. Goodrich, Turning Privacy Leaks into Floods: Surreptitious Discovery of Social Network Friendships and Other Sensitive Binary Attribute Vectors, Proc. Workshop on Privacy in the Electronic Society (WPES), held in conjunction with the 17th ACM Conference on Computer and Communications Security (CCS), 2010.
    • C-148. D. Eppstein, M.T. Goodrich, R. Tamassia, Privacy-Preserving Data-Oblivious Geometric Algorithms for Geographic Data, Proc. 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2010.
    • C-166. M.T. Goodrich, O. Ohrimenko, M. Mitzenmacher, and R. Tamassia, Practical Oblivious Storage," 2nd ACM Conf. on Data and Application Security and Privacy (CODASPY). 13-24, 2012.

    Parallel, Distributed, and External-Memory Algorithms

    • J-19. M.J. Atallah, M.T. Goodrich, and S.R. Kosaraju. Parallel Algorithms for Evaluating Sequences of Set-Manipulation Operations. Journal of the ACM, 41(6), 1994, 1049-1088.
    • J-24. M.T. Goodrich and S.R. Kosaraju. Sorting on a Parallel Pointer Machine with Applications to Set Expression Evaluation, J. ACM, 43(2), 1996, 331-361.
    • J-26. M.H. Nodine, M.T. Goodrich, and J.S. Vitter, Blocking for External Graph Searching, Algorithmica, 16(2), 1996, 181-214.
    • J-27. R. Cole, M.T. Goodrich, C. O'Dunlaing, A Nearly Optimal Deterministic Parallel Voronoi Diagram Algorithm, Algorithmica, 16, 1996, 569-617.
    • M.T. Goodrich, J.J. Tsay, D.E. Vengroff, J.S. Vitter, External-Memory Computational Geometry , 34th IEEE Symp. on Foundations of Computer Science (FOCS), 1993, 714-723.
    • M.T. Goodrich, Y. Matias, and U. Vishkin, Optimal Parallel Approximation Algorithms for Prefix Sums and Integer Sorting, Proc. 5th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1994, 241-250.
    • Y.J. Chiang, M.T. Goodrich, E.F. Grove, R. Tamassia, D.E. Vengroff, J.S. Vitter, External-Memory Graph Algorithms, 6th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1995.
    • N.M. Amato, M.T. Goodrich, E.A. Ramos. Parallel Algorithms for Higher-Dimensional Convex Hulls, Proc. 35th IEEE Symp. on Foundations of Computer Science (FOCS), 1994, 683-694.
    • N.M. Amato, M.T. Goodrich, E.A. Ramos. Computing Faces in Segment and Simplex Arrangements, Proc. 27th ACM Symp. on Theory of Computing (STOC), 1995, 672-682.
    • J-30. M. Ghouse and M.T. Goodrich. "Fast Randomized Parallel Methods for Planar Convex Hull Construction. Computational Geometry: Theory and Applications, 7, 1997, 219-235.
    • J-32. M.T. Goodrich and E.A. Ramos, Bounded-Independence Derandomization of Geometric Partitioning with Applications to Parallel Fixed-Dimensional Linear Programming, Discrete and Computational Geometry, 18, 1997, 397-420. Preliminary version (C-45) appeared in 7th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1996, 132-141.
    • J-36. G. Barequet, S.S. Bridgeman, C.A. Duncan, M.T. Goodrich, and R. Tamassia, GeomNet: Geometric Computing Over the Internet, IEEE Internet Computing, March/April 1999, 2-10.
    • Preliminary version (C-53) appeared in 13th ACM Symp. on Computational Geometry (SCG), 1997.
    • J-38. M.T. Goodrich, Communication-Efficient Parallel Sorting, SIAM Journal on Computing, 29(2), 1999, 416-432. Preliminary version (C-47) appeared in 28th ACM Symp. on Theory of Computing (STOC), 1996.
    • C-49. M.T. Goodrich, Randomized Fully-Scalable BSP Techniques for Multi-Searching and Convex Hull Construction, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • O-6. T.H. Cormen and M.T. Goodrich, A Bridging Model for Parallel Computation, Communication, and I/O,ACM Computing Surveys, 28A(4), December 1996.
    • C-84. A. Bagchi, A. Chaudhary, R. Carg, M.T. Goodrich, and V. Kumar, Seller-Focused Algorithms for Online Auctioning, 2001 Workshop on Algorithms and Data Structures (WADS), Lecture Notes in Computer Science 2125, Springer-Verlag, 2001, 135-147.
    • C-105. L. Arge, D. Eppstein, and M.T. Goodrich, Skip-Webs: Efficient Distributed Data Structures for Multi-Dimensional Data Sets, 24th ACM Symp. on Principles of Distributed Computing (PODC), 2005.
    • C-111. M.T. Goodrich, M.J. Nelson, and J.Z. Sun, The Rainbow Skip Graph: A Fault-Tolerant Constant-Degree Distributed Data Structure, 16th ACM-SIAM Symposium on Discrete Algorithms (SODA), 384-393, 2006.
    • C-114. M.T. Goodrich and D.S. Hirschberg, Efficient Parallel Algorithms for Dead Sensor Diagnosis and Multiple Access Channels, 18th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), 118-127, 2006.
    • J-56. A. Bagchi, A. Chaudhary, M.T. Goodrich, C. Li, and M. Shmueli-Scheuer, Achieving Communication Efficiency through Push-Pull Partitioning of Semantic Spaces to Disseminate Dynamic Information, IEEE Trans. on Knowledge and Data Engineering, 18(10), 1352-1367, 2006.
    • C-126. L. Arge, M.T. Goodrich, M. Nelson, and N. Sitchinava, Fundamental Parallel Algorithms for Private-Cache Chip Multiprocessors, Proc. 20th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), 2008, 197-206.
    • C-138. L. Arge, M.T. Goodrich, and N. Sitchinava, Parallel External Memory Graph Algorithms, 24th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2010.
    • O-12. M.T. Goodrich, Simulating Parallel Algorithms in the MapReduce Framework with Applications to Parallel Computational Geometry, Second Workshop on Massive Data Algorithmics (MASSIVE), 2010.

    Graph and Network Algorithms

    • J-22. M.T. Goodrich. Planar Separators and Parallel Polygon Triangulation. Journal of Computer & System Sciences, 51(3), 1995, 374-389.
    • J-25. A. Garg, M.T. Goodrich, and R. Tamassia. Planar Upward Tree Drawings with Optimal Area. International Journal of Computational Geometry and Applications, 6(3), 1996, 333-356.
    • C-46. M. Chrobak, M.T. Goodrich, and R. Tamassia, Convex Drawings of Graphs in Two and Three Dimensions, 12th ACM Symp. on Computational Geometry (SCG), 1996.
    • J-39. C.A. Duncan, M.T. Goodrich, and S. Kobourov, Balanced Aspect Ratio Trees and Their Use for Drawing Very Large Graphs, Journal of Graph Algorithms and Applications, 4(3), 2000, 19-46. Preliminary version (C-61) appeared in Proceedings of the Sixth Symposium on Graph Drawing, 1998.
    • J-40. M.T. Goodrich and C.G. Wagner, A framework for drawing planar graphs with curves and polylines, Journal of Algorithms, vol. 37, 399-421, 2000. Preliminary version (C-60) appeared in 6th Int. Symp. on Graph Drawing (GD), LNCS 1547, Springer-Verlag, 1998, 153-166.
    • J-43. C.C. Cheng, C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Drawing Planar Graphs with Circular Arcs, Discrete & Computational Geometry 25: 405-418 (2001). Preliminary version (C-69) appeared in 7th Int. Symp. on Graph Drawing (GD), LNCS 1731, Springer-Verlag, 1999, 117-126..
    • J-47. T. Chan, M.T. Goodrich, S.R. Kosaraju, and R. Tamassia, Optimizing Area and Aspect Ratio in Straight-Line Orthogonal Tree Drawings, Computational Geometry: Theory and Applications, Volume 23, Issue 2 , September 2002, Pages 153-162. Preliminary version (C-48) appeared in Graph Drawing '96, 1996.
    • J-48. C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Planarity-Preserving Clustering and Embedding for Large Planar Graphs, CGTA: Computational Geometry: Theory and Applications 24(2), 95-114, 2003. Preliminary version (C-70) appeared in 7th Int. Symp. on Graph Drawing (GD), Lecture Notes in Computer Science 1731, Springer-Verlag, 1999, 186-196.
    • C-96. F. Brandenberg, D. Eppstein, M.T. Goodrich, S.G. Kobourov, G. Liotta, P. Mutzel, Selected Open Problems in Graph Drawing, 11th Int. Symp. on Graph Drawing (GD), LNCS, Springer-Verlag, 2003.
    • J-50. G. Barequet, M.T. Goodrich, and C. Riley, Drawing Planar Graphs with Large Vertices and Thick Edges, J. of Graph Algorithms and Applications (JGAA), 8(1), 3-20, 2004. Preliminary version (C-93) appeared in 2003 Workshop and Data Structures and Algorithms (WADS), LNCS 2748, 2003, 281-293.
    • J-52. P. Gajer, M.T. Goodrich, and S.G. Kobourov, A Multi-Dimensional Approach to Force-Directed Layouts of Large Graphs, Computational Geometry: Theory and Applications (CGTA), 29(1), 3-18, 2004. Preliminary version (C-79) appeared in Graph Drawing 2000, 211-221.
    • J-55. M. Dickerson, D. Eppstein, M.T. Goodrich, J. Meng, Confluent Drawings: Visualizing Non-planar Diagrams in a Planar Way, J. of Graph Algorithms and Applications (JGAA), 2005. Preliminary version (C-95) appeared in 11th Int. Symp. on Graph Drawing (GD), LNCS 2912, 2003, 1-12.
    • J-58. D. Eppstein, M.T. Goodrich, and J.Y. Meng, Confluent Layered Drawings, Algorithmica, 47(4), 439--452, 2007. Preliminary version (C-99) appeared in 12th Int. Symp. on Graph Drawing (GD), Springer, Lecture Notes in Computer Science 3383, 184-194, 2004.
    • C-109. M.T. Goodrich, G.S. Lueker, and J.Z. Sun, C-Planarity of Extrovert Clustered Graphs, Graph Drawing, 211-222, 2005.
    • C-110. D. Eppstein, M.T. Goodrich, J.Y. Meng, Delta-Confluent Drawings, Graph Drawing, 165-176, 2005.
    • C-116. M.B. Dillencourt, D. Eppstein, and M.T. Goodrich, Choosing Colors for Geometric Graphs via Color Space Embeddings, Graph Drawing (GD), 2006.
    • C-127. D. Eppstein and M.T. Goodrich, Succinct Greedy Graph Drawing in the Hyperbolic Plane, Graph Drawing 2008.
    • C-134. C.A. Duncan, M.T. Goodrich, S.G. Kobourov, Planar Drawings of Higher-Genus Graphs, Proc. 17th Int. Symp. on Graph Drawing (GD), Lecture Notes in Computer Science, Springer, 2009.
    • C-135. D. Eppstein, M.T. Goodrich, L. Trott, Going Off-road: Transversal Complexity in Road Networks, Proc. 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2009.
    • C-136. M.T. Goodrich and Darren Strash, Succinct Greedy Geometric Routing in the Euclidean Plane, 13th Int. Symp. on Algorithms and Computation (ISAAC), Lecture Notes in Computer Science, Springer, 2009.
    • C-140. M.T. Dickerson, M.T. Goodrich, and T.D. Dickerson, Round-Trip Voronoi Diagrams and Doubling Density in Geographic Networks, 7th Int. Symp. on Voronoi Diagrams in Science and Engineering (ISVD), IEEE Press, 2010.
    • C-142. C.A. Duncan, D. Eppstein, M.T. Goodrich, S. Kobourov, and M. Nollenburg, Lombardi Drawings of Graphs, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-143. E. Wolf-Chambers, D. Eppstein, M.T. Goodrich, and M. Loffler, Drawing Graphs in the Plane with a Prescribed Outer Face and Polynomial Area, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-144. C.A. Duncan, D. Eppstein, M.T. Goodrich, S. Kobourov, and M. Nollenburg, Drawing Trees with Perfect Angular Resolution and Polynomial Area, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-146. D. Eppstein, M.T. Goodrich, D. Strash, and L. Trott, Extended Dynamic Subgraph Statistics Using h-Index Parameterized Data Structures, Proc. 4th Annual International Conference on Combinatorial Optimization and Applications (COCOA), 2010.

    Data Structures and Algorithms

    • J-23. M.T. Goodrich, M. Ghouse, and J. Bright, Sweep Methods for Parallel Computational Geometry, Algorithmica, 15(2), 1996, 126-153. Preliminary version (C-17) appeared in 2nd ACM Symp. on Parallel Algorithms and Architectures (SPAA), 1990, 280-289.
    • J-29. M.T. Goodrich and R. Tamassia. Dynamic Ray Shooting and Shortest Paths via Balanced Geodesic Triangulations. J. Algorithms, 23, 1997, 51-73.
    • C-51. M.T. Goodrich, M. Orletsky, and K. Ramaiyer, Methods for Achieving Fast Query Times in Point Location Data Structures, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • J-35. M.T. Goodrich and R. Tamassia. Dynamic Trees and Dynamic Point Location, SIAM J. Comput., 28(2), 1999, 612-636.
    • J-41. C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Balanced Aspect Ratio Trees: Combining the Advantages of k-d Trees and Octrees, Journal of Algorithms 38:303-333, 2001. Preliminary version (C-65) appeared at ACM-SIAM Symp. on Discrete Algorithms (SODA), 1999, 300-309.
    • C-71. M.T. Goodrich and J.G. Kloss II, "Tiered Vectors: Efficient Dynamic Arrays for Rank-Based Sequences," 1999 Workshop on Algorithms and Data Structures (WADS), 1999, 205-216.
    • C-72. M.T. Goodrich, Competitive tree-structured dictionaries, 11th ACM/SIAM Symp. on Discrete Algorithms (SODA), 2000, 705-706.
    • C-77. A.L. Buchsbaum, M.T. Goodrich, J.R. Westbrook, Range searching over tree cross products, 8th European Symp. on Algorithms (ESA), 120-131, 2000.
    • C-78. C.A. Duncan, M.T. Dickerson, and M.T. Goodrich, k-D Trees are Better When Cut on the Longest Side, 8th European Symposium on Algorithms (ESA), LNCS 1879, Springer-Verlag, 2000, 179-190.
    • J-45. R. Tamassia, M.T. Goodrich, L. Vismara, M. Handy, G. Shubina, R. Cohen, B. Hudson, R.S. Baker, N. Gelfand, and U. Brandes, JDSL: The Data Structures Library in Java, Dr. Dobbs Journal, 323, 2001, 21-31.
    • C-107. A. Chaudhary and M.T. Goodrich, Balanced Aspect Ratio Trees Revisited, Workshop on Algorithms and Data Structures (WADS), Lecture Notes in Computer Science 3608, Springer, 2005.
    • J-54. A. Bagchi, A.L. Buchsbaum, and M.T. Goodrich, Biased Skip Lists Algorithmica, 42(1), 31-48, 2005. Preliminary version (C-88) appeared in Algorithms and Computation : 13th International Symposium, ISAAC 2002, 1-13.
    • J-57. D. Eppstein, M.T. Goodrich, and J.Z. Sun, The Skip Quadtree: A Simple Dynamic Data Structure for Multidimensional Data, Int. Journal on Computational Geometry and Applications, 18(1/2), 131--160, 2008. Preliminary version (C-102) appeared in 21st ACM Symp. on Computational Geometry (SCG), 2005.
    • J-60. D. Eppstein, M.T. Goodrich, and D. Hirschberg, Improved Combinatorial Group Testing Algorithms for Real-World Problem Sizes, SIAM Journal on Computing, accepted for publication. Preliminary vesion (C-106) appeared in Workshop on Algorithms and Data Structures (WADS), LNCS 3608, Springer, 2005, 86-98.
    • C-120. D. Eppstein and M.T. Goodrich, Space-Efficient Straggler Identification in Round-Trip Data Streams via Newton's Identities and Invertible Bloom Filters, Proc. Workshop on Algorithms and Data Structures (WADS), LNCS 4619, Springer, pp. 638-649, 2007.
    • C-132. W. Du, D. Eppstein, M.T. Goodrich, and G.S. Lueker, On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem, Proc. Algorithms and Data Structures Symposium (formerly WADS), LNCS, Springer, 2009.
    • J-67. M.T. Goodrich, On the Algorithmic Complexity of the Mastermind Game with Black-Peg Results, Information Processing Letters, 2009.
    • C-137. M.T. Goodrich, Randomized Shellsort: A Simple Oblivious Sorting Algorithm, 20th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010.
    • C-147. M.T. Goodrichand D. Strash, Priority Range Trees, Proc. 21st International Symposium on Algorithms and Computation (ISAAC), 2010.
    • C-149. M.T. Goodrich, Spin-the-bottle Sort and Annealing Sort: Oblivious Sorting via Round-robin Random Comparisons, 8th annual Workshop on Analytic Algorithmics and Combinatorics (ANALCO), in conjunction with the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2011.

    Geometric Algorithms

    • J-12. M.T. Goodrich. A Polygonal Approach to Hidden-Line and Hidden-Surface Elimination. Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, 54(1), 1992, 1-12.
    • J-17. M.T. Goodrich, M.J. Atallah, and M. Overmars. Output-Sensitive Methods for Rectilinear Hidden Surface Removal. Information and Computation, 107(1), 1993, 1-24.
    • J-20. M.T. Goodrich. Efficient Piecewise-Linear Function Approximation Using the Uniform Metric, Discrete and Computational Geometry, 14, 1995, 445-462. Preliminary version appeared in Proc. 10th ACM Symp. on Computational Geometry (SCG), 1994, 322-331.
    • J-21. H. Bronnimann and M.T. Goodrich. Almost Optimal Set Covers in Finite VC-Dimension, Discrete and Computational Geometry, 14, 1995, 463.479. Preliminary version (C-35) appeared in Proc. 10th ACM Symp. on Computational Geometry (SCG), 1994, 293-302.
    • J-28. G. Das and M.T. Goodrich, On the Complexity of Optimization Problems for 3-Dimensional Convex Polyhedra and Decision Trees, Computational Geometry: Theory and Applications, 8, 1997, 123-137. Preliminary version appeared in 1995 Workshop on Algorithms and Data Structures (WADS).
    • J-31. L.P. Chew, M.T. Goodrich, D.P. Huttenlocher, K. Kedem, J.M. Kleinberg, and D. Kravets. Geometric Pattern Matching under Euclidean Motion, Computational Geometry: Theory and Applications, 7, 1997, 113-124. Preliminary version appeared in Proc. 5th Canadian Conference on Computational Geometry (CCCG), 1993, 151-156.
    • C-50. C.A. Duncan, M.T. Goodrich, and E.A. Ramos, Efficient Approximation and Optimization Algorithms for Computational Metrology, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • C-52. M.T. Goodrich, L.J. Guibas, J. Hershberger, P.J. Tanenbaum, Snap Rounding Line Segments Efficiently in Two and Three Dimensions, Proc. 13th ACM Symp. on Computational Geometry (SCG), 1997.
    • J-33. M.T. Goodrich. An Improved Ray Shooting Method for Constructive Solid Geometry Models via Tree Contraction. International Journal of Computational Geometry and Applications, 8(1), 1998, 1-23.
    • J-34. G. Barequet, A.J. Briggs, M.T. Dickerson, and M.T. Goodrich, Offset-polygon annulus placement problems, Computational Geometry: Theory and Applications, vol. 11 (3-4), pp. 125-141, 1998. Preliminary version (C-55) appeared in Proc. 5th Workshop on Algorithms and Data Structures (WADS), LNCS 1272, 378-391, 1997.
    • C-54. G. Barequet, A. Briggs, M. Dickerson, C. Dima, and M.T. Goodrich, Animating the Polygon-Offset Distance Function, 13th ACM Symp. on Computational Geometry (SCG), 1997, 479-480, and the Video Review for the 13th ACM Symp. on Computational Geometry.
    • J-37. M.T. Goodrich, J.S.B. Mitchell, and M.W. Orletsky, "Approximate Geometric Pattern Matching under Rigid Motion," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 21(4), 1999, 371-379. A preliminary version (C-38) appeared in ACM Symp. on Computational Geometry, 1994, 103-112.
    • C-68. G. Barequet, C. Duncan, M.T. Goodrich, S. Kumar, M. Pop, Efficient Perspective-Accurate Silhouette Computation, 15th ACM Symp. on Computational Geometry (SCG), 1999, 417-418, and the Video Review for the 15th ACM Symp. on Computational Geometry (SCG).
    • C-73. N.M. Amato, M.T. Goodrich, E.A. Ramos, Computing the Arrangement of Curve Segments: Divide-and-Conquer Algorithms via Sampling, 11th ACM/SIAM Symp. on Discrete Algorithms (SODA), 2000, 705-706.
    • J-42. G. Barequet, M.T. Dickerson, and M.T. Goodrich, Voronoi diagrams for polygon-offset distance functions, Discrete and Computational Geometry, vol. 25 (2), pp. 271-291, 2001. Preliminary version (C-56) appeared at Workshop on Algorithms and Data Structures (WADS), 1997, LNCS, 200-209.
    • C-82. M. Pop, G. Barequet, C.A. Duncan, M.T. Goodrich, W. Hwang, and S. Kumar, Efficient Perspective-Accurate Silhouette Computation and Applications, 17th ACM Symp. on Computational Geometry (SCG), 2001, 60-68.
    • J-44. N.M. Amato, M.T. Goodrich, and E.A. Ramos, A Randomized Algorithm for Triangulating a Simple Polygon in Linear Time, Discrete and Computational Geometry, 26(2), 2001, 245-265. Preliminary version (C-76) appeared in 16th ACM Symp. on Computational Geometry (SCG), 2000, 201-212.
    • J-46. G. Barequet, D.Z. Chen, O. Daescu, M.T. Goodrich, and J. Snoeyink, Efficiently approximating polygonal paths in three and higher dimensions, Algorithmica, vol. 33 (2), pp. 150-167, 2002. Preliminary version (C-59) appeared in ACM Symp. on Computational Geometry (SoCG), 317-326, 1998.
    • J-49. A.L. Buchsbaum and M.T. Goodrich, Three-Dimensional Layers of Maxima, Algorithmica, 39, 275-286, 2004. Preliminary version (C-87) appeared in 10th Annual European Symposium (ESA), 2002, LNCS 2461.
    • J-51. G. Barequet, M.T. Goodrich, A. Levi-Steiner, and D. Steiner, Contour Interpolation by Straight Skeletons, Graphical Models (GM), 66(4), 245-260, 2004. Preliminary version (C-92) appeared in 14th ACM-SIAM Symp. on Discrete Algorithms (SODA), 119-127, 2003.
    • J-53. G. Barequet, P. Bose, M.T. Dickerson, and M.T. Goodrich, Optimizing a Constrained Convex Polygonal Annulus, J. of Discrete Algorithms (JDA), 3(1), 1-26, 2005.
    • J-59. A. Bagchi, A. Chaudhary, D. Eppstein, and M.T. Goodrich, Deterministic sampling and range counting in geometric data streams, ACM Transactions on Algorithms, accepted for publication. Preliminary version (C-97) appeared in 12th ACM Annual Symposium on Computational Geometry (SoCG), 2004.
    • C-117. D. Eppstein, M.T. Goodrich, and Sitchinava, Guard Placement for Wireless Localization, Proc. 23rd ACM Symposium on Computational Geometry (SCG), 27-36, 2007.
    • C-119. M.J. Atallah, M. Blanton, M.T. Goodrich, and S. Polu, Discrepancy-Sensitive Dynamic Fractional Cascading, Dominated Maxima Searching, and 2-d Nearest Neighbors in Any Minkowski Metric, Proc. Workshop on Algorithms and Data Structures (WADS), LNCS 4619, Springer, pp. 114-126, 2007.
    • C-123. D. Eppstein, M.T. Goodrich, E. Kim, and R. Tamstorf, "Approximate Topological Matching of Quadrilateral Meshes," Proc. IEEE Int. Conf. on Shape Modeling and Applications (SMI), 2008, 83--92.
    • C-124. G. Bareqet, D. Eppstein, M.T. Goodrich, and A. Waxman, "Straight Skeletons of Three-Dimensional Polyhedra," Proc. 16th European Symposium on Algorithms (ESA), 2008.
    • J-65. D. Eppstein, M.T. Goodrich, E. Kim, and R. Tamstorf, "Motorcycle Graphs: Canonical Quad Mesh Partitioning," Proc. 6th European Symposium on Geometry Processing (SGP), 2008.
    • C-128. D. Eppstein and M.T. Goodrich, Studying (Non-Planar) Road Networks Through an Algorithmic Lens, Proc. 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2008, 125-134. Best paper award.
    • C-130. D. Eppstein, M.T. Goodrich, and D. Strash, Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Crossings, SODA 2009.
    • C-141. M.T. Dickerson, D. Eppstein, and M.T. Goodrich, Cloning Voronoi Diagrams via Retroactive Data Structures, 18th European Symposium on Algorithms (ESA), 2010.

    Goodrich's Home Page. http://www.ics.uci.edu/~goodrich/projects/ Michael T. Goodrich - Projects

    Michael T. Goodrich
    Projects

    Prof. Goodrich's research is directed at the design of high performance algorithms and data structures for solving large-scale problems motivated from information assurance and security, the Internet, information visualization, and geometric computing.

    • Algorithms for Graphs on Surfaces
    • The Optiputer
    • Information Security Infrastructure
    • Cyber-Security Algorithms
    • Privacy Management, Measurement, and Visualization in Distributed Environments

    • Book sites:
      • Data Structures and Algorithms
      • Data Structures and Algorithms in Java
      • Data Structures and Algorithms in C++
      • Algorithm Design



    Michael T. Goodrich
    Department of Computer Science
    Bren School of Information and Computer Sciences
    University of California, Irvine
    Irvine, CA 92697-3435 USA
    http://www.ics.uci.edu/~goodrich/teach/index.html Michael Goodrich Teaching

    Michael Goodrich - Teaching

    • Position: Professor, Department of Computer Science at the University of California, Irvine
    • Office: DBH 4091

      Photo by Paul Kennedy
    • Office Hours by appt.

    Courses

    • Uni 3--Cyber-Puzzlers (Fall 2013)
    • ICS 8--Practical Computer Security (Fall 2010)
    • CSE/ICS 23--Fundamental Data Structures (Fall, 2008)
    • ICS 160E (EECS 114)--Engineering Data Structures and Algorithms (Spring, 2005)
    • CS 161--Design and Analysis of Algorithms (Winter, 2014)
    • CS 162--Formal Languages and Automata Theory (Fall, 2015)
    • CS 163--Graph Algorithms (Winter, 2003)
    • CS 164--Computational Geometry (Winter, 2016)
    • CS 167--Introduction to Applied Cryptography (Winter, 2008)
    • ICS 247--Computer Security Algorithms (Winter, 2004)
    • CS 263--Analysis of Algorithms (Fall, 2009)
    • CS 266--Computational Geometry (Winter, 2016)
    • CS 269S--Theory Seminar (ongoing)
    • ICS 280--Computer Security Algorithms (Spring 2002)
    • CS 295--Seminar on Algorithms for Cyber-Fraud Prevention and Detection (Spring 2009)

    General Policies

    • Students with Disabilities. Any student who feels that he or she may need an accommodation based on the impact of a disability should contact Dr. Goodrich to discuss his or her specific needs. Also contact the UCI Disability Services Center, at (949)824-7494, as soon as possible to better ensure that such accommodations are implemented in a timely fashion.
    • Lectures. Copyright © 2016 Michael T. Goodrich, as to all course lectures, with all rights reserved. Students are prohibited from recording the audio or video content of lectures and from selling (or being paid for taking) notes during his courses to or by any person or commercial firm without the express written permission of Dr. Goodrich.
    • Cheating. Group work on homeworks is permitted in courses only if it is specifically allowed. Moreover, even if group work is allowed, each student must list his or her collaborators in writing for each assignment. If a student turns in a solution without listing the others who helped produce this solution, this act will be considered cheating (for it is plagarism). Exam performance must be 100% individual effort; no collaboration is allowed on exams. Any collaboration or copying on exams will be considered cheating. In addition to the procedures of the ICS Cheating Policy, any student caught cheating on exams will be given a failing grade in the class.


    Michael T. Goodrich
    Department of Computer Science
    Donald Bren Hall 4091
    University of California, Irvine
    Irvine, CA 92697-3435 USA
    http://ccsw.ics.uci.edu/15/ CCSW 2015
    CCSW
    2015

    The 7th ACM Cloud Computing
    Security Workshop

    October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

    in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

    • Home
    • Submission Info
    • Registration
    • Organizers
    • Program
    • Speakers
    • Past editions

    Overview

    The CCSW workshop brings together researchers and practitioners in all security and privacy aspects of cloud-centric and outsourced computing, including:

    • practical cryptographic protocols for cloud security
    • outsourced privacy-preserving computation
    • secure cloud resource virtualization mechanisms
    • secure data management outsourcing (e.g., database as a service)
    • practical privacy and integrity mechanisms for outsourcing
    • privacy-enhancing technologies for the cloud
    • foundations of cloud-centric threat models
    • secure computation outsourcing
    • remote attestation mechanisms in clouds
    • sandboxing and VM-based enforcements
    • trust and policy management in clouds
    • secure identity management mechanisms
    • new cloud-aware web service security paradigms and mechanisms
    • cloud-centric regulatory compliance issues and mechanisms
    • business and security risk models and clouds
    • cost and usability models and their interaction with security in clouds
    • scalability of security in global-size clouds
    • trusted computing technology and clouds
    • binary analysis of software for remote attestation and cloud protection
    • network security (DOS, IDS etc.) mechanisms for cloud contexts
    • security for emerging cloud programming models
    • energy/cost/efficiency of security in clouds
    • security for software defined networking

    We would like to especially encourage novel paradigms and controversial ideas that are not on the above list. The workshop is to act as a fertile ground for creative debate and interaction in security-sensitive areas of computing impacted by clouds.

    Impact

    CCSW has had a significant impact in our research community. As of April 2015, in the Google Scholar Metrics entry for CCS (which encompasses CCSW), 4 of the top 20 cited papers of the past five years come from CCSW. One way to look at it is that you're as likely or perhaps more likely to have a top-20 paper publishing in CCSW than in CCS! (thanks to Ari Juels for noticing this)

    Organizers

    Program Chairs:

    • Cristina Nita-Rotaru, Purdue University
    • Florian Kerschbaum, SAP

    General Chair:

    • Indrajit Ray, Colorado State University

    Steering Chair:

    • Gene Tsudik, University of California, Irvine

    Sponsors

    http://www.ics.uci.edu/~goodrich/presenting.html Some Tips for Giving Good Research Talks

    Some Tips for Giving Good Research Talks

    Organization

    It is important in a presentation to have a good organization. You don't need to explicitly present your outline to your audience (as this takes up precious time), but you should keep your outline in your mind as you prepare and present your talk. The following list gives a generic outline for a technical research talk:

    1. Introduction. Motivate the problem being presented. Describe the essential issues and why this problem is important.
    2. Previous work. Describe related previous work and how it relates to this work being presented. Refer to previous papers using the last-name and year.
    3. New contributions. Concisely present the main, new results that this research contributed.
    4. Details. Go into a reasonable amount of detail describing the new work, how it functions, why it is correct, and why it is better than the previous, including theoretical and experimental analysis, as appropriate. Rule-of-thumb: if a proof needs to go for more than 1-2 slides, then this proof needs to be broken down into separate lemmas.
    5. Conclusion. Wrap up by reminding the audience what was contributed in this work. Also, include possible directions for future work.

    General Recommendations

    One can gain audience interest and comprehension by using the following techniques:

    1. Try to include a picture, figure, or diagram on every slide.
    2. Try to anticipate what questions your audience will have and be ready with bullet points or slides to address them.
    3. Have a title at the top of every slide, which names the topic being discussed
    4. Don't be afraid to use clip art and cartoons where appropriate, but don't over do it here.

    External Links

    • How to give a good research talk and the slides that went with the lecture
    • How to Do Research
    • Some tips for preparing a research presentation


    Michael T. Goodrich http://www.ics.uci.edu/~goodrich/erdos.html Michael T. Goodrich - Erdős Number

    Michael Goodrich's Erdős Number.

    A person's Erdős number is the degrees of separation from that person to the famous mathematician Paul Erdős through research publications. That is, the distance, in number of collaborations, from that person to Erdős. In fact, we can define a collaboration graph, where we create a vertex for each person and link two people with an edge if they have collaborated on a published research paper.

    Michael Goodrich's Erdős number is 3, which is realized in the following (disjoint) ways in the collaboration graph:

    1. Erdős->Avis->Snoeyink->Goodrich
    2. Erdős->Pach->Bronnimann->Goodrich
    3. Erdős->Pollack->Agarwal->Goodrich
    4. Erdős->Alon->Vishkin->Goodrich
    5. Erdős->Aronov->Kosaraju->Goodrich
    6. Erdős->Wagstaff->Atallah->Goodrich
    7. Erdős->Silverman->Mount->Goodrich
    8. Erdős->Fraenkel->Scheinerman->Goodrich
    9. Erdős->Odlyzko->Guibas->Goodrich
    10. Erdős->Yao->Eppstein->Goodrich
    11. Erdős->Fishburn->Tanenbaum->Goodrich
    For example, Goodrich coauthored a paper with Jack Snoeyink, who coauthored a paper with David Avis, who coauthored a paper with Paul Erdős.



    Michael T. Goodrich
    Department of Computer Science
    Donald Bren School of Information and Computer Sciences
    University of California, Irvine
    Irvine, CA 92697-3435 USA
    http://www.ics.uci.edu/~goodrich/pubs/index.html Selected Publications - Michael T. Goodrich

    Selected Publications - Michael T. Goodrich

    • Book chapters
    • Information Security Algorithms
    • Parallel, Distributed, and External-Memory Algorithms
    • Graph and Network Algorithms
    • Data Structures and Algorithms
    • Geometric Algorithms

    Book Chapters

    • Ch-4. M.T. Goodrich and K. Ramaiyer, Geometric data Structures, in J.-R. Sack and J. Urrutia, eds., Handbook of Computational Geometry, 463-489, Elsevier Science, 2000.
    • Ch-5. M.T. Goodrich and R. Tamassia, Simplified Analyses of Randomized Algorithms for Searching, Sorting, and Selection, in S. Rajasekaran, P.M. Pardalos, J.H. Reif, and J.D.P. Rolim, eds., Handbook of Randomization, Kluwer Academic Publishers, 2001, Vol. 1, 23-34.
    • Ch-6/Ch-3. M.T. Goodrich, Parallel Algorithms in Geometry, in J.E. Goodman and J. O'Rourke, eds., Handbook of Discrete and Computational Geometry, Second Edition, Chapman and Hall/CRC Press, 953-967, 2004.
    • Ch-7. C. Duncan and M.T. Goodrich, Approximate Geometric Query Structures, Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 26-1--26-17, 2005.
    • Ch-8. M.T. Goodrich, R. Tamassia, and L. Vismara, Data Structures in JDSL, Handbook of Data Structures and Applications, Chapman and Hall/CRC Press, 43-1--43-22, 2005.
    • Ch-9. Y. Cho, L. Bao and M.T. Goodrich, Secure Location-Based Access Control in WLAN Systems, From Problem Toward Solution: Wireless and Sensor Networks Security, Zhen Jiang and Yi Pan, eds., Nova Science Publishers, Inc., Chapter 17, 2007.
    • Ch-11. C.A. Duncan and M.T. Goodrich, Planar Orthogonal and Polyline Drawing Algorithms, Handbook of Graph Drawing and Visualization, Chapman & Hall/CRC Press, Inc., 2013.

    Information Security Algorithms

    • C-80. G. Ateniese, B. de Medeiros, and M.T. Goodrich, TRICERT: A Distributed Certified E-mail Scheme, Network and Distributed Systems Security Symposium, 2001, 47-56.
    • C-83. M.T. Goodrich, R. Tamassia, and A. Schwerin, Implementation of an Authenticated Dictionary with Skip Lists and Commutative Hashing, DARPA Information Survivability Conference & Exposition II (DISCEX II), IEEE Press, 2001, 68-82.
    • C-85. A. Anagnostopoulos, M. T. Goodrich, and R. Tamassia, Persistent Authenticated Dictionaries and Their Applications, Proc. Information Security Conference (ISC 2001), Lecture Notes in Computer Science, vol. 2200, Springer-Verlag, pp. 379-393, 2001.
    • C-86. M. T. Goodrich, and R. Tamassia and J. Hasic, An Efficient Dynamic and Distributed Cryptographic Accumulator, Proc. Information Security Conference (ISC 2002) Lecture Notes in Computer Science, vol. 2433, Springer-Verlag, pp. 372-388, 2002.
    • C-90. M. T. Goodrich, R. Tamassia, N. Triandopoulos and R. Cohen, Efficient Authenticated Data Structures for Graph Connectivity and Geometric Search Problems, Algorithmica, to appear. Earlier version appeared in RSA, Cryptographers' Track, 295-313, Springer, LNCS 2612, 2003.
    • C-91. M. T. Goodrich, M. Shin, R. Tamassia, W. H. Winsborough, Authenticated dictionaries for fresh attribute credentials, Proc. Trust Management Conference, 332-347, Springer, LNCS 2692, 2003.
    • C-94. A. Bagchi, A. Chaudhary, M.T. Goodrich, and S. Xu, Constructing disjoint paths for secure communication. 17th International Symposium on Distributed Computing (DISC), 181-195, 2003.
    • C-98. M. T. Goodrich, J. Z. Sun, and R. Tamassia, Efficient Tree-Based Revocation in Groups of Low-State Devices, CRYPTO 2004, Springer, LNCS 3152, 511-527, 2004.
    • C-100. M.J. Atallah, K.B. Frikken, M.T. Goodrich, and R. Tamassia, Secure Biometric Authentication for Weak Computational Devices, 9th Int. Conf. on Financial Cryptograpy and Data Security, 357-371, 2005.
    • C-101. M.T. Goodrich, Leap-Frog Packet Linking and Diverse Key Distributions for Improved Integrity in Network Broadcasts, IEEE Symposium on Security and Privacy (SSP), 196-207, 2005.
    • C-103. M.J. Atallah, M.T. Goodrich, and R. Tamassia, Indexing Information for Data Forensics, 3rd Applied Cryptography and Network Security Conference (ACNS), Lecture Notes in Computer Science 3531, Springer, 206-221, 2005.
    • C-104. W. Du and M.T. Goodrich, Searching for High-Value Rare Events with Uncheatable Grid Computing, 3rd Applied Cryptography and Network Security Conference (ACNS), Lecture Notes in Computer Science 3531, Springer, 122-137, 2005.
    • C-108. M.T. Goodrich, R. Tamassia, and D. Yao, Accredited DomainKeys: A Service Architecture for Improved Email Validation, Conference on Email and Anti-Spam (CEAS), 2005.
    • C-112. M.T. Goodrich, M. Sirivianos, J. Solis, G. Tsudik, E. Uzun, Loud And Clear: Human-Verifiable Authentication Based on Audio, 26th IEEE Int. Conference on Distributed Computing Systems (ICDCS), 2006.
    • C-113. M.T. Goodrich, R. Tamassia, and D. Yao, Notarized Federated Identity Management for Web Services, 20th IFIP WG Working Conference on Data and Application Secuirity (DBSec), Springer, LNCS 4127, 133-147, 2006.
    • J-61. M.T. Goodrich, Probabalistic Packet Marking for Large-Scale IP Traceback, IEEE/ACM Transactions on Networking, 16(1), 15--24, 2008. Preliminary version (C-89) appeared at 9th ACM Conf. on Computer and Communications Security (CCS), 2002, 117-126.
    • C-115. Y. Cho, L. Bao and M.T. Goodrich, LAAC: A Location-Aware Access Control Protocol, In Proc. of International Workshop on Ubiquitous Access Control (IWUAC), San Jose, CA, July 17, 2006.
    • J-64. M.T. Goodrich, Pipelined Algorithms to Detect Cheating in Long-Term Grid Computations, Theoretical Computer Science, vol. 408, 199-207, 2008.
    • C-131. M.T. Goodrich, The Mastermind Attack on Genomic Data, 30th IEEE Symp. on Security and Privacy, 2009.
    • C-133. M.T. Goodrich, R. Tamassia, and N. Triandopoulos, J.Z. Sun, Reliable Resource Searching in P2P Networks, 5th International ICST Conference on Security and Privacy in Communication Networks (SecureComm), Lecture Notes of ICST, Springer, 2009.
    • C-139. W. Du, M.T. Goodrich, T. Luo, and G. Wang, Bureaucratic Protocols for Secure Two-Party Sorting, Selection, and Permuting, 5th ACM Symposium on Information, Computer and Communications Security, 2010.
    • C-145. A.U. Asuncion and M.T. Goodrich, Turning Privacy Leaks into Floods: Surreptitious Discovery of Social Network Friendships and Other Sensitive Binary Attribute Vectors, Proc. Workshop on Privacy in the Electronic Society (WPES), held in conjunction with the 17th ACM Conference on Computer and Communications Security (CCS), 2010.
    • C-148. D. Eppstein, M.T. Goodrich, R. Tamassia, Privacy-Preserving Data-Oblivious Geometric Algorithms for Geographic Data, Proc. 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2010.
    • C-166. M.T. Goodrich, O. Ohrimenko, M. Mitzenmacher, and R. Tamassia, Practical Oblivious Storage," 2nd ACM Conf. on Data and Application Security and Privacy (CODASPY). 13-24, 2012.

    Parallel, Distributed, and External-Memory Algorithms

    • J-19. M.J. Atallah, M.T. Goodrich, and S.R. Kosaraju. Parallel Algorithms for Evaluating Sequences of Set-Manipulation Operations. Journal of the ACM, 41(6), 1994, 1049-1088.
    • J-24. M.T. Goodrich and S.R. Kosaraju. Sorting on a Parallel Pointer Machine with Applications to Set Expression Evaluation, J. ACM, 43(2), 1996, 331-361.
    • J-26. M.H. Nodine, M.T. Goodrich, and J.S. Vitter, Blocking for External Graph Searching, Algorithmica, 16(2), 1996, 181-214.
    • J-27. R. Cole, M.T. Goodrich, C. O'Dunlaing, A Nearly Optimal Deterministic Parallel Voronoi Diagram Algorithm, Algorithmica, 16, 1996, 569-617.
    • M.T. Goodrich, J.J. Tsay, D.E. Vengroff, J.S. Vitter, External-Memory Computational Geometry , 34th IEEE Symp. on Foundations of Computer Science (FOCS), 1993, 714-723.
    • M.T. Goodrich, Y. Matias, and U. Vishkin, Optimal Parallel Approximation Algorithms for Prefix Sums and Integer Sorting, Proc. 5th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1994, 241-250.
    • Y.J. Chiang, M.T. Goodrich, E.F. Grove, R. Tamassia, D.E. Vengroff, J.S. Vitter, External-Memory Graph Algorithms, 6th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1995.
    • N.M. Amato, M.T. Goodrich, E.A. Ramos. Parallel Algorithms for Higher-Dimensional Convex Hulls, Proc. 35th IEEE Symp. on Foundations of Computer Science (FOCS), 1994, 683-694.
    • N.M. Amato, M.T. Goodrich, E.A. Ramos. Computing Faces in Segment and Simplex Arrangements, Proc. 27th ACM Symp. on Theory of Computing (STOC), 1995, 672-682.
    • J-30. M. Ghouse and M.T. Goodrich. "Fast Randomized Parallel Methods for Planar Convex Hull Construction. Computational Geometry: Theory and Applications, 7, 1997, 219-235.
    • J-32. M.T. Goodrich and E.A. Ramos, Bounded-Independence Derandomization of Geometric Partitioning with Applications to Parallel Fixed-Dimensional Linear Programming, Discrete and Computational Geometry, 18, 1997, 397-420. Preliminary version (C-45) appeared in 7th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1996, 132-141.
    • J-36. G. Barequet, S.S. Bridgeman, C.A. Duncan, M.T. Goodrich, and R. Tamassia, GeomNet: Geometric Computing Over the Internet, IEEE Internet Computing, March/April 1999, 2-10.
    • Preliminary version (C-53) appeared in 13th ACM Symp. on Computational Geometry (SCG), 1997.
    • J-38. M.T. Goodrich, Communication-Efficient Parallel Sorting, SIAM Journal on Computing, 29(2), 1999, 416-432. Preliminary version (C-47) appeared in 28th ACM Symp. on Theory of Computing (STOC), 1996.
    • C-49. M.T. Goodrich, Randomized Fully-Scalable BSP Techniques for Multi-Searching and Convex Hull Construction, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • O-6. T.H. Cormen and M.T. Goodrich, A Bridging Model for Parallel Computation, Communication, and I/O,ACM Computing Surveys, 28A(4), December 1996.
    • C-84. A. Bagchi, A. Chaudhary, R. Carg, M.T. Goodrich, and V. Kumar, Seller-Focused Algorithms for Online Auctioning, 2001 Workshop on Algorithms and Data Structures (WADS), Lecture Notes in Computer Science 2125, Springer-Verlag, 2001, 135-147.
    • C-105. L. Arge, D. Eppstein, and M.T. Goodrich, Skip-Webs: Efficient Distributed Data Structures for Multi-Dimensional Data Sets, 24th ACM Symp. on Principles of Distributed Computing (PODC), 2005.
    • C-111. M.T. Goodrich, M.J. Nelson, and J.Z. Sun, The Rainbow Skip Graph: A Fault-Tolerant Constant-Degree Distributed Data Structure, 16th ACM-SIAM Symposium on Discrete Algorithms (SODA), 384-393, 2006.
    • C-114. M.T. Goodrich and D.S. Hirschberg, Efficient Parallel Algorithms for Dead Sensor Diagnosis and Multiple Access Channels, 18th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), 118-127, 2006.
    • J-56. A. Bagchi, A. Chaudhary, M.T. Goodrich, C. Li, and M. Shmueli-Scheuer, Achieving Communication Efficiency through Push-Pull Partitioning of Semantic Spaces to Disseminate Dynamic Information, IEEE Trans. on Knowledge and Data Engineering, 18(10), 1352-1367, 2006.
    • C-126. L. Arge, M.T. Goodrich, M. Nelson, and N. Sitchinava, Fundamental Parallel Algorithms for Private-Cache Chip Multiprocessors, Proc. 20th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA), 2008, 197-206.
    • C-138. L. Arge, M.T. Goodrich, and N. Sitchinava, Parallel External Memory Graph Algorithms, 24th IEEE International Parallel & Distributed Processing Symposium (IPDPS), 2010.
    • O-12. M.T. Goodrich, Simulating Parallel Algorithms in the MapReduce Framework with Applications to Parallel Computational Geometry, Second Workshop on Massive Data Algorithmics (MASSIVE), 2010.

    Graph and Network Algorithms

    • J-22. M.T. Goodrich. Planar Separators and Parallel Polygon Triangulation. Journal of Computer & System Sciences, 51(3), 1995, 374-389.
    • J-25. A. Garg, M.T. Goodrich, and R. Tamassia. Planar Upward Tree Drawings with Optimal Area. International Journal of Computational Geometry and Applications, 6(3), 1996, 333-356.
    • C-46. M. Chrobak, M.T. Goodrich, and R. Tamassia, Convex Drawings of Graphs in Two and Three Dimensions, 12th ACM Symp. on Computational Geometry (SCG), 1996.
    • J-39. C.A. Duncan, M.T. Goodrich, and S. Kobourov, Balanced Aspect Ratio Trees and Their Use for Drawing Very Large Graphs, Journal of Graph Algorithms and Applications, 4(3), 2000, 19-46. Preliminary version (C-61) appeared in Proceedings of the Sixth Symposium on Graph Drawing, 1998.
    • J-40. M.T. Goodrich and C.G. Wagner, A framework for drawing planar graphs with curves and polylines, Journal of Algorithms, vol. 37, 399-421, 2000. Preliminary version (C-60) appeared in 6th Int. Symp. on Graph Drawing (GD), LNCS 1547, Springer-Verlag, 1998, 153-166.
    • J-43. C.C. Cheng, C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Drawing Planar Graphs with Circular Arcs, Discrete & Computational Geometry 25: 405-418 (2001). Preliminary version (C-69) appeared in 7th Int. Symp. on Graph Drawing (GD), LNCS 1731, Springer-Verlag, 1999, 117-126..
    • J-47. T. Chan, M.T. Goodrich, S.R. Kosaraju, and R. Tamassia, Optimizing Area and Aspect Ratio in Straight-Line Orthogonal Tree Drawings, Computational Geometry: Theory and Applications, Volume 23, Issue 2 , September 2002, Pages 153-162. Preliminary version (C-48) appeared in Graph Drawing '96, 1996.
    • J-48. C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Planarity-Preserving Clustering and Embedding for Large Planar Graphs, CGTA: Computational Geometry: Theory and Applications 24(2), 95-114, 2003. Preliminary version (C-70) appeared in 7th Int. Symp. on Graph Drawing (GD), Lecture Notes in Computer Science 1731, Springer-Verlag, 1999, 186-196.
    • C-96. F. Brandenberg, D. Eppstein, M.T. Goodrich, S.G. Kobourov, G. Liotta, P. Mutzel, Selected Open Problems in Graph Drawing, 11th Int. Symp. on Graph Drawing (GD), LNCS, Springer-Verlag, 2003.
    • J-50. G. Barequet, M.T. Goodrich, and C. Riley, Drawing Planar Graphs with Large Vertices and Thick Edges, J. of Graph Algorithms and Applications (JGAA), 8(1), 3-20, 2004. Preliminary version (C-93) appeared in 2003 Workshop and Data Structures and Algorithms (WADS), LNCS 2748, 2003, 281-293.
    • J-52. P. Gajer, M.T. Goodrich, and S.G. Kobourov, A Multi-Dimensional Approach to Force-Directed Layouts of Large Graphs, Computational Geometry: Theory and Applications (CGTA), 29(1), 3-18, 2004. Preliminary version (C-79) appeared in Graph Drawing 2000, 211-221.
    • J-55. M. Dickerson, D. Eppstein, M.T. Goodrich, J. Meng, Confluent Drawings: Visualizing Non-planar Diagrams in a Planar Way, J. of Graph Algorithms and Applications (JGAA), 2005. Preliminary version (C-95) appeared in 11th Int. Symp. on Graph Drawing (GD), LNCS 2912, 2003, 1-12.
    • J-58. D. Eppstein, M.T. Goodrich, and J.Y. Meng, Confluent Layered Drawings, Algorithmica, 47(4), 439--452, 2007. Preliminary version (C-99) appeared in 12th Int. Symp. on Graph Drawing (GD), Springer, Lecture Notes in Computer Science 3383, 184-194, 2004.
    • C-109. M.T. Goodrich, G.S. Lueker, and J.Z. Sun, C-Planarity of Extrovert Clustered Graphs, Graph Drawing, 211-222, 2005.
    • C-110. D. Eppstein, M.T. Goodrich, J.Y. Meng, Delta-Confluent Drawings, Graph Drawing, 165-176, 2005.
    • C-116. M.B. Dillencourt, D. Eppstein, and M.T. Goodrich, Choosing Colors for Geometric Graphs via Color Space Embeddings, Graph Drawing (GD), 2006.
    • C-127. D. Eppstein and M.T. Goodrich, Succinct Greedy Graph Drawing in the Hyperbolic Plane, Graph Drawing 2008.
    • C-134. C.A. Duncan, M.T. Goodrich, S.G. Kobourov, Planar Drawings of Higher-Genus Graphs, Proc. 17th Int. Symp. on Graph Drawing (GD), Lecture Notes in Computer Science, Springer, 2009.
    • C-135. D. Eppstein, M.T. Goodrich, L. Trott, Going Off-road: Transversal Complexity in Road Networks, Proc. 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2009.
    • C-136. M.T. Goodrich and Darren Strash, Succinct Greedy Geometric Routing in the Euclidean Plane, 13th Int. Symp. on Algorithms and Computation (ISAAC), Lecture Notes in Computer Science, Springer, 2009.
    • C-140. M.T. Dickerson, M.T. Goodrich, and T.D. Dickerson, Round-Trip Voronoi Diagrams and Doubling Density in Geographic Networks, 7th Int. Symp. on Voronoi Diagrams in Science and Engineering (ISVD), IEEE Press, 2010.
    • C-142. C.A. Duncan, D. Eppstein, M.T. Goodrich, S. Kobourov, and M. Nollenburg, Lombardi Drawings of Graphs, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-143. E. Wolf-Chambers, D. Eppstein, M.T. Goodrich, and M. Loffler, Drawing Graphs in the Plane with a Prescribed Outer Face and Polynomial Area, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-144. C.A. Duncan, D. Eppstein, M.T. Goodrich, S. Kobourov, and M. Nollenburg, Drawing Trees with Perfect Angular Resolution and Polynomial Area, Proc. 18th Int. Symp. on Graph Drawing (GD), 2010.
    • C-146. D. Eppstein, M.T. Goodrich, D. Strash, and L. Trott, Extended Dynamic Subgraph Statistics Using h-Index Parameterized Data Structures, Proc. 4th Annual International Conference on Combinatorial Optimization and Applications (COCOA), 2010.

    Data Structures and Algorithms

    • J-23. M.T. Goodrich, M. Ghouse, and J. Bright, Sweep Methods for Parallel Computational Geometry, Algorithmica, 15(2), 1996, 126-153. Preliminary version (C-17) appeared in 2nd ACM Symp. on Parallel Algorithms and Architectures (SPAA), 1990, 280-289.
    • J-29. M.T. Goodrich and R. Tamassia. Dynamic Ray Shooting and Shortest Paths via Balanced Geodesic Triangulations. J. Algorithms, 23, 1997, 51-73.
    • C-51. M.T. Goodrich, M. Orletsky, and K. Ramaiyer, Methods for Achieving Fast Query Times in Point Location Data Structures, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • J-35. M.T. Goodrich and R. Tamassia. Dynamic Trees and Dynamic Point Location, SIAM J. Comput., 28(2), 1999, 612-636.
    • J-41. C. A. Duncan, M. T. Goodrich, and S. G. Kobourov, Balanced Aspect Ratio Trees: Combining the Advantages of k-d Trees and Octrees, Journal of Algorithms 38:303-333, 2001. Preliminary version (C-65) appeared at ACM-SIAM Symp. on Discrete Algorithms (SODA), 1999, 300-309.
    • C-71. M.T. Goodrich and J.G. Kloss II, "Tiered Vectors: Efficient Dynamic Arrays for Rank-Based Sequences," 1999 Workshop on Algorithms and Data Structures (WADS), 1999, 205-216.
    • C-72. M.T. Goodrich, Competitive tree-structured dictionaries, 11th ACM/SIAM Symp. on Discrete Algorithms (SODA), 2000, 705-706.
    • C-77. A.L. Buchsbaum, M.T. Goodrich, J.R. Westbrook, Range searching over tree cross products, 8th European Symp. on Algorithms (ESA), 120-131, 2000.
    • C-78. C.A. Duncan, M.T. Dickerson, and M.T. Goodrich, k-D Trees are Better When Cut on the Longest Side, 8th European Symposium on Algorithms (ESA), LNCS 1879, Springer-Verlag, 2000, 179-190.
    • J-45. R. Tamassia, M.T. Goodrich, L. Vismara, M. Handy, G. Shubina, R. Cohen, B. Hudson, R.S. Baker, N. Gelfand, and U. Brandes, JDSL: The Data Structures Library in Java, Dr. Dobbs Journal, 323, 2001, 21-31.
    • C-107. A. Chaudhary and M.T. Goodrich, Balanced Aspect Ratio Trees Revisited, Workshop on Algorithms and Data Structures (WADS), Lecture Notes in Computer Science 3608, Springer, 2005.
    • J-54. A. Bagchi, A.L. Buchsbaum, and M.T. Goodrich, Biased Skip Lists Algorithmica, 42(1), 31-48, 2005. Preliminary version (C-88) appeared in Algorithms and Computation : 13th International Symposium, ISAAC 2002, 1-13.
    • J-57. D. Eppstein, M.T. Goodrich, and J.Z. Sun, The Skip Quadtree: A Simple Dynamic Data Structure for Multidimensional Data, Int. Journal on Computational Geometry and Applications, 18(1/2), 131--160, 2008. Preliminary version (C-102) appeared in 21st ACM Symp. on Computational Geometry (SCG), 2005.
    • J-60. D. Eppstein, M.T. Goodrich, and D. Hirschberg, Improved Combinatorial Group Testing Algorithms for Real-World Problem Sizes, SIAM Journal on Computing, accepted for publication. Preliminary vesion (C-106) appeared in Workshop on Algorithms and Data Structures (WADS), LNCS 3608, Springer, 2005, 86-98.
    • C-120. D. Eppstein and M.T. Goodrich, Space-Efficient Straggler Identification in Round-Trip Data Streams via Newton's Identities and Invertible Bloom Filters, Proc. Workshop on Algorithms and Data Structures (WADS), LNCS 4619, Springer, pp. 638-649, 2007.
    • C-132. W. Du, D. Eppstein, M.T. Goodrich, and G.S. Lueker, On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem, Proc. Algorithms and Data Structures Symposium (formerly WADS), LNCS, Springer, 2009.
    • J-67. M.T. Goodrich, On the Algorithmic Complexity of the Mastermind Game with Black-Peg Results, Information Processing Letters, 2009.
    • C-137. M.T. Goodrich, Randomized Shellsort: A Simple Oblivious Sorting Algorithm, 20th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010.
    • C-147. M.T. Goodrichand D. Strash, Priority Range Trees, Proc. 21st International Symposium on Algorithms and Computation (ISAAC), 2010.
    • C-149. M.T. Goodrich, Spin-the-bottle Sort and Annealing Sort: Oblivious Sorting via Round-robin Random Comparisons, 8th annual Workshop on Analytic Algorithmics and Combinatorics (ANALCO), in conjunction with the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2011.

    Geometric Algorithms

    • J-12. M.T. Goodrich. A Polygonal Approach to Hidden-Line and Hidden-Surface Elimination. Computer Vision, Graphics, and Image Processing: Graphical Models and Image Processing, 54(1), 1992, 1-12.
    • J-17. M.T. Goodrich, M.J. Atallah, and M. Overmars. Output-Sensitive Methods for Rectilinear Hidden Surface Removal. Information and Computation, 107(1), 1993, 1-24.
    • J-20. M.T. Goodrich. Efficient Piecewise-Linear Function Approximation Using the Uniform Metric, Discrete and Computational Geometry, 14, 1995, 445-462. Preliminary version appeared in Proc. 10th ACM Symp. on Computational Geometry (SCG), 1994, 322-331.
    • J-21. H. Bronnimann and M.T. Goodrich. Almost Optimal Set Covers in Finite VC-Dimension, Discrete and Computational Geometry, 14, 1995, 463.479. Preliminary version (C-35) appeared in Proc. 10th ACM Symp. on Computational Geometry (SCG), 1994, 293-302.
    • J-28. G. Das and M.T. Goodrich, On the Complexity of Optimization Problems for 3-Dimensional Convex Polyhedra and Decision Trees, Computational Geometry: Theory and Applications, 8, 1997, 123-137. Preliminary version appeared in 1995 Workshop on Algorithms and Data Structures (WADS).
    • J-31. L.P. Chew, M.T. Goodrich, D.P. Huttenlocher, K. Kedem, J.M. Kleinberg, and D. Kravets. Geometric Pattern Matching under Euclidean Motion, Computational Geometry: Theory and Applications, 7, 1997, 113-124. Preliminary version appeared in Proc. 5th Canadian Conference on Computational Geometry (CCCG), 1993, 151-156.
    • C-50. C.A. Duncan, M.T. Goodrich, and E.A. Ramos, Efficient Approximation and Optimization Algorithms for Computational Metrology, 8th ACM-SIAM Symp. on Discrete Algorithms (SODA), 1997.
    • C-52. M.T. Goodrich, L.J. Guibas, J. Hershberger, P.J. Tanenbaum, Snap Rounding Line Segments Efficiently in Two and Three Dimensions, Proc. 13th ACM Symp. on Computational Geometry (SCG), 1997.
    • J-33. M.T. Goodrich. An Improved Ray Shooting Method for Constructive Solid Geometry Models via Tree Contraction. International Journal of Computational Geometry and Applications, 8(1), 1998, 1-23.
    • J-34. G. Barequet, A.J. Briggs, M.T. Dickerson, and M.T. Goodrich, Offset-polygon annulus placement problems, Computational Geometry: Theory and Applications, vol. 11 (3-4), pp. 125-141, 1998. Preliminary version (C-55) appeared in Proc. 5th Workshop on Algorithms and Data Structures (WADS), LNCS 1272, 378-391, 1997.
    • C-54. G. Barequet, A. Briggs, M. Dickerson, C. Dima, and M.T. Goodrich, Animating the Polygon-Offset Distance Function, 13th ACM Symp. on Computational Geometry (SCG), 1997, 479-480, and the Video Review for the 13th ACM Symp. on Computational Geometry.
    • J-37. M.T. Goodrich, J.S.B. Mitchell, and M.W. Orletsky, "Approximate Geometric Pattern Matching under Rigid Motion," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 21(4), 1999, 371-379. A preliminary version (C-38) appeared in ACM Symp. on Computational Geometry, 1994, 103-112.
    • C-68. G. Barequet, C. Duncan, M.T. Goodrich, S. Kumar, M. Pop, Efficient Perspective-Accurate Silhouette Computation, 15th ACM Symp. on Computational Geometry (SCG), 1999, 417-418, and the Video Review for the 15th ACM Symp. on Computational Geometry (SCG).
    • C-73. N.M. Amato, M.T. Goodrich, E.A. Ramos, Computing the Arrangement of Curve Segments: Divide-and-Conquer Algorithms via Sampling, 11th ACM/SIAM Symp. on Discrete Algorithms (SODA), 2000, 705-706.
    • J-42. G. Barequet, M.T. Dickerson, and M.T. Goodrich, Voronoi diagrams for polygon-offset distance functions, Discrete and Computational Geometry, vol. 25 (2), pp. 271-291, 2001. Preliminary version (C-56) appeared at Workshop on Algorithms and Data Structures (WADS), 1997, LNCS, 200-209.
    • C-82. M. Pop, G. Barequet, C.A. Duncan, M.T. Goodrich, W. Hwang, and S. Kumar, Efficient Perspective-Accurate Silhouette Computation and Applications, 17th ACM Symp. on Computational Geometry (SCG), 2001, 60-68.
    • J-44. N.M. Amato, M.T. Goodrich, and E.A. Ramos, A Randomized Algorithm for Triangulating a Simple Polygon in Linear Time, Discrete and Computational Geometry, 26(2), 2001, 245-265. Preliminary version (C-76) appeared in 16th ACM Symp. on Computational Geometry (SCG), 2000, 201-212.
    • J-46. G. Barequet, D.Z. Chen, O. Daescu, M.T. Goodrich, and J. Snoeyink, Efficiently approximating polygonal paths in three and higher dimensions, Algorithmica, vol. 33 (2), pp. 150-167, 2002. Preliminary version (C-59) appeared in ACM Symp. on Computational Geometry (SoCG), 317-326, 1998.
    • J-49. A.L. Buchsbaum and M.T. Goodrich, Three-Dimensional Layers of Maxima, Algorithmica, 39, 275-286, 2004. Preliminary version (C-87) appeared in 10th Annual European Symposium (ESA), 2002, LNCS 2461.
    • J-51. G. Barequet, M.T. Goodrich, A. Levi-Steiner, and D. Steiner, Contour Interpolation by Straight Skeletons, Graphical Models (GM), 66(4), 245-260, 2004. Preliminary version (C-92) appeared in 14th ACM-SIAM Symp. on Discrete Algorithms (SODA), 119-127, 2003.
    • J-53. G. Barequet, P. Bose, M.T. Dickerson, and M.T. Goodrich, Optimizing a Constrained Convex Polygonal Annulus, J. of Discrete Algorithms (JDA), 3(1), 1-26, 2005.
    • J-59. A. Bagchi, A. Chaudhary, D. Eppstein, and M.T. Goodrich, Deterministic sampling and range counting in geometric data streams, ACM Transactions on Algorithms, accepted for publication. Preliminary version (C-97) appeared in 12th ACM Annual Symposium on Computational Geometry (SoCG), 2004.
    • C-117. D. Eppstein, M.T. Goodrich, and Sitchinava, Guard Placement for Wireless Localization, Proc. 23rd ACM Symposium on Computational Geometry (SCG), 27-36, 2007.
    • C-119. M.J. Atallah, M. Blanton, M.T. Goodrich, and S. Polu, Discrepancy-Sensitive Dynamic Fractional Cascading, Dominated Maxima Searching, and 2-d Nearest Neighbors in Any Minkowski Metric, Proc. Workshop on Algorithms and Data Structures (WADS), LNCS 4619, Springer, pp. 114-126, 2007.
    • C-123. D. Eppstein, M.T. Goodrich, E. Kim, and R. Tamstorf, "Approximate Topological Matching of Quadrilateral Meshes," Proc. IEEE Int. Conf. on Shape Modeling and Applications (SMI), 2008, 83--92.
    • C-124. G. Bareqet, D. Eppstein, M.T. Goodrich, and A. Waxman, "Straight Skeletons of Three-Dimensional Polyhedra," Proc. 16th European Symposium on Algorithms (ESA), 2008.
    • J-65. D. Eppstein, M.T. Goodrich, E. Kim, and R. Tamstorf, "Motorcycle Graphs: Canonical Quad Mesh Partitioning," Proc. 6th European Symposium on Geometry Processing (SGP), 2008.
    • C-128. D. Eppstein and M.T. Goodrich, Studying (Non-Planar) Road Networks Through an Algorithmic Lens, Proc. 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS), 2008, 125-134. Best paper award.
    • C-130. D. Eppstein, M.T. Goodrich, and D. Strash, Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Crossings, SODA 2009.
    • C-141. M.T. Dickerson, D. Eppstein, and M.T. Goodrich, Cloning Voronoi Diagrams via Retroactive Data Structures, 18th European Symposium on Algorithms (ESA), 2010.

    Goodrich's Home Page. http://www.ics.uci.edu/~goodrich/contact.html Address:
    Prof. Michael T. Goodrich
    Dept. of Computer Science, Donald Bren Hall 3019
    Bren School of Information & Computer Sciences
    University of California, Irvine
    Irvine, CA 92697-3435

    Email: my-last-name (at) acm.org
    Office: Bren Hall 4091

    Phone: 949-824-9366

    FAX: (949)824-4056 http://www.ics.uci.edu/~wscacchi/Papers/Vintage/Software_Productivity.html UNDERSTANDING SOFTWARE PRODUCTIVITY

    UNDERSTANDING SOFTWARE PRODUCTIVITY

    WALT SCACCHI
    Information and Operations Management Department
    School of Business Administration
    University of Southern California
    Los Angeles, CA 90089-1421, USA
    (Appears in Advances in Software Engineering and Knowledge Engineering, D. Hurley (ed.),
        Volume 4, pp. 37-70, (1995).
    December 1994
    What affects software productivity and how do we improve it? This report examines the current state of the art in software productivity measurement. In turn, it describes a framework for understanding software productivity, some fundamentals of measurement, surveys empirical studies of software productivity, and identifies challenges involved in measuring software productivity. A radical alternative to current approaches is suggested: to construct, evaluate, deploy, and evolve a knowledge-based `software productivity modeling and simulation system' using tools and techniques from the domain of software process engineering.

    Overview

    What affects software productivity and how do we improve it? This is a concern near and dear to those who are responsible for researching and developing large software systems. For example, Boehm [10] reports that by 1995, a 20% improvement in software productivity will be worth $45 billion in the U.S and $90 billion worldwide. As such, this report examines the current state of the art in understanding software productivity. In turn, this report describes some fundamentals of measurement, presents a survey of studies of software productivity, identifies variables apparently affecting software productivity, and identifies alternative directions for research and practice in understanding what affects software productivity.

    From the survey, it is apparent that existing software productivity measurement studies are fundamentally inadequate and potentially misleading. Depending on how and what indicators of software productivity are measured, it is possible to achieve results that show that modest changes in software development technologies lead to substantial productivity improvements (e.g., 300% in 5 years), while major changes to new technologies can lead to little productivity improvement. Different measurement strategies can show an opposite trend. In short, how and what you measure determines how much productivity improvement you see, whether or not productivity is actually improved.

    This report advocates a radical alternative to current approaches to measuring and understanding what affects software productivity: to construct, evaluate, and deploy knowledge-based software productivity modeling and simulation systems. Accordingly, effort should be directed at developing a knowledge-based system that models and symbolically simulates how software production occurs in a given project setting. Such a modeling facility could be used to simulate software production under various product requirements, development processes, project settings, and computing resource conditions. It could also be used to incrementally capture information about the production dynamics of multiple software projects and thus improve the breadth of its coverage over time. As a result, this modelling technology could be used to articulate and update a computational knowledge-based `corporate memory' of software production practices.

    The potential payoff of such technology is substantial. This technology provides a vehicle for delivering practical feedback that software developers and managers can use prior to and during a development project to help identify what might improve their productivity. Such a knowledge-based technology would enable project managers, developers, or analysts to query a model, conduct `what if' analysis, diagnose project development anomalies, and generate explanations about how certain project conditions affect productivity. Such capabilities are not possible with current productivity measurement technologies.

    Overall, this examination of software productivity primarily focuses on the development of large-scale software systems (LSS). LSS refers to delivered software systems developed by a team of developers, intended to be in sustained operation for a long time, and typically representing 50K-500K+ source code statements. The choice of LSS is motivated by economic and practical considerations. LSS are expensive to develop and maintain so that even modest software productivity improvements can lead to substantial savings. For example, it is reasonable to assume that 10,000 lines of code may cost a development organization $100,000-250,000. For larger systems in the range of 50,000 to 250,000 lines of code, the cost may climb by as much as a factor of 4-25. In turn, it is reasonable to assume that software maintenance costs over the total life of the system dominate software development costs by a factor of 2-10. Small-scale programming productivity measurement often reveals more than an order of magnitude variation for different people, different programs, or both [18,19], while large-scale programming efforts (with large staffs) can mitigate some of this variance.

    An outline of the remainder of this report follows. Section 2 provides a brief exposition of the science of measurement. This section serves to identify some fundamental concerns in evaluating software productivity measures. Section 3 provides a select survey of studies that attempt to identify and measure what affects software productivity. The results of this survey are then summarized in Section 3.14 as a list of software projects attributes that contribute to productivity improvement. These three sections set the stage for Section 4 which provides a discussion of the measurable variables that appear to affect software productivity. Section 5 follows with a new direction for research into identifying what affects software productivity, and how to improve it. We summarize our conclusions and the consequences that follow from this endeavor.

    Notes on the Science of Measurement

    Measurement is ultimately a quest for certainty and control: certainty in understanding the nature of some phenomenon so as to control, influence, or evaluate that phenomenon. In this paper, the phenomenon under study is software production: from system inception through delivery, operation and support. Accordingly, we want to understand how software is produced, how to measure its production, and ultimately, how to positively influence or control the rate of its production. Curtis [18] provides an appropriate background on some fundamental principles involved in measuring software production characteristics, including measure validity and reliability, as well as instrumentation and modeling issues.

    A desire to measure software production implies an encounter with the process of systematic or scientific inquiry. This implies the need to confront fundamental problems such as the role of measurement in theory development, hypothesis testing and verification, and performance evaluation. It also implies understanding the relationship between measurement and instrumentation-the artifacts employed to collect/measure data on the phenomenon under study. Instrumentation in turn raises questions for how to simplify or make trade-offs in:

    • convenience of data collection versus cost of alternative instrumentation, collection, or sampling strategies.
    •  ease of rendering or displaying the results of data analysis for different audiences (e.g., internal management presentations versus journal publication).
    •  how to handle (or delete) anomalous data collected with survey instruments.
    •  use of collected data to monitor, evaluate, and intervene in the phenomenon under study.
    •  developing a narrative, diagrammatic, or operational abstraction of the phenomenon that is the source of the data collected.
    Other fundamental concerns on the use of measurements include how to account for the influence of unmeasured units, the uniformity and consistency of measured units, how to rationalize the construction of composite measures, and how to rationalize measurement scales and normalizations. All of these concerns must be addressed in developing and sustaining an effort for measuring software production.

    As such, what types of measures are appropriate for understanding software productivity? Productivity in most studies inside and out of the software world is usually expressed as a ratio of output units produced per unit of input effort. This simple relation carries some important considerations: for example, that productivity measures are comparable when counting the same kind of outputs (e.g., lines of source code) and inputs (person-months of time). Likewise, that a software development effort with productivity 2X is twice as productive as another effort whose productivity is X. Therefore, how outputs and inputs are defined are critical concerns if they are to be related as a ratio-type measure. As will become apparent through the survey that follows, other measure types - nominal, ordinal, and interval - are also appropriate indicators to characterize the variables that shape software productivity.

    In the next section, a survey of studies of software productivity measurement shows there is often a substantial amount of difference with respect to the degree of rigor and the use of accepted analytical methods.

    A Sample of Software Productivity Measurement Studies

    A number of researchers have sought through empirical investigations to determine whether some software development attribute, tool, technique, or some combination of these has a significant impact on software production. These studies primarily focus on the development of LSS Twelve major software productivity measurement studies are reviewed including those at IBM, TRW, NASA, and ITT, as well as at international sites. In addition, a number of other theoretical and empirical studies of programmer productivity, cost-benefit analysis, software cost estimation, and a software productivity improvement program are reviewed. Together, these studies provide a loosely-grounded basis for identifying a number of project characteristics that affect software productivity.

    IBM Federal Systems Division

    Walston and Felix [56] conducted the classic study in this area. The authors state that a major difficulty arises in trying to identify and measure which independent variables can be used to estimate software development productivity, cost, and size. For example, they measured software productivity in terms of number of lines of code produced per person-hour. However, staff time was measured by the duration of the complete development project, rather than just the coding phase. Thus, we have no information as to what percent of each measured project's effort was dedicated to code production versus other necessary development activities. This omission tends to distort the results of their analysis.

    IBM DP Services Organization

    Albrecht [2,3] developed the `function point' measure to compare the productivity in 24 projects that developed business applications. A function point is a composite measure of a number of program atributes including the number of inputs, outputs, function calls, file accesses, etc. that are multiplied be weighting factors then added together. These systems Albretch examined ranged in size from 3K to 318K lines of code written in either DMS, PL/1 or COBOL and developed over a 5 year period (1974-1978). Albrecht claims that over this period for the programs studied, software productivity, as measured with function points, increased 3 to 1. He finds that developers using DMS (a database management system language) are more productive than those writing in PL/1, who in turn were more productive than those writing COBOL. The application systems developed tended over time to be increasingly interactive (vs. batch), accessing large data files/databases to produce reports. Also, during the 5 year period, developers progressively began to practice structured coding, top-down implementation and HIPO documentation. Such development techniques would seem to lead to more function points appearing in source code. That is, poorly structured code will tend to have fewer functions points than well-structured code conforming to the same specification. Thus, structured code can produce a higher function point measure, and therefore appear to be produced more productively.

    But a number of confounding factors appear in Albrecht's results which undercut the validity of his reported productivity improvement claims. For example, his formula for computing function point values incorporate weighting multipliers which he reports produced reliable results. However, he does not discuss how these weights were determined, or how to determine them when other programming languages and software applications are to be measured. He also indicates that as department manager, he instructed his program supervisors to collect this function point data. To some extent then, his supervisors were encouraged to have their programs developed in ways that would lead to more function points produced per unit of effort. However, it is unclear whether the function point technique works equally well on non-business application systems that do not rely on accessing large files, retrieving selected data, performing some computations on the data, and producing various reports. Thus, it is unclear whether the 3 to 1 productivity improvement that Albrecht claims is due to (a) shifts in the choice of programming language to those that produce more favorable measures, (b) alternative program development techniques, (c) choice of multiplier weights, (d) management encouragement for collecting data that substantiates (and rewards) measured improvement.

    Equitable Life Organizations

    Behrens [5] also utilizes Albrecht's function point measures to compare software productivity in 25 application system projects developed in various life insurance companies from 1980 to 1981. His results are consistent with Albrecht's in supporting the contention that project size, development (computing) environment, and programming language impact software productivity. In particular, he finds that small project teams produce source code with more function points than large teams in a comparable amount of time. He also finds that developers working online are more productive than those working in a batched computing environment. We can also observe that in large projects, software runs tend to become more batch-like as their size grows, and the amount of computing resources they require grows.

    TRW Defense Systems Group

    Boehm [9,12] sought to identify avenues for improving software productivity based primarily on TRW's Software Cost Estimation Program, SCEP. This program served as an aid in developing cost estimates for competitive proposals on large government software projects. The program estimates the cost of a software project as a function of program size expressed in delivered source instructions and a number of other cost drivers. Experience with SCEP in turn gave rise to the development of the COCOMO software cost estimation model presented in [9]. Boehm recognized that software cost drivers are effectively the inverse of productivity (or `benefit') drivers. He found, for example, that personnel/team capability and product complexity had the greatest affect in driving software costs and productivity. Thus, high staff capability and low product complexity lead to high productivity/low cost software production. Conversely, low staff capability and high product complexity similarly imply low productivity/high cost software production. Through his experience with these cost estimation models, Boehm was able to develop quantitative support for the relative contribution of different software development characteristics that affect software cost and productivity.

    Australia-70 Study

    Lawrence [39] conducted a study of 278 commercial applications developed in 23 medium-to-large organizations in Australia. The organizations and applications studies included those in government agencies, manufacturing and mining concerns, and banking and insurance firms. He performed a multivariate analysis of productivity variance using a combination of computing environment and organizational factors. His use of multivariate analysis of variance is in direct contrast to the preceding software productivity studies that employ only univariate analysis.

    Lawrence observed that source lines of code, number of statements, number of procedure invocations, number of functional units, and number of transfers of control are all highly correlated. Other researchers have substantiated this as well. As such, he chose to employ the number of procedural lines of code divided by the total time put into the programming job by the programmer from the receipt of program specifications to completion of program testing. That is, Lawrence was interested in measuring the productivity of individual programmers who in turn were developing small programs (50-10000 lines of code). He found that programmer productivity increases with better turnaround, but decreases with online source code testing and interface to a database. In contrast to Albretch, Lawrence does not define what interface to a database means, nor whether the organizations he studied employed database management systems. Thus, it is not possible to determine whether Albretch and Lawrence agree on the productivity impact of the use of database management systems. However, Lawrence also found that programming experience beyond the first year on the job, structured programming, and walkthroughs contribute little to productivity improvement.

    NASA/SEL

    Bailey and Basili [4] found higher productivity over the entire system life cycle to be associated with the use of a disciplined programming methodology, particularly in the early stages of system development. Their findings indicate that productivity measures, as well as other resource utilization estimates must be specific to the organizational setting and local computing environment to provide the most accurate measures. Standard, program-oriented productivity or cost estimation measures will provide less accurate information than those measures that account for characteristics of the organization and its computing environment. Mohanty [44] and Kemerer [30] also found similar results in their independent examinations of different software cost estimation models.

    IBM

    Thadhani [54] and Lambert [37] examined the effects of good computer services on programmer and project productivity during application program development. In particular, their studies examine the effects of short response times, programmer's skills, and program complexity on programmer productivity. Thadhani reports that programmers were twice as productive when their system's average response time was 0.25 seconds (or less) than when it averaged 2 seconds or more. However, in a review of this and other similar studies, Conte and colleagues [17] report that average response time is not as critical as a narrow variance in expected response time. That is, programmers should be more productive when their system's response time is fast, consistent, and relatively predictable from the computing task at hand.

    Both Thadhani and Lambert assert that unexpected delay in response time to trivial computing tasks (e.g., processing simple editor or shell commands, or compiling a small program) is psychologically disruptive to the programmer. Such delays they argue cause a longer delay than the actual elapsed time. Since LSS development efforts can entail thousands or more of such trivial task transactions, that cumulative time will represent a significant cost to the project. Essentially, they argue that response time has an impact on LSS development projects, so that ample processing resources are critical to enhancing software productivity. Subsequently, this could be viewed as evidence in favor of providing individual programmers more processing resources such as through the adoption of powerful personal computing workstations as a way to improve software productivity. That is, if programmers currently must share a small number of heavily loaded computer systems, then providing each programmer with a workstation should improve their collective productivity [43].

    ITT Advanced Technology Center

    Vosburg and associates [55] produced perhaps the most substantial study of large-scale software productivity to date. They examined software production data for 44 programming projects in 17 different ITT subsidiaries in nine different countries. Data on programming productivity, quality, and cost were collected from the records of completed projects by means of a questionnaire answered by project managers. Software systems ranged in size from 5,000 to 500,000 coded statements, with a median size of 22,000 statements. Statement counts include language processing directives, macro calls, and file inclusion statements, but not comments or blank lines. Their study covered a variety of software systems including telecommunications switches, programming tools, operating systems, electronic defense systems, and process control. In total, they represent more than 2.3 million coded statements and 1500 person-years of effort.

    The authors focused on classifying productivity drivers according to the ability of a software project manager to control them. They identify two types of factors: product-related factors that are not usually controllable by a project manager, and production process-related factors that are controllable by managers and thus provide opportunity for productivity improvement.

    The product-related factors they identify include:

    • computing resource constraints: productivity decreases when software being developed has timing, memory utilization, and CPU occupancy constraints.
    •  program complexity: productivity decreases when software is primarily operating systems, real-time command and control, and fault-tolerant applications that require extensive error detection, rollback and recover routines.
    •  customer participation: productivity increases with customer application experience and participation in requirements and specification articulation.
    •  size of program product: productivity decreases as the number of lines of code increases.
    The production process-related factors they identify include:
    • concurrent hardware-software development: productivity decreases with concurrent development of hardware.
    •  development computer size: productivity increases as computer size (processor speed, main and secondary storage capacity) increases.
    •  requirements and specifications stability: productivity increases with accurate and stable system requirements and specifications.
    •  use of modern programming practices: productivity increases with extensive use of top-down design, modular design, design reviews, code inspections, and quality-assurance programs.
    •  personnel experience: productivity increases with more experienced software development personnel.
    Overall, they find that product-related and process-related factors account for approximately the same amount of variance (one-third for each set) in productivity enhancement.

    In conclusion, the authors suggest that improving programming productivity requires much more than the isolated implementation of new technologies and policies. In their view, `To be successful, a productivity improvement program must address the entire spectrum of productivity issues. Key features of such a program are management commitment and an integrated approach' (pp. 151-152).

    Australia-80 Study

    Jeffrey [26] describes a comparative study of software productivity among small teams in 38 development projects in three Austrialian firms. Each firm used one programming language in its projects, but different from that used by the other two firms. Software systems in the projects ranged from very small (200 LOC) to large (?`\>100K LOC), while their development team size ranged from 1-4 developers for 19 projects, and 3-8 for the other projects. As a result of his analysis, Jeffrey asserts (a) there is an optimal staff level which depends on the language used and the size of the resulting software system, and (b) adding staff beyond the optimal point decreases productivity and increases total development elasped time. However, due to the small sample size (three firms), small team size vis-a-vis individual programmer variations [19], and other common analytical shortcomings in defining input and output measures, the generality of the assertions is limited.

    Commerical U.S. Banks

    Cerveny and Joseph [15] report on their study software enhancement productivity in 200 U.S. commercial banks. Each bank was required by a change in national tax laws to implement new interest reporting requirements. Thus, all banks had to satisfy the same set of tax law requirements. Cerveny and Joseph found that banks which employed structured design and programming techniques took twice the effort as those banks that used non-structured techniques, or that purchased and integrated commercial software packages. Effort in their study represents person hours expended for analysis, programming, and project management activities, which is data apparently collected on a routine basis by the banks in the study. They do not report any measure of source code changes that accompany the measured effort. However, they report that banks that employed structured techniques did so for auditing and control purposes, but generally lacked CASE tools to support the structured techniques. Thus, it is unclear what the net change in software productivity might be if CASE tools that support structured design and programming techniques would have been empolyed.

    U.S. vs. Japan Study

    In a provocative yet systematic comparison of industrial software productivity in the U.S. and Japan, Cusumano and Kemerer [21] argue that Japanese software development capabilities are comparable to those found in the U.S. [20]. Their analyses examined data from 24 U.S. and 16 Japanese development efforts collected from software project managers who completed questionnaires. Their project sample varied in terms of appplication type, programming language used, programming language and application type, and hardware platforms, full-time (versus part-time) staff effort by development phase, percentage of code reuse during development, code defect density, and number of tools/methods used per project. However, the researchers note that their sample of projects was not random, and that the software project managers may have only reported on their best projects. Cusamano and Kemerer employed Fortran-equivalent noncomment source lines of code as the output measure [27], and person-years of effort as the input measure, as well as both parametric and non-parametric statistical test where appropriate. While they report that software productivity appears on the surface to be greater in Japan than in the U.S., the differences that were observed were not found to be statistically significant.

    Other studies of Productivity and Cost Evaluation

    T.C. Jones [27] at IBM was among the first to recognize that measures of programming productivity and quality in terms of lines of code, and cost of detecting and removing code defects are inherently paradoxical. They are paradoxical in that lines of code per unit of effort tend to emphasize longer rather than efficient or high-quality programs. Similarly, high-level programming languages tend to be penalized when compared to assembly programs, since modern programs may utilize fewer lines of code than assembly routines to realize the same computational procedure. Cost of code defect detection and removal tends to indicate that it costs less to repair poor quality programs than high quality programs. Thus, Jones' results undercut the utility of the findings reported by Walston and Felix [55] which are subject to these paradoxes. As an alternative, Jones recommends separating productivity measures into work units and cost units, while program quality be measured by defect removal efficiency and defect prevention.

    Chrysler [16] sought to identify some basic determinants of programming productivity by examining programming activities in a single organization. He sought to identify (1) what characteristics of the time to complete a programming (coding) task can be objectively measured before the task is begun, and (2) what programmer skill attributes are related to time to complete the task. His definition of programming task assumes that the program's specifications, `the instructions to the programmer regarding the performance required by the program', must be sufficiently detailed to incorporate the objective variables that can be measured to determine these relationships. Although he studied a sample of 36 COBOL programs, he does not describe their size, nor account for the number of programmers working on each. His results are similar in kind to those of Albrecht, finding that programming productivity can be estimated primarily from (1) programmer experience at the current computing facility, (2) number of input files, (3) number of input edits, (4) number of procedures and procedure calls, and (5) number of input fields.

    King and Schrems [34] provide the classic survey of problems encountered in applying cost-benefit analysis to system development and operation. To no surprise, the `benefits' they identify represent commonly cited productivity improvements. The authors observe that system development costs are usually underestimated and difficult to control, while productivity improvements are overestimated and difficult to achieve. They observe that cost-benefit (or cost-productivity) analysis can be used as: (a) a planning tool for assistance in choosing among alternative technologies and allocating scarce resources among competing demands; (b) an auditing tool for performing post hoc evaluations of an existing project; and (c) a way to develop `quantitative' support in order to politically influence a resource allocation decision.

    Some of the problems they describe include (a) identifying and measuring costs and benefits, (b) comparing cost-benefit alternatives, (c) cost accounting dilemmas, (d) problems in determining benefits, (e) everyday organizational realities. For example, two cost accounting (or measurement) problems that arise are ommission of significant costs, and hidden costs. Omitting significant costs occurs when certain costs are not measured, such as the time staff spend in design and review meetings, and the effort required to produce system design documents. Hidden costs arise in a number of ways, often as costs displaced either to others in the organization, or to a later time: for example, when a product marketing unit achieves the early release of a software system before the developers have thoroughly tested it that customers find partially defective or suspect. If the developers try to accomodate to the marketing unit's demands, then system testing plans are undercut or compromised, and system integrity is put in question from the developers point of view. The developers might later become demoralized and their productivity decrease if they are viewed by others or senior management as delivering lower quality systems, especially when compared to other software development groups who do not have the same demands from their marketing units.

    King and Schrems also note that conducting quality cost-benefits has direct costs as well. For example, Capers Jones [28] reports that in its software development laboratories, IBM spends the equivalent of 5% of all development costs on software measurement and analysis activities. More typically, he observes, that most companies spend 1.5% to 3% of the cost of developing software to measure the kind of information IBM would collect [cf. 2,3,27,55]. Therefore, this article by King and Schrems can be recommended as background reading to those interested in conducting software cost vs. productivity analysis.

    Mohanty [44] compared the application of 20 software cost estimation models in use by large system development organizations. He entered data collected from a large software project, then entered this data into each of the 20 cost estimation models. He found that the range of costs estimated was nearly uniformly distributed, varying by an order of magnitude! This led him to conclude that almost no model can estimate the true cost of software with any degree of accuracy. However, we could also conclude from his analysis that each cost estimation model might in fact be accurate within the organizational setting where it was created and used. Although two different models may differ in their estimate of software development costs by as much as a factor of 10, each model may reflect the cost accounting structure for the organization where they were created. This means that different cost estimation models, and by logical extension, productivity models, lead to differrent measured values which can show great variation when applied to software development projects. Also, the results of Kemerer's [30] study of software cost estimation models corroborates the same kind of findings that Mohanty`s study shows. However, Kemerer does go so far as to show how function points may be refined to improve their reliability as measures of program size and complexity [31,32], as well as tuned to produce the better cost estimates [30]. But again, function points depend solely upon program source code characteristics, and do not address production process or production setting variations, nor their contributing effects.

    Romeu and Gloss-Soler [48] argue that most software productivity measurement studies employ inappropriate statistical analysis techniques. They argue that the type of productivity data usually reported is ordinal data rather than interval or ratio data. The parametric statistical techniques employed by most software productivity analysts are inappropriate for ordinal data, whereas non-parametric techniques are appropriate. The use of parametric techniques on ordinal data results in apparently stronger relationships (e.g., correlations, regression slopes) than would be found with non-parametric techniques. The consequence is that studies of productivity measurement claiming statistically substantiated relationships based on inappropriate analytical techniques are somewhat dubious, and the strength of the cited relationship may not be as strong as claimed.

    Boehm [9] reported that productivity on a software development project is most keenly affected by who develops the system and how well they are organized and managed as a team. Following this, Scacchi [50] reviewed a number of published reports on the problems of managing large software engineering projects. He found, to no surprise, that when projects were poorly managed or poorly organized, productivity was substantially lower than otherwise possible. Poor management can nullify the potential productivity enhancements attributable to improved development technologies. Scacchi identified a number of strategies for managing software projects that focus on improving the organization of software development work. These strategies identify conditions in the workplace, and the skills and interests of the developers as the basis for project-specific productivity drivers. For example, developers who have a strong commitment to a project and the people associated with it will be more productive, work harder, and produce higher quality software products. This commitment comes from the value the developers expect to find in the products they produce. In contrast, if they do not value the products they are working on, then their commitment will be low and their productivity and quality of work will be lower. So an appropriate strategy is to focus in organizing and managing the project to cultivate staff commitment to each other and to the project's objectives [cf. 33]. When developers are strongly committed to the project and to a team effort [38], they are more than willing to undertake the unplanned for system maintenance and articulation work tasks needed to sustain productive work conditions [6,7]. Scacchi concludes that strategies for managing software development work have been overlooked as a major contributor to software productivity improvement, and thus require further study and experimentation.

    Boehm and associates at TRW [11] described the organization of a software project whose objective was to develop an environment to enhance software productivity by a factor of 2 in 5 years, and 4 in 10 years. The project began in 1981, and the article describes their progress after four years in assembling a software development environment that should be able to support TRW development projects. Surprisingly, their software environment contains many tools for managing project communications and development documentation. This is because much of what gets delivered to a customer in a system is documentation, so tools that help develop what the customers receives should improve customer satisfaction and thus project productivity. However, they do not report any experiences with this environment in a production project. But they report that developers that have used the environment believe it improved their development productivity 25% to 40% [cf. 24,45]. Nonetheless, they report that this productivity improvement was realized at an additional capital investment of $10,000 per programmer. Current investigations in this project include the development and incorporation of a number of knowledge-based software development and project management aids for additional LSS productivity improvements.

    Capers Jones [28] provides the next study in his book on programming productivity. Jones does an effective job at describing some of the problems and paradoxes that plague most software productivity and quality measures based upon his previous studies [27]. For example, he observes that a line of source code is not an economic good, but it is frequently used in software productivity measures as if it were-lines of code (or source statements) produced per unit of time are not a sound indicator of economic productivity. In response, he identifies more than 40 software development project variables that can affect software production. This is the major contribution of this work. However, the work is not without its faults. For example, Jones provides `data' to support his examination of the effects of each variable on comparable development projects. But his data, such as lines of source code is odd is that it is often rounded to the most significant digit (e.g., 500, 10,000, or 500,000), and collected from unnamed sources. Thus, his measurements lack specificity and his data collection techniques lack sufficient detail to substantiate his analysis.

    Jones mentions that he relies upon his data for use in a quantitative software productivity, quality, and reliability estimation model. However, he does not discuss how his model works, or what equations it solves. This is in marked contrast to Boehm's [9] software cost and productivity estimation efforts where he both identifies the software project variables of interest, and also presents the analytical details of the COCOMO software cost estimation model that uses them. Thus, we must regard Jones's reported analysis with some suspicion. Nonetheless, Jones does include an appendix that provides a questionnaire he developed for collecting data for the cost/quality/reliability model his company markets. This questionnaire includes a variety of suggestive questions that people collecting productivity data may find of interest.

    In setting his sights on identifying software productivity improvements opportunities, Boehm [10] also identifies some of the dilemmas encountered in defining what things need to be measured to understand software productivity. In departure from the studies surveyed in the previous section, Boehm observes that software development inputs include: (a) different life cycle development phases each requiring different levels of effort and skill; (b) activities including documentation production, facilities management, staff training, quality assurance, etc.; (c) support personnel such as contract administrators and project managers; and (d) organizational resources such as computing platforms and communications facilities. Similarly, Boehm observes that measuring software development outputs solely in terms of attributes of the delivered software (e.g., delivered source code statements) poses a number of dilemmas: (a) complex source code statements or complex combinations of instructions usually receive the same weight as sequences of simple statements; (b) determining whether to count non-executable code, reused code, and carriage returns as code statements; and (c) whether to count code before or after pre- or post-processing. For example, on this last item, Boehm reports putting a compact Ada program through a pretty-printer frequently may triple the number of source code lines. Even after reviewing other source code metrics, Boehms concludes that none of these measures is fundamentally more imformative than lines of code produced per unit of time. Thus, Boehm's observations add weight to our conclusion that source code statement/line counts should be treated as an ordinal measure, rather than an interval or ratio measure, of software productivity. This conclusion is especially appropriate when comparing such productivity measures across different studies.

    In a comparative field study of software teams developing formal specifications, Bendifallah and Scacchi [7] found that variation in specification teamwork productivity and quality could best be explained in terms of recurring teamwork structures. They found six teamwork structures (ie, patterns of interaction) recurring among all the teams in their study. Further, they found that teams shifted from one structure to another for either planned or unplanned reasons. But more productive teams, as well as higher product quality teams, could be clearly identified in the observed patterns of teamwork structures. Lakhanpal's [38] study corroborates this finding showing workgroup cohesion and collective capability is a more significant factor in team productivity than individual experience. Thus, the structures, cohesiveness, and shifting patterns of teamwork are also salient software productivity variables.

    In a study that does not actually examining the extent to which CASE tools may improve software productivity, Norman and Nunamaker [45] report on what the software engineers they surveyed believed would improve software productivity [cf. 24]. These software engineers answered questions about the desirability and expected effectiveness of a variety of contemporary CASE mechanisms or methods. Norman and Nunamaker found that software engineers believe that CASE tools that enhance their ability to produce various analysis reports, screen displays, and structured diagrams will have the greatest expected boost in software development productivity. But there is no data available that systematically demonstrates if the expected gains are in fact realized, or to what level.

    Kraut and colleagues [35] report on their study of organizational changes in worker productivity and quality of work-life resulting from the introduction of a large automated system. They surveyed the opinions of hundreds of system users in 10 different user sites. Through their analysis of this data, Kraut and colleagues found that the system increased the productivity of certain classes or users, while decreasing it for other user classes. They also found that while recurring user tasks were made easier, uncommon user tasks were reported to be more difficult to complete. Finally, they found that the distribution of user task knowledge shifted from old to new loci within the user sites. So what if anything does this have to do with software development productivity? The introduction of new software development tools and techniques might have a similar differential effect on productivity, software development task configuration, and the locus of development task expertise. This effect might be most apparent in large development organizations employing hundreds or thousands of software developers, rather than in small development teams. In any event, Kraut and colleagues observe that one needs to understand with web of relationships between the organization of work between and among tasks, developers, and users, as well as the computing resources and software system designs in order to understand what affects productivity and quality of work-life [35].

    Last, Bhansali and associates [8] report that programmers are two-to-four times more productive when using Ada versus Fortran or Pascal-like languages according to their study data. However, as Ada contains language constructs not present in these other languages, it is not clear what was significant in explaining the difference in apparent productivity. Similarly, they do not indicate whether any of the source code involved was measured before or after pre-processing, which can affect source line counts, as already observed [10].

    Information Technology and Productivity

    Brynjolfsson [14] provides a comprehensive review of empirical studies that examine the relationship of information technology (IT) and productivity. In this study, IT is broadly defined to include particular kinds of software systems, such as transaction processing and strategic information systems, to general-purpose computing resources and services. Accordingly, he notes that some studies examine the dollars spent on IT or different types of software systems, compared to the overall profitability or productivity of the organizations that have invested in IT. Furthermore, his review examines studies falling into manufacturing and service sectors within the US economy, or in multiple economic sectors. However, none of the studies reviewed in the preceding sections of this report are included in his review.

    The overall focus of his review is to examine the nature of the so-called `productivity paradox' that has emerged in recent public discussions about the economic payoffs resulting from organizational investments in IT. In short, the nature of this paradox indicates that there is little or no measurable contribution of IT to productivity of organizations within an economic sector or to the national economy. His analysis then identifies four issues that account for the apparent productivity paradox. These are:

    • Mismeasurement of IT inputs and outputs,
    •  Lags due to adaptation and learning how to most effectively utilize new IT,
    •  Redistribution of profits or payoffs attributal to IT, and
    •  Mismanagement of IT within industrial organizations.
    In a closer comparative examination of these studies, Brynjolfsson concludes
    `The closer one examines the data behind the studies of IT performance, the more it looks like mismeasurement is at the core of the productivity paradox.' [14, p. 74]
    Thus, once again it appears that measuring and understanding the productivity impact of new software systems or IT remains problematic, and that one significant underlying cause for this is found in the methods for measuring productivity data.

    Summary of Software Development Productivity Drivers

    From a generous though somewhat naive review of the preceding studies, a number of software productivity drivers can be identified. The generosity comes from identifying the positive experiences or results reported in the preceding studies, and the naivete comes from overlooking the fact that many of the reported experiences or results are derived from analytically restricted studies, or from dubious or flawed analytical methods. Further, most studies fail to describe how they account for variation in productive ability among individual programmers, which has been systemtically shown to vary by more than an order of magnitude [19]. That is, for very large software systems (500K+ code statements), it seems likely that `average programmer' productivity dominates individual variations, while in smaller systems (less than 50K code statements) or those developed by only a few programmers, then individual differences may dominate. Nearly all of the studies cited above examined small systems to some extent. Nonetheless, if we take a positivist view, we find the following attributes of the software application product being developed, the process by which it is developed, and the setting in which it is develop contribute favorably to improving software productivity. However, we can neither reliably predict how much productivity improvement should be expected, nor how to measure the individual or collective contribution of the attributes.

    The attributes of a software project that facilitate high productivity include:

    Software Development Environment Attributes:

    • Fast turnaround development activities and high-bandwidth processing throughput (may require more powerful or greater capacity computing resources)
    •  Substantial computing infrastructure (abundant computing resources and easy-to-access support system specialists)
    •  Contemporary software engineering tools and techniques (use of design and code development aids such as rapid prototyping tools, application generators, domain-specific (reusable) software components, etc., used to produce incrementally development and released software products.)
    •  System development aids for coordinating LSS projects (configuration management systems, software testing tools, documentation management systems, electronic mail, networked development systems, etc.)
    •  Programming languages with constructs closely matched to application domain concepts (e.g., object-oriented languages, spreadsheet languages)
    •  Process-centered software development environments that can accomodate multiple shifting patterns of small group work structures
    Software System Product Attributes:
    • Develop small-to-medium complexity systems (complexity indicated by size of source code delivered, functional coupling, and functional cohesion)
    •  Reuse software that supports the information processing tasks required by the application
    •  No real-time or distributed systems software to be developed
    •  Minimal constraints for validation of data processing accuracy, security, and ease of alteration
    •  Stable system requirements and specifications
    •  Short development schedules to minimize chance for project circumstances to change
    Project Staff Attributes:
    • Small, well-organized project teams. Large teams should be organized into small groups of 3-7 experienced developers, comfortable working with each other
    •  Experienced software development staff (better if they are already familiar with application system domain, or similar system development projects)
    •  Software developers and managers who collect and evaluate their own software production data and are rewarded or acknowledged for producing high data value software
    •  A variety of teamwork structures and the patterns of shifts between them during task performance.
    The factors that drive software costs up should be apparent from this list of productivity drivers. Software cost drivers are the opposite of productivity drivers. For example, software without real-time performance should be produced more productively or at lower cost than comparable software with real-time performance requirements.

    Also, it should be clear from this list that it is not always possible or desirable to achieve software productivity enhancements through all of the project characteristics listed above. For example, if the purpose of a project is to convert the operation of a real-time communications system from one computer and operating system to another computer-operating system combination, then only some of the characteristics may apply favorably, while others are inverted, occurring only as inhibitors. In this example, conversion suggests a high potential for substantial reuse of the existing source code. However, if the new code added for the conversion affects the system's real-time performance, or is spread throughout the system, then productivity should decrease and the cost increase. Similarly, if the conversion is performed by a well-organized team of developers already experienced with the system, then they should complete the conversion more productively than if a larger team of newly hired programmers is assigned the same responsibility.

    Finally, if instead of viewing software productivity improvement from a generous and naive point of view, we seek to understand what affects software productivity in a way that project managers and developers find meaningful, then we need an approach fundamentally different than those surveyed above. To achieve this, we must first articulate some of the analytical challenges that must be taken into account. This challenge is the subject of Section 4. We also need to develop analytical instruments or tools that allow us to model and measure software production in ways that managers and developers can employ during LSS projects. This effort may lead us away from numbers and simple quantitative measures, and toward symbolic and qualitative models that incorporate nominal, ordinal, interval and ratio measures of software production. The capacity to accomodate these types of measures is well within the capabilities of symbol processing systems, but generally beyond strictly numerical productivity models. Ultimately, we should articulate an operational knowledge-based model that represents the software production process [22,23,40]. Such an operational model could then provide both a framework and compatible computational vehicle for measuring software production, as well as accomodate simulations of how projects work, and what might happen if certain project attributes were altered. This is the subject of Section 5.

    Challenges for Software Productivity Measurement

    In order to understand the variables that affect software productivity, people interested in measuring it must be able to answer the following five questions: (a) Why measure software productivity? (b) Who (or what) should measure and collect software productivity data? (c) What should be measured? (d) How to measure software productivity? (e) How to improve software productivity? The purpose of asking these questions is to appreciate the complexity of the answers as well as to see that different answers lead to different software production measurement strategies. Unfortunately, as we have begun to see in the preceding section, the answers made in practice can lead to undesirable compromises in the analytical methods employed, or in the reliability of the results claimed.

    Why measure software productivity?

    To date, a number of reasons for measuring software productivity have been reported. In simplest terms, the idea is to identify (and measure) how to reduce software development costs, improve software quality, and improve the rate at which software is developed. In practical terms, this includes alternatives such as:
    • increase the volume of work successfully accomplished by current staff effort,
    •  accomplish the same volume of work with a smaller staff,
    •  develop products of greater complexity or market value with the same staff workload,
    •  avoid hiring additional staff to increase workload,
    •  rationalize higher levels of capital-to-staff investment,
    •  reduce error densities in delivered products, and decreasing the amount of time and effort needed to rectify software errors,
    •  streamline or downsize software production operations,
    •  identify possible product defects earlier in development,
    •  identify resource utilization patterns to discover production bottlenecks and underutilized resources,
    •  identify high-output or responsive personnel to receive rewards, and
    •  identify low-output personnel for additional training or reassignment.
    Clearly, there are many reasons for measuring software productivity. However, once again it may not be desirable to try to accomplish most or all of these objectives through a single productivity measurement program. For example, different people involved in a large software project may value certain of these alternatives more than others. Similarly, each alternative implies certain kinds of data be collected. This diversity may lead to conflicts over why and how to measure software productivity which, in turn, may lead to a situation where the measured results are inaccurate, misleading, or regarded with suspicion [cf. 25,36]. Thus, a productivity measurement program must be carefully design to avoid creating conflicts, mistrust, or other conditions for mismeasurement within the software projects to be studied. Involving the software developers and project managers in the design of the measurement instrument, data collection, and feedback program can help minimize the potential organizational problems as well as gain their support.

    Who should measure software productivity data?

    The choice of who (or what) should collect and report software production data is determined in part by the reasons for measuring productivity noted above. The choices include:
    • Programmer self report,
    •  Project or team manager,
    •  Outside analysts or observers,
    •  Automated performance monitors.
    Programmer or manager self-reported data are the least costly to collect, although they may be of limited accuracy. However, if productivity measures are to be used for personnel evaluation, then one should not expect high reliability or validity in self-reported data. Similarly, if productivity measures are employed as the basis of allocating resources or rewards, then the data reporters will have an incentive to improve their reported production values. This is a form of the Hawthorne Effect, whereby people seek to accomodate their (software production) behavior to generate data values they think the data evaluators want to see.

    Instead, we want to engender a software production measurement capability that can feed back useful information to project managers and developers in a form that enhances their knowledge and experience over time. Outside observers can often collect such information, but at a higher cost than self report. Similarly, automated production performance monitors may be of use, but this is still an emerging area of technology requiring more insight for what should be measured and how. For example, Irving and associates [25] report that use of automated performance monitoring systems is associated with perceived increase in productivity, more accurate assessment of worker performance, and higher levels of organizational control. However, where such mechanisms have been employed, workers indicate that managers overemphasize such quantitative measures, and underemphasize quality of work in evaluating worker performance. Workers also reported increased stress, lower levels of job satisfaction, and a decrease in the quality of relationships with peers and managers. Ultimately, one would then expect that these negative factors would decrease productivity and increase staff turnover. Thus, the findings reported by Irving suggest some caution in the use of automated performance monitors. Collecting high-quality production data and providing ongoing constructive feedback to personnel in the course of a project is thus a longer-term goal.

    Overall, if data quality or accuracy is not at issue, self-reported production data is sufficient. If causal behavior or organizational circumstances do not need to be taken in account, then automated performance monitors can be used. Similarly, if the desire is to show measured improvement in software productivity, whether or not production improves, self-reported or automated data collection procedures will suffice.

    On the other hand, if the intent of the productivity measurement program is to ascertain what affects software production, and what alternative work arrangements might further improve productivity, then reliable and accurate data must be collected. Such data might best be collected by analysts or observers who have no vested interest in particular measured outcomes, nor who will collect data to be used for personnel evaluation. In turn, the collected data should be analyzed and results fed back to project developers and managers in a form they can act upon. Again, this might be best facilitated through the involvement of representative developers and managers in the design of the data collection effort.

    What should be measured?

    The choices over what to measure are many and complex. However, it is clear that focusing on program product attributes such as lines or code, source statements, or function points will not lead to significant insights on the contributing or confounding effects of software production process or production setting characteristics of software productivity, nor vice versa. The studies in earlier sections clearly indicate that different LSS product, process, and production setting characteristics individually and collectively affect software productivity. However, based on this survey, there are some inconsistencies in determining which characteristics affect what increase or decrease in software productivity. As such, an integrated productivity measurement or improvement strategy must account for characteristics of the products, processes, and settings to delineate the potential interrelationships. This is necessary since we cannot predict beforehand which constituent variables will reveal the greatest significance or variance in different projects or computing environments. Similarly, we should expect that software product, process, and setting characteristics will need to be measured using a combination of nominal, ordinal, interval and ratio measures. As such, consider in turn, the following constituents of software products, production processes, and production settings.

    Software Products:

    Software projects produce a variety of outcomes other that source code. Each product is valuable to either the individual developers, project managers, project organization, or the client. Therefore, we should not limit production measurement attention to only one product, especially if comparable effort is committed to producing other closely related products. The point here is that since software projects produce many products along the way, our interest should be focussed on ascertaining the distribution of time, skill, teamwork, and value committed to developing each product. Accordingly, we can see the following kinds of products resulting from a software development project.
    • Delivered (new versus modified) source statements for successive software life cycle development stages, including those automatically transformed or expanded by software tools, such as application generators.
    •  Software development analyses (knowledge about how a particular system was produced): requirements analysis, specifications, architectural and detailed designs, and test plans,
    •  Application-domain knowledge: knowledge generated and made explicit about the problem domain (e.g., how to electronically switch a large volume of telephone message traffic under computer control),
    •  Documents and artifacts: internal and external reports, system diagrams, and terminal displays produced for development schedule milestones, development analyses, user manuals, and system maintenance guides, and
    •  Improved software development skills, new occupational or career opportunities for project personnel, and new ways of cooperating in order to develop other software products.

    Software Production Process:

    LSS are often produced through a multi-stage process commonly understood in terms of the system life cycle: from its inception through delivery, sustained operation, and retirement. But if requirements are frequently renegotiated, if senior software engineers quit after the preliminary architectural design, or if there are no modern software requirements, specification, or design aids employed, then we might expect that the coding phase may show comparatively low productivity, and that test and integration show comparatively high cost. However, we should observe that none of the studies cited in Section 3 collected and analyzed data that addresses such issues.

    Since each production process activity can produce valuable products, why is it conventional to measure the outcome of one of the smallest effort activities in this process of developing large software systems, coding. A number of studies such as those reported by Boehm [9] indicate that coding usually consumes 10-20% of the total LSS development effort. On the other hand, software testing and integration consume the largest share, usually representing 50% of the development effort. Early development activities consume 10-15% each. Clearly, delivered software source code is a valuable product. However, it seems clear that code production depends on the outcomes and products of the activities that precede it.

    In general, software projects do not progress in a simple sequential order from requirements analysis through specification, design, coding, testing, integration, and delivery. However, this does not mean that such activities are not performed with great care and management attention. Quite the contrary. Although the project may be organized and managed to produce requirements, specification, design, and other documents according to planned schedule, the actual course of development work is difficult to accurately predict. Development task breakdowns and rework are common to many projects [6,7,41]. Experience suggests that software specifications get revised during later design stages, requirements change midway through design, software testing reveals inadequacies in certain specifications, and so forth [50,52]. Each of these events leads to redoing previously accomplished development work. As such, the life cycle development process is better understood not as a simple linear process, but rather as one with many possible paths that can lead either forward or backward in the development cycle depending on the circumstantial events that arise, and the project conditions that precede or follow from the occurence of these events.

    If we want to better estimate, measure, and understand the variables that affect software production throughout its life cycle, we need to delineate the activities that constitute the production process. We can then seek to isolate which tasks within the activities can dramatically impact overall productivity. The activities of the software life cycle process to be examined include:

    • System requirements analysis: frequency and distribution of changes in operational system requirements throughout the duration of the project,
    •  Software requirements specifications (possibly including rapid prototypes): number and interrelationship of computational objects, attributes, relations and operations central to the critical components of the system (e.g., those in the kernel),
    •  Software architecture design: complexity of software architecture as measured by the number, interconnections, and functional cohesion of software modules, together with comparable measures of project team organization. Also, as measured by the frequency and distribution of changes in the configuration of both the software architecture and the team work structure.
    •  Detailed software unit design: time spent to design a module given the number of project staff participating, and the amount of existing (or reused) system components incorporated into the system being developed,
    •  Software unit coding (or application generation): time to code designed modules, and density of discovered inconsistencies (bugs) found between a module's detailed design and its source code,
    •  Unit testing, integration, and system testing: ratio of time and effort allocated to (versus actually spent on) testing, effort spent to repair detected errors, density of known error types, and the amount of automated mechanisms employed to generate and evaluate test case results,
    Similar variables for consideration can also be articulated for other system development and evolution activities including quality assurance and configuration management, preliminary customer (beta-site) testing, customer documentation production, delivery turnover, sustained operation and system evolution.

    In addition, we must also appreciate that software production can be organized into different modes of manufacture and organization of work, including:

    • Ad hoc problem solving and articulation work [6,7,33,41]
    •  Project-oriented job shop, which are typical for software development projects [50]
    •  Batched job shops, for producing a family or small volume of related software products
    •  Pipeline, where software production is organized in concurrent multi-stage development, and staff is specialized in particular development crafts such as `software requirement analysts', `software architects', or `coders'.
    •  Flexible software manufacturing systems, which represent one view of a `software factory of the future' [53].
    •  Transfer-line (or assembly line), where raw or unfinished information resources are brought to semi-skilled software craftspeople who perform highly routinized and limited fabrication or assembly tasks.
    Accordingly, the characteristics that distinguish these potential modes of software production from one another are their values along a set of dimensions that include (1) developer skill requirements, (2) ease of productivity measurement and instrumentation, (3) capitalization, (4) flexibility of procedures to accomodate development anomalies, and (5) ability to adopt and assimilate software innovations.

    Software Production Setting:

    Does the setting where software is produced make a difference on productivity? Do we expect that LSS production at, say, TRW Defense Systems Group is different than at the MCC Software Technology Program Center, the Artificial Intelligence Laboratory at MIT, the Data Processing Center at Aetna Life and Casualty, or the Refinery Software Center at Exxon? Do we expect that LSS production in various development organization departments of the same corporation is different? The answer to all is yes. Software production settings differ in a number of ways including:
    • Programming language in use (Assembly, Fortran, Cobol, C, C++, Ada, CommonLisp, Smalltalk, etc.)
    •  Computing applications (telecommunications switch, command and control, AI research application, database management, structural analysis, signal processing, refinery process control, etc.)
    •  Computers (SUN-4 workstations, DEC-VAX, Amdahl 580, Cray Y-MP, Symbolics 3670, PC-clone, etc.) and operating systems (Unix variants, VAX-VMS, IBM-VM, Zetalisp, MS-DOS, etc.)
    •  Differences between host (development) and target (end-user) computing environment and setting, as well as between computing server and client systems
    •  Software development tools (compilers, editors, application generators, report generators, expert systems, etc.) and practices in use (hacking, structured techniques, modular design, formal specification, configuration management and QA, MIL-STD-2167A guidelines, etc.)
    •  Personnel skill base (number of software staff with no college degree, degree in non-technical field, BS CS/EE, MS CS/EE, Ph.D. CS/EE, etc.) and experience in application area.
    •  Dependence on outside organizational units (system vendors, Marketing, Business Analysis and Planning, laboratory directors, change control committees, clients, etc.)
    •  Extent of client participation, their experience with similar application systems, and their expectation for sustained system support
    •  Frequency and history of mid-project innovations in production computing environment (how often does a new release of the operating system, processor memory upgrade, new computing peripherals introduced, etc. occur during prior development projects)
    •  Frequency and history of troublesome anomalies and mistakes that arise during prior system development projects (e.g., schedule overshoot, budget overrun, unexpected high staff turnover, unreliable new release software, etc.)

    How to measure software productivity?

    Measuring software productivity presupposes an ability to construct a measurement program comparable to those employed in experimental designs for behavioral studies [18]. This is necessary to insure that the measures employed are reliable, valid, accurate, and repeatable. This in turn implies that choices must be made with respect to the following concerns:

    Productivity measurement research design and sampling strategy

    Simply put, there are at least three kinds of choices for research design: qualitative case studies, quantitative surveys, and triangulation studies. Qualitative case studies can provide in-depth descriptive knowledge about how software production work occurs, what sort of problems arise and when. Such studies are usually one-shot affairs that are low-cost to initiate, employ open-ended anthropological data collection strategies, usually require outside analysts, and produce rich, but not necessarily generalizable findings. Multiple, comparative case studies are much less common and require a greater sustained field research effort. van den Bosch and associates [13] describe comparative qualitative case study designs for studying software production, while [6,7] provide detailed examples.

    Quantitative survey studies employ some form of instrumentation such as a questionnaire to gather data in a manner well-suited for statistical analysis. The survey sample must be carefully defined to insure reliable and valid statistical results. In constrast to qualitative studies, survey studies require data analysis skills that are more widely available and supported through automated statistical packages. Consider the following scenario of the sequence of activities entailed in the preparation and conduct of a quantitative study:

    1. Develop productivity data collection instrument (form or questionnaire)
    2.  Pilot test and revise initial instrument to be sure that desired data can be collected from subjects with modest effort
    3.  Implement data collection activity and schedule with plans to follow-up on first- and second-round non-respondents (never expect that everyone will gladly cooperate with your data collection program)
    4.  Validate and `clean' collected data (to remove or clarify ambiguous responses)
    5.  Code data into analytical variables, scale, and normalize
    6.  Apply selected variables to univariate analysis to determine first-order descriptive statistics, second-order variables, and factors for analysis of variance
    7.  Select apparently significant first- and second-order variables from univariate analysis for multivariate analysis, partial correlations, and regression analysis
    8.  Formulate an analytical model of apparent quantitative relationship between factors
    9.  Formulate descriptive model of analyzed statistical phenomenon to substantiate findings and recommendations.
    The chief drawback of surveys is that they usually do not capture the description of process phenomena, and so they provide low-resolution indicators of causal relationships among measure variables. Therefore, surveys are best suited for `snap-shot' studies, although multiple or longitudinal survey studies are also possible but more costly and less common.

    Triangulation studies attempt to draw from the relative strengths of both qualitative and quantative studies to maximize analytical depth, generalizability and robustness. In short, triangulation studies seek to use qualitative field studies to gain initial sensitivity to critical issues, use surveys based on the field studies to identify the frequency and distribution of variables that constitute these issues from a larger population, then derive from these a small select sample of projects/work groups for further in-depth examination and verification. Such research designs are quite scarce in software production measurement because of the cost and diversity of skills they require. This may just be a way of saying that high-quality results require a substantial research investment. van den Bosch and associates [13] propose one such study design whose baseline cost is estimated in the range of 1-3 person years of effort.

    Unit of analysis

    The concern here is deciding what are the critical things to study. As we indicated in Section 4.3, there is a multitude of factors that can potentially affect software productivity. However, it should be clear that all these factors are not simultaneously affective. Instead, their influence may be circumstantial or spread out over time and organizational space. Software products, production processes, and production setting characteristics all can be influential but not necessarily  the same time or with the same computing resources. This leads us to recognize that the subject for our analysis of software productivity should be the life history of a software project in terms of its evolving products, processes, and production settings. An awareness of this also impinges on the choice for research design and sampling strategy.

     

    Level and terms of analysis

    Together with the unit of analysis, the level and terms characterize the basis for determining the scope and generalizability of a software productivity analysis. The level of analysis indicates whether the resulting analysis covers micro-level behavior, macro-level behavior, or some span in between. Given the unit of analysis, should software productivity be examined at the level of individual programmers, small work groups, software life cycle activities, development organization, company, or industry? The level(s) chosen determines the granularity of data needed, as well as how it can be aggregated to increase the scope and generalizability of the analysis. Experience suggests that analysis of data collected across three or more consecutive levels provide very strong results (cf. [13,40,41,51]), as opposed to those cited in Section 3 which typically employ only one level. But the greater the desired scope and generalizability, the more carefully systematic the data collection and analysis must be.

    The terms of analysis draw attention to the language and ontology of a productivity analysis and the analyst. Assuming the unit and levels for analysis, choices of which analytical vocabulary or rationale to use foreshadows the outcomes and consequences implied by the analysis. Most analysis of software productivity are framed in terms expressing economic `costs', `benefits', and `organizational impacts.' However, other rationales are commonly employed which broaden the vocabulary and scope of an analysis. For example, Kling and Scacchi [35] observe at least five different kinds of rationale are common: respectively, those whose terms emphasize (a) features of the underlying technology, (b) attributes of the organization setting, (c) improving relations between software people and management, (d) determining who can affect control over, or benefit from, a productivity measurement effort (addressing organizational politics), and (e) the ongoing social interactions and negotiations that characterize software production work. The point of such diverse rationales and their implied terms of analysis is to recognize that no simple account can be rendered which completely describes what affects software productivity in a particular setting. Instead, what might be the best choice is to interpret an analysis in terms of each rationale to better identify which rationale is most informing in a particular situation. But whatever the choice, the analysis will be constrained by the terms built into the data collection instruments.

    How to improve software productivity?

    In addressing this question, Boehm [10] identifies a number of strategies for improving software productivity: get the best from people, make development steps more efficient, eliminate development steps, eliminate rework, build simpler products, and reuse components. These strategies draw attention to the development activities or processes that add value to an emerging software system. Cusumano [20] independently reports on how these same strategies are regularly practiced in various Japanese software factories to achieve software productivity improvements. However, Boehm does not indicate how these productivity improvement opportunities are or should be measured to ascertain their effectiveness, nor can he or anyone else state how much improvement each strategy or a combined strategy might realize.

    Clearly, much more needs to be explained in order to begin to adequately answer this question.

    Summary

    Large-scale studies of software productivity (i.e., across multiple software projects in many different settings) necessitate collecting of a plethora of data. The number and diversity of variables identified above indicate that software productivity cannot be understood simply as a ratio of the amount of source code statements produced for some unit of time. Instead, understanding software productivity requires a more systematic analysis of a variety of types of production measures, as well as their interrelationships. This suggests that we need a more robust theoretical framework, analytical methods, and support tools to address the dilemmas now apparent in understanding and measuring software productivity.

    Alternative Directions for Software Productivity Measurement and Improvement

    We need a fundamental shift in understanding what affects software productivity. In particular, new effort should be directed at the development of a knowledge-based software productivity analysis system capable of modeling and simulating the production dynamics of a software project in a specific setting. In order to develop such a system, it is appropriate to also develop project-specific theories of software production, cultivate software productivity drivers, and develop techniques for utilizing qualitative (symbolic) project data.

    Develop Setting-Specific Theories of Software Production

    Standard measures, such as lines of code produced, represent data that are relatively easy to collect. However, they are also the least useful in informing our understanding for what affects, or how to improve software productivity. We lack an articulated theory of software production. This report identifies a number of elements that could be the constituents of such a theory. In principal, these include the software products, the processes which give rise to these products, and the computational and organizational characteristics that facilitate or inhibit the processes. Clearly, developing such theory is a basic research problem, and a problem that must be informed by systematic empirical examination of current software development projects and practices. Such theory could be used to construct new models, hypotheses, or measures that account for the production of large software systems in different settings [cf. 4,18]. Similarly, such models and measures could be tuned to better account for the mutual influence of product, process, and setting characteristics specific to a project. This in turn could lead to simple, practical, and effective measures of software production that give project managers and developers a source of information they can use to improve the quality characteristics of their products, processes, and settings.

    Identify and Cultivate Software Productivity Drivers

    In the apparent rush to measure software productivity, we may have lost sight of a fundamental concern: why are software developers as productive as they are in the presence of many technical and organizational constraints? The potential for productivity improvement is not an inherent property of any new software development technology [35]. Instead, the people who develop software must effectively mobilize and transform whatever resources they have available to construct software products. Software developers must realize and articulate the potential for productivity improvement. New software development technologies can facilitate this articulation. But other technological impediments and organizational constraints can nullify or inhibit this potential. Thus, a basic concern must be to identify and cultivate software productivity drivers, whether such drivers are manifest as new computing resources, or alternative organizational or work arrangements.

    Section 3.14 identifies a number of productivity drivers that (weakly) follow from a number of software productivity measurement studies. These drivers primarily represent technological resource alternatives. Related research [50,52] also identifies a set of project management strategies that seek to improve software production through alternative social and organizational work arrangements. These strategies are identified through research investigations into the practice of software development in complex settings [e.g., 6,7,35,41,52]. These studies are begining to show that the development project's organizational history, idiosyncratic workplace incentives, investments in prior technologies and work arrangements, local job markets, occupational and career contingencies, and organizational politics can dramatically affect software productivity potential, either positively or negatively [6,29,35,50]. Further, in some cases it appears that such organizational and social conditions dominate the productivity contribution attributable to in-place software development technologies. In other words, in certain circumstances, changing the organization conditions or work arrangements might have far greater an effect in improving software productivity potential than by merely trying to `fix things' by installing new technology. Software productivity improvement will not come from better software development technologies alone. Organizational and project management strategies to improve software productivity potential must be identified, made explicit, and supported.

    Develop Symbolic and Qualitative Measures of Software Productivity

    We should develop a rich understanding of how software production occurs in a small number of representative software projects. From this, we can articulate an initial qualitative process model of software production that incorporates subjective and impressionistic data from local software development experts. Then use this model and data to determine what further quantitative data to collect, as a basis for refining and evolving the process model. Overall, the idea here is to first determine what we should measure before beginning to collect data. Data that is out there and easy to collect, such as lines of code, does not necessarily tell us anything about how those lines of code were produced, what tools were used, what problems were encountered, who wrote what code, etc. Instead, we should seek to be in touch with the people who develop software since it is reasonable to assume that they can identify their beliefs for what works well in their situation, what enhances their productivity, and what improves the quality of their products. Quantitative data can then be used to substantiate or refute the frequency and distribution of the findings described in qualitative terms. Subsequently, this should lead to the development of a family of process models that accounts for a growing range and scope of software production.

    Develop Knowledge-Based Systems that Model Software Production

    We should seek an integrated approach to capture and make explicit an empirically-grounded understanding of software production in a computational model. This model should embody a computational framework for capturing, describing, and applying knowledge of how software development projects are carried out and managed [22,40,41,42]. New software process modelling technology in the form of knowledge-based systems is emerging [e.g., 22,23,40]. This technology appears to be well-suited to support the acquisition, representation, and operationalization of the qualitative knowledge that exists within a software development project. Readers interested in a specific realization of this approach should consult [40,41,42]. However, any software process engineering environment or knowledge engineering system capable of modeling, simulating, and enacting software products, production processes, production settings and their interrelationships could be employed.

    Knowledge acquisition:

    We can acquire knowledge about software projects by conducting in-depth, observational field studies [e.g, 7]. Ideally, such studies should be organized to facilitate comparative analysis. The data to be collected should account for the concerns described in Section 4. This in turn requires the articulation of a scheme for data collection, coding, and analysis. The focus should be directed at gathering and organizing information about the life history of a software development project in terms of its products, processes, and setting attributes described earlier. The goal is to be able to develop a descriptive model of software production such that any analytical conclusion can be traced back to the original data from which it emerged. Subsequently, this descriptive model must capture the knowledge we seek in a form that can then be represented and processed within a knowledge-based system.

    Knowledge representation:

    The area of knowledge representation has long been an active area of research in the field of Artificial Intelligence. Thus discussions of topics or approaches can get easily bogged down in debates over implementation technology, philosophy, and the like. Suffice to say that a knowledge organization scheme is essential, and that such a scheme must again accomodate the kinds software production data outlined in Section 5. A suggestive starting point that others are working from is the Schema Representation Language described by [49], and utilized by [47], or the Software Process Specification Language (SPSL) used in [40,41,42]. For example, in their representation of system development projects, Scacchi and colleagues [22,23,40,41,42,51] developed a scheme for organizing and representing knowledge about organizational settings, resource arrangements, development plans, actions, states, schedules, histories, and expectations. In turn, they elaborate the relationships between these concepts using data derived from detailed narrative descriptions of system development projects [e.g., 6,33] to illustrate their approach. Ultimately, the goal of such a scheme for representing knowledge about software development projects is to facilitate computational analysis, simulation, querying, and explanation [40,41].

    Knowledge operationalization:

    A knowledge base about software production projects provides an initial basis for developing an operational model of software production. Such a knowledge-based system requires (1) a knowledge base for storing facts, hueristics, and reasoning strategies according to the previous scheme, (2) a question-answering subsystem for retrieving facts stored or deduced from known relationships among facts, and (3) a simulator for exploring alternative trajectories for software development projects. Suggestive elaborations of such systems are available [22,23,40,47,49] and recommended. For example, assuming an interesting knowledge base has already been stored, the question-answering subsystem could be used to answer queries of the following kinds: (1) who was the developer responsible for a particular action or situation, (2) what were the project development circumstances during a particular time (schedule) interval, (3) when was a particular circumstance true or when was the action done, (4) where was a specified action performed, (5) how was some software design task accomplished, and (6) why was a certain document produced. More specific questions such as these can be answered by retrieval from the knowledge base, either by direct retrieval, property inheritance, or by inference rules [22,40,49].

    Simulate and measure the effects of productivity enhancements

    The design of a knowledge-based system that simulates software production requires an underlying computational model of development states, actions, plans, schedules, expectations (e.g., requirements), and histories in order to answer `what if' questions [40]. Ultimately, the operation of the simulator depends upon the availability of a relevant knowledge base of facts, hueristics, and reasoning strategies found in development projects.

    Consider the following scenario of the simulator use: We have developed or acquired a knowledge-based software production simulation system of the kind outlined above. The simulator's user is a manager of a new project in a particular setting and wants to determine an acceptable schedule for the project (and thus certain attributes affecting productivity). Knowledge about the setting and the project have not yet been incorporated into the knowledge base. The user interacts with the simulator to elicit the relevant attributes of the setting, project, and schedule then enter them into the knowledge base. The user starts the simulation through an interactive question-answering dialog. The simulator would proceed to compare the particular facts related to the user's queries against prior project knowledge already accumulated in the knowledge base and tries to execute the proposed production schedule. This would give rise to changes in the simulated project states, actions, (sub-)schedules, expectations, and histories consistent with those inferred from the hueristics and reasoning strategies. The simulation finishes when the full schedule is executed, or halt when it reaches a state where it is inhibited. Such a state reflects a point in the project where some bottleneck emerges - for example, a key computing resource is overutilized, or some other precondition for a critical production step cannot be met [6,7,41,42]. Analysis of the conditions prevailing in the simulated project at this point helps the user draw useful conclusions about critical interactions between various organizational units, development groups, and computing resource arrangements that facilitate productive work. The simulation may be redone with different setting or project attributes in order to further explore other hueristics for improving productivity in the project.

    Ultimately, the simulation embodies a deep model of software production that in turn can be further substantiated with quantified data as to the frequency and distribution of actions, states, etc. arising in different software development projects.

    An Approach

    The approach to developing a knowledge-based software productivity modeling and simultion system described above is a radical departure from conventional approaches to understanding and measuring software productivity. Accordingly, the following sequence of activities could be performed as a strategy for evaluating the utility of such an approach:
    • Initiate comparative case studies or surveys of current in-house software production practices. These studies serve to provide an initial baseline data of software project products, production processes, and production setting characteristics.
    •  Clean and analyze collected data using available skills and tools. This is to provide a statement of baseline knowledge about apparent relationships between measured software production variables. This and the preceding step correspond to `knowledge acquisition' activity described above.
    •  Codify subsets of available software project data in a knowledge specification language, such as SPSL [40,41,42]. This step corresponds to an initial realization of the `knowledge representation' and `knowledge operationalization' activities described above.
    •  Demonstrate results in the computational language and processor suggested earlier [40].
    •  Embed the software productivity modeling and simulation system within an advanced CASE environment in order to demostrate its integration, access, and software production guidance on LSS development efforts [22,23,40,41,42,53].

    Conclusions

    What affects software productivity and how do we improve it? This report examines the state of the art in measuring and understanding software productivity. In turn, it descries a framework for understanding software productivity, identifies some fundamentals of measurement, and surveys selected studies of software productivity. This survey helps identify some of the recurring variables that affect software productivity. As a results of the analysis of the shortcomings found in many of the surveyed studies, we then identify an alternative knowledge-based approach for research and practice in understanding what affects software productivity. This approach builds upon recent advances in modeling, simulating, and enacting software engineering processes situated within complex organizational settings. Also, this approach enables the construction of an organizational knowledge base on what affects software productivity and how. Thus, we are optimistic about the potential for developing knowledge-based systems for modeling, simulating, and reasoning about software development projects as a new way to gain insight into what affects software productivity.

    Acknowledgements This work was part of the USC System Factory Project, supported by contracts and grants from AT&T, Northrop Corp., Office of Naval Technology through the Naval Ocean System Center, and Pacific Bell. Additional support provided by the USC Center for Operations Management, Education and Research, and the USC Center for Software Engineering. Preparation of the initial version of this report benefited from discussions and suggestions provided by Dave Belanger, Chandra Kintala, Jerry Schwarz, and Don Swartout of the Advanced Software Concepts Department at AT&T Bell Laboratories, Murray Hill, NJ. Pankaj Garg, Abdulaziz Jazzar, Peiwei Mi, and David Hurley provided helpful comments on subsequent versions of this report. The collective input of these people is appreciated, but not of their doing if misstated or misrepresented.

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    http://www.ics.uci.edu/~wscacchi/Process_Life_Cycle.html Process Life Cycle Engineering

    The life cycle engineering of software/business processes and capabilities:

    An experience report


    Walt Scacchi, Information and Computer Science Dept ., University of California, Irvine, CA 92697-3425
    Copyright © 1997-2002 Walt Scacchi, All Rights Reserved.

    This presentation can be found on the WWW at the URL: http://www.ics.uci.edu/~wscacchi/Process_Life_Cycle.html

    Overview

    • Background, Definitions, and Sources of Experience
    • Upstream Process Engineering
      • Meta-Modeling
      • Definition and Modeling
      • Analysis
      • Simulation
      • Redesign
    • Midstream Process Engineering
      • Prototyping, Walkthrough, and Training
      • Transition Planning and Change Management
      • Administration: Staffing and Scheduling
      • Integration: Data and Systems
      • Target Support Environment Generation
    • Downstream Process Engineering
      • Instantiation and Enactment
      • Monitoring and Measurement
      • Enactment History Capture and Replay
      • Articulation
      • Evolution: Continuous Improvement and Asset Management
    • Advanced Topics and In-Progress Efforts
    • Conclusions

    • A companion paper providing an overall description of this process engineering life cycle can be found at http://www.usc.edu/dept/ATRIUM/Papers/Process_Life_Cycle.html .

    Return to Top

    Background and Definitions

    • Our focus is targeted at the engineering of complex business capabilities or processes like software development across their life cycle.
    • A capability represents the processes, organization staffing, and information infrastructure, as well as their interrelationships, for a recurring business activity that produces products or services.
    • The web of relationships among the objects and attributes of a product, process, organization, infrastructure, or total capability defines its architecture.

    • A paper providing an overall description of a software development capability architecture can be found at http://www.usc.edu/dept/ATRIUM/Papers/Process_Meta_Model.ps .
      The overall architectural strategy for a software production infrastructure that supports the process life cycle was previously presented in the paper W. Scacchi, "The Software Infrastructure for a Distributed System Factory," IEE Software Engineering Journal,, Vol. 6(5), 355-369, (1991).

      Another paper which surveys architectural features of more than 60 software environments can be found at http://www.usc.edu/dept/ATRIUM/Papers/MetaCASE.ps .

      Return to Overview


    Sources of Experiences Encountered

    • Andersen Consulting (now Accenture)
    • AT&T/Lucent Bell Laboratories
    • CoGenTex
    • Computer Technology Associates
    • EDS
    • Hewlett-Packard
    • HoloSoFx (formerly Active Management Inc.)
    • McKesson Water Products Co.
    • Naval Air Warfare Center (China Lake, CA)
    • Northrop(-Grumann) B-2 Division
    • Office of Naval Research
    • Perceptronics
    • SUN Microsystems Computer Corp.
    • USAF Rome Laboratories

    • Return to Overview


    Meta-Modeling

    • Constructing and refining a process concept vocabulary and logic (an ontology) for representing families of capabilities, processes, and their instances in terms of object classes, attributes, relations, constraints, control flow, rules, and computational methods.
    • Experience: key to achieving process-level interoperability , as shown with ERA Product-centered DB (PBI-Softman), Attributed Petri Nets (CACE-PM/DMS*), Rule-Based Databases (AP5, Matisse), Process Programming Language (SynerVision*), Workflow (WORKFLOW/BPR*), Hybrid Composite (SMART), Others {PIF}.
    • * denotes commercial product.

      Image files that show user displays of (a) a conceptual overview of corporate financial operations, (b) a meta-model class hierarchy we use, and (c) a meta-model schema for the "task-force" class , and (d) a hierarchical view of the component functions of an Accounts Payable financial system can be viewed when selected.

      A paper providing a more detailed description of meta-modeling can be found at http://www.usc.edu/dept/ATRIUM/Papers/Process_Meta_Model.ps .

      Return to Overview


    Definition and Modeling

    • Eliciting and capturing of informal process descriptions, and their conversion into formal process models or process model instances.
    • Experience: Most "as-is" processes are ill-defined and not well understood.
    • Experience: Most process redesign efforts want to primarily focus on "to-be" alternatives, without baselining as-is processes, and without defining "transition" process from as-is to to-be.
    • Experience: Capturing as-is processes is labor-intensive and thus represents an area for further R&D innovations.
    • Image files that show user interface displays of (a) a process task model class hierarchy definition conforming to MIL-STD-2167A, and (b) a process model definition for a programming task in a table format can be viewed when selected.

      A description of the definition format and mechanism can be found in the paper P.K. Garg, P. Mi, T. Pham, W. Scacchi, and G. Thunquest, "The SMART Approach to Software Process Engineering," Proc. 16th. Intern. Conf. Software Engineering,, IEEE Computer Society, Sorrento, Italy, 341-350, (1994).

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    Analysis

    • Logical:Evaluating static and dynamic properties of a process/capability model, including its consistency, completeness, internal correctness, traceability, as well as other semantic checks.
    • Feasibility: Determining whether a proposed process or capability architecture can satisfy existing requirements, given available resources.
    • Statistical: Calculating descriptive and inferential statistics the characterize the frequency, distribution, and associations among process step events, transactions, etc.
    • Reasoning: Pattern-matching queries and inferencing to reason about properties of processes, such as spatial and temporal distribution, organization (who, what, where, when, why, how, what-if, how much, etc.), classification (taxonomic, genericity), configuration (composition, scheduling, replanning, generalization, specialization), and diagnosis.
    • Resource Flow:Determining how to transform process flow to reduce resource utilization (e.g., reduce cycle time and cost).
    • Experience: Best source of high-value, short-term results and payoffs.
    • Experience: Easy to produce management reports or presentation materials.
    • Image files that show user interface displays of (a) a sample of process model analysis checks, (b) process model analysis statistics, and (c) process model analysis view, can be viewed when selected.

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    Simulation:

    Knowledge-Based Simulation

    • Symbolically enacting process models in order to determine the path and flow of intermediate state transitions in ways that can be made persistent, replayed, queried, dynamically analyzed, and reconfigured into multiple alternative scenarios.
    • Experience: High-value technology is infrequently used.
    • Experience: Can produce narrative summaries of simulation runs.
    • Image files that show user interface displays of (a) a process model simulation interaction, and a subsequent (b) a process model simulation narrative trace can be viewed when selected.

      A paper describing the initial design and implementation of this simulation mechanism can be found in, P. Mi and W. Scacchi, "A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes," IEEE Trans. Knowledge and Data Engineering, Vol. 2(3), 283-294, (1990). Reprinted in Nikkei Artificial Intelligence, Vol. 20(1), 176-191, (1991, in Japanese).

      This paper can be found at http://www.usc.edu/dept/ATRIUM/Papers/Articulator.ps
       

    Discrete-Event Simulation

    • Computationally enacting a sample of process models as network flows with heuristic or statistical arrival rates and service times so as to determine the overall process performance envelope, throughput, systematic behavior, and resource bottlenecks.
    • Experience: Although less flexible, easy to use to discover process optimizations.
    • Experience: Visual interactions and presentations always impress.
    • Image files that show user interface displays of (a) a discrete-event process model workflow simulation interaction, and a subsequent (b) simulation results display highlighting distribution of costs and activity-based costs can be viewed when selected.

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    Redesign:

    • Transforming the structure and dynamic flow of data, control, or work products so as to reduce process cycle time, number of steps, number of inter-department or inter-organizational hand-offs, number of repetitive manual processing steps, etc.
    • Benefits from systematic measurement of properties of formal process definition/model to determine which redesign transformations or optimizations may apply.
    • Benefits from the development and application of a taxonomy of previously successful process redesign transformation patterns, rules, and consequences.
    • Experience: Cycle time reductions for recurring, routine business processes of a factor of 10-1 or more are common.
    • Experience: Return on Investment in process redesign effort is often greater than 10-1.
    • Experience: Many, but not all, process redesigns fail during organizational implementation and routinization!

    • Image files that show displays of (a) before and (b) after process redesign, and (c) example measurements on a process model that reveal possible redesign optimization opportunities.

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    Visualization

    • Providing users with graphic views of process models and instances that can be viewed, navigationally traversed, interactively edited, and animated to convey process statics and dynamics.
    • Experience: Process visualizations enable intuitive analysis and discovery.
    • Experience: Visualization appears key to acceptability.
    • Image files that show user interface displays of (a) visual process model editor , (b) spreadsheet-like process browser, (c) graphical process model browser, and (d) graphical process object browser can be viewed when selected.

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    Prototyping, Walkthrough, and Performance Support (Training On Demand)

    • Incrementally enacting partially specified process model instances in order to evaluate process presentation scenarios to end users, prior to performing tool and data integration.
    • Experience: Process prototyping and walkthrough is effective enabler for eliciting user feedback.
    • Experience: Can provide a basis for user empowerment in controlling design and improvement of local processes.
    • Experience: Generation of performance support materials in response to process improvements or changes is well-received.
    • Image files that show user interface displays of (a) a process prototype or walkthrough display, and (b) a view of automatically generated process performance support documentation, followed by (c) another subsequent view of this support documentation produced in HTML can be seen when selected.

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    Transition Planning and Change Management

    • Collaboratively developing a plan identifying the incremental steps organizational participants agree to perform in order to implement redesigned processes within their organization.
    • Changing organizational processes takes time, effort, and other resources, as well as the prioritized commitment of participants to make it succeed.
    • Experience: Transition planning is itself a process that is often overlooked, resulting in negative consequences.
    • Experience: Radical process changes can be accomplished in small, incremental steps.

    • Experience: Participants not commited to process change can engage in "counter-implementation" activities that seek to undercut transition efforts.
       

      Examples selected from a recent BPR engagement displaying a process transition plan with prioritized and scheduled transition steps, plus designation of responsible participants, can be found here.

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    Administration: Staffing and Scheduling

    • Assigning and scheduling specified users, tools, and development data objects to modeled user roles, product milestones, and development schedule.
    • Experience: Incremental and heuristic rescheduling functions always impress managers.
    • Experience: This demonstrates scheduling flexibility that may not be available in other tools.
    • Image files that show user interface displays of (a) a process action precedence order to be scheduled, (b) an initial staff schedule assignment, and (c) an optimized schedule assignment after process analysis and improvement transformations, which can be viewed when selected.

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    Integration: Data, Tool, User Interface

    • Encapsulating or wrapping selected information systems, repositories, and data objects that are to be invoked or manipulated when enacting a process instance.
    • Experience: Can entail a lot of difficult technical work, but its relatively easy to finesse when constructing "concept demostrations."
    • Experience: Growing interest in providing support for integration of wide-area heterogeneous information repositories using the Internet.
    • Image files that show user interface displays of (a) multiple tools bound to process actions that are integrated with underlying object manager (not shown) via software broadcast message server (not shown).

      A paper describing the strategy and mechanisms supporting process integration can be found in, P. Mi and W. Scacchi, "Process Integration for CASE Environments," IEEE Software, Vol. 9(2), 45-53 (1992). Reprinted in Computer-Aided Software Engineering, 2nd. Edition, E. Chikofsky (ed.), IEEE Computer Society, (1993).

      A paper describing the mechanisms support data repository integration can be found at http://www.usc.edu/dept/ATRIUM/Papers/Integrating_Software_Repositories.ps .

      A paper describing the mechanisms support data repository integration with adaptive process enactment within a virtual software development enterprise can be found at http://www.usc.edu/dept/ATRIUM/Papers/DHT-95.ps .

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    Target Support Environment Generation

    • Automatically transforming a process model or instance into a process-based computing environment that selectively presents prototyped or integrated information systems to end-users for process enactment.
    • Experience: Considered a unique capability, not available in other process environments.
    • Experience: Simplifies or eliminates low-level process programming via "application generator" techniques.
    • Image files that show user interface displays of (a) process-encapsulated tool environment that was generated via automated transformation of the modeled and integrated process.

      A description of this mechanism can be found in the paper P.K. Garg, P. Mi, T. Pham, W. Scacchi, and G. Thunquest, "The SMART Approach to Software Process Engineering," Proc. 16th. Intern. Conf. Software Engineering, , IEEE Computer Society, Sorrento, Italy, 341-350, (1994).

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    Instantiation and Enactment

    • Performing the modeled process using the environment by a process engine that guides or enforces specified users or user roles to enact the process as planned.
    • An example display of a modeled process during execution.
    • We provide a "process enforcement policy variable" that allows progressive relaxation of process enactment constraints (e.g., relax process step or product pre-conditions). This supports process maturation, but also increases likelihood of process breakdowns.

    • Return to Overview


    Monitoring and Measurement

    • Collecting and measuring process enactment data needed to improve subsequent process enactment iterations, as well as documenting what process actions actually occurred in what order.
    • Experience: Another feature found very attractive by managers.
    • Experience: Key source of data for process improvement, optimization, or evolution.

    • A paper describing different types of process measurements of interest can be found at M. Nissen, Valuing IT through Virtual Process Measurement, Proc. Intern. Conf. Information Systems, (1994) http://www.usc.edu/dept/ATRIUM/Papers/Process_Measurement.ps .

      Return to Overview


    Enactment History Capture and Replay

    • Graphically simulating the re-enactment of a process, in order to more readily observe process state transitions or to intuitively detect possible process enactment anomalies.
    • Experience: Visualizing and replaying process enactment histories is well-received by managers and executives.
    • Experience: Supports "organizational drill-down" when process anomalies are observed.
    • An image file that shows a user interface display of (a) process enactment event history and timing measurements can be viewed when selected.

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    Articulation

    • Diagnosing, repairing, and rescheduling actual or simulated process enactments that have unexpectedly broken down due to some unmet process resource requirement, contention, availability, or other resource failure.
    • Experience: A research result that is ahead of its time.
    • Image files that show user interface displays of (a) a class hierarchy of articulation diagnosis and repair mechanisms, which can be viewed when selected.

      A paper describing the design and implementation of the process articulation support system can be found in the paper P. Mi and W. Scacchi, "Articulation: An Integrated Approach to the Diagnosis, Replanning, and Rescheduling of Software Process Failures", Proc. 8th. Annual Knowledge-Based Software Engineering Conference, IEEE Computer Society, Chicago, IL (1993). http://www.usc.edu/dept/ATRIUM/Papers/Articulation.ps

      Return to Overview


    Evolution: Continuous Improvement and Asset Management

    • Incrementally and iteratively enhancing, restructuring, tuning, migrating, or reengineering process models and process life cycle activities to more effectively meet emerging user requirements, and to capitalize on opportunitistic benefits associated with new tools and techniques.
    • Formalized process descriptions or models are intellectual property that can be protected through copyright or patent.
    • Formalized process assets can be reused, distributed, and tailored for business practices within a corporate setting or  industrial sector.
    • Experience: One of the few process model repositories, which also accomodates process formalization and knowledge-based operations.
    • Experience: Another research result ahead of its time.
    • Image files that show user interface displays of (a) a process class hierarchy for organizing related process models, which can be viewed when selected.

      A paper describing the design and implementation of a knowledge-based process repository supporting these capabilites can be found in P. Mi, M.J. Lee, and W. Scacchi, "A Knowledge-based Software Process Library for Process-Driven Software Development," Proc. 7th. Annual Knowledge-Based Software Engineering Conference, IEEE Computer Society, Washington, DC, pp. 122-131, (September 1992).

      Return to Overview


    Advanced Topics and In-Progress Efforts

    • Modeling and Simulation for Virtual Software Acquisition
    • Process Technology for Virtual Enterprises
    • Reengineering Organizations for Process-Driven Intranets
    • Process-based Interactive Teleradiology Consultation Support
    • Reengineering Procurement Process Architectures and Acquisition and Management of Research Grants for the U.S. Navy
    • Knowledge Web Management Systems
    • Software Process Reengineering
    Return to Top

    Conclusions

    • Business process engineering is a dynamic team-based endeavor that can lead to mature processes through process prototyping, incremental development, iterative refinement, and the reengineering of ad hoc process task instances and models.
    • Process/capability engineering may be most likely to succeed when focused on high frequency or short cycle-time processes.
    • New techniques for rapid process design, trade-off analysis, and customization are needed.
    • There are "pathological" business processes that are resistant to systematic (re)engineering, and thus should be avoided. These processes are characterized by lack of frequent repetition, ad-hoc process structure or flow, highly creative activities, infrequent but long-duration cycle times, and processes whose formalization overwhelms their simplicity.

    This interactive presentation page is maintained by Walt Scacchi who can be reached at the e-mail address noted above. This page was last updated on 28 September 99. 


    http://www.ics.uci.edu/~wscacchi/Pubs-SF.html Pubs-SF Research Publications on The System Factory Project and Environments [SF/E]
     
    1. [VLSI] "A Language-Independent Environment for Software Engineering," VLSI and Software Engineering Workshop Report, IEEE Computer Society, Catalog No.82-82340, pp. 99-103, (1982)
    2. [VLSI] "Developing a Silicon Engineering Environment," Proc. IEEE Intern. Conf. on Computer-Aided Design, San Jose, CA, pp. 221-222, (1983)
    3. [VLSI] "Developing VLSI Systems with a Silicon Engineering Environment," Proc. IEEE Intern. Conf. on Computer Design, Rye Town, NY, pp. 472-475, (1983)
    4. [VLSI] "The System Factory Approach to VLSI and Software Engineering," Proc. Second Conference on Software Engineering, Nice, France, pp. 295-310, (1984)
    5. [VLSI] "Development Environments for VLSI and Software Engineering", (with R. Katz and P. Subramanyham), J. Systems and Software, Vol. 4(1), pp. 15-27, (1984)
    6. [CM] "An Environment for the Development and Maintenance of Large Software Systems," (with K. Narayanaswamy) Proc. IEEE SOFTFAIR II, pp. 11-23, San Francisco, CA (1985)
    7. [P] "The Software Engineering Environment for the System Factory Project," Proc. 19th. Hawaii Intern. Conf. Systems Sciences, Vol. II-B Software, pp. 822-830, (January, 1986)
    8. [P] "A Unix-based Gist Specification Language Processor," (with A. Castillo and S. Corcoran) Proc. 2nd. Intern. Conf. Data Engineering, pp. 582-589, (February, 1986)
    9. [P] "Developing a Knowledge-Based System Factory: Issues and Concepts," (with L. Eliot), IEEE Expert, Vol. 1(4), pp. 51-58, (Winter, 1986)
    10. [CM] "Maintaining the Configuration of Evolving Software Systems," (with K. Narayanaswamy) IEEE Trans. Software Engineering, Vol. SE-13(3), pp. 324-334, (March, 1987)
    11. [CM] "A Database Foundation for Supporting the Evolution of Large Software Systems," (with K. Narayanaswamy) J. Systems and Software, Vol. 7(1), pp. 37-49, (1987)
    12. [HT] "On Designing Intelligent Hypertext Systems for Information Management in Software Engineering," (with P. Garg), pp. 349-369, Proc. Hypertext '87, Chappel Hill, NC, (November 1987).
    13. [HT] "A Hypertext Environment for Engineering Large Software Systems," (with P. Garg), Proc. 21st. Hawaii Intern. Systems Sciences Conf., Kona, Hawaii, Volume II, pp. 337-346, (January, 1988).
    14. [HT,CM] "A Hypertext Environment for Managing Configured Software Descriptions," (with P. Garg), Software Version and Configuration Control, pp. 326-343, B.G. Teubner, Stuttgart, FRG, (January 1988).
    15. [P] "The USC System Factory Project," (Keynote Address) Proc. Software Symposium '88, pp. 11-42, Software Engineers Association, Tokyo, Japan (June, 1988).
    16. [HT] "Composition of Hypertext Nodes," (with P. Garg), Proc. 12th. Intern. ONLINE Conf., London, England, pp. 63-73, (December 1988)
    17. [P] "The System Factory Approach to Software Engineering Education," in R. Fairley and P. Freeman (eds.) Issues in Software Engineering Education, Springer-Verlag, New York, (1989)
    18. [HT] "The Design and Implementation of Intelligent Software Hypertext Systems," (with P.K. Garg), IEEE Expert, Vol. 4(3), pp. 52-63, (Fall 1989).
    19. [CM] "Assuring the Correctness of Configured Software Descriptions," (with S. Choi), Proc. 2nd. Intern. Work. Software Configuration Management, Princeton, NJ. Appears in ACM Software Engineering Notes, Vol. 17(7), pp. 66-75, (November 1989)
    20. [CM] "Extracting and Restructuring the Design of Large Software Systems," (with Song C. Choi) IEEE Software, Vol. 7(1), pp. 66-73, (January 1990)
    21. [P] "Requirements for an Extensible, Object-Oriented Tree/Graph Editors", (with A. Karrer), Proc. ACM SIGGRAPH Symposium on User Interface Software and Technology, ACM Press, pp. 84-91, (October 1990)
    22. [HT] "A Hypertext Environment to Manage Software Life Cycle Descriptions," (with P.K. Garg), IEEE Software, Vol. 7(1), pp. 90-99, (May 1990). Reprinted in Software Change Impact Analysis, Shawn A. Bohner and Robert S. Arnold (eds.), IEEE Computer Society Press, (1996).
    23. [P] "The Software Infrastructure for a Distributed System Factory" Software Engineering Journal, Vol. 6(5), 355-369, (September 1991). IEE and British Computer Society,
    24. [P,CM,HT] "SOFTMAN: An Environment for Forward and Reverse Computer-Aided Software Engineering," (with S.C. Choi), Information and Software Technology, Vol. 33(9), pp. 664-674, (November 1991).
    25. [HT] "Integrating Heterogeneous Information Repositories: A Distributed Hypertext Approach," (with J. Noll), Computer, Vol. 24(12), pp. 38-45, (December 1991).
    26. [HT] "Hypertext", in J. Marciniak (ed.), Encyclopedia of Software Engineering, John Wiley and Sons, New York, pp. 559-567, (1994).
    http://www.ics.uci.edu/~wscacchi/ResearchBio.html PersonalBiography

    Research Biography for Walt Scacchi

    Over the past 20 years, a substantial majority of Dr. Scacchi's research centers about three interrelated categories:
    • Organizational Studies of Computing/Software Development
    • The System Factory Project and Environments
    • Organizational and Software Processes
    He maintains an active interest and research activity in the first and third categories, while work on the second, the System Factory Project, occurred primarily during 1981-1990. This document highlights significant and representative research problems and results he has developed, as well as the concepts, techniques, and tools that gave rise to these results. There are also citations to 25 or so publications in each category that he has produced to document these research efforts. His research contributions in each of the three categories have been recognized both nationally and internationally, as is shown below. Beyond this, there are also some modest details regarding his teaching and service experience.

    Overall, Dr. Scacchi's research efforts have been focused on the question, how do computing/software systems come to be the way they are? Such a question points to his interest in understanding how computing/software systems are produced and consumed within different organizational settings.

    Organizational Studies of Computing/Software Development [OS]

    Dr. Scacchi's earliest research efforts were dominated by his interest in conducting empirical studies of how computing systems are used and evolved--that is, consumed--in various organizational settings [OS1-OS5,OS7]. He observed, studied and conducted interviews in a municipal government MIS shop, a physics research laboratory, an insurance company, and a computer science research institute. These studies focused on characterizing the attributes, describing the processes, and identifying recurring patterns of organizational behavior surrounding the use, maintenance or evolution of local computing environments. Further, these characteristics, descriptions, and patterns were grounded in the goals, relationships and constraints that participants associated with the computing technology at hand within its organizational context. Concomitant with these studies, he worked with his advisor, Rob Kling, to develop new concepts that would serve as an emerging language for analyzing and understanding instrumental computer use. Principal among these concepts was the "computing package" [OS1,OS3,OS5], "multiple perspectives for understanding computing in organizations" [OS4,OS5], and the "web of computing" [OS7,OS5].

    The computing package refers to a recognition that a given computing system is simultaneously embedded within multiple organizational contexts (e.g., user community, developer setting, maintenance organization, administrative unit invoking policies for access/use of computing resources, etc.). This gives rise to a variety and pattern of conditions that made the instrumental use of computing often problematic [OS3]. Therefore, to understand how such a system is consumed, one must understand how it is embedded within each participating context. This includes understanding what resources are in contention, how satisfied users are with computing operations, whether computer use is mandatory or discretionary, etc.), as well as how participants maneuver through or around these contexts in order to get their work done. The significance of the computing package as a contribution and new perspective for understanding computing in organizations was highlighted at length (2 pages) in an 8 page review of the 1979 AFIPS National Computer Conference appearing in IEEE Computer 12(8):79-86, 1979. Such recognition was particularly gratifying for him as this was the first research paper he presented solo at a major research conference. In later studies focused on the production of new software development technologies, he employed the computing package concept to help reveal how it could serve as a design heuristic and project management strategy. His goal was to development new techniques to simplify the deployment, adoption and integration of these new technologies in different organizational settings [OS8,OS9,OS12,OS20,OS21,OS24].

    Through ongoing studies of the instrumental use of computing and computing packages in different settings, Kling and Scacchi recognized that there were a number of alternative perspectives one could employ when trying to understand how people use and evolve computing systems within organizations. By 1980, they had identified six distinct analytical perspectives that could be employed for such purposes [OS4]. In hindsight, the use of multiple perspectives seems obvious, though at the time (1980), there were no other research publications offering or employing such an analytical regimen.

    Rather than attempt to characterize each perspective, two noteworthy points about their existence and usage can be made. First, there is no single best perspective to use to understand computing in all settings or situations. No one perspective guarantees a better truth than any other does. However, people who seek to characterize or explain how a computing systems are used and evolved (whether in good or bad ways) typically choose only one or two perspectives to situate their analysis. This means by having knowledge of other analytical perspectives, it becomes possible to discover holes or blind spots in an analysis drawn from any single perspective. Second, the multiple perspectives can be used prescriptively to anticipate or predict characteristics, patterns or outcomes associated with the development and deployment of new computing technologies [OS2,OS6,OS10,OS13,OS16]. Clearly, prescriptive use is meant to provide a framework for understanding and investigating models/theories that can be empirically substantiated or refuted. Such insights might then favorably influence the production or development of new computing systems in ways that increases the likelihood of their successful use and constructive evolution. For example, Kristen Nygaard (an original developer of Simula-67 programming language) was sufficiently influenced by the analytical power of multiple perspectives that he and his colleagues introduced language constructs for describing perspectives into the programming language Beta in the 1980s. This was an attempt to support the modeling and interpretation of different perspectives. This in turn could facilitate and encourage the work oriented design of computing systems for their users. Thus, multiple analytical perspectives can be employed to describe/model and analyze how computing systems fit into the work patterns and processes of their users.

    The web of computing was a concept developed by Kling and Scacchi to combine the computing package and multiple analytical perspectives to more succinctly account for the organizational patterns and work processes that we were observing in our respective field studies. By this time (1980-1981), they came to find that the configuration of hardware and software resources in an organization, together with the organizational policies governing their use and maintenance were inextricably intertwined with the way people used computing in their work. Computing technology was thus a form of social organization at work [OS7]. Changing (or "innovating") the computing technology at hand within an organization would therefore precipitate change or innovation in the social organization of work for the participants [O5]. This web of computing perspective could thus act as an combination or unification of multiple perspectives for understanding how different kinds of computing systems are consumed in different organizational settings [OS5,OS7]. Dr. Scacchi would later employ this result in understanding and managing the production and use of new software development tools and techniques, as described later. Overall, the web of computing emerged as a dominant perspective within an international community of researchers for analyzing and explaining empirically or theoretically derived research results for how computing fits into or shapes the tempo, place and processes of work in complex organizations. Dozens of papers have employed or cited the web of computing paper [OS7]. For example, work by Curtis and others at the MCC Software Technology Program acknowledged that the web of computing played a central role in their conception of how to understand software design processes using multiple levels of analysis (cf. B. Curtis, H. Krasner, and N. Iscoe, "A Field Study of Software Design Processes for Large Systems", Comunications ACM, 31(11), 1268-1287, 1988). Beyond this, one can also find references to use of the Web of Computing paper (as of 1998) listed in academic course syllabi found on the World Wide Web through a common search engine. Last, a recent paper by Wanda Orlikowski and Suzanne Iacono, "Desperately Seeking the "IT" in IT Research: A Call to Theorizing the IT Artifact", Information Systems Research, 12(2), June 2001, makes frequent reference to the Web of Computing paper and analytical framework. Orlikowski and Iacono, after reviewing the collection of papers published in the past 10 years in this prestigious journal, call for a new research agenda for the Information Systems community that explores and builds on concepts such as those attributed to the Web of Computing, as developed by Kling and Scacchi. Thus, the Web of Computing continues to be one of the most influential in the area of organizational studies of computing and information systems, some 20 years after its original publication.

    Part of Dr. Scacchi's collaborative contribution to the development of the web of computing as an analytical approach lie in his research into understanding the process of innovation in computing [OS5]. Simply stated, one way to understand how computing/software systems are used, maintained or evolve is to empirically examine the processes associated with how such systems change over time within their organizational setting. This work also marked his initial foray into the study of the formal and informal processes people engage when they change or innovate their computing environment. This focus on these processes in general, and those associated specifically with the production of software tools and applications have since become a central category of his research in the last 10 years. In addition, his focus on innovation process later evolved to include studies of software system development (i.e., producing new software innovations) [OS6], software maintenance (i.e., incremental software innovation) [OS11,OS14] and software technology transfer processes and barriers [OS17,OS24].

    Other accomplishments in Dr. Scacchi's organizational studies of computing/software systems draw upon the preceding results, but now focus primarily on examining the different aspects of system production. This move represented his attempt to broaden the scope of organizational studies of computing/software systems to include understanding how complex systems are developed, and whether they can be developed in a manner that constructively exploits an awareness of the computing package, multiple analytical perspectives, and the web of computing. He originated and introduced over a dozen novel strategies/heuristics for how software development projects could be more effectively and efficiently managed to produce successful systems [OS8,OS9,OS12,OS20,OS21]. In general terms, these strategies focus on understanding and managing the computing package/web, the structure and flow of work processes and teamwork [OS19], innovation processes, and handling problems and breakdowns [SP5]. For example, he suggested explicitly mapping/modeling the computing package associated with a software system being developed for a local user base. Later, he suggested how the web of computing associated with a new software system could be described or modeled as a configuration of resources. These resources ("objects") and their configurations ("resource architectures" or webs) can model how systems are produced, used or consumed ("relations") by people ("agents") using computing systems ("tools") and artifacts ("documents") in the course of their work ("tasks" and "processes") [SF/E12,SF/E18,SP1-SP4]. Finally, through a comparative secondary analysis of more than a dozen empirical studies of what affects the productivity of software development organizations/projects, he found some independent support for his strategies, but no contradictory evidence for their refutation [OS18,OS23].

    Overall, Dr. Scacchi's most successful and recognized organizational studies of computing/software development include those papers addressing the computing package [O3], multiple analytical perspectives [O4], web of computing [O7], organizational dynamics of software maintenance work [O14], and organizational characteristics, processes and behavioral patterns that affect software development productivity [O23]. Citations and links to selected papers in this category can be found in his Research Publications on Organizational Studies of Computing/Software Development.

    The System Factory Project and Environments [SF]

    After completing his early studies on the use and evolution of computing systems, Dr. Scacchi decided to turn to look upstream to see if he could apply a similar approach and concepts to understand how computer/software systems were (or could be) developed. The System Factory Project he originated at USC in 1981 marked his first effort in this category. Given the opportunity to teach the graduate software engineering course at USC, he found on the first class meeting that he was facing an eager group of 50 or so M.S. and Ph.D. Computer Science students who wanted to learn about large-scale software engineering. He recognized this as an opportunity to create and enact a novel, large software development organization in which every student would have a role as contributing software engineering and project participant. Such a project would itself be situated within a complex organization setting employing shared access to limited resources, budget and schedule constraints, and a usual assortment of unexpected circumstances that arise in any setting given enough time. He also saw this as an informal experiment in the rapid development of a large software system. As such, he adopted and refined a software development life cycle approach (a process) based on then current software engineering techniques. He also developed a single system architecture for a "software engineering environment" (SEE) which could be factored into 6-10 separate components (stand-alone software tools) and integrated through some common file I/O capabilities.

    Within 15 weeks start to finish, he had 8 teams of 4-7 students each develop, demonstrate, and deliver a prototype version of the tool they had agreed to produce. Teams were organized and managed according to a set of project management strategies that were described earlier. All teams produced a series of documents corresponding to the requirements, specification, architecture design, detailed design, source code, test plan and selected results, user manual and maintenance guide. The initial SEE represented some 3000 pages of printed computer based documentation and about 30,000 source lines of Pascal code. Basically, a large majority of the Project participants considered this initial experiment a success, a valuable learning experience, and something they could take with them (i.e., each student completing their team's project received an electronic copy of all the other teams' components and documentation). In short, this initial investigation suggested that something interested could be observed, learned and shared through a large-scale participatory project with a common set of goals. Dr. Scacchi then went on to repeat, refine and expand Project iterations a total of 11 times through 1988, involving more than 600 M.S. and Ph.D. CS students (about 1.5% of all such students in the U.S. according to figures in the ACM/Taulbee survey) during this period. Five of the Project development cycle iterations were extended to 30 week schedules over nine month academic school year. In total more than 600K lines of source code were delivered, and between 30K to 50K pages of online documentation were produced through this cyclic, evolutionary approach to software system development. Though not noted earlier, one of the main results from his study of the process of innovation in computing [O5] was that once embedded in an organizational setting, computing innovations will continuously arise and be incorporated into local systems and their embedding organization. Said another way, once people in an organization uses computing systems routinely, they will continuously innovate these systems as long as the organization thrives. The SF Project repeatedly produced this kind of finding as well.

    Much has been written about the concepts, management strategies, development techniques and focal software components and composite environments, as can be found in the Research Publications below. The majority of these research results follow from the concepts and research from his organizational studies of computer/software development. Descriptions of the SF Project's objectives, strategies, SEE architecture and so forth can be found in a number of publications [SF7,SF9,SF15,SF17,SF23]. Noteworthy among these is the Keynote Address on the SF Project he gave at the Japan Software Engineering National Conference in Tokyo in 1988 [SF15], which was also invited for presentation at the CMU Software Engineering Institute's annual conference on Software Engineering Education [SF17]. Subsequently, the directors of the SEI's Graduate SE Education Division later identified the USC SF Project as one of the top six exemplary approaches for how to experience software engineering within graduate course work (see G.A. Ford and N.E. Gibbs, "A Masters in Software Engineering Curriculum", Computer, 22(9):59-71, 1989). Finally, the last publication in this segment [SF23] describes the final state of organizational development (i.e., accommodate multiple development teams geographically distributed but interlinked via the Internet) and SEE architecture that was realized in the SF Project when its production was brought to an end.

    The resulting software components and composed SEEs produced in the SF Project were also successful in their respective technical arenas. In particular, pioneering and significant contributions were realized in the development of language based SEEs supporting hardware-software co-production [SF1-SF5], maintaining software architectural designs and source configurations [SF6,SF10,SF11,SF14,SF19,SF20,SF24], and hypertext environments for managing software project documentation [SF12-SF14,SF16,SF18,SF22,SF24-SF26]. Later versions of the SF software technologies were distributed or licensed to various corporate research sponsors and other academic research groups as well.

    Beyond these published results, Dr. Scacchi took the opportunity to give more than 50 invited presentations, academic colloquia, industrial seminars, keynote addresses, and other conference presentations on the SF project in both national and international venues. Audiences throughout the U.S., Canada, Japan, Europe (England, Finland, France, Germany, and Italy) and South America (Columbia) read about, listen to, and engaged in questions and discussions to learn more about what was accomplished in the SF Project and how.

    Overall, the SF Project achieved its many of its objectives as a novel experiment in applying emerging principles from organization studies of computing/software development to the production of software systems. The Project participants collectively produced one of the largest sets of software component tools and composed environments that can be found within any academic setting during the 1980's. It served as a venue that combined educational course work, software development and a research experience that created or enhanced career opportunities for its participants through their hard work, the Project's  publications and international visibility. It also supported the completion of seven doctoral dissertations under his supervision as well. In addition, companies such as AT&T Bell Laboratories set up their own SF-like projects (the software factory laboratory in North Carolina). Accordingly, further details regarding this project can be found in the Research Publications on the System Factory Project and Environments.

    Organizational and Software Processes [SP]

    The third and final category within Dr. Scacchi's research efforts concentrate and build on the results from the preceding two categories. The focus of his research here is directed at understanding organizational processes, software development/use processes, and their interplay. What is novel and unique is his use of a computationally based `web of computing' framework and support environment. As before, his research efforts were influenced by empirical and technology development studies in collaboration with external organizations that serve as field sites or testbed.

    A good deal of Dr. Scacchi's research effort during 1985-1995 focuses on developing a computational (software) environment that would support the modeling, analyzing and simulating of organizational and software processes. This software environment was called the Articulator [SP4]. It was developed as a vehicle for describing and understanding work processes, resource configurations and surrounding organizational circumstances from a web of computing perspective [SP18]. Much like the development of computer based statistical packages enabled quantitative analyses of questionnaire and similar empirical data, the Articulator was designed to facilitate the qualitative analysis of the structure and dynamic behaviors of complex processes. Emphasis within the Articulator was directed at capturing, representing and operationalizing organizational and software processes that could be described primarily using normative, ordinal or interval--i.e., qualitative--data. The Articulator was a breakthrough in that it provided a new class of analytical instrumentation and computational testbed that enabled more robust, computationally tractable and comprehensive analysis than was previously available by the late 1980's. Prior to this time, qualitative studies of organizational or software processes within any discipline were typically limited to describing or representing processes using (a) narrative text or semi-structured hypertext renderings [SF18,SF22,SP15], or (b) procedural or object-oriented programming models or (c) quantitative data and statistical analysis. Text and hypertext descriptions offer the potential for rich, highly contextual descriptions of processes, but "tools" for analysis of large narrative text data sets are usually left to the mind of the analyst/reader. Programming models, while well suited for specifying methodical calculations and detailed computations, are not well suited for describing the work situations or organizational software ecology [OS23] in which software processes were occurring. Quantitative data and statistical tools can be used to readily characterize the frequency, distribution or discrete snapshots of opinions, conditions, events or other circumstances using descriptive or inferential statistical measures. However, the organizational context, structure (configuration of resources, control relationships and division of work), and behavioral flow of processes (what gets done, by who, when, where, how and why, what goes wrong, etc.) over time cannot be easily described or modeled using quantitative data or statistical analysis [OS28]. Examples below to show this.

    In a study he directed for Northrop-Grumman B-2 Division in 1990-1993, Dr. Scacchi had the opportunity to investigate, model and analyze the software development life cycle processes responsible for producing the multi-million lines of code for the Avionics Flight Control software for the B-2 strategic bomber. This B-2 software organization had over 700 software developers and managers working in this effort. It was also said at the time to be 44 months behind schedule and $150M over budget. Furthermore, new schedules slips were recurring, as were multi-million dollar/month development cost overruns. After a collaborative three month empirical study using multi-round interviews, progress briefings, refinement and participatory validations of  Articulator based models by B-2 software personnel, Dr. Scacchi's research team had discovered more than 30 systemic problems ("black holes") and structural conflicts ("miracles") in their development life cycle. These dilemmas made following the current software development process logically and physically impossible. Their analysis and empirical findings provided a foundation for how to restructure the Division's web of computing and reorganize their processes, resources and workflow to resolve the dilemmas. Subsequently, they were then able to reduce their schedule slips and cost overruns leading to a multi-million dollar savings in cost avoidance. In turn, the project sponsors tripled his research funding, scope and duration of this study in acknowledge the significance of these results. Finally, one publication describing how Dr. Scacchi's team modeled, represented and analyzed this software development life cycle process using the Articulator received (in 1997) an international "citation for excellence with the highest quality rating" for original research in the field of Information Systems, from ANBAR Management Intelligence (a European Management Research Rating Service). Furthermore, this publication appears in ANBAR's number 1 rated IS research journal (in 1996) in Europe, Decision Support Systems.

    In a pilot study he directed for U.S.West (the telco) in 1993-94, Dr. Scacchi's research team empirically captured, modeled and refined a software development life cycle process to support 1500 software developers coordinated more than 400 tasks. Through modeling and analysis using the Articulator environment, he discovered the process we captured had 18 levels of process decomposition and project management. The findings were that such a work structure, management coordination hierarchy, and division of labor would be too complex, impractical and unmanageable. Though USWest choose not to heed this advice, the software project that employed this life cycle process eventually failed and reported a loss of over $200M [SP28].

    In addition to these two studies, the Articulator environment has been employed in descriptive or prescriptive studies of  dozens of software development processes at AT&T Bell Laboratories, BellCore, Hewlett-Packard and elsewhere, as well as within the System Factory Project [SP1-SP4,SP7,SP16,SP18]. The Articulator environment also possessed a unique capability (during the early 1990's) to be able to diagnose and suggest repairs for software development processes that had broken down or failed during their enactment [SP5,SP6,SP12]. As before, this novel capability to support "articulation work" was derived from empirical studies of software development work processes [cf. SP6,OS14,OS19,OS26]. More recently, the Articulator environment has been applied in studies of organizational and software use/evolution processes found in other settings. These include corporate financial operations at McKesson Corp. [SP20], supply chain management and order fulfillment for EDS [SP13,SP24], military procurement for the Naval Air Warfare Center, feature film production for USC Entertainment Technology Center and its sponsors (e.g., LucasFilms Ltd.), Tele-medicine for the USC Advanced Biotechnology Consortium and others. Finally, recent (Internet or Web based) technologies and techniques derived from the Articulator have been, or are being used in new studies organizational and software processes. These studies address organizational and software processes associated with: research grants management at the Office of Naval Research [SP19,SP22], organizational transformation [SP22,SP29], software systems acquisition [SP23], distributed multi-site virtual enterprises for software development [SP17,SP21,SP27], Electronic Commerce applications [SP25,SP26,OS27], and the redesign of software processes [SP28].

    How did the Articulator environment help in analyzing these organizational and software development processes? As a process is being captured via interviews, elicitation, artifact review or participant observation, it can be incrementally modeled and analyzed with the Articulator for its consistency, completeness and internal correctness. An emerging "process model" can also be queried about explicit or implicit relationships within the model that follow from the web of computing perspective [SP4]. Similarly such a model could be used as a testbed for experimentation or operationalization through knowledge-based simulation of process dynamics under different hypothetical or empirically grounded scenarios [SP4]. This use of knowledge-based simulation techniques was unique within the software process research community for many years [SP28]. Overall, these analytical capabilities were developed, applied and refined over a number of years through projects noted above. However, the development and use of the Articulator was not limited to only the modeling, analysis and simulation of complex processes.

    The Articulator was also designed to serve as a unifying framework to support and coordinate software development processes, tools, information artifacts/products, and other resources within an operational web of computing. Dr. Scacchi recognized, as his early organizational studies indicate, that the "integration" of computing resources with people at work in a complex setting was a recurring source of problems with computing use [OS3,OS5,OS7]. Accordingly, he directed the design the Articulator environment around its use of an extensible modeling ontology (or process meta-model) [SP18]. This way his team or others could use the Articulator to model arbitrary organizational or technological systems as process models that could then be interlinked or webbed together. With such a capability, his research team was among the first to successfully demonstrate the integration of a legacy, pre-existing software engineering suite of tools and development techniques so as to follow, support and enact a modeled set of software development and project management processes [SP8,SP10,SP16]. This in turn enabled his research team to integrate the Articulator with six other software environment architectural styles [SP11,SP20] and a dozen or so software development/process repositories [SP9,SP15,SP17]. The ability to integrate diverse environments, repositories, and processes using a common (process meta-model) scheme was later characterized as a new class of environment called, "meta-environment" [SP11]. Thus, the Articulator was able to demonstrate and embody the unique result for how the organizational analysis of computing/software development could be integrated into the practice of software engineering through a meta-environment technology based on the web of computing framework. Through additional research effort, this capability evolved into what is now called, "process life cycle engineering" [SP20,SP22,SP24].

    Overall, Dr. Scacchi's research in this category is perhaps his most successful and significant. Five of his research publications (four journals articles, one ICSE paper) in this area have been reprinted in books, which is an indicator of its significance. Furthermore, the ability to take this approach into new organizational and software processes continues to demonstrate that the previous twenty years of his research continues to converge and build on prior results and empirical studies. As such, further information of this research can be found in his Research Publications on Organizational and Software Processes.

    Finally, details for these research publications, including another 15-20 not cited above, can be found in his Curriculum Vita, which is posted on the Web at the location, http://www.ics.uci.edu/~wscacchi//vita.html.

    Courses Taught

    In Computer Science, Dr. Scacchi has taught graduate and undergraduate lecture and project courses in Software Engineering for eight years. He has also taught courses in Management of Computing, Social and Organizational Analysis of Computing,  Introduction to Computer Science, and various research seminars. In Management and Information Systems area , he has taught courses on Electronic Commerce (starting in 1994), Process Analysis and Redesign, Network/Web Information Systems, Decision Support Systems, and Management of Information Technology (Executive Education). In Spring 2000, he will teach a new course on Database Management for Electronic Commerce at the UCI Graduate School of Management.

    Other Contributions and Service

    Dr. Scacchi has supervised nine doctoral dissertations. He has received approximately $3M in external funding as Principal or Co-Principal Investigator in 25 research projects. He has refereed or reviewed approximately 500 research papers and 50 research proposals (NSF, UC MICRO, NSERC, etc.) in areas related to his research. He is member of editorial boards of selected research journals and other compendium in his areas of research interest. He has served on more than two dozen program committees for national or international conferences, workshops, or symposia  in areas related to his research. Dr. Scacchi has served on various departmental and university committees (admissions, computing resources, doctoral dissertations (12 as secondary reader or outside member), inter-discipline planning and coordination, library, recruiting, etc.). Last, he has served as an industry consultant to two dozen business firms or research institutes. Details for these service contributions can be found in his Curriculum Vita, which is posted on the Web at the location, http://www.ics.uci.edu/~wscacchi//vita.html. http://www.ics.uci.edu/~wscacchi/publications.html Walt Scacchi's Publications Page

    Walt Scacchi's Publications Page (Home Page)


    What is the ATRIUM Laboratory?
    Selected Research Papers (in Postscript, HTML, or PDF format) and Interactive Presentations (in HTML format)
    Need a Postscript viewer and printing utility? Get a Postscript viewer and print utility for MS-Windows and OS/2 systems called GSview .
    • Understanding the Social, Technological, and Policy Implications of Open Source Software Development. Interest in open source software development has emerged in many different research communities. Much of this interest has focused attention primarily onto the products of open software development (source code), and secondarily onto the processes and productive units that facilitatre such development. This position paper identifies what I believe are areas, topics, or basic questions requiring further research in the arena of open source software development. Position paper presented at the NSF Workshop on Advancing the Research Agenda on Open Source, 28 January 2002, Revised August 2002.
    • Understanding the Requirements for Developing Open Source Software Systems. This study presents an initial set of findings from an empirical study of social processes, technical system configurations, organizational contexts, and interrelationships that give rise to open software. The focus is directed at understanding the requirements for open software development efforts, and how the development of these requirements differs from those traditional to software engineering and requirements engineering. Four open software development communities are described, examined, and compared to help discover what these differences may be. Eight kinds of software informalisms are found to play a critical role in the elicitation, analysis, specification, validation, and management of requirements for developing open software systems. Subsequently, understanding the roles these software informalisms take in a new formulation of the requirements development process for open source software is the focus of this study. (IEE Proceedings--Software, 149(1), 24-39, February 2002).
    • Hypertext for Software Engineering. This chapter provides a survey of tools, techniques, and concepts for how hypertext capabilities can be employed to support large team-oriented software development projects. (appears in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition , Wiley, 612-621, 2002).
    • Process Models in Software Engineering. This chapter provides a survey of tools, techniques, and concepts for how alternative softwre process models and modeling capabilities can be employed to support large team-oriented software development projects. (revised version appears in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, 993-1005, Wiley, 2002).
    • Software Development Practices in Open Software Development Communities. This study presents an initial set of findings from an empirical study of social processes, technical system configurations, organizational contexts, and interrelationships that give rise to open software. "Open software", or more narrowly, open source software, represents an approach for communities of like-minded participants to develop software system representations that are intended to be shared freely, rather than offered as closed commercial products. While there is a growing popular literature attesting to open software, there are very few systematic empirical studies that informs how these communities produce software. Similarly, little is known about how people in these communities coordinate software development across different settings, or about what software processes, work practices, and organizational contexts are necessary to their success. To the extent that academic research communities and commercial enterprises seek the supposed efficacy of open software, they will need grounded models of the processes and practices of open software development to allow effective investment of their resources. This study investigates four communities engaged in open software development. Case study methods are used to compare practices across communities. (Position paper presented at the 1st Workshop on Open Source Software Engineering, Toronto, Ontario, May 2001).
    • Modeling and Simulating Software Acquisition Process Architectures. (with S. James Choi), In this paper, we describe our efforts to support the modeling and simulation of processes associated with software system acquisition activities. Software acquisition is generally a multi-organization endeavor concerned with the funding, management, engineering, system integration, deployment and long-term support of large software systems. We first describe a language for modeling software acquisition processes at the architectural level. We then describe our approach supporting the simulation of software acquisition processes within a process architecture. Along the way, we introduce how we employ the High-Level Architecture (HLA) and Run-Time Infrastructure (RTI) to support the distribution, concurrent execution and interoperation of multiple software process simulations to address the complexity of software acquisition process architectures. In addition, we also introduce the design and prototyping of a Web-based environment which supports the modeling and simulation of acquisition process architectures. (appears in Journal of Systems and Software , 59(3), 343-354, 15 December 2001).
    • Specifying Process-Oriented Hypertext for Organizational Computing. (with John Noll), Organizations deploy intranets to provide access to documents for those who use them. But the web of computing comprises more than just documents: people, tools, and processes are critical to organizational function. In particular, people may need guidance on how to perform tasks, as well as access to information necessary to carry out those tasks. In this paper, we present a language for describing process-oriented hypertexts. A process-oriented hypertext links information, tools, and activities into a seamless organizational web. Using such a hypertext, and process performer can enact a process by browsing, and receive guidance on how to perform the process activities, where to and relevant information, and what tools to use. The PML process modeling language provides a way for process engineers to specify process models in terms of activities, and the sequence in which they should be performed. The specifcation can be elaborated with descriptions of resources and tools required and provided by activities, and the skills necessary to carry out an activity. The resulting models are then translated into one or more process-oriented hypertexts that represent instances of the process currently being performed. PML includes features that allow the modeler to specify how the process activities should be dynamically linked to information and resource nodes at the time the process is performed. This enables processes to be described as abstract models that can be instantiated as process-oriented hypertexts in different organizational settings. We used PML to model processes in a case study of the grants management process at the US Office of Naval Research. We describe some of our experiences applying PML to this study, and conclude with lessons learned and directions for future study. (appears in Journal of Network Computing and Applications , 24(1):39-61, 2001).
    • Redesigning Contracted Service Procurement for Internet-based Electronic Commerce: A Case Study. This paper describes a case study focused on redesigning procurement processes for research grants at the Office of Naval Research. These processes focus on the procurement of research (R&D) services and how they can be redesigned and supported using Web-based capabilities. By examining these processes, we gain insight into issues and challenges to be addressed in    redesigning service procurement with Internet-based Electronic Commerce capabilities. In collaboration with other participants in this effort, we found that we could contribute to a substantial reduction in process cycle time and operational costs associated with the funding of thousands of research grant procurement actions. Accordingly, this investigation will focus on topics that underlie these results. This includes defining an approach to redesign procurement processes for Internet-based EC, a case study applying this approach and technology at ONR, and a discussion of lessons learned. Along the way, challenges, issues, and possible solutions are identified that foreshadow   the development of a new generation of Internet-based procurement processes and supporting Web-based environments. (Revised version appears in J. Information Technology and Management , 2(3), 313-334, 2001).
    • An Environment for Research in Software Systems Acquisition. (with A. Valente, J. Noll, and J.S. Choi),  In this paper, we present initial results from basic research and exploratory studies in the area of software systems acquisition. This research sought to design a web-based, computer-supported work environment that facilitates research and development in the area of software systems acquisition. This environment supports the capture, representation, and operationalization of various forms of knowledge associated with a new vision for virtual system acquisition, called VISTA. The schemes for organizing and managing knowledge rooted in software feasibility heuristics and informal/formal models of software acquisition and systems engineering processes are called knowledge webs. Accordingly, the environment that provides the mechanisms for capturing, representing, interlinking and operationalizing access to these knowledge webs is called a knowledge web management system (KWMS). Thus, the environment that is the focus of this research effort is designed to provide a KWMS capability that provides access to an incrementally evolving knowledge web for software acquisition research and practice in line with the VISTA vision. This environment for software acquisition web management is called SAWMAN (submitted for publication, June 2001).
    • Experience with Software Architectures and Configured Software Descriptions. (with S. J. Choi), In this position paper, we highlight some of the things we have learned over the past 15 years in our experience with software architectures. Much of what we have learned results from our experience in the specification, design, implementation and evolution of software engineering environments and process-driven software environments. Along the way, we have developed or used a variety of alternative architectural notations to support these efforts. We also have employed architectural design concepts and notations to specify, "code" and evolve a variety of configured software descriptions, including software life cycle documents, software hypertexts, software processes, and others. In this regard, we have found it useful to explore alternative schemes for combining software architecture concepts, techniques, notations and tools with those for software configuration management. Accordingly, we will highlight some of our experiences in these areas or topics. (Position paper presented at the Workshop on Evaluating Software Architectural Solutions  -  2000, Irvine, CA May 2000).
    • Understanding Software Process Redesign using Modeling, Analysis and Simulation. This paper introduces the problem of understanding what software process redesign (SPR) is, and how software process modeling, analysis and simulation may be used to support it. It provides an overview of research results from business process redesign to help draw attention to the importance of treating process redesign as a process of organizational and process transformation. This in turn requires examining and practicing SPR through an approach that combines organizational change management together with process management technologies. A discussion follows which then identifies a number of topic areas that require further study in order to make SPR a subject of software process research and practice. (Presented at ProSim'99 , Silver Falls, OR, June 1999. Revised version appears in Software Process --Improvement and Practice 5(2/3):183-195, 2000).
    • Developing a Knowledge Web for Business Process Redesign. (with Andre Valente), We describe our effort at developing and demonstrating a prototype knowledge-based Web environment for modeling, diagnosing and redesigning complex business processes. Our goal was to investigate how a modern knowledge representation system, Loom [MB95], can favorably leverage the development and evolution of a knowledge web that links narrative, informal and formal descriptions of cases on business process redesign found on the Web. In so doing, we demonstrate concepts, techniques and tools that facilitate the development of a knowledge web management system (KWMS) in an application domain of interest to enterprises throughout the world. (Presented at the 1999 Knowledge Acquisition Workshop, Banff, Canada, October 1999).
    • Supporting Software Development in Virtual Enterprises. (with John Noll), This paper presents recent developments in a distributed semantic hypertext framework called DHT that supports software development projects within virtual enterprises. We show how hypertext functionality embodied in DHT solves the practical problems of project coordination including, collaborative sharing data in a virtual enterprise of distributed teams, integrating existing tools and environments, and enacting software processes to coordinate development activities for teams across wide-area networks. In particular, we describe how software process enactment can be achieved within a virtual enterprise without centralized mechanisms. This is when the process description is represented as a user navigable hypertext graph whose nodes associate process steps, staff roles, and associated tools with designated software products. Overall, these capabilities provide support for coordinating software development projects across a virtual enterprise of teams connected via the Internet. (appears in Journal of Digital Information, 1(4), February 1999). The original version of this paper was presented at The Second International Workshop on Incorporating Hypertext Functionality Into Software Systems, Washington, DC, March 1996.
    • Virtual System Acquisition: Approach and Transitions. (with Barry Boehm), In this paper, we describe a radically new approach for the acquisition of software-intensive systems. We start be reviewing problems and opportunities for improving the acquisition of these systems. We put forward a statement of objective on the need to make the software system acquisition more agile and adaptive, through the evolutionary modeling, simulation, and development of the system being acquired. We describe a new vision for the re-tooling and re-engineering software system acquisition into a form we call, VISTA, denoting an approach to the virtual acquisition of these systems. We then outline the VISTA approach to software acquisition. This is followed by a discussion of the technical and organizational transitions that must be investigated and managed to ensure the eventual success of such a radical change to software system acquisition. (appears in Acquisition Review Quarterly, 5(2):185-216, Spring 1998).
    • Experience with Software Process Simulation and Modeling . This paper describes an approach taken and experiences encountered in developing and applying simulation and modeling technologies to software processes. Processes for both software development and use have been investigated. As such, the focus of this paper is aimed at addressing three aspects of software process simulation and modeling. First, I describe an approach and examples of software simulation and modeling as investigated with the Articulator environment developed at USC. Second, I describe how by focusing on process modeling, analysis, and simulation, we are led to expand the scope of work with software processes toward a more comprehensive software process life cycle engineering. Third, I describe some of the lessons learned from applying modeling and simulation concepts, techniques, and tools to software processes in a variety of organizational settings. Conclusions then stress the complementary value arising from the use of both qualitative and quantitative technologies for software process simulation and modeling. ( Journal of Systems and Software , 46(2/3):183-192,1999. The original version of this paper presented at ProSim'98, Silver Falls, OR, 22-24 June 1998).
    • Computational Business Process Components for Electronic Commerce. In this position paper, I focus on addressing computational business processes as software components for  Electronic Commerce. These components can be configured into an organizational process architecture that serves as a reusable framework for developing an EC information infrastructure. Process-driven Intranets then serve as a distributed development and run-time support environment for the framework. Since PDIs can span organizational boundaries, and since PDIs in different organizations may be interconnected, then process-driven extranets can be created and deployed. PDIs and PDEs enable the design, integration, and enactment of virtual enterprises. When a community of virtual enterprises emerges and begins to support business transactions among these enterprises, then virtual markets can appear, as can different kinds of computational economies. With the exception of this last item, I have experience in developing and applying these capabilities in different organizational settings. My experience leads me to believe that CBPs are viable components for EC. Furthermore, such components address an orthogonal set of issues compared to those addressed by other potential EC technologies such as CORBA/DCOM, EDI X12 transaction standards, Java/ActiveX, UML, and others. Subsequently, the potential value of CBPs can be assessed independent of choices made in using or evolving other EC technologies. Thus, CBPs merit consideration as a foundational software technology for EC. (Presented at the 1998 Intern. Workshop on Component-Based Electronic Commerce, Berkeley, CA, July 1998).
    • Comparative Case Analysis for Understanding Software Processes. This paper provides an exploration of the analysis and use of comparative case studies as an approach to understanding software processes in complex organizational settings. Case studies are well suited to capture and describe how software processes occur in real-world settings, what kinds of problems emerge, how they are addressed, and how software engineering tools, techniques, or concepts are employed. The overall purpose of comparative case analysis is to discover and highlight second- or higher-order phenomena or patterns that transcend the analysis of an individual case. Comparative case analysis provides a strategy that enables the development of more generalizable results and testable theories than individual or disjoint case studies alone can provide. This study incorporates an examination and review of four empirical studies of processes involved in developing, using, or evolving software systems that employ comparative case analyses. Finally, a meta analysis of these four studies then highlights the strengths and weaknesses of comparative case analyses when used to empirically examine and understand software processes. (Draft for review, 1998).
    • Modeling, Integrating, and Enacting Complex Organizational Processes, or an html presentation version can be found here. We describe our approach and mechanisms to support the engineering of organizational processes throughout their life cycle. We describe our current understanding of what activities are included in the process life cycle. We then go on to describe our approach, computational mechanisms, and experiences in supporting many of these life cycle activities, as well as compare it to other related efforts. Along the way, we present examples drawn from a current study aimed at modeling, analyzing, and integrating an order fulfillment process in a product development organization. (appears in K. Carley, L. Gasser, and M. Prietula (eds.), Simulating Organizations: Computational Models of Institutions and Groups, 153-168, MIT Press, 1998).
    • Process Life Cycle Engineering: A Knowledge-Based Approach and Environment. (with P. Mi), We describe our approach and mechanisms to support the engineering of organizational processes throughout their life cycle. We describe our current understanding of what activities are included in the process life cycle. We then go on to describe our approach, computational mechanisms, and experiences in supporting many of these life cycle activities, as well as compare it to other related efforts. Along the way, we present examples drawn from a recent study that uses the approach and the mechanisms of our knowledge-based process engineering environment to support the (re)engineering of corporate financial operations in a mid-size consumer products organization. (NB : Contains 12 image files).(appears in Intelligent Systems in Accounting, Finance, and Management ,  6(1):83-107, 1997). In addition, an interactive presentation on this subject can be found here .
    • Process-Driven Intranets: Life Cycle Support for Process Reengineering. (with John Noll), In this paper, we describe our approach and experience in a case study focused on redesigning processes for research grant funds management at the Office of Naval Research. We found that we could contribute to a substantial reduction in process cycle time and operational costs associated with the funding of thousands of research grant procurement actions. Accordingly, we focus our discussion on topics that underlie these results. This includes defining our approach process design, the technology of process-driven intranets for electronic commerce, our case study applying this approach and technology at ONR, and a discussion of lessons learned. Along the way, we identify challenges, issues, and possible solutions that foreshadow the development of a new generation of intranet-based networked information systems. (appears in IEEE Internet Computing, 1(5):42-49, 1997).
    • Supporting Distributed Configuration Management in Virtual Enterprises. (with John Noll), This paper presents a semantic hypertext-based framework called DHT that supports distributed software configuration management, provides transparent access to heterogeneous, autonomous software repositories, and enables an implementation strategy with low cost and effort. We show how DHT solves the practical problems of sharing and updating heterogenous multi-version software in a virtual enterprise of distributed teams, integrating existing CM tools and environments, executing CM processes to coordinate development activities across wide-area networks. This is when the process model is represented as a user navigable hypertext graph whose nodes associate process steps, user roles, and associated tools with designated software product versions and configurations. Furthermore, we show that this can require the support for alternative policy models for the commitment of software updates into local CM repositories. Overall, these capabilities provide support for product-centered enactment of CM policies and processes across a virtual enterprise of teams connected via the Internet. (appears in R. Conradi (ed.), Software Configuration Management , Lecture Notes in Computer Science, Vol. 1235, Springer-Verlag, New York, 142-160, 1997) .
    • (Re)Engineering Research Grants Management: From Acquisition Reform to Knowledge Brokering at ONR. (with John Noll, Cedric Knight, Capt. J. F. Miller), In this paper, we briefly describe our approach and experience in a research effort focused on (re)engineering the activity of research grants management at the Office of Naval Research. We found that we could contribute to a substantial reduction in process cycle time and operational costs associated with the funding of thousands of research grant procurement actions. Accordingly, we focus our discussion on topics that underlie these results. We also observe that knowledge brokering is an area where a new R&D initiative could lead to more effective and efficient research funding and research program management, as well as serve the mutual self-interests of the Federal research funding agency and researcher communities. (presented at NSF Workshop on Research and Development Opportunities for Federal Information Services, Arlington, VA, http://www.isi.edu/nsf, May 1997).
    • Simulation and Modeling for Software Acquisition (SAMSA). (with Barry Boehm), In this extended final report, we describe the results from a series of workshops and contributions from a Blue Ribbon Panel of government, industry, and academic experts addressing alternative approaches to the acquisition of large software systems. The results focus attention on redesigning the process of software acquisition around the use of (a) a knowledge-based Feasibility Analysis Modeling system to assist assessing the costs, risks, and technical feasibility of a new software system to be acquired; (b) research on software architectures to help better characterize whether proposed system requirements can be satisfied by a given system architecture; and (c) the overall integration of simulation and modeling technologies to support a new approach to the "virtual information systems acquisition" (VISTA). Other reports from the workshops can be accessed through links within this report. (Final Report, March 1996).
    • A knowledge-based environment for modeling and simulating software engineering processes. (with P. Mi), In this paper, we describe the design and representation schemes used in constructing a prototype computational environment for modeling and simulating multi-agent software engineering processes. We refer to this environment as the Articulator. We provide an overview of the Articulator's architecture which identifies five principal components. Three of these components, the knowledge meta-model, the software process behavior simulator, and a knowledge base querying mechanism are detailed and examples are included. The conclusion reiterates what is novel to this approach in applying knowledge engineering techniques to the problems of understanding the statics and dynamics of complex software engineering processes. (appears in IEEE Trans. Data and Knowledge Engineering, 2(3):283-294, September 1990. Reprinted in Nikkei Artificial Intelligence, 20(1):176-191, January 1991, (in Japanese). Reprinted in Process-Centered Software Engineering Environments, P.K. Garg and M. Jazayeri (eds.), IEEE Computer Society, 119-130, 1996).
    • A Meta-Model for Formulating Knowledge-Based Models of Software Development.(with P. Mi), In this paper, we introduce a knowledge-based meta-model which serves as a unified resource model for integrating characteristics of major types of objects appearing in software development models (SDMs). The URM consists of a taxonomy of resource classes and a web of relations that link different types of resources found in different kinds of models of software development. The URM includes specialized models for software systems, documents, agents, tools, and development processes. The URM has served as the basis for integrating and interoperating a number of process-centered CASE environments. The major benefit of the URM is twofold: First, it forms a higher level of abstraction supporting SDM formulation that subsumes many typical models of software development objects. Hence, it enables a higher level of reusability for existing support mechanisms of these models. Second, it provides a basis to support complex reasoning mechanisms that address issues across different types of software objects. To explore these features, we describe the URM both formally and with a detailed example, followed by a characterization of the process of SDM composition, and then by a characterization of the life cycle of activities involved in an overall model formulation process. (appears in Decision Support Systems, 17(4):313-330, 1996).
    • The SMART Approach to Software Process Engineering. (with P. Garg, P. Mi, and G. Thunquest), In this paper, we describe a methodology for software process engineering and an environment, SMART, that supports it. SMART supports a process life-cycle that includes the modeling, analysis, and execution of software processes. SMART's process monitoring capabilities can be used to provide feedback from process execution to the process model. SMART represents the integration of three separately developed process mechanisms, and it uses two modeling formalisms (object-oriented data representation and imperative-style programming language) to bridge the gap between process modeling, analysis, and execution. SMART demonstrates the meta-environment concept, using a process modeling formalism as input specification to a generator that produces Process-Centered Software Engineering Environments (PSEEs). Furthermore, SMART supports a team-oriented approach for process modeling, analysis, and execution. (appears in, Proc. 16th. Intern. Conf. Software Engineering, IEEE Computer Society, Sorrento, Italy, pp. 341-350, May 1994. Reprinted in Process-Centered Software Engineering Environments, P.K. Garg and M. Jazayeri (eds.), IEEE Computer Society, pp. 131-140, 1996).
    • Understanding the Requirements for Information System Documentation: An Empirical Investigation.(with A. Jazzar), Software and Information Systems (IS) documents are a common product of large IS development efforts. These documents are produced and consumed through a variety of documentation processes. These  processes involve developers and users working within complex organizational settings, as well as with the focal system under development. These organizational settings facilitate and constrain IS documentation and development efforts in complicated ways. Accordingly, we present, analyze, and compare cases from field studies of three different IS development efforts in a large industrial corporation. Based on these studies, we identify a new set of variables and hypotheses that we believe represent a more plausible set of requirements for IS documentation products and processes in different organizational settings. In this regard, we utilize the concept of viewing IS documentation requirements as hypotheses to be tested, refined, or refuted. (appears in Proc. 1995 ACM Conf. Organizational Computing Systems, San Jose, CA, 268-279, August 1995).
    • Understanding Software Productivity. What affects software productivity and how do we improve it? This report examines the current state of the art in software productivity measurement. In turn, it describes a framework for understanding software productivity, some fundamentals of measurement, surveys empirical studies of software productivity, and identifies challenges involved in measuring software productivity. A radical alternative to current approaches is suggested: to construct, evaluate, deploy, and evolve a knowledge-based "software productivity modeling and simulation system" using tools and techniques from the domain of software process engineering. (appears in Intern. J. Software Engineering and Knowledge Engineering, 1(3):293-321, 1991. Revised and reprinted in  Advances in Software Engineering and  Knowledge Engineering , D. Hurley (ed.), Volume 4, 37-70, 1995).
    • Meta-Environments for Software Production. (with A. Karrer),  Researchers who create software production environments face considerable problems. Software production environments are large systems that are costly to develop. Furthermore, software production environments which support particular software engineering methods may not be applicable to a large number of software production projects. These conditions have formed a trend towards research into ways which will lessen the cost of developing software production environments. In particular, the trend has been towards the construction of meta-environments from which specific software production environments can be created. In this paper, we attempt to categorize more than 60 meta-environment efforts. For each of the categories, we review research efforts which illustrate different approaches within that category. We conclude by presenting an emerging common thread of requirements which links this field together. (appears in Intern. J. Soft. Engr. and Know. Engr. , 3(2):139-162, May 1993. Revised and reprinted in Advances in Software Engineering and Knowledge Engineering , D. Hurley (ed.), Volume 4,  37-70, 1995).
    • The Emergence of Electronic Commerce on the Internet, also available in HTML here, or for an interactive presentation version with some interesting WWW links, try here .). In this article, three questions are addressed. First, what is the Internet and what are its implications for modern businesses or strategic planners? Second, what are the current opportunities for using the Internet in different business activities? Third, what research is being persued in the USC School of Business Administration that can help better answer the preceding two questions? (appears in USC Business, 5:32-36, Fall 1994).
    • A Hypertext System for Integrating Heterogeneous Autonomous Software Repositories. (with J. Noll), Hypertext is a simple concept for organizing information into a graph structure of linked container objects. This paper examines issues involved in applying hypertext concepts to the integration of heterogeneous, autonomous software repositories, and presents a solution called the Distributed Hypertext System (DHT). Based on a hypertext data model and client-server architecture, DHT features powerful modeling capabilities, integration of heterogeneous, pre-existing repositories, update with concurrency control, and full local autonomy. (appears in Proc. 4th. Irvine Software Symposium, University of California, Irvine, CA, 49-60, April, 1994)
    • Process Integration in CASE Environments.(with P. Mi), Integrated CASE Environments (CASEEs) have been focused on tool and object integration. In this paper, we present a new type of integration called process integration as a strategy for creating process-centered CASEEs. We argue that the major benefits of process integration to software development include explicit process guidance and improved project management. We also present a few key components to implement process integration which form the backbone of a process-centered CASEE. These components include software process models, a process enactment mechanism, a developer's interface and a process manager's interface. Furthermore, our strategy implements process integration by merging these key components with existing CASEEs and creates process-centered CASEEs with reasonable effort. To exemplify this strategy, we have migrated an operational CASEE, the SOFTMAN environment, into a process-centered CASEE. (appears in IEEE Software,9(2):45-53, 1992. Reprinted in E. Chikoski (ed.), Computer-Aided Software Engineering (2nd. Edition), IEEE Computer Society Press, 1993).
    • Articulation: An Integrated Approach to the Diagnosis, Replanning, and Rescheduling of Software Process Failures. (with P. Mi), This paper presents an integrated approach for how to articulate software engineering process plans that fail. The approach, called articulation, repairs a plan when a diagnosed failure occurs and reschedules changes that ensure the plan's continuation. In implementing articulation, we combine knowledge-based diagnosis, replanning, and rescheduling into a powerful mechanism supporting process-based software development. Use of articulation in plan execution supports recovery and repair of unanticipated failures, as well as revising and improving process plans to become more effective. In this paper, we also describe how a prototype knowledge-based system we developed implements this approach to articulation. (appears in Proc. 8th. Knowledge-Based Software Engineering Conference , Chicago, IL, IEEE Computer Society, 77-85, September 1993).
    • A Knowledge-Based Software Process Library for Process-Driven Software Development. (with P. Mi and S. Li), Process-driven software development represents a technique for software production, in which a conceptual knowledge representation. called a software process, is used to represent and guide development activities. Management and reuse of software processes therefore becomes a requirement for process-driven software development. In this paper, we present a knowledge-based process library (SPLib) that supports the organization, access and reuse of software processes. SPLib consists of a knowledge base of software process representations. It also provides a set of process operations that support browsing, searching composition and abstraction. These operations reason about the content of software processes as well as maintain proper interdependency relationships among the software processes. To demonstrate the use of SPLib in process-driven software development, we provide a usage scenario where SPLib facilitates the access and reuse of software processes in real applications. (appears in Proc. 7th. Knowledge-Based Software Engineering Conf. , Washington, DC, IEEE Computer Society, 122-131, September 1992).
    • Integrating Diverse Information Repositories: A Distributed Hypertext Approach. (with J. Noll), Today's networked information systems and software engineering environment is characterized by a multitude of autonomous, heterogeneous information repositories, a variety of incompatible user interfaces, diverse, unconventional data types, including text, graphics, and possibly video and sound, rapid change, both in structure and content, and multiple ways of viewing relationships among the same information items. Existing information storage mechanisms fail to combine diverse data types/models, complex objects and storage structures, personal views and organizations of shared objects, access to distributed, heterogeneous repositories, and ease of evolution. This paper examines these issues and describes a Distributed Hypertext (DHT) architecture that provides transparent access to autonomous, heterogeneous software object repositories, resulting in both a powerful organizational tool and a simple yet effective integration mechanism. (appears in Computer, 24(12):38-45, December 1991).
    • Modeling Articulation Work in Software Engineering Processes.(with P. Mi), Current software process modeling techniques do not generally support articulation work. Articulation work includes the diagnosis, recovery, and resumption of development activities unexpectedly fail. It is an integral part of software process enactment since software processes can sometimes fail or breakdown. This paper presents a knowledge-based model of articulation work in software engineering processes. It uses empirically grounded heuristics to address three problems in articulation work: diagnosing failed development activities, determining appropriate recovery, and resuming software process enactment, We first investigate the role and importance of articulation work with respect to planned software development activities. We then outline a knowledge-based model of articulation work. The model has been implemented in a knowledge-based software process modeling environment called the Articulator. Combining available software process modeling techniques and the model for articulation leads to a better foundation for process improvement and evolution. appears in Proc. 1st. Intern. Conf. Soft. Processes, IEEE Computer Society, Redondo Beach, CA, pp. 188-201, October 1991. 
    • The Software Infrastructure for a Distributed Software Factory. This paper presents an innovative approach to the construction, application and deployment of software factories. Based on experience in creating and evolving the System Factory project at USC, the authors present a new experimental project called Distributed System Factory (DSF) project. The DSF project is intended to provide a software infrastructure suitable for engineering large-scale software systems with dispersed teams working over wide-area networks. This software infrastructure is the central focus of the author. As such, he describes the information structures that can be used to model and create the infrastructure, as well as target software applications. He also describes an electronic market-place of logically centralised software services which populate and execute within this infrastructure. A brief view of how the DSF project can grow to accommodate academic and industrial research groups is also given. (appears in the Software Engineering Journal, 6(5), 355-369, September 1991).
    • A Hypertext System to Manage Software Life-Cycle Documents. (with P. Garg), The Documents Integration Facility, an environment based on objects and relationships between objects that was constructed for the development, use, and maintenance of large-scale systems and their life-cycle documents, is presented. DIF helps integrate and manage the documents produced and used throughout the life cycle: requirements specifications, functional specifications, architectural designs (structural specifications), detailed designs, source code, testing information, and user and maintenance manuals. DIF supports information management in large systems where there is much natural-language text. The documentation method used by DIF and DIF's structure are described. How DIF is used is discussed, and the DIF environment is examined. Issues that were encountered in the design of DIF are considered. (appears in IEEE Software , 7(3), 90-89, May 1990).
    • ISHYS: Designing and Intelligent Software Hypertext System. (with P. Garg), This paper describes the design of ISHYS, an intelligent software hypertext system, and discuss novel applications that such a system can support. In designing ISHYS, they sought to support the software life cycle from a 'web of computing' framework, which necessarily requires the consideration of sociotechnical factors influencing and influenced by the software life cycle. ISHYS supports functionalities that include influencing work interactions on the basis of social interactions and determining tools and their options on the basis of project status information. Implementation of required enhancements to DIF (documents integration facility), the authors' current software hypertext system, has been completed using Prolog, C, and X Windows. (appears in IEEE Intelligent Systems, 4(3), 52-63, Fall 1989).
    • On the Power of Domain-Specific Hypertext Environments. What is the potential power of hypertext technology? This article examines this question and outlines the answer by focussing attention to a domaim-specific view of hypertext environments. I first define what domain-specific hypertext environments (DSHE) represent. Next, I examine DSHE for the domains of encyclopedic and classical studies, creative writing and interactive fiction, journal and book publishing, insurance policy management, and computer-aided software engineering. Then I describe in more detail the structure of information to evolve within a DSHE for software engineering in terms of document products, processing tasks and mechanisms, and workplace attributes. In turn, this examination provides the basis for identifying seven dimensions along which the power of DSHE can be defined, experienced, and accumulated. I also address the organizational costs that may be borne to realize this power. I conclude with observations as to the source of DSHE power as well as identifying topics for further investigation. (appears in Journal American Society Information Science, 40(3):183-191, May, 1989).
    • Work Structures and Shifts: An Empirical Analysis of Software Specification Teamwork.(with S. Bendifallah), The study and support of teamwork in upstream software development activities (e.g., specification, design) have been approached from a variety of perspectives. Those which address aspects of the division of labor typically focus on authority or communication structures. In this paper, we examine how teams of engineers develop software specifications, from a perspective emphasizing the division of labor in terms of the work structures themselves. We present a new typology of work structures and report on an empirical investigation of these work structures. We examine the teamwork process followed by each of five comparable teams of specification developers. The teams worked over a ten-day period with state-of-the- art specification resources to deliver functional specification documents meeting prescribed quality standards. Our data and analysis show the recurrence of various kinds of shifts in the teams' work structures. We discuss the resulting patterns of work structures and shifts and their implications. In particular, separative work structures were associated with improved specification teamwork efficiency, whereas integrative work structures were associated with improved specification product quality. (appears in Proc. 11th. Intern. Conf. Software Engineering , Pittsburgh, PA, ACM and IEEE Computer Society, 260-270, May 1989).
    • Understanding Software Maintenance Work. (with S. Bendifallah), Software maintenance can be successfully accomplished if the computing arrangements of the people doing the maintenance are compatible with their established patterns of work in the setting. To foster and achieve such compatibility requires an understanding of the reasons and the circumstances in which participants carry out maintenance activities. In particular, it requires an understanding of how software users and maintainers act toward the changing circumstances and unexpected events in their work situation that give rise to software system alterations. To contribute to such an understanding, we describe a comparative analysis of the work involved in maintaining and evolving text-processing systems in two academic computer science organizations. This analysis shows that how and why software systems are maintained depends on occupational and workplace contingencies, and vice versa. IEEE Trans. Software Engineering, SE-13(3), 311-323, 1987.
    • Maintaining Configurations of Evolving Software Systems. (with K. Narayanaswamy), Software configuration management ( SCM) is an emerging discipline. An important aspect of realizing SCM is the task of maintaining the configurations of evolving software systems. In this paper, we provide an approach to resolving some of the conceptual and technical problems in maintaining configurations of evolving software systems. The approach provides a formal basis for existing notions of system architecture. The formal properties of this view of configurations provide the underpinnings for a rigorous notion of system integrity, and mechanisms to control the evolution of configurations. This approach is embodied in a language, NuMIL, to describe software system configurations, and a prototype environment to maintain software system configurations. We believe that the approach and the prototype environment offer a firm base to maintain software system configurations and, therefore, to implement SCM. IEEE Trans. Software Engineering, SE-13(3), 324-334, 1987.

       

    Contact Information

    • Walt Scacchi (previously Director of the USC ATRIUM lab )
    • Institute for Software Research
    • Information and Computer Science Dept.
    • University of California, Irvine, CA 92697-3455 USA
    • PHONE: +1-949-824-4130
    • FAX: +1-949-824-1715
    • EMAIL: wscacchi  <<@>> ics (.) uci [.] edu


     


    http://www.ics.uci.edu/~wscacchi/Pubs-Process.html Pubs-Process

    Research Publications on�Organizational and Software Processes [SP]
    �

    1. The Process of Innovation in Computing, Ph.D. dissertation, Information and Computer Science Dept., University of California, Irvine, Irvine, CA 1981.
    2. Modelling Software Evolution: A Knowledge-Based Approach, Proc. 4th. Intern. Workshop Software Process, IEEE Computer Society, Kennebunkport, ME, pp. 153-155, (October 1988)
    3. Engineering Large-Scale Software Systems: An Organizational Knowledge Base Approach, Proc. COMPCON '89, San Francisco, CA, IEEE Computer Society, pp. 232-235, (February 1989)
    4. Experiences with Operational Software Process Modeling in the System Factory Project, Proc. 5th. Intern. Workshop Software Process, IEEE Computer Society, (September 1990)
    5. A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes , (with P. Mi), IEEE Trans. Data and Knowledge Engineering, Vol. 2(3), pp. 283-294, September 1990. Reprinted in Nikkei Artificial Intelligence , Vol. 20(1), pp. 176-191, (January 1991) (in Japanese); also in Process-Centered Software Engineering Environments, P.K. Garg and M. Jazayeri (eds.), IEEE Computer Society, pp. 119-130, (1996)
    6. Modeling Articulation Work in Software Engineering Processes, (with P. Mi), Proc. 1st. Intern. Conf. Soft. Processes, IEEE Computer Society, Redondo Beach, CA, pp. 188-201, (October 1991).
    7. Articulation: Supporting Dynamic Evolution of Software Engineering Processes, (with P. Mi), Proc. 7th. Intern. Workshop Software Process , IEEE Computer Society, Yountville, CA, pp. 94-98, (October 1991)
    8. Experiences in Modeling, Analyzing, and Simulating Software Engineering Processes, Proc. 8th. Intern. Workshop Software Process, IEEE Computer Society, Dagstuhl, Germany (February 1993)
    9. Process Integration in CASE Environments, (with P. Mi), IEEE Software , Vol. 9(2), pp. 45-53, (March 1992). Reprinted in Computer-Aided Software Engineering (CASE), Second Edition, Eliot Chikofsky (ed.), IEEE Computer Society, (1993).
    10. A Knowledge-based Software Process Library for Process-Driven Software Development , (with P. Mi and M. Lee), Proc. 7th. Annual Knowledge-Based Software Engineering Conference, IEEE Computer Society, Washington, DC, pp. 122-131, (September 1992)
    11. Modeling, Integrating, and Enacting Software Engineering Processes, (with P. Mi), Proc. 3rd. Irvine Software Symposium, Costa Mesa, CA, pp. 27-38, (April 1993)
    12. Meta-Environments for Software Production, (with A. Karrer), Intern. J. Soft. Engr. and Know. Engr., Vol. 3(2), pp. 139-162, (May 1993). Reprinted in Advances in Software Engineering and Knowledge Engineering, D. Hurley (ed.), Volume 4, pp. 37-70, (1995).
    13. Understanding Software Productivity, Intern. J. Software Engineering and Knowledge Engineering, 1(3):293-321, 1991. Revised and reprinted in� Advances in Software Engineering and� Knowledge Engineering, D. Hurley (ed.), Volume 4, 37-70, 1995.
    14. Articulation: An Integrative Approach to Diagnosis, Replanning, and Rescheduling, (with P. Mi), Proc. 8th. Annual Knowledge-Based Software Engineering Conference , Chicago, IL, 77-85, (September 1993).
    15. Modeling, Integrating, and Enacting Complex Organizational Processes , (with P. Mi), Proc. 5th. Intern. Symp. Intelligent Systems for Finance, Accounting, and Management, Volume 1, Stanford University, (December 1993)
    16. Process Models in Software Engineering, in J. Marciniak (ed.), Encyclopedia of Software Engineering, John Wiley and Sons, New York, pp. 860-869, (1994)
    17. A Hypertext System for Integrating Heterogeneous, Autonomous Software Repositories, (with J. Noll), Proc. 4th. Irvine Software Symposium , University of California, Irvine, CA, pp.49-60, (April, 1994)
    18. The SMART Approach to Software Process Engineering, (with P.K. Garg, P. Mi, T. Pham, and G. Thunquest), Proc. 16th. Intern. Conf. Software Engineering , IEEE Computer Society, Sorrento, Italy, pp. 341-350, (May 1994). Reprinted in Process-Centered Software Engineering Environments , P.K. Garg and M. Jazayeri (eds.), IEEE Computer Society, pp. 131-140, (1996)
    19. Repository Support for Virtual Software Enterprises, (with J. Noll) Proc. 1996 California Software Symposium, Los Angeles, CA, 78-91, (April 1996).
    20. A Meta-Model for Formulating Knowledge-Based Models of Software Development (with P. Mi), Decision Support Systems, Vol. 17(4):313-330, (1996).
    21. Simulation and Modeling for Software Acquisition (SAMSA), (with B. Boehm), Center for Software Engineering, University of Southern California, Los Angeles, CA, Final Report, March 1996.
    22. (Re)Engineering Research Grants Management: From Acquisition Reform to Knowledge Brokering at ONR, (with J. Noll, C. Knight, and Capt. J. Miller), NSF Workshop on Research and Development Opportunities for Federal Information Services, Arlington, VA,
    23. http://www.usc.edu/dept/ATRIUM/NSF-FIS-Workshop.html , May 1997.
    24. Process Life Cycle Engineering: Approach and Support Environment, (with P. Mi) Intelligent Systems in. Accounting, Finance, and Management, Vol. 6:83-107, (1997).
    25. Supporting Distributed Configuration Management in Virtual Enterprises, (with J. Noll), in R. Conradi (ed.), Software Configuration Management , Lecture Notes in Computer Science, Vol. 1235, Springer-Verlag, New York, pp. 142-160, (1997).
    26. Process-Driven Intranets: Life Cycle Support for Process Reengineering (with J. Noll), IEEE Internet Computing, 1(5):42-49, (1997).
    27. Virtual System Acquisition: Approach and Transitions (with B. Boehm), Acquisition Review Quarterly, 5(2):185-216, Spring 1998.
    28. Modeling, Simulating, and Enacting Complex Organizational Processes: A Life Cycle Approach in M. Prietula, K. Carley, and L. Gasser (eds.), Simulating Organizations: Computational Models of Institutions and Groups , AAAI Press/MIT Press, Menlo Park, CA, 153-168, (1998).
    29. Computational Business Process Components for Electronic Commerce, Intern. Workshop on Component-Based Electronic Commerce, Fisher Center for Information Technology, Berkeley, CA, http://haas.berkeley.edu/~citm/CEC/program1.html , (25 July 1998).
    30. Recent Advances in Process-Driven Intranets and Extranets for Concurrent Engineering, Proc. 1998 Intern. Conf. Systems, Man and Cybernetics (SMC'98), Vol. 3, 2631-2634, San Diego, CA, IEEE Computer Society Press, (October 1998). �
    31. Supporting Software Development in Virtual Enterprises, (with J. Noll), Jour. Digital Information, IEE/British Computing Society, 1(4), (December 1998).
    32. Experience with Software Process Simulation and Modeling, Journal of Systems and Software , 46(2/3):183-192,1999.
    33. Understanding Software Process Redesign using Modeling, Analysis and Simulation, Software Process --Improvement and Practice 5(2/3):183-195, 2000 .
    34. Redesigning Contracted Service Procurement for Internet-based Electronic Commerce: A Case Study, J. Information Technology and Management , 2(3), 313-334, 2001.
    35. Specifying Process-Oriented Hypertext for Organizational Computing, (with J. Noll), Journal of Network Computing and Applications, 24(1):39-61, 2001.
    36. Software Development Practices in Open Software Development Communities, presented at the 1st Workshop on Open Source Software Engineering, Toronto, Ontario, May 2001.
    37. Process Models in Software Engineering, in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, 993-1005, Wiley, 2001.
    38. Modeling and Simulating Software Acquisition Process Architectures (with J.S.C. Choi), Journal of Systems and Software , 59(3), 343-354, 15 December 2001.
    39. Understanding the Requirments for Developing Open Source Software Systems , IEE Proceedings--Software, 2002.
    http://www.ics.uci.edu/~wscacchi/Presentations/OSS-Requirements/ Index of /~wscacchi/Presentations/OSS-Requirements

    Index of /~wscacchi/Presentations/OSS-Requirements

    [ICO]NameLast modifiedSizeDescription

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    [   ]OSS-Req-Design-Process.ppt19-May-2005 12:57 3.4M 
    [   ]OSS-Requirements-Process.pdf01-Oct-2002 09:56 3.2M 
    [   ]OSS-Requirements-Process.ppt06-Jul-2004 12:21 1.1M 
    [   ]Thumbs.db01-Sep-2004 11:14 14K 
    [TXT]Understanding-OSSD-Requirements-Abstract.html15-Oct-2002 12:40 1.9K 

    Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
    http://www.ics.uci.edu/~dan/class/165/index.html CompSci 165: Project in Algorithms and Data Structures

    CompSci 165: Project in Algorithms and Data Structures
    Spring 2016

    • Description
      • Design, implementation, execution, and analysis of algorithms
        Based on material from ICS 46 and CompSci 161, plus some more advanced topics
      • The programming language is C/C++ (each student's choice)
      • There are 3 required projects, no homework and no examinations
      • Prerequisites: CompSci 161 / CSE 161.   Recommended: ICS/CSE 45C

    • Class Meetings
      • Lectures M W F 10-10:50am in ICS 180
        Lectures discuss the theory behind the projects, as well as some implementation concerns
      • The class meets only sporadically during the quarter, typically a couple of times for each of the projects
      • One-on-one discussions with the instructor are available throughout the quarter

    • Instructor
      • Dan Hirschberg   —   dan (at) ics.uci.edu
        Office hours:  in DBH 4226, by appointment
      • Grading Assistant:     TBD

    • Add/drop Policy
      • No adds or drops are permitted after Week 1

    • Schedule

    • Project Submissions and Evaluation

    • Notes

    • Projects
      • Compiler considerations for CompSci 165
      • Some projects need to measure time usage
      • Many projects need a random number generator;  I recommend using dshrandom.c
      • Do you know the meanings of and distinction between precision and accuracy?

      Access to project specifications is limited to enrolled students.
      The user name is your UCInetID (in ALL CAPITAL LETTERS) and the password is your student ID number.

      1. Project 1 – selection of k largest
      2. Project 2 – majority using group counts

    • Communication — If you send me email with course-related questions...
      • Include the string "CompSci 165:" at the start of the subject line
      • Include your name and UCI Student ID number in the message
      • If you are not writing from your official UCI email address, please cc your official UCI email address
      (This protocol enables me to weed out requests for help on problems from non-UCI students.)

    • Discussion Board
      • Students can discuss matters related to this course on Piazza


    Last modified: Feb 8, 2016 http://www.ics.uci.edu/~dan/midi/rock/index.html Best Classical Rock Midi

    Best Classical Rock Midi

    The Angels
    My Boy Friend's Back

    Animals
    House of the Rising Sun ( 2 , 3 )
    Don't Let me Be Misunderstood

    The Beachboys
    Fun Fun Fun
    Surfer Girl
    Good Vibrations
    California Girls

    The Beatles collection

    Bread
    It Don't Matter to Me

    Byrds
    Eight Miles High
    Turn Turn Turn

    Chicago
    25 or 6 to 4 ( 2 )
    Does Anybody Know What Time It Is
    Only the Beginning

    Eric Clapton (also Derek & the Dominoes)
    Layla

    Joe Cocker
    With a Little Help From My Friends
    Here Comes The Sun ( 2 )

    Creedance Clearwater Revival (CCR)
    Down on the Corner

    Crosby, Stills, and Nash
    Our House

    Neil Diamond
    He Ain't Heavy, He's My Brother
    Sweet Caroline

    Dire Straits
    Sultans of Swing

    The Doors
    Light My Fire
    Love Her Madly ( 2 )
    People Are Strange
    Riders On The Storm
    Roadhouse Blues
    Touch Me

    The Drifters
    Under the Boardwalk
    Up on the Roof

    Everly Brothers
    All I Have To Do Is Dream

    Marianne Faithful
    As Tears Go By ( 2 ) ( updated by Warren Porter )

    The Four Seasons
    Alone
    Big Girls Don't Cry
    C'mon Marrianne
    Dawn (Go Away)
    Don't Think Twice
    Ragdoll ( 2 )
    Ronnie
    Sherry
    Silence Is Golden
    Stay
    Walk Like a Man [Nick Jacobi] ( 2 )
    What a Night
    Working My Way Back To You

    Leslie Gore
    It's My Party

    The Guess Who
    No Time

    Richard Harris
    MacArthur's Park

    Herman's Hermits
    I'm Henry the Eighth

    Hollies
    Bus Stop

    Jay and the Americans
    This Magic Moment

    Elton John
    Crocodile Rock

    Kansas
    Carry On
    Dust in the Wind

    Ben E. King
    I, Who Have Nothing
    Stand By Me

    Led Zeppelin
    Stairway to Heaven ( 2, 3 )

    Marcels
    Blue Moon

    Peggy March
    I Will Follow Him

    McCoys
    Hang On Sloopy ( 2 )

    Monkees
    Daydream Believer [Nick Jacobi]

    Moody Blues
    Nights of White Satin
    Tuesday Afternoon
    Just a Singer in a Rock 'n Roll Band ( 2 )

    Van Morrison
    Brown-Eyed Girl

    Roy Orbison
    Crying
    Pretty Woman

    Elvis Presley
    Can't Help Falling in Love
    Suspicious Minds

    Procol Harum
    A Whiter Shade of Pale

    Martha Reeve & the Vandellas
    Heatwave

    Righteous Bros.
    Unchained Melody ( 2 )
    You've Lost that Loving Feeling ( 2 )

    The Rolling Stones collection

    Santana
    Black Magic Woman & Oye Como Va
    Evil Ways

    Sha Na Na ( also Danny & the Juniors )
    At the Hop ( 2 )

    Del Shannon
    Runaway

    Simon & Garfunkel
    Bridge over Troubled Waters ( 2 , 3 )
    Cecilia
    I am a Rock
    Mrs. Robinson ( 2 )
    Sounds of Silence

    Percy Sledge
    When a Man Loves a Woman

    Steppenwolf
    Born to be Wild ( 2 )

    Styx
    Come Sail Away

    Supremes
    Baby Love ( 2 )
    You Can't Hurry Love
    You Keep Me Hanging On

    Surfaris
    Wipeout

    James Taylor
    You've Got a Friend

    Temptations
    My Girl

    Traffic
    Freedom Rider

    Jethro Tull
    Aqualung

    The Turtles
    Happy Together

    Richie Valens
    La Bamba

    The Ventures
    Walk, Don't Run

    Dionne Warwick
    Walk on By

    The Who
    Pinball Wizard


    NOTE: None of these arrangements were constructed by me. These midi files were copied from various websites. I do not know who owns the copyrights.

    Dan Hirschberg
    Computer Science
    University of California, Irvine, CA 92697-3425
    dan at ics.uci.edu
    Last modified: Jan 12, 2009 http://www.ics.uci.edu/~theory/269/ Theory Seminar

    ICS Theory Group

    CompSci 269S, Winter 2016: Theory Seminar


    The Theory Group normally meets Fridays at 1:00pm; this quarter we are in ICS 243.

    Below is this quarter's schedule.

    January 8:
    Organizational meeting
     
    January 15:
    (no seminar)
     
    January 22
    Michael Mitzenmacher:
    More analysis of double hashing with balanced allocations
     
    January 29:
    Timothy Johnson
    Algorithmic complexity of power law networks
     
    February 5:
    Nil Mamano
    On the complexity of an unregulated traffic crossing
     
    February 12:
    Siddhartha Gupta
    Algorithms for two-pass connected component labeling
     
    February 19:
    Tsvi Kopelowitz
    Higher lower bounds from the 3SUM conjecture
     
    February 26:
    Will Devanny
     
    March 4:
    Dmitri Arkhipov
     
    March 11:
    (TBA)

    Previous quarters' theory seminars

    http://www.ics.uci.edu/~dan/vita.html Dan Hirschberg's Curriculum Vitae
    Dan Hirschberg

    EDUCATION

    1975   Ph.D. (Computer Science), Princeton University
    1973 MSE, MA, Princeton University
    1971 BE(EE), City College of New York

    ACADEMIC APPOINTMENTS

    University of California, Irvine
    2003- Professor of Computer Science and EECS
    1994-2003
     
    Professor of Information and Computer Science
    and of Electrical and Computer Engineering
    1987-94 Professor of Information and Computer Science
    1992-93,96-98 Associate Chair of Undergraduate Studies, ICS
    1984-90 Associate Chair of Graduate Studies, ICS
    1981-87 Associate Professor of Information and Computer Science
    Rice University
    1975-81 Assistant Professor of Electrical Engineering

    CONSULTING ACTIVITIES

    1998-2013
     
     
    several law firms
    Consulting expert for intellectual property cases
    Provide expert testimony in judicial proceedings
    1984-94
     
    Manufacturing and Consulting Services, Inc. (Scottsdale, AZ)
    Design and analysis of database structures for CAD/CAM
    1989
     
    A-Chip Co., Inc. (Santa Ana, CA)
    Design of data compression/decompression techniques
    1984-89
     
    Pick Systems, Inc. (Irvine, CA)
    Design of operating system data structures
    1986
     
    Computer Cognition, Inc. (San Diego CA)
    Design of data structures for AI applications
    1978-81
     
    University of Texas Health Science Center (Houston, TX)
    Database development for genetics research
    1976
     
    Argonne National Laboratories (Argonne, IL)
    System simulation, numerical analysis
    1974
     
    IBM Watson Research Center (Yorktown Heights, NY)
    Research in combinatorial algorithms

    PROFESSIONAL SERVICE

    referee of grant proposals for: Army Research Office, NSF,
          Israel Science Foundation, Research Grants Council
    reviewer of textbooks for several publishers
    referee of technical papers for numerous journals
    Associate Editor, ACM Trans. on Mathematical Software (1988-90)
    Associate Editor, Discrete Mathematics, Algorithms and Applications (2009-)
    Member, IEEE Computer Dictionary working group (1990-95)
    Member of the Program Committee
    IEEE Data Compression Conference (1991,1992,1993,1994)
    Combinatorial Pattern Matching (1992,1993,1994,1996,1997,2009) [co-chair 1996]
    String Processing and Information Retrieval (1998)
    Combinatorial Optimization and Applications (2010)

    UC ACADEMIC SENATE SERVICE

    University Committee on Rules and Jurisdiction (2005-07, 12-14), (Chair 1998-2002, 07-09)

    UC IRVINE ACADEMIC SENATE SERVICE

    Campus Parliamentarian (2000-present)
    Committee on Rules and Jurisdiction (1991-92, 96-97, 2000-02, 10-11),
                                    (Chair 1992-94, 97-98, 2003-06, 11-13)
    Committee on Committees (1995-99)
    Executive Committee (1992-94, 97-98)
    Committee on Courses (1992-95)
    Graduate Council (1988)
    Committee on Research (1985)

    PH.D. DISSERTATIONS SUPERVISED

    1986   Lawrence L. Larmore, Methods for Solving Breakpoint Problems
    1988 James H. Hester, Probabilistically Faster Search Structures
    1990 Cheng F. Ng, Computational Complexity of Stable Matching Problems
    1991 Debra A. (Lelewer) Brum, Data Compression on Machines with Limited Memory
    1994 Lynn M. Stauffer, Parallel and High-Speed Data Compression
    1997 Steven S. Seiden, Randomization in Online Computation
    1998 Jonathan Kent Martin, Machine Learning of Classification via Generalized Linear Models:
    Theoretical and Practical Considerations

    PUBLICATIONS


    Last modified: May 29, 2015 http://www.ics.uci.edu/~dan/pub.html Dan Hirschberg's Publications

    Dan Hirschberg's Publications

    A. Book Chapters

    1. D.S. Hirschberg, Recent results on the complexity of common subsequence problems, in Time Warps, String Edits, and Macromolecules, D. Sankoff and J.B. Kruskal, ed., Addison-Wesley, 1983, 323-328.
    2. D.S. Hirschberg and D.A. Lelewer, Context modeling for text compression, in Image and Text Compression, J. A. Storer, ed., Kluwer Academic Publishers, Boston, Mass., 1992, 113-145.
    3. D.S. Hirschberg, M.J. Pazzani and K. Ali, Average case analysis of k-CNF and k-DNF learning algorithms, in Computational Learning Theory and Natural Learning Systems: Constraints and Prospects, S. Hanson, M. Kearns, T. Petsche and R. Rivest, eds., MIT Press, Cambridge, Mass., 1994, 15-28.
    4. D. Hirschberg and G. Myers, eds., Combinatorial Pattern Matching, Proceedings 1996, Lecture Notes in Computer Science, vol. 1075, Springer-Verlag, Berlin, 1996, 392 pp.
    5. D.S. Hirschberg, Serial computations of Levenshtein distances, in Pattern Matching Algorithms, A. Apostolico and Z. Galil, eds., Oxford University Press, 1997, 123-141.

    B. Journal Articles

    1. D.S. Hirschberg, A class of dynamic memory allocation algorithms, Comm. ACM 16,10 (1973) 615-618.
    2. D.S. Hirschberg, A linear space algorithm for computing maximal common subsequences, Comm. ACM 18,6 (1975) 341-343.
    3. A.V. Aho, D.S. Hirschberg and J.D. Ullman, Bounds on the complexity of the longest common subsequence problem, Journal ACM 23,1 (1976) 1-12.
    4. D.S. Hirschberg and C.K. Wong, A polynomial-time algorithm for the knapsack problem with two variables, Journal ACM 23,1 (1976) 147-154.
    5. D.S. Hirschberg, An insertion technique for one-sided height-balanced trees, Comm. ACM 19,8 (1976) 471-473.
    6. A.K. Chandra, D.S. Hirschberg and C.K. Wong, Approximate algorithms for some generalized knapsack problems, Theoretical Computer Science 3,3 (1976) 293-304.
    7. D.S. Hirschberg, Algorithms for the longest common subsequence problem, Journal ACM 24,4 (1977) 664-675.
    8. D.S. Hirschberg, An information theoretic lower bound for the longest common subsequence problem, Information Processing Letters 7,1 (1978) 40-41.
    9. D.S. Hirschberg, Fast parallel sorting algorithms, Comm. ACM 21,8 (1978) 657-661.
    10. A.K. Chandra, D.S. Hirschberg and C.K. Wong, Bin packing with geometric constraints in computer network design, Operations Research 26,5 (1978) 760-772.
    11. D.S. Hirschberg and C.K. Wong, Upper and lower bounds for graph-diameter problems, Journal of Comb. Theory (B) 26,1 (1979) 66-74.
    12. D.S. Hirschberg, A.K. Chandra and D.V. Sarwate, Computing connected components on parallel computers, Comm. ACM 22,8 (1979) 461-464.
    13. D.S. Hirschberg, On the complexity of searching a set of vectors, SIAM Journal on Computing 9,1 (1980) 126-129.
    14. D.S. Hirschberg and J.B. Sinclair, Decentralized extrema-finding in circular configurations of processors, Comm. ACM 23,11 (1980) 627-628.
    15. M. Kumar and D.S. Hirschberg, An efficient implementation of Batcher's odd-even merge algorithm and its application in parallel sorting schemes, IEEE Trans. on Computers C-32,3 (1983) 254-264.
    16. L.L. Larmore and D.S. Hirschberg, Efficient optimal pagination of scrolls, Comm. ACM 28,8 (1985) 854-856.
    17. J. Hester and D.S. Hirschberg, Self-organizing linear search, Computing Surveys 17,3 (1985) 295-311.
    18. J.H. Hester, D.S. Hirschberg, S.-H.S. Huang, and C.K. Wong, Faster construction of optimal binary split trees, Journal of Algorithms 7,3 (1986) 412-424.
    19. D.S. Hirschberg and L.L. Larmore, Average case analysis of marking algorithms, SIAM Journal on Computing 15,4 (1986) 1069-1074.
    20. D.S. Hirschberg and L.L. Larmore, The Set LCS problem, Algorithmica 2 (1987) 91-95.
    21. D.S. Hirschberg and D.J. Volper, Improved update/query algorithms for the interval valuation problem, Information Processing Letters 24 (1987) 307-310.
    22. D.S. Hirschberg and L.L. Larmore, New applications of failure functions, Journal ACM 34,3 (1987) 616-625.
    23. D.S. Hirschberg and L.L. Larmore, The least weight subsequence problem, SIAM J. on Computing 16,4 (1987) 628-638.
    24. J. Hester and D.S. Hirschberg, Self-organizing search lists using probabilistic backpointers, Comm. ACM 30,12 (1987) 1074-1079.
    25. D.A. Lelewer and D.S. Hirschberg, Data compression, Computing Surveys 19,3 (1987) 261-297. Reprinted in Japanese BIT Special issue in Computer Science (1989) 165-195. (in HTML)
    26. J.H. Hester, D.S. Hirschberg, and L.L. Larmore, Construction of optimal binary split trees in the presence of bounded access probabilities, Journal of Algorithms 9,2 (1988) 245-253.
    27. D.S. Hirschberg and L.L. Larmore, The Set-Set LCS problem, Algorithmica 4,4 (1989) 503-510.
    28. C. Ng and D.S. Hirschberg, Lower bounds for the stable marriage problem and its variants, SIAM J. on Computing 19,1 (1990) 71-77.
    29. D.S. Hirschberg and D.A. Lelewer, Efficient decoding of prefix codes, Comm. ACM 33,4 (1990) 449-459.
    30. L.L. Larmore and D.S. Hirschberg, A fast algorithm for optimal length-limited codes, Journal ACM 37,3 (1990) 464-473.
    31. C. Ng and D.S. Hirschberg, Three-dimensional stable matching problems, SIAM J. Discr. Math. 4,2 (1991) 245-252.
    32. D.S. Hirschberg and L.L. Larmore, The traveler's problem, Journal of Algorithms 13 (1992) 148-160.
    33. D.S. Hirschberg and S.S. Seiden, A bounded-space tree traversal algorithm, Information Processing Letters 47 (1993) 215-219.
    34. S.S. Seiden and D.S. Hirschberg, Finding succinct minimal perfect hashing functions, Information Processing Letters 51 (1994) 283-288.
    35. L.M. Stauffer and D.S. Hirschberg, Systolic self-organizing lists under transpose, IEEE Trans. on Parallel and Distributed Systems 6,1 (1995) 102-105.
    36. D.S. Hirschberg and L.M. Stauffer, Dictionary compression on the PRAM, Parallel Processing Letters 7,3 (1997) 297-308.
    37. D. Eppstein and D.S. Hirschberg, Choosing subsets with maximum weighted average, Journal of Algorithms 24 (1997) 177-193.
    38. M. Dillencourt, D. Eppstein, and D. S. Hirschberg, Geometric thickness of complete graphs, J. Graph Algorithms and Applications 4,3 (2000) 5-17. (original version) Reprinted in Graph Algorithms and Applications 2, Giuseppe Liotta, Robert Tamassia, and Ioannis G Tollis, ed., (2004).
    39. D.S. Hirschberg and M. Regnier, Tight bounds on the number of string subsequences, Journal of Discrete Algorithms 1,1 (2000) 123-132.
    40. M. Mamidipaka, D. Hirschberg, and N. Dutt, Adaptive low power address encoding techniques using self-organizing lists, IEEE Trans. on Very Large Scale Integration Systems 11,5 (2003) 827-834.
    41. D. Eppstein, M.T. Goodrich, and D.S. Hirschberg, Improved combinatorial group testing algorithms for real-world problem sizes, SIAM J. on Computing 36,5 (2007) 1360-1375.
    42. G.I. Bell, D.S. Hirschberg, and P. Guerrero-Garcia, The minimum size required of a solitaire army, Integers: Electronic Journal of Combinatorial Number Theory 7 (2007), #G07 http://www.integers-ejcnt.org/vol7.html (22 pages).
    43. P. Baldi, R. Benz, D.S. Hirschberg, and S.J. Swamidass, Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval, Journal of Chemical Information and Modeling 47,6 (2007) 2098-2109.
    44. M.T. Goodrich and D.S. Hirschberg, Improved adaptive group testing algorithms with applications to multiple access channels and dead sensor diagnosis, Journal of Combinatorial Optimization 15,1 (2008) 95-121.
    45. P. Baldi, D.S. Hirschberg, and R. Nasr, Speeding up chemical database searches using a proximity filter based on the logical exclusive-or, Journal of Chemical Information and Modeling 48,7 (2008) 1367-1378.
    46. P. Baldi and D.S. Hirschberg, An intersection inequality sharper than the triangle inequality for the Tanimoto similarity measure, Journal of Chemical Information and Modeling 49,8 (2009) 1866-1870.
    47. R. Nasr, D.S. Hirschberg, and P. Baldi, Hashing algorithms and data structures for rapid searches of fingerprint vectors, Journal of Chemical Information and Modeling 50,8 (2010) 1358-1368.

    C. Conference Articles

    1. A.V. Aho, D.S. Hirschberg and J.D. Ullman, Bounds on the complexity of the longest common subsequence problem, Proc. 15th Annual Symp. on Switching and Automata Theory, New Orleans LA, IEEE (1974) 104-109.
    2. D.S. Hirschberg, A slightly better bound for the vertex connectivity problem, Proc. Conf. of Info. Sci. and Systems, Baltimore MD, Johns Hopkins Univ. (1975) 257-258.
    3. D.S. Hirschberg, Parallel algorithms for the transitive closure and the connected component problems, Proc. 8th Annual Symp. on Theory of Computing, Hershey PA, ACM (1976) 55-57.
    4. D.S. Hirschberg, Complexity of common subsequence problems, Fundamentals of Computation Theory, Poznan Poland, Lecture Notes in Computer Science, 56, Springer-Verlag, Berlin (1977) 393-398.
    5. D.S. Hirschberg, A lower worst-case complexity for searching a dictionary, Proc. 16th Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1978) 50-53.
    6. D.S. Hirschberg, Election processes in distributed systems, Proc. 18th Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1980) p.823.
    7. M. Kumar and D.S. Hirschberg, An efficient implementation of Batcher's odd-even merge algorithm and its application in parallel sorting schemes, Proc. Conf. of Info. Sci. and Systems, Baltimore MD, Johns Hopkins Univ. (1981).
    8. D.S. Hirschberg, Parallel graph algorithms without memory conflicts, Proc. 20th Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1982) 257-263.
    9. D.S. Hirschberg and D.J. Volper, A parallel solution for the minimum spanning tree problem, Proc. Conf. of Info. Sci. and Systems, Baltimore MD, Johns Hopkins Univ. (1983) 680-684.
    10. D.S. Hirschberg and L.L. Larmore, Average case analysis of marking algorithms, Proc. 22nd Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1984) 508-509.
    11. L.L. Larmore and D.S. Hirschberg, Breaking a paragraph into lines in linear time, Proc. 22nd Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1984) 478-487.
    12. D.S. Hirschberg and L.L. Larmore, The Least Weight Subsequence Problem, Proc. 26th Annual Symp. on Foundations of Computer Science, Portland Oregon, IEEE (1985) 137-143.
    13. R.R. Razouk and D.S. Hirschberg, Tools for efficient analysis of concurrent software systems, Proc. of SOFTFAIR II, A Second Conference on Software Development Tools, Techniques, and Alternatives, San Francisco CA (1985).
    14. J.H. Hester and D.S. Hirschberg, Generation of optimal binary split trees, Proc. 24th Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1986) 308-313.
    15. L.L. Larmore and D.S. Hirschberg, Length-limited coding, Proc. First Annual Symposium on Discrete Algorithms, San Francisco, SIAM, Phil. (1990) 310-318.
    16. D.A. Lelewer and D.S. Hirschberg, Streamlining context models for data compression, Proc. IEEE Data Compression Conference, Snowbird UT, IEEE (1991) 313-322.
    17. D.S. Hirschberg, M.J. Pazzani and K. Ali, Average case analysis of k-CNF and k-DNF learning algorithms, Second Annual Workshop on Computational Learning Theory and Natural Learning Systems: Constraints and Prospects, Berkeley CA (1991).
    18. S. Bhatia, D.S. Hirschberg and I.D. Scherson, Shortest paths in orthogonal graphs, Proc. 29th Annual Allerton Conf. on Comm., Control, and Computing, Monticello IL, Univ. of Ill. (1991) 488-497.
    19. L.M. Stauffer and D.S. Hirschberg, Transpose coding on the systolic array, Proc. IEEE Data Compression Conference, Snowbird UT, IEEE (1992) 162-171.
    20. D.S. Hirschberg and M.J. Pazzani, Average case analysis of learning k-CNF concepts, Proc. Ninth International Machine Learning Conference, Aberdeen Scotland, Morgan Kaufmann, San Mateo (1992) 206-211.
    21. D.S. Hirschberg and L.M. Stauffer, Parsing algorithms for dictionary compression on the PRAM, Proc. IEEE Data Compression Conference, Snowbird UT, IEEE (1994) 136-145.
    22. L.M. Stauffer and D.S. Hirschberg, PRAM algorithms for static dictionary compression, Proc. International Parallel Processing Symposium, Cancun Mexico, IEEE (1994) 344-348.
    23. D. Eppstein and D.S. Hirschberg, Choosing subsets with maximum weighted average, Proc. 5th MSI Workshop on Computational Geometry, Stonybrook NY (1995) 7-8.
    24. J.K. Martin and D.S. Hirschberg, On the complexity of learning decision trees, Proc. 4th Int. Symp. on Artif. Intell. and Math., Fort Lauderdale, FL (1996) 112-115.
    25. M.B. Dillencourt, D.E. Eppstein and D.S. Hirschberg, Geometric thickness of complete graphs, Graph Drawing: 6th Int'l Symp. (GD '98), Montreal Canada, Lecture Notes in Computer Science, vol. 1547, Springer-Verlag, Berlin (1998) 102-110.
    26. D.S. Hirschberg, Bounds on the number of string subsequences, Proc. Symp. on Combinatorial Pattern Matching, Warwick UK, Lecture Notes in Computer Science, Springer-Verlag, Berlin (1999) 115-122.
    27. M. Mamidipaka, D. Hirschberg, and N. Dutt, Low power address encoding using self-organizing lists, Proc. ACM/IEEE Int'l Symp. on Low Power Electronics and Design, Huntington Beach CA (2001) 188-193.
    28. M. Mamidipaka, D. Hirschberg, and N. Dutt, Efficient power reduction techniques for time multiplexed address buses, Proc. 15th ACM Int'l Symp. on System Synthesis, Kyoto (2002) 207-212.
    29. D. Eppstein, M.T. Goodrich, and D.S. Hirschberg, Improved combinatorial group testing for real-world problem sizes, Workshop on Algorithms and Data Structures (WADS) (2005), in Lecture Notes in Computer Science, vol. 3608, Springer-Verlag, Berlin, 2005, 86-98.
    30. M.T. Goodrich and D.S. Hirschberg, Efficient parallel algorithms for dead sensor diagnosis and multiple access channels, Proc. 18th ACM Symp. on Parallelism in Algorithms and Architectures (SPAA '06), Cambridge MA (2006) 118-127.
    31. D.S. Hirschberg and P. Baldi, Effective compression of monotone and quasi-monotone sequences of integers, Proc. IEEE Data Compression Conference, Snowbird UT, IEEE (2008) 520.
    32. D.S. Hirschberg, Constructing problems of geometric combinatorics, Gathering for Gardner 9 (G4G9), Atlanta GA (2010), 8 pp.
    33. M.T. Goodrich, D.S. Hirschberg, M. Mitzenmacher, and J. Thaler, Cache-oblivious dictionaries and multimaps with negligible failure probability, Proc. Mediterranean Conference on Algorithms, Kibbutz Ein-Gedi Israel (2012), Lecture Notes in Computer Science, vol. 7659, Springer-Verlag, Berlin (2012) 203-218.
    34. D. Eppstein, M. Goodrich, and D. Hirschberg, Combinatorial pair testing: distinguishing workers from slackers, Algorithms and Data Structures Symposium (WADS) 2013, Lecture Notes in Computer Science, vol. 8037, Springer-Verlag, Berlin (2013) 316-327.
    35. D. Eppstein and D. Hirschberg, From discrepancy to majority, 12th Latin American Theoretical Informatics Symposium (LATIN'16) (2016), Ensenada, Mexico, to appear.

    D. Selected Additional Publications

    1. D.S. Hirschberg and E.C. Horvath, Permutations of the elements of a matrix by column and row rotations, Comp. Sci. Lab. TR-111, Princeton University (July 1972).
    2. D.S. Hirschberg and M. Edelberg, On the complexity of computing graph isomorphism, Comp. Sci. Lab. TR-130, Princeton University (Aug. 1973).
    3. D.S. Hirschberg, L.L. Larmore and M. Molodowitch, Subtree weight ratios for optimal binary search trees, Tech. Rpt. 86-02, ICS Dept., UC Irvine (Jan. 1986).
    4. D.A. Lelewer and D.S. Hirschberg, An order-2 context model for data compression with reduced time and space requirements, Tech. Rpt. 90-33, ICS Dept., UC Irvine (Oct. 1990).
    5. L.M. Stauffer and D.S. Hirschberg, Systolic implementations for transpose coding, Tech. Rpt. 91-69, ICS Dept., UC Irvine (1991).
    6. L.M. Stauffer and D.S. Hirschberg, Self-organizing lists on the Xnet, Tech. Rpt. 92-81, ICS Dept., UC Irvine (1992).
    7. D.S. Hirschberg, Data compression, 1993 McGraw-Hill Yearbook of Science and Technology, McGraw-Hill, New York, 1992, 91-93.
    8. L.M. Stauffer and D.S. Hirschberg, Parallel data compression, Tech. Rpt. 91-44, ICS Dept., UC Irvine (1991). Revised (1993).
    9. J.K. Martin and D.S. Hirschberg, The time complexity of decision tree induction, Tech. Rpt. 95-27, ICS Dept., UC Irvine (1995).
    10. J.K. Martin and D.S. Hirschberg, Small sample statistics for classification error rates, I: Error rate measurements, Tech. Rpt. 96-21, ICS Dept., UC Irvine (1996).
    11. J.K. Martin and D.S. Hirschberg, Small sample statistics for classification error rates, II: Confidence intervals and significance tests, Tech. Rpt. 96-22, ICS Dept., UC Irvine (1996).
    12. D.S. Hirschberg, Data compression, McGraw-Hill Encyclopedia of Science and Technology (8th ed.) vol. 5, McGraw-Hill, New York, 1997, pp. 31-33.

    Last modified: Dec 19, 2015 http://www.ics.uci.edu/~dan/class/260/index.html CompSci 260: Fundamentals of the Design and Analysis of Algorithms

    CompSci 260: Fundamentals of the Design and Analysis of Algorithms

    • Class Meetings (Fall 2015)
      • Lecture:   Tu Th 9:30-10:50am in ICS 180

    • Instructor
      • Professor Dan Hirschberg  —  dan (at) ics.uci.edu
            office hours by appointment in DBH 4226

    • Prerequisites
      • an undergraduate algorithms course

    • Add/Drop Policy
      • Adds/drops handled via the Registrar's WebReg system through the end of week 1
      • Adds/drops handled by authorization code from the instructor during week 2
      • No adds or drops allowed after the second week of classes

    • Textbook
      • Required: Kleinberg and Tardos, Algorithm Design, Addison Wesley, 2006
      • The course will cover the first eight chapters of this book

    • List of Topics
      The following schedule is approximate and may change over the course of the quarter.
      • Week 0: Introduction. The Stable marriage problem. [KT Chapter 1]
      • Week 1: Basics of Algorithm Analysis. [KT Chapter 2]
      • Weeks 2-3: Basics of Graph Algorithms. [KT Chapter 3]
      • Week 4: First midterm examination covers chapters 1-3
      • Weeks 4-5: Greedy Algorithms. Shortest Paths. Minimum Spanning Trees. [KT Chapter 4]
      • Week 6: Divide and Conquer. [KT Sections 5.1-5.6, 13.5]
      • Weeks 7-8: Dynamic Programming. [KT Sections 6.1-6.9]
      • Week 8: Second midterm examination covers chapters 4-6
      • Week 9: Network Flow. [KT Sections 7.1-7.7]
      • Week 10: NP-completeness. [KT Sections 8.1-8.5]
      • Week 11: Comprehensive final examination on Thursday, December 10 (8-10am)

    • Notes (user name is your UCI ID name in ALL CAPS, password is your student number)
      Students are responsible for all material covered in lecture and the relevant portions of the textbook, even if it does not appear in the lecture notes
      • Lecture Slides
      • A paper relevant to NP-completeness:
        • tutorial on complexity
    • Requirements
      • homework
      • two midterms -- tentatively scheduled during weeks 4 and 8
      • final exam -- Th of week 11, 8-10am

    • Homework assignments

    • Masters Comprehensive Exam
      • This course may be used as part of the Comprehensive Exam in the computer science masters program. To pass the Comprehensive Exam, students must obtain an A- or better on the 260 Final Exam.
      • Students who wish to take the Comprehensive Exam but are not enrolled in the course should contact me by email before the end of week 2 of the quarter to reserve a seat in the exam. Include your full name, UCI Student ID #, and which quarter you were previously enrolled in CompSci 260. Send the request from your UCI email account (to prevent identity theft). Students without reservations will not be permitted to sit for the exam.

    • Discussion Board
      • Students can discuss matters related to this course on Piazza

    • Academic Dishonesty
      • Instances of academic dishonesty will be reflected in the final grade (usually an F) because dishonesty devalues the learning experience for the whole class. Additional consequences may occur at the campus level.
      • Examples of academic dishonesty include, but are not limited to:
        • copying from others during an examination
        • communicating exam answers with other students during an examination
        • using unauthorized materials during an examination
        • allowing another student to copy off your work during an examination
        • tampering with an examination after it has been corrected, then returning it for more credit
      • For more complete information about academic honesty policies, consult the
        Academic Senate Policy on Academic Honesty

    • Communication
      If you send me email with course-related questions:
      • include the string "CompSci 260" at the start of the subject line
      • include your name and UCI Student ID number in the message
      • if you are not writing from your official UCI email address, please cc your official UCI email address

      (This protocol enables me to weed out requests for help on problems from non-UCI students)

    Last modified: Feb 1, 2016 http://www.ics.uci.edu/~dan/class/161/index.html CompSci 161: Design and Analysis of Algorithms Home Page

    CompSci 161: Design and Analysis of Algorithms

    • Class meetings (Winter 2016)
      • Lectures: M W F 11:00-11:50am in DBH 1100
      • Discussions: Tu Th 7:00-7:50pm or 8:00-8:50pm in SSL 140

    • Instructor
      • Professor Dan Hirschberg — dan (at) ics.uci.edu
            office hours M W 10-10:45am or by appointment in DBH 4226
      • Teaching Assistant:     David Hirschberg — hirschbd (at) uci.edu
            office hours for midterm 2 TBA
      • Grading Assistant:     Jia Chen — jiac5 (at) uci.edu

    • Prerequisites
      • calculus (Math 2AB), Boolean algebra (ICS 6B), and discrete mathematics (ICS 6D)
      • data structures (ICS 46) with a grade of C or better

    • Course Texts
      • Required: Goodrich and Tamassia, Algorithm Design and Applications, Wiley, 2014
        Wiley's "Direct-to-Student" website   (this is paperback, the nicer hardcover is available on Amazon for $110.67)
      • Recommended: Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms (3rd ed.), MIT Press, 2009
      • Reference: Baase, Van Gelder, Computer Algorithms (3rd ed.), Addison-Wesley, 2000
      • Reference: Dasgupta, Papadimitriou, and Vazirani, Algorithms, McGraw-Hill, 2007
      • List of reference books

    • Course Goals
      To develop an understanding of efficiency of algorithms, to learn some algorithmic design techniques, and to analyze the complexity of the amount of resources required by algorithms for a variety of applications

    • Homeworks and Examinations
      • Policies
      • Schedule

    • Course Outline

    • Lecture Notes

    • Grading
      • Policies
      • 10% -- weekly homework submissions   — lowest homework score is dropped
      • 20% -- three quizzes   — notification given in a previous class lecture
      • 30% -- two midterm examinations   — W of week 4, M of week 8
      • 40% -- final exam   — week 11, F Mar 18 8am
      • To pass the course you must pass the final exam and have an overall passing average
      • Instances of academic dishonesty will be reflected in the final grade
        because dishonesty devalues the learning experience for the whole class.
        For more complete information about academic honesty policies, consult the
        Academic Senate Policy on Academic Honesty

    • Discussion Board
      • Students can discuss matters related to this course on Piazza


    Last modified: Feb 5, 2016 http://www.ics.uci.edu/~dan/genealogy/Krakow/index.html Jewish Krakow genealogical documents

    Jewish Krakow genealogical documents

    • Early Records     before 1810

    • LDS Microfilm     contents   and   on-line record availability
                                    images (35,000 records):     B,     M,     D,     1798-1809   1810-55

    • Census images:   (thanks to Logan Kleinwaks)  
                                          Krakow province 1790-92 (Kazimierz at 273-295): 1790  
                                          Krakow:   1850,   1870,   1880 (surname indices 1, 2),   1890,   1900,   1910
                                          viewing instructions for:   1850,70     1890
            locally stored images:   (thank you Laurie)   1870

                                          Podgorze:   1857, 1880, 1890, 1900, 1910

    • Search Engine     data from over 250,000 records
                                  including over 85,000 extended B/M/D records

    • Early Family Trees   over 700 trees rooted before 1800 ( explanation of tree format )

    • Other Documents

    • Marriage Analysis

    • Free access to records


    Dan Hirschberg       Last modified: Nov 25, 2015 http://www.ics.uci.edu/~dan/class/6b/index.html ICS 6B: Boolean Algebra & Logic - Winter 2010

    ICS 6B: Boolean Algebra & Logic - Winter 2010

    • Class meetings
      • Lecture: MWF 9:00-9:50am, in ICS 174
      • Discussion: MW 2:00-2:50pm, in ICS 174
    • Instructor
      • Professor Dan Hirschberg
      • Office hours: MWF 10:00-10:45am, in 4226 Donald Bren Hall
      • Email: dan (at) ics.uci.edu
    • Teaching assistant
      • Ish Rishabh
      • Office hours: M 10:15-11:30am, 1:00-1:50pm in 2069 Donald Bren Hall
      • Email: irishabh (at) uci.edu
    • Reader
      • Minh Doan
      • Email: daywednes (at) gmail.com
    • Text book
      • [Rosen] Kenneth H. Rosen, Discrete Mathematics and Its Applications, 6th edition, McGraw Hill, 2007.
        This book is required, and it should be available at the UCI bookstore.
        Note: There is an online list of errata.
    • Course announcements
      • Course announcements will be sent via email to the official UCI email address of all students enrolled in the class.
    • Grading
      • Grading will be based on the following weights:
        Final Exam 50%, Quizzes 40%, Homework 10%.
      • You may view your ICS 6B assignment grades on EEE Gradebook
      • Final Exam: Wednesday of week 11 [Mar 17, 8:00-9:45am]
      • Quizzes will be given in Lecture Section, generally on Fridays at the start of the class.
        (Any exceptions will be announced in class.)
        The lowest quiz score will be dropped when computing your quiz average.
      • Homework assignments will generally be due Wednesday, one minute before the start of the lecture (i.e., at 8:59am).
        Homework is to be submitted on the table in the front of the lecture room.
        Late homework, submitted before the end of lecture on the due day, will lose half its score.
        The lowest homework score will be dropped when computing your homework average.
      • Click here for the homework assignments.
        You should be reading sections of the book before those sections are discussed in lecture.
        On occasion, there may be homework questions from sections of the book not yet covered in lecture.
    • List of topics, by week. Numbers in parentheses are sections from [Rosen].
      Note that the following schedule is approximate.
      • Week 1: Logic (1.1), Propositional equivalences (1.2), Predicates and quantifiers (1.3)
      • Week 2: Nested quantifiers (1.4), Rules of inference (1.5), Introduction to proofs (1.6)
      • Week 3: Proof methods and strategy (1.7), Sets (2.1, 2.2), Functions (2.3)
      • Week 4: Relations and their properties (8.1), n-ary relations and their applications (8.2)
      • Week 5: Matrices (3.8), Representing relations (8.3), Closure of relations (8.4)
      • Week 6: Equivalence relations (8.5), Partial orderings (8.6)
      • Week 7: Boolean functions (11.1), Representing Boolean functions (11.2)
      • Week 8: Logic gates (11.3), Languages and grammars (12.1)
      • Week 9: Finite state machines (12.2, 12.3)
      • Week 10: Turing machines (12.5)

    Last modified: Nov 3, 2010 http://www.ics.uci.edu/~dan/class/267/index.html CompSci 267: Data Compression

    CompSci 267: Data Compression -- Spring 2012

    • Lectures: M W 10:00-11:20am in ICS 243

    • Professor: Dan Hirschberg -- dan (at) ics.uci.edu
      • Office: DBH 4226, 824-6480

    • Pre- or co-requisites: CompSci 161 or CompSci 260 or CompSci 261

    • Recommended textbook: K. Sayood, Introduction to Data Compression, 3rd edition. Morgan Kaufmann, San Francisco, 2006.

    Course outline (password to access slides given in first lecture)

    • Introduction
    • Coding Techniques
    • Modeling
    • Text Compression Systems
    • Lossless Image Compression
    • Lossy Compression
    • Lossy Image Compression
    • Audio Compression

    Requirements

    • homework
    • no examinations
    • term project (and 15 minute class presentation)
      • previous years projects:    
        W'00,   W'01,   W'02,   W'03,   F'03,   F'04,   F'05,   F'06,   F'07,   F'08,   F'09
      • Approved student projects for Spring 2012

    Class Datasets

    Links

    References


    Last modified: May 23, 2012 http://www.ics.uci.edu/~dan/class/UniStu3/index.html Freshman Seminar: Puzzlers for Computer Scientists

    Univ Studies 3: Puzzlers for Computer Scientists -- Winter 2009

    • Class meetings
      • Lectures: M 11-11:50am in DBH 1422

    • Instructor
      • Professor Dan Hirschberg

    • Class Requirements
      • no homeworks or examinations
      • grades are given based on class participation, i.e., attendence

    • Course Goals
        This seminar explores problem solving and critical thinking through the study of puzzlers and brain teasers, focusing on problems related to computer science.
        Problem solutions need only high school mathematics and logic.

    • Sample problems
      • It is said that potatoes are 99% water and 1% potato. So, say you take a bunch of potatoes, like 100 pounds of potatoes, and you set them out on your back porch to dry out. As they begin to dry out, the water starts to evaporate. And after a while enough water has evaporated so that the potatoes are now 98% water. If you were to weigh those potatoes at that moment when they are 98% water, how much would they weigh?
      • Three different numbers are chosen at random, and one is written on each of three slips of paper. The slips are then placed face down on the table. The objective is to choose the slip upon which is written the largest number. Here are the rules: You can turn over any slip of paper and look at the amount written on it. If for any reason you think this is the largest, you're done; you keep it. Otherwise you discard it and turn over a second slip. Again, if you think this is the one with the biggest number, you keep that one and the game is over. If you don't, you discard that one too, in which case you're stuck with the third one.
        The chance of getting the highest number is one in three. Or is it? Is there a strategy by which you can improve the odds?
      • How can you identify the one heavy coin out of fifty in just four weighings using a balance scale?
      • In how many ways can you change one dollar (allowing pennies, nickels, dimes, quarters, half dollars)?
      • At one point, a remote island's population of chameleons was divided as follows: 13 red chameleons, 15 green chameleons, 17 blue chameleons. Each time two different colored chameleons would meet, they would change their color to the third color. Is it ever possible for all chameleons to become the same color? Why or why not?


    Last modified: Sep 24, 2009 http://www.ics.uci.edu/~jpd/index-old.shtml Paul Dourish

    Paul Dourish

    Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

    home bio classes students research publications talks software personal schedule

    What's New?

    New papers: Interact 2007, OzCHI 2007, DUX 2007

    I'm on sabbatical 2007-08.

    Paul Dourish

    "Fool" Professor

    Department of Informatics

    Donald Bren School of Information & Computer Sciences

    University of California, Irvine

    Irvine CA 92697-3440

    Office: Bren Hall, room 5086 (Directions)

    Phone: (949) 824-8127

    Fax: (949) 824-4056

    Email: jpd@ics.uci.edu

    Formal bio and photos

    Please note: I am on sabbatical for academic year 2007-08, and will be away from Irvine for roughly a year, beginning August 2007. During this time, I'll be focusing on writing projects, and so I will be turning down all invitations to serve on committees, speak at conferences, etc.

    "It is to Scotland that we look for our idea of civilisation." -- Voltaire

    I am a Professor of Informatics at UC Irvine, with courtesy appointments in Computer Science and Anthropology. According to an anonymous student reviewer, I am "by far the most eccentric professor in ICS," which I choose to take as a compliment. The competition for "most eccentric" is pretty tough around here.

    In addition, I have a number of other roles on campus: a member of the core faculty on our interdisciplinary graduate program in Arts Computation Engineering (ACE); a member of the divisional council of the California Institute for Telecommunications and Information Technology; co-conspirator in the Laboratory for Ubiquitous Computing and Interaction; a member of the Center for Cyber-Security and Privacy, the Institute for Software Research, and the Center for Organizational Research; a faculty associate of the Center for Research on Information Technology and Organizations; a faculty member of the UC Game Culture and Technology Lab; a faculty affiliate of the Center for Unconventional Security Affairs; a member of the Working Group for Science and Technology Studies; and a member of the advisory board of the Center for Ethnography. (Phew.)

    Externally, I serve on the editorial boards of the Journal of Computer-Mediated Communication, the International Journal of Computer-Supported Cooperative Learning, Games and Culture, and Information Technology and People, and on the editorial advisory board of Computer Supported Cooperative Work. In 2008, I was elected to the CHI Academy.

    On this page, I used to list all the conferences for which I was serving on the program committee or the organizing committee. But it was too hard to keep up. Let's just say, "lots."

    I joined the faculty at UCI (or "Ape City") in September 2000, after working at Xerox PARC (now PARC, Inc.), Apple Research Labs (a now-defunct part of Apple Computer), and Rank Xerox EuroPARC (later part of Xerox Research Centre Europe, and now gone... hmm, there seems to be a trend.)

    My principal research interests are in Ubiquitous Computing, Computer-Supported Cooperative Work, Human Computer Interaction, and Social Studies of Science and Technology, all of which basically means that I care not only about cool technology, but also about how ordinary mortals can use it and the consequences for how they live and work. Click on "Research" above for more information, and a list of current and potential projects. My book, "Where the Action Is: The Foundations of Embodied Interaction", was published in 2001 by MIT Press. It is available at all good bookstores (and probably quite a few bad ones). Fortunately, it has not yet been remaindered.

    These days, though, our lives are really defined by numbers. I won't tell you my social security number, but I am an official European "expert", number E73514L, and my Erdos Number is 3.

    Unlike some of my terribly sporty colleagues, I'm someone whose idea of a whole-body workout is to drink standing up. However, I have occasionally been spotted on a bicycle or rollerblades. Watch out if you see me, because I'm probably not in control of my direction.

    Click the links at the top of the page to find more information about courses I'm teaching, my current research activities and projects, and copies of my publications. Other pages you can reach from here are:

    • The ever-popular Microsoft Barney hack.
    • Some photos and quotes.
    • My other home page (actually at home).
    • Recent talks and presentations
    http://www.ics.uci.edu/~mlevorat/index.html Marco Levorato
    ML
    • Home
    • Bio
    • Publications
    • Teaching

    Marco Levorato

    Assistant professor

    Donald Bren School of Information and Computer Science
    Computer Science Department

    3206 Donald Bren Hall,
    University of California, Irvine
    Irvine, CA, 92697-2800
    @ levorato at uci dot edu
    Marco Levorato

    Research

    1. Cognitive networks
    2. Sparse methods and graphical modeling in wireless networks
    3. Energy efficient sensors for remote health-care applications
    4. Demand response and Smart energy grid

    Teaching

    1. Computer and Communication Networks Fall 2013 (COMPSCI232/EECS248A/NETSYS201)
    1. Computer and Communication Networks Fall 2014 (COMPSCI232/EECS248A/NETSYS201)
    1. Wireless Networks Winter 2015 (COMPSCI236/NETSYS230/COMPSCI190)
    1. Computer and Communication Networks Fall 2015 (COMPSCI232/EECS248A/NETSYS201)

    Recent news

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    © Marco Levorato 2013

    http://www.ics.uci.edu/~mlevorat/publications.html Marco Levorato - Publications
    ML
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    • Publications
    • Teaching

    Marco Levorato

    Publications

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    © Marco Levorato 2013

    http://www.ics.uci.edu/~mlevorat/teaching.html Marco Levorato - Teching 2013
    ML
    • Home
    • Bio
    • Publications
    • Teaching

    Marco Levorato

    Teaching 2013/2014

    1. Computer and Communication Networks 2013 (COMPSCI232/EECS248A/NETSYS201)

    © Marco Levorato 2013

    http://www.ics.uci.edu/~mlevorat/bio.html Marco Levorato - Biography
    ML
    • Home
    • Bio
    • Publications
    • Teaching

    Marco Levorato

    Assistant professor

    Donald Bren School of Information and Computer Science
    Computer Science Department

    3206 Donald Bren Hall,
    University of California, Irvine
    Irvine, CA, 92697-2800

    Download full CV

    Marco Levorato

    Marco Levorato joined the Donald Bren School of Information and Computer Science, Computer Science department in August 2013.

    Between 2010-2012, He was a post-doctoral researcher with a joint affiliation at Stanford and the University of Southern California working with prof. Andrea Goldsmith and prof. Urbashi Mitra.

    From January to August 2013, he was an Access post-doctoral affiliate at the Access center, Royal Institute of Technology, Stockholm. He is a member of the IEEE and of the IEEE Comsoc society.

    His research interests are focused on the design and analysis of protocols and algorithms for the next generation wireless networks, mobile heath care networks and smart energy grids.

    He has co-authored over 65 technical articles on these topics, including the paper that has received the best paper award at IEEE GLOBECOM (2012).

    He completed the PhD in Electrical Engineering at the University of Padova, Italy, in 2009.
    He obtained the B.S. and M.S. in Electrical Engineering summa cum laude at the University of Ferrara, Italy in 2005 and 2003, respectively

    Awards

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    © Marco Levorato 2013

    http://ipubmed.ics.uci.edu/ Instant PubMed Search
    Available on the App Store
    iPhone App for Instant PubMed Search iPad App for Instant PubMed Search Follow @ipubmed
    Publications   |   Feedback   |   About

    iPubMed Search

    Fuzzy Search:  OnOn  |  OffOff
    Type in title, author, journal, abstract, MeSH heading and more to search for 25,342,334 MEDLINE publications (as of Feb 19, 2016).

      Search is fuzzy and happens as you type.

      Examples:

    • homocystaine should find articles about homocysteine;
    • marshel kaplan should find publications by Marshal Kaplan;
    • breast cancer vipiwala should find publications about breast cancer by Vapiwala.

      Fuzzy Search can be disabled to find the exact matches only.

    http://www.ics.uci.edu/~chenli/previous-news.html Archived News

    Chen Li

    Archived News

  • From August 2012 to June 2013, I was the Vice Chair of Department of Computer Science.
  • (6/10/2013) We are very excited to release our AsterixDB Beta! Here are some pictures at our celebrarion lunch in Laguna Beach.
  • (4/2013) Attending DASFAA 2013 in Wuhan, China. Sharad and I gave a talk for our 10-year best paper award. Here's a picture at the ceremony. Here are our slides: [Chen's PPT], [Sharad's PPT].
  • (4/2013) Two papers collaborated with my Chinese colleagues were accepted by SIGMOD 2013, one titled "String Similarity Measures and Joins with Synonyms" with Jiaheng Lu and Chunbin Lin at Renming University, and one titled "Improving Regular-Expression Matching on Strings Using Negative Factors" with Xiaochun Yang et al at Northeastern University.
  • (4/6/2013) We are very excited to release our AsterixDB alpha! Here are some pictures at our celebrarion dinner. Stay tuned for the beta release, which is coming soon!
  • (3/2013) Prof. Xiaohui and I received an NIH grant of $662K on assembling complete individual genomes. I am working with a team to do efficient genome assembly using parallel computing in our ASTERIX project.
  • (2/2013) Our PhD student, Alex Behm, has graduated and will join Cloudera. See the pictures taken at his party.
  • (1/13/2013) Our DASFAA 2003 paper titled "Efficient Record Linkage in Large Data Sets" received the 10-year Best Paper Award for DASFAA 2013. It was my first paper in the area of data cleaning and approximiate string search in the context of the Flamingo project.
  • (1/7/2013) This quarter I am teaching CS122B titled "Projects in Databases and Web Applications."
  • (11/6/2012) On the Election Day I gave an invited talk about Election and ASTERIX at the ACM GIS BigSpatial workshop in Redondo Beach, CA.
  • (11/5/2012) I was invited to write an article titled "Entrepreneurship in Data Management Research" at the ACM SIGMOD Blog.
  • (11/1/2012) "Full professor-ed" :-)
  • (9/27/2012) This quarter I am teaching CS222/CS122C titled "Principles of Data Management." For the first time it's co-listed as a undergraduate course CS122C since we want to encourage undergraduate students to get familiar with "what's inside a DBMS system" earlier.
  • (9/2012) I visited several universties and companies in China to talk about our research on powerful search and ASTERIX.
  • (8/2012) I gave a talk titled "Search as You Type: From Research to Commercialization" at the DBRank 2012 workshop at VLDB in Istanbul, Turkey.
  • (8/2012) I gave a talk titled "Supporting Efficient Top-k Queries in Type-Ahead Search" at SIGIR.
  • (5/2012) Our paper titled "Supporting Efficient Top-k Queries in Type-Ahead Search" with Tsinghua colleagues (Guoliang Li, Jiannan Wang, and Jianhua Feng) got accepted by SIGIR. It is amazing to see how reviewers from different communities (Databases and Information Retrieval) have so different tastes :-)
  • (5/2012) Our paper titled "Executing SQL over encrypted data in the database-service-provider model" received ACM SIGMOD 2012 Test-of-Time Award. The paper, published 10 years ago, envisioned the "Database as a service" model.
  • (4/2012) This quarter I am again teaching CS122B titled "Projects in Databases and Web Applications". I am also organizing the CS Seminar Series.
  • (3/2012) I gave a talk at University of Toronto titled Improving Search for Emerging Applications.
  • (3/2012) We recently released a paper titled Analysis of Instant Search Query Logs. It is based on our study to analyze the log of our instant, fuzzy search system called PSearch. We compared it with a traditional search system and showed the benefits of the new search paradigm. Some user behavior patterns are very interesting.
  • (2/2012) I am glad to receive the 2012 ICS Dean's Award for Graduate Student Mentoring.
  • (1/2012) We released an improved version of the source code of the Hobbes project.
  • (12/2011) Our paper titled Hobbes: optimized gram-based methods for efficient read alignment was published by Nucleic Acids Research.
  • (9/2011) This quarter I am teaching CS122B titled "Projects in Databases and Web Applications". I am also organizing the CS Seminar Series.
  • (9/2011) Check OmniPlaces.com, a location-based search engine to demonstrate the technology of Bimaple. It also has an iPhone App.
  • (9/2011) Check a cool system built by our students, Sattam Alsubaiee and Zachary Heilbron, to support spatial aggregation on Twitter data using ASTERIX.
  • (8/26/2011) Our PhD student, Rares Vernica, co-advised by Prof. Mike Carey, has successfully graduated and will join HP Labs. Here's a picture of our celebration. We will surely miss Rares!
  • (8/2011) Our MS student, Nagesh Honnalli, has successfully graduated and will join Amazon. Here's a picture of our celebration.
  • (7/2011) Check my blog on instant search.
  • (7/8/2011) We are glad to release the first software to support instant fuzzy search on large data sets.
  • (6/24/2011) Check the video clip on the Bimaple homepage to show location-based instant, fuzzy search on iPhone and a live demo on more than 17 million records.
  • (6/17/2011) I advised a group of students to participate in the Microsoft Speller Challenge and won the third place. Congratulations to the team! Here is our qSpeller project page for the Microsoft Speller Challenge.
  • (5/18/2011) Bimaple released a prototype to do location-based instant, fuzzy search. To our best knwoledge, it is the first system that can do this type of search in a unified framework.
  • (5/2011) We (my Tsinghua colleagues and I) released our CHIME demo to support error-tolerant Chinese input. It's based on our coming IJCAI 2011 paper.
  • (4/22/2011) I gave an invited talk titled "The Flamingo Software Package on Approximate String Queries" at the DQIS 2011 workshop in Hong Kong. Here is the Powerpoint file.
  • (4/2011) Our paper titled "ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models" by the ASTERIX project has been accepted for publication in Distributed and Parallel Databases.
  • (4/2011) Our paper titled "An Efficient Error-Tolerant Chinese Pinyin Input Method" with Tsinghua collaborators (Yabin Zheng and Maosong Sun) has been accepted for publication in IJCAI 2011. It's my first paper in this conference :-)
  • (4/2011) Our paper titled "Location-Based Instant Search" with my graduated student Shengyue Ji has been accepted by the SSDBM conference.
  • (4/2011) I am glad to launch the Hobbes project on genome sequence mapping.
  • (3/26/2011) This quarter I am again teaching CS122B: Projects in Databases and Web Applications.
  • (2/2011) My PhD student, Shengyue Ji, has just graduated and joined the "Don't be evil" company.
  • (2/2011) Check a new system prototype Bimaple built to support instant, error-tolerant search on Stack Overflow messages.
  • (1/2/2011) This quarter I am teaching CS122B: Projects in Databases and Web Applications.
  • (1/2/2011) The company I am starting, Bimaple, is hiring: http://www.bimaple.com/jobs.html.
  • (12/5/2010) On the weekend of Dec. 4-5, I attended the Random Hacks of Kindness (RhoK) in Chicago. Together with three other people on a team and my UCI students, Manik Sikka, Vijay Rajakumar, and Inci Centindl, we did a project of supporting full-text search on the Person Finder project on the Google App Engine platform. Our project won the third-best-project prize.
  • (11/2010) Check my new "photo" above. Thanks to Heri Ramampiaro for taking the nice picture :-)
  • (10/2010) Our paper titled "Answering Approximate String Queries on Large Data Sets Using External Memory" with Alexander Behm and Michael Carey has been accepted by ICDE 2011.
  • (10/23/2010) We are glad to release the Flamingo Package Version 4.0.
  • (10/2010) My student, Shengyue Ji, received a Yahoo! Best Dissertation Student Award.
  • (9/2010) My student, Alex Behm, received an ARCS scholar award.
  • (9/2010) Together with Professor Xiaohui Xie, I am receving an NIH grant to support our research on the iPubMed system.
  • (9/2010) I am teaching CS222: Principles of Data Management this quarter.
  • (8/2010) On August 14, 2010, I gave a talk about scalable interactive search at the NFIC conference. Here is my talk slides.
  • (6/2010) On June 29, I gave a talk about set-similarity joins using Hadoop at the Yahoo Hadoop Summit. Here is my talk file.
  • (5/2010) Together with Prof. Xiaohui Xie, we received an Intel grant to study compression of personal human genome data. See the ICS news for details. This is a collaboration with our colleagues, Bin Wang and Xiaochun Yang, at the Northeastern University in China.
  • (4/2010) Ray wins a Yahoo! Key Scientific Challenge award: Here is the Yahoo announcement and ICS news.
  • (4/2010) DASFAA excellent demo: Our demo won a DASFAA excellent demo award.
  • (3/2010) Source-code/Demo Releases: My research team released the flamingo package version 3.0, source code of fuzzy joins using MapReduce, and demos of supporting fuzzy keyword search on spatial data (such as maps).
  • (3/2010) Teaching: CS223 - Transaction Processing and Distributed Data Management
  • (3/2010) New NSF Grant: We are glad to receive an NSF award 1030002 to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake.
  • (2/28/2010) Chile Earthquake Family Reunification: My team is working on family reunification in the Chile Earthquake. Here is the project home page.
  • (2/28/2010) ICDE 2010: Busy with local arrangements at ICDE 2010 in Long Beach.
  • (2/2010) Media article on our Haiti Project: On Feb. 8, the UCI homepage published an article to report our Haiti Family Reunification Project
  • (2/2010) SIGMOD 2010 paper: Our paper titled "Efficient Parallel Set-Similarity Joins Using MapReduce" with Rares Vernica and Mike Carey has been accepted by ACM SIGMOD 2010. The paper studies how to do set-similarity joins (such as record linkage) on large amounts of data using MapReduce.
  • (1/2010) Haiti Earthquake Family Reunification: My team is working on getting data about missing people in the Haiti Earthquake and doing powerful search on it. Here is the project home page.
  • (1/2010) Teaching: This quarter I am again teaching CS122B: Projects in Database Management.
  • (11/2009) iPubMed: Check out our new iPubMed system co-developed by my team and Tsinghua University to support type-ahead, fuzzy search on more than 18 million MEDLINE records.
  • (9/2009) Life after Sabbatical: I am teaching two courses this quarter: CS122B: Projects in Database Management, and CS295: Database Management and Information Retrieval .
  • (9/2009) VLDB 2009 Tutorial: Marios Hadjieleftheriou and I gave a tutorial at VLDB 2009 on approximate string matching. Here are the slides: [Part I], [Part II]. Here are the slides of our ICDE09 tutorial: [Part I], [Part II].
  • (9/2009) NSF Funding for ASTERIX: The multi-UC-campus project ASTERIX led by Prof. Mike Caey and me has been funded at $2.7M for three years from the NSF Data Intensive Computing program. The project, based at UCI, also includes UCSD and UCR participants. UCI's share is $1.8M.
  • (6/2009) Summer: I will be visiting colleagues at Tsinghua University, China in the summer. I will also work with several colleagues in China during the visit.
  • (5/2009) PSearch News: Read this NACS news article about our PSearch prototype.
  • (5/2009) Our research needs a student: We are looking for an undergraduate or MS student for a research project. The details are here.
  • (4/2009) Students' award: I am proud that two of our ISG students, Shengyue Ji and Mingya Gao, together with Wen Pu from UIUC, have been selected as one of the five finalist teams for the SIGMOD 2009 programming contest (Main Memory Transactional Index).
  • (4/2009) Dean's Award for Mid-Career Research: I am glad to receive the ICS Dean's Award for Mid-Career Research.
  • (3/27/2009) Pictures of my home where I grew up : I had a trip to my hometown in Jinan, Shandong, China. I took several pictures of the home where I grew up as a child.
  • (3/2009) Startup: I have officially started a company BiMaple to support a novel, powerful way to do search.
  • (3/2009) Launching new project: I am glad to officially launch TASTIER: a joint research project with Tsinghua University on efficient auto-complete and type-ahead search on large data sets. .
  • (3/2009) New SIGMOD 2009 paper: Our paper titled "Type-Ahead Search on Relational Data: a TASTIER Approach" by Guoliang Li, Shengyue Ji, Chen Li, and Jianhua Feng has been accepted by the SIGMOD 2009 conference.
  • (2/2009) New NSF award: We are glad to receive an NSF award IIS-0844574 from the NSF CluE program to support our research on large-scale data cleaning using MapReduce/Hadoop environments. In addition to receiving the NSF support, we will also use software and services on a Google-IBM cluster to explore innovative research ideas in data-intensive computing.
  • (1/2009) New WWW2009 paper: Our paper titled "Efficient Interactive Fuzzy Keyword Search" by Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng has been accepted by the WWW 2009 conference.
  • (11/2008) Launch of our new ISG group home page: Check out this new page of our Information Systems Group (ISG)!
  • (11/2008) First paper on bioinformatics: My first paper on bioinformatics titled "Human genomes as email attachments" has been published on the journal Bioinformatics. We used novel techniques to compress a human genome from 3.2GB to 4.1MB. From the date we submitted the paper (Oct. 7, 2008) to the date it was published online (Nov. 7, 2008), it took just one month! The PDF is available at here. It was once the No. 1 most-frequently read article in the Journal of Bioinformatics in January and February of 2009 according to the following link (as of March 2009).
  • (10/2008) Flamingo Release 2.0: we are glad to release version 2.0 of the package to sup\ port fuzzy string search. Version 2.0.1 (released on Nov. 7, 2008) fixed compatibility issues for GCC 4.3.2.
  • (9/2008) New funding award from China: Together with Prof. Xiaochun Yang from Northeastern University of China, I received a funding award from the "Research Funds for Oversea Scholars" program of the National Natural Science Foundation of China. It will support our research on fuzzy search on text documents.
  • (9/2008) Sabbatical: I am on sabbatical this year. I will be mainly at UCI.
  • (9/2008) New PhD students: Two new PhD students, Minh Doan and Sattam Mubark Alsubaiee, have joined our research team.
  • (9/2008) New ICDE2009 Publications: We have two full research papers accepted by ICDE 2009: "Space-Constrained Gram-Based Indexing for Efficient Approximate String Search," by Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu; "Best-Effort Top-k Query Processing Under Budgetary Constraints," by Michal Shmueli-Scheuer, Chen Li, Yosi Mass, Haggai Roitman, Ralf Schenkel, and Gerhard Weikum. In addition, I will be presenting a tutorial titled "Efficient Approximate Search on String Collections" with Marios Hadjieleftheriou (from AT&T Labs--Research).
  • (8/2008) Mike Carey joined us! We are extremely happy that Prof. Mike Carey has joined our department.
  • (7/3/2008) Launching Search@ICS: I am glad to our research prototype has been launched on the ICS Homepage that can support interactive, fuzzy search for ICS people and general pages at ICS.UCI.EDU.
  • (4/1/2008) Launching PSearch: I am glad to release the PSearch Prototype to support interactive, fuzzy search for UCI Directory.
  • (3/31/2008) This quarter I am teaching CS122B and CS224.
  • (2/22/2008) New SIGMOD08 paper: The conference has accepted our paper titled "Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently", a joint work with Bin Wang and Xiaochun Yang when they visited our place last fall. The paper solves several open, important problems not addressed in our VLDB07 VGRAM paper.
  • (2/1/2008) New Visitor: I am glad that Guoliang Li from Tsinghua University is visiting my research team for about four months.
  • (12/12/2007) Today I attended a local computer industry forum about the computer cluster workforce in Orange County. There is an excellent survey on the needs of computer cluster workforce in the county. One interesting finding is that the county is facing the challenge of not being able to find enough workers in the IT industry. The survey also gives us some thoughts on how we design our education curriculum to meet the need of the industry.
  • (12/2007) I am looking for a motivated BS/MS student for an independent research project. Requirements: strong java programming skills. Please contact me if you are interested.
  • (10/2007) New paper on approximate string matching: Our recent paper titled "Efficient Merging and Filtering Algorithms for Approximate String Searches" by Chen Li, Jiaheng Lu, and Yiming Lu will appear in ICDE 2008. We developed new algorithms and indexing structures that can significantly improve the performance of approximate string search.
  • (10/2007) New NSF Grant: We received an NSF grant of $95K for our proposal titled "SGER: Answering Approximate String Queries Using Variable-Length Grams."
  • (8/2007) Visitors: Bin Wang and Xiaochun Yang are visiting our team again this summer. We will continue working on topics related to approximate query answering.
  • (8/2007) New PhD student: I am glad that Alex Behm has joined our research team as a new PhD student.
  • (6/2007) Summer: My students, Ray and Yiming, will be doing summer internships at Microsoft Research and IBM T.J. Watson, respectively. I will be traveling early summer in China, attending conferences and visting schools and companies. After that, I will be working with my students, postdoc, and visitors at UCI. There are several very exciting ideas I would like to pursue.
  • (6/2007) Tenured.
  • (6/2007) VGRAM for VLDB07: Our paper titled "VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams" by Chen Li, Bin Wang, and Xiaochun Yang will appear in VLDB 2007. I am glad that the reviewers liked the work as much as we do.
  • (4/17/2007) Flamingo 1.0 Release: I am glad to release our Flamingo Package 1.0 on approximate string matching.
  • (4/17/2007) Release of Web-object-history data: I am glad to release our data set of the history of data objects collected from 6 web sites in 1.5 years.
  • (4/2007) SIGMOD07 Undergraduate Scholarship Program: I am chairing this program. Click here for more information.
  • (4/2007) Teaching: This quarter I am teaching CS223 (formerly ICS214B) - Transaction Processing and Distributed Data Management.
  • (1/2007) Teaching: This quarter I am teaching CS122B (formerly ICS185), Projects in Database Management.
  • (12/2006) Research Funds: I received an ICS Ted & Janice Smith Faculty Seed Fund and an ICS CORCLR research/travel fund.
  • (12/2006) NSF Proposals: My team and I submitted two proposals to the NSF IIS program. Both proposals are based on our observations on several critical problems the solutions of which are greatly needed by many real applications.
  • (9/2006) New Project on Family Reunification: Ray and I have started working on a new project called Family Reunification. It's a data-integration project using real data from many Web sources. It's part of the RESCUE project. More information will come soon.
  • (9/2006) Release of SEPIA 1.0: Ray has released SEPIA 1.0 on selectivity estimation of fuzzy string predicases based on our VLDB 2005 paper.
  • (9/2006) New Junior Specialist: We have a new junior specialist, Jiaheng Lu, who is joining our research team. He's expecting his PhD from the National University of Singapore. He will be working on projects related to data integration.
  • (9/2006) Google Research Award: I received a Google Research Award in the amount of $37,500 renewable for a second year. It will be used to support my research on data cleaning, especially on approximate string searching. I am very thankful for their support, especially since this is the largest support I received from the industry.
  • (7/2006) Work on Data Exchange: Recently I finished a technical report with Foto Afrati and Vassia Pavlaki (at NTUA, Greece) titled "Data Exchange with Arithmetic Comparisons." It is a work we have been working on for almost one year: all of us went to Stanford for one week, and Vassia visited UCI twice. It took us a lot of time to think about all the subtle issues that are not covered in the excellent paper on data exchange by Fagin et al. I am glad that finally we completed the work, and I really like it.
  • (6/2006) Summer: My student, Ray, is doing a summer internship at Yahoo!. My other students are working with me during the summer. I will have two visitors (Xiaochun Yang and Bin Wang). I will visit a few places (IBM, SRI, Yahoo, Google, possibly Toronto, and VLDB in Korea). Well, these will keep me busy enough, not to mention I have two sons to play with :-)
  • (5/2006) New PhD Student: I am glad that a new student, Yiming Lu, is joining our PhD program soon. He graduated from Shanghai Jiaotong University with a BS and an MS, and has been working on data quality at Microsoft Research Asia.
  • (5/2006) Work on Query Relaxation: Our paper titled Relaxing Join and Selection Queries (joint work with Nick Koudas, Anthony Tung, and my student, Rares Vernica) will appear in VLDB 2006, Seoul, Korea. It is about how to relax empty-answer SQL queries in RDBMS in order to compute answers for users with a minimal relaxation. We use skyline as our relaxation framework, in which we need to consider join conditions as well. The work extends our previous work on supporting approximate query answering in applications such as data cleaning. See our two VLDB'2005 papers on similar topics.
  • (5/2006) CleanDB Workshop: I am currently organizing the CleanDB Workshop with Dongwon Lee. It will be colocated with VLDB2006 in Seoul, Korea.
  • (5/2006) New Release of StringMap: I spent some days cleaning the StringMap code that supports approximate string searches and joins. The new release is available at here.
  • (4/2006) $$ from M$R: In April 2006, I received an unrestricted gift fund from Microsoft Research. I want to thank them for their generous support. It's very encouraging, and I wish to receive more support from the industry in the future. http://www.ics.uci.edu/~raccoon/ The Raccoon Project: Peer-Based Data Integration and Sharing <body> <p>This page uses frames, but your browser doesn't support them.</p> </body> http://tastier.ics.uci.edu/ The TASTIER Project on Efficient Auto-Completion, Type-Ahead Search

    TASTIER: Efficient Auto-Completion, Type-Ahead Search

    Mirror Site at Tsinghua University

    Objective

    TASTIER is a joint research project between Tsinghua University and UC Irvine. It focuses on efficient autocompletion, type-ahead search on large data sets of various types, such as relational data, documents, semi-structured data. "TASTIER" stands for type-ahead search techniques in large data sets.

    People

    • Inci Cetindil (Ph.D. Student, UCI)
    • Jianhua Feng (Faculty, Tsinghua)
    • Shengyue Ji (Ph.D. Student, UCI)
    • Chen Li (Faculty, UCI)
    • Guoliang Li (Ph.D. Student, Tsinghua)
    • Jiannan Wang (Ph.D. Student, Tsinghua)

    Systems

    • DBLPSearch: Type-ahead, fuzzy search on about one million computer science publication records.
    • DBLP author search: Type-ahead, fuzzy search on DBLP authors.
    • Haiti Project: Type-ahead, fuzzy search on missing people in Haiti Earthquake.
    • iPubmed: Type-ahead, fuzzy search on about 19 million MEDLINE publication records.
    • Learning: Interactive, fuzzy search for learning.
    • PSearch: Type-ahead, fuzzy search on the UCI directory.
    • UCI-ICS Search: Type-ahead, fuzzy search on important entries in the School of ICS at UCI. Try the "Search" box at the top of the school homepage.

    Publications

    • Interactive and fuzzy search: a dynamic way to explore MEDLINE.
      Jiannan Wang, Inci Cetindil, Shengyue Ji, Chen Li, Xiaohui Xie, Guoliang Li, Jianhua Feng
      Bioinformatics 2010.
    • Seaform: Search-As-You-Type in Forms
      Hao Wu, Guoliang Li, Chen Li, Lizhu Zhou
      VLDB 2010 (Demo).
    • Efficient Fuzzy Type-Ahead Search in TASTIER
      Guoliang Li, Shengyue Ji, Chen Li, Jiannan Wang, Jianhua Feng
      ICDE 2010 (Demo).
    • Efficient Type-Ahead Search on Relational Data: a TASTIER Approach
      Guoliang Li, Shengyue Ji, Chen Li, and Jianhua Feng
      SIGMOD 2009. (PDF, PPTX)
    • Automatic URL Completion and Prediction Using Fuzzy Type-Ahead Search
      Jiannan Wang, Guoliang Li, Jianhua Feng, Chen Li
      SIGIR 2009 (Poster).
    • Interactive Search in XML Data
      Guoliang Li, Jianhua Feng, Lizhu Zhou
      WWW 2009 (Poster).
    • Efficient Interactive Fuzzy Keyword Search
      Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng
      WWW 2009. (PDF, PPTX, ConferenceLink)

    Acknowledgements: This project is partly supported by the National Natural Science Foundation of China under Grant No. 60873065, the National High Technology Development 863 Program of China under Grant No. 2007AA01Z152, the US NSF award No. IIS-0742960, and a Google Research Award.


    http://hobbes.ics.uci.edu/ Hobbes Genome Sequence Mapping, UC Irvine

    Hobbes

    Genome Sequence Mapping

    Information Systems Group

    Institute for Genomics and Bioinformatics

    Bren School of ICSUC Irvine

    • About
    • Downloads
    • Quick Start
    • Examples
    • Manual
    • People
    • FAQ
    • Contact

    About Hobbes

    Hobbes is a sequence mapping software in development at School of Information and Computer Sciences at UC Irvine.

    News

    07/13/2015

    We released Hobbes3 (Hobbes3 source code). Hobbes3 significantly improves accuracy and mapping speed for edit distance in all mapping mode (and m mapping mode as well).

    12/03/2013

    A new version of Hobbes2 has been released (Hobbes 2.1 source code). This version supports m mapping mode.

    06/17/2013

    We released Hobbes2 (Hobbes 2.0 source code) under a BSD license.

    01/25/2013

    We released a new version of Hobbes source code under a BSD license. The new version is faster and return more mappings

    01/23/2012

    We released the Hobbes source code under a BSD license.

    11/01/2011

    We released Hobbes 1.3 which includes mostly bugfixes and usability improvements.

    09/01/2011

    A new version of Hobbes has been released. This version supports indels and paired end reads.

    03/31/2011

    Hobbes has been released for the first time. The current version supports hamming distance. The Hobbes team is trying to add support for indels and paired end reads in the next version.

    Let Us Know What You Think

    The Hobbes team would appreciate your feedback.

    � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

    Last Updated on Jul 13, 2015

    http://www.ics.uci.edu/~rares/ Rares Vernica, PhD - University of California, Irvine

    Rares Vernica

    [ra'-resh]
    PhD
    Rares Vernica photo
    • Contact
    • Overview
    • Projects
    • Publications
    • Awards
    • Hobby

    Contact



    UCI Logo

    Overview

    PhD received in Summer 2011

    Disertation: Efficient Processing of Set-Similarity Joins on Large Clusters
    Advisers: Prof. Michael J. Carey and Prof. Chen Li

    My research area is large-scale data management and data-intensive computing. Since September 2011 I am a Research Scientist at HP Labs in Palo Alto, CA.

    Curriculum Vitae (CV)

    Projects

    ASTERIX
    Developing a highly scalable parallel platform for semi-structured data management and analysis;
    • Source code for Parallel Set-Similarity Joins Using MapReduce;
    Family Reunification
    Developing data integration, indexing, and search techniques to help people find their loved ones during or after a disaster;
    • Haiti and Chile Earthquake, online services and data for finding missing people;
    • UCI article featuring the Haiti Earthquake service;
    Flamingo
    Developing data cleaning techniques to deal with errors and inconsistencies in information systems;
    • Flamingo Package, open source library for approximate string matching;
    • Flamingo Toolkit, open source UDF library for approximate string matching for MySQL database;
    • PSearch, UC Irvine online directory search service;
    • UCI article featuring the PSearch service.

    Publications

    1. Adaptive MapReduce using Situation-Aware Mappers.
      Rares Vernica, Andrey Balmin, Kevin S. Beyer, Vuk Ercegovac.
      EDBT 2012
      paper slides
    2. Efficient Processing of Set-Similarity Joins on Large Clusters.
      Rares Vernica.
      Ph.D. Thesis, University of California, Irvine, 2011.
      Advisers: Prof. Michael J. Carey and Prof. Chen Li
      paper
    3. CIRCUMFLEX: A Scheduling Optimizer for MapReduce Workloads Involving Shared Scans.
      Joel Wolf, Deepak Rajan, Kirsten Hildrum, Rohit Khandekar, Sujay Parekh, Kun-Lung Wu, Andrey Balmin, Rares Vernica.
      LADIS 2011. (Workshop on Large Scale Distributed Systems and Middleware, collocated with VLDB 2011)
      paper doi
    4. ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving World Models.
      Alexander Behm, Vinayak R. Borkar, Michael J. Carey, Chen Li, Nicola Onose, Rares Vernica, Alin Deutsch, Yannis Papakonstantinou, Vassilis J. Tsotras
      Journal of Distributed and Parallel Databases, Special Issue on Cloud Computing, 2011
      paper doi
    5. Hyracks: A Flexible and Extensible Foundation for Data-Intensive Computing
      Vinayak R. Borkar, Michael J. Carey, Raman Grover, Nicola Onose, Rares Vernica
      ICDE 2011
      paper (long version)   doi
    6. AKYRA: Efficient Keyword-Query Cleaning in Relational Databases.
      Rares Vernica, Chen Li
      Technical Report, University of California, Irvine, 2011
      paper
    7. Efficient Parallel Set-Similarity Joins Using MapReduce.
      Rares Vernica, Michael J. Carey, Chen Li
      SIGMOD 2010
      paper (long version)   doi   slides (long version)   poster    source code
    8. Efficient Top-k Algorithms for Fuzzy Search in String Collections.
      Rares Vernica, Chen Li
      KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)
      paper   doi   slides
    9. Entity Categorization Over Large Document Collections.
      Venkatesh Ganti, Arnd Christian König, Rares Vernica
      KDD 2008: 274-282.
      paper   doi   slides
    10. SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases.
      Liang Jin, Chen Li, Rares Vernica
      VLDB J. 17(5): 1213-1229 (2008).
      paper   doi   slides   source code
    11. Relaxing Join and Selection Queries.
      Nick Koudas, Chen Li, Anthony K. H. Tung, Rares Vernica
      VLDB 2006: 199-210.
      paper   slides   source code

    Awards

    2010
    Yahoo! Key Scientific Challenges Winner in the Web Information Management area - UCI article
    2009
    Microsoft Student Travel Award for KEYS 2009, Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009
    2005-2006
    Chair's Fellowship, Department of Computer Science, University of California, Irvine
    2005
    California Institute for Telecommunications and Information Technology (Calit2) Fellowship
    2005
    Second place, Pentalog programming contest, Brasov, Romania
    1999 - 2004
    Merit-Based Scholarship, Politehnica University of Bucharest, Romania

    Hobby

    Kendo
    • Member All United States Kendo Federation (AUSKF), Southern California Kendo Federation (SCKF), Costa Mesa Kendo Dojo;
    • Rank 2 Dan;
    Rares Vernica - kendo
    http://www.ics.uci.edu/~icetindi/ Inci Cetindil's Homepage

    Inci Cetindil



    PhD student

    Resume

    Advisor: Prof. Chen Li

    Department of Computer Science
    Information Systems Group
    Donald Bren Hall, Room 2062
    University of California, Irvine

    Research

    Information Management, Instant and Fuzzy Search, Data Cleaning

    Education

    • PhD student, Computer Science Department, University of California, Irvine, USA.
    • M.S. of Computer Science, University of California, Riverside, USA.
    • B.S. of Computer Science, Bilkent University, TURKEY.

    Projects

    • AsterixDB
    • iPubMed
    • TASTIER: Efficient Auto-Completion, Type-Ahead Search.
    • qSpell: Spelling Correction of Web Search Queries

    Publications

    • Efficient Instant-Fuzzy Search with Proximity Ranking.
      Inci Cetindil, Jamshid Esmaelnezhad, Taewoo Kim, Chen Li
      ICDE 2014 (to appear).
    • Analysis of Instant Search Query Logs.
      Inci Cetindil, Jamshid Esmaelnezhad, Chen Li, David Newman
      WebDB 2012.
    • qSpell: Spelling Correction of Web Search Queries using Ranking Models and Iterative Correction.
      Y. Ganjisaffar, A. Zilio, S. Javanmardi, I. Cetindil, M. Sikka, S. Katumalla, N. Khatib, C. Li, C. Lopes
      Spelling Alteration for Web Search Workshop, Bellevue, WA, USA, July 2011.
    • Interactive and fuzzy search: a dynamic way to explore MEDLINE.
      Jiannan Wang, Inci Cetindil, Shengyue Ji, Chen Li, Xiaohui Xie, Guoliang Li, Jianhua Feng
      Bioinformatics 2010.
    • Making Instant Fuzzy Search Faster with Less Memory.
      Shengyue Ji, Inci Cetindil, Chen Li
      Technical Report, UC Irvine 2010.
    http://flamingo.ics.uci.edu/spellchecker/ qSpell - Spelling Correction of Web Search Queries


    qSpell: Spelling Correction of Web Search Queries

    Analysis of Web search query logs shows that roughly 10-15% of queries sent to search engines contain spelling errors. A query speller is crucial to search engines in improving Web search relevance, because it is hard for a search engine to retrieve relevant contents with misspelled keywords. In this project we explore leveraging Web-scale data for implementing a highly accurate and efficient spell checker. To implement a Web-scale spell checker that can handle diverse needs of Web users, we process massive amounts of data such as query logs and Web ngrams to build accurate language models. (PDF)


    University of California, Irvine
    School of Information and Computer Sciences


    News
    • (06/2011) We won the 3rd Prize in Microsoft's speller challenge.
    • (02/2011) We are participating in Microsoft's speller challenge.
    Datasets
    Name: JDB2011 [Download]
    Size:11,134 queries
    Last update:June 20, 2011
    Description: We have randomly sampled 11,134 queries from the publicly available AOL and 2009 Million Query Track query sets and asked 8 human assessors to provide spelling corrections for these queries. Each query was judged by at least one human assessor and queries for which the human assessors had provided at least one suggestion which was different from the original query were reviewed by at least another human assessor. In order to assist the human assessors in providing the most plausible suggestions for each query, we had designed an interface that was showing Google and Bing search results for each query.
    Format: The queries of this data set are split into 6,000 queries that can be used for trainig and cross-fold validation. The final models should be evaluated on the three test splits (each 1711 queries) and average performance across these three splits should be reported.

    Each line has the following format:
    query <tab> suggestion1 <tab> suggestion2 <tab> ...
    Citation Policy: If you use this data set for a research purpose, please use the following citation:

    Y. Ganjisaffar et al., qSpell: Spelling Correction of Web Search Queries using Ranking Models and Iterative Correction, in Spelling Alteration for Web Search Workshop, Bellevue, WA, USA, July 2011.

    Bibtex:

    @inproceedings{Ganji:2011:Speller,
        author = {Yasser Ganjisaffar and Andrea Zilio and Sara Javanmardi and Inci Cetindil and Manik Sikka and Sandeep Paul Katumalla and Narges Khatib-Astaneh and Chen Li and Cristina Lopes},
        title = {{qSpell}: Spelling Correction of Web Search Queries using Ranking Models and Iterative Correction},
        booktitle = {Spelling Alteration for Web Search Workshop},
        month = {July},
        year = {2011},
        location = {Bellevue, WA, USA},
    }
    People
    • Inci Cetindil, Phd Student
    • Yasser Ganjisaffar, Phd Candidate
    • Sara Javanmardi, Phd Candidate
    • Sandeep Paul Katumalla, MS Student
    • Narges Khatib-Astaneh, Visitor Student
    • Chen Li, Faculty
    • Manik Sikka, MS Student
    • Andrea Zilio, Visitor Student
    Acknowledgments
    We would like to thank Amazon.com for a research grant that allowed us to use their MapReduce cluster. Our research has been also partially supported by NIH grant 1R21LM010143-01A1 and NSF grants, OCI-074806 and IIS-1030002.
    Contact spellchecker AT ics.uci.edu
    © Copyright 2011 Bren School of Information and Computer Sciences, UC Irvine. All Rights Reserved.


    http://chime.ics.uci.edu/ CHIME: An Efficient Error-Tolerant Chinese Pinyin Input Method

    CHIME: An Efficient Error-Tolerant Chinese Pinyin Input Method













    Yabin Zheng1 Chen Li2 Maosong Sun1
    1Department of Computer Science, Tsinghua University
    2Department of Computer Science, University of California, Irvine
    A paper to appear in IJCAI 2011.
    Paper in PDF.

    Abstract

    Chinese Pinyin input methods are very important for Chinese language processing. In many cases, users may make typing errors. For example, a user wants to type in "shenme" (什么, meaning "what" in English) but may type in "shenem" instead. Existing Pinyin input methods fail in converting such a Pinyin sequence with errors to the right Chinese words. To solve this problem, we developed an efficient error-tolerant Pinyin input method called "CHIME" that can handle typing errors. By incorporating state-of-the-art techniques and language-specific features, the method achieves a better performance than state-of-the-art input methods. It can efficiently find relevant words in milliseconds for an input Pinyin sequence.

    Framework

    For an input Pinyin sequence with typing errrors, CHIME works in three steps:

    • Detect mistyped Pinyins that are not included in the predefined Pinyin dictionary.
    • For each mistyped Pinyin, CHIME find top-k similar candidate Pinyins.
    • CHIME converts the corrected Pinyin sequence to the most likely sequence of Chinese words.

    Evaluation Dataset

    Bellow are the datasets used in the study. (To see the text properly in your browser, please make sure to change the encoding of your browser to UTF-8.)

    • inputPinyin: contains the 2,000 input Pinyin sequences (679 sequences contain at least one typo).
    • outputHanzi: contains the corresponding Chinese word sequences that users intend to type in.
    • SogouResult: contains the conversion results from Sogou.
    • CHIMEResult: contains the conversion results from CHIME.

    Just for Fun!

    Check this page (in Chinese) as a motivation of our research! :-)

    Acknowledgments

    Most of the work was done while Yabin Zheng was visiting UCI. We thank Alexander Behm and Shengyue Ji for their insightful discussions at UCI. This work is partially supported by the National Natural Science Foundation of China (No. 60873174 and 60828004).

    CHIME: An Efficient Error-Tolerant Chinese Pinyin Input Method
    CHIME: 一种高效的容错中文拼音输入法

    Type in Pinyins with errors, and we are trying to predict what you intend to type in.
    If you type in "shenem", we can find "什么". If you type in "xiexe", we can find "谢谢".
    如果您输入的拼音序列中包含错误,我们可以将错误进行纠正并转换为您所需要的汉字序列。
    例如,当您想输入"谢谢"(xiexie)或"shenme"(shenme),却误输入为"xiexe"或"shenem",我们可以将您需要的目标词条返回。

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{mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1027"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body lang=EN-US link=blue vlink=blue style='tab-interval:.5in'> <div class=WordSection1> <h1 align=center style='text-align:center'><span style='mso-fareast-font-family: "Times New Roman"'>Chen Li's Publications<o:p></o:p></span></h1> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo1;tab-stops:list .5in'><a href="#confs">Refereed Conference Full Papers</a> </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo1;tab-stops:list .5in'><a href="#journals">Refereed Journal Articles</a> </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo1;tab-stops:list .5in'><a href="#workshopdemo">Refereed Workshop, Conference Demo Papers, and Other Publications </a><span style="mso-spacerun:yes">�</span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo1;tab-stops:list .5in'><a href="#other">Ph.D. Thesis</a></li> </ul> <p class=MsoNormal><a name=confs></a><b>Refereed Conference Full Papers</b> </p> <p class=MsoListParagraphCxSpFirst style='text-indent:-.25in;mso-list:l3 level1 lfo2; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>1.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span class=SpellE><b style='mso-bidi-font-weight: normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222; background:white'>AsterixDB</span></b></span><b style='mso-bidi-font-weight: normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222; background:white'>: A Scalable, Open Source BDMS</span></b><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>, <span class=SpellE>Sattam</span> <span class=SpellE>Alsubaiee</span>, Yasser <span class=SpellE>Altowim</span>, <span class=SpellE>Hotham</span> <span class=SpellE>Altwaijry</span>, Alexander <span class=SpellE>Behm</span>, <span class=SpellE>Vinayak</span> R. <span class=SpellE>Borkar</span>, <span class=SpellE>Yingyi</span> Bu, Michael J. Carey, <span class=SpellE>Inci</span> <span class=SpellE>Cetindil</span>, <span class=SpellE>Madhusudan</span> <span class=SpellE>Cheelangi</span>, <span class=SpellE>Khurram</span> <span class=SpellE>Faraaz</span>, Eugenia <span class=SpellE>Gabrielova</span>, Raman Grover, Zachary <span class=SpellE>Heilbron</span>, Young-<span class=SpellE>Seok</span> Kim, Chen Li, <span class=SpellE>Guangqiang</span> Li, <span class=SpellE>Ji</span> <span class=SpellE>Mahn</span> Ok, Nicola <span class=SpellE>Onose</span>, <span class=SpellE>Pouria</span> <span class=SpellE>Pirzadeh</span>, <span class=SpellE>Vassilis</span> J. <span class=SpellE>Tsotras</span>, <span class=SpellE>Rares</span> <span class=SpellE>Vernica</span>, <span class=SpellE>Jian</span> Wen, Till <span class=SpellE>Westmann</span><span class=GramE>:.</span> PVLDB 7(14): 1905-1916 (2014)</span><span style='mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='text-indent:-.25in;mso-list:l3 level1 lfo2; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>2.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>Storage Management in <span class=SpellE>AsterixDB</span></span></b><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>, <span class=SpellE>Sattam</span> <span class=SpellE>Alsubaiee</span>, Alexander <span class=SpellE>Behm</span>, <span class=SpellE>Vinayak</span> R. <span class=SpellE>Borkar</span>, Zachary <span class=SpellE>Heilbron</span>, Young-<span class=SpellE>Seok</span> Kim, Michael J. Carey, Markus <span class=SpellE>Dreseler</span>, Chen Li, PVLDB 7(10): 841-852 (2014)</span><span style='mso-fareast-font-family: "Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='text-indent:-.25in;mso-list:l3 level1 lfo2; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>3.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>Efficient instant-fuzzy search with proximity ranking</span></b><span style='mso-fareast-font-family: "Times New Roman";color:#222222;background:white'>, <span class=SpellE>Inci</span> <span class=SpellE>Cetindil</span>, <span class=SpellE>Jamshid</span> <span class=SpellE>Esmaelnezhad</span>, <span class=SpellE>Taewoo</span> Kim, Chen Li. ICDE 2014: 328-339</span><span style='mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='text-indent:-.25in;mso-list:l3 level1 lfo2; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>4.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>Efficient direct search on compressed genomic data</span></b><span style='mso-fareast-font-family: "Times New Roman";color:#222222;background:white'>, <span class=SpellE>Xiaochun</span> Yang, Bin Wang, Chen Li, <span class=SpellE>Jiaying</span> Wang, <span class=SpellE>Xiaohui</span> <span class=SpellE>Xie</span>, ICDE 2013: 961-972</span><span style='mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='text-indent:-.25in;mso-list:l3 level1 lfo2; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>5.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>Improving regular-expression matching on strings using negative factors</span></b><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>, <span class=SpellE>Xiaochun</span> Yang, Bin Wang, Tao <span class=SpellE>Qiu</span>, <span class=SpellE>Yaoshu</span> Wang, Chen Li, SIGMOD Conference 2013: 361-372</span><span style='mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;mso-add-space:auto;text-indent:-.25in;mso-list:l3 level1 lfo2;tab-stops: list .5in'><![if !supportLists]><span style='mso-fareast-font-family:"Times New Roman"'><span style='mso-list:Ignore'>6.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>String similarity measures and joins with synonyms</span></b><span style='mso-fareast-font-family: "Times New Roman";color:#222222;background:white'>, <span class=SpellE>Jiaheng</span> Lu, <span class=SpellE>Chunbin</span> Lin, Wei Wang, Chen Li, <span class=SpellE>Haiyong</span> Wang, SIGMOD Conference 2013: 373-384</span></p> <p class=MsoListParagraphCxSpLast style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;mso-add-space:auto;text-indent:-.25in;mso-list:l3 level1 lfo2;tab-stops: list .5in'><![if !supportLists]><span style='mso-fareast-font-family:"Times New Roman"'><span style='mso-list:Ignore'>7.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'>Supporting Efficient Top-k Queries in Type-Ahead Search,</b> Guoliang Li, Jiannan Wang, Chen Li, Jianhua Feng, <span class=GramE>SIGIR</span> 2012. [<a href="pub/sigir2012-ipubmed-topk.pdf">PDF</a>], [<a href="pub/sigir2012-ipubmed-topk.pptx">PPTX</a>], [<a href="http://ipubmed.ics.uci.edu/">Demo</a>]</p> <ol start=8 type=1> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Inside  Big Data Management : Ogres, Onions, or Parfaits?</b> Vinayak Borkar, Michael J. Carey, and Chen Li, EDBT 2012. [<a href="pub/edbt2012-asterix.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Location-Based Instant Search, </b>Shengyue Ji, Chen Li, SSDBM 2011: 17-36. [<a href="pub/2011-SSDBM.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>CHIME: An Efficient Error-Tolerant Chinese Pinyin Input Method, </b>Yabin Zheng, Chen Li, Maosong Sun<b style='mso-bidi-font-weight:normal'>, </b>IJCAI 2011, 2551-2556. [<a href="pub/ijcai11-chime.pdf">PDF</a>], [<a href="http://chime.ics.uci.edu/">Demo</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Answering Approximate String Queries on Large Data Sets Using External Memory</b>, Alexander Behm, Chen Li, and Michael Carey, ICDE 2011. [<a href="pub/2011-icde.pdf">PDF</a>] [<a href="http://flamingo.ics.uci.edu/releases/latest/">Source Code</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Supporting Location-Based Approximate-Keyword Queries</b>, Sattam Alsubaiee, Alexander Behm, and Chen Li, ACM GIS 2010. [<a href="http://www.ics.uci.edu/~salsubai/pub/gis2010-LBAKTree.pdf">PDF</a>] [<a href="http://www.ics.uci.edu/~salsubai/pub/Alsubaiee_GIS2010_LBAK-Tree.pptx">PPT</a>] [<a href="http://jujube.ics.uci.edu/localsearch/fuzzysearch/">Source Code and Demos</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Hybrid Indexing and Seamless Ranking of Spatial and Textual Features of Web Documents</b>, Ali Khodaei, Cyrus Shahabi, Chen Li, DEXA 2010. <span class=q>[<a href="pub/2010-dexa.pdf">PDF</a>]</span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient Parallel Set-Similarity Joins Using MapReduce.</b> Rares Vernica, Michael J. Carey, Chen Li, SIGMOD 2010, [<a href="pub/sigmod10-p495-vernica.pdf">PDF</a>], [<a href="http://asterix.ics.uci.edu/fuzzyjoin-mapreduce/"> source code</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Type-Ahead Search on Relational Data: a TASTIER Approach,</b> Guoliang Li, Shengyue Ji, Chen Li, and Jianhua Feng, SIGMOD 2009. [<a href="pub/sigmod2009-tastier.pdf">PDF</a>], [<a href="pub/sigmod2009-tastier.pptx">PPTX</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient Interactive Fuzzy Keyword Search</b>, Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng, WWW 2009. [<a href="pub/www2009-tastier-fuzzy.pdf">PDF</a>], [<a href="pub/www2009-tastier-fuzzy.pptx">PPTX</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Best-Effort Top-k Query Processing Under Budgetary Constraints</b>, Michal <span class=SpellE>Shmueli-Scheuer</span>, Chen Li, <span class=SpellE>Yosi</span> Mass, Haggai Roitman, Ralf Schenkel, and Gerhard Weikum, ICDE 2009. [<a href="pub/2009-icde-topk.pdf">PDF</a>], [<a href="pub/2009-icde-topk.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Space-Constrained Gram-Based Indexing for Efficient Approximate String Search</b>, Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu, ICDE 2009. [<a href="pub/icde2009-memreducer.pdf">PDF</a>], [<a href="pub/icde2009-memreducer.pptx">PPTX</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently</b>, Xiaochun Yang, Bin Wang, and Chen Li, ACM SIGMOD 2008. [<a href="pub/sigmod08.pdf">PDF</a>], [<a href="pub/sigmod08.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient Merging and Filtering Algorithms for Approximate String Searches</b>, Chen Li, Jiaheng Lu, and <span class=SpellE>Yiming</span> Lu. ICDE 2008. [<a href="pub/icde08-stringsearch.pdf">PDF</a>], [<a href="pub/icde08-stringsearch.ppt">PPT</a>], [<a href="http://flamingo.ics.uci.edu/releases/latest">Source Code</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Data Exchange with Arithmetic Comparisons</b>, <span class=SpellE>Foto</span> Afrati, Chen Li, and <span class=SpellE>Vassia</span> Pavlaki. EDBT 2008. [<a href="pub/edbt08-deac.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams</b>, Chen Li, Bin Wang, and Xiaochun Yang. VLDB 2007. [<a href="pub/vldb07-vgram.pdf">PDF</a>], [<a href="pub/vldb07-vgram.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Processing Spatial-Keyword (SK) Queries in Geographic Information Retrieval (GIR) Systems</b>, Ramaswamy Hariharan, Bijit Hore, Chen Li, Sharad Mehrotra, <span class=GramE>SSDBM</span> 2007. [<a href="pub/2007-SSDBM.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Protecting Individual Information Against Inference Attacks in Data Publishing</b>, Chen Li, <span class=SpellE><span class=GramE>Houtan</span></span><span class=GramE><span style="mso-spacerun:yes">� </span>Shirani</span>-Mehr, and Xiaochun<span style="mso-spacerun:yes">� </span>Yang. DASFAA 2007. [<a href="pub/2007-dasfaa.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Supporting Approximate Similarity Queries with Quality Guarantees in P2P Systems</b>, Qi <span class=SpellE>Zhong</span>, <span class=SpellE>Iosif</span> <span class=SpellE>Lazaridis</span>, <span class=SpellE>Mayur</span> <span class=SpellE>Deshpande</span>, Chen Li, Sharad Mehrotra, Hal Stern, COMAD 2006, December 14-16, 2006, Delhi, India. [<a href="pub/2006-comad.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Relaxing Join and Selection Queries. </b>Nick Koudas, Chen Li, Anthony Tung, and Rares Vernica. VLDB 2006, <ns0:place><span><ns0:City><span>Seoul</ns0:City></span>, <ns0:country-region><span>Korea</ns0:country-region></span></ns0:place></span>, 2006.<span style="mso-spacerun:yes">� </span>(<b style='mso-bidi-font-weight: normal'>13.2% accepted</b>) [<a href="pub/vldb06.pdf">PDF</a>], [<a href="pub/VLDB06-RelaxingJoinandSelectionQueries.ppt">PPT</a>], [<a href="http://flamingo.ics.uci.edu/src/queryrelax-0.1.tgz">Source Code</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Selectivity Estimation for Fuzzy String Predicates in Large Data Sets</b>, Liang Jin and Chen Li. VLDB 2005, <ns0:place><span><ns0:City><span>Trondheim</ns0:City></span>, <ns0:country-region><span>Norway</ns0:country-region></span></ns0:place></span>, August 30 - September 2, 2005. (<b>16% accepted</b>) [<a href="pub/vldb05-sepia.pdf">PDF</a>], [<a href="pub/vldb05-sepia.ppt">PPT</a>], [<a href="http://flamingo.ics.uci.edu/releases/latest">Source Code</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Indexing Mixed Types for Approximate Retrieval</b>, Liang Jin, Nick <span class=SpellE>Koudas</span>, Chen Li, Anthony K.H. <span class=SpellE>Tung.VLDB</span> 2005, <ns0:place><span><ns0:City><span>Trondheim</ns0:City></span>, <ns0:country-region><span>Norway</ns0:country-region></span></ns0:place></span>, August 30 - September 2, 2005. (<b>16% accepted</b>) [<a href="pub/vldb05-mat.pdf">PDF</a>], [<a href="pub/vldb05-mat.ppt">PPT</a>], [<a href="http://flamingo.ics.uci.edu/releases/latest">Source Code</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Secure XML Publishing without Information Leakage in the Presence of Data Inference</b>. Xiaochun Yang and Chen Li. VLDB, <ns0:place><span><ns0:City><span>Toronto</ns0:City></span>, <ns0:country-region><span>Canada</ns0:country-region></span></ns0:place></span>, August 29 - September 3, 2004. [<a href="pub/vldb04.pdf">PDF</a>], [<a href="pub/vldb04.ppt">PPT</a>]. (<b>16% accepted</b>) </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms</b>. Liang Jin, Nick Koudas, Chen Li. EDBT, <ns0:place><span><ns0:City><span>Crete</ns0:City></span>, <ns0:country-region><span>Greece</ns0:country-region></span></ns0:place></span>, March 2004. (<b>14% accepted</b>) [<a href="pub/edbt04-nnh.pdf">PDF</a>], [<a href="pub/edbt04-nnh-full.pdf">Full version</a>], [<a href="pub/edbt04-nnh.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>On Containment of Conjunctive Queries with Arithmetic Comparisons</b>. <span class=SpellE>Foto</span> Afrati, Chen Li, <span class=SpellE>Prasenjit</span> <span class=SpellE>Mitra</span>. EDBT, <ns0:place><span><ns0:City><span>Crete</ns0:City></span>, <ns0:country-region><span>Greece</ns0:country-region></span></ns0:place></span>, March 2004. (<b>14% accepted</b>) [<a href="pub/edbt04-alm.pdf">PDF</a>]. </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Materializing Views with Minimal Size to Answer Queries</b>. <span class=SpellE>Rada</span> <span class=SpellE>Chirkova</span> and Chen Li. ACM PODS, June 2003, <ns0:place><span><ns0:City><span>San Diego</ns0:City></span></ns0:place></span>, CA. (<b>20% accepted</b>). [<a href="pub/pods2003.pdf">PDF</a>], [<a href="pub/pods2003.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Efficient Record Linkage in Large Data Sets</b>, Liang Jin, Chen Li, and Sharad Mehrotra, in the 8th International Conference on Database Systems for Advanced Applications (DASFAA 2003) 26 - 28 March, 2003, Kyoto, Japan. (<b>33% accepted</b>) [<a href="pub/dasfaa03.ps">PS</a>], [<a href="pub/dasfaa03.pdf">PDF</a>], [<a href="pub/dasfaa03.ppt">PPT</a>], [<a href="http://flamingo.ics.uci.edu/releases/latest">Source Code</a>]. <b>Received DASFAA 2013 10-year Best Paper Award.</b></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Executing SQL over Encrypted Data in the Database-Service-Provider Model</b>. <span class=SpellE>Hakan</span> <span class=SpellE>Hacigumus</span>, <span class=SpellE>Bala</span> <span class=SpellE>Iyer</span>, Chen Li, and Sharad Mehrotra. In ACM SIGMOD, June 3-6, 2002 <ns0:place><span><ns0:City><span>Madison</ns0:City></span>, <ns0:State><span>Wisconsin</ns0:State></span></ns0:place></span>. (<b>18% accepted</b>). <b>Received <a href="http://www.sigmod.org/all-news/2012-acm-sigmod-awards-1">SIGMOD 2012 10-year Test-of-Time Award</a>.</b> [<a href="pub/sigmod02.pdf">PDF</a>] </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Answering Queries Using Views with Arithmetic Comparisons</b>. <span class=SpellE>Foto</span> Afrati, Chen Li, and <span class=SpellE>Prasenjit</span> <span class=SpellE>Mitra</span>. In ACM Symposium on Principles of Database Systems (PODS), June 3-6, 2002 <ns0:place><span><ns0:City><span>Madison</ns0:City></span>, <ns0:State><span>Wisconsin</ns0:State></span></ns0:place></span>. (<b>22% accepted</b>)</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Generating Efficient Plans for Queries Using Views.</b> <span class=SpellE>Foto</span> Afrati, Chen Li, and Jeff Ullman. In the Proc. of the 30th ACM SIGMOD Conference, <ns0:place><span><ns0:City><span>Santa Barbara</ns0:City></span>, <ns0:State><span>CA</ns0:State></span></ns0:place></span>, May, 2001. (<b>15% accepted</b>) [<a href="pub/SIGMOD01-aquv.ps">PS</a>] [<a href="pub/SIGMOD01-aquv.pdf">PDF</a>] [<a href="pub/sigmod01.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Minimizing View Sets without Losing Query-Answering Power.</b> Chen Li, <span class=SpellE>Mayank</span> <span class=SpellE>Bawa</span>, and Jeff Ullman. In the 8th International Conference on Database Theory (ICDT), <ns0:place><span><ns0:City><span>London</ns0:City></span>, <ns0:country-region><span>UK</ns0:country-region></span></ns0:place></span>, January, 2001. [<a href="pub/ICDT2001-minviews.ps">PS</a>] [<a href="pub/ICDT2001-minviews.pdf">PDF</a>], [<a href="pub/icdt01-minviews.ppt">PPT</a>]. Full version: [<a href="pub/ICDT2001-minviews-long.ps">PS</a>] [<a href="pub/ICDT2001-minviews-long.pdf">PDF</a>]. (<b>35% accepted</b>)</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>On Answering Queries in the Presence of Limited Access Patterns.</b> Chen Li and Edward Chang. In the 8th International Conference on Database Theory (ICDT), <ns0:place><span><ns0:City><span>London</ns0:City></span>, <ns0:country-region><span>UK</ns0:country-region></span></ns0:place></span>, January, 2001. [<a href="pub/ICDT2001-aqbp.ps">PS</a>] [<a href="pub/ICDT2001-aqbp.pdf">PDF</a>] [<a href="pub/icdt01-aqbp.ppt">PPT</a>]. (<b>35% accepted</b>)</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Query Planning with Limited Source Capabilities.</b> Chen Li and Edward Chang.<i> International Conference on Database Engineering (ICDE), pages 401-412, San Diego, CA, February, 2000.</i> (<b>14% accepted</b>) [<a href="pub/ICDE2000-qplsc.ps">PS</a>] [<a href="pub/ICDE2000-qplsc.pdf">PDF</a>] [<a href="pub/icde00.ppt">PPT</a>]. Full version: [<a href="pub/ICDE2000-qplsc-long.ps">PS</a>] [<a href="pub/ICDE2000-qplsc-long.pdf">PDF</a>] </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Computing Capabilities of Mediators.</b> <span class=SpellE>Ramana</span> <span class=SpellE>Yerneni</span>, Chen Li, Hector Garcia-Molina, Jeffrey Ullman. SIGMOD'99, <ns0:place><span><ns0:City><span>Philadelphia</ns0:City></span>, <ns0:State><span>PA</ns0:State></span></ns0:place></span>, May 1999. (<b>20% accepted</b>) [<a href="pub/SIGMOD99-med.ps">PS</a>] [<a href="pub/SIGMOD99-med.pdf">PDF</a>]. Full version: [<a href="pub/SIGMOD99-med-long.ps">PS</a>] [<a href="pub/SIGMOD99-med-long.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Optimizing Large Join Queries in Mediation Systems.</b> <span class=SpellE>Ramana</span> <span class=SpellE>Yerneni</span>, Chen Li, Jeffrey Ullman, Hector Garcia-Molina. International Conference on Database Theory (ICDT), <ns0:place><span><ns0:City><span>Jerusalem</ns0:City></span>, <ns0:country-region><span>Israel</ns0:country-region></span></ns0:place></span>, January, 1999. (<b>29% accepted</b>) [<a href="pub/ICDT99-oljq.ps">PS</a>] [<a href="pub/ICDT99-oljq.pdf">PDF</a>]. Full version: [<a href="pub/ICDT99-oljq-long.ps">PS</a>] [<a href="pub/ICDT99-oljq-long.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Searching Near-Replicas of Images via Clustering.</b> Edward Chang, Chen Li, James Wang, Peter <span class=SpellE>Mork</span>, and <span class=SpellE>Gio</span> <span class=SpellE>Wiederhold</span>. Proc. of SPIE Symposium of Voice, Video, and Data Communications, Multimedia Storage and Archiving Systems VI, pages 281-292, Boston, MA, September, 1999. [<a href="pub/SPIE99-RIME.ps">PS</a>] [<a href="pub/SPIE99-RIME.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>RIME: A Replicated Image Detector for the World-Wide Web.</b> Edward Chang, James <span class=SpellE>Ze</span> Wang, Chen Li, and <span class=SpellE>Gio</span> <span class=SpellE>Wiederhold</span>. Proceedings of SPIE Symposium of Voice, Video, and Data Communications, pages 58--67, Boston, MA, November 1998. [<a href="pub/SPIE98-RIME.ps">PS</a>] [<a href="pub/SPIE98-RIME.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b><span lang=NO-BOK style='mso-ansi-language:NO-BOK'>2D BubbleUp: Managing Parallel Disks for Media Servers.</span></b><span lang=NO-BOK style='mso-ansi-language:NO-BOK'> </span>Edward Chang, Hector Garcia-Molina, and Chen Li. The 5th International Conference of Foundations of Data Organization (FODO), pages 221-230, <ns0:place><span><ns0:City><span>Kobe</ns0:City></span>, <ns0:country-region><span>Japan</ns0:country-region></span></ns0:place></span>, 1998. [<a href="pub/FODO98-2DBubbleUp.ps">PS</a>] [<a href="pub/FODO98-2DBubbleUp.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo2;tab-stops:list .5in'><b>Performance Analysis of the Communication Mechanism for POE Workstation Cluster.</b> <span class=SpellE>Weiqiang</span> <span class=SpellE>Zhuang</span>, Chen Li, Meiming Shen.<i> Microcomputer &amp; Micro-system, Jan, 1995</i></li> </ol> <p class=MsoNormal><a name=tr></a><a name=journals></a><b><span style='font-size:13.5pt'>Refereed Journal Articles</span></b> </p> <ol start=1 type=1> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Hobbes: optimized gram-based methods for efficient read alignment</b>, Athena Ahmadi, Alexander Behm, <span class=SpellE>Nagesh</span> <span class=SpellE>Honnalli</span>, Chen Li, <span class=SpellE>Lingjie</span> <span class=SpellE>Weng</span>, and Xiaohui Xie, Nucleic Acids Research 2011; <span class=SpellE>doi</span>: 10.1093/<span class=SpellE>nar</span>/gkr1246. [<a href="pub/2011-hobbes.pdf">PDF</a>]<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>SKIF-P: a point-based indexing and ranking of web&nbsp;documents for spatial-keyword search</b>, Ali Khodaei, Cyrus Shahabi, and Chen Li, <span class=SpellE>Geoinformatica</span>, Springer, 2011. [<a href="pub/2011-geoinformatica.pdf">PDF</a>]<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Supporting BioMedical Information Retrieval: The BioTracer Approach, </b>Heri Ramampiaro and Chen Li, In Transactions on Large-Scale Data- and Knowledge-Centered Systems (TLDKS), 2011, No.4. Vol. 6990, Springer. pp. 73 94. [<a href="pub/2011-TLDKS.pdf">PDF</a>]<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>ASTERIX: towards a scalable, semistructured data platform for evolving-world models. </b>Alexander Behm, Vinayak R. Borkar, Michael J. Carey, Raman Grover, Chen Li, Nicola Onose, Rares Vernica, Alin Deutsch, Yannis Papakonstantinou, Vassilis J. Tsotras, Distributed and Parallel Databases,<span style="mso-spacerun:yes">� </span>2011, 29(3), 185-216. [<a href="http://asterix.ics.uci.edu/pub/ASTERIX-vision.pdf">PDF</a>]<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Efficient fuzzy full-text type-ahead search</b>, Guoliang Li, Shengyue Ji, Chen Li, Jianhua Feng:. VLDB J. 20(4): 617-640 (2011). [<a href="pub/2011-vldbj-fuzzy-search.pdf">PDF</a>]<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Interactive and Fuzzy Search: A Dynamic Way to Explore MEDLINE</b>, Jiannan Wang, Inci Cetindil, ShengyueJi, Chen Li, Xiaohui Xie, Guoliang Li, Jianhua Feng, Journal of Bioinformatics, 2010. </span>[<a href="pub/2010-ipubmed.pdf">PDF</a>]<span class=q><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Rewriting Queries using Views</b>, Chen Li: Encyclopedia of Database Systems 2009: 2438-2441. </span>[<a href="pub/2009-queryrewriting.pdf">PDF</a>]<span class=q><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents, </b>Guoliang Li, Chen Li, Jianhua Feng, <span class=SpellE>Lizhu</span> Zhou: Inf. Sci. 179(21): 3745-3762 (2009). </span>[<a href="pub/2009-sail.pdf">PDF</a>]<span class=q><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Human genomes as email attachments.</b> Scott Christley, <span class=SpellE>Yiming</span> Lu, Chen Li, and Xiaohui Xie, Bioinformatics 25: 274-275 (2009). </span>[<a href="pub/2008-bioinformatics.pdf">PDF</a>]. [<a href="http://silver.ics.uci.edu/~dnazip/index.html">Source Code</a>]. <span class=q>It was the most downloaded article on the Web site of the Journal of Bioinformatics for two months.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases.</b> Liang Jin, Chen Li, and Rares Vernica.<span style="mso-spacerun:yes">� </span>VLDB Journal, Volume 17, Number 5, pages 1213-1229, August 2008. </span>[<a href="pub/2008-vldbj-sepia.pdf">PDF</a>]<span class=q><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><span class=q><b style='mso-bidi-font-weight:normal'>Using Views to Generate Efficient Evaluation Plans for Queries</b></span> <span class=SpellE>Foto</span> Afrati, <span class=GramE>Chen&nbsp;&nbsp;Li</span>, and Jeff Ullman, Journal of Computer and System Sciences, Volume 73, Issue 5, pages 703-724,<span style="mso-spacerun:yes">� </span>August 2007. [<a href="pub/2007-alu.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Rewriting Queries Using Views in the Presence of Arithmetic Comparisons</b>, <span class=SpellE>Foto</span> Afrati, <span class=GramE>Chen&nbsp;&nbsp;Li</span>, and <span class=SpellE>Prasenjit</span> <span class=SpellE>Mitra</span>, Theoretical Computer Science, Volume 368, Numbers 1-2, pages 88-123, 2006. [<a href="pub/2006-alm.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques</b>, Chen Li, Liang Jin, and Sharad Mehrotra, World Wide Web Journal, Volume 9, Number 4, pages 557-584, December 2006. [<a href="pub/2006-ljm.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Achieving Communication Efficiency through Push-Pull Partitioning of Semantic Spaces to Disseminate Dynamic Information</b>, <span class=SpellE>Amitabha</span> <span class=SpellE>Bagchi</span>, Amitabh Chaudhary, Michael T. Goodrich, Chen Li, and Michal Shmueli-Scheuer. IEEE Transaction on Knowledge and Data Engineering (TKDE), October 2006 (Vol. 18, No. 10). [<a href="pub/2006-gm.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Answering Queries Using Materialized Views with Minimum Size</b>. <span class=SpellE>Rada</span> <span class=SpellE>Chirkova</span>, Chen Li, and <span class=SpellE>Jia</span> Li. VLDB Journal (2006), Volume 15, Number 3, 191-210. [<a href="pub/2005-cll.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Recent Progress on Selected Topics on Database Research -- A Report from Nine Young Chinese Researchers Working in the <ns0:place><span><ns0:country-region><span>United States</ns0:country-region></span></ns0:place></span>.</b> <span class=SpellE>Zhiyuan</span> Chen, Chen Li, <span class=SpellE>Jian</span> Pei, <span class=SpellE>Yufei</span> Tao, <span class=SpellE>Haixun</span> Wang, Wei Wang, <span class=SpellE>Jiong</span> Yang, Jun Yang, and <span class=SpellE>Donghui</span> Zhang. The Journal of Computer Science and Technology. Vol. 18, No. 5, Pages 538 - 552, September 2003. [<a href="pub/jcst03.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Computing Complete Answers to Queries in the Presence of Limited Access Patterns.</b> Chen Li. The VLDB Journal (2003) 12: 211-227 [<a href="pub/jvldb03.ps">PS</a>] [<a href="pub/jvldb03.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Answering Queries with Useful Bindings.</b> Chen Li and Edward Chang. ACM Transactions on Database Systems (TODS), Volume 26 , Issue 3 (September 2001).[<a href="pub/TODS2001.ps">PS</a>] [<a href="pub/TODS2001.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l1 level1 lfo3;tab-stops:list .5in'><b>Clustering for Approximate Similarity Search in High-Dimensional Spaces.</b> Chen Li, Edward Chang, Hector Garcia-Molina, and <span class=SpellE>Gio</span> <span class=SpellE>Wiederhold</span>. IEEE Transaction on Knowledge and Data Engineering, Volume 14, Number 4, pp.792-808, July/August 2002 [<a href="pub/TR-highdim.ps">PS</a>] [<a href="pub/TR-highdim.pdf">PDF</a>]</li> </ol> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; margin-left:.5in'><o:p>&nbsp;</o:p></p> <p class=MsoNormal><a name=workshopdemo></a><b>Refereed Workshop, Conference Demo Papers</b><b style='mso-bidi-font-weight:normal'>, Tutorials, and Other Publications<o:p></o:p></b></p> <p class=MsoListParagraphCxSpFirst style='text-indent:-.25in;mso-list:l0 level1 lfo4; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>1.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>ASTERIX: An Open Source System for &quot;Big Data&quot; Management and Analysis</span></b><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>, <span class=SpellE>Sattam</span> <span class=SpellE>Alsubaiee</span>, Yasser <span class=SpellE>Altowim</span>, <span class=SpellE>Hotham</span> <span class=SpellE>Altwaijry</span>, Alexander <span class=SpellE>Behm</span>, <span class=SpellE>Vinayak</span> R. <span class=SpellE>Borkar</span>, <span class=SpellE>Yingyi</span> Bu, Michael J. Carey, Raman Grover, Zachary <span class=SpellE>Heilbron</span>, Young-<span class=SpellE>Seok</span> Kim, Chen Li, Nicola <span class=SpellE>Onose</span>, <span class=SpellE>Pouria</span> <span class=SpellE>Pirzadeh</span>, <span class=SpellE>Rares</span> <span class=SpellE>Vernica</span>, <span class=SpellE>Jian</span> Wen. PVLDB 2012 (demo).</span><span style='mso-fareast-font-family:"Times New Roman"'><o:p></o:p></span></p> <p class=MsoListParagraphCxSpMiddle style='text-indent:-.25in;mso-list:l0 level1 lfo4; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>2.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b style='mso-bidi-font-weight:normal'><span style='mso-fareast-font-family:"Times New Roman";color:#222222;background:white'>Big data platforms: what's next?</span></b><span style='mso-fareast-font-family: "Times New Roman";color:#222222;background:white'> <span class=SpellE>Vinayak</span> R. <span class=SpellE>Borkar</span>, Michael J. Carey, Chen Li. ACM Crossroads 19(1): 44-49, 2012.</span></p> <p class=MsoListParagraphCxSpLast style='text-indent:-.25in;mso-list:l0 level1 lfo4; tab-stops:list .5in'><![if !supportLists]><span style='mso-fareast-font-family: "Times New Roman"'><span style='mso-list:Ignore'>3.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span class=SpellE><span class=GramE><b style='mso-bidi-font-weight:normal'>qSpell</b></span></span><b style='mso-bidi-font-weight:normal'>: Spelling Correction of Web Search Queries using Ranking Models and Iterative Correction. </b>Yasser <span class=SpellE>Ganjisaffar</span>, Andrea <span class=SpellE>Zilio</span>, Sara <span class=SpellE>Javanmardi</span>, Inci Cetindil, <span class=SpellE>Manik</span> <span class=SpellE>Sikka</span>, <span class=SpellE>Sandeep</span> <span class=SpellE>Katumalla</span>, <span class=SpellE>Narges</span> <span class=SpellE>Khatib</span>, Chen Li, Cristina Lopes, Spelling Alteration for Web Search Workshop, July 2011. [<a href="pub/2011-Speller.pdf">PDF</a>], [<a href="http://flamingo.ics.uci.edu/spellchecker/">Dataset</a>] (The authors won the third place in the <a href="http://web-ngram.research.microsoft.com/spellerchallenge/">Microsoft's speller challenge</a> in 2011.)</p> <ol start=4 type=1> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>The Flamingo Software Package on Approximate String Queries. </b>Chen Li, DASFAA Workshops 2011, 477. [<a href="pub/2011-dqis-flamingo.pdf">PDF</a>], [<a href="http://flamingo.ics.uci.edu/releases/latest/">Source Code</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Seaform: Search-As-You-Type in Forms</b>, <span class=SpellE>Hao</span> Wu, Guoliang Li, Chen Li, <span class=SpellE>Lizhu</span> Zhou, VLDB 2010 (Demo). [<a href="pub/2010-seaform.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Search-As-You-Type: Opportunities and Challenges</b>, Chen Li, Guoliang Li, IEEE Data Eng. Bull. 33(1): 37-45 (2010). [<a href="pub/2010-search-as-you-type-overview.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Fuzzy Keyword Search on Spatial Data</b>, Sattam Alsubaiee, Chen Li: DASFAA, Excellent Demo Award, 2010: 464-467. [<a href="pub/dasfaa10-alsubaiee.pdf">PDF</a>], [<a href="http://jujube.ics.uci.edu/localsearch/fuzzysearch/">Demos</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient top-k algorithms for fuzzy search in string collections</b>, Rares Vernica, Chen Li, KEYS 2009: 9-14, [<a href="pub/keys09-p9-vernica.pdf">PDF</a>], [<a href="pub/keys09-p9-vernica-slides.pdf">Talk Slides</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient Approximate Search on String Collections (Tutorial), </b><span class=SpellE>Marios</span> <span class=SpellE>Hadjeleftheriou</span> and<b style='mso-bidi-font-weight:normal'> </b>Chen Li, VLDB 2009. [<a href="pub/vldb09-tutorial.pdf">PDF</a>], [<a href="pub/vldb09-tutorial-part1.ppt">Part I</a>], [<a href="pub/vldb09-tutorial-part2.ppt">Part II</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Efficient Approximate Search on String Collections (Tutorial), </b><span class=SpellE>Marios</span> Hadjieleftheriou, Chen Li, ICDE 2009, [<a href="pub/icde2009-tutorial-part1.ppt">PPT-Part1</a>], [<a href="pub/icde2009-tutorial-part2.ppt">PPT-part2</a>].</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Quality-Aware Retrieval of Data Objects from Autonomous Sources for Web-Based Repositories,</b> <span class=SpellE>Houtan</span> Shirani-Mehr, Chen Li, Gang Liang, Michal Shmueli-Scheuer, ICDE 2008 (poster). [<a href="pub/icde08-crawling.pdf">PDF</a>] [<a href="pub/crawling-technical-report-2007.pdf">Technical Report</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b style='mso-bidi-font-weight: normal'>Communication-Efficient Query Answering with Quality Guarantees in Client-Server Applications</b>.<span style="mso-spacerun:yes">� </span>Michal <span class=SpellE>Shmueli-Scheuer</span>, Amitabh Chaudhary, <span class=SpellE>Avigdor</span> Gal, Chen Li.<span style="mso-spacerun:yes">� </span>WebDB 2007. [<a href="pub/2007-weddb.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Quality-Driven Approximate Methods for GIS Data Integration</b>. Ramaswamy Hariharan, Michal <span class=SpellE>Schmueli-Scheuer</span>, Chen Li, and Sharad Mehrotra. ACM GIS 2005, November 4-5th, 2005 <ns0:place><span><ns0:City><span>Bremen</ns0:City></span>, <ns0:country-region><span>Germany</ns0:country-region></span></ns0:place></span>. [<a href="pub/gis05.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Answering Aggregation Queries on Hierarchical Web Sites Using Adaptive Sampling</b>. <span class=SpellE>Foto</span> Afrati, <span class=SpellE>Paraskevas</span> <span class=SpellE>Lekeas</span>, and Chen Li. <a href="pub/sampling05.pdf">Technical Report</a>, UCI ICS, August 2005. A <a href="pub/cikm05.pdf">short version</a> appears in CIKM'2005, 31st October - 5th November, 2005 <ns0:State><span>Bremen</ns0:State></span>, <ns0:place><span><ns0:country-region><span>Germany</ns0:country-region></span></ns0:place></span>. </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><span class=SpellE><b>XGuard</b></span><b>: A System for Publishing XML Documents without Information Leakage in the Presence of Data Inference</b>. Xiaochun Yang, Chen Li, <span class=SpellE>Ge</span> Yu, and Lei Shi. Proc. of ICDE'2005, demo track, <ns0:place><span><ns0:City><span>Tokyo</ns0:City></span>, <ns0:country-region><span>Japan</ns0:country-region></span></ns0:place></span>, March 2005. </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>RACCOON: A Peer-Based System for Data Integration and Sharing</b>. <span lang=FI style='mso-ansi-language:FI'>Chen Li, Jia Li, Qi Zhong. </span>Proc. of ICDE'2004, demo track. [<a href="pub/icde04-demo.pdf">PDF</a>] </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Schema-Guided Wrapper Maintenance for Web-Data Extraction</b>. <span lang=NO-BOK style='mso-ansi-language:NO-BOK'>Xiaofeng Meng, Dongdong Hu, Chen Li. </span>To appear in the Fifth International Workshop on Web Information and Data Management (WIDM'03), <ns0:City><span>New Orleans</ns0:City></span>, <ns0:place><span><ns0:State><span>Louisiana</ns0:State></span></ns0:place></span>. [<a href="pub/widm03.pdf">PDF</a>] [<a href="pub/widm03.ppt">PPT</a>]. </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>A Supervised Visual Wrapper Generator for Web-Data Extraction. </b>. <span class=SpellE>Xiaofeng</span> <span class=SpellE>Meng</span>, <span class=SpellE>Haiyan</span> Wang, <span class=SpellE>Dongdong</span> Hu, Chen Li. COMPSAC 2003: 657-662. [<a href="pub/compsac2003.pdf">PDF</a>] </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Using Constraints to Describe Source Contents in Data Integration Systems</b>. Chen Li. IEEE Intelligent Systems 18(5): 49-53 (2003). [<a href="pub/2003-is.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Describing and Utilizing Constraints to Answer Queries in Data-Integration Systems</b>. Chen Li. IJCAI 2003 workshop on Information Integration on the Web, August 2003, <ns0:place><span><ns0:City><span>Acapulco</ns0:City></span>, <ns0:country-region><span>Mexico</ns0:country-region></span></ns0:place></span>. [<a href="pub/iiweb2003.pdf">PDF</a>], [<a href="pub/iiweb2003.ppt">PPT</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Towards Perception-Based Image Retrieval.</b> Edward Chang, <span class=SpellE>Beitao</span> Li, and Chen Li. Proceedings of IEEE Workshop on Content-based Access of Image and Video Libraries, p. 401-412, South Carolina, June, 2000. [<a href="pub/cbaivl.ps">PS</a>] [<a href="pub/cbaivl.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Managing Parallel Disks for Continuous Media Data.</b> Edward Chang, Chen Li, and Hector Garcia-Molina. A Book Chapter in Information Organization &amp; Databases, p.107-120, Kluwer Publisher, 2000. [<a href="pub/2DBubbleup-book.ps">PS</a>] [<a href="pub/2DBubbleup-book.pdf">PDF</a>]<b>Answering Queries with Database Restrictions (Research Summary).</b> Chen Li. <i>Symposium on Abstraction, Reformulation and Approximation (SARA)</i>, pages 328 - 329, July, 2000, <ns0:PlaceName><span>Horseshoe</ns0:PlaceName></span> <ns0:PlaceType><span>Bay</ns0:PlaceType></span> (<ns0:PlaceType><span>Lake</ns0:PlaceType></span> <ns0:PlaceName><span>LBJ</ns0:PlaceName></span>), <ns0:place><span><ns0:State><span>Texas</ns0:State></span></ns0:place></span>. [<a href="pub/SARA2000-summary.ps">PS</a>] [<a href="pub/SARA2000-summary.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'>I wrote a report of the <a href="http://dimacs.rutgers.edu/Workshops/DataMining/">Workshop on Data Mining in the Internet Age</a>, which was held May 1 - 2, 2000, IBM Almaden Center, <span class=GramE>San</span> Jose, California. [<a href="pub/DIMACS-report.ps">PS</a>] [<a href="pub/DIMACS-report.pdf">PDF</a>]</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><b>Capability Based Mediation in TSIMMIS.</b> <span lang=FI style='mso-ansi-language:FI'>Chen Li, Ramana Yerneni, Vasilis Vassalos, Hector Garcia-Molina, Yannis Papakonstantinou, Jeffrey Ullman, Murty Valiveti. </span>Proc. of ACM SIGMOD'98, demo track, pages 564 - 566, <ns0:place><span><ns0:City><span>Seattle</ns0:City></span>, <ns0:State><span>WA</ns0:State></span></ns0:place></span>, June, 1998. [<a href="pub/SIGMOD98-cbmt.ps">PS</a>] [<a href="pub/SIGMOD98-cbmt.pdf">PDF</a>] </li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo4;tab-stops:list .5in'><span class=SpellE><b>HiComm</b></span><b> -- A New Technique for Improving Communication Performance in Workstation Cluster.</b> Chen Li, <span class=SpellE>Weiqiang</span> <span class=SpellE>Zhuang</span>, Meiming Shen, <span class=SpellE>Dingxing</span> Wang, <span class=SpellE>Weimin</span> Zheng,<i> Proc. of International Workshop on Advanced Parallel Processing Technologies (APPT), October, 1995, Beijing, China.</i></li> </ol> <p class=MsoNormal><a name=techmag></a><b><span style='font-size:13.5pt'><o:p>&nbsp;</o:p></span></b></p> <p class=MsoNormal><a name=other></a><b><span style='font-size:13.5pt'>Ph.D. Thesis</span></b> </p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; margin-left:.25in'><b>Query Processing and Optimization in Information-Integration Systems.</b> Chen Li. <a href="thesis:thesis.html">Ph.D. Thesis</a>, Computer Science Department, Stanford University, August, 2001.</p> </div> </body> <script src="http://www.google-analytics.com/urchin.js" type="text/javascript"> </script> <script type="text/javascript"> _uacct = "UA-1409310-2"; urchinTracker(); </script> </html> http://jujube.ics.uci.edu/www/learning.html Interactive, fuzzy search for learning

    Interactive, fuzzy search for learning


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    http://www.ics.uci.edu/~chenli/misc.html Chen Li's Miscellaneous Pages

    Chen Li's Miscellaneous Pages

    • Personal
    • Here is a page about the instructions for PODS'2005 Camera-Ready Submissions
    • I wrote a page about how to use type-1 fonts in PDF paper submissions
    • I am helping a nonprofit organization called Angel Heart International to help children with congenital heart diseases (CHD) in China and other developing countries.
    http://www.ics.uci.edu/~dnazip/ DNAzip

    DNAzip: DNA sequence compression using a reference genome

    The demonstration code can be found here DNAzip.

    The code is written in standard C++. We have used GNU C++ to compile in the provided makefile, but other C++ compilers should work. The resulting executable program is "perftest", currently as a demonstration program it is hard-coded to look for specific reference genome, reference SNP map, and genome to be compressed.

    To run the code, you will need the following two datasets:

    • The human reference genome (hg18, 840MB). This is the exact same data from UCSC goldenPath, you will likely achieve faster download directly from UCSC. There are 25 FASTA files, one for each chromosome and the mitochondrial genome (which typically isn't used), that should be uncompressed and placed in a "chr" subdirectory under the source code.

    • dbSNP database, 140MB, used as the refernece SNP map. The SNP data is based upon NCBI dbSNP build 129, but we use the UCSC SNP track as it has pre-processed all of the needed data into a single table. The UCSC Table Browser can be used to acquire the SNPs (group: Variation and Repeats, track: SNPs (129), table: snp129). There are 25 FASTA files, one for each chromosome and the mitochondrial genome. These should be in a "dbSNP" subdirectory under the source code.

    • James Watson's genome, 418M, used as the example genome to be compressed. The data is from the Nature publication and was kindly provided by David Wheeler. There are two files, one which lists all of the indels (JWB-indels-submission) and the other which lists the SNPs (JWB-snps-submission.txt). These should be in a "files" subdirectory under the source code.
    All files generated by the "perftest" executable will be created in the "files" subdiretory. The program compresses the genome then uncompresses it. The following files will be created:
    • JWB-unified-file.txt: Post-processing of the variation data for JW genome into a more compact text representation; this removes the unneeded fields from the data set. This data is the basis for compression and is ~84MB for JW genome.

    • JWB-in-posFreq4.txt: The 4-mer frequency table for JW genome.

    • JWB-unified-compression.txt: This is the resulting compressed genome, it should ~4MB for JW genome.

    • JWB-unified-DeCompression.txt: This is the resulting decompressed genome, it should be equivalent to the original file (JWB-unified-file.txt).

    Future work

    We have plans to enhance the code into a more flexible genome compression library.

    Any questions about use of this code should be directed to Xiaohui Xie or Chen Li

    For citation, please refer to the following paper

    Human genomes as email attachments, Christley S, Lu Y, Li C, and Xie X, Bioinformatics. 2009 25:274-5. It was the most downloaded article on the Web site of the Journal of Bioinformatics for two months.

    Additional info

    • One of the top 20 papers in translational bioinformatics chosen by Dr. Russ Altman of Stanford in his 2009 The Year in Review.
    • So Long, Data Depression -- Genome Technology, Sept, 2009
    • You've Got Email -- A Human Genome, Bio-IT World

    Funding

    Development of DNAzip is partially supported by funding from National Science Foundaton.

    http://flamingo.ics.uci.edu/ The FLAMINGO Project on Data Cleaning

    The FLAMINGO Project on Data Cleaning

    Department of Computer Science, UC Irvine

    Objective

    The Flamingo Project focuses on data cleaning, i.e., how to deal with errors and inconsistencies in information systems. As an example, in many applications such as data integration, commercial organizations need to collect data from various sources to conduct analysis and make decisions. Often, the data from these different sources can have inconsistencies. For instance, we use first name, last name, SSN, and birthday to identify a person. However, the same name, e.g., "Schwarzenegger", may be misspelled as "Swarzzengaer" or other forms. Such errors make it more challenging to link records from different places and answer queries approximately. We are developing algorithms in order to make query answering and information retrieval efficient in the presence of such inconsistencies and errors.

    With the NSF award IIS-0844574, we plan to study the following problems. Supporting fuzzy queries is becoming increasingly more important in applications that need to deal with a variety of data inconsistencies in structures, representations, or semantics. Many existing algorithms require an offline analysis of data sets to construct an efficient index structure to support online query processing. Fuzzy join queries of data sets are more time consuming due to the computational complexity. The PI is studying three research problems: (1) constructing high-quality inverted lists for fuzzy search queries using Hadoop; (2) supporting fuzzy joins of large data sets using Hadoop; and (3) using the developed techniques to improve data quality of large collections of documents.

    With the NSF award 1030002, we will study how to support powerful keyword search with efficient indexing structures and algorithms in a clouding-computing infrastructure. A main application is supporting family reunification in disasters such as the Haiti Earthquake. Check our portals for the Haiti Earthquake and Chile Earthquake. The main challenge is how to use limited programming primitives in the cloud to implement index structures and search algorithms.

    Our qSpeller project page for the Microsoft Speller Challenge.

    News

    • (1/13/2013) Our DASFAA 2003 paper titled "Efficient Record Linkage in Large Data Sets" received the 10-year Best Paper Award for DASFAA 2013. It was my first paper in the area of data cleaning and approximiate string search in the context of the Flamingo project.
    • (2/2012) We are glad to release version of our Flamingo Package on approximate string matching.
    • (7/2011) Our team won the third prize at the Microsoft Speller Challenge. Here is our project page.
    • (4/22/2011) Chen Li gave an invited talk titled "The Flamingo Software Package on Approximate String Queries" at the DQIS 2011 workshop in Hong Kong. Here is the Powerpoint file.
    • (10/2010) Out paper titled "Answering Approximate String Queries on Large Data Sets Using External Memory" has been accepted for publication in ICDE 2011.
    • (9/2010) Our paper titled "Supporting Location-Based Approximate-Keyword Queries" has been accepted for publication in ACM SIGSPATIAL GIS 2010.
    • (3/2010) We are glad to release the third version of our Flamingo Package on approximate string matching.
    • (3/2010) We are glad to release the source code of our SIGMOD 2010 paper titled "Efficient Parallel Set-Similarity Joins Using MapReduce"
    • (3/2010) We are glad to release two Fuzzy Keyword Search on Spatial Data demos.
    • (3/2010) We are glad to receive an NSF award 1030002 to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake.
    • (2/2010) Our paper titled "Efficient Parallel Set-Similarity Joins Using MapReduce" has been accepted by the SIGMOD 2010 conference.
    • (2/2009) We are glad to receive an NSF award IIS-0844574 from the NSF CluE program to support our research on large-scale data cleaning using MapReduce/Hadoop environments. In addition to receiving the NSF support, we will also use software and services on a Google-IBM cluster to explore innovative research ideas in data-intensive computing.
    • (11/07/2008) We updated our Flamingo Package (2.0.1) for compatibility with the latest GCC version (4.3.2).
    • (10/14/2008) We are glad to release the second version of our Flamingo Package on approximate string matching.
    • (10/14/2008) We are glad to release the Flamingo Toolkit that contains UDF functions for MySQL.
    • (4/1/2008) We are glad to release the PSearch Prototype to support interactive, fuzzy search for UCI Directory.
    • (4/17/2007) We are glad to release the first version of our Flamingo Package on approximate string matching.

    Fuzzy Keyword Search on Spatial Data

    We present a solution to support Fuzzy Keyword Search on Spatial Data.

    Releases

    • Latest

    • 4.1 (February 22nd, 2012)
    • 4.0 (October 23rd, 2010)
    • 3.0 (March 29th, 2010)
    • 2.0.1 (November 7th, 2008)
    • 2.0 (October 14th, 2008)
    • 1.0 (April 17th, 2007)

    • Toolkit (October 14th, 2008), UDF functions for MySQL

    People

    • Sattam Alsubaiee (Ph.D. Student)
    • Alexander Behm (Ph.D. Student)
    • Shengyue Ji (Ph.D. Student)
    • Chen Li (Faculty)
    • Rares Vernica (Ph.D. Student)

    Alumni and Visitors

    • Guoliang Li, spring of 2008, visitor from Tsinghua University, China.
    • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
    • Yiming Lu, graduated from UC Irvine in 2008
    • Bin Wang and Xiaochun Yang, summers of 2006, 2007, and 2008, visitors from Northeastern University, China
    • Liang Jin, graduated from UC Irvine in 2005

    Publications

    • Answering Approximate String Queries on Large Data Sets Using External Memory
      Alexander Behm, Chen Li, Michael J. Carey.
      ICDE 2011 (accepted for publication).
    • Supporting Location-Based Approximate-Keyword Queries
      Sattam Alsubaiee, Alexander Behm, Chen Li. PDF PPTX Source Code
      ACM SIGSPATIAL GIS 2010.
    • Efficient Parallel Set-Similarity Joins Using MapReduce
      Rares Vernica, Michael J. Carey, Chen Li. PDF Full Version Source Code
      SIGMOD 2010.
    • Fuzzy Keyword Search on Spatial Data (Demo)
      Sattam Alsubaiee and Chen Li PDF Demo
      DASFAA 2010.
    • Efficient top-k algorithms for fuzzy search in string collections.
      Rares Vernica, Chen Li. PDF PDF slides Source Code
      KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)
    • Efficient Interactive Fuzzy Keyword Search
      Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng PDF PPTX ConferenceLink
      WWW 2009.
    • Space-Constrained Gram-Based Indexing for Efficient Approximate String Search
      Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu PDF Full Version PPTX Source Code
      ICDE 2009.
    • Efficient Approximate Search on String Collections (Tutorial)
      Marios Hadjieleftheriou, Chen Li PPT Part1, PPT Part2
      ICDE 2009.
    • Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently PDF PPT
      Xiaochun Yang, Bin Wang, Chen Li.
      SIGMOD 2008.
    • Efficient Merging and Filtering Algorithms for Approximate String Searches PDF PPT Source Code
      Chen Li, Jiaheng Lu, and Yiming Lu.
      ICDE 2008.
    • SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases Source Code
      Liang Jin, Chen Li, and Rares Vernica.
      VLDB Journal 2007. It's an extended version of the SEPIA paper in VLDB05.
    • VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams. PDF PPT
      Chen Li, Bin Wang, and Xiaochun Yang.
      VLDB 2007, Vienna, Austria
    • Relaxing Join and Selection Queries. PDF PPT Source Code
      Nick Koudas, Chen Li, Anthony Tung, and Rares Vernica.
      VLDB 2006, Seoul, Korea.
    • Selectivity Estimation for Fuzzy String Predicates in Large Data Sets. PDF PPT Source Code
      Liang Jin and Chen Li.
      VLDB 2005, Trondheim, Norway.
    • Indexing Mixed Types for Approximate Retrieval. PDF PPT Source Code
      Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung.
      VLDB 2005, Trondheim, Norway.
    • NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms. PDF Full Version PPT
      Liang Jin, Nick Koudas, Chen Li.
      EDBT 2004, Heraklion - Crete, Greece.
    • Efficient Record Linkage in Large Data Sets. PDF, PPT Source Code
      Liang Jin, Chen Li, and Sharad Mehrotra.
      8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
      Received 10-year Best Paper Award for DASFAA 2013.
    • Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques
      Chen Li, Liang Jin, and Sharad Mehrotra
      World Wide Web Journal, Volume 9, Number 4, pages 557-584, December 2006.
      This journal article is an extended version of the DASFAA03 paper.

    Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF award IIS-0844574, the NSF award 1030002, the NSF-funded RESCUE project, the NIH grant 1R21LM010143-01A1, a Google Research Award, a gift fund from Microsoft, a research grant from Amazon.com to allow us to use their MapReduce cluster, and a fund from CalIt2.
    Many thanks to Minh Doan and Kensuke Ohta for their valuable testing and feedback on the code and documentation.


    For any questions regarding this project, please send email to flamingo AT ics.uci.edu

    http://jujube.ics.uci.edu/localsearch/fuzzysearch/ Fuzzy Keyword Search on Spatial Data

    Powered by Flamingo Project

    Fuzzy Keyword Search on Spatial Data

    Sattam Alsubaiee Chen Li
    Department of Computer Science
    University of California, Irvine

    Abstract

    In recent years, many websites have started providing keyword-search services on maps. In these systems, users may experience difficulties finding the entities they are looking for if they do not know their exact spelling, such as the name of a restaurant. In this paper, we present a solution to support fuzzy keyword search on spatial data. We combine a spatial index structure with inverted indexes on grams to efficiently answer fuzzy queries on maps. We show two system prototypes to demonstrate the practicality of our solution.

    Publications

    • Supporting Location-Based Approximate-Keyword Queries, Sattam Alsubaiee, Alexander Behm, and Chen Li, ACM SIGSPATIAL GIS 2010. [PDF] [PPT] [Source Code]
    • Fuzzy Keyword Search on Spatial Data, Sattam Alsubaiee, Chen Li, DASFAA 2010 (demo paper) (Excellent Demo Award) [PDF]

    Demos

    We used two real datasets to develop two prototypes for demonstration:

    • Demo on the CoPhIR dataset which is a multimedia metadata collection extracted from Flickr pages.
    • Demo using the GeoNames geographical database which contains geographical objects such as lakes and hills.

    Acknowledgments

    This study is supported by NSF award No. IIS-0742960 , as well as a Google Research Award and a gift fund from Microsoft.

    For questions about this work, please contact Sattam Alsubaiee.


    http://fr.ics.uci.edu/haiti/ Family Reunification for Haiti Earthquake

    FAMILY REUNIFICATION
    FOR HAITI EARTHQUAKE

     

    Search Interfaces

    • Search interfaces for the Person Finder: Haiti Earthquake website:
      • Search Page
        Interactive and error-tolerant search page;
      • Search Widget
        Interactive and error-tolerant search widget that can be embedded into any website.

    • Interactive and error-tolerant search widget for the Red Cross FamilyLinks website.

    • Interactive and error-tolerant search engine for over 40,000 missing person records crawled from Red Cross, CNN iReport and other websites.

    Data (January 2010)

    • CNN iReport
      • HTML pages: www.ireport.com.tgz (7,861 files)
      • SQL dump:
        • 2010-01-19: IREPORT_PERSON_88131.sql.gz (604 new records)
        • 2010-01-18: IREPORT_PERSON_86188.sql.gz (952 new records)
        • 2010-01-17: IREPORT_PERSON_83755.sql.gz (659 new records)
        • 2010-01-16: IREPORT_PERSON_64235.sql.gz (5,645 records)
    • Family Links International Committee of the Red Cross
      • New Schemata
        • SQL dump (1 table, denormalized):
          • 2010-02-28: ICRC_FAMILYLINKS_2010-02-08_13-55-29.sql.gz (28,034 records)
        • SQL dump (2 tables, normalized):
          • 2010-02-28: ICRC_PEOPLE_2010-02-08_13-55-54.sql.gz (27,401 records)
          • 2010-02-28: ICRC_SEEKER_2010-02-08_13-55-54.sql.gz (28,034 records)
      • Layout after Jan 20
        • SQL dump:
          • 2010-01-21: ICRC_FAMILYLINKS_20100121.sql.gz (23,813 records)
      • Layout before Jan 20
        • HTML pages: www.familylinks.icrc.org.tgz (23,840 files)
        • SQL dump:
          • 2010-01-19: ICRC_PERSON_88131.sql.gz (489 new records)
          • 2010-01-18: ICRC_PERSON_86188.sql.gz (1,034 new records)
          • 2010-01-17: ICRC_PERSON_83755.sql.gz (1,127 new records)
          • 2010-01-16: ICRC_PERSON_64235.sql.gz (21,188 records)
    • CBC News
      • SQL dump:
        • 2010-01-18: CBC_PERSON_86188.sql.gz (285 records)
    • Koneksyon.com
      • HTML pages: www.koneksyon.com.tgz (4,571 pages)
      • SQL dump:
        • 2010-01-22: KONEKSYON_PERSON_64235.sql.gz (4,571 records)
    • Knexu Forum Posts
      • HTML pages: www.knexu.org.tgz (817 files)
      • SQL dump:
        • 2010-01-19: KNEXU_PERSON_88131.sql.gz (816 records)

    People

    • Chen Li (Faculty)

    • Sattam Alsubaiee (Student)
    • Alexander Behm (Student)
    • Inci Cetindil (icetindil ..AT.. gmail.com) (Student)
    • Shengyue Ji (Student)
    • Dustin Lakin (dustin.lakin ..AT.. gmail.com) (Student)
    • Rares Vernica (Student)

    Websites powered by our search interfaces

    • Haiti Earthquake and Recovery - Miami Herald

    In the news

    • Helping Haitians find family, February 8, 2010 - University of California, Irvine
    • Search Engine Developed to Help Haiti Victims, January 27, 2010 - Donald Bren School of Information and Computer Sciences, University of California, Irvine
    • UCI aids hunt for missing Haitians, January 16, 2010 - The Orange County Register

    « Back to homepage

    http://fr.ics.uci.edu/ Family Reunification

    FAMILY REUNIFICATION

     

    Objective

    The goal of this project is to develop techniques to help people find their loved ones during or after a disaster.

    Many people can be affected by disasters such as Hurricane Katrina. There are many Web sites available during and after a disaster, with the same goal of helping people find their loved ones, or report they are OK. For a parent who wants to find online information about his missing kid, he needs to go to many such sites to search. This process is very time consuming, especially when information from the sources keeps changing.

    Another problem is that, a lot of data at the source has inconsistencies and even errors. For instance, a kid called Michael could be stored as Micheal, since the person who submitted the data mistyped the name. A person called William Smith could also be stored as Bill Smyth. The last name of a kid could be missing. For these reasons, the parent might not be able to find the information, even if the source does include valuable information about his missing kid.

    We study these research issues in this project. As a prototype, we crawl and extract information from these Web sources, and put the data into a central repository. We also provide interfaces for people to submit information about missing people, or report they are OK. We allow users to search for information in the repository, instead of going to those individual sources.

    Due to the low quality of the data, it is very important for the system to allow users to find persons whose description matches a search query approximately. We are developing research techniques to answer such search queries efficiently. In addition, we also need to study issues such as crawling, information extraction, and answering subscribed queries.

    With the NSF award 1030002, we will study how to support powerful keyword search with efficient indexing structures and algorithms in a clouding-computing infrastructure. A main application is supporting family reunification in disasters such as the Haiti Earthquake. Check our portals for the Haiti Earthquake and Chile Earthquake. The main challenge is how to use limited programming primitives in the cloud to implement index structures and search algorithms.

    Family Reunification - PPT

    Chile Earthquake (February, 2010)

    Check out the our detailed page available here.

    Haiti Earthquake (January, 2010)

    Check out the our detailed page available here.

    Hurricane Katrina (2005) (offline)

    Data Crawled (2005-2007)

    We crawled around 35,000 entries from the following websites:

    • MissingKids.com
    • PublicPeopleLocator.com
    • FamilyMessages.org
    • NOLA.com

    People

    • Chen Li (Faculty)
    • Rares Vernica (Student)

    Alumni

    • Sharad Mehrotra (Faculty)

    • Jay Lickfett (Software Engineer)
    • Jiaheng Lu (Postdoctoral Researcher)

    • Yiming Lu (Student)
    • Ajay Shah (Student)
    • Chris Trezzo (Student)

    Wiki (username/password required)

    For any questions regarding this project please contact Rares Vernica

    This project is supported by:

    http://asterix.ics.uci.edu/fuzzyjoin/ Efficient Parallel Set-Similarity Joins Using MapReduce

    Efficient Parallel Set-Similarity Joins Using MapReduce

    Rares Vernica Michael J. Carey Chen Li
    Department of Computer Science
    University of California, Irvine

    Abstract

    In this paper we study how to efficiently perform set-similarity joins in parallel using the popular MapReduce framework. We propose a 3-stage approach for end-to-end set-similarity joins. We take as input a set of records and output a set of joined records based on a set-similarity condition. We efficiently partition the data across nodes in order to balance the workload and minimize the need for replication. We study both self-join and R-S join cases, and show how to carefully control the amount of data kept in main memory on each node. We also propose solutions for the case where, even if we use the most fine-grained partitioning, the data still does not fit in the main memory of a node. We report results from extensive experiments on real datasets, synthetically increased in size, to evaluate the speedup and scaleup properties of the proposed algorithms using Hadoop.

    • Efficient Parallel Set-Similarity Joins Using MapReduce.
      Paper Long Version
      Slides Long Version Hadoop Summit 2010
      Poster
      Rares Vernica, Michael J. Carey, Chen Li
      SIGMOD 2010

    Source Code

    All the algorithms are implemented in Java. The source code is licensed under the Apache License, Version 2.0.
    • fuzzyjoin-0.0.2.tgz (April 12th, 2011)
    • fuzzyjoin-0.0.2-patch-2011-11-09.tgz
    • README (also available in the package)
    • FAQ (last updated April 12th, 2011)
    • CHANGELOG (also available in the package)
    Previous releases:
    • fuzzyjoin-mapreduce-RWE-2010-04-23.tgz (April 23rd, 2010)
    • fuzzyjoin-mapreduce-1.0.tgz (March 24th, 2010)

    ACM SIGMOD 2010 Repeatability & Workability Evaluation

    Our fuzzyjoin-mapreduce-RWE-2010-04-23.tgz (April 23rd, 2010) release of the code was verified by the ACM SIGMOD 2010 Repeatability & Workability Evaluation committee against the claims in SIGMOD 2010 "Efficient Parallel Set-Similarity Joins Using MapReduce" paper. The Repeatability was Fully confirmed and the Workability was Partly confirmed. The reviews of the code are available here:
    • Review 1
    • Review 2

    Datasets

    Bellow are the two datasets used in the study:

    • DBLP dblp.raw.txt.gz (83MB, 1.2M records)
    • CITESEERX csx.raw.txt.gz (591MB, 1.3M records)

    Acknowledgments

    This study is supported by NSF IIS awards 0844574 and 0910989, as well as a grant from the UC Discovery program and a donation from eBay.

    This study is part of the ASTERIX and Flamingo projects.
    For any questions about this study, please contact Rares Vernica.
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    http://isg.ics.uci.edu/ ISG

    ISG

    Information Systems Group

    Bren School of ICS

    UC Irvine

    • About
    • News
    • People
    • Research
    • Publications
    • Events
    • Courses
    • Partnerships
    • Visitors

    About ISG

    Photo by Heri Ramampiaro. © 2010 ISG

    The Information Systems Group (ISG) at UC Irvine is part of the Department of Computer Science within the Donald Bren School of Information and Computer Sciences.

    ISG is a broad and vital group consisting of five UCI CS faculty members (Profs. Carey, Jain, Li, Mehrotra, and Venkatasubramanian), adjunct faculty member (Prof. Kalashnikov), students, visitors, project staff, and other affiliates. The mission of ISG is broad-ranging, as the combination of the next-generation Web, diverse forms of multimodal data, and new devices have created a world rich with heterogeneous forms of information that need to be located, accessed, queried, managed, archived, and integrated much like the more traditional (yet ever important) enterprise data of yesteryear. ISG's mission is to address this rapidly evolving information infrastructure by conducting research on all aspects of modern data and information systems.

    ISG faculty interests range from theory to systems, from principles to practice, and from design and experimentation to deployment and applications. Application focus areas include emergency response, situational awareness, and cyber-physical systems. Research topics with ISG cover architectures, algorithms, and performance evaluation of a variety of next-generation information systems and techologies. Current topics include database systems, data analysis and data cleansing, data warehousing systems, information integration tools, multimedia information systems and search, semantic Web, adaptive middleware, experiential computing, peer-to-peer systems, mobile and pervasive computing, service-oriented architectures, search techniques, scalable data-intensive computing, and data sharing and dissemination. Current projects within ISG are exploring challenges in the realization of such systems stemming from information diversity and heterogeneity (e.g., multidimensional data, XML, text, multimedia data, and sensor streams) and due to emerging application needs (including privacy and security, mobility, quality of data, quality of service, and reliability and robustness in extreme situations).

    M.S./Ph.D. Studies in Information Systems at UC Irvine

    The faculty of the Information Systems Group (ISG) at UC Irvine is looking for a handful of excellent prospective students who are seeking an exciting, active place to study and do research on databases and information systems starting each Fall. Several large projects are underway and several new ones are just beginning and will provide excellent opportunities for incoming students to "get in on the ground floor" of interesting new research initiatives.

    ISG is a part of the CS Department within the Bren School of Information and Computer Sciences at UC Irvine, an environment that offers a number of unique advantages and opportunities. Being one of just a handful of such schools across the country, ICS offers a broad, stimulating intellectual environment for graduate studies. ICS is comprised of three departments - Computer Science, Informatics, and Statistics - covering all traditional computer science areas as well as related areas such as software engineering, human-computer interaction and usability, collaboration technologies, and statistical machine learning and data mining and analysis.

    UC Irvine itself, in the words of our current chancellor, "combines the strengths of a major research university with the bounty of an incomparable Southern California location." Thus, in addition to a stimulating research and educational environment, graduate study in ISG at UC Irvine offers attractive off-hours opportunities. Our location in Orange County offers easy access to a wide variety of outdoor and indoor entertainment and sporting activities.

    More information about graduate study in CS at UC Irvine can be found on the CS Department's graduate studies page. Applications for admission for a given Fall are due by January 15th of that calendar year. More specifics about ISG, its faculty, and its projects can be found on our new ISG web site. Check us out!

    © 2009 ISG | Website maintained by Yingyi Bu | Created by Yun Huang | Original design Andreas Viklund

    Last Updated on January 07, 2011

    http://www.ics.uci.edu/~chenli/webobjects/ Datasets of Dynamic Objects on the Web

    Datasets of Dynamic Objects on the Web

    Department of Computer Science, UC Irvine

    Contributors�����������������������������������������������������������������������������������������������������

    • Chen Li (Faculty)
    • Gang Liang (Faculty)
    • Houtan Shirani-Mehr (PhD student)
    • Michal Shmueli-Scheuer (PhD student)

    Overview

    Many applications need to retrieve information about objects from remote Web sources that are autonomous and non-collaborative.� An example is a service provider (e.g., Monster.com) that is specific to a certain application domain such as jobs. For such applications, it is critical to understand how the objects at the remote sources change over time. Such information can help the application decide a good crawling schedule in order to maintain a high quality of its data while it does not overuse network resources.

     

    In order to gain an insight on how data objects on the Web change over time, we have collected data objects from six Web sources in four domains, including books, cars, forums, and jobs. Our crawlers collected data objects from these sites during a period of one and a half years on a daily basis. The following table gives an overview of the collected data (with the Web sites anonymized):

     

    Table 1: Overview of the collected data objects

    Domain

    Web Source (# of categories)

    Crawling Period

    # of Objects

    Cars

    Car Web source 1 (10 models)

    10 months (2006/1 - 2006/11)

    42,543

    Cars

    Car Web source 2 (10 models)

    13 months (2005/10 - 2006/11)

    46,311

    Jobs

    Job Web source 1 (7 categories)

    4 months (2006/6 - 2006/10)

    31,157

    Jobs

    Jobs Web source 2 (2 categories)

    2 months (2006/12 - 2007/2)

    2,500

    Books

    Book Web source (3 categories)

    4 months (2005/8 - 2005/12)

    3,315

    Forums

    Forums Web source (4 categories)

    2 months (2006/12-2007/02 )

    25,392

    We publish the collected data for researchers who want to study dynamics of Web objects.


    Publications������������������������������������������������������������������������������������������������������

    1. Quality-Aware Retrieval of Data Objects from Autonomous Sources for Web-Based Repositories, Houtan Shirani-Mehr, Chen Li, Gang Liang, Michal Shmueli-Scheuer, ICDE 2008 (poster). [PDF]

     

    Index

    �         Web Sources

    o        Car Web source 1

    o        Car Web source 2

    o        Job Web source 1

    o        Job Web source 2

    o        Book Web source

    o        Forums Web source

    �         Reference

    �         Acknowledgements


    Car Web source 1 (Download the dataset)

    This Web source is one of the Web sources in the car domain we crawled. We crawled the data for the 10 different car models shown in Table 2.

    Table 2: Different Model and make of the crawled cars

    Make

    Model

    BMW

    3

    BMW

    5

    Ford

    Explorer

    Ford

    Focus

    Ford

    F150

    Honda

    Accord

    Honda

    Civic

    Toyota

    Camry

    Toyota

    Corolla

    Dodge

    Durango

     The schema of the collected data is the following:

    Table 3:  Schema of collected data

    cid

    mileage

    year

    crDate

    price

    make

    model

    Internal Web source database ID of the car entry

    Mileage of the car

    The year in which the car is built

    The crawling date

    The price of the car

    Make of the car

    Model of the car

     The crawled data covers 42,543 different cars from the following intervals (dates are in YYYY-MM-DD format):

    • 2006-1-9 to 2006-2-19
    • 2006-4-14 to 2006-6-22
    • 2006-7-2 to 2006-11-14

     


    Car Web source 2 (Download the dataset)

    Ten different car models at this Web source were crawled (the models are the same as the models in Table 2).  The schema of the collected data is the following:

    Table 4:  Schema of collected data

    cid

    year

    price

    make

    model

    crDate

    Internal Web source database ID of the car entry

    The year in which the car is built

    The price of the car

    Make of the car

    Model of the car

    The crawling date

    The crawled data covers 46,311 different cars from the following intervals (dates are in YYYY-MM-DD format):

    • 2005-10-22 to 2006-1-5  (10 models)
    • 2006-1-9 to 2006-2-17  (10 models)
    • 2006-2-19 to 2006-2-22  (10 models)
    • 2006-4-14 to 2006-6-23  (10 models)
    • 2006-7-2 to 2006-9-19  (8 models consisting of all model in Table 6 except BMW 3 and Dodge Durango)
    • 2006-9-20 to 2006-11-6  (10 models)

     


    Jobs Web source 1 (Download the dataset)

    We crawled seven different categories of jobs. We anonymized the category of object to make data confidential. The schema of the collected objects is the following:

    Table 5:  Schema of collected data

    id

    cat

    crDate

    postDate

    Internal Web source database ID of the job entry

    Job category

    The crawling date

    Posting date of the job (as shown on the Web source)

    The collected data includes 31,157 jobs and spans the following intervals (dates are in YYYY-MM-DD format):

    • 2006-6-16 to 2006-7-24
    • 2006-8-4 to 2006-10-7

    Job Web source 2 (Download the dataset)

    The jobs with the category of management and technology were crawled from this Web source. The schema of the data is the following:

    Table 6:  Schema of collected data

    postDate

    id

    cat

    crDate

    Posting date of the job which is shown on the Web source

    Internal Web source database ID of the job entry (job ID) 

    Job category

    The crawling date

    The collected data contains 2,500 jobs and spans the following intervals (dates are in YYYY-MM-DD format):

    • 2006-12-19 to 2007-2-25

    Book Web source (Download the dataset)

    Books with three different subjects are crawled from this Web source: books on Java, Linux and DBMS (database management systems).  The schema of the data is the following:

    Table 7:  Schema of collected data

    id

    price

    topic

    crDate

    Internal Web source database ID of the book entry

    Price of the book

    The topic of the book (subject of the book)

    The crawling date

    The collected data contains 3,315 books and spans the following intervals (dates are in YYYY-MM-DD format):

    • 2005-8-23 to 2005-9-28
    • 2005-10-1 to 2005-12-6

    Forums Web source (Download the dataset)

    Four different kinds of posts were crawled which are anonymized to make it data confidential. The schema of the collected objects is the following:

    Table 8:  Schema of collected data

    id

    cat

    crDate

    replies

    Internal Web source database ID of the post entry

    Post category

    The crawling date

    Number of replies to the post

    The collected data objects for this Web source contains 25,392 posts and spans the following intervals (dates are in YYYY-MM-DD format):

    • 2006-12-24 to 2007-2-12

     

    Reference:

    • Quality Aware Retrieval of Data Objects from Autonomous Sources for Web-Based Repositories, Houtan Shirnai-Mehr, Chen Li, Gang Liang, Michal Shmueli-Scheuer, UCI ICS Technical Report, March 2007.

     


    This project is partially supported by the NSF CAREER Award, No. IIS-0238586 and a Smith Faculty Seed Fund of ICS at UCI.

    If you have any questions about these datasets, please contact Houtan Shirani-Mehr (hshirani AT uci.edu) or Chen Li (chenli AT ics.uci.edu).

     

     

     

    http://flamingo.ics.uci.edu/releases/4.0/ FLAMINGO Package (Approximate String Matching) Release 4.0

    FLAMINGO Package
    (Approximate String Matching)

    Release 4.0 (October 23, 2010)

    Department of Computer Science, UC Irvine

    Contributors

    • Sattam Alsubaiee (Ph.D. Student)
    • Alexander Behm (Ph.D. Student)
    • Shengyue Ji (Ph.D. Student)
    • Liang Jin, graduated from UC Irvine in 2005.
    • Chen Li (Faculty)
    • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
    • Yiming Lu, graduated from UC Irvine in 2008.
    • Rares Vernica (Ph.D. Student)
    « Back to Flamingo Main Page

    Getting Started

    Please refer to the Flamingo Getting Started Guide.

    Introduction

    This release (in C++) includes the source code of several algorithms for approximate string matching developed at UC Irvine. It includes algorithms for approximate selection queries, location-based approximate keyword search, selectivity estimation for approximate selection queries, approximate queries on mixed types, and others. Although an implementation for approximate joins is included, the focus of this release is on approximate selection queries.

    Here is a brief explanation of the terms used above:

    • Approximate String Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
      This functionality is implemented by the modules Common, FilterTree, Listmerger, StringMap, and PartEnum. We recommend getting started with the FilterTree module for this purpose.
    • Selectivity Estimation for Approximate String Search: Given a collection of strings and a single string, how can we estimate the number of strings that are "similar to" the given string? This functionality is implemented in the SEPIA module.
    • Approximate String Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?
    • Location-Based Approximate Keyword Search: Given a collection of spatial objects with descriptive keywords, find those objects within a given spatial region that have a given set of keywords. In addition, the keywords don't need to match exactly, but approximately.
      This functionality is implemented by the module: LBAK-Tree

    There are various string similarity functions, such as Levenshtein Distance (aka the Edit Distance), Jaccard Similarity, Cosine Similarity, and Dice Similarity. The following is a description of the modules corresponding to the source directory structure:

    • Common: This module contains classes for supporting the following similarity functions / distance measures: Levenshtein Distance (aka Edit Distance), Jaccard Similarity, Cosine Similarity, Dice Similarity. It also provides functionality for decomposing strings into grams.
    • FilterTree: This module provides functionality for approximate string search using an inverted-list index. Furthermore, query performance can be improved by adding filters, i.e. partitioning the string collection into disjoint subsets according to some property (e.g. the length of the strings). The use of filters is facilitated by a hierarchical structure (the FilterTree), in which each level in the tree corresponds to one filter. We have implemented the length and charsum filter. This package contains three flavors of indexes: in-memory indexes compressed & uncompressed and a disk-based index.
    • ListMerger: Answering approximate string queries based on an inverted-list index requires finding elements that occur at least T times on the inverted lists belonging to the grams in the query string (T depends on the similarity metric and the similarity threshold). This problem is commonly referred to as the T-occurrence problem. This module implements several algorithms for solving the T-occurrence problem as described in "Efficient Merging and Filtering Algorithms for Approximate String Searches", Chen Li, Jiaheng Lu and Yiming Lu, ICDE 2008. In addition, we have implemented efficient algorithms for disk-based indexes.
    • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
    • SEPIA: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published in the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
    • StringMap: This algorithm maps strings from the edit-distance metric space to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
    • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate string matching queries, excluding approximate joins.
    • TopK: This package contains algorithms for efficient Top-K approximate string search.
    • LBAK-Tree: This module implements location-based approximate keyword search as described in "Supporting Location-Based Approximate Keyword Search", by Sattam Alsubaiee, Alexander Behm and Chen Li. It enhances an R*-Tree with inverted indexes for approximate selection queries. It implements various algorithms for choosing R*-Tree nodes to place inverted indexes in. The FilterTree module is used to provide the inverted indexes for approximate selection queries.
    In addition, we have provided some commonly used functions in the util directory.

    Changes in Version 4.0 (compared to Version 3.0)

    • Added LBAK-Tree for location-based approximate keyword search from:
      "Supporting Location-Based Approximate Keyword Search",
      by Sattam Alsubaiee, Alexander Behm and Chen Li, ACM SIGSPATIAL GIS 2010
    • Major performance improvements for approximate selection queries
    • Added insert/delete functionality for in-memory inverted indexes
    • Added support for 64-bit operating systems
    • Switched to CMake build system

    Bibtex

    @misc{misc/flamingo4.0-2010,
          author = {Alexander Behm and Rares Vernica and Sattam Alsubaiee and Shengyue Ji and Jiaheng Lu and Liang Jin and Yiming Lu and Chen Li},
          year = {2010},
          title = {{UCI} {Flamingo} {Package} 4.0},
          url = {http://flamingo.ics.uci.edu/releases/4.0/},
          institution = {University of California, Irvine, School of Information and Computer Sciences}
    } 
    
    [ICO]Name

    [DIR]Parent Directory
    [DIR]docs/
    [DIR]src/
    [DIR]flamingo-4.0.tgz2.8M
    [DIR]README.txt

    Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft, a fund from CalIt2, the NSF CluE Project and the ASTERIX Project funded by the NSF.
    Many thanks to Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

    License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, FilterTree, and LBAK-Tree is permitted under the terms of the BSD license. Permission to use, copy, modify, and distribute the implementations of the compression techniques DiscardLists and CombineLists is permitted under the terms of the following Academic BSD License. The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.

    Academic BSD License:
    The (compression techniques) DiscardLists and CombineLists are the proprietary property of The Regents of the University of California (“The Regents.”)
    Copyright © 2009 The Regents of the University of California, Irvine. All Rights Reserved.
    Redistribution and use in source and binary forms, with or without modification, are permitted by nonprofit, research institutions for research use only, provided that the following conditions are met:

    • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
    • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
    • Neither the name of The Regents nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

    The end-user understands that the program was developed for research purposes and is advised not to rely exclusively on the program for any reason.

    THE SOFTWARE PROVIDED IS ON AN "AS IS" BASIS, AND THE REGENTS AND CONTRIBUTORS HAVE NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. THE REGENTS AND CONTRIBUTORS SPECIFICALLY DISCLAIM ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES, INCLUDING BUT NOT LIMITED TO PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, LOSE OF USE, DATA OR PROFITS, OR BUSINESS INTERRUPTION, HOWEVER CAUSED AND UNDER ANY THEORY OF LIABILITY WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

    If you do not agree to these terms, do not download or use the software. This license may be modified only in a writing signed by authorized signatory of both parties.


    For any questions regarding this release, please send email to flamingo AT ics.uci.edu

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    http://www.ics.uci.edu/~kibler/GenomicPapers.htm untitled

    Bioinformatics Papers

    • April 8:
      • Steve Hampson: Extracting Regulatory Sites from the Upstream Region of Yeast Genes by Computational Analysis of Oligonucleotide Frequencies. Jacques van Helden, B. Andre and J. Collado-Vides. JMB (1998) 281,827-842
    • April 15:
      • Dennis Kibler: Molecular Classification fo Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. T.R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J.P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, E. S. Lander. Science, vol 286 October 1999.
    • April 22:
      • Bob Chan: Multiclass cancer diagnosis using tumor gene expression signatures. S. Ramaswamy, P. Tamayo, R. Rifkin, S. Mukherjee, C-H. Yeang, M. Angelo, C. Ladd, M. Reich, E. Latulippe, J. P. Mesirov, T. Poggio, W. Gerald, M. Loda, E. S. Lander, and T. R. Golub. PNAS December 18, no. 26, 15149-15154.
      • Jennifer Miok Rhough: Beyond Synexpression Relationships: Local Clustering of Time-shifted and Inverted Gene Expression Patterns Expression Profiles Identifies New, Biologically Relevant Interactions. J. Qian, M. Dolled-Filhart, J Lin, M. Gertstein (Yale) JMB 2001 314, 1053-1066.
    • April 29:
      • Sean Lee: Multi-alphabet consensus algorithm for identification of low specificity protein-DNA interactions. Anatoly Ulyanov and Gary Stormo. Nucleic Acids Research, vol 23, no 8 (1995) 1434-1440.
      • Gianluca Pollastri: Machine Learning Structural and Functional Proteomics. P.Baldi, G.Pollastri. IEEE Intelligent Systems (Intelligent Systems in Biology II), March/April 2002. Download at: ftp://promoter.ics.uci.edu/Papers/2002_IEEE.pdf
    • May 13:
      • Gianluca Pollastri: Prediction of Contact Maps by Recurrent Neural Network Architectures and Hidden Context Propagation from All Four Cardinal Corners. G.Pollastri, P.Baldi. Bioinformatics (ISMB2002 special issue), in press. Download at ftp://promoter.ics.uci.edu/Papers/2002_ISMB.pdf
      • Son Dang: Hidden Markov models that use predicted secondary structures for fold recognition. Hargbo J, Elofsson. PROTEINS-STRUCTURE FUNCTION AND GENETICS. v36 (1): 68-76 JUL 1 1999.
    • May 20:
      • Li Zhang: Support Vector Machine Classifciation of Microarray data. S. Murherjee, P. Tamayou, D. Slonim, A. Verri, T. Golub, J. P. Mesirov, and T. Poggio. A.I. Memo No 1677 MIT.
      • Karalyn Ramon: Genome-wide transcript profiled in aging and calorically restricted D. melanogaster. : S. Pletcher et al. Current Biology, Vol.12, 1-20, April 30, 2002.
    • May 27: (Memorial Day) Class?
    • June 3:
      • Hilda Yu: Predicted highly expressed genes of diverse prokaryotic genomes. Karlin, S., Mrazek, J.: Jour. of Bacteriology 182: 5238-5250 (2000) (available on pub med)
      • Steve Hampson Ribosome Binding Sites: Probability Matrices and Weight Matrices. Kibler and Hampson. METMBS-2002. (available on my web page)
    • June 10 Last Presentations
    http://www.ics.uci.edu/~kibler/ICS23/syllabus.html Honors ICS 23

    ICS23: Data Structures and Algorithms

    A collaborative problem-solving approach

    • Professor: Dennis Kibler kibler@ics.uci.edu
    • Class Meetings: 1:00- 3:50 Mondays &Wednesdays (Social Science Plaza A Rm 1100)
    • Office Hours: 4-5 Mondays,Wednesdays (414D ICS)
    • Teaching Assistants: Li Zhang (lzhang1@ics.uci.edu), Javid Huseynov (javid@ics.uci.edu)
    • Tutor: Chang (Charles) Liu (liu@ics.uci.edu) Office hours: MW 8-11; T/Ths 8-12 ICS2: Rm 249
    • Discussions: 1:00-2:50 Tues/Thurs in ICS 183, 189, 192


    Course Goals

    Using the Java language students will learn the properties and implementation details of the fundamental data structures (arrays, lists, queues, stacks, dictionaries, hashtables, trees, graphs) that often are at the heart of any program. Students will also learn basic problem solving methods, such as divide and conquer, separate and conquer, dynamic programming, greedy algorithms, tree and search algorithms as well as some useful applications. As part of the coding assigments, students will be expected to analyse their code and follow good object-oriented design.


    Grading

    There will be four programming assignments, 4 quizzes and a final. The course is not graded on a curve. Roughly the homeworks count 40%, the quizzes 20%, and the final 40%. I don't use a formula to assign grades. Trends matter. You need to pass both the homeworks and the final to pass the class. The lowest quiz score will be dropped. If you can't make a quiz, then that quiz is the one that is dropped. The coding assignments will be in Java. Roughly, the exams and homeworks will count equally. The final exam will be based on the text, lecture notes, and homeworks. Each chapter ends with a summary. Be sure that you know every concept discussed in the summary. The fastest way to get questions answered is to email either the TAs or me. Answers of general interest will be posted on the bulletin board (ics.23). You may also use the bulletin board to ask classmates for appropriate help. Any form of inappropriate help will result in an F grade in the class and a letter in your file. If you are unsure whether the type of help you are seeking is appropriate, imagine that you are video taped and the tape was shown to me.

    The homeworks focus on using arrays, lists, trees, hashing, and sorting.

    Grades are posted at: http://www.ics.uci.edu/~javid/ICS23/grades.htm.


    Knowledge Prerequisites

    You should be familiar with arrays, linked lists, and (unbalanced) binary trees. By familiar I mean that you have implemented these data structures and are confident with dealing with them. Additional you should have the a rudimentary knowledge of O-notation, in particular the ability to analyze programs that involve multiple loops, but not programs that involve recursion. It is also expected that you are familiar with Java, but not with Gui interfaces. My experience is that students who know C++ can learn enought of Java in just a few hours to write programs required for this class.


    Text

    Text Data Structures and Problem Solving using Java by Mark Allen Weiss


    Suggested for those interested in GUI's: : UP to speed in Swing by Steven Gutz


    Web Course Resources

    Lecture notes for the Java Language are available on-line Java Notes . Please tell me if you find any errors or misrepresentations.

    Li Zhang has lab notes on course at www.ags.uci.edu/~lzhang

    Class lectures notes are available at \\masterhit\instructional\ics-23\Files. Click on network neighborhood to get to masterhit.

    Current class questions/answer are put on the newsgroup bulletin board ics23.

    Note: Questions asked after 4pm on Friday are not guaranteed to be answered. Sometimes I take the weekends off.

    Note: Because of time constraints in the summer, no understanding of proofs will be necessary. They are in the notes for those who are interested.

    Lecture 1 Design

    Lecture 2 Analysis

    Lecture 3 Data Structure Overview

    Lecture 4: Trees

    Lecture 5: Compression

    Lecture 6: Priority Queues

    Lecture 7: Hashing

    Lecture 8:Recursion

    Lecture 9: Guis

    Lecture 10&11:Sorting

    Lecture Tree Searching

    Lectures 12: Graphs

    Lecture 13: Greedy Algorithms


    Computer Resources

    Everyone needs to have an ICS computer account in order to access Masterhit and Visual Cafe. You can get this account in the main computer room, 364.

    Lab 364 Summer Hours:

    Monday - Sunday 10 - 6pm


    Tests

    Quizzes: Every Monday (Homeworks due on Monday also)


    (except Sept 3, when quiz and homework due date is Sept 5)


    Final: Sept 12 1:00- 3:50 same room as lecture


    Lecture Schedule

    Week 1 Design and analysis, O notation, Linked Lists, Collections
    Collections: Arrays, Lists, Trees, Queues, HashTables
    Week 2 Binary trees, Balanced Trees, Huffman Trees
    Tree Search, rotations, expected cost

    Week 3 Priority Queues & Hashing

    Heaps, array representations, collisions

    Week4 Sorting

    Insertion, QuickSort, MergeSort, Radix Sorting, BubbleSort
    Week 5 Holiday + Graphs
    Representations, basic algorithms: Kruskal's, Prim's and Dijsktra's algorithm, topological sorting:
    Week 6 Greedy Algorithms + Final
    Local Improvement, Simulated Annealing, Branch and Bound


    You should know the complexity of the algorithms/methods that we have covered in class. You should also understand how the algorithms work and be able to illustrate what they do. For example, given a specific binary tree you should be able to show the changes in the tree that would remove a specific element.


    Monday Lectures Wednesday Lectures Homework: Due Monday
    8/6: OOD+Complexity Analysis

    Chapter 5: key pages 107-112, 121-122, 128-129. Some of 437-447
    8/10: Linked List, Stack,Queue, Collections

    Chapter 6: all of it
    Cache as a List, Array, and Binary Tree.
    8/13:Binary Trees+Balanced Trees

    Quiz on Complexity
    8/15: Huffman Trees + Start PQs Huffman code for text
    8/20: Priority Queues and Hashing

    Quiz on Trees and Huffman Codes
    8/22: Recursion + Gui's Surprising Kmers
    8/27: Sorting

    Quiz on PQs & Hashing & Recursion
    8/29: Tree Search + Graphs Experimental Comparison of sorting
    9/3: Holiday 9/5: Graphs/ Quiz on Sorting  
    9/10: Greedy Algorithms 9/12: FINAL Exam  

    http://www.ics.uci.edu/~kibler/ics277a/syllabus/ untitled

      The Main Goal of this class is to enable biologists and computer scientists to work together. This requires learning something of each others concepts and methodologies.

      Grading Your grade is based on your homework (minor), your project presentation and write-up (major). If you believe your homework assignment has been misgraded (a distinct possibility), simply resubmit with an explanation and I will look at it again. Or see me during office hours. A project does not have to be successful. It need only be a reasonable effort. The project write-up should follow the standard paper format.

      NOTE: The following is a tentative schedule. The general flow is correct, but some later topics may be replaced. Time may vary with the requirements for understanding the work. Questions in class or via email are welcomed. Only next week's homework is guarantee to be correct.

      Default assignment: For each paper or chapter that is required, hand in a short typed observation, question, correction or criticism about the work. These are due on first meeting of the class for the week, usually tuesdays. A paragraph is plenty. Default assignments may be overruled or supplanted

      The best type of comment would take the form of constructive criticism: how could the author make the paper more convincing or more significant. Pretend it is a relative that has written the paper and you are helping them get their ideas published.

      Example Projects: Projects do not need to be successful, in the sense of discovering something new. WARNING: Use other peoples data and other peoples algorithm. If data is not in hand, the project will not finish. If the algorithms are not known, the time to code them is too short. A project may reconfirm what is already known.

      1. Working with a biologist. Understand the data and the question that the biologist has and apply either some machine learning algorithm or download existing software and apply it to the problem. This is a 2-person project and the final report should reflect this. The write-up will be about 8 pages including a description of the data, the problem, and the algorithm.

      2. Working another CS student. Using existing biocomputational programs. This is similar to 1, but with much less chance of success. An example would be to apply some gene-finding program to some genome. You would explain the algorithm, the results and alternative approaches.

      3. Working with another CS student. Instead of using existing bioinformatics tools, use machine learning methods. Numerous algorithms are available over the web. A suite of such algorithms is available at the Weka site, a unified collection of about 30 machine learning algorithms. Write-up would be similar.

      4. Working with another CS students or alone. Any idea you have for analyzing genomic information (genomes, gene expression data, protein data, metabolic data etc). You may implement your own algorithm, but the aim of the project, which may not be realized, should be the discovery of new Biological knowledge.


      I will be suggested many projects in the lectures. Unless noted, readings are from the text "Bioinformatics" by Mount. There are many sites with lectures notes for Computational Biology, but I think the notes from Martin Tompa's class are particular useful. Here's the url: http://www.cs.washington.edu/homes/tompa/. Tompa is a computer scientist and Mount is a biologist. The differences in the way they think should be apparent. Another good source for lectures notes is from Princeton at http://www.cs.princeton.edu/courses/archive/fall01/cs551/. For notes on mathematical aspects of this course , such as probability, entropy and hidden markov models see: http://www-2.cs.cmu.edu/~awm/tutorials/.


      Algorithms can be and should be understood at multiple levels. At the minimum you should clearly understand the inputs, outputs, and assumptions, i.e. you should know what the algorithm computes. A different level of understanding is how the computation is carried out. That level is necessary if you want to code or improve the algorithm.


    • Week 1: Introduction to Molecular biology and Computation
      • Read Chapter 1 and the first 11 pages of Chapter 10.
      • Read Lecture 1 and Lecture 2 from Tompa's lecture notes.
      • Optional: Computer Scientists: use search engine on "Computation Molecular Biology at NIH", then Cold Springs Harbor, then Dolan Learning Center: look at mini-lesson 19.
      • Find and talk with your bio-computer mate
      • Optional: Look at Jacques van Helden's site
      • Homework: Hand in next Monday a paragraph about chapter 1 and the first part of chapter 10.
    • Week 2 Pairwise Alignment
      • Read Chapter 3
      • Optional: Read Tompa's lecture notes 3 and 4.
      • Dot-matrix
      • Needleman-Wunsh
      • Smith-Waterman
      • Homework due next Monday: (replaces default assignment) Hand in:
        1. Compute (by hand) the Dot-Matrix for the strings actgact and gactatca.
        2. What do you notice?
        3. Compute (by hand) the global alignment of aact and gatc. Show the matrix.
        4. What is the minimum and maximum global similarity for two strings of length n and m?
    • Week 3: Multiple Alignment
      • Claimed gentle introduction at http://www.techfak.uni-bielefeld.de/bcd/Curric/MulAli/mulali.html
      • Read Chapter 4 and Tompa's notes, lecture 6.
      • Gusfield's star-alignment
      • ClustalW available on web
      • Markov Models
      • Homework
        1. Run ClustalW on the 500 bp upstream region of the NIT family. The genes are listed in Van Helden's paper and you can retrieve the sequences from his site. There are many servers for Clustalw. One is at http://www.ch.embnet.org/software/ClustalW.html.
        2. Report the results. In particular can you identify the regulatory elements noted in Van Helden's paper.
    • Week 4 & 5 Finding Regulatory Elements
      • Van Helden (Extracting Regulatory Sites form Upstream Region of Yeast Genes by Computational Analysis of Oligonucletide Frequencies; JMB 1998, 827-842)
      • Consensus (Stormo)
      • EM Algorithm (Elkan paper)
      • Homework: Hand in next monday
        1. Get the upstream regions for the Hap family of Yeast genes (in his paper) from Van Helden's site.
        2. Run the Oligo-nucleotide analysis program on these genes using different models of what is expected, i.e. different background models.
        3. Report(hand in) your results with some explanation and comparison with Van Helden's results.
    • Week 6: Micro-Array Analysis
      • Read
        • Cluster Analysis and display of genome-wide expression patterns, Eisen , Spellman, Brown, Botstein. PNAS 1998
        • Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Golub, ... Lander. Science 1999
      • Visit the Dolan Learning site, lesson 36
      • Clustering Approaches: SOM, K-Means, Hierarchical Clustering
      • Homework: This homework is cancelled. Concentrate on Project.
        1. Hand in critique of Golub et al paper
        2. Consider the data {1, 2, 5,6, 9,10,11}, i.e these 7 points.
        3. a) Do a hierarchical clustering of this data (draw the tree)
        4. b) Show the steps in k-means when k = 2 and the starting points are two initial centroids are the points 1 and 2. Again, draw the pictures.
        5. Note: drawings should be done by hand.
    • Week 7: Phylogeny analysis
      • No assignment: work on project
      • Read Chapter 6
    • Week 8: Structure Prediction
      • No assignment: work on project
      • Read Chapter 8
      • RNA structure prediction
      • DNA structure prediction
      • Protein Folding: contact maps, threading
    • Week 9: November 26 Evaluating Weka algorithms for analyzing SNPs
      Eric Wang, Scott Murphy, & Peter Hebben

      Identify candidate response elements to Androgen Receptor
      Chris Wasserman, Chin-Yi Chu, & Greg Kodama

    • Happy Thanksgiving.

    • Week 10: December 3

      Analyze Life-Cycle Gene Expression data for Chlamydia (1000 ORFS) to determine which genes are responsible for transforming from RB (reticular body/non-infectious) to EB(elementary body/infectious). Next analyze upstream regions for regulatory binding sites.
      Johnny Akers, Jianlin Cheng & Arlo Randall

      Study of protein-protein interactions in yeast
      Kevin Lin, Yimeng Dou & Haiying Deng

    • December 5

      Correlate Gene Expression data with Protein-Protein Interaction Data
      Lin Wu & Yu-Chyuan Su

    http://www.ics.uci.edu/~kibler/WorkSchedule.html

    Weekly Work Schedule

    • Discussion of possible projects and course mechanics.
      You need to select your group members at this time. To help find group members you may use the course email address is: 36485-F05@classes.uci.edu. For example, you might say what you are interested in working on. Also you can email me (kibler@ics.uci.edu) with suggested projects.
    • By October 10, noon, everyone should send me and the TA a description of their project. Also you should contact the TA to set up a meeting with him.
    • By the fifth week, groups will make a short power point presentation of the goal of their project to the entire class. Here one only talk about what they hope to achieve. Also, by this time, students will submit their power point slides to the TA to be displayed on a class website.
    • Sixth week. Hand in a design document. Identification of Major objects, methods and assignment of objects to individuals. Include a screen-shot of the GUI interface, as appropriate. Your code should be documented only at very high level. However one requirement is who was responsible for the initial version of a particular object. Anyone else who changes the code should have a short description of their contribution.
    • Week 10: Very short in-class power-point presentation of the main thing that you accomplished or learned. Also during this week there will be a poster session where you will demonstrate your program. You should also have a full Power-point presentation which you put on your website. In most cases, your code will be executable from your website.
    • Final: At the final time, or before, your are required to hand in a final report and the your code. The final report includes the background material, what you accomplished, what difficulties you faced, and an evaluation of the project. This should be about 8-10 pages long.

      Examples of Project Description


      Projects comes in two flavors: POPs (Problem-Oriented Projects) and MOPs (Method-Oriented Projects). Most will likely choose a POP.
      • Team Members: names with email addresses
        Project: POP: Othello Game Player
        Method(s): Alpha-beta search
        Evaluation: Program will play against itself using various heuristic and depth of search
        Very High Level Design (Major Pieces): Gui interface, Game Manager, Game Player
      • Team Members:...
        Project: POP: Bridge Bidder
        Method: Forward Chaining Expert System
        Evaluation: Problems from a standard text
        Very High Level Design: Gui, Rule Interpreter, Execution Explainer
      • Team members:...
        Project: MOP: Improved Decision Tree Algorithm
        Method: Replace entropy by better measure
        Evaluation: Compare with other decision tree learning algorithm and maybe other learning algorithms. Use some form of out-of-sample testing.
        Very High Level Design: Gui, Decision tree learner, Experimenter or evaluator
      In general evaluation can be done in several ways: comparison with existing programs, comparison with people or comparison with some standard, such as defined in a text.

      Example of Web Site

      1. Project: A Program to Invent Mathematics
      2. Team Members: Al Einstein (aeinstein@yahoo.com) and Fred Gauss (fgauss@yahoo.com)
      3. Abstract: Starting with no knowledge the program uses a genetic-neural net algorithm to construct mathematical conjectures. These conjectures are extensively tested and then proven.
      4. Background: TBD
      5. Code: TBD
      6. Presentation: TBD (about 12-15 slides)

      Important Dates (UPDATED)

      1. Week 2: Hand in project team, description and website.
      2. Week 3: Hand in background/history on project. This should be added to website. Present in class
      3. Weeks 4&5, 6&7, 8&9 Meet with tas to give progress reports.
      4. Week 5: Hand in Design document. High level design with objects assigned to people.
      5. Week 10: Poster presentations of project plus demonstrations of program. Hand in a copy of your power-point presentation, which should also be added to your web site.
      6. Final: hand in final report and code.
      http://www.ics.uci.edu/~kibler/freshmanSyllabus.html

      Discussion topics

      1. Introduction to class format and informal discussion of intelligence.
      2. 1950 Can A Machine Think in Computing Machinery and Intelligence by Alan Turing. Poses the problem of deciding whether an artifact can exhibit intelligence.
      3. 1950 Chess Playing Programs and the Problem of Complexity by Allen Newell, J Shaw, and Herbert Simon. Can programs play difficult games well? Discusses approaches to game playing, including minimax, static evaluation, quiescence, and goals. Does this demonstrate intelligence? What else might be needed? How hard is chess?
      4. 1956 Realization of a Geometry-theorem Proving Machine by H. Gelernter. Mathematical ability is often thought of as sign of intelligence. Can programs prove theorems? Presents theorem proving as a search through a space of goals and subgoals. Shows how to limit search by using models. Assignment question: Assume that you have a program that can prove theorems. Is this sufficient/not sufficient to indicate that you have created an intelligence program. Why or why not?
      5. 1961 GPS: A Program that simulates Human Thought by Allen Newell and Herbert Simon. Can intelligence be revealed by any one ability? Does it require a host of abilities? N&S provide a general problem solving algorithm that relies on states, goals and operators. Assignment Question: How is this program similar or different from the way you solve problems?
      6. 1977 Computers and Thought Lecture by Douglas Lenat. The ubiquity of discovery. Lenat surveys several programs that do scientific discovery, or do they? Assignment question: Are these programs doing scientific discovery? Argue for or against.
      7. 1981 Rodney Brooks: Brooks argues that intelligence can be achieved without reason or representation, two key assumptions behind most AI research. Darpa issued a million dollar challenge for an autonomous vehicle that would traverse natural environments. Search for Humanoid robots via google. Asimov and Wakamaru are two. Others? Assignment question(s): Can we build Hal? Why or why not? What can programs do? Use specifics from readings.
      8. 1982 Marvin Minsky Why People Think Computers Can't You may find Minsky's web site interesting. Assignment Questions Do you agree that his "web of meaning" yields meaning? Do you think computers can be conscious?
      9. 1999 Tom Mitchell Machine Learning and Data Mining Assignment questions: Are these programs learning? Why or why not?
      10. Tieing it all together: Assignment questions: What papers did you find most valuable? Is AI achieving intelligence?

      Other Potential Topics/Papers

      • What is Bioinformatics?
      • Can Programs do medical diagnosis?
      • Practical application of learning
      • AM: A Mathematician by Douglas Lenat. Is proving theorems or solving differential equations an indication of intelligence? A more creative task is discovering theorems. Lenat demonstrates a heuristic approach to generate mathematical conjectures and definitions by examining examples.
      • Some Studies in Machine Learning Using the Game of Checkers by Arthur Samuel. (1954) Can programs learn? Demonstrates an effective learning algorithm and discusses the problem of representation. Does this convince you that programs can be intelligence? Why or Why not? What can programs do?
      • The Principle Acts of Conceptual Dependency by Roger Schank. (1969) Provides a core conceptual language that he hoped would allow for representing the meaning of sentences. Representation is a key problem in AI.
      • Mapping Ontologies into Cyc by Douglas Lenat. (2002) An attempt to store everything a child knows by the age of five or common sense knowledge. Cyc stores world knowledge in multiple forms, including first and second order logic. Inference is handled by special algorithms for efficiency. Currently has several million facts and rules.
      • Prolog by Kowalski. Prolog is a programming language based on logic. Computation is viewed as logical deduction (formally resolution). In this context a program is simply a collection of facts and rules. You query the program and it used the facts and rules to generate a proof. <

      Notes

      • Complexity: $1,000,000 Clay (mathematics prize) for settling tennis pickup problem. Suppose you want pick up N tennis balls and return your spot, with the least amount of work. What is an upper bound on the number of operations you need to figure out the best path?
      • Lenat on why he went into AI. (1971)
        One was that it was positively reinforcing --- you would be building something like a mental amplifier that would make you smarter, hence would enable you to do even more and better things.
        The second interesting property was that it was clear researchers in the field didn't know what the hell they were doing.
      • Charlie Brown on Natural Language Processing:
        Lucy and Charlie are on the baseball field and it is raining. Lucy is holding an umbrella.
        Charlie says: "You can't catch a baseball holding an umbrella".
        Lucy says: "How did he know that?"
      http://www.ics.uci.edu/~kibler/H23Homeworks.htm H23 Homeworks

      H23 Homeworks: Typicallyl these homeworks have a theory portion and a design or coding portion.

      Warning: Only the homework for the next assigment is guaranteed to be correct and complete. Look at the assignment early and ask questions if you do not understand what you are supposed to do.

      NOTE: Due Time: Coding assignments are due (i.e. need to be deposited) by 10pm on tuesday of the week that the homework is due. Work that should be handed in is due at the beginning of class on Wednesday of the week that homework is due. Late homeworks will be marked down by 20% for each day that it is late.

      Regrades: For a regrade you must resubmit your homework within 1 week of receiving your score. Also you must explain what part of your homework needs to be regraded. The entire assigment will be regraded so it is possible to lower your score on a regrade.


      Special Homework 0: Due wednesday by 5pm.

      Email your answers to me (kibler@ics.uci.edu) and cc the ta Li Zhang (lzhang1@uci.edu). Restrict your answer to a maximum of a paragraph.

      1. Tell me about any program that you have written purely for fun, i.e. not for any class assignment. If none, say so.

      2. Tell me about a program that you would like to write.



      Homework 1: Goal: Review + practice object-oriented design + simple Gui interface

      Read chapter 1. chapter 1 has some useful program examples as well as a review of some basic mathematical techniques.

      1. . This is a finite induction problem.

      Prove: sum(i = 1 to i = N ) { 2*i -1} = N^2.

      2. This part requires no coding, only design. Suppose that you are coding a very limited banking systems. Customers can only: open a savings account, close a savings account, add money to an account, and withdrawn money from an account. You may assume that this is a new bank which begins with no customers. In the style of the lectures, define appropriate objects and methods. You should only turn in the your final design, not the steps you used to create the design. Also include the driver program, ie. the program which puts the objects together. This should look like a code, but would not execute since the needed objects have not been defined. Some people like to start by writing the driver program, guessing what objects they might need.

      3. This part of the assigment involves writing a simple GUI interface. From the user get a file name. The file you will use is WM.txt which is in the class folder XX. Your program will display: a) the total number of letters (characters from a through z), b) for each letter the number of times it occurs, and c) a graphical plot of the frequency of each letter. More specifically, you plot (i-th letter, frequency of i-th letter). Upper case letters should be "normalized" to lower case letters. This can be conveniently done by transforming every read String into a String of lower case letter via a String method.



      Homework 2: Goal: Reinforce familiarity with O notation + Proofs

      Read chapter 5

      1. Do problem 5.14, only part a. ( 6 code fragments to analyze)

      2. Suppose T(N) = O(f(N)) and S(N) = O(g(N)).

      a) What is T(N)*S(N)?

      b) Prove it.

      c) What is T(N)+S(N)?

      d) Prove it.

      3. Definition: A set S is convex if whenever points p and q belong to the set, then for any x between 0 and 1, the point x*p + (1-x)*q belongs to S.

      Prove that if S1 and S2 are convex, then S1 intersect S2 is convex. The proof is straightforward, as long as you don't lose your head. Do not attempt a geometric proof, although that might provide insight/confidence in the result. Algebraic proofs are usually easier, once you know what might be true.

      Homework 3: Goal: Use Gui Interface and standard Collection Classes

      This assignment is somewhat similar to assignment 1, but adds the uses of Collection classes. In this assignment you will build a Gui interface that performs a simple statistical analysis of a text document. Use the swing classes as the awt library is deprecated. Using BorderLayout, in the north panel ask the user for a file to be processed. In the west panel display, in sorted order, the frequency of all the words in the document. In the center panel display a graph of the 100 most frequent words, in sorted order. The y-axis measures frequency of occurrence as a percentage of the i-th most frequent word. So you plot the points (i-th word, frequences of i-th word). On the north panel display the total number of words and the total number of different words.

      You will read the file WM.txt again. One important question is: What constitutes a word? For the purposes of this assignment a word is a sequence of characters surrounded by white-space where the first and last characters are letters. Also you need to remove final (not internal) punctuation. Hence Jackson7 is not a word and won't be counted. Use StringTokenizer to identify candidate words. Use a Hashtable or HashMap, provided in the Collections package, to keep track of the number of occurences of every word. Then use TreeSet, also provided in the Collections package, to sort the words. You will sort the words by frequency so you need to define a Comparator.

      Start this assignment early. Do not write this is one fell swoop. Instead, start by writing a few of the simplest throw-away programs that you can imagine that develop your understanding and confidence in individual features of the Java language.


      To help you with this assignment, I'm giving you a class I wrote called FileTokenizer that you might find useful. Conceptually it is much like StringTokenizer on a file. It basically provides an iterator, but I did not make it implement Iterator. Why not? If you find errors or improvements in this code, please let me know.


      import java.util.*;

      class FileTokenizer
      {
      BufferedReader br;
      String line;
      StringTokenizer stok;

      FileTokenizer(String s)

      {
      try
      {
      br = new BufferedReader(new FileReader(s));
      line = br.readLine();
      stok = new StringTokenizer(line);
      }
      catch(IOException ioe) {};

      }

      String nextWord()
      {
      return stok.nextToken();
      }
      boolean hasWord()
      {
      try{
      if (stok.hasMoreElements()) return true;
      line = br.readLine();

      if (line == null) return false;

      stok = new StringTokenizer(line); // misses blank lines
      while ( !stok.hasMoreElements())
      {
      line = br.readLine();
      if (line == null) return false;
      stok = new StringTokenizer(line);
      }
      return true;

      }
      catch(IOException ioe){};
      return false;
      }

      }



      Homework 4
      : Goal: Review and compare use of lists, stacks and arrays.

      Due Date extended to May 8, 10pm

      Read chapter 3+ 4.3 Chapter 3 is on lists, stacks, and queues. 4.3 covers binary trees. Also read handout on Collections.

      In this assignment you will implement a Cache (the inteface will be defined) in 5 different ways: namely as an array, a linked list, an ordered tree, as a linked list from the collections package (in java.util), and as a tree from the collections package. For each implementation give an O-notation analysis. Additional compare the implementation by generating 1,000,000 random numbers and putting them into a cache of size 1000. If these numbers are too small (depends on your computer) you may increase them. Or run the experiment multiple times. Which implementation of cache is performs best? What can you say about memory use, i.e. how much memory does each technique require (give more than an O(n) analysis).

      A Cache is a bounded, ordered container for objects. Since it only stores objects, you will actually store Double objects. A concrete cache has a single constructor, cache(int bound) where bound is the number of elements to be stored. Only objects that are comparable are allowed to be entered into a cache. Duplicated objects (in this case doubles) are not stored.

      The interface Cache has only one required method.

      void add(Object o) .. may store the comparable object o. Object o is added if either the cache is not full or the object o is greater than some object currently stored.

      The constructor is Cache(int bound) where bound is the maximum number of elements to be stored.

      It is suggested that you also define

      Iterator iterator() .. this will allow you to view see what is stored. For this, define remove() to do nothing. This is not trivial to define an Iterator() for Trees.

      import java.util.*;
      interface Cache
      {
      void add(Comparable o); // note that wrappers such as Double, Integer and the String class all implement Comparable.
      }


      You may defined additional methods, such as isFull() or remove(Object o) or whatever if you find them useful.

      You may use your Cache class in later assignments.

      Note: one homework has been cancelled.


      Homework 5: Goals: Processing files, using hashtables and a cache. Different data structures for different goals.

      Read chapter 5 or chapter on hashing

      The program finds interesting kmers. A kmer is contiguous string of exactly k letters. Your program will read in two files and the size of k. Each file consists of characters from the alphabet {a,c,g,t}. The first file we will call the family.The second file we will call the background. To create a filereader from the Masterhit directory, you should use new File("\\\\Masterhit\\Instructional\\ics-h23\\files\\nit.txt"); You may think of the background as dna strings from the normal population and the family file as dna from people with some genetic disease.Your program goal is to find unusual (really statistically significant) kmers that occur suprisingly often in the family with respect to the background. For the purposes of this homework we define the unusualness of a kmer in the family as:

      (number of times kmer occurs in family) - ( number of times kmer occurs in background)* (size of family)/(size of background).

      This is the difference between he actual number of occurrences and the expected number of occurrences.

      There are more appropriate statistical founded definitions, but involve more work and arrive at nearly the same results.

      This value defines how to kmers should be compared.

      To count the number of times every kmer in a family occurs use a hashtable which is part of the collections package (in java.util.*). For computing the background counts, you should only count the kmers that occur in the family. Again a hashtable is suitable. If you define it properly, you can use the same hashtable as before. For example an entry in the hashtable could consist of a pair of integers (family count, background count) and a real-values score of the unusualness.

      There are three major steps (b,c,d) to do this task, plus a few minor ones (a,e).

      a) You need to read into memory two files and store them as strings. (This isn't entirely necessary, but otherwise you will have to worry about substrings wrapping around the end of one line and the beginning of the next line). Both files are in Masterhit\Instructional\icsh23\files. The family file is in "nit.txt". This file is in standard "Fasta" format, the form that molecular biologists use to store information about genes or their surrounding regions.To process this file you need "skip" the comment lines. The net effect is that you read this file and form a single long string, which should have 3500 characters. The second file is the complete chromosome I of yeast (which has 16 chromosomes) and is called "chri_230203.txt". You can guess how many characters it has. Again you need to read the file into a single very long string. You can define the same reader class to process either file. (I will provide that little bit of code).


      b) computing the number of times each kmer occurs in the family ( use a Hashtable or a HashMap). You need to define a class I call entry. Its what goes into the hashtable. The key to the entry is the kmer, a string. Since string have good predefined hashcode you don't need to define anything special. The class entry require at least two fields: int family count and int background count. To process the family file you consider each kmer in turn, and either enter it into the hashtable or update the family count if it is already their. After you process the family file, the family count will hold the number of occurrences in the family and the background count will be zero. The complexity of all this should be linear. The general rule is that you also avoid processing a file. Twice through a file is once too many.

      The entry into the hashtable might have the fields: (string kmer, int familyCount, int backgroundCount, double score).

      c) computing the number of times kmers in the family occur in the background (use the same hashtable). Processing this file is a a little different. For each kmer in the file, check to see if it occurs in the hashtable. If it isn't there you don't care. Otherwise you update the background count.

      d) computing unusualness and sorting the kmers by this value. Luckily hashtables have enumerators and Hashmaps have iterators associated with them. Now you go thru the hashtable and enter the best scoring kmers into your cache (sorted, bounded) from a previous assignment.

      e) finally you print out the top 20 kmers from your cache with kmersize of 6 (minor step) Instead of using your cache class, you may use TreeSet from the collections package. Actually you should print out the entry associated with each of these kmers. So the output would look something like:

      Kmer # of times in family # of times in background Score

      aaaaaa 13 121 .... (not the real answer)

      etc.

      ++++++++++++++++++ Code to follow ++++++++++++


      Here is the code that will concatenate all the upstream regions into a single string. If you write this with s += br.readLine() you will have a huge (unacceptable) cost overhead. Instead (and in fact better) you could just read in and process each upstream region. This code has worked for me, but no guarantees that is errorfree. Complaints/improvements welcomed.


      import java.io.*;

      class FastaReader
      {
      String data;

      FastaReader(String fileName)
      {
      try
      {
      File file = new File(fileName);
      BufferedReader bf = new BufferedReader( new FileReader (file));
      StringBuffer sbuf = new StringBuffer((int)file.length());

      String line = bf.readLine();
      while (line != null)
      {
      if (line.charAt(0) != '>')
      sbuf.append(line);
      line = bf.readLine();
      }

      data = new String(sbuf);
      bf.close();
      }

      catch(IOException e)
      {
      System.out.println("bad file or something");
      }

      }


      }


      .



      Homework 6: Goal: Dynamic programming: the Needleman-Wunsch Algorithm.

      Read chapter 10.3 Not covered in depth in either text.

      The Needleman-Wunsch algorithm has many applications. The most famous application is helping to discover the function of proteins by finding similar proteins with known function. It could also be applied to spelling correction. It is the basis of time-warping algorithms in speech recognition. Your task also includes extending the algorithm so that it also produces an alignment. You can do this either with a graphical interface or a command line interface or using terminal io. The input to the program consists of two strings. The output is a) the Needleman-Wunsch similarity score and the alignment. The alignment can be illustrated via dashes, as in the following example: If using graphics, use a fixed size font.

      Here's an example:

      input string1: heagawghee

      input string2: pawheae

      output: score = -1

      heagawghe-e

      --p-aw-heae

      A dash indicates that the character was skipped. The final score is unique, but their may be several alignments that achieve that score.

      The Needleman-Wunsch algorithm will be discussed in class. To compute the alignment I suggest using a separate two dimensional array to record the "backpointers", although this is not necessary. One can also implement the algorithm to use linear space, but that takes more effort and care.


      Last Assignment due June 5: Two week assignment. A Competition!

      Top 5 performers, scored by length of the path produced, will get double grades, i.e grade will also replace another homework score, assuming it is a better grade.


      Homework 7: Goal: Use local improvements algorithm on the traveling salesman problem. And Graphics.

      Read Chapter 10.1 and 10.2 The topic is greedy algorithms

      This assigment requires a graphical display. The inputs to the program are two integers. The first integer is a seed for the random number generator and the second integer is the number of cities. The code to generate the 2-d Points is provided below. Each point will have values that range from 0 to 100. Your program should display the initial path together with its length. There are a number of local improvements methods that you might try. At the minimum you should implement the "uncrossing" heuristic. You may add other operators or approaches as you choose. Your code will be evaluated on a random set of 40 cities.

      The best "operator" I've found for improving a tour is to "remove crosses". Other operators, that are also useful , are swapping a pair of cities or moving a single city to a new point in the tour. You need only implement the "remove crosses" heuristic. A formula for detecting crosses is fairly simple. Let d[i][j] represent the distance from city i to city j. For convenience let i' and j' be the next city in the tour (you need to worry about wrap around). Then a "cross" exists between i and i' and j and j' if:

      d[i][j]+d[i'][j'] < d[i][i'] + d[j][j'].

      If you implement this with doubly linked lists, you can uncross the path with a few pointer moves. If you use an array to store the cities, then you will need to swap a number of cities. You may use the collection class.

      The performance measure is the length of the tour that your program finds. Your program is constrained to not take too long, say not more than 2 cpu minutes for either problem.

      Note the grade on the assignment is determined in the standard way, i.e. the code is correct and clean. However for the competition, its no holds bar. The only thing that counts is the length of the tour you find. However you would not want to double a poor grade. :)

      Code for Generating an array of Points. Note this is like the Math functions, i.e. it is really just a long name for a function. may have This code guarantees that each city have unique coordinates.

      =================== Code ===================

      import java.util.*; // for the class Random
      import java.awt.*; // for the class Point

      class PointGenerator
      {
      Point[] pts;
      PointGenerator(int seed, int size)
      {
      pts = new Point[size];
      int [] xcoord = shuffle(new Random(seed));
      int [] ycoord = shuffle(new Random(seed+1));
      for (int i = 0; i<size; i++)
      pts[i] = new Point(xcoord[i],ycoord[i]);
      }

      int[] shuffle(Random r)
      // returns 100 random integers with no repeats from 0..99
      {
      int[] ans = new int[100];
      for (int i = 0; i<100; i++)
      ans[i] = i;
      for (int i = 99, bound = 100; i>0; i--, bound--)
      swap(ans, i, modulo(r.nextInt(),bound));
      return ans;
      }

      int modulo(int i, int m) // because % doesn't compute modulo correctly
      {
      int temp = i%m;
      if (temp<0) return temp+m;
      return temp;
      }

      void swap(int[] a, int i, int j)
      {
      int temp = a[i];
      a[i]= a[j];
      a[j] = temp;
      }

      Point[] getPoints()
      {
      return pts;
      }
      }

      ================end of code =======================


      Final Exam: June 11

      Quiz 2 gives a reasonable idea of the form of the final, except it will be longer and cover the entire course material.









      Homework Z:

      Read Chapter 10.1 and 10.2 The topic is greedy algorithms

      This assigment requires a graphical display. The input to the program is the number of cities. Each city will be placed randomly at (i,j) where i and j are between 0-100. Display the random path together with its length. Implement two ways of solving the problem: an exhaustive method and a local improvement method. The user should be able to specify which method. Each time a method improves the path, show the new path and the new length. Compare the effectiveness of each method on problems of size 10. To do this run each method on 10 problems. Of course the exhaustive method will find the best solution, but how good is the heuristic approach. Also record the amount of time each method makes. Also try both methods on larger problems. When does the exhaustive approach fail - i.e.when is it unable to solve the problem in a reasonable amount of time (reasonable = 1 minute)? Run the heuristic 10 times on the same problem of size 50, but randomize the initial ordering of the cities before each run.

      The best "operator" I've found for improving a tour is to "remove crosses". Other operators, that are also useful , are swapping a pair of cities and moving a single city to a new point in the tour. You need only implement the "remove crosses" heuristic. A formula for detecting is fairly simple. Let d[i][j] represent the distance from city i to city j. For convenience let i' and j' be the next city in the tour (you need to worrry about wrapping around). Then a "cross" exists between i and i' and j and j' if:

      d[i][j]+d[i'][j'] < d[i][i'] + d[j][j'].

      If you implement this with doubly linked lists, you can uncross the path with a few pointer moves. If you use an array to store the cities, then you will need to swap a number of cities.




      Homework X: N-Queens Problem

      This assignment requires a graphics. Optionally you may let the user choose a board size, but you can set it at some reasonable number between 20 and 100. Display the board with a random placement of N-queens. Implement a greedy local improvement algorithm. For each cycle of the algorithm display the number of queen moves. Provide appropriate summary information once a solution is reached.



      Homework X: Goals: Graphics, binary search

      Read Appropriate chapter

      In this assignment you will implement a gui interface that displays a polynomial and some information about the polynomial. The input to the program is a line like: 2*x^3+5*x+3 and a pair of real numbers, say 3.1 and 6.1. The constructor for your polynomial class is Polynomial(String s).

      You should define a method double evaluate(double d) which evaluates the polynomial on the given real number. There is a clever way to evaluate a polynomial.

      Your program will graph the function over the range provided. Finally your program will display a solution to the problem of polynomial(x) = 0. To do this part of the problem you need to assume a solution exists. To solve it, use binary search. In order to apply binary search you will need to have two values x1 and x2 in the domain such that poly(x1) > 0 and poly(x2) <0, or vice versa. To find such values I suggest a simple linear search over the domain, i.e. test poly(x[i]) and poly(x[i+1] where the x[i]'s break the domain into 100 equal sized parts. Once these values are found, use binary search to yield an approximate solution to poly(x) = 0.

      This following is a possible and simple layout for the GUI interface. The interface contains three text fields for the inputting the polynomial ( a string) and the left and right bound. It a button that causes the graphing of the polynomial on someJPanel.. When the polynomial is drawn, a solution to p(x) = 0 (if it has a solution) should be indicated graphically and numerically, i.e. in a JTextField.

      As always you should provide time and space analysis as appropraite. http://www.ics.uci.edu/~kibler/SW/SW.html Smith-Waterman Dynamic Program
      Author: Dennis Kibler and Ray Klefstad April 17, 1997.
      This code follows the dynamic programming algorithm given in Sebutal/Meidanis. Numerous important extensions are described in Sebutal and M. In this implementation, the gap penality is -2, the mismatch penalty is -1, and the match credit is +1. Source http://www.ics.uci.edu/~kibler/ics171/homeworks/ Index of /~kibler/ics171/homeworks

      Index of /~kibler/ics171/homeworks

      [ICO]NameLast modifiedSizeDescription

      [DIR]Parent Directory  -  
      [TXT]LearningHwk.htm27-Aug-2004 09:35 2.3K 
      [TXT]Logic.htm28-Oct-2001 09:19 1.5K 
      [   ]LogicQuizAns.doc02-Aug-2004 09:02 38K 
      [   ]LogicSolutions.doc03-Aug-2004 10:59 26K 
      [TXT]Probability.htm13-Aug-2004 13:34 1.9K 
      [   ]ProbabilityHWKSolutions.doc18-Aug-2004 09:10 30K 
      [TXT]SearchCoding.html06-Jul-2004 11:37 2.6K 
      [   ]SearchCoding.tex25-Jun-2004 13:11 932  

      Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
      http://www.ics.uci.edu/~kibler/H23syllabus.html Honors ICS 23

      Honors Data Structures and Algorithms

      A collaborative problem-solving approach

      • Professor: Dennis Kibler kibler@ics.uci.edu
      • Class Meetings: 10-10:50 Mondays, Wednesdays, Fridays (ET204)
      • Office Hours: 11-12 Mondays,Wednesdays (414 CS)
      • Teaching Assistant: Li Zhang
      • Discussion: 11:30-12:50 Mondays, Wednesdays (CS193)


      Course Goals

      Using the Java language students will learn the properties and implementation details of the fundamental data structures (arrays, lists, queues, stacks, dictionaries, hashtables, trees, graphs) that often are at the heart of any program. Students will also learn basic problem solving methods, such as divide and conquer, separate and conquer, dynamic programming, greedy algorithms, tree and search algorithms as well as some useful applications. As part of the coding assigments, students will be expected to analyse their code and follow good object-oriented design.


      Grading

      There will be approximately 8 programming assignments two quizzes and a final. The coding assignments will be in Java. Your lowest homework score will be dropped. Roughly, the exams and homeworks will count equally. The final exam will be based on the text, lecture notes, and homeworks. Each chapter ends with a summary. Be sure that you know every concept discussed in the summary. The fastest way to get questions answered is to email either the TA or me. Answers of general interest will be posted on the bulletin board (ics.H23). You may also use the bulletin board to ask classmates for appropriate help. Any form of inappropriate help will result in an F grade in the class and a letter in your file.


      Text

      Text Data Structures and Problem Solving using Java by Mark Allen Weiss

      Recommended: UP to speed in Swing by Steven Gutz


      Java Language Notes

      Lecture notes for the Java Language are available on-line Java Notes . Please tell me if you find any errors or misrepresentations.


      Tests

      Quiz: April 23

      Quiz: May 21 12:00- 1:00

      Final: June 11 10:30am-12:30, same room as lecture


      Tentative schedule.

      Week 1 GUI interfaces and Object Oriented Design
      Telephone Address book
      Instructor grading program
      Hwk:Banking: deposits, withdrawals, opening, closing
      Week 2 O notation and Fundamental Data Structures via Collections
      Collections: Lists, Trees, HashTables
      Hwk: Lexicon with Gui
      Week 3 Problem Solving I: Divide and Conquer
      Dynamic Programming: fibonacci, longest common subsequece, matrix multiplication
      Hwk: Needleman-Wunsch
      Week 4 Problem Solving II: Backtracking, Local Improvement
      Heuristic search, boolean satisifiability, np-completeness
      Hwk: N-queens (exhaustive and heuristic)
      Week 5 Linked Lists Implementation Ch. 16
      singly, doubly, circular, sorted
      Hwk: Traveling Salesman Problem (arrays and lists)
      Week 6 Hash Tables Ch. 19
      hash functions, collisions, linear and quadratic probing
      Midterm!
      Week 7 Queue, Dequeues, Priority Queues Ch. 15, 20
      heap, priority queue, heapsort
      Hwk: Bounded priority queue
      Week 8 Balanced Trees Ch. 13
      AVL trees, red-black trees, AA-trees, B-trees
      Hwk: build, evaluate, and display expression trees
      Week 9 Tree Search
      Depth first, breadth first, iterative deepening
      Hwk: Evaluation of efficiency
      Week 10 Graphs Ch. 14
      Adjacency representation, Kruskal's, Prim's and Dijsktra's algorithm, topological sorting
      Hwk: You gotta be kidding


      Homework Details http://www.ics.uci.edu/~kibler/H23Homeworks.htm

      http://www.ics.uci.edu/~kibler/ics172/syllabus/ AI in Java

      AI Programming Techniques (ICS 172)
      Instructor: Dennis Kibler
      Teaching Assistant: To Be Determined

      • Java Text: Any one you like. I recommend, without passion, Core Java by Horstmann and Cornell (latest edition).
      • AI Text: Any one you like. I recommend, with passion, Artificial Intelligence: A Modern Approach by Peter Norvig and Stuart Russell.
      • Computer Language: Java with JDK1.1 or JDK1.2.
      • Computer Environment: Symantec Cafe on PC under Windows NT.
      • Course Prerequisite: ICS171
      • Knowledge Prerequisites
        • Java Applications
        • Mathematics: Vector Algebra, Boolean Algerbra
        • Computer Science: Standard data structures, search methods, etc
      • Bboard: Read ics.172 for latest class announcements.

      Course Goal

      Enable students to write Applets that reveal the structure and performance of several AI methods, including those dealing with search, problem solving, learning, and decision making. These areas have had significant contributions to either scientific or industrial applications. Object-oriented design will be stressed. There will be five modules. Each module will have two lectures on AI theory and one lecture on design. Another lecture is reserved for whatever is most needed, including Java, design, implementation, or AI theory. For each module, you will do both a design and an implementation. The design consists of your guesses to the objects and their methods and fields. The design has no code associated with it. The design is due on the first class of the week after the assigmnent. No credit will be given for late designs. Designs will be graded on the basis on their rationality. It is not expected that they will be completey correct. Nor do they need be followed. Your implementation should be readily runnable over the net. Consequently no credit will be given for late implementations. Your implementation is due on the first class of the second week after the assigment. Implementations will be graded on the correctness, style and efficiency. Efficiency is not a major goal, but if some computation is done particularly poorly, such as an O(n^3) sort, credit will be taken off.

      AI Modules

      1. Linear Threshold Learning: Perceptron and Winnow.
      2. Traveling Salesman Problem: Heuristic search, A*, and branch-and-bound.
      3. Clustering: kmeans, expectation maximization (EM)
      4. Game-playing: Perfect and non-perfect information games. Alpha-beta search. Sampling.
      5. Decision-Tree Learning: automatic creation of expert systems.

      Work Load

      Each module will take several hundred lines of Java code. Weekly assignments alternate between design and implementation.

      Grading

      Assignments will alternate between design and implementation. The design consists of the objects, method prototypes and data-members. It is not anticipated that designs will be followed exactly, but they should be reasonable. Similarly code does not have to optimal, but again must be reasonable clear, correct, and efficient. For each module, design and implementation with be worth 15% of your grade. Assignments receive no credit if handed in late.

      Any code copying will result in letter in your file besides receiving no credit for the assignment.

      There will be a final exam which must be passed to pass the class. When averaged with the other scores, the final will count for 25% of your grade. The final will be based on an understand of the AI concepts that are covered in the lectures.

      Notes

      Lecture notes for the Java Language are available on-line Java Notes . Please tell me if you find any errors or misrepresentations.
      By the end of the course students are expected to competent in:
      • writing applications and applets in Java.
      • doing an object-oriented design.
      • judging whether an ai method is appropriate.
      • coding several standard ai methods.
      • evaluating the performance of an ai methods.
      http://www.ics.uci.edu/~kibler/ICS174/Assignments.html ICS174 Assignments
      • Assignment 1: Finding Candidate Genes" (coding)
      • Assignment 2: Have a BLAST: Gene Verification
      • Assignment 3: Similarity computation by dynamic programming.(coding)
      • Assignment 4: Multiple Sequence Alignment using ClustalW
      • Assignment 5: Hierarchical Clustering (Coding!)
      • Assignment 6: Almost Markov Modeling (Coding!)
      • Assignment 7: Discovery Regulatory Elements
      • Assignment 8: Proteomic disease diagnosis
      http://www.ics.uci.edu/~kibler/ics270a/syllabus/ untitled

      Roughly the course will require that you read and understand 2 chapters per week. Some sections of the chapters will not be required. You are expected to learn the assigned reading material even if it is not covered in the class. lectures. The pace of the course will vary with the difficulty of the material.

      There are three coding projects, dealing with problem solving, logical reasoning, and learning.

      The first assignment has been updated and is correct.


      Homework Grading: The homeworks are deliberately open-ended. If you do a minimal job, your grade will be a B. If you do more and it is mostly correctly, then the grade is A What counts here is the intelligence/thought that you add to the assignment. If less, then C.


      Week 1. Read Chapters 1, 2(summary only), and 3. Introduction to AI. State-Space Representations and Search. DFS, BFS, IDS, Uniform-Cost. Bidirectional search.

      Lecture 1. AI Intro

      Lecture 2. State-Space Representation

      Week 2. Read Chapter 4 (not 4.3) Informed Search and more: Best First, A*, Beam, Stochastic, Iterative Improvement. Generating heuristics. Simulated annealing.

      Lecture 3. UnInformed Search

      Lecture 4&5. Informed Search

      Week 3. Constraint Satisfaction

      Week 4. Game Playing and Propositional Logic

      Your first homework is due at class time.

      Week 5. Read Chapters 8 & 9 Propositional Logic and Prolog.

      Week 6. First-Order Logic

      Week 7. Read Chapters 14 & 15. Probabilistic Reasoning

      Week 8. Read Chapters 18 & Bayesian Networks Weka Suite of ML algorithms Learning in Pictures

      Week 9. Read Chapter 18. Decision Trees.

      Week 10. Read Chapter 18 & 20. Linear Discriminators and Nearest Neighbor or Instance-Based Learning



      Homework 1. Search. Due at the beginning of the fourth week. Due Date is October 21. In this problem you will evaluate several different search algorithms. The problem to be solved is the traveling salesman problem. Each city has two integer co-ordinates and every city is connected to every other city by a straight-line edge. The search algorithms that you need to code are:

      a) A non-informed exhaustive search algorithm such as depth first, breadth first, or iterative deepening. Hint: because of the repeated states, I suggest that you just define a function/method that generates all permutations of the cities.

      b) The A* algorithm with a suitable heuristic function. CHANGE: it is extra credit to code this algorithm because of the problem with repeated states. You still need to define a non-trivial admissible heuristic h.

      c) The simulated annealing algorithm.

      d)The iterative improvement algorithm (also called local improvement) with the "uncross" heuristic. Uncross will be explained in class.

      You may be asked to turn in your code or demonstrate your algorithm. What you need to turn in is an evaluation of the algorithms. Here is a minimal evaluation.

      a) For ten problems of size 10, compare the performance of your algorithms, in terms of speed and quality of solution or any other measure that you can think of. If problems of size 10 take too long, you may choose a slightly smaller number.

      b) What is the largest problem (number of cities) that you can "solve" by every algorithm in less than 1 minute?

      Your typed write-up should be as brief as possible, but no briefer. Your conclusions should be supported by the experiments that you have run, i.e. the data from the experiments that you discuss should be included in your write-up. You may assume I know the algorithms. However you should explain the operators (which may change with the algorithm) and the heuristic you've chosen for A*. You should explain your evaluation of the algorithms. At the end you should discuss the results.


      Homework 2. Logic. Due at the beginning of the seventh week. Because of the holiday on Nov 11, this is due on Nov 13. This assignment requires the use Prolog.

      1. Do problem 7.8 page 237 in the new edition. (Propositional Logic) Just the answers.
      2. Do problem 8.6 page 269 in the edition. Write the FOL form.
      3. This problem is less structured. You are to write Prolog code for the micro-world of spatial reasoning. At a minimum you should include predicates: at(X,Y), in(X,Y), between(X,Y,Z), location(X), object(Y), on(X,Y). What types of reasoning does your micro-world support? What types of reasoning within this domain does it not support?

        Here's an example: ( you should do 5-10 different inferences)

        % John has a picture of his wife in his wallet. John is at the spectrum. Inference: picture is at the Spectrum, i.e. the answer to the question at(picture, X) binds X to spectrum.

        Now provide the Prolog code to do it.

        You should provide your code and trace outputs that demonstrate the capability of your program.



      Homework 3.
      Learning. Due at the beginning of the tenth week.

      1. Do problems 13.6, 13.8, 13,10 from chapter 13 (new edition).
      2. Do problem 18.7 from chapter 18 (new edition).
      3. Download the Weka software (I suggest 3.2.3 but you could try the newer version) onto your computer and prepare the dermatology dataset from the UCI machine learning database(http;// www.ics.uci.edu/~mlearn) for analysis by the Weka learning programs. From the Weka suite of algorithms use decision trees (j48), rule learning (j48.part), nearest neighbor( IB1 or IBK) and Naive Bayes on the data. Report how you evaluated the algorithms, which worked best, whether you were surprised and anything you thought was interesting.
      http://www.ics.uci.edu/~kibler/H22syllabus.html Honors ICS 23

      Honors Data Structures and Algorithms


      Schedule Change: Sandy Irani is teaching this class!


      • Professor: Sandy Iraniu
      • Class Meetings: 3:30-4:50 Tuesday and Thursdays (PSCB 140)
      • Office Hours: 5-6 Tuesdays and Thursdays + by Appt. (414D CS)
      • Teaching Assistant: Steve Moret smoret@uci.edu
      • Required Text: Data Structures and Problems Solving with Java by Mark Allen Weiss
      • Discussion: TBA

      Teaching Philosophy

      I am your guide and collaborator in learning. However the primary responsibility in learning rests with you. Evaluate yourself before I do. Use all the available resources. Ask questions. Writes lots of very simple programs to verify and deepen your understanding of the various concepts. Doing only the homeworks assignments is not sufficient. If you can't use a concept, you don't know it. Explain the concept to friend. Teaching often reveals holes in understanding. Everytime you make an error, it is an oppportunity to learn. Enjoy the process of learning.

      Course Goals

      Learn the properties and implementation details of the fundamental data structures (arrays, lists, queues, stacks, dictionaries, trees) that often are at the heart of any program. Implementations will be done in Java. Learn the elements of analysis of algorithms, including O notation. as well as basic elements of object-oriented design and implementation.

      Grading

      There will be approximately 4 quizzes and 8 programming assignments. The coding assignments are all Java. Your lowest quiz score and your lowest homework score will be dropped. Approximately, the final, quizzes, and homework will count equally. The final exam will be based on the text, lecture notes, homeworks, and quizzes. Each chapter ends with a summary. Be sure that you know every concept discussed in the summary in the Preiss text. The fastest way to get questions answers is to email either Steve or myself. Answers of general interest will be posted on the bulletin board (ics.H22). You may also use the bulletin board to ask classmates for appropriate help. Any form of inappropriate help will result in an F grade in the class and a letter in your file.

      Required Text

      Text Data Structures and Algorithms with Object-Oriented Design Patterns in Java by Bruno Preiss.

      Suggested Text

      Any book on Java that you can learn from. Many students like Core Java but I like The Java Programming Language 3rd edition by Ken Gosling et al.

      Java Language Notes

      Lecture notes for the Java Language are available on-line Java Notes . Please tell me if you find any errors or misrepresentations.

      Intended Schedule.


      Quick Overview: We will approximately cover the first 8 chapters of Preiss plus introductory material on the Java language. Use your Java text to fill in gaps. I will not assign reading in the your Java book; instead use the index to look-up what you need.
    • Why Java?
      • Goals of a programming language.
      • Goals of program
      • Achieve goals
      • Program design
      • Program implementation
      • Object definition in Java
      • Object decomposition
      • examples: complex numbers, polynomials,
      • Object hierarchies: triangles.
      • Read: Appendix 1 in Preiss + select from Java text.
    • Using Other Peoples Classes
      • File io: BufferedReader, PrintWriter
      • Simple Lexical Analysis: String Tokenizer
      • Sorting: TreeSet: how fast is it?
      • Formatting
      • Linked Lists: polynomials again
      • HashTables: making a lexicon
      • Reading: On-line documentation on classes via Cafe.
    • GUI interface. Event-driven programming.
      • Inheritance
      • Interfaces
      • Inner classes
      • Frames
      • Layout Managers
      • Buttons, TextFields, Labels, TextAreas,
      • ActionListeners
      • Reading: online documentation + Java text as needed.
    • Why Analysis?
      • Program model.
      • O notation.
      • Finite Induction.
      • Programming Timing
      • Henceforth, all your code requires space and time complexity analysis, if it is not O(1).
      • Read Chapter 3 in Preiss.
    • Fundamental Data Structures: Arrays
      • Dynamic arrays
      • Dynamic Programming
      • Smith-Waterman Algorithm
      • Read Chapter 4.1-4.2
    • Week 6. Fundamental Data Strucutres: Linked Lists
      • Single linked Lists
      • Double linked lists
      • Extra pointers
      • Read Preiss 4.3
    • Week 7. Stacks and Queues
      • Stack as array
      • Stack as list
      • Queue as array
      • Read Chapter 6.
    • Week 8. Ordered Lists
      • List as array
      • Ordered List
      • Sorted list
      • Read Chapter 7.
    • Week 9. Hash Tables
      • Hash Functions
      • Chaining
      • Scatter Tables
      • Linear Probing
      • Quadratic Probing
      • Double Hashing
      • Removing items
      • Read Chapter 8
    • Week 10. Trees
      • Binary Trees
      • Tree Traversals
      • Evaluating Expression
      • NP-completeness: Million dollar question
      • N-ary trees
      • Read Chapter 9.


      Homework Assignments

      Homeworks are duly weekly and need to typed. No handwritten assignments will be accepted.
      1. Object design

        • Use Visual Cafe's IDE and write any program at all. Hand in the code. You may have help in using Visual Cafe, but not in writing the program.
        • Suppose you are design an computer implementation of a personal telephone book. In English, provide a list of the constructors and methods you would expect to have. Include a sentence describing each method/constructor. Do not write any code. For example:
          TelephoneBook() : constructs an empty telephone directory. We have done this in class for complex numbers as part of the design process.
        • Suppose a math professor hires you to write a class for polynomials. As above provide in English a list of the constructors and the methods. Do not write any code.
      2. Other Peoples classes

        • Using BufferedReader and StringTokenizer, write a program to count the number of words in a file. Precisely define what a word is.
        • Use Double, TreeSet, and CurrentTimeMillis to time sorting 100, 200, ... 12800 random doubles ( Math.random() ) Printout a table of times, times/n, times/n*logn, times/(n*n)
        • Using BufferedReader and Hashtable, write a program to count the number of words of length equal to the length of your last name in the file XX.
      3. Gui Interface

      4. Analysis

        • Do problems XX
        • Use the linked list class provided in the Collections class to implement a sparse polynomial. Implement the following:
          1. Poly(String s) where s has looks like 2x3+11x14 etc.
            Use StringTokenizer and add "x" to the delimiter set.
          2. void add(Poly p) adds p to the current polynomial
          3. int degree(Poly p) returns the highest degree of the polynomial
          4. Extra credit: void mult(Poly p) multiplies p with current polynomial
          5. Extra credit: double solve(P, int a, int b) returns a solution to P within the range [a,b].
      5. Arrays

        • Write a dynamic arrays as an array, a singly linked list, a doubly linked list pointer, and an array. Time the results of adding the elements 1...1000 to it.
      6. LinkedLists

        • Expand the program from the previous assignment to include deletion. To time deletion, first create an fixed array of size 1000, with the the integers in random order. Now you can delete elements from each representation in the same order.
      7. Stacks and Queues

      8. Ordered Lists

        In this program you will create a book index of all the "content" words. We will somewhat arbitrarily define a content word as one containing at least 7 characters. Your program will accept a file and output an ordered list of word linenumber, linenumber etc. For example a typically output line might be: government at lines: 15, 123, 3004
      9. Hash tables

      10. Trees

      http://www.ics.uci.edu/~kibler/ICS23/Homeworks.htm H23 Homeworks

      ICS23 Summer Homeworks: Typically these homeworks have a theoretical portion and a coding portion. For all methods of all classes, you are required to provide a O-notation space and time complexity analysis unless the space and time are O(1).

      Warning: Only the homework for the next assigment is guaranteed to be correct and complete. Look at the assignment early and ask questions if you do not understand what you are supposed to do.

      NOTE: Due Time: Coding assignments are due (i.e. need to be deposited) by NOON of the first class day of the week that the homework is due. Another handout will explain how this should be done. Work that should be handed in is due at the beginning of class of the week that homework is due. Late homeworks will be marked down by 20% for each day that it is late.


      In general, if questions are asked about your code, you should answer them in the documentation. For example you should document the time and space complexity of all methods when not O(1).

      Regrades: For a regrade you must resubmit your homework within 1 week of receiving your score. Also you must explain what part of your homework needs to be regraded. The entire assigment will be regraded so it is possible to lower your score on a regrade.

      Note: Submit only *.java and *.vep files, no *.class files (which are large). See Visual Cafe Lab guide on Li Zhang's web site.


      Homework 1: Goal: O-notation+ review of arrays, lists and unbalanced trees.

      Read chapter 1. chapter 1 has some useful program examples as well as a review of some basic mathematical techniques.


      1. Do problem 5.14, only part a. ( 6 code fragments to analyze). The solutions to these problems can be submittted in a separate file *.txt file. This should be fairly short, such as: // analysis: 1. O(n^3) 2. O(nlogn) 3. O(n^100) etc. Note; not real answers. The name should be analysis.txt

      The quiz will contain problem similar to these.



      Read
      chapter 3+ 4.3 Chapter 3 is on lists, stacks, and queues. 4.3 covers binary trees.

      In this assignment you will implement a Cache in 4 different ways: namely as an array, a linked list, as binary tree, and as a tree (new TreeSet()) from the Collection package (in Java.util). The goal of a cache is to store the best values. Teh best values are the highest values. They do not have to be stored in order, but this is sometimes convenient. Your program will have a single input: an integer that defines the seed for a general random niumber generator, i.e. Random r = new Random(seed) which in the util.* library. Then you can get random integers view r.nextInt(). For each implementation give an O-notation analysis. Additional compare your implementations by generating 10,000 random integers and putting them into a cache of size 20. Duplicated integers can be stored or not, as your choice. Just comment which you do. Which implementation of cache performs best? What can you say about memory use, i.e. how much memory does each technique require? For each algorithm print out the final state of the cache. Should the caches be identical?

      Note: To insure that each cache is given the same problem, you should reinitialize the random number generate with the given seed for each Cache that you define. Also, to get timing results, you should the function System.currentTimeMillis(), which returns a long.


      Summary: input: single integer that defines the seed for the Random.

      Output: for each cache algorithm, it's time and the final elements in the cache.

      Questions should be answered in the documentation for your code. In particular give the O analysis for the cache where the number of data elements to be examined is N and the size of the Cache is K. The answer should be O(some function in terms of K and N).


      You may use your Cache class in later assignments.

      Code Hints: TreeSet only allows you add objects, hence you may want to use the wrapper Integer to store the values. Look at the methods on TreeSet so that you don't store too many elements.

      Homework 2. Goal: Practice creating and using Trees. Huffman trees are discussed in 12,1-12,4. Chapter 18 discusses trees. Key sections are 18.4 on AVL trees and 18.7 on B-trees.

      In this assignment you will create optimal codes for the characters a,b,....z by generated a Huffman Tree. You may use any of the classes in util that you find useful. The input to your program is the name of a text file. The text file will be used to evaluate your program. Uppercase letters should be counted as their lowercase equivalent. Numbers, punctuations etc are not to be counted. I suggest that you create several simple text files to check that your program is working correctly. The output of your program is

      a) 26 lines consisting of the letter and its frequency of the letter, in alphabetic order

      b) 26 lines consisting of the letter and its code. The letters may be in any order.

      . These entries should be separated by a tab.


      So an example output might look:

      a 132

      b 15

      etc

      and

      a 001

      e 10001

      etc.

      Summary: input: name of a file

      output: list of letters a..z with their frequence and a list of letters with their Huffman codes.

      Coding Hints: The main two activities are reading a file, counting the frequency of the characters and building a Huffman Tree. Hence I expect that there are two primary classes. Here would be my plan. You do not need to follow my plan.

      Monday: write the driver program. Hence I would have at least two auxiliary classes: CountCharacters(String fileName) and HuffmanTree(int[] array).

      Tuesday: write the CountCharacter class and test. Note if br is a BufferedReader, then br.read() returns -1 if you are at the end of the file. By using char ch = (char)br.read() you will get the next character in the file. The Character method isLetter(char c) will tell you if it is a letter. The Character method toLowerCase(char c) returns the lower case equivalent of the letter. Assume ch is a lower case letter, then (ch-'a') will be 0 and (ch)('a'+i) will be the ith character of the alphabet. Note that you can test this class independent of creating the Huffman class.

      Wed/Thurs: write the Huffman class, which generates codes for each character. Since the Huffman codes are based on building a special type of tree, I expect to have additional classes such as HuffmanNode. These trees are built bottom-up, which is not the usual way. You may find the LinkedList or Vector class useful here. You should think carefully about what information you store in each node. Also note that you could write and test this class without writing CountCharacters, e.g. by inputting simple test arrays where you know what the answer is, such as:

      int[] test = {2,2,2,2} and then running HuffmanTree(test).

      I find that when I make a logical error in coding, its very difficult to find the same day. Hence it's best for me to get a night's sleep and retry the next day. That happened to me in coding the Huffman tree.


      Homework 3. Goals: Processing files, using hashtables and a cache. Using different data structures for different goals.

      Read chapter 5 or chapter on hashing

      The program finds interesting kmers. A kmer is contiguous string of exactly k letters. For example in the string ACTACTA there are 4 4-mers, namely ACTA, CTAC, TACT, and ACTA. Note that we allow overlaps. Your program will read in two files and the size of k. Each file consists of characters from the alphabet {a,c,g,t}. The first file we will call the family.The second file we will call the background. To create a filereader from the Masterhit directory, you should use new File("\\\\Masterhit\\Instructional\\ics-23\\files\\nit.txt"); You may think of the background as dna strings from the normal population and the family file as dna from people with some genetic disease.Your program goal is to find unusual (really statistically significant) kmers that occur suprisingly often in the family with respect to the background. For the purposes of this homework we define the unusualness of a kmer in the family as:

      (size of background)*(number of times kmer occurs in family) - ( number of times kmer occurs in background)* (size of family)

      This is the difference between the actual number of occurrences and the expected number of occurrences, normalized to be an integer.

      There are more appropriate statistical well-founded definitions of surprisingness, but involve more work and arrive at nearly the same results.This value defines how to kmers should be compared. The size of the background is the length of the string that the FastaReader generates. Same for the size of the family.

      To count the number of times every kmer in a family occurs use a hashtable which is part of the collections package (in java.util.*). For computing the background counts, you should only count the kmers that occur in the family. Again a hashtable is suitable. If you define it properly, you can use the same hashtable as before. For example an entry in the hashtable could consist of a pair of integers (family count, background count) and a real-values score of the unusualness.

      The first step: Make a schedule. Write the driver the first day and then allocate days for the rest. If you do not write the driver by Wednesday, then I think you are very far behind schedule. By writing the driver you can focus your questions.

      There are three major steps (b,c,d) to do this task, plus a few minor ones (a,e).

      a) You need to read into memory two files and store them as strings. (This isn't entirely necessary, but otherwise you will have to worry about substrings wrapping around the end of one line and the beginning of the next line). Both files are in Masterhit\Instructional\icsh23\files. The family file is in "nit.txt". This file is in standard "Fasta" format, the form that molecular biologists use to store information about genes or their surrounding regions.To process this file you need "skip" the comment lines. The net effect is that you read this file and form a single long string, which should have 3500 characters. The second file is the complete chromosome I of yeast (which has 16 chromosomes) and is called "chri_230203.txt". You can guess how many characters it has. Again you can do this by reading the file into a single very long string. You can define the same reader class to process either file. (I will provide that little bit of code).


      b) computing the number of times each kmer occurs in the family ( use a Hashtable or a HashMap). You need to define a class I call entry. Its what goes into the hashtable. The key to the entry is the kmer, a string. Since strings have well predefined hashcodes you don't need to define anything special. The class entry requires at least two fields: int family count and int background count. To process the family file you consider each kmer in turn, and either enter it into the hashtable or update the family count if it is already there. After you process the family file, the family count will hold the number of occurrences in the family and the background count will be zero. The complexity of all this should be linear. The general rule is that you also avoid processing a file. Twice through a file is once too many.

      The entry into the hashtable might have the fields: (string kmer, int familyCount, int backgroundCount, long score). Note: The Java hashtable uses rehashing, so the table size will automatically be expanded as needed. However since you may have as many as 3000 entries, you could use new Hashtable(6000) to initialize the hashtable with size 6000.

      c) computing the number of times kmers in the family occur in the background (use the same hashtable). Processing this file is a a little different. For each kmer in the file, check to see if it occurs in the hashtable. If it isn't there you don't care. Otherwise you update the background count.

      d) computing unusualness and sorting the kmers by this value. Luckily hashtables have enumerators and Hashmaps have iterators associated with them. Now you go thru the hashtable and enter the best scoring kmers into your cache (sorted, bounded) from a previous assignment. It is not necessary to use a cache.

      e) finally you print out ( to the console) the top 20 kmers from your cache with kmersize of 6 (minor step) Instead of using your cache class, you may use TreeSet from the collections package. Actually you should print out the entry associated with each of these kmers. So the output would look something like:

      Kmer # of times in family # of times in background Score

      aaaaaa 13 121 ... (not the real answer)

      etc.

      ++++++++++++++++++ Code to follow ++++++++++++


      Here is the code that will concatenate all the upstream regions into a single string. If you write this with s += br.readLine() you will have a huge (unacceptable) cost overhead. Instead (and in fact better) you could just read in and process each upstream region. This code has worked for me, but no guarantees that it is errorfree. Students have used it without problems. Complaints/improvements welcomed.


      import java.io.*;

      class FastaReader
      {
      String data;

      FastaReader(String fileName)
      {
      try
      {
      File file = new File(fileName);
      BufferedReader bf = new BufferedReader( new FileReader (file));
      StringBuffer sbuf = new StringBuffer((int)file.length());

      String line = bf.readLine();
      while (line != null)
      {
      if (line.charAt(0) != '>')
      sbuf.append(line);
      line = bf.readLine();
      }

      data = new String(sbuf);
      bf.close();
      }

      catch(IOException e)
      {
      System.out.println("bad file or something");
      }

      String getData()

      {

      return data;

      }

      }


      Homework 4: Comparison of Sorting Routines (Last homework!)

      Note: As always form a work plan. If you implement one sorting routine and the driver, you will have a good estimate for thw work involved.

      Use Random r= new Random(0) for constructing the random number generator. Using this r, construct 4 test arrays of size 1000, 2000, 4000 and 8000 filling them using r.nextInt(). You will implement four sorting algorithms and time their performance on each array. The four sorting algorithms are: BubbleSort, Mergesort, HeapSort, and QuickSort. However the text code is for arrays of objects and you will be sorting an array of integers. The text code provides a strong outline for the code, but you should be able to greatly simplify it. Or you can write your own code for the algorithms.. Verify the O-notation formula for the running time of each algorithm by creating the following table for each algorithm where n is the number of data items. Use this data to estimate the constant in the O-notation for the running time, i.e instead of saying O(n^2) say 23.5*n^2.

      BubbleSort time/1k time/2k time/4k time/8k meaning the running time for to sort 1k, 2k, 4k and 8k entries divided by the size of the array.

      time/klogk time/2klog(2k) time/4klog(4k) time/8klog*8k)

      time/k^2 time /4k^2 time/(16k^2) time/64k^2

      If some row has an approximately constant value, then you can hypothesize that the running is time is that constant*(appropriate polynomial).

      output: For each algorithm, a table of its performance.

      Also (in the document of the code) the precise polynomial you estimate for the running time of each algorithm.

      Besides the Driver program, this programshould have the obvious 4 classes, one for each sorting routine. It may be convenient to have additional, auxiliary classes for the sorting routines or for measuring performance. http://www.ics.uci.edu/~kibler/ics273/syllabus/ untitled

      Workload: There will be weekly written assignments and reading assignments. The reading will primarily be from the text, but some additional papers will be assigned. Homeworks are due at class time on tuesdays. No late homework, but the lowest scoring homework will be dropped. There will be an open book final exam. All homework should be typed, except for drawings of figures. Students are required to do an individual project. Homeworks will be returned in the distribution center by the time of the next assignment. Many assignments will involve running algorithms from the Weka suite of Machine Learning programs and making intelligent comments about their behavior. You need not know Java to do this, and Weka has been installed on the ICS computers - or maybe not. The Weka site is at www.cs.waikato.ac.nz/ml/weka/index.html. You can also download the source from that site.



      Grading: Your grade will be determined by your homework scores, the final, your class participation and your project. The structure of the final will be similar to the homeworks. In particular you can expect to:

  • write pseudo-code for a variation of an algorithm covered in class
  • do a space and time complexity analysis of some algorithm
  • do a "gedanken" experiment where you predict the behavior of an algorithm without running it.
  • identify which algorithm would be most appropriate for a problem and explain why


    Responsibility: I will be roughly following the text, but you are responsible for the content of the text even if it is not covered in lecture. In particular you should know and understand all the emphasized words. You are responsible for the content of the lectures.


    Goals: By the end of this course you will be familiar with the main effective methods for classification and regression learning. In particular for classification you will have learned about nearest neighbor, naive bayes, rules, decision trees, perceptron, and support vector approaches. Many of these methods can be also be adapted to regression. Finally the latest research in Machine Learning shows how to combine these methods to increase their effectiveness. We will also cover some exploratory data analysis methods such as clustering and association rule finding.


    With respect to each learning method you should understand its assumption, limitations, goals, and effectiveness. Although you may not implement any algorithms, you should understand how the algorithms are implemented in sufficient detail to carry out imaginary variations, applications and evaluations. You should develop a good intuitive understanding of the algorithms. You should feel that you could implement the algorithm if you had sufficient time.

    A nice set of notes which cover the more mathematical approaches to machine learning are at: http://www-2.cs.cmu.edu/~awm/tutorials/ These notes also have additional examples on some of the topics we will cover. Andrew Moore phd thesis was on regression and regression is emphasized in his view of Machine Learning.


    Tentative Reading Schedule:

    Final Presentation/Project: Presentations should be short (15-20 minutes) and done in powerpoint slides. The project should be accompanied by an appropriate paper. Each project should be based on some machine learning algorithm that is not covered in the text or on the analysis of some novel data set. The algorithm may be your own, but more likely it will be one from the literature. Presentations/papers of algorithms from the literature will not simply be a regurgitation of a paper, but include appropriate background information. You should clearly identify the main contribution, the significance of the contribution, and the evidence supporting the conclusion. Place the paper in context. You might view this as a tutorial for the algorithm. Projects may also be an analysis of novel data. I have one such data set: protein spectrum on mice with and without intestinal cancer, but many exist on the web or perhaps from your own research project.

    Week

    Readings Topic
    1 Chapters 1 & 2 Intro to ML and Weka
    2 Chapter 4 Decision Trees
    3 Chapter 5.1 Rule Learning
    4 Chapter 5.2 &: 5.3 Nearest Neighbor and Naive Bayes
    5 Chapter 5.4 & 5.5 Perceptron & Neural Nets
    6 Chapter 5.6 SVM
    7 Chapter 8 Clustering
    8-10 Student Presentations


    Assignments should always be typed and are due at class time. Your lowest assignment will be dropped. No late homework. Chapters and page numbers refer to the Data Mining text. Do not write more than you need to. Do not include irrelevant information. If you believe your assignment was mis-graded, a distinct possibility, simply resubmit the homework with an explanation and I will look at it again. This needs to done within 1 week of the returned assignment. Or see me during office hours.

    Warning: only the assignment for the next week is guaranteed to be accurate. The other assignments may change as the class progresses.

    Project. The Weka software has substantial changed since the text was written. The documentation is woeful behind. Your project is to explain a machine learning algorithm that is in the software and not explained or a machine learning algorithm that has appeared since 2000. A good source would be papers from late Machine Learning conferences, Journal of Machine Learning Research, Machine Learning or IJCAI or AAAI. You should expect to read several background papers. Your presentation, done in power-point, will be about 15-20 minutes long, so you need to concentrate on the idea. You will also have a write-up where you have room to explain the details of the algorithm. Your write-up should not be just a regurgitation of the paper, but include whatever background material is necessary. Typically papers assume that the reader has a good background in the subject. Instead, your paper should assume that the reader knows introductory ai and what we have covered in the Machine Learning course. Everyone will do a different algorithm, so you need to clear the project with me by the third week of class. You need only identify an algorithm plus include the appropriate reference(s) which will be the basis of your paper and talk.


    The Final: This is an open book, open notes exam. You can expect to do some problems that are similar to the homeworks. For example I will describe WHAT should be computed and you will need to write pseudo-code for the algorithm and do both a time and space complexity analysis. You will also be asked general questions about the various algorithms that we have studied. You may be asked to modify an algorithm to achieve a different result. If you have been attending class and doing ok on the homework, you should do ok on the final also. I welcome questions in class or via email.


    Week 1. Read chapters 1 and 2. This assignment has two points. You are to generate or find two data sets for classification. In one data set, the decision tree algorithm (j48) does well and nearest neighbor (ib1) does poorly. In the other data set, the converse is true. You will need to download the open source free Weka software to do this. You should define the two data sets, give an argument why one algorithm is better than the other for the particular data set, and finally verify your conclusion by doing a 10-fold cross-validation. Weka outputs a lot of information - only include that information which you use to support your arguments. For example, don't report the kappa statistic unless you tell me what it means and why you used it. This is important. Do not include superfluous information. Ok?


    Week 2.

    This problem is typical of the type of problem I use on the final. You could make up your similar problems.
    1. The various measures of impurity have concentrated on generating small decision trees. Give a precise definition of a new heuristic measure that likely creates large decision trees. Give a qualitative argument why this is true and estimate its performance relative to the standard decision tree algorithm as well as random decision trees. By estimate I only mean whether its performance is better, worse or the same. Provide a qualitative argument for your decision.
    2. Provide pseudo-code for an extension of the standard decision tree algorithm that does a k-move lookahead. The standard hill-climbing algorithm is a 1-move lookahead algorithm. Assume that you have N instances in the training set and each instance is defined by M binary attributes. Provide a computational analysis of the time-complexity of the algorithm with a 2-move lookahead. This can be done in a similar manner to the one in class. Hypothesize whether this algorithm will perform better or worse than the standard decision tree and give a qualitative argument why.
    3. Assume that all attributes are numeric. Let N be the number of examples and A be the number of attributes. What is the time complexity of forming a decision tree in this case?

    Week 3.

    1. Describe the algorithm with associated paper or project that you intend to do.
    2. Fill out the Machine Learning Review form for the paper by Domingos on Rise.
      MACHINE LEARNING: REVIEW FORM

      Title:
      Authors:
      Reviewer's Name:____________________________

      GOALS. Does the author clearly specify the learning task on which he/she is focusing? Does he/she state the research goals he/she is trying to achieve?

      DESCRIPTION. Does the paper describe the method(s) in sufficient detail for readers to replicate the work? For instance, does it describe the inputs and outputs of the system? Does it clearly explain the algorithms used for performance and learning? Does the paper include enough examples?

      EVALUATION. Does the author carefully evaluate the approach to learning? For instance, does the author run systematic experiments, provide a careful theoretical analysis, show psychological validity, or give evidence of generality?

      DISCUSSION. Does the paper make contact with relevant earlier work, noting similarities, differences, and progress? Does it discuss the limitations of the approach along with its advantages? Does it consider the implications of the approach and outline directions for future work?

      GENERAL. Does the paper make a significant, technically sound contribution? Is the paper well-organized and well-written? Does it use standard terminology? Feel free to give additional comments.

      RECOMMENDATION: Accept____ Borderline____ Reject____

      CONFIDENCE: High _____ Medium _______ Low ______


    Week 4. In this homework you are asked to extend the 1R algorithm covered in class. Assume that the data is in Weka format and that all attributes are nominal.

    a. Write pseudo-code for a "2R" algorithm, i.e. an algorithm which finds the optimal theory where each rule in the theory has the form: if (Attibute1 = value) and (Attribute2 = value' ) then classK. Let T be the number of elements in the training data. Optimality is defined in the same way as for 1R. Note that the result of 2R is a set of rules, but each rule in the "theory" has the same two attributes.

    b. Given a time/space complexity analysis of your pseudo-code.

    c. Without giving an algorithm, write down the time/space complexity of the "kR"algorithm. As before this algorithm yields the optimal theory where each rule has k conditions.



    Week 5.

    1. Suppose a data set has only numeric data with two classes. Also suppose that it is linearly separable. Prove that the space of solutions (linear separators) is convex. Recall: a set S is convex if for two points p and q in S, the point a*p+(1-a)q also belongs to S, where 0<=a<=1.
    2. The standard Perceptron algorithm was presented after doing a data transformation: augmented the data with a constant. This made the algorithm simpler to state and prove theorems about. Provide pseudo-code for the standard Perceptron algorithm where these data transformation have not occurred. Now you will need to explicitly deal updating the threshold. Your code should correspond exactly to code presented in class.
    3. Using the cpu data that is provided with Weka, display a table compare the performance of the following regression algorithms (use 10-fold CV): least mean squares (LMS or Linear Regression),IB1 and IB3, M5, SMOreg with the exponent set to 1 (default) and 2. Does it matter which of the 5 performance measures are used? Which result is most interpretable?


    Weeks 6-7-8. Prepare your paper and presentation

    Weeks 9-10: Make presentation in class. You should submit your powerpoint slides to me the day before your presentation. This will make for smoother transitions between talks. In some cases you may want to use your own laptop, if you have a specific demo to run. Clear this with me first.
    Besides making a presentation, every student is required to comment on each presentation in writing. The goal of this exercise is to give helpful feedback to the speaker so that the presentation can be improved. These comments will be anonymous and address issues such as: goal, clarity, significance, and evidence. The comment form is given below. For each talk, your comments should take no more than 1 page in total. At the next class period paper-clip your comment pages together and add a cover sheet which just has your name on it. Please put your comment pages in alphabetical order by the name of the speaker. I will resort the papers and return all comments via the distribution center.
    Speaker Name:

    • What was the main point of the talk?
    • What was the best part of the talk?
    • What, if anything, was confusing?
    • How might the presenter improve his talk?
    • Anything else?

    Speaker Dates
    • March 7
      • CRISTOFORETTI, JONATHAN
      • WORCESTER, JAMES BRIAN
      • Bhasker, Ezekiel Suneel
      • LEE, DWIGHT JI WEI
      • AZENCOTT, CHLOE AGATHE
    • March 9
      • LINSTEAD, ERIK JOSEPH
      • SHIRANI-MEHR, HOUTAN
      • JOHNSON, MATTHEW E
      • NG, WENG LEONG
      • PORTEOUS, IAN
    • March 14
      • BICHUTSKIY, VADIM Y.
      • KIM, DOUGLAS JUNIOR
      • LIU, SHAOSHAN
      • JUANG, RADFORD RAY
      • VERNICA, RARES
    • March 16
      • Olsen, Robert Alan
      • KUMAR, VASANTH
      • RODRIGUEZ, LUIS ALBERTO
      • CHEN, JONATHAN HAELIN
      • Partida, Augusto Rafael

    http://www.ics.uci.edu/~kibler/AIProjects.html

    AI Project Examples

    In theory, algorithms are evaluated by performance measures such as time and space complexity. In AI these concerns are secondary to how the algorithm performs relative to people. You should consider how you will evaluate your program from the outset. You should also pay a lot of attention to the user-interface. Problems or confusing aspects of your interface are most easily found by those not involved with the project. The suggestions below only provide some examples. You might look through any AI text for more ideas. It's best to choose a topic that you are very interested in. All code will be done in Java. Each object should be documented with who wrote the code. Moreover and very important, each method should be documented with time and space complexity when it is not constant. Usually this is not difficult, but occasionally it is very hard. If you can't do it, say so or discuss it with me or the class. In the first lectures I describe some possible projects. You may do your own project, but it needs to be well-defined, demonstratably doable within one quarter, involves some AI method, and approved by me.

      Machine Learning

      Note: Weka is a freely available, open source Java program for Machine Learning.
    • Decision Trees. (Possible research result). Extend Weka to handle another version of DT algorithms that better deals with continuous values.
    • Clustering. (Possible research result). MOP. Extend Weka to handle co-occurrence evaluation of clustering. Currently no program properly guesses the number of clusters.
    • Function Hierarchical Clustering (Possible research result). MOP. Extend hierarchial clustering to use functional information, such as the Gene Ontology (GO)
    • Kernel Perceptron: (Possible research result) MOP. Combine perceptron training with Kernel method as developed within the Support vector context.
    • FingerPrint Identification: (Possible research result) POP. Choose a representation and apply Machine Learning methods. High visibility problem. Web site for data sets and algorithms.
    • Face Recognition. (Possible research result). POP. Similar to fingerprint recognition. Will become increasingly important. Web site for data sets and algorithms.

      Bioinformatics

    • Visualization. Code various alignment algorithms (local, global, multiple) and display results in a user friendly way.
    • BiMotif finding: (Possible Research results). POP. Reimplement and improve upon the BioProspector approach for finding pairs of short patterns
    • Motif finding: (possible Research result). POP. Reimplement Gibbs sampling and evaluate various alternatives for finding motifs.

      Game Playing

    • Perfect Information two person game. Implement alpha-beta for, say, the Othello game.
    • Multiple person game (e.g. hearts/ scrabble) or incomplete information game (e.g. backgammon, poker) Implement simulation-based decision making algorithm
    • Bridge Bidder. Adopt an expert system approach to bidding. This needs to support explanation.

      Natural Language Processing

    • Form the most likely function from several text descriptions. Blast returns several similar genes with different functions. Using Gene Ontology, form a summary description.
    • Take a simple child story and try to make common sense inferences about the story. This might best be done in Prolog.

      Projects from Russell and Norvig

    • CAI: Illustrate the various search algorithms, i.e. provide a graphical demonstration of how the dfs, bfs, iterative deepening, A* work.
    • Hidden Markov Models: Develop the algorithm and provide illustrative examples of how it would works. Also allow users to select their own data for analysis by the HMM.
    • Neural Nets: Similar to above. Some illustrate examples (data sets to illustrate certain problems) plus ability to enter own data sets.
    • Planners: Choose an example planner from R&N and build several problem domains that the planner would work on.
    • Template idea: take any chapter or two from Russell and Norvig an build a system to illustrate the basic algorithm, whether it be in logical reasoning, constraint satisfaction, image processing, etc.
    http://www.ics.uci.edu/~kibler/Ethics.htm untitled

    Ethical Behavior

    A simple test:

    If your behavior were shown to the class, your family, the tas or the instructor would you be proud or ashamed?



    Scenairo's


    The professor has an assignment involving recursion. You didn't understand recursion in the text or the lecture. Which of the following behaviors is dishonest?

    1. You have a friend explain his solution to the problem. You do not copy it.
    2. You have a friend go over the lecture notes on recursion.
    3. You have a friend get you started on the problem, by giving you some hints.
    4. You have a friend do some different problems involving recursion.

    One way to judge is to see whether the work you present as a solution is your own, or was partly generated by someone else. In cases 1 and 3, the friend provided needed information. It is dishonest and could result in a grade of F, a letter in your file, and possible expulsion from ICS and even UCI. This type of unethical behavior would be difficult to spot, although the damage would eventually accumulate. By misrepresenting your knowledge you are fooling yourself. At some point you may find that computer science is inappropriate for you - then you will have wasted many years. Also employers will recognize your lack of ability and that is unlikely to make you very happy. http://www.ics.uci.edu/~kibler/JavaTexts.html Java Texts

    • The Java Programming Language by Ken Arnold, James Gosling and David Holmes. 3rd Edition.
      Covers fundamentals of language, but not important packages. Accurate description of language. Where I go when I need a deeper understanding of some feature of the language. Covers Collections. This is the book I recommend.
    • Java: An Introduction to Computer Science and Programming by Walter Savitch.
      Straightforward and complete introduction to language that can be used by people with no programming experience. Students like this text.
    • Introduction to Programming Using Java: An Object-Oriented approach by David Arnold and David Weiss.
      Introduction to object-oriented programming using Java. Meant as a first CS course text. I liked it.
    • Up to Speed with Swing by Steven Gutz. Assumes you know Java and introduces basic Swing classes. Readable.
    • Computing Concepts using Java Essentials by Cay Horstmann.
      Covers Java for the beginning programming student. Used in 1A.
    • Core Java 2nd Ed by Cay Horstmann and Gary Cornell
      language + applets, user-interface, delegation event-model (JDK 1.1), and new graphical widgets (e.g. scrollpane).
    • Java in a Nutshell (2nd Edition) (JDK 1.1)
      A complete brief description of the language plus a list of the methods and classes in the JDK 1.1 packages. JDK 1.0 had 8 packages and JDK 1.1 has 23. Moreover the old packages have been extended. These new packages cover important extensions such as JavaBeans, reflections, serialization, JAR, and a new delegation event-model which replaces the old model.
    • Thinking in Java by Bruce Eckel.
      This text covers JDK 1.1 with some information about JDK 1.2. It is meant for the serious programmer who has already programmed in some language, preferably C++. I like this text a lot. He provides practical advice. The text covers the topics in Java in a Nutshell in greater depth.
    • Graphic Java 1.1: Mastering the AWT 2nd Ed.
      thorough discussion of components, lightweight components, custom components. More than you want to know. Good reference.
    • Symantic Visual Café Sourcebook by Cary Jardin and Pam Dixon. A complete guide to Creating Java Applets and applications with Visual Café . Note: only similar to Visual Cafe Pro.
    • For an up-to-date discussion of the Java and its use, see http://www.javasoft.com/.
    • Any book that covers JDK1.1 that you can learn from. Everyone learns differently.
    http://www.ics.uci.edu/~kibler/ics171/syllabus/ untitled

    Grading:

    The total number of quizzes for the class will be 4. The quiz topics are search, logic, probabilistic reasoning and learning. The quizzes are each worth 15% of your grade. The number of homeworks for the class will be 4, divided between one coding problems and three written assignments. There is no cumulative final: instead the final will be a quiz on learning on the last day of class, as requested by Summer Session.

    Regrades: There is no option to resubmit new homework for a new grade. If you believe a homework or quiz is misgraded, ask for a regrade by resubmitting your paper with an explanation of why you believe the grade is incorrect. The entire paper will be regraded, so it is possible to lose credit. You are welcomed to submit homewowk early.


    Homework are to be submitted according to the TA's policy. Late homework will be marked down 20% per day. Graded homework will be available one week after submission. Homework must be typed.

    Tentative Schedule
    • Week 1: Introduction to AI: goals, history, evaluation. Read Chapter 1.
    • Week 2: State-Space Representation, Uninformed Search: DFS, BFS, IDS
    • Week 3: Informed Search: Hill-climbing, A*, Best-First, Iterative Improvement, Genetic Algorithm, Simulated Annealing; Constraint Satisfaction
    • Week 4: Game Playing, Propositional Logic, Quiz on Search
    • Week 5: Prolog, First-Order Logic
    • Week 6: Probabilistic Reasoning, Quiz on Logic
    • Week 7: Bayesian Networks, Weka ML suite
    • Week 8: Quiz on Probabilistic Reasoning
    • Week 9: Nearest Neighbor, Decision Trees,
    • Week 10: Perceptron, Neural Nets


    Quiz Dates: Quizzes are 30 minutes long.

    1. Monday July 19: Quiz on Search
    2. Monday August 2: Quiz on Logic
    3. Wednesday August 18: Quiz on Probabilistic reasoning
    4. Friday September 3: Quiz on Learning
    Homework Due Dates
    1. July 16: Search Homework (code + output)
    2. July 30: Logic Homework (problems)
    3. August 16: Probability Homework (problems)
    4. September 3: Learning Homework (problems)
        http://www.ics.uci.edu/~pattis/misc/cheatingarticle/index.html On Plagiarism

        On Plagiarism

        In 2010 I read a New York Times editorial on Plagiarism (in which I have highlighted two sections in yellow) that accompanied a much longer article about cheating in college. Please read the entire editorial. I focused on two items:
        1. There is a difference between education and training. In education, we are not looking just for the right answer. Instead, we are learning to think critically, to acquire skills, tools, and the knowledge to use them, all towards the goal of being able to use fundamental principles to find the answers to new, unanswered questions.

        2. By cheating, real learning/education is undermined. One doesn't acquire the educational objectives mentioned above by avoiding hard work and just finding and submitting the right answers (even if you end-up understanding these answers).

        What is interesting about this reading is that the author does not say cheating is bad because it is unethical (which it is, and the consequences of cheating can affect the cheaters and other students in class as well), but he does say that cheating is bad becuase it prevents students from acquiring the knowledge/experience they came to college to get, not only in the class in which they cheat, but in later classes as well. http://www.ics.uci.edu/~pattis/ICS-33/index.html ICS-33 <A HREF="frameindex.html"> no frames link to programs </A>


        Richard Pattis
        http://www.ics.uci.edu/~pattis/ICS-46/index.html ICS-46 <A HREF="frameindex.html"> no frames link to programs </A>
        Richard Pattis
        http://www.ics.uci.edu/~pattis/educationvideoclips.html Education Video Clips

        Education Video Clips

        I am starting to index, annotate, and put on the web various video clips focussing on education. So far the collection is small, but I've started with some of my favorites.


        Father Guido Sarducci (Don Novello) discussing his Five Minute University
        Format: Microsoft Media Player; playing time: ~4 minutes.


        Apple Computer's first advertisement for the Macintosh (circa 1984). This ad caused quite a stir at the time. Apple paid to have it broadcast only once, during the superbowl. But, because it was so captivating and clever, it was rebroadcast many times by news outlets, thereby giving Apple a tremendous amount of exposure for free.
        Format: Quicktime Movie; playing time: ~1 minute.


        Ben Stein plays an incredibly inept and boring teacher in the movie Ferris Bueller's Day Off. For a time, Stein had many roles like this one in movies and on TV.
        Format: Microsoft Media Player; playing time: ~1 minute.


        The UCI Race: How to Think About and Win the UCI Race.
        Format: Microsoft Media Player; playing time: ~10 seconds.

        Computer Science is Hard: Tom Hanks explains why, from the Movie, A League of Their Own. Crank up the volume when playing this one.
        Format: Microsoft Media Player; playing time: ~10 seconds.


        The Paper Chase is my all-time favorite movie for students (for teachers, it is Dead Poets Society see below). Kingsfield and Keating, while appearing adjacent in the alphabet, are poles apart in their teaching styles: in their own ways, both are mesmerizing and unforgetable educators.

        The Paper Chase follows James Hart through his first year at Harvard Law School, concentrating on Professor Kingsfield's course on contract law. While a bit dated for students in the new millenium (it was released in 1973), I believe that this movie makes more interesting observations and raises more interesting questions about college life than any other film that I have seen. It especially speaks to survival skills at a school populated by smart students with high expectations. Mr. Hart spends a lot of time trying to understand Kingsfield, but in the end, he understands himself much better.

        The clips below are a few of my favorite (short) scenes, in the order they appeared in the film, providing a limited narrative. Watched individually, they cannot do justice to the film, which has a much richer and more detailed texture. So of course, I recommend skipping these clips now and watching the entire movie first. Watch the whole movie now, before even reading the descriptions of the clips below. I have tried to remove references to the most interesting plot twists.

        Clip 1: At the start of the first day of classes, Kingsfield randomly calls on Mr. Hart to discuss a case that was assigned before school began.
        Format: Microsoft Media Player; playing time: ~3 minutes.

        Clip 2: Kingsfield explains the Socratic method of teaching.
        Format: Microsoft Media Player; playing time: ~1.5 minutes.

        Clip 3: Hart tells his girfriend (they are in bed together, but there is no nudity) about the three factions of students at Harvard, and vows to enter the upper echelon in Kingsfield's class.
        Format: Microsoft Media Player; playing time: ~1.5 minutes.

        Clip 4: Kingfield responds to one of Hart's classmates, who has a photographic memory.
        Format: Microsoft Media Player; playing time: ~.5 minutes.

        Clip 5: Hart and and his friend/confidant Ford break into the "red set room" in the Law Library to read the notes that Kingfield took in his contract law class, when he was a first year student.
        Format: Microsoft Media Player; playing time: ~3.5 minutes.

        Clip 6: Hart approaches Kingsfield after class to further discuss a point, and gets a job from him.
        Format: Microsoft Media Player; playing time: ~2.5 minutes.

        Note: I had to edit out a few words to ensure that this clip doesn't disclose a plot twist.
        Clip 7: Hart discusses Kingsfield with his girlfriend, and in the next scene "passes" when asked a question by Kingsfield in class.
        Format: Microsoft Media Player; playing time: ~3.5 minutes.

        Clip 8: After taking his final exam in contracts, Hart runs into Kingsfield in the elevator, and tells him the impact Kingsfield and his class has had on Hart. A major part of the movie concerns the many levels of interaction between Hart and Kingsfield (many of those interactions are missing from these clips). Note what happens when Kingsfield asks Hart a surprising question: the slow take and look on Hart's face is all-telling.
        Format: Microsoft Media Player; playing time: ~1 minute.


        Dead Poets Society is my all-time favorite movie for teachers (for students, it is The Paper Chase see above). I try to watch it before every semester starts. The older I get the further away I get from the aspirations and life-experiences of my students, who are mostly barely out of high school, and now at a new and wonderful place, with millions of "distractions" competing for their attention. This movie helps me recenter my thoughts. Kingsfield and Keating, while appearing adjacent in the alphabet, are poles apart in their teaching styles: in their own ways, both are mesmerizing and unforgetable educators.

        Dead Poets Society follows a group of seniors through the first half of their last year at a New England prep school, and the effect a new English teacher, Mr. Keating, has on their lives.

        The clips below are a few of my favorite (short) scenes, in the order they appeared in the film, providing a glimpse of Mr. Keating's teaching style. Watched individually, they do not provide much of a narrative of the film, as most of this "coming of age" story occurs outside of the classroom. So of course, I recommend skipping these clips now and watching the entire movie first.

        Clip 1: On the first day of classes, Keating introduces himself to the boys and introduces them to the phrase "Carpe Diem" -seize the day- and tells them to "make their lives extraordinary".
        Format: Microsoft Media Player; playing time: ~5.5 minutes.

        Clip 2: The boys read an essay about measuring poetry, titled "Understanding Poetry" by Dr. J. Evans Pritchard, Ph.D. in the introduction to their textook. Warning: includes scenes of extreme violence to textbooks.
        Format: Microsoft Media Player; playing time: ~6 minutes.

        Clip 3: By standing on his desk, Keating reminds the boys that "we must constantly look at things in a different way." He assigns each to compose a poem of his own, to be delivered aloud in class.
        Format: Microsoft Media Player; playing time: ~2 minutes.

        Clip 4: Keating teaches a short lesson on the soccer field.
        Format: Microsoft Media Player; playing time: ~1.5 minutes.

        Clip 5: In class, the boys read the poems that they composed.
        Format: Microsoft Media Player; playing time: ~4 minutes.

        Clip 6: Keating teaches a lesson on conformity.
        Format: Microsoft Media Player; playing time: ~2 minutes.


        The following videos show demos that changed the face of Computer Science.

        Douglas Engelbart: "This is an edited record of Douglas Engelbart's historic presentation of the NLS System at the Fall Joint Computer Conference in San Francisco, on December 8, 1968. The video captuers what was projected onto a 22'x18/ screen at a 2000-chair convention center. The soundtrack reproduces what came over the loud speakers. On stage was Doug Enbelbart at the controls of an on-line computer display whose output was projected onto the screen. Behind the scenes, Bill English and the crew manned cameras and signal switchers, connecting the conference center with SRI, 30 miles away. Many concepts in today's interfaces were first introduced in the NLS system and this presentation. These include: modern word processing, outlining and hypermedia, a mouse and 1-hand keyboard, shared files, messaging, email and filtering, video desktop conferencing."

        Engelbart is most well know as the inventor of the mouse. In the early 60s, he did groundbreaking work at SRI (formerly the Stanford Research Institute) on the augmentation of human intellect. This work was a direct outgrowth of the Vannevar Bush's Memex, and the work done by J.C.R. Licklider on Man-Computer symbiosis. For more information on Engelbart, see Bardini, Boostrapping: Douglas Engelbart, Coevolution, and the Origins of Personal Computing, Stanford University, 2000.
        Format: Microsoft Media Player; playing time: ~21 minutes.

        Ivan Sutherlad - Sketchpad: This video is a TV show made about the software Ivan Sutherland developed in his 1963 thesis at MIT's Lincoln Labs, "Sketchpad, A Man-Machine Graphical Communication System", described as one of the most influential computer programs ever written. This work was seminal in Human-Computer Interaction, Graphics and Graphical User Interfaces (GUIs), Computer Aided Design (CAD), and contraint/object-oriented programming. While watching this video, remember that the TX-2 computer (built circa 1958) on which the software ran was built from discrete transistors (not integrated circuits -it was room-sized) and contained just 64K of 36-bit words (~272k bytes). For more information, you can view/download a .pdf file of Sutherland's Thesis a remarkably clear presentation of his ideas.
        Format: Microsoft Media Player; playing time: ~21 minutes. http://www.ics.uci.edu/~pattis/quotations.html Quotations for CS1

        Quotations for Learning and Programming

        I have enjoyed reading (and writing), collecting, and pondering the following quotations, which I think are all relevant to teaching and learning programming. If you know any others that you think I might like, please email them to pattis@ics.uci.edu. If you have corrections or further information on the source of one of these quotations, please let me know that too.


        A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

        Quotations are indexed alphabetically by their author. For authors with multiple quotations, the quotations are alphabetized by the first word in the quotations.


        A

        Programs must be written for people to read, and only incidentally for machines to execute.

        - H. Abelson and G. Sussman (in "The Structure and Interpretation of Computer Programs)

        The computer revolution is a revolution in the way we think and in the way we express what we think. The essence of this change is the emergence of what might best be called procedural epistemology--the study of the structure of knowledge from an imperative point of view, as opposed to the more declarative point of view taken by classical mathematical subjects.

        - H. Abelson and G. Sussman (in "The Structure and Interpretation of Computer Programs)

        We have also obtained a glimpse of another crucial idea about languages and program design. This is the approach of stratified design, the notion that a complex system should be structured as a sequence of levels that are described using a sequence of languages. Each level is constructed by combining parts that are regarded as primitive at that level, and the parts constructed at each level are used as primitives at the next level. The language used at each level of a stratified design has primitives, means of combination, and means of abstraction appropriate to that level of detail.

        - H. Abelson and G. Sussman (in "The Structure and Interpretation of Computer Programs")

        A common mistake people make when trying to design something completely foolproof is to underestimate the ingenuity of complete fools.

        - D. Adams

        Good teaching is more a giving of the right questions than a giving of the right answers.

        - J. Albers

        They know enough who know how to learn

        - J. Adams

        Computer Science is a science of abstraction -creating the right model for a problem and devising the appropriate mechanizable techniques to solve it.

        - A. Aho and J. Ullman

        Each pattern describes a problem which occurs over and over again in our environment, and then describes the core of the solution to that problem, in such a way that you can use this solution a million times over, without ever doing it in the same way twice.

        - C. Alexander

        Don't worry about people stealing your ideas. If your ideas are any good, you'll have to ram them down people's throats.

        - H. Aiken

        Eighty percent of success is showing up.

        - W. Allen

        If you're not failing every now and again, it's a sign you're not doing anything very innovative.

        - W. Allen

        More is different.

        - P. Anderson (attacking the reductionist hypothesis in science, by concluding that quantitatively larger systems can be qualitatively different)

        Today, most software exists, not to solve a problem, but to interface with other software.

        - I. O. Angell

        All programmers are playwrights and all computers are lousy actors.

        - Anonymous

        Bad code isn't bad, its just misunderstood.

        - Anonymous

        Debugging is anticipated with distaste, performed with reluctance, and bragged about forever.

        - Anonymous

        Distance education begins at the 8th row in a classroom.

        - Anonymous

        Experience is a poor teacher: it gives its tests before it teaches its lessons.

        - Anonymous

        If you understand what you're doing, you're not learning anything.

        - Anonymous

        In theory, there is no difference between theory and practice, but not in practice.

        - Anonymous

        It is easier to measure something than to understand what you have measured.

        - Anonymous

        Measure twice, cut once.

        - Anonymous

        Microsoft, where quality is job 1.1

        - Anonymous

        On a visit to the NASA space center, President Kennedy spoke to a man sweeping up in one of the buildings. "What's your job here?" asked Kennedy. "Well Mr. President," the janitor replied, "I'm helping to put a man on the moon".

        - Anonymous

        One day a mother comes home from work and asks her son, "What did you do today?" The son replied, "I taught our dog how to play the piano." The mother, incredulous, asked, "Our dog can play the piano?", to which the son laughed and replied, "Of course not mom. I said that I taught him; I didn't say that he learned how."

        - Anonymous

        Here is how a true educator deals with a problem.

        The principal of a small middle school had a problem with a few of the older girls, who were starting to use lipstick. They were applying it in the bathroom, and then they would press their lips to the mirror to smooth it out, leaving lip prints.

        He invited the girls to the ladies room, where they met with the custodian. He said he wanted them to witness just how hard it was to clean these mirrors.

        The custodian took a long brush on a handle out of a box, dipped it into the nearest toilet, moved to the mirror, and proceeded to scrub it with the brush.

        That was the last day the girls pressed their lips on the mirror.

        - Anonymous

        Programming languages should be designed not by piling feature on top of feature, but by removing the weaknesses and restrictions that make additional features appear necessary.

        - Anonymous, Revised Report on the Algorithmic Language Scheme

        Programs for sale: Fast, Reliable, Cheap: choose two.

        - Anonymous

        Ready, fire, aim (the fast approach to software development).
        Ready, aim, aim, aim, aim ... (the slow approach to software development).

        - Anonymous

        Real programmers don't comment their code. If it was hard to write, it should be hard to understand.

        - Anonymous

        Recurses! Called again.

        - Anonymous

        The huge printing presses of a major Chicago newspaper began malfunctioning on the Saturday before Christmas, putting all the revenue for advertising that was to appear in the Sunday paper in jeopardy. None of the technicians could track down the problem. Finally, a frantic call was made to the retired printer who had worked with these presses for over 40 years. "We'll pay anything; just come in and fix them," he was told.

        When he arrived, he walked around for a few minutes, surveying the presses; then he approached one of the control panels and opened it. He removed a dime from his pocket, turned a screw 1/4 of a turn, and said, "The presses will now work correctly." After being profusely thanked, he was told to submit a bill for his work.

        The bill arrived a few days later, for $10,000.00! Not wanting to pay such a huge amount for so little work, the printer was told to please itemize his charges, with the hope that he would reduce the amount once he had to identify his services. The revised bill arrived: $1.00 for turning the screw; $9,999.00 for knowing which screw to turn.

        Commentary: most debugging problems are fixed easily; identifying the location of the problem is hard.

        - Anonymous

        The person who knows HOW will always have a job. The person who knows WHY will always be his/her boss.

        - Anonymous

        The sooner you get behind in your work, the more time you have to catch up.

        - Anonymous

        There are only 10 different kinds of people in the world: those who know binary and those who don't.

        - Anonymous

        Think (design) globally; act (code) locally.

        - Anonymous

        Think twice, code once.

        - Anonymous

        Time is an excellent teacher; but eventually it kills all its students.

        - Anonymous

        Weeks of programming can save you hours of planning.

        - Anonymous

        When a programming language is created that allows programmers to program in simple English, it will be discovered that programmers cannot speak English.

        - Anonymous

        Why do we never have time to do it right, but always have time to do it over?

        - Anonymous

        By viewing the old we learn the new.

        - Anonymous Chinese Proverb

        Give me a fish and I eat for a day. Teach me to fish and I eat for a lifetime.

        - Anonymous Chinese Proverb

        He who asks is a fool for five minutes; he who does not ask remains a fool forever.

        - Anonymous Chinese Proverb

        Teachers open the door, but you must enter by yourself.

        - Anonymous Chinese Proverb

        Tell me and I forget. Show me and I remember. Involve me and I understand.

        - Anonymous Chinese Proverb

        The first step towards wisdom is calling things by their right names.

        - Anonymous Chinese Proverb

        The person who says it cannot be done should not interrupt the person doing it.

        - Anonymous Chinese Proverb

        He who is ashamed of asking is ashamed of learning.

        - Anonymous Danish Proverb

        No matter how far down the wrong road you have gone, turn back now.

        - Anonymous Turkish Proverb

        Those who know, do. Those who understand, teach.

        - Aristotle

        We are what we repeatedly do. Excellence, then, is not an act, but a habit.

        - Aristotle

        The most exciting phrase to hear in science -the one that heralds new discoveries- is not "Eureka!" but "That's funny...".

        - I. Asimov

        The Analytical Engine weaves Algebraical patterns just as the Jacquard loom weaves flowers and leaves.

        - A. Augusta, Countess of Lovelace, on Babbage's Analytical Engine


        B

        On two occasions, I have been asked [by members of Parliament], "Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?" I am not able to rightly apprehend the kind of confusion of ideas that could provoke such a question.

        - C. Babbage

        I, myself, have had many failures and I've learned that if you are not failing a lot, you are probably not being as creative as you could be -you aren't stretching your imagination.

        - J. Backus

        You need the willingness to fail all the time. You have to generate many ideas and then you have to work very hard only to discover that they don't work. And you keep doing that over and over until you find one that does work.

        - J. Backus (NYT Obituary )

        A prudent question is one-half of wisdom.

        - F. Bacon

        I can only think that the book is read because it deals with the difficulties of schooling, which do not change. Please note: the difficulties, not the problems. Problems are solved or disappear with the revolving times. Difficulties remain. It will always be difficult to teach well, to learn accurately; to read, write, and count readily and competently; to acquire a sense of history and start one's education or anothers.

        - J. Barzun ("Begin Here", pp 14),

        The American university is built on two false premises: that all teachers must add to the existing stock of knowledge by research, and that all self-respecting institutions fulfill this role only by employing productive scholars...Of course, the teacher must keep reading and thinking abreast of his time, but this does not mean that he must write and publish. The confusion hides a further absurd assumption, which is that when a man writes a scholarly book that reaches a dozen specialists he adds immeasurably to the world's knowledge; whereas if he imparts his thoughts and his reading to one hundred and fifty students every year, he is wasting his time and leaving the world in darkness. One is tempted to ask what blinkered pedant ever launched the notion that students in coming to college seceded from the human race and may therefore be safely left out when knowledge is being broadcast.

        - J. Barzun ("Teacher in America"),

        The sole justification of teaching, of the school itself, is that the student comes out of it able to do something he could not do before. I say do and not know, because knowledge that doesn't lead to doing something new or doing something better is not knowledge at all.

        - J. Barzun ("Begin Here", pp 112),

        The truth is, when all is said and done, one does not teach a subject, one teaches a student how to learn it. Teaching may look like administering a dose, but even a dose must be worked on by the body if it is to cure. Each individual must cure his or her own ignorance.

        - J. Barzun ("Begin Here", pp 35),

        Optimism is an occupational hazard of programming: testing is the treatment.

        - K. Beck

        Ever tried. Ever failed. No matter. Try Again. Fail again. Fail better.

        - S. Beckett

        The cheapest, fastest, and most reliable components of a computer system are those that aren't there.

        - G. Bell

        Dakin (to Irwin, his teacher): Do you really believe that, sir, or are you just trying to make us think?

        - A. Bennett (in "The History Boys")

        The key to performance is elegance, not battalions of special cases.

        - J. Bentley & D. McIlroy

        Walking on water and developing software from a specification are easy if both are frozen.

        - E. Berard

        More than the act of testing, the act of designing tests is one of the best bug preventers known. The thinking that must be done to create a useful test can discover and eliminate bugs before they are coded - indeed, test-design thinking can discover and eliminate bugs at every stage in the creation of software, from conception to specification, to design, coding and the rest.

        - B. Bezier

        Percy:  You know, they do say that the Infanta's eyes are more beautiful
                than the famous Stone of Galveston. 
        Edmund: Mm! ... What?
        Percy:  The famous Stone of Galveston, My Lord.
        Edmund: And what's that, exactly?
        Percy:  Well, it's a famous blue stone, and it comes ... from Galveston.
        Edmund: I see. And what about it?
        Percy:  Well, My Lord, the Infanta's eyes are bluer than it, for a start.
        Edmund: I see. And have you ever seen this stone?
        Percy:  (nods) No, not as such, My Lord, but I know a couple of people who
                 have, and they say it's very very blue indeed. 
        Edmund: And have these people seen the Infanta's eyes?
        Percy:  No, I shouldn't think so, My Lord.
        Edmund: And neither have you, presumably.
        Percy:  No, My Lord.
        Edmund: So, what you're telling me, Percy, is that something you have never
                seen is slightly less blue than something else you have never seen.
        Percy:  (finally begins to grasp) Yes, My Lord.
        
        I sometimes feel this way when trying to explain a new programming concept.

        - From the Queen of Spain's Beard episode of Blackadder.

        When I grew up, my Dad used to encourage my brother and me to fail. So I would come home from school, and sit at the dinner table and my dad would say kids what did you fail at this week. And if I didn't have something to tell him, he would be disappointed. So if we weren't failing enough in our home, it meant we weren't trying new things.

        - S. Blakely (founder of Spanx, youngest self-made woman billionaire 2013) from CNN interview

        There is a division in the student population between those who go to college to learn and those who go to college to earn a diploma.

        - J. Blau (letter to the editor, Chronicle of Higher Education, May 24, 2002)

        The cleaner and nicer the program, the faster it's going to run. And if it doesn't, it'll be easy to make it fast.

        - J. Bloch (in Seibel, "Coders at Work")

        It is wrong to think that the task of physics is to find out how nature is. Physics concerns what we say about nature.

        - N. Bohr

        Opposites are not contradictory but complementary.

        - N. Bohr

        Software development has been, is, and will likely remain fundamentally hard. Building quality systems involves an essential and irreducible complexity, which is why the entire history of software engineering can be characterized as one of rising levels of abstraction. As such, the task of the software development team is to engineer the illusion of simplicity. Nonetheless, software-intensive systems can amplify human intelligence, but they cannot replace human judgment; software-intensive systems can fuse, co-ordinate, classify, and analyze information, but they cannot create knowledge. In other words, not everything we want to build can be built: there exist pragmatic theoretical and technical limits that make software development hard if not impossible. Furthermore, not everything we want to build should be built: there exist moral economic, social, and political limits that govern human industry. From fundamental to human, these are the factors that define the limits of software, factors that separate our vision from execution.

        - G. Booch (in a blurb from a talk, "The Limits of Software")

        That language is an instrument of human reason, and not merely a medium for the expression of thought, is a truth generally admitted.

        - G. Boole

        The greatest obstacle to discovery is not ignorance, but the illusion of knowledge.

        - D. Boorstin

        The most likely way for the world to be destroyed, most experts agree, is by accident. That's where we come in; we're computer professionals. We cause accidents.

        - N. Borenstein

        All models are wrong; some models are useful.

        - G. Box

        Simplicity and flexibility will trump optimization and power in a world where connectivity is the key.

        - A. Bosworth (paraphrased by David Bank in Breaking Windows, page 203)

        The important thing in science is not so much to obtain new facts as to discover new ways of think about them.

        - W. Bragg

        Information wants too be free. Information also wants to be expensive. Information wants to be free because it has become so cheap to distribute, copy, and recombine---too cheap to meter. It wants to be expensive because it can be immeasurably valuable to the recipient. That tension will not go away. It leads to endless wrenching debate about price, copyright, "intellectual property", the moral rightness of casual distribution, because each round of new devices makes the tension worse, not better.

        - S. Brand (see the Information Wants to Be Free web page)

        It is important that students bring a certain ragamuffin, barefoot irreverence to their studies; they are not here to worship what is known, but to question it.

        - J. Bronowski

        That is the essence of science: ask an impertinent question, and you are on the way to a pertinent answer.

        - J. Bronowski

        Welcome to Yale. Yale will be for you and to you what you make of it. Despite the gloom of the times this is not a gloomy place. It is a place where life can have purpose without being a society of driven men and women. It is also a place where there is no escape from argument, for the next person you meet will not be likely to agree with the last person talked to. This is a community which rewards participation but does not expect conformity. This is primarily a place for learning, but not all learning is in books or laboratories or classrooms. You probably have not been as free before. You may not be as free again. Enjoy the privilege of doubt. Make the most of it.

        - K. Brewster

        Good judgment comes from experience; experience comes from bad judgment.

        - F. Brooks

        Plan to throw one away; you will anyhow.

        - F. Brooks ("The Mythical Man-Month", Chapter 11)

        If you plan to throw one away, you will throw away two.

        - C. Zerouni

        Scientists build to learn; Engineers learn to build.

        - F. Brooks

        The hardest part of the software task is arriving at a complete and consistent specification, and much of the essence of building a program is in fact the debugging of the specification.

        - F. Brooks

        The programmer, like the poet, works only slightly removed from pure thought-stuff. He builds castles in the air, from air, creating by exertion of the imagination. Few media of creation are so flexible, so easy to polish and rework, so readily capable of realizing grand conceptual structures. Yet the program construct, unlike the poet's words, is real in the sense that it moves and works, producing visible outputs separate from the construct itself. It prints results, draws pictures, produces sounds, moves arms. The magic of myth and legend has come true in our time. One types the correct incantation on a keyboard, and a display screen comes to life, showing things that never were nor could be. ... The computer resembles the magic of legend in this respect, too. If one character, one pause, of the incantation is not strictly in proper form, the magic doesn't work. Human beings are not accustomed to being perfect, and few areas of human activity demand it. Adjusting to the requirement for perfection is, I think, the most difficult part of learning to program.

        - F. Brooks ("The Mythical Man Month", pages 7-8)

        Successful software always gets changed.

        - F. Brooks

        A man's reach should exceed his grasp, or what's heaven for?

        - R. Browning

        They may forget what you said, but they will never forget how you made them feel.

        - C. Buchner

        ...and then it occurred to me that a computer is a stupid machine with the ability to do incredibly smart things, while computer programmers are smart people with the ability to do incredibly stupid things. They are, in short, a perfect match.

        - B. Bryson

        Learning how to learn is life's most important skill.

        - T. Buzan


        C

        The first 90% of the code accounts for the first 90% of the development time. The remaining 10% of the code accounts for the other 90% of the development time.

        - T. Cargill

        The sooner you start to code, the longer the program will take.

        - R. Carlson

        The important point is that the cost of adding a feature isn't just the time it takes to code it. The cost also includes the addition of an obstacle to future expansion. Sure, any given feature list can be implemented, given enough coding time. But in addition to coming out late, you will usually wind up with a code base that is so fragile that new ideas that should be dead-simple wind up taking longer and longer to work into the tangled existing web. The trick is to pick the features that don't fight each other.

        - J. Carmack

        We think too much about effective methods of teaching and not enough about effective methods of learning. No matter how good teaching may be, each student must take the responsibility for his own education.

        - J. Carolus S.J.

        In a way, math isn't the art of answering mathematical questions, it is the art of asking the right questions, the questions that give you insight, the ones that lead you in interesting directions, the ones that connect with lots of other interesting questions -the ones with beautiful answers.

        - G. Chaitin (pg. 23, in "Meta Math: The Quest for Omega")

        Mathematical truth is not totally objective. If a mathematical statement is false, there will be no proofs, but if it is true, there will be an endless variety of proofs, not just one! Proofs are not impersonal, they express the personality of their creator/discoverer just as much as literary efforts do. If something important is true, there will be many reasons that it is true, many proofs of that fact. Math is the music of reason, and some proofs sound like jazz, others sound like a fugue. Which is better, the jazz or the fugue? Neither: it's all a matter of taste...each proof will emphasize different aspects of the problem, each proof will lead in a different direction. Each one will have different corollaries, different generalizations...Mathematical facts are not isolated, they are woven into a vast spider's web of interconnections.

        - G. Chaitin (pg. 23, in "Meta Math: The Quest for Omega")

        Any sufficiently advanced technology is indistinguishable from magic.

        - A. Clarke

        We don't have time to stop for gas, we're already late.

        - M. Cleron (Commenting on how Software Projects are often Run)

        To be truly educated from this point of view means to be in a position to inquire and create on the basis of the resources available to you, which you've come to appreciate and comprehend. To know where to look, to know how to formulate serious questions, to question a standard doctrine, if that's appropriate, to find your own way, to shape the questions that are worth pursuing, and to develop the path to pursue them. That means knowing, understanding many things, but also, much more important than what you have stored in your mind, to know where to look, how to look, how to question, how to challenge, how to proceed independently to deal with the challenges that the world presents to you....

        - N. Chomsky (Video )

        Whenever there is a hard job to be done I assign it to a lazy man; he is sure to find an easy way of doing it.

        - W. Chrysler

        The real technology -behind all our other technologies- is language. It actually creates the world our consciousness lives in.

        - A. Codrescu

        I hear and I forget; I see and I remember; I do and I understand.

        - Confucius

        Never hesitate to ask a lesser person.

        - Confucius

        Any organization that designs a system (defined broadly) will produce a design whose structure is a copy of the organization's communication structure.
        (commonly expressed as Conway's law, "Systems resemble the organizations which produce them.")

        - M. Conway

        Press on. Nothing in th world can take the place of persistence. Talent will not; nothing is more common than unsuccessful men with talent. Genius will not; unrewarded genius is almost a proverb. Education alone will not; the world is full of educated derelicts. Persistence and determination alone are omnipotent.

        - C. Coolidge

        Six jokes: "What do you get when you cross a computer with an airplane? What do you get when you cross a computer with a camera? What do you get when you cross a computer with an alarm clock? What do you get when you cross a computer with a car? What do you get when you cross a computer with a bank? What do you get when you cross a computer with a warship?"

        In all six cases the answer is "A computer." [Cooper illustrates how the nature of these systems becomes dominated by the nature of their computer components]

        - A. Cooper (I'm paraphrasing some pictures and text appearing in "The Inmates are Running the Asylum")

        The value of a prototype is in the education it gives you, not in the code itself.

        - A. Cooper (in "The Inmates are Running the Asylum")

        When the words are fuzzy, the programmers reflexively retreat to the most precise method of articulation available: source code. Although there is nothing more precise than code, there is also nothing more permanent or resistant to change. So the situation frequently crops up where nomenclature confusion drives programmers to begin coding prematurely, and that code becomes the de facto design, regardless of its appropriateness or correctness.

        - A. Cooper (in "The Inmates are Running the Asylum")

        Doing more things faster is no substitute for doing the right things.

        - S. R. Covey

        The generation of random numbers is too important to be left to chance.

        - R. Coveyou

        If you don't think carefully, you might believe that programming is just typing statements in a programming language.

        - W. Cunningham


        D

        Who dares to teach must never cease to learn.

        - J.C. Dana

        Every now and then go away, have a little relaxation, for when you come back to your work your judgment will be surer. Go some distance away because then the work appears smaller and more of it can be taken in at a glance and a lack of harmony and proportion is more readily seen.

        - L. Da Vinci

        Simplicity is the ultimate sophistication.

        - L. Da Vinci

        If you cannot describe what you are doing as a process, you don't know what you're doing.

        - W. E. Deming

        Question authority; but, raise your hand first.

        - A. Dershowitz

        One can think effectively only when one is willing to endure suspense and to undergo the trouble of searching.

        - J. Dewey

        As long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now [1972] that we have gigantic computers, programming has become a gigantic problem. As the power of available machines grew by a factor of more than a thousand, society's ambition to apply these new machines grew in proportion, and it was the poor programmer who found his job in this exploded field of tension between the ends and the means. The increased power of the hardware, together with the perhaps more dramatic increase in its reliability, made solutions feasible that the programmer had not dared to dream about a few years before. And now, a few years later, he had to dream about them and even worse, he had to transform such dreams into reality! It is no wonder that we found ourselves in a software crisis

        - E. Dijkstra (The Humble Programmer, "ACM Turing Award Lectures: The First 25 Years", Addison-Wesley, 1987, pages 17-32)

        A most important, but also most elusive, aspect of any tool is its influence on the habits of those who train themselves in its use. If the tool is a programming language this influence is, whether we like it or not, an influence on our thinking habits.... A programming language is a tool that has profound influence on our thinking habits.

        - E. Dijkstra

        Being abstract is something profoundly different from being vague... The purpose of abstraction is not to be vague, but to create a new semantic level in which one can be absolutely precise.

        - E. Dijkstra

        Besides a mathematical inclination, an exceptionally good mastery of one's native tongue is the most vital asset of a competent programmer.

        - E. Dijkstra

        Computer Science is no more about computers than astronomy is about telescopes.

        - E. Dijkstra

        If we wish to count lines of code, we should not regard them as lines produced but as lines spent.

        - E. Dijkstra

        John von Neumann draws attention to what seemed to him a contrast. He remarked that for simple mechanisms, it is often easier to describe how they work than what they do, while for more complicated mechanisms, it is usually the other way around.

        - E. Dijkstra (Trip Reports, 213)

        Object-oriented programming is an exceptionally bad idea which could only have originated in California

        - E. Dijkstra (note: OOP originated in Norway, quite near Holland -Dijkstra's home)

        ...our intellectual powers are rather geared to master static relations and that our powers to visualize processes evolving in time are relatively poorly developed. For that reason we should do (as wise programmers aware of our limitations) our utmost to shorten the conceptual gap between the static program and the dynamic process, to make the correspondence between the program (spread out in text space) and the process (spread out in time) as trivial as possible.

        - E. Dijkstra (in "Goto Considered Harmful")

        Program testing can be used to show the presence of bugs, but never to show their absence!

        - E. Dijkstra

        Progress is possible only if we train ourselves to think about programs without thinking of them as pieces of executable code.

        - E. Dijkstra

        Simplicity is prerequisite for reliability.

        - E. Dijkstra

        ...Simplifications have had a much greater long-range scientific impact than individual feats of ingenuity. The opportunity for simplification is very encouraging, because in all examples that come to mind the simple and elegant systems tend to be easier and faster to design and get right, more efficient in execution, and much more reliable than the more contrived contraptions that have to be debugged into some degree of acceptability....Simplicity and elegance are unpopular because they require hard work and discipline to achieve and education to be appreciated.

        - E. Dijkstra (The Tide, not the waves; in Denning/Metcalfe: Beyond Calculation, Springer-Verlag 1997)

        The tools we use have a profound (and devious!) influence on our thinking habits, and, therefore, on our thinking abilities.

        - E. Dijkstra

        The competent programmer is fully aware of the strictly limited size of his own skull; therefore he approaches the programming task in full humility, and among other things he avoids clever tricks like the plague.

        - E. Dijkstra (in "The Humble Programmer", his 1972 Turing Award Lecture)

        The art of programming is the art of organizing complexity, of mastering multitude and avoiding its bastard chaos.

        - E. Dijkstra (in "Notes on Structured Programming")

        We are all shaped by the tools we use, in particular: the formalisms we use shape our thinking habits, for better or for worse, and that means that we have to be very careful in the choice of what we learn and teach, for unlearning is not really possible.

        - E. Dijkstra (in Answers to Questions from Students of Software Engineering)

        We shall do a much better programming job, provided that we approach the task with a full appreciation of its tremendous difficulty, provided that we stick to modest and elegant programming languages, provided that we respect the intrinsic limitations of the human mind and approach the task as Very Humble Programmers.

        - E. Dijkstra (in "The Humble Programmer", his 1972 Turing Award Lecture)

        Yes, I share your concern: how to program well -though a teachable topic- is hardly taught. The situation is similar to that in mathematics, where the explicit curriculum is confined to mathematical results; how to do mathematics is something the student must absorb by osmosis, so to speak. One reason for preferring symbol-manipulating, calculating arguments is that their design is much better teachable than the design of verbal/pictorial arguments. Large-scale introduction of courses on such calculational methodology, however, would encounter insurmountable political problems.

        - E. Dijkstra (in Answers to Questions from Students of Software Engineering)

        Text is linear; it is black and white; it doesn't zoom around the page in 3-D; it isn't intelligent by itself; in fact, in terms of immediate reaction it is quite boring. I can't imagine a single preliterate was ever wowed at the first sight of text, and yet text has been the basis of arguably the most fundamental intellectual transformation of the human species. It and its subforms, such as algebra, have made science education for all a plausible goal.

        - A.diSessa ("Changing Minds: Computers, Learning, and Literacy", MIT Press, 2000; page 112)

        There is nothing so useless as doing efficiently that which should not be done at all.

        - P. Drucker

        The first step in fixing a broken program is getting it to fail repeatably [on the simplest example possible].

        - T. Duff

        It's supposed to be hard! If it wasn't hard, everyone would do it. The hard... is what makes it great!

        - J. Dugan (said by Tom Hanks' character in "A League of Their Own" in response to a complaint from one of his ball players)

        In a fixed mindset students believe their basic abilities, their intelligence, their talents, are just fixed traits. They have a certain amount and that's that, and then their goal becomes to look smart all the time and never look dumb. In a growth mindset students understand that their talents and abilities can be developed through effort, good teaching and persistence. They don't necessarily think everyone's the same or anyone can be Einstein, but they believe everyone can get smarter if they work at it.

        - C. Dweck (see also the quote by J. Hamilton)

        It's when something fails that you learn. If it doesn't fail, you don't learn anything; you haven't made any progress. Everything I do is a mistake; it fails.

        - J. Dyson (interview in Wired Magazine, p. 112, Dec. 2012)


        E

        Genius is 1 percent inspiration and 99 percent perspiration. As a result, genius is often a talented person who has simply done all of his homework.

        - T. Edison

        Any intelligent fool can make things bigger, more complex, and more violent. It takes a touch of genius - and a lot of courage - to move in the opposite direction.

        - A. Einstein

        Everything should be made as simple as possible, but not simpler.

        - A. Einstein

        Example isn't another way to teach. It is the only way to teach.

        - A. Einstein

        The formulation of a problem is often more essential than its solution, which may be merely a matter of mathematical or experimental skill.

        - A. Einstein

        If you can't explain it simply, you don't understand it well enough.

        - A. Einstein

        Imagination is more important than knowledge.

        - A. Einstein

        Out of clutter, find simplicity. From discord, find harmony. In the middle of difficulty, lies opportunity.

        - A. Einstein

        Reinventing the wheel is a process.

        - R. Elisha

        To arrive at the simple is difficult.

        - R. Elisha

        Shall I tell you the secret of the true scholar? It is this: every man I meet is my master in some point, and in that I learn of him.

        - R.W. Emerson

        The proof of a high education is the ability to speak about complex matters as simply as possible.

        - R.W. Emerson


        F

        In those days [batch processing] programmers never even documented their programs, because it was assumed that nobody else would ever use them. Now, however, time-sharing had made exchanging software trivial: you just stored one copy in the public repository and thereby effectively gave it to the world. Immediately people began to document their programs and to think of them as being usable by others. They started to build on each others work.

        - R. Fano (in Waldrop, "The Dream Machine", pp. 232)

        ..a function approach [to teaching programming] has numerous, beneficial side-effects.

        - M. Felleisen (in What's TeachScheme!)

        Thus, writing a clever piece of code that works is one thing; designing something that can support a long-lasting business is quite another. Commercial software design and production is, or should be, a rigorous, capital-intensive activity. Software products should be based on a broad, deep structure that can support much more than whatever the product contains at any given time. In addition to code that works, you need documentation, help functions, error handling, multi-platform support, and multiple languages. You also need an underlying architecture that allows you to add and change features, purchase and integrated external software components, and allows other software vendors to make their products talk to yours, add customized widgets to it, embed your product inside something larger of their own. A good architecture, one that will carry you for a decade's worth of unpredictable technology and marked changes, take months to develop. But if you skip this step, as Netscape did, you have made a truly Faustian bargain.

        The problem with this [building just what you need, instead of planning ahead] is that these systems start getting ferociously complicated. It comes time to fix a mistake, add a feature, replace something and you discover that everything is connected to everything else in ways you can't even begin to understand. Because you're doing something more ambitious than the initial academic prototype, it's big enough that you need to partition it across a team. The members of the team need to have a clear idea of how their work relates to everyone else's, and they need to be able to communicate to the testers whose job it is to find errors. Otherwise [in other words], you give the patient a kidney transplant and his heart suddenly fails; then you give him a heart drug, but that makes his lungs collapse. You don't know why, and you're screwed.

        And then the future comes, and you're really screwed. Later releases of the product inevitably are more complex, because they must continue to support previous version while adding new capabilities. You discover that the original developers have quit or been promoted or have forgotten what they did, and it's time to keep up with the competition by adding new features, supporting more platforms, translating into Japanese, and so forth. The engineering team has to quadruple in size. You start discovering things like three different groups need to change the same piece of code, and each set of changes causes problems for the others, and nobody else can test their work until that piece of code is stable, so a hundred people twiddle their thumbs for a week. Or you want to use an existing function for some new purpose, but you can't isolate it from everything else, so you have to write it all over again. This not only means that you have the extra time and cost of writing and maintaining twice as much code, but you probably have to ensure that the two versions work exactly alike, which they almost certainly won't.

        With each successive release, these problems get worse. By the time you're on your fifth release, the decision to do your first product the quick and dirty way has probably cost you ten times what it originally saved. A program like Microsoft's Windows 98 is tens of millions of lines of code. Nobody can keep that much complexity in their head or hope to manage it effectively. So you need an architecture that says to everyone, "Here's how this thing works, and to do your part, you need to understand only these five things, and don't you dare touch anything else."

        - C. Ferguson (High Stakes, No Prisoners; Times Business Press, page 107-109)

        I can live with doubt and uncertainty. I think it's much more interesting to live not knowing than to have answers which might be wrong... In physics the truth is rarely perfectly clear, and that is certainly universally the case in human affairs. Hence, what is not surrounded by uncertainty cannot be the truth.

        - R. Feynman (in "Perfectly Reasonable Deviations from the Beaten Track: The Letters of Richard P. Feynman")

        If you're teaching a class, you can think about the elementary things that you know very well. These things are kind of fun and delightful. It doesn't do any harm to think them over again. Is there a better way to present them? The elementary things are easy to think about; if you can't think of a new thought, no harm done; what you thought about it before is good enough for the class. If you do think of something new, you're rather pleased that you have a new way of looking at it.

        The questions of the students are often the source of new research. They often ask profound questions that I've thought about at times and then given up on, so to speak, for a while. It wouldn't do me any harm to think about them again and see if I can go any further now. The students may not be able to see the thing I want to answer, or the subtleties I want to think about, but they remind me of a problem by asking questions in the neighborhood of that problem. It's not so easy to remind yourself of these things.

        - R. Feynman (from "The Dignified Professor" in "Surely You're Joking Mr. Feynman": pg. 166)

        Precise language is not the problem. Clear language is the problem.

        - R. Feynman

        The inside of a computer is as dumb as hell but it goes like mad!

        - R. Feynman

        What I cannot create I do not understand.

        - R. Feynman

        When the problem [quantum chromodynamics] is finally solved, it will all be by imagination. Then there will be some big thing about the great way it was done. But it's simple -it will all be by imagination, and persistence.

        - R. Feynman (in Mlodinow's "Feynman's Rainbow")

        When a Caltech student asked the eminent cosmologist Michael Turner what his "bias" was in favoring one or another particle as a likely candidate to compromise dark matter in the universe, Feynman snapped, "Why do you want to know his bias? Form your own bias!"

        - R. Feynman (related by Timothy Ferris in "Perfectly Reasonable Deviations from the Beaten Track: The Letters of Richard P. Feynman")

        There does not now, nor will there ever exist, a programming language in which it is the least bit hard to write bad programs.

        - L. Flon

        An engineer can do for a nickel what any damn fool can do for a dollar.

        - H. Ford

        Failure is the opportunity to begin again more intelligently.

        - H. Ford

        Education's purpose is to replace an empty mind with an open one.

        - M. Forbes

        Any fool can write code that a computer can understand. Good programmers write code that humans can understand.

        - M. Fowler, "Refactoring: Improving the Design of Existing Code"

        [Describing JUnit] Never in the field of program testing, was so much owed by so many to so few lines of code.

        - M. Fowler (apologizing to Winston Churchill)

        Awaken people's curiosity. It is enough to open minds, do not overload them. Put there just a spark.

        - A. France

        Simplicity is the soul of efficiency.

        - A. Freeman (in The Eye of Osiris)

        When I am working on a problem, I never think about beauty. I think only of how to solve the problem. But when I have finished, if the solution is not beautiful, I know it is wrong.

        - B. Fuller

        The biggest difference between time and space is that you can't reuse time.

        - M. Furst


        G

        Habitability is the characteristic of source code that enables programmers coming to the code later in its life to understand its construction and intentions and to change it comfortably and confidently... Software needs to be habitable because it always has to change...Programs are written and maintained, bugs are fixed, features are added, performance is tuned, and a whole variety of changes are made both by the original and new programming team members... What is important is that it be easy for programmers to come up to speed with the code, to be able to navigate through it effectively, to be able to understand what changes to make, and to be able to make them safely and correctly.

        - R. Gabriel (Patterns of Software, Oxford Press 1996)

        All truths are easy to understand once they are discovered; the point is to discover them.

        - G. Galilei

        Mathematics is the language with which G-d has written the universe.

        - G. Galilei

        A complex system that works in invariably found to have evolved from a simple system that worked.

        - J. Gall

        An excellent plumber is infinitely more admirable than an incompetent philosopher. The society that scorns excellence in plumbing because plumbing is a humble activity and tolerates shoddiness in philosophy because it is exalted activity will have neither good plumbing or good philosophy. Neither its pipes or its theories will hold water.

        - J. Gardner

        640K [of main memory] ought to be enough for anybody.

        - W. Gates (Founder and CEO Microsoft), 1981 - disclaimed

        A great lathe operator commands several times the wage of an average lathe operator, but a great writer of software code is worth 10,000 times the price of an average software writer.

        - W. Gates

        The best way to prepare [to be a programmer] is to write programs, and to study great programs that other people have written. In my case, I went to the garbage cans at the Computer Science Center and fished out listings of their operating systems.

        - W. Gates

        We flew down weekly to meet with IBM, but they thought the way to measure software was the amount of code we wrote, when really the better the software, the fewer lines of code.

        - W. Gates

        It is not knowledge, but the act of learning, not possession, but the act of getting there which generates the greatest satisfaction.

        - F. Gauss

        Beauty is more important in computing than anywhere else in technology because software is so complicated. Beauty is the ultimate defense against complexity. ... The geniuses of the computer field, on the other hand, are the people with the keenest aesthetic senses, the ones who are capable of creating beauty. Beauty is decisive at every level: the most important interfaces, the most important programming languages, the winning algorithms are the beautiful ones.

        - D. Gelernter ("Machine Beauty", Basic Books, 1998)

        Good programmers know what's beautiful and bad ones don't.

        - D. Gelernter ("Machine Beauty", Basic Books, 1998)

        Object-oriented programming as it emerged in Simula 67 allows software structure to be based on real-world structures, and gives programmers a powerful way to simplify the design and construction of complex programs.

        - D. Gelernter ("Machine Beauty", Basic Books, 1998)

        One of the principal objects of theoretical research in any department of knowledge is to find the point of view from which the subject appears in its greatest simplicity.

        - J. W. Gibbs

        Vague and nebulous is the beginning of all things, but not their end.

        - K. Gibran

        At the source of every error which is blamed on the computer, you will find at least two human errors, one of which is the error of blaming it on the computer.

        - T. Gilb (in "Laws of Unreliability", Datamation March 1975)

        Indirection is the right direction.

        - A. Glew

        Never put off until run time what can be done at compile time.

        - A. Glew

        Always code as if the guy who ends up maintaining your code will be a violent psychopath who knows where you live.

        - M. Golding

        This attitude [the abstract method in mathematics] can be encapsulated in the following slogan: a mathematical object is what it does.

        - T. Gowers (in "Mathematics: A Very Short Introduction" pg. 18)

        A really good language should be both clean and dirty: cleanly designed, with a small core of well understood and highly orthogonal operators, but dirty in the sense that it lets hackers have their way with it....A real hacker's language will always have a slightly raffish character.

        - P. Graham (in "Hackers and Painters" pg. 204)

        Fixing fresh bugs is easier than fixing old ones. It's usually fairly quick to find a bug in code you just wrote. When it turns up you often know what's wrong before you even look at the source, because you were already worrying about it subconsciously. Fixing a bug in something you wrote six months ago (the average case if you release once a year) is a lot more work. And since you don't understand the code as well, you're more likely to fix it in an ugly way, or even introduce more bugs.

        When you catch bugs early, you also get fewer compound bugs. Compound bugs are two separate bugs that interact: you trip going downstairs, and when you reach for the handrail it come off in your hand. In software this kind of bug is the hardest to find, and also tends to have the worst consequences. The traditional "break everything and then filter out the bugs" approach inherently yields a lot of compound bugs. And software released in a series of small chances inherently tends not to. The floors are constantly being swept clean of any loose objects that might later get stuck to something.

        - P. Graham (in "Hackers and Painters" pg. 65-66)

        Great software, likewise, requires a fanatical devotion to beauty. If you look inside good software, you find that parts that no one is ever supposed to see are beautiful too. When it comes to code I behave in a way that would make me eligible for prescription drugs if I approached everyday life the same way. It drives me crazy to see code that's badly indented, or that uses ugly variable names.

        - P. Graham (in "Hackers and Painters" pg. 29)

        Imagine the kind of conversation you would have with someone so far away that there was a transmission delay of one minute. Now imagine speaking to someone in the next room. You wouldn't just have the same conversation faster, you would have a different kind of conversation. In Lisp, developing software is like speaking face-to-face. You can test code as you're writing it. And instant turnaround has just as dramatic an effect on development as it does on conversation. You don't just write the same program faster; you write a different kind of program.

        - P. Graham (in "On Lisp")

        Simplicity is the most important thing in technology. And it's only getting more important.

        - P. Graham

        The difference between design and research seems to be a question of the good versus the new. Design doesn't have to be new, but it has to be good. Research doesn't have to be good, but it has to be new. I think these two paths converge at the top: the best design surpasses its predecessors by using new ideas, and the best research solves problems that are not only new, but worth solving. So ultimately design and research are aiming for the same destination, just approaching it from different directions.

        - P. Graham (in "Hackers and Painters" footnote 9, pg. 224)

        The way to make programs easy to read is not to stuff them with comments... A good programming language ought to be better for explaining software than English. You should only need comments when there is some kind of kludge you need to warn readers about, just as on a road there are only arrows on parts with unexpectedly sharp curves.

        - P. Graham (in "Hackers and Painters" footnote 9, pg. 224)

        You should figure our programs as you're writing them, just as writers and painters and architects do. Realizing this [programming as sketching] has real implications for software design. It means that a programming language should, above all, be malleable. A programming language is for thinking of programs, not for expressing programs you've already thought of. It should be a pencil, not pen.
        ...
        Remember too that languages are not primarily a form for finished programs, but something that programs have to be developed in.
        ...
        A good programming language should, like oil paint, make it easy to change your mind.
        ...
        Paintings usually begin with a sketch. Gradually the details get filled in. But it is not merely a process of filling in. Sometimes the original plans turn out to be mistaken. Countless paintings, when you look at them in x-rays, turn out to have limbs that have been moved or facial features that have been readjusted.
        ...
        So the test of a language is not simply how clean the finished program looks in it, but how clean the path to the finished program was.
        ...
        What made oil paint so exciting, when it first became popular in the fifteenth century, was that you could make the finished work from the prototype. You could make a preliminary drawing if you wanted to, but you weren't held to it; you could work out all the details, and even make major changes as you finished the painting. You can do this with software too. A prototype doesn't have to be just a model; you can refine it into the finished product....it's good for morale.
        ...
        Building something by gradually refining a prototype is good for morale because it keeps you engaged. In software, my rule is: always have working code. If you're writing something you'll be able to test in an hour, you have the prospect of an immediate reward to motivate you.

        - P. Graham (in "Hackers and Painters" pg. 22, 27, 218, 219, 220, 221)

        Incidentally, when we're faced with a "prove or disprove," we're usually better off trying first to disprove with a counterexample, for two reasons: A disproof is potentially easier (we need just one counterexample); and nitpicking arouses our creative juices. Even if the given assertion is true, our search for a counterexample often leads to a proof, as soon as we see why a counterexample is impossible. Besides, it's healthy to be skeptical.

        - R. Graham, D. Knuth and O. Patashnik (in "Concrete Mathematics: A Foundation for Computer Science")

        Any sufficiently complicated C or FORTRAN program contains an ad hoc informally specified bug-ridden slow implementation of half of Lisp.

        - P. Greenspun

        Writing is nature's way of letting you know how sloppy your thinking is.

        - R. Guindon (cartoon, San Francisco Chronincle, January 1989)

        Mathematics is nature's way of letting you know how sloppy your writing is.

        - L. Lamport, ("Specifying Systems", 2002, p. 2

        Formal mathematics is nature's way of letting you know how sloppy your mathematics is.

        - L. Lamport, ("Specifying Systems", 2002, p. 2

        UNIX was not designed to stop people from doing stupid things, because that would also stop them from doing clever things.

        - D. Gwyn


        H

        Any performance problem can be solved by removing a level of indirection. (also see "Any programming problem..." by Anonymous).

        - M. Haertel

        The tendency to err that programmers have been noticed to share with other human beings has often been treated as though it were an awkwardness attendant upon programming's adolescence, which like acne would disappear with the craft's coming of age. It has proved otherwise.

        - M. Halpern

        Research by Stanford pyschology professor Carol Dweck suggests that a student who has been raised to believe that his or her success stems from special abilities rather than hard work and learning from mistakes may become fixated on maintaining the "gifted" status at any cost, which could include cheating.

        - J. Hamilton (from "Why We Cheat" in "Stanford Magazine", Sep/Oct 2015)

        The purpose of computing is insight, not numbers.

        - R. Hamming

        The mathematician's patterns, like those of the painter's or the poet's, the ideas, like the colours or words, must fit together in a harmonious way. There is no permanent place in this world for ugly mathematics.

        - G.H. Hardy (in "A Mathematician's Apology")

        Programming is an explanatory activity.

        - R. Harper

        It is very interesting to me how quickly the class has divided up into three factions. One faction being the students who sit in the back of the class, given up sitting in their assigned seats, preparing the cases. What is it, only October? They've already given up trying -the cowards. The second group are the ones who won't raise their hands or volunteer an answer, but will try when they're called upon. That's where I am right now, living in a state of constant fear. And then there's the third echelon: the upper echelon; the volunteers. They raise their hands in class. They thrust themselves into the fray. I don't think they're smarter than anyone else, but they have courage. And, they'll achieve the final recognition, that teachers will get to know their names, and they'll get better grades. The past couple weeks I've been preparing to enter the upper echelon, and this weekend -if I can get all my work done- I'm going to enter it Monday morning, in Kingsfield's contract law class.

        - James Hart (a law student in "The Paper Chase")

        [Breaking into the "red" room in the Harvard law library, which contains the notes of Harvard professors from their school days, and drafts of their articles and books] Do you realize what this is? This is it. This is the unbroken chain. The ageless passing of wisdom. Hey [what is it?] listen to this. "Kingsfield, Charles W. notes on contract law in the course on contracts by Professor Williston at the Harvard Law School, 1927". What the hell is it. They're just notes: and they look just like mine. Look [reading from Kingsfield's notes]. "Questions: Does everybody have a contract to obey everybody else's rights. What is a contract? What do you owe to others?" Look, there are even doodles. [C'mon, let's get outta here.] Wait, wait. "Can we make a contract with G-d that is biding to man? ... After all, I am almost the living extension of the old judges. Where would they be without me. I carry in my mind the cases they wrote. Where the hell would they be if it wasn't for me? Who would hang their pictures if there were no law students? It's hard being the living extension of tradition."

        - James Hart (a law student in "The Paper Chase")

        There are features that should not be used. There are concepts that should not be exploited. There are problems that should not be solved. There are programs that should not be written.

        - R. Harter

        Eventually hardware fails; eventually software works.

        - M. Hartung

        PROBLEMS
        Problems worthy
        of attack
        prove their worth
        by hitting back.

        - P.Hein (in "Grooks")

        THE ROAD TO WISDOM
        The road to wisdom? - Well, it's plain
        and simple to express:
        Err
        and err
        and err again
        but less
        and less
        and less.

        - P.Hein (in "Grooks")

        The one who insists on never uttering an error must remain silent.

        - W. Heisenberg

        The speed of a non-working program is irrelevant.

        - S. Heller (in "Efficient C/C++ Programming")

        Refactoring provides enough energy to a system for it to relax into a new and more comfortable state, a new local minimum. The effect of refactoring commonality is to tame the complexity of your system.

        - K. Henney (in Minimalism: The Imperial Clothing Crisis)

        A process cannot be understood by stopping it. Understanding must move with the flow of the process, must join and flow with it.

        - F. Herbert (The First Law of Mentat in "Dune")

        It's [programming] the only job I can think of where I get to be both an engineer and an artist. There's an incredible, rigorous, technical element to it, which I like because you have to do very precise thinking. On the other hand, it has a wildly creative side where the boundaries of imagination are the only real limitation.

        - A. Hertzfeld (original Mac programmer)

        ...At first I hoped that such a technically unsound project would collapse but I soon realized it was doomed to success. Almost anything in software can be implemented, sold, and even used given enough determination. There is nothing a mere scientist can say that will stand against the flood of a hundred million dollars. But there is one quality that cannot be purchased in this way -and that is reliability. The price of reliability is the pursuit of the utmost simplicity. It is a price which the very rich find most hard to pay.

        - C.A.R. Hoare

        I was eventually persuaded of the need to design programming notations so as to maximize the number of errors which cannot be made, or if made, can be reliably detected at compile time.

        - C.A.R. Hoare

        In the development of the understanding of complex phenomena, the most powerful tool available to the human intellect is abstraction. Abstraction arises from the recognition of similarities between certain objects, situations, or processes in the real world and the decision to concentrate on these similarities and to ignore, for the time being, their differences.

        - C.A.R. Hoare

        Inside every well-written large program is a well-written small program.

        - C.A.R. Hoare

        Premature optimization is the root of all evil in programming.

        - C.A.R. Hoare

        The unavoidable price of reliability is simplicity.

        - C.A.R. Hoare

        There are two ways of constructing a software design. One way is to make it so simple that there are obviously no deficiencies. And the other way is to make it so complicated that there are no obvious deficiencies.

        - C.A.R. Hoare

        What is the central core of the subject [computer science]? What is it that distinguishes it from the separate subjects with which it is related? What is the linking thread which gathers these disparate branches into a single discipline. My answer to these questions is simple -it is the art of programming a computer. It is the art of designing efficient and elegant methods of getting a computer to solve problems, theoretical or practical, small or large, simple or complex. It is the art of translating this design into an effective and accurate computer program.

        - C.A.R. Hoare

        When examining the detail of the algorithm, it seems probable that the proof will be helpful in explaining not only what is happening but why.

        - C.A.R. Hoare (in "An Axiomatic Basis for Computer Programming", 1969)

        You cannot teach beginners top-down programming, because they don't know which end is up.

        - C.A.R. Hoare (private communication)

        All thought is a kind of computation.

        - D. Hobbes

        The problem is never how to get new, innovative thoughts into your mind, but how to get old ones out!

        - D. Hock (founder of VISA)

        Using a language we may create models of phenomena of interest, and by using models, phenomena may be studied for purposes of understanding or prediction. Models may be used for analysis focused on a close examination of individual parts of the model and for synthesis aimed at understanding the interplay of the parts, that is, understanding the model as a whole. A novel is like a model of the real world expressed in a written language like English. In a novel, the characters may be analyzed and the interaction between people may be displayed and studied. (in "Dreams of Calculus: Perspectives on Mathematics Education")

        - J. Hoffman, C. Johnson, A. Logg

        The ability to simplify means to eliminate the unnecessary so that the necessary may speak.

        - H. Hofmann (in "Introduction to the Bootstrap")

        This sequence [of languages, SP/1 through SP/8] solves one of the perennial problems of introductory programming. As J.J. Horning once put it, the subject requires that everything must be taught first.

        - R. Holt, D. Wortman, D. Barnard and J. Cordy (quoting J.J. Horning) in "SP/k: A System for Teaching Computer Programming", CACM 20/5 (May 77) pg. 303

        A ship in port is safe, but that is not what ships are built for. I want all the youngsters to sail out to sea and be good ships.

        - G. Hopper

        When you have a good idea and you've tried it and you know it's going to work, go ahead and do it -because it's much easier to apologize afterwards than it is to get permission.

        - G. Hopper

        Computer Science is the only discipline in which we view adding a new wing to a building as being maintenance.

        - J. Horning

        To treat programming scientifically, it must be possible to specify the required properties of programs precisely. Formality is certainly not an end in itself. The importance of formal specifications must ultimately rest in their utility -in whether or not they are used to improve the quality of software or to reduce the cost of producing and maintaining software.

        - J. Horning

        One purpose of CRC cards [a design tool] is to fail early, to fail often, and to fail inexpensively. It is a lot cheaper to tear up a bunch of cards that it would be to reorganize a large amount of source code.

        - C. Horstmann (in Object-Oriented Design with Java)

        An examination should not be confused with an education.

        - C. Hsi

        We [teachers] make the road, others will make the journey.

        - V. Hugo

        The greatest mistake you can make in life is to be continually fearing you will make one.

        - E. Hubbard


        I


        J

        Rules of Optimization:
          Rule 1: Don't do it.
          Rule 2 (for experts only): Don't do it yet.

        - M. A. Jackson

        In the practical use of our intellect, forgetting is as important as remembering.

        - W. James

        "Do you want to sell sugar water for the rest of your life, or do you want to change the world?" (what Steve Jobs said to John Sculley when trying to recruit him from Pepsi to work at Apple in 1983).

        - S. Jobs

        "Learning to program teaches you how to think. Computer science is a liberal art"

        - S. Jobs

        That simplicity is the ultimate sophistication. What we meant by that was when you start looking at a problem and it seems really simple with all these simple solutions, you don't really understand the complexity of the problem. And your solutions are way too oversimplified, and they don't work. Then you get into the problem, and you see it's really complicated. And you come up with all these convoluted solutions. That's sort of the middle, and that's where most people stop, and the solutions tend to work for a while. But the really great person will keep on going and find, sort of, the key, underlying principle of the problem. And come up with a beautiful elegant solution that works.

        - S. Jobs (in "The Perfect Thing" by Steven Levy, pg. 67-68)

        That's been one of my mantras -focus and simplicity. Simple can be harder than complex: you have to work hard to get your thinking clean to make it simple. But it's worth it in the end because once you get there, you can move mountains.

        - S. Jobs (BusinessWeek interview, May 1998)

        The only problem with Microsoft is they just have no taste. They have absolutely no taste. What that means is -I don't mean that in a small way; I mean that in a big way- is the sense that they don't think of original ideas. They don't bring much culture into their product. ... So I guess I am saddened not by Microsoft's success -I have no problem with their success; they've earned their success (for the most part). I have a problem with the fact that the just make really third-rate products.

        - S. Jobs (transcribed from "Triumph of the Nerds")

        Before software can be reusable it first has to be usable.

        - R. Johnson

        What we hope ever to do with ease, we must first learn to do with diligence.

        - S. Johnson

        The fastest algorithm can frequently be replaced by one that is almost as fast and much easier to understand.

        - D. Jones

        To teach is to learn twice.

        - J. Joubert

        The honest truth is that having a lot of people staring at the code does not find the really nasty bugs. The really nasty bugs are found by a couple of really smart people who just kill themselves. (also see "Given enough eyeballs..." by E. Raymond).

        - B. Joy

        We don't manage our time as well as we manage our space. There's an overhead of starting and an overhead of stopping a project because you kind of lose your momentum. And you've got to bracket and put aside all the things you're already doing. So you need reasonably large blocks of uninterrupted time if you're going to be successful at doing some of these things. That's why hackers tend to stay up late.

        If you stay up late and you have another hour of work to do, you can just stay up another hour later without running into a wall and having to stop. Whereas it might take three or four hours if you start over, you might finish if you just work that extra hour. If you're a morning person, the day always intrudes a fixed amount of time in the future. So it's much less efficient. Which is why I think computer people tend to be night people -because a machine doesn't get sleepy.

        - B. Joy

        In the particular is contained the universal.

        - J. Joyce

        Mistakes are the portals of discovery.

        - J. Joyce


        K

        Following the thin song of mathematics and not the heavy voice of experience proved to be another victory for engineering science.

        - T. von Karman

        90% of code written today is getting around other people's mistakes.

        - A. Kay

        Computer Science today keeps reinventing the flat tire.

        - A. Kay

        Computers are to computing as instruments are to music. Software is the score whose interpretations amplifies our reach and lifts our spirits. Leonardo da Vinci called music the shaping of the invisible, and his phrase is even more apt as a description of software.

        - A. Kay

        [In a programming language] Simple things should be simple and complex things should be possible.

        - A. Kay

        The best way to predict the future is to invent it.

        - A. Kay

        Once we start learning something, it is very hard for us to see what else is going on... Probably the most disastrous thing that you can ever learn is your first programming language, even if it is a good programming language. And the reason is that it tends to become computing. [And] So it might be a better idea to learn two or three programming languages at the same time, even though that is a different kind of struggle, but it would at least relativize what people think computing might be.

        - A. Kay

        Until real software engineering is developed, the next best practice is to develop with a dynamic system that has extreme late binding in all aspects.

        - A. Kay

        When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind.

        - Lord Kelvin

        Should array indices start at 0 or 1? My compromise of 0.5 was rejected without, I thought, proper consideration.

        - S. Kelly-Bootle

        If you have a large number of unrelated ideas, you have to get quite a distance away from them to get a view of all of them, and this is the role of abstraction. If you look at each too closely you see too many details. If you get far away things may appear simpler because you can only see the large, broad outlines; you do not get lost in petty details.

        - J. Kemeny (co-creator of the BASIC programming language)

        Act in haste and repent at leisure; code too soon and debug forever.

        - R. Kennington

        Controlling complexity is the essence of computer programming.

        - B. Kernighan

        Everyone knows that debugging is twice as hard as writing a program in the first place. So if you are as clever as you can be when you write it, how will you ever debug it?

        - B. Kernighan

        Another effective [debugging] technique is to explain your code to someone else. This will often cause you to explain the bug to yourself. Sometimes it takes no more than a few sentences, followed by an embarrassed "Never mind, I see what's wrong. Sorry to bother you." This works remarkably well; you can even use non-programmers as listeners. One university computer center kept a teddy bear near the help desk. Students with mysterious bugs were required to explain them to the bear before they could speak to a human counselor.

        - B. Kernighan & D. Pike (in "The Practice of Programming" pp. 123)

        The only way to learn a new programming language is by writing programs in it.

        - B. Kernighan & D. Ritchie

        The study of law is something new and unfamiliar to most of you -unlike any schooling you have ever been through before. We use the Socratic method here: I call on you, ask you a question, and you answer it. Why don't I just give you a lecture? Because through my questions you learn to teach yourselves. Through this method of questioning-answering, questioning-answering, we seek to develop in you the ability to analyze that vast complex of facts that constitute the relationships of members within a given society. Questioning and answering. At times, you may feel that you have found the correct answer. I assure you that this is a total delusion on your part. You will never find the correct, absolute, and final answer. In my classroom, there is always another question, another question to follow your answer. Yes, you are on a treadmill. My little questions spin the tumblers of your mind. You are on an operating table; my little questions are the fingers probing your brain. We do brain surgery here. You teach yourselves the law, but I train your mind. You come in here with a skull full of mush, and you leave thinking like a lawyer.

        - Professor Kingsfield (addressing 1st year Harvard Law Students in "The Paper Chase")

        A charlatan makes obscure what is clear; a thinker makes clear what is obscure.

        - H. Kingsmill

        I made mistakes, but I never did make the same mistake.

        - B. Kirk

        The best theory is motivated by practice, and the set practice is motivated by theory.

        - D. Knuth

        Beware of bugs in the above code; I have only proved it correct, not tried it.

        - D. Knuth

        Computers are good at following instructions, but not at reading your mind.

        - D. Knuth (Tex, pg. 9)

        Debugging is an art that needs much further study .... The most effective debugging techniques seem to be those which are designed and built into the program itself -many of today's best programmers will devote nearly half of their programs to facilitating the debugging process on the other half; the first half... will eventually be thrown away, but the net result is a surprising gain in productivity.

        Another good debugging practice is to keep a record of every mistake that is made. Even though this will probably be quite embarrassing, such information is invaluable to anyone doing research on the debugging problem, and it will also help you learn how to reduce the number of future errors.

        - D. Knuth (The Art of Computer Programming, Volume 1)

        Let us change our traditional attitude to the construction of programs. Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to human beings what we want a computer to do.

        - D. Knuth

        ...methods are more important than facts. The educational value of a problem given to a student depends mostly on how often the thought processes that are invoked to solve it will be helpful in later situations. It has little to do with how useful the answer to the problem may be. On the other hand, a good problem must also motivate the students; they should be interested in seeing the answer. Since students differ so greatly, I cannot expect everyone to like the problems that please me.

        - D. Knuth (in "Are Toy Problems Useful", Chapter 10 of "Selected Papers on Computer Science", pg. 176)

        ...One of the most important lessons, perhaps, is the fact that SOFTWARE IS HARD. From now on I shall have significantly greater respect for every successful software tool that I encounter. During the past decade I was surprised to learn that the writing of programs for TeX and Metafont proved to be much more difficult than all the other things I had done (like proving theorems or writing books). The creation of good software demands a significantly higher standard of accuracy than those other things do, and it requires a longer attention span than other intellectual tasks.

        - D. Knuth (in "Selected Papers on Computer Science", pp 161)

        We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.

        - D. Knuth

        When I speak about computer programming as an art, I am thinking primarily of it as an art form, in an aesthetic sense. The chief goal of my work as an educator and author is to help people learn how to write beautiful programs...My feeling is that when we prepare a program, the experience can be just like composing poetry or music...Some programs are elegant, some are exquisite, some are sparkling. My claim is that it is possible to write grand programs, noble programs, truly magnificent ones!...computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty. Programmers who subconsciously view themselves as artists will enjoy what they do and will do it better.

        - D. Knuth (Computer Programming as an Art. Turing Award Speech 1974)

        When certain concepts of TeX are introduced informally, general rules will be stated; afterwards you will find that the rules aren't strictly true. In general, the later chapters contain more reliable information than the earlier ones do. The author feels that this technique of deliberate lying will actually make it easier for you to learn the ideas. Once you understand a simple but false rule, it will not be hard to supplement that rule with its exceptions.

        - D. Knuth (Tex, pg. vi)

        Steve [Jobs] wanted to see if we couldn't make it [the circuit board of the first Mac] more aesthetic...He wanted the inside of the computer to look good even if no one would see it.

        - D. Kottke (early Apple Engineer)

        If we really understand the problem, the answer will come out of it, because the answer is not separate from the problem.

        - J. Krishnamurti

        Any sufficiently advanced bug is indistinguishable from a feature.

        - R. Kulawiec


        L

        There is a race between the increasing complexity of the systems we build and our ability to develop intellectual tools for understanding their complexity. If the race is won by our tools, then systems will eventually become easier to use and more reliable. If not, they will continue to become harder to use and less reliable for all but a relatively small set of common tasks. Given how hard thinking is, if those intellectual tools are to succeed, they will have to substitute calculation for thought.

        - L. Lamport

        Writing is nature's way of letting you know how sloppy your thinking is.

        - R. Guindon (cartoon, San Francisco Chronincle, January 1989)

        Mathematics is nature's way of letting you know how sloppy your writing is.

        - L. Lamport, ("Specifying Systems", 2002, p. 2

        Formal mathematics is nature's way of letting you know how sloppy your mathematics is.

        - L. Lamport, ("Specifying Systems", 2002, p. 2

        "Where have you performed?" Murray asked me from behind a copy of Variety. "Well, I don't perform, exactly," I answered, "but I have spoken at synagogues, and I lecture from time to time at colleges and universities."

        "Universities?" Murray sputtered. "Did he say universities? Worst audience in the world. I spoke at a university once. They invited me to speak about the hotel industry. Believe me, I've got a pretty good routine on this; I've used it many times before, and I know where I'm supposed to get reactions: chuckles, laughs, applause. Son of a bitch, I stand up there and it's a grim audience! All these young people in jeans and sneakers. I open it up for questions. These bastards are dead serious -they're taking notes! I realized later, they weren't an audience, they were students. They take notes and get grades. They're not there to laugh. Who wants to perform for an audience like that?"

        - A. Lansky, relating his experiences lecturing at resorts in the Catskills while raising money for the National Yiddish Book Center (described in his book, "Outwitting History")

        We think only through the medium of words. Languages are true analytical methods. Algebra, which is adapted to its purpose in every species of expression, in the most simple, most exact, and best manner possible, is at the same time a language and an analytical method. The art of reasoning is nothing more than a language well arranged.

        - A. Lavoisier

        We don't have much time, so we don't teach them; we acquaint them with things that they can learn.

        - C. Leiserson (on "The Undergraduate Practicum" at MIT, from a talk at CMU)

        Learning is never done without errors and defeat.

        - V. Lenin

        If you want truly to understand something, try to change it.

        - K. Lewin

        A vivid confirmation of this analysis [the importance of extra-curricular activities] occurred during a lunchtime conversation I had with three computer science graduates of the classes of 1994/1995 who started a company together and sold it in 1998 for more than $250 million. Over sandwiches I asked them what part of their computer science education had been most important to the success of their software enterprise. After a moment of tight smiles and awkward silence, one of the young graduates spoke up. "The computer science courses I took were really terrific," he said in an attempt to reassure me, "but I didn't learn much that I could not have learned on my own. The most important things I learned were from managing the Quincy House Grill." It made perfect sense -hiring, firing, and inspiring colleagues, working under pressure in close quarters- all were very much the same in the cheeseburger-flipping business he had run at Harvard as in the software business the three had started in a tiny apartment.

        - H. Lewis (in "Excellence Without Soul", pg. 88)

        ...fielding statistics made sense only as numbers, not as language. Language, not numbers, is what interested him [Bill James, a baseball writer]. Words, and the meaning they were designed to convey. "When the numbers acquire the significance of the language," he later wrote, "they acquire the power to do all the things which language can do: to become fiction and drama and poetry."

        - M. Lewis (in Moneyball: The Art of Winning an Unfair Game, page 67)

        Give me six hours to chop down a tree and I will spend the first four sharpening the axe.

        - A. Lincoln

        I can't think of a job I'd rather do than computer programming. All day, you create patterns and structure out of the formless void, and you solve dozens of smaller puzzles along the way.

        - P. van Der Linden

        Complexity is a sign of technical immaturity. Simplicity of use is the real sign of a well design product whether it is an ATM or a Patriot missile.

        - D. Ling

        Gentlemen, we are going to relentlessly chase perfection, knowing full well we will not catch it, because nothing is perfect. But we are going to relentlessly chase it, because in the process we will catch excellence. I am not remotely interested in just being good.

        - V. Lombardi (Greenbay Packers Coach 1959-1967)

        Composing computer programs to solve scientific problems is like writing poetry. You must choose every word with care and link it with the other words in perfect syntax. There is no place for verbosity or carelessness. To become fluent in a computer language demands almost the antithesis of modern loose thinking. It requires many interactive sessions, the hands-on use of the device. You do not learn a foreign language from a book, rather you have to live in the country for year to let the language become an automatic part of you, and the same is true for computer languages.

        - James Lovelock (Originator of the Gaia Theory)


        M

        In the beginning we must simplify the subject, thus unavoidably falsifying it, and later we must sophisticate away the falsely simple beginning.

        - M. Maimonides

        Development is maintenance.

        - B. Marick

        A teacher's job is to take a bunch of live wires and see that they are well-grounded.

        - D. Martin

        If our designs are failing due to the constant rain of changing requirements, it is our designs that are at fault. We must somehow find a way to make our designs resilient to such changes and protect them from rotting.

        - R. Martin

        If the only tool you have is a hammer, you tend to see every problem as a nail.

        - A. Maslow

        Much of the beauty that arises in art comes from the struggle an artist wages with his limited medium.

        - H. Matisse

        Optimization is a funny game, the more you optimize the code the uglier it gets. Programs that have been optimized are ... a lot harder to maintain and a lot harder to debug as well.

        - J. . Mattheij

        Any clod can have the facts; having opinions is an art.

        - C. McCabe

        Good code is its own best documentation. As you're about to add a comment, ask yourself, "How can I improve the code so that this comment isn't needed?" Improve the code and then document it to make it even clearer.

        - S. McConnell

        It's hard enough to find an error in your code when you're looking for it; it's even harder when you've assumed your code is error-free.

        - S. McConnell

        It's OK to figure out murder mysteries, but you shouldn't need to figure out code. You should be able to read it.

        - S. McConnell

        Testing by itself does not improve software quality. Test results are an indicator of quality, but in and of themselves, they don't improve it. Trying to improve software quality by increasing the amount of testing is like try to lose weight by weighing yourself more often. What you eat before you step onto the scale determines how much you will weigh, and the software development techniques you use determine how many errors testing will find. If you want to lose weight, don't buy a new scale; change your diet. If you want to improve your software, don't test more; develop better.

        - S. McConnell

        Formal methods will never have a significant impact until they can be used by people who don't understand them.

        - T. Melham

        Programming is similar to a game of golf. The point is not getting the ball in the hole but how many strokes it takes.

        - H. Mills

        The only way for errors to occur in a program is by being put there by the author. No other mechanisms are known. Programs can't acquire bugs by sitting around with other buggy programs. Right practice aims at preventing insertion of errors and, failing that, removing them before testing or any other running of the program.

        - H. Mills

        Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that's creativity.

        - C. Mingus

        A computer is like a violin. You can imagine a novice trying first a phonograph and then a violin. The latter, he says, sounds terrible. That is the argument we have heard from our humanists and most of our computer scientists. Computer programs are good, they say, for particular purposes, but they aren't flexible. Neither is a violin, or a typewriter, until you learn how to use it.

        - M. Minsky (in "Why Programming Is a Good Medium for Expressing Poorly-Understood and Sloppily-Formulated Ideas")

        The key to understanding randomness and all of mathematics is not being able to intuit the answer to every problem immediately but merely having the tools to figure out the answer.

        - L. Mlodinow (in "The Drunkar's Walk")

        Language designers are not intellectuals. They're not as interested in thinking as you might hope. They just want to get a language done and start using it.

        - D. Moon

        He who hasn't hacked assembly language as a youth has no heart. He who does so as an adult has no brain.

        - J Moore

        Computer science is to biology what calculus is to physics. It's the natural mathematical technique that best maps the character of the subject.

        - H. Morowitz

        A little inaccuracy sometimes saves tons of explanation.

        - H.H. Munro


        N

        You have to honor failure, because failure is just the negative space around success.

        - R. Nelson (in Wired 06/2004 page 166)

        Computing is not about computers any more. It is about living.

        - N. Negroponte

        Between 1892 say, and 1904, movies were made by the cameraman because he understood the equipment. And that is exactly where we are now [in software design]. In 1904 they invented the director; what was the director? It was the guy who didn't have to know how to load the camera didn't have to know how to sew costumes, play a violin, dance, fence, or hang the lights. But, he had to know how to make those effects come together in a unified experience... Why are video games so much better designed than office software? Video games are designed by people who love to play video games. Office software is designed by people who want to do something else on the weekend... What does show business teach you. It teaches you that design is war; it is a power struggle between the producers, directors, authors, everyone who wants to be involved.

        - T. Nelson (transcribed from a talk at Engelbart's Unfinished Revolution a Stanford University Symposium)

        In mathematics you don't understand things. You just get used to them.

        - J. von Neumann

        Millions for compilers, but hardly a penny for understanding human programming language use. Now, programming languages are obviously symmetrical, the computer on one side, the human on the other. In an appropriate science of computer languages, one would expect that half the effort would be on the computer side, understanding how to translate the languages into executable form, and half on the human side, understanding how to design languages that are easy or productive to use. Yet, we do not even have an enumeration of all the psychological functions programing languages serve for the user. Of course, there is lots of programming language design, but it comes from computer scientists. And though technical papers on languages contain mainly appeals to ease of use and learning, they patently contain almost no psychological evidence nor any appeal to psychological science.

        - A. Newell and S. Card

        "Two monologues do not make a dialog"...a technology that gives no opportunity for discussion, explanation, or debate is a poor technology.

        - D. Norman (in The Design of Future Things, Basic Books, 2007, pg. 6)


        O

        There is no reason anyone would want a computer in their home.

        - K. Olsen (Founder and President, Digital Equipment Corporation), 1977

        The best performance improvement is the transition from the nonworking state to the working state

        - J. Osterhout

        Complexity kills. It sucks the life out of developers, it makes products difficult to plan, build and test, it introduces security challenges and it causes end-user and administrator frustration. ...[we should] explore and embrace techniques to reduce complexity.

        - R. Ozzie


        P

        As a rule, software systems do not work well until they have been used, and have failed repeatedly, in real applications.

        - D. Parnas

        A programming language is like a natural, human language in that it favors certain metaphors, images, and ways of thinking.

        - S. Papert (in "Mindstorms: Children, Computers, and Powerful Ideas", 1980)

        My basic idea is that programming is the most powerful medium of developing the sophisticated and rigorous thinking needed for mathematics, for grammar, for physics, for statistics, for all the "hard" subjects.... In short, I believe more than ever that programming should be a key part of the intellectual development of people growing up.

        - S. Papert (in "CACM January 2005 (Vol 24, #1, pp38)")

        I have made this letter longer than usual, only because I have not had the time to make it shorter.

        - B. Pascal

        Chance favors the prepared mind.

        - L. Pasteur

        A class, in Java, is where we teach objects how to behave.

        - R. Pattis

        Code should run as fast as necessary, but no faster; something important is always traded away to increase speed.

        - R. Pattis

        He who runs an av-rage pace,
        runs alone throughout the race.
        [I know I'm teaching at the right pace when I please no one: half the students say I'm going too slow, half too fast.]

        - R. Pattis

        If you cannot grok the overall structure of a program while taking a shower [e.g., with no external memory aids], you are not ready to code it.

        - R. Pattis

        Mistakes lead to failure only if you don't learn from them.

        - R. Pattis

        Paradoxically, it is often harder to understand a program than the programming language in which it is written.

        - R. Pattis

        Programming languages, like pizzas, come in only too sizes; too big and too small.

        - R. Pattis

        The discipline of programming is most like sorcery. Both use precise language to instruct inanimate objects to do our bidding. Small mistakes in programs or spells can lead to completely unforeseen behavior: e.g., see the story, "The Sorcerer's Apprentice". Neither study is easy: "...her [Galinda's] early appetite for sorcery had waned once she'd heard what a grind it was to learn spells and, worse, to understand them." from the book "Wicked" by G. Maguire.

        - R. Pattis

        The purpose of brakes on a car is to allow you to go fast. Although the gas pedal makes you go fast, the brake pedal allows you to drive safely while going fast. The purpose of a strict compiler (one that performs type checking, uninitialized variable checking, reachability analysis, etc.) is to allow you to program fast. Programmers -like all humans- have limited intellects: when they focus on one aspect of a program, they must ignore others. Focusing on the right aspect at the right time is critical. By understanding those aspects that the compiler can check, you can ignore them, and focus on more important ones. Some programmers think that such an approach is reckless; they believe that you must pay close attention to everything at once. They are right -for them; but I'm just not that smart, so I must use my tools more effectively.

        - R. Pattis

        The structure of a software system provides the ecology in which code is born, matures, and dies. A well-designed habitat allows for the successful evolution of all the components needed in a software system.

        - R. Pattis

        The three most important aspects of debugging and real estate are the same: Location, Location, and Location.

        - R. Pattis

        There is a famous rule in performance optimization called the 90/10 rule: 90% of a program's execution time is spent in only 10% of its code. The standard inference from this rule is that programmers should find that 10% of the code and optimize it, because that's the only code where improvements make a difference in the overall system performance. But a second inference is just as important: programmers can deoptimize the other 90% of the code (in order to make it easier to use, maintain, etc.), because deterioration (of performance) of that code won't make much of a difference in the overall system performance.

        - R. Pattis

        When debugging, novices insert corrective code; experts remove defective code.

        - R. Pattis

        When teaching a rapidly changing technology, perspective is more important than content.

        - R. Pattis

        When building a complex system, having crackerjack programmers (who can make any design work, even a bad one) can be a liability. The result, after lots of effort, is a working system that cannot be easily maintained or upgraded. Good -but not great- programmers would fail early, causing a realization that the system must be redesigned, and then reimplemented. The extra cost is paid once, early in the system's cycle (when it is cheap), instead of repeatedly paid late in the system's cycle (when it is more expensive).

        - R. Pattis

        Don't tell people how to do things. Tell them what to do and let them surprise you with their results.

        - G. Patton

        The best way to get a good idea is to get a lot of ideas.

        - L. Pauling

        Brick walls are there for a reason. The brick walls are not there to keep us out. The brick walls are there to show how badly we want something. Because the brick walls are there to stop the people who don't want something badly enough. They are there to keep out the other people.

        - R. Pausch (see The Last Lecture)

        A good programming language is a conceptual universe for thinking about programming.

        - A. Perlis

        A language that doesn't affect the way you think about programming is not worth knowing.

        - A. Perlis

        Any noun can be verbed.

        - A. Perlis

        Fools ignore complexity; pragmatists suffer it; experts avoid it; geniuses remove it.

        - A. Perlis

        I think it is inevitable that people program poorly. Training will not substantially help matters. We have to learn to live with it.

        - A. Perlis

        I think that it's extraordinarily important that we in computer science keep fun in computing. When it started out, it was an awful lot of fun. Of course, the paying customers got shafted every now and then, and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful, error-free perfect use of these machines. I don't think we are. I think we're responsible for stretching them, setting them off in new directions, and keeping fun in the house. I hope the field of computer science never loses its sense of fun.

        - A. Perlis

        It goes against the grain of modern education to teach students to program. What fun is there to making plans, acquiring discipline, organizing thoughts, devoting attention to detail, and learning to be self critical.

        - A. Perlis

        It is easier to write an incorrect program than understand a correct one.

        - A. Perlis

        Optimization hinders evolution.

        - A. Perlis

        Simplicity does not precede complexity, but follows it.

        - A. Perlis

        There are two ways to write error-free programs, but only the third one works.

        - A. Perlis

        To understand a program, you must become both the machine and the program.

        - A. Perlis

        You think you KNOW when you learn, are more sure when you can write, even more when you can teach, but certain when you can program.

        - A. Perlis

        Computers are useless. They only give you answers.

        - P. Picasso

        I am always doing that which I cannot do, in order that I may learn how to do it.

        - P. Picasso

        Fancy algorithms are slow when N is small, and N is usually small.

        - R. Pike

        Fancy optimizers have fancy bugs.

        - R. Pike

        Thinking and spoken discourse are the same thing, except that what we call thinking is, precisely, the inward dialog carried on by the mind with itself without spoken sound.

        - Plato

        The mind is not a vessel to be filled, but a fire to be ignited.

        - Plutarch (See W.B. Yeats: Education...)

        Life is good only for two things: to study mathematics and to teach it.

        - M. Poisson

        Computers in the future may weigh no more than 1.5 tons.

        - Popular Science (1959)

        Knowledge of a subject means knowledge of the language of that subject, which includes not only what its words mean, but far more important, how its words mean. As one learns the language of a subject, one is also learning what the subject is. It cannot be said often enough that what we call a subject consists mostly, if not entirely, of its language. If you eliminate all the words of a subject, you have eliminated the subject. Biology is not plants and animals. It is language about plants and animals. History is not events. It is language describing and interpreting events. Astronomy is not planets and stars. It is a way of talking about planets and stars.

        - N. Postman

        Gates has always understood Moore's Law better than anyone else in the industry. If you can make something run at all, get it out there -it may be slow and clunky, but hardware improvements will bail you out. If you wait until it's running perfectly on the hardware already in the field, it will be obsolete before it's released. This philosophy built Microsoft and is the main reason Microsoft won the war IBM declared back in the OS/2 days.

        - J. Pournelle (Dr. Dobbs Journal, Feb. 2004, pp. 89)

        The voyage of discovery is not in seeking new landscapes but in having new eyes.

        - M. Proust

        I really hate this darn machine;
            I wish that they would sell it.
        It won't do what I want it to,
            but only what I tell it.

        - Programmer's Lament


        Q

        The underlying complexity of a given problem is constant. It can be hidden, but it does not go away. Complexity is conserved by abstractions. In fact, apparent complexity can be increased by abstractions, but the underlying complexity can never be reduced.

        - M. Quail (in his blog)


        R

        Given enough eyeballs, all bugs are shallow (e.g., given a large enough beta-tester and co-developer base, almost every problem will be characterized quickly and the fix will be obvious to someone). (also see "The honest truth..." by B. Joy).

        - E. Raymond (Lesson 8 in The Cathedral and the Bazaar)

        Good programmers know what to write. Great ones know what to use. [I'd add: Exceptional programmers know how to write code that others can use. -REP]

        - E. Raymond

        Ugly programs are like ugly suspension bridges: they're much more liable to collapse than pretty ones, because the way humans (especially engineer-humans) perceive beauty is intimately related to our ability to process and understand complexity. A language that makes it hard to write elegant code makes it hard to write good code.

        - E. Raymond

        Computer Science is the first engineering discipline in which the complexity of the objects created is limited solely by the skill of the creator, and not by the strength of raw materials.

        - B. Reid

        All of us had been trained by Kelly Johnson [designer of the Lockheed SR-71] and believed fanatically in his insistence that an airplane that looked beautiful would fly the same way.

        - B. Rich (in "Skunk Works")

        In time of profound change, the learners inherit the earth, while the learned find themselves beautifully equipped to deal with a world that no longer exists.

        - A. Rogers

        G-d is in the details.

        - M. van der Rohe

        Less is more.

        - M. van der Rohe

        Software is abstract and therefore seems as if it should be infinitely malleable. And yet, for all its ethereal flexibility, it can be stubbornly, maddeningly intractable, and it is constantly surprising us with his rigidity.

        - S. Rosenberg (in "Dreaming in Code", pp 58)

        Don't you hate code that's not properly indented? Making it [indenting] part of the syntax guarantees that all code is properly indented.

        - G. van Rossum(designer of the Python)

        The highest reward for a person's toil is not what they get for it, but what they become by it.

        - J. Ruskin

        Language serves not only to express thought but to make possible thoughts which could not exist without it.

        - B. Russell


        S

        A designer knows he's achieved perfection not when there is nothing left to add, but when there is nothing left to take away.

        - A. de Saint-Exupery

        If you want to build a ship, don't drum up the men to gather wood, divide the work and give orders. Instead, teach them to yearn for the vast and endless sea.

        - A. de Saint-Exupery

        Questions are the important thing, answers are less important. Learning to ask a good question is the heart of intelligence. Learning the answer---well, answers are for students. Questions are for thinkers.

        - R. Schank (in "The Connoisseur's Guide to the Mind")

        There is one very good reason to learn programming, but it has nothing to do with preparing for high-tech careers or with making sure one is computer literate in order to avoid being cynically manipulated by the computers of the future. The real value of learning to program can only be understood if we look at learning to program as an exercise of the intellect, as a kind of modern-day Latin that we learn to sharpen our minds.

        - R. Schank (in "The Cognitive Computer")

        Always do the hard part first. If the hard part is impossible, why waste time on the easy part? Once the hard part is done, you're home free.

        Always do the easy part first. What you think at first is the easy part often turns out to be the hard part. Once the easy part is done, you can concentrate all your efforts on the hard part.

        - A. Schapira

        The skill of writing is to create a context in which other people can think.

        - E. Schlossberg

        Machines are simple: a hammer, a door hinge, a steak knife. Systems are much more complicated; they have components, feedback loops, mean times between failure, infrastructure. Digital systems are daedal; even a simple computer program has hundreds of thousands of lines of computer code doing all sorts of different things. A complex computer program has thousands of components, each of which has to work by itself and in interaction with all the other components. This is why object-oriented programming was developed: to deal with the complexity of digital systems...systems have bugs. A bug is a particular kind of failure...It's different from a malfunction. When something malfunctions, it no longer works properly. When something has a bug, it misbehaves in a particular way, possibly unrepeatable, and possibly unexplainable. Bugs are unique to systems. Machines can break, or fail, or not work, but only a system can have a bug.

        - B. Schneier (in Secrets & Lies: Digital Security in a Networked World).

        Microsoft knows that reliable software is not cost effective. According to studies, 90% to 95% of all bugs are harmless. They're never discovered by users, and they don't affect performance. It's much cheaper to release buggy software and fix the 5% to 10% of bugs people find and complain about.

        - B. Schneier

        If the code and the comments disagree, then both are probably wrong.

        - N. Schryer

        Always to see the general in the particular is the very foundation of genius.

        - A. Schopenhauer

        Thus the task is not so much to see what no one has yet seen, but to think what no one has yet thought about that which everybody sees.

        - E. Schrodinger

        Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom.

        - G. Schubert (extending Cliff Stoll in "Silicon Snake Oil"). Also see F. Zappa

        Any intelligent fool can make things bigger and more complex. It takes a touch of genius -and a lot of courage- to move in the opposite direction

        - E.F. Schumacher

        And simple truth miscalled simplicity,

        - W. Shakespeare (Sonnet 66)

        Learning results from what the student does and thinks, and only from what the student does and thinks. The teacher can advance learning only by influencing the student to learn.

        - H. Simon

        Mathematics is a language. We want scientists to be able to read it, speak it, and write it. But we are are not training them to be grammarians.

        - H. Simon

        His philosophy of aesthetics reminds me of a quote that went something like this: "Fashion is what seems beautiful now but looks ugly later; art can be ugly at first but it becomes beautiful later." Steve always aspired to make beautiful later.

        - M. Simpson (Steve Jobs' sister, quoted from her eulogy)

        I'd rather write programs to write programs than write programs.

        - D. Sites

        I cannot teach anybody anything, I can only make them think.

        - Socrates

        All understanding begins with our not accepting the world as it appears.

        - S. Sontag

        One must learn by doing the thing; for though you think you know it, you have no certainty, until you try.

        - Sophocles

        [On being asked, "How do we hold on to dreams that, statistically, are impossible (like aspiring to be Supreme Court Justice)?"] ... Experience has taught me that you cannot value dreams according to the odds of their coming true. Their real value is in stirring within us the will to aspire. That will, wherever it finally leads, does at least move yo forward. And after a time you may recognize that the proper measure of success is not how much you've closed the distance to some far-off goal but the quiality of what you've done today.

        - S. Sotomayor (in "My Beloved World" pp. viii)

        I told him [a cousin] how I had been dazzled by his brilliance and his limitless curiosity about how the world works. And how I despaired of ever matching up to him. He looked at me and shook his head. "You don't understand, do you? I've always been in awe of you. There was nothing you couldn't learn if you set your mind to it. You would just study until you figured it out. ... The determination you have is special. It's a different kind of intelligence."

        - S. Sotomayor (in "My Beloved World" pp. 151)

        I've spent my whole life learning how to do things that were hard for me. None of it has ever been easy. ... I've honestly never felt fully prepared at the outset. Yet each time I've survived, I've learned, and I've thrived. I'm not intimidated by challenges. My whole life has been one. I look forward to enganging in the work [a judgeship] and learning how to do it well.

        - S. Sotomayor (in "My Beloved World" pp. 288)

        Computers do not solve problems -computers carry out solutions, specified by people, to problems.

        - D. D. Spencer

        EMACS could not have been reached by a process of careful design, because such processes arrive only at goals which are visible at the outset, and whose desirability is established on the bottom line at the outset. Neither I nor anyone else visualized an extensible editor until I had made one, nor appreciated its value until he had experienced it. EMACS exists because I felt free to make individually useful small improvements on a path whose end was not in sight.

        - R. Stallman

        Giving the Linus Torvalds Award to the Free Software Foundation is a bit like giving the Han Solo Award to the Rebel Alliance.

        - R. Stallman

        Being forced to write comments actually improves code, because it is easier to fix a crock than to explain it.

        - G. Steele

        A real failure does not need an excuse. It is an end in itself.

        - G. Stein

        [teaching]is rather artificial. The world is complicated and messy, with lots of loose ends, and the teacher's job is to impose order on the confusion, to convert a chaotic set of episodes into a coherent narrative.

        - I. Stewart (in "Letters to a Young Mathematician")

        Don't get suckered in by the comments -they can be terribly misleading: Debug only the code.

        - D. Storer

        Design and programming are human activities; forget that and all is lost.

        - B. Stroustrup

        It is my firm belief that all successful languages are grown and not merely designed from first principles

        - B. Stroustrup (in "The Design and Evolution of C++")

        More good code has been written in languages denounced as "bad" than in languages proclaimed "wonderful" -much more.

        - B. Stroustrup (in The Design and Evolution of C++)

        The most important single aspect of software development is to be clear about what you are trying to build.

        - B. Stroustrup

        There are only two kinds of programming languages: those people always bitch about and those nobody uses.

        - B. Stroustrup

        I have learned throughout my life as a composer chiefly through my mistakes and pursuits of false assumptions, not my exposure to founts of wisdom and knowledge.

        - I. Stravinsky

        Omit needless words. Vigorous writing is concise. A sentence should contain no unnecessary words, a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts.

        - W Strunk Jr (in The Elements of Style)

        Rewrite and revise. Do not be afraid to seize what you have and cut it to ribbons ... Good writing means good revising.

        - W Strunk Jr (in The Elements of Style)

        For me, great algorithms are the poetry of computation. Just like verse, they can be terse, allusive, dense, and even mysterious. But once unlocked, they cast a brilliant new light on some aspect of computing.

        - F Sullivan

        In engineering, as in other creative arts, we must learn to do analysis to support our efforts in synthesis. One cannot build a beautiful and functional bridge without a knowledge of steel and dirt, and a considerable mathematical technique for using this knowledge to compute the properties of structures. Similarly, one cannot build a beautiful computer system without a deep understanding of how to "previsualize" the process generated by the code one writes.

        - G. Sussman

        It is a bad plan that admits of no modification.

        - P. Syrus

        If you want to succeed in this world you don't have to be much cleverer than other people; you just have to be one day earlier than most people.

        - L. Szilard

        In life you must often choose between getting a job done or getting credit for it.

        - L. Szilard

        It is better to be clear and wrong than right and confused.

        - L. Szilard

        The most important step in getting a job done...is the recognition of the problem.

        - L. Szilard


        T

        Once you succeed in writing the programs for [these] complicated algorithms, they usually run extremely fast. The computer doesn't need to understand the algorithm, its task is only to run the programs.

        - R. Tarjan

        I believe in excellence. It is a basic need of every human soul. All of us can be excellent, because, fortunately, we are exceedingly diverse in our ambitions and talents.

        - E. Teller

        We must learn to live with contradictions, because they lead to deeper and more effective understanding.

        - E. Teller

        A programming language is a system of notation for describing computations. A useful programming language must therefore be suited for both description(i.e., for human writers and readers of programs) and for computation (i.e., for efficient implementation on computers). But human beings and computers are so different that it is difficult to find notational devices that are well suited to the capabilities of both.

        - R. Tennant (Principles of Programming Languages, Prentice Hall, 1981)

        A lot of people think mastering complexity is the goal. But once you have gotten your Master of Complexity merit badge, you don't have to keep winning it. Complexity is the enemy of computer science and it behooves us, as designers, to minimize it

        - C. Thacker

        One of my most productive days was throwing away 1,000 lines of code.

        - K. Thompson (in Walden)

        But lo! men have become the tools of their tools.

        - H. Thoreau (in Walden)

        Simplify, simplify, simplify!

        - H. Thoreau (in Walden)

        Character is created by encountering and overcoming failure.

        - P. Tough (in How Children Succeed)

        The function of genius is not to give new answers, but to pose new questions which time and mediocrity can resolve.

        - H. Trevor-Howard

        It is better to have an approximate answer to the right question than an exact answer to the wrong one.

        - J. Tukey

        Unless in communicating with it [a computer] one says exactly what one means, trouble is bound to result.

        - A. Turing


        U

        He [John von Neumann] had the invaluable faculty of being able to take the most difficult problem and separate it into its components, whereupon everything looked brilliantly simple.

        - S. Ulam (Bull. of American Mathematical Society, May 1958)


        V

        It's easy to cry "bug" when the truth is that you've got a complex system and sometimes it takes a while to get all the components to co-exist peacefully.

        - D. Vargas

        The outcome of any serious research can only be to make two questions grow where only one grew before.

        - T. Veblen

        Real education happens only by failing, changing, challenging, and adjusting. All of those gerunds apply to teachers as well as students. No person is an "educator," because education is not something one person does to another. Education is an imprecise process, a dance, and a collaborative experience.

        Education is the creation of habits of thought and methods of inquiry that yield unpredictable results. We offer diplomas to people upon completion of a rigorous and diverse set of intellectual experiences, not the mere accumulation of a series of facts and techniques. Education is certainly not an injection of information into a passive receptacle.

        - S. Vaidhyanathan ( in The Chronicle of Higher Education)

        The best is the enemy of the good.

        - Voltaire ("Dramatic Art" in _Philosophical Dictionary, 1764)

        Judge a man by his questions, rather than his answers.

        - Voltaire


        W

        The problem with using C++... is that there's already a strong tendency in the language to require you to know everything before you can do anything.

        - L. Wall

        The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires.

        - W. A. Ward

        I think there is a world market for maybe five computers.

        - T. J. Watson (Founder and Chairman, IBM), 1943

        If you want to increase your success rate, double your failure rate.

        - T. J. Watson

        The computer programmer ... is a creator of universes for which he alone is the lawgiver ... universes of virtually unlimited complexity can be created in the form of computer programs. Moreover ... systems so formulated and elaborated act out their programmed scripts. They compliantly obey their laws and vividly exhibit their obedient behavior. No playwright, no stage director, no emperor, however powerful, has ever exercised such absolute authority to arrange a stage or a field of battle and to command such unswervingly dutiful actors or troops.

        - J. Weizenbaum (Computer Power and Human Reason, page 115)

        Newton was a genius, but not because of the superior computational power of his brain. Newton's genius was, on the contrary, his ability to simplify, idealize, and streamline the world so that it became, in some measure, tractable to the brains of perfectly ordinary men.

        - G. M. Weinberg

        Any problem in computing can be solved by adding another level of indirection. (also see "Any performance problem..." by M. Haertel).

        - D. Wheeler (inventor of the subroutine)

        The best writing is rewriting.

        - E. B. White

        By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and in effect increases the mental power of the race.

        - A. N. Whitehead

        It is a profoundly erroneous truism, repeated by all the copybooks, and by eminent people when they are making speeches, that we should cultivate the habit of thinking what we are doing. The precise opposite is the case. Civilization advances by extending the number of operations which we can perform without thinking about them. Operations of thought are like cavalry charges in a battle -they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.

        - A. N. Whitehead (in "An Introduction to Mathematics")

        I have never learned anything except from people younger than myself.

        - O. Wilde

        As soon as we started programming, we found out to our surprise that it wasn't as easy to get programs right as we had thought. Debugging had to be discovered. I can remember the exact instant when I realized that a large part of my life from then on was going to be spent in finding mistakes in my own programs.

        - M. Wilkes

        Furious activity is no substitute for understanding.

        - H. H. Williams

        From a programmer's point of view, the user is a peripheral that types when you issue a read request.

        - P. Williams

        He knows the course forwards and backwards. He teaches it backwards

        - S. Willoughby (commenting on Garrett Birkhoff's freshman calculus course at Harvard
        in "The Other End of the Log: Memoirs of an Education Rebel")

        Complexity has and will maintain a strong fascination for many people. It is true that we live in a complex world and strive to solve inherently complex problems, which often do require complex mechanisms. However, this should not diminish our desire for elegant solutions, which convince by their clarity and effectiveness. Simple, elegant solutions are more effective, but they are harder to find than complex ones, and they require more time, which we too often believe to be unaffordable

        - N. Wirth

        It is a sad manifestation of the spirit of modern times, in which an individulal's pride in his/her work has become rare. The idea that one might derive satisfaction from his or her successful work, because that worrk is ingenious, beautiful, or just pleasing, has become ridiculed. Nothing but economic success and monetary reward is acceptable. Hence our occupations have become mere jbos. But quality of work can be expected only through personal satisfaction, dedication, and enjoyment.

        - N. Wirth

        Increasingly, people seem to misinterpret complexity as sophistication, which is baffling -the incomprehensible should cause suspicion rather than admiration. Possibly this trend results from a mistaken belief that using a somewhat mysterious device confers an aura of power on the user.

        - N. Wirth

        My being a teacher had a decisive influence on making language and systems [that I designed] as simple as possible so that in my teaching, I could concentrate on the essential issues of programming rather than on details of language and notation.

        - N. Wirth

        Software gets slower faster than hardware gets faster. (Or, sometimes known by] Grove [the head of Intel] giveth and Gates [the head of Microsoft] taketh away.)

        - N. Wirth

        Don't ask what it means, but rather how it is used.

        - L. Wittgenstein

        If we spoke a different language, we would perceive a somewhat different world.

        - L. Wittgenstein

        The limits of your language are the limits of your world.

        - L. Wittgenstein

        More bugs have been introduced into programs through premature optimization than any other cause, including pure stupidity. (sometimes quoted as, "More computing sins are committed in the name of efficiency (without necessarily achieving it) than for any other single reason -including blind stupidity.")

        - W. Wulf


        X


        Y

        Education is not the filling of a pail, but the lighting of a fire.

        - W.B. Yeats (See Plutarch: The mind...)

        Do or do not...there is no try

        - Yoda


        Z

        Cutler, armed with a schedule [for finishing MS Windows NT], was urging the team to "eat its own dog food." Part macho stunt and part common sense, the "dog food diet" was the cornerstone of Cutler's philosophy. "We're going to run on the program we build," he insisted. Eating dog food meant there would be no escape from facing the flaws and imperfections of NT. Even while immersed in his own piece of NT, a code writer would confront all of its weaknesses. By controlling the operations of a code writer's computer, NT would define the quality of his life. If at first NT tasted no better than dog food, all the better. Code writers would feel an urgent need to raise the dietary level by quickly fixing the errant code and writing more durable code in the first place.

        - G. P. Zachary (in "Show-Stopper: The Breakneck Race to Create Windows NT and the Next Generation at Microsoft")

        Information is not knowledge, Knowledge is not wisdom, Wisdom is not truth, Truth is not beauty, Beauty is not love, Love is not music, and Music is THE BEST.

        - F. Zappa (Packard Goose). Also see G. Schubert

        Technical skill is mastery of complexity, while creativity is mastery of simplicity.

        - E. C. Zeeman

        One day Chao-Chou fell down in the snow, and called out: "Help me! Help Me!" A monk came and lay down beside him. Chao-Chou got up and went away.

        - Zen koan

        Who is wise? He who learns from all people...as it is said: "From all my teachers I gained understanding".

        - B. Zoma (Psalms 119:99)


        Thanks to the following contributors of quotes I've missed and felt worthy of appearing here, or of digging up the right attribution for quotes that I once listed as anonymous: Joel Adams, Jerry B. Altzman, Caroline Bauer, David Bell, Teresa Carrigan, David Edelheit, Sarah Fix, Rick Gee, Hakon (see his SoftwareQuotes.com web site), John Harrison, Herbert Holland, Randy Howe (see his book The Quotable Teacher, The Lyons Press, 2003), Jim Huggins (see his Short Quotes web site), Dalton Hunkins, David Kay, Pekka Kilpelainen, Butler Lampson, Jiajun Lim, Chris Lowell, Ben Mauer, Sean McLaughlin, Mary-Alice Muraski, Brad Osgood, Robert Noonan, Nick Parlante Dan Resler, Niseeth Sharma, Jonathan Shelly, Mark Steward, Eugene Wallingford, Richard D. Zakia (see his Quotes for Teachers web site), the Lambda the Ultimate web site, the quoteland.com web site, the Andy "Krazy" Glew's Favorite Quotes and Sayings web site, the Bumper-Sticker Computer Science web site, a Programming Quotations web site, the sysprog.net Quotations for Programmers web site, the Alan Perlis Epigrams in Programming web site, the Paul Graham's Quotes (mostly about Lisp) web site, the Quotations on simplicity in software design web site, Programming Quotations web site, Eugene Fink's web site, Glenn Vanderburg: Quotations on Software Design web site, Glenn Vanderburg: Quotations on Software Design web site, jbox.dk Quotations on simplicity in software design web site. Mathematical and Educational Quotation Server at Westfield State College Quotations Related to University Studies of Computer Science (some in Finnish) http://www.ics.uci.edu/~pattis/misc/sbsarticle/index.html Short Bowel Syndrome

        A Short Reading on Problem Solving

        On August 4th, 2003, right before the start of school that year, I ran across an interesting article highlihgted on the firt page of the New York Times: Brainstorm to Breakthrough: A Surgical Procedure is Born. Since then, I have introduced and discussed this article during my first few lectures (I primarily teach freshmen) every semester. The article discusses the history of a simple operation for treating "Short Bowel Syndrome."

        It comprises many interesting educational themes that resonate with me:

        • Simplicity vs. Complexity.

        • The difficulty for unorthodox ideas to find acceptance by authority ([Students]are not here to worship what is known, but to question it -Jacob Bronowski).

        • Questions vs. Answers -or how asking a better question leads to a better answer. The name "Short Bowel Syndrome" itself implies that the question that must be answered is, "How can we make the bowel longer." By recasting the problem into more general terms, admitting to different kinds of questions, Dr. Kim was able to explore radically different -and better- kinds of solutions.
        The article is of moderate size, but easy to read. It illustrates a common pattern: first an idea is dismissed; eventually it is seriously examined; finally it is thought obvious -and no one can understand why it wasn't discovered sooner. By observing this pattern we might be able to avoid it -although as humans, the odds are against us. http://www.ics.uci.edu/~bic/messengers/ Project: MESSENGERS

        Project: MESSENGERS AND NAVIGATIONAL PROGRAMMING

        This project is developing a new programming paradigm, called Navigational Programming, for distributed systems based on the principles of autonomously migrating processes, called Messengers. Each Messenger is able to migrate among nodes of a local area network using explicit hop statements, which support strong migration. Unlike mobile agents used for a variety of services on the Internet, the MESSENGERS system is intended for general-purpose scientific computing.

        Some of the advantages of migrating processes, as compared to stationary message-passing processes, are highlighted in the following Project Summary.

        Participants

        Faculty:

          • Lubomir Bic
          • Michael Dillencourt

        PhD Students:

          • Wendy Zhang

        Recent PhD/MS Graduates:

          • Munehiro Fukuda (University of Washington, Bothel)

        Ph.D. Dissertation: MESSENGERS: A Distributed Computing System Based on Autonomous Objects (Postscript), 1997

          • Fehmina Merchant (IBM, New York)

        Ph.D. Dissertation: Load Balancing in Spatial Individual-Based Systems using Autonomous Objects (Postscript), 1998

          • Susan Mabry (Whitworth College, Spokane, WA)

        Ph.D. Dissertation: SimAgents: Migrating Agents for Simulation Models, 1999

          • Katherine Morse (SAIC, San Diego, CA)

        Ph.D. Dissertation: An Adaptive, Distributed Algorithm for Interest Management (Postscript), 2000

          • Adam Chi-Lun Chang

        M.S. Thesis: Graphical Interface for Paradigm Oriented Distributed Computing (PDF), 2000

          • Eugene Gendelman (Bloomberg, New York, NY)

        Ph.D. Dissertation: Virtual Infrastructure for Mobile Agent Computing (Postscript), 2002

          • Hairong Kuang (Yahoo, Sunnyvale, CA)

        Ph.D. Dissertation: Paradigm-Oriented Distributed Computing Using Mobile Agents (Postscript), 2002

          • Lei Pan (JPL, Pasadena, CA)

        Ph.D. Dissertation (abstract): Navigational Programming (PDF), 2005

          • Richard Utter (NSA, Washington, D.C.)

        Ph.D. Dissertation: Diactoros: Full State Migration (PDF), 2006

          • Koji Noguchi (Yahoo, Sunnyvale, CA)

        Ph.D. Dissertation: Spontaneous Process Migration with Global Pointers (PDF), 2006

          • Javid J. Huseynov

        Ph.D. Dissertation: Distributed Localization of Ultrasonic Sources of Gas Leak (PDF), 2008

          • Jiming Liu

        Ph.D. Dissertation: Distributed Individual-Based Simulation (PDF), 2008

          • Ming Kin Lai

        Ph.D. Dissertation: State-Migration Shared-Variable Programming (PDF), 2009

          • Matthew Badin

        Ph.D. Dissertation: Methods for Mitigating and Eliminating Error in Hybrid Matrix Multiply Algorithms, 2014

          • Kiyoshi Nakayama

        Ph.D. Dissertation: A Distributed Smart Grid Control Model for Integration of Renewables, 2014

         

        Visiting Students:

          • Dominik Jergus (Fachhochshule Darmstadt, Germany) Diploma Thesis (61 pages, Postscript), 1997
          • Christian Wicke (University of Karlsruhe, Germany) Diploma Thesis (81 pages, Postscript), 1998

         

        Publications

          1. Simulating Autonomous Objects in a Spatial Database (5 pages, Postscript), 9th European Simulation Multiconference, Prague, Czech Republic, June 1995

        (Shows how autonomous objects can navigate in a simulated spatial environment)

          1. Distributed Computing using Autonomous Objects (10 pages, Postscript), 5th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′95), Cheju Island, Korea, Aug. 1995

        (This is similar to the subsequent article in IEEE COMPUTER (below) -- it presents potential application areas and surveys related approaches)

          1. A Novel Approach to Toxicology Simulation based on Autonomous Objects (6 pages, Postscript), Conf. on Simulation in the Medical Sciences (part of SCS Western MultiConference), San Diego, Jan. 1996

        (Discusses a specific simulation application for Toxicology)

          1. Intra- and Inter-Object Coordination with MESSENGERS (18 pages, Postscript), First Int′l Conf. on Coordination Models and Languages (COORDINATION′96), Cesena, Italy, April 1996

        (Presents MESSENGERS as a coordination paradigm for constructing distributed applications)

          1. Distributed Computing using Autonomous Objects (16 pages, Postscript), IEEE COMPUTER, August 1996

        (Surveys several lines of research related to aunonmous objects and coordination)

          1. Performance of the MESSENGERS Autonomous-Objects-Based System (15 pages, Postscript), First Int′l Conf. on Worldwide Computing and Its Applications ′97 (WWCA97), Tsukuba, Japan, March 10-11, 1997 (LNCS 1274, Springer-Verlag)

        (Presents initial performance results)

          1. Messages versus Messengers in Distributed Programming (8 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-97), Baltimore, MD, May 1997

        (Illustrates how navigational programming differs from message-passing and shows the advantages)

          1. A Hierarchical Mapping Scheme for Mobile Agent Systems (6 pages, Postscript), 6th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′97), Tunis, Tunesia, Oct. 1997

        (Discusses the mapping problem for MESSENGERS, that is, mapping of Messengers to logical nodes, logical nodes to daemon nodes, and daemon nodes to physical nodes. Presents new ways of load balancing, resource utilization, and granularity control)

          1. Distributed Coordination with MESSENGERS (24 pages, Postscript), Science of Computer Programming Journal, Special Issue on Coordination Models, Languages, and Applications, 31(2), July 1998

        (Presents the navigational calculus and shows how it is used to dynamically compose an application)

          1. Mobile Network Objects (23 pages, Postscript), Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, Inc., 1998

        (An introductory overview of mobile agents technologies)

          1. Octopus -- Interactive Visualization and Contol Environment for Mobile-Objects-Based Systems (61 pages, Postscript), Diploma thesis by D. Jergus (Fachhochschule Darmstadt, Germany), 1997

        (Octopus is a tool superimposed on the MESSENGERS environment. It permits individual Messengers to periodically report information about themselves, e.g., their current position in a simulated space, which Octopus is able to display in real time for the purposes of visualization of the ongoing computation)

          1. CVSys: A Coordination Framework for Dynamic and Fully Distributed Cardiovascular Modeling and Simulation

        (8 pages, PDF), Int′l Biomedical Optics Symposium (BIOS′98), special section on Biomedical Sensing, Imaging and Tracking Technologoes, San Jose, CA, Jan. 1998
        (Discusses a large biomedical application -- the simulation of a cardiovascular system)

          1. Automatic State Capture of Self-Migrating Computations (6 pages, Postscript), ICSE98 Int′l Workshop on Computing and Communication in the Presence of Mobility, (Part of Int′l Conference on Software Engineering), Kyoto, Japan, April 1998

        (Presents an approach to automatic state capture by restricting migration to the top coordination layer as implemented in the MESSENGERS system)

          1. Global Virtual Time Support for Individual-Based Simulations (8 pages, Postscript), Int′l Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA′98), Las Vegas, Nevada, July 1998

        (Discusses the advantages of having global virtual time support provided by the system when implementing individual-based simulations)

          1. Automatic State Capture of Self-Migrating Computations in MESSENGERS (12 pages, Postscript), Second Int′l Workshop on Mobile Agents 98 (MA′98), Stuttgart, Germany, September 1998

        (Discusses the implementation of fully transparent state capture and restoration for the purposes of migration or local context switch. This is possible even under a fully compiled version of the MESSENGERS system)

          1. Load Balancing in Individual-Based Spatial Applications (8 pages, Postscript), Int′l Conf. on Parallel Architectures and Compilation Techniques (PACT′98), Paris, France, Oct. 1998

        (Presents three specific algorithms to do load balancing in applications where a group of individuals (or particles) move autonomously through a simulated environment and perform some coordinated group movement. Shows performance evaluation of these algorithms.)

          1. Messages versus Messengers in Distributed Programming (33 pages, Postscript), Journal of Parallel and Distributed Computing, 57, 188-211, 1999

        (A more extensive version of the paper with the same title presented at ICDCS′97 -- see above -- it illustrates how navigational programming differs from message-passing and shows the advantages both qualitatively and quantitatively)

          1. Introducing Dynamic Data Structure into Mobile Agents (7 pages, Postscript), Int′l Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA′99), Las Vegas, Nevada, July 1999

        (Introduces a special abstract data type into MESSENGERS. The dynamic structures belonging to a Messenger are carried automatically whenever the Messenger hops between nodes)

          1. Compiling for Fast State Capture of Mobile Agents (8 pages, Postscript), Parallel Computing ′99 (ParCo99), Delft, The Netherlands, Aug. 1999

        (A new approach to state capture/restoration of a mobile agent during migration)

          1. Self-Migrating Threads for Multi-Agent Applications (8 pages, Postscript), Int′l Workshop on Cluster Computing (IWCC′99), Melbourne, Australia, Dec. 2, 1999

        (A cluster computing paradigm that combines navigational autonomy of agents with fine granutality of threads)

          1. Efficient Checkpointing Algorithm for Distributed Systems with Reliable Communication Channels (2 pages, Postscript), IEEE Symp. on Reliable Distributed Systems (SRDS′99), Lausanne, Switzerland, Oct. 1999
          2. Bridging Semantics Gaps with Migrating Agents (6 pages, PDF), Int′l Conf. on Parallel and Distributed Computing Systems (PDCS′99), Cambridge, MA, Nov. 1999
          3. PODS: Paradigm-Oriented Distributed Computing (7 pages, Postscript), 7th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′99), Cape Town, South Africa, Dec. 1999
          4. Modeling Cardiovascular Flow with a Migrating Agent Systems (6 pages, PDF), Int′l Conf. on Health Sciences Simulation, San Diego, CA, Jan. 2000
          5. Paradigm-Oriented Distributed Computing Using Mobile Agents (9 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-2000), Taipei, Taiwan, April 2000
          6. An Application-Transparent, Platform-Independent Approach to Rollback-Recovery for Mobile Agent Systems (8 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-2000), Taipei, Taiwan, April 2000
          7. Superboundary Exchange: A Technique for Reducing Communication in Distributed Implementations of Interactive Computations (15 pages, Postscript), Int′l Conf. on Algorithms and Architectures for Parallel Processing (ICA3PP-2000), Hong Kong, December 2000
          8. Process Interconnection Structures in Dynamically Changing Topologies (10 pages, Postscript), Int′l Conf. on High Performance Computing (HiPC-2000), Bangalore, India, December 2000
          9. Distributed Sequential Computing Using Mobile Code: Moving Computation to Data (8 pages, PDF), Int′l Conf. on Parallel Processing (ICPP-01), Valencia, Spain, September 2001
          10. Fast File Access for Fast Agents (15 pages, Postscript), Int′l Conf. on Mobile Agents (MA2001), Atlanta, GA, Dec. 2001
          11. MESSENGERS: Distributed Programming Using Mobile Agents (17 pages, PDF), Transaction of the Society for Design and Process Science (SDPS), Vol. 5, No. 4, Dec. 2001
          12. Shared Variable Programming Beyond Shared Memory: Bridging Distributed Memory with Mobile Agents (11 pages, PDF), The Sixth International Conference on Integrated Design and Process Technology (IDPT02), Pasadena, CA, June 2002
          13. Communication Reduction in Iterative Grid-based Computing Using SuperBoundary Exchange Technique (6 pages, Postscript), The 20th IASTED International Multi-Conference Applied Informatics (AI-2002), Innsbruck, Austria, February, 2002
          14. Iterative Grid-based Computing Using Mobile Agents (9 pages. Postscript), International Conference on Parallel Processing (ICPP-2002), Vancouver, British Columbia, Canada, August, 2002
          15. Mobile Agents -- The Right Vehicle for Distributed Sequential Computing (10 pages, PDF), Int′l Conf. on High Performance Computing (HiPC-2002), Bangalore, India, December 2002
          16. Distributed Parallel Computing Using Navigational Programming: Orchestrating Computations Around Data (6 pages, PDF), Int′l Conf. on Parallel and Distributed Computing and Systems (PDCS 2002), Cambridge, MA, November 2002
          17. Estimation of Multimedia Inorganic Arsenic Intake in the U.S. Population (25 pages, PDF), Human and Ecological Risk Assessment, Vol. 8, No. 7, pp. 1697-1721, 2002
          18. GIDM: Globally-Indexed Distributed Memory (7 pages, Postscript), 9th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS 2003), San Juan, Puerto Rico, May 2003
          19. Facilitating Agent Navigation Using DSM - High Level Designs (11 pages, PDF), The Seventh World Conference on Intergrated Design & Process Technology (IDPT03), Houston, TX, Dec 2003
          20. A Mobile-Agent-Based PC Grid (9 pages, Postscript), The 5th Annual International Workshop on Active Middleware Services (AMS2003), Seattle, WA, June 2003
          21. From Distributed Sequential Computing to Distributed Parallel Computing (8 pages, PDF), The 5th Workshop on High Performance Scientific and Engineering Computing with Applications (HPSECA-03), Kaohsiung, Taiwan, ROC, October 2003
          22. NavP Versus SPMD: Two Views of Distributed Computation (8 pages, PDF), Int′l Conf. on Parallel and Distributed Computing and Systems (PDCS 2003), Marina del Ray, CA, November 2003
          23. Distributed Sequential Computing (18 pages, PDF), Advanced Parallel and Distributed Computing. Series: Advances in Computation: Theory and Practice, Vol. 16, (Y. Pan and L.T. Yang, Eds.), Nova Science Publishers, Inc., New York, 2004
          24. Distributed Parallel Computing Using Navigational Programming (37 pages, PDF), International Journal of Parallel Programming, Vol. 32, No. 1, Feb. 2004
          25. PODC: Paradigm-Oriented Distributed Computing (13 pages, PDF), Journal of Parallel and Distributed Computing, No. 65, 2005 (www.sciencedirect.com)
          26. Incremental Parallelization Using Navigational Programming: A Case Study (10 pages, PDF), International Conference on Parallel Processing (ICPP-2005), Oslo, Norway, June 2005
          27. Mobile Pipelines: Parallelizing Left-Looking Algorithms Using Navigational Programming (12 pages, PDF), 12th IEEE Int′l Conf. on High Performance Computing (HiPC-2005), Goa, India, December 2005
          28. Toward Incremental Parallelization, (9 pages PDF), IEICE Trans. Inf. & Syst., Vol. E89-D, No. 2, pp. 390-398, Feb. 2006
          29. Toward Automatic Data Distribution for Migrating Computations (8 pages, PDF), Int′l Conf. on Parallel Processing (ICPP 07), Xian, China, Sept. 2007
          30. Efficient Global Pointers With Spontaneous Process Migration (8 pages, PDF), The 16th Euromicro Conference on Parallel Distributed and Network-based Processing (PDP 2008), Toulouse, France, February 2008
          31. Mobile Agents, DSM, Coordination, and Self-Migrating Threads: A Common Framework (6 pages, PDF), Int′l Conference on Data Networks, Communications, and Computers (DNCOCO′08), Bucharest, Romania, November 2008
          32. Distributed Individual-Based Simulation (12 pages, PDF), 15th International European Conference on Parallel and Distributed Computing (Euro-Par 2009), Delft, The Netherlands, August 2009
          33. Gas-Leak Localization Using Distributed Ultrasonic Sensors, Proc. SPIE, Vol. 7293, 72930Z, San Diego, March 2009
          34. Automatic Resource Management in Multi-site Mobile Computing, The 5th International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2010), Seattle, WA, April 2010
          35. Pretty Good Accuracy in Matrix Multiplication with GPUs, 9th Int′l Symp. Parallel and Distributed Computing (ISPDC 2010 ), Istanbul, Turkey, July 2010
          36. JaMes: A Java-based System for Navigational Programming, Int′l Conference on Computational Problem-Solving (ICCP), Chengdu, China, October 2011
          37. Improving Accuracy for Matrix Multiplications on GPUs, Scientific Programming. Volume 19 (2011)
          38. Improving the Accuracy of High Performance BLAS Implementations using Adaptive Blocked Algorithms. The 23rd Int′l Symp. on Computer Architecture and High Performance Computing, Vitoria, Espirito Santo, Brazil, October 2011
          39. Incremental Parallelization with Migration, IEEE Int′l Symp. on Parallel and Distributed Processing with Applications, Madrid, Spain, July 2012
          40. Complete Automation of Future Grid for Optimal Real-Time Distribution of Renewables, IEEE Int′l Conf. on Smart Grid Communication, Tainan City, Taiwan, Nov. 2012 (Best Paper Award)
          41. Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms, 27th Int′l Conf. on Supercomputing (ICS-2013) June 2013, Eugene, OR
          42. Tie-Set Based Fault Tolerance for Autonomous Recovery of Double Link Failures, IEEE Symp. on Computers and Communications (ISCC′13), July 2013, Split, Croatia
          43. Distributed Real-Time Power Flow Control with Renewable Integration, IEEE Int′l Conf. on Smart Grid Communication, Vancouver, Canada, Oct. 2013
          44. Distributed Flow Optimization Control for Energy-Harvesting Wireless Sensor Networks, IEEE Int′l Conf. on Communications (ICC), Sydney, Australia, 2014

         

        Technical Reports

          • MESSENGERS: A Distributed Computing Environment for Autonomous Objects
            • UCI Technical Report: TR-96-20, 1996
              (Contains additional details of MESSENGERS and its implementation)
          • Interest Management in Large-Scale Distributed Simulations
            • UCI Technical Report: TR-96-27, 1996
              (Surveys approaches to information filtering in distributed interactive simulation, which is one application area for MESSENGERS)
          • Distributed Individual-Based Simulation Using Autonomous Objects
            • UCI Technical Report: TR-97-46, 1998
              (Describes the MESSENGERS virtual time environment and demonstrates its programmability and performance)
          • CVSys: A First Prototype of a Distributed and Dynamic Cardiovascular Simulation System
            • UCI Technical Report: TR-98-08, 1998
              (Presents the design of a MESSENGERS-based simulation of the cardiovascular system)
          • Mobile Agents - The Right Vehicle for Distributed Sequential Computing
            • UCI Technical Report: TR-01-68, 2001
          • Incremental Parallelization Using Navigational Programming: A Case Study
            • UCI Technical Report: TR-05-04, 2005
          • Mobile Pipelines: Parallelizing Left-Looking Algorithms Using Navigational Programming
            • UCI Technical Report: TR-05-12, 2005

         

         

         

        There are two independent versions of MESSENGERS: MESSENGERS-I, and MESSENGERS-C, where "I" stands for "interpreted", and "C" stands for "compiled". The MESSENGERS-C is faster than MESSENGERS-I, but MESSENGERS-I has a Virtual Time support.

        User Manuals and System Installation for MESSENGERS-C

          • MESSENGERS-C (Version 2.1) User Manual provides a description of the MESSENGERS-C (Version 2.1) functionality and installation.
          • MESSENGERS-C (Version 1.2.04) User Manual provides a description of the MESSENGERS-C (Version 1.2.04) functionality and installation (stable version).
          • MESSENGERS-C (Version 1.2.05) User Manual provides a description of the MESSENGERS-C (Version 1.2.05) functionality and installation (experimental version).
          • Net Creator User Manual describes tools that can be used to automatically create a logical network for MESSENGERS-C system
          • Graph Creator User Manual describes yet another, graphical tool that can be used for logical network creation. The files output by GraphCreator should be used as input to the NetScheduler program, described in the "Net Creator User Manual"
          • The MESSENGERS software may be obtained free of charge for non-commercial purposes.

        User Manuals and System Installation for MESSENGERS-I

          • MSGR01 MESSENGERS User′s Manual
          • MSGR02 MESSENGERS System Library
          • MSGR03 MESSENGERS Daemon Design Book
          • MSGR04 MESSENGERS: Intermediate Code Specification
          • MSGR05 MLEX: The MESSENGERS Assembler
          • MSGR06 MESSENGERS-C Compiler
          • The MESSENGERS software may be obtained free of charge for non-commercial purposes. Installation instructions are given in MSGR01: MESSENGERS User′s Manual. If you are interested in installing/using MESSENGERS on your system, please send a message to mfukuda@u.washington.edu to receive a decryption keyword.
        http://www.ics.uci.edu/~bic/courses/6B/index.html Lubomir Bic

         

         
        Lubomir Bic

        • email: bic@ics.uci.edu
        • office: ICS3 (Bren Hall), Room 3224
        • phone/fax: 949-824-5248

        This course page will open approximately one week prior to instruction begin

         

        School of Information and Computer Sciences,


        University of California, Irvine CA 92717-3425
         

        http://www.ics.uci.edu/~kobsa/left.htm Homepage Alfred Kobsa

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        http://www.ics.uci.edu/~kobsa/right.htm Homepage of Alfred Kobsa

         


        Alfred Kobsa

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        +49 180 548 20102442 reach me by email]
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        Dr Alfred Kobsa is a Professor in the Donald Bren School of Information and Computer Sciences of the University of California, Irvine. Before he was an Institute Director at the German National Research Center for Information Technology (GMD-FIT, now part of Fraunhofer), and a Professor of Computer Science at the University of Essen, Germany. He was also an Associate Professor of Information Systems at the Department of Information Science at the University of Konstanz, Germany, and a Senior Researcher at the Department of Computer Science of the University of Saarbrücken. He received his master degrees in Computer Science and in the Social and Economic Sciences from the Johannes Kepler University Linz, Austria, and his Ph.D. in Computer Science from the University of Vienna, Austria and the Vienna University of Technology.

        Dr Kobsa's research lies in the areas of user modeling and personalized systems, privacy, support for personal health maintenance, and in information visualization. He is the editor of User Modeling and User-Adapted Interaction: The Journal of Personalization Research, and was the founding president of User Modeling Inc. Dr. Kobsa edited several books and authored numerous publications in the areas of user-adaptive systems, privacy, human-computer interaction and knowledge representation. He also co-founded a national workshop series and an international conference series in these areas. He received research awards from the Humboldt Foundation, Google, and several other organizations.


        Donald Bren School of Information and Computer Sciences
        University of California, Irvine
        Irvine, CA 92697-3440, U.S.A.
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        http://www.ics.uci.edu/employment/employ_lecturer.php lecturer positions @ the bren school of information and computer sciences
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        Bren school home > Employment
        Lecturer Positions
        Donald Bren School of Information and Computer Sciences
        Department of Informatics
        Part-Time Lecturer Position

        The Department of Informatics in the Donald Bren School of Information and Computer Sciences has the occasional need for part-time lecturers to assist us in delivering a diverse curriculum.

        Applicants must submit a curriculum vitae, statement of teaching, and names and contact information of three references. All materials must be uploaded at:
        https://recruit.ap.uci.edu/apply/JPF03005

        Salaries are commensurate with technical and teaching experience and/or level of education. Appointments may be made at varying percentages of time.

        Applications for Fall Quarter positions are due on or before August 31, 2015.
        Applications for Winter Quarter positions are due on or before December 2, 2015.
        Applications for Spring Quarter positions are due on or before March 1, 2016.

        UCI is an equal opportunity employer committed to excellence through diversity.

        The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy.


        Donald Bren School of Information and Computer Sciences
        Department of Statistics
        Part-Time Lecturer Position

        The Department of Statistics has the occasional need for part-time lecturers in the various areas of Statistics.

        Desirable qualifications include a Ph.D. or M.S in Statistics, or equivalent education or experience in a related field plus successful university-level teaching experience.

        Salaries are commensurate with technical and teaching experience and/or level of education. Appointments may be made at varying percentages of time. Application information may be found at recruit.ap.uci.edu/apply/JPF02853

        The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy.

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        http://www.ics.uci.edu/employment/ employment @ the bren school of information and computer sciences
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        Bren school home > Employment
        Employment

        » Research positions
        Our faculty occasionally recruit top researchers to assist them with their innovative research projects. Apply today.

        » Student employment
        Our recently revamped Tech Jobs portal connects employers with students of the Bren School of Information and Computer Sciences. Students can create a profile and search for jobs that meet their specific criteria; employers can post and manage jobs or internships.

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        http://www.ics.uci.edu/community/icsjobs/index.php ics.jobs @ the bren school of information and computer sciences
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        Bren school home > Community > ics.jobs
        ics.jobs

        Thank you for visiting ics.jobs. We have changed the face of our recruitment web site and hope you will like its new features, which include listing your position, logo, and also the opportunity to link in as an ICS Corporate Partner too. To post a full time, part-time or internship opportunity, and learn more about Tech Jobs and our Corporate Partners Program, please visit https://techjobs.uci.edu/.

        If you have this link bookmarked, please be sure to change it to the new web link.

        Thank you for your interest in our students and our School!

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        http://www.ics.uci.edu/employment/employ_faculty.php faculty positions @ the bren school of information and computer sciences
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        Bren school home > Employment
        Faculty Positions

        Department of Statistics
        Assistant or Associate Professor

        The Department of Statistics (www.stat.uci.edu) at the University of California, Irvine (UCI), invites applications for a tenure-track assistant or tenured associate professor position beginning July 1, 2016. The department has an interdisciplinary focus, with an emphasis on developing methods to solve applied problems and advancing the statistical theory that underlies those methods. We are searching for faculty with strong research potential, a commitment to excellence in teaching, and enthusiasm for helping our department continue to grow. Applicants must hold a Ph.D. degree in Statistics, Biostatistics or a related field. The Department is interested in individuals with research interests in all areas of statistics.

        The Department of Statistics is part of the Donald Bren School of Information and Computer Sciences at UCI. UC Irvine celebrated its 50th anniversary in 2015, just after being ranked first in the United States and seventh in the world among universities less than 50 years old by a Times Higher Education survey. Compensation is competitive, and includes priority access to purchase on-campus faculty housing at below-market prices. UC Irvine is located 4 miles from the Pacific Ocean and 45 miles south of Los Angeles. The area offers a very pleasant year-round climate, numerous recreational and cultural opportunities, and one of the highest-ranked public school systems in the nation.

        Completed applications containing a cover letter, curriculum vita, graduate transcripts (for assistant professor candidates), statements on teaching and research, sample research publications, and three letters of recommendation should be uploaded electronically. Complete your application by also submitting a statement on previous and/or potential contributions to diversity, equity and inclusion. Please refer to the following web site for instructions: https://recruit.ap.uci.edu/JPF03075 (assistant) or https://recruit.ap.uci.edu/JPF03076 (associate). The review of applications will begin December 15, 2015.

        The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy.


        Computer Science Department
        Tenure-track Assistant Professor position

        The Department of Computer Science (CS) at the University of California, Irvine (UC Irvine) invites applications for a tenure-track Assistant Professor position. We are particularly interested in candidates with expertise in areas such as Artificial Intelligence, Machine Learning, and Natural Language Processing. Exceptionally qualified more advanced candidates may also be considered for a tenured position.

        The department has 46 faculty members and 297 graduate students. Faculty research spans a broad spectrum of areas in CS. Prospective applicants are invited to visit our webpages at http://www.cs.uci.edu. Applicants must have an earned Ph.D. or equivalent degree. Screening will begin immediately upon receipt of a completed application. Applications will be accepted until the position is filled, although maximum consideration will be given to applications received by January 1, 2016. Each application must contain: a cover letter, CV, up to 3 key publications, a statement of research and teaching interests, and 3-5 letters of recommendation. A separate statement that addresses past and/or potential contributions to diversity, equity and inclusion should also be included in the application materials. All materials must be uploaded using the links below.

        Tenure-Track Assistant Professors may apply at: https://recruit.ap.uci.edu/apply/JPF03163

        Tenured Associate Professors may apply at: https://recruit.ap.uci.edu/apply/JPF03166

        The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy. A recipient of an NSF ADVANCE award for gender equity, UCI is responsive to the needs of dual career couples, supports work-life balance through an array of family-friendly policies, and is dedicated to broadening participation in higher education.

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        http://www.ics.uci.edu/employment/employ_distinguished.php endowed bren chairs @ the bren school of information and computer sciences
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        Bren school home > Employment
        Endowed Bren Chairs

        The Donald Bren School of Information and Computer Sciences was endowed with a transformational gift that included ten “Bren Chairs” to be filled with scholars who are internationally-recognized as leaders in emerging issues of any area of information and computer sciences, including cross-disciplinary research integrating information and computer sciences with other disciplines. The Bren Professors are some of the most distinguished appointments at UC Irvine.

        Candidates should bring an integrative outlook to the discipline, enthusiasm in engaging with professional and business communities and the general public, collaborating with UCI scholars who study issues of information and computing technology, and support for the development of innovative technologies and applications. We envision Bren chair-holders to serve as catalysts on campus to establish educational and research programs that foster an interdisciplinary perspective. Accordingly, candidates should not only have a strong disciplinary background with a distinguished record of scholarly publications and extramural funding, but also a proven track record of innovation, collaboration, stimulation and leadership in both education and research.

        Appointments will be in the Department of Computer Science, Informatics, or Statistics at the rank of senior, distinguished professor. Scholars doing truly cross-disciplinary research may be jointly appointed with another school at UC Irvine. Four chairs are currently filled.

        The Bren School of ICS has excellent faculty, innovative programs, high quality students and outstanding graduates as well as strong relationships with local and national high tech industry.

        As one of eleven academic units at UC Irvine. an independent school with three departments – Computer Science, Informatics, and Statistics – the Bren School has a unique perspective that provides a broad foundation from which to build initiatives that explore the full extent of the computing and information disciplines.

        With 68 regular-rank faculty members, seven full-time lecturers, approximately 250 doctoral, 150 masters, and 1000 undergraduate students, ICS is one of the largest computing programs in the country. Many faculty in the school engage in interdisciplinary research through various organizations such as the California Institute for Telecommunications and Information Technology (Calit2), the Institute for Genomics and Bioinformatics (IGB), Institute for Software Research (ISR), to name but a few.

        Outstanding candidates in all relevant areas and at other ranks are encouraged to contact the Dean (icsdean@ics.uci.edu)

        UC Irvine (http://www.uci.edu)  is one of the youngest UC campuses, yet consistently ranks among the nation’s best public universities. UCI is located three miles from the ocean in southern California with an excellent year-round Mediterranean climate. The area surrounding campus offers numerous outdoor and fine arts opportunities and the public school system in Irvine is ranked one of the highest in the nation.

         

        UCI is an equal opportunity employer committed to excellence through diversity and strongly encourages applications from all qualified candidates, including women and minorities. UCI is responsive to the needs of dual career couples, is dedicated to work-life balance through an array of family-friendly policies, and is the recipient of a National Science Foundation ADVANCE award for gender equity.

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        http://www.ics.uci.edu/employment/employ_research.php research positions @ the bren school of information and computer sciences
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        Donald Bren School of Information and Computer Sciences
        Department of Informatics
        Junior Specialist Position
        University of California, Irvine

        The Department of Informatics at UC Irvine has one position available for a Junior Specialist. This positions requires a BS degree in Informatics, Software Engineering, Computer Science, or a related area. The position entails performing research in the area of code search, specifically in enhancing the CodeExchange prototype to address multiple languages, and to design and implement additional novel functionality to turn it into a social-technical code search engine.

        The salary for this position is $36,432/year. The appointment will be at 71% time for 10 months starting in January 2015.

        Please apply online at the following website https://recruit.ap.uci.edu/apply
        Applicants should respond no later than November 6, 2014.

        Andre van der Hoek
        Professor and Chair
        Department of Informatics
        Donald Bren School of Information and Computer Sciences
        University of California, Irvine
        Irvine, CA 92697-3440

        The University of California, Irvine is an Equal Opportunity/Affirmative Action Employer advancing inclusive excellence. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, age, protected veteran status, or other protected categories covered by the UC nondiscrimination policy.

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        http://www.ics.uci.edu/employment/employ_staff.php staff positions @ the bren school of information and computer sciences
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        Bren school home > Employment
        Staff Positions

        Since 1965, a spirit of innovation has distinguished the University of California, Irvine as a research-driven university and an inspiring place to work, earning us international respect. To review current open staff positions and apply online, please visit UCI’s Job Website.

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        http://www.ics.uci.edu/~dechter/talks/tutorial-ijcai2013/ CP and Probabilisitic Reasoning Tutorial @ IJCAI 2013

        IJCAI 2013 Tutorial
        Constraint Processing and Probabilistic Reasoning from a Graphical Models Perspective
         

         

        Abstract

        Constraint networks and Bayesian networks can be viewed within the general framework of graphical models which includes also Markov random fields, cost networks and influence diagrams. Graphical models are knowledge representation schemes that capture independencies in the knowledge base and support efficient, graph-based algorithms for a variety of tasks, including scheduling, planning, diagnosis and situation assessment, design, and hardware and software verification.

        Algorithms for processing constraints and probabilistic models are of two primary types:inference-based and search-based and they support exact and approximate algorithms. Exact Inference (e.g., variable elimination, join-tree clustering) are time and space exponentially bounded by the tree-width of the problem's graph. Exact Search-based algorithms (e.g., backtracking search) can be executed in linear space and often outperform their worst-case predictions. Constraint propagation schemes that approximate inference, can be applied with a bounded time and space. Likewise stochastic scheme (e.g, local search and sampling schemes) can be interpreted as approximate full search. The thrust of advanced schemes is in finding the right balance between search and inference within a hybrid scheme.

        The tutorial will present the algorithmic principles behind the progress that has been made in the past decades in constraint processing and probabilistic reasoning for answering a variety of queries such as: determining consistency and finding one or all solutions, finding optimal solutions and answering likelihood and counting queries. Complexity analysis and empirical demonstration will be presented on variety of benchmarks. Example benchmarks include radio-frequency problems, linkage analysis, combinatorial auctions, and coding networks.

        Speaker Bio

        Rina Dechter is a professor of Computer Science at the University of California, Irvine. She received her PhD in Computer Science at UCLA in 1985, an MS degree in Applied Mathematic from the Weizmann Institute and a B.S in Mathematics and Statistics from the Hebrew University, Jerusalem. Her research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning.

        Professor Dechter is an author of Constraint Processing published by Morgan Kaufmann, 2003, has authored over 150 research papers, and has served on the editorial boards of: Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research and Logical Method in Computer Science (LMCS). She was awarded the Presidential Young investigator award in 1991, is a fellow of the American association of Artificial Intelligence since 1994, was a Radcliffe Fellowship 2005-2006 and received the 2007 Association of Constraint Programming (ACP) research excellence award. She has been Co-Editor-in-Chief of Artificial Intelligence, since 2011.

        Slides


        Please send any questions, concerns or comments to Rina Dechter.

        http://www.ics.uci.edu/~dechter/software.html Dr. Rina Dechter @ UCI :: Software

        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Software
        • My group's software page with a variety of algorithms and problem instances.
        • REES software (article / poster, manual, install notes).
        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/talks.html Dr. Rina Dechter @ UCI :: Selected Talks
        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Selected Talks

        2015

        Advances in Combinatorial Optimization for Graphical Models (PDF)
        Tutorial at the 24th International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, July 27, 2015.

        Advances in Combinatorial Optimization for Graphical Models (PDF)
        Tutorial at the 25th International Conference for Automated Planning and Scheduling, Jerusalem, Israel, June 8, 2015.

        2014

        Modern Exact and Approximate MAP Algorithms for Graphical Models (PDF)
        Invited Talk at Toyota Technological Institute at Chicago (TTIC), Chicago, Illinois, May 22, 2014

        Inference and Search for Discrete Graphical Models: A Tutorial and Recent Work (PDF)
        Grant Meeting, Cambridge, Massachusetts, April 2014

        2013

        From AND/OR Search to AND/OR Sampling (PDF)
        Invited Talk at Department of Computer Science, George Mason University, Fairfax, Virginia, November 11, 2013

        Inference and Search for Graphical Models (PDF)
        Invited Talk at Center for Nonlinear Studeies, Los Alamos National Laboratory, Los Alamos, New Mexico, September 25, 2013

        Constraint Processing and Probabilistic Reasoning from a Graphical Models Perspective (PDF)
        Tutorial at IJCAI 2013, Beijing, China, August 4, 2013

        Weighted AND/OR Multivalued Decision Diagrams (AOMDD) and the Semantic-Width (PDF)
        Invited Talk, in the First Symposium on Structure in Hard Combinatorial Problems, Vienna Center for Logic and Algorithms, Vienna, Austria, May 18, 2013

        Weighted AND/OR Graphs/Diagrams for Probabilistic and Constraints Databases (PDF) Abstract
        Invited Talk, IBM Research Tel-Aviv, Tel-Aviv, Israel, January 2, 2013

        2012

        Modern Exact and Approximate MAP Algorithms for Graphical Models (PDF) Abstract
        In AIRG (Artificial Intelligence Research Group), SEAS, Harvard University, Cambridge, Massachusetts, November 2, 2012

        Judea Pearl: Turing Award, 2011 (PDF)
        In the Symposium on Nobel Prizes and Turing Award, 2011 Open University of Israel, Ra'anana, Israel, June 13, 2012

        Finding Most Likely Haplotypes in General Pedigrees through Parallel Branch and Bound Search (PDF) Abstract
        In the Clinical Genomic Analysis Workshop 2012 IBM, Haifa, Israel, May 13, 2012

        Principles of Reasoning with Graphical Models (PDF)
        In the IAAI 2012 Symposium Ashkelon, Israel, February 22, 2012

        Bayesian Networks and Belief Propagation: From Rumelhart to Pearl to Today (PDF) Abstract
        In the ELSC-ICNC Retreat 2012 Ein Gedi, Israel, February 6, 2012

        Inference and Search for Probabilistic and Determinisitic Graphical Models (PDF)
        Colloquium, Hebrew University, Jerusalem, Israel, January 28, 2012

        2011

        Graph-guided Sampling (PDF) Abstract
        Part of Learning Club, Hebrew University, Jerusalem, Israel, December 2011

        Advances in Combinatorial Optimization Tasks over Graphical Models (PDF) Abstract
        Colloquium, Ben Gurion University, Beer-Sheva, Israel, November 22, 2011

        Problem Solving with Graphical Models (PDF) Abstract
        Part of Computer Science & Problem Solving: New Foundations, Dagsthul, Germany, August 30, 2011

        Constraint Processing from the Graphical Model Perspective (PDF)
        Tutorial at IJCAI 2011, Barcelona, Spain, July 18, 2011

        Anytime AND/OR Depth First Search for Combinatorial Optimization (PDF)
        Symposium on Combinatoial Search 2011, Barcelona, Spain, July 15, 2011

        Reasoning with Graphical Models (PDF)
        Part of Bioinformatics Summer School 2011 Computational Methods for RN, 2011

        2010

        On the Power of Belief Propagation: A Constraint Propagation Perspective (PDF) PPTX | Video
        Part of Judea Pearl Festschrift, 2010

        Tutorial: Advances in Search and Inference for Graphical Models (PDF) Abstract
        Lisbon, Portugal, August 16-20, 2010

        Sampling Techniques for Probabilistic and Deterministic Graphical models (PDF)
        Tutorial, AAAI 2010, Atlanta, GA, July 12, 2010.

        SampleSearch: Importance Sampling in the presence of Determinism (PDF) Abstract
        IBM, Israel, August 3rd, 2010

        On the Power of Belief Propagation:A Constraint Propagation Perspective (pdf)
        The symposium in honor of Judea Pearl, March, 2010.

        AND/OR Search for Probabilistic and Deterministic Graphical Models (pdf)
        University of Washington, Seattle, January 27, 2010.

        2009

        Combinatorial Optimization for Graphical Models (pdf)
        Tutorial, IJCAI 2009. Rina Dechter, Simon de Givry, Radu Marinescu, and Thomas Schiex. Pasadena, CA, July 2009.

        2008

        How I Entered Constraints (pdf)
        Dedicated to Ugo Montanari on the occasion of his 65th Birthday, June 2008

        2007

        ACP 2007 Award talk (pdf)
        Acceptance talk for the ACP Research Excellence Award, Providence, Rhode Island, September 2007.

        From Constraint Programming to Graphical Models (pdf)
        DoD Workshop on Satisfiability, March 2008

        Advances in Search and Inference for Combinatorial Optimization (ppt)
        Tutorial, ICAPS 2007. Rina Dechter, Radu Marinescu and Robert Mateescu. Providence, RI, September 2007

        2006

        From AND/OR Search to AND/OR BDDs (ppt)
        Workshop on Constraints and Verificiaction in the Newton Institute, Cambridge, England, May 2006

        Representation and Reasoning with Graphical Models (ppt)
        Talk given to Radcliffe fellows, February 2006

        2005

        Principles of AI Problem Solving (pdf)
        Tutorial, IJCAI 2005. Adnan Darwiche, Rina Dechter and Hector Geffner

        2004

        Exploiting Tree-Decomposition in Search: The AND/OR Paradigm (pdf)
        Workshop on Graph and Hypergraph Decompositions, Vienna, Austria, December 2004.

        Advanced algorithms for Graphical Models (pdf)
        University of Maryland, September 2004.

        Constraint Processing; The Graphical Models Perspective (pdf)
        Invited Tutorial, UAI 2004.

        2003

        Systematic vs non-systematic algorithms for constraint optimization (pdf)
        Artificial Intelligence Laboratory at the Swiss Federal Institute of Technology in Lausanne, July 2003.

        On The Feasibility Of Distributed Constraint Satisfaction (pdf)
        Workshop on distributed CSP" in IJCAI-2003, August 2003.

        2002

        Constraints and Probabilistic networks: a look at the interface (pdf)
        Invited talk at CP 2002, September 2002.

        2000

        Approximation Techniques for Automated Reasoning (pdf)
        Invited Tutorial, AAAI 2000.

        1998

        Principles and Methods for Automated Inference (pdf)
        Invited Tutorial, AAAI 1998.

        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/research.html Dr. Rina Dechter @ UCI :: Research Overview
        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Automated Reasoning in Artificial Intelligence

        My research is focused on automated reasoning in Artificial Intelligence, particularly in the areas of search, constraint-based reasoning and reasoning under uncertainty.

        My ongoing focus is on constraint processing, a field which unifies themes cutting across many traditional areas in Artificial Intelligence. A variety of techniques have been developed for processing different kinds of constraint expressions and are being applied to diverse tasks such as vision, design, diagnosis, truth maintenance, scheduling, spatio-temporal reasoning, logic programming, and user interface. Many of these methods were incorporated into constraint programming languages which enhance practical applications substantially.

        Since most reasoning tasks are computationally intractable, my primary approach is to devise methods through the understanding and exploitation of tractable reasoning tasks. My previous works on greedy problems, the mechanical generation of heuristics, the identification of tractable constraint models via topological decompositions, and the establishment of boundaries of local computations have been driven by this principal concern. With my students, I analyze algorithms both analytically and empirically using real life applications such as scheduling, planning, and diagnosis.

        In the past decade I extended my research to general graphical models and especially to reasoning under uncertainty using Bayesian networks. We introduced the two unifying algorithmic frameworks of bucket-elimination and AND/OR search which capture the most common styles of human reasoning (e.g., Inference, and conditioning). Bucket elimination unifies dynamic programming for combinatorial optimization with algorithms for theorem proving, logic programs, temporal reasoning, probabilistic reasoning and planning under uncertainty. AND/OR search allows exploiting problem decomposition during search and is the basis to many recent algorithmic advances in graphical models. Within these two frameworks we develop efficient exact and approximate algorithms with potential impact across many computational disciplines.


        Selected Work
        R. Dechter and J. Pearl, "Tree-clustering schemes for constraint-processing" Artificial Intelligence, Vol. 38(3), April 1989, pp.353-366.

        R. Dechter and J. Pearl, "Network-based heuristics for constraint-satisfaction problems" Artficial Intelligence, Vol 34(1), December 1987 pp. 1-38.

        R. Dechter. "Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition" Artificial Intelligence, Vol. 41(3), 1990, pp. 273-312.

        R. Dechter, and J. Pearl, "Structure identification in relational data." In Artificial Intelligence, Vol. 58, 1992, pp. 237-270.

        Pinkas, G., and Dechter, R., "On Improving Connectionist Energy Minimization" In Journal of 157 Artificial Intelligence Research (JAIR), Vol. 3, 1995, pp. 223-248.

        Ben-Eliyahu, R., and Dechter, R., "Default reasoning using classical logic" In Artificial Intelligence, Vol. 84, 1996, pp. 113-150.

        van Beek, P., and Dechter, R., "On the minimality and decomposability of row-convex constraint networks" Journal of the ACM, Vol. 42, No. 3, May 1995, pp. 543-561.

        van Beek, P., and Dechter, R., "Constraint restrictiveness versus local and global consistency" In Journal of the Association of Computing Memory.

        Dechter, R., and van Beek, P., "Local and global relational consistency" In Journal of Theoretical Computer Science, 1996

        Schwalb, E., and Dechter, R., "Processing Disjunctions in Temporal Constraint Networks" In Artificial Intelligence, volume 93, pp. 29-61, 1997.

        Dechter, R., "Bucket Elimination: A unifying framework for probabilistic inference" In Uncertainty in Artificial Intelligence, UA196, 1996, pp. 211-219.

        Dechter, R., and Rish, I., " A scheme for approximating probabilistic inference" In Uncertainty in Artificial Intelligence (UAI97), August 1997.

        Frost, D., and Dechter, R. "Maintenance scheduling problems as benchmarks for constraint algorithms" in Annals of Math and AI, 1999

        I Rish, and R. Dechter., "Resolution versus Search: Two Strategies for SAT" in Journal of Automated Reasoning, Volume 24, Issue 1/2, pp. 225-275, January, 2000.

        K. Kask, R. Dechter, "A General Scheme for Automatic Generation of Search Heuristics from Specification Dependencies", in Artificial Intelligence, 129:91-131, 2001.

        R. Dechter, I. Rish "Mini-Buckets: A General Scheme For Approximating Inferance" In the journal of ACM, 2003.

        K. Kask, Rina Dechter, Javier Larrosa and Avi Dechter. "Unifying Cluster-Tree Decompositions for Reasoning in Graphical Models" in Artificial Intelligence Journal, 2005.

        Bozhena Bidyuk and Rina Dechter. "Cutset Sampling for Bayesian Networks". in JAIR, 2006.

        Rina Dechter and Robert Mateescu. "AND/OR Search Spaces for Graphical Models", in Artificial Intelligence 171 (2-3), pp. 73-106, 2007.

        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/index.html Dr. Rina Dechter @ UCI :: Home
        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Research in my group: Automated reasoning in Artificial Intelligence

        My research is in the field of Automated Reasoning in Artificial Intelligence and focused on Graphical Models. Graph based models (e.g., Bayesian and constraint networks, influence diagrams and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both Artificial Intelligence and Computer Science in general. These models are used to accomplish many science and engineering tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification and bioinformatics. These reasoning problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization and probabilistic inference. It is well known that these tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques for significantly advancing the state of the art. Our approach is to devise methods through the understanding and exploitation of tractable reasoning tasks and use those islands of tractability in the design of general anytime algorithms. As their name implies, anytime methods provide a solution anytime during the processing, with the added provision that the quality of the solution improves if more time is available.

        To summarize, my research interests are in the areas of Automated Reasoning, Knowledge-Representation, Planning and Learning.
        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/publications.html Dr. Rina Dechter @ UCI


         

        Publications

         
        2016
         

        [R227] Abstract | PDF | Natalia Flerova, Radu Marinescu, and Rina Dechter. "Weighted heuristic anytime search: new schemes for optimization over graphical models" in Annals of Mathematics and Artificial Intelligence.

        [R226] Abstract | PDF | Slides Junkyu Lee, Radu Marinescu, and Rina Dechter. "Applying Search Based Probabilistic Inference Algorithms to Probabilistic Conformant Planning: Preliminary Results" in proceedings of International Symposium on Artificial Intelligence and Mathematics (ISAIM 2016).

        [R225] Abstract | PDF Rina Dechter, Kalev Kask, William Lam, and Javier Larrosa. "Look-ahead with Mini-Bucket Heuristics for MPE" in proceedings of AAAI 2016.

        [R224] Abstract | PDF Junkyu Lee, Radu Marinescu, Rina Dechter and Alexander Ihler. "From Exact to Anytime Solutions for Marginal MAP" in proceedings of AAAI 2016.

        2015
         

        [R223] Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Searching For M Best Solutions In Graphical Models" Submitted to Journal of Artificial Intelligence Research.

        [R222] Abstract | PDF Lars Otten and Rina Dechter. "Parallelizing AND/OR Branch-and-Bound" Submitted to Journal of Artificial Intelligence Research.

        [R221] Abstract | PDF Rodrigo de Salvo Braz, Claran O'Reilly, Vibhav Gogate, and Rina Dechter. "Probabilistic Inference Modulo Theories" in Workshop on Hybrid Reasoning @IJCAI 2015.
        Alternate version: [R221a] Abstract | PDF Rodrigo de Salvo Braz, Claran O'Reilly, Vibhav Gogate, and Rina Dechter. "Probabilistic Inference Modulo Theories" in Tenth International Workshop on Neural-Symbolic Learning and Reasoning (NeSy'15) @IJCAI 2015

        [R220] Abstract | PDF William Lam, Kalev Kask, and Rina Dechter. "Empowering Mini-Bucket in Anytime Heuristic Search with Look-Ahead: Preliminary Evaluation" in Proceedings of the Symposium on Combinatorial Search (SoCS 2015).
        Supplemental Experiments: PDF

        [R219] Abstract | PDF Radu Marinescu, Rina Dechter, and Alexander Ihler. "Pushing Forward Marginal MAP with Best-First Search" in Proceedings of the International Joint Conference on Artificial Intelligence 2015 (IJCAI 2015).
        Extended Experimental Results: PDF

        [R218] Abstract | PDF | Rina Dechter, Levi H. S. Lelis, and Lars Otten. "Caching in Context-Minimal OR Spaces" in Proceedings of the Symposium on Combinatorial Search 2015 (SoCS 2015).
        Longer version: [R218a] Abstract | PDF

        2014
          [R217] Abstract | PDF Alon Milchgrub. "BPLS: Cutset-Driven Local Search For MPE and Improved Bounds for Minimal Cutsets in Grids." M.A. thesis, Computer Science, Hebrew University, Jerusalem.

        [R216] Abstract | PDF Natalia Flerova, Radu Marinescu, Pratyaksh Sharma and Rina Dechter. "Weighted Best-First Search for W-Optimal Solutions over Graphical Models." in Proceedings of Planning, optimization and search (PlanSOpt) 2015 (a workshop of AAAI'15).

        [R215] Abstract | PDF Alon Milchgrub and Rina Dechter. "STLS: Cutset-Driven Local Search For MPE." in Proceedings of SoCS 2014.

        [R214] Abstract | PDF Pratyaksh Sharma, Natalia Flerova and Rina Dechter. "Empirical Evaluation of weighted Heuristic Search with advanced Mini-Bucket Heuristics for Graphical Models." ICS Technical Report, 2014.

        [R213] Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Evaluating Weighted DFS Branch and Bound over Graphical Models " in Proceedings of SoCS 2014.

        [R212] Abstract | PDF William Lam, Kalev Kask, Rina Dechter, and Alexander Ihler. "Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics" in Proceedings of SoCS 2014.

        [R211] Abstract | PDF Levi H. S. Lelis, Lars Otten, and Rina Dechter. "Memory-Efficient Tree Size Prediction for Depth-First Search in Graphical Models" in Constraint Programming 2014 (CP-2014)

        [R210] Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Weighted anytime search: new schemes for optimization over graphical models" Progress report.

        [R209] Abstract | PDF Radu Marinescu, Rina Dechter, and Alexander Ihler. "AND/OR Search for Marginal MAP Search." In UAI 2014.

        [R208] Abstract | PDF Junkyu Lee, Radu Marinescu, and Rina Dechter. "Applying Marginal MAP Search to Probabilistic Conformant Planning: Initial Results." to appear in the 4th International Workshop on Statistical Realtional AI (STARAI-2014).
        Longer version (M.S Thesis): [R208a] Abstract | PDF | Slides

        [R207] Abstract | PDF Andrew Gelfand. "Bottom-Up Approaches to Approximate Inference and Learning in Discrete Graphical Models." Ph.D. Thesis, 2014.

        [R206] Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter, "Weighted Best First Search for MAP." in proceedings of International Symposium on Artificial Intelligence and Mathematics (ISAIM 2014), January, 2014.

        2013
         

        [R205] Rina Dechter. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms.Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers 2013, pp. 1-192. Link

        [R204] Abstract | PDF Andrew E. Gelfand, Rina Dechter, and Alexander Ihler. "Does Better Inference Mean Better Learning?" in Perturbations, Optimization, and Statistics (POS) (a workshop of NIPS 2013).

        [R203] Abstract | PDF Alon Milchgrub and Rina Dechter. "On Minimal Tree-Inducing Cycle-Cutsets and Their Use in a Cutset-Driven Local Search" in DISCML 2013 (a workshop of NIPS 2013).
        Longer version: [R203a] Abstract | PDF

        [R202] Abstract | PDF | Poster Junkyu Lee, William Lam, and Rina Dechter. "Benchmark on DAOOPT and GUROBI with the PASCAL2 Inference Challenge Problems" in DISCML 2013 (a workshop of NIPS 2013).

        [R201] Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Anytime AND/OR Best-First Search for Optimization in Graphical Models." to appear in Inferning'13 (a workshop of ICML'2013).

        [R200] Abstract | PDF Emma Rollon, Javier Larrosa and Rina Dechter. "Semiring-Based Mini-Bucket Partitioning Schemes." to appear in IJCAI'13.

        [R199] Abstract | PDF | Poster Levi H.S. Lelis, Lars Otten and Rina Dechter. "Predicting the Size of Depth-First Branch and Bound Search Trees." to appear in IJCAI'13.

        [R198] Abstract | PDF Lars Otten. "Extending the Reach of AND/OR Search for Optimization in Graphical Models." Ph.D. Thesis, 2013.

        2012
         

        [R197] Abstract | PDF Natalia Flerova, Radu Marinescu, and Rina Dechter. "Preliminary Empirical Evaluation of Anytime Weighted AND/OR Best-First Search for MAP" in Proceedings of DISCML 2012 (a workshop of NIPS 2012).

        [R196] Abstract | PDF Lars Otten, Alexander Ihler, Kalev Kask, and Rina Dechter. "Winning the PASCAL 2011 MAP Challenge with Enhanced AND/OR Branch-and-Bound" in Proceedings of DISCML 2012 (a workshop of NIPS 2012).

        [R195] Abstract | PDF | Web Lars Otten and Rina Dechter. "Anytime AND/OR Depth-first Search for Combinatorial Optimization" in AI Communications Journal, 2012.

        [R194] Abstract | PDF William Lam and Rina Dechter. "Empirical Evaluation of AND/OR Multivalued Decision Diagrams for Inference" in Doctoral Programme of CP 2012.
        Longer version: [R194a] Abstract | PDF

        [R193] Abstract | PDF | Poster Alexander Ihler, Natalia Flerova, Rina Dechter, and Lars Otten. "Join-graph based cost-shifting schemes" in Proceedings of UAI 2012.

        [R192] Abstract | PDF | Poster Lars Otten and Rina Dechter. "A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models" in Proceedings of UAI 2012.

        [R191] Abstract | PDF | Slides Lars Otten and Rina Dechter. "Advances in Distributed Branch and Bound" in Proceedings of ECAI 2012.

        [R190] Abstract | PDF | Slides Rina Dechter, Natalia Flerova, and Radu Marinescu. "Search Algorithms for m Best Solutions for Graphical Models" in Proceedings of AAAI 2012.

        [R189] Abstract | PDF | Suppl. | Web Mark Silberstein, Omer Weissbrod, Lars Otten, Anna Tzemach, Andrei Anisenia, Oren Shtark, Dvir Tuberg, Eddie Galfrin, Irena Gannon, Adel Shalata, Zvi U. Borochowitz, Rina Dechter, Elizabeth Thompson, Dan Geiger. "A System for Exact and Approximate Genetic Linkage Analysis of SNP Data in Large Pedigrees" in Bioinformatics, 2012.

        2011
         

        [R188] Abstract | PDF Lars Otten and Rina Dechter. "Learning Subproblem Complexities in Distributed Branch and Bound" in Proceedings of DISCML 2011 (a workshop of NIPS 2011).

        [R187] Abstract | PDF Natalia Flerova, Alexander Ihler, Rina Dechter, and Lars Otten. "Mini-bucket Elimination with Moment Matching" in Proceedings of DISCML 2011 (a workshop of NIPS 2011).

        [R186] Abstract | PDF Vibhav Gogate and Rina Dechter. "Sampling-based Lower Bounds for Counting Queries" in Submitted to Intelligenza Artificiale.

        [R185] Abstract | PDF | Slides Emma Rollon, Natalia Flerova, and Rina Dechter. "Inference Schemes for M Best Solutions for Soft CSPs" in Proceedings of Soft 2011 (a workshop of CP 2011).

        [R184] Abstract | PDF | Slides Rina Dechter and Natalia Flerova. "Heuristic Search for m Best Solutions with Applications to Graphical Models" in Proceedings of Soft 2011 (a workshop of CP 2011).

        [R183] Abstract | PDF | Slides Lars Otten and Rina Dechter. "Anytime AND/OR Depth-first Search for Combinatorial Optimization" in Proceedings of SoCS 2011.

        [R182] Abstract | PDF | Long version Natalia Flerova, Emma Rollon, and Rina Dechter. "Bucket and mini-bucket Schemes for M Best Solutions over Graphical Models" in in GKR 2011 (a workshop of IJCAI 2011).

        [R181] Abstract | PDF | Slides Kalev Kask, Andrew E. Gelfand, Lars Otten, and Rina Dechter. "Pushing the Power of Stochastic Greedy Ordering Schemes for Inference in Graphical Models" in Proceedings of AAAI 2011.

        [R180] Abstract | PDF Andrew E. Gelfand, Kalev Kask, and Rina Dechter. "Stopping Rules for Randomized Greedy Triangulation Schemes" in Proceedings of AAAI 2011.

        2010
          [R179a] PDF Rina Dechter, Bozhena Bidyuk, Robert Mateescu, and Emma Rollon. "On the Power of Belief Propagation: A Constraint Propagation Perspective" in Festschrift book in honor of Judea Pearl, 2010

        [R179] PDF Rina Dechter, Dan Geiger, and Elizabeth Thompson. "A Constraint View of IBD Graphs" in ICS Internal report, September, 2010

        [R178] PDF Natalia Flerova. "Calculating LOD score: experimental comparison" in ICS Internal report, September, 2010

        [R177] Abstract | PDF Natalia Flerova and Rina Dechter. "M best solutions over Graphical Models" in in CRAGS 2010 (a workshop of CP 2010)

        [R176] Abstract | PDF Lars Otten and Rina Dechter. "Finding Most Likely Haplotypes in General Pedigrees through Parallel Search with Dynamic Load Balancing" in PSB 2011, Pacific Symposium on Biocomputing.
        Workshop version, presented in SofT'10 and CRAGS'10: [R176a] Abstract | PDF

        [R175] Abstract | PDF Kalev Kask, Rina Dechter, and Andrew E. Gelfand. "BEEM : Bucket Elimination with External Memory" in UAI'2010 Proceedings

        [R174b] Abstract | PDF Bozhena Bidyuk, Rina Dechter, and Emma Rollon. "Active Tuples-based Scheme for Bounding Posterior Beliefs" In JAIR'2010

        [R173] Abstract | PDF |Color PDF
        Emma Rollon and Rina Dechter. "New Mini-Bucket Partitioning Heuristics for Bounding the Probability of Evidence" forthcoming in AAAI 2010

        [R172] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "Importance Sampling based Estimation over AND/OR Search Spaces for Graphical Models" To be published in Artificial Intelligence, 2012

        [R171] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "On Combining Graph-based Variance Reduction schemes" In AISTATS'2010.

        [R170] Abstract | PDF
        Emma Rollon and Rina Dechter. "Some New Empirical Analisys of Evaluating Iterative Join-Graph Propagation." ICS technical report. January 2010.

        [R169] Abstract | PDF
        Emma Rollon and Rina Dechter. "Evaluating Partition Strategies for Mini-Bucket Elimination." In ISAIM '2010.

        [R168] Abstract | PDF
        Lars Otten and Rina Dechter. "Towards Parallel Search for Optimization in Graphical Models." In ISAIM '2010.

        2009
         

        [R167] Abstract | PDF
        Maria Silvia Pini, Francesca Rossi, Kristen Brent Venable, and Rina Dechter. "Robust Solutions in Unstable Optimization Problems." 13th Annual ERCIM International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP'08 in Rome, Italy. Revised Selected Papers appeared as Recent Advances in Constraints, July 2009.

        [R166] Abstract | PDF | Slides
        Vibhav Gogate and Rina Dechter. "SampleSearch: Importance Sampling in presence of Determinism." ICS technical report (accepted), 2010.

        [R165] Abstract | PDF
        Vibhav Gogate. "Sampling Algorithms for Probabilistic Graphical Models with Determinism." Ph.D. Thesis, 2009.

        [R164] Abstract | PDF
        Robert Mateescu, Kalev Kask, Vibhav Gogate, and Rina Dechter. "Join-Graph Propagation Algorithms." JAIR'2009

        [R163] Abstract | PDF
        Lars Otten, Rina Dechter, Mark Silberstein, and Dan Geiger. "Maximum Likelihood Haplotyping through Parallelized Search on a Grid of Computers." In RECOMB'09.

        2008
         

        [R162] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Advancing AND/OR Search for Optimization Using Diverse Principles." In Workshop on Inference Methods based on Graphical Structures of Knowledge of the European Conference on Artificial Intelligence (ECAI), 2008.
        Longer version: [R162a] Abstract | PDF

        [R161] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "Approximate Solution Sampling (and Counting) on AND/OR spaces" In CP'08.
        Longer version: [R161a] Abstract | PDF

        [R160] Abstract | PDF
        Lars Otten and Rina Dechter. "Refined Bounds for Instance-Based Search Complexity of Counting and Other #P Problems." In CP'08.
        Longer version: [R160a] Abstract | PDF

        [R159] Abstract | PDF
        Robert Mateescu and Rina Dechter. "Mixed deterministic and probabilistic networks". In Annals of Mathematics and Artificial Intelligence. Special Issue: Probabilistic Relational Learning, Volume 54 (1-3), pages 3-51, 2008.

        [R158] Abstract | PDF
        Radu Marinescu. "AND/OR Search Strategies for Combinatorial Optimization in Graphical Models." Ph.D. Thesis, 2008.

        [R157] Abstract | PDF
        Rina Dechter, Lars Otten, and Radu Marinescu. "On the Practical Significance of Hypertree vs. Tree Width." In ECAI'08.
        Longer version: [R157a] Abstract | PDF
        Workshop version: [R157b] Abstract | PDF

        [R156] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "AND/OR Importance Sampling." In UAI'08.

        [R155] Abstract | PDF
        Lars Otten and Rina Dechter. "Bounding Search Space Size via (Hyper)tree Decompositions." In UAI'08.

        [R154] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Evaluating the Impact of AND/OR Search on 0-1 Integer Linear Programming." In Constraints, 2009.
        [R154a] Abstract | PDF ICS Technical Report, May 2008.

        [R153] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models." In Artificial Intelligence, Volume 173 (16-17), pages 1492-1524, 2009.
        [R153a] Abstract | PDF ICS Technical Report, April 2008.

        [R152] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "Studies in Solution Sampling" In AAAI'08.

        [R151] Abstract | PDF
        Radu Marinescu and Rina Dechter. "AND/OR Branch-and-Bound Search for Combinatorial Optimization in Graphical Models." In In Artificial Intelligence, Volume 173 (16-17), pages 1457-1491, 2009.
        [R151a] Abstract | PDF ICS Technical Report, April 2008.

        [R149] Abstract | PDF
        Robert Mateescu, Rina Dechter and Radu Marinescu. "AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models". Forthcoming, Journal of Artificial Intelligence Research (JAIR), 2008.

        2007
          [R148] Abstract | PDF
        Robert Mateescu, Radu Marinescu and Rina Dechter. "AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Constraint Optimization". In CP'07.

        [R147] Abstract | PDF
        Robert Mateescu. "AND/OR Search Spaces for Graphical Models". Ph.D. Thesis, 2007.

        [R146] Abstract | PDF
        Robert Mateescu and Rina Dechter. "AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical Models". In UAI'07.

        [R145] Abstract | PDF
        Vibhav Gogate, Bozhena Bidyuk and Rina Dechter. "Studies in Lower Bounding Probability of Evidence using the Markov Inequality". In UAI'07.

        [R144] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Best-First AND/OR Search for Most Probable Explanations". In UAI'07.

        [R143] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Best-First AND/OR Search for Graphical Models". In AAAI'07.

        [R142] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "Approximate Counting by Sampling the Backtrack-free Search Space". In AAAI'07.

        [R141] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "SampleSearch: A Scheme that Searches for Consistent Samples". In AISTATS'07.

        [R140] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Best-first AND/OR Search for 0/1 Integer Programming". In CPAIOR'07. To appear

        [R139] PDF
        Rina Dechter. "Tractable Structures for Constraint Satisfaction Problems". Chapter in the "Handbook of Constraint Programming". F. Rossi, T. Walsh and P. van Beek, editors. Elsevier, 2006.

        [R138] Abstract | PDF
        Robert Mateescu and Rina Dechter. "A Comparison of Time-Space Schemes for Graphical Models". In IJCAI'07.

        2006
         

        [R137] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "Cutset Sampling for Bayesian Networks". In JAIR, 2006.

        [R136] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "A New Algorithm for Sampling CSP Solutions Uniformly at Random". In CP'06.
        Longer version: [R136a] Abstract | PDF

        [R135] Abstract | PDF
        Robert Mateescu and Rina Dechter. "Compiling Constraint Networks into AND/OR Multi-Valued Decision Diagrams (AOMDDs)". In CP'06.

        [R134] Abstract | PDF
        Bozhena Bidyuk. "Exploiting Graph Cutsets for Sampling-Based Approximations in Bayesian Networks". Ph.D. Thesis, 2006

        [R133] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "Cutset Sampling with Likelihood Weighting". In UAI'06.

        [R132] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "Improving Bound Propagation". In ECAI'06.

        [R131] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Dynamic Orderings for AND/OR Branch-and-Bound Search in Graphical Models". In ECAI'06.

        [R130] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "An Anytime Scheme for Bounding Posterior Beliefs". In AAAI'06.
        Longer version: [R130a] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "An Anytime Scheme for Bounding Posterior Belief". Extended version, ICS Technical Report, January 2006.

        [R129] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Memory Intensive Branch-and-Bound Search for Graphical Models". In AAAI'06.

        [R128] Abstract | PDF
        Radu Marinescu and Rina Dechter. "AND/OR Graph Search for Genetic Linkage Analysis". In Workshop on Heuristic Search, Memory Based Heuristics and Their Applications, AAAI'06.
        Longer version: [R128a] Abstract | PDF

        [R127] Abstract | PDF
        Radu Marinescu and Rina Dechter. "AND/OR Branch-and-Bound Search for Pure 0/1 Integer Linear Programming Problems". In CPAIOR 2006.

        [R126] Abstract | PDF
        Rina Dechter and Robert Mateescu. "AND/OR Search Spaces for Graphical Models". Artificial Intelligence 171 (2-3), pp. 73-106, 2006.

        2005
         

        [R125] Abstract | PDF
        Radu Marinescu and Rina Dechter. "Advances in AND/OR Branch-and-Bound Search for Constraint Optimization". In The 7th International Workshop on Preferences and Soft Constraints of the Eleventh International Conference on Principles and Practice of Constraint Programming , CP'2005.

        [R124] Abstract | PDF
        Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt and James Marca. "Modeling Transportation Routines using Hybrid Dynamic Mixed Networks". In UAI 2005.

        [R123] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints". In UAI 2005.

        [R122] Abstract | PDF
        Robert Mateescu and Rina Dechter. "The Relationship Between AND/OR Search Spaces and Variable Elimination". In UAI 2005.

        [R121] Abstract | PDF
        Robert Mateescu and Rina Dechter. "AND/OR Cutset Conditioning". In proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI'2005.

        [R120] Abstract | PDF
        Radu Marinescu and Rina Dechter. "AND/OR Branch-and-Bound for Graphical Models". In proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI'2005.

        [R109] Abstract | PDF
        Kalev Kask, Rina Dechter, Javier Larrosa and Avi Dechter. "Unifying Cluster-Tree Decompositions for Reasoning in Graphical Models". In Artificial Intelligence Journal, 2005.

        2004
         

        [R119] Abstract | PDF
        Radu Marinescu and Rina Dechter. "AND/OR Tree Search for Constraint Optimization". In the 6th International Workshop on Preferences and Soft Constraints, of the Tenth International Conference on Principles and Practice of Constraint Programming , CP'2004.

        [R118] Abstract | PDF
        Kalev Kask, Rina Dechter, and Vibhav Gogate. "Counting-Based Look-Ahead Schemes for Constraint Satisfaction". In Constraint Programming, CP'2004.

        [R117] Abstract | PDF
        Rina Dechter and Robert Mateescu. "The Impact of AND/OR Search Spaces on Constraint Satisfaction and Counting". In Constraint Programming, CP'2004.
        Longer version: [R117a] Abstract | PDF

        [R116] Abstract | PDF
        Bozhena Bidyuk. "Approximation Algorithms for Probabilistic Reasoning: Sampling and Iterative Inference". Ph.D. Proposal, May 2004.

        [R115] Abstract | PDF
        Rina Dechter."AND/OR Search Spaces for Graphical Models". ICS Technical Report, March, 2004.

        [R114] Abstract | PDF
        Rina Dechter and Robert Mateescu. "Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search Space". In UAI 2004.

        [R113] Abstract | PDF
        Bozhena Bidyuk and Rina Dechter. "On finding minimal w-cutset problem". In UAI 2004.

        [R112] Abstract | PDF
        Vibhav Gogate and Rina Dechter. "A Complete Anytime Algorithm for Treewidth". In UAI 2004.

        [R111] Abstract | PDF
        Kalev Kask, Rina Dechter and Vibhav Gogate. "New Look-Ahead Schemes for Constraint Satisfaction". In The Eighth International Symposium on Artificial Intelligence and Mathematics, 2004.

        [R110] Abstract | PDF
        Robert Mateescu. "Iterative Algorithms for Graphical Models". Ph.D. Proposal, June 2003.

        2003
         

        [R108] Abstract | PDF
        David Larkin. "Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries". In UAI 2003.

        [R107] Abstract | PDF
        David Larkin. "Semi-Independent Partitioning: A Method for Approximating the Solution to Constraint Optimization Problems". In CP2003.

        [R106] Abstract | PDF
        Rina Dechter, and Robert Mateescu. "A Simple Insight Into Properties Of Iterative Belief Propagation". In UAI 2003.

        [R105] Abstract | PDF
        Bozhena Bidyuk, and Rina Dechter. "An Empirical Study of w-Cutset Sampling for Bayesian Networks". In UAI 2003

        [R104] Abstract | PDF
        Radu Marinescu, Kalev Kask, and Rina Dechter. "Systematic vs. Non-systematic Algorithms for Solving the MPE Task". In UAI 2003.

        [R103] Abstract | PostScript | PDF
        David Larkin. "Generating Random Solutions from a Constraint Satisfaction Problem with Controlled Probability", In the 1st International Workshop on Constraints in Functional Verification of Principles and Practice of Constraint Programming (CP 2002).

        [R102] Abstract | PostScript | PDF
        Bozhena Bidyuk, and Rina Dechter. "Cycle-Cutset sampling for Bayesian Networks", In The Canadian AI Conference, June 2003.

        [R101] Abstract | PostScript | PDF
        Rina Dechter, Kalev Kask and Robert Mateescu. "Iterative Join-Graph Propagation", In proceedings of Uncertainty in Artificial Intelligence (UAI 2002), pp. 128-136.

        [R100] Abstract | PostScript | PDF
        David Larkin and Rina Dechter. "Bayesian Inference in the Presence of Determinism", In AI and Statistics, AI-STAT, 2003.

        2002
         

        [R99] Abstract | PostScript | PDF
        Robert Mateescu, Rina Dechter, Kalev Kask. "Tree Approximation for Belief Updating", In proceedings of AAAI-2002.

        [R98] Abstract | PostScript | PDF
        Rina Dechter, Kalev Kask, Eyal Bin, Roy Emek. "Generating Random Solutions for Constraint Satisfaction Problems", In proceedings of AAAI-2002.

        2001
         

        [R97] Abstract | PostScript | PDF
        Bozhena Bidyuk, Rina Dechter. "The Epsilon-cutset Effect in Bayesian Networks", An ICS Technical Report, July, 2001.
        Longer version: [R97a] PostScript | PDF

        [R96] Abstract | PDF
        R. Dechter, D. Larkin, "Hybrid Processing of Beliefs and Constraints", In proceedings of UAI '01.
        Longer version: [R96a] PostScript | PDF

        [R95] Abstract | PostScript | PDF
        R. Dechter, K. Kask, J. Larrosa, "A General Scheme for Multiple Lower Bound Computation in Constraint Optimization", Appears in Constraint Programming (CP2001).
        Longer version: [R95a] PostScript | PDF

        [R94] Abstract | PostScript | PDF
        K. Kask, R. Dechter, "A General Scheme for Automatic Generation of Search Heuristics from Specification Dependencies", In Artificial Intelligence 129:91-131, 2001.

        [R93] Abstract | PostScript | PDF
        J. Larrosa, "On the Time Complexity of Bucket Elimination Algorithms", An ICS technical report, January, 2001.

        [R92] Abstract | PostScript | PDF
        K. Kask, R. Dechter, J. Larrosa and F. Cozman, "Bucket-Tree Elimination for Automated Reasoning", ICS Technical Report.
        Longer version: [R92a] Abstract | PostScript | PDF

        [R91] Abstract | PostScript | PDF
        K. Kask, J. Larrosa, R. Dechter, "Up and Down Mini-Buckets: A Scheme for Approximating Combinatorial Optimization Tasks" Technical Report, January, 2001.

        2000
         

        [R90] Abstract | PostScript | PDF
        J. Larrosa, R. Dechter, "On The Duel Representation of Non-Binary Semiring-Based CSPs" In workshop 1 (Soft Constraints) of the "Sixth International Conference on Principles and Practice of Constraint Programming" (CP2000), September, 2000.

        [R89a] Abstract | PostScript | PDF
        J. Larrosa, R. Dechter, "Boosting Search with Variable Elimination in Constraint Optimization and Constraint Satisfaction Problems." In "Constraints", 2003, volume 3, number 8, pp. 303-326.

        [R89] Abstract | PostScript | PDF
        J. Larrosa, R. Dechter, "Boosting Search With Variable Elimination" In the proceedings of the "Sixth International Conference on Principles and Practice of Constraint Programming" (CP2000), September, 2000."

        [R88] Abstract | PostScript | PDF
        R. Menke, R. Dechter, "An Implementation of the Combinatorial Auction Problem in ECLIPSE" An ICS Technical Report, July, 2000

        [R87] Abstract | PostScript | PDF
        K. Kask. "New Search Heuristics For Max-CSP" In the proceedings of the "Sixth International Conference on Principles and Practice of Constraint Programming" (CP2000), September, 2000, pp.255-269

        [R87a] Abstract | PostScript | PDF
        K. Kask, R. Dechter "Using Mini-Bucket Heuristics For Max-CSP" An ICS Technical Report, June, 2000
        Longer version: [R86] Abstract |PDF

        [R85] Abstract | PostScript | PDF
        Dechter, R., Rossi, F., "Constraint Satisfaction" Survey ECS, March, 2000.

        [R84] Abstract | PostScript | PDF
        Dechter, R. "An Anytime Approximation For Optimizing Policies Under Uncertainty" In workshop of Decision Theoretic Planning, "AIPS2000", April, 2000.

        [R83] Abstract | PostScript | PDF
        Dechter, R., Smyth, P. "Processing Boolean Queries Over Belief Networks" An ICS Technical report.

        [R82] Abstract | PostScript | PDF
        Dechter, R., "A New Perspective on Algorithims for Optimizing Policies Under Uncertainty" In "AI and Planning" (AIPS2000) April, 2000. Technical Reports (AIPS-2000)".

        [R81] Abstract | PostScript | PDF
        Rish, I. "Efficient Reasoning in Graphical Models" Ph.D. Thesis, 1999

        [R80] Abstract | PostScript | PDF
        Rish, I., and Dechter, R., "Resolution versus Search: Two Strategies for SAT" In "Journal of Automated Reasoning", special issue on SAT, Volume 24, Issue 1/2, pp. 225-275, Januray, 2000.

        1999
         

        [R79] Abstract | PostScript | PDF
        Kask, K., and Dechter, R., "Mini-Bucket Heuristics for Improved Search" In proceedings of "Uncertainty in AI", February, 1999.

        [R78] Abstract | PostScript | PDF
        Students of 275B Fall Quarter 1998, "Empirical Evaluations of Some Benchmark Bayesian Networks"

        [R77] Abstract | PostScript | PDF
        Kask, K., and Dechter, R., "Branch and Bound with Mini-Bucket Heuristics" In "International Joint Conference on Artificial Intelligence" (IJCAI99), pp. 426­433, Stockholm, August 1999.

        [R76] Abstract | PostScript | PDF
        Shapiro, R., Feldman, Y. A., and Dechter, R., "On the Complexity of Interval-Based Constraint Networks" In "MISC'99 Workshop on Applications of Interval Analysis to Systems and Control", February, 1999 pp. 389-399.

        [R76A] Abstract | PDF
        Rina Dechter"Bucket elimination: A unifying framework for reasoning" In Artificial Intelligence, Volume 113, pages 41–85, 1999.

        1998
         

        [R75] Abstract | PostScript | PDF
        Schwalb, E., "Temporal Reasoning With Constraints" A Ph.D. thesis, UCI, ICS, June 1998.

        [R74] Abstract | PostScript | PDF
        D. Frost and R. Dechter, "Evaluating Constraint Processing Algorithms" In Workshop on Combinatorial Search and Planning of the Fourth International Conference on Artificial Intelligence, In "Artificial Intelligence Planning Systems" (AIPS '98), Carnegie Mellon University, June 7-10, 1998

        [R73]
        Dechter, R. and the Constraint Programming Workgroup, "Constraint Programming" In "Constraints Journal", 1997.

        [R72] Abstract | PostScript | PDF
        K. Kask and R. Dechter, "Stochastic Local Search For Bayesian Networks" In Workshop on "AI and Statistics 99", D. Heckerman and J. Whittaker Ed. Morgan Kaufmann Publishers, pp 113­122, January 1999.

        [R71] Abstract | PostScript | PDF
        Rish, I., Kask, K., and R. Dechter, "Empirical Evaluation Of Approximation Algorithms For Probabilistic Decoding" In "Uncertainty in AI" (UAI98).

        [R70] Abstract | PostScript | PDF
        Dechter, R., and D. Frost, "Optimizing With Constraints: A Case Study In Scheduling Maintenance Of Electric Power Units" In the "Fifth International Symposium on Artificial Intelligence and Mathematics", January, 1998.
        Longer version: [R70a] Abstract | PostScript | PDF,
        Alternate version: [R70b] Abstract | PostScript | PDF,

        [R69] Abstract | PostScript | PDF
        Frost, D., "Algorithms And Heuristics For Constraint Satisfaction Problems" A Ph.D. thesis, ICS, UCI, October 1997.

        [R68] Abstract | PostScript | PDF
        Dechter, R., "Constraint Satisfaction" In the "MIT Encyclopedia of the Cognitive Sciences" (MITECS), 1998.

        [R67] Abstract | PostScript | PDF
        Dechter, R., and I. Rish, "On The Impact Of Causal Independence" An ICS Technical Report, October 1998.

        1997
         

        [R66] Abstract | PostScript | PDF
        E. Schwalb, L. Vila "Temporal Constraints: A Survey" An ICS Technical Report, October 1997.

        [R65]
        Rish, I., and Frost, D., "Statistical Analysis Of Backtracking OnInconsistent CSPs" In proceedings of "Constraint Programming (CP97), October 29 - November 1, 1997, Schloss Hagenberg, Austria.

        [R64] Abstract | PostScript | PDF
        Dechter, R., "Bucket Elimination: A Unifying Framework For Processing Hard And Soft Constraints" In "Constraints: An International Journal", No. 2, pp. 51-55, 1997.

        [R63] Abstract | PostScript | PDF
        Dechter, R., and Rish, I., "A Scheme For Approximating Probabilistic Inference" In "Uncertainty in Artificial Intelligence" (UAI97), August 1997.

        [R62] Abstract | PDF
        Dechter, R. and Rish, I., "Mini-Buckets: A General Scheme for Bounded Inference" In "Journal of the ACM", Vol. 50, Issue 2: pages 107-153, March 2003.
        Short conference version:
        [R62a] Abstract | PostScript | PDF In "Fifteenth International Joint Conference of Artificial Intelligence" (IJCAI97), Japan, 1997.

        [R61]
        Dechter, R., Vila, L., and Schwalb, E., "Temporal Constraint Logic Programming" An ICS Technical Report.

        [R60]
        Schwalb, E., "Temporal Reasoning With Resolution And Constraint Propagation" in AAAI '97

        [R59]
        Frost, D., Rish, I., and Vila, L., "Summarizing CSP Hardness With Continuous Probability Distributions" An ICS Technical Report.

        [R58]
        Frost, D., Dechter, R., "Full Looking Ahead For Constraint Satisfaction Problems" An ICS Technical Report.

        [R57]
        Dechter, R., Vila, L., and Schwalb, E., "Decidability And Finite Representability Of Deductive Databases With Temporal Constraints" An ICS Technical Report.

        [R56] Abstract | PostScript | PDF
        Dechter R. and Frost, D., "Backjump-based Backtracking for Constraint Satisfaction Problems", in Artificial Intelligence.

        1996
         

        [R54] Abstract | PostScript | PDF
        Rish, I., Dechter, R., "Finding A Balance Between Guessing and Thinking: Combining Conditioning with Variable Elimination" (A Ph.D. proposal), August 14, 1996.

        [R53]
        Abstract | PostScript | PDF
        Frost, D., and Dechter, R., "Looking At Full Look-Ahead" In "International Conference on Constraint Programming" (CP96), Boston, September 1996.

        [R52] Abstract | PostScript | PDF
        Dechter, R., and Dechter, A., "Structure Driven Algorithms For Truth Maintenance" In the "Artificial Intelligence Journal", Volume 82, 1996, pp. 1-20.

        [R51] Abstract | PostScript | PDF
        Pearl, J., and Dechter, R., "Identifying Independencies In Causal Graphs With Feedback" In "Uncertainty in Artificial Intelligence", UA196, Portland, Oregon, August 1996, pp. 420-426.

        [R50] Dechter, R., "Constraint-Based Computing; A Position Paper" For "ACM Strategic Direction", June 1996.

        [R49] Abstract | PostScript | PDF
        Rish, I., and Dechter, R., "To Guess Or To Think? Hybrid Algorithms For SAT" In "International Conference on Constraint Programming" (CP96), Boston, Massachusetts, September 1996.
        Longer version: [R49a] Abstract | PostScript | PDF

        [R48] Abstract | PostScript | PDF
        R. Dechter, "Bucket Elimination: A Unifying Framework for Probabilistic Inference" In "Uncertainty in Artificial Intelligence", UA196, 1996, pp. 211-219.
        Longer version:[R48a] Abstract | PostScript | PDF,
        Alternate version: [R48b] PostScript | PDF

        [R47] Abstract | PostScript | PDF
        El Fattah, Y., and Dechter, R., "An evaluation of structural parameters for probabilistic reasoning: results on benchmark circuits." In "Uncertainty in Artificial Intelligence", UA196, Portland, Oregon, August 1996 pp. 244-251.

        [R46] Abstract | PostScript | PDF
        Kask, K., and Dechter, R., "Graph-based methods for improving GSAT." In proceedings of "National Conference of Artificial Intelligence" (AAAI-96), Portland, Oregon, August 1996.

        1995
         

        [R45] Abstract | PostScript | PDF
        C. Freuder, R. Dechter, B. Selman, M. Ginsberg, and E. Tsang, "Systematic versus stochastic constraint satisfaction" Panel, IJCAI-95, pp.2027-2032.

        [R44] Abstract | PostScript | PDF
        Dechter, R., "Topological parameters for time-space tradeoff." In "Uncertainty in Artificial Intelligence" (UA196), Portland, Oregon, August 1996 pp. 220-227.
        Longer version: [R44a] Abstract | PDF
        Dechter, R. and El Fattah, Y."Topological parameters for time-space tradeoff." Artificial Intelligence, Volume 125, Issues 1-2. January 2001, pp. 93-118

        [R43] Thimor, D., "GSAT vs. LVO vs. DVO experimental report." A Technical Report.

        [R42] Abstract | PostScript | PDF
        I. Rish, and R. Dechter, "Variable Independence In Markov Decision Problems" A Technical Report.
        Longer version: [R42a] Abstract | PostScript | PDF

        [R41] Abstract | PostScript | PDF
        Dechter, R., and van Beek, P., "Local and global relational consistency." In "Journal of Theoretical Computer Science", 1996.
        Longer version: [R41a] Abstract | PostScript | PDF

        [R40] Abstract | PostScript | PDF
        Schwalb, E., and Dechter, R., "Processing Disjunctions in Temporal Constraint Networks." In "Artificial Intelligence", volume 93, pp. 29-61, 1997.

        [R39] Abstract | PostScript | PDF
        Frost, D., and Dechter, R., "Look-ahead value ordering for constraint satisfaction problems." In "International Joint Conference on Artificial Intelligence" (IJCAI-95), Montreal, Canada, August 1995, pp. 572-578.

        1994 and earlier
         

        [R38] Abstract | PostScript | PDF
        Schwalb, E., Kask, K., and Dechter, R., "Temporal Reasoning With Constraints On Fluents And Event." In The Twelfth National Conference of Artificial Intelligence" (AAAI-94), Seattle, WA, August 1994, pp. 1067-1072.

        [R37] Schwalb, E., Pazzani, M., and Dechter, R., "Using Identifiability for learning horn logic programs." Machine Learning Workshop, Canada.
        Longer version: [R37a] Also appears as a UCI Technical Report #94-13.

        [R36] Abstract | PostScript | PDF
        van Beek, P., and Dechter, R., "Constraint Restrictiveness Versus Local and Global Consistency." In "Journal of the Association of Computing Memory".
        Longer version: [R36a] "Constraint Tightness and Looseness Versus Local and Global Consistency." In "Principles of Knowledge Representation and Reasoning" (KR-94), Bonn, Germany, May 1994, pp. 572-582.

        [R35] Abstract | PostScript | PDF
        Frost, D., and Dechter, R., "Dead-End Driven Learning." In Proceedings of the "Twelfth National Conference of Artificial Intelligence" (AAAI-94), Seattle, WA, August 1994, pp. 294-300.
        Longer version: [R35a] Abstract | PostScript | PDF
        "In Search Of The Best Constraint Satisfaction Search." In Proceedings of the "Twelfth National Conference of Artificial Intelligence" (AAAI-94), Seattle, WA, August 1994, pp. 301-306.

        [R34] Abstract | PostScript | PDF
        Kask, K., and Dechter, R., "GSAT and Local Consistency." In "International Joint Conference on Artificial Intelligence" (IJCAI-95), Montreal, Canada, August 1995, pp. 616-622.

        [R33] Abstract | PostScript | PDF
        van Beek, P., and Dechter, R., "On The Minimality And Global Consistency Of Row-Convex Constraint Networks." Journal of the ACM, Vol. 42, No. 3, May 1995, pp. 543-561.

        [R32] Abstract | PostScript | PDF
        Schwalb, E., and Dechter, R., "Compiling Relational Data Into Disjunctive Structure: Empirical Evaluation." In AI/GI/VI-94, Banff, Alberta, Canada, May 1994, pp. 71-78.

        [R31] Dechter, R., and Dechter, A., "On the representation of general constraint networks." November, 1992.

        [R30] Abstract | PostScript | PDF
        Schwalb, E., and Dechter, R., "Coping With Disjunctions In Temporal Constraint Satisfaction Problems." In "The National Conference on Artificial Intelligence" (AAAI-93), Washington, D.C., July 1993, pp. 127-132.

        [R29] Abstract | PostScript |
        Dechter, R., and Rish, I., "Directional Resolution: The Davis Putnam Procedure, Revisited." In Principles of Knowledge Representation (KR-94), Bonn, Germany, May 1994, pp. 134-145.
        Longer version: [R29a] Abstract | PostScript | PDF

        [R28] Abstract | PostScript | PDF
        Ben-Eliyahu, R., and Dechter, R., "On Computing Minimal Models." In the "Journal of Annals of Mathematics and Artificial Intelligence", 1995.
        Longer version: [R28a] A preliminary version in "The National Conference on Artificial Intelligence", AAAI-93, Washington, D.C.,July 1993, pp. 2-8.

        [R27] Abstract | PostScript | PDF
        El Fattah, Y., and Dechter, R., "Diagnosing Tree-Decomposable Circuits." In "International Joint Conference on Artificial Intelligence", IJCAI-95, Montreal, Canada, August 1995, pp. 572-578.

        [R26] Abstract | PostScript | PDF
        Ben-Eliyahu, R., and Dechter, R., "Default Reasoning Using Classical Logic" In "Artificial Intelligence", Vol. 84, 1996, pp. 113-150.
        Longer version: [R26a] Abstract | PostScript | PDF

        [R25] Abstract | PostScript | PDF
        Ben-Eliyahu, R., and Dechter, R., "Propositional Semantics for Disjunctive Logic Programs." In the "Journal of Annals of Mathematics and Artificial Intelligence", Vol. 12, 1994, pp. 53-87.

        [R24] Abstract | PostScript | PDF
        Pinkas, G., and Dechter, R., "On Improving Connectionist Energy Minimization." In "Journal of Artificial Intelligence Research" (JAIR), Vol. 3, 1995, pp. 223-248.
        Longer version: [R24a] "An improved connectionist activation function for energy minimization." In "The National Conference on Artificial Intelligence", San Jose, CA, July 1992, pp. 434-439.

        [R23] Abstract | PostScript | PDF
        Dechter, R., and Itai, A., "Finding All Solutions If You Can Find One." Presented in the Workshop on Tractable Reasoning (AAAI-92), San Jose, CA, July 1992, pp. 35-39.

        [R22] Dechter, R., and Pearl, J., "Structure identification in relational data." In Artificial Intelligence, Vol. 58, 1992, pp. 237-270.
        Longer version: [R22a] A preliminary version in the Canadian Artificial Intelligence Conference, May 1992, Vancouver, British Columbia, pp. 176-182.

        [R21] Baram, Y., and Dechter, R., "Processing constraints by neural networks."

        [R20] Pearl, J., and Dechter, R., "Learning structure from data: A survey." In Proceedings of the Workshop on Computational Learning Theory (COLT'89), Santa Cruz, CA, July 1989, pp. 230-244.

        [R19] Abstract | PostScript | PDF
        Meiri, I., "Combining qualitative and quantitative constraints in temporal reasoning." In Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, California, July 1991, pp. 1-8.

        [R18] Dechter, R., and Pearl, J., "Directed constraint networks: A relational framework for causal modeling." In Proceedings of the Twelfth International Joint Conference of Artificial Intelligence [IJCAI-91], Sydney, Australia, August 1991, pp. 1164-1170.

        [R17] Abstract | PostScript | PDF
        Dechter, R., "Constraint Networks (Survey)." In Encyclopedia of Artificial Intelligence, 2nd edition, 1992, John Wiley & Sons, Inc., pp. 276-285.

        [R16] Cohen, S., and Dechter, R., "Evaluating production systems in a multiprocessing environment." In Y.A. Feldman and A. Bruckstein (Eds.), Artificial Intelligence and Computer Vision, Elsevier Science Publishers (North Holland), 1991, pp. 285-300.

        [R15] Abstract | PostScript | PDF
        Collin, Z., Dechter, R., and Katz, S., "Self-Stabilizing Distributed Constraint Satisfaction." In Chicago J. Theor. Comput. Sci., 1999
        Shorter version: [R15a] Abstract | PostScript | PDF

        [R14] PDF
        Dechter, R., "From local to global consistency." In Artificial Intelligence Journal, Vol. 55, 1992, pp. 87-107.
        Longer version: [R14a] A preliminary version in Proceedings of the 8th CSCSI (Canadian AI Conference), Ottawa, Canada, May 1990, (best paper award), pp. 231-237.

        [R13] PDF
        Dechter, R., "On the expressiveness of networks with hidden variables." In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-90), July 1990, Boston, MA, pp. 556-562.

        [R12] Abstract | PostScript | PDF
        Meiri, I., Dechter, R., and Pearl, J., "Uncovering trees in constraint networks." In Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI-1990), pp. 10-16, July 1990, Boston, MA.
        Longer version: [R12a] Abstract | PDF

        [R11] Dechter, R., and Meiri, I., "Experimental evaluation of preprocessing algorithms for constraint satisfaction problems" Artificial Intelligence Journal, Vol. 68(2), 1994, pp. 211-241.
        Longer version: [R11a] A preliminary version appears in Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-89), Detroit, MI, August 1989, pp. 271-277.

        [R10] PDF
        Dechter, R., Meiri, I., and Pearl, J., "Temporal constraint networks." Artificial Intelligence, Vol. 49, 1991, pp. 61-95.
        Longer version: [R10a] A preliminary version appears in Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning (KR'89), Toronto, Canada, May 1989, pp. 83-93.

        [R9] Dechter, R., "Decomposing a relation into a tree of binary relations." Journal of Computer and System Sciences, Special Issue on the Theory of Relational Databases, Vol. 41, 1990, pp. 2-24.
        Longer version: [R9a] A preliminary version appears in Proceedings of the 6th Conference on Principles of Database Systems, San Diego, CA, March 1987, pp. 185-189.

        [R8] PDF
        Dechter, R., "Enhancement schemes for constraint processing: Backjumping, learning and cutset decomposition." Artificial Intelligence, Vol. 41(3), January 1990, pp.273-312.

        [R7] Dechter, R., Dechter, A., and Pearl, J., "Optimization in constraint networks." In R.M. Oliver and J.Q. Smith (Eds.), Influence Diagrams, Belief Nets and Decision Analysis, Sussex, England, John Wiley & Sons, Ltd., 1990, pp. 411-425.

        [R6] PDF
        Dechter, R., and Pearl, J., "Tree Clustering schemes for constraint-processing." Artificial Intelligence, Vol. 38(3), April 1989, pp. 353-366.
        Longer version: [R6a] Appears in Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), St. Paul, MN, 1988, pp. 150-154.

        [R5] PDF
        A. Dechter and R. Dechter, "Belief Maintenance in dynamic constraint networks." In Proceedings of the Seventh National Conference on Artificial Intelligence (AAAI-88), St. Paul, MN, August 1988, pp. 37-42. (A full extended version appears in R52)    
        Longer version: [R5a] PDF
        Longer version: [R5b] Dechter, R., "A distributed algorithm for ATMS." In Bar-Ilan Symposium on Foundation of AI (BISFAI-89), June 1989.

        [R4] PDF
        Dechter, A., and Dechter, R., "On the greedy solution of ordering problems." ORSA Journal on Computing , Vol. 1(3), Summer 1989, pp. 181-189.

        [R3] PDF
        Dechter R., and Pearl, J., "Network-based heuristics for constraint-satisfaction problems." In Artificial Intelligence, Vol. 34 (1), December 1987, pp. 1-38.
        Longer version: [R3a] Appears in Search in AI , Springer-Verlag, 1988, pp. 370-422, L. Kanal and V. Kumar (Eds.).

        [R2] A. Dechter and R. Dechter, "Minimal Constraint Graphs," Technical Report CSD-870007(R-74). UCLA, Cognitive Systems Laboratory, December 1986.
        Longer version: [R2a] "Removing redundancies in constraint networks." In Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87), Seattle, WA, July 1987, pp. 105-109.

        [R1] Dechter, R., and Michie, D., "Structured induction of plans and programs." Lab Report , IBM Scientific Center, Los Angeles, CA, October 1984.

        [R0] Abstract | PDF
        Dechter, R., and Pearl, J., "Generalized Best-First Search Strategies and the Optimality of A*." Journal of the Association for Computing Machinery , Vol. 32, No. 3, July 1985, pp. 505-536.


        Other Publications

        http://www.ics.uci.edu/~dechter/awards.html Dr. Rina Dechter @ UCI :: Awards

        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Awards
        • Radcliffe Fellowship
        • ACP 2007 Award
        • AAAI Fellow
        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/biographical.html Dr. Rina Dechter @ UCI :: Biographical Info
        Biographical Sketch

        Rina Dechter is a professor of Computer Science at the University of California, Irvine. She received her PhD in Computer Science at UCLA in 1985, an MS degree in Applied Mathematics from the Weizmann Institute and a B.S in Mathematics and Statistics from the Hebrew University, Jerusalem. Her research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning.

        Professor Dechter is an author of Constraint Processing published by Morgan Kaufmann, 2003, and Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms by Morgan and Claypool publishers, 2013, has authored over 150 research papers, and has served on the editorial boards of: Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research and journal of Machine Learning (JLMR). She was awarded the Presidential Young investigator award in 1991, is a fellow of the American association of Artificial Intelligence since 1994, was a Radcliffe Fellowship 2005-2006, received the 2007 Association of Constraint Programming (ACP) research excellence award and is a 2013 Fellow of the ACM. She has been Co-Editor-in-Chief of Artificial Intelligence, since 2011.

        Full CV
        http://www.ics.uci.edu/~dechter/books/ Dr. Rina Dechter @ UCI :: Constraint Processing, the book
        Dr. Rina Dechter
        Prof. Rina Dechter, Ph.D
        Artificial Intelligence
        Office: DBH 4232
        Phone: 1.949.824.6556
        Email: dechter_at_ics.uci.edu
        Highlights and News
        BOOK (2013)
        Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
        AWARD
        2013 ACM Fellow (link 1 / link 2)
        PASCAL CHALLENGE (2012)
        Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
        UAI COMPETITION (2010)
        Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
        CS 275
        Constraint Networks Course Page
        MINI-SCHOOL
        UCI Lifted Algorithms Mini-School (November 3-6)
        BOOK (2010)
        'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
        IJCAI 2013 TUTORIAL
        Constraint Processing and Probabilistic Reasoning
        More
        Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

        Constraint Processing

        by Rina Dechter
        Published by Morgan Kaufmann


        About the Book

        Material for Instructors

        Commentary

        Review (Journal of Logic Programming, by Roland H.C. Yap)

        Review (Artificial Intelligence Journal, by Roman Bart�k)

        Review (SIAM Review, by Maarten van Emden)

        Review (AI Magazine, by Peter van Beek and Toby Walsh)


        Buy Online:

        Morgan Kaufmann

        Amazon.com

        School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
        http://www.ics.uci.edu/~dechter/courses/ics-275a/winter-2016/ Dr. Rina Dechter @ UCI
        Dr. Rina Dechter - University of California at Irvine ZOT!
        home | publications | book | courses | research Revised on Feb. 18, 2016



        CompSci 275 Winter 2016, Constraint Networks
        [ main | project |

        • Instructor: Rina Dechter
        • Section: 34985
        • Classoom: DBH 1423
        • Days: Tuesday & Thursday
        • Time: 12:30 pm - 1:50 pm
        • Office hours: Tuesday 3:00 pm - 4:00 pm
        • Exam: Mar 3rd, in class


        Course Goals
        Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics.

        The purpose of this course is to familiarize students with the theory and techniques of constraint processing, using the constraint graphical model. This model offers a natural language for encoding world knowledge in areas such as scheduling, vision, diagnosis, prediction, design, hardware and software verification, and bio-informatics, and it facilitates many computational tasks relevant to these domains such as constraint satisfaction, constraint optimization, counting and sampling . The course will focus on techniques for constraint processing. It will cover search and inference algorithms, consistency algorithms and structure based techniques and will focus on properties that facilitate efficient solutions. Extensions to general graphical models such as probabilistic networks, cost networks, and influence diagrams will be discussed as well as example applications such as temporal reasoning, diagnosis, scheduling, and prediction.


        Textbook

        Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann


        Grading Policy
        Homeworks and projects (80%), midterm (20%).


        Assignments:
        There will be weekly homework-assignments, a project, and an exam.


        Syllabus:

        Project Information

        Week Topic Slides
        Lecture
        Homework
        Additional Reading
        Date  
        Week 1
        • Chapters 1,2: Introductions to constraint network model. Graph representations, binary constraint networks.
        Set 1

        Numberjack Tutorial
        Homework 1
        (due 01-14)
        Numberjack
        MiniZinc

        Code Examples
        01-05

        01-07
        Week 2
        • Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency
        Set 2

        Lecture 3

        Lecture 4
        Homework 2
        (due 01-21)
        Constraint Propragation by Christian Bessiere 01-12

        01-14

        Week 3
        • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
        Set 3 Lecture 5

        Lecture 6
        Homework 3
        (due 01-28)
        The Sat Solving Revolution: Solving, Sampling and Counting by Moshe Vardi 01-19

        01-21

        Week 4
        • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
        Set 4 Lecture 7

        Lecture 8
        Homework 4
        (due 02-04)
        Complete Algorithms by Darwiche and Pipatsrisawat 01-26

        01-28
        Week 5
        • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
        Set 5 Lecture 9

        Lecture 10
        Homework 5
        (due 02-11)
        Minisat
        WALKSAT
        RSAT
        02-02

        02-04
        Week 6
        • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
        • Satisfiability solving
        Satisfiability

        Set 6
        Lecture 11

        Lecture 12
        SATHandbook-CDCL 02-09

        02-11
        Week 7
        • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.
        Set 7 Lecture 13

        Lecture 14
        Homework 6
        (due 02-23)
        02-16

        02-18
        Week 8
        • Chapter 13: Constraint Optimization, soft constraints
        02-23

        02-25
        Week 9
        • Chapter 13: Constraint Optimization, soft constraints (continued)


        Exam, in class
        03-01

        03-03
        Week 10


          Week 11



            Resources on the Internet
            • Books
              • Francesca Rossi, Peter van Beek, Toby Walsh. Handbook of Constraint Programming. Elsevier Science, 2006
              • Kimball Marriott, Peter Stuckey. Programming with Constraints: An Introduction. The MIT Press, 1998
            • Software links
              • Numberjack modeling language
                • Numberjack main page
                • Tutorial
              • MiniZinc modeling language
                • MiniZinc page
            • Links for Satisfiability
              • Satisfiability tutorials by Joao Marques-Silva
              • SAT Solving : A Mini Course
              • A satisfiability tutorial by Youssef Hamadi



              School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
              http://www.ics.uci.edu/community/news/notes/index noteworthy achievements @ the bren school of information and computer sciences
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              Bren school home > Community > News > Noteworthy achievements
              Noteworthy achievements

              Bren School faculty, students and research initiatives are some of the most well regarded successes on the UC Irvine campus. We are pleased to announce the following noteworthy achievements.

              Be sure to subscribe to the Bren School's RSS (Really Simple Syndication) feed to get noteworthy news, press releases and articles about the Bren School delivered directly to your desktop!

              Awards, grants and other honors can be sent to communications@ics.uci.edu to be considered for publication.


              WINTER 2016

              Gary Olson, 2 ICS Alums receive SIGCHI honors

              photo: Gary Olson

              Gary
              Olson

              Informatics professor Gary Olson has received a Lifetime Service Award from the ACM Special Interest Group on Computer–Human Interaction (SIGCHI), as part of the group’s annual effort to recognize and honor leaders and shapers within the field of human-computer interaction.

              According to the award website, recipients of the Lifetime Service Award are individuals who have contributed to the growth and success of SIGCHI in a variety of capacities over a number of years. Olson has worked in the human-computer interaction (HCI) field since 1983, when he and colleagues Judy Olson (a fellow informatics professor), Paul Green and Marilyn Mantei taught the first graduate course on the subject at the University of Michigan.

              Olson’s contribution to HCI has largely revolved around the concept of distance. From the mid-1980s he and Judy Olson began researching the role technology plays in collaboration. The pair published their highly cited paper, “Distance Matters,” on the subject in 2000 and later authored “Working Together Apart.” Olson has long played an active role in SIGCHI, co-chairing and chairing numerous conferences, as well as award and steering committees. SIGCHI previously elected Olson to the CHI academy and, along with Judy Olson, awarded him a Lifetime Achievement Award in 2006.

              In addition to Olson, two ICS alumni were also honored in this year’s round of awards. Leysia Palen, professor and founding chair of the newly established Department of Information Science at the University of Colorado Boulder, was elected to the CHI academy. She earned her Ph.D. in information and computer science in 1998. Daniel Russell, a senior research scientist at Google, was also elected to the CHI academy. He earned his B.S. in information and computer science in 1977 and has been recognized as a UC Irvine Lauds & Laurels Distinguished Alumnus.

               


              Kobsa receives Mercator Fellowship

              photo: Alfred Kobsa

              Alfred
              Kobsa

              Informatics professor Alfred Kobsa has received a Mercator Fellowship from the German Research Foundation (DFG), the largest research funding organization in Germany. The Mercator fellowship will enable Kobsa—whose research focuses on the areas of user modeling and personalized systems, privacy, support for personal health maintenance, and information visualization—to participate in “intensive, long-term project-based collaboration between researchers from both domestic and foreign institutions,” according to the DFG. Throughout the duration of the fellowship, Kobsa will work both on-site at a German institution and continue his project collaboration here in Irvine. “Foreign Mercator Fellowship holders are awarded the title of Mercator Fellows in recognition of their dedication,” the DFG notes.

              As the largest independent research funding organization in Germany, the DFG “promotes the advancement of science and the humanities by funding research projects, research [centers] and networks, and facilitating cooperation among researchers,” according to its website. It also joins major international funding counterparts like the National Science Foundation and the Royal Society as a member of the International Council for Science (ICSU).

               


              FALL 2015

              Franz named 2016 IEEE Fellow

              photo: Michael Franz

              Michael
              Franz

              The Institute of Electrical and Electronics Engineers (IEEE) has named Computer Science Professor Michael Franz a 2016 IEEE Fellow. Franz is being recognized by IEEE for his contributions to just-in-time compilation as well as his contributions to computer security through compiler-generated software diversity.

              The IEEE Grade of Fellow is conferred by the IEEE Board of Directors upon a person with an outstanding record of accomplishments in any of the IEEE fields of interest. It is the highest grade of membership and is recognized by the technical community as a prestigious honor and an important career achievement. The total number of fellows selected in any one year cannot exceed one-tenth of 1 percent of the total voting membership. “It is a great achievement receiving recognition from one's peers and being included among such a distinguished group of IEEE members," says Franz.

              The IEEE is the world’s leading professional association for advancing technology for humanity with 400,000 members in 160 countries. Dedicated to the advancement of technology, the IEEE publishes 30 percent of the world’s literature in the electrical and electronics engineering and computer science fields, and has developed more than 900 active industry standards.

               


              Tsudik elected to The Academy of Europe

              photo: Gene Tsudik

              Gene
              Tsudik

              Chancellor's Professor of Computer Science Gene Tsudik has been elected a member of The Academy of Europe (Academia Europaea), the organization dedicated to the “advancement and propagation of excellence in scholarship in the humanities, law, the economic, social, and political sciences, mathematics, medicine, and all branches of natural and technological sciences anywhere in the world for the public benefit and for the advancement of the education of the public of all ages in the aforesaid subjects in Europe,” according to the organization’s website.

              Tsudik was elected to the computational and information science-focused section—dubbed the Informatics section—of the academy. Membership is by invitation only, with invitations made only after a peer group nomination and rigorous scrutiny of the eminence and scholarship of the potential member. Tsudik is the only United States-based member elected to the Informatics section in 2015, joining a total of 11 U.S. members in the 237 member total section.

              The Academy of Europe endeavors to encourage the highest possible standards in scholarship, identifying topics of trans-European importance to science and scholarship, as well as making recommendations to national governments and international agencies concerning matters affecting science, scholarship and academic life in Europe. It counts among its members some of the foremost scholars in the world. Tsudik joins in the Informatics section eminent U.S.-based scholars like Victor Vianu, computer scientist and editor-in-chief of the Journal of the ACM, and Mihalis Yannakakis, professor of computer science at Columbia University and winner of the 2015 Donald E. Knuth prize—awarded to those who have made outstanding contributions to the foundations of computer science.

               


              NSA grants Tsudik $286K for cybersecurity research

              photo: Gene Tsudik

              Gene
              Tsudik

              Chancellor's Professor of computer science Gene Tsudik has received a $286,000 grant from the National Security Agency (NSA) for his project “ERADS: Efficient Remote Attestation of Dynamic Swarms.”

              As embedded devices—including automotive sensors or controllers, drones, household appliances, and factory automation components—proliferate into many aspects of everyday life, they also become targets for attacks. This project aims to develop techniques for detecting and mitigating malware infestations of networks consisting of a myriad of such embedded devices.

              The NSA designates UC Irvine as a National Center of Academic Excellence (CAE), with a focus in Information Assurance Research. Institutions with CAE designations promote higher education in information assurance—the management of risks related to the use, processing, storage, and transmission of data—and cyber defense, while helping to meet the need to reduce vulnerabilities in the Nation’s networks. The grant comes out of the CAE cybersecurity research program.

               


              Informatics Ph.D. student to present at ACM-DEV 2015

              photo: Ankita Raturi

              Ankita
              Raturi

              Informatics Ph.D. student Ankita Raturi received an ACM Women in Computing (ACM-W) scholarship to attend the ACM Symposium on Computing for Development (ACM DEV), held at the Queen Mary University of London in December. ACM-W provides scholarships to enable women in computer science to attend research conferences around the world.

              At ACM DEV, Raturi will present on a paper she co-authored with current and former UC Irvine faculty Bill Tomlinson, Bonnie Nardi, Donald J. Patterson, Debra Richardson, Jean-Daniel Saphores and Dan Stokols. The paper, “Toward Alternative Decentralized Infrastructures,” looks at how we can build interfaces between infrastructures to improve robustness, reliability and resilience. “Enabling communities to transition to a more resilient configuration of infrastructures is crucial for establishing a distributed portfolio of processes and systems by which human needs may be met,” Raturi says.

              This will be Raturi’s first time at the conference, “an ideal venue for this work to be presented,” Raturi says. The conference is a platform for “original and innovative work on the applications, technologies, architectures and protocols for computing in developing regions,” according to the ACM DEV website.

              “Having the opportunity to present my work, engage with the community and learn from leading researchers in my field is a major part of my professional growth," Raturi says. "Discussing our work with experts who have been working on computing for development will be incredibly valuable.”

               


              Ziv collaborates in groundbreaking NSF-funded privacy research

              photo: Hadar Ziv

              Hadar
              Ziv

              Informatics lecturer Hadar Ziv will be a research collaborator in a groundbreaking NSF-funded project titled “Privacy Compliance by Design: Ideation Techniques to Facilitate System Design Compliant with Privacy Laws and Regulations.”

              The project attempts to bring privacy protection to the forefront of software developer’s minds in the wake of the explosion of big data. “Software professionals typically have no formal training or education on sociotechnical aspects of privacy. As a result, addressing privacy issues raised by a system is frequently an afterthought and/or a matter of compliance-check during the late phases of the system development lifecycle,” the project’s abstract explains. To tackle this challenge, the project’s research team will develop “privacy ideation cards” based on relevant U.S. laws and regulations, which “can potentially transform how privacy-relevant aspects are handled in real-world software solutions built by industry and inform how students are taught these issues in undergraduate software curricula.” The team includes Principal Investigator Sameer Patil from New York University, who received a $175,000 Early-concept Grant for Exploratory Research (EAGER) for the project, Ziv, Janice Tsai of Microsoft and Jonathan Fox of Intel.

              In addition to the deck of privacy ideation cards, the project will promote privacy by design, making privacy protection a built-in framework for all software development. Ziv will connect the research team with students in his senior Capstone Informatics project course, “as a test-bed for ideas and presentations related to privacy,” Ziv says. “Their engagement will affect change in the students' projects. I will likely participate in collecting and analyzing data about those changes.”

               


              Professor Tsudik Keynoting Two Conferences in November

              photo: Gene Tsudik

              Gene
              Tsudik

              Chancellor’s Professor of Computer Science Gene Tsudik is delivering two keynote addresses on “Secure and Private Proximity-Based Discovery of Common Factors in Social Networks” at conferences in November. First, on November 4, he will be speaking at the 9th International Conference on Network and System Security in New York City, before traveling to Sydney, Australia to speak at the 25th International Telecommunication Networks and Applications Conference on November 20.

               


              Study reveals ICS degree-friendly jobs have the best work-life balance

              photo: Glassdoor logo

              Careers in data science, user experience design, web development and software engineering promote excellent work-life balance, according to a survey from Glassdoor, a job rankings website.

              Glassdoor notes that, across the board, employee satisfaction with work-life balance has been declining in the past few years, but there are a number of careers that won’t leave employees working 24/7—many of these careers bolstered by skills learned at the Donald Bren School of Information and Computer Sciences (ICS).

              Glassdoor analyzed feedback from around 60,000 company reviews to determine the top 25 careers where employees report balance between their personal lives and the workplace. Among the 25, 10 were careers in tech, including data scientist (#1), user experience (UX) designer (#7), web developer (#10), instructional designer (#14), software quality assurance (QA) engineer (#16), web designer (#17), data analyst (#20), solutions engineer (#22), software developer (#24), and front-end developer (#25).

              ICS is well-placed to foster future careers in tech. As the only school focused on computer and information sciences in the University of California system, ICS offers undergraduate programs of study in business information management, computer game science, computer science, computer science and engineering, informatics, and software engineering. The newly established data science major is unique at the undergraduate level, equipping budding data scientists—Glassdoor’s career with the highest work-life balance—with the necessary combined skills in computing and statistics. The major is part of UC Irvine’s Data Science Initiative, a coordinated effort to bring together researchers and students across campus involved in various aspects of data science.

              At the graduate level, students at ICS can pursue deeper educational opportunities in computer science, informatics, embedded systems, networked systems, software engineering, and statistics.

               


              Tsudik part of panel at UCI-Nossaman Cybersecurity Symposium

              photo: Gene Tsudik

              Gene
              Tsudik

              Chancellor’s Professor of Computer Science Gene Tsudik took part in a panel at the 2015 UCI-Nossaman Cybersecurity Symposium at the City Club Los Angeles on Oct. 12. The symposium, titled “Cybersecurity, Data Breach and Privacy: A Dialogue on the Rising Risks and Evolving Legal Landscape,” was a joint effort by the UC Irvine School of Law and Nossaman LLP, a nationwide law firm that has made privacy and security one of its focus areas. The emphasis of the panel that Tsudik spoke on was “Not If, But When — Hack Offensives, Investigating Breaches, and Closing the Gaps on Data Leaks.”

               


              Postdoctoral scholar Per Larsen recognized as “DARPA Riser”

              photo: Per Larsen

              Defense Advanced Research Projects Agency (DARPA) has recognized assistant project scientist in computer science Per Larsen as a “DARPA Riser.” The early-career honor is conferred to “up-and-coming standouts in their fields, capable of discovering and leveraging innovative opportunities for technological surprise—the heart of DARPA’s national security mission,” DARPA says.

              Larsen, along with 54 other honorees from around the country, attended “Wait, What? A Future Technology Forum,” in September with special guest U.S. Secretary of Defense Ashton Carter (the gentleman on the left in the photo). The forum, which drew more than 1,200 participants from around the world, explored future technologies “on their potential to radically change how we live and work, and on the opportunities and challenges these technologies will raise within the broadly defined domain of national security,” according to the event website. Larsen was among a small subset of honorees who were treated to lunch with the U.S. Secretary of Defense.

              “DARPA organized Wait, What? to bring together forward-looking thinkers across a host of fields that are abundant with possibilities,” DARPA Director Arati Prabhakar said in the event press release. “In particular, our DARPA Rising effort aimed to identify and inspire some of the nation’s emerging leaders in research and technology—so we at DARPA can learn from them, and to make them aware of opportunities to apply their expertise in the important domain of national security.”

              Larsen works as a postdoctoral scholar with Computer Science Professor Michael Franz. His research interests include information security, including software diversity and exploits and mitigations; compilers, including profiling, randomization and control-flow integrity; and systems software, including interpreters and virtual machines.

               


              Van der Hoek to speak at SCSIM Fall Event

              photo: André van der Hoek

              André
              van der Hoek

              Department of Informatics Chair André van der Hoek will be speaking at the Southern California Society for Information Management (SCSIM) Fall Event: “The Southern California Disruptors—How Startups and the New Innovation Culture in Southern California are affecting IT” on Sept. 30 at the Long Beach Marriott. As the head of the UCI Software Design and Collaboration Lab, van der Hoek is part of a three-person panel that will relate their applicable experiences crucial to participating in the new business environment developing around us.

               


              SUMMER 2015

              Franz amasses $3.9 million in research funding

              photo: Michael Franz

              Michael
              Franz

              This year alone, Computer Science Professor Michael Franz has accumulated over $3.9 million in research funding from prestigious organizations such as the Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), Qualcomm, Oracle and Mozilla. This follows his trend of more than $1 million per year on average in research expenditures.

              Franz currently runs two projects funded by DARPA’s Cyber Fault-Tolerant Attack Recovery (CFAR) Program, for which he received nearly $2 million and roughly $700,000 in May, respectively. The CFAR Program aims to “produce revolutionary breakthroughs in defensive cyber techniques that can be deployed to protect existing and planned software systems in both military and civilian contexts without requiring changes to the concept of operations of these systems,” according to a statement by program manager John Everett.

              Franz also runs a project funded by DARPA’s Vetting Commodity IT Software and Firmware Program (VET), which addresses “the threat of hidden malicious functionality in COTS (Commercial Off-the-Shelf) IT devices ... including mobile phones, printers, computer workstations and many other everyday items,” according to a statement by program manager Timothy Fraser. He received nearly $65,000 for this project.

              Finally, in July, Franz received nearly $620,000 from the NSF for a collaborative project titled “ENCORE—ENhanced program protection through COmpiler-REwriter cooperation.” According to the abstract, the project will produce “a prototype implementation consisting of a producer-side metadata derivation engine, and a consumer-side binary rewriting engine using this metadata to safely perform binary code manipulation.” In the past year, Franz has also received unrestricted gifts from Qualcomm, Oracle and Mozilla totaling $263,000.

               


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              http://www.ics.uci.edu/~dechter/courses.html Dr. Rina Dechter @ UCI :: Courses
              Current Courses
                2016
                • CompSci 275 Constraint Networks
              • All Past Courses
              http://www.ics.uci.edu/~dechter/gradstudents.html Dr. Rina Dechter @ UCI :: My Group
              My Group
              Our blog: Automated reasoning goup

              Some papers
              Current Graduate Students and Postdocs
              • Kalev Kask [ mail ]
              • William Lam [ www ]
              • Junkyu Lee [ www ]
              • Filjor Broka [ mail ]
              • Zhengli Zhao [ www ]

              Graduated Students and Postdocs
              • Natalia Flerova [ www ] Graduated June 2015
              • Andrew Gelfand [ www ] Graduated June 2014
              • Lars Otten [ www ] Graduated March 2013
              • Emma Rollon [mail ] 2009
              • Vibhav Gogate [ www ] Graduated June 2009
              • Tuan Nguyen [ www ] Graduated 2009
              • Radu Marinescu [ www ] Graduated June 2008
              • Robert Mateescu [ www ] Graduated June 2007
              • Bozhena Biduk [ www ] Graduated June 2006
              • Javier Larrosa [ www | mail ] 2000-2001
              • Kalev Kask [ www | mail ] Graduated June 2002
              • Irina Rish [ www | mail ] Graduated June 1999
              • Eddie Schwalb [ www | mail ] Graduated May, 1998
              • Dan Frost [ www | mail ] Graduated June 1997
              • Lluis Vila [ www | mail ]
              • Rachel Ben Eliyahu [ www | mail ] Graduated 1994 (UCLA)
              • Itay Meiri [ www | mail ] Graduated 1992 (UCLA)
              • Zeev Collin [ www | mail ] MSc, Technion, 1990
              http://www.ics.uci.edu/~magda/Projects/WIP.html Magda El Zarki
              Real Time Services over Wireless Networks

              Research Abstract


              Future wireless communication systems are expected to provide a broad range of multimedia services including voice, video, and data. A cellular architecture is envisioned in order to support the service over a wide area. Due to heterogeneity of the traffic, a network based on the packet switching technology may provide a more flexible framework and allow a simpler architecture and significant cost savings. Those have been the experiences with the wireline networks, which have moved recently to such an integrated architecture. By having all data in a packetized form, there is no need to maintain a different type of network for specialized applications, such as a circuit-switched network for voice telephony, which may require different types of equipment and administrative overhead. In addition, there is the possibility of using statistical multiplexing to increase the utilization of often-limited transmission resources. Since network resources are not pre-assigned in a packet-switched network, different users or flows can share the network resources and higher utilization of may be achieved. In particular, networks based on the Internet Protocol (IP) have begun to gain momentum as the platform of choice due to their ubiquity and scalability.
              There are significant hurdles to overcome, however, in building a packet-switched wireless network that can serve all types of traffic. Not only must the communication link be shared among many users and flows while providing adequate QoS, the sharing must be done over the wireless channel which is prone to frequent errors and sometimes prolonged outages. Transport of packet data traffic over the wireless channel has received a lot of attention lately due to increasing use of the Internet from wireless devices. However, much of the work has dealt with delay-insensitive data, as most researchers have assumed that delay-sensitive applications such as voice telephony will continue to be served using a separate circuit-switched network dedicated to such applications. To make the idea of extending the packet-switched framework to the wireless network more feasible, there remains much work to be done on how to efficiently serve different types of data over a medium that is relatively scarce and fraught with errors. In this resesarch project, we focus on the transport of real-time packets such as those from voice over IP (VoIP) applications which may make up an important subset of traffic over the future packet-switched wireless networks. http://www.ics.uci.edu/~magda/Projects/Auto.html Magda El Zarki
              Inter Vehicular Communication Systems

              Research Abstract


              Our vision is focused on utilizing the intrinsic properties of vehicular traffic, coupled with modern communication, computing and information management technologies, in order to facilitate increased safety and situational awareness on high-speed highways. We anticipate an environment where traveling vehicles communicate among themselves, forming rapidly changing ad hoc network topologies. Several cars travel on a highway while communicating locally via an ad hoc wireless network Each car is equipped with a laptop or PDA equipped with a wireless LAN card (e.g., 802.11) for local communication and forms, around itself, a local area of communication. Cars that are further away, although they may constitute part of a neighbor's local area, are not part of that particular car's communication network. All cars broadcast information omni-directionally and receive data from any and every direction. There is no point-to-point communication link. The purpose of the ad hoc network is to impart information, i.e., the car's vital signs, to vehicles in close proximity and to receive the same data from them. The information is processed locally to provide the driver with a map indicating the status of each car in the immediate vicinity, e.g., acceleration, turning signal status, braking, etc.

              We are faced with a very dynamic environment composed of fast-moving vehicles, which, from the communication perspective, translates into rapidly changing network topologies. In addition, the data that is exchanged between the vehicles is time sensitive. Since the communication devices are mounted in vehicles, power supply is practically unlimited thus making it possible to use fairly large antennas and on-board GPS devices. Also, the data is really of interest only to a small circle of neighboring vehicles.

              Although the problem is simplified by the single-hop nature of the network and the consequent lack of routing, it also raises some concerns, primarily, the throughput of the system and the delays involved. Since data is time-sensitive, and in many cases, urgent in nature (e.g., speeding vehicle approaching in left lane), the system must be robust and capable to support the traffic load and its time critical needs. Tradeoffs to be investigated involve the type of data, its urgency and the practical limits of the system before it degenerates into chaos and data flow comes to a halt. More complex, multi-channel systems capable of coping with the traffic load and its delay constraints, pose a different set of problems associated with the speed with which vehicles can associate themselves to the different channels and maintain an up-to-date picture of their surroundings. http://www.ics.uci.edu/~magda/presentations.html Presentations

              Presentations



              MPEG Tutorial I

              MPEG Tutorial II

              MPEG Tutorial III

              NREN Worskhop: Wireless & Mobile Systems

              CENIC-QoS Workshop: Video Coding and Quality Issues
              http://www.ics.uci.edu/~magda/courses.html Courses

              Courses



              CS 133: Networking Lab

              CS 233/NetSys 202: Networking Lab

              ICS 167: Multiplayer Online Systems

              ICS 190/CS 295/ NetSys 270: Principles of Data Transmission

              CS 236/NetSys 230: Mobile and Wireless Networking

              CS 620 Trends in IT: Online Game Systems
              http://www.ics.uci.edu/~magda/Projects/VIP.html Video over IP (VIP)

              Video over IP (VIP)

               

              Research Abstract:

               

              The focus of this research is on problems that arise in the context of QoS enabled IP networks and their use by applications.� In particular, the main theme of the research, is the development of a greater synergy between potential users of network QoS capabilities, i.e., applications, and the design and implementations of those QoS capabilities (signaling, scheduling and access algorithms) within the network.� In order to accomplish such a goal we are looking specifically at the use and selection of network QoS services and parameters by applications. The goal here is to identify performance parameters that are of most significance to applications, and translate that knowledge into utility curves that can then be used by both applications to select the appropriate network service, and by the network to make intelligent decisions on how to best handle an application's traffic in case of resource contention.� This includes not only assessing sensitivity to traditional QoS parameters such as bandwidth, loss, delay, and jitter of different types of applications traffic, e.g., transactions, audio, video, etc., but also considering more complex scenarios involving applications with multiple traffic streams which might involve different (dynamic) resource sharing rules depending on the availability of network resources.

              The research focuses on the identification of both network and end system� QoS capabilities, that are explicitly aimed at better application support.� This requires an iterative process between applications and the network in order to identify applications requirements and determine how they can be best met by the network. Of particular interest in the context of this research, is to develop an understanding of which engineering support is most useful to applications when accessing a QoS enabled IP.� The investigation of these issues is being carried out using a rich multimedia client-server application that combines multiple types of streams, i.e., audio, video, and data. The benefit of using such an application is not only that the different requirements of its individual components exercise a wide range of network QoS capabilities, but also that the dependencies that exist between streams create a new set of resource sharing requirements. For example, the choice of which streams to degrade and how degradation should be applied across streams in the presence of congestion, is likely to depend not only on the level of congestion, but also on application level semantic.

              Another very important aspect of the research relates to the pricing of services and the impact that it will have on user behaviour. For that one needs to understand the range of possibilities presented by the multimedia services in conjunction with the capabilities of the end system, and how that can be presented to the end user who will make a choice based upon availability, need and price.

              In order to gain an understanding of these issues, and develop and test the necessary network mechanisms, this research relies heavily on experiments. Experimentation will take place in the context of a lab devoted to multimedia and networking, that includes end-systems as sources of application traffic, and networking equipment made available by several vendors. Furthermore, work on the design and implementation of new mechanisms to better support application service requirements, will and must be done in close collaboration with equipment vendors so as to facilitate their incorporation and testing on the available platforms.

              Research Issues:

               

              • Understanding video services
                • Real-time versus non real-time
                • Content based: news, sports, movie, cartoon, etc.
                • End system specific
                • Quality range
              • Extend our experience with MPEG 1 & 2 video encoding algorithms
                • How to survive network idiosyncranies (delay, jitter, losses, out or order)
                • Intelligent error concealment
                • Feedback control based on received quality.
                • New session layer protocols that are better suited to video transmission over IP
              • MPEG 4
                • object oriented
                • multi stream
                • interactive
              • Quality Measurements
                • Subjective techniques - Double Stimulus Continuous Quality Scale (DSCQS)
                • Objective techniques - Perceptual Objective Measurement Metrics - NTIA/ITS model (efficient, allows for real-time quality processing, can use a feedback channel for control)

               

              • End user needs and expectations -> how they translate into quality parameters
              • Pricing and its role in quality and service provisioning
              • Understanding network mechanisms for use with Video
                • Diffserv
                • MPLS
                • Multicasting

               

              • Interoperability between different QoS mechanisms: MPLS, Diffserv, 802.3q/p
              • Interoperability between QoS brokers in different autonomous systems

               


               

              http://www.ics.uci.edu/~magda/Projects/Online%20Games.html Online Games
              Online Games and Virtual Environments (VE)

              It is believed that online games and VEs will become the pervasive “3D web” or “3D Internet” that will enable real-time interactions and to access information, in a way that we cannot do with the current 2D web. By making 3D immersive environments well integrated with everyday computing activities, it becomes possible to develop engaging new applications for Science, Education and Health that can reach the widest audiences possible.
              The large-scale virtual environments of tomorrow have to be sufficiently flexible (to support varied environments), powerful (to not limit functionality to the least common denominator), and extensible (to support decades of continued evolution, much as the Internet and Web have). MMVEs will have a tremendous impact on the way the Internet will evolve in the future due to their interactive and real-time nature. Online computer gaming/simulation is forecasted to become one of the most demanding applications on the Internet. Researchers in the networking arena need to be prepared to meet that challenge, and our success will depend very much on how well we understand the requirements of this new class of applications.
              With the anticipated growth in this application area, we will be seeing more and more stress being placed on the underlying transport infrastructure to provide the kind of quality of experience (QoE) that these applications and their users expect. The quality of an end user’s experience is the true litmus test of a proper online game deployment. Only by understanding both the application needs and the underlying network facilities can one design networking mechanisms and develop pre and post processing schemes to ensure the required QoE. The next section gives an overview of some recent work that I have conducted and plan to continue pursuing in QoE.

              http://www.ics.uci.edu/~magda/Projects/ghana.html Ghana

              Ghana Slave Trade


              Research Abstract

              The story of the slave trade is not often told to the masses, the world over, in its entirety, beginning from the villages in the heart of the African continent and ending on the shores of the New World. This project proposes to create a learning experience using virtual world technology that will bring the slave trade experience from its origins the West Coast of Africa
              Motivation
              The motivation for this virtual tour project - for making the history of the slave trade come alive, is to give true meaning to the proverb: ``let us forgive but let us never forget.’’ By creating a virtual environment that renders the story of the slave trade, from its origins on the Western Coast of Africa, to its final destinations on the shores of the western side of the Atlantic, with a near to realistic experience of the journey suffered by so many, we will foster a stronger appreciation and understanding of one of history’s most shameful eras. A virtual 3D immersive tour will
              •    spark the interest of our children and youth as they learn about it in school,
              •    serve as a valuable tool for historians and scholars the world over, and
              •    make the history of the African slave trade accessible to all.

              Zip Files
              Elmina
              More 1
              More 2
              Video
              Gold Coast and Jago Castle
              http://www.ics.uci.edu/~ziv/ooad/intro_to_se/index.htm Object-Oriented Software Engineering Lecture 1

              Object-Oriented Software Engineering Lecture 1

              Hadar Ziv

              Ziv Research and Consulting

              Object-Oriented Software Engineering Lecture 1

              Why Study OO Software?

              An Object-Oriented World

              Classes and Objects

              Class Inheritance

              Why is Software Even Important?

              What is Software Engineering?

              Software Engineering Vs. Programming

              Why is Software Development Hard?

              The Software Crisis

              Examples of Software Crisis

              Myths of Software Development (1)

              Project Cost by task

              Myths of Software Development (2)

              Relative Cost of Fixing a Fault

              Myths of Software Development (3)

              The Software Process

              The Software Process

              The Software Process

              The Waterfall Lifecycle Model

              Faking It

              Software Engineering Principles

              Software Engineering Principles

              Separation of Concerns

              Abstraction

              Anticipation of Change

              Summary: Software Engineering

              Lecture Summary

              Read more about it (1)

              Read more about it (2)

              http://www.ics.uci.edu/~ziv/diss/conclusionpaper/conclusionpaper.html No Title
              next up previous
              Next: Conclusions and Future Work



              • Conclusions and Future Work
                • Summary of Contributions
                • Future Work
                  • Comprehensive Case Study
                  • Monitoring the testing process
                  • Other software qualities
                  • Other uncertainty modeling techniques
                  • Modeling uncertainty in software processes
                • Final Thoughts
              • References
              • About this document ...


              Hadar Ziv
              Fri Jun 20 16:25:19 PDT 1997
              http://www.ics.uci.edu/~ziv/diss/intropaper/intropaper.html No Title next up previous
              Next: Introduction



              • Introduction
                • Software Uncertainty Modeling
                • Premises and Hypothesis
                • Dissertation Contributions
                • Dissertation Organization
              • References
              • About this document ...


              Hadar Ziv
              Fri Jun 20 16:22:31 PDT 1997
              http://www.ics.uci.edu/~neno/interests.html My Interests

              Some Stuff I Like To Do

              Find out more about the greatest sport that ever was!

              BTW, I can't wait for the 1998 World Cup to begin!

              In the meantime, the European Championships will have to do - and they are already over! How I'm going to keep myself busy now is anybody's guess.

              I'm a big fan, having lived in Phoenix and all.

              They stink these days, but I'll never be one of them fairweather fans.

              Whenever I get a chance, I try and listen to The Jungle. Jim Rome is pretty funny, notwithstanding the fact that (or especially because) Jim "Chris" Everett decked him on live TV a couple of years back.

              I watch way too much TV. With the notable exception of , I don't believe any show on TV is nearly as funny as .

              and are much more intelligent than people give them credit. I sometimes think that what bothers some about them is that, as repulsive as they find these two, B&B-H provide both funny and accurate social commentary. The truth hurts sometimes!

              are not dead.

              They are just roaming an alternate universe.

              I'm a certified buff.

              Click here to find out about the movies playing in your area .

              I recently had a chance to finally see Picasso's Guernica in Madrid. It left every bit as powerful an impression on me as I had expected.

              http://www.ics.uci.edu/~ziv/diss/abstractpaper/abstractpaper.html No Title next up previous
              Next: References

              Software development in practice is still human intensive, involving human error, human judgment and subjective assessment. The outcome is often uncertain and unpredictable and, along with uncertainty in natural phenomena, inevitably leads to software risks and uncertainties. Despite the pervasiveness of human involvement, surprisingly few techniques exist that model related software uncertainties explicitly. This dissertation begins to remedy this situation by presenting the Maxim of Uncertainty in Software Engineering, stating that ``Uncertainty is inherent and inevitable in software processes and products,'' and, as corollary, urging that software uncertainties be modeled explicitly.

              Ideally, an uncertainty modeling technique should include probabilistic notions of uncertainty and confidence, support multiple factors of influence, and allow dynamic updating of uncertainty values during development. Here, we claim that Bayesian Belief Networks meet these desiderata and are therefore suitable for software uncertainty modeling.

              We are specifically interested in software traceability and how it may be improved through notions of judgment, confidence, belief, evidence, and causal influence. To this end, we posit a research hypothesis, stating that software uncertainty modeling, specifically Bayesian network modeling of developers' confidence levels, allows improved traceability of software artifacts and relations.

              This requires that Bayesian network models of confidence levels be constructed. We describe how, given a system of interrelated software artifacts, an isomorphic Bayesian network is constructed. The structure and belief values in this network are later revised and refined as the network is validated against project data and developer judgment.

              For validation, we studied the artifacts and relations of a system being developed at Beckman Instruments. Bayesian networks were constructed to capture and confirm developers' confidences in software artifacts and relationships. An example scenario of confidence fluctuation was used to study developers' ability to trace and track artifacts and associated confidences with and without the Bayesian information. The study showed that improvement in developer scores was, as hypothesized, achieved with statistical significance. Additional contributions include a prototype implementation of a Java applet for software belief networks and a collection of confidence factors that may influence levels of developer confidence in software requirements.



              • References
              • About this document ...

              next up previous
              Next: References

              Hadar Ziv
              Fri Jun 20 16:27:13 PDT 1997
              http://www.ics.uci.edu/~ziv/resume.html Hadar Ziv Resume 1997

              Hadar Ziv


              Objective

              Seeking full-time tenure-track opportunity as faculty member in software engineering, specifically development of complex software including object-oriented systems, data mining, and intelligent agents.

              Summary of qualifications

              I have been a software developer since 1983, a teacher and instructor since 1986, a researcher since 1988, and a consultant and mentor since 1993. My professional expertise is in development of complex software systems, including object-oriented (OO) analysis and design methods (Booch, Rumbaugh), OO programming languages (C++, Ada), and the corresponding development tools (Visual C++, Rational Rose, Select OMT, Ada compilers). I am also proficient with Microsoft technologies including Windows 95/NT, MFC 4.x, and COM/OLE programming. My research experience includes all aspects of the software development lifecycle, as well as software process modeling, software understanding, software visualization, hypertext and hypermedia.

              My teaching experience includes close to 1,000 computer professionals in Southern California trained in OO Analysis and Design methods and Microsoft Windows programming using Visual C++ and MFC. Currently I am consulting and mentoring in object-oriented software development to Beckman Instruments, a Fortune 500 company in Fullerton CA, and to ObjectAutomation, a promising start-up in Irvine CA.

              Upcoming Books

              "Intelligent Software Agents: A Compendium of Theory and Practice," to be published by Addison-Wesley in early 1998.

              "The Coherent Object-Oriented Lifecycle: From Analysis and Design to Successful Implementations using Visual C++ and MFC," to be published by Addison-Wesley in late 1997.

              Education

              1990 - 1997 University of California, Irvine Irvine, CA

              Ph.D., awarded June 1997.

              • Information and Computer Science, specializing in software engineering. My Ph.D. thesis offers a novel approach to software uncertainty modeling using Bayesian Belief Networks. The work also includes a tool for defining Bayesian networks for software systems.

              1988 - 1990 University of California, Irvine Irvine, CA

              M.S., awarded March 1990.

              • Information and Computer Science, specializing in software engineering.

              1980 - 1983 Technion, Israel Institute of Technology Haifa, Israel

              B.Sc., Cum Laude, awarded April 1983.

              • Computer Science.

              Work and Business Experience

              1. Owner, Object Oriented Training, formerly Ziv Research & Consulting, since 1993.
              2. Supervised and participated in the development of IVAN, a hypertext browser for navigating networks of software artifacts, 1994-1996.
              3. Participated in many design, implementation and demonstration activities within the Arcadia research project at UC Irvine, 1988-1997, including the Triton object management system and the Process Viewer system.
              4. Served as a systems programmer and an instructor for the central computing facility of the Israeli Defense Force, Israel, 1983-1988.

              Areas of Proficiency

              • Software engineering, object-oriented software development, software process models, hypertext and hypermedia, uncertainty modeling
              • Methods: Booch, Rumbaugh, Jacobson, Unified Modeling Language
              • CASE: Rational Rose/C++, Select OMT Workbench, IDE StP, SeeObject
              • Microsoft: Windows 95 / NT, Visual C++ / MFC 4, COM / OLE 2, WOSA
              • C/C++, Ada/Ada95, Java, HTML, UNIX, X Windows, IBM MVS

              Consulting Experience

              Provided training and consulting services to the following companies in the areas of using Booch, Rumbaugh and the proposed UML methods for OO analysis and design, CASE tools from Rational and Select Software Tools, and Microsoft technologies such Visual C++, MFC, and COM/OLE.

              1. Beckman Instruments, Fullerton, CA, May - March 1997.
              2. ObjectAutomation, Irvine, CA May - December 1996.

              Additional in-house Training

              1. Volt Delta Resources, Inc., Orange, CA. A 411-directory application for Windows 3.1/NT with Visual C++/MFC, January - February 1996.

              Provided in-house training in object-oriented analysis and design methods, C++, Visual C++ and MFC, and OLE to the following companies:

              Beckman Instruments, ObjectAutomation, MCA/Universal, Ericsson, NetSoft, Sony Transcom, and Phoenix Technologies.

              UCI Extension Instruction

              My teaching experience at UCI Extension includes a 30-hour class on Object-Oriented Analysis and Design in September - December 1996, June - August 1996, April 1 - June 10, 1996, January 10 - February 28, 1996, September 20 - November 22, 1995, April 4 - June 6, 1995, January 20 - 28, 1995, and September 29 - December 8, 1994.

              Also included is a 30-hour class on Microsoft Visual C++ Programming with MFC 4.0 in October - November 1996, June - August 1996, February 2 - 16, 1996, September 25 - November 27, 1995, September 8 - 16, 1995, July 7 - 15, 1995, June 2 - 10, 1995, and April 5 - June 7, 1995.

              Refereed Papers

              1. "Constructing Bayesian Network Models of software testing and maintenance uncertainties", with Debra J. Richardson. ICSM-97 International Conf. on Software Maintenance (ICSM97), Bari, Italy, September-October 1997.
              2. "Research Issues in the Intersection of Hypertext and Software Development Environments", with Leon J. Osterweil. Workshop on Software Engineering and Human-Computer Interaction, adjacent to ICSE-16, Sorrento, Italy, May 1994. Available in Lecture Notes in Computer Science 896, pp. 268-279, Springer-Verlag, 1995.
              3. "Software Visualization and Yosemite National Park", with Dani Steinberg. In HICSS-25, Hawaii Int. Conference on System Sciences, Volume 2, pp. 607-618, Kauai, Hawaii, January 1992.
              4. "Programming a Software Requirements-Specification Process", with Stanley M. Sutton, Jr., Dennis Heimbigner, Harry E. Yessayan, Mark Maybee, Leon J. Osterweil, and Xiping Song. In First Int. Conference on the Software Process, Redondo Beach, CA, October 1991.

              Research Papers in Progress

              • "The Uncertainty Principle in Software Engineering", with Debra J. Richardson and René Kloesch. Submitted to ICSE-19, Int. Conference on Software Engineering, August 1996.
              • "A Hypertext Browser for Software Traceability", with Debra J. Richardson, to be submitted to Hypertext97, In September 1996.
              • "Software Re-Architecting in the presence of Partial Documentation", with René Kloesch and Debra J. Richardson, submitted to ISAW-2, July 1996.
              • "Modeling Uncertainty in Software Engineering using Bayesian Belief Networks", to be submitted to SEKE'97, in December 1996.

              References

              References are available upon request. The first two chapters of my book are available for review, beginning September 19, 1996. http://www.ics.uci.edu/~ziv/ooad/classes/index.htm Classes and Objects Lecture 3

              Classes and Objects Lecture 3

              Hadar Ziv

              Ziv Research and Consulting

              Classes and Objects Lecture 3

              What is an Object (1)

              What is an Object (2)

              State

              Definition of State

              Behavior

              Identity

              Classes and Objects

              Examples of Classes

              Attributes

              Operations

              The Class Diagram

              Example UML Classes

              Example UML Classes

              Abstraction and Encapsulation

              Mechanics of Encapsulation

              Reminder: Binary Tree Client Code

              Class Implementation

              Chapter Summary

        • http://www.ics.uci.edu/~zhaoxia/publications/publications.htm

          REFEREED JOURNAL ARTICLES

          1.      Jain T, Peshock R, McGuire DK, Willett D, Yu Z, Vega GL, Guerra R, Hobbs HH, Grundy SM, the Dallas Heart Study Investigators (2004). African Americans and Caucasians have a similar prevalence of coronary calcium in the Dallas Heart Study, Journal of the American College of Cardiology, 44: 1011-1017.

          2.      Levine R, Yu Z, Hanley W, Nitao J. (2005). Implementing componentwise Hastings algorithms, Computational Statistics & Data Analysis, 48: 363-389.

          3.      Levine R, Yu Z, Hanley W, Nitao J. (2005). Implementing the random scan Gibbs sampler, Computational Statistics, 20: 177-196.

          4.      Guerra R, Yu Z, Marcovina S, Peshock R, Cohen JC, Hobbs HH. (2005). Lipoprotein(a) and apolipoprotein(a) isoforms: no association with coronary artery calcification in the Dallas Heart Study, Circulation, 111: 1471-1479.

          5.      Wilcox MA, Li Z, Tapper W, Browning S, Curtin K, Ding J, Ding Y, Gagnon F, He Q, Kuo TY, Li M, Matthew G, Mei L, Rao S, Shaw J, Wei Z, Yu Z, Zhang W, Zheng T, Zhu G. (2007). Genetic association with rheumatoid arthritis-Genetic Analysis Workshop 15: summary of contributions from Group 2. Genet Epidemiology, 31 Suppl 1:S12-21.

          6.      Yu Z, Schaid D. (2007). Sequential haplotype scan methods for association analysis. Genetic Epidemiology, 31: 553-564.

          7.      Yu Z, Schaid D. (2007). Application of haplotype sequential scan methods to case-control data. BMC Proceedings, 1 Suppl 1:S21.

          8.      Yu Z, Schaid D. (2007). Methods to impute missing genotypes for population data. Human Genetics, 122:495-504.

          9.      Yu Z, Wang L, Hildebrandt MAT, Schaid D. (2008). Testing whether genetic variation explains correlation of quantitative measures of gene expression, and application to genetic network analysis. Statistics in Medicine, 27:3847-3867.

          10.  Jian Z, Yu Z, Yu L, Rao B, Chen Z, Tromberg BJ. (2009). Speckle attenuation in optical coherence tomography by curvelet shrinkage. Optics Letters, 34:1516-1518.

          11.  Yu Z*, Garner C, Ziogas A, Anton-Culver H, Schaid D. (2009). Genotype determination for polymorphisms in linkage disequilibrium. BMC Bioinformatics, 10:63.

          12.  Browning B, Yu Z. (2009). Simultaneous genotype calling and haplotype phasing improves genotype accuracy and reduces false positive associations for genome-wide association studies. American Journal of Human Genetics, 85:847-861.

          13.  Weng L, Macciardi F, Subramanian A, Guffanti G, Potkin SG, Yu Z*, Xie X.  (2011) SNP-based pathway enrichment analysis for genome-wide association studies. BMC Bioinformatics, 12:99.

          14.  Mkhikian H, Grigorian A, Li GF, Chen HL, Newton B, Zhou RW, Beeton C, Torossian S, Tatarian GG, Lee SU, Lau K, Walker E, Siminovitch KA, Chandy KG, Yu Z, Dennis JW, Demetriou M. (2011) Environmental and genetic dysregulation of N-glycosylation is a unifying mechanism in Multiple Sclerosis. Nature Communications, 2:334.

          15.  Meng L, Cannesson M, Alexander BS, Yu Z, Kain ZN, Cerussi AE, Tromberg BJ, Mantulin WW. (2011) Effect of phenylephrine and ephedrine bolus treatment on cerebral oxygenation in anaesthetized patients. British Journal of Anaesthesia, 107:209-17.

          16.  Yu Z* (2011). Testing gene-gene interactions in the case-parents design. Human Heredity, 71:171-179.

          17.  Yu Z*, Wang S (2011). Contrasting linkage-disequilibrium as a multi-locus family-based association test. Genetic Epidemiology, 35:487-498.

          18.  Wang S, Yu Z, Miller RL, Tang D, Perera FP. (2011). Methods for detecting interactions between imprinted genes and environmental exposures using birth cohort designs with mother-offspring pairs. Human Heredity, 71:196-208.

          19.  Yu Z*, Deng L. (2011) Pseudosibship methods in the case-parents design. Statistics in Medicine, 30:3236-3251.

          20.  Shahbaba B, Shachaf CM, Yu Z*. (2012) A pathway analysis method for genome-wide association studies, Statistics in Medicine, 31:988-1000.

          21.  Yu Z* (2012). Family-based association tests using genotype data with uncertainty. Biostatistics, 13:228-240.

          22.  Meng L, Mantulin WW, Alexander BS, Cerussi AE, Tromberg BJ, Yu Z, Laning K, Kain ZN, Cannesson M, Gelb AW. (2012) Head-up tilt and hyperventilation produce similar changes in cerebral oxygenation and blood volume: an observational comparison study using frequency-domain near-infrared spectroscopy. Canadian Journal of Anesthesia, 59:357-365.

          23.  Meng L, Gelb AW, Alexander BS, Cerussi AE, Tromberg BJ, Yu Z, Mantulin WW. (2012) Impact of phenylephrine administration on cerebral tissue oxygen saturation and blood volume is modulated by carbon dioxide in anaesthetized patients. British Journal of Anaesthesia, 108:815-822.

          24.  Yu Z*, Gillen D, Li CF, Demetriou M. (2013) Incorporating parental information into family-based association tests. Biostatistics, 14: 556-572., supplementary material

          1. Alexander BS, Gelb AW, Mantulin WW, Cerussi AE, Tromberg BJ, Yu Z, Lee C, Meng L. (2013) Impact of stepwise hyperventilation on cerebral tissue oxygen saturation in anesthetized patients: a mechanistic study. Acta Anaesthesiologica Scandinavica, 57:604-612.
          2. Li GF, Zhou RW, Mkhikian H, Newton BL, Yu Z, Demetriou M. (2013) Hypomorphic MGAT5 polymorphisms promote multiple sclerosis cooperatively with MGAT1 and Interleukin-2 and 7 receptor variants. Journal of Neuroimmunology, 256:71-76.
          3. Kim SH, Lilot M, Sidhu KS, Rinehart J, Yu Z, Canales C, Cannesson M. (2014) Accuracy and Precision of Continuous Non-Invasive Arterial Pressure Monitoring Compared to Invasive Arterial Pressure: A Systematic Review and Meta-Analysis. Anesthesiology, 120:1080-1097.
          4. Kim SH, Lilot M, Sidhu KS, Murphy LSL, Rinehart J, Yu Z, Canales C, Cannesson M. (2014) Accuracy of Continuous Non-Invasive Hemoglobin Monitoring: A Systematic Review and Meta-Analysis. Anesthesia Analgesia, 119:332-346.
          5. Yu Z, Li CF, Mkhikian H, Zhou RW, Newton BL, Demetriou M. (2014) Family studies of Type 1 diabetes reveal additive and epistatic effects between MGAT1 and three other polymorphisms. Genes and Immunity, 15:218-223.
          6. Yu Z*, Demetriou M, Gillen D. (2015) Genome-wide analysis of gene-gene and gene-environment interactions using closed-form Wald tests. Genetic Epidemiology, 39: 446-455. (software: GG_Wald)
          7. Cannesson M, Ramsingh D, Rinehart J, Demirjian A; Vu T, Vakharia S, Imagawa D, Yu Z, Greenfield S, Kain Z. (2015) Perioperative goal directed therapy and postoperative outcomes in patients undergoing high-risk abdominal surgery:� A historical-prospective, comparative effectiveness study. Critical Care, 19:261.
          8. Meng L, Settecase F, Xiao J, Yu Z, Flexman A, Higashida R. (2016) Initial Clinical Experience with Near-infrared Spectroscopy in Assessing Cerebral Tissue Oxygen Saturation in Cerebral Vasospasm before and after Intraarterial Verapamil Injection. Journal of Clinical Neuroscience (in press).
          9. Wu K, Chen C, Moyzis RK, Greenberger E, Yu Z. (2016) Gender Interacts with Opioid Receptor Polymorphism A118G and Serotonin Receptor Polymorphism -1438A/G on Speed-dating Success. Human Nature (in press).

           

           

          *: corresponding author

           

          OTHER PUBLICATIONS

          1.      Guerra R, Yu Z. (2005). Single nucleotide polymorphisms and their applications, book chapter in Computational and Statistical Approaches to Genomics, W. Zhang and I. Shmulevich (editors). Second Edition, Boston: Kluwer Academic Publishers.

          2.      Yu Z. (2012). Estimating genotype-specific call rates from offspring-parents trio data. JSM Proceedings.

           

           

          http://www.ics.uci.edu/~zhaoxia/publications/GGWald_manuscript/index.htm

          gg_wald: closed-form Wald tests for genome-wide analysis of gene-gene interactions

           

          gg_wald is a C++ program that conducts Wald tests for testing gene-gene interactions.

           

          Benefits of gg_wald:

          o   Feasible for genome-wide interaction analysis. It runs as fast as other computationally efficient tests of gene-gene interactions

          o   Flexible parameterization. It can conduct both 4-degree-of-freedom and 1-degree-of-freedom tests. Tests with other parameterization, such as dominant or recessive interaction effects, can be obtained by slightly modifying the source code

          o   Statistically valid. The results are based upon Wald tests, which are asymptotically equivalent to likelihood ratio tests.

           

          C++ source code

           

          The manuscript, supporting text: S1, S2

          http://www.ics.uci.edu/~gbowker/pubs.htm Bowker's Papers http://www.ics.uci.edu/~gbowker/records.html The Multiple Bodies of the Medical RecordTHE MULTIPLE BODIES OF THE MEDICAL RECORD:
          Towards a Sociology of an Artifact

          Marc Berg

          University of Maastricht

          Geoffrey Bowker

          University of Illinois, Champaign/Urbana

          Contact address:

          Marc Berg

          Dept. of Health Ethics and Philosophy

          Maastricht University

          P.O. Box 616

          6200 MD Maastricht

          The Netherlands

          Phone: +31-43-3881234/3881144

          Fax: + 31-43-3670932

          E-Mail: Marc.Berg@GW.UniMaas.NL

          October 1996

          To appear in Sociological Quarterly

          THE MULTIPLE BODIES OF THE MEDIC AL RECORD:

          Towards a Sociology of an Artifact

          This paper argues that the medical record is an important focus for sociological research. In medical work, the modern patient's body Foucault has so aptly described is produced throug h embodied, materially heterogeneous work - and the medical record plays a crucial role in this production. It does not simply represent this body's history and geography: it is a central element in the material re-writing of these. Simultaneously, the re cord fulfills a core role in the production of a body politic. As the record is involved in the performance of the patient's body, it is also involved in the performance of the clinic in which that body comes to life. Finally, we argue that< i>different records, different practices of reading and writing are intertwined with the production of different patient's bodies, bodies politic, and bodies of knowledge. As organizational infrastructure, the medical record affords the interplay and coordination of divergent worlds. Seen in this light, as a site where multiple stories about patients and about organizations are at stake (including the interoperability between these stories), the medical record becomes highly relevant both analytically and politically.

          In his Birth of the Clinic, Foucault argues that the classical, pre-modern "medicine of species" required a two-dimensional table as an intermediary between the individual body and medical knowledge (1973/1963). T he table would translate individual symptoms: it would yield the true nature of the disease by showing how they fitted into the eternal scheme of things. Symptoms were not the disease itself: they were pointers to this higher truth, which merely "precipit ated" in individual bodies, and which the table could decode. In contrast to the medicine of species, Foucault argues, the modern clinical gaze requires no such intermediary. Truth is no longer found and organized elsewhere in some grand nosological schem e, but rather in the pathological processes of individual bodies. The gaze deciphers this truth by following the symptoms inwards, eliciting signs, and differentiating the pathological reality that now is the disease.

          Yet the development of thi s gaze depended crucially on the development of some new intermediaries. Writing was crucial in this new configuration. "Medicine no longer tried to see the essential truth beneath the sensible individuality; it was faced by the task of perceiving, and to infinity, the events of an open domain" (Foucault 1973, p. 98). In order to develop a body of true knowledge, medicine had to record individual cases: only in the accumulation of such experience, only in the totality of observers/observations coul d true knowledge be generated (Fagot-Largeault, 1989; Dagognet 1970). A cascade of inscriptions, to use Latour's term, typified and produced the possibility of this means of knowing (Latour 1987; 1993, pp. 171-225).

          Although Foucault does not discuss this in the Birth of the Clinic, knowing in the practice of medicine is similarly dependent on writing. The power of the gaze, in other words, would not go very far if it stood isolated (cf. Atkinson 1995, pp. 60-65). No longer typifi ed by the metaphorical two-dimensional table Foucault describes, modern medicine could not be imagined without that other object of consultation: the medical record. In this paper, we argue that the medical record is fundamental to the everyday production of that contemporary body whose archeology Foucault describes (a body which hides the essences of the disease in the pathological processes taking place in its tissues; where the symptoms and signs attest to a reality which is never completely accessible in life) and to the everyday production of the organizations which enact and treat it.

          How is the patient's body produced in hospital wards? How is its specific geometry and its historicity created? How is the patient's body transformed from the liv ed body of Mr. Thompson into a juxtaposition of organs, parameters, rows of numbers, graphs, and so forth? Is this through a specific way of looking? Or is this a discursive transformation, achieved through talk (as is a frequent focus of so cial constructivist medical sociology)?[1](see e.g. Davis 1986; ten Have 1994) We will maintain that the body is produced through embodied, materially heterogeneous work (Hirschauer 1991; Cussins 1996). Much comes into pla y here: urine containers, infusion pumps, nursing routines, doctor's consultations, and so forth. All these artifacts, individuals and organizational routines are intermediaries which together perform the medical body. Following recent developments in sci ence and technology studies (Bijker and Law 1992; Clarke and Fujimura 1992; Latour 1996; Star 1995), we argue that they constitute the network, or the dispositif within which the body acquires its specific ontology. We focus on the medical record b ecause this artifact occupies a central niche in this network: it is where many of the nurses and physician's tasks begin, end, and are coordinated, where inscriptions accumulate, and where the specific spaces and times we will describe unfold. The record does not merely mirror the bodies it maps, we argue - but neither does it determine them. To emphasize the active role of artifacts without falling into technological determinism, we use the term "mediation" (Latour 1994): the record mediates the relations that it organizes, the bodies that are configured through it.

          Following Foucault, we stress that these practices of reading and writing are not only central to the production of a patient's body: they also fulfill a core role in the producti on of a body politic. As the record is involved in the performance of the patient's body Foucault so aptly describes, it is also involved in the performance of the clinic in which that body comes to life. A specific configuration of the body cannot be cut loose from the specific social position different health professionals have within hospitals, and with the type of stories about the work done that can emerge from the records. To strengthen both these points, we make a third argument which departs from the Foucauldian scheme.[2] We argue that different records, different practices of reading and writing are intertwined with the production of different patient's bodies, different bodies politic , and different bodies of knowledge.

          In the first section, we look in some detail at a medical record taken from an oncology ward from a Dutch University Hospital. Here we concentrate on the production of patient's bodies. In the second section, we c oncentrate on the way in which the medical record shapes various bodies politic. We explore some more general developments in the area of medical record keeping such as the increasing attention to coding and classification, and the attempts to produce an electronic medical record. In each section we look first at the general process of production of the body/the body politic and then look at the practice of multiplicity (managing the articulation of multiple bodies/bodies politic).

          Throughout, we will be using a broad definition of the medical "record" as all written, typed or electronically stored traces of any aspect of patient treatment that has official status within the hospital system and is in principle stored for a period of time (at least equ al to the patient's stay in the hospital; see e.g. Huffman 1990). More often than not, this implies that the "medical record" is not one single object: rather, it is the record the physicians keep in one folder at the outpatient clinic together with the ( also often physically separate) nursing and physician's "in house" record, with the separate forms created and used in the hospital administration offices - and so forth.

          This paper is not primarily concerned with practices of reading and writing whic h bring the record to life (see Berg 1996 for this approach); it is concerned, rather, with mapping the configurations it helps bring into being. We will thus not be looking at the real-time articulation work that links the record to the ongoing work, or at the informal organizations that interpenetrate the more formal body politics. These are crucial issues (see e.g. Suchman 1987; Star 1995), but we want to explore here how the structuring of the record speaks to the structuring of the bodies we investig ate. It is a synchronic exercise to explore just how a mundane, boring artifact like a medical record is involved in the politically charged production of human bodies, organizational hierarchies, and selective memories.

          The paper is based on two line s of research. Marc Berg has spent several months in different wards in various hospitals in the Netherlands, focusing on the role of artifacts such as records and protocols in the ongoing processes of medical work (see e.g. Berg 1997). Geoffrey Bowker ha s studied the history of medical records and has through a series of interviews and observations traced the development of a nursing classification scheme designed for incorporation within hospital information systems.

          PRODUCING BODI ES

          Mr. Wood is a patient on the Dutch University Hospital oncology ward. Imagine a newly arrived oncologist, who begins her shift on this ward. She meets Mr. Wood without having his medical record to hand. She sees a middle aged, somber looking man, probably suffering from cancer since this is the oncology ward. Questioning him or the personnel on the ward, she may elicit a story of Hodgkin's disease, which has been treated once before, but which has recurred. Investigating him m ight yield some more clues as to the spread of the disease, the side effects of his treatment, his general condition. Without the record, she might sense the damage the cancer has done, and conclude that the prognosis is poor. Yet she would excuse herself and not take any action before having seen the record. Without the record, she is without memory, without a device to structure her thought; despite all her years of experience, she is barely more able to proceed than the recently graduated resident who stands besides her.[3]

          With the record, things are different. This record comprises some 120 pages, producing a fascinating, detailed yet jagged and dispersed memory of the patient. It starts with the temperature list (Figure 1): an unfolding sheet which is structured like a flow-chart, wherein blood pressure, pulse, temperature, medications and so forth are logged. Next is the order form, on which physicians write down any diagnostic steps and changes in treatment for nursing staff to effectuate. Then come the physician's progress notes, starting with a few pre-structured forms which summarize the patient's medical history at the date of admission, and followed by over twenty, unstructured pages of daily notes. These are followed by computer print outs from the laboratory information system (listing rows of numbers indicating outcomes of laboratory tests performed on the patient's blood and urine), results from bacteriological tests and X-rays (altogether over twenty pages), letters written about Mr. Wood, and so forth.

          FIGURE 1 HERE

          History

          The record produces a patient with a medical history: the accumulation of sets of traces configures a medical past for a specific pa tient.[4] The temperature list maps the different parameters against time - the x-axis of this flow chart. Lines divide the sheet into weeks and days, and thinner lines divide the upper, graphical part of the sheet even further into eight-hour periods. Temperature, pulse and respiration rate can be entered along the y-axis. Below this, the y-axis changes its role. It becomes a list of different parameters: tension, weight, specific gravity of the urine , bleeding time (both not filled in on this form), and the medication given. Presenting them in this way clarifies how changes in one are temporally related to changes in another - yet their vertical order is arbitrary. Further below, the y-axis ch anges again: here it incorporates a mathematical operation. The different infusions listed add up to total fluid intake ("totale vochtopname"), followed by the different varieties of fluid loss that result in total fluid loss. These totals are then subtra cted, resulting in the last row on this sheet: the fluid balance.

          The forms listing the laboratory results are structured in a similar fashion: columns of figures, each column indicating a time at which the blood was tested. Reading such forms from le ft to right, one enters the past in a most orderly fashion: step by step, day by day, or even hour by hour, the same variable is followed back through time. This produces a linear, stable history; this activity performs the temporality that Foucaul t saw as a crucial innovation of the modern, clinical gaze. It generates a body with a set of variables whose mutual interrelations and deviations can be traced. The body acquires a double continuity: it acquires a past through recurring variables distrib uted throughout an evenly flowing time, and this past itself remains constant over time. Whether a doctor looks at these traces one day or one month later, this past does not change. (As we will see later, this constancy is not characteristic of al l the histories produced in the record, nor does it imply a unified history).

          History is also produced by putting new forms (such as X-ray reports) in front of the previous form in that section. This does not produce the graphical oversight that a flo w sheet format provides, but through for example flipping through the bacteriology reports, the doctor again finds herself traveling back and forth through linear time, accelerating and decelerating where necessary. Through compressing several weeks of re ports into a few seconds, the doctor leaves the time zones of everyday medical work and enters the temporal order of pathological processes of Foucault's body: the record produces textbook time. The growth of a tumor as witnessed on successive X-ra ys, the battle between microorganisms and antibiotic regimes, and the rise and fall of blood cells as the chemotherapy does its work - these are not events which take place within the time zones of a working day. Fluctuations in blood cell levels only bec ome meaningful over a several day period, and the "growth of a tumor" unfolds over several weeks. Since the rows of blood cell levels on one page cover some ten days, and the X-ray reports over a two month period usually only cover a few pages, the record elegantly affords this crossing between time zones.[5]

          The physician's progress notes and letters, finally, generate a history in a somewhat different way. As the pages of the progress notes fill up, the sequence of e ntries can certainly be read much as the sequence of bacteriology reports or laboratory tests: re-winding time backwards to generate an image of events taking place in a different temporal order. Yet most entries themselves start out with a short "summary " of the case, as follows:

          Now 8 days post-reinfusion (of his bone marrow cells)[6]

          Last night started amica 2 x 700 (these are antibiotics)

          ripera 4 x 4 gr.

          Origin? (... of the fever)

          (From Mr. Wood's record)

          In these summaries, the "current situation" is deduced from other entries in the record, the new laboratory results, information from nurses and the patient, and so forth. The starting point of that d ay's deliberations is formulated in a single phrase uninterpretable out of the context of the other entries. These summaries are read, re-read, re-written in the light of new events, summarized again, and continually constitute a brief patient's history a s relevant at that particular juncture (Berg 1996). Glancing through a few recent pages of progress notes, then, is often the preferred way for a novel physician to "eyeball" a patient's current situation: to build a multi-layered history of pathological processes, diagnostic and therapeutic procedures, intertwined with organizational routines (such as who was the responsible physician when, where was the patient sent to), and so forth.

          Geography

          The record also produces an anato mical geography. Its different sections detach the blood from its vessels, split its constituents, juxtapose the organs. The case history form separates several "organ systems", as do the nursing history forms; the X-ray reports delineate the "thor ax", the "abdomen", and all the structures that can be seen within these bodily cavities. Orthogonal to the creation of a linear history, here anatomical orders are produced which perform the textbook medical body. The record thus also performs a dislocat ion in space (Latour 1987): it performs an in-vivo dissection, fleshing out a map to the terrain that is hidden under the patient's skin. This map affords a linkage of anatomical realities analogous to the time travel described above. Juxtaposing t he radiographic reports from Mr. Wood with the physical examination and the laboratory results allows the physician to trace the spread of the cancer and to weigh the damage done by the chemotherapy.

          But what does it mean, exactly, to say that the rec ord "produces" a patient's history or geography? Of course, Mr. Wood's record is productive of narrative in the banal sense that people tend to read a narrative structure into any set of facts and figures: Boland (1993) has shown how, read by accountants, two different sets of balances sheets can give rise to the most varied conjectures about the personal lives and professional competence of their peers. Yet we want to go further. We are not arguing that the record produces a history or geography b y creating a specific representation of the body, while leaving the "real" patient's body untouched. It is not a matter of merely producing a particular discourse, a specific rhetoric about a body. The medical record is not simply a post hoc depiction of times passed and spaces explored: it feeds into the very constitution of these times and spaces. The medical record is a distributing and collecting device (Berg 1996): work tasks begin and end there. It produces the patient's history by sequential ly demanding that the same measurements be made again and again (according to the density of the x-axis). In order to produce the evenly distributed graphs and tables, these measurements have to be meticulously timed: every day, standard blood test s are performed, bacteriological cultures are taken; and every eight hours, nurses measure the temperature and the pulse. Every day, physicians produce a new summary of the situation which forms the basis for that day's diagnostic and therapeutic interven tions. The end result is not a "medical" history which is clearly distinguishable from the history of Mr. Wood. The production of this representation can only occur with the concurrent transformation of that which is represented. The meticulous organizati onal routines into which the patient is "hooked" totally transform and control the patient's previous time zones - determining when the patient sleeps, structuring the days, and transforming the very experience of the flow of time (Frankenberg 1992; Roth 1979/1963; Star and Bowker 1994). The body is thus re-written in bureaucratic format: the weekly, daily and eight-hour cycles which now structure the patient's time closely match the doctor's and nurses' shifts, the opening and closing hours of the labora tory, and the measurement units of the financial administration (Zerubavel 1979). Again, this is not merely a different, professional "reading" of the body. The body is materially reconfigured - its flesh is part and parcel of the discursive transformatio ns we witness here. In Star and Bowker's terms, we witness a convergence between body and representation: in its production, the representation inscribes itself in the body it represents (1994).

          It is this re-written body, subsequently, which i s the site of the diagnostic and therapeutic interventions. At this point, it becomes meaningless to debate whether these interventions address the body "itself" or its representation, since it is in and through this representation that the body "i tself" is known, surveyed and intervened upon. Peaks in the temperature curve will prompt the administration of a certain antibiotic - whose effect subsequently is also monitored in that graph, and in the temporal changes in the laboratory values register ing the organ functions that might be affected by this drug. Not only do the record's pre-formatted time zones inscribe itself into the patient's body: all medical activities are started, followed up and evaluated within the time zones produced by the rec ord. Most of the relevant historical events in the hospitalized patient's life become events triggered by orders written in the record - procedures set in motion by specific forms; therapeutic dosages re-adjusted because of an increase in a certain labora tory variable - as gleaned from the record.

          While the patient's time is morphed into the record's time, the patient's geography is rewritten as well. As Hirschauer has so beautifully argued, the surgeon who operates on a body meticulously prepares and carves out the tissues so that they map the anatomical atlas. The atlas does not simply figure as the "ground" of objective knowledge, immediately visible in individual patient's bodies. Rather, this visibility is the outcome of the surgeon's "scu lptured practice": "the proper anatomy of the ideal body is engraved ... into the patient-body" (1991). In an analogous fashion, the record inscribes a geography. The forms physicians and nurses use in their investigations of the patient body invariably d elineate the organ systems. A form used in an intensive care unit, for example, listed the categories under "physical examination" as follows:

          general impression

          central nervous system

          cardial

          pulmonal

          abdomen

          urogenital

          extremiti es

          Similarly, many laboratory tests are categorized anatomically: "liver function", "renal function", "pulmonary function". In this way, through categorizing observations and substances from the body, the record carves out an anatomy. And it is thi s "geographicalized" body which is subsequently intervened upon: drug dosages are adjusted to correct a decreasing liver function, antibiotics are geared towards a possible focus for infection seen on an X-ray, and tubes and monitor cables are put in plac e to monitor the cardio-vascular circulation. As above, in an ever tightening cycle, the patient's body becomes its representation. Information required by the record or written in the record leads to interventions on the patient's body as it is re presented in the record. Again, the record does not simply describe a patient's body: it structures the way the patient's body is rewritten. The categorization of observations and substances is only a first step in a continuing sequence of events, in whic h the individual anatomical systems become the center for specific interventions by different specialists. Fluids and medication are given to correct urogenital problems, a chest is punctured to restore a deflated lung to its proper form, and postures hav e to be taken to shoot the X-ray right: in mapping the patient, the patient's body is reconfigured so that it matches its map.

          The record is of course not the only element active in these rewriting processes. Only as a part of a interlocked se ries of elements does the record come to life; only when linked to nursing and laboratory routines, tubes, infusion bags and cables, the hospital information system and so forth does a network emerge which as a whole performs the transformations de scribed. And the record's structure does not "determine" the nursing routines in any simple way: these routines and the record's current structure have emerged together, mirroring one another and interlocking in historically specific ways (Berg forthcomin g). Yet the record is one turning point in the cycle of inscriptions that circulate through these interlinked entities. It is where the inscriptions end up, are matched and rearranged, and where new inscription-yielding activities begin.

          Mul tiplicity

          The rewriting processes described above do not produce a single coherent and transparent patient's body, mapped into Euclidean space. Medical practice is not a unitary apparatus "disciplining" the patient's body into some free-floati ng, similarly unitary biomedical entity (Haraway 1991; Hirschauer and Mol 1995). Focusing again on the medical record, we could say that it constitutes multiple histories. It encompasses multiple layers: time flows faster in some sections than in o thers. The temperature curve exists in time measured in hours, while X-ray's exist in time measured in days. Some pages capture a time span of two months, while other pages cannot hold even a single day.

          And these histories do not simply fold into one another. The flow chart links several histories by mapping them on a similar x-axis - yet even here the histories remain relatively self-contained. The fluid balance, for example, is produced as a closed system, with a rhythm and an internal logic which is transparent. Daily, a single number results which "represents" the fluid balance of this particular patient. And the temperature curve is similarly clear-cut: it is a graph mapping the evolution of the patient's body temperature over eight-hour periods. Yet the interrelation between these two histories is not self-evident. The only direct linkage (Mol forthcoming) produced by the record is a temporal co-occurence. But how are the peaks related to the numbers? Are they? What is their joint histor y?

          Such links are sometimes made in other parts of the record: especially in progress notes. A high temperature reading may be linked to a negative fluid balance record by the story that the former causes dehydration. Yet the progress notes only refer to a fraction of all the data listed in the record. More often than not, the X-ray reports, temperature curve, fluid balance, laboratory values and so forth remain unconnected, in their own time zones. And even if they are connected through the pr ogress notes, another complication of the image of a fully integrated, unitary, Euclidean body arises. Where the flow chart and the series of X-ray reports produce a series of - often separate - linear histories, the progress notes contain non-linear histories. The summaries extract and condense time to match the current situation. Moreover, they continually reconstruct these histories whenever the current situation is changed. The progress notes do not build up linear histories like a flow chart d oes. Rather, a history of the patient evolves in the progress notes, changing whenever the current situation changes. Links are forged to the linear histories evolving elsewhere in the record, but these links are constantly reconstructed, removed a nd retrofitted whenever the current situation alters. What is a crucial temperature peak at time t, may become a singular, non-relevant reading later, and be forgotten in the next summary. And what is described as a sequenced chain of events at som e stage in the progress notes, gets summarized into a singular "event" later on. At the eighth day after the reinfusion of Wood's bone marrow cells, the summary in the progress notes starts out with "now 8 days post-reinfusion", and then states "last nigh t started amica & ripera" (see the fragment of his record above). A whole evening of doctor's deliberations on the right antibiotics to prescribe is summarized in this last sentence. And the eight days between reinfusion and last night flash by uneven tfully, only to slow down "last night", and to stop in the present, where the search for the "origin" of the fever is the order of the day. Time loops and swirls in these reconstructions: post hoc rationalizations after a sequence of dispersed occurrences become prior reasons for a "decision", and episodes get condensed, stretched and rearranged. Although the progress notes do sometimes tie the histories evolving in different sections of the record together, they produce a history which is non-linear, con stantly rewritten, and constantly emergent.

          There is, then, no simple mapping between these histories. Mr. Wood's record portrays an array of histories, which do not fold into one another to produce one overall history of a medical body. Rather, the d ifferent histories co-exist, self-contained, sometimes touching and interlinked, but often going their own way. The record performs multiple histories: a series of parallel trajectories, through parallel but also often disconnected time-zones, and a series of statements in which a integrative, historical narrative constantly rewrites itself.

          The multiplicity of the patient performed by the medical record is prominent when we look at the patient's geography. Juxtaposing the different parts of th e record does not provide an evenly distributed, three dimensional map of the body. The travel through bodily space is discontinuous, and filled with indeterminacy. The different sections all stand for a series of probes (the blood tests, the radiographic procedures, the physical examination) that are let down into body that, in Foucault's felicitous phrase, hides its inner pathological truth as long as it remains in the "night of life" (1973, p. 165). Different sections perform different geographies: som e organ systems are lumped together in one, and separated in other parts. The blood test forms meticulously separate different cell types, which are lumped together in the fluid loss forms (all under the category "blood"). The bacteriological forms distin guish different sites from which cultures are taken: the urine, the blood, the throat, the prepuce, the feces. The X-ray reports speak about the thorax (including the lungs, the heart) and the abdomen (including liver, spleen, pancreas, and so forth). And sometimes different geographies are performed on one form: the "organ systems" listed on the intensive care's physical examination form (see above), for instance, mapped out regions (the abdomen, the extremities), functional systems (cardial, pulmonal, c entral nervous system) as well as an overall "others" category (general impressions).

          Here as well, the different geographies do not unequivocally fold into one biomedical body. There are spaces that remain empty, and there are spaces that are filled with competing tales. A differentiation in "regions" cross-sects a differentiation in "fluids" (blood, urine), which cross-cuts a differentiation in "functional systems"; some "functional systems" might be grouped in one "region", while others (e.g. the c ardio-vascular system, or the nervous system) might not. How are these related? How is what is seen on the X-thorax related to the "cardial system" as felt and heard during the physical examination? Does fluid loss through blood loss result in a loss of e quivalent amounts of the different blood cell types, or does bleeding result in a depletion of some cells more than others? As above, these links are not self-evident. They may be created, in the record, through an explicit juxtaposition and interpretatio n of these observations in the progress notes, or through additional examinations (a more frequently repeated series of blood tests, for example). The point is that these links are often not made; that they are absent, thus leaving the geographies in thei r unconnected realms (Mol forthcoming). And when such attempts are made, the geographies may well contradict each other. The blood might seem infected according to the changing ratio in blood cell types, but not according to the bacterial examination of t he blood; while the physical examination may carve out lungs that are "clean", an X-ray may simultaneously show lungs that are "massively infiltrated". Again, the record does not contain mechanisms for preventing such gaps and conflicts. It performs multi ple geographies, sometimes linked, sometimes oblivious to one another, sometimes conflicting.

          MULTIPLE BODIES POLITIC

          The record is not only crucial for the planning of interventions and the coordinati on and interaction between care providers. Other parties are also highly interested in the record. Insurance companies want more information about the indications for interventions, researchers want data on the prevalence of combinations of symptoms and t he efficacy of therapies, and governments and hospital boards want to know how many days patients of a specific diagnostic category are hospitalized - and for what reasons. All these parties claim that the patient record can deliver the information they r equire (see e.g. Ball and Collen 1992). In these discussions, the record is often portrayed as a "repository of information" (Dick and Steen 1991), as a vast treasure of facts which only needs to be tapped into. In such a view, the building blocks, the "facts", can only be more or less "adequate" and "complete".

          One dream of an electronic medical record from the 1970s was that such a record would provide an unmediated picture of doctors' thought, and produce an untainted, complete, error-free d ocument:

          To put a secretary or a computer technician between the physician and the computer is defeating the purpose of a direct natural language physician-computer conversation. If a girl has to interpret what you have said and then put this in s ome form that she thinks the computer will understand, I think we increase the error rate. More important, you prevent the physician from talking to the computer. When the technology becomes sophisticated enough and the expense becomes low enough t hat the physician-computer interface should be direct and in a natural language, there should be no girl, no mark sense card, no keyboard or any other type of artificial instrument between him and his computer. This is why a light pen was devised. It look s like a pencil, it has the physical form of a pencil. The physician has been using the same instrument since he was three years old. (Anderson and Forsythe, 1970, p. 257)

          Current, "paper" medical records are harshly criticized precisely for not p roperly reflecting what has happened, and what physicians think:

          The medical record is an abomination. ... It is a disgrace to the profession that created it. More often than not, the chart is thick, tattered, disorganized, and illegible; progress notes, consultant's notes, radiology reports, and nurses notes are all co-mingled in accession sequence. The charts confuse rather than enlighten; they provide a forbidding challenge to anyone who tries to understand what is happening to a patient (Bleic h 1993).

          The dream of the unmediated record dates from the earliest medical computing applications such as MUMPS (Barnett, 1975) - and has of course never been realized. There are many steps between "what happened" and the final record of t hese events: the summarizing processes described, but also the often invisible but crucial pool of typists who transform the physicians' taped reports into legible, grammatically correct and understandable reports. And most medical records work in hospita ls is actually done by the equally invisible yet crucial medical records professionals - there are some fifty schools in the States training people for bachelor's degrees in Medical Records Administration (Huffman 1990, p. 53). Such professionals signific antly change aspects of the record. For example, Ann Fagot-Largeault (1989, pp. 139-140) traces the mediation process for French death certificates. She cites cases where, like doctors, coders (who take the medical history and apply it to the death certif icate) have their own favorite codes - leading to an "epidemic" of "diffuse intravascular coagulation" in the 1970s (p. 145). These mediations add up: she cites a study from 1970 in Stockholm, where it was shown that the final cause of death was wrong (as demonstrated by autopsy; itself of course a specific mediation) in 45% of cases for "probable" causes of death and in 25% of "certain" causes... (p. 145). It is estimated in France that about one in four certificates filled in by doctors need to be "re-i nterpreted"; with doctors agreeing with the coder's choice only between 80% and 95% of the time (p. 218).7 The image of the record as a more or less adequate representation of facts and events is also problematic since, a s we have seen, the record feeds into the very constitution of these facts and events. We have seen how the record contains many emergent stories about the patient's body: both its temporality and geography. Any one implementation of the record actualizes one type of story; and a set of implementations employing a standardized story format (it is easier to tell some stories than others in the record) inscribes its specific time and space in the patient's body. In this section, we shall see that the same a nalysis can be made of the organizational work done by the medical record.

          What do medical records record? The record traces what Strauss and his co-workers have called the "illness trajectory": the course of the illness, the total organization of work done over that period and the impact of that work on those involved (1985, p. 8). In this section, we will take one cut through this totality, arguing that medical records always describe past action in the context of a set of organizational arrangements . It would be meaningless to cite a temperature reading from a record out of the context of what that reading entailed organizationally - a high reading in this kind of chart at this point entrains this kind of response, and so forth. The medical record o f its nature only exists within a certain organizational context: as do all records of past action. But it at the same time feeds into the reproduction of these organizational contexts: the act of recording both enacts the organization that deals with the phenomenon and creates the past within which the phenomenon has occurred. Medical records produce a series of well-defined narrative structures; each of which reflects and produces a particular kind of social organization. We want to look at some of thes e structures to elaborate how the record is involved in the reflection and production of different body politics; how the records are involved in the very production of the descriptions under which they function.8 Here as well as above, the crucial observ ation is that the stories the record produces are not mererepresentations of the work - they are a core, constituent part of it. We will concentrate on three dimensions of the organizational life of medicine in turn: institution al (legal and insurance arrangements), work practice (the organization of the clinic) and processes of professionalization (nursing); at each point we shall be concerned with the deployment of medical records in building a "body politic".[9] Finally, we will - making the same move as in the previous section - discuss the question of multiplicity: how one infrastructural form (the medical record) can produce a range of configurations of these bodies politic.

          The body of the law

          The medical record is one of the records that produces the legal entity the citizen-in-the-state (Ewald 1986). In order for the record to work, the object of the story has to be a registered entity (the need for registration often being the need for medical attention). Here as elsewhere the primary issue is not whether the records tells a "true" story; though in this case the act of registering in a sense makes it so - it is constitutive of citizenship.H utchins (1995) reminds us of this in the case of navigation: the primary motive for the rich set of records kept by every ship is that the captain needs to be able to show, in case of accident, that everything possible was done.[10] And in a textbook on medical records, the first thing that is said about medical data entry is that these "formulate a legal document that demonstrates the following:

          * the patient has a need for service

          * the correct service is provided< p> * the service is provided in the proper manner by the proper person". (Waters and Murphy 1979, p. 54).

          In current medical work, this legal story is simultaneously the story produced for the insurance companies' interest: it legitimates the actions p erformed as well as their costs. These are particular kind of stories: the legal record should not contain inconsistencies or lacunae (Bowker 1994). It should not refer to things which "just happened". Everything has to be explicable and justifiable. If t he patient died it was despite the full operation of due process; if a test was performed it was because there was a clear-cut medical indication. The outcome of the legal record (the moral of the story) is the meta-affirmation that the medical sys tem is fully penetrated and underwritten by the legal system: that the doctors did everything that they could be expected to, and that all these affirmations were just.

          Typically, these "preferred" accounts centralize the physician's agency as core d ecision maker, and reduce the role of the nurse to the provision of primary data and the execution of the doctor's plans. Although nurses spend far more time with the patient, their presence in the medical record is limited to filling in the temperature l ist; whilst the final story emphasizes the logical work (rational reconstruction) of physicians, who are geographically much more removed from the patient. The story that is told over and over again is that patients are cured by doctors' actions; and that doctors' actions reflect a rational thought process. In producing these stories about doctors as heroes and patients as objects, the record configures chains of delegation that render the doctor the representative of the entire heterogeneous assembly com prising the hospital. S/he is performed as the head of the organizational work comprising the crafting of the patient's body, which is done according to the logic of textbook science. This "repository of information", in other words, embodies a differenti al evaluation of whose time counts, whose information matters, and whose work deserves to be mentioned and made visible (Egger and Wagner 1993; Frankenberg 1992; Star 1991).[11]

          How do these preferred accounts evolve? It would be wrong to assume that medical personnel "lie", or that there are "cover ups" going on somewhere. Preferred accounts emerge from the record only gradually, through small steps and reconstructions which by themselves are just ordinary moments in the ongoing work. The ordering and polishing of accounts is a natural process - it is a prerequisite for the smooth progression of complex, interactive work processes (Garfinkel 1967).

          One of the active elements in the production of such accoun ts is the medical record (Berg 1996). The records lead to a rational reconstruction of the past - just the same kind of rational reconstruction that has been so successfully deconstructed within science studies (see for example Latour and Woolgar 1986). T he record is instrumental in producing such stories that tell "rational", "textbook" narratives about a patient entering a hospital with complaint X, being diagnosed with Y, treated with Z, and who was discharged with or died from Q. Its very forms are st ructured towards such a sequence: the complaint should be filled in first, then the diagnosis, and then the therapy. The forms suggest the reasoning process which should have taken place: the conclusions are logically derived from the data gathered (Barrett 1988).

          In addition, the record is the very place where a public account of "what has happened" is created. It is when writing into this potential source for retrospective inspection that physicians and nurses construe narratives that align w hat actually happened with what should have happened - no matter how insignificant these occurrences may seem (Garfinkel 1967, pp. 197-207; Hunter 1991). If a patient has been hospitalized for several days, for example, nurses may omit measuring the blood pressure and just fill in yesterday's measurement in today's column (see Figure 1). Likewise, residents often ask nurses what to prescribe while they complete the order form in the regular fashion: as if it is they who have told the nurses what to do (cf. Hughes 1988). The same phenomenon occurs in and through the summaries that are continually being produced. In this process, details are omitted, and the story is simplified and retold in ways that fit the present situation at hand. This results i n an increasing stylization of past events into a standard cannon: a sign leading to a diagnosis leading to a therapy leading to an outcome. A sentence like "admitted with Hodgkin, now 8 days post-reinfusion" effectively sets the focus of the current atte ntion. Yet in doing so, it also smoothes over any diagnostic uncertainties which might have played a role, deleting the deliberations that went into the selection of this therapy and Mr. Wood's fears and anxieties.

          Finally, all this adds to the peculi ar feature of written text that once written, it tends to have a privileged position vis a vis other recollections of these events (see Clanchy, 1993 for the historical genesis of this privilege). Wherever it travels (from the audit committee to the insur ance inspector's desk to the court room), it becomes the trace to the "original event". As Smith aptly sums these issues up, accounts enter "document time" once they are written: "that crucial point at which much if not every trace of what has gone into the making of that account is obliterated and what remains is only the text which aims at being read as `what actually happened'" (1974, p. 260).12 Legal concerns are not the only relevant cross-cutting interferenc es here: the fact that these documents are also (formally and informally) used for quality control purposes and for the evaluation of colleagues and trainees yields similar consequences. And these are not new interferences: the first official, early twent ieth century attempts to standardize and improve medical record keeping in the USA did not primarily derive from the urge to improve the primary care process. Rather, they were driven by the absence of means to decide upon certification and accreditation of hospitals and individual doctors (Stevens 1989; Waters and Murphy 1979, pp. 4-5). From early on, then, the record was a crucial element in the generation and reproduction of this complex body politic.

          The body politic of the clinic

          Analogous to its involvement in the production of a patient's body, the medical record is involved in the production and reproduction of the body politic of the clinic. First, it plays a core role in the ongoing reproduction of the hospi tal's temporal structure. Triggering sequenced measurements produces both a graph in a record and circumscribed, cyclical work tasks for nurses. The unstructured progress notes forms require physicians to regularly summarize the current state of af fairs, and thus create an organizing structure for their work days. Without the record's core role as a distributing and collecting device, the complex temporal organization of this body politic could not exist. The record lists which actions are undertak en and which actions still to undertake, when which requests or interventions will yield outcomes, and who is responsible for the completion of such a task. The flow sheet's medication and infusion lists, for example, function for both nurses and physicia ns as a list of what has been administered, and as a means to see at a glance how some crucial measurements have been affected by these interventions. In addition, they also organize the work: the ward secretary can see what type of medication might need to be ordered, and nurses can see when to give what, and can subsequently add the infusion dosages for the calculation of the fluid balance. The record, in other words, affords a highly complex, temporally distributed division of labor.

          And it thereby also maps a complex geography of the hospital. The record discussed here is a so-called "source-oriented" record: by far the most common means of record keeping. It entails ordering the different sections in the record according to the different institut ional sources the data are derived from: nursing notes, doctor's notes, laboratory results, bacteriology reports, consultations of different specialties, and so forth.[13] Again, this does not yield a map which merely "rep resents": it feeds into the reproduction of these geographies. Having their own forms and sections within the record, the nurses are separated from the physicians - both physically and intellectually. Notwithstanding other alliances or differences, the re cord clearly differentiates the bacteriologists vis-à-vis the radiologists, and sets them both apart, as two isolated groups, from the specialists in charge.

          The record, moreover, feeds into the reproduction of the hierarchies between these geo graphical sections. The treating physicians are on top: they write the central pages of the record, defining the outlines of the illness trajectory and incorporating and summarizing information coming from all other sources. They have unlimited access to all the sections, but only write there when the overall course is set. And the other sources are not equal either. Specialists called in for consultation will usually fill in their own, yellow consultation forms, but they are allowed to write their findin gs in the progress notes. No other discipline is allowed to do this. In addition, the hierarchy is evident in the way just how much a form is pre-structured. An unstructured forms leaves it to a named author to create order through the production of a nar rative, and it does not exert demands on the content of these narratives by listing pre-formatted labels - which may remain visibly empty. The physicians' progress notes are totally unstructured, and the consultancy forms have a few, large sections with l abels such as "findings, conclusions and advice" - all designed for free text. The radiographic reports are more brief, but the radiologist is free to describe the image as s/he pleases, as long as it is followed by a conclusion which answers the treating physician's request. The bacteriologist, however, is limited to saying "no growth", "a little growth" or "much growth", and the laboratory workers and nurses can only produce the numbers which end up in the pre-formatted graphs and columns. As Hunter phr ases it, "the hierarchy of disciplines is very alive in the chart" (1991, p. 88).[14]

          The record not only plays a generative role in the organization of the clinic: it also legitimates this organization's design. As we have pointed at above, the record tells the story of the work this body politic performs. One way of structuring the record has been to make every record a description of a working organization operating at peak efficiency. Thus record keepers are advised that the purpose of the record is to tell a story that begins with admission, describes an every more precise diagnostic process, goes on to treatment and ends with discharge - or, even more "complete" from the medical record keepers' perspective , death (Waters and Murphy 1979, p. 97). In this case the outcome of the record is again the meta-affirmation that allopathic medicine works - and, ipso facto, that the sites where medicine is performed work. Every record - as individual as may be - is, t hrough its very form, testament to this assertion.

          The professional nursing body

          The fact that the chart maps - and thus feeds into - the hierarchy of disciplines in medical work has not gone unnoticed by these disciplines themse lves. Turning to our third example, we encounter a professional group striving to enhance its status through attempting to change these record-keeping practices. Here, the constitution of a body politic in and through the medical record has become a site of active contestation. The record-keeping practices at stake concern the nursing classification scheme designed for incorporation within hospital information systems. The scheme, NIC (Nursing Interventions Classification) divides up all of nursing work i nto a series of well-defined actions which are then to be coded (allowing nursing administrators to track the work of their staff). The group at Iowa University School of Nursing published a first edition of their system in 1992, and a revised and expande d version came out in 1996 (Cohen et al. 1991; McCloskey and Bulechek 1996).

          NIC itself is a fascinating system. Some categories, like "Bleeding Reduction - Nasal - 4024", are on the surface relatively obvious and codable into discrete units of work practice to be carried out on specific occasions. But what about the equally important categories of hope installation and humor? "Hope Installation - 5310" includes the subcategory of "Avoid masking the truth". This is not so much something that nurses d o on a regular basis, as something that they should not do constantly. It also includes: "Help the patient expand spiritual self". Here the contribution that the nurse is making is to an implicit lifelong program of spiritual development. With respect to" Humor - 5320", the very definition of the category suggests the operation of a paradigm shift: "Facilitating the patient to perceive, appreciate, and express what is funny, amusing, or ludicrous in order to establish relationships"; and it is unclear how this could ever be attached to a time line: it is something the nurse should always do while doing other things. Further, contained within the nursing classification is an anatomy of what it is to be humorous, and a theory of what humor does. The recommen ded procedures break humor down into subelements. One should determine the types of humor appreciated by the patient; determine the patient's typical response to humor (e.g. laughter or smiles); select humorous materials that create moderate arousal for t he individual (for example picture a forbidding authority figure dressed only in underwear); encourage silliness and playfulness and so on to make a total of fifteen sub-activities: any one of which might be scientifically relevant. A feature traditionall y attached to the personality of the nurse (being a cheerful and supportive person) is now attached through the classification to the job description as an intervention which can be accounted for. These process categories fit into the same numbering schem e as more punctual categories such as "Bottle Feeding - 1052" or "Suturing - 3620" - once in the record, there is no distinction between process and single interventions.

          The Iowa group (the kernel of whom are teachers of nursing administration) made essentially three arguments for the creation of a nursing classification, initially to nursing informaticians and thence to the hospital information systems community. First, it was argued that without a standard language to describe nursing interventions , there would be no way of producing a scientific body of knowledge about nursing. NIC in theory would be articulated with two other classification systems: NOC (the nursing sensitive patient outcomes classification scheme) and NANDA (the nursing diagnosi s scheme). The three could work together: one could perform studies over a set of hospitals employing the three schemes in order to check if a given category of patient responded well to a given category of nursing intervention. Rather than this comparati ve work being done anecdotally as in the past through the accumulation of experience, it could be done scientifically through the conduct of experiments. The second argument for classifying nursing interventions was that it was a key strategy for defendin g the professional autonomy of nursing. The Iowa nurses are very aware of the literature on professionalization - notably Schön (1983) - and are aware of the force of having an accepted body of scientific knowledge as their domain. The third argument was that nursing, alongside other medical professions, was moving into the new world of computers. As the representational medium changed, it was important to be able to talk about nursing in a language that computers could understand - else nursing work would not be represented at all in the future, and would risk being even further marginalized than it was at present.

          The intervention in turn can then be linked by nursing academics to nursing outcomes in a series of clinical studies. The outcome o f the records will be the integration of nursing work into the medical establishment . The expressed fear of professional leaders is that without representation in the information system, the profession of nursing will become ever more marginalized. So th e record is producing a patient - as we stressed in the first part of this work - but it is also producing an organizational result. For the nurses, the record constitutes, then, building blocks for a body of knowledge, which in turn loops back int o the hierarchy. In current storage practices, nurses' notes are frequently separated from doctors' notes, and are often destroyed immediately the patient leaves the hospital (Huffman 1990). A professionalized form of reporting would create a continuing r ecord of their activities - which could feed into the discipline of nursing science and be used to unseat the current hierarchy.15 Nurses who met to develop a "nursing minimum data set" (modeled on the successful medical minimum data set) to be produced by all hospitals in their medical records argued that: "the most frequently focused upon language is that developed by medicine and the numerous natural or physical sciences that contribute to medicine's scientific knowled ge base. This language is focused upon most frequently by nurses, because of medicine's long history of dominance in nursing, its relative specificity about physiological phenomena, its apparent measurability, and its familiarity. It is also valued for it s social status as a scientific language, used by a politically powerful professional group, physicians" (Kritek, 1988, p. 25). It is not enough for nurses to accurately record what they do - they must record it in a language acceptable to physicians and hospital administrators, and productive of a new, higher status. For this reason, "leech treatment" was not initially recognized as a NIC category - even though it was indeed a treatment and was indeed carried out by a number of nurses (Neumann, forthcomi ng). The nursing record had to be seen to be "professional" in order for it to do its work. That work is, in part, the modeling of nursing on the example of medicine; itself modeled (with the help of the medical record, as we saw above) in the image of pu re science.

          Multiplicity: Interoperability and Infrastructure

          It is turning into a complex thing, our medical record. It produces multiple bodies and underwrites a number of ever-shifting organizational configurations. For the nurses, the medical record is a crucial tool in their organizational struggle for status at the same time as it is a record of what happened to the patient, a legal document, and a tool which organizes their daily work. It must be able to do all these jobs at once - and for all those working with it. In the multiplicity of context the record is drawn into (and helps to constitute), materially different objects are at play: most physicians never get to see the coding forms used by t he coders; most record administration coders know little of the elaborate nursing coding schemes; most nurses primarily draw on the nursing record during their work, and so forth.

          As we have seen, medical records are involved in the production of mult iple bodies politic. Consider the legal reading of the record that we have discussed. In this reading, the legal narrative is the chief concern, and some features of the record are activated as relevant for the story to be properly told. It may not matter for the nursing record whether or not a given temperature reading was taken, but this may make a large difference legally (as indicative of contributory negligence). So medical records do not have to do a single job, they have to be configured in such a way that a set of different organizations are serviced by them. To borrow the vocabulary of information science they have to serve as infrastructure (Star and Ruhleder, 1996) to a set of organizations. They have to do this in such a way that these organizations can use the medical records in conjunction with other records that they generate. Thus for example you want the medical record to store information about ethnicity in a way which makes sense to epidemiologists and to public health off icials - so that in turn these groups can deploy the records in conjunction with other records that they produce to track diseases. Again to borrow the vocabulary of information science, we can say that the medical record needs to guarantee interoperab ility. In this section, we shall see that fashioning of medical records into infrastructural tools guaranteeing interoperability involves a series of organizational alignments of great political and ethical importance. Just as for the patients in thep revious section above, there is a real multiplicity of organizations produced by the records. Just as for the patients, this conflicts with a rhetoric of singularity (the convergence of traces on a single ideal patient temporality and geography and on a single ideal organizational context).

          A recent history of medical informatics notes that: "... by the 1980s, the provision of health care in the United States had three dimensions of integration for patient care information: local integration of information in hospitals or in physicians' offices; vertical integration of information between affiliated hospitals and medical offices; and horizontal integration among associated hospitals, clinics, and community health and welfar e support groups" (Collen 1995, p. 82). Each of these forms of integration (which cannot be as neatly separated off from each other as Collen implies) entail a different form of interoperability. The record has to be aligned with the needs of the organiza tions associated with them. This involves, for one thing, matching of the different timetables and geographies the records produce. Roth (1979) discusses the difficulty of a tuberculosis patient traveling to a new sanatorium without there being an agreed- upon language for describing the patient's condition; the NIC nurses despair of ever producing nursing knowledge without comparability (Timmermans et al. forthcoming). But this integration also extends to relationships with health care providers, public h ealth officials, the World Health Organization (which maintains the International Classification of Diseases), the legal profession and so forth: as we have pointed out, the record is increasingly seen as a vital source for these "third parties" as well. With the development of computer based records, a further dimension is introduced by the needs of the programmers. Thus at a meeting discussing the development of the NIC classification, for example, nursing informaticians protested the existence of "nest ed classifications" within NIC - that is to say an intervention containing another within it. They argued that this made for difficulties in programming the appropriate screens on the electronic nursing record.

          With the extension of the record's reach through different times, spaces and into novel domains, much work is done on the record to ensure that these multiple configurations continue to be possible. Much of this work seems "purely technical"; appears far removed from organizational or po litical issues. Consider the sets of committees that have been involved with standardizing medical informatics:

          The American Standard for Testing Materials (ASTM) subcommittee E31.12 subcommittee framed nomenclatures and medical records. In 1988, A STM sub-co E31.12 published standards for patient discharge and transfer data. Health Level Seven (HL7) an organization made up of vendors, hospitals and consultants was organized in 1987 to develop interface standards for transmitting data between applic ations that used different computers within HISs. The message content of HL7 was to conform to the International Standards Organization (ISO) standards for the applications level 7 of the Open Systems Interconnection (OSI) model; the HL7 standard used the same message syntax, the same data types and some of the same segment definitions as ASTM. The Medical Data Interchange (MEDIX) P1157 committee of the IEEE, formed at the SCAMC 1987 was also developing a set of standards, based on the ISO application-lev el standards, for the transferring of clinical data over large networks from mixed sources, such as from a clinical lab and a pharmacy, for both intra- and interhospital communications. (Collen 1995, p. 109)

          Each of these interconnections was the product of a continuing effort to maintain and stabilize an infrastructure. Janet Abbate (forthcoming) has demonstrated how the OSI standards are themselves the fragile outcome of a series of negotiations between a computing industry wanting to retain the proprietary edge of a closed system, government regulators, public pressure groups and so forth. As you spiral down into the infrastructure you get beyond a particular database in a hospital to the technical standards (OSI) underwriting that database; an d as you follow OSI in development you witness a titanic struggle between company interest and the public good being written in code. This is typical for infrastructure: the "harder" you go, the closer you get to organizational and political concer ns (see also Hanseth et al. 1996, and see Carlson 1991 who makes this point brilliantly for innovation in the electrical industry).

          All these standardization efforts do not do away with the continuing need to balance the needs exerted within the diffe rent networks that interconnect through the infrastructure (Bowker and Star 1994; Timmermans and Berg 1997). The ever unattainable ideal of pure, quantifiable knowledge remains a driving organizing force - even when it is patently absurd. Consider the cas e of fluid balance - this is precisely measured to the milliliter in intensive care units, and then a fudge factor out of all proportion to the data is added in for sweating (which cannot be so simply measured). Why is this ideal so successful in organizi ng infrastructures and guaranteeing interoperability? One answer is that it generates alignments. If we are all striving in the same direction then, whether or not the direction itself is a good one, our sets of fudge factors and uncertainties will tend t o align with each other. An analogy here would be to the story of the emperor's new clothes. If everyone seeks to describe the world in ways acceptable to the others around them, then the emperor will be seen to well dressed - whether or not he is - if on e person is out of alignment, the whole may crumble. This, we would argue, is one reason why formal records entail such deep organizational discipline.

          The nurses producing NIC believe deeply that the current system of accounting inscribed in the hosp ital information systems is wrong-headed. Thus in a June 1995 meeting, co-leader of the NIC team JoAnne McCloskey mentioned the rise of activity-based accounting, meaning that individual activities had to be costed. She went on to effectively deconstruct this in much the same vein as Dick Boland (1996) has done from within the field of Management and Information Science. Nurse practitioners, she noted, see patients for longer than doctors - since they are teaching, and preventing re-occurrence etc. Until you pull out productivity against performance one cannot see this. This was also a problem, she noted, for women physicians (who see patients longer, and need to see them less as a result - so on a productivity measure it looks like they are working more slowly). However the productivity measures being used were based on the factory measure of how many widgets an hour you produce. Crucially, she went on to say that: "Nursing needs to speak the language used elsewhere. This is why we need to use a restrict ed definition of productivity". In our terms, she is pointing to the need for interoperability of the nursing records with other medical records. And indeed in the NIC email list she soon floated an attempt to pin times to interventions (difficult in the case of emotional support and active listening; but not obvious even for bowel incontinence care...). And indeed in order to speak the language of accountancy well enough to justify a new nursing slot, the organization had to be recorded as having worked in a particularly well defined set of temporal slots. Here is a sample argument that uses the assignation of time slots to NIC interventions to justify the creation of a new nursing position in a given hospital. This argment was presented to nursing admin istrators and nursing information specialists at the same meeting:

          Position justification

          Teaching/ discussion/ process time

          individ. teaching 8

          prescribed medic. 15

          prescribed procedure 10

          treatment 30

          63 mi nutes by 620 new patients per year = 39,060 minutes or 651 hours.

          651 hours divided by 2000 = .3 FTE. (FTE being "full time equivalent").

          What is happening here is that a key player in the production and maintenance of the medical record - the nurse - does not believe in the validity of the traces that they are producing of their own work in the record; and yet they have to both write records as if such traces were true and write them well enough so that nursing should be seen to be a key playe r in the efficient scientific treatment of patients (rather than a prime candidate for cost cutting). What a strange irony! It is, we would argue, an extreme example of a very common phenomenon. Consider the case discussed at length by Young (1995) and Kirk and Kutchins (1992) of psychoanalysts who in order to receive reimbursement for this procedures need to couch them in a biomedical language that is anathema to them, but is the lingua franca of the medical insurance companies. The nurses have po tential allies - there are radical accountants, radical doctors, critical medical informaticians: but the need to align is immediate; the risks of disappearing forever from the medical record too great to make the risk of challenging the common language w orthwhile. Local use of the record underwrites organizational multiplicity (nurses will continue to be concerned with process; psychoanalysts with the id) at the same time as the constrained form of the record evokes a single large medical organization wi th guaranteed interoperability of its "departments" (nursing, epidemiology, public health, medical insurance and so forth) - just as the patient's body is at once conjured into a single time and space while remaining locally multiple.

          Infrastructures underwrite alignment of the organizations that they are infrastructural to; and a key feature of this alignment in the case of the medical record is the evanescent ideal of the scientific operation of medicine. As Latour (1988) has argued in an analysis of Pasteur, much of the power of science comes from such alignment. This alignment is not ever completed or entirely successful: however large techno-economic networks cannot exist without it. The medical record produces organizations which can configure their own records such that a privileged set of stories (due process; medical science; nursing efficiency) can be told again and again.

          Conclusion

          The medical record, it has become clear, is a complex object, and a fascinating a nd crucial focus for sociological research. It feeds into that what it merely seems to represent; it is a crucial element in the constitution of the patient's body (inscribed in a set of spaces and times) and of the hospital as a formal organization (equa lly inscribed in a set of spaces and times). It is a site where both the constitution of the patient and the constitution of the hospital worker is at work: the bodies and bodies politic they inhabit are reinscribed into novel places, broken apart in new ways, and configured into spaces and times out of their control. Seen in this light, as a site where multiple stories about patients and about organizations are at stake (including the interoperability between these stories), the medical record becomes hi ghly relevant both analytically and politically. There is a strong current "push", for example, towards the implementation of the electronic medical record - and when the record is seen as an innocuous storage device, the appropriate implementation is oft en seen as a "technical problem", or as a matter of finding the "appropriate interface". When it is acknowledged that the medical record is interwoven with the structure of medical work in fundamental ways, that different medical record systems embody dif ferent notions of how work is organized, different modes of configuring patient bodies, and so forth, we are in a position to better understand and intervene upon the issues that are at stake. We are reminded of Latour's (1987) observation that science is bureaucracy or Serres' equation of law and science (1987): the site of the creation of organization and body is the bureaucratic work of creating inscriptions. To end where we began: this site thereby becomes a key site for the arrogation and exercise of power.

          Notes

          Acknowledgements

          Marc Berg would like to thank the Dutch Association for Scientific Research (NWO) for making this research possible. We thank the two anonymous referees, Emilie Gomart, Leigh Star an d the members of the Xerox PARC Work, Practice and Technology group for their valuable comments.

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          < /body> http://www.ics.uci.edu/~gbowker/actnet.html Actor Network Theory and Classification

          How things (actor-net)work: Classification, magic and the ubiquity of standards

          Geoffrey C. Bowker

          Susan Leigh Star

          Graduate School of Library and Information Science

          University of Illinois at Urbana-Champaign

          November 18, 1996

          to appear in a special issue of Philosophia

          INTRODUCTION

          "A classified and hierarchically ordered set of pluralities, of variants, has none of the sting of the miscellaneous and uncoordinated plurals of our actual world." (Dewey, 1989: 49)

          "We do many things today that a few hundred years ago would have looked like magic". We all know versions of this banal assertion - we've probably all made it ourselves at some point or another. And if we don't understand a given technology it looks like magic: we are perpetually surprised by the mellifluous tones read off our favorite CDs by (we believe) a laser. Star (1995b) notes that even engineers black box and think of technology `as if by magic' in their everyday practical dealings with machines. A common description of a good waiter or butler (one thinks of Jeeves in the Wodehouse stories) is that she clears a table `as if by magic'. Are these two kinds of magic or one or none?

          The following paper is an attempt to answer this question, which can be posed more prosaically as:

          * What work do classifications and standards do? We want to look at what goes into making things work like magic: making them fit together so that we can buy a radio built by someone we have never met in Japan, plug it into a wall in Champaign and hear the world news from the BBC.

          * Who does that work? We want to explore the fact that all this magic involves much work: there is a lot of hard labor in effortless ease[1]. Such invisible work is often not only underpaid - it is severely underrepresented in theoretical literature (Star and Strauss, in press). We will discuss where all the `missing work' that makes things look magical goes.

          * What happens to the cases that don't fit? We want to draw attention to cases that don't fit easily into our created world of standards and classifications: the left handers in the world of right-handed magic, chronic disease sufferers in the world of allopathic acute medicine, the onion-hater in MacDonalds (Star, 1991b) and so forth.

          These are issues of great epistemological, political and ethical import. It is easy to get lost in Baudrillard's (1990) cool memories of simulacra. The hype of our times is that we don't need to think about the work any more: the real issues are scientific and technological - in artificial life, thinking machines, nanotechnology, genetic manipulation... Clearly each of these are important. However, we endeavor to demonstrate that there is rather more at stake - epistemologically, politically and ethically - in the day to day work of building classification system and producing and maintaining standards than in these philosophical high-fliers. The pyrotechnics may hold our fascinated gaze; they cannot provide any path to answering our questions.

          Through looking at classification systems and standards, we will move towards an understanding of the stuff which makes up the networks of actor network theory. Latour, Callon and others within the actor-network approach have developed an array of concepts in order to describe the development and operation of technoscience. Their valuable concepts include: regimes of delegation; the centrality of mediation; and the position that nature and society are not causes but consequences of human scientific and technical work. The position that a fact may be seen as a consequence, and not as an antecedent, is axiomatic to the American pragmatist approach as well, particularly in the work of John Dewey (e.g., Dewey, 1929). As he noted in his Experience and Nature:

          For things are objects to be treated, used, acted upon and with, enjoyed and endured, even more than things to be known. They are things HAD before they are things cognized....the isolation of traits characteristic of objects known, and then defined as the sole ultimate realities, accounts for the denial to nature of the characters which make things lovable and contemptible, beautiful and ugly, adorable and awful. It accounts for the belief that nature is an indifferent, dead mechanism; it explains why characteristics that are the valuable and valued traits of objects in actual experience are thought to creative a fundamentally troublesome philosophical problem. (1989 [1925]: p. 21)

          We draw attention here to the places where the work gets done of assuring that delegation and mediation will work: to the places where human and non-human are constructed to be operationally and analytically equivalent. And following both Dewey and Latour, we also question the indifference -- of nature, and of machines. So doing, we explore the political and ethical dimensions of actor-network theory, restoring the interlinked and webbed relationships between people, things, and infrastructure.

          TWO DEFINITIONS

          We will take a `classification' to be a spatial, temporal or spatio-temporal segmentation of the world. A `classification system' is a set of boxes, metaphorical or not, into which things can be put in order to then do some kind of work - bureaucratic or knowledge production. We will not demand of a classification system that it has properties such as:

          * the operation of consistent classificatory principles (for example being solely a genetic classification (Tort, 1989) classifying things by their origin);

          * mutual exclusivity of categories;

          * completeness (total coverage of the world being described).

          No working classification system that we have looked at meets these `simple' requirements and we doubt that any ever could (Desrosières and Thevenot, 1988).

          For example, consider the International Classification of Diseases, which will be one of our major examples throughout this paper. The full title of the current (10th) edition of the ICD, is: "ICD-10 - International Statistical Classification of Diseases and Related Health Problems; Tenth Revision". Note that it is designated a `statistical' classification. By this is meant that only diseases which are statistically significant are to be entered in (it is not an attempt to classify all disease). It calls itself a `classification', even though many have said that it is a `nomenclature' since it has no single classificatory principle (it has at least four; which are not mutually exclusive (Bowker and Star, 1994). In many cases it represents a compromise between conflicting schemes: "The terms used in categories C82-C85 for non-Hodgkin's lymphomas are those of the Working Formulation, which attempted to find common ground among several major classification systems. The terms used in these schemes are not given in the Tabular List but appear in the Alphabetical Index; exact equivalence with the terms appearing in the Tabular List is not always possible". (ICD-10, 1, 215). However, it presents itself clearly as a classification scheme and not a nomenclature. Since 1970, there has been an effort underway by the World Health Organization to build a distinct International Nomenclature of Diseases, whose main purpose will be to provide: "a single recommended name for every disease entity" (ICD-10, 1, 25). The point here is that we want to take a broad enough definition so that anything that is consistently called a classification system can be included. If we took a purist view, the ICD would be a nomenclature and who knows what the IND would be. With a broad definition we can look at the work that is involved in building and maintaining a family of entities that people call classification systems - rather than attempt the Herculean, Sisyphian task of purifying the (un)stable systems in place. Howard Becker makes the point here: "Epistemology has been a ... negative discipline, mostly devoted to saying what you shouldn't do if you want your activity to merit the title of science, and to keeping unworthy pretenders from successfully appropriating it. The sociology of science, the empirical descendant of epistemology, gives up trying to decide what should and shouldn't count as science, and tells what people who claim to be doing science do..." (1996: 54-55).

          We will take a `standard' to be any set of agreed-upon rules for the production of (textual or material) objects. There are a number of histories of standards which point to the development and maintenance of standards as being a key to industrial production. Thus, as David Turnbull points out, it was possible to build a cathedral like Chartres without standard representations (blueprints) and standard building materials (regular sizes for stones, tools etc.) (1993). However it is not possible to build a modern housing development without them: too much needs to come together - electricity, gas, sewer, timber sizes, screws, nails and so on. The control of standards is a central, often underanalyzed (but see the work of Paul David - for example David and Rothwell, 1994 - for a rich treatment) feature of economic life. They are key to knowledge production as well - Latour (1987) speculates that far more economic resources are spent creating and maintaining standards than in producing `pure' science. Key dimensions of standards are:

          * They are often deployed in the context of making things work together - computer protocols for Internet communication involve a cascade of standards (cf. Abbate and Kahin, 1995) which need to work together well in order for the average user to gain seamless access to the web of information. There are standards for the components to link from your computer to the phone network, for coding and decoding binary streams as sound, for sending messages from one network to another, for attaching documents to messages and so forth;

          * They are often enforced by legal bodies - be these professional organizations; manufacturers' organizations or the State. We can say tomorrow that volapük (a universal language that boasted some 23 journals in 1889[2]) or its successor Esperanto shall henceforth be the standard language for international diplomacy; without a mechanism of enforcement we shall probably fail.

          * There is no natural law that the best (technically superior) standard shall win - the QWERTY keyboard, Lotus 123, DOS and VHS are often cited in this context. Standards have significant inertia, and can be very difficult to change.

          Classifications and standards are two sides of the same coin. The distinction between them (as we are defining them) is that classifications are containers for the descriptions of events - they are an aspect of organizational, social and personal memory - whereas standards are procedures for how to do things - they are an aspect of acting in the world. Every successful standard imposes a classification system.

          UNDERSTANDING CLASSIFYING AND STANDARDIZING

          This paper will offer four major themes for understanding classifying, standardizing (and the related processes of formalizing) and their politics and histories. Each theme operates as a gestalt switch - it comes in the form of an infrastructural inversion (Bowker, 1994). Inverting our commonsense notion of infrastructure means taking what have often been seen as behind the scenes, boring, background processes to the real work of politics and knowledge production[3] and bringing their contribution to the foreground. The first two, ubiquity and material texture, speak to the space of actor-networks; the second two, the indeterminate past and the practical politics, speak to their time. Taken together, they sketch out features of the historically creation of the infrastructure which (ever partially, ever incompletely) orders the world in such a way that actor-network theory becomes a reasonable description.

          The first major theme is seeing the ubiquity of classifying and standardizing. Classification schemes and standards literally saturate the worlds we live in. This saturation is furthermore intertwined, or webbed together. While it is possible to pull out a single classification scheme or standard for reference purposes, in reality none of them stand alone. So a subproperty of ubiquity is interdependence, if not smooth integration.

          The second major theme is to see classifications and standards as materially textured. Under the sway of cognitivism, it is easy to see classifications as properties of mind and standards as ideal numbers or settings. But both have material force in the world, and are built into and embedded in every feature of the built environment (and many of the borderlands, such as with engineered genetic organisms). When we think of classifications and standards as material, we can afford ourselves of what we know about material structures, such as structural integrity, enclosures and confinements, permeability, and durability, among many others. We see people doing this all the time in describing organizational settings, and a common way to hear people's experience of this materiality is through metaphors. So the generation of metaphors is closely linked with the shift to texture.

          The third major theme is to see the past as indeterminate[4]. This is not a new idea to historiography, but is important in understanding the evolution of ubiquitous classification/standardization and the multiple voices that are represented in any scheme. No one classification orders reality for everyone -- e.g. the red light-green light-yellow light categories don't work for blind people or those who are red-green color blind. In looking to classification schemes as ways of ordering the past, it is easy to forget those who are overlooked in this way. Thus, the indeterminacy of the past implies recovering multi-vocality; it also means understanding how standard narratives that seem universal have been constructed (Star, 1991a).

          The fourth major theme is uncovering the practical politics of classifying and standardizing. There are two aspects of these politics: arriving at categories and standards, and, in the process, deciding what will be visible within the system (and of course what will thus then be invisible). The negotiated nature of standards and classifications follows from indeterminacy and multiplicity that whatever appears as universal or, indeed, standard, is the result of negotiations or conflict. How do these negotiations take place? Who determines the final outcome in preparing a formal classification? Visibility issues arise as one decides where to make the cuts in the system, for example, down to what level of detail one specifies a description of work, of an illness, of a setting. Because there are always advantages and disadvantages to being visible, this becomes crucial in the workability of the schema.

          Ubiquity

          In the built world we inhabit, thousands and thousands of standards are used everywhere, from setting up the plumbing in a house to assembling a car engine to transferring a file from one computer to another. Consider the canonically simple act of writing a letter longhand, putting it in an envelope and mailing it. There are standards for (inter alia): paper size, the distance that lines are apart if it is lined paper, envelope size, the glue on the envelope, the size of stamps, their glue, the ink in the pen that you wrote with, the sharpness of its nib, the composition of the paper (which in turn can be broken down to the nature of the watermark, if any; the degree of recycled material used in its production, the definition of what counts as recycling). And so forth.

          Similarly, in any bureaucracy, classifications abound -- consider the simple but increasingly common classifications that are used when you dial an airline for information now ("if you are traveling domestically, press 1"; "if you want information about flight arrivals and departures, press 2...."). And once the airline has hold of you, you are classified by them as a frequent flyer (normal, gold or platinum); corporate or individual; tourist or business class; short haul or long haul (different fare rates and scheduling applies); irate or not (different hand-offs to the supervisor when you complain).

          A systems approach would see the proliferation of both standards and classifications as a matter of integration -- almost like a gigantic web of interoperability. Yet the sheer density of these phenomena go beyond questions of interoperability. They are layered, tangled, textured; they interact to form an ecology as well as a flat set of compatibilities. There ARE spaces between (unclassified, non-standard areas), of course, and these are equally important to the analysis. A question: it seems that increasingly these spaces are marked as unclassified and non-standard. How does that change their qualities?

          It is a struggle to step back from this complexity and think about the issue of ubiquity broadly, rather than try to trace the myriad connections in any one case. We need concepts for understanding movements, textures, shifts that will grasp larger patterns in this. For instance, the distribution of residual categories ("not elsewhere classified" or "other"), is one such concept. "Others" are everywhere. The analysis of any one instance of a residual category might yield information about biases or what is valued in any given circumstance; seeing that residual categories are ubiquitous offers a much more general sweep on the categorizing tendencies of most modern cultures. Another class of concepts which are found ubiquitously, and which speak to the general pervasiveness of standards and classification schemes, concern those which describe tangles or mismatches between subsystems. For instance, what Strauss calls a "cumulative mess trajectory" is a useful notion (Strauss, et al., 1985). In medicine, this occurs when one has an illness, is given a medicine to cure the illness, but incurs a serious side effect, which then needs to be treated with another medicine, etc. If the trajectory becomes so tangled that you can't return and the interactions multiply, "cumulative mess" results. We see this phenomenon in the interaction of categories and standards all the time -- ecological examples are particularly rich places to look.

          Texturing Classification and Standardization

          How do we "see" this densely saturated classified world? We are commonly used to casually black-boxing this behind-the-scenes machinery, even to the point, as we noted above, of ascribing a casual magic to it. All classification and standardization schemes are a mixture of physical entities such as paper forms, plugs, or software instructions encoded in silicon and conventional arrangements such as speed and rhythm, dimension, and how specifications are implemented. Perhaps because of this mixture, the web of intertwined schemes can be difficult to "see." In general, the trick is to question every apparently natural easiness in the world around us and look for the work involved in making it easy. Within a project or on a desktop, the seeing consists in seamlessly moving between the physical and the conventional. So when a computer programmer writes some lines of C code, she moves within conventional constraints and makes innovations based on them; at the same time, she strikes plastic keys, shifts notes around on a desktop, and consults manuals for various standards and other information. If we were to try to list out all the classifications and standards involved in writing a program, the list could run to pages. Classifications include types of objects, types of hardware, matches between requirements categories and code categories, and meta-categories such as the goodness of fit of the piece of code with the larger system under development. Standards range from the precise integration of the underlying hardware to the 60Hz power coming out of the wall through a standard size plug.

          Merely reducing the description to the physical aspect such as the plugs does not get us anywhere interesting in terms of the actual mixture of physical and conventional. A good operations researcher could describe how and whether things would work together, often purposefully blurring the physical/conventional boundaries in making the analysis. But what is missing there is a sense of the landscape of work as experienced by those within it. It gives no sense of something as important as the texture of an organization: it is smooth or rough? Bare or knotty? What is needed is a sense of the topography of all of the arrangements -- are they colliding? co-extensive? gappy? orthogonal? One way to begin to get at these questions is to begin to take quite literally the kinds of metaphors that people use when describing their experience of organizations, bureaucracies, and information systems (Star, in press). So, for example, when someone says something simple like "things are running smoothly," the smoothness is descriptive of an array of articulations of people, things, work and standards. When someone says, "I feel as though the whole project is moving through thick molasses," it points to the opposite experience. These are not merely poetic expressions, although at some level they are that, too. As Schon pointed out in his seminal book, Displacement of Concepts, a metaphor is an import, meant to illuminate aspects of a current situation via juxtaposition (1963). It is also a rich and often unmined source of knowledge about people's experience of the densely classified world.

          The Indeterminacy of the Past

          There is no way of ever getting access to the past except through classification systems of one sort or another - formal or informal, hierarchical or not ... . Take the unproblematic statement: "In 1640, the English Revolution occurred; this led to a twenty year period in which the English had no monarchy". The classifications involved here include:

          * The current segmentation of time into days, months and years. Accounts of the English revolution generally use the Gregorian calendar, which was adopted some hundred years later - so causing translation problems with contemporary documents;

          * The classification of `peoples' into English, Irish, Scots, French and so on. These designations were by no means so clear at the time - the whole discourse of national genius really only arose in the nineteenth century;

          * The classification of events into revolutions, reforms, revolts, rebellions and so forth (cf. Furet, 1978 on thinking the French revolution). There really was no concept of `revolution' at the time; our current conception is marked by the historiographical work of Karl Marx.

          * And then, what do we classify as being a `monarchy'? There is a strong historiographical tradition which says that Oliver Cromwell was a monarch - he walked, talked and acted like one after all. Under this view, there is no hiatus at all in this English institution; rather a usurper took the throne.

          There are two major schools of thought with respect to using classification systems on the past - one saying that we should only use classifications available to actors at the time (authors in this tradition warn against the dangers of anachronism - Hacking (1995) on child abuse is a sophisticated version) and the other that we should use the real classifications that progress in the arts and sciences has uncovered (typically history informed by current sociology will take this path - for example Tort's (1989) work on `genetic' classification systems, which were not so called at the time, but which are of vital interest to the Foucaldian problematic). Whichever we choose, it is clear that we should always understand classification systems according to the work that they are doing -the network within which they are embedded.

          When we ask historical questions about the deeply and heterogeneously structured space of classification systems and standards, we are dealing with a 4-dimensional archaeology - some of the structures it uncovers are stable, some in motion; some evolving, some decaying. An institutional memory, about say, an epidemic, can be held simultaneously and with internal contradictions (sometimes piecemeal or distributed and sometimes with entirely different stories at different locations) across [a given institutional] space.

          In the case of AIDS, for example, there are shifting classifications over the last 20 years, including the invention of the category in the first place. There is then a backwards look at cases which might have been AIDS before we had the category (a problematic gaze to be sure, as Bruno Latour (forthcoming) has written about tuberculosis; see also Star and Bowker, 1997). There are the stories about collecting information about a shameful disease, and a wealth of personal narratives about living with it. There is a public health story and a virology story, which use different category systems. There are the standardized forms of insurance companies and the categories and standards of the census bureau; when an attempt was made to combine them in the 80s to disenfranchise young men living in San Francisco from getting health insurance, the resultant political challenge stopped the combination of this data from being so used. At the same time, the blood banks refused for years to employ HIV screening, thus refusing the admission of another category to their blood labeling -- as Shilts (1987) tells us, with many casualties as a result.

          Practical Politics

          Someone, somewhere, often a body of people in the proverbial gray suits and smoke-filled rooms, must decide and argue over the minutiae of classifying and standardizing. The negotiations themselves form the basis for a fascinating practical ontology -- our favorite example is when is someone really alive? Is it breathing, attempts at breathing, movement....? And how long must each of those last? Whose voice will determine the outcome is sometimes an exercise of pure power: we, the holders of Western medicine and of colonialism, will decide what a disease is, and simply obviate systems such as acupuncture or Ayruvedic medicine. Sometimes the negotiations are more subtle, involving questions such as the disparate viewpoints of an immunologist and a surgeon, or a public health official (interested in even ONE case of the plague) and a statistician (for whom one case is not relevant) (Neumann and Star, 1996).

          Once a system is in place, the practical politics of these decisions are often forgotten, literally buried in archives (when records are kept at all) or built into software or the sizes and compositions of things. In addition to our archaeological expeditions into the records of such negotiations, we provide here some observations of the negotiations in action. Finally, even where everyone agrees on the way the classifications or standards should be established, there are often practical difficulties about how to craft their architecture. For example, a classification system with 20,000 "bins" on every form is practically unusable. (The original International Classification of Diseases had some 200 diseases not because of the nature of the human body and its problems but because this was the maximum number that would fit the large census sheets then in use). Sometimes the decision about how fine-grained to make the system has political consequences as well. For instance, in describing and recording the tasks someone does, as in the case of nursing work, may mean controlling or surveilling their work as well, and may imply an attempt to take away discretion. After all, the loosest classification of work is accorded to those with the most power and discretion, who are able to set their own terms.

          These ubiquitous, textured classifications and standards help frame our representation of the past and the sequencing of events in the present. They can best be understood as doing the ever-local, ever-partial work of making it appear that science describes nature (and nature alone) and that politics is about social power (and social power alone). Consider the case discussed at length by Young (1995) and Kirk and Kutchins (1992) of psychoanalysts who in order to receive reimbursement for this procedures need to couch them in a biomedical language (the DSM) that is anathema to them, but is the lingua franca of the medical insurance companies. There are local translation mechanisms that allow the DSM to continue to operate and to provide the sole legal, recognized representation of mental disorder. A `reverse engineering' of the DSM or the ICD reveals the multitude of local political and social struggles and compromises which go into the constitution of a `universal' classification.

          INFRASTRUCTURE AND ACTOR NETWORK THEORY

          We have, then, looked briefly at the space and time of the infrastructures that subtend actor-networks. Our position is that through due attention to these infrastructures, we can achieve an understanding of how it is that actor network theory comes to be a useful way of describing the nature scientific knowledge on the one hand and the (increasing) convergence of human and non-human on the other.

          The converging sameness of humans and non-humans, and in general the construction of a world in which actor-network theory is true, is a political and ethical question. Work by scholars such as Joan Fujimura (1991), Valerie Singleton and Mike Michael (1993) and Leigh Star (1991b; 1995) has pointed to the fact that actor-network theory can be read as an uncritical celebration of the power of modern science and technology. There are certainly readings of Latour's Science in Action or The Pasteurization of France which could support such an assertion. Through our concentration on the work of standardization and classification - a concentration fully consonant with the analysis of Latour and Callon - we are pointing to a place where actor-network theory can be further developed; and to a place where its political side meets its philosophical underpinnings.

          In order to clarify our position here, let us take an analogy. In the early nineteenth century in England there were a huge number of capital crimes - starting from stealing a loaf of bread and going up... . However, precisely because the penalties were so draconian, few juries would ever impose the maximum sentence; and indeed there was actually a drastic reduction in the number of executions even as the penal code was progressively strengthened. There are two ways of writing this history - one can either concentrate on the creation of the law; or one can concentrate on the way things worked out in practice. This is very similar to the position taken in Latour's We have never been modern: where he says we can either look at what scientists say that they are doing (working within a purified realm of knowledge) or at what they actually are doing (manufacturing hybrids). Actor network theory has looked in detail at the role of relatively black-boxed hybrids in creating the discourse of pure science as endpoint; we are advocating a development of the theory that pays more attention to the classification and standardization work that allows for hybrids to be manufactured and so explores the terrain of the politics of science in action.

          The point for us is that both of these are valid kinds of account. Early actor-network theory concentrated on the ways in which it comes to seem that science gives an objective account of natural order: trials of strength, enrolling of allies, cascades of inscriptions and the operation of immutable mobiles. It drew attention to the importance of the development of standards (though not to the linked development of classification systems); but did not look at these in detail. We were invited to look at the process of producing something which looked like what the positivists alleged science to be. We got to see the `Janus face' of science. In so doing we `followed the actors'. We shared their insights (allies must be enrolled, translation mechanisms must be set in train so that, in the canonical case, Pasteur's laboratory work can be seen as a direct translation of the quest for French honor after defeat in the battlefield).

          However, by the very nature of the method, we also shared their blindness. The actors being followed did not see what was excluded: they constructed a world in which that exclusion could occur. Thus if we just follow the doctors who create the International Classification of Diseases at the World Health Organization in Geneva, we will not see the variety of representation systems that other cultures have for classifying diseases of the body and spirit; and we will not see the fragile networks these classification systems subtend. Rather, we will see only those actants who are strong enough, and shaped in the right way, to impact the fragile actor-networks of allopathic medicine. We will see the blind leading the blind.

          We ascribe to Latour's (1987) definition of reality as `that which resists' (again, a concept with strong American pragmatist resonances, se e.g. Dewey, 1916). The actor-network will be changed by the resistances that it encounters. We have suggested that the work of dealing with resistance is twofold:

          * Changing the world such that the actor-network's description of reality becomes true. Thus if all diseases (of the mind and body) are classified purely physiologically and systems of medical observation and treatment are set up such the physical manifestations are the only manifestations recorded and physical treatments are the only treatments available then it is of course possible that the world will be such that schizophrenia, say, results purely and simply from a chemical imbalance in the brain. It will be impossible to think or act otherwise. We have called this the principle of convergence (Star and Bowker, 1994; Neumann, Bowker and Star, in press).

          * Distributing the resistance in such a way that it becomes marginalized and can be overlooked.

          A good example of responses to resistances comes from the nursing administrators we are studying at present. We will see how they are producing a classification of nursing work whose political edge is in the technical work of meshing this classification system with those already operating within the sociotechnical framework of the hospital. There is a play of resistances around this political of representation.

          The Iowa Intervention Team are producing a classification of all nursing work - a nursing interventions classification (NIC) (McCloskey and Bulechek, 1996). NIC itself is a fascinating system. Those of us studying it see it as an ethnomethodological nirvana. Some categories, like bleeding reduction - nasal, are on the surface relatively obvious and codable into discrete units of work practice to be carried out on specific occasions. But what about the equally important categories of hope installation and humor? Hope installation includes the subcategory of `Avoid masking the truth'. This is not so much something that nurses do on a regular basis, as something that they should not do constantly. It also includes: `Help the patient expand spiritual self'. Here the contribution that the nurse is making is to an implicit lifelong program of spiritual development. With respect to humor, the very definition of the category suggests the operation of a paradigm shift: "Facilitating the patient to perceive, appreciate, and express what is funny, amusing, or ludicrous in order to establish relationships"; and it is unclear how this could ever be attached to a time line: it is something the nurse should always do while doing other things. Further, contained within the nursing classification is an anatomy of what it is to be humorous, and a theory of what humor does. The recommended procedures break humor down into subelements. One should determine the types of humor appreciated by the patient; determine the patient's typical response to humor (e.g. laughter or smiles); select humorous materials that create moderate arousal for the individual (for example `picture a forbidding authority figure dressed only in underwear'); encourage silliness and playfulness and so on to make a total of fifteen sub-activities: any one of which might be scientifically relevant. A feature traditionally attached to the personality of the nurse (being a cheerful and supportive person) is now attached through the classification to the job description as an intervention which can be accounted for.

          Within the context of the hospital's sociotechnical system, nursing work has been deemed irrelevant to any possible future reconstruction; it has been canonically invisible, in Star's (1991a) term. The logic of NIC's advocators is that what has been excluded from the representational space of medical practice should be included. The Iowa group, the kernel of whom were teachers of nursing administration, made essentially three arguments for the creation of a nursing classification. First, it was argued that without a standard language to describe nursing interventions, there would be no way of producing a scientific body of knowledge about nursing. NIC in theory would be articulated with two other classification systems: NOC (the nursing sensitive patient outcomes classification scheme) and NANDA (the nursing diagnosis scheme). The three could work together thusly. One could perform studies over a set of hospitals employing the three schemes in order to check if a given category of patient responded well to a given category of nursing intervention. Rather than this comparative work being done anecdotally as in the past through the accumulation of experience, it could be done scientifically through the conduct of experiments. The Iowa Intervention project made up a jingle: NANDA, NIC and NOC to the tune of Hickory, Dickory, Dock to stress this interrelationship of the three schemes. The second argument for classifying nursing interventions was that it was a key strategy for defending the professional autonomy of nursing. The Iowa nurses are very aware of the literature on professionalization - notably Schon (1983) - and are aware of the force of having an accepted body of scientific knowledge as their domain. (Indeed Andrew Abbott, taking as his central case the professionalization of medicine, makes this one of his key attributes of a profession [1988].) The third argument was that nursing, alongside other medical professions, was moving into the new world of computers. As the representational medium changed, it was important to be able to talk about nursing in a language that computers could understand - else nursing work would not be represented at all in the future, and would risk being even further marginalized than it was at present.

          However, there is also a danger in representing. It is more difficult to hive off aspects of nursing duties and give them to lower paid adjuncts, if nursing work is relatively opaque. The test sites that are implementing NIC have provided some degree of resistance here, arguing that activities should be specified - so that, within a soft decision support model a given diagnosis can trigger a nursing intervention constituted of a single, well-defined set of activities. As Marc Berg (in press) has noted in his study of medical expert systems, such decision support can only work universally if local practices are rendered fully standard. A key professional strategy for nursing - particularly in the face of the ubiquitous process re-engineer - is realized by deliberate non-representation in the information infrastructure. What is remembered in the formal information systems resulting is attuned to professional strategy and to the information requisites of the nurses' take on what nursing science is.

          Further, there is a brick wall that they come up against when dealing with nurses on the spot: if they overspecify an intervention (that is break it down into too many constituent parts), then it gets called, in the field, an NSS classification - where NSS stands for `No shit, Sherlock' and is not used (Bowker, Star and Timmermans, 1996). It is assumed that any reasonable education in nursing or medicine should lead to a common language wherein things do not need spelling out to the ultimate degree. The information space will be sufficiently well pre-structured that some details can be assumed. Attention to the finer-grained details is delegated to the educational system, where it is overdetermined.

          These NIC-related strategies of dealing with overspecification and the political drive to relative autonomy by dropping things out of the representational space - are essential for the development of a successful actor-network system that includes nursing. These two forms of erasure of local context are needed in order to create the very infrastructure in which nursing can both appear as a science like any other and yet nursing as a profession can continue to develop as a rich, local practice. The ongoing erasure is guaranteed by the classification system: only information about nursing practice recognized by NIC can be coded on the forms fed into a hospital's computers or stored in a file cabinet.

          Nursing informaticians agree as a body that in order for proper health care to be given and for nurses to be recognized as a profession, hospitals as organizations should code for nursing within the framework of their memory systems: nursing work should be classified and forms should be generated which utilize these classifications. However, there has been disagreement with respect to strategy. To understand the difference that has emerged, recall one of those forms you have filled in (we have all experienced one) which do not allow you to say what you think. You may, in a standard case, have been offered a choice of several racial origins; but may not believe in any such categorization. There is no room on the form to write an essay on race identity politics. So you either you make an uncomfortable choice in order to get counted, and hope that enough of your complexity will be preserved by your set of answers to the form; or you don't answer the question and perhaps decide to devote some time to lobbying the producers of the offending form to reconsider their categorization of people. The NIC group has wrestled with the same strategic choice: fitting their classification system into the Procrustean bed of all the other classification systems that they have to articulate with in any given medical setting in order to form part a given organization's potential memory; or rejecting the ways in which memory is structured in the organizations that they are dealing with. We will now look in turn at each of these strategies.

          Let us look first at the argument for including NIC within the information infrastructural framework of the hospital's sociotechnical system. They argue that NIC has to respond to multiple important agendas simultaneously. Consider the following description of needs for a standard vocabulary of nursing practice:

          It is essential to develop a standardized nomenclature of nursing diagnoses in order to name without ambiguity those conditions in clients that nurses identify and treat without prescription from other disciplines; such identification is not possible without agreement as to the meaning of terms. Professional standards review boards require discipline-specific accountability; some urgency in developing a discipline-specific nomenclature is provided by the impending National Health Insurance legislation, since demands for accountability are likely both to increase and become more stringent following passage of the legislation. Adoption of a standardized nomenclature of nursing diagnoses may also alleviate problems in communication between nurses and members of other disciplines, and improvement in interdisciplinary communication can only lead to improvement in patient care. Standardization of the nomenclature of nursing diagnoses will promote health care delivery by identifying, for legal and reimbursement purposes, the evaluation of the quality of care provided by nurses; facilitate the development of a taxonomy of nursing diagnoses; provide the element for storage and retrieval of nursing data; and facilitate the teaching of nursing by providing content areas that are discrete, inclusive, logical, and consistent . (Castles, 1981, 38)

          We have cited this passage at length since it unites most of the motivations for the development of NIC. The development of a new information infrastructure for nursing, heralded in this passage, will make nursing more `memorable'. It will also lead to a clearance of past nursing knowledge - henceforth prescientific - from the textbooks; it will lead to changes in the practice of nursing (a redefinition of disciplinary boundaries) - a shaping of nursing so that future practice converges on its representation.

          Many nurses and nursing informaticians are concerned that the profession itself may have to change too much in order to meet the requirements of the information infrastructure. We murder, they note, to dissect. In her study of nursing information systems in France, Ina Wagner (1993) speaks as follows of the gamble of computerizing nursing records:

          Nurses might gain greater recognition for their work and more control over the definition of patients' problems while finding out that their practice is increasingly shaped by the necessity to comply with regulators' and employers' definitions of 'billable categories.'

          Indeed, a specific feature of this 'thought world' into which nurses are gradually socialized through the use of computer systems is the integration of management criteria into the practice of nursing. She continues: "Working with a patient classification system with time units associated with each care activity enforces a specific time discipline on nurses. They learn to assess patients' needs in terms of working time." This analytic perspective is shared by the Iowa nurses. They argue that documentation is centrally important; it not only provides a record of nursing activity but structures same:

          While nurses do complain about paperwork, they structure their care so that the required forms get filled out. If the forms reflect a philosophy of the nurse as a dependent assistant to the doctor who delivers technical care in a functional manner, this is to some extent the way the nurse will act. If the forms reflect a philosophy of the nurse as a professional member of the health team with a unique independent function, the nurse will act accordingly. In the future, with the implementation of price-per-case reimbursement vis-à-vis diagnosis related groups, documentation will become more important than ever. (Bulechek and McCloskey, 1985, 406).

          As the NIC classification has developed, observes Joanne McCloskey, the traditional category of `nursing process' has been replaced by `clinical decision making plus knowledge classification'. And in a representation of NIC that she produced both the patient and the nurse had dropped entirely out of the picture (both were, she said, located within the `clinical decision making box' on her diagram) (Iowa Intervention Project meeting, 6/8/95). A recent book about the next generation nursing information system argued that the new system:

          Cannot be assembled like a patchwork quilt, by piecing together components of existing technologies and software programs. Instead, the system must be rebuilt on a design different from that of most approaches used today: it must be a data-driven rather than a process-driven system. A dominant feature of the new system is its focus on the acquisition, management, processing, and presentation of 'atomic-level' data that can be used across multiple settings for multiple purposes. The paradigm shift to a data-driven system represents a new generation of information technology; it provides strategic resources for clinical nursing practice, rather than just support for various nursing tasks. (Zielstorff et al., 1993, 1).

          This speaks to the progressive denial of process and continuity through the segmentation of nursing practice into activity units. Many argue that in order to `speak with' databases at a national and international level just such segmentation is needed. The fear is that unless nurses can describe their process this way (at the risk of losing the essence of that process in the description), then it will not be described at all. They can only have there own actions remembered at the price of having others forget, and possibly forgetting themselves, precisely what it is that they do.

          Some nursing informaticians have chosen instead to challenge the informational framework existing in the medical organizations they deal with. They have adopted a Batesonian strategy of responding to the threat of the new information infrastructure by moving the whole argument up one level of generality and trying to supplant `data-driven' categories with categories that recognize process on their own terms. Thus the Iowa team pointed to the fact that women physicians often spend longer with patients than male doctors, but they need to see patients less often as a result: they argue that just such a process-sensitive definition of productivity needs to argued for and implemented in medical information systems in order that nursing work gets fairly represented (Iowa Intervention Project meeting, 6/8/95). They draw from their secret (because unrepresented) reservoir of knowledge about process in order to challenge the data-driven models from within.

          Within this strategy, the choice of allies is by no means obvious. Since with the development of NIC we are dealing with the creation of an information infrastructure, the whole question of how and what to challenge becomes very difficult. Scientists can only, willy nilly, deal with data as presented to them by their information base, just as historians of previous centuries must, alas, rely on written traces. When creating a new information infrastructure for an old activity, questions have a habit of running away from one: a technical issue about how to code process can become a challenge to organizational theory (and its database). A defense of process can become an attack on the scientific world view. One of the chief attacks on the NIC scheme has been made by a nursing informatician, Susan Grobe, who believes that rather than standardize nursing language computer scientists should develop natural language processing tools so that nurse narratives can be interpreted. Grobe argues for the abandonment of any goal of producing: "A single coherent account of the pattern of action and beliefs in science" (1992, 92); she goes on to say that: "philosophers of science have long acknowledged the value of a multiplicity of scientific views" (92). She excoriates Bulechek and McCloskey, architects of NIC, for having produced work: "derived from the natural science view with its hierarchical structures and mutually exclusive and distinct categories." (93). She on the other hand is drawing from cognitive science, library science and social science (94). Or again, a recent paper on conceptual considerations, decision criteria and guidelines for the Nursing Minimum Data Set cited Fritjof Capra against reductionism, Steven Jay Gould on the social embeddedness of scientific truth and praised Foucault for having developed a philosophical system to "grapple with this reality" (Kritek, 1988, 24). Nurse scientists, it is argued, "have become quite reductionistic and mechanistic in their approach to knowledge generation, at a time when numerous others, particularly physicists, are reversing that pattern" (p. 27). And nursing has to find allies amongst these physicists:

          Nurses who deliver care engage in a process. It is actually the cyclic, continuous repetition of a complex process. It is difficult, therefore, to sketch the boundaries of a discrete nursing event, a unit of service, and, therefore, a unit of analysis. Time is clearly a central force in nursing care and nursing outcomes. Nurses have only begun to struggle with this factor. It has a centrality that eludes explication when placed in the context of quantum physics. (Kritek, 1988: 28)

          The point here is not whether this argument is right or wrong. It is an interesting position. It can only be maintained, as can many of the other possible links that bristle through the NIC literature, because the information infrastructure itself is in flux. When the infrastructure is not in place to provide a `natural' hierarchy of levels, then discourses can and do make strange connections between themselves.

          If they want to prove a case within a given hospital for the opening-up of a new nursing position, they need to demonstrate that nursing is cost-effective according to the dominant accountancy paradigm. Now they in fact disagree with this paradigm (arguing, for example, that `quality of care' is not quantifiable but is still significant); and yet they feel that they must act as if they accept it - or else their voice will not be heard at all. There are a group of radical accountants who argue for the kinds of position that the NIC nurses are taking; however, these accountants are tied in to a different series of local battles about classification and standardization. The resistance to such cost accounting might be large in the aggregate while its impact, because of effective distribution, is minimal.

          In order to not be continually erased from the record, nursing informaticians are risking either modifying their own practice (making it more data driven) or waging a Quixotic war on database designers. The corresponding gain is great, however. If the infrastructure itself is designed in such a way that nursing information has to be present as an independent, well defined category, then nursing itself as a profession will have a much better chance of surviving through rounds of process re-engineering and nursing science as a discipline will have a firm foundation. The infrastructure assumes the position of Bishop Berkeley's God: as long as it pays attention to nurses, they will continue to exist. Having ensured that all nursing acts are potentially remembered by any medical organization, the NIC team will have gone a long way to ensuring the future of nursing.

          What actor-network theory has to offer in its approach to resistance is a reading of where and how political work is done in the world of technoscience; and how such work can be problematized and challenged. Donald MacKenzie's wonderful study of `missile accuracy' furnishes the best example of this approach. In a concluding chapter to his book, he discusses the possibility of `uninventing the bomb', by which he means changing society and technology in such a way that the atomic bomb becomes an impossibility. Such change, he suggests, can be carried out in part at the overt level of political organizations. However, and crucially for our purposes, he also sensitizes the reader to the site of the development and maintenance of technical standards as a site of political decisions and struggle. Standards and classifications, however dry and formal on the surface are suffused with traces of political and social work.

          CONCLUSION

          It is difficult when discussing any theory to adopt the appropriate degree of reflexivity. Actor-network theory tells us quite clearly that a theory should not be judged according to an absolute set of indicators, but according to the work that it does in the world. How does the theory itself stand up against this criterion?

          We have argued that it can do a good job in drawing our attention to the real political work that is being done in the development of technoscience; and can provide us with some useful concepts for analyzing that work. We have not in this paper argued, but would maintain (in accordance especially with Michel Serres' corpus; and to an extent Latour's We have never been modern and Dieux Faitiches) the symmetrical position that there is real philosophical and scientific work being done in the realm traditionally seen as the purely political. The central point is that technoscientific societies are powerful precisely because they are so good at delegating and distributing; and that actor-network theory is well position to track and describe the work of delegation and distribution.

          Does this mean that actor-network theory is the theory for our times? Indeed not. However, it is a theory which takes the work of classification and standardization seriously; and so provides one way of understanding the development of a master narrative (Western science) which is not a master narrative (because it frequently breaks down locally as postmodernists would remind us) and yet which act likes one (in that it enacts the very exclusions and silencing that allow it to appear to be true). The magic of modern technoscience is a lot of hard work.

          References:

          Abbate, J. and Kahin, B. (eds) (1995). Standards policy for information infrastructure. Cambridge, Ma : MIT Press.

          Abbott, A. (1988).The system of professions : an essay on the division of expert labor. Chicago: University of Chicago Press.

          Baudrillard, Jean. (1990). Cool memories . New York: Verso.

          Becker, H.S. (1996). `The epistemology of qualitative research' in R. Jessor, A. Colby and R. A. Shweder, eds., Ethnography and human development; Context and meaning in social inquiry, Chicago: University of Chicago. pp. 53-71.

          Berg, M. (forthcoming 1996). Rationalizing medical work - decision support techniques and medical problems. Cambridge, MA: MIT Press.

          Bowker, G. (1994). Science on the run: Information management and industrial geophysics at Schlumberger, 1920-1940. Cambridge, MA: MIT Press.

          Bowker, G. and Star, S.L. (1994). Knowledge and Infrastructure in international information management: Problems of classification and coding. In L. Bud-Frierman (ed), Information acumen: The understanding and use of knowledge in modern business (Pp.187-216). London: Routledge.

          Bowker, G., Star, S.L. and Timmermans, S. (1996). "Infrastructure and organizational transformation: Classifying nurses' work" in W. Orlikowski, G. Walsham, M. Jones and J. DeGross, eds. Information technology and changes in organizational work. (Proceedings IFIP WG8.2 Conference, Cambridge, England.) (Pp.. 344-370) London: Chapman and Hall.

          Bulechek, G. and McCloskey, J. (1985). Future directions. In Gloria M. Bulechek, Joanne C. McCloskey, Nursing Interventions: treatments for nursing diagnoses (Pp.401-408). Philadelphia, PA: Saunders.

          Callon, M. (1986). Some elements of a sociology of translation. In J. Law (Ed.), Power, action, and belief: A new sociology of knowledge? (Pp. 196-233). London: Routledge and Kegan Paul.

          Castles, M.R. (1981). Nursing Diagnosis: standardization of nomenclature. In H. H. Werley and M. R. Grier (eds), Nursing information systems (Pp. 36-44), New York: Springer.

          David, P. and Rothwell, G. S. (1994). Standardization, diversity and learning : strategies for the coevolution of technology and industrial capacity. Stanford, CA : Center for Economic Policy Research, Stanford University.

          Desrosières, A. and Thévenot, L. (1988). Les catégories socio-professionnelles. Paris: Découverte.

          Dewey, J. (1916). Logic: The theory of inquiry. New York: Holt, Rinehart and Winston.

          Dewey, J. (1929). The quest for certainty. NY: Open Court.

          Dewey, John. (1989). [Originally published in 1925). Experience and nature. La Salle, IL: Open Court Press.

          Cody, W. (1995). Letter from William K. Cody, Nursing outlook (Pp.93-94), 43 (2).

          Fujimura, J. (1991). On methods, ontologies, and representation in the sociology of science: Where do we stand?," In David Maines, ed. Social organization and social process: Essays in honor of Anselm L. Strauss. Hawthorne, NY: Aldine de Gruyter.

          Furet, F. (1978). Penser la Revolution francaise. Paris: Gallimard.

          Grobe, S. (1992). Response to J.C. McCloskey's and G.M. Bulechek's Paper on Nursing Intervention Scheme. In The Canadian Nurses Association, Papers from the Nursing Minimum Data Set Conference, October 27-29, 1992, Edmonton, Alberta: The Canadian Nurses Association.

          Hacking, I. (1995). Rewriting the soul: multiple personality and the sciences of memory. Princeton, N.J.: Princeton University Press.

          Huffman, E. (1990). Medical record management. Berwyn, IL: Physicians' Record Company.

          Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.

          ICD-10. (1992). ICD-10. International Statistical Classification of Diseases and Related Health Problems, tenth revision, Volume 1. Geneva: World Health Organization.

          Jenkins, T. (1988). New roles for nursing professionals. In M.J.Ball, K.J. Hannah, U. Gerdin Jelger, H. Peterson (eds), Nursing Informatics: Here caring and technology meet (Pp.88-95). NY: Springer.

          Kirk, S. A. and Kutchins, H. (1992). The selling of DSM : the rhetoric of science in psychiatry . New York: A. de Gruyter.

          Kritek, P.B, (1988). Conceptual considerations, decision criteria and guidelines for the nursing minimum data set from a practice perspective. In H. H. Werley and N. M. Lang (eds), Identification of the nursing minimum data set (Pp.22-33). New York: Springer.

          Latour, B. (1987). Science in action: How to follow scientists and engineers through society. Milton Keynes: Open University Press.

          Latour, B. (1993). We have never been modern . Cambridge, Mass. : Harvard University Press.

          Latour, B. (1996a). Aramis or the love of technology. Cambridge, MA: Harvard University Press.

          Latour, B. (1996b). Petite réflexion sur le culte moderne des dieux faitiches. Paris: Les Empecheurs de penser en rond.

          Latour, B. (forthcoming). `Did Ramses II die of tuberculosis? On the partial existence of existing and non-existing objects'. Typescript from author.

          McCloskey, J. and Bulechek, G. (1996). Iowa Intervention Project - Nursing Interventions Classification. (NIC) Second Edition. St Louis, MO: Mosby.

          MacKenzie, Donald A. (1990). Inventing accuracy : an historical sociology of nuclear missile guidance . Cambridge, MA : MIT Press.

          Neumann, L. J. and Star, S. L. (1996). Making infrastructure: The dream of a common language, Proceedings of PDC `96 (Participatory Design Conference), Eds. J. Blomberg, F. Kensing and E. Dykstra-Erickson. Palo Alto, CA: Computer Professionals for Social Responsibility, pp. pp. 231-240.

          Neumann, L., Star, S.L. and Bowker, Geoffrey C. (in press). `Information convergence'. Submitted to Journal of the American Society for Information Science (JASIS).

          Proust, Marcel. (1989) A la recherche du temps perdu, tome iv, Paris: Pleiade.

          Schon, D. (1983). The reflective practitioner : how professionals think in action. New York: Basic Books.

          Schon, D. (1963). Displacement of concepts. London: Tavistock Publications.

          Serres, M. (1993). Les origines de la géométrie. Paris: Flammarion.

          Singleton, V. and M. Michael. (1993). Actor-networks and ambivalence: General practitioners in the UK Cervical Screening Programme. Social studies of science 23: 227-64.

          Star, S.L. (1989). Regions of the mind: brain research and the quest for scientific certainty. Stanford, CA: Stanford University Press.

          Star, S.L. (1991a). The Sociology of the invisible: The primacy of work in the writings of Anselm Strauss. In David Maines (Ed.), Social organization and social process: Essays in honor of Anselm Strauss (Pp.265-283). Hawthorne, NY: Aldine de Gruyter.

          Star, S.L. (1991b). "Power, technologies and the phenomenology of standards: On being allergic to onions," in A sociology of monsters? Power, technology and the modern world, John Law, ed. Sociological Review Monograph. (Pp. 27-57 ). No. 38, 1991. Oxford: Basil Blackwell.

          Star, S.L. (1995a) Introduction. In S.L.Star, ed. Ecologies of knowledge: Work and politics in science and technology. Albany, NY: SUNY Press.

          Star, S.L. (1995b). The politics of formal representations: Wizards, gurus and organizational complexity," Pp. 88-118 in S. L. Star, Ed. Ecologies of knowledge: Work and politics in science and technology. Albany, NY: SUNY Press.

          Star, S.L. (In press). Leaks of experience: The link between science and knowledge. To appear in Thinking Practices, eds. Shelley Goldman and James Greeno. Hillsdale, NJ: Lawrence Erlbaum Associates.

          Star, S. L. and G. C. Bowker. (1997). Of lungs and lungers: The classified story of tuberculosis. Mind, Culture and Activity.

          Star, S.L. and A. L. Strauss. (In press.) Layers of silence, arenas of voice: The dialogues between visible and invisible work. In B. Nardi and Y. Engeström, eds. A Web on the Wind: The Structure of Invisible Work.

          Strauss, A., S. Fagerhaugh, B. Suczek and C. Wiener. (1985). Social organization of medical work. Chicago: University of Chicago Press.

          Tort, P. (1989). La raison classificatoire : les complexes discursifs : quinze etudes Paris: Aubier.

          Turnbull, D. (1993). `The ad hoc collective work of building gothic cathedrals with templates, string, and geometry' in Science, technology, & uman values. (Pp. 315-343). Vol. 18(3).

          Wagner, I., 1993.Women's voice: The case of nursing information systems. In AI and society. Vol. 7(4).

          Weick, K.E. and Roberts, K.H., 1993. Collective mind in organizations: Heedful interrelating on flight decks. In Administrative science quarterly (Pp.357-381). Vol. 38.

          Young, A. The harmony of illusions : inventing post-traumatic stress disorder . Princeton, N.J. : Princeton University Press.

          Zielstorff, R.D., Hudgings, C.I., Grobe, S. J.and The National Commission on Nursing Implementation Project (NCNIP) Task Force on Nursing Information Systems (1993). Next-Generation nursing information systems: Essential characteristics for professional practice, Washington, DC: American Nurses Publishing.

          http://www.ics.uci.edu/~lopes/teaching/cs221W12/index.html CS 221 - Information Retrieval

          CS 221 Information Retrieval

          Homework Projects
          Paper Summaries
          Syllabus
           
          Academic Honesty
           
          Students with Disability
          Synopsis

          Purpose. An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents.

          Book. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze

          Evaluation. Homework/lab projects (1/2) + Summaries (1/4) + Class participation (1/4)

          Pedagogy:
          - Lectures cover the material in the reading materials by placing it in context, giving examples, and engaging in Q&As.
          - Homework projects are hands-on vehicles for learning the material. Collaboration and knowledge exchange are encouraged in the projects, but mindless copy of solutions (aka cheating) is not allowed.
          - Papers cover the foundations of the field of Information Retrieval, right from the original source.
          - Class discussions push students beyond text book materials, and into research territory.

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Reader: Nitin Shantharam

          Lectures: Mon & Web 3:30-4:50pm, PSCB 120
          Office hours: Mondays and Wednesdays, 1:30pm-3pm, DBH 5076


          Projects

          Project descriptions

          Support for this courses's projects kindly provided by

           


          Quizzes

          There will be 4 quizzes throughout the course. Quizzes are on Mondays during the lecture. They cover material that has been taught the previous weeks since the last quizz. No quiz make-ups.

          Quiz Date
          1 1/30
          2 2/13
          3 2/27
          4 3/12

           


          Paper Summaries

          Summaries are due Fridays. Summaries submitted up to one week late will have a penalty of 35%.

          Please name your paper summary files like this:
          LastName_SummaryNumber.pdf
          starting with SummaryNumber=1 for the first summary.
          Files that don't follow this convention may be missed by the instructors.

          Include your full name and student ID in the summary itself.

          Turn in summaries in EEE Dropbox.


          Syllabus:

          Week Date Topic Weekly materials Deliverables Notes
          1 1/9 Web Search Basics Textbook Chapter 19: Web Search Basics (no need to summarize)

          1. Wikipedia entry on Vannevar Bush

          2. "As We May Think" The Atlantic Monthly, July, 1945. (reprinted in ACM CHI Interactions, March 1996)

          Summaries Slides

          Slides

          Slides

          1/11 Projects Overview

          Slides

          2 1/16* Web Search Basics

          3. "Stuff I've seen: A system for personal information retrieval and re-use " by S. Dumais, E. Cutell, J. Cadiz, G. Jancke, R. Sarin, and D. Robbins, SIGIR, 2003

          Commentary: "This paper addresses an increasingly important problem - how to search and manage personal collections of electronic information. ... it addresses an important user-centered problem. ...this paper presents a practical user interface to make the system useful. ..., the paper includes large scale, user-oriented testing that demonstrates the efficacy of the system. ..., the evaluation uses both quantitative and qualitative data to make its case. I think this paper is destined to be a classic because it may eventually define how people manage their files for a decade. Moreover, it is well-written and can serve as a good model for developers doing system design and evaluation, and for students learning about IR systems and evaluation."

          4. "Simple, Proven Approaches to Text Retrieval" by Robertson and Jones

          Commentary: "This paper provides a brief but well informed and technically accurate overview of the state of the art in text retrieval, at least up to 1997. It introduces the ideas of terms and matching, term weighting strategies, relevance weighting, a little on data structures and the evidence for their effectiveness. In my view it does an exemplary job of introducing the terminology of IR and the main issues in text retrieval for a numerate and technically well informed audience. It also has a very well chosen list of references."

          Summaries

          No class
          1/18 Discussion
          3 1/23 Web crawling
          and
          Evaluation in IR
          Textbook Chapter 20 : Web Crawling and Indices (no need to summarize)
          Textbook Chapter 8 : Evaluation in information retrieval (no need to summarize)

          5. "The Web As a Graph" by R. Kumar, P Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, E. Upfal, PODS 2000

          Abstract: "The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph has about a billion nodes today, several billion links, and appears to grow exponentially with time. There are many reasons -- mathematical, sociological, and commercial -- for studying the evolution of this graph. We first review a set of algorithms that operate on the Web graph, addressing problems from Web search, automatic community discovery, and classification. We then recall a number of measurements and properties of the Web graph. Noting that traditional random graph models do not explain these observations, we propose a new family of random graph models."

          Summaries Slides

          Slides

          More

          1/25 Evaluation
          4 1/30 Index Construction Textbook Chapter 4 : Index Construction

          6. "How Google Code Search Worked " by Russ Cox (January 2012)

          Commentary: Google code search has been a great resource for developers, but it has just been shut down. This blog post explains how it worked.

          Summaries Slides
          2/1 Slides

          More

          5 2/6 Index Construction and Scoring

          7. The unreasonable effectiveness of data

          Commentary: Three Google researchers summarize the benefits of data-driven problem-solving in an essay that borrows the title from another famous paper that proposes the opposite.

          Summaries Slides Slides
          2/8 Slides Slides
          6 2/13 Querying, Scoring, Term Weighting and the Vector Space model Textbook Chapter 1 : Boolean Retrieval

          Textbook Chapter 6 : Scoring, term weighting & the vector space model

          8.A vector space model for automatic indexing by Salton, Wong, Yang

          Summaries Slides
          Slides
          2/15 Slides
          7 2/20* Hadoop

          10. "Map Reduce: Simplified Data Processing on Large Clusters" by Jeffrey Dean and Sanjay Ghemawat

          Commentary: the paper that revolutionized modern data processing, made "cloud computing" trendy, and a great example of how programming language concepts can be applied to the design of real systems.

           

          Summaries *no class
          2/22

          Slides

          8 2/27 Link Analysis

          Textbook Chapter 21 : Link Analysis

          9. "The Anatomy of a Large-Scale Hypertextual Web Search Engine" by S. Brin and L. Page (this link is to the long version, the short version was publishied in WWW1998)

          Commentary: "This paper (and the work it reports) has had more impact on everyday life than any other in the IR area. A major contribution of the paper is the recognition that some relevant search results are greatly more valued by searchers than others. By reflecting this in their evaluation procedures, Brin and Page were able to see the true value of web-specific methods like anchor text. The paper presents a highly efficient, scalable implementation of a ranking method which now delivers very high quality results to a billion people over billions of pages at about 6,000 queries per second. It also hints at the technology which Google users now take for granted: spam rejection, high speed query-based summaries, source clustering, and context(location)-sensitive search. IR and bibliometrics researchers had done it all (relevance, proximity, link analysis, efficiency, scalability, summarization, evaluation) before 1998 but this paper showed how to make it work on the web. For any non-IR engineer attempting to build a web-based retrieval system from scratch, this must be the first port of call."

          Summaries Slides
          Slides
          2/29  
          9 3/5 Matrix decompositions and latent semantic indexing Textbook Chapter 18 : Matrix Decompositions and latent semantic indexing

          Additional tutorial on LSA, with code

          11. "Indexing by latent semantic analysis" by (Deerwester, Dumais, et.al)

          Commentary: " IR, as a field, hasn't directly considered the issue of semantic knowledge representation. The above paper is one of the few that does in the following way. LSI is latent semantic analysis (LSA) applied to document retrieval. LSA is actually a variant of a growing ensemble of cognitively-motivated models referred to by the term "semantic space". LSA has an encouraging track record of compatibility with human information processing across a variety of information processing tasks. LSA seems to capture the meaning of words in a way which accords with the representations we carry around in our heads. Finally, the above paper is often cited and interest in LSI seems to have increased markedly in recent years. The above paper has also made an impact outside our field. For example, recent work on latent semantic kernels (machine learning) draws heavily on LSI. "

          Summaries

          Slides
          3/7  
          10 3/12 Matrix decompositions and latent semantic indexing Textbook Chapter 8 : Evaluation in Information Retrieval

          12. " Unsupervised Named-Entity Extraction from the Web: An Experimental Study " (Etzioni, et.al.)

          Commentary: "This paper represents a new generation of IR work that attempts to do more than build a bag of words for information retrieval, but also attempts to make some sense of the information as well."

          Summaries

          Slides

          Slides
          3/14

          Exam: no exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.

          So don't risk it! If, for some reason, you can't do the homework on time or can't study for the Quiz, you're better off skipping it than cheating it. Do the math!


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/teaching/inf212W14/index.html INF 212 - Analysis of Programming Languages

          INF 212 Analysis of Programming Languages

          Synopsis

          Purpose. The study of programming languages and their use in software systems engineering. See beyond hypes, know the past, grasp fundamental concepts.

          Textbook: Exercises in Programming Style
          Code: Github

          Evaluation. Projects (70%) + Readings (20%) + Class participation (10%)

          Pedagogy:
          - Lectures give overview of the material
          - Projects are hands-on vehicles for learning the material.

          Course policies: Policies

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Office hours: Mondays, Wednesdays 11am--12pm


          Weekly Assignments

          There will be 6 to 9 assignments. Each one is a small set of programming exercises plus paper summaries

          Submission: 1 single zip file submitted to EEE

          Project Topic Due date
          1 - Sign Course policies. Turn in to "Policies" DropBox.
          - Exercises from book 1.1, 1.2, 2.1, 2.2 + Week 1 readings (*). Turn in to "Homework 1" DropBox
          1/11
          2 - Exercises from book 3.4, 4.3, 5.1, 5.2, 6.1 + Week 2 readings (*). Turn in to "Homework 2" DropBox 1/18
          3 - Exercises from book 7.1, 7.2, 8.1, 9.1 + Week 3 readings (*). Turn in to "Homework 3" DropBox 1/25
          4 - Exercises from book (11.1, 11.2) or (12.2, 12.3) + 13.1 + (14.1, 14.2) or (15.1, 15.2) + Week 4 readings (*). Turn in to "Homework 4" DropBox 2/1
          5 - Exercises from book 18.4 + 19.1 + Week 5 readings (*). Turn in to "Homework 5" DropBox 2/8
          6 - Exercises from book 22.3 + 24.1 + 24.2 + 24.3 + Week 6 readings (*). Turn in to "Homework 6" DropBox 2/15
          7 - Exercises from book 26.1+26.2 (one single solution) + 27.1+27.2 (one single solution)+ Week 7 readings (*). Turn in to "Homework 7" DropBox 2/22
          8 - Exercises from book 28.1 + 29.1+ 31.1 + 31.3 + Week 8 readings (*). Turn in to "Homework 8" DropBox 3/1
          9 - Exercises from book 32.1 + 32.3 + 33.1 + 33.2 + Week 9 readings (*). Turn in to "Homework 9" DropBox 3/8


          Syllabus:

          Week Date Topic Weekly materials Notes
          1 1/6 Historical Overview of PLs 1. Turing's Machine (*)
          2. von Neumann's architecture
          3. Forth (*)
          Intro
          Turing Machines
          Forth
          1/8* *no class*
          2 1/13/9 Basics of PLs: control flow, procedures, functions, expressions, statements, side effects, libraries 1. Dijkstra's GOTO Considered Harmful
          2. McCabe's Complexity metric (*)
          3. Dijkstra's Notes
          4. Global Variables considered harmful
          5. Church's Lambda Calculus (*)
          6. LISP
          7. Stratchey's lectures -- semantics
          8. Backus' case for functional programming
          Imperative
          1/15 Procedures & Functions
          Lambda Calculus
          3 1/20* Function Composition 1. Dijkstra's Recursive Programming (*)
          2. The discoveries of continuations
          3. Moggi's Monads
          4. Wadler's The essence of functional programming (*)
          *no class*
          1/22 Function Composition

          4 1/27 Objects and Object Interactions 1. Simula
          2. Smalltalk (*)
          3. Self (*)
          4. ADTs
          5. Inversion of Control in Smalltalk
          6. Fowler's Inversion of Control
          7. The information bus
          OOP Basics
          Smalltalk
          JavaScript
          1/29 ADTs
          Frameworks
          5 2/3 Reflection and Metaprogramming 1. Reflection and Semantics in LISP (*)
          2. Concepts and Experiments in Computational Reflection
          3. Reflection in logic, functional and OO programming
          4. Aspect-Oriented Programming (*)
          5. Aspects as latent topics
          6. Conic
          7. Fowler's Dependency Injection
          Reflection
          2/5 Plugins
          6 2/10 Adversity: dealing with the outside world 1. Proto-exceptions: error handling in PL/I (pages 105--114)
          2. Cardelli's Type Systems (*)
          3. Hanenberg's Static vs. Dynamic empirical study
          4. Imperative Functional Programming (*)
          5. Wadler's How to declare an imperative
          Adversity
          2/12 Type Systems
          7 2/17* Data-centric: relational model, spreadsheets, reactive, dataflow. Iterators and generators. 1. Codd's Relational Model for data banks (*)
          2. A Brief History of Spreadsheets
          3. Coroutines
          4. CLU's "iterators" (*)
          *no class*
          2/19 SQL / Spreadsheets
          Iterators, Generators, Coroutines
          8 2/24 Concurrency 1. Actors
          2. Linda (*)
          3. CLOS Map/reduce (Chapter 14.2)
          4. Google's MapReduce (*)
          Concurrency I
          2/26 Concurrency II
          9 3/3 Interactivity 1. MVC in Smalltalk (*)
          2. REST and Fielding's blog post (*).
          Interactivity
          3/5

           

          10 3/10 Recap   Wrap up
          3/12 Slides

          No exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/teaching/inf212W12/index.html INF 212 - Analysis of Programming Languages

          INF 212 Analysis of Programming Languages

          Synopsis

          Purpose. The study of programming languages and their use in software systems engineering -- partly theoretical, partly empircal. Includes an introduction to the formal aspects of programming languages, program analysis techniques and large-scale analysis of software projects.

          Evaluation. Projects (90%) + Class participation (10%)

          Pedagogy:
          The course's 20 or so lecture slots will be half formal lectures and half discussion sessions.
          - Lectures give overview of the material
          - Projects are hands-on vehicles for learning the material.

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu


          Projects

          There will be 6 to 9 projects.

          Submission

          EEE plus F2F discussion.

          Important dates

          Project Topic Due date Demo date
          1 Lambda Calculus 4/15 4/16
          2 Operational Semantics 4/15 4/16
          3 Haskell 4/29 4/30
          4 JavaScript 4/29 4/30
          5 Reflection 5/13 5/14
          6 Type Systems 5/13 5/14
          7 Modularity 5/27 6/4
          8 Virtual Machines 6/3 6/4
          9 Program Analysis 6/12 6/13


          Syllabus:

          Week Date Topic Weekly materials Notes
          1 4/1 Lambda Calculus 1. Alonzo Church and Lambda Calculus
          2. Church's original paper
          3. Handout 1
          4. Handout 2
          Slides
          4/4 Slides
          2 4/9 Operational Semantics 1. Pierce
          2. Moore and Grossman
          3. Wasserrab et al
          4. Mathews and Findler
          Slides
          4/11  
          3 4/16 Closures, continuations, monads,
          and assorted functional programming goodies
          1. Goto Considered harmful
          2. The discoveries of continuations
          3. Programming with continuations
          *demos
          4/18 Slides

          4 4/23 Haskell and JavaScript 1. How to declare an imperative
          2. You could have invented monads
          Slides
          4/25 Slides
          5 4/30 Reflection and Metaprogramming 1. Reflection and Semantics in LISP
          2. Concepts and Experiments in Computational Reflection
          3. Reflection in logic, functional and OO programming
          4. Reflection in Smalltalk
          *demos
          5/2 Slides
          6 5/7 Type Systems 1. Type Systems
          2. Types and Programming Languages
          3. Dependent Types
          4. The End of the Cold War
          Slides
          5/9 Slides
          7 5/14 Modularity 1. Parnas' "On the Criteria..."
          2. AOP
          3. Execution in the Kingdom of Nouns
          *demos
          5/16 Slides
          8 5/21 Virtual Machines 1. Lean and Mean JVM
          2. The Jalapeno VM
          3. SlimVM
          Slides
          5/23 Prof. Xu's slides
          9 5/28* Program Analysis 1. Representation and Analysis of Software
          2. A Survey of Program Analysis Techniques
          3. Abstract Interpretation
          4. Dynamic Program Slicing
          *no class
          5/30

          Slides

          10 6/4 Recap   *demos
          6/7 Slides

          No exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/patents.html Crista Lopes' patents

          Patents

          • "Design by Contract with Aspect-Oriented Programming"
            Cristina Lopes; Martin Lippert; and Erik Hilsdale.
            U.S. Patent No. 6,442,750.  Issued August 27, 2002.
             
          • "Aspect-Oriented Programming"
            Gregor Kiczales; John Lamping; Cristina Lopes; James Hugunin; Erik Hilsdale; and Chandrasekar Boyapati.
            U.S. Patent No. 6,467,086.  Issued October 15, 2002.
             
          • "Aspect-Oriented System Monitoring and Tracing"
            Cristina Lopes; Gregor Kiczales; John Lamping; Erik Hilsdale; Venkatesh Choppella; and Taher Haveliwala.
            U.S. Patent No.  6,473,895.  Issued October 29, 2002.
             
          • Integrated Development Environment for Aspect-Oriented Programming"
            Gregor Kiczales; Erik Hilsdale; Cristina Lopes; John Lamping; and James Hugunin.
            U.S. Patent No. 6,539,390. Issued March 25, 2003.
             
          • "Systems and Methods For Authenticating Communications In A Network Medium"
            Dirk Balfanz; Cristina Lopes; Diana Smetters; Paul Stewart; and Hao-Chi Wong.
            U.S. Application No. 10/066,699. Filed February 2002. Pending.
             
          http://www.ics.uci.edu/~lopes/teaching/cs221W15/index.html CS 221 - Information Retrieval

          CS 221 Information Retrieval

          Homework Projects
          Paper Summaries
          Syllabus
           
          Academic Honesty
           
          Students with Disability
          Synopsis

          Purpose. An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents.

          Book. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze

          Evaluation. Homework/lab projects (1/2) + Summaries (1/4) + Quizzes (1/4)

          Pedagogy:
          - Lectures cover the material in the reading materials by placing it in context, giving examples, and engaging in Q&As.
          - Homework projects are hands-on vehicles for learning the material. Collaboration and knowledge exchange are encouraged in the projects, but mindless copy of solutions (aka cheating) is not allowed.
          - Papers cover the foundations of the field of Information Retrieval, right from the original source.

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Office hours: Mondays 1pm-2pm; Wednesdays 11am-12pm. DBH 5076

          Readers: Wen Shen and Oliver Wang

          Lectures: Mon & Wed 3:30-4:50pm, SH 134


          Projects

          There will be 4 projects, the first one being trivial and the last one having several milestones with their own deadlines. Projects are due by midnight on the due date. Late projects will be accepted with penalties -- see course policies.

          Submission

          See instructions in each project description.

          Important dates

          Assignment Topic Due date Weight
          0 Course policies: read, sign, scan (or take picture) and upload to EEE 1/11 1%
          1 Text processing 1/19 9%
          2 Web crawling 2/8 30%
          3 Search Engine 2/18, 2/28, 3/14 60%

           


          Quizzes

          There will be 4 quizzes throughout the course. Quizzes are on Mondays during the lecture. They cover material that has been taught the previous weeks since the last quizz. The quiz with the worst score will be discarded. No quiz make-ups.

          Quiz Date
          1 1/26
          2 2/9
          3 2/23
          4 3/9

           


          Paper Summaries

          Summaries are due Saturdays. Summaries submitted up to one week late will have a penalty of 25%. No summaries will be accepted past 1 week of their due date.

          Each article should be summarized in no more than one page, with the following structure: (a) objective summary of the article (do not inject your views here, be objective); (b) short personal commentary about the article (your views here).

          Submit one pdf file per week with all the summaries for that week on that file.

          Please name your paper summary files like this:
          LastName_WeekNumber.pdf
          starting with WeekNumber=1 for the first week.
          Files that don't follow this convention may be missed by the instructors.

          Include your full name and student ID in the summary itself.

          Turn in summaries in EEE Dropbox.


          Syllabus:

          Week Date Topic Weekly materials Deliverables Notes
          1 1/5 Web Search Basics Textbook Chapter 19: Web Search Basics (no need to summarize)

          1. Wikipedia entry on Vannevar Bush

          2. "As We May Think" The Atlantic Monthly, July, 1945. (reprinted in ACM CHI Interactions, March 1996) (*)

          Summaries

          Slides
          The Web

          1/7 Slides
          Slides
          2 1/12

          Text Processing

          Search Engine Optimization

          3. "Stuff I've seen: A system for personal information retrieval and re-use " by S. Dumais, E. Cutell, J. Cadiz, G. Jancke, R. Sarin, and D. Robbins, SIGIR, 2003

          Commentary: "This paper addresses an increasingly important problem - how to search and manage personal collections of electronic information. ... it addresses an important user-centered problem. ...this paper presents a practical user interface to make the system useful. ..., the paper includes large scale, user-oriented testing that demonstrates the efficacy of the system. ..., the evaluation uses both quantitative and qualitative data to make its case. I think this paper is destined to be a classic because it may eventually define how people manage their files for a decade. Moreover, it is well-written and can serve as a good model for developers doing system design and evaluation, and for students learning about IR systems and evaluation."

          4. "Simple, Proven Approaches to Text Retrieval" by Robertson and Jones (*)

          Commentary: "This paper provides a brief but well informed and technically accurate overview of the state of the art in text retrieval, at least up to 1997. It introduces the ideas of terms and matching, term weighting strategies, relevance weighting, a little on data structures and the evidence for their effectiveness. In my view it does an exemplary job of introducing the terminology of IR and the main issues in text retrieval for a numerate and technically well informed audience. It also has a very well chosen list of references."

          Summaries

          Slides
          Slides
          1/14 Slides

          3 1/19* Web crawling Textbook Chapter 20 : Web Crawling and Indices (no need to summarize)

          5. "The Web As a Graph" by R. Kumar, P Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, E. Upfal, PODS 2000 (*)

          Abstract: "The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph has about a billion nodes today, several billion links, and appears to grow exponentially with time. There are many reasons -- mathematical, sociological, and commercial -- for studying the evolution of this graph. We first review a set of algorithms that operate on the Web graph, addressing problems from Web search, automatic community discovery, and classification. We then recall a number of measurements and properties of the Web graph. Noting that traditional random graph models do not explain these observations, we propose a new family of random graph models."

          Summaries *no class*
          1/21 Slides
          Slides
          4 1/26 Web Crawling

          6. "How Google Code Search Worked " by Russ Cox (January 2012) (*)
          Commentary: Google code search has been a great resource for developers, but it has just been shut down. This blog post explains how it worked.

          Quiz Slides
          1/28 Summaries Slides
          5 2/2 Index Construction and Scoring

          Prof. Chen Li will share his experiences of building a commercial enterprise search engine at his startup SRCH2.

          Textbook Chapter 4 : Index Construction

          7. The unreasonable effectiveness of data (*)

          Commentary: Three Google researchers summarize the benefits of data-driven problem-solving in an essay that borrows the title from another famous paper that proposes the opposite.

          Summaries Slides
          Slides
          2/4 Compression
          MapReduce
          Hadoop
          6 2/9 Querying, Scoring, Term Weighting and the Vector Space model Textbook Chapter 1 : Boolean Retrieval

          Textbook Chapter 6 : Scoring, term weighting & the vector space model

          8.A vector space model for automatic indexing by Salton, Wong, Yang (*)

          Quiz Slides
          Slides
          2/11 Summaries Slides
          7 2/16* Search Engine Evaluation
          Vector Space Model

          9. "Map Reduce: Simplified Data Processing on Large Clusters" by Jeffrey Dean and Sanjay Ghemawat (*)

          Commentary: the paper that revolutionized modern data processing, made "cloud computing" trendy, and a great example of how programming language concepts can be applied to the design of real systems.

           

          Summaries *no class*
          2/18 Slides
          8 2/23 Link Analysis

          Textbook Chapter 21 : Link Analysis

          10. "The Anatomy of a Large-Scale Hypertextual Web Search Engine" by S. Brin and L. Page (this link is to the long version, the short version was publishied in WWW1998)(*)

          Commentary: "This paper (and the work it reports) has had more impact on everyday life than any other in the IR area. A major contribution of the paper is the recognition that some relevant search results are greatly more valued by searchers than others. By reflecting this in their evaluation procedures, Brin and Page were able to see the true value of web-specific methods like anchor text. The paper presents a highly efficient, scalable implementation of a ranking method which now delivers very high quality results to a billion people over billions of pages at about 6,000 queries per second. It also hints at the technology which Google users now take for granted: spam rejection, high speed query-based summaries, source clustering, and context(location)-sensitive search. IR and bibliometrics researchers had done it all (relevance, proximity, link analysis, efficiency, scalability, summarization, evaluation) before 1998 but this paper showed how to make it work on the web. For any non-IR engineer attempting to build a web-based retrieval system from scratch, this must be the first port of call."

          Quiz Slides
          2/25 Summaries Slides
          9 3/2 Matrix decompositions and latent semantic indexing Textbook Chapter 18 : Matrix Decompositions and latent semantic indexing

          Additional tutorial on LSA, with code

          11. "Indexing by latent semantic analysis" by (Deerwester, Dumais, et.al) (*)

          Commentary: " IR, as a field, hasn't directly considered the issue of semantic knowledge representation. The above paper is one of the few that does in the following way. LSI is latent semantic analysis (LSA) applied to document retrieval. LSA is actually a variant of a growing ensemble of cognitively-motivated models referred to by the term "semantic space". LSA has an encouraging track record of compatibility with human information processing across a variety of information processing tasks. LSA seems to capture the meaning of words in a way which accords with the representations we carry around in our heads. Finally, the above paper is often cited and interest in LSI seems to have increased markedly in recent years. The above paper has also made an impact outside our field. For example, recent work on latent semantic kernels (machine learning) draws heavily on LSI. "

          Summaries

          Slides
          3/4 Guest lecturer
          10 3/9 Matrix decompositions and latent semantic indexing Textbook Chapter 8 : Evaluation in Information Retrieval

          12. " Unsupervised Named-Entity Extraction from the Web: An Experimental Study " (Etzioni, et.al.) (*)

          Commentary: "This paper represents a new generation of IR work that attempts to do more than build a bag of words for information retrieval, but also attempts to make some sense of the information as well."

          Quiz

          Slides

          Slides
          3/11 Summaries Slides

          Exam: no exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.

          So don't risk it! If, for some reason, you can't do the homework on time or can't study for the Quiz, you're better off skipping it than cheating it. Do the math!


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://mondego.ics.uci.edu/ Mondego Group @ UC Irvine

          Theme >

          LARGE.
          We do research in large systems and large data.
             

          Projects >

          Sourcerer
          SourcererCC
          R-MUVE
          Clone Detection
          Yelp dataset challenge
          OpenSimulator

          Software >
          and
          Datasets

          Source Code Datasets
          Wikipedia Editing Events
          crawler4j
          jforests
          lasso4j
          Digital Voices

          Papers >

          Show

          Mondego Group Publications

          Show

          Mondego Group Awards

          2011 Cristina V. Lopes, ACM Distinguished Scientist
          2011 Arthur Valadares, Jackie Doong and Boaz Gurdin, Winners of ICS/UCI Butterworth Competition
          2011 Ganjisaffar, Y., Zilio, A., Javanmardi, S., Cetindil, I., Sikka, M., Katumalla, S., Khatib, N., Li, C., Lopes, C. V. 3rd Place in Microsoft Speller Challenge (100+ teams competing)
          2009 Sushil Krishna Bajracharya and Cristina Videira Lopes, Best Paper Award at Mining Software Repositories
          2007 Sara Javanmardi, Winner of ICS/UCI Butterworth Competition
          2007 Erik Linstead, Paul Rigor, Sushil Bajracharya, Cristina Lopes, and Pierre Baldi. Mining Challenge Winner at Mining Software Repositories
          2007 Cristina V. Lopes, ICS Dean's Award for Excellence in Service
          2004 Cristina V. Lopes, NSF CAREER Award
          2004 Cristina V. Lopes, UCI Chancellor's Award for Excellence in Undergraduate Research

          < Awards

          People >

          Crista Videira Lopes, advisor
          Rohan Achar, PhD student
          Thomas Debeauvais, PhD student
          Eugenia Gabrielova, PhD student
          Vaibhav Saini, PhD student
          Hitesh Sajnani, PhD student
          Wen Shen, PhD student
          Arthur Valadares, PhD student
          Di Yang, Ph.D. student
          Institute for Software Research
          Institute for Genomics and Bioinformatics
          Institute for Virtual Environments and Games
          Center for Environmental Biology
          Encitra

          < Collabs

          Alumni >

          Maryam Khademi, PhD 2015
          Xi Sun, MS 2014
          Pramit Choudhary, MS 2013
          Joel Ossher, PhD 2013
          Shengwei (William) Li, MS 2012.
          Praneet Mahtre, M.S. 2012
          Yasser Ganjisaffar, PhD 2011
          Sara Javanmardi, PhD 2011
          Sushil Bajracharya, PhD 2010
          Amy Henckel, MS 2009
          Trung Ngo, MS 2007
          Amir Haghighat, MS 2007
          Raja Jurdak, PhD 2005
          National Science Foundation
          CCF-0347902, CCF-0725370, OCI-0724806, IIS-0808783, CCF-1018374, CCF-1218228
          American Heart Association 13GRNT16990060
          DARPA MUSE
          Additional gifts from Intel, Amazon AWS, Northrop Grumman, Unimodal Inc, Calit2

          Thank you!

          < Funding

          Graduate School?

          I am always looking for good, highly-motivated individuals who have what it takes to spend 5-6 years diving into a specific problem in my areas of interest. If you are interested in working with me and the students in the Mondego Group, consider applying to one of these Ph.D. programs in the Bren School of ICS:

          Software Engineering: choose this if your main interest is the production of software systems. Work that I advise under this program includes: software architectures for large-scale systems, mining software repositories, programming languages and environments. In other words, the server side and development tools.

          Informatics: choose this if your main interest is HCI. Work that I advise under this program includes: user experience for 3D spaces, virtual-physical interaction. In other words, the front end.

          Computer Science: choose this if your main interests are Web data or scientific data. Work that I advise under this program includes: mining the Web, analyzing large scientific data sets. In other words, the data side.

          http://www.ics.uci.edu/~lopes/teaching/cs221W14/index.html CS 221 - Information Retrieval

          CS 221 Information Retrieval

          Homework Projects
          Paper Summaries
          Syllabus
           
          Academic Honesty
           
          Students with Disability
          Synopsis

          Purpose. An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents.

          Book. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze

          Evaluation. Homework/lab projects (1/2) + Summaries (1/4) + Quizzes (1/4)

          Pedagogy:
          - Lectures cover the material in the reading materials by placing it in context, giving examples, and engaging in Q&As.
          - Homework projects are hands-on vehicles for learning the material. Collaboration and knowledge exchange are encouraged in the projects, but mindless copy of solutions (aka cheating) is not allowed.
          - Papers cover the foundations of the field of Information Retrieval, right from the original source.

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Reader: TBD

          Lectures: Mon & Wed 2-3:50pm, ICS 174
          Office hours: Mondays and Wednesdays, 11am-12pm, ICS 408


          Projects

          Project descriptions

          There will be 4 projects, the first one being trivial and the last one having several milestones with their own deadlines. Projects are due by midnight on the due date. Late projects will be accepted with penalties -- see course policies.

          Submission

          See instructions in each project description.

          Important dates

          Assignment Topic Due date Weight
          0 Course policies: read, sign, scan (or take picture) and upload to EEE 1/11 1%
          1 Text processing 1/21 9%
          2 Web crawling 2/8 30%
          3 Search Engine 2/22, 3/1, 3/15 60%

           


          Quizzes

          There will be 4 quizzes throughout the course. Quizzes are on Mondays during the lecture. They cover material that has been taught the previous weeks since the last quizz. The quiz with the worst score will be discarded. No quiz make-ups.

          Quiz Date
          1 1/27
          2 2/10
          3 2/24
          4 3/10

           


          Paper Summaries

          Summaries are due Saturdays. Summaries submitted up to one week late will have a penalty of 25%. No summaries will be accepted past 1 week of their due date.

          Each article should be summarized in no more than one page, with the following structure: (a) objective summary of the article (do not inject your views here, be objective); (b) short personal commentary about the article (your views here).

          Submit one pdf file per week with all the summaries for that week on that file.

          Please name your paper summary files like this:
          LastName_WeekNumber.pdf
          starting with WeekNumber=1 for the first week.
          Files that don't follow this convention may be missed by the instructors.

          Include your full name and student ID in the summary itself.

          Turn in summaries in EEE Dropbox.


          Syllabus:

          Week Date Topic Weekly materials Deliverables Notes
          1 1/6 Web Search Basics Textbook Chapter 19: Web Search Basics (no need to summarize)

          1. Wikipedia entry on Vannevar Bush

          2. "As We May Think" The Atlantic Monthly, July, 1945. (reprinted in ACM CHI Interactions, March 1996)

          Summaries

          Slides
          The Web
          Slides

          1/8* *no class*
          2 1/13

          Text Processing

          Search Engine Optimization

          3. "Stuff I've seen: A system for personal information retrieval and re-use " by S. Dumais, E. Cutell, J. Cadiz, G. Jancke, R. Sarin, and D. Robbins, SIGIR, 2003

          Commentary: "This paper addresses an increasingly important problem - how to search and manage personal collections of electronic information. ... it addresses an important user-centered problem. ...this paper presents a practical user interface to make the system useful. ..., the paper includes large scale, user-oriented testing that demonstrates the efficacy of the system. ..., the evaluation uses both quantitative and qualitative data to make its case. I think this paper is destined to be a classic because it may eventually define how people manage their files for a decade. Moreover, it is well-written and can serve as a good model for developers doing system design and evaluation, and for students learning about IR systems and evaluation."

          4. "Simple, Proven Approaches to Text Retrieval" by Robertson and Jones (*)

          Commentary: "This paper provides a brief but well informed and technically accurate overview of the state of the art in text retrieval, at least up to 1997. It introduces the ideas of terms and matching, term weighting strategies, relevance weighting, a little on data structures and the evidence for their effectiveness. In my view it does an exemplary job of introducing the terminology of IR and the main issues in text retrieval for a numerate and technically well informed audience. It also has a very well chosen list of references."

          Summaries

          Slides

          Slides

          1/15

          Slides

          Slides

          3 1/20* Web crawling Textbook Chapter 20 : Web Crawling and Indices (no need to summarize)

          5. "The Web As a Graph" by R. Kumar, P Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, E. Upfal, PODS 2000 (*)

          Abstract: "The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph has about a billion nodes today, several billion links, and appears to grow exponentially with time. There are many reasons -- mathematical, sociological, and commercial -- for studying the evolution of this graph. We first review a set of algorithms that operate on the Web graph, addressing problems from Web search, automatic community discovery, and classification. We then recall a number of measurements and properties of the Web graph. Noting that traditional random graph models do not explain these observations, we propose a new family of random graph models."

          Summaries *no class*
          1/22 Slides
          4 1/27 Web Crawling

          6. "How Google Code Search Worked " by Russ Cox (January 2012) (*)
          Commentary: Google code search has been a great resource for developers, but it has just been shut down. This blog post explains how it worked.

          Summaries Slides
          More
          1/29 Slides
          5 2/3 Index Construction and Scoring

          Prof. Chen Li will share his experiences of building a commercial enterprise search engine at his startup SRCH2.

          Textbook Chapter 4 : Index Construction

          7. The unreasonable effectiveness of data (*)

          Commentary: Three Google researchers summarize the benefits of data-driven problem-solving in an essay that borrows the title from another famous paper that proposes the opposite.

          Summaries Slides
          2/5 Compression
          MapReduce
          Hadoop
          6 2/10 Querying, Scoring, Term Weighting and the Vector Space model Textbook Chapter 1 : Boolean Retrieval

          Textbook Chapter 6 : Scoring, term weighting & the vector space model

          8.A vector space model for automatic indexing by Salton, Wong, Yang (*)

          Summaries Slides
          Slides
          2/12 Slides
          7 2/17* Search Engine Evaluation
          Vector Space Model

          9. "Map Reduce: Simplified Data Processing on Large Clusters" by Jeffrey Dean and Sanjay Ghemawat (*)

          Commentary: the paper that revolutionized modern data processing, made "cloud computing" trendy, and a great example of how programming language concepts can be applied to the design of real systems.

           

          Summaries *no class*
          2/19 Slides
          8 2/24 Link Analysis

          Textbook Chapter 21 : Link Analysis

          10. "The Anatomy of a Large-Scale Hypertextual Web Search Engine" by S. Brin and L. Page (this link is to the long version, the short version was publishied in WWW1998)(*)

          Commentary: "This paper (and the work it reports) has had more impact on everyday life than any other in the IR area. A major contribution of the paper is the recognition that some relevant search results are greatly more valued by searchers than others. By reflecting this in their evaluation procedures, Brin and Page were able to see the true value of web-specific methods like anchor text. The paper presents a highly efficient, scalable implementation of a ranking method which now delivers very high quality results to a billion people over billions of pages at about 6,000 queries per second. It also hints at the technology which Google users now take for granted: spam rejection, high speed query-based summaries, source clustering, and context(location)-sensitive search. IR and bibliometrics researchers had done it all (relevance, proximity, link analysis, efficiency, scalability, summarization, evaluation) before 1998 but this paper showed how to make it work on the web. For any non-IR engineer attempting to build a web-based retrieval system from scratch, this must be the first port of call."

          Summaries Slides
          2/26 Slides
          9 3/3 Matrix decompositions and latent semantic indexing Textbook Chapter 18 : Matrix Decompositions and latent semantic indexing

          Additional tutorial on LSA, with code

          11. "Indexing by latent semantic analysis" by (Deerwester, Dumais, et.al) (*)

          Commentary: " IR, as a field, hasn't directly considered the issue of semantic knowledge representation. The above paper is one of the few that does in the following way. LSI is latent semantic analysis (LSA) applied to document retrieval. LSA is actually a variant of a growing ensemble of cognitively-motivated models referred to by the term "semantic space". LSA has an encouraging track record of compatibility with human information processing across a variety of information processing tasks. LSA seems to capture the meaning of words in a way which accords with the representations we carry around in our heads. Finally, the above paper is often cited and interest in LSI seems to have increased markedly in recent years. The above paper has also made an impact outside our field. For example, recent work on latent semantic kernels (machine learning) draws heavily on LSI. "

          Summaries

          Slides
          3/5 Guest lecturer
          10 3/10 Matrix decompositions and latent semantic indexing Textbook Chapter 8 : Evaluation in Information Retrieval

          12. " Unsupervised Named-Entity Extraction from the Web: An Experimental Study " (Etzioni, et.al.) (*)

          Commentary: "This paper represents a new generation of IR work that attempts to do more than build a bag of words for information retrieval, but also attempts to make some sense of the information as well."

          Summaries

          Slides

          Slides
          3/12 Slides

          Exam: no exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.

          So don't risk it! If, for some reason, you can't do the homework on time or can't study for the Quiz, you're better off skipping it than cheating it. Do the math!


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/teaching/inf212W15/index.html INF 212 - Analysis of Programming Languages

          INF 212 Analysis of Programming Languages

          Synopsis

          Purpose. The study of programming languages and their use in software systems engineering. See beyond hypes, know the past, grasp fundamental concepts.

          Textbook: Exercises in Programming Style
          Code: Github

          Evaluation. Projects (80%) + Readings (20%)

          Up to 9 projects+papers:
          • Each project is worth up to 80 points
          • Each paper is worth up to 20 points
          Level Points   Level Points   Level Points   Level Points
          A+900 B+765 C+630 D 495
          A 855 B 720 C 585   
          A-810 B-675 C-540   

          Course policies: Policies

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Office hours: Mondays 1pm--2pm; Wednesdays 11am-12pm. DBH 5076.

          Teaching Assistant: Matias Giorgio, DBH 5209, mgiorgio at ics dot uci dot edu
          Office hours: Thursdays 3pm-5pm, DBH 5209.


          Weekly Assignments

          Submission: Instructions

          Project Topic Due date
          1 Sign Course policies.
          Homework Project 1.
          Week 1 paper (*)
          1/10
          2 Exercises from the book: 3.4, 4.1+4.4, 5.1+5.2, 6.1 (4 files)
          Week 2 paper (*)
          1/17
          3 Exercises from the book: 14.1, 14.2, 15.1, 15.2 (4 files)
          Week 3 paper (*)
          1/24
          4 Exercises from the book: 7.1, 8.1, 9.1 (3 files)
          Week 4 paper (*)
          1/31
          5 Exercises from the book: 20.1, 21.1, 23.1+23.2, 24.1+24.3 (4 files)
          Week 5 paper (*)
          2/7
          6 Exercises from the book: 25.1+25.2, 26.1, 27.1+27.2 (3+1 files)
          Week 6 paper (*)
          2/14
          7 Exercises from the book: 19.1+19.4, 19.3
          Week 7 paper (*)
          2/21
          8 Exercises from the book: 28.1+28.2, 29.1+29.2, 31.1+31.3
          Week 8 paper (*)
          2/28
          9 Exercises from the book: 32.1, 33.1
          Week 9 paper (*)
          3/7
          10 Extra! 32.2 (5 points) or 32.3 (8 points) 3/14

          Syllabus:

          Week Date Topic Weekly materials Notes
          1 1/5 Historical Overview of PLs 1. Turing's Machine (*)
          2. von Neumann's architecture
          3. Forth
          Intro
          1/7 Turing Machines
          Forth
          2 1/12 Basics of PLs: control flow, procedures, functions, expressions, statements, side effects, libraries 1. Dijkstra's GOTO Considered Harmful
          2. McCabe's Complexity metric
          3. Dijkstra's Notes
          4. Global Variables considered harmful
          5. Church's Lambda Calculus (*)
          6. LISP
          7. Stratchey's lectures -- semantics
          8. Backus' case for functional programming
          Imperative
          1/14 Procedures & Functions
          Lambda Calculus
          3 1/19* Objects and Object Interactions 1. Simula (*)
          2. Smalltalk
          3. Self
          4. ADTs
          5. Inversion of Control in Smalltalk
          6. Fowler's Inversion of Control
          7. The information bus
          *no class*
          1/21 OOP Basics
          Smalltalk
          JavaScript
          ADTs
          Frameworks
          4 1/26 Function Composition 1. Dijkstra's Recursive Programming
          2. The discoveries of continuations
          3. Moggi's Monads
          4. Wadler's The essence of functional programming (*)
          Function Composition
          1/28  
          5 2/2 Adversity: dealing with the outside world 1. Proto-exceptions: error handling in PL/I (pages 105--114)
          2. Cardelli's Type Systems (*)
          3. Hanenberg's Static vs. Dynamic empirical study
          4. Imperative Functional Programming
          5. Wadler's How to declare an imperative
          Adversity
          2/4 Type Systems
          6 2/9 Data-centric: relational model, spreadsheets, reactive, dataflow. Iterators and generators. 1. Codd's Relational Model for data banks (*)
          2. A Brief History of Spreadsheets
          3. Coroutines
          4. CLU's "iterators"
          SQL / Spreadsheets
          2/11 Iterators, Generators, Coroutines
          7 2/16* Reflection and Metaprogramming 1. Reflection and Semantics in LISP
          2. Concepts and Experiments in Computational Reflection (*)
          3. Reflection in logic, functional and OO programming
          4. Aspect-Oriented Programming
          5. Aspects as latent topics
          6. Conic
          7. Fowler's Dependency Injection
          *no class*
          2/18 Reflection
          Plugins
          8 2/23 Concurrency 1. Actors
          2. Linda (*)
          3. CLOS Map/reduce (Chapter 14.2)
          4. Google's MapReduce
          Concurrency I
          2/25 Concurrency II
          9 3/2 Interactivity 1. MVC in Smalltalk
          2. REST and Fielding's blog post (*).
          Interactivity
          3/4

           

          10 3/9 Recap   Wrap up
          3/11  

          No exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/teaching/inf102S14/ INF 102 - Concepts of Programming Languages II

          INF 102 Concept of Programming Languages II

          Synopsis

          Purpose. In-depth study of major programming paradigms: imperative, functional, declarative, object-oriented, aspect-oriented, and much more! Understanding the role of programming languages in software development and the suitability of languages in context.

          Textbook: Exercises in Programming Style
          Code: Github

          Evaluation. Up to 9 projects. Each project is worth up to 100 points.
          Level Points   Level Points   Level Points   Level Points
          A+810 B+675 C+540 D 350
          A 765 B 630 C 495   
          A-720 B-585 C-450   

          Course policies: Policies

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Office hours: Mondays, Wednesdays 11am--12pm

          Teaching Assistant: Matias Giorgio, DBH 5209, mgiorgio at ics dot uci dot edu
          Office hours: Fridays 12:30-2:30pm


          Weekly Assignments

          Submission: 1 single file submitted to EEE

          Project Topic Due date
          1 Sign Course policies.
          Homework Project 1.
          4/5
          2 Exercises from the book: 3.4, 4.1+4.4, 5.1+5.2, 6.1, one file per chapter named Three, Four, etc. with proper extensions.
          Zip the 4 files and upload to Homework 2.
          4/12
          3 Exercises from the book: 7.1+7.2, 8.1, 9.1 4/19
          4 Exercises from the book: 12.1+12.2+12.3, 15.1+15.2+15.3 4/26
          5 Exercises from the book: 16.3 (but use the program from exercise 15, instead of 10 or 11), 19.1.
          Extra credit (30 points!): 19.3 (note that by "show" I mean "show the code that works")
          5/3
          6 Exercises from the book: 23.1+23.2, 24.1+24.3 5/10
          7 Exercises from the book: 25.1+25.2, 27.1+27.2 5/17
          8 Exercises from the book: 28.1, 29.1+29.2
          Extra credit: 31.1+31.3
          5/24
          9 Exercises from the book: 32.2 (not in Python)
          (Extra credit 31.1+31.3 still valid this week)
          5/31


          Syllabus:

          Week Date Topic Weekly materials Notes
          1 3/31 Historical Overview of PLs 1. Turing's Machine
          2. von Neumann's architecture
          3. Forth
          Intro
          4/2 Turing Machines
          Forth
          2 4/7 Basics of PLs: control flow, procedures, functions, expressions, statements, side effects, libraries 1. Dijkstra's GOTO Considered Harmful
          2. McCabe's Complexity metric
          3. Dijkstra's Notes
          4. Global Variables considered harmful
          5. Church's Lambda Calculus
          6. LISP
          7. Stratchey's lectures -- semantics
          8. Backus' case for functional programming
          Imperative
          4/9 Procedures & Functions
          Lambda Calculus
          3 4/14 Function Composition 1. Dijkstra's Recursive Programming
          2. The discoveries of continuations
          3. Moggi's Monads
          4. Wadler's The essence of functional programming
           
          4/16 Function Composition

          4 4/21 Objects and Object Interactions 1. Simula
          2. Smalltalk
          3. Self
          4. ADTs
          5. Inversion of Control in Smalltalk
          6. Fowler's Inversion of Control
          7. The information bus
          OOP Basics
          Smalltalk
          JavaScript
          4/23 ADTs
          Frameworks
          5 4/28 Reflection and Metaprogramming 1. Reflection and Semantics in LISP
          2. Concepts and Experiments in Computational Reflection
          3. Reflection in logic, functional and OO programming
          4. Aspect-Oriented Programming
          5. Aspects as latent topics
          6. Conic
          7. Fowler's Dependency Injection
          Reflection
          4/30 Plugins
          6 5/5 Adversity: dealing with the outside world 1. Proto-exceptions: error handling in PL/I (pages 105--114)
          2. Cardelli's Type Systems
          3. Hanenberg's Static vs. Dynamic empirical study
          4. Imperative Functional Programming
          5. Wadler's How to declare an imperative
          Adversity
          5/7 Type Systems
          7 5/12 Data-centric: relational model, spreadsheets, reactive, dataflow. Iterators and generators. 1. Codd's Relational Model for data banks
          2. A Brief History of Spreadsheets
          3. Coroutines
          4. CLU's "iterators"
          SQL / Spreadsheets
          5/14 Iterators, Generators, Coroutines
          8 5/19 Concurrency 1. Actors
          2. Linda
          3. CLOS Map/reduce (Chapter 14.2)
          4. Google's MapReduce
          Concurrency I
          5/21 Concurrency II
          9 5/26* Interactivity 1. MVC in Smalltalk
          2. REST and Fielding's blog post .
          *no class
          5/28

          Interactivity

          10 6/2 Recap   Wrap up
          6/4  

          No exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~lopes/dv/dv.html Digital Voices Home Page at UCI

          Digital Voices

          In the spotlight
            PARC Forum
            (11/2001)
           
          Listen
            This is R2D2!
           
            http://parcweb.parc.xerox.com
            
            The answer is ...
            same message
            (~100 characters)
           
            An exhaustive analysis ...
            (~400 characters)
           
            There is only ...
            (~40 characters)

            Experiments with
            musical instruments:
                piano, bell, clarinet
                piano+bell+clarinet 

          Sample Source Code (in Java)
             codec.zip

          Read more
            WASPAA'01
            MMSP'02
            IEEE Pervasive'03
           
          Research topics
            Sound and security
            The acoustic channel
            Coding
            Alternatives to speech
            Auditory interfaces
            Bio-inspired comm.
              

          Inter-machine communications have always been kept away from our own communication channel, audible sound in air. There are good reasons for this: the data rates are relatively low when compared to other media (e.g. electric wires, radio) and the sounds tend to be annoying. But as more and more devices support an audio channel for voice or music, that channel becomes a cheap option for transferring arbitrary information among devices that happen to be near each other. Sound is attractive for applications that do not require high bit rates and for which it is expensive to extend the hardware infrastructure with radio or infrared transmitters. Some examples of those applications are: toys; broadcasting information through the sound of TV and radio that can be picked up by devices at home or in the car; transferring names and phone numbers between cell phones; transferring business cards between PDAs; and broadcasting location-dependent information from rooms into PDAs and laptops. Sound also has some natural advantages over other media when security is at stake.

          Motivated by the specific characteristics of the aerial acoustic communication paradigm used by humans and other animals, the Digital Voices project explores the use of sound as a communication medium in ubiquitous computing environments. The inter-machine aerial acoustic communications are designed along the following criteria:

          • The messages of these communication systems are pleasant to humans. They are either imperceptible or, if perceivable, they sound like music or familiar environment sounds such as birds, wind or water drops.
          • The systems are to be deployed in ordinary hardware. We utilize the existing infrastructure for voice, avoiding extra costs.
          • The systems are used in ordinary environments. This means that the communication has to be reasonably robust in the presence of noise such as people talking.

          Listen to the sound samples on the left. For those of you who like challenges, try to decode the whole of the messages in the "Listen" list on the left. If you can do it, I would like to hear from you! For checking your result and for general information, please contact Prof. Crista Lopes.

           

          http://www.ics.uci.edu/~lopes/teaching/cs221W13/index.html CS 221 - Information Retrieval

          CS 221 Information Retrieval

          Homework Projects
          Paper Summaries
          Syllabus
           
          Academic Honesty
           
          Students with Disability
          Synopsis

          Purpose. An introduction to information retrieval including indexing, retrieval, classifying, and clustering text and multimedia documents.

          Book. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schutze

          Evaluation. Homework/lab projects (1/2) + Summaries (1/4) + Quizzes (1/4)

          Pedagogy:
          - Lectures cover the material in the reading materials by placing it in context, giving examples, and engaging in Q&As.
          - Homework projects are hands-on vehicles for learning the material. Collaboration and knowledge exchange are encouraged in the projects, but mindless copy of solutions (aka cheating) is not allowed.
          - Papers cover the foundations of the field of Information Retrieval, right from the original source.

          Instructor: Prof. Cristina Lopes, DBH 5076, lopes at ics dot uci dot edu
          Reader: TBD

          Lectures: Tue & Thu 9:30-10:50am, ICS 174
          Office hours: Mondays and Wednesdays, 11am-12pm, ICS 408


          Projects

          Project descriptions

          There will be 3 projects, the last one with several milestones. Projects are due by midnight on the due date. Late projects will be accepted with penalties.

          Submission

          See instructions in each project description.

          Important dates

          Assignment Topic Due date Weight
          1 Text processing 1/20 20%
          2 Web crawling 2/3 20%
          3 Search Engine 2/17, 3/3, 3/15 60%

           


          Quizzes

          There will be 4 quizzes throughout the course. Quizzes are on Tuesdays during the lecture. They cover material that has been taught the previous weeks since the last quizz. The quiz with the worst score will be discarded. No quiz make-ups.

          Quiz Date
          1 1/22
          2 2/5
          3 2/19
          4 3/5

           


          Paper Summaries

          Summaries are due Fridays. Summaries submitted up to one week late will have a penalty of 35%. No summaries will be accepted past 1 week of their due date.

          Each article should be summarized in no more than one page, with the following structure: (a) objective summary of the article (do not inject your views here, be objective); (b) short personal commentary about the article (your views here).

          Submit one pdf file per week with all the summaries for that week on that file.

          Please name your paper summary files like this:
          LastName_WeekNumber.pdf
          starting with WeekNumber=1 for the first week.
          Files that don't follow this convention may be missed by the instructors.

          Include your full name and student ID in the summary itself.

          Turn in summaries in EEE Dropbox.


          Syllabus:

          Week Date Topic Weekly materials Deliverables Notes
          1 1/8 Web Search Basics Textbook Chapter 19: Web Search Basics (no need to summarize)

          1. Wikipedia entry on Vannevar Bush

          2. "As We May Think" The Atlantic Monthly, July, 1945. (reprinted in ACM CHI Interactions, March 1996)

          Summaries

          Slides

          1/10

          The Web

          Slides

          2 1/15

          Text Processing

          Search Engine Optimization

          3. "Stuff I've seen: A system for personal information retrieval and re-use " by S. Dumais, E. Cutell, J. Cadiz, G. Jancke, R. Sarin, and D. Robbins, SIGIR, 2003

          Commentary: "This paper addresses an increasingly important problem - how to search and manage personal collections of electronic information. ... it addresses an important user-centered problem. ...this paper presents a practical user interface to make the system useful. ..., the paper includes large scale, user-oriented testing that demonstrates the efficacy of the system. ..., the evaluation uses both quantitative and qualitative data to make its case. I think this paper is destined to be a classic because it may eventually define how people manage their files for a decade. Moreover, it is well-written and can serve as a good model for developers doing system design and evaluation, and for students learning about IR systems and evaluation."

          4. "Simple, Proven Approaches to Text Retrieval" by Robertson and Jones

          Commentary: "This paper provides a brief but well informed and technically accurate overview of the state of the art in text retrieval, at least up to 1997. It introduces the ideas of terms and matching, term weighting strategies, relevance weighting, a little on data structures and the evidence for their effectiveness. In my view it does an exemplary job of introducing the terminology of IR and the main issues in text retrieval for a numerate and technically well informed audience. It also has a very well chosen list of references."

          Summaries

          Slides

          Slides

          1/17

          Slides

          Slides

          3 1/22 Web crawling Textbook Chapter 20 : Web Crawling and Indices (no need to summarize)

          5. "The Web As a Graph" by R. Kumar, P Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, E. Upfal, PODS 2000

          Abstract: "The pages and hyperlinks of the World-Wide Web may be viewed as nodes and edges in a directed graph. This graph has about a billion nodes today, several billion links, and appears to grow exponentially with time. There are many reasons -- mathematical, sociological, and commercial -- for studying the evolution of this graph. We first review a set of algorithms that operate on the Web graph, addressing problems from Web search, automatic community discovery, and classification. We then recall a number of measurements and properties of the Web graph. Noting that traditional random graph models do not explain these observations, we propose a new family of random graph models."

          Summaries Slides
          1/24 Slides

          More

          4 1/29* Web Crawling

          On Tuesday, 1/29 Prof. Chen Li will share his experiences of doing search-related research and commercializing the results. The work is mainly conducted in the iPubmed project (http://ipubmed.ics.uci.edu), which can support instant, fuzzy search on more than 21 million medical publications. He has been doing a startup, called SRCH2, to commercialize the techniques. The company has spent the last few years developing a new full text search software from the ground up. Here are some of the things SRCH2 can do: it resides wholly in-memory, deploys multi-threaded queries, uses cached forward indexing to enable instant recommendations, does rapid geo search, error correction, customizable rankings, with real-time updates, all in parallel, in scale. Each feature addresses significant pain points for our growing list of enterprise mobile, social, and e-commerce clients. He will share experiences of doing research commercialization.

          6. "How Google Code Search Worked " by Russ Cox (January 2012)
          Commentary: Google code search has been a great resource for developers, but it has just been shut down. This blog post explains how it worked.

          Summaries *invited lecturer
          1/31 Slides
          5 2/5 Index Construction and Scoring Textbook Chapter 4 : Index Construction

          7. The unreasonable effectiveness of data

          Commentary: Three Google researchers summarize the benefits of data-driven problem-solving in an essay that borrows the title from another famous paper that proposes the opposite.

          Summaries Slides
          2/7 Compression
          MapReduce
          Hadoop
          6 2/12 Querying, Scoring, Term Weighting and the Vector Space model Textbook Chapter 1 : Boolean Retrieval

          Textbook Chapter 6 : Scoring, term weighting & the vector space model

          8.A vector space model for automatic indexing by Salton, Wong, Yang

          Summaries Slides
          Slides
          2/14 Slides
          7 2/19 Search Engine Evaluation
          Vector Space Model

          10. "Map Reduce: Simplified Data Processing on Large Clusters" by Jeffrey Dean and Sanjay Ghemawat

          Commentary: the paper that revolutionized modern data processing, made "cloud computing" trendy, and a great example of how programming language concepts can be applied to the design of real systems.

           

          Summaries Slides
          2/21

          Slides

          8 2/26 Link Analysis

          Textbook Chapter 21 : Link Analysis

          9. "The Anatomy of a Large-Scale Hypertextual Web Search Engine" by S. Brin and L. Page (this link is to the long version, the short version was publishied in WWW1998)

          Commentary: "This paper (and the work it reports) has had more impact on everyday life than any other in the IR area. A major contribution of the paper is the recognition that some relevant search results are greatly more valued by searchers than others. By reflecting this in their evaluation procedures, Brin and Page were able to see the true value of web-specific methods like anchor text. The paper presents a highly efficient, scalable implementation of a ranking method which now delivers very high quality results to a billion people over billions of pages at about 6,000 queries per second. It also hints at the technology which Google users now take for granted: spam rejection, high speed query-based summaries, source clustering, and context(location)-sensitive search. IR and bibliometrics researchers had done it all (relevance, proximity, link analysis, efficiency, scalability, summarization, evaluation) before 1998 but this paper showed how to make it work on the web. For any non-IR engineer attempting to build a web-based retrieval system from scratch, this must be the first port of call."

          Summaries Slides
          2/28  
          9 3/5 Matrix decompositions and latent semantic indexing Textbook Chapter 18 : Matrix Decompositions and latent semantic indexing

          Additional tutorial on LSA, with code

          11. "Indexing by latent semantic analysis" by (Deerwester, Dumais, et.al)

          Commentary: " IR, as a field, hasn't directly considered the issue of semantic knowledge representation. The above paper is one of the few that does in the following way. LSI is latent semantic analysis (LSA) applied to document retrieval. LSA is actually a variant of a growing ensemble of cognitively-motivated models referred to by the term "semantic space". LSA has an encouraging track record of compatibility with human information processing across a variety of information processing tasks. LSA seems to capture the meaning of words in a way which accords with the representations we carry around in our heads. Finally, the above paper is often cited and interest in LSI seems to have increased markedly in recent years. The above paper has also made an impact outside our field. For example, recent work on latent semantic kernels (machine learning) draws heavily on LSI. "

          Summaries

          Slides
          3/7  
          10 3/12 Matrix decompositions and latent semantic indexing Textbook Chapter 8 : Evaluation in Information Retrieval

          12. " Unsupervised Named-Entity Extraction from the Web: An Experimental Study " (Etzioni, et.al.)

          Commentary: "This paper represents a new generation of IR work that attempts to do more than build a bag of words for information retrieval, but also attempts to make some sense of the information as well."

          Summaries

          Slides

          Slides
          3/14

          Exam: no exam

           


          Academic Honesty

          I trust all students are honest and do not cheat. Those who break my trust at any point will get an F in the course - no excuses or apologies will be accepted.Additional penalties may also be imposed by the department and the university. Very severe incidents of academic dishonesty can result in suspension or expulsion from the university.

          So don't risk it! If, for some reason, you can't do the homework on time or can't study for the Quiz, you're better off skipping it than cheating it. Do the math!


          Students with Disability

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

          http://www.ics.uci.edu/~ardalan/index.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

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          I joined the CS department at UC Irvine in July 2015. I received my M.Sc. and Ph.D. from the ECE department at Rice University, and my B.Sc. from Sharif University of Technology.

          My research involves building efficient, high performance, and reliable systems. I enjoy solving operating system problems related to both mobile devices and datacenter servers.

          Prospective Students

          I'm looking for motivated graduate and undergraduate students who enjoy building systems. If you're interested, send me an email or drop by my office.

          Selected Publications

          • Ardalan Amiri Sani, Kevin Boos, Min Hong Yun, Lin Zhong, "Rio: A System Solution for Sharing I/O between Mobile Systems," in Proc. ACM Int. Conf. Mobile Systems, Applications and Services (MobiSys), June 2014.
            Best Paper Award. (PDF) (video demo) (source code)
          • Ardalan Amiri Sani, Kevin Boos, Shaopu Qin, Lin Zhong, "I/O Paravirtualization at the Device File Boundary," in Proc. ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2014. (PDF) (source code)
          • Ardalan Amiri Sani, Lin Zhong, Ashutosh Sabharwal, "Directional Antenna Diversity for Mobile Devices: Characterizations and Solutions," in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), September 2010. (PDF) (data traces)

          http://www.ics.uci.edu/~ardalan/downloads.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

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          • Rio's source code (MobiSys 2014 paper) (released on November 2014)
          • Paradice's source code (ASPLOS 2014 paper) (Xen support released on May 2014, KVM support released on September 2014)
          • HTC G1 kinetic sensor data traces (Mobicom 2010 paper) (11 users, one week each, 2.2 GB) (released on September 2010) (also available from CRAWDAD)

          http://www.ics.uci.edu/~ardalan/courses.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

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          • Winter 2016: CompSci 295/190, Advanced Operating Systems

          http://www.ics.uci.edu/~ardalan/publications.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

          • Home
          • Students
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          • Kevin Boos, Ardalan Amiri Sani, Lin Zhong, "Eliminating State Entanglement with Checkpoint-based Virtualization of Mobile OS Services," in Proc. ACM SIGOPS Asia-Pacific Workshop on Systems (APSys), July 2015.
          • Ardalan Amiri Sani, Lin Zhong, Dan S. Wallach, "Glider: A GPU Library Driver for Improved System Security," Technical Report 2014-11-14, Rice University, November 2014. (arXiv)
          • Ardalan Amiri Sani, Kevin Boos, Min Hong Yun, Lin Zhong, "Rio: A System Solution for Sharing I/O between Mobile Systems," in Proc. ACM Int. Conf. Mobile Systems, Applications and Services (MobiSys), June 2014.
            Best Paper Award. (PDF) (video demo) (source code)
          • Ardalan Amiri Sani, Kevin Boos, Shaopu Qin, Lin Zhong, "I/O Paravirtualization at the Device File Boundary," in Proc. ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2014. (PDF) (source code)
          • Ardalan Amiri Sani, Zhiyong Tan, Peter Washington, Mira Chen, Sharad Agarwal, Lin Zhong, Ming Zhang, "The wireless data drain of users, apps, & platforms, " in ACM SIGMOBILE Mobile Computing and Communications Review (MC2R), October 2013. (PDF)
          • Hang Yu, Ahmad Rahmati, Ardalan Amiri Sani, Lin Zhong, Jehan Wickramasuriya, Venu Vasudevan, "Data Broadcasting using Mobile FM Radio: Design, Realization and Application," in Proc. ACM Int. Conf. Ubiquitous Computing (UbiComp), September 2011. (PDF)
          • Ardalan Amiri Sani, Wolfgang Richter, Xuan Bao, Trevor Narayan, Mahadev Satyanarayanan, Lin Zhong, Romit Roy Choudhury, "Opportunistic Content Search of Smartphone Photos," Technical Report TR0627-2011, Rice University, June 2011. (arXiv)
          • Xuan Bao, Trevor Narayan, Ardalan Amiri Sani, Wolfgang Richter, Romit Roy Choudhury, Lin Zhong, Mahadev Satyanarayanan, "The Case for Context-Aware Compression," in Proc. ACM Int. Workshop on Mobile Computing Systems and Applications (HotMobile), March 2011. (PDF)
          • Ardalan Amiri Sani, Lin Zhong, Ashutosh Sabharwal, "Directional Antenna Diversity for Mobile Devices: Characterizations and Solutions," in Proc. ACM Int. Conf. Mobile Computing and Networking (MobiCom), September 2010. (PDF) (data traces)
          • Ardalan Amiri Sani, Hasan Dumanli, Lin Zhong, Ashutosh Sabharwal, "Power-Efficient Directional Wireless Communication on Small Form-Factor Mobile Devices," in Proc. ACM/IEEE Int. Symp. Low Power Electronics and Design (ISLPED), August 2010. (PDF)
          • Stephen So, Ardalan Amiri Sani, Lin Zhong, Frank Tittel, Gerard Wysocki, "Laser Spectroscopic Trace-Gas Sensor Networks for Atmospheric Monitoring Applications," in Proc. IPSN Wrkshp. Sensor Networks for Earth and Space Science Applications (ESSA), April 2009.

          http://www.ics.uci.edu/~ardalan/students.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

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          Ph.D. Students

          • Saeed Mirzamohammadi

          Undergraduate Students

          • Vinh Hoang Vu

          http://www.ics.uci.edu/~ardalan/services.html Ardalan Amiri Sani

          Ardalan Amiri Sani

          Assistant Professor
          Computer Science Department
          University of California, Irvine
          Email: ardalan at uci dot edu
          Office: Donald Bren Hall #3062

          • Home
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          • ACM WearSys 2016 (Technical program committee member)
          • ACM MobiSys 2016 (Technical program committee member)
          • ACM MobiSys Ph.D. Forum 2014 (Co-chair)
          • ACM Workshop on Wireless of the Students, by the Students, for the Students (S3) 2013 (Co-chair)
          • ACM MobiSys Ph.D. Forum 2012 (Student committee member)
          • ACM Workshop on Wireless of the Students, by the Students, for the Students (S3) 2011 and 2012 (Student committee member)

          http://www.ics.uci.edu/~sjordan/research/wireless.html Dynamic Resource Allocation for Wireless CDMA Networks
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Dynamic Resource Allocation for Wireless CDMA Networks

          Diagram of Wireless PricingOur focus in this work is on dynamic pricing of network resources for cellular networks. The work is characterized by four aspects:

          • Pricing is dynamic, on a time scale that is slower than fast power control but quicker than connection access control.
          • Resources are allocated to users in a fashion that maximizes social welfare or service provider revenue.
          • Prices are used to signal the relationship between the wireless channel and the resources required to obtain desired QoS.
          • Users are assumed to be cooperative, not game-playing. Cooperation may be induced through charges to users.

          Our goal is use pricing to dynamically allocate radio resources (power, codes, and/or data rate) to competing users to optimize an overall network objective criterion while satisfying Quality of Service constraints. We explicitly consider the effect of load-based interference between neighboring cells..

           

          The first paper presents some of the earliest work in the field of wireless pricing. We consider here only voice service in a single cell, and show how pricing of power and codes can be used to dynamically allocate resources in a distributed fashion.

          Single-Cell Forward Link Power Allocation Using Pricing in Wireless Networks (with P. Liu, P. Zhang, M.L. Honig), IEEE Transactions on Wireless Communications, vol. 3 no. 2, March 2004, pp. 533-543.

           

          The second set of papers explicitly considers the effect of neighboring cells upon each other. We characterize when coordination is required between the two cells to achieve the system-wide optimum allocation, and how this coordination can be achieved through exchange of a price per unit power representing the externality.

          Two-Cell Power Allocation for Downlink CDMA (with C. Zhou, P. Zhang, M.L. Honig), IEEE Transactions on Wireless Communications, vol. 3 no. 6, November 2004, pp. 2256-2266.

          Forward-Link Resource Allocation for a Two-Cell Voice Networks with Multiple Service Classes (with C. Zhou, M.L. Honig, R. Berry), IEEE Wireless Communications and Networking Conference, New Orleans, Louisiana, March 2003, pp. 1060-1065.

          The third set of papers considers data rather than voice service. Data applications are assumed to have utility that increases concavely with SINR. Prices are associated with power in each cell. We prove that if the power budget is low, then no coordination is required between neighboring cells. However, if the power budget is high, then not only is coordination required, but it can not be accomplished through exchange of an externality price.

          Utility-Based Power Control for a Two-Cell CDMA Data Network (with C. Zhou, M.L. Honig), IEEE Transactions on Wireless Communications, vol. 4 no. 6, November 2005, pp. 2764-2776.

          Utility-Based Resource Allocation for Wireless Networks with Mixed Voice and Data Services (with C. Zhou, M.L. Honig, R. Berry), IEEE International Conference on Computer Communications and Networks, Miami, Florida, October 2002, pp. 485-488.

          Dynamic Resource Allocation for Integrated Voice and Data Traffic in DS-CDMA (with J.B. Kim, M.L. Honig), IEEE Vehicular Technology Conference, Atlantic City, New Jersey, October 2001, pp. 42-46.

          The next paper considers the effect of dynamic arrivals and departures of data users upon allocation of power. Utility is associated with the time required for file transfer. Pricing is used to allocate power to competing data streams.

          Packet-Based Power Allocation for Forward Link Data Traffic (with P. Liu, R. Berry, and M. Honig), IEEE Transactions on Wireless Communications, vol. 6 no. 8, August 2007, pp. 2894-2903.

          The next set of papers consider applications which are in between the extremes of inelastic real-time and elastic best-effort. Here, we attempt to provide QoS to such interactive applications. In the first paper, interactive users can specify a target througput to be achieved over the duration of the connection. We propose a packet scheduler that balances efficiency with fairness by combining a max-min policy with a policy of serving users with relatively good channels. In the second paper, interactive users can specify both a minimum throughput and a maximum rate variance. We characterize the power and rate control policy that minimizes the peak total transmit power. In the third paper, users can specify thresholds on short term throughputs. We present algorithms that maximize the probability of satisfying these thresholds.

          Cross Layer Dynamic Resource Allocation with Targeted Throughput for WCDMA Data (with P. Zhang), IEEE Transactions on Wireless Communications, vol. 7 no. 12, December 2008, pp. 4896-4906.

          Downlink Power Control with Throughput and Rate Variation Guarantees (with N. Chen), IEEE Wireless Communications and Networking Conference (WCNC), Hong Kong, China, March 2007, pp. 3430-3435.

          Downlink Scheduling with Guarantees on the Probability of Short-term Throughput (with N. Chen), IEEE Transactions on Wireless Communications, vol. 8 no. 2, February 2009, pp. 593-598.

           

          The next set of papers continues the focus on applications which are in between the extremes of inelastic real-time and elastic best-effort. However, unlike the papers above, these applications are now explicitly modeled using sigmoid (s-shaped) utility functions. Whereas elastic applications only require rate scheduling, we find that semi-elastic applications require both rate scheduling and connection access control. The first paper considers the case in which utility is a semi-elastic function of the rate achieved in each time slot. We propose a near-optimal algorithm that iteratively finds optimal shadow prices for power and rates. The second paper considers the case in which utility is a sigmoid function of the average bit rate over multiple time slots. We are particularly motivated here by video conferencing. We show that greedy allocation to maximize incremental utility in the current time slot can be implemented in a distributed fashion by an exchange of price and demand amongst users, the network, and an intermediate power allocation module. We then propose resource allocation that considers both the average rate achieved so far and the future expected rate, and show how future expected rate can be estimated by modeling the probability that a user will be allocated a subcarrier in a future time slot.

          Downlink User Selection and Resource Allocation for Semi-Elastic Flows in an OFDM Cell (with C. Yang), ACM Journal of Wireless Networks, vol. 19 no. 6, August 2013, pp. 1407-1421.

          Power and Rate Allocation for Video Conferencing in Cellular Networks (with C.Yang), EURASIP Journal on Wireless Communications and Networking, February 2013, 2013:31.

          Resource Allocation for Semi-elastic Applications with Outage Constraints in Cellular Networks (with C. Yang), IEEE Transactions on Vehicular Technology, vol. 64 no. 4, April 2015, pp. 1591-1606.

          A Novel Coordinated Connection Access Control and Resource Allocation Framework for 4G Wireless Networks (wth C. Yang), IEEE/ACM Transactions on Networking, to appear.

           

          Portions of this work were supported by DARPA and NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, DARPA, or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/applicants.html Information for Prospective Graduate Students
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Information for Prospective Graduate Students

          Our research group does not currently have openings for students pursuing M.S. and Ph.D. degrees with a primary interest in computer networks. (In the future, when we do, applicants to our research group are expected to have an exceptional background and interest in applying probability and random process theory to computer network and telecommunications problems.)

          The CS Department and the Networked Systems programs are considering applicants. For best consideration, prospective graduate students should apply by Jan. 1.

          At UCI, computer networks can be pursued as a research topic in four different programs:

          Networked Systems: The Networked Systems Program grants a M.S. and/or Ph.D. in Networked Systems. Students in the Networked Systems program typically take core courses in networks, breadth courses selected from technical courses (including distributed systems, algorithms, data structures, operating systems, databases, random processes, and linear systems) and management and applications of technology (including educational technology, management of information technology, and social impact), and concentration courses selected from a long list including courses on networks, performance, middleware, communications, and operations research. Apply directly to the Networked Systems Program.

          Computer Science: The Department of Computer Science grants a M.S. and/or Ph.D. in Information and Computer Science with a Concentration in Computer Science. Students in the Concentration in Computer Science typically take courses in networks, algorithms, databases, and other computer science areas. Apply directly to the Department of Computer Science.

          Electrical and Computer Engineering: The Department of Electrical Engineering and Computer Science grants a M.S. and/or Ph.D in Electrical and Computer Engineering with either a Concentration in Computers Networks & Distributed Systems (CNDC) or a Concentration in Electrical Engineering (EE). Students in the concentration in CNDC typically take courses in networks, operating systems, algorithms, distributed systems, and other computer engineering areas. Students in the concentration in EE typically take courses in networks, random processes, communications, signal processing, and other electrical engineering areas. Apply directly to the Deparment of Electrical Engineering and Computer Science.

          Our research group reviews applications for Networked Systems and for Computer Science, but not usually for Electrical and Computer Engineering.

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/resalloc.html Admission Control in Multimedia Networks
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Connection Access Control in Multimedia Networks

          Our focus in this work is on connection access control in networks supporting multiple types of services. The approach taken is fundamental and generic.

           

          In the first set of papers, we consider connection-oriented services that require different amounts of network resources, e.g. integrated services in a circuit-switched or reservation-based network. We first characterize the combination of service loads that can be supported by a set of network resources, and the relationships between the load and resource requirements of each service class and the network revenue that can be obtained from these loads. We then characterize the connection access control policy that maximizes network revenue. Finally, we derive numerical techniques to increase the size of the networks for which optimal connection access control policies can be determined.

          Throughput in Multiple Service Multiple Resource Communication Networks (with P.P. Varaiya), IEEE Transactions on Communications, vol. 39 no. 8, August 1991, pp. 1216-1222.

          Control of Multiple Service Multiple Resource Communication Networks (with P.P. Varaiya), IEEE Transactions on Communications, vol. 42 no. 11, November 1994, pp. 2979-2988.

          A Continuous State Space Model of Multiple Service Multiple Resource Communication Networks, IEEE Transactions on Communications, vol. 43 no. 2, February 1995, pp. 477-484.

          A Recursive Algorithm for Bandwidth Partitioning, IEEE Transactions on Communications, vol. 58 no. 4, April 2010, pp. 1026-1030.

           

          The second set of papers considers applications that require sequential use of network resources. The results characterize the connection access control policy that maximizes network revenue.

          Access Control to Two Multiserver Loss Queues in Series (with C.-Y. Ku), IEEE Transactions on Automatic Control, vol. 42 no. 7, July 1997, pp. 1017-1023.

          Near Optimal Admission Control for Multiserver Loss Queues in Series (with C.-Y. Ku), European Journal of Operations Research, vol. 144 no. 1, November 2002, pp. 166-178.

          Access Control of Parallel Multiserver Loss Queues (with C.-Y. Ku), Performance Evaluation, vol. 50 no. 4, December 2002, pp 219-231.

          Near-optimal Control Policy for Loss Networks (with C.Y. Ku, D.C. Yen, I.-C. Chang, and S.-H.Huang), Omega: The International Journal of Management Science, vol. 34 no. 4, August 2006, pp. 406-416.

           

          Portions of this work were supported by NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

           

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/index.html Scott Jordan
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Scott Jordan

          Office:
          3214 Bren Hall
          Department of Computer Science
          University of California, Irvine

          Mailing address:
          3019 Bren Hall
          University of California, Irvine
          Irvine, CA 92697-3435

          sjordan at uci dot edu

          During 2014-2016, I am on leave from UCI, and serving as the Chief Technology Officer of the Federal Communications Comission

            Courses

          I will not be teaching during 2015-2016. Here are archived webpages from past courses:


          ICS 11 The Internet and Public Policy

          CS 132 Computer Networks

          CS 232 Computer and Communication Networks

           

            Information about our Graduate Programs

          Networked Systems Graduate Program

          Computer Science Graduate Program

          Information for Prospective Graduate Students

            Research and Publications

          ISP Service Tiers and Data Caps

          Device Attachment

          Net Neutrality

          Pricing of Internet Resources

          Dynamic Resource Allocation for Wireless Multimedia DS-CDMA

          Universal Service

          Network QoS

          Connection Access Control in Multimedia Networks

          Dynamic Channel Allocation in Cellular Networks

          Web Server Performance under User Impatience

           

           
          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/tiers.html ISP Service Tiers and QoS
          Scott Jordan
          Department of Computer Science University of California, Irvine
            ISP Service Tiers and Data Caps

          Diagram of Internet PricingThirty years of networking research on Quality of Service (QoS) has resulted in standards that have been incorporated into routers of all shapes and sizes. And yet, despite all of this work, use of QoS is very limited. Internet Service Providers (ISPs) have implemented QoS to support their own voice and video services, but do not offer QoS to their subscribers for use by any other Internet applications. ISPs have not incorporated QoS into their interconnection agreements, and hence end-to-end QoS is not available. The limited deployment of QoS has ignited vigorous debate over Net Neutrality, which has become the most contentious telecommunications public policy issue in decades.

          End-to-end QoS deployment requires an interdisciplinary approach that integrates network architecture, economics, and law. Internet architecture has not adequately considered the economic motivations of ISPs; QoS will be deployed and used only when there is a business case for it. The network economics literature has only modeled network access, not QoS. Law will dictate what ISPs can do; it may make QoS illegal, may allow QoS but prohibit charging for it, or may allow ISPs to not offer QoS to competitors. This project will address the lack of QoS availability to applications, the lack of availability of end-toend QoS, and the effects of possible net neutrality laws.

          Service Tier Design

          The initial work is focused on how Internet Service Providers design tier rates, tier prices, and network capacity. Internet Service Provider design of service tiers are modeled and analyzed, based on demand for web browsing and video streaming. A basic model that considers user willingness to pay, network capacity, and application performance is formulated to determine when multiple tiers maximize profit. An extended model that also considers the time that users devote to each application is formulated to determine the optimal network capacity, tier rates, and tier prices. We show that an Internet Service Provider may simplify tier and capacity design by allowing its engineering department to set network capacity, its marketing department to set tier prices, and both to jointly set tier rates. Numerical results are presented to illustrate the magnitude of the decrease in profit compared to the optimal profit resulting from such a simplified design.

          ISP Service Tier Design (with W. Dai), IEEE/ACM Transactions on Networking, forthcoming.

          Data Caps

          The next work is focused on how ISPs choose data caps and the resulting impact on users. We propose models to analyze the effect of data caps upon Internet subscribers. Novel utility functions that consider the time users devote to Internet applications and the opportunity cost of a user’s free time are designed for web browsing and video streaming. A monopoly ISP is presumed to maximize its profit by controlling tier prices, tier rates, data caps and overage charges in a basic tier targeting web browsing service and a premium tier targeting video streaming service. We show how users fall into five
          categories: non-Internet subscribers, basic tier subscribers, premium tier subscribers unaffected by a data cap, premium tier subscribers who are capped but do not choose to exceed the cap, and premium tier subscribers who exceed the cap and pay overage charges. When data caps are used for profit maximization, we find that the monopoly ISP has the incentive to keep the basic tier price and basic tier rate unchanged, to increase the premium tier rate, and to reduce the premium tier price. The ISP also has the incentive to set smaller caps and higher overage charges than when caps are used only to ensure that heavy users pay for their usage. Based on the change in the tiered pricing plan, we give analytical and numerical results to show the change in user tier choice, user surplus and social welfare.

          This paper is intended for people with a background in communications policy:

          How do ISP Data Caps Affect Subscribers? (with W. Dai), Research Conference on Communication, Information and Internet Policy (TPRC), Arlington, Virginia, September 2013.

          This paper is intended for people with a technical background in networking:

          Design and Impact of Data Caps (with W.Dai), IEEE Global Communications Conference (Globecom), Atlanta, Georgia, December 2013.

          The next work is focused on how data caps affect competition between a cable ISP and a DSL ISP. Utility is a function of users’ relative interest, time devoted to each application, and application performance. Users maximize surplus by making ISP subscription choices and by controlling the time devoted to Internet activities. ISPs maximize profit by competing through tier prices, tier rates, network capacities, data caps and overage charges. We illustrate how users’ utilities are affected by data caps, and the resulting impact upon ISP market shares. The initial incentives for both ISPs to update tier prices and tier rates are predicted. The final Nash equilibrium with data caps is analyzed through simulation, and compared to the Nash equilibrium without data caps. We find that a DSL ISP may not set data caps, and that the use of data caps may thus result in an advantage for a cable ISP over a DSL ISP.

          The Impact of Data Caps on ISP Competition (with W. Dai and J. Baek),IEEE International Conference on Game Theory for Networks (GameNets), Beijing, China, November 2014.

          Portions of this work were supported by NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/qos.html Network QoS
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Network QoS

          Our focus in this work is on the fundamental relationship between network resources and the QoS that the network can support. A widely used folk theorem is that multiplexing increases efficiency, and that the magnitude of the efficiency gain depends on the burstiness of the traffic, but often these relationships are often difficult to analytically characterize.

           

          The first paper addresses the gains obtained by multiplexing bursty traffic operating under QoS contraints. The results establish a basis for the belief that although bursty sources require more bandwidth, multiplexing gains are increasing with burstiness. When heterogeneous sources are partially multiplexed, it also demonstrates that it is most efficient to group similar source types.

          Multiplexing Gains in Bit Stream Multiplexors (with I. Sidhu), IEEE/ACM Transactions on Networking, vol. 3 no. 6, December 1995, pp. 785-797.

           

          The second paper addresses a simple question: how do the optimal allocations of bandwidth and buffer vary with the number of sources? For on/off fluid flows, the optimal buffer allocation is proven to be proportional to the square root of the number of sources, and hence that allocation of a fixed buffer, or allocation of buffer linearly with the number of sources, can lead to substantial inefficiency.

          The Variation of Optimal Bandwidth and Buffer Allocation with the Number of Sources (with K. Jogi, C. Shi and I. Sidhu), IEEE/ACM Transactions on Networking, vol. 12 no. 6, December 2004, pp. 1093-1104.

           

          The third set of papers address the relationship between the network capacity and the number of data users that can be supported under a QoS criterion. Two types of QoS are considered: the average rate achieved and the probability that the rate is above a specified threshold. These results can be used to establish the cost associated with a QoS contraint on the basis of the network resources required.

          Throughput in Processor-Sharing Queues (with N. Chen), IEEE Transactions on Automatic Control, vol. 52 no. 2, February 2007, pp. 299-305.

          Violation Probability in Processor-Sharing Queues (with N. Chen), IEEE Transactions on Automatic Control, vol. 53 no. 8, September 2008, pp. 1956-1961.

           

          Portions of this work were supported by DARPA and NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, DARPA, or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

           

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/courses/ics11/index.html Econ 11 / ICS 11
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Econ 11 / ICS 11 The Internet and Public Policy

          Lecture: TuTh 3:30-4:50 in 1304 SE2

          Professor: Scott Jordan (sjordan at uci dot edu). Office Hours: I can be found most days in my office in 3214 Bren Hall. The most likely times to find me in the office are Mondays, Wednesdays, and Fridays during 10:30-12 and Mondays and Fridays during 3-4. Please stop by, or email me a list of a few days/times that you can meet.

          Discussion Section:

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          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/courses/cs132/index.html CS 132
          Scott Jordan
          Department of Computer Science University of California, Irvine
            CS 132 Computer Networks

          Lecture: TuTh 2:00-3:20 in ICS174

          Discussion Sections: F 12-12:50, F 1:00-1:50 in ICS174

          Professor: Scott Jordan [sjordan at uci dot edu] Office Hours: I can be found most days in my office in 3214 Bren Hall. The most likely times to find me in the office are Mondays and Wednesdays during 2:00-3:30 and Tuesdays and Thursdays after class. Please stop by, or email me a list of a few days/times that you can meet.

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          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/web.html Web Server Performance under User Impatience
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Web Server Performance under User Impatience

          Web users are notoriously impatient, willing to wait only a few seconds before aborting a pending web request. This behavior unnecessarily taxes web server resources, which are dedicated to a connection that ultimately does not complete. While this is not as problematic at well-provisioned or lightly-trafficked web servers, where all incoming requests can be serviced with little or no queuing time required, such behavior is extremely detrimental to poorly-provisioned or very busy web servers, where server deadlock may occur and no users are adequately served.

          In this work, we analytically derive, using queuing models, a service ordering for web servers that is useful under these extreme conditions and that takes into account the impatience of web users. The ordering is based on a revenue generation model that assumes a web server earns some amount from processing a user request to completion. This amount diminishes exponentially with the time spent in queue and in service at the web server. In such cases, a greedy algorithm maximizes server revenue and outperforms "fair" algorithms such a first-in-first-out by several orders of magnitude when server load is high. These performance gains continue even when the server passes into an overload situation, allowing the web server to operate even when completely saturated. These results are verified both analytically and via simulation, and have been shown to be useful for general Markovian queuing systems as well.

          An Optimal Service Ordering for a World Wide Web Server (with A.C. Dalal), ACM Performance Evaluation Reviews, vol. 29 no. 2, September 2001, pp. 8-13.

          Optimal Scheduling in a Queue with Differentiated Impatient Users (with A.C. Dalal), Performance Evaluation, vol. 59 no. 1, January 2005, pp. 73-84.

          Portions of this work were supported by NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/courses/cs232/index.html CS 232
          Scott Jordan
          Department of Computer Science University of California, Irvine
            CS 232 Computer and Communication Networks

          Lecture: TuTh 2:00-3:20 in DBH 1600

          Professor: Scott Jordan [sjordan at uci dot edu] Office Hours: I can be found most days in my office in 3214 Bren Hall. The most likely times to find me in the office are Mondays, Wednesdays, and Fridays during 10:30-12 and Tuesdays and Thursdays during 4-5. Please stop by, or email me a list of a few days/times that you can meet.

          Readers:

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          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/pricing.html Pricing of Internet Resources
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Pricing of Internet Resources

          Diagram of Internet PricingOur focus in this work is on dynamic pricing of network resources. The work is characterized by four aspects:

          • Pricing is dynamic, on a time scale that is slower than congestion control but quicker than connection access control.
          • Resources are allocated to users in a fashion that maximizes social welfare. Prices are used to signal the marginal utility of each network resource.
          • The network is assumed to support integrated services, and resource prices are often translated into QoS prices to efficiently support a diverse range of applications.
          • Users are assumed to be cooperative, not game-playing. Cooperation may be induced through charges to users.

          Our goal is use network pricing to connect traffic characterization, scheduling, and connection access control. There is now a substantial body of work on traffic characterization and application requirements characterization, which will allow users to describe the QoS requirements of their traffic flows. There is also a substantial body of work on integrated and differentiated services network architectures, which will allow networks to give differentiated performance to these flows.

          However, without some policy to manage QoS within the network, the network can not efficiently transform such user characterizations into network resource allocations and make intelligent decisions as to how to treat each traffic flow.

           

          The following papers are some of the earliest work in the field of pricing of network resources. They assume the network has an architecture that allows for reservation of network resources, e.g. Internet's IntServ or ATM's virtual paths. Real-time services measure utility by the loss experienced on the network path, while non real-time services measure utility by the time required to transmit a file. Prices are set per unit effective bandwidth on each network link, so that each application can determine the QoS it should receive.

          Connection Establishment in High Speed Networks (with H. Jiang), IEEE Journal on Selected Areas in Communications, vol. 13 no. 7, September 1995, pp. 1150-1161.

          The Role of Price in the Connection Establishment Process (with H. Jiang), European Transactions on Telecommunications, vol. 6 no. 4, July-August 1995, pp. 421-429.

          A Pricing Model for High Speed Networks with Guaranteed Quality of Service (with H. Jiang), IEEE InfoCom, March 1996, pp. 888-895.

           

          The next set of papers expands this framework to associate prices with both bandwidth and buffer. Real-time services measure utility by the combination of end-to-end loss and delay, and they request the combination of bandwidth and buffer that most efficiently achieves their desired loss and delay.

          The Effect of Bandwidth and Buffer Pricing on Resource Allocation and QoS (with N. Jin), Computer Networks, Special Issue on Internet Economics: Pricing and Policies, vol. 46 no. 1, September 2004, pp. 53-71.

          Dynamic Congestion-Based Pricing of Bandwidth and Buffer (with N. Jin and G. Venkitachalam), IEEE/ACM Transactions on Networking, vol. 13 no. 6, December 2005, pp. 1233-1246.

           

          The final set of papers considers an architecture that allows for prioritization (rather than reservation), e.g. Internet's diffServ. The premise is that network pricing may be able to decide what priority each packet should receive, i.e. how to assign codepoints. Prices are associated with each codepoint, and users choose traffic rates on each codepoint on the basis of price and QoS.

          On the Feasibility of Dynamic Congestion-Based Pricing in Differentiated Services Networks, IEEE/ACM Transactions on Networking, vol. 16 no. 5, October 2008, pp. 1001-1014.

          A Pricing Model for Networks with Priorities (with H. Jiang and I. Sidhu), Allerton Conference on Communication, Control and Computing, September 1996, pp. 269-275.

           

          Portions of this work were supported by DARPA and NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, DARPA, or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~sjordan/research/dca.html Overview of Networked Systems
          Scott Jordan
          Department of Computer Science University of California, Irvine
            Dynamic Channel Allocation in Cellular Networks

          Diagram of a Cellular Network

          Our focus in this work is dynamic channel allocation in cellular networks. Many DCA algorithms have been proposed in the literature. However, they are often based on heuristics, and optimal policies are rarely considered. Our goal here is to present a framework within which DCA policies can be presented and evaluated.

          In the first paper, we suggest a categorization of DCA policies on the basis how the policy determines connection access control, channel assignment, and channel reassignment. We hope that this categorization will help distill the concepts involved and encourage the research that is necessary to extend these concepts to future integrated service wireless systems.

          Resource Allocation in Wireless Networks, Journal of High Speed Networks, vol. 5, no. 1, 1996, pp. 23-34.

          In the second paper, we suggest that the optimal DCA policy varies from maximum packing at low loads to fixed allocation at high loads. This policy is often impractical to implement, but can be considered as a performance bound on practical systems.

          A Performance Bound on Dynamic Channel Allocation in Cellular Systems: Equal Load (with A. Khan), IEEE Transactions on Vehicular Technology, vol. 43 no. 3, April 1994, pp. 333-344.

          In the third paper, we introduce two worst-base performance metrics. We prove a lower bound on any DCA policy, and demonstrate the tradeoff between the performance and the complexity of a channel allocation policy.

          Worst-Case Performance of Cellular Channel Assignment Policies (with E. Schwabe.), ACM Journal of Wireless Networks, vol. 2, 1996, pp. 265-275.

          In the final paper, we consider DCA in interference-limited systems such as CDMA. We focus on the relationship between system performance and the amount of imbalance in load among neighboring cells. We find that with use of C/I information, the difference in performance between FCA and DCA (in terms of throughput or blocking probability) is increasing with load imbalance.

          Dynamic Channel Allocation in Interference-Limited Cellular Systems with Uneven Traffic Distribution, (with Y. Argyropoulos, S. Kumar) IEEE Transactions on Vehicular Technology, vol. 48 no. 1, January 1999, pp. 224-232.

          Portions of this work were supported by NSF. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. One print or electronic copy may be made for personal use only. Permission must be obtained from the copyright holder for systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in these papers for a fee or for commercial purposes, modification of the content of these papers, reprinting or republishing of this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, and to reuse any copyrighted component of this work in other works.

           

          Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
          http://www.ics.uci.edu/~mjcarey/index.html Home : index

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

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          • EDUCATION

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          • INVITED LECTURES and PANELS

          • CONTACT



          Professional Interests

          Database management systems, data-intensive computing, information integration, middleware, distributed systems, and computer system performance evaluation.

          Education

          Ph.D. in Computer Science, December 1983.
          University of California at Berkeley

          M.S. in Electrical Engineering (Computer Engineering), May 1981.
          Carnegie-Mellon University

          B.S. (University Honors) in Electrical Engineering and Mathematics, May 1979.
          Carnegie-Mellon University

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Prior_Research_Funding.html Home : MJCarey Prior Research Funding

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

          • PROFESSIONAL EXPERIENCE

          • PUBLICATIONS

          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

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          • CONTACT

          Research Funding

          • Principal Investigator (with C. Li), ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis, NSF, UC Irvine, 8/09-7/12.
          • Principal Investigator, ASTERIX: A Scalable Platform for XML Information Analysis, UC Discovery Program and eBay, UC Irvine, 4/09-3/11.
          • Principal Investigator, A Declarative and Open Source Data Mapping Tool for OpenII, Google Faculty Award, UC Irvine, 2/09.
          • Co-Investigator (with D. DeWitt, J. Naughton, and M. Solomon), SHORE - A High-Performance, Scalable, Heterogeneous Object Repository for Mach and Touchstone, DARPA, UW-Madison, 8/91-7/94.
          • Co-Investigator (with D. Maier, D. DeWitt, and S. Zdonik), Architectures for Query Processing in Persistent Object Bases, subcontract of a DARPA contract at the Oregon Graduate Institute, UW-Madison, 10/91-9/94.
          • Co-Investigator (with D. DeWitt and J. Naughton), Scheduling and Complex Query Processing in Highly Parallel Database Machines, IBM Corporation, UW-Madison, 1/91-12/93.
          • Co-Investigator (with M. Vernon, C. Dyer, M. Hill, R. Meyer, and B. Miller), PRISM: A Laboratory for Research in Future High-Performance Parallel Computing, NSF, UW-Madison, 7/91 - 6/96.
          • Co-Investigator (with M. Livny), Client-Server DBMS Architectures, IBM Corporation, UW-Madison, 12/90-12/91.
          • Co-Investigator (with D. DeWitt), Porting EXODUS to Camelot and Mach, DARPA, UW-Madison, 8/89-9/91.
          • Co-Investigator (with D. DeWitt), Extensible Database Systems, DARPA, UW-Madison, 8/85-1/91.
          • Co-Investigator (with M. Livny), HiPAC - A High Performance Active Data Manager for Time-Constrained Knowledge Processing, subcontract of a DARPA contract at Computer Corporation of America, UW-Madison, 5/87-4/89.
          • Principal Investigator, Presidential Young Investigator Grant, NSF, UW-Madison, 9/87-8/92.
          • Principal Investigator, Incentives for Excellence Award, Digital Equipment Corporation, UW-Madison, 11/86-10/89.
          • Principal Investigator, The Performance of Algorithms for Shared Relational Database Systems, NSF, UW-Madison, 9/84-2/87.
          • Principal Investigator, IBM Faculty Development Award, IBM Corporation, UW-Madison, 6/84-6/86.

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Patents.html Home : MJCarey Patents

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

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          Patents

          • Using an XML query language to publish relational data as XML (with J. Shanmugasundaram, E. Shekita, and N. Iyer), US6947945 (09/20/2005).
          • Identification of vacuous predicates in computer programs (with S. Rielau and B. Vance), US6728952 (04/27/2004).
          • Query optimization using a multi-layered object cache (with G. Kiernan), US6457020 (09/24/2002).
          • Efficient implementation of typed view hierarchies for ORDBMS (with G. Lapis, H. Pirahesh, S. Rielau, and B. Vance), US6421658 (07/16/2002).
          • Query optimization with deferred update and autonomous sources (with G. Kiernan), US6285997 (09/04/2001), US6574639 (06/03/2003).
          • System, method, and program for applying query rewrite technology to object building (with G. Kiernan), US6134540 (10/17/2000).
          • System, method, and program for object building in queries over object views (with G. Kiernan), US6122627 (09/19/2000), US6226637 (05/01/2001), US6477527 (11/05/2002).
          • Database management system, method, and program for providing query rewrite transformations for nested set elimination in database views (with G. Kiernan), US6006214 (12/21/1999).
          • Method and system for limiting the cardinality of an SQL query result(with D. Kossmann), US5956706 (09/21/1999).
          • Handling null values in SQL queries over object-oriented data (with G. Kiernan), US5905982 (05/18/1999).
          • Database method and apparatus for interactively retrieving data members and related members from a collection of data (with P. Boyer and G. Kiernan), US5778355 (07/07/1998).

          Home


          http://www.ics.uci.edu/~mjcarey/MJCarey_Invited_Lectures.html Home : MJCarey Invited Lectures

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

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          Invited Lectures and Panels

          • Thoughts on the Future: Opportunities and Roadblocks, panel presentation(s), 2010 NSF Information Integration and Informatics Workshop (4/2010), ACM SIGMOD New Researcher Symposium (6/2010).
          • Cloudy Skies for Analytics, panel presentation, Annual Stanford InfoLab Workshop (4/2010).
          • Beyond MapReduce: The Hyrax Platform for Data-Intensive Computing (and an ASTERIX Overview), Facebook (3/2010).
          • Data Services: Past, Present, and Future, Keynote Talk, Second IEEE Workshop on Information & Software as Services (3/2010).
          • Whither Storm Clouds?, panel chair for “Cloud Computing for Information Management and Analysis in Emergency Response Scenarios”, DHS Workshop on Emergency Management: Incident, Resource, and Supply Chain Management, UC Irvine (11/2009).
          • ASTERIX: A New Platform for Scalable Semistructured Data Management and Analysis, Teradata Corporation (8/2009), Cloudera Corporation (11/2009).
          • XQuery Is The Answer! (What Was the Question?), Computer Science Colloquium, UC Irvine (9/2008), Computer Science Colloquium, Cornell University (10/2008), SAP Laboratories (6/2009).
          • Services Modeling: Data Modeling in the SOA Age, Data Services World 2008 West (11/2008).
          • Declarative Data Services: This Is Your Data on SOA, Distinguished Lecture, USC Computer Science Department (10/2007), Distinguished Lecture, UC Irvine Computer Science Department (11/2007), Colloquium, UC Riverside Computer Science Department (12/2007).
          • SOA What?  2007 Symposium on High-Performance Transaction Systems (10/2007).
          • Declarative Data Services: This Is Your Data on SOA, Keynote Talk, IEEE SOCA Symposium (6/2007).
          • Data Delivery in a Service-Oriented World: The BEA AquaLogic Data Services Platform, New England Database Society (3/2006), UC Santa Cruz (5/2006), IEEE Shannon Lecture at Stanford University (3/2007), HP (3/2007).
          • Integrating Data and Services: Products and Challenges at BEA, Keynote Talk, Penn Engineering Workshop on Information Integration (10/2006).
          • This Is Your Data On SOA, UC Berkeley (12/2005), eBay (2/2006).
          • XML Data Services, Keynote Talk, 2005 IEEE ICWS Conference (7/2005).
          • EAI ... EII ... Aye Yi Yi!, panel session on "Enterprise Information Integration: Successes, Challenges, and Controversies", 2005 SIGMOD Conference (6/2005).
          • XQuery Crystal Ball, panel on "What is the Future of XQuery?", ACM XIME-P Workshop (6/2005).
          • XML Data Services: Data Modeling in the Web Services Era, UC San Diego (2/2005), San Jose State University (3/2005), UC Santa Barbara (5/2005), UCLA (5/2005).
          • Integrating Services and Data with XQuery and XML Schema, Keynote Talk, 2005 ACM PLAN-X Workshop (1/2005).
          • Enterprise Data Services: Data Access in an SOA World, San Francisco DAMA Day (11/2004).
          • Fun With XQuery: Some Industrial/Implementation Panel Musings, panel on "What is the Vision for XQuery in the IT/DB Industry?", ACM XIME-P Workshop (6/2004).
          • Enterprise Information Integration - XML to the Rescue!, 2003 ER Conference (10/2003), UW-Madison (10/2003), HP Laboratories (10/2003), New England Database Society (12/2003), Stanford University (1/2004).
          • _XML: Finally, a Cure for Data?,_Distinguished Lecture, UC Riverside (5/2003).
          • Middleware Is 'Ware It's At, panel session on "Where Are Our Promising Research Directions: Database Server, Middleware, or Applications?", 2002 ICDE Conference (3/2002).
          • The Propel Distributed Services Platform, 2001 VLDB Conference (9/2001).
          • Toto, We're Not in Kansas Anymore: On Transitioning from Research to the Real World, VLDB Conference invited talk (9/2000), UW-Madison (11/2000), UC Berkeley (5/2001), Stanford University (5/2001).
          • On XML and Databases: Where's the Beef?, panel session, 2000 SIGMOD Conference (5/2000).
          • XML + Databases = ?, panel session, 2000 ICDE Conference (2/2000).
          • O-O, What Have They Done to DB2?, IBM Santa Teresa Laboratory (5/98), IBM Toronto Laboratory (10/98), UCLA (11/98), UC Berkeley (1/99).
          • Web Site Management is an Object-Relational Database Problem, panel on "Is Web-site Management a Database Problem?", 1998 VLDB Conference (8/98).
          • DB2 UDB Is the Answer; What Was the Question?, panel on "Looking for the Objects in Object-Relational DBMSs", 1997 OOPSLA Conference (10/97).
          • Of Objects and Databases: Revolution or Evolution?, ETH Latsis Symposium (8/97).
          • Semi-Structured Data Considered Harmful, panel on "Semi-Structured Data: Useful or Harmful?", SIGMOD/PODS Workshop on Management of Semistructured Data (5/97).
          • Object-Relational Database Systems: Evolution Beats Revolution, Intel Microcomputer Research Laboratory (2/97), Oregon Graduate Institute (3/97).
          • Of Objects and Databases: A Decade of Turmoil, 1996 VLDB Conference (8/96),
          • Stanford University (10/96), Oregon Graduate Institute (3/97).
          • PESTO: An Integrated Query/Browser for the Garlic Multimedia Information System, Stanford University (5/96).
          • The EXODUS Project: An Overview and Retrospective, Stanford University (5/94).
          • How Would You Like Your Objects Served?, IBM Almaden Research Center (3/94), Oregon Graduate Institute (3/94), HP Laboratories (4/94), Stanford University (5/94).
          • An Update on the OO7 OODBMS Benchmark, Sequent Corporation (3/94).
          • An Overview of the SHORE Project, MIT (4/93), DARPA POB Workshop (5/93), IBM Santa Teresa Laboratory (9/93).
          • Database System Technology: Where Is It Headed?, IBM Rochester (5/93).
          • The OO7 Benchmark, Cornell University (3/93), IBM Almaden Research Center (4/93), MIT (4/93), Tandem (7/93).
          • OODB Implementation and Performance Issues, Oregon Graduate Institute short course on Object-Oriented Database Systems (7/92), UC Berkeley Extension (5/93).
          • Alternatives to OODBMS: The Extended Relational and Extensible DBMS Approaches, Oregon Graduate Institute short course on Object-Oriented Database Systems (7/92),UC Berkeley Extension (5/93).
          • Data Caching Tradeoffs in Client-Server DBMS Architectures, Univ. of Colorado at Colorado Springs (5/91), IBM Hawthorne Research Center (1/91), Univ. of Waterloo (1/91), IBM Almaden Research Center (1/91), Univ. of Arizona (1/91).
          • The EXODUS Extensible DBMS Project, Univ. of Colorado at Colorado Springs (4/90), Univ. of Southern California (4/90), Univ. of California at Santa Barbara (1/90), Carnegie-Mellon Univ. (12/89), Air Force Institute of Technology (3/89), AT&T Bell Laboratories (3/89).
          • Load Control for Locking: The `Half-and-Half' Approach, IBM Almaden Research Center (8/89).
          • Extensible Database Systems, invited tutorial at the 5th Int'l. Conf. on Data Engineering (2/89).
          • The Extensible DBMS Approach, panel on "Extensible, Object-Oriented, or Semantic?  Sorting Out the Isues," 1988 VLDB Conf. (8/88).
          • Distributed Concurrency Control Performance: A Study of Algorithms, Distribution, and Replication, IBM Almaden Research Center (8/88).
          • Extensible Database Systems, Oregon Database Forum short course on Next Generation Database Systems (held at Portland State Univ., 2/88).
          • Extensible Database Systems: An Overview, 1987 GTE Software Conf. (10/87).
          • The EXODUS Extensible Database System Project, Computer Corporation of America (11/86), Wang Institute of Graduate Studies (11/86), Microelectronics and Computer Technology Consortium (10/86).
          • The EXODUS Project, panel on "Extensible Database Systems," 1986 SIGMOD Conf. (5/86).
          • Load Balancing in a Locally Distributed Database System, AT&T Bell Laboratories (1/86).
          • Concurrency Control Performance Modeling: Alternatives and Implications, Computer Corporation of America (9/85).
          • To Block or to Restart?  Concurrency Control Policies and Performance, IEEE Int'l. Workshop on Data Management in Distributed Real Time Telecommunications Systems (12/83).

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Honors.html Home : MJCarey Honors

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

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          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

          • PATENTS

          • INVITED LECTURES and PANELS

          • CONTACT

          Honors and Awards

          • 2010 Chancellor's Award for Excellence in Fostering Undergraduate Research, UC Irvine, 2010.
          • ICS Faculty Research Incentive Award, UC Irvine, 2009.
          • Senior Member, IEEE, 2009.
          • ACM SIGMOD Edgar F. Codd Innovations Award, 2005.
          • Test of Time Paper Award, ACM SIGMOD Conference, 2004.
          • Member, National Academy of Engineering, 2002.
          • Distinguished Alumnus Award, EECS Department, UC Berkeley, 2002.
          • Fellow, Association for Computing Machinery (ACM), 2000.
          • ACM SIGMOD Contributions Award (co-recipient with L. Haas), 2000.
          • IBM Outstanding Technical Achievement Award, 1999.
          • Stonebraker Visiting Fellow, UC Berkeley CS Department, 1999.
          • IBM Technical Group Award, 1998.
          • IBM Second Plateau Invention Achievement Award, 1998.
          • IBM First Plateau Invention Achievement Award, 1997.
          • 10-Year Best Paper Award, VLDB Conference, 1996.
          • NSF Presidential Young Investigator Award, 1987.
          • Incentives for Excellence Award, Digital Equipment Corporation, 1986.
          • Computer Sciences Department Teaching Award, UW-Madison, 1985.
          • IBM Faculty Development Award, 1984.
          • California MICRO Fellowship, UC Berkeley, Spring 1982 and 1983.
          • Stanley M. Tasheira Scholarship, UC Berkeley, 1980-1981.
          • Outstanding Student Award, IEEE Student Branch of Carnegie-Mellon University 1979.
          • Member, Tau Beta Pi and Eta Kappa Nu honor societies, Carnegie-Mellon University chapters.

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Professional_Experience.html Home : MJCarey Professional Experience

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

          • PROFESSIONAL EXPERIENCE

          • PUBLICATIONS

          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

          • PATENTS

          • INVITED LECTURES and PANELS

          • CONTACT

          Professional Experience

          07/08-present:  Donald Bren Professor of Computer and Information Sciences, Computer Science Department, University of California, Irvine.

          11/01-7/08:  Senior Engineering Director, BEA Systems, Inc. (later Oracle Corporation), San Jose, CA.  (Technical Director 11/01-8/05; Senior Engineering Director 8/05-5/08; Architect, 6/08-7/08.)

          12/07-present:  Adjunct Professor, Computer Science Department, University of Southern California.

          4/07-6-07:  Visiting Lecturer (part time), Computer Science Department, Stanford University.

          3/00-11/01:  Fellow, Propel Software Corporation, Santa Clara, CA.

          5/95-3/00:  Research Staff Member/Manager, IBM Almaden Research Center, San Jose, California.

          1/99-5/99:  Stonebraker Visiting Fellow (part time), Computer Science Division, EECS Department, UC Berkeley.

          8/83-5/95:  Professor, Computer Sciences Department, University of Wisconsin-Madison.  (Assistant Professor through 8/88; Associate Professor through 8/91;  Professor from 8/91 onwards.)

          8/93-7/94:  Visiting Scientist, IBM Almaden Research Center, San Jose, California.

          6/89-8/89:  Visiting Scientist, IBM Almaden Research Center, San Jose, California.

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_PhD_Students_Supervised.html Home : MJCarey PhD Students Supervised

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

          • PROFESSIONAL EXPERIENCE

          • PUBLICATIONS

          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

          • PATENTS

          • INVITED LECTURES and PANELS

          • CONTACT

          PhD Students Supervised

          • J. Shafer, Parallelization of Data Mining Operations, UW-Madison, May 1998.***
          • M. McAuliffe, Storage Management Methods for Object Database Systems, UW-Madison, June 1997.**
          • M. Zaharioudakis, Highly Concurrent Cache Consistency for Object-Oriented Database Systems, UW-Madison, April 1997.
          • K. Brown, Goal-Oriented Memory Allocation in Database Management Systems, UW-Madison, August 1995.*
          • H. Pang, Query Processing in Firm Real-Time Database Systems, UW-Madison, March 1994.*
          • P. Bober, Towards Practical Multiversion Locking Techniques for On-Line Query Processing, UW-Madison, June 1993.
          • M. Franklin, Caching and Memory Management in Client-Server Database Systems, UW-Madison, June 1993.*
          • V. Srinivasan, On-Line Processing in Large-Scale Transaction Systems, UW-Madison, January 1992.
          • J. Haritsa, Transaction Scheduling in Firm Real-Time Database Systems, UW-Madison, August 1991.*
          • E. Shekita, High-Performance Implementation Techniques for Next-Generation Database Systems, UW-Madison, December 1990.
          • R. Jauhari, Priority Scheduling in Database Management Systems, UW-Madison, August 1990.*
          • J. Richardson, E: A Persistent Systems Implementation Language, UW-Madison, August 1989.
          • T. Lehman, Design and Performance Evaluation of a Main Memory Relational Database System, UW-Madison, August 1986.
          • H. Lu, Distributed Query Processing with Load Balancing in Local Area Networks, UW-Madison, December 1985.

            * Supervised jointly with Miron Livny.
            ** Supervised jointly with Marvin Solomon.
            *** Supervised jointly with Rakesh Agrawal.

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Professional_Activities.html Home : MJCarey Professional Activities

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

          • PROFESSIONAL EXPERIENCE

          • PUBLICATIONS

          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

          • PATENTS

          • INVITED LECTURES and PANELS

          • CONTACT

          Professional Activities

          Offices Held

          • Secretary/Treasurer, ACM Special Interest Group on Management of Data (SIGMOD), 1989-1997.
          • Chair, ACM SIGMOD Advisory Board, 1998-2001.
          • Trustee, VLDB Endowment, 1996-2002, 2010-present.
          • Technical Advisory Board Member, FLOWR Foundation, 2006-present.

          External Committees

          • Member, NRC Committee on the Social Security Administration's E-Government Strategy and Planning for the Future, 2006-2007.
          • Member, ACM SIGMOD Awards Committee, 2006-present.
          • Chair, ACM SIGMOD Awards Committee, 2007.
          • Member, IEEE Data Engineering Awards Committee, 2007-present.

          Journal/Book Editing

          • Associate Editor, Database Engineering, 1987-1990.
          • Editorial Board, VLDB Journal, 1996-2003.
          • Associate Editor, ACM Trans. on Database Systems, 1992-2004.
          • Co-Editor-in-Chief, Data-Centric Systems and Applications book series, Springer-Verlag, 2002-present.
          • Editorial Board, Foundations and Trends in Databases, Now Publishers, 2009-present.

          Conference Program Committee Chairing
          Program Co-Chair for 1993 PDIS Conf., Program Chair for 1995 ACM SIGMOD Conf., Americas Program Chair for 1997 VLDB Conf., Infrastructure Program Chair for 2003 VLDB Conf., Co-Chair for 2006 XIME-P Workshop, Industrial Applications and Experience Program Co-chair for 2009 VLDB Conf., Industry Track Co-Chair for 2010 ICDE Conf., Program Chair for 2011 HPTS Symposium, Program Co-Chair for 2012 ACM Symposium on Cloud Computing.

          Other Conference Chairing:
          Research Prototype Exhibits Chair for 1988 ACM SIGMOD Conf., Tutorial Chair for 1993 VLDB Conf., Panel Chair for 1993 ACM SIGMOD Conf., Industrial Program Co-Chair for 1994 ACM SIGMOD Conf., Co-Organizer of 1998 NSF Workshop on Academic/Industrial Cooperation in Database Systems, Panel Chair for 2000 ICDE Conf., Industrial Program Chair for 2002 ACM SIGMOD Conf, Tutorial Co-Chair for 2005 ICDE Conf., Panel Co-Chair for 2006 VLDB Conf., Seminars Co-Chair for ICDE 2008.

          Conference Program Committees:
          1985 ACM SIGMOD Conf., 1987 ACM SIGMOD Conf., 1988 ACM SIGMOD Conf., 1988 VLDB Conf., 1989 ACM SIGMOD Conf., 1989 VLDB Conf., 1989 Conf. on Data and Knowledge Sys. For Manuf. and Eng., 1990 ACM SIGMETRICS Conf., 1990 VLDB Conf., 1991 VLDB Conf., 1991 Conf. on Parallel and Dist. Info. Sys., 1992 IEEE Workshop on Rsch. Issues on Data Eng.: Trans. and Query Proc., 1992 ACM SIGMOD Conf., 1992 IEEE Workshop on Mgmt. of Replicated Data, 1994 Conf. on Extending Database Technology, 1994 VLDB Conf., 1996 ACM SIGMOD Conf., 1999 VLDB Conf., 2000 ACM SIGMOD Conf., 2000 COMAD Conf, 2002 VLDB Conf., 2003 Conf. on Innovative Data Systems Research (CIDR), 2003 ACM SIGMOD Conf. (Industrial Track), 2004 VLDB Conf. (Core Committee), 2005 Conf. on Innovative Data Systems Research (CIDR), 2005 IEEE Int'l. Conf. on Web Services (ICWS). 2006 ACM SIGMOD Conf., 2006 ICDE Conf. (Industrial Track), 2007 ICDE Conf. (Industrial Track), 2007 ACM SIGMOD Conf. (Industrial Track), 2007 VLDB Conf. (Infrastructure for Information Systems Track), 2008 VLDB Conf. (Industrial Track), 2008 Int'l. Workshop on Business Intelligence for the Real-Time Enterprise (BIRTE 08), 2008 Int'l. Conf. on Service-Oriented Computing (Tool Demonstration Track). 2009 ICDE Conf. (Industrial Track), 2009 Int’l. Workshop on Information & Software as a Service (WISS 10), 2010 WWW Conf. (Structured and Unstructured Data Track), 2010 ACM SOCC Conf, 2010 VLDB Conf. (Infrastructure for Information Systems Track), 2010 ACM/IFIP/USENIX Middleware Conf. (Industrial Track), 2011 CIDR Conference.

          Journal Refereeing:
          The VLDB Journal, ACM Trans. on Database Systems, ACM Trans. on Computer Systems, ACM Trans. on Information Systems, ACM Trans. on Software Eng. Methodology., ACM Computing Surveys, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Parallel and Dist. Sys., IEEE Trans. on Software Engineering, IEEE Computer, IEEE Trans. on Computers, Proc. of the IEEE, Dist. and Parallel Database Sys. Journal, Information Systems, Software---Practice & Experience, Information Processing Letters, Computer Networks.

          Other Conference Refereeing:
          1984 VLDB Conf., 1985 IEEE Parallel Processing Conf., 1986 Fall Joint Computer Conf., 1986 VLDB Conf., 1988 Conf. on Data and Knowledge Bases, 1987 EDBT Conf., 1989 Conf. on Manufacturing Data and Knowledge Management, 1990 Parbase Conf., 1990 ACM PODS Conf., 1991 ACM SIGMETRICS Conf., 1992 Data Eng. Conf., 1992 VLDB Conf., 1993 Data Eng. Conf., 1993 SIGMOD Conf.

          Proposal Reviewing:
          National Science Foundation, California MICRO Program, Australian National Research Council, IBM Corporation.

          Society Memberships:
          ACM (SIGMOD), IEEE, Eta Kappa Nu, Tau Beta Pi.

          Home

          http://www.ics.uci.edu/~mjcarey/MJCarey_Publications.html Home : MJCarey Publications

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

          • PROFESSIONAL INTERESTS

          • EDUCATION

          • PROFESSIONAL EXPERIENCE

          • PUBLICATIONS

          • PROFESSIONAL ACTIVITIES

          • PhD STUDENTS SUPERVISED

          • RESEARCH FUNDING

          • HONORS and AWARDS

          • PATENTS

          • INVITED LECTURES and PANELS

          • CONTACT

          Publications

          Refereed Conference Papers

          1. “Efficient Parallel Set-Similarity Joins Using MapReduce” (with R. Vernica and C. Li), Proc. of the ACM SIGMOD Int’l. Conf. on Management of Data, Indianapolis, IN, June 2010.
          2. “Graphical XQuery in the AquaLogic Data Services Platform” (with V. Borkar, S. Koleth, A. Kotopoulis, K. Mehta, J. Spiegel, S. Thatte, and T. Westmann), Proc. of the ACM SIGMOD Int’l. Conf. on Management of Data, Indianapolis, IN, June 2010.
          3. “Access Control in the AquaLogic Data Services Platform” (with. V. Borkar, D. Engovatov, D. Lychagin, P, Reveliotis, J. Spiegel, S. Thatte, and T. Westmann), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Providence, RI, June/July 2009.
          4. “Updates in the AquaLogic Data Services Platform” (with. V. Borkar, C. Hillery, A. Kotopoulis, D. Lychagin, R. Preotiuc-Pietro, P, Reveliotis, J. Spiegel, and T. Westmann), Proc. of the 25th Int'l. Conf. on Data Engineering, Shanghai, China, March/April 2009.
          5. "XQSE: An XQuery Scripting Extension for the AquaLogic Data Services Platform" (with. V. Borkar, D. Engovatov, D. Lychagin, T. Westmann, and W. Wong), Proc. of the 24th Int'l. Conf. on Data Engineering, Cancun, Mexico, April 2008.
          6. "Inverse Functions in the AquaLogic Data Services Platform" (with N. Onose and V. Borkar), Proc. of the 33rd Int'l. Conf. On Very Large Data Bases, Vienna, Austria, September 2007.
          7. "Query Processing in the AquaLogic Data Services Platform" (with V. Borkar, D. Lychagin, T. Westmann, D. Engovatov, and N. Onose), Proc. of the 32nd Int'l. Conf. On Very Large Data Bases, Seoul, Korea, September 2006.
          8. "Data Delivery in a Service-Oriented World: The BEA AquaLogic Data Services Platform", Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Chicago, IL, June 2006.  (Invited paper.)
          9. "SQL to XQuery Translation in the AquaLogic Data Services Platform" (with S. Jigyasu, S. Banerjee, V. Borkar, K. Dixit, A. Malkani, and S. Thatte), Proc. of the 22nd Int'l. Conf. on Data Engineering, Atlanta, GA, April 2006.
          10. "Enterprise Information Integration: Successes, Challenges, and Controversies" (with A. Halevy, N. Ashish, D. Bitton, D. Draper, J. Pollock, and A. Rosenthal), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Baltimore, MD, June 2005.
          11. "Implementing Memoization in a Streaming XQuery Processor" (with Y. Diao, D. Florescu, D. Kossmann, and M. Franklin), Proc. of the 2nd Int'l. XML Database Symposium, Toronto, Canada, August 2004.
          12. "XML in the Middle: XQuery in the WebLogic Platform", invited industrial paper, Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Paris, France, June 2004.
          13. "Liquid Data for WebLogic: XML-Based Enterprise Information Integration" (with the BEA Liquid Data Team), invited industrial paper, Proc. of the 20th Int'l. Conf. on Data Engineering, Boston, MA, March 2004.
          14. "The BEA/XQRL Streaming XQuery Processor" (with D. Florescu, C. Hillery, D. Kossmann, P. Lucas, F. Riccardi, T. Westmann, A. Sundararajan, and G. Agrawal), Proc. of the 29thInt'l. Conf. On Very Large Data Bases, Berlin, Germany, September 2003.
          15. "XMark: A Benchmark for XML Data Management" (with A. Schmidt, F. Waas, M. Kersten, I. Manolescu, and R. Busse), Proc. of the 28thInt'l. Conf. On Very Large Data Bases, Hong Kong, September 2002.
          16. The Propel Distributed Services Platform (with S. Kirsch, M. Roth, B. Van der Linden, N. Adiba, M. Blow, D. Florescu, D. Li, I. Oprencak, R. Panwar, R. Qi, D. Rieber, J. Shafer, B. Sterling, T. Urhan, V. Vickery, D. Wineman, and K. Yee), short paper, Proc. of the 27th Int'l. Conf. On Very Large Data Bases, Rome, Italy, September 2001.
          17. Middleware Object Query Processing with Deferred Updates and Autonomous Sources (with J. Kiernan), Proc. of the ACM Int'l. Conf. On Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), Minneapolis, MN, October 2000.
          18. Efficiently Publishing Relational Data as XML Documents (with J. Shanmugasundaram, E. Shekita, R. Barr, B. Lindsay, H. Pirahesh, and B. Reinwald), Proc. of the 26th Int'l. Conf. on Very Large Data Bases, Cairo, Egypt, September 2000.
          19. "Object View Hierarchies in DB2 UDB" (with S. Rielau and B. Vance), Proc. of the Int'l. Conf. on Extending Database Technology, Konstanz, Germany, March 2000.
          20. "O-O, What Have They Done to DB2?" (with D. Chamberlin, S. Narayanan, B. Vance, D. Doole, S. Rielau, R. Swagerman, and N. Mattos), Proc. of the 25th Int'l. Conf. on Very Large Data Bases, Edinburgh, Scotland, September 1999.
          21. "Vclusters: A Flexible Clustering Mechanism for Object Databases" (with M. McAuliffe and M. Solomon), Proc. of the ACM Int'l. Conf. On Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), Vancouver, Canada, October 1998.
          22. "Reducing the Braking Distance of an SQL Query Engine" (with D. Kossman), Proc. of the 24th Int'l. Conf. on Very Large Data Bases, New York, NY, August 1998.
          23. "On Saying `Enough Already!' in SQL" (with D. Kossman), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Tucson, AZ, May 1997.
          24. "The BUCKY Object-Relational Benchmark" (with D. DeWitt, J. Gehrke, J. Naughton, D. Shah, and M. Asgarian), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Tucson, AZ, May 1997.
          25. "Highly Concurrent Cache Consistency for Indices in Client-Server Database Systems" (with M. Zaharioudakis), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Tucson, AZ, May 1997.
          26. "Hierarchical, Adaptive Cache Consistency in a Page Server OODBMS" (with M. Zaharioudakis), Proc. of the 17th Int'l. Conf. on Distributed Computing Systems, Baltimore, Maryland, May 1997.
          27. "Of Objects and Databases: A Decade of Turmoil" (with D. DeWitt), Proc. of the 22nd Int'l. Conf. on Very Large Data Bases, Bombay, India, September 1996.  (Invited paper.)
          28. "PESTO: An Integrated Query/Browser for Object Databases" (with L. Haas, V. Maganty, and J. Williams), Proc. of the 22nd Int'l. Conf. on Very Large Data Bases, Bombay, India, September 1996.
          29. "Towards Effective and Efficient Free Space Management" (with M. McAuliffe and M. Solomon), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Montreal, Canada, June 1996.
          30. "Goal-Oriented Buffer Management Revisited" (with K. Brown and M. Livny), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Montreal, Canada, June 1996.
          31. "Extending SQL-92 for OODB Access: Design and Implementation Experience" (with J. Kiernan), Proc. of the ACM Int'l. Conf. on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), Austin, TX, October 1995.
          32. "Querying Multimedia Data From Multiple Repositories By Content: The Garlic Project" (with W. Cody, L. Haas, W. Niblack, M. Arya, R. Fagin, M. Flickner, D. Lee, D. Petkovic, P. Schwarz, J. Thomas, M. Tork Roth, J. Williams, and E. Wimmers), Proc. IFIP Working Conference on Visual Database Systems, Lausanne, Switzerland, March 1995.
          33. "Towards Heterogeneous Multimedia Information Systems: The Garlic Approach" (with L. Haas, P. Schwarz, M. Arya, W. Cody, R. Fagin, M. Flickner, A. Luniewski, W. Niblack, D. Petkovic, J. Thomas, J. Williams, and E. Wimmers), Proc. 1995 IEEE Workshop on Research Issues in Data Engineering (RIDE-95), Taipei, Taiwan, March 1995.
          34. "A Status Report on the OO7 OODBMS Benchmarking Effort" (invited paper with D. DeWitt, C. Kant, and J. Naughton), Proc. of the ACM Int'l. Conf. On Object-Oriented Programming Systems, Languages, and Applications (OOPSLA), Portland, OR, October 1994.
          35. "Towards Automated Performance Tuning for Complex Workloads" (with K. Brown, M. Mehta, and M. Livny), Proc. of the 20th Int'l. Conf. On Very Large Data Bases, Santiago, Chile, September 1994.
          36. "Fine-Grained Sharing in a Page Server OODBMS" (with M. Franklin and M. Zaharioudakis), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Minneapolis, MN, May 1994.
          37. "Managing Memory for Real-Time Queries" (with H. Pang and M. Livny), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Minneapolis, MN, May 1994.
          38. "Shoring Up Persistent Applications" (with D. DeWitt, M. Franklin, N. Hall, M. McAuliffe, J. Naughton, D. Schuh, M. Solomon, C. Tan, O. Tsatalos, S. White, and M. Zwilling), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Minneapolis, MN, May 1994.
          39. "Accurate Modeling of the Hybrid Hash Join Algorithm" (with J. Patel and M. Vernon), Proc. Of the ACM SIGMETRICS Conf. on Measurement and Modeling of Computer Systems, Nashville, TN, May 1994.
          40. "Indexing Alternatives for Multiversion Locking" (with P. Bober), Proc. of the Int'l. Conf. on Extending Database Technology, Cambridge, England, March 1994.
          41. "Managing Memory to Meet Multiclass Workload Response Time Goals" (with K. Brown and M. Livny), Proc. of the 19th Int'l. Conf. on Very Large Data Bases, Dublin, Ireland, August 1993.
          42. "Local Disk Caching for Client-Server Database Systems" (with M. Franklin and M. Livny), Proc. of the 19th Int'l. Conf. on Very Large Data Bases, Dublin, Ireland, August 1993.
          43. "Memory-Adaptive External Sorting" (with H. Pang and M. Livny), Proc. Of the 19th Int'l. Conf. on Very Large Data Bases, Dublin, Ireland, August 1993.
          44. "The OO7 Benchmark" (with D. DeWitt and J. Naughton), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington, DC, May 1993.
          45. "Partially Preemptible Hash Joins" (with H. Pang and M. Livny), Proc. Of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington, DC, May 1993.
          46. "Tapes Hold Data, Too: Challenges of Tuples on Tertiary Store" (with L. Haas and M. Livny), Database Challenges Session (short paper), Proc. of the ACM SIGMOD Int'l. Conf. On Management of Data, Washington, DC, May 1993.
          47. "Transaction Scheduling in Multiclass Real-Time Database Systems" (with H. Pang and M. Livny), Proc. of the 13th Real-Time Systems Symposium, Phoenix, AZ, December 1992.
          48. "Global Memory Management in Client-Server DBMS Architectures" (with M. Franklin and M. Livny), Proc. of the 18th Int'l. Conf. on Very Large Data Bases, Vancouver, BC, Canada, August 1992.
          49. "Multiversion Query Locking" (with P. Bober), Proc. of the 18th Int'l. Conf. on Very Large Data Bases, Vancouver, BC, Canada, August 1992.
          50. "Compensation-Based On-Line Query Processing" (with V. Srinivasan), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, San Diego, CA, June 1992.
          51. "Crash Recovery in Client-Server EXODUS" (with M. Franklin, M. Zwilling, C. Tan, and D. DeWitt), Proc. of the ACM SIGMOD Int'l. Conf. On Management of Data, San Diego, CA, June 1992.
          52. "Performance of On-Line Index Construction Algorithms" (with V. Srinivasan), Proc. of the Int'l. Conf. on Extending Database Technology, Vienna, Austria, March 1992.
          53. "On Mixing Queries and Transactions via Multiversion Locking" (with P. Bober), Proc. of the 8th Int'l. Conf. on Data Engineering, Phoenix, AZ, February 1992.
          54. "Earliest Deadline Scheduling for Real-Time Database Systems" (with J. Haritsa and M. Livny), Proc. of the 12th Real-Time Systems Symposium, San Antonio, TX, December 1991.
          55. "Preliminary Results on Combining Value and Deadline in Real-Time Database Systems" (with J. Haritsa and M. Livny), Proc. of the Int'l. Conf. On Management of Data (COMAD), Bombay, India, December 1991.
          56. "On-Line Index Construction Algorithms" (with V. Srinivasan), Proc. Of the Fourth Int'l. Workshop on High Performance Transaction Systems, Pacific Grove, CA, September 1991.
          57. "Data Caching Tradeoffs in Client-Server DBMS Architectures" (with M. Franklin, M. Livny, and E. Shekita), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Denver, CO, May 1991.
          58. "Performance of B-Tree Concurrency Control Algorithms" (with V. Srinivasan), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Denver, CO, May 1991.
          59. "Dynamic Real-Time Optimistic Concurrency Control" (with J. Haritsa and M. Livny), Proc. of the 11th Real-Time Systems Symposium, Orlando, FL, December 1990.
          60. "An Incremental Join Attachment for Starburst" (with E. Shekita, G. Lapis, B. Lindsay, and J. McPherson ), Proc. of the 16th Int'l. Conf. on Very Large Data Bases, Brisbane, Australia, August 1990.
          61. "Priority-Hints: An Algorithm for Priority-Based Buffer Management" (with R. Jauhari and M. Livny), Proc. of the 16th Int'l. Conf. On Very Large Data Bases, Brisbane, Australia, August 1990.
          62. "A Performance Evaluation of Pointer-Based Joins" (with E. Shekita), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Atlantic City, NJ, May 1990.
          63. "Load Control for Locking: The 'Half-and-Half' Approach" (with S. Krishnamurthi and M. Livny), Proc. of the 9th ACM Symposium on Principles of Database Systems, Nashville, TN, April 1990.
          64. "On Being Optimistic about Real-Time Constraints" (with J. Haritsa and M. Livny), Proc. of the 9th ACM Symposium on Principles of Database Systems, Nashville, TN, April 1990.
          65. "Priority in DBMS Resource Scheduling" (with R. Jauhari and M. Livny), Proc. of the 15th Int'l. Conf. on Very Large Data Bases, Amsterdam, The Netherlands, August 1989.
          66. "Parallelism and Concurrency Control Performance in Distributed Database Machines" (with M. Livny), Proc. of the ACM SIGMOD Int'l. Conf. On Management of Data, Portland, OR, June 1989.
          67. "Performance Enhancement Through Replication in an Object-Oriented DBMS" (with E. Shekita), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Portland, OR, June 1989.
          68. "A Concurrency Control Algorithm for Memory-Resident Database Systems" (with T. Lehman), Proc. of the 3rd Int'l. Conf. on Foundations of Data Organization and Algorithms, Paris, France, June 1989.
          69. "Distributed Concurrency Control Performance: A Study of Algorithms, Distribution, and Replication" (with M. Livny), Proc. of the 14th Int'l. Conf. on Very Large Data Bases, Los Angeles, CA, August 1988.
          70. "A Data Model and Query Language for EXODUS" (with D. DeWitt and S. Vandenberg), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Chicago, IL, June 1988.
          71. "Programming Constructs for Database System Implementation in EXODUS" (with J. Richardson), Proc. of the ACM SIGMOD Int'l. Conf. On Management of Data, San Francisco, CA, May 1987.
          72. "A Recovery Algorithm for a High-Performance Memory-Resident Database System" (with T. Lehman), Proc. of the ACM SIGMOD Int'l. Conf. O Management of Data, San Francisco, CA, May 1987.
          73. "The Architecture of the EXODUS Extensible DBMS" (with D. DeWitt, D. Frank, G. Graefe, J. Richardson, E. Shekita, and M. Muralikrishna), Proc. Of the 1st Int'l. Workshop on Object-Oriented Database Systems, Pacific Grove, CA, September 1986.
          74. "Object and File Management in the EXODUS Extensible Database System" (with D. DeWitt, J. Richardson, and E. Shekita), Proc. of the 12th Int'l. Conf. on Very Large Data Bases, Kyoto, Japan, August 1986.
          75. "A Study of Index Structures for Main Memory Database Management Systems" (with T. Lehman), Proc. of the 12th Int'l. Conf. on Very Large Data Bases, Kyoto, Japan, August 1986.
          76. "Load-Balanced Task Allocation in Locally Distributed Computer Systems" (with H. Lu), Proc. of the 1986 Int'l. Conf. on Parallel Processing, St. Charles, IL, August 1986.
          77. "Load Balancing in a Locally Distributed Database System" (with H. Lu), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington, DC, May 1986.
          78. "Query Processing in Main Memory Database Management Systems" (with T. Lehman), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Washington, DC, May 1986.
          79. "Some Experimental Results on Distributed Join Algorithms in a Local Network" (with H. Lu), Proc. of the 11th Int'l. Conf. on Very Large Data Bases, Stockholm, Sweden, August 1985.
          80. "Models for Studying Concurrency Control Performance: Alternatives and Implications" (with R. Agrawal and M. Livny), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Austin, TX, May 1985.
          81. "Dynamic Task Allocation in a Distributed Database System" (with M. Livny and H. Lu), Proc. of the 5th Int'l. Conf. on Distributed Computing Systems, Denver, CO, May 1985.
          82. "The Performance of Concurrency Control Algorithms for Database Management Systems" (with M. Stonebraker), Proc. of the 10th Int'l. Conf. on Very Large Data Bases, Singapore, August 1984.
          83. "An Abstract Model of Database Concurrency Control Algorithms," Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, San Jose, CA, May 1983.
          84. "Granularity Hierarchies in Concurrency Control," Proc. of the 2nd ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, Atlanta, Georgia, March 1983.

          Refereed Journal Articles

          1. “The Claremont Report on Database Research” (with R. Agrawal and many others), Comm. of the ACM, Vol. 52, No. 6, June 2009.
          2. “SOA What?”, IEEE Computer, Vol. 41, No. 3, March 2008.
          3. "XML Data Services" (with V. Borkar, N. Mangtani, D. McKinney, R. Patel, and S. Thatte), Int'l. Journal of Web Service Research, Vol. 3, No. 1, January-March 2006.
          4. "The Lowell Database Research Self-Assessment" (with S. Abiteboul and many others), Comm. of the ACM, Vol. 48, No. 5, May 2005.
          5. "The BEA Streaming XQuery Processor" (with D. Florescu, C. Hillery, D. Kossmann, P. Lucas, F. Riccardi, T. Westmann, and A. Sundararajan), VLDB Journal, Vol. 13, No. 3, September 2004.
          6. "Efficiently Publishing Relational Data as XML Documents" (with J. Shanmugasundaram, E. Shekita, R. Barr, B. Lindsay, H. Pirahesh, and B. Reinwald), VLDB Journal, Vol. 10, No. 2, April 2001.
          7. "Hierarchical, Adaptive Cache Consistency in a Page Server OODBMS" (with M. Zaharioudakis), IEEE Trans. on Computers, Vol. 47, No. 4, April 1998.
          8. "Adaptive, Fine-Grained Sharing in a Client-Server OODBMS: A Callback-Based Approach" (with M. Zaharioudakis and M. Franklin), ACM Trans. on Database Systems, Vol. 22, No. 4, December 1997.
          9. "Transactional Client-Server Cache Consistency: Alternatives and  Performance" (with M. Franklin and M. Livny), ACM Trans. on Database Systems, Vol. 22, No. 3, September 1997.
          10. "SEEKing the Truth About Ad Hoc Join Costs" (with L. Haas, M. Livny, and A. Shukla), VLDB Journal, Vol. 6, No. 3, July 1997.
          11. "Indexing for Multiversion Locking: Alternatives and Performance Evaluation" (with P. Bober), IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 1, January-February 1997.
          12. "Multi-Class Query Scheduling in Real-Time Database Systems" (with H. Pang and M. Livny), IEEE Trans. on Knowledge and Data Engineering, Vol. 7, No. 4, August 1995.
          13. "Performance of B-Tree Concurrency Control Algorithms" (with V. Srinivasan), VLDB Journal, Vol. 2, No. 4, October 1993.
          14. "The Design of the E Programming Language" (with J. Richardson and D. Schuh), ACM Trans. on Programming Languages and Systems, Vol. 15, No. 3, July 1993.
          15. "Value-Based Scheduling in Real-Time Database Systems" (with J. Haritsa and M. Livny), VLDB Journal, Vol. 2, No. 2, April 1993.
          16. "Performance Evaluation of Algorithms for Transitive Closure" (with R. Kabler and Y. Ioannidis), Information Systems, Vol. 17, No. 5, September 1992.
          17. "Data Access Scheduling in Firm Real-Time Database Systems" (with J. Haritsa and M. Livny), Journal of Real-Time Systems, Vol. 4, No. 3, September 1992.
          18. "Conflict Detection Tradeoffs for Replicated Data" (with M. Livny), ACM Trans. on Database Systems, Vol. 16, No. 4, December 1991.
          19. "Third-Generation Data Base System Manifesto" (with M. Stonebraker, L. Rowe, B. Lindsay, J. Gray, M. Brodie, P. Bernstein, and D. Beech), Computer Standards & Interfaces, No. 12, December 1991.
          20. "On Transaction Boundaries in Active Databases: A Performance Perspective" (with R. Jauhari and M. Livny), IEEE Trans. on Knowledge and Data Engineering, Vol. 3, No. 3, September 1991.
          21. "Storage Management for Persistent Complex Objects" (with S. Khoshafian and M. Franklin), Information Systems, Vol. 15, No. 3, March 1990.
          22. "Starburst Mid-Flight: As the Dust Clears" (with L. Haas, W. Chang, G. Lohman, J. McPherson, P. Wilms, G. Lapis, H. Pirahesh, and E. Shekita), IEEE Trans. on Knowledge and Data Engineering, Vol. 2, No. 1, March 1990.
          23. "Persistence in the E Language: Issues and Implementation" (with J. Richardson), Software---Practice & Experience, Vol. 19, No. 12, December 1989.
          24. "Concurrency Control Performance Modeling: Alternatives and Implications" (with R. Agrawal and M. Livny), ACM Trans. on Database Systems, Vol. 12, No. 4, December 1987.
          25. "The Performance of Alternative Strategies for Dealing with Deadlocks in Database Management Systems" (with R. Agrawal and L. McVoy), IEEE Trans. on Software Engineering, Vol. SE-13, No. 12, December 1987.
          26. "Improving the Performance of an Optimistic Concurrency Control Algorithm through Timestamps and Versions," IEEE Trans. on Software Engineering, Vol. SE-13, No. 6, June 1987.
          27. "The Performance of Multiversion Concurrency Control Algorithms" (with W. Muhanna), ACM Trans. on Computer Systems, Vol. 4, No. 4, November 1986.
          28. "An Efficient Implementation of Search Trees on (lclg N + 1(rc Processors" (with C. Thompson), IEEE Trans. on Computer Systems, Vol. C-33, No. 11, November 1984.
          29. "Multiprocessors for Power System Problems" (with S. Talukdar and S. Pyo), Joho-Shori, Vol. 22, No. 12, Info. Proc. Society of Japan, December 1981.

          Trade Journal Articles

          1. "Data Services: This is Your Data on SOA", Business Integration Journal, November/ December 2005.
          2. "Keep your Data Flowing: Accessing Multiple Data Sources Made Easy" (with N. Mangtani), BEA WebLogic Developer's Journal, Vol. 2, No. 10, October 2003.
          3. "Integrating Enterprise Information on Demand with XQuery (Part II)" (with D. Florescu and N. Mangtani), XML Journal, Vol. 2, No. 7, July 2003.
          4. "Integrating Enterprise Information on Demand with XQuery (Part I)" (with D. Florescu and N. Mangtani),XML Journal, Vol. 2, No. 6, June 2003.
          5. "Liquid Data: XQuery-Based Enterprise Information Integration" (with N. Mangtani), BEA WebLogic Developer's Journal, Vol. 2, No. 4, April 2003.

          Book Chapters

          1. "Client-Server Caching Revisited" (with M. Franklin), in Distributed Object Management, M. Oszu, U. Dayal, and P. Valduriez, eds., Morgan-Kaufmann Publishers, 1994.
          2. "The EXODUS Extensible DBMS Project: An Overview" (with D. DeWitt, G. Graefe, D. Haight, J. Richardson, D. Schuh, E. Shekita, and S. Vandenberg), in Readings in Object-Oriented Databases, S. Zdonik and D. Maier, eds., Morgan-Kaufman, 1990.
          3. "Storage Management for Objects in EXODUS" (with D. DeWitt, J. Richardson, and E. Shekita), in Object-Oriented Concepts, Databases, and Applications, W. Kim and F. Lochovsky, eds., Addison-Wesley Publishing Co., 1989. (Extended/updated version of a conference paper.)
          4. "Extensible Database Systems" (with D. DeWitt), in On Knowledge Base Management: Integrating Artificial Intelligence and Database Technologies, M. Brodie and J. Myopoulos, eds., Springer-Verlag, 1986.
          5. "Concurrency Control and Recovery for Prolog - A Proposal" (with D. DeWitt and G. Graefe), in Expert Database Systems, Benjamin/Cummings Publishing Company, L. Kerschberg, ed., 1986.
          6. "Logic Programming and Databases" (with D. Parker, F. Golshani, M. Jarke, E. Sciore, and A. Walker), in Expert Database Systems, Benjamin/Cummings Publishing Company, L. Kerschberg, ed., 1986.
          7. "A Pipelined Architecture for Search Tree Maintenance" (with C. Thompson), in Algorithmically Specialized Parallel Computers, L. Snyder, L. Jamieson, D. Gannon, and H. Siegel, eds., Academic Press, 1985.
          8. "Sorting Records in VLSI" (with P. Hansen and C. Thompson), in Algorithmically Specialized Parallel Computers, L. Snyder, L. Jamieson, D. Gannon, and H. Siegel, eds., Academic Press, 1985.

          Other Publications

          1. “Hyrax: A Flexible and Extensible Foundation for Data-Intensive Computing” (with V. Borkar, R. Grover, N. Onose, and R. Vernica), submitted for publication.
          2. “Answering Set-Similarity Selection Queries on Large Disk-Resident Data Sets” (with A. Behm and C. Li), submitted for publication.
          3. “EXRT: Towards a Simple Benchmark for XML Readiness Testing” (with L. Ling, M. Nicola, and L. Shao), 2nd TPC Technology Conf. on Performance Evaluation & Benchmarking (TPC TC), September 2010.
          4. “OpenII: An Open Source Information Integration Toolkit” (with L. Seligman, P. Mork, A. Halevy, K. Smith, K. Chen, C. Wolf, J. Madhavan, and A. Kannan), Industrial Abstract, Proc. of the ACM SIGMOD Int’l. Conf. on Management of Data, Indianapolis, IN, June 2010.
          5. “Experiences with XQuery Processing for Data and Service Federation (with M. Blow, V. Borkar, D. Engovatov, D. Lychagin, P. Reveliotis, J. Spiegel, and T. Westmann), IEEE Data Engineering Bulletin (Special Issue on XQuery Processing: Practice and Experience), Vol. 31, No. 4, December 2008.
          6. "XDM + SDO = XXDM: Getting Change Back From XDM" (with V. Borkar, D. Lychagin, R. Preotiuc-Pietro, P. Reveliotis, J. Spiegel, and T. Westmann),Proc. of the 5th Int'l. Workshop on XQuery Implementation, Experience, and Perspectives, Vancouver, Canada, June 2008.
          7. "Semantically-Assisted Integration Query Editing in the AquaLogic Data Services Platform (Demonstration)" (with S. Ghandeharizadeh, K. Mehta, P. Mork L. Seligman, S. Srivastava, and  S. Thatte),  IEEE International Conference on Semantic Computing 2008 (ICSC 2008), August 2008.
          8. "Data Service Modeling in the AquaLogic Data Services Platform (Extended Abstract)" (with P. Reveliotis, S. Thatte, and T. Westmann), 2008 IEEE SOA Industry Summit (SOAIS 2008), July 2008.
          9. "AL$MONY: Exploring Semantically-Assisted Matching in an XQuery-Based Data Mapping Tool" (with S. Ghandeharizadeh, K. Mehta, P. Mork, L. Seligman, and S. Thatte), Proc. of the Int'l. Workshop on Semantic Data and Service Integration, Vienna, Austria, September 2007.
          10. "XQuery-P: An XML Application Development Language" (with D. Chamberlin, M. Fernandez, D. Florescu, G. Ghelli, D. Kossmann, J. Robie, and J. Simeon), XML 2006 Conference, Boston, MA, December 2006.
          11. "Report on the Third International Workshop on XQuery Implementation, Experience, and Perspectives (XIME-P 2006)" (with T. Grust), ACM SIGMOD Record, to appear.
          12. "Integrating Data and Services: Products and Challenges at BEA", Position Paper, Penn Engineering Workshop on Information Integration, Philadephia, PA, October 2006.
          13. "XQuery-P: Programming with XQuery" (with D. Chamberlin, D. Florescu, D. Kossmann, and J. Robie),Proc. of the 3rd Int'l Workshop on XQuery Implementation, Experience, and Perspectives (XIME-P), Chicago, IL, June 2006.
          14. "The BEA AquaLogic Data Services Platform (Demo)" (with V. Borkar, D. Lychagin, and T. Westmann), Proc. of the ACM SIGMOD Int'l. Conf. on Management of Data, Chicago, IL, June 2006.
          15. "Your Enterprise on XQuery and XML Schema: XML-based Data and Metadata Integration" (with P. Reveliotis), Proc.of the 3rd Int'l. Workshop on XML Schema and Data Management (XSDM), Atlanta, GA, April 2006.
          16. "Service Data Objects, Version 2.0" (with J. Beatty, S. Brodsky, R. Ellersick, M. Nally, and R. Preotiuc-Pietro), IBM/BEA Joint Specification, June 2005.
          17. "Extending XQuery with Grouping, Duplicate Elimination and Outerjoins" (with. V. Borkar), XML 2004 Conference, Washington, DC, November 2004.
          18. "Integration, Web Services Style" (with M. Blevins and P. Takacsi-Nagy), Data Engineering Bulletin (Special Issue on Web Services), Vol. 25, No. 4, December 2001.
          19. "Why and How to Benchmark XML Databases" (with A. Schmidt, F. Waas, M. Kersten, D. Florescu, I. Manolescu, and R. Busse), ACM SIGMOD Record, Vol. 30, No. 3, September 2001.
          20. "Towards a Scalable Infrastructure for Advanced E-Services" (with the Propel Platform Team), Data Engineering Bulletin (Special Issue on Infrastructure for Advanced E-Services), Vol. 24, No. 1, March 2001.
          21. "XPERANTO: Efficiently Publishing Object-Relational Data as XML" (with D. Florescu, Z. Ives, Y. Lu, J. Shanmugasundaram, E. Shekita, S. Subramanian), Third Int'l. Workshop on the Web and Databases (WebDB'2000), May 2000.
          22. "Experiences in Implementing a Java Binding for an Object-Relational Database System" (with C.M. Park), 1999 OOPSLA Workshop on Java and Databases: Persistence Options, Denver, CO, November 1999.
          23. "NSF Workshop on Industrial/Academic Cooperation in Database Systems" (with L. Seligman), ACM SIGMOD Record, Vol. 28, No. 1, March 1999.
          24. "MAJOR: A Java Language Binding for Object-Relational Databases" (with C.M. Park and S. Dessloch), Proc. of the Persistent Object Systems Workshop, Napa, CA, September 1998.
          25. "Data Access Interoperability in the IBM Database Family" (with L. Haas, J. Kleewein, and B. Reinwald), Data Engineering Bulletin (Special Issue on Interoperability), Vol. 21, No. 3, September 1998.
          26. "Processing Top N and Bottom N Queries" (with D. Kossman), Data Engineering Bulletin (Special Issue on Improving Query Responsiveness), Vol. 20, No. 3, September 1997.
          27. "Accessing OODB Data from Legacy (Relational) Tools" (with J. Kiernan), ACM OOPSLA Legacy Systems and Object Technology Workshop, Austin, TX, October 1995.
          28. "Making Real Data Persistent: Initial Experiences with SMRC" (with B. Reinwald, S. Desslock, T. Lehman, H. Pirahesh, and V. Srinivasan), Proc. of the Persistent Object Systems Workshop, Tarascon, Provence, France, September 1994.
          29. "Towards an Autopilot in the DBMS Performance Cockpit" (with K. Brown and M. Livny), position paper, Fifth Int'l. Workshop on High Performance Transaction Systems Workshop, Pacific Grove, CA, September 1993.
          30. "The OO7 Benchmark: Current Status & Future Directions" (with D. DeWitt and J. Naughton), position paper, Fifth Int'l. Workshop on High Performance Transaction Systems Workshop, Pacific Grove, CA, September 1993.
          31. "Resource Allocation and Scheduling for Mixed Database Workloads" (with K. Brown, D. DeWitt, M. Mehta, and J. Naughton), Computer Sciences Technical Report No. 1095, University of Wisconsin-Madison, July 1992.
          32. "Extensible Database Management Systems" (with L. Haas), ACM SIGMOD Record, Vol. 19, No. 4, December 1990.
          33. "Persistence in E Revisited - Implementation Experiences" (with D. Schuh and D. DeWitt), Proc. of the Persistent Object Systems Workshop, Martha's Vineyard, MA, September 1990.
          34. "Implementing Persistence in E" (with J. Richardson), Proc. of the Persistent Object Systems Workshop, Newcastle, Australia, January 1989.
          35. "The HiPAC Project: Combining Active Databases and Timing Constraints" (with U. Dayal, B. Blaustein, A. Buchmann, U. Chakravarthy, M. Hsu, R. Ladin, D. McCarthy, A. Rosenthal, S. Sarin, M. Livny, and R. Jauhari),ACM SIGMOD Record (Special Issue on Real Time Data Base Systems), Vol. 17, No. 1, March 1988.
          36. "Persistence in EXODUS" (with J. Richardson, D. DeWitt, and D. Schuh), Proc. of Persistent Object Systems Workshop, Appin, Scotland,August 1987.
          37. "An Overview of the EXODUS Project" (with D. DeWitt), Database Engineering (Special Issue on Extensible Database Systems), Vol. 10, No. 2, June 1987.
          38. "The Performance of Concurrency Control and Recovery Algorithms for Transaction-Oriented Database Systems" (with R. Agrawal), Database Engineering, Vol. 8, No. 2, June 1985.
          39. Modeling and Evaluation of Database Concurrency Control Algorithms, Ph.D. Thesis, Computer Science Division, Department of Electrical Engineering and Computer Science, Univ. of California, Berkeley, September 1983.
          40. "Deadlock Detection is Cheap" (with R. Agrawal and D. DeWitt), ACM SIGMOD Record, Vol. 13, No. 2, January 1983.
          41. "Performance Analysis of Distributed Database Systems" (with M. Stonebraker, J. Woodfill, J. Ranstrom, M. Murphy, J. Kalash, and K. Arnold), Database Engineering, Vol. 5, No. 4, December 1982.
            #Parallel Processing for Power System Transient Simulation - A Case Study, M.S. Thesis, Department of Electrical Engineering, Carnegie-Mellon Univ., May 1981.

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          Michael J. Carey

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          University of California, Irvine

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          Michael J. Carey

          2091 Donald Bren Hall
          Department of Computer Science
          University of California, Irvine
          Irvine, CA 92697-3435
          (949) 824-2302

          mjcarey@ics.uci.edu

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          http://www.ics.uci.edu/~andre/address.html André van der Hoek's Address
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          University of California, Irvine
          Donald Bren School of Information and Computer Sciences
          Department of Informatics
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          Irvine, CA 92697-3440
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          http://www.ics.uci.edu/~andre/publications.html André van der Hoek's Publications
          home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
          projects
          Calico
          CodeExchange
          Crowd Development
          PorchLight
          Ph.D. students
          Christian Adriano
          Gerald Bortis
          Lee Martie
          J.18 T.D. LaToza and A. van der Hoek, Crowdsourcing in Software Engineering, IEEE Software (to appear).
          J.17 N. Mangano, T.D. LaToza, M. Petre, and A. van der Hoek, How Software Designers Interact with Informal Sketches at the Whiteboard, IEEE Transactions on Software Engineering, 41(2):2015, pages 135–156.
          J.16 N. Mangano and A. van der Hoek, The design and evaluation of a tool to support software designers at the whiteboard, Automated Software Engineering, 19(4): 2012, pages 381–421.
          J.15 A. Sarma, D. Redmiles, and A. van der Hoek, Palantír: Early Detection of Development Conflicts Arising from Parallel Code Changes, IEEE Transactions on Software Engineering, 38(4): 2012, pages 889–908.
          J.14 A. Baker and A. van der Hoek, Ideas, Subjects, and Cycles as Lenses for Understanding the Software Design Process, Design Studies, 31(6):2010, pages 590–613.
          J.13 A. Sarma, D. Redmiles, and A. van der Hoek, Categorizing the Spectrum of Coordination Technology, IEEE Computer, 43(6): 2010, pages 61–67.
          J.12 J. Georgas, A. van der Hoek, and R.N. Taylor, Using Architectural Models to Manage and Visualize Runtime Adaptation, IEEE Computer, 42(10):2009, pages 52–60.
          J.11 L.P.G. Murta, A. van der Hoek, and C.M.L Werner, Continuous and Automated Evolution of Architecture-to-Implementation Traceability Links, Automated Software Engineering Journal, 15(1):2008, pages 75–107.
          J.10 B. Al-Ani, D. Redmiles, A. van der Hoek, M. Alvim, I. Almeida da Silva, N. Mangano, E. Trainer, A. Sarma, Continuous Coordination within Software Engineering Teams: Concepts and Tool Support, Journal of Computer Science and Engineering in Arabic, 1(3):2008, pages 10–33.
          J.9 D. Redmiles, A. van der Hoek, B. Al-Ani, T. Hildenbrand, S. Quirk, A. Sarma, R. Silva Filho, C. de Souza, and E. Trainer, Continuous Coordination: A New Paradigm to Support Globally Distributed Software Development Projects, Wirtschaftsinformatik, 49:2007, pages S28–S38.
          J.8 J. Estublier, D. Leblang, G. Clemm, R. Conradi, A. van der Hoek, W. Tichy, D. Wiborg-Weber, Impact of the Research Community on the Field of Software Configuration Management, ACM Transactions on Software Engineering and Methodology, 14(4):2005, pages 1–48.
          J.7 E. Oh Navarro and A. van der Hoek, Software Process Modeling for an Educational Software Engineering Simulation Game, Software Process Improvement and Practice special issue containing expanded best papers from the Fifth International Workshop on Software Process Simulation and Modeling, 10(3):2004, pages 311–325.
          J.6 E. Dashofy, A. van der Hoek, and R.N. Taylor, A Comprehensive Approach for the Development of XML-Based Software Architecture Description Languages, ACM Transactions on Software Engineering and Methodology, 14(2):2005, pages 199–245.
          J.5 R. Roshandel, A. van der Hoek, M. Mikic-Rakic, N. Medvidovic, Mae – A System Model and Environment for Managing Architectural Evolution, ACM Transactions on Software Engineering and Methodology, 13(2):2004, pages 240–276.
          J.4 A. van der Hoek, Design-Time Product Line Architectures for Any-Time Variability, Science of Computer Programming special issue on Software Variability Management, 53(30):2004, pages 285–304.
          J.3 A. Baker, E. Oh Navarro, and A. van der Hoek, An Experimental Card Game for Teaching Software Engineering Processes, Journal of Systems and Software special issue containing invited and expanded best papers from the 2003 International Conference on Software Engineering & Training, 75:1-2, 2005, pages 3–16.
          J.2 A. van der Hoek and A.L. Wolf, Software Release Management for Component-Based Software, Software – Practice and Experience, 33:2003, pages 77–98.
          J.1 A. van der Hoek, A. Carzaniga, D. Heimbigner, and A.L. Wolf, A Testbed for Configuration Management Policy Programming, IEEE Transactions on Software Engineering, 28(1):2002, pages 79–99.
          B.2 M. Petre and A. van der Hoek, Software Designers in Action: A Human-Centric Look at Design Work, CRC Press, 2013.
          B.1 I. Mistrik, J. Grundy, A. van der Hoek, and J. Whitehead, Collaborative Software Engineering, Springer-Verlag, 2010.
          C.106 L. Martie. T.D LaToza, and A. van der Hoek, CodeExchange: Supporting Reformulation of Internet-Scale Code Queries in Context, Thirtieth International Conference on Automated Software Engineering, November 2015, pages 24–35
          C.105 T.D. LaToza, A. Di Lecce, F. Ricci, W.B. Towne, and A. van der Hoek, Ask the Crowd: Scaffolding Coordination and Knowledge Sharing in Microtask Programming, Symposium on Visual Languages and Human-Centric Computing, October 2015 (to appear)
          C.104 L. Martie and A. van der Hoek, Sameness: An Experiment in Code Search, Twelfth Working Conference on Mining Software Repositories, May 2015, pages 76–87
          C.103 M. Zhao and A. van der Hoek, A Brief Perspective on Microtask Crowdsourcing Workflows for Interface Design, Second International Workshop on Crowdsourcing in Software Engineering, May 2015, pages 45–46
          C.102 T.D. LaToza and A. van der Hoek, A Vision of Crowd Development, Thirty-seventh International Conference on Software Engineering New Ideas and Emerging Results Track, May 2015, pages 563–566
          C.101 T.D. LaToza, M. Chen, l. Jiang, M. Zhao, and A. van der Hoek, Borrowing from the Crowd: A Study of Recombination in Software Design Competitions, Thirty-seventh International Conference on Software Engineering, May 2015, pages 551–562.
          C.100 T.D. LaToza, W.B. Towne, C. Adriano, A. van der Hoek, Microtask Programming: Building Software with a Crowd, Symposium on User Interface Software and Technology, October 2014, pages 43–54.
          C.99 B. Penzenstadler, B. Tomlinson, E. Baumer, M. Pufal, A. Raturi, D. Richardson, B. Cakici, R. Chitchyan, G. Da Costa, L. Dombrowski, M. Picha Edwardsson, E. Eriksson, X. Franch, G.R. Hayes, C. Herzog, W. Lohmann, M. Mahaux, A. Mavin, M. Mazmanian, S. Nayebaziz, J. Norton, D. Pargman, D.J. Patterson, J.-M. Pierson, K. Roher, M. Silberman, K. Simonson, A. Torrance, and A. van der Hoek, ICT4S 2029: What Will Be the Systems Supporting Sustainability in 15 Years?, Second International Conference on ICT for Sustainability, August 2014, pages 30–39.
          C.98 N. Mangano, T.D. LaToza, M. Petre, and A. van der Hoek, Supporting Informal Design with Interactive Whiteboards, Conference on Human Factors in Computing Systems, April 2014, pages 331–340.
          C.97 T.D. LaToza, W. B. Towne, A. van der Hoek, J. D. Herbsleb, Crowd Development, Sixth International Workshop on Cooperative and Human Aspects of Software Engineering, May 2013, pages 85–88.
          C.96 L. Martie and A. van der Hoek, Toward Social-Technical Code Search, Sixth International Workshop on Cooperative and Human Aspects of Software Engineering, May 2013, pages 101–104.
          C.95 T.D. LaToza, E. Shabani, and A. van der Hoek, A Study of Architectural Decision Practices, Sixth International Workshop on Cooperative and Human Aspects of Software Engineering, May 2013, pages 77–80.
          C.94 A. Motta, N. Mangano, and A. van der Hoek, Light-weight Analysis of Software Design Models at the Whiteboard, Fifth International Workshop on Modeling in Software Engineering, May 2013, pages 18–23.
          C.93 D. Loksa, N. Mangano, T.D. LaToza, and A. van der Hoek, Enabling a Classroom Design Studio with a Collaborative Sketch Design Tool, Thirty-third International Conference on Software Engineering Education Track, May 2013, pages 1073–1082.
          C.92 G. Bortis and A. van der Hoek, PorchLight: a Tag-Based Approach to Bug Triaging, Thirty-fifth International Conference on Software Engineering, Thirty-fifth International Conference on Software Engineering, May 2013, pages 342–351.
          C.91 N. Mangano, M. Dempsey, N. Lopez, and A. van der Hoek, A Demonstration of a Distributed Software Design Sketching Tool, Thirty-third International Conference on Software Engineering Demonstration Track, May 2011, pages 1028–1030.
          C.90 N. Lopez and A. van der Hoek, The Code Orb - Supporting Contextualized Coding via At-a-Glance Views (NIER Track), Thirty-third International Conference on Software Engineering New Ideas and Emerging Results Track, May 2011, pages 824–827.
          C.89 G. Bortis and A. van der Hoek, TeamBugs: A Collaborative Bug Tracking Tool, Fourth International Workshop on Cooperative and Human Aspects of Software Engineering, May 2011, pages 69–71.
          C.88 A. van der Hoek and N. Lopez, A Design Perspective on Modularity, Tenth International Conference on Aspect-Oriented Software Development, March 2011, pages 265–279 (keynote paper).
          C.87 N. Lopez and A. van der Hoek, An Agenda for Concern-Oriented Software Engineering, FSE/SDP Workshop on Future of Software Engineering Research, November 2010, 5 pages.
          C.86 M. Grechanik, J.A. Jones, A. Orso, and A. van der Hoek, Bridging Gaps between Developers and Testers in Globally-Distributed Software Development, FSE/SDP Workshop on Future of Software Engineering Research, November 2010, 5 pages.
          C.85 N. Mangano, A. Baker, M. Dempsey, E. Navarro, and A. van der Hoek, Software Design Sketching with Calico, Twenty-fifth IEEE/ACM International Conference on Automated Software Engineering, September 2010, pages 23–32.
          C.84 S. Jansen, W. Buts, Sjaak Brinkkemper, and A. van der Hoek, Benchmarking the Customer Configuration Updating Process of the International Product Software Industry, International Conference on Software Process 2010, July 2010, pages 369–380.
          C.83 F. Servant, J.A. Jones, and A. van der Hoek, CASI: Preventing Indirect Conflicts through a Live Visualization, Third International Workshop on Cooperative and Human Aspects of Software Engineering, May 2010, pages 39–46.
          C.82 T. Proença, N. Moura, and A. van der Hoek, On the Use of Emerging Design as a Basis for Knowledge Collaboration, Third International Workshop on Knowledge Collaboration in Software Development, November 2009, pages 91–99.
          C.81 G. Bortis and A. van der Hoek, DesignMinders: A Design Knowledge Collaboration Approach, Third International Workshop on Knowledge Collaboration in Software Development, November 2009, pages 84–90.
          C.80 E. Nistor and A. van der Hoek, Explicit Concern-Driven Development in ArchEvol, Twenty-fourth IEEE/ACM International Conference on Automated Software Engineering, November 2009, pages 185–196.
          C.79 G. Bortis and A. van der Hoek, DesignMinders: Preserving and Sharing Informal Software Design Knowledge, Second Workshop on Knowledge Reuse, September 2009 (electronic proceedings, 8 pages).
          C.78 N. Lopéz, R. Casallas, and A. van der Hoek, Issues in Mapping Change-Based Product Line Architectures to Configuration Management Systems, Thirteenth International Software Product Line Conference, August 2009, pages 21–30.
          C.77 S.A Hendrickson, Y. Wang, A. van der Hoek, R.N. Taylor, and A. Kobsa, Modeling PLA Variation of Privacy-Enhancing Personalized Systems, Thirteenth International Software Product Line Conference, August 2009, pages 71–80.
          C.76 J.A. Jones, M. Grechanik, and A. van der Hoek, Enabling and Enhancing Collaborations between Software Development Organizations and Independent Test Agencies, Workshop on Cooperative and Human Aspects on Software Engineering, May 2009, pages 56–59.
          C.75 A. Baker and A. van der Hoek, An Experience Report on the Design and Delivery of Two New Software Design Courses, Fortieth ACM Technical Symposium on Computer Science Education, March 2009, pages 319–323.
          C.74 E. Navarro and A. van der Hoek, A Multi-Site Evaluation of SimSE, an Educational Software Engineering Simulation Game, Fortieth ACM Technical Symposium on Computer Science Education, March 2009, pages 326–330.
          C.73 A. Sarma, D. Redmiles, and A. van der Hoek, Empirical Evidence of the Benefits of Workspace Awareness in Software Configuration Management, Sixteenth ACM SIGSOFT International Symposium on the Foundations of Software Engineering, November 2008, 113–123.
          C.72 R. Ripley and A. van der Hoek, Decisions and Rationale during the Evolution of a Coordination Infrastructure, Workshop on Infrastructure for Research in Collaborative Software Engineering, November 2008 (electronic proceedings, 4 pages).
          C.71 N. Mangano, A. Baker, M. Dempsey, E. Navarro, and A. van der Hoek, Calico: A Tool for Early Software Design Sketching, Workshop on Sketch Tools for Diagramming, September 2008, pages 51–56.
          C.70 N. Mangano, A. Baker, and A. van der Hoek, Calico: A Prototype Sketching Tool for Modeling in Early Design, International Workshop on Modeling in Software Engineering, May 2008, pages 63–68.
          C.69 G. Bortis and A. van der Hoek, Software Pre-Patterns as Architectural Knowledge, Third International Workshop on Sharing and Reusing Architectural Knowledge, May 2008, pages 19–22
          C.68 S.A. Hendrickson, S. Subramanian, and A. van der Hoek, Multi-Tiered Design Rationale for Change Set Based Product Line Architectures, Third International Workshop on Sharing and Reusing Architectural Knowledge, May 2008, pages 41–44.
          C.67 B. Al-Ani, E. Trainer, R. Ripley, A. Sarma, A. van der Hoek, and David Redmiles, Continuous Coordination within the Context of Cooperative and Human Aspects of Software Engineering, First International Workshop on Cooperative and Human Aspects of Software Engineering, May 2008, pages 1–4.
          C.66 A. Sarma, G. Bortis, and A. van der Hoek, Towards Supporting Awareness of Indirect Conflicts across Software Configuration Management Workspaces, Twenty-second IEEE/ACM International Conference on Automated Software Engineering, November 2007, pages 94–103.
          C.65 A. Sarma, D. Redmiles, and A. van der Hoek, A Comprehensive Evaluation of Workspace Awareness in Software Configuration Management Systems, 2007 IEEE Symposium on Visual Languages and Human-Centric Computing, September 2007, pages 23–26.
          C.64 I. Almeida da Silva, M. Alvim, R. Ripley, A. Sarma, C.M.L. Werner, and A. van der Hoek, Designing Software Cockpits for Coordinating Distributed Software Development, First Workshop on Measurement-based Cockpits for Distributed Software and Systems Engineering Projects, August 2007, pages 14–19.
          C.63 E. Oh Navarro and A. van der Hoek, Comprehensive Evaluation of an Educational Software Engineering Simulation Environment, Twentieth Conference on Software Engineering Education and Training, July 2007, pages 195–202.
          C.62 R. Ripley, A. Sarma, and A. van der Hoek, A Visualization for Software Project Awareness and Evolution, Fourth IEEE International Workshop on Visualizing Software for Understanding and Analysis, June 2007, pages 137–144.
          C.61 S.A. Hendrickson and A. van der Hoek, Modeling Product Line Architectures through Change Sets and Relationships, Twenty-ninth International Conference on Software Engineering, May 2007, pages 189–198.
          C.60 I.A. da Silva, P. Chen, C. Van der Westhuizen, R. Ripley, and A. van der Hoek, Lighthouse: Coordination through Emerging Design, OOPSLA Eclipse Technology Exchange Workshop, October 2006, pages 11–15.
          C.59 A. Sarma and A. van der Hoek, Towards Awareness in the Large, First International Conference on Global Software Engineering, October 2006, pages 127–131.
          C.58 S.A. Hendrickson, B. Jett, and A. van der Hoek, Layered Class Diagrams: Supporting the Design Process, Ninth International Conference on Model Driven Engineering Languages and Systems, October 2006, pages 722–736.
          C.57 L.G.P. Murta, A. van der Hoek, and C.M.L. Werner, ArchTrace: Policy-Based Support for Managing Evolving Architecture-to-Implementation Traceability Links, Twenty-first IEEE/ACM International Conference on Automated Software Engineering, September 2006, pages 135–144.
          C.56 Y. Wang, A. Kobsa, A. van der Hoek, and J. White, PLA-based Runtime Dynamism in Support of Privacy-Enhanced Web Personalization, Tenth International Software Product Line Conference, August 2006, pages 151–160.
          C.55 L. Xu, S.A. Hendrickson, E. Hettwer, H. Ziv, A. van der Hoek, and D.J. Richardson, Towards Supporting the Architecture Design Process through Evaluation of Design Alternatives, Second International Workshop on the Role of Software Architecture for Testing and Analysis, July 2006, pages 38–44.
          C.54 C. Van der Westhuizen, P. Chen, and A. van der Hoek, Emerging Design: New Roles and Uses for Abstraction, Workshop on the Role of Abstraction in Software Engineering: Organizational, Managerial and Cognitive Perspectives, May 2006 (electronic proceedings, 6 pages).
          C.53 E. Nistor and A. van der Hoek, Concern Highlight: A Tool for Concern Exploration and Visualization, Workshop on Linking Aspect Technology and Evolution, March 2006 (electronic proceedings, 5 pages).
          C.52 D. Carrington, A. Baker, and A. van der Hoek, It�s All in the Game: Teaching Software Process Concepts, Frontiers in Education, October 2005, pages T1A1–T1A6.
          C.51 E. Nistor, J. Erenkrantz, S. Hendrickson, and A. van der Hoek, ArchEvol: Versioning Architectural-Implementation Relationships, Twelfth International Workshop on Software Configuration Management, September 2005, pages 99–111.
          C.50 J.C. Georgas, A. van der Hoek, and R.N. Taylor, Architectural Runtime Configuration Management in Support of Dependable Self-Adaptive Software, Workshop on Architecting Dependable Systems, May 2005, pages 48–53.
          C.49 T. Birkh�lzer, E. Oh Navarro, and A. van der Hoek, Teaching by Modeling instead of by Models, Sixth International Workshop on Software Process Simulation and Modeling, May 2005.
          C.48 A. van der Hoek, D.G. Kay, and D.J. Richardson, A B.S. Degree in Informatics: Contextualizing Software Engineering Education, Twenty-seventh International Conference on Software Engineering, May 2005, pages 641–642.
          C.47 E. Oh Navarro and A. van der Hoek, Scaling up: How Thirty-two Students Collaborated and Succeeded in Developing a Prototype Software Design Environment, Eighteenth Conference on Software Engineering Education & Training, February 2005, pages 155–162.
          C.46 E. Oh Navarro and A. van der Hoek, Design and Evaluation of an Educational Software Process Simulation Environment and Associated Model, Eighteenth Conference on Software Engineering Education & Training, February 2005, pages 25–32.
          C.45 D.G. Kay, A. van der Hoek, and D.J. Richardson, Informatics: A Focus on Computer Science in Context, SIGCSE 2005 Technical Symposium on Computer Science Education, February 2005, pages 551–555.
          C.44 A. Sarma, A. van der Hoek, and L.-T. Cheng, A Need-Based Collaboration Classification Framework, Workshop on Eclipse as a Vehicle for CSCW Research, November 2004, pages 16–20 (also available as IBM Technical Report RC23339).
          C.43 R. Ripley, R. Yasui, A. Sarma, and A. van der Hoek, Workspace Awareness in Application Development, OOPSLA Eclipse Technology Exchange Workshop, October 2004, pages 17–21.
          C.42 A. Baker, P. Chen, C. Van der Westhuizen, and A. van der Hoek, A Call for the Use of Display Technology to Support Software Development, Workshop on Ubiquitous Display Environments, September 2004.
          C.41 E. Oh Navarro and A. van der Hoek, SimSE: An Interactive Simulation Game For Software Engineering Education, IASTED Conference on Computers and Advanced Technology in Education, August 2004, pages 12–17.
          C.40 A. van der Hoek, D. Redmiles, P. Dourish, A. Sarma, R. Silva Filho, and C. de Souza, Continuous Coordination: A New Paradigm for Collaborative Software Engineering Tools, Workshop on Directions in Software Engineering Environments, May 2004, pages 29–36.
          C.39 A. Sarma and A. van der Hoek, A Conflict Detected Earlier is a Conflict Resolved Easier, Fourth Workshop on Open Source Software Engineering, May 2004, pages 82–86.
          C.38 E. Oh Navarro and A. van der Hoek, Software Process Modeling for an Interactive, Graphical, Educational Software Engineering Simulation Game, Fifth International Workshop on Software Process Simulation and Modeling, May 2004, pages 171–176.
          C.37 C. Lüer and A. van der Hoek, JPloy: User-Centric Deployment Support in a Component Platform, Second International Working Conference on Component Deployment, May 2004, pages 190–204.
          C.36 R. van der Lingen and A. van der Hoek, An Experimental, Pluggable Infrastructure for Modular Configuration Management Policy Composition, Twenty-Sixth International Conference on Software Engineering, May 2004, pages 573–582.
          C.35 A. Baker, E. Oh Navarro, and A. van der Hoek, Teaching Software Engineering using Simulation Games, International Conference on Simulation in Education, January 2004, pages 9–14.
          C.34 M. Critchlow, K. Dodd, J. Chou, and A. van der Hoek, Refactoring Product Line Architectures, First International Workshop on Refactoring: Achievements, Challenges, and Effects, November 2003, pages 23–26.
          C.33 A. Sarma and A. van der Hoek, Visualizing Parallel Workspace Activities, IASTED International Conference on Software Engineering and Applications, November 2003, pages 435–440.
          C.32 P. Chen, M. Critchlow, A. Garg, C. Van der Westhuizen, and A. van der Hoek, Differencing and Merging within an Evolving Product Line Architecture, Fifth International Workshop on Product Family Engineering, November 2003, pages 269–281.
          C.31 A. Garg, M. Critchlow, P. Chen, C. Van der Westhuizen, and A. van der Hoek, An Environment for Managing Evolving Product Line Architectures, International Conference on Software Maintenance 2003, September 2003, pages 358–367.
          C.30 A. van der Hoek, E. Dincel, and N. Medvidovic, Using Service Utilization Metrics to Assess the Structure of Product Line Architectures, Ninth IEEE Software Metrics Symposium, September 2003, pages 298–308.
          C.29 R. van der Lingen and A. van der Hoek, Dissecting Configuration Management Policies, Software Configuration Management: ICSE Workshops SCM 2001 and SCM 2003 Selected Papers, May 2003, pages 177–190.
          C.28 S. Sowrirajan and A. van der Hoek, Managing the Evolution of Distributed and Inter-related Components, Software Configuration Management: ICSE Workshops SCM 2001 and SCM 2003 Selected Papers, May 2003, pages 217–230.
          C.27 H. Muccini and A. van der Hoek, Towards Testing Product Line Architectures, International Workshop on Test and Analysis of Component Based Systems, April 2003, pages 111–121.
          C.26 A. Baker, E. Oh Navarro, and A. van der Hoek, Problems and Programmers: An Educational Software Engineering Card Game, Twenty-fifth International Conference on Software Engineering, May 2003, pages 614–619.
          C.25 A. Sarma, Z. Noroozi, and A. van der Hoek, Palantír: Raising Awareness among Configuration Management Workspaces, Twenty-fifth International Conference on Software Engineering, May 2003, pages 444–453.
          C.24 A. Baker, E. Oh Navarro, and A. van der Hoek, An Experimental Card Game for Teaching Software Engineering, Sixteenth International Conference on Software Engineering Education and Training, March 2003, pages 216–223.
          C.23 E. Dashofy, A. van der Hoek, and R.N. Taylor, Towards Architecture-Based Self-Healing Systems, First ACM SIGSOFT Workshop on Self-Healing Systems, November 2002, pages 21–26.
          C.22 P. Dourish and A. van der Hoek, Émigré: Metalevel Architecture and Migratory Work, Fourth International Symposium on Human Computer Interaction with Mobile Devices, September 2002, pages 281–285.
          C.21 C. Van der Westhuizen and A. van der Hoek, Understanding and Propagating Architectural Changes, Third Working IEEE/IFIP Conference on Software Architecture, August 2002, pages 95–109.
          C.20 A. Sarma and A. van der Hoek, Palantír: Coordinating Distributed Workspaces, Workshop on Cooperative Supports for Distributed Software Engineering Processes, August 2002, pages 1093–1097.
          C.19 E. Dashofy, A. van der Hoek, and R.N. Taylor, An Infrastructure for the Rapid Development of XML-Based Architecture Description Languages, Twenty-fourth International Conference on Software Engineering, May 2002, pages 266–276.
          C.18 A. Sarma and A. van der Hoek, Palantír: Increasing Awareness in Distributed Software Development, 2002 ICSE Workshop on Global Software Development, May 2002, pages 28–32.
          C.17 A. van der Hoek, Integrating Configuration Management and Software Deployment, Working Conference on Complex and Dynamic Systems Architecture, December 2001, pages 230–233.
          C.16 E. Dincel, N. Medvidovic, and A. van der Hoek, Measuring Product Line Architectures, Fourth International Workshop on Product Family Engineering, October 2001, pages 346–352.
          C.15 E. Dashofy and A. van der Hoek, Representing Product Family Architectures in an Extensible Architecture Description Language, Fourth International Workshop on Product Family Engineering, October 2001, pages 330–341.
          C.14 A. van der Hoek, M. Mikic-Rakic, R. Roshandel, and N. Medvidovic, Taming Architectural Evolution, Eighth European Software Engineering Conference with the Ninth International Symposium on the Foundations of Software Engineering, September 2001, pages 1–10.
          C.13 E. Dashofy, A. van der Hoek, and R.N. Taylor, A Highly-Extensible, XML-Based Architecture Description Language, Working IEEE/IFIP Conference on Software Architecture, September 2001, pages 103–112.
          C.12 C. Lüer, D. Rosenblum, and A. van der Hoek, The Evolution of Software Evolvability, International Workshop on the Principles of Software Evolution, September 2001, pages 131–134.
          C.11 E. Oh and A. van der Hoek, Adapting Game Technology to Support Individual and Organizational Learning, 2001 International Conference on Software Engineering and Knowledge Engineering, June 2001, pages 347–354.
          C.10 E. Oh and A. van der Hoek, Challenges in Using an Economic Cost Model for Software Engineering Simulation, Third International Workshop on Economics-Driven Software Engineering Research, May 2001, pages 45–49 (reprinted in Projects & Profits, 4 (8), pages 43–50).
          C.9 A. van der Hoek, Capturing Product Line Architectures, Fourth International Software Architecture Workshop, June 2000, pages 95–99.
          C.8 A. van der Hoek, Configuration Management and Open Source Projects, Third Workshop on Software Engineering over the Internet, June 2000, pages 41–45.
          C.7 A. van der Hoek, D. Heimbigner, and A.L. Wolf, Versioned Software Architecture, Third International Software Architecture Workshop, November 1998, pages 73–76.
          C.6 A. van der Hoek, D. Heimbigner, and A.L. Wolf, System Modeling Resurrected, Eighth International Symposium on System Configuration Management, July 1998, pages 140–145.
          C.5 A. van der Hoek, R.S. Hall, D. Heimbigner, and A.L. Wolf, Software Release Management, Sixth European Software Engineering Conference with the Fifth ACM SIGSOFT Symposium on the Foundations of Software Engineering, September 1997, pages 159–175.
          C.4 R.S. Hall, D. Heimbigner, A. van der Hoek, and A.L. Wolf, An Architecture for Post-Development Configuration Management in a Wide Area Network, Seventeenth International Conference on Distributed Computing Systems, May 1997, pages 269–278.
          C.3 A. van der Hoek, D. Heimbigner, and A.L. Wolf, A Generic, Peer-to-Peer Repository for Distributed Configuration Management, Eighteenth International Conference on Software Engineering, March 1996, pages 308–317.
          C.2 A. van der Hoek, D. Heimbigner, and A.L. Wolf, Does Configuration Management Have a Future?, Software Configuration Management: ICSE SCM�4 and SCM�5 Workshops Selected Papers, May 1995, pages 305–309.
          C.1 R.H. Byrd, E. Eskow, A. van der Hoek, R.B. Schnabel, and K.P.B. Oldenkamp, A Parallel Global Optimization Method for Solving Molecular Cluster and Polymer Conformation Problems, Seventh SIAM Conference on Parallel Processing for Scientific Computing, February 1995, pages 72–77.
          BC.7 A. Baker and A. van der Hoek, Ideas, Subjects, and Cycles as Lenses for Understanding the Software Design Processn, M. Petre and A. van der Hoek (Eds), Software Designers in Action: A Human-Centric Look at Design Work, CRC Press, 2013 (reprint from J.14).
          BC.6 T. Proença, N. Moura, and A. van der Hoek, On the Use of Emerging Design as a Basis for Knowledge Collaboration, K. Nakakoji, Y. Murakami, and E. McCready (Eds), New Frontiers in Artificial Intelligence: JSAI-isAI 2009 Workshops, LENLS, JURISIN, KCSD, LLLL, Springer-Verlag, 2010, pages 124–134.
          BC.5 A. Sarma, B. Al-Ani, E. Trainer, R. S. Silva Filho, I. da Silva, D. Redmiles, and A. van der Hoek, Continuous Coordination Tools and their Evaluation, I. Mistrik, J. Grundy, J. Whitehead, and A. van der Hoek (Eds), Collaborative Software Engineering, Springer-Verlag, 2010, pages 153–178.
          BC.4 E. Navarro and A. van der Hoek, On the Role of Learning Theories in Furthering Software Engineering Education, H.J.C. Ellis, S.A. Demurjian, and J.F. Naveda (Eds), Software Engineering: Effective Teaching and Learning Approaches and Practices, IGI Global, 2008, pages 38–59.
          BC.3 R.N. Taylor and A. van der Hoek, Software Design and Architecture: The Once and Future Focus of Software Engineering, L. Briand and A.L. Wolf (Eds), Future of Software Engineering 2007, IEEE Computer Society Press, May 2007, pages 226–243.
          BC.2 A. van der Hoek, D.G. Kay, and D.J. Richardson, Informatics: A Novel, Contextualized Approach to Software Engineering Education, P. Inverardi and M. Jazayeri (Eds), Software Engineering Education in the Modern Age: Challenges and Possibilities, PostProceedings of ICSE '05 Education and Training Track, Lecture Notes in Computer Science 4309, Springer, November 2006, pages 147–165.
          BC.1 R.H. Byrd, E. Eskow, A. van der Hoek, R.B. Schnabel, C.-S. Shao, and Z. Zou, Global Optimization Methods for Protein Folding Problems, DIMACS Series in Discrete Mathematics and Theoretical Computer Science � Global Minimization of Nonconvex Energy Functions: Molecular Conformation and Protein Folding, 1995, pages 29–39.
          T.12 A. Sarma, J. Herbsleb, and A. van der Hoek, Challenges in Measuring, Understanding, and Achieving Social-Technical Congruence, Technical Report CMU-ISR-08-106, Carnegie Mellon University, Institute for Software Research International, Pittsburg, 2008.
          T.11 A. Baker and A. van der Hoek, Examining Software Design from a General Design Perspective, Technical Report UCI-ISR-06-15, Institute for Software Research, University of California, Irvine, 2006.
          T.10 A. Baker and A. van der Hoek, Framing Software Design with the Design Diamond, Technical Report UCI-ISR-06-11, Institute for Software Research, University of California, Irvine, 2006.
          T.9 A. Baker and A. van der Hoek, Reframing Software Design: Perspectives on Advancing an Elusive Discipline, Technical Report UCI-ISR-06-10, Institute for Software Research, University of California, Irvine, 2006.
          T.8 R. Ripley, A. Sarma, and A. van der Hoek, Using Visualizations to Analyze Workspace Activity and Discern Software Project Evolution, Technical Report UCI-ISR-06-01, Institute for Software Research, University of California, Irvine, 2006.
          T.7 C. Lüer and A. van der Hoek, Composition Environments for Deployable Software Components, Technical Report UCI-ICS-02-18, Department of Information and Computer Science, University of California, Irvine, 2002.
          T.6 A. van der Hoek, D. Heimbigner, and A.L. Wolf, Global Optimization Methods for Protein Folding Problems, Technical Report CU-CS-895-99, Department of Computer Science, University of Colorado at Boulder, 1998.
          T.5 A. van der Hoek, A. Carzaniga, D. Heimbigner, and A.L. Wolf, A Reusable, Distributed Repository for Configuration Management Policy Programming, Technical Report CU-CS-864-98, Department of Computer Science, University of Colorado at Boulder, 1998.
          T.4 A. van der Hoek, D. Heimbigner, and A.L. Wolf, Investigating the Applicability of Architecture Description in Configuration Management and Software Deployment, Technical Report CU-CS-862-98, Department of Computer Science, University of Colorado at Boulder, 1998.
          T.3 A. Carzaniga, A. Fuggetta, R.S. Hall, D. Heimbigner, A. van der Hoek, and A.L. Wolf, A Characterization Framework for Software Deployment Technologies, Technical Report CU-CS-857-98, Department of Computer Science, University of Colorado at Boulder, 1998.
          T.2 A. van der Hoek, D. Heimbigner, A.L. Wolf, Software Architecture, Configuration Management, and Configurable Distributed Systems: A Ménage a Trois, Technical Report CU-CS-849-98, Department of Computer Science, University of Colorado at Boulder, 1998.
          T.1 R.S. Hall, D. Heimbigner, A. van der Hoek, and A.L. Wolf, The Software Dock: A Distributed, Agent-based Software Deployment System, Technical Report CU-CS-832-97, Department of Computer Science, University of Colorado at Boulder, 1997.
          PHD A. van der Hoek, A Reusable, Distributed Repository for Configuration Management Policy Programming, Ph.D. Dissertation, University of Colorado at Boulder, 2000.
          MS A. van der Hoek, Parallel Global Optimization of Proteins, M.S. Thesis, Erasmus Universiteit Rotterdam, 1994.
          Andre's picture
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          AW van der Hoek
          http://www.ics.uci.edu/~andre/index.html André van der Hoek
          home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
          projects
          Calico
          CodeExchange
          Crowd Development
          PorchLight
          Ph.D. students
          Christian Adriano
          Gerald Bortis
          Lee Martie
          André van der Hoek is a professor in and serves as chair of the Department of Informatics at the University of California, Irvine. He holds a joint B.S. and M.S. degree in Business-Oriented Computer Science from Erasmus University Rotterdam, the Netherlands, and a Ph.D. degree in Computer Science from the University of Colorado at Boulder.

          André heads the Software Design and Collaboration Laboratory, which focuses on understanding and advancing the role of design, coordination, and education in software development. His graduate work addressed distributed configuration management and versioned software architecture from a strictly technical perspective, but since his arrival at UC Irvine he has been positively corrupted by his colleagues in the Department of Informatics to address a broader research agenda that integrates a strong focus on people and how they work.

          Education is a key interest of André. He was the principal designer of the new B.S. in Informatics at UC Irvine, and is responsible for delivering several courses in this innovative curriculum. His research bridges into the educational realm by developing and critically evaluating new approaches to teaching software engineering, particularly for those topics that traditionally are difficult to address in the classroom.

          Andre's picture
          contact
          email
          andre@ics.uci.edu

          skype
          awvanderhoek

          aim
          AW van der Hoek
          http://www.ics.uci.edu/~andre/teaching.html André van der Hoek's Teaching
          home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
          projects
          Calico
          CodeExchange
          Crowd Development
          PorchLight
          Ph.D. students
          Christian Adriano
          Gerald Bortis
          Lee Martie
          Fall 2015
          Informatics 121: Software Design I
          Informatics 291s: Literature Survey in Software Engineering

          Winter 2015
          (no classes)
          Spring 2015
          Informatics 291s: Literature Survey in Software Engineering
          Fall 2013
          Informatics 121: Software Design I
          Informatics 221: Software Architecture

          Winter 2014
          (no classes)
          Spring 2014
          (no classes)
          Fall 2012
          Informatics 43: Introduction to Software Engineering
          Informatics 121: Software Design I

          Winter 2013
          (no classes)
          Spring 2013
          (no classes)
          Fall 2011
          Informatics 121: Software Design I

          Winter 2012
          ICS 52: Introduction to Software Engineering
          Spring 2011
          (no classes)
          Fall 2010
          Informatics 121: Software Design I

          Winter 2011
          (no classes)
          Spring 2011
          Informatics 223: Applied Software Design Techniques

          Fall 2009
          Informatics 121: Software Design I

          Winter 2010
          Informatics 122: Software Design II
          Spring 2010
          (no classes)
          Fall 2008
          (no classes)

          Winter 2009
          Informatics 122: Software Design II
          Spring 2009
          Informatics 117: Project in Software System Design
          Informatics 223: Applied Software Design Techniques
          Fall 2007
          Informatics 122: Software Design II

          Winter 2008
          (no classes)
          Spring 2008
          (no classes)
          Fall 2006
          Informatics 122: Software Design II
          Informatics 209S: Seminar in Informatics
          Winter 2007
          (no classes)
          Spring 2007
          Informatics 121: Software Design I
          Informatics 223: Applied Software Design Techniques
          Fall 2005
          (no classes)
          Winter 2006
          ICS 223 Software Architecture
          Spring 2006
          Informatics 121: Software Design I
          ICS 228: Software Environments
          Andre's picture
          contact
          email
          andre@ics.uci.edu

          skype
          awvanderhoek

          aim
          AW van der Hoek
          http://www.ics.uci.edu/~andre/bio.html André van der Hoek's Bio
          home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
          projects
          Calico
          CodeExchange
          Crowd Development
          PorchLight
          Ph.D. students
          Christian Adriano
          Gerald Bortis
          Lee Martie
          André van der Hoek serves as chair of the Department of Informatics at the University of California, Irvine. He holds a joint B.S. and M.S. degree in Business-Oriented Computer Science from Erasmus University Rotterdam, the Netherlands, and a Ph.D. degree in Computer Science from the University of Colorado at Boulder. He heads the Software Design and Collaboration Laboratory, which focuses on understanding and advancing the roles of design, collaboration, and education in software development. He has authored and co-authored over 100 peer-reviewed journal and conference publications, and in 2006 was a recipient of an ACM SIGSOFT Distinguished Paper Award. He is a co-author of the 2005 Configuration Management Impact Report as well as the 2007 Futures of Software Engineering Report on Software Design and Architecture. He has served on numerous international program committees, was a member of the editorial board of ACM Transactions on Software Engineering and Methodology from 2008 to 2014, was program chair of the 2010 ACM SIGSOFT International Symposium on the Foundations of Software Engineering, and was program co-chair of the 2014 International Conference on Software Engineering. He was recognized as an ACM Distinguished Scientist in 2013, and in 2009 he was a recipient of the Premier Award for Excellence in Engineering Education Courseware. He is the principal designer of the B.S. in Informatics at UC Irvine and was honored, in 2005, as UC Irvine Professor of the Year for his outstanding and innovative educational contributions.
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          AW van der Hoek
          http://www.ics.uci.edu/~andre/research.html André van der Hoek's Research
          home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
          projects
          Calico
          CodeExchange
          Crowd Development
          PorchLight
          Ph.D. students
          Christian Adriano
          Gerald Bortis
          Lee Martie
          A synopsis of my research, current projects, and opportunities to get involved can be found on the Software Design and Collaboration Laboratory pages.
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          http://www.ics.uci.edu/~theory/ Center for Algorithms and Theory of Computation Center for Algorithms and Theory of Computation

          Faculty

          • Michael Dillencourt, Professor
          • David Eppstein, Chancellor's Professor and Center Director
          • Michael Goodrich, Chancellor's Professor and Center Technical Director
          • Dan Hirschberg, Professor
          • Sandy Irani, Professor
          • George Lueker, Professor Emeritus
          • Amelia Regan, Professor

          Students

          • Dmitri Arkhipov
          • Will Devanny
          • Siddharth Gupta
          • Timothy Johnson
          • Nil Mamano

          Former students

          Faculty research interests

          Weekly seminar (CompSci 269)

          Workshop on Cloud Security, February 18, 2015

          CS theory student wiki (access limited to UCI campus)



          Department of Computer Science
          University of California, Irvine, CA 92697-3425
          http://www.ics.uci.edu/~dillenco/pubs/ Publications

          Publications

          Journal Articles

          The versions that are available on-line may not be quite as recent as the actual published versions. For example, they do not reflect changes to the galleys.

          1. M. B. Dillencourt, Traveling Salesman Cycles Are Not Always Subgraphs of Delaunay Triangulations or of Minimum Weight Triangulations, Information Processing Letters, 24(5), March 1987, 339-342.
          2. M. B. Dillencourt, A Non-Hamiltonian Delaunay Triangulation, Information Processing Letters, 25(3), May 1987, 149-151.
          3. M. B. Dillencourt, An Upper Bound on the Shortness Exponent of Inscribable Polytopes, Journal of Combinatorial Theory Series B, 46(1), February 1989, 66-83.
          4. R. E. Webber and M. B. Dillencourt, Compressing Quadtrees via Common Subtree Merging, Pattern Recognition Letters, 9(3), April 1989, 193-200.
          5. M. B. Dillencourt, Realizability of Delaunay Triangulations, Information Processing Letters, 33(6), February 1990, 283-287.
          6. M. B. Dillencourt, Hamiltonian Cycles in Planar Triangulations with No Separating Triangles, Journal of Graph Theory, 14(1), March 1990, 31-49.
          7. M. B. Dillencourt, Toughness and Delaunay Triangulations, Discrete and Computational Geometry, 5(6), 1990, 575-601.
          8. M. B. Dillencourt, An Upper Bound on the Shortness Exponent of 1-tough, Maximal Planar Graphs, Discrete Mathematics, 90(1), June 1991, 93-97.
          9. M. B. Dillencourt, D. M. Mount, and N. S. Netanyahu, A Randomized Algorithm for Slope Selection International Journal of Computational Geometry & Applications, 2(1), March 1992, 1-27.
          10. M. B. Dillencourt, H. Samet and M. Tamminen, A General Approach to Connected-Component Labeling for Arbitrary Image Representations, Journal of the ACM, 39(2), April 1992, 253-280.
          11. M. B. Dillencourt, On the toughness index of planar graphs, Journal of Graph Theory, 18(1), 1994, 103-107.
          12. M. B. Dillencourt and W. D. Smith, A linear-time algorithm for testing the inscribability of trivalent polyhedra, International Journal of Computational Geometry & Applications, 5(1-2), March-June 1995, 21-36.
          13. M. B. Dillencourt and H. Samet, Using Topological Sweep To Extract the Boundaries of Regions in Maps Represented by Region Quadtrees, Algorithmica, 15(1), January 1996, 82-102.
          14. M. B. Dillencourt, Polyhedra of small order and their Hamiltonian properties, Journal of Combinatorial Theory Series B, 66(1), January 1996, 87-122.
          15. M. B. Dillencourt, Finding Hamiltonian Cycles in Delaunay Triangulations Is NP-Complete, Discrete Applied Mathematics, 64(3), February 1996, 207-217.
          16. W. R. Shankle, P. Datta, M. Dillencourt, and M. Pazzani, Improving Dementia Screening Tests with Machine Learning Methods, Alzheimer's Research 2(3), June 1996, 95-99.
          17. L .F. Bic, M. Fukuda, and M. B. Dillencourt, Distributed Computing using Autonomous Objects, IEEE Computer, 29(8), August 1996, 55-61.
          18. M. B. Dillencourt and W. D. Smith, Graph-theoretical conditions for inscribability and Delaunay realizability, Discrete Mathematics, 161(1-3), December 1996, 63-77.
          19. T. K. Dey, M. B. Dillencourt, S. K. Ghosh, and J. M. Cahill, Triangulating with High Connectivity, Computational Geometry: Theory & Applications, 8(1), June 1997, 39-56.
          20. M. Fukuda, L. F. Bic, M. B. Dillencourt, and F. Merchant, Distributed Coordination with MESSENGERS, Science of Computer Programming, 31(2-3), July 1998, 291-311.
          21. M. Fukuda, L. F. Bic, M. B. Dillencourt, and J. M. Cahill, Messages versus Messengers in Distributed Programming, Journal of Parallel and Distributed Computing, 57(2), May 1999, 188-211.
          22. K. L. Morse, L. F. Bic, and M. Dillencourt, Interest Management in Large-Scale Virtual Environments, Presence: Teleoperators and Virtual Environments, 9(1), February 2000, 52-68.
          23. M. B. Dillencourt, D. Eppstein, and D. H. Hirschberg, Geometric Thickness of Complete Graphs, Journal of Graph Algorithms and Applications, 4(3), October 2000, 5-17.
          24. M. Fukuda, L. F. Bic, M. B. Dillencourt, and F Merchant, MESSENGERS: Distributed Programming Using Mobile Agents, Transactions of the Society for Design and Process Science, 5(4), December 2001, 95-112.
          25. L. Pan, M K. Lai, K. Noguchi, J. J. Huseynov, L. F. Bic, and M. B. Dillencourt, Distributed Parallel Computing Using Navigational Programming, International Journal of Parallel Programming, 32(1), February 2004, 1-37.

          Conferences

          1. R. N. Meeson, M. B. Dillencourt, and A. M. Rogerson, Executable Data Flow Diagrams, in Advance Papers, First International Workshop on Computer-Aided Software Engineering (CASE '87), Cambridge, MA, May 1987, 445-454.
          2. M. B. Dillencourt, Toughness and Delaunay Triangulations, in Proceedings of the Third ACM Symposium on Computational Geometry, Waterloo, Ontario, June 1987, 186-194.
          3. M. B. Dillencourt and H. Samet, Extracting Region Boundaries from Maps Stored as Linear Quadtrees, in Proceedings of the Third International Conference on Spatial Data Handling, Sydney, Australia, August 1988, 65-77.
          4. M. B. Dillencourt, H. Samet, and M. Tamminen, Connected Component Labeling for Arbitrary Binary Image Representations, Fifth International Conference on Image Analysis and Processing (5CIAP), Positano, Italy, September 1989. (Progress in Image Analysis and Processing, V. Cantoni, L. P. Cordella, S. Levialdi, and G. Sanniti di Baja Eds., World Scientific Publishing Co. Ltd., Singapore, 1990, 131-146).
          5. M. B. Dillencourt, D. M. Mount, and N. S. Netanyahu, A randomized algorithm for slope selection, in Proceedings of the Third Canadian Conference on Computational Geometry, Vancouver, British Columbia August, 1991, 135-140.
          6. V. J. Leung, M. B. Dillencourt, and A. L. Bliss, GraphTool: A Tool for Interactive Design and Manipulation of Graphs and Graph Algorithms, in N. Dean and G. E. Shannon, editors, Computational Support for Discrete Mathematics: DIMACS Workshop, New Brunswick, NJ, March 1992. American Mathematical Society, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, Volume 15.
          7. M. B. Dillencourt and W. D. Smith, A linear-time algorithm for testing the inscribability of trivalent polyhedra, in Proceedings of the Eighth ACM Symposium on Computational Geometry, Berlin, Germany, June 1992, 177-185.
          8. M. B. Dillencourt, Finding Hamiltonian Cycles in Delaunay Triangulations Is NP-Complete, in Proceedings of the Fourth Canadian Conference on Computational Geometry, St. Johns, Newfoundland, August, 1992, 223-228.
          9. M. B. Dillencourt and W. D. Smith, A Simple Method for Resolving Degeneracies in Delaunay Triangulations, in Automata, Languages, and Programming: Proceedings of the 20th International Colloquium (ICALP 93), Lund, Sweden, July, 1993, 177-188.
          10. M. B. Dillencourt, D. M. Mount, and A. J. Saalfeld, On the Maximum Number of Intersections of Two Polyhedra in 2 and 3 Dimensions, in Proceedings of the Fifth Canadian Conference on Computational Geometry, Waterloo, Ontario, August, 1993, 49-54.
          11. R. Jayakrishnan, M. B. Dillencourt, V. Leung, and P. Oreizy, Simulation Framework for Distributed Traffic Control Algorithms in ATMS, 26th International Symposium on Automotive Technology and Automation, Aachen, Germany, September, 1993.
          12. M. B. Dillencourt and W. D. Smith, Graph-theoretical conditions for inscribability and Delaunay realizability, in Proceedings of the Sixth Canadian Conference on Computational Geometry, Saskatoon, Saskatchewan, August, 1994, 287-292.
          13. T. K. Dey, M. B. Dillencourt, and S. K. Ghosh, Triangulating with High Connectivity, in Proceedings of the Sixth Canadian Conference on Computational Geometry, Saskatoon, Saskatchewan, August, 1994, 339-343.
          14. F. Merchant, L. Bic, P. Borst, M. Corbin, M. B. Dillencourt, M. Fukuda, and P. Sapaty, Simulating Autonomous Objects in a Spatial Database, in 9th European Simulation Multiconference, Prague, Czech Republic, June 1995, 768-772.
          15. M. Fukuda, K. L. Morse, L. Bic, M. Dillencourt, E. Lee, and D. Menzel, A Novel Approach to Toxicology Simulation Based on Autonomous Objects, SCS Western Multiconference, San Diego, January 1996.
          16. M. Fukuda, L. F. Bic, M. B. Dillencourt, and F. Merchant, Intra- and Inter-Object Coordination with MESSENGERS, COORDINATION '96: First International Conference on Coordination Models and Languages\/, Cesena, Italy, April, 1996 179-196.
          17. M. Fukuda, L. F. Bic, and M. B. Dillencourt, Performance of the MESSENGERS Autonomous-Objects Based System, World-Wide Computing and its Applications, WWCA '97, Tsukuba, Japan, March, 1997, 43-57.
          18. M. Fukuda, L. F. Bic, M. B. Dillencourt, and F. Merchant, Messages versus Messengers in Distributed Programming, 17th International Conference on Distributed Computing Systems, ICDCS'97, Baltimore, Maryland, May, 1997.
          19. S. Seiden, M. Dillencourt, S. Irani, R. Borrey, and T. Murphy, Logo Detection in Document Images, International Conference on Imaging Science, Systems, and Technology, CISST'97, Las Vegas, Nevada, June/July, 1997, 446-449.
          20. M. Fukuda, L. F. Bic, M. B. Dillencourt, and F. Merchant, A Hierarchical Mapping Scheme for Mobile Agent Systems , 6th IEEE Workshop on Future Trends of Distributed Computing Systems, FTDCS'97, Tunis, Tunisia, October, 1997, 66-71.
          21. C. Wicke, L. F. Bic, M. B. Dillencourt, and M. Fukuda, Automatic State Capture of Self-Migrating Computations, International Workshop on Computing and Communication in the Presence of Mobility (ICSE '98), Kyoto, Japan, April 1998.
          22. M. Fukuda, L. F. Bic, and M. B. Dillencourt, Global Virtual Time Support for Individual-Based Simulations, International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA '98), Las Vegas, Nevada, July 1998, 9-16.
          23. M. B. Dillencourt, D. Eppstein, and D. H. Hirschberg, Geometric Thickness of Complete Graphs, Sixth Symposium on Graph Drawing (GD '98), Montreal, Canada, August 1998, 102-110.
          24. C. Wicke, L. F. Bic, M. B. Dillencourt, and M. Fukuda, Automatic State Capture of Self-Migrating Computations in MESSENGERS, Second International Workshop on Mobile Agents (MA'98), Stuttgart, Germany, September 1998, 68-79.
          25. F. Merchant, L. F. Bic, and M. B. Dillencourt, Load Balancing in Individual-Based Spatial Applications, International Conference on Parallel Architectures and Compilation Techniques (PACT`98), Paris, France, October 1998, 350-357.
          26. K. L. Morse, L. F. Bic, and M. B. Dillencourt, Characterizing Scenarios for DDM Performance and Benchmarking RTI's, Spring Simulation Interoperability Workshop, Orlando, Florida, March, 1999, 1-6.
          27. K. L. Morse, L. Bic, M. Dillencourt, and K. Tsai, Multicast Grouping for Dynamic Data Distribution Management, Summer Computer Simulation Conference (SCSC 1999), Chicago, Illinois, July, 1999, 312-318.
          28. C. Wicke, L. F. Bic, and M. B. Dillencourt, Compiling for Fast State Capture of Mobile Agents, Parallel Computing 99 (ParCo99), Delft, The Netherlands, August, 1999, 714-721.
          29. E. Gendelman, L. F. Bic, and M. B. Dillencourt, Efficient Checkpointing Algorithm for Distributed Systems Implementing Reliable Communication Channels, 18th IEEE Symposium on Reliable Distributed Systems (SRDS '99), Lausanne, Switzerland, October, 1999, 290-291.
          30. H. Kuang, L. F. Bic, M. B. Dillencourt, and A. C. Chang, PODC: Paradigm-Oriented Distributed Computing, 7th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems (FTDCS '99), Cape Town, South Africa, December 1999, 169-175.
          31. H. Kuang, L. F. Bic, and M. B. Dillencourt, Paradigm-Oriented Distributed Computing Using Mobile Agents, 20th International Conference on Distributed Computing Systems (ICDCS 2000), Taipei, Taiwan, April, 2000, 11-19.
          32. E. Gendelman, L. F. Bic, and M. B. Dillencourt, An Application-Transparent, Platform-Independent Approach to Rollback-Recovery for Mobile-Agent Systems, 20th International Conference on Distributed Computing Systems (ICDCS 2000), Taipei, Taiwan, April, 2000, 564-571.
          33. E. Gendelman, L. F. Bic, and M. B. Dillencourt, Process Interconnection Structures in Dynamically Changing Topologies, 7th International Conference on High-Performance Computing (HiPC 2000), Bangalore, India, December, 2000, 405-414.
          34. H. Kuang, L. F. Bic, and M. B. Dillencourt, SuperBoundary Exchange: A Technique for Reducing Communication in Distributed Implementations of Iterative Computations, 4th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP 2000), Hong Kong, December, 2000, 421-435.
          35. L. Pan, L. F. Bic, and M. B. Dillencourt, Distributed Sequential Numerical Computing Using Mobile Agents: Moving Computation to Data, 2001 International Conference on Parallel Processing (ICPP '01), Valencia, Spain, September, 2001, 77-84.
          36. E. Gendelman, L. F. Bic, and M. B. Dillencourt, Fast File Access for Fast Agents, Fifth IEEE International Conference on Mobile Agents (MA 2001), Atlanta, Georgia December, 2001, 88-102.
          37. H. Kuang, L. F. Bic, and M. B. Dillencourt, Communication Reduction in Iterative Grid-based Computing Using SuperBoundary Exchange Technique, 20th IASTED International Conference on Applied Informatics, (AI 2002), Innsbruck, Austria February, 2002, 179-184.
          38. L. Pan, L. F. Bic, and M. B. Dillencourt, Shared Variable Programming Beyond Shared Memory: Bridging Distributed Memory with Mobile Agents, 6th World Conference on Integrated Design and Process Technology (IDPT 2002), Pasadena, CA, June, 2002.
          39. H. Kuang, L. F. Bic, and M. B. Dillencourt, Iterative Grid-Based Computing Using Mobile Agents, 31st IEEE International Conference on Parallel Processing (ICPP 2002), Vancouver, BC, Canada, August 2002, 109-117.
          40. L. Pan, M. K. Lai, J. J. Huseynov, L. F. Bic, and M. B. Dillencourt, Distributed Parallel Computing Using Navigational Programming: Orchestrating Computations Around Data, International Conference on Parallel and Distributed Computing and Systems (PDCS 2002), Cambridge, MA, November, 2002, 458-463.
          41. L. Pan, L. F. Bic, M. B. Dillencourt, and M. K. Lai, Mobile Agents-The Right Vehicle for Distributed Sequential Computing, 9th International Conference on High Performance Computing (HiPC 2002), Bangalore, India, December, 2002, 575-586.
          42. L. Pan, L. F. Bic, M. B. Dillencourt, J. J. Huseynov, and M. K. Lai, Facilitating Agent Navigation Using DSM--High Level Designs, 7th World Conference on Integrated Design and Process Technology (IDPT 2003), Beijing, PRC, June, 2003.
          43. L. Pan, L. F. Bic, M. B. Dillencourt, and M. K. Lai, From Distributed Sequential Computing to Distributed Parallel Computing, 5th Workshop on High Performance Scientific and Engineering Computing with Applications (HPSECA-03), Kaohsiung, ROC, October, 2003, 255-262.
          44. L. Pan, L. F. Bic, M. B. Dillencourt, and M. K. Lai, NavP versus SPMD: Two Views of Distributed Computing, International Conference on Parallel and Distributed Computing and Systems (PDCS 2003), Marina del Ray, CA, November, 2003, 666-673.
          45. H. Kuang, L. F. Bic, and M. B. Dillencourt, GIDM: Globally Indexed Distributed Memory, 9th IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems (FTDCS 2003), San Juan, Puerto Rico, December 2003, 108-114.

          Book Chapters

          1. L. F. Bic, M. B. Dillencourt, and M. Fukuda, Mobile Network Objects, Encyclopedia of Electrical and Electronics Engineering, J. Webster, Ed., John Wiley & Sons, Inc., New York, 1998.
          2. L. Pan, L. F. Bic, M. B. Dillencourt, and M. K. Lai, Distributed Sequential Computing, Parallel and Distributed Scientific and Engineering Computing: Practice and Experience, Y. Pan and L. T. Yang, Eds., Advances in Computation: Theory and Practice Volume 15, Nova Science Publishers, Inc., New York, 2003, 237-254.

          Michael B. Dillencourt
          Computer Science Department
          Donald Bren School of Information and Computer Sciences
          University of California, Irvine
          444 Computer Science Building
          Irvine, CA 92697-3425 USA
          dillenco@ics.uci.edu
          Last modified: December 16, 2004 http://www.ics.uci.edu/~dillenco/officehrs/ Office Hrs - Prof. Michael Dillencourt

          Office Hrs - Prof. Michael Dillencourt

          • When to find me (Winter, 2016):
            • Office Hrs:
              • 4:15-5:00 PM Wednesday
              • 2:15-3:00 PM Friday
              • I am available after class for questions about CompSci 260. I will stay after class (or immediately outside the classroom) as long as there are questions.
          • Where/How to find me
            • Office: DBH 4086
            • Mailing address:
              Michael B. Dillencourt
              Computer Science Department
              University of California, Irvine
            • Email: dillenco at ics dot uci dot edu
          Last modified: October 5, 2015 http://www.ics.uci.edu/~bic/messengers/index.html Project: MESSENGERS

          Project: MESSENGERS AND NAVIGATIONAL PROGRAMMING

          This project is developing a new programming paradigm, called Navigational Programming, for distributed systems based on the principles of autonomously migrating processes, called Messengers. Each Messenger is able to migrate among nodes of a local area network using explicit hop statements, which support strong migration. Unlike mobile agents used for a variety of services on the Internet, the MESSENGERS system is intended for general-purpose scientific computing.

          Some of the advantages of migrating processes, as compared to stationary message-passing processes, are highlighted in the following Project Summary.

          Participants

          Faculty:

            • Lubomir Bic
            • Michael Dillencourt

          PhD Students:

            • Wendy Zhang

          Recent PhD/MS Graduates:

            • Munehiro Fukuda (University of Washington, Bothel)

          Ph.D. Dissertation: MESSENGERS: A Distributed Computing System Based on Autonomous Objects (Postscript), 1997

            • Fehmina Merchant (IBM, New York)

          Ph.D. Dissertation: Load Balancing in Spatial Individual-Based Systems using Autonomous Objects (Postscript), 1998

            • Susan Mabry (Whitworth College, Spokane, WA)

          Ph.D. Dissertation: SimAgents: Migrating Agents for Simulation Models, 1999

            • Katherine Morse (SAIC, San Diego, CA)

          Ph.D. Dissertation: An Adaptive, Distributed Algorithm for Interest Management (Postscript), 2000

            • Adam Chi-Lun Chang

          M.S. Thesis: Graphical Interface for Paradigm Oriented Distributed Computing (PDF), 2000

            • Eugene Gendelman (Bloomberg, New York, NY)

          Ph.D. Dissertation: Virtual Infrastructure for Mobile Agent Computing (Postscript), 2002

            • Hairong Kuang (Yahoo, Sunnyvale, CA)

          Ph.D. Dissertation: Paradigm-Oriented Distributed Computing Using Mobile Agents (Postscript), 2002

            • Lei Pan (JPL, Pasadena, CA)

          Ph.D. Dissertation (abstract): Navigational Programming (PDF), 2005

            • Richard Utter (NSA, Washington, D.C.)

          Ph.D. Dissertation: Diactoros: Full State Migration (PDF), 2006

            • Koji Noguchi (Yahoo, Sunnyvale, CA)

          Ph.D. Dissertation: Spontaneous Process Migration with Global Pointers (PDF), 2006

            • Javid J. Huseynov

          Ph.D. Dissertation: Distributed Localization of Ultrasonic Sources of Gas Leak (PDF), 2008

            • Jiming Liu

          Ph.D. Dissertation: Distributed Individual-Based Simulation (PDF), 2008

            • Ming Kin Lai

          Ph.D. Dissertation: State-Migration Shared-Variable Programming (PDF), 2009

            • Matthew Badin

          Ph.D. Dissertation: Methods for Mitigating and Eliminating Error in Hybrid Matrix Multiply Algorithms, 2014

            • Kiyoshi Nakayama

          Ph.D. Dissertation: A Distributed Smart Grid Control Model for Integration of Renewables, 2014

           

          Visiting Students:

            • Dominik Jergus (Fachhochshule Darmstadt, Germany) Diploma Thesis (61 pages, Postscript), 1997
            • Christian Wicke (University of Karlsruhe, Germany) Diploma Thesis (81 pages, Postscript), 1998

           

          Publications

            1. Simulating Autonomous Objects in a Spatial Database (5 pages, Postscript), 9th European Simulation Multiconference, Prague, Czech Republic, June 1995

          (Shows how autonomous objects can navigate in a simulated spatial environment)

            1. Distributed Computing using Autonomous Objects (10 pages, Postscript), 5th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′95), Cheju Island, Korea, Aug. 1995

          (This is similar to the subsequent article in IEEE COMPUTER (below) -- it presents potential application areas and surveys related approaches)

            1. A Novel Approach to Toxicology Simulation based on Autonomous Objects (6 pages, Postscript), Conf. on Simulation in the Medical Sciences (part of SCS Western MultiConference), San Diego, Jan. 1996

          (Discusses a specific simulation application for Toxicology)

            1. Intra- and Inter-Object Coordination with MESSENGERS (18 pages, Postscript), First Int′l Conf. on Coordination Models and Languages (COORDINATION′96), Cesena, Italy, April 1996

          (Presents MESSENGERS as a coordination paradigm for constructing distributed applications)

            1. Distributed Computing using Autonomous Objects (16 pages, Postscript), IEEE COMPUTER, August 1996

          (Surveys several lines of research related to aunonmous objects and coordination)

            1. Performance of the MESSENGERS Autonomous-Objects-Based System (15 pages, Postscript), First Int′l Conf. on Worldwide Computing and Its Applications ′97 (WWCA97), Tsukuba, Japan, March 10-11, 1997 (LNCS 1274, Springer-Verlag)

          (Presents initial performance results)

            1. Messages versus Messengers in Distributed Programming (8 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-97), Baltimore, MD, May 1997

          (Illustrates how navigational programming differs from message-passing and shows the advantages)

            1. A Hierarchical Mapping Scheme for Mobile Agent Systems (6 pages, Postscript), 6th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′97), Tunis, Tunesia, Oct. 1997

          (Discusses the mapping problem for MESSENGERS, that is, mapping of Messengers to logical nodes, logical nodes to daemon nodes, and daemon nodes to physical nodes. Presents new ways of load balancing, resource utilization, and granularity control)

            1. Distributed Coordination with MESSENGERS (24 pages, Postscript), Science of Computer Programming Journal, Special Issue on Coordination Models, Languages, and Applications, 31(2), July 1998

          (Presents the navigational calculus and shows how it is used to dynamically compose an application)

            1. Mobile Network Objects (23 pages, Postscript), Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, Inc., 1998

          (An introductory overview of mobile agents technologies)

            1. Octopus -- Interactive Visualization and Contol Environment for Mobile-Objects-Based Systems (61 pages, Postscript), Diploma thesis by D. Jergus (Fachhochschule Darmstadt, Germany), 1997

          (Octopus is a tool superimposed on the MESSENGERS environment. It permits individual Messengers to periodically report information about themselves, e.g., their current position in a simulated space, which Octopus is able to display in real time for the purposes of visualization of the ongoing computation)

            1. CVSys: A Coordination Framework for Dynamic and Fully Distributed Cardiovascular Modeling and Simulation

          (8 pages, PDF), Int′l Biomedical Optics Symposium (BIOS′98), special section on Biomedical Sensing, Imaging and Tracking Technologoes, San Jose, CA, Jan. 1998
          (Discusses a large biomedical application -- the simulation of a cardiovascular system)

            1. Automatic State Capture of Self-Migrating Computations (6 pages, Postscript), ICSE98 Int′l Workshop on Computing and Communication in the Presence of Mobility, (Part of Int′l Conference on Software Engineering), Kyoto, Japan, April 1998

          (Presents an approach to automatic state capture by restricting migration to the top coordination layer as implemented in the MESSENGERS system)

            1. Global Virtual Time Support for Individual-Based Simulations (8 pages, Postscript), Int′l Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA′98), Las Vegas, Nevada, July 1998

          (Discusses the advantages of having global virtual time support provided by the system when implementing individual-based simulations)

            1. Automatic State Capture of Self-Migrating Computations in MESSENGERS (12 pages, Postscript), Second Int′l Workshop on Mobile Agents 98 (MA′98), Stuttgart, Germany, September 1998

          (Discusses the implementation of fully transparent state capture and restoration for the purposes of migration or local context switch. This is possible even under a fully compiled version of the MESSENGERS system)

            1. Load Balancing in Individual-Based Spatial Applications (8 pages, Postscript), Int′l Conf. on Parallel Architectures and Compilation Techniques (PACT′98), Paris, France, Oct. 1998

          (Presents three specific algorithms to do load balancing in applications where a group of individuals (or particles) move autonomously through a simulated environment and perform some coordinated group movement. Shows performance evaluation of these algorithms.)

            1. Messages versus Messengers in Distributed Programming (33 pages, Postscript), Journal of Parallel and Distributed Computing, 57, 188-211, 1999

          (A more extensive version of the paper with the same title presented at ICDCS′97 -- see above -- it illustrates how navigational programming differs from message-passing and shows the advantages both qualitatively and quantitatively)

            1. Introducing Dynamic Data Structure into Mobile Agents (7 pages, Postscript), Int′l Conf. on Parallel and Distributed Processing Techniques and Applications (PDPTA′99), Las Vegas, Nevada, July 1999

          (Introduces a special abstract data type into MESSENGERS. The dynamic structures belonging to a Messenger are carried automatically whenever the Messenger hops between nodes)

            1. Compiling for Fast State Capture of Mobile Agents (8 pages, Postscript), Parallel Computing ′99 (ParCo99), Delft, The Netherlands, Aug. 1999

          (A new approach to state capture/restoration of a mobile agent during migration)

            1. Self-Migrating Threads for Multi-Agent Applications (8 pages, Postscript), Int′l Workshop on Cluster Computing (IWCC′99), Melbourne, Australia, Dec. 2, 1999

          (A cluster computing paradigm that combines navigational autonomy of agents with fine granutality of threads)

            1. Efficient Checkpointing Algorithm for Distributed Systems with Reliable Communication Channels (2 pages, Postscript), IEEE Symp. on Reliable Distributed Systems (SRDS′99), Lausanne, Switzerland, Oct. 1999
            2. Bridging Semantics Gaps with Migrating Agents (6 pages, PDF), Int′l Conf. on Parallel and Distributed Computing Systems (PDCS′99), Cambridge, MA, Nov. 1999
            3. PODS: Paradigm-Oriented Distributed Computing (7 pages, Postscript), 7th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS′99), Cape Town, South Africa, Dec. 1999
            4. Modeling Cardiovascular Flow with a Migrating Agent Systems (6 pages, PDF), Int′l Conf. on Health Sciences Simulation, San Diego, CA, Jan. 2000
            5. Paradigm-Oriented Distributed Computing Using Mobile Agents (9 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-2000), Taipei, Taiwan, April 2000
            6. An Application-Transparent, Platform-Independent Approach to Rollback-Recovery for Mobile Agent Systems (8 pages, Postscript), Int′l Conf. on Distributed Computing Systems (ICDCS-2000), Taipei, Taiwan, April 2000
            7. Superboundary Exchange: A Technique for Reducing Communication in Distributed Implementations of Interactive Computations (15 pages, Postscript), Int′l Conf. on Algorithms and Architectures for Parallel Processing (ICA3PP-2000), Hong Kong, December 2000
            8. Process Interconnection Structures in Dynamically Changing Topologies (10 pages, Postscript), Int′l Conf. on High Performance Computing (HiPC-2000), Bangalore, India, December 2000
            9. Distributed Sequential Computing Using Mobile Code: Moving Computation to Data (8 pages, PDF), Int′l Conf. on Parallel Processing (ICPP-01), Valencia, Spain, September 2001
            10. Fast File Access for Fast Agents (15 pages, Postscript), Int′l Conf. on Mobile Agents (MA2001), Atlanta, GA, Dec. 2001
            11. MESSENGERS: Distributed Programming Using Mobile Agents (17 pages, PDF), Transaction of the Society for Design and Process Science (SDPS), Vol. 5, No. 4, Dec. 2001
            12. Shared Variable Programming Beyond Shared Memory: Bridging Distributed Memory with Mobile Agents (11 pages, PDF), The Sixth International Conference on Integrated Design and Process Technology (IDPT02), Pasadena, CA, June 2002
            13. Communication Reduction in Iterative Grid-based Computing Using SuperBoundary Exchange Technique (6 pages, Postscript), The 20th IASTED International Multi-Conference Applied Informatics (AI-2002), Innsbruck, Austria, February, 2002
            14. Iterative Grid-based Computing Using Mobile Agents (9 pages. Postscript), International Conference on Parallel Processing (ICPP-2002), Vancouver, British Columbia, Canada, August, 2002
            15. Mobile Agents -- The Right Vehicle for Distributed Sequential Computing (10 pages, PDF), Int′l Conf. on High Performance Computing (HiPC-2002), Bangalore, India, December 2002
            16. Distributed Parallel Computing Using Navigational Programming: Orchestrating Computations Around Data (6 pages, PDF), Int′l Conf. on Parallel and Distributed Computing and Systems (PDCS 2002), Cambridge, MA, November 2002
            17. Estimation of Multimedia Inorganic Arsenic Intake in the U.S. Population (25 pages, PDF), Human and Ecological Risk Assessment, Vol. 8, No. 7, pp. 1697-1721, 2002
            18. GIDM: Globally-Indexed Distributed Memory (7 pages, Postscript), 9th IEEE Workshop on Future Trends of Distributed Computing Systems (FTDCS 2003), San Juan, Puerto Rico, May 2003
            19. Facilitating Agent Navigation Using DSM - High Level Designs (11 pages, PDF), The Seventh World Conference on Intergrated Design & Process Technology (IDPT03), Houston, TX, Dec 2003
            20. A Mobile-Agent-Based PC Grid (9 pages, Postscript), The 5th Annual International Workshop on Active Middleware Services (AMS2003), Seattle, WA, June 2003
            21. From Distributed Sequential Computing to Distributed Parallel Computing (8 pages, PDF), The 5th Workshop on High Performance Scientific and Engineering Computing with Applications (HPSECA-03), Kaohsiung, Taiwan, ROC, October 2003
            22. NavP Versus SPMD: Two Views of Distributed Computation (8 pages, PDF), Int′l Conf. on Parallel and Distributed Computing and Systems (PDCS 2003), Marina del Ray, CA, November 2003
            23. Distributed Sequential Computing (18 pages, PDF), Advanced Parallel and Distributed Computing. Series: Advances in Computation: Theory and Practice, Vol. 16, (Y. Pan and L.T. Yang, Eds.), Nova Science Publishers, Inc., New York, 2004
            24. Distributed Parallel Computing Using Navigational Programming (37 pages, PDF), International Journal of Parallel Programming, Vol. 32, No. 1, Feb. 2004
            25. PODC: Paradigm-Oriented Distributed Computing (13 pages, PDF), Journal of Parallel and Distributed Computing, No. 65, 2005 (www.sciencedirect.com)
            26. Incremental Parallelization Using Navigational Programming: A Case Study (10 pages, PDF), International Conference on Parallel Processing (ICPP-2005), Oslo, Norway, June 2005
            27. Mobile Pipelines: Parallelizing Left-Looking Algorithms Using Navigational Programming (12 pages, PDF), 12th IEEE Int′l Conf. on High Performance Computing (HiPC-2005), Goa, India, December 2005
            28. Toward Incremental Parallelization, (9 pages PDF), IEICE Trans. Inf. & Syst., Vol. E89-D, No. 2, pp. 390-398, Feb. 2006
            29. Toward Automatic Data Distribution for Migrating Computations (8 pages, PDF), Int′l Conf. on Parallel Processing (ICPP 07), Xian, China, Sept. 2007
            30. Efficient Global Pointers With Spontaneous Process Migration (8 pages, PDF), The 16th Euromicro Conference on Parallel Distributed and Network-based Processing (PDP 2008), Toulouse, France, February 2008
            31. Mobile Agents, DSM, Coordination, and Self-Migrating Threads: A Common Framework (6 pages, PDF), Int′l Conference on Data Networks, Communications, and Computers (DNCOCO′08), Bucharest, Romania, November 2008
            32. Distributed Individual-Based Simulation (12 pages, PDF), 15th International European Conference on Parallel and Distributed Computing (Euro-Par 2009), Delft, The Netherlands, August 2009
            33. Gas-Leak Localization Using Distributed Ultrasonic Sensors, Proc. SPIE, Vol. 7293, 72930Z, San Diego, March 2009
            34. Automatic Resource Management in Multi-site Mobile Computing, The 5th International Conference on Mobile Computing and Ubiquitous Networking (ICMU 2010), Seattle, WA, April 2010
            35. Pretty Good Accuracy in Matrix Multiplication with GPUs, 9th Int′l Symp. Parallel and Distributed Computing (ISPDC 2010 ), Istanbul, Turkey, July 2010
            36. JaMes: A Java-based System for Navigational Programming, Int′l Conference on Computational Problem-Solving (ICCP), Chengdu, China, October 2011
            37. Improving Accuracy for Matrix Multiplications on GPUs, Scientific Programming. Volume 19 (2011)
            38. Improving the Accuracy of High Performance BLAS Implementations using Adaptive Blocked Algorithms. The 23rd Int′l Symp. on Computer Architecture and High Performance Computing, Vitoria, Espirito Santo, Brazil, October 2011
            39. Incremental Parallelization with Migration, IEEE Int′l Symp. on Parallel and Distributed Processing with Applications, Madrid, Spain, July 2012
            40. Complete Automation of Future Grid for Optimal Real-Time Distribution of Renewables, IEEE Int′l Conf. on Smart Grid Communication, Tainan City, Taiwan, Nov. 2012 (Best Paper Award)
            41. Improving Numerical Accuracy for Non-Negative Matrix Multiplication on GPUs using Recursive Algorithms, 27th Int′l Conf. on Supercomputing (ICS-2013) June 2013, Eugene, OR
            42. Tie-Set Based Fault Tolerance for Autonomous Recovery of Double Link Failures, IEEE Symp. on Computers and Communications (ISCC′13), July 2013, Split, Croatia
            43. Distributed Real-Time Power Flow Control with Renewable Integration, IEEE Int′l Conf. on Smart Grid Communication, Vancouver, Canada, Oct. 2013
            44. Distributed Flow Optimization Control for Energy-Harvesting Wireless Sensor Networks, IEEE Int′l Conf. on Communications (ICC), Sydney, Australia, 2014

           

          Technical Reports

            • MESSENGERS: A Distributed Computing Environment for Autonomous Objects
              • UCI Technical Report: TR-96-20, 1996
                (Contains additional details of MESSENGERS and its implementation)
            • Interest Management in Large-Scale Distributed Simulations
              • UCI Technical Report: TR-96-27, 1996
                (Surveys approaches to information filtering in distributed interactive simulation, which is one application area for MESSENGERS)
            • Distributed Individual-Based Simulation Using Autonomous Objects
              • UCI Technical Report: TR-97-46, 1998
                (Describes the MESSENGERS virtual time environment and demonstrates its programmability and performance)
            • CVSys: A First Prototype of a Distributed and Dynamic Cardiovascular Simulation System
              • UCI Technical Report: TR-98-08, 1998
                (Presents the design of a MESSENGERS-based simulation of the cardiovascular system)
            • Mobile Agents - The Right Vehicle for Distributed Sequential Computing
              • UCI Technical Report: TR-01-68, 2001
            • Incremental Parallelization Using Navigational Programming: A Case Study
              • UCI Technical Report: TR-05-04, 2005
            • Mobile Pipelines: Parallelizing Left-Looking Algorithms Using Navigational Programming
              • UCI Technical Report: TR-05-12, 2005

           

           

           

          There are two independent versions of MESSENGERS: MESSENGERS-I, and MESSENGERS-C, where "I" stands for "interpreted", and "C" stands for "compiled". The MESSENGERS-C is faster than MESSENGERS-I, but MESSENGERS-I has a Virtual Time support.

          User Manuals and System Installation for MESSENGERS-C

            • MESSENGERS-C (Version 2.1) User Manual provides a description of the MESSENGERS-C (Version 2.1) functionality and installation.
            • MESSENGERS-C (Version 1.2.04) User Manual provides a description of the MESSENGERS-C (Version 1.2.04) functionality and installation (stable version).
            • MESSENGERS-C (Version 1.2.05) User Manual provides a description of the MESSENGERS-C (Version 1.2.05) functionality and installation (experimental version).
            • Net Creator User Manual describes tools that can be used to automatically create a logical network for MESSENGERS-C system
            • Graph Creator User Manual describes yet another, graphical tool that can be used for logical network creation. The files output by GraphCreator should be used as input to the NetScheduler program, described in the "Net Creator User Manual"
            • The MESSENGERS software may be obtained free of charge for non-commercial purposes.

          User Manuals and System Installation for MESSENGERS-I

            • MSGR01 MESSENGERS User′s Manual
            • MSGR02 MESSENGERS System Library
            • MSGR03 MESSENGERS Daemon Design Book
            • MSGR04 MESSENGERS: Intermediate Code Specification
            • MSGR05 MLEX: The MESSENGERS Assembler
            • MSGR06 MESSENGERS-C Compiler
            • The MESSENGERS software may be obtained free of charge for non-commercial purposes. Installation instructions are given in MSGR01: MESSENGERS User′s Manual. If you are interested in installing/using MESSENGERS on your system, please send a message to mfukuda@u.washington.edu to receive a decryption keyword.
          http://www.ics.uci.edu/~dgillen/Welcome.html Welcome
           
           
           
          CV
           
           
           
           

          Daniel L. Gillen, PhD

          Professor

          Department of Statistics,

          Program in Public Health, &

          Department of Epidemiology

          University of California, Irvine 

          Contact Information:

          Office:

          2226 Bren Hall

          Department of Statistics

          University of California, Irvine

          Irvine, CA  92697-1250

          Phone:

          949.824.9862

          Fax:

          949.824.9863

          e-mail:

          dgillen@uci.edu

          Welcome

           
           
          http://sli.ics.uci.edu/Group/Group SLI | Group / Group
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          Current Group Members


          Ihler      
          Prof. Alexander Ihler
          BH 4066, x4-3645
          Ping      
          Wei Ping
          BH 4051
          Gallo      
          Nick Gallo
          BH 4059
          Lou      
          Qi Lou
          BH 4051


          Graduated Students

          Doctoral

          Forouzan

          Sholeh Forouzan
          PhD, Fall 2015
          Thesis, "Approximate Inference in Graphical Models".


          Keator

          David Keator
          PhD, Spring 2015
          Thesis, "Probabilistic Models for Brain Image Collection, Classification and Functional Connectivity".


          Liu

          Qiang Liu
          PhD, Fall 2014
          Thesis, "Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework".


          Gelfand

          Andrew Gelfand
          PhD, Spring 2014
          Thesis, "Bottom-Up Approaches to Approximate Inference and Learning in Discrete Graphical Models".


          Frank

          Drew Frank
          PhD, Spring 2013
          Thesis, "Variational Message-Passing: Extension to Continuous Variables and Applications in Multi-target Tracking".


          Sumer

          Ozgur Sumer
          PhD, U Chicago, 2011 (unofficial advisor)
          Thesis, "Adaptive Inference for Graphical Models".


          Hutchins

          Jon Hutchins
          PhD, Fall 2010
          Thesis, "Probabilistic Learning for Analysis of Sensor-Based Human Activity Data".


          Master's

          Venkateshan

          Priya Venkateshan
          MS, Spring 2011
          Thesis, "Graphical models for entity coreference resolution".


          Shekhar

          Sidharth Shekhar
          MS, Spring 2009
          Thesis, "Fixing and extending the Multiplicative Approximation Scheme".


          Undergraduate Research

          Ponmalai      
          Ravi Ponmalai
          Vorobyov      
          Michael Vorobyov
          Ma      
          Yuhao Ma
          Stroud      
          Jonathan Stroud
          Last modified January 12, 2016, at 04:50 PM
          Bren School of Information and Computer Science
          University of California, Irvine
          http://sli.ics.uci.edu/ SLI | Main / HomePage
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          Welcome to the webpage for Prof. Alexander Ihler's Statistical Learning and Inference group at UCI.

          We are interested in using probabilistic models to represent and understand real-world phenomena, using approximation algorithms to estimate probabilistic quantities efficiently, and learning probabilistic models from data. We focus primarily on algorithms for learning and estimation in graphical models, with applications to sensor networks, data mining, image processing and computer vision, and computational biology.


           
          Group
          Group Members
                Research
          Research Areas
                Teaching
          Teaching and Classes
           
          Pubs
          Publications
                Code
          Open-Source Code

          More links:

          • UCI's Center for Machine Learning
            • Our AI & Machine Learning seminar series
          • UCI's Machine Learning Repository for data sets
          Last modified July 06, 2010, at 10:17 PM
          Bren School of Information and Computer Science
          University of California, Irvine
          http://sli.ics.uci.edu/Ihler-Photos/Main SLI | Ihler-Photos / Main
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          Travel

          Africa (2008) Japan (2006)

          Family

          Bobby (2010)
          Last modified February 07, 2010, at 06:56 PM
          Bren School of Information and Computer Science
          University of California, Irvine
          http://www.ics.uci.edu/~sforouza/ Index of /~sforouza

          Index of /~sforouza

          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  

          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~johutchi/

          Jon Hutchins



          Personal Information:

          CV
          [DOC]

          Student Profile
          [DOC]

          Brief (3 page) introduction to my research
          [PDF]


          Publications:

          Probabilistic analysis of a large-scale urban traffic data set
          J. Hutchins, A. Ihler, and P. Smyth
            Second International Workshop on Knowledge Discovery from Sensor Data (ACM SIGKDD Conference, KDD-08), August 2008
          [PDF]

          Modeling count data from multiple sensors:A building occupancy model
          Jon Hutchins, Alexander Ihler, Padhraic Smyth
            Proc. 2nd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP 2007), December 2007.
          [PDF]

          Learning to detect events with Markov-modulated Poisson processes
          A. T. Ihler, J. Hutchins, and P. Smyth
             ACM Transactions on Knowledge Discovery from Data, December 2007.
          [PDF]

          Adaptive event detection with time-varying Poisson processes
          A. Ihler, J. Hutchins, and P. Smyth
             Proceedings of the 12th ACM SIGKDD Conference (KDD-06), August 2006.
          [PDF]

          Prediction and ranking algorithms for event-based network data
          J. O Madadhain, J. Hutchins, P. Smyth
            ACM SIGKDD Explorations: Special Issue on Link Mining, 7(2), 23-30, December 2006.
          [PDF]


          KDD Slides
          [PPT]

          http://www.ics.uci.edu/~ihler/code/index.html

          Code Packages


          KDE Toolbox

          Event Detection

          Adaptive Inference

          Gaussian Process Regression with Time-shifts


           
           
          This page contains toolboxes and other code written by myself or our group for our research and made available for others. Although we hope you find it helpful, please do not expect a significant amount of technical support or debugging.
           
           

           
          Matlab Toolboxes
           
          • Kernel Density Estimation (KDE) Toolbox for Matlab
            • A reasonably efficient implementation of spatial data structures for kernel or Parzen window density estimation and similar functions. Most work is done by a k-d tree data structure; versions of the "fast Gauss transform" and nearest neighbor searches are also included. Written as a Matlab class, with methods for many standard distributional operations (estimating entropy, evaluating, etc.)
           
           

           
          Other Matlab Code
           
          • Anomaly and Event Detection in Count Data
          • Adaptive Inference in Graphical Models
          • Gaussian Process Regression with Time-shifts
           
           
          http://www.ics.uci.edu/~qliu1/ Qiang Liu
          Qiang Liu
          home
          Publications
          CV (PDF)
          TA (CS178)

          Qiang Liu

          alt text 

          Qiang Liu
          Ph.D. Candidate
          Advisor: Prof. Alexander Ihler
          Information & Computer Science
          University of California at Irvine
          qliu1(at)uci.edu

          ("Qiang" sounds like "Chee-ah-ng", and "Liu" as "l-yo")


          New.

          I will be an assistant professor in the Department of Computer Science at Dartmouth College starting in Summer 2015.

          PhD positions are available. Please email me if interested.

          Workshops co-organized:

          • Crowdsourcing: Theory, Algorithms and Applications, NIPS’13,

          • Machine Learning Meets Crowdsourcing, ICML’13.

                    

          Research

          My research area is machine learning and statistics, with interests spreading over the pipeline of data collection (mainly crowdsourcing), learning, inference, decision making, and various applications under the framework of probabilistic graphical models.

          Crowdsourcing. All machine learning processes start from data collection. Crowdsourcing is a modern approach to collect large amounts of labeled data by hiring anonymous workers through online platforms such as Amazon Mechanical Turk. Unfortunately, the crowdsourced workers are often unreliable and uncontrollable, raising many challenging computational questions, such as how to aggregate labels from workers with different expertise, how to combine and balance noisy (but cheap) crowdsourced labels and accurate (but expensive) expert labels, and how to crowdsource complicated objectives such as protein structures.

          • We reform the problem of aggregating crowdsourced labels into a standard inference problem on a factor graph, which we solve using a class of variational inference algorithms. We show that both the naïve majority voting method and a previous algorithm by Karger et al. 2012 are special cases of one of our belief-propagation-type algorithms with special priors. We demonstrate significant improvement on the performance by using better priors, see NIPS2012, code.

          • Control items with known answers can be used to evaluate workers’ performance, and hence improve the combined results on the target items with unknown answers. This raises the problem of how many control items to use when the total number of items each workers can answer is limited: more control items evaluates the workers better, but leaves fewer resources for the target items that are of direct interest, and vice versa. We perform theoretical analysis and provide surprisingly simple answers for this problem, see here.

          • A preliminary thought on combining structured labels such as the protein folding, see here.

          Learning. Learning refers to constructing probabilistic models from empirical data, either to estimate the model parameters with predefined model structures, or even to estimate the model structures solely from data? I am interested in developing efficient, possibly distributed, learning algorithms, that perform well on real world data.

          • Here is an efficient distributed learning algorithm based on smartly combining local estimators defined by pseudo-likelihood components: ICML2012.

          • Here is a structure learning algorithm for recovering scale-free networks, thought to appear commonly in the real world: AISTATS2011 (notable paper award).

          • Here are some earlier works on contrastive divergence and MCMC-MLE: ICML2010; AISTATS2010.

          Inference. With given graphical models, either handcrafted or learned from data, inference refers to answering queries, such as marginal probability (or partition function), maximum a posteriori (MAP) estimation, or marginal MAP, the hybrid of marginalization and MAP. I am interested in developing efficient inference algorithms, mostly based on variational methods and in the form of belief-propagation-like message passing algorithms.

          • Marginal MAP is notoriously difficult even on tree-structured graphs. We developed a general variational dual representation for marginal MAP, and propose a set of variational approximation algorithms, including an interesting “mixed-product” BP that is a hybrid of max-product, sum-product and a special “argmax-product” message updates, and a convergent proximal point algorithm that works by iteratively solving pure marginalization tasks. See JMLR2013; UAI2011 (Slides).

          • We proposed an efficient approximate inference algorithm for calculating the log-partition function that unifies Rina Dechter's “one-pass” mini-bucket algorithm with iterative variational algorithms, such as tree reweighted BP. Our method inherits the advantages of both, and easily scales to large clique sizes. Our algorithm can provide both upper and lower bounds for the log-partition function. See ICML2011.

          • Tree reweighted BP provides an upper bound on the log-partition function, while naïve mean field and structured mean field give lower bounds. We show that tree reweighted BP provably gives a lower bound if its weights are set to take negative values in a particular way. We also show that such “negative” tree reweighted BP reduces to structured mean field as the weights approach infinity. For the full story, see UAI2010.

          Structured decision making. In practice, we often need to take a sequence of actions to achieve a predefined goal, usually under uncertain environments where information is observed sequentially and interactively as we progress. Decision networks (also called influence diagrams) are graphical model style representations of such structured decision making problems under uncertainty. Just like Bayesian networks generalize Markov chains or hidden Markov chains, decision networks generalize Markov decision processes (MDP), or partially observable decision processes (POMDP). Unfortunately, the problem of finding the optimal actions for decision networks is much more challenging than answering queries on Bayesian networks, especially in cases where limited information is observed or where multi-agent cooperation is required (such as in robot soccer games).

          • We extend the powerful variational inference framework for solving decision networks, based on which we propose an efficient BP-type algorithm and a convergent proximal point algorithm. Our framework enables us to translate basically any variational algorithm to solve influence diagrams. See UAI2012.

          Applications. I am interested in applying these machine learning methods in many application areas.

          • Natural language processing:

            • How well can computers solve the SAT sentence completion question? This is the work I involved when I was interning in Microsoft Research Redmond, ACL2012.

          • Sensor networks:

            • Here is a distributed algorithm for learning parameters in sensor networks: ICML2012.

            • My algorithm for solving influence diagrams provides a powerful way to design optimal decentralized detection networks: UAI2012.

          • Bioinformatics:

            • PNAS2012; AISTATS2011; Bioinformatics2010 .











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          Classes

          Current

          Winter 2016
          CS178, Machine Learning & Data Mining (undergrad)

          Past

          Fall 2015
          CS179, Introduction to Graphical Models (undergrad)
          Winter 2015
          CS178, Machine Learning & Data Mining (undergrad)
          CS273a, Intro to Machine Learning (graduate)
          Spring 2014
          CS274b, Learning in Graphical Models (graduate)
          Winter 2014
          CS178, Machine Learning & Data Mining (undergrad)
          Fall 2013
          CS273a, Intro to Machine Learning (graduate)
          Summer 2013
          iCAMP, Collaborative Filtering undergraduate summer research
          Spring 2013
          ICS77B/Math77B, Recommender Systems and Collaborative Filtering (undergraduate)
          Fall 2012
          CS178, Machine Learning & Data Mining (undergraduate)
          CS273a, Intro to Machine Learning (graduate)
          Spring 2012
          CS274b, Learning in Graphical Models
          Winter 2012
          CS178, Machine Learning & Data Mining
          Fall 2011
          CS171, Introduction to Artificial Intelligence
          Spring 2011
          CS271, Introduction to Artificial Intelligence
          CS274a, Probabilistic Learning
          Winter 2011
          CS178, Machine Learning & Data Mining
          Spring 2010
          CS295, Advanced Methods in Graphical Models
          Winter 2010
          CS178, Machine Learning & Data Mining
          CS274a, Probabilistic Learning
          Spring 2009
          CS177, Applications of Probability in Computer Science
          Winter 2009
          CS271, Introduction to Artificial Intelligence
          Fall 2008
          CS295, Research Projects in Machine Learning
          CSE Projects, Senior projects in the CS/E program
          Spring 2008
          CS274A, Probabilistic Learning
          Winter 2008
          CS295, Advanced methods in graphical models

          Last modified January 05, 2016, at 01:49 PM
          Bren School of Information and Computer Science
          University of California, Irvine
          http://www.ics.uci.edu/~ajfrank/ Andrew Frank - Home

          Andrew Frank - Home

          Andrew J. Frank PhD Candidate in Computer Science, University of California, Irvine
          • Home
          • Research
          ajfrank@ics.uci.edu View Andrew Frank's LinkedIn profile
          Notice: I defended my thesis in summer 2013 and now work at Google in London, UK.

          About

          I am a graduate student in the Computer Science Department at the University of California, Irvine. I am fortunate to be co-advised by Alex Ihler and Padhraic Smyth, and as such I enjoy "dual citizenship" in the Statistical Learning and Inference and DataLab groups at UCI. I plan to defend in the summer of 2013.

          My research is in the area of approximate inference for probabilistic graphical models. On the theoretical side, I am interested combining sampling-based approximations with variational message-passing algorithms. On the applied side, I am interested in using graphical models to manage association uncertainty in multi-target tracking. Check out my research page for short blurbs about my major projects, along with publications and code.

          News

          07/01/2013
          Thesis: Frank, A. Variational Message-Passing: Extension to Continuous Variables and Applications in Multi-Target Tracking. PhD Thesis. University of California, Irvine, 2013. (pdf) (bib)
          04/20/2012
          Publication: Frank, A.; Smyth, P.; Ihler, A.; , "A graphical model representation of the track-oriented multiple hypothesis tracker," Statistical Signal Processing Workshop (SSP), 2012 IEEE , vol., no., pp.768-771, 5-8 Aug. 2012. (pdf) (bib) (poster)
          04/02/2012
          Talk: New applications of graphical models for multitarget tracking. Presented at the UCI AI/ML seminar series. (slides)
          09/29/2011
          I am co-organizing a new Machine Learning Reading Group with Andrew Gelfand and Chris Dubois. Join us on Wednesdays from 12-1 to discuss the week's paper and bring suggestions for what to read next. You can subscribe to the mailing list to receive weekly notifications with links to the paper.
          09/12/2011
          Internship: LinkedIn Product Analytics team with mentor Monica Rogati.
          04/15/2011
          Publication: van Leeuwen, T. T., A. J. Frank, Y. Jin, P. Smyth, M. L. Goulden, G. R. van der Werf, and J. T. Randerson (2011), Optimal use of land surface temperature data to detect changes in tropical forest cover, J. Geophys. Res., 116, G02002, <doi:10.1029/2010JG001488>. (pdf) (bib)
          06/16/2010
          I am now the curator for the UCI Machine Learning Repository.
          11/02/2009
          Talk: Belief Propagation in a Continuous World. Presented at the UCI AI/ML seminar series. (slides)
          09/04/2009
          Publication: A. Ihler, A. Frank, and P. Smyth. Particle-based variational inference for continuous systems. Neural Information Processing Systems, 2009. (pdf) (bib) (poster)
          06/15/2008
          Third place: UCSD Data Mining Contest, supervised learning category. Fellow team members: Todd Johnson, David Orendorff, Julien Neel.
          http://www.ics.uci.edu/~ihler/bio.html Alexander Ihler

          Alexander Ihler

          Associate Professor

          Information & Computer Science, UC Irvine


          Bren Hall 4066
          ph: 949-824-3645
          fx: 949-824-4056
          ihler (at) ics.uci.edu /
          ihler (at) alum.mit.edu


          Home
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          Piano
           
          Short Bio
           

          Alexander Ihler is an Associate Professor in the Department of Computer Science at the University of California, Irvine. He received his Ph.D. in Electrical Engineering and Computer Science from MIT in 2005 and a B.S. with honors from Caltech in 1998. His research focuses on machine learning, graphical models, and algorithms for exact and approximate inference, with applications to areas such as sensor networks, computer vision, data mining, and computational biology. He is the recipient of an NSF CAREER award and several best paper awards at conferences including NIPS, IPSN, and AISTATS.

           
           
          http://www.ics.uci.edu/~ihler/index.html Alexander Ihler

          Alexander Ihler

          Associate Professor

          Information & Computer Science, UC Irvine


          Bren Hall 4066
          ph: 949-824-3645
          fx: 949-824-4056
          ihler (at) ics.uci.edu /
          ihler (at) alum.mit.edu


          Home
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            CS179, Graphical Models
            ... archive of older offerings
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          Piano
           
           
          I work in artificial intelligence and machine learning, focusing on statistical methods for learning from data and on approximate inference techniques for graphical models. Applications of my work include data mining and information fusion in sensor networks, computer vision and image processing, and computational biology.
           
           

           
          Research themes
           

          Graphical models are used to organize and structure probability distributions over large systems, and enable efficient approximate or exact reasoning. My group balances developing theoretical and algorithmic advances with applications to the real-world systems of our collaborators.

          Algorithms. One of our main focuses is on finding maxima or computing probabilities using variational methods, including the family of belief propagation (BP) message-passing algorithms. Our contributions include analyzing the convergence and accuracy properties of BP, developing new BP-like bounds, extending BP techniques to continuous valued systems, improving the efficiency of "adaptive" or incremental inference, and extending variational algorithms to ``mixed'' inference tasks such as marginal MAP and decision making problems, including influence diagrams (or decision networks) and distributed team decision problems.

          Applications. We have applied our algorithms to a wide variety of problems, including tracking and understanding data from sensor networks, efficient representations for large text corpora, computer vision and image processing, and gene expression data in biology.

           
           

           
          News
           
          Our solver ("ai") won first place in five categories of UAI's 2014 Approximate Inference Challenge. Congratulations also to Rina Dechter's group ("daoopt"), which won several other categories.

          We co-organized the NIPS'13 workshop, "Crowdsourcing: Theory, Algorithms and Applications".

          I received an NSF CAREER award, "Estimation and Decisions in Graphical Models" (IIS-1254071)

          I was awarded the 2013 Chancellor's Award for Excellence in Fostering Undergraduate Research, and my student Michael Vorobyov the Award for Excellence in Undergraduate Research for his honors thesis work.

          Qiang Liu co-organized the workshop, "Machine Learning Meets Crowdsourcing" at ICML 2013 in Atlanta.

          Our collaboration with Rina Dechter's group won the MAP task components of the 2011 Probabilistic Inference Challenge, and our group's entry was runner-up in the marginalization tasks.

          Qiang Liu has received a 2011 Microsoft Research Fellowship award.
           
           

           
          Students (group page)
           
          Current:
          • David Keator
          • Sholeh Forouzan
          • Wei Ping
          • Nick Gallo
          • Qi Lou
          Graduated:
          • Qiang Liu (PhD, 2014)
          • Andrew Frank (PhD, 2013)
          • Ozgur Sumer (PhD, 2012)
          • Jonathan Hutchins (PhD, 2010)
          • Sidharth Shekhar (MS, 2009)
          • Priya Venkateshan (MS, 2011)
           
           

           
          Other links
           
          • UCI's Center for Machine Learning and our AI/ML seminar series
          • UCI Machine Learning Dataset Repository
           
           

           
          Funding Acknowledgements
           
          We gratefully acknowledge support for our current and recent research from the National Science Foundation, DARPA, Microsoft Research, the National Institute of Health, NIAMS, and UCI's Center for Complex Biological Systems.
           
           
          http://www.ics.uci.edu/~dbkeator/GradSchool/David%20Keator.html David Keator
           
           
           
           
          Projects
          Currently, I’m working on applying probabilistic learning techniques to improve automatic tuning and boundary detection  of lutetium oxyorthosilicate (LSO) scintillation crystal arrays in the High Resolution Research Tomograph (HRRT) brain PET scanner.  Manufacturer supplied tuning software performs poorly in situations where there is low signal-to-noise characteristics or when there is significant “cross-talk” from neighboring scintillators in the array.
           
          Other Interests
          I am interested in applying AI and machine learning techniques to medical imaging data, modeling brain circuitry (both nuclei and interconnections between nuclei) abnormalities in a variety of neurological disorders.  Further interests include biomedical informatic methods for enabling large-scale distributed data federations.  For the past 6 years I’ve been working on tools to enable the documentation and  federation of distributed fMRI data across multi-site collaboratories.
          David Keator
          Position: Graduate Student
          Area: AI/ML
          Advisors: Padhraic Smyth
                            Alex Iher
          Office: Bren Hall 4051
                       Irvine Hall 163
          Office Tel: 949-824-7870
          Office Fax: 949-824-7873
          Email: dbkeator@uci.edu
          CV: CV.pdf
           
           
           
           
           
          My favorite links
          1. •www.ics.uci.edu
          2. •www.bic.uci.edu
          3. •www.nbirn.net
          4. •www.brainimage.net
           
           
          Photos
           
           
           
           
          Alaska 2008
           
           
           
          Hawaii 2007
          Glacier National Park
          Chianti
           
          Selected Publications
          1. • A National Human Neuroimaging Collaboratory Enabled By The Biomedical Informatics Research Network (BIRN). Keator, D.; Grethe, J.S.; Marcus, D.; Ozyurt, B.; et. al.  IEEE Transactions on Information Technology in Biomedicine. 2008 Mar;12(2):162-72.
          2. • High Resolution PET Listmode Motion Correction Using 3D Motion Data. Lee K.; Hong I; Potkin S.; Burbar Z.;Keator, D.. Abstract and poster presentation at Joint Molecular Imaging Conference, 2007.
          3. • A General XML Schema and Associated SPM Toolbox for Storage and Retrieval of Neuro-Imaging Results and Anatomical Labels. Keator, D.; Gadde, S; Grethe, J ; Taylor, D; FIRST BIRN; Potkin, S.  Neuroinformatics, 2006; 4(2):199-212.
          4. • A General and Extensible Multi-Site Database and XML based Informatics System for the Storage, Retrieval, Transport, and Maintenance of Human Brain Imaging and Clinical Data. Keator D; Ozyurt BI; Wei D; Gadde S; Potkin SG; Brown G; MBIRN; FBIRN; Grethe J. Abstract and poster (253 TH-AM) presentation at Organization of Human Brain Mapping, 2006.
           
           
           
          http://www.ics.uci.edu/~qliu1/MLcrowd_ICML_workshop/ ICML ’13 Workshop: Machine Learning Meets Crowdsourcing
          Main
          Home
          Overview
          Call For Papers
          Organizers
          Schedule
          Invited Speakers
          Keynotes
          Accepted Papers
          Related Links

          ICML ’13 Workshop: Machine Learning Meets Crowdsourcing

          Important Dates: MSR

          • ICML Workshop, June 21, 2013

          • Room: Lobby 506-7 (right behind the registration desk)

          • Poster session: 10th floor area D (map)

          • [Schedule]   [Keynotes]   [Papers]

          Overview

          Our ability to solve challenging scientific and engineering problems relies on a mix of human and machine intelligence. The machine learning (ML) research in the past two decades has created a set of powerful theoretical and empirical tools for exploiting machine intelligence. On the other side, the recent rise of human computation and crowdsourcing approaches enables us to systematically harvest and organize human intelligence, for solving problems that are easy for human but difficult for computers. The past few years have witnessed widespread use of the crowdsourcing paradigm, including task-solving platforms like Amazon Mechanical Turk and CrowdFlower, crowd-powered scientific projects like GalaxyZoo and Foldit game, as well as various successful crowdsourcing business such as crowdfunding and open Innovation, to name a few.

          This trend yields both new opportunities and challenges for the machine learning community. On one side, crowdsourcing systems provide machine learning researchers with the ability to gather large amount of valuable data and information, leading advances in challenging problems in areas like computer vision and natural language processing. On the other side, crowdsourcing confronts challenges on increasing its reliability, efficiency and scalability, for which machine learning can provide power computational tools. More importantly, building systems that seamlessly integrate machine learning and crowdsourcing techniques can greatly push the frontier of our ability to solve challenging and large-scale problems.

          The goal of this workshop is to bring together experts on fields related to crowdsourcing such as economics, game theory, cognitive science and human-computer interaction with the machine learning community to have a workshop focused on areas where crowdsourcing can contribute to machine learning and vice versa. We are interested in a wide variety of topics, including but not limited to:

          State of the field. What are the emerging crowdsourcing tasks and new opportunities for machine learning? What are the latest and greatest tasks being tackled by crowdsourcing and human intelligence and how do these tasks highlight the need for new machine learning approaches that aren’t being studied already?

          Integrating machine and human intelligence. How to build practical systems that seamlessly integrate machine and human intelligence? Machine learning algorithms can help the crowdsourcing component to manage work flows and control workers’ qualities, while the crowds can be used to handle the tasks that are difficult for machines to adaptively boost the performance of machine learning algorithms.

          Machine learning for crowdsourcing. Many machine learning approaches have been applied to crowdsourcing on problems such as output aggregation, quality control, work flow management and incentive mechanism design. We expect to see more machine learning contribution to crowdsourcing, either by novel ML methods, or on new crowdsourcing problems.

          Crowdsourcing for machine learning. Machine learning largely relies on big and high quality data, which can be provided by crowdsourcing systems, perhaps in an automatic and adaptive way. Also, most machine learning algorithms have many design choices that require human intelligence, including tuning hyper-parameters, selecting score functions, and designing kernel functions. How can we systematically “outsource” these typically expert-level design choices to the crowds in order to achieve results that match expert-level human experience?

          Crowdsourcing complicated tasks. How to design work flows and aggregate answers in crowdsourcing systems that collect structured labels, such as bounding box annotations in computer vision, protein folding structures in biology, or solve complicated tasks such as proof reading, and machine translation? How can machine learning provide help in these cases?

          Theoretical analysis. There are many open theoretical questions in crowdsourcing that can be addressed by statistics and learning theory. Examples include analyzing label aggregation algorithms such as EM, or budget allocation strategies.

          Invited Speakers

          • Jeffrey P. Bigham. University of Rochester

          • Yiling Chen. Harvard University

          • Panagiotis G. Ipeirotis. NYU Stern School of Business

          • Edith Law. Harvard University

          • Mark Steyvers. UC Irvine

          Call for Papers

          Submissions should follow the ICML format and are encouraged to be up to eight pages. Papers submitted for review do not need to be anonymized. There will be no official proceedings, but the accepted papers will be made available on the workshop website. Accepted papers will be either presented as a talk or poster.

          We welcome submissions both on novel research work as well as extended abstracts on work recently published or under review in another conference or journal (please state the venue of publication in the later case); we particularly encourage submission of visionary position papers on the emerging trends on crowdsourcing and machine learning.

          Please submit papers in PDF format here.

          Organizers

          • Paul Bennett, Dengyong Zhou, John Platt. Microsoft Research, Redmond

          • Qiang Liu. UC Irvine

          • Xi Chen, Qihang Lin. CMU

          Abstracts of Invited Talks

          Jeffrey P. Bigham : Crowd Agents: Interactive Crowd-Powered Systems in the Real World

          Over the past few years, we have been developing and deploying interactive crowd-powered systems that help people get things done in their everyday lives. For instance, VizWiz answers visual questions for blind people in less than a minute, Legion drives robots in response to natural language commands, Chorus supports consistent dialog between end users and the crowd, and Scribe converts streaming speech to text in less than five seconds. Overall, thousands of people have engaged with these systems, providing an interesting look at how end users interact with crowd work in their everyday lives. These systems have collectively informed a new model for real-time crowd work that I call “crowd agents,” which is proving to be especially useful for building interactive crowd-powered systems. In this model, a diverse and changing crowd – the kind easily recruited on the web – is made to act as a single high-quality actor through interface support and computational mediation of each individual’s work. These systems allow us to deploy truly intelligent interactive systems today, and present challenging problems for machine learning going forward to support and eventually replace the humans in the loop.

          Yiling Chen: Financial Incentives and Crowd Work

          Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance-contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increase the effectiveness of financial incentives.

          Panagiotis G. Ipeirotis : Rewarding Crowdsourced Workers

          We describe techniques for rewarding workers in a crowdsourcing setting. We describe a real-time monetary payment scheme that rewards workers according to their quality, in the presence of uncertainty in quality estimation, while at the same time guaranteeing stable (or increasing) salaries. We report experimental results indicating that the proposed scheme encourages long-term engagement, avoiding churn, and avoiding the common problem of adverse selection and moral hazard. We also describe a set of non-monetary, psychological schemes that actively discourage low-quality workers from participating in tasks. We finish showing that mice and crowdsourced workers are not that different after all.

          Edith Law : Mixed-Expertise Crowdsourcing

          To date, most of the research in human computation focuses on tasks that can be performed by any person with basic perceptual capabilities and common sense knowledge. In this talk, I will discuss new directions towards mixed-expertise crowdsourcing, where the crowd consists of people with drastically different motivations, levels and domains of expertise, as well as availabilities. I will illustrate the new opportunities and challenges in mixed-expertise crowdsourcing, by outlining existing work and describing my two ongoing projects – Curio, a micro-task marketplace for crowdsourcing scientific tasks, and SimplyPut, a crowdsourcing platform for improving health literacy through the collaborative summarization of medical information.

          Mark Steyvers: Aggregating Human Judgments in Combinatorial Problems

          We analyze the collective performance of individuals in combinatorial problems involving the rankings of events and items (e.g. “what is the order of US presidents?”) as well as traveling salesperson and minimum spanning tree problems. We compare situations in which a group of individuals independently answer these questions with an iterated learning environment in which individuals pass their solution to the next person in a chain. We introduce Bayesian information aggregation models for both the independent and information-sharing environments and treat the collective group knowledge as a latent variable that can be estimated from the observed judgments across individuals. The models allow for individual differences in expertise and confidence in other individuals’ judgments. Initial results suggest that information-sharing environments lead to better collective performance despite the fact that information-sharing increases correlations between judgments. In addition, the models’ estimates of expertise are more indicative of actual performance than the users’ self-rated expertise. Finally, we study situations where the same individual solves the same problem at different points in time. We show that the consistency in answers across repeated problems provides an additional signal to estimate expertise.

          Accepted Papers

          • Sivan Sabato, Adam Kalai; Feature Multi-Selection among Subjective Features.

          • Adish Singla, Andreas Krause; Truthful Incentives for Privacy Tradeoff: Mechanisms for Data Gathering in Community Sensing.

          • Hongwei Li, Bin Yu, Dengyong Zhou; Error Rate Analysis of Labeling by Crowdsourcing; (Supplementary).

          • Peng Ye, David Doermann; Combining preference and absolute judgements in a crowd-sourced setting.

          • Vaibhav Rajan, Sakyajit Bhattacharya, L. Elisa Celis, Deepthi Chander, Koustuv Dasgupta, Saraschandra Karanam; CrowdControl: An online learning approach for optimal task scheduling in a dynamic crowd platform.

          • Alexey Tarasov, Sarah Jane Delany, Brian Mac Namee; Improving Performance by Re-Rating in the Dynamic Estimation of Rater Reliability.

          • Michael Wick, Ari Kobren, Andrew McCallum; Probabilistic Reasoning about Human Edits in Information Integration.

          • Joel Lehman, Risto Miikkulainen; Leveraging Human Computation Markets for Interactive Evolution.

          • Jian Peng, Qiang Liu, Alexander Ihler, Bonnie Berger; Crowdsourcing for structured labeling with applications to protein folding.

          Related Workshops, Conferences and Resources

          • ICML 2013 Workshop on Machine Learning Meets Crowdsourcing.

          • Conference on Human Computation & Crowdsourcing (HCOMP), 2013.

          • HCOMP 2013 Workshop on Crowdsourcing at Scale.

          • ICML 2012 Workshop on Machine Learning in Human Computation & Crowdsourcing.

          • ICML 2011 Workshop on Combining Learning Strategies to Reduce Label Cost.

          • NIPS 2012 Workshop on Human Computation for Science and Computational Sustainability.

          • NIPS 2011 Workshop on Computational Social Science and the Wisdom of Crowds.

          • NIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds.

          • CVPR 2010 Workshop on Advancing Computer Vision with Humans in the Loop (ACVHL)

          • 1st-4th Human Computation Workshop (HCOMP).

          • CrowdCamp 2012, 2013.

          • CHI 2011 Workshop on Crowdsourcing and Human Computation

          • See more information on CrowdResearch.org or Mathew Lease's crowdsourcing site.

          Page generated 2013-10-31 00:19:29 PDT, by jemdoc. (source)
          http://www.ics.uci.edu/~ihler/pubs.html Alexander Ihler Publications

          Alexander Ihler

          Associate Professor

          Information & Computer Science, UC Irvine


          Bren Hall 4066
          ph: 949-824-3645
          fx: 949-824-4056
          ihler (at) ics.uci.edu /
          ihler (at) alum.mit.edu


          Home
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            CS179, Graphical Models
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          Piano

          Publications

          Showing by

          2016

          "From Exact to Anytime Solutions for Marginal MAP"; Lee, Marinescu, Dechter, Ihler; Conference on Artificial Intelligence (AAAI), Feb 2016
          C58: [ Abstract ] | [ BibTex ]

          2015

          "Probabilistic Variational Bounds for Graphical Models"; Liu, Fisher, Ihler; Neural Information Processing Systems (NIPS), Dec 2015
          C57: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Decomposition Bounds for Marginal MAP"; Ping, Liu, Ihler; Neural Information Processing Systems (NIPS), Dec 2015
          C56: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Estimating the Partition Function by Discriminance Sampling"; Liu, Peng, Ihler, Fisher; Uncertainty in Artificial Intelligence (UAI), July 2015
          C55: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Incremental Region Selection for Mini-bucket Elimination Bounds"; Forouzan, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2015
          C54: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Boosting Crowdsourcing with Expert Labels: Local vs. Global Effects"; Liu, Ihler, Fisher; Int'l Conference on Information Fusion, July 2015
          C53: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Pushing Forward Marginal MAP with Best-First Search"; Marinescu, Dechter, Ihler; Int'l Joint Conference on Artificial Intelligence (IJCAI), July 2015
          C52: [ Abstract ] | [ BibTex ] | [ PDF ]

          2014

          "Distributed Estimation, Information Loss and Exponential Families"; Liu, Ihler; Neural Information Processing Systems (NIPS), Dec. 2014
          C51: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics"; Lam, Kask, Dechter, Ihler; Symposium on Combinatorial Search (SoCS), Aug. 2014
          C50: [ Abstract ] | [ BibTex ] | [ PDF ]
          "AND/OR Search for Marginal MAP"; Marinescu, Dechter, Ihler; Conference on Uncertainty in Artificial Intelligence (UAI), July 2014
          C49: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Marginal structured SVM with hidden variables"; Ping, Liu, Ihler; International Conference on Machine Learning (ICML), June 2014
          C48: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Beyond MAP estimation with the track-oriented multiple-hypothesis tracker"; Frank, Smyth, Ihler; IEEE Trans. Signal Processing 62(9):2413--2423.
          J17: [ Abstract ] | [ BibTex ] | [ Link ]
          "Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification"; Keator, Fallon, Lakatos, Fowlkes, Potkin, Ihler; Human Brain Mapping (HBM) 35(1):38--52.
          J16: [ Abstract ] | [ BibTex ] | [ Link ]

          2013

          "Does better inference mean better learning?"; Gelfand, Dechter, Ihler; NIPS Workshop on Perturbations, Optimization, and Statistics (POS), Dec. 2013
          R8: [ Abstract ] | [ BibTex ]
          "Scoring workers in crowdsourcing: How many control questions are enough?"; Liu, Steyvers, Ihler; Neural Information Processing Systems (NIPS), Dec. 2013
          C47: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Variational planning for graph-based MDPs"; Cheng, Liu, Chen, Ihler; Neural Information Processing Systems (NIPS), Dec. 2013
          C46: [ Abstract ] | [ BibTex ] | [ PDF ]
          "On reliable crowdsourcing and the use of ground-truth information"; Liu, Steyvers, Fisher, Ihler; Workshop on Crowdsourcing at Scale, HCOMP, Nov. 2013
          R7: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Linear Approximation to ADMM for MAP inference"; Forouzan, Ihler; Asian Conf. on Machine Learning (ACML), JMLR W&CP 29, pp48-61, Nov. 2013
          C45: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Variational Algorithms for Marginal MAP"; Liu, Ihler; Journal of Machine Learning Research (JMLR), 14, pp. 3165-3200.
          J15: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]
          "Image enhancement in projectors via optical pixel shift and overlay"; Sajadi, Qoc-Lai, Ihler, Gopi, Majumder; Int'l Conference on Computational Photography 2013
          C44: [ Abstract ] | [ BibTex ] | [ PDF ]

          2012

          "Winning the PASCAL 2011 MAP Challenge with Enhanced AND/OR Branch-and-Bound"; Otten, Ihler, Kask, Dechter; NIPS Workshop on Discrete Optimization (DiscML), Dec. 2012
          R6: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Variational inference for crowdsourcing"; Liu, Peng, Ihler; Neural Information Processing Systems (NIPS), Dec. 2012
          C43: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Fast planar correlation clustering for image segmentation"; Yarkony, Ihler, Fowlkes; European Conference on Computer Vision (ECCV), Oct. 2012
          C42: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Belief propagation for structured decision making"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
          C41: [ Abstract ] | [ BibTex ] | [ PDF ]
          "A cluster-cumulant expansion at the fixed points of belief propagation"; Welling, Gelfand, Ihler; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
          C40: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Join-graph based cost-shifting schemes"; Ihler, Flerova, Dechter, Otten; Uncertainty in Artificial Intelligence (UAI), Aug. 2012
          C39: [ Abstract ] | [ BibTex ] | [ PDF ]
          "A graphical model representation of the track-oriented multiple hypothesis tracker"; Frank, Smyth, Ihler; IEEE Conference on Statistical Signal Processing (SSP), Aug. 2012
          C38: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis"; Geyfman et al.; Proc. National Academy of Sciences (PNAS), 109 (29), pp. 11758-11763.
          J14: [ Abstract ] | [ BibTex ] | [ Link ]
          "Approximating the sum operation for marginal-MAP inference"; Cheng, Chen, Dong, Xu, Ihler; Conference on Artificial Intelligence (AAAI), July 2012
          C37: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Distributed parameter estimation via pseudo-likelihood"; Liu, Ihler; International Conference on Machine Learning (ICML), June 2012
          C36: [ Abstract ] | [ BibTex ] | [ PDF ]

          2011

          "Mini-bucket elimination with moment matching"; Flerova, Ihler, Dechter, Otten; NIPS Workshop on Discrete Optimization (DiscML), 2011
          R5: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Adaptive exact inference in graphical models"; Sumer, Acar, Ihler, Mettu; J. Machine Learning Res. (JMLR) 12, Nov. 2011, pp. 3147-3186.
          J13: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Variational algorithms for marginal MAP"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), 2011
          C35: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Planar cycle covering graphs"; Yarkony, Ihler, Fowlkes; Uncertainty in Artificial Intelligence (UAI), 2011 (earlier arXiv version)
          C34: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Tightening MRF relaxations with planar subproblems"; Yarkony, Morshed, Ihler, Fowlkes; Uncertainty in Artificial Intelligence (UAI), 2011 (corrected)
          C33: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Fast parallel and adaptive updates for dual-decomposition solvers"; Sumer, Acar, Ihler, Mettu; Conference on Artificial Intelligence (AAAI), 2011
          C32: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Bounding the Partition Function using Holder's Inequality"; Liu, Ihler; International Conference on Machine Learning (ICML), 2011
          C31: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Fault Detection via Nonparametric Belief Propagation"; Bickson, Baron, Ihler, Avissar, Dolev; IEEE Trans. Signal Proc. 59(6), June 2011. (arXiv version)
          J12: [ Abstract ] | [ BibTex ] | [ Link ]
          "Learning Scale Free Networks by Reweighted L1 regularization"; Liu, Ihler; AI & Statistics, April 2011. (Notable paper award)
          C30: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Multicore Gibbs Sampling in Dense, Unstructured Graphs"; Xu, Ihler; AI & Statistics, April 2011.
          C29: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Revisiting MAP Estimation, Message Passing and Perfect Graphs"; Foulds, Navaroli, Smyth, Ihler; AI & Statistics, April 2011.
          C28: [ Abstract ] | [ BibTex ] | [ PDF ]

          2010

          "Understanding Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; IEEE Trans. Knowledge Data Engineering, 24(5), pp.952-960, May 2012. (Preliminary version, 2009: TR-09-06, PDF)
          J11: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]
          "Nonparametric Belief Propagation"; Sudderth, Ihler, Isard, Freeman, Willsky; Communications of the ACM 53(10), Oct. 2010 pp. 95-103.
          J10: [ Abstract ] | [ BibTex ] | [ Link ]
          "Negative Tree-reweighted Belief Propagation"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2010
          C27: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Covering Trees and Lower Bounds on Quadratic Assignment"; Yarkony, Fowlkes, Ihler; Computer Vision & Pattern Recognition (CVPR), June 2010
          C26: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Particle Filtered MCMC-MLE with Connections to Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; Int'l Conf on Machine Learning (ICML), June 2010
          C25: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Learning with Blocks: Composite Likelihood and Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; AI & Statistics (AISTATS), April 2010
          C24: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Estimating Replicate Time-Shifts Using Gaussian Process Regression"; Liu, Lin, Anderson, Smyth, Ihler; Bioinformatics 26(6), Mar. 2010, pp. 770-776; doi:10.1093/bioinformatics/btq022
          J9: [ Abstract ] | [ BibTex ] | [ Link ]

          2009

          "Bayesian detection of non-sinusoidal periodic patterns in circadian expression data"; Chudova, Ihler, Lin, Andersen, Smyth; Bioinformatics 25(23), Dec. 2009, pp. 3114-3120; doi: 10.1093/bioinformatics/btp547.
          J8: [ Abstract ] | [ BibTex ] | [ Link ]
          "Particle-Based Variational Inference for Continuous Systems"; Ihler, Frank, Smyth; Neural Information Processing Systems, Dec. 2009
          C23: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; ICS Technical Report 09-06, Oct. 2009.
          R4: [ Abstract ] | [ BibTex ] | [ PDF ]
          "A Low Density Lattice Decoder via Non-parametric Belief Propagation"; Bickson, Ihler, Avissar, Dolev; Allerton Conference on Communication, Control, and Computing, Sept. 2009
          C22: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Adaptive Updates for MAP Configurations with Applications to Bioinformatics"; Acar, Ihler, Mettu, Sumer; in IEEE Statistical Signal Processing (SSP), Sept. 2009
          C21: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Circadian Clock Genes Contribute to the Regulation of Hair Follicle Cycling"; Lin, Kumar, Geyfman, Chudova, Ihler, Smyth, Paus, Takahashi, Andersen; PLoS Genetics, 5(7):e1000573. July 2009. doi:10.1371/journal.pgen.1000573
          J7: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]
          "Particle Belief Propagation"; Ihler, McAllester; in Twelfth International Conference on Artificial Intelligence and Statistics (AIStats), April 2009.
          C20: [ Abstract ] | [ BibTex ] | [ PDF ]

          2008

          "Probabilistic Analysis of a Large Scale Urban Traffic Sensor Data Set"; Hutchins, Ihler, Smyth; in Second International Workshop on Knowledge Discovery from Sensor Data 2008, LNCS series #5840, pp. 94-114, 2010.
          C19: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]
          "Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation"; Porteous, Newman, Ihler, Asuncion, Smyth, Welling; in ACM Knowledge Discovery and Data Mining (KDD) 2008.
          C18: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Adaptive Inference in General Graphical Models"; Acar, Ihler, Mettu, Sumer; in Uncertainty in Artificial Intelligence (UAI) 2008.
          C17: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

          2007

          "Learning to detect events with Markov-modulated Poisson processes"; Ihler, Hutchins, Smyth; ACM Transactions on Knowledge Discovery from Data, Vol 1 Issue 3, Dec. 2007.
          J6: [ Abstract ] | [ BibTex ] | [ Link ]
          "Graphical Models and Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Kreidl, Moses, Wainwright, Williams, Willsky; in Wireless Sensor Networks: Signal Processing and Communications, Wiley 2007.
          B1: [ Abstract ] | [ BibTex ] | [ Link ]
          "Adaptive Bayesian Inference"; Acar, Ihler, Mettu, Sumer; in Neural Information Processing Systems (NIPS) 2007.
          C16: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Modeling Count Data from Multiple Sensors: A Building Occupancy Model"; Hutchins, Ihler, Smyth; in Computational Advances in Multisensor Adaptive Processing (CAMSAP) 2007.
          C15: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Accuracy Bounds for Belief Propagation"; Ihler; in Uncertainty in Artificial Intelligence (UAI) 2007.
          C14: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Graphical Models for Statistical Inference and Data Assimilation"; Ihler, Kirshner, Ghil, Robertson, Smyth; Physica D: Nonlinear Phenomena, June 2007. (Survey of graphical model methods)
          J5: [ Abstract ] | [ BibTex ] | [ PDF ] | [ Link ]

          2006

          "Learning Time-Intensity Profiles of Human Activity Using Nonparametric Bayesian Models"; Ihler, Smyth; in Neural Information Processing Systems (NIPS) 2006.
          C13: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Adaptive Event Detection with Time-Varying Poisson Processes"; Ihler, Hutchins, Smyth; in Knoweldge Discovery and Data Mining (KDD) 2006.
          C12: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Distributed Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Moses, Wainwright, Willsky; IEEE Signal Processing Magazine, July 2006.
          J4: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation"; Porteous, Ihler, Smyth, Welling; in Uncertainty in Artificial Intelligence (UAI) 2006.
          C11: [ Abstract ] | [ BibTex ] | [ PDF ]

          2005

          "Particle Filtering Under Communications Constraints"; Ihler, Fisher, Willsky; in Statistical Signal Processing (SSP) 2005.
          C10: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Loopy Belief Propagation: Convergence and Effects of Message Errors"; Ihler, Fisher, Willsky; Journal of Machine Learning Research, May 2005. (Full version of NIPS'04 paper)
          J3: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; Journal of Selected Areas in Communication, Apr. 2005. (Expanded version of IPSN/ICASSP papers)
          J2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Estimating Dependency and Significance for High-Dimensional Data"; Siracusa, Tieu, Ihler, Fisher, Willsky; in ICASSP 2005.
          C9: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Inference in Sensor Networks: Graphical Models and Particle Methods"; Ihler; Ph.D. Thesis, MIT, 2005
          T2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS (zipped) ]

          2004

          "Message Errors in Belief Propagation"; Ihler, Fisher, Willsky; in Neural Information Processing Systems (NIPS) 2004. (Outstanding Student Paper Award)
          C8: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Nonparametric Hypothesis Tests for Statistical Dependency"; Ihler, Fisher, Willsky; IEEE Transactions on Signal Processing, Aug. 2004.
          J1: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Communications-Constrained Inference"; Ihler, Fisher, Willsky; LIDS Tech Report 2601 (Lossless and lossy encoding of sample-based density estimates)
          R3: [ Abstract ] | [ BibTex ] | [ PDF ]
          "Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; in ICASSP 2004.
          C7: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "An Overview of Fast Multipole Methods"; Ihler; 2004 (MIT Area Exam)
          R1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Nonparametric Belief Propagation for Self-Calibration in Sensor Networks"; Ihler, Fisher, Moses, Willsky; in Information Processing in Sensor Networks (IPSN) 2004. (Best Student Paper Award)
          C6: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

          2003

          "Efficient Multiscale Sampling from Products of Gaussian Mixtures"; Ihler, Sudderth, Freeman, Willsky; in Neural Information Processing Systems (NIPS) 2003.
          C5: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; in Computer Vision and Pattern Recognition (CVPR) 2003. (also AI Memo # AIM-2002-020)
          C4: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]
          "Hypothesis Testing over Factorizations for Data Association"; Ihler, Fisher, Willsky; in Information Processing in Sensor Networks (IPSN) 2003.
          C3: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

          2002

          "Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; LIDS Technical Report # 2551, Aug. 2002.
          R2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

          2001

          "Nonparametric Estimators for Online Signature Authentication"; Ihler, Fisher, Willsky; in ICASSP 2001.
          C2: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]

          2000

          "Maximally Informative Subspaces: Nonparametric Estimation for Dynamical Systems"; Ihler; Masters' Thesis, MIT, 2000
          T1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS (zipped) ]

          1999

          "Learning Informative Statistics: A Nonparametric Approach"; Fisher, Ihler, Viola; in Neural Information Processing Systems (NIPS) 1999.
          C1: [ Abstract ] | [ BibTex ] | [ PDF ] | [ PS ]


          Publications and citation info also on Google Scholar or Microsoft Academic Search. http://sli.ics.uci.edu/Classes/2015F-179 SLI | Classes / CS179: Introduction to Graphical Models

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          Classes /

          CS179: Introduction to Graphical Models

          Assignments and Exams:

          HW1Code10/06/15Soln 
          HW2 10/13/15Soln 
          HW3 10/20/15Soln 
          HW4 10/27/15Soln 
          HW5Code,Data11/11/15Soln 
          HW6Alarm11/20/15Soln 
          HW7 12/09/15Soln 

          Lecture: M/W/F 3pm-3:50pm, SSL 290

          Weekly review session: W 6-6:50pm, DBH1600

          Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

          • Office Hours: Fri 10:30-12:00pm, Bren Hall 4066, or by appointment

          Teaching Assistant: Qi Lou

          • Office Hours: Mon 10:30-12:00pm in Bren Hall 3013; or by appointment (Bren Hall 4051)

          Reader: Shu Kong


          Introduction to graphical models

          Graphical models are a powerful framework for efficiently representing and reasoning about large systems. Common examples include solving constraint satisfaction and constraint optimization problems, learning and reasoning about high-dimensional, multivariate probability models, and structured prediction tasks. Graphical models are used in a broad spectrum of scientific fields, including computer vision, natural language processing, computational biology, communications and information theory, physics, and more. In this course we will study the common representations of graphical models, including Bayesian networks and Markov random fields; methods for and the complexity of exact reasoning in these models; computationally efficient algorithms for approximate reasoning; and techniques for learning models from data.

          Background

          We will assume basic familiarity with the concepts of probability. Some programming will be required; we will primarily use Python.

          Textbook and Reading

          There is no required textbook for the class. However, some useful books on the subject for supplementary reading, on hold at the science library, include:

          • Russel & Norvig, AI: A Modern Approach
          • Dechter, Constraint Processing
          • Murphy, Machine Learning: A Probabilistic Perspective
          • Koller & Friedman, Probabilistic Graphical Models: Principles and Techniques

          and available online,

          • Dechter, Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
          • Wainwright & Jordan, Graphical Models, Exponential Families, and Variational Inference
          • Poole & Mackworth, Artificial Intelligence: FCA

          Discussion on Piazza

          I prefer that students ask questions within a discussion forum so that everyone (staff and other students) can see the questions, participate in answering them, and learn from each other. I also expect students to read the posts regularly to keep current on the class issues. Please avoid duplicate posts: before posting, first check whether another student has already posted on that topic. When you do post a question, choose a clear and descriptive title. I currently prefer to use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/fall2015/cs179/home.

          Collaboration Policy

          I encourage you to collaborate in the sense of discussing the course material and ideas with your fellow students. However, it is an important part of the learning process to do your work yourself. To this end, do not show your homework solutions or code to your fellow students, or examine another students solutions or code. Similarly, do not post solution code on Piazza (unless privately to myself and the TA). An excellent guide is the ICS-33 academic integrity handout.


          Syllabus (subject to change)

          DateSlidesTopicsReading
          W0: 09/25pdfIntroduction
          W1: 09/28pdfCSPs, Local SearchR&N CSPs (2nd Ed Ch5), pyGM CSP Example, Dechter Ch1-2
          W1: 09/30pdfLocal SearchpyGM Local Search Example
          W1: 10/02pdfBacktracking Search
          W2: 10/05pdfVariable Elimination
          W2: 10/07pdfProbability
          W2: 10/09  
          W3: 10/12pdfBayesian Networks
          W3: 10/14  
          W3: 10/16pdfUndirected graphical models
          W4: 10/19pdfMarkov chains
          W4: 10/21  python HMM example
          W4: 10/23 Hidden Markov modelsR&N Ch15, P&M 6.5
          W5: 10/26 Variable elimination (2)python Bayes Net & variable elimination example
          W5: 10/28pdfLearning from dataR&N 20.2
          W5: 10/30  python maximum likelihood example
          W6: 11/02 Learning in undirected models
          W6: 11/04 Learning structure in Bayesian networks
          W6: 11/06pdfMonte Carlo methods IR&N 14.5
          W7: 11/09 Monte Carlo methods II
          W7: 11/11 Veteran's Day
          W7: 11/13 Mini-bucket elimination
          W8: 11/16pdfVariational methods Ipython dual decomposition
          W8: 11/18 Variational methods IIpython loopy BP
          W8: 11/20 Variational methods III
          W9: 11/23  
          W9: 11/25 Thanksgiving
          W9: 11/27 Thanksgiving
          W10: 11/30  
          W10: 12/02pdfLatent Dirichlet Allocation
          W10: 12/04 Boltzmann machines; RBMs, DBMs

          External resources of interest

          • UAI 2015 Tutorials and Keynotes
          • Coursera PGM lectures

          Last modified February 03, 2016, at 01:25 PM
          Bren School of Information and Computer Science
          University of California, Irvine
          http://www.ics.uci.edu/~qliu1/nips13_workshop/ NIPS ’13 Workshop on Crowdsourcing: Theory, Algorithms and Applications
          Main
          Home
          Overview
          Schedule
          Invited Speakers
          Keynotes
          Call For Papers
          Accepted Papers
          Organizers
          Related Links

          NIPS ’13 Workshop on Crowdsourcing: Theory, Algorithms and Applications


          Workshop in conjunction with NIPS 2013.

          Important Dates MSR

          • Submission deadline: October 15, 2013

          • Acceptance Notification: October 30, 2013

          • Workshop: December 9, 2013

          Overview

          Machine learning systems involve an integration of data representing human or physical knowledge, and algorithms that discover patterns in this data and make predictions about new instances. While machine learning research usually focuses on developing more efficient learning algorithms, it is often the quality and amount of training data that predominately govern the performance of real-world systems. This is only amplified by the recent popularity of large scale and complicated learning systems such as deep networks, which can require millions to billions of training instances to perform well. Unfortunately, traditional methods of collecting data from specialized workers are usually expensive and slow. In recent years, however, a potential for change has emerged thanks to crowdsourcing, which enables huge amounts of labeled data to be collected from large groups of (usually online) workers for a low cost or no cost at all. Many machine learning tasks, such as computer vision and natural language processing, increasingly benefit from data gathered on crowdsourcing platforms such as Amazon Mechanical Turk and CrowdFlower. On the other hand, tools in machine learning, game theory, and mechanism design can help to address many challenging problems in crowdsourcing systems, such as making them more reliable, more efficient, and less expensive.

          In this workshop, we call attention to crowdsourcing as a source of data, discussing cheap and fast data collection methods based on crowdsourcing, and how these methods could impact subsequent stages of machine learning. Furthermore, we will emphasize how the data sourcing paradigm interacts with the most recent emerging trends in the NIPS community.

          Examples of topics of interest in the workshop include (but are not limited to):


          • Applications of crowdsourcing to machine learning

          • Reliable crowdsourcing, e.g., label aggregation, quality control

          • Optimal budget allocation or active learning in crowdsourcing

          • Pricing and incentives in crowdsourcing markets

          • Workflow design and answer aggregation for complex tasks (e.g., machine translation, proofreading)

          • Prediction markets / information markets and their connection to learning

          • Theoretical analyses of crowdsourcing algorithms, e.g., error rates and sample complexities for label aggregation and budget allocation algorithms

          Invited Speakers

          • Michael Bernstein. Stanford University. [abstract], [slides]

          • Evgeniy Gabrilovich. Google. [abstract]

          • Arpita Ghosh. Cornell University. [abstract], [slides]

          • Devavrat Shah. MIT. [abstract]

          • Dengyong Zhou. Microsoft Research. [abstract], [slides]

          Call for Papers

          Submissions should follow the NIPS format and are encouraged to be up to eight pages, excluding references. Additional appendices and supporting materials are allowed. Papers submitted for review do not need to be anonymized. There will be no official proceedings, but the accepted papers will be made available on the workshop website. Accepted papers will be either presented as a talk or poster. We welcome submissions both on novel research work as well as extended abstracts on work recently published or under review in another conference or journal (please state the venue of publication in the later case); we particularly encourage submission of visionary position papers on the emerging trends on the field.

          Please submit papers in PDF format here.



          Accepted Papers

          • Gagan Goel, Afshin Nikzad, Adish Singla; Matching Workers Expertise with Tasks: Incentives in Heterogeneous Crowdsourcing Markets.

          • Hossein Azari Soufiani, William Z. Chen, David C. Parkes, Lirong Xia; Generalized Method-of-Moments for Rank Aggregation.

          • Genevieve Patterson, Grant Van Horn, Serge Belongie, Pietro Perona, James Hays; Bootstrapping Fine-Grained Classifiers: Active Learning with a Crowd in the Loop.

          • Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan; Adaptive Contract Design for Crowdsourcing.

          • Paul Ruvolo, Jacob Whitehill, Javier R. Movellan; Exploiting Commonality and Interaction Effects in Crowdsourcing Tasks Using Latent Factor Models.

          • Ashwinkumar Badanidiyuru, Robert Kleinberg, Aleksandrs Slivkins; Bandits with Knapsacks: Dynamic procurement for crowdsourcing.

          • Nicole Immorlica, Greg Stoddard, Vasilis Syrgkanis; Social Status and the Design of Optimal Badges.

          • Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause; On Actively Teaching the Crowd to Classify.



          Organizers

          • Dengyong Zhou, Jenn Wortman Vaughan, Nikhil R. Devanur. Microsoft Research

          • Qiang Liu, Alexander Ihler. UC Irvine

          • Xi Chen. UC Berkeley & NYU



          Related Workshops, Conferences and Resources

          • ICML 2013 Workshop on Machine Learning Meets Crowdsourcing.

          • Conference on Human Computation & Crowdsourcing (HCOMP), 2013.

          • HCOMP 2013 Workshop on Crowdsourcing at Scale.

          • ICML 2012 Workshop on Machine Learning in Human Computation & Crowdsourcing.

          • ICML 2011 Workshop on Combining Learning Strategies to Reduce Label Cost.

          • NIPS 2012 Workshop on Human Computation for Science and Computational Sustainability.

          • NIPS 2011 Workshop on Computational Social Science and the Wisdom of Crowds.

          • NIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds.

          • CVPR 2010 Workshop on Advancing Computer Vision with Humans in the Loop (ACVHL)

          • 1st-4th Human Computation Workshop (HCOMP).

          • CrowdCamp 2012, 2013.

          • CHI 2011 Workshop on Crowdsourcing and Human Computation

          • See more information on CrowdResearch.org or Mathew Lease's crowdsourcing site.

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font-family:"Times New Roman";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1027"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor=ivory lang=EN-US link=blue vlink=purple style='tab-interval:.5in'> <div class=WordSection1> <h2><span style='font-size:22.0pt;mso-bidi-font-size:18.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Research Interests:<o:p></o:p></span></h2> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Architectures and Compilers for Embedded Systems <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Memory architecture exploration for Systems-on-Chip<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>System specification techniques <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Software/Hardware synthesis, analysis and verification <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Architectural exploration for SOC and domain-specific problems <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Low-power/low-energy analysis and design techniques <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Hardware/software interfaces for distributed embedded systems <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Electronic Design Automation <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Brain-inspired computing and architectures<o:p></o:p></span></li> </ul> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt'>&nbsp;</span><span style='font-size:22.0pt;mso-bidi-font-size:18.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'>Current NSF Projects:<o:p></o:p></span></h2> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'><a href="http://www.variability.org/">Variability Expedition</a>:<span style="mso-spacerun:yes">� </span>Variability-Aware Software for Efficient <span class=SpellE>Nanoscale</span> Devices<span style='mso-tab-count: 2'>������������ </span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"'><span style='mso-tab-count:3'>����������������������������������� </span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo3;tab-stops:list .5in'><span style='font-size:12.0pt; mso-bidi-font-size:10.0pt;mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman";color:blue'><a href="http://www.ics.uci.edu/~dsm/cypress/">CYPRESS</a></span><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>:<span style="mso-spacerun:yes">� </span><span class=SpellE><span style='color:blue'>CY</span>ber</span>-<span style='color:blue'>P</span>hysical <span class=SpellE><span style='color:blue'>RES</span>ilience</span> &amp; <span style='color:blue'>S</span>ustainability</span><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt;mso-bidi-font-family: "Times New Roman"'><o:p></o:p></span></li> </ul> <h2><span style='font-size:22.0pt;mso-bidi-font-size:18.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Other Active UCI Projects:<o:p></o:p></span></h2> <p style='margin-left:.5in;text-indent:-.25in;mso-list:l0 level1 lfo6'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 10.0pt;mso-fareast-font-family:"Times New Roman"'>Spiking Neural Networks:<span style="mso-spacerun:yes">� </span>with <a href="http://www.socsci.uci.edu/~jkrichma/">Prof. Jeff <span class=SpellE>Krichmar</span> </a><span style="mso-spacerun:yes">�</span>(CARL Lab) in Cognitive Sciences </span><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt'><o:p></o:p></span></p> <p style='margin-left:.5in;text-indent:-.25in;mso-list:l0 level1 lfo6'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 10.0pt'>Ultimately Reliable and Safe (Zero-Recall) Software Technology Development: with <a href="http://english.kookmin.ac.kr/site/campus_life/new_hot/news.htm?mode=view&amp;num=64">Prof. Sung-<span class=SpellE>Soo</span> Lim,</a> <span class=SpellE>Kookmin</span> University, Korea<o:p></o:p></span></p> <p><span style='font-size:12.0pt;mso-bidi-font-size:10.0pt'>For more details on other research projects, visit the <a href="https://duttgroup.ics.uci.edu//doku.php">Dutt Research Group</a> webpage.<o:p></o:p></span></p> </div> </body> </html> http://www.ics.uci.edu/~dutt/teaching.htm Nikil Dutt's Teaching

          Teaching: Spring 2010

          CS 259 (Special Topics in Embedded Systems):  Seminar on Cyber-Physical Systems   Th 2-4:50 pm, DBH 4011 

           


          Course Offerings:

          University Studies 3 (1) Freshmen Seminar

           

          CS 145A Embedded Computing Systems (4). Principles of embedded computing systems: embedded systems architecture, hardware/software components, system software and interfacing, real-time operating systems, hardware/software co-development, and communication issues. Examples of embedded computing in real-world application domains. Simple programming using an embedded systems development environment. Prerequisites: ICS 21/CSE21, ICS 22/CSE22, ICS 23/CSE23, ICS 51. Formerly ICS 53.

           

          CS 151 Digital Logic Design (4) Boolean algebra. Design/analysis of combinational and sequential systems using SSI/MSI/LSI modules. Number systems. Error detecting and correction codes. Arithmetic algorithms. Hardware/firmware implementation of algorithms. Prerequisites: ICS 23 and 51; Mathematics 6A-B-C; satisfactory completion of the lower-division writing requirement.

          ICS 153 Logic Design Laboratory (4) Introduction to standard integrated circuits: gates, flip-flops, shift registers, counters, latches. Construction and debugging techniques. Design of digital systems using LSI and MSI components. Practical use of circuits in a laboratory environment, including implementation of small digital systems such as arithmetic modules, displays, and timers. Prerequisite: ICS 151.

          CS H198 Honors Research (4) Directed independent research in computer science for honors students. Prerequisites: ICS H197; upper-division standing and satisfactory completion of the lower-division writing requirement; participation in the ICS Honors Program or Campuswide Honors Program; consent of instructor.

          CS 244: Introduction to Embedded and Ubiquitous Systems (4). Survey of the basic components of embedded and ubiquitous computing systems. Introduction to digital signal processing, control systems, software for embedded systems, and ad-hoc as well as wireless and distributed networked embedded systems, human interaction with embedded and ubiquitous systems, as well as special topics on applications of embedded and ubiquitous systems. Prerequisites: B.S. degree in computer science; or ICS 51, CS 152; Mathematics 3A or 6C or ICS 6A; CS 161. Same as Informatics 244. Formerly ICS 212.

          CS 251 Digital System Verification and Testing (4). Techniques for simulation, verification, and testing of hardware and mixed-mode systems. Fault models, test generation, algorithms, and functional testing. Design for testability. Prerequisite: consent of instructor.

          CS 253 Design Description and Modeling (4). Introduction to design modeling. Overview of design description languages and demonstration of design modeling at different abstraction levels. Techniques and methodologies for simulating and testing of design. Prerequisites: consent of instructor. Formerly ICS 251.

          CS 256 Design Synthesis (4). Methods, algorithms, and tools for design synthesis on different levels of design: logic, register-transfer, behavioral, and system . CAD laboratory assignments using design tools for exploration of different synthesis algorithms. Prerequisites: ICS 152 (or 241), or 252, or consent of instructor. Formerly ICS 227.

          CS 259S Seminar in Design Science (2) Current research and research trends in design science. Forum for presentation and criticism by students of research work in progress. May be repeated for credit. Formerly ICS 259

          CS 295 Special Topics in Embedded Systems (4)

          CS 290 Research Seminar (2) Forum for presentation and criticism by students of research work in progress. Presentation of problem areas and related work. Specific goals and progress of research. Satisfactory/Unsatisfactory Only.

          CS 298 Thesis Supervision (2 to 12). Individual research or investigation conducted in preparation for the M.S. thesis option or the dissertation requirements for the Ph.D. program.

          ICS 299 Individual Study (2 to 12). Individual research or investigation under the direction of an individual faculty member.

          EECS199 Individual Study (1 to 4) For undergraduate Engineering majors in supervised but independent reading, research, or design. Students taking individual study for design credit are to submit a written paper to the instructor and to the Undergraduate Student Affairs Office in the School of Engineering. May be taken for credit for a total of six units. Formerly ECE199. (Design units: varies)

          EECS199P Individual Study (1 to 4) Same description as EECS199. Pass/Not Pass grading only. May be repeated for credit as topics vary. Formerly ECE199P. (Design units: varies)

          EECSH199 Individual Study for Honors Students (1 to 5). For undergraduate honor students majoring in Electrical Engineering. Independent reading, research, or design under the direction of a faculty member or group of faculty members in Electrical and Computer Engineering. Students taking individual study for design credit are to submit a written paper to the instructor and to the Undergraduate Student Affairs Office in the School of Engineering. Prerequisite: consent of instructor; open only to Campuswide Honors students. May be taken for credit four times. Formerly ECEH199. (Design units: varies)

          EECS296 Master of Science Thesis Research (4 to 12) Individual research or investigation conducted in the pursuit of preparing and completing the thesis required for the M.S. degree in Engineering. Prerequisite: consent of instructor. May be repeated for credit. Formerly ECE296.

          EECS297 Doctor of Philosophy Dissertation Research (4 to 12) Individual research or investigation conducted in preparing and completing the dissertation required for the Ph.D. degree in Engineering. Prerequisite: consent of instructor. May be repeated for credit. Formerly ECE297.

          EECS298 Topics in Electrical Engineering and Computer Science (3) Study of Electrical and Computer Engineering concepts. Prerequisite: consent of instructor. May be repeated for credit as topics vary. Formerly ECE298.

          EECS299 Individual Research (1 to 12)  Individual research or investigation under the direction of an individual faculty member. Prerequisite: consent of instructor. May be repeated for credit. Formerly ECE299.

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gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor=ivory lang=EN-US link=blue vlink=purple style='tab-interval:.5in'> <div class=WordSection1> <h2><span style='mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman"'>Short Biography of Nikil D. Dutt<o:p></o:p></span></h2> <p style='text-align:justify;text-justify:inter-ideograph'>Nikil D. Dutt is a Chancellor s Professor at the <ns0:PlaceType><span>University</ns0:PlaceType></span> of <ns0:PlaceName><span>California</ns0:PlaceName></span>, <ns0:place><span><ns0:City><span>Irvine</ns0:City></span></ns0:place></span>, with academic appointments in the CS, EECS, and Cognitive Sciences departments.<span style="mso-spacerun:yes">� </span>He received a B.E<span class=GramE>.(</span><span class=SpellE>Hons</span>) in Mechanical Engineering from the Birla Institute of Technology and Science, <ns0:City><span><span class=SpellE>Pilani</span></ns0:City></span>, <ns0:country-region><span>India</ns0:country-region></span> in 1980, an M.S. in Computer Science from the <ns0:PlaceName><span>Pennsylvania</ns0:PlaceName></span> <ns0:PlaceType><span>State</ns0:PlaceType></span> <ns0:PlaceType><span>University</ns0:PlaceType></span> in 1983, and a Ph.D. in Computer Science from the <ns0:place><span><ns0:PlaceType><span>University</ns0:PlaceType></span> of <ns0:PlaceName><span>Illinois</ns0:PlaceName></span></ns0:place></span> at Urbana-Champaign in 1989. He is affiliated with the following Centers at UCI: Center for Embedded Computer Systems (CECS), Center for Cognitive Neuroscience and Engineering (CENCE), California Institute for Telecommunications and Information Technology (Calit2), the Center for Pervasive Communications and Computing (CPCC), and the Laboratory for Ubiquitous Computing and Interaction (LUCI).</p> <p style='text-align:justify;text-justify:inter-ideograph'>Professor <span class=SpellE>Dutt s</span> research interests are in embedded systems, electronic design automation, computer architecture, optimizing compilers, system specification techniques, distributed systems, formal methods, and brain-inspired architectures and computing. He is a coauthor of seven books: &quot;<i><span style='color:blue'>High-Level Synthesis: Introduction to Chip and System Design</span></i>&quot;, Kluwer Academic Publishers, 1992, &quot;<i><span style='color:blue'>Memory Issues in Embedded Systems-on-Chip: Optimizations and Exploration</span></i>&quot;, Kluwer Academic Publishers, 1999, &quot;<i><span style='color:blue'>Memory Architecture Exploration for Programmable Embedded System</span>s</i>&quot;, Kluwer Academic Publishers, 2003, &quot;<i><span style='color:blue'>SPARK: A Parallelizing Approach to the High-Level Synthesis of Digital Circuits</span></i>&quot;, Kluwer Academic Publishers, 2004, &quot;<i><span style='color:blue'>Functional Validation of Programmable Embedded Architectures: A Top-Down Approach</span></i>&quot;, Springer-<span class=SpellE>Verlag</span>, 2005,  <i><span style='color:blue'>On-chip Communication Architectures: Current Practice, Research and Future Trends</span></i>, Morgan Kaufman/Elsevier Systems-on-Silicon Series, 2008, and  <i><span style='color:blue'>Processor Description Languages: Applications and Methodologies</span></i>, Morgan Kaufman/Elsevier Systems-on-Silicon Series, 2008.</p> <p style='text-align:justify;text-justify:inter-ideograph'>Professor <span class=SpellE>Dutt s</span> research has been recognized by Best Paper Awards at the following conferences: CHDL 89, CHDL 91, VLSI Design 2003, CODES+ISSS 2003, CNCC 2006, ASPDAC 2006, IJCNN 2009, and DATE 2012; and Best Paper Award Nominations at: WASP 2004, DAC 2005, VLSI Design 2006, and CASES 2011.<span style="mso-spacerun:yes">� </span>He has also received a number of departmental and campus awards for excellence in teaching at UC Irvine.</p> <p style='text-align:justify;text-justify:inter-ideograph'>Professor Dutt currently serves as Associate Editor of ACM Transactions on Embedded Computer Systems (TECS) and of IEEE Transactions on VLSI Systems (TVLSI). He served as Editor-in-Chief of ACM Transactions on Design Automation of Electronic Systems (TODAES) between 2004-2008. He was an ACM SIGDA Distinguished Lecturer during 2001-2002, and an IEEE Computer Society Distinguished Visitor for 2003-2005.<span style="mso-spacerun:yes">� </span>He has served on the steering, organizing, and program committees of several premier CAD and Embedded System conferences and workshops.<span style="mso-spacerun:yes">� </span>His recent major conference activity includes: <span class=SpellE><i style='mso-bidi-font-style: normal'><span style='color:blue'>ESWeek</span></i></span><i style='mso-bidi-font-style: normal'><span style='color:blue'> Steering Committee Chair</span></i> and <i style='mso-bidi-font-style:normal'><span style='color:blue'>TPC Co-Chair DAC-2010/2011</span></i>. He currently serves on, or has served on the ACM Publications Board, the advisory boards of ACM SIGBED, ACM SIGDA, and IFIP WG 10.5. He is a Fellow of the IEEE, an ACM Distinguished Scientist, and an IFIP Silver Core awardee.</p> <p><b style='mso-bidi-font-weight:normal'><span style='font-size:20.0pt'><a href="jun09-vita-web.pdf">Recent CV (<span class=SpellE>pdf</span>, June 2009)</a><span class=MsoHyperlink><o:p></o:p></span></span></b></p> <p><b style='mso-bidi-font-weight:normal'><span style='font-size:20.0pt'><a href="dutt-short-journal-bio-jun09.txt">Journal Biography</a></span></b><span class=MsoHyperlink><o:p></o:p></span></p> <p><b style='mso-bidi-font-weight:normal'><span style='font-size:20.0pt'><a href="Dutt-portrait-2007.tif.gz">Journal Photograph (<span class=SpellE>gzipped</span> tiff file)</a><o:p></o:p></span></b></p> </div> </body> </html> > http://www.ics.uci.edu/~dutt/uci-directions-dutt-office.htm Directions to Dutt's office: 3086 Bren Hall

          Directions to Dutt's office: 3086 Bren Hall

           

          View on UCI map, Select Action Locate Buildings -> Bren Hall DBH

          http://www.parking.uci.edu/maps/

           

          Parking near Bren Hall:

          Use the Anteater Parking Structure (APS):

          View on UCI map, Select Action Locate Parking Lots/Structures -> Anteater Parking Structure

          http://www.parking.uci.edu/maps/

          You can obtain a parking permit at the kiosk (intersection of Anteater Road and E. Peltason Drive)

           

          Driving directions to Bren Hall:

          http://www.ics.uci.edu/about/visit/index.php

           

          Taxi from Orange County (SNA) airport:

          Ask the taxi driver to drop you off at the UCI University Club,

          at the intersection of Peltason and Los Trancos on campus.

          (This is Parking Lot 18B in the UCI map.)

          Bren Hall is right next to the University Club.

           

          Shuttle from local area hotels:

          Most area hotels offer complimentary shuttle service to UCI.

          Ask the shuttle driver to drop you off at the UCI University Club

          at the intersection of Peltason and Los Trancos on campus.

          (This is Parking Lot 18B in the UCI map.)

          Bren Hall is right next to the University Club.

           

           

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mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1027"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor=ivory lang=EN-US style='tab-interval:.5in'> <div class=WordSection1> <h2><span style='font-size:20.0pt;mso-bidi-font-size:18.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Professional Activities:<o:p></o:p></span></h2> <h4><i><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Recent Major Conference Committees</span></i><span style='font-size:14.0pt;mso-bidi-font-size: 12.0pt;mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"'><o:p></o:p></span></h4> <ul type=disc> <li class=MsoNormal 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mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"'>Program Committee Member (various editions): ASPDAC, CODES+ISSS, CASES, DATE, EMSOFT, ICCAD, ISLPED, LCTES, RTAS, RTSS, etc.<o:p></o:p></span></li> </ul> <h4><i><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Professional Society Service</span></i><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"'><o:p></o:p></span></h4> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo6;tab-stops:list .5in'><span style='font-size:14.0pt; mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"'>ACM Publications Board<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo6;tab-stops:list .5in'><span 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style='font-size:14.0pt;mso-bidi-font-size:12.0pt;mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'>Major Editorial Activity</span></i><span style='font-size:14.0pt;mso-bidi-font-size:12.0pt; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"'><o:p></o:p></span></h4> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo9;tab-stops:list .5in'><span style='font-size:14.0pt; mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"'>Editor-in-Chief, ACM Transactions on Design Automation of Electronic Systems (ACM-TODAES).<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l3 level1 lfo9;tab-stops:list .5in'><span style='font-size:14.0pt; mso-bidi-font-size:12.0pt;mso-bidi-font-family:"Times New Roman"'>Associate Editor, ACM Transactions on Embedded Computer Systems (ACM-TECS).<o:p></o:p></span></li> 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mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1027"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor=ivory lang=EN-US link=blue vlink=blue style='tab-interval:.5in'> <div class=WordSection1> <h2><span style='mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman"'>Affiliated <a href="https://duttgroup.ics.uci.edu/doku.php/group">Ph.D. Students</a>:</span><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;mso-bidi-font-family:"Times New Roman"'><span style='mso-tab-count:1'>����� </span></span><span style='mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman"'><o:p></o:p></span></h2> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Abbas <span class=SpellE>Banaiyan-Mofrad</span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Jun Yong Shin<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Santanu</span></span><span style='mso-bidi-font-family:"Times New Roman"'> <span class=SpellE>Sarma</span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Hossein</span></span><span style='mso-bidi-font-family:"Times New Roman"'> <span class=SpellE>Tajikh</span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Jurngyu</span></span><span style='mso-bidi-font-family:"Times New Roman"'> Park<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Majid</span></span><span style='mso-bidi-font-family:"Times New Roman"'> <span class=SpellE>Namaki</span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Bryan <span class=SpellE>Donyanavard</span><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l0 level1 lfo3;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Michael <span class=SpellE>Beyeler</span> (jointly advised with Prof. Jeff <span class=SpellE>Krichmar</span> in <span class=SpellE>CogSci</span>)<span style="mso-spacerun:yes">� </span><o:p></o:p></span></li> </ul> <div class=MsoNormal align=center style='text-align:center'><span style='mso-bidi-font-family:"Times New Roman"'> <hr size=2 width="100%" align=center> </span></div> <h2><span style='mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman"'>Ph.D. Students Graduated:<o:p></o:p></span></h2> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="mailto:pradip@hierdesign.com"><span class=SpellE>Pradip</span> <span class=SpellE>Jha</span>, </a>Xilinx Inc., <span style='mso-tab-count:4'>��������������������������������������������� </span><span style='mso-tab-count:1'>����������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>High-Level Library Mapping for RT Components</i>, October 1995. <a href="http://www.ics.uci.edu/~dutt/pubs/pradip-jha-thesis.ps.gz"><span class=SpellE><span class=GramE>gzip'ed</span></span> PostScript file </a><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="mailto:rogerang@cadence.com">Roger <span class=SpellE>Ang</span>,</a> <span class=SpellE>Synplicity</span> Inc., <span style='mso-tab-count:4'>�������������������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Library Insertion and Reuse of <span class=SpellE>Datapath</span> Components in High-Level Synthesis</i>, June 1996. <a href="http://www.ics.uci.edu/~dutt/pubs/roger-ang-thesis.ps.gz"><span class=SpellE><span class=GramE>gzip'ed</span></span> PostScript file </a><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.cse.iitd.ernet.in/~panda"><span class=SpellE>Preeti</span> <span class=SpellE>Ranjan</span> Panda,</a> Professor at IIT-Delhi, <span style='mso-tab-count:1'>����������� </span><span style="mso-spacerun:yes">�</span><span style='mso-tab-count:1'>���������� </span>THESIS: <i style='mso-bidi-font-style:normal'>Memory Optimizations and Exploration for Embedded Systems</i>, January 1998. <a href="http://www.ics.uci.edu/~dutt/pubs/preeti-panda-thesis.ps.gz"><span class=SpellE><span class=GramE>gzip'ed</span></span> PostScript file </a><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~pgrun">Peter <span class=SpellE>Grun</span>, </a>ARM Inc., <span style='mso-tab-count:4'>��������������������������������������������� </span><span style='mso-tab-count:1'>����������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Hardware/Software Memory Customizations for Programmable Embedded Systems</i>, September 2001. <o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.cise.ufl.edu/~prabhat"><span class=SpellE>Prabhat</span> Mishra, </a>Associate Professor at <ns0:place><span><ns0:PlaceType><span>University</ns0:PlaceType></span> of <ns0:PlaceName><span>Florida</ns0:PlaceName></span></ns0:place></span><span class=GramE>,<span style="mso-spacerun:yes">� </span>THESIS</span>: <i style='mso-bidi-font-style:normal'>Specification Driven Validation of Programmable Embedded Systems</i>, June 2004. <b><span style='color:red'>(Winner of 2004 EDAA Outstanding Dissertation Award)</span></b><o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~maheshmn">Mahesh <span class=SpellE>Mamidipaka</span></a>, Intel, Inc., <span style='mso-tab-count: 3'>����������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Power Estimation in High-Performance Memory Structures,</i> October 2004.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~partha/"><span class=SpellE>Partha</span> <span class=SpellE>Biswas</span></a>, <span class=SpellE>MathWorks</span>, Inc., <span style='mso-tab-count:3'>���������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i>Acceleration beyond Memory Barriers in IS-extensible Processors</i>, March 2006.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~aviral/"><span class=SpellE>Aviral</span> <span class=SpellE>Shrivastava</span></a>, Assistant Professor at <ns0:place><span><ns0:PlaceName><span>Arizona</ns0:PlaceName></span> <ns0:PlaceType><span>State</ns0:PlaceType></span> <ns0:PlaceType><span>University</ns0:PlaceType></span></ns0:place></span><span class=GramE>,<span style="mso-spacerun:yes">� </span>THESIS</span>:<span style="mso-spacerun:yes">� </span><i>Compiler-in-the-Loop Exploration of Programmable Embedded Systems</i>, June 2006.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~banerjee"><span class=SpellE>Sudarshan</span> Banerjee</a>, Cadence, Inc.,<span style='mso-tab-count:3'>���������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i>Application mapping for FPGAs with Partial Dynamic Reconfiguration</i>, March 2007.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'><a href="http://www.ics.uci.edu/~isse"><span class=SpellE>Ilya</span> <span class=SpellE>Issenin</span></a>, <span style='mso-tab-count:6'>�������������������������������������������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i>Multiprocessor System-on-Chip Data Memory Customization for Embedded Array-Intensive Applications</i>, March 2007.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Sudeep</span></span><span style='mso-bidi-font-family:"Times New Roman"'> <span class=SpellE>Pasricha</span>, Assistant Professor, Colorado State University<span class=GramE>,<span style="mso-spacerun:yes">� </span>THESIS</span>:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>On-Chip Communication Architecture Synthesis for Multi-Processor System-on-Chips</i>, June 2008.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Minyoung</span></span><span style='mso-bidi-font-family:"Times New Roman"'> Kim<span class=GramE>,<span style="mso-spacerun:yes">� </span>SRI</span>, <span style='mso-tab-count: 4'>����������������������������������������������� </span><span style='mso-tab-count:1'>����������� </span>THESIS:<span style="mso-spacerun:yes">� </span><span class=SpellE><i style='mso-bidi-font-style: normal'>xTune</i></span><i style='mso-bidi-font-style:normal'>: A Formal Methodology for Cross-layer Tuning of Mobile Real-time Embedded Systems</i>, September 2008.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Kyoungwoo</span></span><span style='mso-bidi-font-family:"Times New Roman"'> Lee, Assistant Professor, <span class=SpellE>Yonsei</span> University<span class=GramE>,<span style="mso-spacerun:yes">� </span>THESIS</span>:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Cooperative Cross-Layer Protection for Resource Constrained Mobile Multimedia Systems</i>, December 2008.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Gabor <span class=SpellE>Madl</span>, Honeywell Research Center,<span style='mso-tab-count:2'>���������������� </span><span class=GramE>THESIS</span>:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Model-based Analysis of Event-driven Distributed Real-time Embedded Systems,</i> June 2009.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Ashok <span class=SpellE>Halambi</span>, Yahoo, Inc.,<span style='mso-tab-count:4'>������������������������������������� </span>THESIS:<span style="mso-spacerun:yes">� </span><span class=SpellE><i style='mso-bidi-font-style: normal'>TransMutations</i></span><i style='mso-bidi-font-style:normal'>: A framework for dynamic customization of <span class=SpellE>retargetable</span> compilers for embedded systems</i>, June 2009.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Aseem</span></span><span style='mso-bidi-font-family:"Times New Roman"'> Gupta, <span class=SpellE>Freescale</span>, Inc.,<span style='mso-tab-count:3'>����������������������������������� </span><span style='mso-tab-count:1'>����������� </span>THESIS:<span style="mso-spacerun:yes">� </span><i style='mso-bidi-font-style:normal'>Temperature Aware VLSI Design for Reduced Power and Reliability Enhancement</i><span class=GramE>,<span style="mso-spacerun:yes">� </span>June</span> 2009.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span class=SpellE><span style='mso-bidi-font-family:"Times New Roman"'>Jayram</span></span><span style='mso-bidi-font-family:"Times New Roman"'> <span class=SpellE>Moorkanikara</span>, The Brain Corp.,<span style='mso-tab-count:2'>������������������ </span>THESIS: <i style='mso-bidi-font-style:normal'>Framework for Spiking Neural Network Modeling on High-Performance Architectures</i><span class=GramE>,<span style="mso-spacerun:yes">� </span>September</span> 2010.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Luis Angel <span class=SpellE>Bathen</span>, SPAWAR<span style='mso-tab-count:3'>����������������������������������� </span><span style='mso-tab-count:1'>����������� </span>THESIS: <span class=SpellE><i style='mso-bidi-font-style:normal'>PHiLOSoftware</i></span><i style='mso-bidi-font-style:normal'>: A Low Power, High Performance, Reliable, and Secure Virtualization Layer for On-Chip Software-Controlled Memories, </i>July 2012.<o:p></o:p></span></li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto; mso-list:l2 level1 lfo6;tab-stops:list .5in'><span style='mso-bidi-font-family: "Times New Roman"'>Kazuyuki Tanimura, <span class=SpellE>BloomReach</span> Inc.<span style='mso-tab-count:3'>��������������������������������� </span>THESIS: <i style='mso-bidi-font-style:normal'>PARADE: Power Analysis Resistive Architecture Design,</i> August 2012.<o:p></o:p></span></li> </ul> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='mso-bidi-font-family:"Times New Roman"'><o:p>&nbsp;</o:p></span></p> <div class=MsoNormal align=center style='text-align:center'><span style='mso-bidi-font-family:"Times New Roman"'> <hr size=2 width="100%" align=center> </span></div> <h2><span style='mso-fareast-font-family:"Times New Roman";mso-bidi-font-family: "Times New Roman"'><a href="https://duttgroup.ics.uci.edu/doku.php/group">Visitors</a> </span><span class=MsoHyperlink><o:p></o:p></span></h2> <p class=MsoNormal><o:p>&nbsp;</o:p></p> </div> </body> </html> http://www.ics.uci.edu/~gts/students.html STUDENTS

          Hard-working, long-suffering but always appreciated...

          Current PhD Students:

          • Christopher Wood, from 2013.
          • Tyler Kaczmarek, from 2013.
          • Marc Schlosberg, from 2014.
          • Sky Faber, from 2011.
          • Cesar Ghali, from 2012.
          • Ekin Oguz, from 2012.

          Former PhD Students (advisor):

          1. Mishari Almishari, UCI, 2012, Assistant Professor, King Saud University, Saudi Arabia.
          2. Yanbin Lu, UCI, 2012, Software Engineer, Google.
          3. Emiliano De Cristofaro, UCI, 2011, Research Staff, Xerox PARC ==> Associate Professor, University College London, UK.
          4. John Solis, UCI, 2010, Research Staff, Sandia National Labs.
          5. Karim El Defrawy, UCI, 2010, Research Staff, Hughes Research Labs.
          6. Ersin Uzun, UCI, 2010, Research Staff, Xerox PARC.
          7. Di Ma, UCI, 2009, Assistant Professor, University of Michigan, Dearborn.
          8. Claudio Soriente, UCI, 2009, Researcher, ETH Zuerich, Switzerland.
          9. Jihye Kim, UCI, 2008, Assistant Professor, Kookmin University, Korea (co-advised with S. Jarecki).
          10. Nitesh Saxena, UCI, 2006, Associate Professor, University of Alabama (co-advised with S. Jarecki).
          11. Maithili Narasimha, UCI, 2006, Member of Technical Staff, CISCO.
          12. Einar Mykletun, UCI, 2006, Security and Compliance Architect, Quest Software.
          13. Jeong Hyun Yi, UCI, 2005, Associate Professor, Soongsil University, Korea.
          14. Xuhua Ding, USC, 2003, Associate Professor, Singapore Management University, Singapore.
          15. Yongdae Kim, USC, 2002, Associate Professor, University of Minnesota ==> Professor, KAIST, Korea.
          16. Michael Steiner, Universität des Saarlandes (Germany), 2002, Research Staff Member, IBM Research Watson (co-advised with B. Pfitzmann).
          17. Giuseppe Ateniese, Universitá degli Studi di Genova (Italy), 1999, Associate Professor, Johns Hopkins University ==> Professor, University of Rome ("La Sapienza), Italy.
          18. Brenda Timmerman, USC, 1998, Professor, California State University, Northridge.

          Former PhD Students (committee member):

          1. Philip Ginzboorg, Aalto University, Finland, 2014.
          2. Abdelberi Chaabane, INRIA Rhone-Alpes, France, 2014.
          3. Steffen Schulz, Macquarie University, Australia, 2014.
          4. Lei Wei, UNC Chapel Hill, 2013.
          5. Luis Angel Bathen, UCI, 2012.
          6. Anh Le, UCI, 2012.
          7. Todd Jackson, UCI, 2012.
          8. Ali Bagherzandi, UCI, 2011.
          9. Ahren Studer, CMU, 2011.
          10. Alessandro Colantonio, Universitá di Roma Tre (Italy), 2011.
          11. Kasper Bonne Rasmussen, ETH Zuerich (Switzerland), 2011.
          12. Dan Forsberg, Aalto University (Finland), 2010.
          13. Julien Freudiger, EPFL (Switzerland), 2010.
          14. Xiaomin Liu, UCI, 2010.
          15. Bryan Parno, CMU, 2010.
          16. Gabriele Oligieri, Universitá di Pisa (Italy), 2010.
          17. Enrique Caiuch, UCI, 2009.
          18. Gergely Acs, Budapest University of Technology and Economics (Hungary), 2009.
          19. Bijit Hore, UCI, 2007.
          20. Walid Bagga, ENST / Institut Eurecom (France), 2006.
          21. Vivek Haldar, UCI, 2006.
          22. Kumar Viswanath, UC Santa Cruz, 2005.
          23. Pietro Michiardi, ENST / Institut Eurecom (France), 2004.
          24. Alain Pannetrat, ENST / Institut Eurecom (France), 2002.
          25. Adrian Perrig, CMU, 2003.
          http://www.ics.uci.edu/~gts/pubs.html Gene Tsudik -- Publications Gene Tsudik -- Publications

          Publications of all sorts, shapes and sizes...

            2016

          1. I. Martinovic, K. Rasmussen, M. Roeschlin and G. Tsudik,
            Authentication Using Pulse-Response Biometrics,
            Communications of the ACM (CACM): Research Highlights, to appear in February 2015.
            contatt-CACM16.pdf

            2015

          2. E. Ayday, E. De Cristofaro, JP Hubaux and G. Tsudik,
            Whole Genome Sequencing: Revolutionary Medicine or Privacy Nightmare?,
            IEEE Computer, Vol 48, No. 2, February 2015.
            WGS-Computer15.pdf

          3. T. Kaczmarek, A. Kobsa, R. Sy and G. Tsudik,
            An Unattended Study of Users Performing Security Critical Tasks Under Adversarial Noise,
            NDSS Workshop on Usable Security (USEC), 2015.
            noise-USEC15.pdf

          4. A. Compagno, M. Conti, P. Gasti, L. Mancini and G. Tsudik,
            Violating Consumer Anonymity: Geo-locating Nodes in Named Data Networking,
            Applied Cryptography and Network Security (ACNS), 2015. Best Student Paper Award.
            geoloc-ACNS15.pdf

          5. K. El Defrawy, G. Holland and G. Tsudik,
            Remote Attestation of Heterogeneous Cyber-Physical Systems: The Automotive Use Case,
            Conference on Embedded Security in Cars (ESCAR), 2015.
            attest-ESCAR15.pdf

          6. A. Compagno, M. Conti, C. Ghali, G. Tsudik,
            To NACK or not to NACK? Negative Acknowledgments in Information-Centric Networking,
            IEEE Conference on Computer Communications and Networks (ICCCN), 2015.
            ndn-nacks-ICCCN15.pdf

          7. S. Faber, R. Petrlic and G. Tsudik,
            UnLinked: Private Proximity-based Oline OSN Interaction,
            ACM Workshop on Privacy in the Electronic Society (WPES), 2015.
            unlinked-WPES15.pdf

          8. C. Ghali, M. Schlosberg, G. Tsudik and C. Wood,
            Interest-Based Access Control for Content Centric Networks,
            ACM Conference on Information-Centric Networking (ICN), 2015.
            ibac-ICN15.pdf

          9. N. Asokan, F. Brasser, A. Ibrahim, A. Sadeghi, M. Schunter, G. Tsudik and C. Wachsmann,
            SEDA: Scalable Embedded Device Attestation,
            ACM Conference on Computer and Communications Security (CCS), 2015.
            seda-CCS15.pdf

          10. C. Ghali, A. Narayanan, D. Oran, G. Tsudik and C. Wood,
            Secure Fragmentation for Content-Centric Networks,
            IEEE Symposium on Network Computing and Applications (NCA), 2015. Best Paper Award.
            frag-NCA15.pdf

            2014

          11. M. Almishari, P. Gasti, N. Nathan and G. Tsudik,
            Optimizing Bi-Directional Low-Latency Communication in Named-Data Networking,
            ACM Computer Communications Review, Vol. 44, No. 1, January 2014.
            bidir-CCR14.pdf

          12. C. Ghali, G. Tsudik and E. Uzun,
            Elements of Trust in Named-Data Networking,
            ACM Computer Communications Review, Vol. 44, No. 3, October 2014.
            ndn-trust-CCR14.pdf

          13. S. Ali, S. Jha, D. Ostry, V. Sivaraman and G. Tsudik,
            Securing First-Hop Data Provenance for Bodyworn Devices using Wireless Link Fingerprints,
            IEEE Transactions on Information Forensics and Security, Vol. 9, No. 12, September 2014.
            prov-TIFS14.pdf

          14. I. Martinovic, K. Rasmussen, M. Roeschlin and G. Tsudik,
            Authentication Using Pulse-Response Biometrics,
            ISOC Symposium on Network and Distributed System Security (NDSS), 2014. Distinguished Paper Award.
            contatt-NDSS14.pdf

          15. A. Francillon, Q. Nguyen, K. Rasmussen and G. Tsudik,
            A Minimalist Approach to Remote Attestation,
            IEEE/ACM Design, Automation, and Test in Europe (DATE), 2014.
            minimal-DATE14.pdf

          16. C. Ghali, G. Tsudik and E. Uzun,
            Needle in a Haystack: Mitigating Content Poisoning in Named-Data Networking,
            NDSS Workshop on Security of Emerging Networking Technologies (SENT), 2014.
            needle-SENT14.pdf

          17. M. Ambrosin, M. Conti, P. Gasti and G. Tsudik,
            Covert Ephemeral Communication in Named Data Networking,
            ACM Symposium on Information, Computer and Communications Security (ASIACCS), 2014.
            covert-ASIACCS14.pdf

          18. J. Burke, P. Gasti, N. Nathan and G. Tsudik,
            Secure Sensing over Named Data Networking,
            IEEE Symposium on Network Computing and Applications (NCA), 2014.
            ndn-sense-NCA14.pdf

          19. M. Almishari, E. Oguz and G. Tsudik,
            Fighting Authorship Linkability with Crowdsourcing,
            ACM Conference on Online Social Networks (COSN), 2014.
            crowd-COSN14.pdf

          20. M. Almishari, E. Oguz, D. Kaafar and G. Tsudik,
            Stylometric Linkability of Tweets,
            ACM Workshop on Privacy in the Electronic Society (WPES), 2014.
            tweet-WPES14.pdf

            2013

          21. R. Di Pietro, G. Oligeri, C. Soriente and G. Tsudik,
            United We Stand: Intrusion-Resilience in Mobile Unattended WSNs,
            IEEE Transactions on Mobile Computing, Vol. 12, No. 7, July 2013.
            united-TMC13.pdf

          22. A. Kobsa, R. Nithyanand, G. Tsudik and E. Uzun,
            Can Jannie Verify: Usability of Display-Equipped RFID Tags for Security Purposes,
            Journal of Computer Security, Vol. 21, No. 3, July 2013.
            jannie-JCS13.pdf

          23. J. Burke, P. Gasti, N. Nathan and G. Tsudik,
            Securing Instrumented Environments over Content-Centric Networking,
            IEEE Workshop on Emerging Design Choices in Name-Oriented Networking (NOMEN), 2013.
            ndn-inst-NOMEN13.pdf

          24. G. Acs, M. Conti, C. Ghali, P. Gasti and G. Tsudik,
            Cache Privacy in Name-Data Networking,
            IEEE International Conference on Distributed Computing Systems (ICDCS), 2013.
            ndn-cache-ICDCS13.pdf

          25. P. Gasti, G. Tsudik, E. Uzun and L. Zhang,
            DoS & DDoS in Named Data Networking,
            IEEE Conference on Computer Communications and Networks (ICCCN), 2013.
            ndn-dos-ICCCN13.pdf

          26. M. Almishari, P. Gasti, E. Oguz and G. Tsudik,
            Privacy-Preserving Matching of Community-Contributed Content,
            European Symposium on Research in Computer Security (ESORICS), 2013.
            ppmcc-ESORICS13.pdf

          27. A. Compagno, M. Conti, P. Gasti and G. Tsudik,
            Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data
            Networking, IEEE Local Computer Networks (LCN), 2013.
            poseidon-LCN13.pdf

          28. E. Ayday, E. De Cristofaro, JP Hubaux and G. Tsudik,
            Privacy Considerations of Genome Sequencing,
            USENIX Summit on Hot Topics in Security (HotSec) , 2013.
            wgs-HOTSEC13.pdf

          29. E. De Cristofaro, S. Faber, and G. Tsudik,
            Secure Genomic Testing via Size- and Position-Hiding Private Substring Matching,
            ACM Workshop on Privacy in the Electronic Society (WPES), 2013.
            sphpsm-WPES13.pdf

            2012

          30. S. DiBenedetto, P. Gasti, G. Tsudik and E. Uzun,
            ANDaNA: Anonymous Named Data Networking Application,
            ISOC Symposium on Network and Distributed System Security (NDSS), 2012.
            andana.pdf

          31. K. Eldefrawy, A. Francillon, D. Perito and G. Tsudik,
            SMART: Secure and Minimal Architecture for (Establishing Dynamic) Root of Trust,
            ISOC Symposium on Network and Distributed System Security (NDSS), 2012.
            smart.pdf

          32. E. De Cristofaro, C. Soriente and G. Tsudik,
            Hummingbird: Privacy at the time of Twitter,
            IEEE Symposium on Research in Security and Privacy (S&P), 2012.
            hummingbird.pdf

          33. E. De Cristofaro and G. Tsudik,
            Experimenting with Fast Private Set Intersection,
            International Conference on Trust and Trustworthy Computing (TRUST), 2012.
            psi-TRUST12.pdf

          34. Z. Erkin and G. Tsudik,
            Private Computation of Spatial and Temporal Power Consumption with Smart Meters,
            Applied Cryptography and Network Security (ACNS), 2012.
            ACNS12.pdf

          35. M. Almishari and G. Tsudik,
            Exploring Linkability of Community Reviewing,
            European Symposium on Research in Computer Security (ESORICS), 2012.
            reviewlink.pdf

          36. E. De Cristofaro, S. Faber, P. Gasti and G. Tsudik,
            GenoDroid: Are Privacy-Preserving Genomic Tests Ready for Prime Time?,
            ACM CCS Workshop on Privacy in the Electronic Society (WPES), 2012.
            genodroid.pdf

          37. E. De Cristofaro, P. Gasti and G. Tsudik,
            Fast and Private Computation of Cardinality of Set Intersection and Union,
            Conference on Cryptology and Network Security (CANS), 2012.
            psi-ca.pdf

          38. M. Conti, S. Das, C. Bisdikian, M. Kumar, L. Ni, A. Passarella, G. Roussos, G. Trster, G. Tsudik and F. Zambonelli,
            Looking Ahead in Pervasive Computing: Challenges and Opportunities in the Era of Cyber-Physical Convergence,
            Pervasive and Mobile Computing, Vol. 8, No. 12, February 2012.

          39. R. Di Pietro, D. Ma, C. Soriente and G. Tsudik,
            Self-Healing in Unattended Wireless Sensor Networks,
            ACM Transactions on Sensor Networks, Vol. 9, No. 1, February 2012.
            self-healing-TOSN12.pdf

          40. J. Cheon, C. Lim and G. Tsudik,
            Reducing RFID Reader Load with the Meet-in-the-Middle Strategy,
            Journal of Communications and Networks, Vol. 14, No. 1, February 2012.
            rfid-mitm-JCN12.pdf

          41. M. Al Mishari, E. De Cristofaro, K. El Defrawy and G. Tsudik,
            Harvesting SSL Certificate Data to Mitigate Web-Fraud,
            International Journal of Network Security, Vol. 14, No. 6, 2012.
            ssl-IJNS12.pdf

            2011

          42. G. Ateniese, E. De Cristofaro and G. Tsudik,
            (If) Size Matters: Size-Hiding Private Set Intersection,
            IACR Public Key Cryptography (PKC), 2011.

          43. Y. Lu and G. Tsudik,
            Enhancing Data Privacy in the Cloud,
            IFIP International Conference on Trust Management (IFIPTM), 2011.

          44. K. El Defrawy, S. Capkun and G. Tsudik,
            Group Distance Bounding Protocols,
            International Conference on Trust and Trustworthy Computing (TRUST), 2011.

          45. E. De Cristofaro, Y. Lu and G. Tsudik,
            Efficient Techniques for Privacy-Preserving Sharing of Sensitive Information,
            International Conference on Trust and Trustworthy Computing (TRUST), 2011.

          46. A. Kobsa, R. Nithyanand, G. Tsudik and E. Uzun,
            Usability of Display-Equipped RFID Tags for Security Purposes,
            European Symposium on Research in Computer Security (ESORICS), 2011.

          47. P. Baldi, R. Baronio, E. De Cristofaro, P. Gasti and G. Tsudik,
            Countering GATTACA: Efficient and Secure Testing of Fully-Sequenced Human Genomes,
            ACM Computer and Communication Security Conference (CCS), 2011.

          48. K. El Defrawy and G. Tsudik,
            ALARM: Anonymous Location-Aided Routing in Suspicious MANETs,
            IEEE Transactions on Mobile Computing, Vol. 10, No. 9, September 2011.

          49. R. Di Pietro, C. Soriente, A. Spognardi and G. Tsudik,
            Intrusion-Resilient Integrity in Data-Centric Unattended WSNs,
            Pervasive and Mobile Computing, Vol. 7, No. 4, August 2011.

          50. R. Nithyanand, G. Tsudik and E. Uzun,
            User-aided Reader Revocation in PKI-Based RFID Systems,
            Journal of Computer Security, Vol. 19, No. 6, December 2011.

          51. K. El Defrawy and G. Tsudik,
            PRISM: Privacy-friendly Routing In Suspicious MANETs,
            IEEE JSAC, Special Issue on Advances in Military Communications and Networking, Vol. 29, No. 10, December 2011.

          52. Y. Lu and G. Tsudik,
            Privacy-Preserving Cloud Database Querying,
            Journal of Internet Services and Information Security (JISIS), Vol. 1 No. 4, November 2011.

            2010

          53. E. De Cristofaro and G. Tsudik,
            Practical Private Set Intersection Protocols,
            Financial Cryptography and Data Security Conference (FC), 2010.

          54. R. Di Pietro, G. Oligeri, C. Soriente and G. Tsudik
            Intrusion-Resilience in Mobile Unattended WSNs,
            IEEE INFOCOM, 2010.

          55. C. Catellucia, K. El Defrawy and G. Tsudik
            Link-Layer Encryption Effect on the Capacity of Network Coding in Wireless Networks,
            IEEE INFOCOM WiP Session, 2010.

          56. D. Ma and G. Tsudik
            IRRES: Intrusion Resilient Remote Email Storage,
            ICDCS Workshop on Security and Privacy in Cloud Computing (SPCC), 2010.

          57. M. Manulis, B. Poettering and G. Tsudik
            Affiliation-Hiding Key Exchange with Untrusted Group Authorities,
            Applied Cryptography and Network Security (ACNS), 2010.

          58. M. Manulis, B. Poettering and G. Tsudik
            Taming Big Brother Ambitions: More Privacy for Secret Handshakes,
            Privacy Enhancing Technologies Symposium (PETS), 2010.

          59. R. Nithyanand, N. Saxena, G. Tsudik and E. Uzun
            Groupthink: On the Usability of Secure Group Association of Wireless Devices,
            ACM International Conference on Ubiquitous Computing (UBICOMP), 2010.

          60. R. Nithyanand, G. Tsudik and E. Uzun
            Readers Behaving Badly: Reader Revocation in PKI-Based RFID Systems,
            European Symposium on Research in Computer Security (ESORICS), 2010.

          61. D. Perito and G. Tsudik
            Secure Code Update for Embedded Devices via Proofs of Secure Erasure,
            European Symposium on Research in Computer Security (ESORICS), 2010.

          62. Y. Lu and G. Tsudik
            Towards Plugging Privacy Leaks in Domain Name System,
            IEEE International Conference on Peer-to-Peer Computing (P2P), 2010.

          63. R. Di Pietro, G. Oligeri, C. Soriente and G. Tsudik
            Securing Mobile Unattended WSNs against a Mobile Adversary,
            IEEE Symposium on Reliable Distributed Systems (SRDS), 2010.

          64. E. De Cristofaro, J. Kim and G. Tsudik
            Linear-Complexity Private Set Intersection Protocols Secure in Malicious Model,
            IACR ASIACRYPT, 2010.

          65. C. Badea, M. Cesarano, A. Nicolau, G. Tsudik and A. Veidenbaum
            Leveraging Virtualization Towards Improving Cloud Security for Compute-Intensive Applications,
            International Conference on Cloud Computing (CloudComp), 2010.

          66. S. Jarecki, J. Kim and G. Tsudik,
            Flexible Robust Group Key Agreement,
            IEEE Transactions on Parallel and Distributed Systems, to appear in 2010.

          67. D. Ma and G. Tsudik,
            Security and Privacy in Emerging Wireless Networks,
            IEEE Wireless Communications, to appear in October 2010.

          68. K. El Defrawy and G. Tsudik
            ALARM: Anonymous Location-Aided Routing in Suspicious MANETs,
            IEEE Transactions on Mobile Computing, to appear in 2011.

            2009

          69. E. De Cristofaro, X.Ding and G.Tsudik
            Privacy-Preserving Querying in Sensor Networks,
            IEEE Conference on Computer Communications and Networks (ICCCN), 2009.

          70. F. Massacci, G. Tsudik and A. Yautsiukhin
            Extended Abstract: Logging Key Assurance Indicators in an Enterprise,
            ACM Symposium on Information, Computer & Communication Security (ASIACCS), 2009.

          71. A. Kumar, N. Saxena, G. Tsudik and E. Uzun
            Caveat Emptor: A Comparative Study of Secure Device Pairing Methods,
            IEEE Conference on Pervasive Computing and Communications (PERCOM), 2009.

          72. R. Di Pietro, C. Soriente, A. Spognardi and G. Tsudik
            Intrusion-Resilience via Collaborative Authentication in Unattended WSNs,
            ACM Conference on Wireless Network Security (WiSec), 2009.

          73. K. El Defrawy, J. Solis and G. Tsudik
            Leveraging Social Contacts for Message Confidentiality in Delay Tolerant Networks,
            IEEE Computer Software and Applications Conference (COMPSAC), 2009.

          74. E. De Cristofaro, S. Jarecki, J. Kim and G. Tsudik
            Privacy-Preserving Policy-based Information Transfer,
            Privacy Enhancing Technologies Symposium (PETS), 2009.

          75. A. Kobsa, R. Sonawalla, G. Tsudik, E. Uzun and Y. Wang
            Serial Hook-Ups: A Comparative Usability Study of Secure Device Pairing Methods,
            Symposium On Usable Privacy and Security (SOUPS), 2009.

          76. N. Saxena, G. Tsudik and J. Yi
            Efficient Node Admission and Certificate-less Secure Communication in Short-Lived MANETs,
            IEEE Transactions on Parallel and Distributed Systems, Vol. 20, No. 2. 2009.

          77. M. Goodrich, M. Sirivianos, J. Solis, C. Soriente, G. Tsudik and E. Uzun
            Loud and Clear: Human-Verifiable Authentication Based on Audio,
            International Journal of Security and Networks, Vol. 4, No. 1, 2009.

          78. C. Soriente, G. Tsudik and E. Uzun
            Secure Pairing of Interface-Constrained Devices,
            International Journal of Security and Networks, Vol. 4, No. 1, 2009.

          79. M. Narasimha, J. Solis and G. Tsudik
            Privacy-Preserving Revocation Checking,
            International Journal of Information Security, Vol. 8, No. 1, 2009.

          80. A. Chan, C. Castelluccia, E. Mykletun and G. Tsudik
            Efficient and Provably Secure Aggregation of Encrypted Data in WSN-s,
            ACM Transactions on Sensor Networks, Vol. 5, No. 3., 2009.

          81. X. Ding, G. Tsudik and S. Xu
            Leak-Free Group Signatures: Definitions and Construction,
            Journal of Computer Security, Vol. 17, No. 4, 2009.

          82. J. Kim and G. Tsudik
            SRDP: Secure Route Discovery for Dynamic Source Routing in MANETs,
            Ad Hoc Networks, Vol. 7, No. 6, 2009.

          83. D. Ma and G. Tsudik
            A New Approach to Secure Logging,
            ACM Transactions on Storage, Vol. 5, No. 1, 2009.

          84. M. Burmester, T. Van Le, B. de Medeiros and G. Tsudik
            Universally Composable RFID Identification and Authentication Protocols,
            ACM Transactions on Information Systems Security, Vol. 12, No. 4, 2009.

          85. R. Di Pietro, L. Mancini, C. Soriente, A. Spognardi and G. Tsudik
            Data Security in Unattended Wireless Sensor Networks,
            IEEE Transactions on Computers, Special Issue on Autonomic Network Computing, Vol. 7, No. 8, 2009.

          86. D. Ma, C. Soriente and G. Tsudik
            New Adversary and New Threats in Unattended Sensors Networks,
            IEEE Network, Vol. 23 No. 2, 2009.

          87. R. Di Pietro, L. Mancini, C. Soriente, A. Spognardi and G. Tsudik,
            Maximizing Data Survival in Unattended Wireless Sensor Networks against a Focused Mobile Adversary,
            Ad Hoc Networks, Vol. 7, No. 8, 2009.

          88. G. Ateniese, J. Camenisch, M. Joye and G. Tsudik,
            Remarks on "Analysis of One Popular Group Signature Scheme" in Asiacrypt 2006,
            Journal of Applied Cryptography, Vol. 1, No. 4, 2009.

          89. A. Kumar, N. Saxena, G. Tsudik and E. Uzun,
            A Comparative Study of Secure Device Pairing Methods,
            Pervasive and Mobile Computing, Vol. 5, No. 6, 2009.

            2008:

          90. D. Ma and G. Tsudik
            A New Approach to Secure Logging,
            IFIP WG11.3 Working Conference on Data and Applications Security (DBSec), 2008.

          91. C. Soriente, G. Tsudik and E. Uzun
            HAPADEP: Human-Assisted Pure Audio Device Pairing,
            Information Security Conference (ISC), 2008.

          92. G. Ateniese, R. Di Pietro, L. Mancini and G. Tsudik
            Scalable and Efficient Provable Data Possession,
            Conference on Security and Privacy in Communication Networks (Securecomm), 2008.

          93. R. Di Pietro, D. Ma, C. Soriente and G. Tsudik
            POSH: Proactive co-Operative Self-Healing in Unattended Wireless Sensor Networks,
            IEEE Symposium on Reliable Distributed Systems (SRDS), 2008.

          94. K. Eldefrawy and G. Tsudik
            PRISM: Private Routing In Suspicious MANETs,
            IEEE International Conference on Network Protocols (ICNP), 2008.

          95. D. Ma and G. Tsudik,
            DISH: Distributed Self-Healing (in Unattended Sensor Networks)
            Symposium on Stabilization, Safety and Security of Distributed Systems (SSS), 2008.

          96. J. Kim and G. Tsudik
            Survival in the Wild: Robust Group Key Agreement in Wide-Area Networks,
            International Conference on Information Security and Cryptology (ICISC), 2008.

          97. R. di Pietro, L. Mancini, C.Soriente, A. Spognardi and G. Tsudik
            Catch Me (If You Can): Data Survival in Unattended Sensor Networks,
            IEEE International Conference on Pervasive Computing and Communications (PERCOM), 2008.
            dmsst08.pdf

          98. S. Jarecki, J. Kim and G. Tsudik
            Beyond Secret Handshakes: Affiliation-Hiding Authenticated Key Agreement,
            RSA Conference, Cryptographers' Track (CT-RSA), 2008.
            jkt08.pdf

            2007:

          99. S. Jarecki, J. Kim and G. Tsudik
            Group Secret Handshakes or Affiliation-Hiding Authenticated Group Key Agreement,
            RSA Conference, Cryptographers' Track (CT-RSA), 2007.
            jkt07.pdf

          100. D. Ma and G. Tsudik
            Forward-Secure Aggregate Authentication,
            IEEE Symposium on Research in Security and Privacy (S&P), 2007.
            mt07.pdf

          101. K. Eldefrawy and G. Tsudik
            ALARM: Anonymous Location-Aided Routing in MANETs,
            IEEE International Conference on Network Protocols (ICNP), 2007.
            alarm07.pdf

          102. S. Jarecki, J. Kim and G. Tsudik
            Robust Group Key Agreement using Short Broadcasts,
            ACM Computer and Communication Security Conference (CCS), 2007.
            rbd07.pdf

          103. G. Tsudik,
            A Family of Dunces: Trivial RFID Identification and Authentication Protocols,
            Symposium on Privacy-Enhancing Technologies (PET'07), 2007.
            t07.pdf

          104. M. Narasimha and G. Tsudik
            Privacy-Preserving Revocation Checking with Modified CRLs,
            European PKI Workshop: Theory and Practice (EuroPKI), 2007.

          105. K. Eldefrawy, M. El Zarki and G. Tsudik, Incentive-Based Cooperative and Secure Inter-Personal Networking,
            ACM Workshop on Mobile Opportunistic Networking (MobiOpp), 2007.
            ezt07.pdf

          106. X. Ding, D. Mazzocchi and G. Tsudik
            Equipping "Smart" Devices With Public Key Signatures,
            ACM Transactions on Internet Technology, Vol. 7, No. 1, February 2007.
            DMT07.pdf

          107. N. Saxena, G. Tsudik and J. Yi
            Threshold Cryptography in P2P and MANETs: the Case of Access Control,
            Computer Networks, Vol. 51, No. 13, August 2007, pp. 3632-3649.
            COMPNW3557.pdf

            2006:

          108. M. Goodrich, M. Sirivianos, J. Solis, G. Tsudik and E. Uzun
            Loud And Clear: Human Verifiable Authentication Based on Audio,
            IEEE ICDCS'06, 2006.
            gsstu.pdf

          109. S. Jarecki, J. Kim and G. Tsudik
            Authentication for Paranoids: Multi-Party Secret Handshakes
            ACNS'06, 2006.
            jkt06.pdf

          110. E. Mykletun and G. Tsudik
            Aggregation Queries in the Database-As-a-Service Model
            IFIP WG 11.3 Working Conference on Data and Applications Security (DBSEC), 2006.
            mt06.pdf

          111. G. Tsudik
            YA-TRAP: Yet Another Trivial RFID Authentication Protocol (WiP paper)
            IEEE PerCom, 2006.
            t-2006.pdf

          112. M. Narasimha and G. Tsudik
            Authentication of Outsourced Databases using Signature Aggregation and Chaining
            International Conference on Database Systems for Advanced Applications (DASFAA), 2006.
            nt-2006.pdf

          113. C. Castelluccia, E. Mykletun and G. Tsudik
            Improving Secure Server Performance by Re-balancing SSL/TLS Handshakes
            ACM AsiaCCS, 2006.
            cmt-2006.pdf

          114. J. Solis and G. Tsudik
            Simple and Flexible Private Revocation Checking
            Privacy-Enhancing Technologies (PETS), 2006.
            st-2006.pdf

          115. G. Tsudik and S. Xu
            A Flexible Framework for Secret Handshakes
            Privacy-Enhancing Technologies (PETS), 2006.
            tx-2006.pdf

          116. X. Li, G. Tsudik and X. Yang
            A Technical Approach to Net Neutrality
            ACM Workshop on Hot Topics in Networks (Hotnets-V), 2006.
            lty-2006.pdf

          117. M. Narasimha, E. Mykletun and G. Tsudik
            Authentication and Integrity in Outsourced Databases,
            ACM Transactions on Storage, , Vol. 2, No. 2, May 2006. pp. 107-138.
            NMT06.pdf

          118. C. Castelluccia, S. Jarecki, J. Kim and G. Tsudik
            Secure Acknowledgement Aggregation and Multisignatures with Limited Robustness,
            Computer Networks, Vol. 50, No. 10, July 2006, pp. 1639-1652.
            scn04-journal.ps.gz

          119. K. Obraczka, G. Tsudik and K. Viswanath
            Exploring Mesh- and Tree-Based Multicast Routing Protocols for MANETs,
            IEEE Transactions on Mobile Computing, Vol. 5, No. 1, pp. 28-42, January 2006.
            tmc-2005.ps.gz

            2005:

          120. E. Mykletun and G. Tsudik
            On using Secure Hardware in Outsourced Databases
            International Workshop on Innovative Architecture for Future Generation High-Performance Processors and Systems (IWIA), 2005.
            iwia-2005.pdf

          121. C. Castelluccia, E. Mykletun and G. Tsudik,
            Efficient Aggregation of Encrypted Data in Wireless Sensor Networks
            IEEE Mobiquitous, 2005.
            mobiq-2005.pdf

          122. J. Kim and G. Tsudik,
            Securing Route Discovery in DSR
            IEEE Mobiquitous, 2005.
            sdsr-2005.pdf

          123. G. Tsudik and S. Xu,
            A Flexible Framework for Secret Handshakes (Short Paper / Brief Announcement)
            ACM Conference on Principles of Distributed Computing (PODC), 2005.
            podc-2005.pdf

          124. G. Ateniese, D. Chou, B. de Medeiros and G. Tsudik,
            Sanitizable Signatures
            European Symposium on Research in Computer Security (ESORICS), 2005.
            esorics-2005.pdf

          125. N. Saxena, G. Tsudik and J. Yi
            Efficient Node Admission for Short-lived Mobile Ad Hoc Networks
            IEEE International Conference on Network Protocols (ICNP), 2005.
            icnp-2005.pdf

          126. M. Narasimha and G. Tsudik
            DSAC: Integrity of Outsourced Databases with Signature Aggregation and Chaining (Short Paper/Poster)
            ACM Conference on Information and Knowledge Management (CIKM), 2005.
            cikm-2005.pdf

          127. Y. Amir, C. Nita-Rotaru, J. Stanton and G. Tsudik
            Secure Spread: An Integrated Architecture for Secure Group Communication,
            IEEE Transaction on Dependable and Secure Computing Systems, Vol. 2, No. 3, July-September 2005.
            TDSC-0113-0704.pdf

            2004:

          128. B. Hore, S. Mehrotra and G. Tsudik
            A Privacy-Preserving Index for Range Queries
            International Conference on Very Large Databases (VLDB), August 2004.
            vldb-2004.pdf

          129. E. Mykletun, M. Narasimha and G. Tsudik
            Signature "Bouquets": Immutability of Aggregated Signatures
            European Symposium on Research in Computer Security (ESORICS), 2004.
            esorics-2004.pdf

          130. C. Castelluccia, S. Jarecki and G. Tsudik
            Secret Handshakes from CA-oblivious Encryption
            IACR ASIACRYPT, 2004.
            asiacrypt-2004.pdf

          131. C. Castelluccia, S. Jarecki and G. Tsudik
            Verifiable and Secure Acknowledgement Aggregation
            Security in Computer Networks Conference (SCN), 2004.
            scn-2004.pdf

          132. C. Castelluccia, S. Jarecki and G. Tsudik
            Secret Handshakes from CA-oblivious Encryption (Short paper)
            ACM Conference on Principles of Distributed Computing (PODC), 2004.
            podc-2004.pdf

          133. N. Saxena, G. Tsudik and J. Yi
            Identity-based Access Control for Ad Hoc Groups
            International Conference on Information Security and Cryptology (ICISC'04), December 2004.
            icisc-2004.pdf

          134. S. Mehrotra, E. Mykletun, G. Tsudik and Y. Wu
            A Framework for Efficient Storage Security in RDBMS
            International Conference on Extending Database Technology (EDBT), 2004.
            edbt.pdf

          135. E. Mykletun, M. Narasimha and G. Tsudik
            Authentication and Integrity in Outsourced Databases
            Symposium on Network and Distributed Systems Security (NDSS), 2004.
            ndss-2004.pdf

          136. X. Ding, G. Tsudik and S. Xu
            Leak-Free Mediated Group Signatures
            IEEE ICDCS, 2004.
            icdcs-2004.pdf

          137. N. Saxena, G. Tsudik and J. Yi
            Access Control Mechanisms for Ad Hoc Networks
            IEEE Workshop on Hot Topics in Peer-to-Peer (HOT-P2P'04), 2004.
            p2p-2004.pdf

          138. K. Bicakci, G. Tsudik and B. Tung
            On Constructing Optimal One-Time Signatures,
            Computer Networks, Vol. 43, No. 3, pp. 339-349, October 2003.
            comnet-2003.pdf

          139. Y. Kim, F. Maino, M. Narasimha, K. Rhee and G. Tsudik
            Secure Group Services for Storage-Area Networks,
            IEEE Communications, Vol. 41, No. 8, August 2003.
            kmnrt04.pdf

          140. K. Rhee, Y. Park and G. Tsudik
            A Group Key Management Architecture in Mobile Ad-hoc Wireless Networks,
            Journal Of Communication and Networks, Vol. 6, No. 2, pp. 156-162, June 2004.
            jcn-2004.pdf

          141. Y. Amir, Y. Kim, C. Nita-Rotaru and G. Tsudik
            On the Performance of Group Key Agreement Protocols
            ACM Transactions on Information Systems Security, Vol. 7, No. 3, pp. 457-488, August 2004.
            tissec-2004.pdf

          142. D. Boneh, X. Ding and G. Tsudik
            A Method for Fast Credentials Revocation,
            ACM Transactions on Internet Technology, vol. 4, No. 1, pp. 60-82, February 2004.
            bdt04.pdf

          143. Y. Kim, A. Perrig and G. Tsudik
            Communication-Efficient Group Key Agreement,
            IEEE Transactions on Computers, Vol. 53, No. 7, July 2004.
            kpt04.pdf

          144. Y. Amir, Y. Kim, C. Nita-Rotaru, J. Stanton, J. Schultz and G.Tsudik
            Secure Group Communication Using Robust Contributory Key Agreement,
            IEEE Transaction on Parallel and Distributed Systems, Vol. 15, No. 5, pp. 468-480, May 2004.
            aknsst04.pdf

          145. Y. Kim, A. Perrig and G. Tsudik
            Tree-Based Group Key Agreement,
            ACM Transactions on Information Systems Security, Vol. 7, No. 1, pp. 60-96, May 2004.
            kpt04a.pdf

            2003:

          146. G. Tsudik and S. Xu
            Accumulating Composites and Improved Group Signing
            IACR ASIACRYPT, 2003.
            tx03.ps.gz

          147. M. Narasimha, G. Tsudik and J. Yi,
            On the Utility of Distributed Cryptography in P2P Settings and MANETs
            IEEE International Conference on Network Protocols (ICNP), 2003.
            nty03.pdf

          148. X. Ding and G. Tsudik
            Simple Identity-Based Cryptography with Mediated RSA
            RSA Conference, Cryptographers' Track, 2003.
            bdt03.pdf

          149. Y. Amir, C. Nita-Rotaru, J. Stanton and G. Tsudik
            Scaling Secure Group Communications: Beyond Peer-to-Peer
            DARPA Information Security Conference and Exposition (DISCEX), 2003.
            anst03.pdf

          150. Y. Kim, D. Mazzocchi and G. Tsudik
            Admission Control in Collaborative Groups
            IEEE Symposium on Network Computing and Applications (NCA), 2003.
            kmt03.pdf

          151. N. Saxena, G. Tsudik and J. Yi
            Experimenting with Peer Group Admission Control
            Workshop on Advanced Developments in Software and Systems Security (WADIS), 2003.
            wadis-2003.pdf

          152. N. Saxena, G. Tsudik and J. Yi
            Admission Control in Peer-to-Peer: Design and Performance Evaluation
            ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN), 2003.
            sty03.pdf

          153. Y. Kim and G. Tsudik
            Admission Control in Peer Groups
            NSF/NASA/DOE Workshop on New Directions on Scalable Cyber-Security, 2003.

            2002:

          154. G. Tsudik
            Weak Forward Security in Mediated RSA
            Security in Computer Networks Conference (SCN), 2002.
            t02.pdf

          155. G. Ateniese, D. Song and G. Tsudik
            Quasi-Efficient Revocation in Group Signatures
            Financial Cryptography (FC), 2002.
            ast02.pdf

          156. Y. Amir, Y. Kim, C. Nita-Rotaru, and G. Tsudik
            On the Performance of Group Key Agreement Protocols
            IEEE ICDCS, 2002.
            aknt02.pdf

          157. M. El Zarki, S. Mehrotra, G. Tsudik and N. Venkatasubramanian
            Security Issues in a Future Vehicular Network
            EuroWireless, 2002.
            zmtv02.pdf

          158. X. Ding, D. Mazzocchi and G. Tsudik
            Experimenting with Server-Aided Signatures
            Network and Distributed Systems Security Symposium (NDSS), 2002.
            dmt02.pdf

          159. Y. Park, K. Rhee and G. Tsudik
            Group Key Management in Hierarchical Ad-hoc Wireless Networks
            Workshop on Information Security Applications (WISA), 2002.
            prt02.pdf

          160. Y. Kim, F. Maino, M. Narasimha and G. Tsudik
            Secure Group Services for Storage-Area Networks
            IEEE International Workshop on Storage Security (IWSS), 2002.
            kmnt02.pdf

          161. Y. Kim, D. Mazzocchi and G. Tsudik
            Admission Control in Collaborative Groups,
            ACM Mobile Computing and Communications Review, Vol. 6, No. 4, October 2002.

            2001:

          162. D. Boneh, X. Ding, G. Tsudik and B. Wong
            Fast Revocation of Security Capabilities
            Usenix Security Symposium, 2001.
            bdtw.pdf

          163. D. Agarwal, O. Chevassut, M. Thompson and G. Tsudik
            An Integrated Solution for Secure Group Communication in Wide-Area Networks
            IEEE Symposium on Computers and Communications (ISCC), 2001.
            actt01.pdf

          164. Y. Kim. A. Perrig and G. Tsudik Communication-Efficient Group Key Agreement
            IFIP-SEC, 2001.
            kpt2001.pdf

          165. Y. Amir, Y. Kim, C. Nita-Rotaru, J. Schultz, J. Stanton and G. Tsudik
            Exploring Robustness in Group Key Agreement
            IEEE ICDCS, 2001.
            aknsst00.pdf

          166. K. Obraczka, G. Tsudik and K. Viswanath
            Pushing the Limit of Multicast in Ad Hoc Networks
            IEEE ICDCS, April 2001.
            otv2000.ps.gz

          167. K. Obraczka, G. Tsudik and K Viswanath
            Towards Reliable Multicast in Multi-Hop Ad Hoc Networks,
            ACM/Balzer Wireless Networks, Vol. 7, No. 6, 2001.
            hotv2000.ps.gz

            2000:

          168. Y. Kim. A. Perrig and G. Tsudik
            Simple and Fault-Tolerant Key Agreement for Dynamic Collaborative Groups
            ACM CCS, 2000.
            kpt2000.pdf

          169. G. Ateniese, J. Camenisch, M. Joye and G. Tsudik
            A Practical and Provably Secure Coalition-Resistant Group Signature Scheme
            IACR CRYPTO. 2000.
            acjt00.pdf

          170. Bicakci, B. Tung and G. Tsudik
            On Constructing Optimal One-Time Signatures
            International Symposium on Computer and Information Sciences (ISCIS), 2000.
            iscis-2000.pdf

          171. Y. Amir, G. Ateniese, D. Hasse, Y. Kim, C. Nita-Rotaru, T. Schlossnagle, J. Schultz, J. Stanton and G. Tsudik
            Secure Group Communication in Asynchronous Networks with Failures: Integration and Experiments
            IEEE ICDCS, 2000.
            aahknssst99.pdf

          172. G. Ateniese, O. Chevassut, D. Hasse, Y. Kim and G. Tsudik
            The Design of a Group Key Management API
            DARPA DISCEX, 2000.
            achkt99.ps.gz

          173. C. Landwehr, M. Reed, P. Syverson and G. Tsudik
            Towards and Analysis of Onion Routing Security
            Workshop on Design Issues in Anonymity and Unobservability, 2000.
            lrtst2000.ps.gz

          174. M. Bellare, J. Garay, R. Hauser, A. Herzberg, H. Krawczyk, M. Steiner, G. Tsudik, E. Van Herreweghen, and M. Waidner
            Design, Implementation and Deployment of a Secure Account-Based Electronic Payment System,
            IEEE JSAC, special issue on Secure Communication, May 2000.
            ikpj.pdf

          175. G. Ateniese, M. Steiner and G. Tsudik
            New Multi-party Authentication Services and Key Agreement Protocols,
            IEEE JSAC, special issue on Secure Communication, May 2000.
            ast99.ps.gz

          176. M. Steiner, G. Tsudik and M. Waidner
            Key Agreement in Dynamic Peer Groups,
            IEEE Transactions on Parallel and Distributed Systems, August 2000.
            stw99.ps.gz

          177. T. Hardjono and G. Tsudik
            IP Multicast Security: Issues and Directions,
            Annales de Telecom, 2000.
            ht99.ps.gz

            1999 and earlier:

          178. G. Ateniese and G. Tsudik
            Some Open Issues and New Directions in Group Signatures
            Financial Cryptography (FC), 1999.
            at98-2.ps.gz

          179. G. Ateniese and G. Tsudik
            Group Signatures a' la carte
            ACM Symposium on Discrete Algorithms (SODA), 1999.
            at98-1.ps.gz

          180. C. Ho, K. Obraczka, G. Tsudik and K. Viswanath
            Flooding for Reliable Multicast in Multi-Hop Ad Hoc Networks,
            ACM Workshop on Discrete Algorithms for Mobility (DIAL-M), 1999.
            hotv99.ps.gz

          181. G. Ateniese, M. Joye and G. Tsudik
            On the Difficulty of Coalition-Resistance in Group Signature Schemes
            Security in Computer Networks (SCN), 1999.
            ajt99.ps.gz

          182. R. Hauser, A. Przygienda and G. Tsudik
            Lowering Security Overhead in Link State Routing,
            Computer Networks and ISDN Systems, April 1999.
            hpt98.ps.gz

          183. G. Ateniese, A. Herzberg, H. Krawczyk and G. Tsudik
            Untraceable Mobility: On Travelling Incognito,
            Computer Networks and ISDN Systems, April 1999.
            hkt97.ps.gz

          184. I. Foster, C. Kesselman, G. Tsudik and S. Tuecke
            Security Architecture for Large-Scale Distributed Computations
            ACM Conference on Computer and Communications Security (CCS), 1998.
            fktt98.ps.gz

          185. G. Ateniese, M. Steiner and G. Tsudik
            Authenticated Group Key Agreement and Related Issues
            ACM Conference on Computer and Communications Security (CCS), 1998.
            ast98.ps.gz

          186. M. Franklin and G. Tsudik
            Secure Group Barter
            Financial Cryptography (FC), 1998.
            ft97.ps.gz

          187. M. Steiner, G. Tsudik and M. Waidner
            CLIQUES: A New Approach to Group Key Agreement
            IEEE ICDCS, 1998.
            stw97.ps.gz

          188. K. Obraczka and G. Tsudik
            Multicast Routing Issues in Ad Hoc Networks
            IEEE International Conference on Universal Personal Communication (ICUPC), 1998.
            ot98.ps.gz

          189. R. Molva and G. Tsudik
            Secret Sets and Applications,
            Information Processing Letters, Vol. 65, No. 1, pp. 47-55, 1998.
            mt96.ps.gz

          190. R. Hauser, A. Przygienda and G. Tsudik
            Reducing the Cost of Security in Link State Routing
            Symposium on Network and Distributed System Security (NDSS), 1997.
            hpt96.ps.gz

          191. N. Asokan, G. Tsudik and M. Waidner
            Server-Supported Signatures,
            Journal of Computer Security, November 1997.
            atw97.ps.gz

          192. J. Gray, A. Kshemkalyani, M. Matyas, M. Peyravian and G. Tsudik
            ATM Cell Encryption and Key Update Synchronization,
            Telecommunication Systems Journal, Vol. 7, No. 4, pp. 391-408, 1997.
            gkmpt96.ps.gz

          193. P. Janson, G. Tsudik and M. Yung
            Scalability and Flexibility in Authentication Services: The KryptoKnight Approach
            IEEE INFOCOM, 1997.
            jty94.ps.gz

          194. R. Hauser, A. Przygienda and G. Tsudik
            Low-cost Security in Link State Routing
            Security in Communication Networks, 1996.

          195. C. Gulcu and G. Tsudik
            Mixing E-mail with BABEL
            Symposium on Network and Distributed System Security (NDSS), 1996.
            gguts96.ps.gz

          196. N. Asokan, G. Tsudik and M. Waidner
            Server-Supported Signatures
            European Symposium on Research in Computer Security (ESORICS), 1996.
            atw96.ps.gz

          197. R. Hauser and G. Tsudik
            On Shopping Incognito
            USENIX Conference on Electronic Commerce, 1996.
            hats96.ps.gz

          198. M. Steiner, G. Tsudik and M. Waidner
            Diffie-Hellman Key Distribution Extended to Groups
            ACM Conference on Computer and Communications Security (CCS), 1996.
            stw96.ps.gz

          199. R. Hauser, P. Janson, R. Molva, G. Tsudik and E. Van Herreweghen,
            Robust and Secure Password/Key Change Method,
            Journal of Computer Security, November 1996.
            hjmtv96.ps.gz

          200. M. Bellare, R. Hauser, A. Herzberg, J. Garay, H. Krawczyk, M. Steiner, G. Tsudik and M. Waidner
            iKP -- A Family of Secure Electronic Payment Protocols
            USENIX Conference on Electronic Commerce, 1995.
            bhhgkstw95.ps.gz

          201. R. Molva, D. Samfat and G. Tsudik
            An Authentication Protocol for Mobile Users
            IEE Colloquium on Security and Cryptography Applications to Radio Systems, 1995

          202. D. Chess, B. Grosof, C. Harrison, D. Levine, C. Parris and G. Tsudik
            Itinerant Programs for Mobile Computing,
            IEEE Personal Communication Systems, September 1995.
            cghlpt95.ps.gz

          203. P. Janson and G. Tsudik
            Secure and Minimal Protocols for Authenticated Key Distribution
            Computer Communications Journal, September 1995.
            jt94.ps.gz

          204. R. Hauser, P. Janson, R. Molva, G. Tsudik and E. Van Herreweghen
            Robust and Secure Password/Key Change Method
            European Symposium on Research in Computer Security (ESORICS), 1994.
            hjmtv94.ps.gz

          205. A. Herzberg, H. Krawczyk and G. Tsudik
            On Travelling Incognito
            IEEE Workshop on Mobile Systems and Applications, 1994.
            hkt94.ps.gz

          206. R. Molva, D. Samfat and G. Tsudik
            Authentication of Mobile Users,
            IEEE Network, Special Issue on Mobile Communications, March/April 1994.
            mst94.ps.gz

          207. G. Tsudik and E. Van Herreweghen
            Some Remarks on Protecting Weak Secrets and Poorly-Chosen Keys from Guessing Attacks
            IEEE Symposium on Reliable Distributed Systems (SRDS), 1993.
            tv93a.ps.gz

          208. G. Tsudik and E. Van Herreweghen
            On Simple and Secure Key Distribution
            ACM Conference on Computer and Communications Security (CCS), 1993.
            tv93.ps.gz

          209. R. Molva and G. Tsudik
            Authentication Method with Impersonal Token Cards
            IEEE Symposium on Security and Privacy (S&P), 1993.
            mt93.ps.gz

          210. D. Estrin, M. Steenstrup and G. Tsudik
            Protocols for Route Establishment and Packet Forwarding Across Multi-Domain Internetworks
            ACM/IEEE Transactions on Networking, February 1993.

          211. G. Tsudik
            Policy Enforcement in Stub Autonomous Domains
            European Symposium on Research in Computer Security (ESORICS), 1992.
            t92a.ps.gz

          212. R. Molva, G. Tsudik, E. Van Herreweghen and S. Zatti
            KryptoKnight Authentication and Key Distribution System
            European Symposium on Research in Computer Security (ESORICS), 1992.
            mtvz92.ps.gz

          213. G. Tsudik
            Message Authentication with One-Way Hash Functions
            IEEE INFOCOM, 1992.
            t92.ps.gz

          214. R. Summers and G. Tsudik
            AudES: An Expert System for Security Auditing,
            Computer Security Journal Vol. 6, No. 1, June 1991.

          215. D. Estrin and G. Tsudik
            An End-to-End Argument for Network Layer Inter-Domain Access Controls,
            Journal of Internetworking: Research and Experience, June 1991.
            et91.ps.gz

          216. D. Estrin and G. Tsudik
            Secure Control of Transit Internetwork Traffic,
            Computer Networks and ISDN Systems, October 1991.

          217. R. Summers and G. Tsudik
            AudES: An Expert System for Security Auditing
            AAAI Conference on Innovative Applications in AI, 1990.
            st90.pdf

          218. D. Estrin, J. Mogul and G. Tsudik
            VISA Protocols for Controlling Inter-Organizational Datagram Flow
            IEEE Journal on Selected Areas in CommunicationsMay 1989.

          219. G. Tsudik
            Datagram Authentication in Internet Gateways
            IEEE Journal on Selected Areas in Communications, May 1989.

          220. D. Estrin and G. Tsudik
            Policy Enforcement in Interconnected Autonomous Networks
            DIMACS Workshop on Connections Between Distributed Computing and Cryptography, 1989.

          221. D. Estrin and G. Tsudik
            Security Issues in Policy Routing
            IEEE Symposium on Security and Privacy (S&P), 1989.
            et89.pdf

          222. D. Estrin and G. Tsudik
            VISA Scheme for Inter-Organizational Network Security
            IEEE Symposium on Security and Privacy (S&P), 1987.
            et87.pdf

          http://www.ics.uci.edu/~gts/stupid.html Gene Tsudik's Info Page

          Gene Tsudik

          Lois and Peter Griffin Professor 
          Computer Science Department
          Pewterschmidt School of Information and Computer Sciences

          University of Caliphoneya, Irvine

          Some of my favorite moronic expressions polluting modern-day (American) English:
          • Disenfranchised minority -- a tragedy in three acts:
            1. once, there was a minority,
            2. it was then happily en-franchised, and then,
            3. some (evil) force dis-en-franchised it
          • Innocent women and children -- either/or:
            1. men are guilty, one way or another, or
            2. there are some women and children who are not innocent
          • Vicious assault -- there must be soft, considerate and gentle ways to assault someone
          • Brutal murder -- somewhere, someplace murders are comfy, cozy and pleasant
          • Aid and abet / rot and decay / cease and desist / lewd and lascivious -- redundancy never, ever, ever, ever, ever hurts in aiding comprehension, right?
          • Mean-spirited attack -- when attacking someone, please strive to do so in a kind-hearted and well-meaning manner
          • Hard-working people/immigrants/etc. -- as opposed to lazy and slovenly (people/immigrants/etc.)
          • Country/nation/ethnic-group with a long and rich history -- in contrast to those others with short, lousy and poor history
          • Civilian population -- some populations must be purely military
          • Intelligent design -- a mythical alternative to totally stupid, brainless design
          • Saggy and droopy -- whatever this phrase refers to is usually sad enough, so why rub it in redundantly?
          • Proud nation/country/people/ethnic/group -- better than those who are ashamed of themselves and generally depressed about being what they are
          • What doesn't kill you, makes you stronger -- an idiotic adage that holds particularly well with people in a coma as well as para- and quadra-plegics.
          • An eye for an eye leaves the whole world blind -- a moronic proverb (attributed to Gandhi) that can be interpreted as: No eye for an eye leaves only criminals sighted.
          • Hate Crime -- a real linguistic gem that refers to any crime committed while bearing ill will towards the victim(s). Makes one wonder why similarly appealing concepts of "Love Crime", "Adulation Crime", "Contempt Crime" or "Diss Crime" are not being popularized.
          http://www.ics.uci.edu/~gts/contact.html Gene Tsudik's Info Page

          CONTACT INFO:
          Email
          gts [AT] ics.uci.edu
          Phone
          +1 (949)824-3410
          Fax 
          +1 (949)824-4056
          Mail
          Computer Science Dept.
          Bren Hall, 3rd Floor  

          Irvine CA 92697-3435
           

          http://sprout.ics.uci.edu/

          Welcome to Sprout

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          [2015-08-26] Paper Accepted: "Authentication Using Pulse-Response Biometrics" at CACM
          [2015-08-26] Paper Accepted: "Three-Party ORAM for Secure Computation" at ASIACRYPT
          [2015-08-26] Paper Accepted: "SEDA: Scalable Embedded Device Attestation" at CCS

          UCI | ICS

          http://www.ics.uci.edu/~gts/pats.html Gene Tsudik -- Patents

            Gene Tsudik -- Patents

            Gene Tsudik -- Patents

          1. Encryption of Low Data Content ATM Cells
            United States Patent 5,642,421
            Issued June 24, 1997.

          2. Authentication Method and System with a Smartcard
            United States Patent 5,347,580
            Issued September 13, 1994.

          3. Personal Key Archive
            United States Patent 5,495,533
            Issued September 13, 1994.

          4. System and Method for Changing the Key or Password in a Secure Distributed Communication Network
            European Patent 0720796
            Issued July 16, 1997.
            United States Patent 5,778,065
            Issued July 7, 1998.

          5. Method and System for Providing Secure Authenticated Key Distribution in a Communication System
            European Patent 0711480
            Issued June 11, 1997.
            United States Patent 5,729,608
            Issued March 17, 1998.

          6. Secure Anonymous Information Exchange in a Network
            European Patent 0876722
            Issued July 17, 1997.
            United States Patent 6,061,789
            Issued May 9, 2000.

          7. Method and Apparatus for Secure Identification of a Mobile User in a Communication Network
            European Patent 0788688
            Issued May 9, 1996.
            United States Patent 6,072,875
            Issued June 6, 2000.

          8. RFID Reader Revocation Checking using Low Power Attached Displays,
            United States Patent Pending,
            September 2010.
          http://www.ics.uci.edu/~gts/words.html Gene Tsudik's Info Page

          Gene Tsudik

          Lois and Peter Griffin Professor 
          Computer Science Department
          Pewterschmidt School of Information and Computer Sciences

          University of Caliphoneya, Irvine

          Some words I invented, but (sadly) never trademarked:
          • Collabortion (noun)
            :an attempt at (e.g., scientific) collaboration that has failed or never took off.
          • Shizza (noun)
            :a form of pseudo-nourishment typically offered at academic gatherings (faculty- and/or student-attended) in the United States. It is rumored to be distantly related to pizza.
          • Cryptosexual (adj, opt. noun)
            :a person whose sexual orientation can not be guessed with probability greater than (0.5+\epsilon), where \epsilon is a security parameter.
          • Donorrhea (noun)
            :the state of uncontrolled donation to political, religious or other causes. Sadly, it has no cure.
          • Mediot (noun)
            :a person who uses fancy multi-media tools in a presentation (or publication) to obscure lack of substance/content.
          • Glopeteria (noun)
            :a food-serving establishment offering a rich assortment of (usually overpriced) glop, e.g., certain eateries at UCI.
          • Homocitational (adj)
            :refers to the behavior of a person who chronically cites him/her-self (a common academic disease). Use example: Joe's recent publications exhibit signs of homocitational behavior.
          • Cryptonecrophilia (noun)
            :an act of repeatedly finding "holes" or "bugs" in a long-dead (broken) cryptosystem, scheme or protocol.
          • Cryptolibel (noun)
            :an act of besmirching the reputation of a cryptosystem, scheme or protocol by any combination of: (1) empty claims, (2) broken attacks, (3) attacks on properties never claimed by the "victim".
          • Sloppid (adj)
            :refers to a person who is both stupid and sloppy .
          • Teutological (adj)
            :refers to Teutonic logic or reasoning which is, simulteneosly: pedantic, rigid, self-righteous and dull.
          • Obsecure (verb)
            :to apply ineffective or inappropriate security techniques in order to address non-existent or exaggerated security threats.
          • Mediocrat (noun)
            :a medio-cre (usually self-important, seemingly irreplaceable and perennially busy) bureau-crat
          • Econdomy (noun)
            :E-condom-y: nefarious cottage industry that uses scare tactics and borderline malware to peddle its (usually ineffective and over-priced) "security" products to naive users.
          • Youthanasia (noun)
            :Youth-an-asia: activity (popular in Orange County) that involves repeated voluntary submission to plastic surgery; usually yields extremely grotesque results that can prompt fear and panic attacks in small children and household pets.
          • Geekosystem (noun)
            :Geek-oh-system : microcosm, often within the confines of a small lab, inhabited by k>2 earnest geeks who "feed off each other" in terms of humor, hygiene, dietary habits and entertainment choices.
          http://www.ics.uci.edu/~gts/facts.html Gene Tsudik's Info Page

          Gene Tsudik

          Lois and Peter Griffin Professor 
          Computer Science Department
          Pewterschmidt School of Information and Computer Sciences

          University of Caliphoneya, Irvine
          Gene Tsudik:
          • does not know his Erdos number
          • is ignorant of his IQ
          • authored and proved the following theorem:
            "The number of skeletons in one's closet is directly proportional to the degree of one's self-righteousness, political correctness and/or piety."
          • thinks that people who like to "turn the other cheek" are members of (or secretly wish to join) a spanking club
          • has never been elected to to any office
          • is not a democrat or a republican
          • does not publish acceptance rates for his publication venues
          • does not support gratuitous authorship of scientific papers
          • thinks that Intelligent Design is an oxymoron
          • has smoked -- and may have even inadvertently inhaled -- some substances of dubious legal status
          • is a native of Slobonia (formerly known as Crappystan)
          • is an impromptu marathon runner
          • is a staunch believer in limitless idiocy of bureaucracy
          • does not speak any language without an accent
          • is allergic to BLOGs and even less offensive forms of exhibitionism
          • is not actually an endowed "Lois and Peter Griffin" professor
          • is not a member of NRA, NAMBLA, FDIC, NCAA, NFL, BFD, WTF or AFL-CIO
          • has never learned how to dive
          • is an enthusiastically mediocre squash player
          • does not know his neighbors
          • never been accused of being subtle
          • never bitten a dog (though one time in Korea he may have eaten one)
          • unashamedly believes that some cultures are clearly superior to others
          • has cross-dressed on at least one occasion
          • has never poisoned anyone with mushrooms
          • is only modestly self-important
          • regrettably, has no piercings, tattoos or a vestigal tail
          • is not an early adopter
          • has never been abducted by aliens or social science researchers
          • has never received a letter bomb or an anonymous love letter
          • thinks that ethnic pride is just a better-looking fraternal twin of racism
          • can do one-handed push-ups
          • has never parachuted, para-sailed, hang-glided, bungee-jumped, base-jumped, surfed, water-skied or played basketball
          • loves Grand Canyon in July
          • has written some vile limericks
          • has come up with some atrocious analogies
          • won a runner-up prize in the 2nd Midwestern Invitational Inspector Clouseau Imitation Tournament
          • believes that "Underwater Basket-Weaving in the Arctic" is not part of Computer Science
          • eats his apples whole
          • is ideologically opposed to celery consumption
          • can subsist on olives, peppers and eggplant
          • does not possess a high school diploma
          • says a daily prayer for all vegans
          • is a believer in the unmatched healing power of habaneros
          • is a strong supporter of animail rights (unless he is hungry)
          • has not been arrested to-date
          • writes unfashionably short recommendation letters
          • is oblivious of his BMI
          • is a web luddite (this page serves as proof)
          • has no talents as a thespian
          • sometimes exhibits homosocial behavior
          • suffers from a rare form of Munchausen-without-proxy syndrome
          • is inconcistenst in his dedication to recycling and saving the planet
          • believes thar one's religion is a "private part" and -- like other private parts -- it is best kept hidden from public view
          • does not believe in your deity
          • would happily allow anyone to burn copies of his papers (or any papers for that matter) while successfully repressing barbaric urge to kill
          • is a pioneer in the field of "Computational Gastronomy" and "Veterinary Computing"
          • started a renowned international conference on "Pedantics and Pomposity in Computing"
          • wonders how the desire to be "different" (or to be noticed) leads people to conform to the fad of tattoos and piercings
          • asserts that most of the above claims are true
          http://www.ics.uci.edu/~gmark/Home_page/Welcome.html Gloria Mark
           
           
           

          Gloria Mark

           
           

          The world is continuing to shrink as social computing grows. Never before have people been able to connect to others and to information sources so rapidly and on such a global scale. This is a fascinating time to study how technology use affects people. My research interest is in what is known as social computing: studying how individuals, groups, society and technology mutually influence each other. I am particularly interested in studying how information technology use affects multi-tasking, attention, mood, and above all, stress. Rather than bring people into a laboratory to study, I go to where people are--the real world is a living laboratory. To study people and their technology use, I employ a method called precision tracking, which involves a combination of sensors, bio-sensors, experience sampling, surveys, and ethnographic techniques to gain a very detailed, comprehensive, and in-depth understanding of what people experience when they use computer technology. I study real-world technology usage in the workplace and also with college students, who are of the Millennial Generation, who grew up with the Internet. I also study how people use social media and computer systems to be resilient during and after environmental crises. I invite you to take a closer look at some of the projects that my students and I are involved in.

           

          Professor

          Department of Informatics


          Donald Bren School of Information and Computer Sciences


          University of California, Irvine

          Research


          Students/researchers


          Curriculum vitae


          Media reports


          Videos of talks

          Department of Informatics

          5212 Donald Bren Hall

          University of California, Irvine

          Irvine, CA 92697


          Office:  +1-949-824-5955


          Fax:      +1-949-824-1715


          g m a r k  [at]  u c i . e d u

           
           
          Made on a Mac
          http://www.ics.uci.edu/~babaks/Site/Home.html Babak Shahbaba, PhD Associate Professor & director, the center for statistical Consulting 
           

          Babak Shahbaba, PhD

          Associate Professor

          &

          director, the center for statistical Consulting

           
           
           
           
           

          RESEARCH INTEREST

          My research interest is related to developing new Bayesian methods and applying them to real-world problems. I am currently focusing on the following areas:

          1. 1.Scalable Bayesian inference: fast MCMC methods that can be applied to large datasets

          2. 2.Nonparametric Bayesian models: developing statistical models that are sufficiently flexible

          3. 3.Statistical Methods in Neuroscience: applying statistical methods to answer research questions in neuroscience

          NEWS AND HIGHLIGHTS

          1. •Zhou, B., Moorman, D. E., Behseta, S., Ombao, H., and Shahbaba, B. (2015), A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision Making, Journal of American Statistical Association (to appear).

          2. •Lan, S., Palacios, J., Karcher, M., Minin, V., Shahbaba, B. (2015) An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics, Bioinformatics, 31(20), 3282-3289.

          3. •Shahbaba, B., Behseta, S., and Vandenberg-Rodes, A. (2015), Neuronal Spike Train Analysis Using Gaussian Process Models,  in Nonparametric Bayesian Methods in Biostatistics and Bioinformatics, Mitra, R. and Muller, P. (Eds.), Springer-Verlog.

          4. •Vandenberg-Rodes, A. and Shahbaba, B. (2015), Dependent Matérn Processes for Multivariate Time Series, , arXiv:1502.03466.

          5. •Quinlan, E.B., Dodakian, L., See, J., McKenzie, A., Le, V., Wojnowicz, M., Shahbaba, B., Cramer, S.C. (2015), Neural function, injury, and stroke subtype predict treatment gains after stroke, Annals of Neurology, 77(1), 132-45.

          6. •Lan, S., Stathopoulos, V., Shahbaba, B., and Girolami, M., Lagrangian dynamical Monte Carlo: Journal of Computational and Graphical Statistics (to appear): arXiv:1211.3759.

          7. BulletShahbaba, B., Lan, S., Johnson, W.O. , Neal, R.M.,  Split Hamiltonian Monte Carlo, Statistics and Computing (to appear): arXiv:1106.5941

          8. •Shahbaba, B., Zhou, B., Lan, S., Ombao, H., Moorman, D., and Behseta, S., A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons, Neural Computation (to appear): arXiv:1306.6103.

          9. BulletLan, S., Zhou, B., and Shahbaba, B., Spherical Hamiltonian Monte Carlo for Constrained Target Distributions, ICML 2014: pdf.

          10. BulletAhn, S., Shahbaba, B., and Welling, M., Distributed Stochastic Gradient MCMC, ICML 2014: D-SGLD.pdf

          11. •Lan, S., Streets, J., and Shahbaba, B., Wormhole Hamiltonian Monte Carlo, AAAI 2014: arXiv:1306.0063.


          RECENT & UPCOMING COURSES

          1. BulletComputational Statistics, Stats 230, Winter 2016

          2. BulletBayesian Statistics, Stats 225, Winter 2015

          3. BulletStatistical Consulting, Stats 275, Winter 2015


          RECENT & UPCOMING WORKSHOPS

          1. •We have organized a NIPS workshop on Scalable Monte Carlo: ScalableMonteCarlo

          2. •We had our second workshop on BigBayes at Oxford in June: BIBiD-2015

          3. BulletIntroduction to Biostatistics: announcement (repeated annually)

          4. BulletIntroduction to linear and logistic regression models: announcement (repeated annually)


          RECENT & UPCOMING INVITED TALKS

          1. BulletUCLA, Biostatistics, October 28, 2015

          2. BulletUniversity of Texas at Austin, October 16, 2015

          3. BulletJoint Statistical Meetings, Seattle, August 13, 2015

          4. Bullet3rd Meeting on Statistics, June 2015, Athens: click here

          5. BulletUCI Neurology Grand Rounds, October 2014

          6. BulletCSUF September 2014

          7. BulletISBA 2014, July 14-18

          8. BulletUCSD, May 7, 2014

          9. BulletUCSC, April 7, 2014

          10. BulletENAR 2014, Match 18, 2014

           
           
          http://www.ics.uci.edu/~harris/projects.html Ian G. Harris, Projects

          Ian G. Harris

          Associate Professor, Department of Computer Science

          University of California Irvine

          • Home
          • Publications
          • Courses
          • Projects
          • Contact

          Short descriptions of some of our current projects.

          Hardware Assisted Host-based Intrusion Detection

          We are investigating a technique to implement host-based intrusion detection (HIDS) in hardware so that attacks can be detected as soon as their behavior deviates from correct system behavior. Our system is anomaly-based; a model of the correct system behavior is generated at compile-time and any deviations from the correct behavior must indicate an attack. We characterize correct system behavior as a finite state machine which accepts all legal system call sequences.

          The execution of system calls is detected in hardware (Syscall Detector) by examining the instruction at each clock cycle, and the contents of specific internal registers. The legal system call sequences are captured in as a finite state machine which is implemented in hardware (Syscall Sequence Recognizer).

          In this way, the execution of an illegal call sequence can be detected a single clock cycle after it occurs.

          Directed-Random Security Testing of Network Applications

          We propose a new directed-random fuzzing system which applies static analysis of the target source code to generate fuzzing constraints to rapidly expose vulnerabilities. Constraints are identified which will increase the execution frequency of potential vulnerabilities.

          Networked applications, which receive network messages as input and respond to those messages, are the most common source of software security vulnerabilities because they are directly exposed to attack via the internet. Networked applications have the property that a large part of their code execution depends directly on the values of fields of the network messages received as input. For example, the behavior of an HTTP server will depend on the request method and header fields, and a TFTP server will depend on the opcode and mode fields. We analyze the source code of the networked application to identify these dependencies and use them to constrain test generation.

          Specification-based Hardware Verification

          Misunderanding the specification is a significant source of design errors. Detection of these errors requires that tests be generated directly from the specification, in order to identify differences between the specification and the implementation. Transaction Level Models (TLMs) are used to abstractly describe system behavior as a set of functions which encapsulate details of function and communication. TLMs are the most abstract formal description of the specification which we use to generate specification-based test sequences.

          Transactions describe sequences of input events which trigger a behavior in the correct system. The behavior of a design with a specification-based error would match that of a mutated transaction. We generate tests by mutating existing transactions to create tests which will differentiate teh behavior of correct and erroneous designs.



          http://www.ics.uci.edu/~harris/courses.html Ian G. Harris, Courses

          Ian G. Harris

          Associate Professor, Department of Computer Science

          University of California Irvine

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          Courses

          • CS 244 Introduction to Embedded and Ubiquitous Systems - Spring 2011
          • CS 151 Digital Logic Design - Winter 2011
          • CSE 181 AB Senior Design Project - Fall 2010/Winter 2011
          • CS 151 Digital Logic Design - Summer Session 1 2010
          • Hobby Electronics, Freshman Seminar - Spring 2010
          • Hobby Electronics, Transfer Seminar - Winter 2010
          • CSE 181 AB Senior Design Project - Fall 2009/Winter 2010
          • Hobby Electronics, Transfer Seminar - Fall 2009


          http://www.ics.uci.edu/~harris/publications.html Ian G. Harris, Publications

          Ian G. Harris

          Associate Professor, Department of Computer Science

          University of California Irvine

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          Publications

          • Z. Guo, I. G. Harris, L.-F. Tsaur, X. Chen, "An On-demand Scatternet Formation and Multi-hop Routing Protocol for BLE-based wireless Sensor Networks", IEEE Wireless Communications and Networking Conference , 2015.
          • H. R. Bhakta, I. G. Harris, "Semantic Analysis of Dialogs to Detect Social Engineering Attacks", IEEE Conference on Semantic Computing , 2015.
          • C. B. Harris, I. G. Harris, "Generating Formal Hardware Verification Properties from Natural Language Documentation", IEEE Conference on Semantic Computing , 2015.
          • Z. Guo, H. R. Bhakta, I. G. Harris, "Control-flow Checking for Intrusion Detection via a Real-Time Debug Interface", IEEE International Workshop on Smart Embedded Systems , 2014.
          • M. Soeken, C. B. Harris, N. Abdessaied, I. G. Harris, R. Drechsler, "Automating the Translation of Assertions Using Natural Language Processing Techniques", Forum on Specification and Design Languages , 2014.
          • D. Binkley, D. Lawrie, E. Hill, J. Burge, I. G. Harris, R. Hebig, O. Keszcze, K. Reed, J. Slankas, "Task Driven Software Summarization", IEEE International Conference on Software Maintenance, 2013.
          • Ian G. Harris, "Capturing Assertions from Natural Language Descriptions", Workshop on Natural Language Analysis in Software Engineering (NaturaLiSE) , May 2013.
          • Zi-Shun Huang and Ian G. Harris, "Return-Oriented Vulnerabilities in ARM Executables", Homeland Security Affairs Journal, 2013.
          • M. Rahmatian, H. Kooti, Ian G. Harris and E. Bozorgzadeh, "Hardware-Assisted Detection of Malicious Software in Embedded Systems", IEEE Embedded Systems Letters (ESL), vol. 4, num. 4
          • Ian G. Harris, "Generating Formal System Models from Natural Language Descriptions", IEEE High Level Design Validation and Test Workshop (HLDVT), November 2012.
          • Best Paper Award , Zi-Shun Huang and Ian G. Harris, "Return-Oriented Vulnerabilities in ARM Executables", IEEE International Conference on Technologies for Homeland Security (HST), November 2012.
          • M. Rahmatian, H. Kooti, Ian G. Harris and E. Bozorgzadeh, "Adaptable Intrusion Detection Using Partial Runtime Reconfiguration", IEEE International Conference on Computer Design (ICCD), October 2012.
          • M. Rahmatian, H. Kooti, Ian G. Harris and E. Bozorgzadeh, "Minimization of Trojan Footprint by Reducing Delay and Area Impact", IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology (DFTS), October 2012.
          • Ian G. Harris, "Extracting Design Information from Natural Language Specifications", IEEE/ACM Design Automation Conference (DAC), June 2012.
          • Patricia S. Lee and Ian G. Harris, "Test Generation for Subtractive Specification Errors", IEEE VLSI Test Symposium (VTS), April 2012.
          • Sharon Barner, Ian G. Harris, Daniel Kroening, and Orna Raz eds., "Hardware and Software: Verification and Testing, 6th International Haifa Verification Conference, HVC 2010 Haifa, Israel, October 2010 Revised Selected Papers, Lecture Notes in Computer Science", vol. 6504, Springer, 2010.
          • Dhiraj K. Pradhan and Ian G. Harris eds., Practical Design Verification, Cambridge University Press, 2009
          • S. Verma and Ian G. Harris, "SystemVerilog and Vera in a Verification Flow", in Practical Design Verification, Cambridge University Press, 2009
          • Ian G. Harris and Dhiraj Pradhan eds. "Design Verification and Validation", Special Section of IEEE Transactions on VLSI Systems, April 2008.
          • K. Ramineni, S. Verma, and I. G. Harris "Evaluation of an Efficient Control Oriented Coverage Metric", IEEE High Level Design Validation and Test Workshop, 2008.
          • S. Verma, I. G. Harris, and K. Ramineni, "Automatic Generation of Functional Coverage Models from Behavioral Verilog Descriptions", IEEE/ACM Design Automation and Test in Europe (DATE) Conference, 2007.
          • T. Alrahem, A. Chen, N. DiGiussepe, J. Gee, S.-P. Hsiao, S. Mattox, T. Park, A. Tam, I. G. Harris, and M. Carlsson, "INTERSTATE: A Stateful Protocol Fuzzer for SIP" , DEFCON 15, 2007.
          • F. Fummi, I. G. Harris, C. Marconcini, and G. Pravadelli, "A CLP-based Functional ATPG for Extended FSMs", IEEE Microprocessor Test and Verification Workshop, 2007.
          • K. Ramineni, I. G. Harris, and S. Verma, "Improving Feasible Interactions Among Multiple Processes", IEEE High Level Design Validation and Test Workshop, 2007.
          • S. Verma, I. G. Harris, and K. Ramineni, "Automatic Generation of Functional Coverage Models from CTL", IEEE High Level Design Validation and Test Workshop, 2007.
          • I. G. Harris, "Covalidation of Complex Hardware/Software Systems" System-on-Chip: Next Generation Electronics, Institution of Electrical Engineers Publishing (Bashir M. Al-Hashimi ed.), 2006.
          • M. Heath, W. Burleson, I. G. Harris, "Synchro-Tokens: A Deterministic GALS Methodology for Chip-Level Debug and Test" , IEEE Transactions on Computers, vol. 54, num. 12, December 2005.
          • I. G. Harris, "Hardware/Software Covalidation" , IEE Proceedings on Computers and Digital Techniques, vol. 152, num. 3, May 2005.
          • S. Verma, K. Ramineni, and I. G. Harris, "An Efficient Control-Oriented Coverage Metric" , IEEE Asian South Pacific Design Automation Conference (ASPDAC), January 2005.
          • I. G. Harris, "Tacking Concurrency and Timing Problems" Test and Validation of Hardware/Software Systems Starting with System-Level Descriptions, Springer-Verlag Publishing, Matteo Sonza Reorda and Zebo Peng eds., 2005.
          • M. Heath, W. Burleson, and I. G. Harris, "Eliminating Nondeterminism to Enable Chip-Level Test of Globally-Asynchronous Locally-Synchronous SoCs" , IEEE/ACM Design Automation and Test in Europe (DATE) Conference, February 2004.
          • E. Gaudette, M. Moussa, and I. G. Harris, "A Method for the Evaluation of Behavioral Fault Models" , IEEE High-Level Design, Validation, and Test Workshop (HLDVT), November 2003.
          • D. A. Fernandes and I. G. Harris, "Application of Built in Self-Test for Interconnect Testing of FPGAs" , IEEE International Test Conference, September 2003.
          • I. G. Harris, "Fault Models and Test Generation for Hardware-Software Covalidation", IEEE Design and Test of Computers, volume 20, number 4, July-August 2003.
          • S. Arekapudi, F. Xin, J. Peng, I. G. Harris, "ATPG for Timing Errors in Globally Asynchronous Locally Synchronous Systems", Journal for Circuits, Systems and Computers, volume 12, number 3, June 2003.
          • M. Heath and I. G. Harris "A Deterministic Globally Asynchronous Locally Synchronous Microprocessor Architecture", IEEE Microprocessor Test and Verification Workshop (MTV), May 2003.
          • Z. Zeng, Q. Zhang, I. G. Harris, and M. Ciesielski, "Fast Computation of Data Correlation Using BDDs", IEEE/ACM Design Automation and Test in Europe (DATE) Conference, March 2003.
          • Q. Zhang and I. G. Harris, "Partial BIST Insertion to Eliminate Data Correlation", IEEE Transactions on Computer-Aided Design, March 2003.
          • I. G. Harris and R. Tessier, "Testing and Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures", IEEE Transactions on Computer-Aided Design, November 2002.
          • F. Xin and I. G. Harris, "Test Generation for Hardware-Software Covalidation Using Non-Linear Programming", IEEE Workshop on High Level Design Validation and Test (HLDVT), October 2002.
          • S. Arekapudi, F. Xin, J. Peng, I. G. Harris, "ATPG for Timing-Induced Functional Errors on Trigger Events in Hardware-Software Systems" , IEEE European Test Workshop (ETW), May 2002
          • I. G. Harris, "Hardware-Software Covalidation: Fault Models and Test Generation", IEEE Workshop on High Level Design Validation and Test (HLDVT), November 2001
          • S. Arekapudi, F. Xin, J. Peng, I. G. Harris, "Test Pattern Generation for Timing-Induced Errors in Hardware-Software Systems", IEEE Workshop on High Level Design Validation and Test (HLDVT), November 2001
          • I. G. Harris, P. Menon, and R. Tessier, "BIST-Based Path Delay Testing in FPGA Arichitectures", IEEE International Test Conference (ITC), October 2001.
          • Q. Zhang and I. G. Harris, "A Validation Fault Model for Timing-Induced Functional Errors", IEEE International Test Conference (ITC), October 2001.
          • W. Burleson, A. Ganz, and I. G. Harris, "Educational Innovations in Multimedia Systems", ASEE Journal of Engineering Education, January 2001.
          • Q. Zhang and I. G. Harris, "A Data Flow Coverage Metric For Validation of Behavioral HDL Descriptions", International Conference on Computer-Aided Design (ICCAD), 2000.
          • I. G. Harris and R. Tessier, "Diagnosis of Interconnect Faults in Cluster-Based FPGA Architectures", International Conference on Computer-Aided Design (ICCAD), 2000.
          • Q. Zhang and I. G. Harris, "A Domain Coverage Metric for the Validation of Behavioral VHDL Descriptions" , International Test Conference (ITC), October 2000.
          • I. G. Harris and Russell Tessier, "Interconnect Testing of Cluster-based FPGA Architectures", Design Automation Conference (DAC), 2000.
          • Q. Zhang and I. G. Harris, "Partial BIST Insertion to Eliminate Data Correlation", International Conference on Computer-Aided Design (ICCAD), 1999.
          • Q. Zhang and I. G. Harris, "Mutation Analysis for the Evaluation of Functional Fault Models", High-Level Design, Validation, and Test Workshop (HLDVT), 1999.

          http://www.ics.uci.edu/~harris/contact.html Ian G. Harris, Contact

          Ian G. Harris

          Associate Professor, Department of Computer Science

          University of California Irvine

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          Contact Information

          • Department of Computer Science
          • University of California Irvine
          • Donald Bren Hall, Room 3088
          • Irvine, CA 92697, USA
          • Phone: +1 949 824 8842
          • Fax: +1 949 824 4056
          • Email: harris@ics.uci.edu


          http://www.ics.uci.edu/~harris/index.html Ian G. Harris, Associate Professor

          Ian G. Harris

          Associate Professor, Department of Computer Science

          University of California Irvine

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          Short Bio

          Ian G. Harris is currently Vice Chair of Undergraduate Education in the Computer Science Department at the University of California Irvine. He received his BS degree in Computer Science from Massachusetts Institute of Technology in 1990. He received his MS and PhD degrees in Computer Science from the University of California San Diego in 1992 and 1997 respectively. He was a member of the faculty in the Electrical and Computer Engineering Department at the University of Massachusetts Amherst from 1997 until June 2003.

          Research Areas

          • Functional Verification
          • Electronic Design Automation from Natural Language
          • Embedded Systems Security
          • Social Engineering Attack Detection

          Research projects in Professor Harris' group is related to testing of hardware and software systems. His field of interest includes validation of hardware systems to ensure that the behavior of the system matches the intentions of the designer. He also investigates the application of testing for computer security. Natural Language Processing (NLP) is a prominent theme in Professor Harris' work in both security and verification. NLP techniques are used to extract information from hardware specifications, and NLP techniques are used to identify social engineering attacks in a dialog between two speakers.

          Service

          Professor Harris serves on the program committees of several leading conferences in verification and security including IEEE Hardware Oriented Security and Trust (HOST), Haifa Verification Conference (HVC), and the IEEE Conference on Technologies for Homeland Security.

          Professor Harris is also co-organizer of the Workshop for Design Automation for Understanding of Hardware Designs (DUHDe) held in conjunction with IEEE/ACM Design Automation and Test in Europe 2016. The aim of the workshop is to consolidate the research community for problems related to design understanding across multiple levels of abstraction. Problems of interest include Automatic Feature Extraction, Reverse Engineering, and Synthesis/Verification from Natural Language.



          http://riscit.ics.uci.edu/ RiSCIT: The Center for Research in Sustainability, Collapse-Preparedness and Information Technology
          RiSCIT
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          • LUCI
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          The Center for Research in Sustainability,
          Collapse-Preparedness & Information Technology

          Meeting Needs with Reduced Resources

          In his keynote address at a 2012 NSF-funded National Academies symposium, John Holdren, head of the US Office of Science and Technology Policy and chief science advisor to the nation, spoke at length about climate change, and described a need for both mitigation, the reduction of the magnitude of change, and adaptation, the mobilization of responses to change. Holdren advocated for the development of technology that focuses on "meeting human needs [and] wants at lower cost with reduced use of material resources [and] reduced environmental impact." The Center for Research on Sustainability, Collapse-Preparedness & Information Technology (RiSCIT) seeks to engage with this challenge, in part due to the potential for making civilizations more environmentally sustainable via IT interventions and in part as means of preparing for civilizational collapse. The goal of the RiSCIT center is to provide a central focus for research on the role of informatics and computing in supporting the transition to sustainability and addressing the potential to prepare for civilization-scale collapse.

          Research

          Our members engage in cutting edge research on topics relating to sustainability and collapse.

          Teaching

          We teach courses and supervise independent study students related to the center's research focuses.

          Living

          We compost, vermicompost, grow food, mine bitcoin, travel less, and generally try to "walk the walk".

          Events

          RiSCIT is pleased to announce the LIMITS 2015 Public Discussion on Computing with Limits to be held at the UCI University Club Library in conjuction with the Newkirk Center for Science & Society on 3 December, 2015.
          Details here.

          LIMITS 2015 Public Discussion

          Past Events

          RiSCIT hosted LIMITS 2015 at UCI on June 15-16, 2015.
          Details here.

          LIMITS 2015
          Joseph Tainter

          RiSCIT was pleased to host anthropologist, historian and author Joseph Tainter on February 14th, 2014 in DBH 6011.
          Details here.

          The Collapse of Complex Societies

          Projects

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          Plant Guild Composer

          Transition Towns

          SE 4 Sustainability

          Bitcoin

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          People

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          Debra Richardson

          Bill Tomlinson

          Don Patterson

          Bonnie Nardi

          Paul Dourish

          Eli Bozorgzadeh

          Geof Bowker

          Eric Mjolsness

          Nalini Venkatasubramanian

          Padhraic Smyth

          Walt Scacchi

          Katie Pine

          ›
          http://www.ics.uci.edu/~wmt/officeHours.html Bill Tomlinson: Office Hours
          Office Hours



          By appointment. Please send me an email at wmt at uci dot edu.




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          http://www.ics.uci.edu/~wmt/emailFormat.html Bill Tomlinson: Funny Email Format
          Funny Email Format




          Listing my email address in a form that is only readily understood by people helps avoid having it harvested by wily autonomous computational systems.



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          http://www.ics.uci.edu/~wmt/publications.html Bill Tomlinson: Publications
          Publications




           
           
          Books
          2010 B. Tomlinson. 2010. Greening through IT. Cambridge, MA: MIT Press.
           
           
           
          Journals and Law Review Articles
          2012 B. Tomlinson, M. S. Silberman. 2012. "The Cognitive Surplus Is Made of Fossil Fuels." In: First Monday, Vol. 17, No. 11. Online. 5502 words.
           
          2012 A. Torrance, B. Tomlinson. 2012. "Property Rules, Liability Rules, and Patents: One Experimental View of the Cathedral." In: Yale Journal of Law and Technology.
           
          2011 J. Ross, R. Simpson, B. Tomlinson. 2011. "Media Richness, Interactivity, and Retargeting to Mobile Devices." In: International Journal of Arts and Technology, Special Issue on Interactive Experiences in Multimedia and Augmented Environments.
           
          2011 J. Ross, B. Tomlinson. 2011. "Negabehaviors and Environmental Sustainability." In: Journal of Sustainability Education. Vol. 2 (1).
           
          2011 E. P. S. Baumer, M. Sueyoshi, B. Tomlinson. 2011. "Bloggers and Readers Blogging Together: Collaborative Co-Creation of Political Blogs." In: Computer Supported Cooperative Work. Vol. 20(1-2) p. 1-36.
           
          2010 J. Ross, B. Tomlinson. 2010. "How Games Can Redirect Humanity's Cognitive Surplus for Social Good." In: ACM Computers In Entertainment. December Vol. 8(4). 4pp.
           
          2009 A. Torrance, B. Tomlinson. 2009. "Patent Expertise and the Regress of Useful Arts." In: Southern Illinois University Law Journal. Vol. 33. p. 239-278.
           
          2009 L. Lewis, R. Black, B. Tomlinson. 2009. "Let Everyone Play: An Educational Perspective on Why Fan Fiction Is, or Should Be, Legal." In: International Journal of Learning and Media. Vol. 1(1). MacArthur Foundation/MIT Press. p. 67-81.
           
          2009 B. Tomlinson, M. L. Yau, E. Baumer, J. Ross, A. Correa, G. Ji. 2009. "Richly Connected Systems and Multi-Device Worlds." In: PRESENCE: Teleoperators and Virtual Environments. Vol. 18, No. 1. p. 54-71. MIT Press.
           
          2009 B. Tomlinson. 2009. "A Proximate Mechanism for Communities of Agents to Commemorate Long Dead Ancestors." In: Journal of Artificial Societies and Social Simulation. Vol. 12, No. 1, online. 6,494 words.
           
          2009 A. Torrance, B. Tomlinson. 2009. "Patents and the Regress of Useful Arts." In: Columbia Science and Technology Law Review. Vol. 10. 39pp.
           
          2008 B. Tomlinson, E. Baumer, M. L. Yau, F. L Carpenter, R. Black. 2008. "A Participatory Simulation for Informal Education in Restoration Ecology." In: E-Learning. Vol. 5, No. 3. Online. p. 238-255.
           
          2005 Tomlinson, B. 2005. "Social Characters for Computer Games." In: International Journal of Interactive Technology and Smart Education, Special Issue on Social Learning through Gaming. Vol. 2, No. 2, p. 101-115.
           
          2005 Tomlinson, B. 2005. "From Linear to Interactive Animation: How Autonomous Characters Change the Process and Product of Animating." In: ACM Computers in Entertainment. Vol 3. No. 1. Online. 20 pages.
           
          2002 Blumberg, B., Downie, M., Ivanov, Y., Berlin, M., Johnson, M. P., and Tomlinson, B. 2002. "Integrated Learning for Interactive Synthetic Characters.” Computer Graphics, Proceedings of SIGGRAPH 2002. San Antonio, TX.
           
          1999 Tomlinson, B. 1999. "Dead Technology." Style Vol. 33 No. 2, p. 316-335
           
           
           
          Full Conference & Symposium Papers
          2012 B. Tomlinson, M. S. Silberman, D. Patterson, Y. Pan, E. Blevis. 2012. "Collapse Informatics: Augmenting the Sustainability & ICT4D Discourse in HCI." in ACM Conference on Human Factors in Computing Systems (CHI 2012). (Austin, TX.) (Best Paper Honorable Mention, CCC Sustainability Award)
           
          2011 E. P. S. Baumer and B. Tomlinson. 2011. "Comparing Activity Theory with Distributed Cognition for Video Analysis: Beyond 'Kicking the Tires.'" in ACM Conference on Human Factors in Computing Systems (CHI 2011). (Vancouver, BC, Canada). (Best Paper Honorable Mention)
           
          2011 Hansen, J., Baumer, E.P.S., Richland, L., and Tomlinson, B. 2011. "Metaphor and Creativity in Learning Science." in American Educational Researchers Association Annual Conference (AERA). (New Orleans, Louisiana).
           
          2010 J. Ross, N. Shantharam, and B. Tomlinson. 2010. "Collaborative Filtering and Carbon Footprint Calculation." in IEEE International Symposium on Sustainable Systems and Technology (ISSST 2010). (Washington, DC).
           
          2010 E. P. S. Baumer, J. Sinclair, and B. Tomlinson. 2010. "'America Is Like Metamucil': Critical and Creative Thinking about Metaphor in Political Blogs." in ACM Conference on Human Factors in Computing Systems (CHI 2010). (Atlanta, GA).
           
          2009 E. Baumer, B. Tomlinson, J. Hansen, and L. Richland. 2009. "Fostering Metaphorical Creativity Using Computational Metaphor Identification." In: ACM Conference on Creativity & Cognition. (Berkeley, CA).
           
          2009 E. Baumer, J. Sinclair, D. Hubin, and B. Tomlinson. 2009. "metaViz: Visualizing Computationally Identified Metaphors in Political Blogs." In: The IEEE Symposium on Social Computing (SocialCom). (Vancouver, BC, Canada).
           
          2009 E. Baumer, B. Tomlinson, and L. Richland. 2009. "Computational Metaphor Identification: A Method for Identifying Conceptual Metaphors in Written Text." In: The Second International Analogy Conference (Analogy'09). (Sofia, Bulgaria).
           
          2009 E. Baumer, L. Richland, and B. Tomlinson. 2009. "Applying Computational Metaphor Identification to Middle School Students' Writing about Cellular Reproduction." National Association for Research in Science Teaching Annual Conference (NARST). (Garden Grove, CA).
           
          2008 Baumer, E., M. Sueyoshi, B. Tomlinson. 2008. "Exploring the Role of the Reader in the Activity of Blogging." In: ACM Conference on Human Factors in Computing Systems (CHI 2008). Florence, Italy. 10 pages.
           
          2008 Tomlinson, B. 2008. "Prototyping a Community-Generated, Mobile Device-Enabled Database of Environmental Impact Reviews of Consumer Products." In: Hawai'i International Conference on System Sciences (HICSS), Social Spaces Minitrack. Big Island, HI. 10 pages.
           
          2006 Baumer, E. and B. Tomlinson. 2006. "The Interconnected Roles of Abstraction and Emergence in Artificial Societies." In: AAAI Fall Symposium. Interaction and Emergent Phenomena in Societies of Agents. Arlington, VA. 9 pages.
           
          2006 Alspaugh, T. Tomlinson, B., Baumer, E. 2006. "Using Social Agents to Visualize Software Scenarios." ACM Symposium on Software Visualization (SOFTVIS'06), September 2006.
           
          2006 Baumer, E., Tomlinson, B. 2006. "The Interconnected Roles of Abstraction and Emergence in Artificial Societies." In: AAAI Fall Symposium. Interaction and Emergent Phenomena in Societies of Agents. Arlington, VA.
           
          2006 Tomlinson, B., Yau, M. L. and Baumer, E. 2006. "Embodied Mobile Agents." In: Proceedings of the Fifth International Joint Conference on Autonomous Agents & Multi Agent Systems.
           
          2002 Tomlinson, B., Downie, M., Berlin, M., Gray, J., Lyons, D., Cochran, J., and Blumberg, B. 2002. "Leashing the AlphaWolves: Mixing User Direction with Autonomous Emotion in a Pack of Semi-Autonomous Virtual Characters." Proceedings of the 2002 ACM SIGGRAPH Symposium on Computer Animation. San Antonio, TX.
           
          2001 Tomlinson, B., and Blumberg, B. 2001. "Social Behavior, Emotion and Learning in a Pack of Virtual Wolves." 2001 AAAI Fall Symposium "Emotional and Intelligent II: The Tangled Knot of Social Cognition". November 2-4, 2001, North Falmouth, MA.
           
          2001 Blumberg, B., Tomlinson, B., and Downie, M. 2001. "Multiple Conceptions of Character-Based Interactive Installations." Computer Graphics International 2001. p. 5-11
           
          2000 Tomlinson, B., B. Blumberg and B. Rhodes. 2000. "How Is an Agent Like a Wolf?: Dominance and Submission in Multi-Agent Systems." International ICSC Symposium on Multi-Agents and Mobile Agents in Virtual Organizations and E-Commerce (MAMA '2000), Wollongong, Australia
           
          2000 Tomlinson, B., B. Blumberg, and D. Nain. 2000. "Expressive Autonomous Cinematography for Interactive Virtual Environments." Fourth International Conference on Autonomous Agents (Agents 2000), Barcelona, Catalonia, Spain
           
           
           
          Short Conference & Symposium Papers
          2012 B. Tomlinson, J. Ross, P. André, E.P.S. Baumer, D.J. Patterson, J. Corneli, M. Mahaux, S. Nobarany, M. Lazzari, P. Penzenstadler, A.W. Torrance, D.J. Callele, G.M. Olson, M.S. Silberman, M. Ständer, F.R. Palamedi, A. Salah, E. Morrill, X. Franch, F. Mueller, J. Kaye, R.W. Black, M.L. Cohn, P.C. Shih, J. Brewer, N. Goyal, P. Näkki, J. Huang, N. Baghaei, and C. Saper. 2012. "Massively Distributed Authorship of Academic Papers." ACM Conference on Human Factors in Computing Systems (CHI 2012) Extended Abstracts (alt.chi). 10 pages.
           
          2011 N. Amsel, Z. Ibrahim, A. Malik, B. Tomlinson. 2011. "Toward Sustainable Software Engineering." In: 33rd International Conference on Software Engineering (ICSE 2011), New Ideas and Emerging Results track. Honolulu, HI. 4 pages.
           
          2010 N. Amsel, B. Tomlinson. 2010. "Green Tracker: A Tool for Estimating the Energy Consumption of Software." In: ACM Conference On Human Factors In Computing Systems (CHI 2010), Work in Progress. Atlanta, GA. 6 pages.
           
          2010 M. S. Silberman, B. Tomlinson. 2010. "Toward an ecological sensibility: tools for evaluating sustainable HCI." In: ACM Conference On Human Factors In Computing Systems (CHI 2010), Work in Progress. Atlanta, GA. 6 pages.
           
          2010 J. Ross, L. Irani, M. S. Silberman, A. Zaldivar, B. Tomlinson. 2010. "Who are the Crowdworkers? Shifting Demographics in Mechanical Turk." ACM Conference on Human Factors in Computing Systems (CHI 2010) Extended Abstracts (alt.chi). 10 pages.
           
          2010 Huh, J., Nathan, L., Silberman, S., Blevis, E., Tomlinson, B., Sengers, P., and Busse, D., (2010). "Workshop: Examining Appropriation, Re-use, and Maintenance for Sustainability." In Extended Abstracts of the Twenty-Eighth Annual SIGCHI Conference on Human Factors in Computing Systems (Atlanta, GA, USA, April 10-15, 2010). CHI '10. ACM: New York, NY.
           
          2009 Huang, E.M., Blevis, E., Mankoff, J., Nathan, L., & Tomlinson, B. 2009. Workshop: Defining the Role of HCI in the Challenges of Sustainability. In Extended Abstracts of the Twenty-Seventh Annual SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA, April 04-09, 2009). CHI '09. ACM, New York, NY.
           
          2008 Dourish, P., G. Hayes, L. Irani, C. Lee, S. Lindtner, B. Nardi, D. Patterson, B. Tomlinson. 2008. "Informatics at UC Irvine." ACM Conference on Human Factors in Computing Systems (CHI 2008) Extended Abstracts (Research Landscapes). 6 pages.
           
          2007 Baumer, E. and B. Tomlinson. 2007. "Questioning the Technological Panacea: Three Reflective Questions for Designers." ACM Conference on Human Factors in Computing Systems (CHI 2007) Extended Abstracts (alt.chi). 9 pages.
           
          2007 Tomlinson, B., E. Baumer, M. L. Yau, P. MacAlpine, L. Canales, A. Correa, B. Hornick, A. Sharma. 2007. "Dreaming of Adaptive Interface Agents." ACM Conference on Human Factors in Computing Systems (CHI 2007) Extended Abstracts (Trends - Interactivity). 6 pages.
           
          2006 Tomlinson, B., Yau, M. L., Baumer, E., Goetz, S., Carpenter, F. L., Pratt, R. T., Young, K., May-Tobin, C. 2006. "The EcoRaft Project: A Multi-Device Interactive Graphical Exhibit for Learning About Restoration Ecology." In: ACM Conference On Human Factors In Computing Systems (CHI 2006), Work in Progress. Montreal, Canada.
           
          2006 Elliott, G., Tomlinson, B. 2006. "PersonalSoundtrack: Context-aware playlists that adapt to user pace." In: ACM Conference On Human Factors In Computing Systems (CHI 2006), Work in Progress. Montreal, Canada.
           
          2005 Tomlinson, B., Yau, M. L. and Gray, J. 2005. "Heterogeneous Character Animation: How to make an interactive character jump between stationary and mobile graphical computers" In: SIGGRAPH 05 Sketches.
           
          2005 Tomlinson, B. 2005. "Negative Behavior Space in the Design of Interactive Agents." In: Artificial Intelligence and Interactive Digital Entertainment (AIIDE 05) Conference, Marina del Rey, CA. AAAI Press.
           
          2005 Tomlinson, B. 2005. "A Heterogeneous Animated Platform for Educational Participatory Simulations." In: 10th Computer Supported Collaborative Learning Conference, Taipei, Taiwan.
           
          2005 Tomlinson, B., Gray, J., Yau, M. L. 2005. "Multiple Virtual Rafts: A Multi-User Paradigm for Interacting with Communities of Autonomous Characters." In: ACM Conference On Human Factors In Computing Systems (CHI 2005), Late Breaking Results (Interactive Poster), Portland, OR.
           
          2005 Tomlinson, B., Yau, M. L., O'Connell, J., Williams, K., Yamaoka, S. 2005. "The Virtual Raft Project: A Mobile Interface for Interacting with Communities of Autonomous Characters." Conference Abstracts and Applications, ACM Conference On Human Factors In Computing Systems (CHI 2005). Portland, OR.
           
          2002 Tomlinson, B., and Blumberg, B. 2002. "Synthetic Social Relationships in Animated Virtual Characters." From Animals to Animats 7. Proceedings of the Seventh International Conference on the Simulation of Adaptive Behavior (SAB ’02). Edinburgh, UK.
           
           
           
          Workshop Papers & Posters
          2012 B. Penzenstadler, B. Tomlinson, D. Richardson. 2012. "RE4ES: Support Environmental Sustainability by Requirements Engineering." International Workshop on "Requirements Engineering for Sustainable Systems". 6 pages.
           
          2010 A. Torrance, B. Tomlinson. 2010. "Property Rules, Liability Rules, and Patents: One Experimental View of the Cathedral." Conference on Empirical Legal Studies (CELS). Poster session. 28 pages.
           
          2010 J. Ross, M. S. Silberman, L. Irani, B. Tomlinson. 2010. "Sellers' problems in human computation markets." Human Computation Workshop (HComp 2010). 4 pages.
           
          2010 E. P. S. Baumer, J. P. White, and B. Tomlinson. 2010. "Comparing Semantic Role Labeling with Typed Dependency Parsing in Computational Metaphor Identification." in Computational Approaches to Linguistic Creativity (CALC-10) Workshop at Human Language Technologies (NAACL/HLT), (Los Angeles, CA). 10 pages.
           
          2009 M. S. Silberman and B. Tomlinson. 2009. "Precarious infrastructure and postapocalyptic computing." In ACM CHI 2010 Conference on Human Factors in Computing Systems, Workshop on Examining Appropriation, Re-use, and Maintenance for Sustainability. Boston, MA. 3 pages.
           
          2009 B. Tomlinson. 2009. "Broadening Human Horizons through Green IT." In ACM CHI 2009 Conference on Human Factors in Computing Systems, Workshop on Defining the Role of HCI in the Challenges of Sustainability. Boston, MA. 3 pages.
           
          2008 Baumer, E., B. Tomlinson. 2008. "Dream-like Interfaces and Computational Dreaming." In: ACM Conference On Human Factors In Computing Systems (CHI 2008), Workshop on Night and Darkness: Interaction after Dark. Florence, Italy. 2 pages.
           
          2008 Baumer, E., M. Sueyoshi, and B. Tomlinson. 2008. "Examining Privacy in Blogging from the Reader's Perspective." In Poster Session, Second International Conference on Weblogs and Social Media (ICWSM 2008). Seattle, WA. 2 pages.
           
          2008 Baumer, E. and B. Tomlinson. 2008. "Computational Identification of Conceptual Metaphors in Communities of Blogs." In Poster Session, Second International Conference on Weblogs and Social Media (ICWSM 2008). Seattle, WA. 2 pages.
           
          2007 Torrance, A. and B. Tomlinson. 2007. "A Multi-User Interactive Patent Simulation (PatentSim)." Fifth Annual Works in Progress Intellectual Property Colloquium. Washington, DC.
           
          2007 Yau, M. L., J. Moore, Z. Ji, M. Roland, B. Tomlinson. 2007. "Persistence and Propagation of Shadow Direction in Mobile and Multi-Device Graphics." In: SIGGRAPH 07, Research Posters. San Diego, CA.
           
          2007 Baumer, E. and B. Tomlinson. 2007. "Advocating Polytheoretic Evaluation of Interactive Art and New Media." In: ACM Conference On Human Factors In Computing Systems (CHI 2007), Workshop on HCI and New Media: Methodology and Evaluation. San Jose, CA. 4 pages.
           
          2006 Alspaugh, T., Baumer, E., Tomlinson, B. 2006. "On a Mixed-Methods Evaluation of a Social-Agent Scenario Visualization." Fourth International Workshop on Comparative Evaluation in Requirements Engineering (CERE '06)
           
          2006 Tomlinson, B., Baumer, E., Yau, M. L. 2006. "The Island Metaphor." In: SIGGRAPH 2006 Posters.
           
          2006 Yau, M. L., Moore, J. Z., Tomlinson, B. 2006. "Interactive Lighting Design for Multi-device Virtual Environments." In: SIGGRAPH 2006 Posters.
           
          2005 Baumer, E. and Tomlinson, B. 2005. "Institutionalization Through Reciprocal Habitualization and Typification." Second NASA/JPL Workshop or Radical Agent Concepts (WRAC). September, 2005.
           
          2005 Baumer, E., Tomlinson, B. 2005. "Synthetic Social Construction for Autonomous Characters." In: AAAI 05 Conference, Workshop on Modular Construction of Human-Like Intelligence. Pittsburgh, PA.
           
          2005 Tomlinson, B. 2005. “Designing Affective Interaction Paradigms for Animated Characters.” In: Proceedings of the Human Computer Interaction Consortium Winter Meeting (HCIC 05), Fraser, CO.
           
          2004 Tomlinson, B. 2004. "Using Human Acting Skill to Measure Empathic Value in Heterogeneous Characters.” Third International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), Workshop on Empathic Agents. New York, NY.
           
           
           
          Invited Workshop Proceedings
          2011 B. Tomlinson. 2011. "IT and (Un)sustainable Cultures." NSF/CCC Workshop on IT and the Sustainability Enterprise, Role of Information Sciences and Engineering in Sustainability (RISES), 3 pages.
           
           
           
          Peer-Reviewed Book Chapters
          2009 E. Baumer and B. Tomlinson. 2009. "Relationships between the Processes of Emergence and Abstraction in Societies." In Trajkovski, G. and Collins, S. (eds.) Handbook of Research on Agent-Based Societies: Social and Cultural Interactions. Hershey, PA: IGI-Global.
           
          2003 Synthetic Characters Group (B. Tomlinson, M. Downie, M. Berlin, J. Gray, A. Wong, R. Burke, D. Isla, Y. Ivanov, M. P. Johnson, D. Lyons, J. Cochran, B. Yong, B. Blumberg). 2003. "AlphaWolf.” in J. Shaw and P. Weibel (eds.) Future Cinema: The Cinematic Imaginary after Film. MIT Press: Cambridge, MA.
           
          2003 Tomlinson, B., and Blumberg, B. 2003. "AlphaWolf: Social Learning, Emotion and Development in Autonomous Virtual Agents." Innovative Concepts in Agent-Based Systems, Lecture Notes in Computer Science, Vol. 2564, p. 35-45, Springer-Verlag, 2003. Presented at First GSFC/JPL Workshop on Radical Agent Concepts. 2002. NASA Goddard Space Flight Center, Greenbelt, MD.
           
           
           
          Invited Book Chapters
          to appear B. Tomlinson. (forthcoming). "Technology and City Sustainability." In: H. Blanco and D. Mazmanian (eds.). Handbook on Sustainable Cities, Edward Elgar Publishing Ltd.
           
           
           
          Professional Newsletters & Magazines
          2012 B. Tomlinson, D. J. Patterson, Y. Pan, E. Blevis, B. Nardi, S. Silberman, J. Norton, J. J. LaViola Jr. (2012). "What If Sustainability Doesn't Work Out? An Informatics Perspective on Adaptation to Global Change." In: ACM Interactions.
           
          2011 B. Tomlinson, M. S. Silberman, J. White. 2011. "Can More Efficient IT Be Worse For The Environment?" In: IEEE Computer, Green IT column. Vol. 44, No. 1. January 2011.
           
          2010 B. Tomlinson. 2010. "Future Workplaces to Support Environmental Sustainability." In: ACM Interactions. Vol. 17, No. 6. Nov/Dec 2010.
           
          2008 B. Tomlinson. 2008. "A Call for Pro-Environmental Conspicuous Consumption in the Online World." In: ACM Interactions. Vol. 15, No. 6. Nov/Dec 2008. Sustainably Ours forum, edited by E. Blevis.
           
          2006 Pratt, R. T., Carpenter, F. L. and Tomlinson, B. 2006. "The EcoRaft Project: An Interdisciplinary Approach to Teaching Lessons in Ecological Restoration." In: Bulletin of the Ecological Society of America. Vol. 87, No. 2. April 2006, p. 176-182.
           
          2002 Tomlinson, B., and Blumberg, B. 2002. "Social Synthetic Characters." (Visfiles column, edited by Bill Hibbard). Computer Graphics. Vol. 26, No. 2. (May 2002).
           
          2002 Downie, M., Tomlinson, B., and Blumberg, B. 2002. "Developing an Aesthetic: Character-Based Interactive Installations." Computer Graphics. Vol. 26, No. 2. (May 2002).
           
          1999 Synthetic Characters Group (B. Tomlinson, M. Downie, A. Benbasat, J. Wahl, W. Stiehl, B. Blumberg). 1999. "sand:stone - Artist Statement." Leonardo Vol. 32, No. 5, p. 462-463
           
           
           
          Technical Reports
          2012 J. Ross, O. Holmes, B. Tomlinson. 2012. "Playing with Genre: User-Generated Game Design in LittleBigPlanet 2." LUCI-2012-003, Laboratory for Ubiquitous Computing and Interaction. Irvine, CA: University of California, Irvine.
           
          2012 D. E. Lyons, J. J. Long, R. S. Goraya, J. Lu, & B. Tomlinson. 2012. "Cultivating Environmental Systems Thinking with Karunatree." LUCI-2012-002, Laboratory for Ubiquitous Computing and Interaction. Irvine, CA: University of California, Irvine.
           
          2010 E. P. S. Baumer, D. Hubin, B. Tomlinson. 2010. "Computational Metaphor Identification." LUCI-2010-002, Laboratory for Ubiquitous Computing and Interaction. Irvine, CA: University of California, Irvine.
           
          2010 J. Ross, N. Amsel, R. Beckman, and B. Tomlinson, B. 2010. "EcoPath: Adding Spatial, Social, and Gaming Contexts to Personal Tracking Systems." Social Code Report 2010-01.
           
          2009 J. Ross, A. Zaldivar, L. Irani, and B. Tomlinson. 2009. "Who are the Turkers? Worker Demographics in Amazon Mechanical Turk." Social Code Report 2009-01.
           
          2008 C. P. Lee, B. Hornick, J. Chen, M. Blonk, B. Tomlinson, and B. Nardi. 2008. "The Technology Garden." LUCI-2008-001, Laboratory for Ubiquitous Computing and Interaction. Irvine, CA: University of California, Irvine.
           
           
           
          Theses
          2002 Tomlinson, W. 2002. "Synthetic Social Relationships for Computational Entities." Doctoral Dissertation. MIT Program in Media Arts & Sciences. May 7, 2002.
           
          1999 Tomlinson, W. 1999. "Interactivity and Emotion through Cinematography." Master's Thesis. MIT Media Arts and Sciences.
           
           
           
          Popular Press
          1997 Tomlinson, B. 1997. "The Sundance Kid." Philadelphia City Paper, 1/30/97






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          http://www.ics.uci.edu/~wmt/students.html Bill Tomlinson: Students
          Students



          PhD Students
          Eric Baumer
          Joel Ross
          Nadine Amsel

          MS Students
          Faith Dang
          Ajey Shah

          Undergraduates
          Lauren Lewis
          Nitin Shantharam
          Andrew Zaldivar
          Jordan Sinclair
          Robert Simpson

          Alumni
          Man Lok (Simon) Yau
          Matt Berlin
          Jesse Gray
          Dan Stiehl
          Delphine Nain
          Jennie Cochran
          Jed Wahl
          Bryan Yong
          Peter Stepniewicz
          So Yamaoka
          Sara Goetz
          Joe Rojas
          Robert Moodey
          Jessica O'Connell
          Dung Nguyen
          Ksatria Williams
          Nathan Ie
          Grace Chiang
          Stefan Marinov
          Martin Schmidt
          Erika Ramos
          Andrew Gee
          Kenneth Chua
          Craig Yoho
          Chris Schmitz
          Uel (Jack) McMahan, III
          Elena Koriakina
          Jared Beam
          Jennifer Law
          TJ Thinakaran
          Greg Elliott
          Lorenzo Canales
          Andrew Correa
          Zack (Gang) Ji
          Paul Mac Alpine
          Anju Sharma
          David Hubin
          Mark Sueyoshi
          Rodrigo Lois
          Bryant Hornick
          Ellen Eramya
          Michael Riccobono
          Bruno Nadeau

          If you are a current student or alumni and would like to be listed on (or removed from) the above list, please email Bill to let him know. Also, if you have a link you'd like him to add to your name, email him for that too.






          Return to Main Site
          http://www.ics.uci.edu/~wmt/socialCodeGroup/projects.html Social Code Group
          Social Code Group
          Home
          People
          Projects
          Publications
          Support
          Press
          Get Involved
          Greening through IT (2005-present)
          see also various individual projects below

          Project Lead: Bill Tomlinson

          Green IT is a field that explores the juncture between two growing trends - the spread of environmental concern across many human communities, and the rapid adoption of digital tools and techniques for manipulating information. Information technology is transforming societies around the world, affecting many different topics from communication between people to the workings of international politics. Green IT brings together these two areas, examining the role of information technology in supporting human responses to the world's current environmental issues. Human minds are not well suited to thinking about problems that occur over long periods of time, large distances, and vast complexity; nevertheless, environmental problems often occur on these scales. By helping bridge from human scales to environmental scales, information technology can help our civilization launch an appropriate response to these critical concerns. The intent of this book is to help IT researchers and practitioners understand how they can enable human civilizations to move toward environmental sustainability, and help environmentally-minded researchers and concerned citizens see how they can use IT in support of these same goals. This is the first book to explore the ways in which IT broadens human horizons, and thereby can help us orchestrate a viable response to the significant environmental problems currently facing the world.

          MIT Press book | UCI Course | NSF REU award | CHI 2010 WIP paper | CHI 2010 workshop paper | CHI 2009 workshop

          Software Engineering for Sustainability (2008-present)
          Project Leads: Debra Richardson, Bill Tomlinson, Birgit Penzenstadler, Ankita Raturi

          Current software engineering practices lead to significant environmental impacts, such as power consumption of software operation and e-waste from computers made obsolete by software upgrades. In an exploratory survey, we found that most software users did not think about environmental sustainability when choosing a software package to perform a certain task. To compare environmental impacts of different software packages we measured energy consumption across three types of software: Internet browsers, word processing software, and audio software. We found that different software systems do have different levels of consumption. These results point toward a new research area - sustainable software engineering - that aims to create reliable, long-lasting software systems that meet the needs of users while reducing environmental impacts.

          RE4SuSy Paper | ICSE NIER paper

          Collapse Informatics (2009-present)
          Project Leads: Bill Tomlinson, Don Patterson, Bonnie Nardi

          Research in many fields argues that contemporary global industrial civilization will not persist indefinitely in its current form, and may, like many past human societies, eventually collapse. Arguments in environmental studies, anthropology, and other fields indicate that this transformation could begin within the next half-century. While imminent collapse is far from certain, it is prudent to consider now how to develop sociotechnical systems for use in these scenarios. This research effort explores the notion of collapse informatics - the study, design, and development of sociotechnical systems in the abundant present for use in a future of scarcity.

          CHI 2012 paper | Interactions magazine article | Collapse-O-Matic Blog

          Resource Sharing (2010-present)
          Project Lead: Bill Tomlinson

          One of the most important functions of an organized society is to determine how to allocate limited resources to, and encourage optimal use of resources by, individual members of the society. There are many cases, across many societies, in which resources do not appear to be optimally utilized. One way of using resources more efficiently is to share them. There are many examples of successful resource sharing across different cultures, from reciprocal food sharing to CouchSurfing.com. However, there are many cases where resources could be shared more effectively, but are not. We are examining several different online and offline resource allocation systems to explore which system characteristics most effectively encourage sharing.

          Online System

          Games, Education, and Sustainability (2005-2012)
          Project Leads: Bill Tomlinson, Joel Ross

          Computer games have significant potential as tools for education, in particular in the sustainability domain. Our research group has pursued a variety of projects in this area, including projects about restoration ecology, systems thinking, and environmental causality.

          ACM CIE paper | LUCI tech report | Joel Ross's dissertation

          Patent Game (2008-2010)
          Project Leads: Bill Tomlinson and Andrew Torrance (KU School of Law)

          The Constitution empowers such spending to "provide for...the general Welfare," and further includes the Patent Clause, which authorizes Congress "To promote the Progress of Science and useful Arts, by securing for limited Times to...Inventors the exclusive Right to their...Discoveries." These clauses suggest a crucial role for scientific progress in providing for the general welfare of the U.S. However, the rationale underlying the Patent Clause, that monopoly exclusion rights encourage technological innovation, is based on centuries-old assumptions about human motivation, rather than experimental evidence. We used a participatory simulation to operationalize a legal system and explore how people interact with three different variations of patent protection. Experimental results showed that a non-patent system generated significantly more productivity and social wealth than systems offering patent protection, suggesting that patents may not serve the social ends for which they are designed. By applying experimental evidence to theoretical issues of law, government, and society, participatory simulations offer a mechanism for helping institutions reconcile their societal goals with the particular laws and policies they use to achieve them.

          YJOLT Paper | CSTLR Paper | SIULJ Paper

          Better Carbon (2009-2010)
          Project Lead: Joel Ross

          A popular way for people to understand their environmental impact is by using an online carbon footprint calculator. Although there are a variety of such calculators available, the majority share the same form of user interaction. We analyze how, with this mode of interaction, most calculators focus on the environmental impact of individual actions without drawing attention to the broader impacts of those actions on the surrounding community and world. To address these problems, we present the Better Carbon calculator, which uses collaborative filtering and location- based calculation to provide an individual footprint estimate while simultaneously affecting and improving the estimates for other people in a user's community. This method also allows Better Carbon to be more extendable, as additional forms of impact can be considered without requiring additional user effort. Better Carbon can thus provide quicker and easier footprint estimates, and help the process of calculating a carbon footprint create stronger linkages within the communities of its users.

          Site | ISSST 2010 Paper

          GreenTracker (2008-2010)
          Project Lead: Nadine Amsel

          Green Tracker is a software system currently implemented for Mac OS X that estimates the energy consumption of software in order to help concerned users make informed decisions about the software they use. Ultimately the information gathered from this tool will be used to raise awareness and help make the energy consumption of software an important concern among software developers.

          CHI WIP paper

          Computational Metaphor Identification (2008-2010)
          Project Lead: Eric Baumer

          People often understand abstract or unfamiliar ideas, concepts, or experiences in terms of more familiar, concrete ones. These conceptual framings are often evidenced by systemic patterns of language, such as talking about money as if it were a liquid. Computational metaphor identification is a technique for finding such patterns and suggesting potential metaphors they might indicate. The goal in this work is not to state definitely what metaphors are being used in a given text, but rather to draw these patterns to a user's attention in order to encourage critical thinking and reflection about potential metaphors, as well as creative generation of alternative metaphors.

          Analogy 09 paper

          CMI in Science Education (2008-2010)
          Project Lead: Eric Baumer

          Using computational metaphor identification to promote critical thinking about metaphors and creative generation of alternative metaphors in the context of middle grades science education.

          Project Details | Creativity and Cognition Paper

          MetaViz (2008-2010)
          Project Lead: Eric Baumer

          metaViz is a tool designed to encourage critical thinking about conceptual metaphors among readers of political blogs. Using computational metaphor identification, a technique to identify potential metaphors in written text based on linguist patterns, metaViz displays potential metaphors in a readily accessible, interactive fashion. The visualization is updated regularly, populated with data obtained from 28 different blogs across the political spectrum. metaViz is also designed to be used collaboratively; users can share URLs linking to metaphors they find interesting, as well as post comments on the visualization itself.

          Site | Project Details | CHI Paper | SocialCom Paper

          ResearchWatch (2009-2010)
          Project Lead: Tommy Chheng

          ResearchWatch is a tool for any researcher to find and analyze the distribution of federal NSF grants.

          Site

          Turkopticon (2009-2010)
          Project Lead: Six Silberman

          Allows workers on Mechanical Turk to rate employers. Rails web application with Firefox add-on.

          Site | Press coverage

          Understanding Mechanical Turk (2008-2010)
          Project Lead: Joel Ross

          Amazon.com's Mechanical Turk (http://www.mturk.com) is an online system in which workers are paid small sums of money to work on projects that, while quick and easy for a human, are very difficult for computers to perform correctly and efficiently--for example, identifying and object in an image or categorizing data. Furthermore, workers have little or not context for the work they perform through this highly mediated system. In this project, we explore the question of who are the Mechanical Turkers, looking to identify the kinds of people who work on MTurk and understand the reasons (both intrinsic and extrinsic) for their participation. We are also considering how the ubiquity of monetary rewards may shape the interactions with and potential exploitations within this crowdsourcing system.

          alt.CHI Paper

          Flood Risk (2009-2010)
          Project Lead: Bill Tomlinson

          Many people find it difficult to engage with environmental issues, in part because global change occurs on scales of time and space that are relatively large compared to the usual scope of human decision making. People respond enthusiastically to fast-acting disasters such as fires and earthquakes, but less so to issues that occur more gradually over many years, even when the consequences are far greater. To date, there has been little research on how to connect long-term global environmental change to human scales of time and space in a systematic way, thereby enabling behavioral change. Our efforts will focus on the science and public perception of sea level rise.

          UCI Environment Institute grant

          Twitter Following (2009-2010)
          Project Lead: Eric Baumer

          A study exploring the practices of, and motivations for, following on Twitter.

          Project Details | CHI Microblogging Workshop Paper


          Web Browser Environmental Sustainability Toolkit (2008-2009)
          Project Lead: Andrew Zaldivar

          The Web Browser Environmental Sustainability Toolkit (WebBEST) is a system that uses the capability of browsers to integrate popular online services (e.g., Amazon.com, Google Maps, Albertsons, Cars.com) with existing environmental databases (e.g., Electronic Product Environmental Assessment Tool, Travel Matters, Skin Deep Cosmetic Safety Database, Fuel Economy). The system subtly integrates relevant environmental information from these databases into the online services. WebBEST also features a web presence through which people can share ideas, and a framework for other developers to contribute new plugins connecting services and databases.

          Site

          EcoRaft (2005-2008)
          Project Lead: Bill Tomlinson

          Learning about system sciences such as ecology is challenging due to the large amount of time and space over which these processes occur. To address these difficulties, we worked with ecologists and educators to create the EcoRaft Project, a participatory simulation that helps children learn about ecology by collaborating to restore a virtual rain forest. The project integrates monitors with "virtual islands" of animated plants and animals on them, and mobile tabletPCs that allow participants to move species between islands. Through a combination of lifelike creatures, colorful graphics and a novel interface, EcoRaft helps enable learning about complex ecological processes.

          Site | Video | E-Learning Journal Paper | ESA Paper | CHI Paper | AAMAS Paper | CSCL Paper | NSF CAREER award

          Trackulous (2007-2008)
          Project Lead: Bill Tomlinson

          Corporations and governments have powerful tools for tracking information to accomplish their goals. Regular people, though, lack resources to work effectively with their own information. Trackulous provides a suite of web-based tools that help people track, analyze and share their own personal information - anything from their weight, to their gas mileage, to their children's health - in ways that they themselves find useful, rather than in ways that only benefit corporations. By enabling people to work with the vast bodies of information that are important to them, Trackulous can help people improve themselves and live well-informed lives.

          Site | MIT Press book, Ch. 7

          Blog Readers (2007-2008)
          Project Lead: Eric Baumer

          Despite the growing research on bloggers, little work has focused on blog readers. This project explores blogging from the reader's perspective.

          CHI Paper | ICWSM Poster

          GreenScanner (2005-2007)
          Project Lead: Bill Tomlinson

          GreenScanner is a system that helps people engage in environmentally preferable purchasing during their everyday consumer transactions. This system includes an online database of community-generated environmental impact reviews, and a mobile phone application to enable consumers to access these reviews at a point of purchase. The vision for this system is to provide a forum for exchange of environmental information in a format that is reliable and exceedingly easy to access. By doing so, the site can help people around the world make more informed decisions, and incentivize companies to engage in more environmentally sound practices.

          Site | HICSS Paper | MSR equipment grant

          CalFireHelp (2007)
          Project Lead: Bill Tomlinson

          Professor Tomlinson's work on CalFireHelp further demonstrates the importance of real-world impact in his work. He was teaching an undergraduate class on the social impact of information technology when the 2007 California wildfires struck. He quickly worked with his students to produce a website within days that not only provided information about the state of the disaster, but helped people to find accommodation and other services when they were displaced from their homes. This system was up and running within days of the onset of the emergency.

          Press Release
          Informatics Department | Bren School of ICS | Calit2 | UC Irvine
          http://www.ics.uci.edu/~wmt/courses.html Bill Tomlinson: Classes
          Classes



          ICS 5: Environmental Issues in Information Technology, Winter '11

          Informatics 161: Social Analysis of Computerization, Fall '09

          Informatics 295: Environmental Issues in Information Technology, Fall '09

          ICS 5: Environmental Issues in Information Technology, Spring '09

          Informatics 161: Social Analysis of Computerization, Fall '08

          University Studies 12: Computer Games as Art, Culture and Technology, Fall '08 - Spring '09

          Informatics 235: Advanced User Interface Architecture, Spring '08

          Informatics 161: Social Analysis of Computerization, Fall '07

          University Studies 12: Computer Games as Art, Culture and Technology, Fall '07 - Spring '08

          Informatics 161: Social Analysis of Computerization, Spring '07

          University Studies 12: Computer Games as Art, Culture and Technology, Fall '06 - Spring '07

          ICS 131: Social Analysis of Computerization, Spring '06

          Drama 235: Script Analysis, Winter '06

          Informatics/Arts/Engineering 277: Programming for Interactivity, Fall '05

          ICS 187: Computer Game Development, Spring '05

          ICS 131: Social Analysis of Computerization, Winter '05

          Performance Theory: Emotion, Fall '04

          Biomorphic Computing, Winter '04

          Autonomous Characters, Fall '03






          Return to Main Site
          http://www.ics.uci.edu/~wmt/bio.html Bill Tomlinson: Biography
          Biography



          Bill Tomlinson is a Professor of Informatics at the University of California, Irvine, and a researcher in the California Institute for Telecommunications and Information Technology. He studies the fields of environmental informatics, human-computer interaction, multi-agent systems and computer-supported learning. His book Greening through IT (MIT Press, 2010) examines the ways in which information technology can help people think and act on the broad scales of time, space, and complexity necessary for us to address the world's current environmental issues. In addition, he has authored dozens of papers across a range of journals and conferences in computing, the learning sciences, and the law. His work has been reviewed by the Wall Street Journal, the Washington Post, the LA Times, Wired.com, Scientific American Frontiers, CNN, and the BBC. In 2007, he received an NSF CAREER award, and in 2008 he was selected as a Sloan Research Fellow. In 2014, he was named to the EPA's Board of Scientific Counselors, joining the Sustainable and Healthy Communities subcommittee. He holds an A.B. in Biology from Harvard College, an M.F.A. in Experimental Animation from CalArts, and S.M. and Ph.D. degrees from the MIT Media Lab.

          Professor Tomlinson's Erdös Number is 4 (Erdös, Specker, Lieberherr, Dourish, Tomlinson), and his Bacon Number is 3 (Bacon, Kornelis, Underhill, Tomlinson), thus making his Erdös-Bacon Number a 7. (Allowing for television, the Bacon Number drops to a 2 (Bacon, Alda, Tomlinson), and the Erdös-Bacon Number drops to a 6.) His animated film, Shaft of Light, screened at the 1997 Sundance Film Festival and dozens of other film festivals around the world. His 2009 paper with Andrew Torrance on patent systems has been cited in amicus briefs and in a writ filed with the United States Supreme Court. He has also had the honor to share a lab with one of the world's most educated parrots (Alex), and to exchange nose-blows with one of the world's most educated sea lions (Rio, video here).





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          http://frost.ics.uci.edu/cs113/ CS 113 / Informatics 125 Syllabus

          CS 113 / Informatics 125: Computer Game Development

          Fall, 2015
          Tu Th 7:00pm - 8:20pm, SSPA 1100, Course codes: 34030 (CS) and 37060 (Inf)

          Instructor: Dan Frost frost@ics.uci.edu   Office: Donald Bren Hall 5058 (949) 824-1588 (Why UCI? 1 Luv!) Office hours: Tuesdays, 2:00pm - 3:00pm, and by appointment, in DBH 5058
          Teaching assistant: Mehdi Rahimzadeh   m.rahimzadeh at uci.edu

          Goal: To learn about the science, art, craft, and industry of computer games.

           


          Useful links:

          • Team Information Form
          • How To Make A Game
          • Game Loop Pattern
          • Notes from class on how the final game project is graded
          • 3D modeling and rigging.
          • TextureDemo.zip
          • A form of AI
          • Design Document drafts and pitches -- schedule and assignments
          • Final presentation schedule and information
          • Wonders of GPUs
          • Game Megatrends
          • Need space on the web? Consider ICS's web space and UCI Webfiles


          Ground rules

          • Your grade will be based mostly on your team's accomplishments, which are primarily the design document, the delivered game, and the final presentation. No quizzes, tests, or final exam. Usually all team members get the same grade for the team game.
          • If you add the course after Sept. 29, or if you do not fill out and turn in the Team Information Form, or if you drop from your team and do not find another team, then you will not be able to work on a team and the best possible grade you can get for the course will be a B.

          Important Dates

          Attendance during the lecture period is required on certain days, often those with student or guest speakers:
          • Thursday, Oct. 1: team announcements and first team meetings
          • Team Pitches: Tuesday, Oct. 27, Thursday, Oct. 29, Tuesday, Nov. 3
          • Tuesday, Nov. 10: guest speaker Graham Harwood. Links: http://v2.nl/archive/people/graham-harwood, vimeo.com/32030825
          • Thursday, Nov. 12: guest speaker Thomas Lee, owner and founder of Ying Ying Apps
          • Tuesday, Dec. 8, 7:00pm - 9:00pm in DBH 4011 and DBH 5011: Open House during the course's final exam period time

          Assignments and projects

          • First individual assignment, due Tuesday, 9/29/2015 7:00pm: completed Team Information Form.
          • Second individual assignment, due Sunday, 10/25/2015: Comment on another team's draft design.
          • Third individual assignment: six weekly status reports, due Mondays of sixth through Finals weeks.

          • The major part of the course is a project, to be completed by a team. The project will be to design and implement substantial portions of a computer game. The project consists of the following:
            1. A statement of team composition. Name the students on the team, the grade option of each student, the major of each student, the team name, brief descriptions of two ideas you are considering for the team's game, and the time and place your team will meet for three hours every week. If you plan to work with students from outside of the class, please mention that as well. Send by email to m.rahimzadeh at uci.edu, Friday, 10/2/2015, by noon. Put "CS 113/Inf 125" at the start of the email's subject line.
            2. A draft design document for the game. This should be between 20 and 40 pages in length. It should be on the World Wide Web using HTML, PDF, or Google Docs. The format must permit scrolling through the entire document; therefore a wiki is not acceptable. Due Wednesday, 10/21/2015 (by the end of the day). Email the URL of your draft to Prof. Frost. Structure your Design Doc based on our recommended Design Doc structure.
            3. A presentation, or "pitch," of the team's game idea in the fifth or sixth week of the class Tuesday, 10/27/2015, or Thursday, 10/29/2015, or Tuesday, 11/3/2015 (subject to change).
            4. A final design document for the game. This should be about 30 to 40 pages in length. In addition to describing the game, technologies, art, and game play in detail, it should describe the specific responsibilities of each team member. Due Wednesday, 11/4/2015 (for teams pitching in week 5) or due Friday, 11/6/2015 (for teams pitching in week 6). This is also on the web, and the URL should be emailed to Prof. Frost.
            5. A presentation of the game development status in eighth week. (Cancelled)
            6. A presentation of the game at the end of week 10 or in Finals Week. To be scheduled; each team will have 30 minutes. Your team's grade will be based on the game as of this presentation.
            7. A two to three minute video of the game and the team, ideally posted on YouTube.
            8. A completion document, which includes an optional users manual, revised design document, individual statements about the course of the project (optional), and a CD with source code, executables, art and music files, a link to the game video, and at least two screen shots. Due at final presentation.
            9. Participation in the CS 113/Informatics 125 Open House, held during the course's Final Exam period, Tuesday, 12/8/2015 7:00pm-9:00pm.

          Scoring and grading

          Grading will be on a straight scale, based on total points. (A+ 97.0 or higher; A 93.0 to 96.9; A- 90.0 to 92.9; B+ 87.0 to 89.9; B 83.0 to 86.9; B- 80.0 to 82.9; and so on.)

          Each game project will receive a letter grade, e.g. A, A-, B+, B (these are by far the most common grades). The game project is worth at most 75 points, as follows: A+, 73 to 75; A, 71; A-, 68; B+, 65; B, 62; and so on.

          The game design document (including pitch and 8th week status reports) is worth up to 10 points; most teams will get 9 (an A). Students who miss part or all of their team's pitch will receive less credit.

          Individual accomplishments are worth up to 15 points:

          5 - attendance (roll will be taken on six or more dates)
          2 - comment on another team's draft design document
          2 - attendance at and energy demonstrated in eighth week presentation
          6 - six status reports


          Special Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to ensure that such accommodations are implementationed in a timely fashion.


          At the Computer Game Design Conference on May 6, 1998, there was a panel on design docs. Moderator Alex Dunne referenced a real design document which was submitted by panelist John Jack, a producer at Monolith. This design doc was from the company's recent computer game, "Claw". Claw Design Document.

           
           
           
           
           
          http://frost.ics.uci.edu/ics169/ ICS 169B — Capstone Game Project II

          ICS 169B — Capstone Game Project II

          Winter, 2016

          Syllabus

          In this course, and its first part ICS 169A, students work in teams to design and develop a computer game.

          The course has no textbook, tests, or final exam. The final exam period, Tuesday, March 15, from 4:00pm to 6:00pm will be used for final presentations.

          ICS 169A and 169B are graded together; your grade for ICS 169B will "count" for both courses. The grade for ICS 169B will be determined as follows:

          80%: Game quality and individual contribution to the game, where "quality" includes many but probably not all of
        • Good gameplay - well balanced, fun, challenging but not too hard, engaging, replayable, immersive, addictive
        • Art and art design, including animation - polished, integrated with theme and gameplay
        • Music and sound design - polished, integrated with theme and gameplay
        • (Note: credit is given for art and sound assets created by team members, and for the work required to wrangle external artists and their creations)
        • Completeness - such as menus, ending screens, transitions between levels, save/restore, help screens and tutorial levels, cut scenes, GUI elements
        • Technical challenges - such as complex AI, networking
        • Extensiveness - multiple levels/quests/puzzles, many characters, weapons, attacks, etc.
        • Narrative - background story, integration with game play, good writing and proofreading
        • 20%: Course process, including
        • Attendance
        • Completing surveys
        • Timely completion of weekly status reports
        • You have access to the COGS lab (ICS-2 room 170). For now, use the code that was sent via an email. Make sure to read and follow the COGS lab rules.

          Prof. Frost's office hours: tba

          Prof. Wang's office hours: tba

          Schedule

          Tentative schedule for Winter quarter:

          WeekTuesdayThursday
          11/5
          Introductions and announcements
          1/7
          Profs meet with teams
          21/12
          Meetings with mentors
          1/14
          Team meetings, no lecture
          31/19
          Visit from UCSC's game program*
          1/22
          Guest speaker: Michael Cullen, Audio Engineer**
          41/26
          Bus trip to Blizzard
          1/28
          Team meetings, no lecture
          52/2
          Play testing (in DBH 5011)
          2/4
          Team meetings, no lecture
          62/9
          Professors meet with teams
          2/11
          Meetings with mentors
          72/16
          Team meetings
          2/18
          Guest speakers from Blind Squirrel Games: Dominic Camargo, an Engineer, and Chris Kagel, Lead Designer
          82/23
          Meetings with mentors
          2/25
          Play testing
          93/1
           
          3/3
          Blizzard Tour
          10 3/8
          Dress Rehearsals
          Wed., 3/9
          Game Day
          3/10
           
          F3/15
          Presentations, 4pm-6pm in DBH 5011
           

          *Visit from UCSC's game program: UC Santa Cruz has several options for graduate study in games. After a brief outline of those options from Prof. Jim Whitehead, Michael John, the Program Director for the Games and Playable Media MS program, will give a talk. Here's the abstract:

          Dynamic tuning, game analytics, and purple dragons, ca. 1999

          In 1999, the teams of Insomniac Games and Universal Interactive Studios had successfully launched Spyro the Dragon, with the help of Sony, and we realized we had found success at our goal of reaching a very large and diverse audience. However, this came at a cost - tuning the difficulty of the game was extremely difficult, and resulted in a game that was not correct for any particular cohort of the audience.

          As we developed Spyro 2, we added a system which would adjust the difficulty of the play experience on-the-fly according to the player's historical performance. However as we attempted to seed the system with performance metrics, we realized we needed more accuracy than we could get from simple observation. So we implemented a primitive data analytics system into the pre-release builds of Spyro 2 and gathered play performance data from our playtesters.

          Both of these systems were created out of pure necessity, but ended up being well ahead of their time. I'll detail our thought processes in how they were created, show some of the data we collected and how it was used, and talk about how adaptive difficulty is at work in a great many of the games you play, whether you know it or not.

          **Michael Cullen has generously shared with us his slides and two C# scripts: Basics of Sound Design for Video Games, AudioPlayer.cs, AU_PlayOneShot.cs.
           
           
           
            http://www.ics.uci.edu/~rickl/rickl-summary.html Richard H. Lathrop summary

          Richard H. Lathrop, Ph.D., is a professor in the Computer Science Department (ICS) of the Donald Bren School of Information and Computer Sciences (ICS) at the University of California, Irvine (UCI). He received his Ph.D. in 1990 from the Massachusetts Institute of Technology (MIT) in Artificial Intelligence (AI), and afterwards was first a post-doc and then a research scientist at the MIT AI Lab. He also holds graduate degrees in electrical engineering and in computer science from MIT, and an undergraduate degree in mathematics from Reed College.


          Dr. Lathrop's research for the last 15 years has involved applying intelligent systems and advanced computation to problems in molecular biology. He has broad interests in computational molecular biology, including current research interests in protein structure prediction from sequence, protein-DNA interactions and genetic regulation, rational drug design and discovery, and other molecular structure/function relationships. He also has broad interests in intelligent systems, especially machine learning, constraint systems, and optimal heuristic search. Recently he has begun to explore DNA self-assembly, with applications to nanotechnology and biotechnology.


          Nick Steffen, then Dr. Lathrop's graduate student, and Miriam Raphael and Sophia Deeds-Rubin, then Dr. Lathrop's undergraduate students, shared in the 1998 AAAI/IAAI Innovative Application Award and the 1999 cover of AI Magazine. Nick Steffen, then Dr. Lathrop's graduate student, and Anton Sazhin and Ye Sun, then Dr. Lathrop's graduate students co-advised with Dr. Irani, shared in the Best Paper Award at the 2001 Genome Informatics Conference. Mac Casale, then Dr. Lathrop's graduate student co-advised with Dr. Eppstein, won the Best Student Paper Presentation award at the 1998 ISMB Conference.


          Dr. Lathrop was a co-founding scientist of CODA Genomics, Inc., (CODA renamed itself to Verdezyne Inc. in 2008) and of Arris Pharmaceutical Corp. (Arris merged with Sequana to form AxyS Pharmaceuticals in Jan., 1998, which was acquired by Celera Therapeutics in Nov., 2001). He was on the Scientific Advisory Boards of CombiChem, Inc. (now DuPont Pharmaceuticals Research Labs) and of GeneFormatics, Inc. (now defunct). He is on the Editorial Boards of J. Molecular and Cellular Proteomics and IEEE Intelligent Systems. He has published over 75 scientific and technical papers. His research has appeared on the covers of Communications of the ACM (1987), the Journal of Molecular Biology (1996), and AI Magazine (1999). He is a co-inventor of US Patents No. 7,262,031 (``Method for Producing a Synthetic Gene or Other DNA Sequence'') and 5,526,281 ("Machine Learning Approach to Modeling Biological Activity for Molecular Design and to Modeling Other Characteristics"). His Ph.D. thesis ("Efficient Methods for Massively Parallel Symbolic Induction: Algorithms and Implementation") received MIT's EECS George M. Sprowl departmental award and was nominated by MIT for the ACM Distinguished Doctoral Dissertation award (1990). He was elected to Phi Beta Kappa (the national academic honor society) and to Sigma Xi (the national scientific research society). His biography is listed in "Who's Who in the World 2002." He has been licensed as a nuclear reactor operator by the US Nuclear Regulatory Commission (1977). His GRE scores are in the 99th percentiles of all three categories simultaneously: Verbal, Quantitative, and Analytic. He has received Best Paper Awards at the ACM/IEEE Design Automation Conference (1987) and the International Conference on Genome Informatics (2001), a Graduate Fellowship (1980) and a CAREER grant award (1996) from the National Science Foundation (NSF), UCI/ICS's Departmental Outstanding Faculty Award (1997), UCI's Excellence in Teaching Award for undergraduate teaching (1998), the ICS Dean's Award for Undergraduate Teaching (2009), an Innovative Application Award at the AAAI/IAAI Conference (1998), an Innovation Award from UCI (2005), and Certificates of Appreciation from MathCounts, the US National Institutes of Health, and the International Society for Computational Biology (of which he was the founding Treasurer and a member of the founding Board of Directors).



          Dr. Lathrop is affiliated with the ICS Ph.D. concentrations in Informatics in Biology and Medicine and Artificial Intelligence. He is the Director of the undergraduate ICS Honors Program. His current course offerings include ICS-H197, "Honors Seminar". Dr. Lathrop adheres scrupulously to the UCI Senate Academic Honesty Policies and the ICS Department's Cheating Policy. Any student who engages in cheating, plagiarism, or collusion in dishonest activities, will receive an academic evaluation of "F" for the entire course with a letter of explanation to the student's permanent file. The ICS Student Affairs Office will be involved at every step of the process. Dr. Lathrop seeks to create a level playing field for all students.


          Return to Richard H. Lathrop's home page. http://www.ics.uci.edu/~rickl/rickl-students.html Richard H. Lathrop students

          Dr. Richard H. Lathrop --- Students



          Dr. Nick Steffen, then Dr. Lathrop's graduate student, and Anton Sazhin and Ye Sun, then Dr. Lathrop's graduate students co-advised with Dr. Irani, shared in the Genome Informatics Conference Best Paper Award.


          Dr. Nick Steffen, then Dr. Lathrop's graduate student, and Sophia Deeds-Rubin and Miriam Raphael, then Dr. Lathrop's undergraduate students, shared in the AAAI/IAAI Innovative Application Award and the cover article of AI Magazine.


          Dr. Mac Casale, then Dr. Lathrop's graduate student co-advised with Dr. Eppstein, won the Best Student Presentation award at the Intelligent Systems for Molecular Biology Conference.


          Dr. Sam Danziger, then Dr. Lathrop's graduate student, developed the Most Informative Positive (MIP) active machine learning system that performed at a level statistically indistinguishable from a human expert cancer biologist at the task of predicting novel p53 cancer mutant genetic reactivation regions.


          Dr. Chris Wassman, then Dr. Lathrop's graduate student, discovered the L1/S3 binding pocket for p53 cancer mutant reactivation by small drug-like molecules.


          Dr. Faezeh Salehi, then Dr. Lathrop's graduate student, developed a high-throughput method for p53 cancer mutant reactivation by genetic reactivation..


          Max Ho, David Inglish, Dong Le, Thuan (Tim) Quoc Truong, Alex Van Buskirk, and Sean King, then Dr. Lathrop's undergraduate students, wrote the teaching software used in Dr. Lathrop's course CS-171, "Introduction to Artificial Intelligence." Dr. Lathrop has ongoing independent study opportunities for other undergraduates interested in developing innovative software for teaching AI. Prerequisite: a grade of "A-" or better in CS-171.



          Return to Richard H. Lathrop's home page. http://www.ics.uci.edu/~rickl/rickl-other.html Richard H. Lathrop other

          Dr. Richard H. Lathrop --- Other Pursuits

          THIS PAGE IS UNDER CONSTRUCTION.


          Dr. Lathrop has hitch-hiked through all 50 states of the USA and once hitch-hiked from Sarasota, Florida, to Anchorage, Alaska, where he lived for over two years. He has visited most major national parks in the USA. He has been around the world twice and set foot on all seven continents, including Canada, Mexico, French Polynesia, Raratonga, Fiji, New Zealand, Australia, Indonesia, Singapore, Malaysia, Thailand, Myanamar (Burma), Hungary, Austria, France, Germany, Belgium, Spain, Morocco, India, Sri Lanka, Norway, Italy, England, Denmark, Sweden, Japan, Portugal, Andorra, Monaco, Colombia, Venezuela, Brazil, Argentina, Uruguay, Netherland Antilles, Switzerland, Greece, Holland, the British Antarctic Territories, Chile, Madagascar, S. Korea, and Vietnam.


          Dr. Lathrop has seen the midnight sun and the aurora borealis; ridden an elephant into a tiger forest in India; crossed the Amazon jungle by truck sleeping in a tent; watched a molten red lava flow from six feet away; ridden the Marakesh Express in Morocco and the Orient Express in Vienna; boated on the Irrawady River to Mandalay; danced in the streets of Rio de Janeiro during Carnaval, and of New Orleans during Mardi Gras; seen two total solar eclipses; watched flying fish across the lagoon at sunset from the deck of a Tahitian freighter; seen glaciers as large as Connecticut and been at temperatures below -50 degrees (F or C); been across the Bridge on the River Kwai; caught and eaten a piranha; seen a snake charmed; flown in to Angel Falls, the world's tallest waterfall; climbed Ayer's Rock, the world's largest monolith; been awestruck by the Taj Mahal, the world's most beautiful building; driven more than 20 times coast-to-coast across the USA by car; gone white-water rafting down the Upper and Lower Grand Canyon, and the Wild and Scenic part of the Rogue River; been sky-diving (once!); swum in the Antarctic Ocean on New Year's Day, 2001; voted in every eligible national election; heard the Grateful Dead and Santana in the same live concert; crossed the Arctic Circle, the Tropic of Cancer, the Equator, the Tropic of Capricorn, the Prime Meridian, and the International Date Line; and done several other things that are not on his C.V.


          Dr. Lathrop is a certified SCUBA diver; a blackbelt (sho-dan) in Shotokan karate; a former Civil Air Patrol Earhart cadet (Cadet officer rank of Captain), who has been Cadet Squadron Commander (Chico Squadron 76, California Wing), drill team commander in a parade, and search base radio operator and search-and-rescue mobile ground team for missing civilian airplanes; the former Class C Chess Champion of Alaska; and the former President of the Reed College Fencing Club.


          In addition to his scientific and technical employment, Dr. Lathrop also has supported himself by doing road construction; installing fiberglass insulation; short order fry cook; dishwasher; bus boy; ditch digger; plumber's helper; carpenter's helper; seismic crew member; and day laborer. His most memorable job was dishwasher on the Alaska Railroad, working the Anchorage-Fairbanks line. Twelve hours of heartbreakingly beautiful Alaskan wilderness where not even roads go, sleep overnight in the railroad depot in Fairbanks, and twelve more hours of wilderness back to Anchorage, all viewed from a window above the dishwashing sink. The train passed close to Denali, the highest mountain on the North American continent; and sometimes when Denali was shining cloudless and snowcovered in the sky and reflected in full majesty by a mirror-still wilderness lake, the engineers would just stop the train and let everyone drink in the beauty.



          Return to Richard H. Lathrop's home page. http://www.ics.uci.edu/~rickl/courses/cs-171/2016-wq-cs171/CS-171-WQ-2016.htm CS-171 Winter Quarter 2016

          CompSci (CS) 171 — Introduction to Artificial Intelligence — Winter 2016


          Jump to section:

                     Current Announcements

                     Important Dates

                      Place, Time, Instructors

                      Goal

                      Class Setup

                      Textbook

                      Grading

                     Study Habits

                      Syllabus

                                  Week 1

                                  Week 2

                                  Week 3

                                  Week 4

                                  Week 5

                                  Week 6

                                  Week 7

                                  Week 8

                                  Week 9

                                  Week 10

                                  Final Project Deadline

                                  Final Exam

                      Project

                      Study Guides --- Previous CS-171 Quizzes, Mid-term, and Final exams

                      Online Resources

                      Academic Honesty


          Current Announcements:

           

          v  16Feb2016: The Project deadlines have been extended to give you more time to code. (1) The Forward Checking deadline has been extended to next Sunday, 21 Feb, and will be regraded. Please fix any problems with your code and resubmit if needed. (2) The AC-3/ACP/MAC part has been made optional for extra credit. (3) The MRV/DH part has been extended to Sunday, 6 March, and combined with the LCV deadline.

          v  11Feb2016: The Mid-term Exam key has been posted below and is available here.

          v  11Feb2016: Thanks to the good efforts of Junkyu Lee, the TA, a revised and more detailed the Sudoku Project Assignments document is available here.

          v  9Feb2016: The Midterm exam will cover chapters 1-6 in your textbook.

          v  9Feb2016: Marvin Minsky, one of the early pioneers of AI, has passed away recently (e.g., see here and here).

          v  6Feb2016: Thanks to the good efforts of Minhaeng Lee, the Reader, we have updated the Quiz #2 key to include the approximate percentage and number of students who scored Perfect, Partial, and Zero on each question (available here and in the Study Guides section below).

          v  3Feb2016: Thanks to the good efforts of Minhaeng Lee, the Reader, we have released a revised Testing Shell v.0.2 (available here, and also in the Project section below). This testing shell should accommodate your Sudoku solver.

                      Minhaeng 's fervent hope is that you will discover all of the problems with the testing shell quickly, and report them to him promptly, so that he may fix them and improve your experience.   Please send feedback email to minhaenl@uci.edu, and CC me when you do so.

                      Because we did not provide the promised testing shell for your code prior to the due date, and since many of your programs had problems, the CS-171 Teaching Staff after discussion has decided to treat this assignment very gently and charitably.  Provided that you turned in something that was binary, source, and documentation, you will not be penalized if your code did not work in our scripts, because it is partially our fault since you were not provided with the promised testing shell.

                      ****  You are responsible for fixing your code so that on the *next* assignment it runs correctly in our scripts (= it runs correctly in the current released Testing Shell).  Otherwise, you will lose points on the *next* assignment.

          v  3Feb2016: The answer key to Quiz #2 has been posted below and also is available here.

          v  28Jan2016: Thanks to the good efforts of Minhaeng Lee, the Reader, a test shell to validate your programs on openlab is available here. This shell is only for testing whether student's program is runnable on the openlab environment. Note that, currently, the testing shell doesn't contain tests for whether or not the output is correct. We will update later.

          v  28Jan2016: Thanks to the good efforts of Junkyu Lee, the TA, a detailed description of exactly what to turn in for your Sudoku Project Assignments is available here.  It will be updated as we go along. Please note that we are still discussing what to do about the Generator portion of the project, which was canceled (see below) because the provided Java shell included a generator. However, apparently some students have written their own generator anyway. Most likely we will give extra credit for extra work; but the matter is still under discussion. In the meantime, it is OK if your program accepts the GEN and BT option tokens, even if they do not appear in the current description.

          v  28Jan2016: A former CS-171 student has shared some news that you may find of interest.

          "... a few hours ago, Google announced that one of their DeepMind AI's, AlphaGo, was able to successfully defeat a Go master in a game without any handicap."

          Related links, which you may find of interest (you must access the links below from within your UCI login or Nature may try to charge you for them):

          Ø  Nature | Editorial:  Digital intuition:  A computer program that can outplay humans in the abstract game of Go will redefine our relationship with machines.

          Ø  Nature | News:  Google AI algorithm masters ancient game of Go:  Deep-learning software defeats human professional for first time.

          Ø  Nature | Article:  Mastering the game of Go with deep neural networks and tree search

          v  Every project team, including students who code in Java, must turn in the Backtracking Search assignment by Sunday, 31 Jan. Erroneously, an announcement below (now canceled) previously said that “For students who code in Java, *nothing* will be due on Sun., 31 Jan.” We later realized that even students who code in Java must submit code and executables that function correctly under our project scripts.

          v  In order to make the Project more fair to students who code in Python or C++, the CS-171 Teaching Staff has decided to offer Extra Credit points to any student who writes their own project shell without using or copying code from the Java shell provided (it is OK to look at it for ideas, but not to copy it directly). In order to be fair, this offer is available to all students, including those who prefer to code in Java. The exact amount of Extra Credit will be decided and posted to EEE GradeBook shortly.

          v  EEE DropBoxes for your project assignments are now available. Please follow the file format and naming conventions stated below.

          v  The Syllabus part of the class website has been reorganized to be more user-friendly and easier to navigate. For each week, material has been grouped and ordered as: Lecture Slides, Discussion Slides, Project Deadlines, Optional Homework, Optional Reading, and Optional Cultural Interest.

          v  The quiz #1 key has been posted below and is available here.

          v  A description of what to turn in for your first assignment is available here. It currently covers Backtracking Search (due Sun., 31 Jan., 11:59pm), and will be expanded as we go along.

          v  Sun., 24 Jan., 11:59pm: DEADLINE CANCELLED --- SEE CLASS EMAIL.  *NOTHING* IS DUE THIS SUN., 24 JAN.  Project Problem Generator Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          v  I have revised the class website to cancel the Sun., Jan. 24, deadline.  The Java coding shell that was provided has rendered the generator unnecessary, even for students who prefer C++ or Python.

                      I am and remain embarrassed that there is a Java shell but no C++ nor Python shell (which are expected to be written this quarter by former CS-171 students who scored A- or better).

                      Nevertheless, upon discussion, the CS-171 Teaching Staff (the TA, Reader, and I) became worried about the possibly excessive workload if we made you code *everything* from scratch with no guidance whatsoever.

                      In the end, our worries about a possibly excessive workload outweighed our concerns that releasing a Java-only coding shell would favor students who preferred Java.  It became yet another engineering trade-off, and concerns about excessive workload became more important.

                      For students who prefer C++ or Python, please recognize that your task has been greatly simplified by the release of the Java shell. Even if you do not prefer Java, surely you can read it, and porting/translating existing code is always easier than writing it from scratch.

                      ****  For students who code in C++ or Python, I apologize that your ported/translated backtracking search working code still will be due on  Sun., 31 Jan., 11:59pm.  The reason is so that you do not fall behind in your coding project.  Your submitted code should be essentially a port/translation of the provided Java backtracking search shell.  I apologize that I have only a Java shell now; but it is what I have do now, and porting/translating it will move you greatly forward relative to the workload of coding it from scratch.

                      For students who code in Java, *nothing* will be due on Sun., 31 Jan., 11:59pm, because the shell provides it all.  Please do not rest on your laurels.  Instead, please use this time to get further ahead in your project.

                      All remaining Project deadlines will persist unchanged.  The TA and the Reader (Junkyu Lee and Minhaeng Lee) are in charge of the Project, and shortly will release relative weights of the several Project components.

          v  The CS-171 Teaching Staff discussed the Project and decided to release a Java coding shell for Sudoku. I do not yet have C++ or Python shells (they will be written this quarter), and it has never been tested by “live” use in a CS-171 class. In spite of these limitations, we thought it might be helpful to you as an example.

          v  The CS-171 Teaching Staff discussed Project grading and decided to temper justice with mercy. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above. (Previously, you lost 10% for every day or fraction thereof that it was late.)

          v  The Google form (http://goo.gl/forms/YLixJ7ep5j)  REPLACES the EEE DropBox that was previously mentioned.  Please deposit your team information in the Google form AND NOT in the EEE DropBox.

          v  Merged with Sudoku Project Assignments (above). A Specification for your Monster Sudoku problem Reader and Generator is available here.

          v  Please submit your team information to the following Google form.

          http://goo.gl/forms/YLixJ7ep5j

          1) As announced, the submission is due by upcoming Friday, Jan 15th, 23:59 PM.

          2) It is okay to submit your team information ONCE.

          NO SPACES, NOR ANY OTHER UNIX SPECIAL CHARACTER, in yourTeamName. They break our scripts and waste time and effort. You may lose points if yourTeamName breaks our scripts. You are guaranteed to be safe if yourTeamName contains no spaces nor any other Unix special character. Letters, numbers, hyphens, and underscores are safe.

          v  A slideshow about the Monster Sudoku coding project is available here.  More information will be posted shortly.

          v  There is an EEE CS-171 MessageBoard forum "Seeking CS-171 Coding Project Partner" intended for use by students who seek a project partner.

          v  There is an EEE CS-171 MessageBoard forum “Class Material Discussion” intended for use by students who wish to discuss the class material.

          v  Current announcements will appear here, at top-level, for quick and easy inspection.

           

           


          Important Dates:

           

          ·        Fri., 15 Jan., 11:59pm: Project Team Formation Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Tue., 19 Jan.: Quiz #1.

          ·        Sun., 24 Jan., 11:59pm: DEADLINE CANCELLED --- SEE CLASS EMAIL.  *NOTHING* IS DUE THIS SUN., 24 JAN.  Project Problem Generator Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Sun., 31 Jan., 11:59pm: Project Backtracking Search Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Tue., 2 Feb.: Quiz #2.

          ·        Tue., 9 Feb.: Catch-up, Review for Mid-term Exam.

          ·        Thu., 11 Feb., Mid-term Exam.

          ·        REVISED: Sun., 21 Feb., 11:59pm: Project Forward Checking & Bookkeeping Deadline. Extended deadline. Fix your code, and resubmit clean working code. Your resubmitted clean assignment will be regraded anew.  Sun., 14 Feb., 11:59pm: You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        REVISED: optional for extra credit under “advanced techniques.” Sun., 21 Feb., 11:59pm: Project Arc Consistency & Bookkeeping Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Tue., 23 Feb.: Quiz #3.

          ·        REVISED: Merged with the LCV deadline Sun, 6 Mar. Deadline Sun., 28 Feb., 11:59pm: Project MRV & DH Heuristic Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        REVISED: Merged in MRV & DH. Sun., 6 Mar.., 11:59pm: Project MRV, DH, & LCV Heuristic Deadline. You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Tue., 8 Mar.: Quiz #4.

          ·        Thu., 10 Mar.: Catch-up, Review for Final Exam.

          ·        Sun., 13 Mar., 11:59pm: Final Project Deadline. You will lose 10% for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

          ·        Fri., 18 Mar., 10:30am-12:30pm: Final Exam.

           

           


          Place, Time, Instructors:

           

          Lecture:

          Place: RH 104 (Building 400 on the UCI campus map)
          Time: Tuesday/Thursday, 12:30- 1:50pm

          Discussion sections:

          Dis 1: Friday, 9:00-9:50pm in ICS 174 (Building 302 on the UCI campus map)

          Dis 2: Friday, 10:00-10:50pm in ICS 174 (same building as above)

           

          Instructor:

          Richard Lathrop
          Office hours: Tuesday 2:00-3:00pm, or anytime by appointment, in DBH-4224.

          Email:  rickl@uci.edu

          (If you send email, please put “CS-171” somewhere in the Subject line.)

           

          TA:

          Junkyu Lee

          Office hours: Friday, 11am-noon, or anytime by appointment, in DBH-4099.

          Email: mailto:junkyul@uci.edu

          (If you send email, please put “CS-171” somewhere in the Subject line.)

           

          Reader:

          Minhaeng Lee

          Office hours: Thursday, 2:00-3:00pm, or anytime by appointment, in DBH-4219.

          Email: minhaenl@uci.edu

          (If you send email, please put “CS-171” somewhere in the Subject line.)

           


          Goal:

          The goal of this class is to familiarize you with the basic principles of artificial intelligence. You will learn some basic AI techniques, the problems for which they are applicable, and their limitations.

          The course content is organized roughly around what are often considered to be three central pillars of AI: Search, Logic, and Learning. Topics covered include basic search, heuristic search, game search, constraint satisfaction, knowledge representation, logic and inference, probabilistic modeling, and machine learning algorithms.


          Class Setup:

          The course will be primarily lecture-based.  There will be a Mid-term and a Final Exam.  On most second Tuesdays, adjusting for the the Mid-term Exam, the first 20 minutes will be an in-class pop quiz, followed by lecture (see specific dates in Important Dates above).  The frequent quizzes are intended to encourage you to stay current with the course material.  All exams and quizzes may cover all material presented in class, including lectures and assigned textbook reading.  Quizzes will cover mostly material presented since the last quiz, and also may include questions that many students missed on the previous quiz.  The Final Exam will cover mostly material since the Mid-term Exam, and also will include many questions intended to encourage you to remember the earlier material (i.e., it will be comprehensive). Please study the previous CS-171 quizzes and exams (below), which are made available as study guides to help you learn and master the class material; they are important guides about the performance that will be expected from you now.

          There will be an AI coding project (see Project section below).  You are allowed to do the project by yourself, or you may form project teams of two students following the “Pair Programming” paradigm.  Please note that you are encouraged to discuss concepts, methods, algorithms, etc.; but you are forbidden to copy: (1) source code from any source, or (2) text from any source unless properly cited and set off as a quote.  Except for class materials provided from this class website, you must invent and write all of your own code by yourself with your partner.  Except for properly referenced material, you must write all of your own project report by yourself with your partner. Please note that your source code and project report are subject to analysis by automated plagiarism detection programs, and that direct copying will be treated as an act of academic dishonesty (please see the section on “Academic Honesty” below). Please start your AI coding project earlier than you believe necessary; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

          All my AI project shells were written by former CS-171 students who became interested in AI and signed up for CS-199 in order to pursue their interest and write more interesting AI project shells.  Please let me know if this is of interest to you (CS-171 grade of A- or better required).

          Homework will be assigned, but is not graded. The reason is that prior student course evaluations alerted me to the existence of student cheating by way of copying the homework answers.  I deplore this degree of personal degradation in dishonest students, but I cannot control it, and so I avoid the opportunity.  I remain determined to create a fair and honest educational experience for all students, as best I can.


          Textbook

          Required:  Russell & Norvig : Artificial Intelligence; A Modern Approach, 3rd edition.

          The course is based on, and the UCI bookstore has, the 3rd edition. The assigned textbook reading is required, and is fair game for quizzes and exams.  You place yourself at a distinct disadvantage if you do not have the textbook.  I expect that you have a personal copy of the textbook, and quizzes and exams are written accordingly.

          Please purchase or rent your own personal textbook for the quarter (and then resell it back to the UCI Bookstore at the end if you don't want it for reference). Please do not jeopardize your precious educational experience with the false economy of trying to save a few dollars by not having a personal copy of the textbook.

          Also, for your convenience, I have requested that a copy of the textbook be placed on reserve in the UCI Science Library. There is a two-hour check-out limit. However, please understand that with high student enrollments, it is unrealistic to expect that these thin reserves always will be available when you need them.  Please purchase or rent your own personal textbook.

          I do deplore the high cost of textbooks.  You are likely to find the book cheaper if you search online at EBay.com, Amazon.com, and related sites.

          A helpful student kindly contributed this link to a blog that offers a PDF of the course textbook, for which I cannot vouch:

                      http://crazy-readers.blogspot.com/2013/08/artificial-intelligence-modern-approach.html

           

          Another helpful student kindly contributed this link, which also offers a PDF of the course textbook, again for which I cannot vouch:

                      https://www.dropbox.com/s/gq9gatmroagrsf2/Artificial%20Intelligence%20A%20Modern%20Approach%20%283rd%20Edition%29.pdf?dl=0

           

          You can also try to search the Internet for “artificial intelligence a modern approach pdf 3rd edition”. Several more hits turned up the last time I did so.

           

          A helpful student kindly contributed the following suggestion, for which I cannot vouch:

          Hello,
          I just wanted to point out that there does exist an international edition of the book which can be bought for around $40-50. I cannot comment on what specific differences there are for this particular book, though they are usually very small (exercises moved around, etc). Obviously, it is in paperback.
                      http://www.valorebooks.com/affiliate/buy/siteID=e79mzf/ISBN=0136042597
                      http://www.abebooks.com/servlet/BookDetailsPL?bi=4161131466&cm_ven=sws&cm_cat=sws&cm_pla=sws&cm_ite=4161131466&afn_sr=para&para_l=1
                      http://www.biblio.com/books/360025589.html
          Personally I plan on using this book for a while so I bought the hardcover version, but I just wanted to point out that this is an option for those looking for a more 'economical' route.
          ~ XXXXXX [name anonymized to protect student privacy]


          Grading:

          Your grade will be based on the bi-weekly quizzes (20%), a project (20%), a mid-term exam (25%), and a final exam (35%). Homework is assigned but ungraded.

           

          ·        Quizzes will be given the first 20 minutes of class on the dates listed in Important Dates above, and are closed-book, closed-notes.  Your lowest quiz score will be discarded in computing your grade.  It is not possible to make-up missed quizzes, but one missed quiz may be discarded as your lowest quiz score.

          ·        The mid-term exam will be given in class on Thursday, Feb. 11, and is closed-book, closed-notes.  It is not possible to make-up a missed mid-term exam.

          ·        The final exam will be given on Friday, Mar. 18, 10:30am-12:30pm, and is closed-book, closed-notes.  The final exam will cover all course material from the entire quarter, with emphasis on the second half.  It is not possible to make-up a missed final exam.

                      * Dates and times for all final exams are set by the UCI Registrar (Final Exam Schedule 2015-16)

           

          I make exceptions for:

                      * genuine medical conditions (I require a signed note from your doctor on official letterhead),

                      * births/deaths in the family (I require a copy of the birth/death certificate),

                      * jury duty or other court proceedings (I require a copy of your jury service papers or other official court documents), or

                      * field maneuvers of the US military or National Guard (I require a copy of your official orders).

          Also, I honor all requests made by the UCI Disability Services Center.

           

          ·        The AI coding project will be a “Monster Sudoku” solver.  “Monster Sudoku” is played on a board larger than 9x9 and is harder than regulation Sodoku.  Previously, when I gave students a coding project of regulation 9x9 Sudoku, they complained that it was too easy.  So, we have made it harder.

           

          ·        Every student who fills out a course evaluation for CS-171 will receive a bonus of 1% added to their final grade, free and clear, off the curve, simply a bonus.

                  EEE will return to me the names of students who fill out evaluations (but not the content, which remains anonymous), provided that enough students fill out evaluations so that anonymity is not compromised.  I will add 1% free bonus to the final grade of each such named student.

                  Student course evaluations are very important to me for monitoring and improving the course content, and very important to UCI for evaluating our success at our educational mission.  *Please* fill out your student course evaluations.

           

          ·        “Bonus Points” will be awarded, at my sole discretion, (1) to the first student who spots a genuine technical error (typos don’t count) in any of the course materials before I spot it too, and (2) for helpful contributions to the class as we go along.  One bonus point is equivalent to one quiz point.

           

                      Your Bonus Points, if any, should be visible to you in EEE GradeBook. If for some reason you have been awarded a Bonus Point, but you did not get a notification from me or it did not appear in EEE GradeBook, please do not hesitate to send an email message to me as a reminder.

           


          Study Habits:

           

          This course is technical, rigorous, and demanding. You will be expected to learn and master a large body of technical material in a very short period of time. You must demonstrate your mastery by (1) accurate performance on frequent quizzes and exams, and (2) successful implementation of an AI coding project.

          I deliberately treat you as adults who are responsible for your own educational decisions, and so Lecture and Discussion Sections are optional. Nevertheless, students who do not attend both Lecture and Discussion Sections are at a serious disadvantage and do not succeed as well in this class.  Students who spend Lectures and Discussion Sections sleeping, on cell phones, surfing the Web, or on social media are wasting their time and might as well be absent.  Such students send me email messages to ask questions that already were covered thoroughly and in detail during Lecture and again in Discussion Section.  On quizzes and exams, they miss points that already have been covered thoroughly.

          Your educational moments are precious, and your education now will be the single most important factor in your future career success or failure.  Please, make the most of your precious educational moments now. Please, attend both Lecture and Discussion Section, pay attention, ask questions, and master the material.

          Please do not ever fall behind in the class material; instead, study frequently and diligently. Please begin your AI coding project earlier than you believe necessary; it will take longer and be more difficult than you expect (as is true of all coding projects everywhere at all times).

          Please work harder and study longer.  Please understand thoroughly all class material, and ask questions when you do not understand.  Please attend all lectures and discussion sections.  Please come to lectures and discussion sections prepared with questions about any material that is not clear.  Please do all assigned reading, both before and again after lecture. Please review the lecture notes, several times over, both before and again after lecture, until you understand every detail. Please regularly attend office hours with me and the TA. Please ask questions about any class material that is not absolutely crystal clear.

          Please work and understand all past quizzes and exams; they are important guides about the performance that will be expected from you now. Please work and understand all the optional homework.

          Please OVERSTUDY!!


          Syllabus:

          The following represents a preliminary syllabus. Some changes in the lecture sequence may occur due to earthquakes, fires, floods, wars, natural disasters, unnatural disasters, or the discretion of the instructor based on class progress.

          Background Reading and Lecture Slides will be changed or revised as the class progresses at the discretion of the instructor.  Please note:  I often tweak or revise the lecture slides prior to the lecture; please ensure that you have the current version.

          Please read the assigned textbook reading and review the lecture notes in advance of each lecture, then again after each lecture.

           

          Week 1:

                      Tue., 5 Jan., Introduction, Agents.

                                  Read in Advance: Textbook Chapters 1-2.

                                  Lecture slides: Introduction, Agents [PDF; PPT].

           

                      Thu., 7 Jan., start Constraint Satisfaction.

          Read in advance: Textbook Chapter 6.1-6.4, except 6.3.3.

                                  Lecture slides: Constraint Satisfaction Problems [PDF; PPT].

           

                      Fri., 8 Jan., Discussion Section.

                                  Discussion Section slides [PDF].

           

                      Optional Reading:

           

                                  John McCarthy, “What Is Artificial Intelligence?”

                                 

                                  AAAI, AI Overview.

           

                                  “Technological singularity” --- Wikipedia.

                                              “The technological singularity hypothesis is that accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization in an event called the singularity.”

           

                                  “The Coming Technological Singularity: How to Survive in the Post-Human Era” (c) 1993 by Vernor Vinge.

                                              (Verbatim copying/translation and distribution of this entire article is permitted in any medium, provided this notice is preserved.)   

                                                          “Abstract:     Within thirty years, we will have the technological means to create superhuman intelligence. Shortly after, the human era will be ended.  Is such progress avoidable? If not to be avoided, can events be guided so that we may survive?  These questions are investigated. Some possible answers (and some further dangers) are presented.”

                                                          “.... Just so I'm not guilty of a relative-time ambiguity, let me more specific: I'll be surprised if this event occurs before 2005 or after 2030....”

           

                                  Rumors, and rumors of rumors.  You get to make up your own mind.  ;-)


                      Optional Cultural Interest:

           

                                  IBM Watson: Final Jeopardy! and the Future of Watson

                                  AI vs. AI. Two chatbots talking to each other.

           

                                  Silicon Valley Kingpins Commit $1 Billion to Create Artificial Intelligence Without Profit Motive

           

                                  Google Goggles

           

          Week 2:

           

                      Tue., 12 Jan., finish Constraint Satisfaction.

                                  Read in advance: Textbook Chapter 6.1-6.4, except 6.3.3.

                                  Lecture slides: Constraint Propagation  [PDF; PPT].

           

                      Thu., 14 Jan., Uninformed Search.

                                  Read in Advance: Textbook Chapter 3.1-3.4.

                                  Lecture slides (three parts):

                                              (1) Introduction to Search [PDF; PPT]; and

                                              (2) Uninformed Search [PDF; PPT].

           

                      Fri., 15 Jan., Discussion Section.

          Discussion Section slides [PPT].

           

                      Fri., 15 Jan., 11:59pm: Project Team Formation Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Notify us about your team status. Put your team name and partner status in the EEE Dropbox named “Project Team Status.” PLEASE DO NOT PUT SPACES IN YOUR TEAM NAME, as spaces may break our Unix scripts.

                                  (1) What is your team name?  creativity is encouraged!

                                  (2) Who is your partner?  or are you a solo team?

           

          Filename:

                      “<your last name>_< UCI numeric ID>_< team name>.txt”

          File format:

                      NAME = <your full name>

                      UCIID = <your UCI numeric ID>

                      TEAMNAME = <your team name with NO SPACES>

                      PARTNER = {“solo” | your partner’s full name and UCI numeric ID}

           

          There is an EEE CS-171 MessageBoard forum "Seeking CS-171 Coding Project Partner" intended for use by students who seek a project partner.

           

          Optional Reading:

           

                      Newell & Simon’s “Symbols and Search” Turing Award Lecture (1976).

                      Herbert Simon is the only computer scientist to be awarded a Nobel Prize (in economics, 1978).

           

                                  “Flexible Muscle-Based Locomotion for Bipedal Creatures” --- paper.

           

                      Optional Cultural Interest:

           

                                  “Flexible Muscle Based Locomotion for Bipedal Creatures” --- video

           

                                  Boston Dynamics Big Dog (new video March 2008)

                                  Cheetah Robot runs 28.3 mph; a bit faster than Usain Bolt

                                  Amazing Bike Riding Robot!

                                  Honda's robot ASIMO

           

          Week 3:

           

                      Tue., 19 Jan., Quiz #1 (answer key here);  Heuristic Search.

                                  Read in advance:  Textbook Chapter 3.5-3.7.

                                  Lecture slides: Heuristic Search [PDF; PPT].

           

                      Thu., 21 Jan., Local Search.

          Read in advance:  Textbook Chapter 4.1-4.2.

                                  Lecture slides (two parts):

                                              (1) Local Search [PDF; PPT]; and

                                              (2) Representation [PDF; PPT].

           

                      Fri., 22 Jan., Discussion Section.

                                  Discussion Section slides [PPT].

           

                      Optional Ungraded Homework:

                                  Homework #1; answer key.

           

                      Optional Reading:

           

          Alan Turing’s classic paper on AI (1950).

                      Alan Turing is the most famous computer scientist of all time.

          The Turing Award is the highest honor in computer science.

          The Turing Machine is still our fundamental theoretical model of computation.

          Turing’s work on the Enigma code in WWII led to programmable computers.

           

                      AAAI/AI Topics: The Turing Test: “Can Machines Think?”

           

                                  Wikipedia “Computing Machinery and Intelligence”


                                  Minton, et. al., 1990, AAAI "Classic Paper" Award recipient in 2008.

                                              How to solve the 1 Million Queens problem and schedule space telescopes.

           

                      Optional Cultural Interest:

           

                                  Infinite Mario AI - Long Level

                                  An attempt at a Mario AI using the A* path-finding algorithm.

                                              It claims the bot won both Mario AI competitions in 2009.

                                              “You can see the path it plans to go as a red line, which updates when it detects new obstacles at the right screen border. It uses only information visible on screen.”

                                  See also http://www.marioai.org/.

           

                                  Interesting search algorithm visualization web page.

           

                               A* Search in Interplanetary Trajectory Design, courtesy of Eric Trumbauer, former CS-271 student.

                                              Eric comments, “One thing to possibly discuss with the last slide is that the itinerary it settles on does stay at a higher energy for a little bit until it passes closest to Europa, maximizing the velocity before the insertion sequence to the lower energy.  This is indeed optimal behavior, as opposed to immediately reducing its energy as a Greedy Best First algorithm using this heuristic would want to do.”

           

                                  A* Search in Protein Structure Prediction, Lathrop and Smith, J. Mol. Biol. 255(1996)641-665

                                 

                                  “Hill Climbing with Simulated Annealing”

           

                      “Boxcar 2D”

                                              The program learns to build a car using a genetic algorithm.

                                              If you let this program run for a long time (>> 30 generations), you will see that eventually it produces cars well suited to the terrain. This outcome illustrates a general theme of genetic algorithms: very, very slow; but, eventually, good performance. After all, it took ~3.6 billion years to evolve humans from bacteria (http://en.wikipedia.org/wiki/Timeline_of_evolutionary_history_of_life). Please note that this eventual good performance of genetic algorithms is conditional upon a representation that allows good solutions to sub-problems to be combined simply, by cross-over, into a globally good solution; if the vector position of the features is completely randomized within the chromosome, any such good performance is lost.

           

          Week 4:

           

           

          Tue. 26 Jan., start Games/Adversarial Search.

          Read in advance: Textbook Chapter 5.1, 5.2, 5.4.

                                  Lecture slides: Games/Adversarial Search/MiniMax Search [PDF; PPT].

           

                      Thu., 28 Jan., finish Games/Adversarial Search.

          Special 2 minute Blood Donor Center presentation.

          Read in advance: Textbook Chapter 5.3. (Optional: Chapter 5.5 and beyond.)

                      Lecture slides: Games/Adversarial Search/Alpha-Beta Pruning [PDF; PPT].

           

          Fri., 29 Jan., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 31 Jan., 11:59pm: Project Backtracking Search Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Optional Ungraded Homework:

                                  Homework #2; answer key.

           

                      Optional Reading:

           

                                  Campbell, et al., 2002, Artificial Intelligence, “Deep Blue.” [PDF]

                                              (URL http://www.sciencedirect.com/science/article/pii/S0004370201001291)

                                              Details about the AI system that beat the human chess champion.

           

                                  Arthur C. Clarke “Quarantine.”

                                              A science fiction short story written by a classic master, in 188 words.

                                              He was challenged to write a science fiction short story that would fit on a postcard.

           

                      Chaslot, et al., “Monte-Carlo Tree Search: A New Framework for Game AI,”

          in Proceedings of the Fourth Artificial Intelligence and Interactive Digital Entertainment Conference, AAAI Press, Menlo Park, pp. 216-217, 2008.

                      An interesting combination of Local Search (Chapter 4) and Game Search (Chapter 5).

                      Related URL: “Everything Monte Carlo Tree Search” website.

           

                      Optional Cultural Interest:

           

                                  RoboCup 2012 Standard Platform: USA / Germany (Final).

                                  RoboCup Home Page.

           

                                  “Complete Map of Optimal Tic-Tac-Toe Moves.”

           

          Week 5:

           

                      Tue., 2 Feb., HAPPY GROUNDHOG DAY!!

                                  Quiz #2 (answer key here); start Propositional Logic.

                                  Read in advance: Textbook Chapter 7.1-7.4.

                                  Lecture slides: Propositional Logic A [PDF; PPT].

           

                      Thu., 4 Feb., finish Propositional Logic.

                                  Read in advance: Textbook Chapter 7.5 (optional: 7.6-7.8).

                                  Lecture slides: Propositional Logic B [PDF; PPT].

                                              Additional Discussion lecture slides [PDF].

           

          Fri., 5 Feb., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Optional Reading:

           

                                  Autonomous car - Wikipedia, the free encyclopedia

                                  “Autonomous Driving in Traffic: Boss and the Urban Challenge” (2009).

           

                                  “Evolution” by R. H. Lathrop.

           

                      Optional Cultural Interest:

           

                                  Audi Piloted Parking (Audi's self-parking car)

                                  Tesla Model S P85D AWD and auto-pilot demo

                                  Google Car: It Drives Itself - ABC News

                                  [Part 1/3] The Evolution of Self-Driving Vehicles

                                  [Part 2/3] How Google's Self-Driving Car Works                  

                                  [Part 3/3] Google's Self-Driving Golf Carts

                                  DARPA Urban Challenge Highlights

                                  DARPA Urban Challenge: Ga Tech hits curb

                                  DARPA Urban Challenge - Sting Racing crash

                                  [DARPA] Team Oshkosh attempts forced Entry to Main Exchange

                                  [DARPA] Alice's Crash (spectator view)

                                  [DARPA] Alice's Crash (road-finding camera) [different view of above; long]

                                  DARPA Urban Challenge Crash Cornell MIT

                                  DARPA Urban Challenge - robot car wreck [different view of above]

           

          Week 6:

           

                      Tue., 9 Feb., Catch-up, Review for Mid-term Exam.

          Read in advance: Textbook Chapters 1-7 (only sections assigned above).

                                  Lecture slides: Catch-up, Review, Question&Answer [PDF; PPT].

           

                      Thu., 11 Feb., Mid-term Exam (answer key here).

                      Read in advance: Textbook Chapters 1-7 (only sections assigned above).

                                  Lecture slides: Catch-up, Review, Question&Answer (above).

           

          Fri., 12 Feb., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 14 Feb., 11:59pm: Project Forward Checking Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      No homework --- study for the Mid-term Exam.

           

                      Optional Cultural Interest:

           

                                  “Quadrocopter Pole Acrobatics”

                                  “Nano Quadcopter Robots swarm video” [need to fix link]

           

                                  The Stanford Autonomous Helicopter performing an aerobatic airshow under computer control:

                                              “Stanford Autonomous Helicopter - Airshow #1”

                                              “Stanford Autonomous Helicopter - Airshow #2 Redux”

           

          Week 7:

           

                      Tue., 16 Jan., Review Mid-term Exam; start First Order Logic

                      Read in advance: Textbook Chapter 8.1-8.2.

                                  Lecture slides: First Order Logic Syntax [PDF; PPT].

           

                      Thu., 18 Jan., finish First Order Logic; Knowledge Representation.

          Read in advance: Textbook Chapter 8.3-8.5.

                                  Lecture slides (two parts):

          (1) First Order Logic Semantics [PDF; PPT]; and

          (2) First Order Logic Knowledge Representation [PDF; PPT].

           

                                  Optional Lecture slides: First Order Logic Inference [PDF; PPT].

          Optional read in advance: Textbook Chapter 9.1-9.2, 9.5.1-9.5.5.

           

          Fri., 19 Feb., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 21 Feb., 11:59pm: Project Arc Consistency Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Optional Ungraded Homework:

                                  Homework #3; answer key.

           

                      Optional Reading:

           

                                  Cyc is a large-scale knowledge-engineering project:

                                              “CYC: A Large-Scale Investment in Knowledge Infrastructure,” Lenat, 1995

                                              “Searching for Commonsense: Populating Cyc from the Web,” Matuszek et al, AAAI 2005

                                              Cyc home page.

                                              Cyc - Wikipedia, the free encyclopedia.

           

                      Optional Cultural Interest:

           

                                  hitchBOT

                                  hitchBOT FaceBook

                                  hitchBOT Instagram

                                  Hitting the road: Hitchbot begins cross-Canada journey

                                  “Canada's hitchBOT travels 4,000 miles to test human-robot bonds --- LA Times.”

                                  HitchBOT, the hitchhiking robot, gets beheaded in Philadelphia

           

           

          Week 8:

           

           

                      Tue., 23 Feb., Quiz #3 (answer key here); Probability, Uncertainty

          Read in advance: Textbook Chapter 13.

                                  Lecture slides:

                                              Reasoning Under Uncertainty [PDF; PPT].

           

                      Thu., 25 Feb., Graphical Models, Bayesian Networks.

          Read in advance: Textbook Chapters 14.1-14.5.

                                  Lecture slides:

                                              Bayesian Networks [PDF; PPT].

           

          Fri., 26 Feb., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 28 Feb., 11:59pm: Project MRV & DH Heuristic Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Optional Ungraded Homework:

                                  Homework #4; answer key.

           

                      Optional Cultural Interest:

           

                                  Video of Judea Pearl’s 2011 Turing Award lecture.

                                  The Mechanization of Causal Inference: A “mini” Turing Test and Beyond.

           

                                  “Peter Norvig 12. Tools of AI: from logic to probability.”

           

                                  “High-Speed Robot Hand”

                                  “Janken (rock-paper-scissors) Robot with 100% winning rate”

                                  “CubeStormer II”

           

                                  (Snakes, spiders, and a talking head!):

                                  “Snake Robot Climbs a Tree”

                                  “Asterisk - Omni-directional Insect Robot Picks Up Prey #DigInfo”

                                  “Freaky AI robot, taken from Nova science now”

                     

                     

           

          Week 9:

           

                      Tue, 1 Mar., start Learning from Examples.

          Read in advance: Textbook Chapter 18.1-18.4.

                                  Lecture slides: Intro to Machine Learning [PDF; PPT].

           

                      Thu., 3 Mar., finish Learning from Examples.

          Read in advance: Textbook Chapter 18.5-18.12, 20.1-20.3.2.

                                  Lecture slides:

                                              Learning Classifiers [PDF; PPT].

           

                                  Optional Lecture slides: Viola & Jones, Learning, Boosting, Vision [PDF; PPT] (read the two papers immediately below)

                                  Optional Associated Reading: Viola & Jones, 2004, “Robust Real-Time Face Detection”

                                  Optional Associated Reading: Freund & Schapire, 1999, “A Short Introduction to Boosting”

           

          Fri., 4 Mar., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 6 Mar., 11:59pm: Project LCV Heuristic Deadline.

                                  You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Optional Ungraded Homework:

                                  Homework #5; answer key.

           

                      Optional Reading:

           

                                  “Machine learning” - Wikipedia, the free encyclopedia

                                  “Data mining” - Wikipedia, the free encyclopedia

           

          Ferrucci, et al., 2010, “Building Watson: An Overview of the DeepQA Project”

           

                                  Proof that Decision Tree information gain is always non-negative (problem 3, pp. 4-5).

           

                                  “Google reveals it is developing a computer so smart it can program ITSELF.”

           

                                  Danziger, et al., 2009, “Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning”

           

                                  Kim & Xie, 2014, “Handwritten Hangul recognition using deep convolutional neural networks”

           

                                  Baldi, Sadowski, & Whiteson, 2014, “Searching for Exotic Particles in High-Energy Physics with Deep Learning”

           

           

          Week 10:

           

                      Tue., 8 Mar., Quiz #4 (answer key here); Clustering (unsupervised learning) and Regression (statistical numeric learning).

          Read in advance: Textbook Chapter 18.6.1-2, 20.3.1.

                                  Lecture slides:

                                              Clustering (Unsupervised Learning) [PDF; PPT].

                                              Linear Regression [PDF; PPT].

           

                      Thu., 10 Mar., Catch-up, Review for Final Exam.

          Read in advance: Textbook, review all assigned reading.

                                  Lecture slides: Review, Catch-up, Question&Answer [PDF; PPT].

           

          Fri., 11 Mar., Discussion Section.

                      Discussion Section slides [PPT].

           

                      Sun., 13 Mar., 11:59pm, Project Final Report Deadline:

                      Deadline to deposit your project Final Report in EEE Dropbox.

                      You will lose 10% of your Project grade for every day it is late, pro-rated for fractional days and rounded up to the nearest integer percentage point above.

           

                      Wed., 16 Mar., 11:59pm:

                      No Project Final Reports accepted hereafter.

           

                      Optional Ungraded Homework:

                                  Homework #6; answer key.

           

                      Optional Reading:

           

                                  Gaffney, et al., 2007, “Probabilistic clustering of extratropical cyclones using regression mixture models”

           

                      Optional Cultural Interest:

           

                                  “IBM simulates 530 billon neurons, 100 trillion synapses on supercomputer”

           

                                  “Speech Recognition Breakthrough for the Spoken, Translated Word”

           

           

          Final Exam:

           

                      Fri., 18 Mar., 10:30am-12:30pm. (answer key here)

           

           


           


           

          Project:

          (1) A Java coding shell for Sudoku is available in case it may be helpful to you as an example (shell is here). I do not yet have C++ or Python shells (they will be written this quarter), and it has never been tested by “live” use in a CS-171 class. In spite of these limitations, we thought it might be helpful to you as an example.

          (2) (revised 3 Feb 2016) Thanks to the good efforts of Minhaeng Lee, the Reader, a test shell to validate your programs on openlab.ics.uci.edu is available here. This shell is only for testing whether student's program is runnable on the openlab environment. Please note that, currently, the testing shell doesn't contain tests for whether or not the output is correct. We will update it later.

          (3) Thanks to the good efforts of Junkyu Lee, the TA, a detailed description of exactly what to turn in for your Sudoku Project Assignments is available here.  It will be updated as we go along. Please note that we are still discussing what to do about the Generator portion of the project, which was canceled because the provided Java shell included a generator. However, apparently some students have written their own generator anyway. Most likely we will give extra credit for extra work; but the matter is still under discussion. In the meantime, it is OK if your program accepts the GEN and BT option tokens. If you wrote your own generator then exactly one of the GEN and BT tokens must be present (one is required but they are mutually exclusive), and they govern whether your program is being run in generate (GEN) or solver (BT) mode. If you did not write your own generator then you may ignore, but must tolerate, both the GEN and BT tokens. Please see the Sudoku Project Assignments document described in this paragraph for more details.

          (4) A slideshow about the Monster Sudoku coding project is available here. 

          (5) Merged with Sudoku Project Assignments (above). A Specification for your Monster Sudoku problem Reader and Generator is available here. It is largely superseded because the provided Java shell already contains a Generator.

          (6) Just for fun, once upon a time I wrote LISP code (available here) that computes the "odometer" style tokens up to any arbitrarily large N (example of the resulting tokens available here).  Please note that this "odometer" material is purely for cultural interest, i.e., you will never be responsible for "odometer" style tokens above 35 on quizzes, exams, or the project.  Nevertheless, it is a curious wrinkle that is interesting to see, especially if you are interested in bigger and better.

           

           

          Please note: The C++ target platform should be x86. You should write your code to run on any x86 machine. The OS is CentOS 6. We most likely will need to compile your code with CentOS 6 (RHEL 6) x86_64. Machines in the openlab.ics.uci.edu (family-guy.ics.uci.edu) are CentOS 6. Your code should run on openlab.ics.uci.edu.

           

           

           

          Project Deadlines:

          ·        Project deadlines are given above in the Important Dates section.

           

          ·        Your EEE DropBox submission must be a single “zipped” file named “yourLastName_yourUCINumericID_yourTeamName.”  NO SPACES in yourTeamName.

          ·        It should have three subdirectories: src, bin, & doc; for source, executable, and documents (‘doc’ must contain your Project Report).

          ·        Please deposit only one submission per team.

          ·        If your partner has deposited your submission, please deposit a text file stating your name/ID, your partner’s name/ID, and your team name. Please use the same filename format given above.

           

          ·        You will lose 10% of your project score for each day (or part thereof) that your project is late for any deadline. Please submit your project early, well ahead of the deadline, and avoid the last-minute rush. If system problems, web congestion, or other unavoidable Internet delays make your project late, it is still late and will be penalized.

           

           


           

          Study Guides --- Previous CS-171 Quizzes, Mid-term, and Final exams:

          Previous CS-171 Quizzes, Mid-term exams, and Final exams are available here as study guides.

           

          As an incentive to study this material, at least one question from a previous Quiz or Exam will appear on every new Quiz or Exam. In particular, questions that many students missed are likely to appear again. If you missed a question, please study it carefully and learn from your mistake --- so that if it appears again, you will understand it perfectly.

           

          Please note that some of the very old tests below reflect different textbooks that may define some things differently than does your current textbook. In case of conflict, your current textbook is deemed correct and will prevail. Some of your visualization systems may not display the red PDF overlays used to correct errors in very old tests. For example, in problems #2a, #2c, #3a, and #3b on Quiz #2 from SQ’2004, the PDF overlay is invisible on a Mac (iPad), and possibly on some other systems or printers.  The PDF overlays just do not seem to work as advertised (sorry!!), but this problem seems only to afflict very old tests (i.e., from over a decade ago). If you are confused by any of the answers below, please bring your questions to the TA in Discussion Section.  If you find a genuine error anywhere, please send me email and you will receive a Bonus Point if correct.

           

          Also, a student has recommended ‘quizlet.com’ as a good online study resource. While I cannot vouch for it, apparently it contains several good study aids for your textbook.

           

          Winter Quarter 2016:

          Quiz #1 and key.

          Quiz #2 and key.

          Mid-term Exam and key.

           

          Fall Quarter 2015:

          Quiz #1 and key.

          Quiz #2 and key.

          Quiz #3 and key.

          Quiz #4 and key.

          Mid-term Exam and key.

          Final Exam and key.

           

          Winter Quarter 2015:

          Quiz #1 and key.

          Quiz #2 and key.

          Quiz #3 and key.

          Quiz #4 and key.

          Mid-term Exam and key.

          Final Exam and key.

           

          Fall Quarter 2014:

          Quiz #1 and key.

          Quiz #2 and key.

          Quiz #3 and key.

          Quiz #4 and key.

          Mid-term Exam and key.

          Final Exam and key.

           

          Winter Quarter 2014:

          Quiz #1 and key

          Quiz #2 and key

          Quiz #3 and key

          Quiz #4 and key

          Mid-term Exam and key

          Final Exam and key

           

          Fall Quarter 2013:

          Quiz #1 and key

          Quiz #2 and key

          Quiz #3 and key

          Quiz #4 and key

          Mid-term Exam and key

          Final Exam and key

           

          Fall Quarter 2012:

          Quiz #1 and key

          Quiz #2 and key

          Quiz #3 and key

          Quiz #4 and key

          Mid-term Exam and key

          Final Exam and key

           

          Winter Quarter 2012:

          Quiz #1 and key

          Quiz #2 and key

          Quiz #3 and key

          Quiz #4 and key

          Mid-term Exam and key

          Final Exam and key

           

          Spring Quarter 2011:

          Quiz #1 and key

          Quiz #2 and key

          Quiz #3 and key

          Quiz #4 and key

          Quiz #5 and key

          Mid-term Exam and key

          Final Exam and key

           

          Spring Quarter 2004:

          Quiz #1 key

          Quiz #2 key

          The correct answer to Quiz #2 (2a) is A B D E C G.

          The correct answer to Quiz #2 (2c) is A; A B C G.

          The correct answer to Quiz #2 (3a) is N.

          The correct answer to Quiz #2 (3b) is N.

          These emendations to Quiz #2 have been corrected by overlays to the old PDF files, but apparently those corrections may not be not visible on some systems (MAC/iPAD?) or when printed on some printers (?). Please be warned.

          Quiz #3 key

          Quiz #4 key

          Quiz #5 key

          Quiz #6 key

           

          Spring Quarter 2000:

          Quiz #1 key

          Quiz #2 key

          Quiz #3 key

          Quiz #4 key

          Quiz #5 key

          Final Exam key

           


           

          Online Resources:

          Additional Online Resources may be posted as the class progresses.

          Textbook website for Artificial Intelligence: A Modern Approach (AIMA).

                      AIMA page for additional online resources.        

           

          Website for American Association for Artificial Intelligence (AAAI).

                      AAAI page of AI Topics.

                      AAAI AI in the News.

                      AAAI Digital Library of more than 10,000 AI technical papers.

                      AAAI AI Magazine.

                      AAAI Author Kit.

                      AAAI Student Resources.

                      AAAI Classic Papers.

                      AAAI Annual AAAI Conference.

                      AAAI Innovative Applications of Artificial Intelligence Conference.

           


           

          Academic Honesty:

          Academic dishonesty is unacceptable and will not be tolerated at the University of California, Irvine. It is the responsibility of each student to be familiar with UCI's current academic honesty policies. Please take the time to read the current UCI Academic Senate Policy On Academic Integrity and the ICS School Policy on Academic Honesty.

          The policies in these documents will be adhered to scrupulously. Any student who engages in cheating, forgery, dishonest conduct, plagiarism, or collusion in dishonest activities, will receive an academic evaluation of ``F'' for the entire course, with a letter of explanation to the student's permanent file. The ICS Student Affairs Office will be involved at every step of the process. Dr. Lathrop seeks to create a level playing field for all students.

           


           

           

           

          http://www.ics.uci.edu/~rickl/rickl-publications.html Richard H. Lathrop publications

          Dr. Richard H. Lathrop --- Publications

           

          PUBLIC SERVICE JOURNAL ARTICLES:

          On behalf of the membership of the International Society for Computational Biology (ISCB): This "ISCB Public Policy Statement on Open Access to Scientific and Technical Research Literature" was approved unanimously by each of the ISCB Executive Committee, the ISCB Board of Directors, and the ISCB Public Affairs Committee. At the time, I was the Chair of the ISCB Public Affairs Committee and a member of the ISCB Board of Directors; Burkhard Rost was President of the ISCB. The statement was simultaneously co-published by the two official journals of the ISCB, Bioinformatics and PLoS Computational Biology.

          "ISCB Public Policy Statement on Open Access to Scientific and Technical Research Literature." Lathrop RH, Rost B; ISCB Membership; ISCB Executive Committee; ISCB Board of Directors; ISCB Public Affairs Committee. PLoS Comput Biol; 7(2):e1002014. Epub 2011 Feb 24. PMID: 21390278.

          (PDF)

           

          "ISCB public policy statement on open access to scientific and technical research literature." Lathrop RH, Rost B; ISCB Membership, Executive Committee, Board of Directors, and Public Affairs Committee. Bioinformatics; 27(3):291-4. 2011 Feb 1. PMID: 21282182. PMCID: PMC3031043.

          (PDF)

           

          REFEREED JOURNAL ARTICLES:

          "Tuning cellular response by modular design of bioactive domains in collagen." Que, R.A., Chan, S.W., Jabaiah, A.M., Lathrop, R.H., Da Silva, N.A., Wang, S.W. Biomaterials, 53:309-317, 2015. doi: 10.1016/j.biomaterials.2015.02.074.

          (PDF)

           

          "CHOPER filters enable rare mutation detection in complex mutagenesis populations by next-generation sequencing." Salehi, F., Baronio, R., Idrogo-Lam, R., Vu, H., Hall, L.V., Kaiser, P., Lathrop, R.H. PLoS One, 10(2), 2015. doi: 10.1371/journal.pone.0116877.

          (PDF)

           

          "Directed DNA Shuffling of Retrovirus and Retrotransposon Integrase Protein Domains." Qi, X., Vargas, E., Larsen, L., Knapp, W., Hatfield, G., Lathrop, R. H., Sandmeyer, S. Plos One, 8(5), 2013. doi: 10.1371/journal.pone.0063957

          (PDF)

           

          "Computational Identification of a Transiently Open L1/S3 Pocket for Reactivation of Mutant p53." Wassman, C., Baronio, R., Demir, O., Wallentine, B., Hall, L., Salehi, F., Lin, D., Chung, B., Hatfield, G., Chamberlin, R. A., Luecke, H., Lathrop, R. H., Kaiser, P., Amaro, R. Nat Commun., 4, 1407, 2013. doi: 10.1038/ncomms2361

          (PDF)

           

          "Ensemble-Based Computational Approach Discriminates Functional Activity of p53 Cancer and Rescue Mutants." Demir O, Baronio R, Salehi F, Wassman CD, Hall L, Hatfield GW, Chamberlin R, Kaiser P, Lathrop RH, Amaro RE. PLoS Comput Biol. 2011 Oct;7(10):e1002238. Epub 2011 Oct 20. PMID: 22028641.

          (PDF)

           

          "Identification and functional analysis of CT069 as a novel transcriptional regulator in Chlamydia." Akers JC, Hodac H, Lathrop RH, Tan M. J Bacteriol. Epub 2011 Sep 9. PMID: 21908669.

          (PDF)

           

          "iCODA: RNAi-based inducible knock-in system in Trypanosoma brucei." Ringpis GE, Lathrop RH, Aphasizhev R. Methods Mol Biol;718:23-37, 2011.

          (PDF)

           

          "All-codon scanning identifies p53 cancer rescue mutations." Baronio R, Danziger SA, Hall LV, Salmon K, Hatfield GW, Lathrop RH, Kaiser P. Nucleic Acids Res. Epub 2010 Jun 25. PMID: 20581117.

          (PDF)

           

          "Mechanism of U insertion RNA editing in trypanosome mitochondria: the bimodal TUTase activity of the core complex." Ringpis GE, Aphasizheva I, Wang X, Huang L, Lathrop RH, Hatfield GW, Aphasizhev R. J Mol Biol. 2010 Jun 25;399(5):680-95. Epub 2010 Apr 1. PMID: 20362585.

          (PDF)

           

          "Recombinant human collagen and biomimetic variants using a de novo gene optimized for modular assembly." Chan SW, Hung SP, Raman SK, Hatfield GW, Lathrop RH, Da Silva NA, Wang SW. Biomacromolecules, 11(6):1460-9. 2010 Jun 14. PMID: 20481478.

          (PDF)

           

          "Novel TUTase associates with an editosome-like complex in mitochondria of Trypanosoma brucei." Aphasizheva I, Ringpis GE, Weng J, Gershon PD, Lathrop RH, Aphasizhev R. RNA. 2009 Jul;15(7):1322-37. Epub 2009 May 22. PMID: 19465686.

          (PDF)

           

          "Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning." Danziger SA, Baronio R, Ho L, Hall L, Salmon K, Hatfield GW, Kaiser P, Lathrop RH. PLoS Comput Biol. 2009 Sep;5(9):e1000498. Epub 2009 Sep 4. PMID: 19756158.

          (PDF)

           

          "Genome wide screens in yeast to identify potential binding sites and target genes of DNA binding proteins," Zheng J., Yan J., Wang T., Mosbrook-Davis D., Dolan K., Christensen R., Stormo G., Haussler D., Lathrop R.H., Brachmann R., Burgess S., Nucleic Acids Research, Jan; 36(1):e8, 2008.
          (PDF)

           

          "Computationally Optimised DNA Assembly of synthetic genes," Larsen, L.S.Z.; Wassman, C.D.; Hatfield, G.W.; Lathrop, R.H.; Intl. J. Bioinformatics Research and Applications, Vol. 4, No. 3, 2008.
          (PDF)

           

          "Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants," Danziger SA, Zeng J, Wang Y, Brachmann RK, Lathrop RH, Bioinformatics, Jul 1;23(13), 2007; co-published in 2007 Proc. Intl. Conf. on Intelligent Systems for Molecular Biology.
          (PDF)

           

          "A statistical phylogeography of influenza A H5N1," Wallace, R.G., HoDac, H., Lathrop, R.H., Fitch, W.M., Proc. Natl. Acad. Sci., USA, Mar 13;104(11):4473-8, 2007.
          (PDF)

           

          "DNA Deformation Energy as an Indirect Recognition Mechanism in Protein-DNA Interactions," Aeling, K., Steffen, N.R., Johnson, M., Hatfield, G.W., Lathrop, R.H., Senear, D.F., IEEE Trans. on Computational Biology and Bioinformatics, Jan-Mar;4(1):117-25, 2007.
          (PDF)

           

          "GRA1 protein vaccine confers better immune response compared to codon-optimized GRA1 DNA vaccine," Doskaya M., Kalantari-Dehaghi M., Walsh C.M., Hiszczynska-Sawicka E., Davies D.H., Felgner P.L., Larsen L.S., Lathrop R.H., Hatfield G.W., Schulz J.R., Guruz Y., Jurnak F., Vaccine, 25(10):1824-37, Feb. 26, 2007. Epub Nov. 20, 2006.
          (PDF)

           

          "Functional census of mutation sequence spaces: The example of p53 cancer rescue mutants," Danziger, S.A., Swamidass, S.J., Zeng, J., Dearth, L.R., Lu, Q., Chen, J.H., Cheng, J., Hoang, V.P., Saigo, H., Luo, R., Baldi, P., Brachmann, R.K., and Lathrop, R.H., IEEE Transactions on Computational Biology and Bioinformatics, 3(2):114-25, Apr.-Jun., 2006.
          (PDF)

           

          "Heterogeneous Biomedical Database Integration Using a Hybrid Strategy: A p53 Cancer Research Database," Bichutskiy, V., Colman, R., Brachmann, R.K., Lathrop, R.H., Cancer Informatics, 2:277-287, 2006.
          (PDF)

           

          "Indirect recognition in sequence-specific DNA binding by Escherichi coli integration host factor: the role of DNA deformation energy," Aeling, K.A., Opel, M.L., Steffen, N.R., Tretyachenko-Ladokhina, V., Hatfield, G.W., Lathrop, R.H., Senear, D.F., J. Biol. Chem., 281(51):39236-48, Dec. 22, 2006. Epub 2006 Oct 11.
          (PDF)

           

          "HB tag modules for PCR-based gene tagging and tandem affinity purification in Saccharomyces cerevisiae," Tagwerker, C., Zhang, H., Wang, X., Larsen, L.S., Lathrop, R.H., Hatfield, G.W., Auer, B., Huang, L., Kaiser, P., Yeast, 23(8):623-32, Jun., 2006.
          (PDF)

           

          "Predicting oligonucleotide-directed mutagenesis failures in protein engineering," Wassman, C.D., Tam, P.Y., Lathrop, R.H., Weiss, G.A., Nucleic Acids Res., 32(21):6407--6413, 2004.
          (PDF)

           

          "Information-Theoretic Dissection of Pairwise Contact Potentials," Cline, M.S., Karplus, K., Lathrop, R.H., Smith, T.F., Rogers, R.G. Jr., Haussler, D., Proteins: Structure, Function, Genetics, 49(1):7-14, 2002.
          (PDF)

           

          "Knowledge-based Avoidance of Drug-Resistant HIV Mutants," Lathrop, R.H., Steffen, N.R., Raphael, M., Deeds-Rubin, S., Pazzani, M.J., Cimoch, P.J., See, D.M., Tilles, J.G., ( cover illustration; invited article from IAAI Conference paper of same title, below), AI Magazine, 20(1):13-25, Spring, 1999.
          (PDF)

           

          "Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses," Lathrop, R.H., Pazzani, M.J., J. Combinatorial Optimization, 3:301-320, 1999.
          (PDF)

           

          "An Anytime Local-To-Global Optimization Algorithm for Protein Threading in O(m2n2) Space," Lathrop, R.H. J. Computational Biology, 6(3/4):405-418, 1999.
          (PDF)

           

          "A Bayes-Optimal Probability Theory that Unifies Protein Sequence-Structure Recognition and Alignment." Lathrop, R.H., Rogers, R.G. Jr., Smith, T.F., White, J.V., Bull. Math. Biol., 60:1039-1071, 1998.
          (preprint version. Postscript, PDF)

           

          "Current Limitations to Protein Threading Approaches," Smith, T.F., Lo Conte, L., Bienkowska, J., Gaitatzes, C., Rogers, R.G. Jr., and Lathrop, R.H., J. Computational Biol., 4(3):217-225, 1997.

           

          "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles," Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T., Artificial Intelligence, 89:31-71, 1997.
          (PDF)

           

          "Global Optimum Protein Threading with Gapped Alignment and Empirical Pair Potentials," Lathrop, R.H. and Smith, T.F., J. Mol. Biol. (cover article), 255:641-665, Feb., 1996.
          (PDF)

           

          "Compass: A Shape-Based Machine Learning Tool for Drug Design," Jain, A.N., Dietterich, T.G., Lathrop, R.H., Chapman, D.E., Critchlow, R.E., Bauer, B.E., Webster, T.A., Lozano-Perez, T., J. Computer-Aided Molecular Design, 8:635-652, Dec., 1994.

           

          "The Protein Threading Problem With Sequence Amino Acid Interaction Preferences Is NP-Complete," Lathrop, R.H., Protein Engineering, 7(9):1059-1068, Sept., 1994.
          PDF

           

          "Acid Helix-Turn Activator Motif," Zhu, Q.-L., Smith, T.F., Lathrop, R.H., and Figge, J., Proteins: Structure, Function, and Genetics, 8:156-163, 1990.

           

          "Potential Structural Motifs for Reverse Transcriptases", Webster, T.A., Patarca, R., Lathrop, R.H., Smith, T.F., Molecular Biology and Evolution, 6(3):317-320, 1989.
          (PDF)

           

          "Pattern Descriptors and the Unidentified Reading Frame 6 Human mtDNA Dinucleotide-Binding Site", Webster, T.A., Lathrop, R.H., Smith, T.F., Proteins: Structure, Function, and Genetics, 3:97-101, 1988.

           

          "ARIADNE: Pattern-Directed Inference and Hierarchical Abstraction in Protein Structure Recognition", Lathrop, R.H., Webster, T.A., Smith, T.F., Communications of the ACM, (cover article) 30(11):909-921, Nov., 1987.
          PDF

           

          "Prediction of a Common Structural Domain in Aminoacyl-tRNA Synthetases through Use of a New Pattern-Directed Inference System", Webster, T.A., Lathrop, R.H., Smith, T.F., Biochemistry, 26(22):6950-6957, Nov., 1987.

           

          "Consensus Topography in the ATP Binding Site of the Simian Virus 40 and Polyomavirus Large Tumor Antigens", Bradley, M.K., Smith, T.F., Lathrop, R.H., Livingston, D.M., Webster, T.A., Proc. National Academy of Science (PNAS), 84:4026-4030, June, 1987.
          (PDF)

           

          "Parallelism in Manipulator Dynamics", Lathrop, R.H., Intl. J. of Robotics Research, Summer, 1985, pp. 80-102; also appeared in Proc. IEEE Intl. Conf. on Robotics and Automation, pp. 772-778, St. Louis, MO, March 25-28, 1985.

           

           

          UNREVIEWED JOURNAL ARTICLES:

           

          " `Functional abstraction' anticipates timing glitches," Lathrop, R.H., Hall, R.J., Duffy, G., Alexander, K.M., and Kirk, R.S., IEEE Spectrum, 27(4):41-42, April, 1990.

           

           

          REFEREED CONFERENCE PROCEEDINGS:


          (excludes workshop notes, posters, unrefereed abstracts, etc.)

           

          "The Role of DNA Deformation Energy at Individual Base Steps for the Identification of DNA-Protein Binding Sites," Steffen, N.R., Murphy, S.D., Lathrop, R.H., Opel, M.L., Tolleri, L., Hatfield, G.W., p. 153-162 in Proc. Intl. Conf. on Genome Informatics, Tokyo, Japan, Dec. 16-18, 2002. In Genome Informatics 2002 (Genome Informatics Series No. 13), Universal Academy Press, Inc.
          (PDF)

           

          "DNA Sequence and Structure: Direct and Indirect Recognition in Protein-DNA Binding," Steffen, N.R., Murphy, S.D., Tolleri, L., Hatfield, G.W., Lathrop, R.H., p. 22-30 in Proc. Intl. Conf. on Intelligent Systems for Molecular Biology (ISMB'02), Edmonton, Canada, Aug 3--7, 2002. In Bioinformatics, 18 Suppl 1:S22-S30.
          (PDF)

           

          "Multi-Queue Branch-and-Bound Algorithm for Anytime Optimal Search with Biological Applications," Lathrop, R.H., Sazhin, A., Sun, Y., Steffen, N., Irani, S., (Best Paper Award), p. 73-82 in Proc. Intl. Conf. on Genome Informatics, Tokyo, Japan, Dec. 17-19, 2001. In Genome Informatics 2001 (Genome Informatics Series No. 12), Universal Academy Press, Inc.
          (PDF)

           

          "An Anytime Algorithm for Gapped Block Protein Threading with Pair Interactions," Lathrop, R.H., p. 238-249 in Proc. Intl. Conf. on Computational Molecular Biology (RECOMB'99), Lyon, France, April 11-14, 1999.
          (Postscript, PDF)

           

          "Knowledge-based Avoidance of Drug-Resistant HIV Mutants," Lathrop, R.H., Steffen, N.R., Raphael, M., Deeds-Rubin, S., Pazzani, M.J., Cimoch, P.J., See, D.M., Tilles, J.G., (Innovative Application Award winner), pp. 1071-1078 in Proc. Innovative Applications of Artificial Intelligence Conf., Madison, WI, USA, July 27-29, 1998.
          (Postscript, PDF)

           

          "Modeling Protein Homopolymeric Repeats: Possible Poly Glutamine Structural Motifs For Huntington's Disease," Lathrop, R.H., Casale, M., Tobias, D.J., Marsh, J.L., Thompson, L.M., pp. 105-114 in Proc. Intl. Conf. on Intelligent Systems and Molecular Biology, Montreal, Quebec, Canada, June 28-July 1, 1998.

           

          "The Threading Approach to the Inverse Protein Folding Problem," Smith, T.F., Lo Conte, L., Bienkowska, J., Rogers, B., Gaitatzes, C., Lathrop, R.H., pp. 287-292 in Proc. Intl. Conf. on Computational Molecular Biology, Santa Fe, NM, Jan. 20-23, 1997.

           

          "On the Learnability of the Uncomputable," Lathrop, R.H., pp. 302-309 in Proc. Intl. Conf. on Machine Learning, Bari, Italy, July 3-6, 1996.
          (Postscript, PDF)

           

          "From Electron Density and Sequence to Structure: Integrating Protein Image Analysis and Threading for Structure Determination," Baxter, K., Steeg, E., Lathrop, R.H., Glasgow, J., Fortier, S., pp. 25-33 in Proc. Intl. Conf. on Intelligent Systems and Molecular Biology, St. Louis, MO, USA, June 12-15, 1996.

           

          "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction," Dietterich, T.G., Jain, A.N., Lathrop, R.H., Lozano-Perez, T., pp. 216-223 in Advances in Neural Information Processing Systems, 6, ed. Cowan, J.D., Tesauro, G., and Alspector, J., Morgan Kaufmann Press, April, 1994.

           

          "A Branch-and-Bound Algorithm for Optimal Protein Threading with Pairwise (Contact Potential) Amino Acid Interactions," Lathrop, R.H. and Smith, T.F., Proc. 27th Hawaii Intl. Conf. on System Sciences, IEEE Computer Soc. Press, 1994.

           

          "Advances in Functional Abstraction From Structure", Lathrop, R.H., Hall, R.J., Duffy, G., Alexander, K.M., Kirk, R.S., Proc. 25th IEEE/ACM Intl. Design Automation Conf. (DAC'88), Anaheim, CA, Jun 13-15, 1988.

           

          "Automatic Generation of Behavioral Simulation Models Using Functional Abstraction", Alexander, K.M., Kirk, R.S., Lathrop, R.H., Hall, R.J., Duffy, G., Proc. Custom Integrated Circuits Conf. (CICC'88), 1988.

           

          "Functional Abstraction From Structure in VLSI Simulation Models", Lathrop, R.H., Hall, R.J., Kirk, R.S., (Best Paper Award), Proc. 24th ACM / IEEE Intl. Design Automation Conf. (DAC'87), Miami Beach, FL, pp. 822-828, 1987.

           

          "A Multiple Representation Approach to Understanding the Time Behavior of Digital Circuits", Hall, R.J., Lathrop, R.H., and Kirk, R.S., Proc. 6th Natl. Conf. on Artificial Intelligence (AAAI'87), Seattle, WA, 13-17 July, 1987.

           

          "SCORE Cell Development Environment", Kirk, R.S., Lathrop, R.H., Hall, R.J., Proc. 1987 Custom Integrated Circuits Conf. (CICC'87), May, 1987.

           

          "Constrained (Closed-Loop) Robot Simulation by Local Constraint Propagation", Lathrop, R.H., Proc. IEEE Intl. Conf. on Robotics and Automation, pp. 689-694, San Francisco, CA, April 7-10, 1986.

           

          "A System Which Uses Examples to Learn VLSI Structure Manipulations", Lathrop, R.H., Kirk, R.S., Proc. 5th Natl. Conf. on Artificial Intelligence (AAAI'86), pp. 1024-1028, Philadelphia, August 11-15, 1986.

           

          "Precedent-based Manipulation of VLSI Structures", Lathrop, R.H., Kirk, R.S., Proc. 23rd ACM / IEEE Intl. Design Automation Conf. (DAC'86), pp. 667-670, Las Vegas, June 29-July 2, 1986.

           

          "Circuit Improvement Using Precedent-based Reasoning", Kirk, R.S., Lathrop, R.H., Proc. 1986 Custom Integrated Circuits Conf. (CICC'86), Rochester, NY, May 12-15, 1986.

           

          "An Extensible Object-Oriented Mixed-Mode Functional Simulation System", Lathrop, R.H., and Kirk, R.S., Proc. 22nd ACM / IEEE Intl. Design Automation Conf. (DAC'85), paper 39.2, pp. 630-636, Las Vegas, Nev., 23-26 June, 1985.

           

          "An Extensible Datapath Generator", Barrett, W.A., Rogers, R.G., Lathrop, R.H., Kuchinsky, A., Proc. IEEE Intl. Conf. on Computer-Aided Design (ICCAD-83), paper 1.1, Santa Clara, CA, Sept. 12-15, 1983.

           

          UNREVIEWED CONFERENCE PROCEEDINGS:

           

          "DNA Structure, Protein-DNA Interactions, and DNA-Protein Expression," Baldi, P., Lathrop, R.H., Proc. Pacific Symposium on Biocomputing'01, World Scientific Press, 2001.

           

          "Protein Structure Prediction in the Post Genomic Era," Skolnick, J., Lathrop, R.H., Proc. Pacific Symposium on Biocomputing'99, World Scientific Press, 1999.

           

          "Application of a Genotypic Driven Rule-Based Expert Artificial Intelligence Computer System in Treatment Experienced HIV-Infected Patients. Immunologic and Virologic Response," Cimoch, P.J., See, D.M., Pazzani, M.J., Reiter, W.M., Lathrop, R.H., Fasone, W.A., Tilles, J.G., poster abstract for the Twelfth World AIDS Conf., Geneva, Switzerland, 1998.
          (Word)

           

          "Protein Structure Prediction," Lathrop, R.H., Proc. Pacific Symposium on Biocomputing'98, World Scientific Press, 1998.

           

          "Understanding and Predicting Protein Structure: Introduction to Session," Fischer, D., Godzik, A., Chung, S., Subbiah, S., Lathrop, R., Proc. Pacific Symposium on Biocomputing'97, World Scientific Press, 1997.

           

          "Discovering, Learning, Analyzing and Predicting Protein Structure: Introduction to Session," Dunker, A.K., Lathrop, R.H., Proc. Pacific Symposium on Biocomputing'96, World Scientific Press, 1996.

           

          "Protein Structure Prediction: Introduction to Session," Lathrop, R.H., Dunker, A.K., Proc. 28th Hawaii Intl. Conf. on System Sciences, IEEE Computer Soc. Press, 1995.

           

          "Protein Structure Prediction: Introduction to Session," Dunker, A.K., Lathrop, R.H., Proc. 27th Hawaii Intl. Conf. on System Sciences, IEEE Computer Soc. Press, 1994.

           

          "Protein Structure Prediction: Introduction to Session," Lathrop, R.H., Proc. 26th Hawaii Intl. Conf. on System Sciences, IEEE Computer Soc. Press, 1993.

           

          "Framework Report", Lathrop, R.H., and Winston, P.H., in Report of the Matrix of Biological Knowledge Workshop, Santa Fe Institute, Santa Fe, NM, eds. Morowitz, H.J., Smith, T.F., Oct., 1987.

           

          UNREVIEWED BOOK CHAPTERS, EDITED VOLUMES:

           

          "Protein Threading," in Encyclopedia of Genetics, Genomics, Proteomics, and Bioinformatics, eds. Dunn, M., Jorde, L., Little, P., Subramaniam, S., John Wiley & Sons, West Essex, UK (to appear). Word file (preprint version),

           

          Proceedings, Genome Informatics 2002, eds. Nakai, K., Lathrop, R., Miyano, S., Takagi, T., Universal Academy Press, Tokyo, 2002.

           

          (Guest Editor of Special Issue of Journal)."Intelligent Systems in Biology II," ed. Lathrop, R.H., IEEE Intelligent Systems, 17(2), March/April, 2002.

           

          (Guest Editor of Special Issue of Journal)."Intelligent Systems in Biology," ed. Lathrop, R.H., IEEE Intelligent Systems, 16(6), November/December, 2001.

           

          Proceedings, Sixth Intl. Conf. on Intelligent Systems for Molecular Biology, eds. Glasgow, J., Lathrop, R., Littlejohn, T., Major, F., Peitsch, M., Sankoff, D., Sensen, C., AAAI Press, Menlo Park, 1998.

           

          "Analysis and Algorithms for Protein Sequence-Structure Alignment," Lathrop, R.H., Rogers, R.G. Jr., Bienkowska, J., Bryant, B.K.M., Buturovic, L.J., Gaitatzes, C., Nambudripad, R., White, J.V., and Smith, T.F., Chapter 12, pp. 227--283, in Computational Methods in Molecular Biology, ed. Salzberg, S.L., Searls, D.B., Kasif, S., Elsevier Science, Amsterdam, 1998.
          (preprint version. Postscript, PDF)

           

          "Predicting Protein Structure with Probabilistic Models," Stultz, C.M., Nambudripad, R., Lathrop, R.H., and White, J.V., in Protein Structural Biology in Biomedical Research, ed. Allewell, N., Woodward, C., Vol. 22B of Advances in Molecular and Cell Biology, 1997, series ed. Bittar, E.E., JAI Press, Greenwich, CT, USA, 1997.

           

          "The Identification of Protein Functional Patterns," Smith, T.F., Lathrop, R.H. and Cohen, F.E., in Integrative Approaches to Molecular Biology, eds. Collado-Vides, J., Magasanik, B., and Smith, T.F., MIT Press, Cambridge, MA, 1996.

           

          Proceedings, Second Intl. Conf. on Intelligent Systems for Molecular Biology, eds. Altman, R., Brutlag, D., Karp, P., Lathrop, R.H., and Searls, D., AAAI Press, Menlo Park, 1994.

           

          "Massively Parallel Symbolic Induction of Protein Structure / Function Relationships," Lathrop, R.H., Webster, T.A., Smith, T.F., and Winston, P.H., pp. 157-173 in Machine Learning: From Theory to Applications, ed. Hanson, S., Remmele, W., and Rivest, R., Springer-Verlag, Berlin, 1993; also appeared in Proc. 27th Hawaii Intl. Conf. on System Sciences, IEEE Computer Soc. Press, 1991.

           

          "Integrating AI With Sequence Analysis," Lathrop, R.H., Webster, T.A., Smith, R., Winston, P.H., and Smith, T.F., pp. 210-258 in Artificial Intelligence and Molecular Biology, ed. Hunter, L., AAAI Press, Menlo Park, 1993.

           

          "ARIEL: A Massively Parallel Symbolic Learning Assistant for Protein Structure / Function," Lathrop, R.H., Webster, T.A., Smith, T.F., and Winston, P.H., in Artificial Intelligence at MIT: Expanding Frontiers, ed. Winston, P.H., and Shellard, S., MIT Press, Cambridge, MA, 1990.

           

           


          Return to Richard H. Lathrop's home page.

          http://www.ics.uci.edu/ugrad/honors/index.php honors opportunities @ the bren school of information and computer sciences
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          http://www.ics.uci.edu/~rickl/rickl-industry.html Richard H. Lathrop industrial experience

          Dr. Richard H. Lathrop --- Industrial Experience
          (excludes ad hoc consulting arrangements, often involving due diligence for bioinformatics-related start-ups)


          (PAGE UNDER CONSTRUCTION)



          Apr. 2004--present (part-time): Co-Founder, Scientific Advisory Board, Consultant.

          CODA Genomics, Inc. (Irvine, CA). (CODA renamed itself to Verdezyne Inc. in 2008.)

          I was a co-inventor of US Patent No.\ 7,262,031 (``Method for producing a synthetic gene or other DNA sequence'').



          July 2001--Dec. 2002 (part-time): Scientific Advisory Board.

          GeneFormatics, Inc. (San Diego, CA).

          The company was dedicated to a proteomics approach to drug discovery. The company is defunct.



          April 1997--Dec. 1999 (part-time): Scientific Advisory Board, Consultant.

          CombiChem, Inc. (San Diego, CA).

          The company is dedicated to drug discovery by computational analysis and combinatorial chemistry. The company went public with 78 full-time employees in May, 1998, and was bought by Dupont Co. for $95 million in Oct., 1999.



          May 1989--Dec. 1993 (part-time): Co-Founder, Senior Scientist, Consultant.

          Arris Pharmaceutical Corp., (South San Francisco, CA).

          The company is dedicated to drug discovery by integrating artificial intelligence and machine learning with advanced biology and chemistry. I was a co-inventor of US Patent No. 5,526,281 (``Machine Learning Approach to Modeling Biological Activity for Molecular Design and to Modeling Other Characteristics''). Arris went public in Nov., 1993, with 59 full-time employees and a valuation of approximately $60 million. It merged with Sequana to form AxyS Pharmaceuticals in Jan., 1998, which was acquired by Celera Therapeutics in Nov., 2001.



          June 1984--Aug. 1991 (summers): Software Engineer, Consultant.

          Gould/A.M.I. VLSI CAD Research Laboratory, (Twain Harte, CA).

          Researched relation of structure to function in VLSI. Designed and implemented a mixed-mode functional simulator, a structural circuit optimizer, and a functional abstraction from structure prototype.



          June 1982--Sept. 1983 (summers): Summer Staff.

          Hewlett-Packard Engineering Productivity Division (Cupertino, CA).

          Assisted the design and implementation of a silicon compiler (DPG, the Data-Path Generator).



          Jan.--May 1980: Business Software Consultant.

          Solid State Equipment Ltd. (Lower Hutt, New Zealand).

          Developed inventory control/job status monitoring package for small businesses.



          Aug. 1978--April 1979: Data Telecommunications Programmer/Analyst.

          Progress Electronics (Portland, OR).

          Wrote entire assembler telecommunications sub-system for the master controller of a remote water-level telemetry system for the National Oceanic and Atmospheric Administration, and a remote air quality and meteorological sensor system for Oregon Department of Environmental Quality.



          May--Sept. 1977: Chief Programmer.

          NSF-SOS research project (Portland OR).

          Designed and implemented complete system for gamma spectra analysis and statistical processing of data.



          May--Oct. 1975, May--Sept. 1976: Business Programmer/Analyst.

          Alaskan Data Systems (Anchorage AK).

          Designed and implemented all programs for each phase of internal bookkeeping system.



          April 1974--May 1975: Programmer and production control supervisor.

          U.S. Postal Service (Anchorage, AK).

          Wrote numerous programs pertaining to productivity analysis and manpower utilization. Developed a computer-assisted training system later adopted by Western Region HQ.




          Return to Richard H. Lathrop's home page. http://www.ics.uci.edu/~kay/labtutors/ ICS 193 Course Reference

          Spring Quarter 2015 — Information and Computer Sciences — UC Irvine

          Lab Tutor Seminar (ICS 193)

          COURSE REFERENCE

          Instructors: David G. Kay (kay@uci.edu), 5056 Donald Bren Hall; Rich Pattis (pattis@uci.edu), 4062 Donald Bren Hall

          Tutor coordinators: Ada Guan (adag@uci.edu) and Andrew Yang (akyang1@uci.edu). Jointly we're reachable at tutors@ics.uci.edu.

          Meeting time and place: Tuesdays 2:00 to 3:20 in 5011 DBH. We expect to skip the meetings in weeks 5, 7, and 9; stay tuned to Email and Piazza for the final determination.

          Course requirements: It's simple: Everyone can get an A who performs professionally. Unprofessional things (like missing scheduled lab sessions or missing the ICS 193 meetings or not preparing and editing some teaching materials [see below]) will lower the grade and pretty quickly result in a tutor being asked not to be a tutor any more. Of course real emergencies come up (but normal academic obligations like midterms and projects aren't real emergencies; you can plan around them); in a real emergency, contact the coordinators and the TA of your section as soon as you possibly can.

          To reiterate: Every tutor is expected to show up to each assigned lab session, to attend each scheduled meeting of ICS 193 [or make alternative arrangement with the coordinators in case of a class conflict], and to prepare and submit and edit some teaching materials as described below.

          Topics to be covered in ICS 193 meetings: Primarily we will discuss issues that have come up or may come up in your tutoring. We will talk some about computer science curricula and teaching in general, and we will also cover relevant aspects of Python and other computer science principles. Refer frequently to the Lab Tutor Guide, http://www.ics.uci.edu/~kay/labtutor.html

          Resources for tutors: This syllabus (with all its hyperlinks) is available on line at http://www.ics.uci.edu/~kay/labtutors/193.s15.html. There are other useful resources on David Kay's Teaching in ICS page.

          We're making heavy use of forums at Piazza.com this quarter (for tutors, as well as for the students). This is a site where questions can be posted and answers can be created collaboratively, wiki-style, both by other students in the class and by instructors.

          • Some or all of ICS 31/32/33/45C/45J/46/51 will have Piazza groups. You should go ahead and add yourself as students to the course(s) you'll be tutoring for, and as time permits, feel free to answer students' postings. Just remember three things: (a) It's better to say nothing than to post something wrong; recovering from incorrect postings is like putting toothpaste back in the tube. (b) Hundreds of students will read what you post, so you want to be sure you don't confuse more people than you help. If the questioner has done something an unusual way, preface your response with something like, "There are other ways to do this; nobody should adopt this approach if they have another approach that's working for them." (c) TAs have a little leeway to clarify assignment requirements for their own sections. So if a student asks, "Do we have to handle the X case?", the answer for that student may not be the same as it was for you when you took the course. If the answer isn't clearly written in the problem specification you should tell them to check with their own TA. Finally, be careful about posting code that actually solves the lab problems; give examples of similar usage or something, but don't write all of the students' code for them. [This is more of an issue after the first couple of weeks; early in the course, it's appropriate to be more concrete.]
          • ICS 193 also has a Piazza group where you can post questions and observations about tutoring. You can use it to ask things you'd otherwise have to postpone to the next Tuesday's ICS 193 class meeting. There's just one thing: If you're tutoring for ICS 32, realize that some of the ICS 31 tutors are students in ICS 32 this quarter. It's not appropriate to post solutions or approaches to the ICS 32 lab problems (or identifiable references to ICS 32 students) on the main ICS 193 Piazza group; it would deprive the ICS 32 students who are ICS 31 tutors of the opportunity to work it out for themselves (and especially since they're the strongest students, they should have that opportunity). The same goes for ICS 33 tutors not posting to the main ICS 193 group information that ICS 33 students who are ICS 31 or 32 tutors shouldn't see. But there's a solution: ICS 32 tutors have been added to the "ICS 32 Section" of the ICS 193 class on Piazza and likewise for ICS 33 tutors and an "ICS 33 Section". General tutoring issues, everyone should post and answer in the main ICS 193 Piazza group. But ICS 32 tutors who want to discuss sensitive ICS 32 issues should have those discussions in the ICS 32 Section, which is visible only to members (and likewise for ICS 33, 45C, and 45J).

          Resources by tutors: As you know, ICS 193 must exist to give tutors units for tutoring; moreover, ICS 193 has to be enough like an academic course so that when the program is eventually reviewed, nobody will object that we're giving units away for nonacademic purposes. At the same time, we'd like ICS 193 to be valuable and stimulating for you as tutors and helpful in making you more effective tutors, thereby improving the students' experience.

          This quarter, instead of just having a weekly seminar meeting, we'll cancel the meeting some weeks and instead ask each tutor to design some teaching materials (that will help students with skills and concepts you've observed they need help with). This is all experimental, so any of the "rules" below can be altered if there's a good reason.

          Basic requirement: During the quarter, each tutor will produce one set of "teaching materials" and will review and edit at least two sets of materials written by other tutors (so that each item is reviewed at least twice). First-time tutors may skip the production part and just edit two or three sets of other materials.

          Quality control: We do two reviews of each item because eventually we will post them in the "Resources" section of the tutors.ics.uci.edu site. That means they have to be clear and grammatical, using correct CS terminology, with fully tested code, following good programming style—in short, as close to perfect as possible. Eventually we'll have a rich resource we can direct students to. Another upside is that you get the glory of seeing your name as the author of the published items.

          Teaching materials can be exercises, programming problems, focused explanations of specific topics, illustrations, animations, summary tables, videos, games, or anything else that might help a student learn a topic in the course you're tutoring for. We're thinking mostly of text-based programming exercises, but if you think a more unconventional form will help you get your point across, go right ahead.

          Stay tuned for logistical and administrative details.


          David G. Kay, kay@uci.edu

          Monday, March 30, 2015 5:04 PM

          http://www.ics.uci.edu/~kay/labtutors/193.f15.html ICS 193 Course Reference

          Fall Quarter 2015 — Information and Computer Sciences — UC Irvine

          Lab Tutor Seminar (ICS 193)

          COURSE REFERENCE

          Instructors: David G. Kay (kay@uci.edu), 5056 Donald Bren Hall; Rich Pattis (pattis@uci.edu), 4062 Donald Bren Hall

          Tutor coordinators: Andrew Yang (akyang1@uci.edu) and German Krikorian (gkrikori@uci.edu). Jointly we're reachable at tutors@ics.uci.edu.

          Meeting time and place: Tuesdays 2:00 to 3:20 in 5011 DBH. We expect to skip the meetings in weeks 5, 7, and 9; stay tuned to Email and Piazza for the final determination.

          Course requirements: It's simple: Everyone can get an A who performs professionally. Unprofessional things (like missing scheduled lab sessions or missing the ICS 193 meetings or not preparing and editing some teaching materials [see below]) will lower the grade and pretty quickly result in a tutor being asked not to be a tutor any more. Of course real emergencies come up (but normal academic obligations like midterms and projects aren't real emergencies; you can plan around them); in a real emergency, contact the coordinators and the TA of your section as soon as you possibly can. 

          To reiterate: Every tutor is expected to show up to each assigned lab session, to attend each scheduled meeting of ICS 193 [or make alternative arrangement with the coordinators in case of a class conflict], and to prepare and submit and edit some teaching materials as described below.

          Topics to be covered in ICS 193 meetings: Primarily we will discuss issues that have come up or may come up in your tutoring. We will talk some about computer science curricula and teaching in general, and we will also cover relevant aspects of Python and other computer science principles. Refer frequently to the Lab Tutor Guide, http://www.ics.uci.edu/~kay/labtutor.html

          Resources for tutors: This syllabus (with all its hyperlinks) is available on line at http://www.ics.uci.edu/~kay/labtutors/193.f15.html. There are other useful resources on David Kay's Teaching in ICS page.

          We're making heavy use of forums at Piazza.com this quarter (for tutors, as well as for the students). This is a site where questions can be posted and answers can be created collaboratively, wiki-style, both by other students in the class and by instructors.

          • Some or all of ICS 31/32/33/45C/45J/46/51 will have Piazza groups. You should go ahead and add yourself as students to the course(s) you'll be tutoring for, and as time permits, feel free to answer students' postings. Just remember three things: (a) It's better to say nothing than to post something wrong; recovering from incorrect postings is like putting toothpaste back in the tube. (b) Hundreds of students will read what you post, so you want to be sure you don't confuse more people than you help. If the questioner has done something an unusual way, preface your response with something like, "There are other ways to do this; nobody should adopt this approach if they have another approach that's working for them." (c) TAs have a little leeway to clarify assignment requirements for their own sections. So if a student asks, "Do we have to handle the X case?", the answer for that student may not be the same as it was for you when you took the course. If the answer isn't clearly written in the problem specification you should tell them to check with their own TA. Finally, be careful about posting code that actually solves the lab problems; give examples of similar usage or something, but don't write all of the students' code for them. [This is more of an issue after the first couple of weeks; early in the course, it's appropriate to be more concrete.]
          • ICS 193 also has a Piazza group where you can post questions and observations about tutoring. You can use it to ask things you'd otherwise have to postpone to the next Tuesday's ICS 193 class meeting. There's just one thing: If you're tutoring for ICS 32, realize that some of the ICS 31 tutors are students in ICS 32 this quarter. It's not appropriate to post solutions or approaches to the ICS 32 lab problems (or identifiable references to ICS 32 students) on the main ICS 193 Piazza group; it would deprive the ICS 32 students who are ICS 31 tutors of the opportunity to work it out for themselves (and especially since they're the strongest students, they should have that opportunity). The same goes for ICS 33 tutors not posting to the main ICS 193 group information that ICS 33 students who are ICS 31 or 32 tutors shouldn't see. But there's a solution: ICS 32 tutors have been added to the "ICS 32 Section" of the ICS 193 class on Piazza and likewise for ICS 33 tutors and an "ICS 33 Section". General tutoring issues, everyone should post and answer in the main ICS 193 Piazza group. But ICS 32 tutors who want to discuss sensitive ICS 32 issues should have those discussions in the ICS 32 Section, which is visible only to members (and likewise for ICS 33, 45C, and 45J).

          Resources by tutors: As you know, ICS 193 must exist to give tutors units for tutoring; moreover, ICS 193 has to be enough like an academic course so that when the program is eventually reviewed, nobody will object that we're giving units away for nonacademic purposes. At the same time, we'd like ICS 193 to be valuable and stimulating for you as tutors and helpful in making you more effective tutors, thereby improving the students' experience. 

          This quarter, instead of just having a weekly seminar meeting, we'll cancel the meeting some weeks and instead ask each tutor to design some teaching materials (that will help students with skills and concepts you've observed they need help with). This is all experimental, so any of the "rules" below can be altered if there's a good reason. 

          Basic requirement: During the quarter, each tutor will produce one set of "teaching materials" and will review and edit at least two sets of materials written by other tutors (so that each item is reviewed at least twice). First-time tutors may skip the production part and just edit two or three sets of other materials. 

          Quality control: We do two reviews of each item because eventually we will post them in the "Resources" section of the tutors.ics.uci.edu site. That means they have to be clear and grammatical, using correct CS terminology, with fully tested code, following good programming style—in short, as close to perfect as possible. Eventually we'll have a rich resource we can direct students to. Another upside is that you get the glory of seeing your name as the author of the published items. 

          Teaching materials can be exercises, programming problems, focused explanations of specific topics, illustrations, animations, summary tables, videos, games, or anything else that might help a student learn a topic in the course you're tutoring for. We're thinking mostly of text-based programming exercises, but if you think a more unconventional form will help you get your point across, go right ahead. 

          Stay tuned for logistical and administrative details. 


          David G. Kay, kay@uci.edu 

          Monday, March 30, 2015 5:04 PM 

          http://www.ics.uci.edu/~kay/courses/31/f15.html ICS 31 Syllabus • Fall 2015

          ICS 31 & CSE 41 • FALL 2015 • DONALD BREN SCHOOL OF INFORMATION AND COMPUTER SCIENCES • UC IRVINE

          ICS 31: Introduction to Programming
          COURSE REFERENCE

          Instructor: David G. Kay, 5056 Donald Bren Hall (DBH) (kay@uci.edu)

          Quick links: Assignments Partner App Quizzes Piazza (Q&A) Textbook home page Resources Final Exam

          Course goals: This course is the first of a three-quarter sequence introducing computer science through computer programming. This course will broaden your technical horizons, focus on systematic problem solving, and possibly change the way you look at the world. We welcome you and we hope you enjoy it.

          Prerequisite concepts: This course does not expect any previous experience in computer science or computer programming. If you do have some experience, you will find some topics familiar but many others will certainly be new to you.

          We do expect each student to be able to navigate the Windows systems in our labs, to navigate the World-Wide Web, to download and read documents in Adobe Acrobat (pdf) format, and to read and send electronic mail. Some of our assignments will require these skills. If you need to pick these skills up or sharpen them, do it in the first week of the quarter; speak with us if you need a hand.

          Meeting place and times: Tuesdays and Thursdays, lecture section A meets 8:00 a.m. to 9:20 a.m. in Social Science Lecture Hall 100 and lecture section B meets 11:00 a.m. to 12:20 p.m. in Howard Schneiderman Lecture Hall 100A. Each student must enroll in one lecture section and attend the section he or she is enrolled in.

          Lab sections, TAs, and tutors: Each student must enroll in one of these lab sections:

          Lab Time Room TA (Email @uci.edu) Tutor(s) (subject to change)
          1 MWF 8–9:50am ICS 183 Prateek Basavpur Swamy (pbasavap) Matthew Downing, Phuc Pham
          2 MWF 10–11:50 ICS 183 Prateek Basavpur Swamy (pbasavap) Emory Jiang, Wilmer Domingo
          3 MWF 12–1:50 ICS 183 Rohit Malhotra (rohitm) Andrew Yang (ex F 1), Rachel Lee (ex MW 1),
          Ji Yeon Kim (MF1)
          4 MWF 2–3:20 ICS 183 Rohit Malhotra (rohitm) Madeline Chan, Michelle Tjoa
          5 MWF 4–5:50 ICS 183 Roeland Singer-Heinze (rsingerh) Max Shapiro, Noah Djenguerian
          6 MWF 6–7:50 ICS 183 Roeland Singer-Heinze (rsingerh) Nathan Padrid, Omar Morales
          7 MWF 8–9:50pm ICS 183 Akshat Amrish Patel (akshatp)  
          8 MWF 8–9:50am ICS 364A Shibani Konchady (skonchad) Weikuan Sun
          9 MWF 10–11:50 ICS 364A Shibani Konchady (skonchad) ChanWoo Park, Tina Yang
          10 MWF 12–1:50 ICS 364A Vignesh Raghunathan (raghunav) Yiteng Zhang, Jean Truong
          11 MWF 2–3:20 ICS 364A Vignesh Raghunathan (raghunav) Yiqiao Zhao, Ji Yeon Kim (MWF 3)
          12 MWF 4–5:50 ICS 364A Akshat Amrish Patel (akshatp) Yanlin Lu, Huy Pham
          13 MWF 8–9:50am ICS 189 Kartik Saxena (saxenak) Sharon Babu, Haoming Li
          14 MWF 10–11:50 ICS 189 Kartik Saxena (saxenak) Aaron Ching, Nicky Huynh
          15 MWF 12–1:50 ICS 189 Archit Dey (architd1) Philip Yun, Alyssa Lingad
          16 MWF 2–3:20 ICS 189 Archit Dey (architd1) Justin Dong, Rachel Lee (MW 3)

          None of the six scheduled lab hours each week are optional. It is essential that you attend the lecture and lab you're officially enrolled in; the class is too full to accommodate "visiting." Most of your lab work will be done in pairs, so the scheduled lab times are the best times to work with your partner. Because the labs are full, switching lab or lecture sections is not automatically supported by the WebReg system. We can't promise there's a way around this, but we're looking and if we find one we'll let the class know. Note that lab sections 8 through 12 are "laptop only sections"; in accordance with the ICS Laptop Policy, students in those sections are required to bring a laptop computer to every section meeting.

          Office hours: You are welcome to drop by my office at any time. If I'm there and not immersed in something else, I'll be glad to chat about the course material or other topics. I will definitely be in or near my office during these scheduled hours, during which course-related matters will have first priority: Tuesdays from 12:30 to 1:15 and Wednesdays from 11:30 to 12:20 (starting the first day of class and ending the last day of class). I may adjust these times at the end of the first week. Of course emergencies may come up, but I will try to give advance notice of any change. I'll also be happy to make arrangements for other times during the week; "making an appointment" is no big deal (but if you make one, don't skip it without getting in touch). The quickest and most effective way to reach me is by electronic mail, as described below.

          Questions and announcements: You can get a response to your course-related questions most quickly by posting them at Piazza.com. If you need to reach one of us privately, use our individual IDs listed above. I will never intentionally ignore a message, so if you don't receive a response, write again; sometimes overactive spam filters snag a legitmate message. Using course-specific subject lines and your UCInet Email address will help your messages get noticed.

          We will also send course announcements by Email to the official course mailing list, so you should check your Email daily. Note that this mailing list goes to the Email address that the registrar has for you (your UCInet ID). If you prefer to read your Email on another account, forwarding instructions appear below. Don't let this slide; if you miss official announcements, your grade could suffer.

          This course's web page is at http://www.ics.uci.edu/~kay/courses/31 and official course Email is archived at eee.uci.edu; follow the link on your MyEEE page.

          Textbooks and software: Our textbook this quarter is Introduction to Computing Using Python: An Application Development Focus, second edition., by Ljubomir Perkovic.

          The course resource page has more information about these alternatives.

          The Python software you need for your work (Python 3.5 and the IDLE environment) is installed on every machine in the ICS labs. Because every lab assignment is done with a partner, you'll do much of your work in the scheduled labs. You are also welcome to install Python on your personal machine; we just have to say that because everyone's computer is configured a little differently, we can't promise to fix the installation problems that may come up on your own machine. At http://www.ics.uci.edu/~kay/courses/31/refs.html is a list of supplementary resources on course topics, including information about installing Python.

          Labs and lab hours: Of course you will need to do some of your work outside of the scheduled Monday/Wednesday/Friday lab hours. Students in ICS 31 may use any of the school's instructional computing labs except for times when another course is scheduled in the lab exclusively. See http://www.ics.uci.edu/~lab for lab hours and other information. Note in particular that all ICS labs are closed on university holidays such as the Friday after Thanksgiving (but not the Saturday and Sunday).

          Please remember that programming tasks always take longer to complete than you think they will, no matter how much experience you have. You must account for this as you plan your time; we cannot accept busy schedules or time mismanagement as an excuse for late or incomplete assignments.

          Course structure:
          • Weekly lab assignments (30% of the course grade). All the assignments will be available at
          http://www.ics.uci.edu/~kay/courses/31/hw/.
          • Class participation: Based primarily on participating consistently and effectively in the lab, including turning in a partner evaluation for each lab assignment. Responding to various electronic surveys and contributing on Piazza.com may help this score, too. (10%)
          • Weekly quizzes: Given on line through EEE, available from Sunday afternoon through 11:00 p.m. Tuesday. (10% together) Because these are self-graded, you get credit for completing them, whatever your answers. We can't support make-ups for missed quizzes, since all you have to do is read them and submit answers each week on EEE. This is easy credit to get; just don't forget to do each quiz by Tuesday. There is more information at http://www.ics.uci.edu/~kay/courses/31/quiz/ .
          • Two midterms: Given in lecture, the first will be on Tuesday, October 20, and the second on Thursday, November 12 (20%, with the second being weighted more than the first). We can't give make-ups for missed midterms, but if you miss one for a good reason (and let us know about it), we won't count it against your grade.
          • One final exam: On Tuesday, December 8. Lecture section A's exam is from 8:00 to 10:00 a.m.; section B's exam is from 10:30 a.m. to 12:30 p.m. (30%).

          Your TA has primary responsibility for evaluating your work; see the TA first for any questions about grading or scoring. If that does not resolve your question, then see the instructor. To compensate for differences in grading between different TAs, we will calculate final grades for each TA's students separately rather than for the entire class as one group. Thus, comparison of scores between TAs is meaningless.

          Lab scores will be recorded on the web in the EEE gradebook. We will be happy to correct any errors that do occur, but we must ask that you bring your TA your grading questions within a week after the item is returned; the course moves quickly and we simply can't deal with assignments long past.

          Our goal is to obtain the fairest overall grade outcomes. We find that we achieve this best by not committing in advance to a specific fixed point scale for assigning final letter grades in the course. Thus, no letter grades apply to anything until the end of the course and it is not possible to calculate your letter grade precisely until all the work has been done. Don't ask, "What grade am I getting so far?"; instead ask, "What more can I do to master the material?" or "What should I have done differently on this assignment (or exam)?" We urge everyone to focus not on letter grades but on learning what's necessary to earn high scores; the grades will follow from that. You should check the EEE gradebook periodically to make sure your assignment scores have been recorded correctly. EEE also indicates where your score falls compared to other students; if you find yourself in the bottom quarter consistently, talk with your TA or the instructor.

          We're required to say that in unusual circumstances, these policies may change, but we do not expect that to happen.

          Special needs: Any student who feels he or she may need an accommodation due to a disability should contact the UCI Disability Services Center at (949) 824-7494 as soon as possible to explore the possible range of accommodations. We encourage all students having difficulty, whether or not due to a disability, to consult privately with the TA or instructor at any time.

          What to do this week to get started in ICS 31:
          — If you do not have a UCInet ID, get one (see http://activate.uci.edu/).
          — Learn how to read electronic mail sent to your UCInet ID (see http://www.oit.uci.edu/email/). If you prefer to read your electronic mail on an account other than your UCInet account, redirect your mail at http://www.oit.uci.edu/email/deliverypoint.html. If you don't read your course Email most every day, you may miss vital announcements.
          — If you do not have an ICS account for access to the Windows network in ICS, get one. See http://www.ics.uci.edu/~lab/students/acct_activate.php. The labs will have temporary IDs for the first week or two; get your own ID some time in that period.
          — On the Web, go to checkmate.ics.uci.edu, log in with your UCInet ID, choose "Fall 2015" and "Course Listing," click "Go" next to ICS 31, and then click "List me for this course." Use your UCInet ID (your Email adddress @uci.edu) for this. You'll submit most of your work electronically; this step is necessary to set that up.
          — Sign yourself up for ICS 31 at Piazza.com and read a little bit there about how the site works.
          — Get a lab printing key-card if you plan to do any printing in the lab. See http://www.ics.uci.edu/~lab/students/printing.php.
          — Complete the survey at http://eee.uci.edu/survey/ics31.f15.q.

          Good advice and helpful hints:

          Make every effort to attend each class meeting; we will often cover material that isn't directly in the textbook. It's not fair to ask the TA simply to repeat lecture material you missed, though of course the TA will always answer questions about it. And even though UCI Replay recordings will be available for most class sessions, they don't capture everything that goes on in class and they aren't 100% reliable (i.e., some days there will be no recording).

          Attend your lab section, too; you will do most of your lab work there, and you can get a fresh perspective from the TA or lab tutor. Don't hesitate to ask your TA to address topics that will help you. Since most of your work will be done with a partner, your partner also depends on your consistent presence.

          Check your electronic mail regularly; this is an official channel for course announcements.

          Keep up with the reading; you'll need it to do your assignments, and the quarter system goes so fast that a few missed pages can quickly become a few chapters if you're not careful. You will want to read the assigned sections early so you can ask us about parts that aren't clear.

          The assignments, like all technical specifications, require careful and thorough reading and re-reading. Expect to refer back to the assignment often, and check it first when you have questions about what's required or how to proceed. Before you come to lab, be sure to read the assignment to get an idea of what's required.

          Start each assignment early. Assignments will be due weekly, but you'll need to spend some time on them nearly every day, especially after the first couple of weeks. Programming always takes longer to complete than you think it will, even if you have previous programming experience. By starting early, you'll have time to ask in discussion section about problems you encounter.

          If you find yourself having trouble or getting behind, speak with your TA or the instructor. We have lots of ways to help. But never take the shortcut of copying someone else's work and turning it in; the consequences can be far worse than just a low score on a lab assignment or exam. ICS takes academic honesty very seriously; for a more complete discussion, see our course collaboration guidelines (http://www.ics.uci.edu/~kay/courses/31/collab.html) and the ICS departmental web page covering academic honesty issues (http://www.ics.uci.edu/ugrad/policies/index.php#academic_honesty).

          Turn in whatever portion of an assignment you have completed on the due date. It's much better to turn in something rather than nothing; zeroes are hard to make up. In some circumstances you may arrange with your TA to work further on an assignment after the due date, but you must turn in whatever you have when the official due date comes. Likewise, don't skip any quizzes or partner evaluations if you can help it; too many zeroes will significantly lower your overall score.

          Always keep your own copy of each assignment, both electronically and on paper; if an assignment should get lost in the shuffle (or if a file server in the lab should crash, which has happened), we'll expect you to be able to supply a replacement easily.

          Approximate course outline:

          Week Date Topics
          0. 24 September Introduction to the course and computing • Models and abstraction
          1. 29 September Things (nouns)/objects/data/information and actions (verbs)/statements/operations/functions
          1 October Evaluating expressions • Numbers, strings • Variables • Input/output, imperative programming
          2. 6 October Multiple-valued data • Namedtuples • Lists • Programmer-defined functions, design recipe
          8 October Programming with multiple-valued data • Basic selection (if) and repetition (for)
          3. 13 October Arguments and parameters • Extended example
          15 October Mutable and immutable data • Mutable parameters and function side-effects
          4. 20 October — First Midterm —
          23 October Programming with nested data structures
          5. 27 October Strings and text processing
          29 October Formatting output
          6. 3 November Files
          5 November Control structures revisited
          7. 10 November Combining data structures
          12 November — Second Midterm —
          8. 17 November Two-dimensional tables
          19 November Dictionaries
          9. 24 November Tuples and sets
          26 November — No class meeting: Thanksgiving —
          10. 1 December Extended example
          3 December Looking back and looking forward
          F. 8 December Final exam, Tuesday, sec. A from 8:00 to 10:00 a.m., sec. B from 10:30 a.m. to 12:30 p.m.


          David G. Kay, kay@uci.edu
          Tuesday, December 1, 2015 4:44 PM
          http://www.ics.uci.edu/~kay/uteach/w16.html US 197B Course Reference
          Winter 2016 — UTeach Program — Division of Undergraduate Education — UC Irvine

          UTeach Seminar (University Studies 197B)

          UTeach: Theory and Practice

          Course Reference

          Instructors: David G. Kay, 5056 Donald Bren Hall (kay@uci.edu); Gail K. Hart, 229 Humanities Instructional Building (gkhart@uci.edu); Jacky Schlegel (schlegej@uci.edu)

          Quick links: Assignments Piazza Q&A Resources

          Course goals and learning outcomes: This course is exclusively for participants in the 2015–16 UTeach program. [Applications for the 2016–17 program will be available in Spring 2016 at the UTeach Program site.] We will cover course organization, presentation techniques and skills, classroom management, motivating students, giving and receiving feedback, and other aspects of college teaching.

          Meeting place and times: Class meets Tuesdays from 5:00 to 6:20 in Donald Bren Hall 1420 (but not both days every week).

          Office hours: We're happy to hear from UTeachers any time. Drop by, send a message, arrange a specific time if you like. You can also get pretty apid feedback from your colleagues and instructors by posting on Piazza.com.

          We will never intentionally ignore a message, so if you don't receive a response, write again; sometimes overactive spam filters snag a legitmate message.

          We will also send course announcements by Email to the official course mailing list, so you should check your Email regularly. Note that this mailing list goes to the Email address that the registrar has for you (your UCInet ID). If you prefer to read your electronic mail on an account other than your UCInet account (which is a good idea, since if your UCInet account's mailbox fills up, you stop receiving mail), redirect your mail at http://www.oit.uci.edu/email/deliverypoint.html .

          Course structure: This course is graded P/NP. Passing requires professionanl performance in (a) attendance and participation [Most of your learning will come from class discussions, presentations, activities, and feedback. We expect you to attend and participate actively in every class meeting except for schedule conflicts we know about and real emergencies], (b) scheduled presentations [Deliver each of your three presentations—5-minute, 10-minute, and 30-minute—well prepared on the scheduled date], and (c) written materials [Submit your final syllabus and detailed course plan according to the course schedule. Both documents must address previous comments of reviewers and demonstrate your readiness to teach your course.].

          Special needs: Any student who feels he or she may need an accommodation due to a disability should contact the UCI Disability Services Center at (949) 824-7494 as soon as possible to explore the possible range of accommodations. We encourage all students having difficulty, whether or not due to a disability, to consult privately with the instructor at any time.

          Approximate course schedule: Dates that are empty, we won't meet.

          Week Date Topics
          1. Tuesday
          5 January
           
          Thursday
          7 January
          Introductions • Five-minute presentations
          2. Tuesday
          12 January
          Five-minute presentations • Syllabi and workloads • Attendance policies
          Sign up for your 10-minute presentations.
          Thursday
          14 January
           
          3. Tuesday
          19 January
          Learning styles • Active learning • Learning outcomes
          Thursday
          21 January
           
          4. Tuesday
          26 January
           
          Thursday
          28 January
          Ten-minute presentations (#1–#4)
          5. Tuesday
          2 February
          Ten-minute presentations (#5–#8)
          Sign up for thirty-minute presentations.
          Thursday
          4 February
          Peer editing of syllabi • Promoting discussion
          Bring two paper copies of your syllabus.
          6. Tuesday
          9 February
          Classroom management • Classroom technologies • Other issues
          Thursday
          11 February
          Thirty-minute presentations (#1, #2)
          Final revisions of syllabus and course plan due, Friday 5:00 on EEE dropbox.
          7. Tuesday
          16 February
          Thirty-minute presentations (#3, #4)
          Thursday
          18 February
           
          8. Tuesday
          23 February
          Thirty-minute presentations (#5, #6)
          Thursday
          25 February
          Thirty-minute presentations (#7, #8)
          9. Tuesday
          1 March
          Remaining issues [tentative]
          Syllabus and course plan with final comments addressed, due Friday 5:00 on EEE dropbox.
          Thursday
          3 March
           
          10. Tuesday
          8 March
           
          Thursday
          10 March
          Wrap-up • Last-minute concerns
          F. Thursday
          17 March, 4-6
          TBA (We won't have an exam, but we'll reserve this time in case we need it for extra presentations or something)


          http://www.ics.uci.edu/~kay/courses/398a/f15.html ICS TA Seminar

          Fall Quarter 2015 — Information and Computer Sciences — UC Irvine

          Teaching Assistant Seminar (ICS 398A)

          COURSE REFERENCE

          Instructor: David G. Kay, 5056 Donald Bren Hall. Stop by any time, or send electronic mail to "kay".

          Meeting time and place: Fridays, 1:00 to 2:50 p.m. in room MSTB 118. As a two-unit class, ICS 398A will meet for about 15 hours in total, though we're expecting a large class so we may have a couple of semi--optional meetings on Wednesdays.

          Enrollment information: All new or prospective ICS teaching assistants are required to attend ICS 398A, which is offered this year only in the fall quarter. It's ideal to take ICS 398 in the first quarter you're actually teaching, although that isn't always possible. But if you know you won't be teaching until next year, you could wait until then.

          ICS 398B, the Advanced TA Seminar, is offered some years in the spring quarter. ICS 398B covers such issues as designing assignments, exams, and courses, assigning grades, and other issues in running your own course; it is valuable for everyone, and required for TAs who may wish to teach their own classes (as many grad students do in summer session or through University Extension, not to mention in their own academic jobs). You may take ICS 398B later in your career, but you must complete it before you can be appointed as an instructor in summer session or Extension.

          Course requirements: This seminar is not designed to impose a time burden on the participants beyond the class meetings themselves. Grading is satisfactory/unsatisfactory or pass/not-pass, and we ask only that you attend the meetings, participate in the discussions and activities, give one or two short presentations on material you expect to be teaching, and arrange through the campus Teaching, Learning & Technology Center (x46188) to be videotaped in your class (whichever quarter you will be teaching).

          Any benefit you get from this course (unlike most computer science classes, but like most seminars) comes from the in-class discussions and activities. We expect everyone to attend every meeting; that's not asking very much. You may have to miss a meeting (e.g., because you have to travel to a conference), but missing more than one will not be looked on favorably. Please check your calendar for the rest of the quarter and resolve any conflicts now.

          Topics to be covered: In a seminar like this, the topics don't all come in a predetermined order. Over the course of the quarter we will cover the following, and more: motivating students, teaching techniques and styles, grading exams and assignments, dealing with cheating and problem students, working with faculty, departmental and university policies. Most of our time will be devoted to discussion and hands-on participatory activities.

          Approximate course schedule: 

          Week 0

          25 September

          Introductions and overview of university teaching

          Week 1

          2 October 

          TA presentations and presentation strategies

          Week 2

          9 October

          TA presentations 

          Week 3

          16 October

          TA presentations

          Week 4

          23 October

          Grading policies, practices, philosophies

          Week 5

          30 October

          Exam grading

          Week 6

          6 November

          Grading students' programs and projects

          Week 7

          13 November

          Preventing and detecting academic dishonesty

          Week 8

          20 November

          Motivation, problem students, teaching styles, epilogue and looking ahead

          Week 9

          27 November

          — HOLIDAY —

          Week 10

          4 December 

          — No class meeting —


          Quick resource guide:

          This syllabus (with all its hyperlinks) is available on line at http://www.ics.uci.edu/~kay/courses/398a/. There are other useful resources on David Kay's Teaching in ICS page.

          If you don't have an assigned office (or prefer not to hold your scheduled office hours there), you may use a designated room (ICS 406A or 406B) by advance reservation with the appropriate staff support person in your department: in Statistics, Rosemary Busta [rbusta@ics.uci.edu]; in Computer Science, Carolyn Simpson [cmsimpso@ics.uci.edu]; in Informatics, Suzie Barrows [sbarrows@uci.edu]),

          For information about network-based class support (class Email lists, home pages, rosters, and so on), see http://eee.uci.edu. Within ICS, we support automatic assignment submission via http://checkmate.ics.uci.edu (have your instructor contact checkmate@ics.uci.edu) and automated detection of plagiarism in prose and code (see http://www.ics.uci.edu/~kay/checker.html).

          For a UCInet ID (an Email account @uci.edu—you'll need one for access to some EEE features and Checkmate), see http://activate.uci.edu/. You can redirect Email from this account to another account (@ics.uci.edu, for example) via the web: http://www.oit.uci.edu/email/deliverypoint.html.

          We have set up a forum at Piazza.com for you to make comments or ask questions about anything relating to teaching in ICS. Sign yourself up and then participate!

          David G. Kay, kay@uci.edu 

          Monday, September 29, 2014 9:22 AM 

          http://www.ics.uci.edu/~kay/teachingics.html Teaching in ICS

          Students Teaching Computer Science

          in ICS at UCI


          Students have a variety of opportunities to help other students learn computer science at UCI:

          • Graduate students may be awarded TAships; contact your advisor or the ICS Graduate Office (Kris Bolcer or Karina Bocanegra).
          • Undergraduate students help students as lab tutors for some first-year courses (more information).
          • Undergraduates occasionally work as graders; contact the departmental office (Computer Science, Informatics, or Statistics).
          • Graduate students may be hired as TAs on a quarterly basis; contact your departmental office. In certain cases, advanced undergraduates may also be hired as TAs.
          • Students may also gain teaching experience by working as a tutor at the Learning and Academic Resources Center (LARC)

          TAs whose native language isn't English can use these strategies for effective communication in the classroom.

          Instructors who think electronic mail makes their life harder might enjoy these tips for responding to student e-mail.

          Lab tutors (current and prospective) should read ICS 31/32/33 Lab Tutor Guide. For a detailed view of the duties of a TA or section leader in a lower-division class, consult the ICS 31/32/33 Guide. (Many of the details vary from one course to another.)

          For a detailed list of logistical details a TA or reader should work out with a course instructor early in the quarter, see What to Ask the Instructor.

          The UCI Teaching, Learning & Technology Center (TLTC) provides a wide variety of services relating to teaching.

          UCI's Electronic Educational Environment (EEE) at e3.uci.edu provides course rosters, course mailing lists, electronic grade distribution, web-based course evaluations, and other services.


          http://www.ics.uci.edu/~kay/choosing.courses.html Choosing Your Courses

          UCI is a great place, and here's how I think you can get the most out of it:

          • Take every opportunity to become a good writer—not necessarily a literary writer, but a clear, cogent, persuasive writer. Your success at anything you do as an educated person will depend on your ability to write coherently.

            This kind of good writing is made, not born; you have to work at it, practicing continually. Classes that require written assignments and projects are a perfect opportunity to sharpen these skills; avoiding them is a cowardly mistake.

          • Read everything you can get your hands on—not just textbooks, but fiction, magazines, and newspapers. Reading will help you become a better writer, and will widen your knowledge far beyond what you can experience personally.

            Instead of watching the TV news, read the newspaper; instead of entertainment TV, read an entertaining novel.

            Someone who doesn't read is hardly better off than someone who can't read.

          • Take as wide a variety of courses as you can—not just in your major, but from every corner of the campus.

            You should use your undergraduate years as an opportunity to learn about the world, about the achievements and diversity of humanity. After you graduate, you will find yourself concentrating more on your career and your family, but while you're here at UCI, it couldn't be easier: Just sign up for a course, be it botany, Russian drama, art history, or Chinese politics.

          One of UCI's greatest strengths is its diversity; take advantage of it, and good luck!


          David G. Kay, kay@uci.edu
          http://www.ics.uci.edu/~kay/courses/31/w16.html ICS 31 Syllabus • Winter 2016

          ICS 31 & CSE 41 • WINTER 2016 • DONALD BREN SCHOOL OF INFORMATION AND COMPUTER SCIENCES • UC IRVINE

          ICS 31: Introduction to Programming
          COURSE REFERENCE

          Instructor: David G. Kay, 5056 Donald Bren Hall (DBH) (kay@uci.edu)

          Quick links: Assignments Partner App Quizzes Piazza (Q&A) Textbook home page Resources

          Course goals: This course is the first of a three-quarter sequence introducing computer science through computer programming. This course will broaden your technical horizons, focus on systematic problem solving, and possibly change the way you look at the world. We welcome you and we hope you enjoy it.

          Prerequisite concepts: This course does not expect any previous experience in computer science or computer programming. If you do have some experience, you will find some topics familiar but many others will certainly be new to you.

          We do expect each student to be able to navigate the Windows systems in our labs, to navigate the World-Wide Web, to download and read documents in Adobe Acrobat (pdf) format, and to read and send electronic mail. Some of our assignments will require these skills. If you need to pick these skills up or sharpen them, do it in the first week of the quarter; speak with us if you need a hand.

          Meeting place and times: Tuesdays and Thursdays from 8:00 to 9:20 a.m. in room 1200, Biological Sciences 3. Lab sections meet for the first time on Wednesday, January 6, after the first lecture.

          Lab Time Room TA (Email @uci.edu) Tutor(s) (subject to change)
          1 MWF 8–9:50am ICS 183 Shibani Konchady (skonchad) Brenda La, Sheila Truong
          2 MWF 10–11:50 ICS 183 Shibani Konchady (skonchad) Haoming Li, Anshul Singhal
          3 MWF 4–5:50 ICS 183 Akshat Amrish Patel (akshatp) Aps Jakhanwal, Gustavo Lopez
          4 MWF 6–7:50 ICS 183 Akshat Amrish Patel (akshatp) Kevin Chow, Linyu Wang
          5 MWF 8–9:50am ICS 192 Aniket Shivam (aniketsh) Brian Huynh
          6 MWF 10–11:50 ICS 192 Aniket Shivam (aniketsh) Leona He, Vatsal Rustagi
          7 MWF 4–5:50 ICS 364A Roeland Singer-Heinze (rsingerh) Sid Kasat, Huy Pham
          8 MWF 6–7:50 ICS 364A Roeland Singer-Heinze (rsingerh) Christian Villlamea, Kevin Yin

          None of the six scheduled lab hours each week are optional. It is essential that you attend the lecture and lab you're officially enrolled in; the class is too full to accommodate "visiting." Most of your lab work will be done in pairs, so the scheduled lab times are the best times to work with your partner. Because the labs are full, switching lab or lecture sections is not automatically supported by the WebReg system. We can't promise there's a way around this, but we're looking and if we find one we'll let the class know. Note that lab sections 7 and 8 are "laptop only sections"; in accordance with the ICS Laptop Policy, students in those sections are required to bring a laptop computer to every section meeting.

          Office hours: You are welcome to drop by my office at any time. If I'm there and not immersed in something else, I'll be glad to chat about the course material or other topics. I will definitely be in or near my office during these scheduled hours, during which course-related matters will have first priority: Tuesdays from 11:00 to 11:30 and Thursdays from 9:30 to 10:00 (starting the first day of class and ending the last day of class). I may adjust these times at the end of the first week. Of course emergencies may come up, but I will try to give advance notice of any change. I'll also be happy to make arrangements for other times during the week; "making an appointment" is no big deal (but if you make one, don't skip it without getting in touch). The quickest and most effective way to reach me is by electronic mail, as described below.

          Questions and announcements: You can get a response to your course-related questions most quickly by posting them at Piazza.com. If you need to reach one of us privately, use our individual IDs listed above. I will never intentionally ignore a message, so if you don't receive a response, write again; sometimes overactive spam filters snag a legitmate message. Using course-specific subject lines and your UCInet Email address will help your messages get noticed.

          We will also send course announcements by Email to the official course mailing list, so you should check your Email daily. Note that this mailing list goes to the Email address that the registrar has for you (your UCInet ID). If you prefer to read your Email on another account, forwarding instructions appear below. Don't let this slide; if you miss official announcements, your grade could suffer.

          This course's web page is at http://www.ics.uci.edu/~kay/courses/31 and official course Email is archived at eee.uci.edu; follow the link on your MyEEE page.

          Textbooks and software: Our textbook this quarter is Introduction to Computing Using Python: An Application Development Focus, second edition., by Ljubomir Perkovic. The course resource page has more information.

          The Python software you need for your work (Python 3.5 and the IDLE environment) is installed on every machine in the ICS labs. Because every lab assignment is done with a partner, you'll do much of your work in the scheduled labs. You are also welcome to install Python on your personal machine; we just have to say that because everyone's computer is configured a little differently, we can't promise to fix the installation problems that may come up on your own machine. At http://www.ics.uci.edu/~kay/courses/31/refs.html is a list of supplementary resources on course topics, including information about installing Python.

          Labs and lab hours: Of course you will need to do some of your work outside of the scheduled Monday/Wednesday/Friday lab hours. Students in ICS 31 may use any of the school's instructional computing labs except for times when another course is scheduled in the lab exclusively. See http://www.ics.uci.edu/~lab for lab hours and other information. Note in particular that all ICS labs are closed on university holidays such as the Friday after Thanksgiving (but not the Saturday and Sunday).

          Please remember that programming tasks always take longer to complete than you think they will, no matter how much experience you have. You must account for this as you plan your time; we cannot accept busy schedules or time mismanagement as an excuse for late or incomplete assignments.

          Course structure:
          • Weekly lab assignments (30% of the course grade). All the assignments will be available at
          http://www.ics.uci.edu/~kay/courses/31/hw/.
          • Class participation: Based primarily on participating consistently and effectively in the lab, including registering a partner each week in the Partner App, following the pair programming method, and turning in a partner evaluation for each lab assignment. Responding to various electronic surveys and contributing on Piazza.com may help this score, too. (10%)
          • Weekly quizzes: Given on line through EEE, available from Sunday afternoon through 11:00 p.m. Tuesday. (10% together) Because these are self-graded, you get credit for completing them, whatever your answers. We can't support make-ups for missed quizzes, since all you have to do is read them and submit answers each week on EEE. This is easy credit to get; that means everyone else is getting it and your grade may be surprisingly low if you skip them. Just don't forget to do each quiz by Tuesday. There is more information at http://www.ics.uci.edu/~kay/courses/31/quiz/ .
          • Two midterms: Given in lecture, the first will be on Tuesday, January 26, and the second on Thursday, February 18 (20%, with the second being weighted more than the first). We can't give make-ups for missed midterms, but if you miss one for a good reason (and let us know about it), we won't count it against your grade.
          • One final exam: On Tuesday, March 15, from 8:00 to 10:00 a.m. (30%).

          Your TA has primary responsibility for evaluating your work; see the TA first for any questions about grading or scoring. If that does not resolve your question, then see the instructor. To compensate for differences in grading between different TAs, we will calculate final grades for each TA's students separately rather than for the entire class as one group. Thus, comparison of scores between TAs is meaningless.

          Lab scores will be recorded on the web in the EEE gradebook. We will be happy to correct any errors that do occur, but we must ask that you bring your TA your grading questions within a week after the item is returned; the course moves quickly and we simply can't deal with assignments long past.

          Our goal is to obtain the fairest overall grade outcomes. We find that we achieve this best by not committing in advance to a specific fixed point scale for assigning final letter grades in the course. Thus, no letter grades apply to anything until the end of the course and it is not possible to calculate your letter grade precisely until all the work has been done. Don't ask, "What grade am I getting so far?"; instead ask, "What more can I do to master the material?" or "What should I have done differently on this assignment (or exam)?" We urge everyone to focus not on letter grades but on learning what's necessary to earn high scores; the grades will follow from that. You should check the EEE gradebook periodically to make sure your assignment scores have been recorded correctly. EEE also indicates where your score falls compared to other students; if you find yourself in the bottom quarter consistently, talk with your TA or the instructor. Likewise, check the EEE Quiz tool and the Partner App periodically to make sure you're getting credit for the quizzes and your lab partnerships and evaluations.

          We're required to say that in unusual circumstances, these policies may change, but we do not expect that to happen.

          Special needs: Any student who feels he or she may need an accommodation due to a disability should contact the UCI Disability Services Center at (949) 824-7494 as soon as possible to explore the possible range of accommodations. We encourage all students having difficulty, whether or not due to a disability, to consult privately with the TA or instructor at any time.

          What to do this week to get started in ICS 31:
          — If you do not have a UCInet ID, get one (see http://activate.uci.edu/).
          — Learn how to read electronic mail sent to your UCInet ID (see http://www.oit.uci.edu/email/). If you prefer to read your electronic mail on an account other than your UCInet account, redirect your mail at http://www.oit.uci.edu/email/deliverypoint.html. If you don't read your course Email most every day, you may miss vital announcements.
          — If you do not have an ICS account for access to the Windows network in ICS, get one. See http://www.ics.uci.edu/~lab/students/acct_activate.php. The labs will have temporary IDs for the first week or two; get your own ID some time in that period.
          — Go to the ICS Partner App and fill out your personal profile. You will do each lab assignment with one partner, a different person every week, with no repetitions. You can pick a partner using the Partner App or in person, but either way you will use the Partner App to register each partnership officially (or you won't get credit).
          — On the Web, go to checkmate.ics.uci.edu, log in with your UCInet ID, choose "Winter 2016" and "Course Listing," click "Go" next to ICS 31, and then click "List me for this course." Use your UCInet ID (your Email adddress @uci.edu) for this. You'll submit most of your work electronically; this step is necessary to set that up.
          — Sign yourself up for ICS 31 at Piazza.com and read a little bit there about how the site works.
          — Get a lab printing key-card if you plan to do any printing in the lab. See http://www.ics.uci.edu/~lab/students/printing.php.
          — Complete the survey at http://eee.uci.edu/survey/ics31.w16.q.

          Good advice and helpful hints:

          Make every effort to attend each class meeting; we will often cover material that isn't directly in the textbook. It's not fair to ask the TA simply to repeat lecture material you missed, though of course the TA will always answer questions about it. And even though UCI Replay recordings will be available for most class sessions, they don't capture everything that goes on in class and they aren't 100% reliable (i.e., some days there will be no recording).

          Attend your lab section, too; you will do most of your lab work there, and you can get a fresh perspective from the TA or lab tutor. Don't hesitate to ask your TA to address topics that will help you. Since most of your work will be done with a partner, your partner also depends on your consistent presence.

          Check your electronic mail regularly; this is an official channel for course announcements.

          Keep up with the reading; you'll need it to do your assignments, and the quarter system goes so fast that a few missed pages can quickly become a few chapters if you're not careful. You will want to read the assigned sections early so you can ask us about parts that aren't clear.

          The assignments, like all technical specifications, require careful and thorough reading and re-reading. Expect to refer back to the assignment often, and check it first when you have questions about what's required or how to proceed. Before you come to lab, be sure to read the assignment to get an idea of what's required.

          Start each assignment early. Assignments will be due weekly, but you'll need to spend some time on them nearly every day, especially after the first couple of weeks. Programming always takes longer to complete than you think it will, even if you have previous programming experience. By starting early, you'll have time to ask in discussion section about problems you encounter.

          If you find yourself having trouble or getting behind, speak with your TA or the instructor. We have lots of ways to help. But never take the shortcut of copying someone else's work and turning it in; the consequences can be far worse than just a low score on a lab assignment or exam. ICS takes academic honesty very seriously; for a more complete discussion, see our course collaboration policies (http://www.ics.uci.edu/~kay/courses/31/collab.html) and the ICS departmental web page covering academic honesty issues (http://www.ics.uci.edu/ugrad/policies/index.php#academic_honesty).

          Turn in whatever portion of an assignment you have completed on the due date. It's much better to turn in something rather than nothing; zeroes are hard to make up. In some circumstances you may arrange with your TA to work further on an assignment after the due date, but you must turn in whatever you have when the official due date comes. Likewise, don't skip any quizzes or partner evaluations if you can help it; too many zeroes will significantly lower your overall score.

          Always keep your own copy of each assignment, both electronically and on paper; if an assignment should get lost in the shuffle (or if a file server in the lab should crash, which has happened), we'll expect you to be able to supply a replacement easily.

          Approximate course outline:

          Week Date Topics
          1. 5 January Introduction to the course and computing • Models and abstraction •
          Things (nouns)/objects/data/information and actions (verbs)/statements/operations/functions
          7 January Evaluating expressions • Numbers, strings • Variables • Input/output, imperative programming
          2. 12 January Multiple-valued data • Namedtuples • Lists • Programmer-defined functions, design recipe
          14 January Programming with multiple-valued data • Basic selection (if) and repetition (for)
          3. 19 January Arguments and parameters • Extended example
          21 January Mutable and immutable data • Mutable parameters and function side-effects
          4. 26 January — First Midterm —
          28 January Programming with nested data structures
          5. 2 February Strings and text processing
          4 February Formatting output
          6. 9 February Files
          11 February Control structures revisited
          7. 16 February Combining data structures
          18 February — Second Midterm —
          8. 23 February Two-dimensional tables
          25 February Dictionaries
          9. 1 March Tuples and sets
          3 March — No class meeting —
          10. 8 March Extended example
          10 March Looking back and looking forward
          F. 16 March Final exam, Tuesday, from 8:00 to 10:00 a.m.


          David G. Kay, kay@uci.edu
          Wednesday, January 6, 2016 10:32 AM
          http://www.ics.uci.edu/~kay/pubs/index.html Publications/Presentations

          Some items here are available in PostScript or PDF (Adobe Acrobat Reader) form. Either form will preserve the formatting and references of the original more accurately than the HTML version.


          Publications  

          A. van der Hoek, D. G. Kay, D. J. Richardson, "Informatics: A Novel, Contextualized Approach to Software Engineering Education,” in P. Inverardi and M. Jazayeri (eds.), Software Engineering Education in the Modern Age: Challenges and Possibilities, post-proceedings of ICSE ’05 Education and Training Track, Lecture Notes in Computer Science 4309, Springer (pages 147–165)

          "Higher-Order Functions Incarnate: Parameterization via Graduated Examples and a Simple Machine," TeachScheme! Project Anniversary Workshop, June 2005. [PDF]

          A. van der Hoek, D. G. Kay, D. J. Richardson, "A B.S. Degree in Informatics: Contextualizing Software Engineering Education," Twenty-Seventh International Conference on Software Engineering, May 2005. [PDF]

          D. G. Kay, A. van der Hoek, D. J. Richardson, "Informatics: A Focus On Computer Science In Context," SIGCSE Bulletin, February 2005 (Proceedings of Thirty-Sixth SIGCSE Technical Symposium on Computer Science Education) [PDF]

          W. C. Cheng, L. Golubchik, D. G. Kay, "Total Recall: Are Privacy Changes Inevitable?", First ACM Workshop on Continuous Archival and Retrieval of Personal Experiences (October 2004). [PDF]

          M. S. Guntersdorfer and D. G. Kay, "How Software Patents Can Support COTS Component Business," IEEE Software (May/June 2002) [UCI Link, also accessible from off campus via VPN]

          M. S. Guntersdorfer, D. G. Kay, and D. S. Rosenblum. "Using Software Patents to Support the Business Model of Software Components," Proceedings of the ICSE 2000 Second Workshop on Commercial Off-The-Shelf Software, Limerick, Ireland, June 2000

          "A Course in Computer Law," Forum for Advancing Software engineering Education (FASE), April 1998 (http://www.cs.ttu.edu/fase/v8n04.txt)

          "Large Introductory Computer Science Classes: Strategies for Effective Course Management," SIGCSE Bulletin, February 1998 (Proceedings of Twenty-Ninth SIGCSE Technical Symposium on Computer Science Education). [PostScript] [PDF] [Slides in PostScript] [Slides in PDF]

          "Computer Scientists Can Teach Writing: An Upper Division Course for Computer Science Majors," SIGCSE Bulletin, February 1998 (Proceedings of Twenty-Ninth SIGCSE Technical Symposium on Computer Science Education). [PostScript] [PDF] [Slides in PostScript] [Slides in PDF]

          "Bandwagons Considered Harmful, or The Past as Prologue in Curriculum Change" SIGCSE Bulletin, December 1996. [PostScript] [PDF]

          "Training Computer Science Teaching Assistants: A Seminar for New TAs," SIGCSE Bulletin, March 1995 (Proceedings of Twenty-Sixth SIGCSE Technical Symposium on Computer Science Education).

          "An Honors Computer Science Seminar for Undergraduate Non-Majors," SIGCSE Bulletin, February 1993 (Proceedings of Twenty-Fourth SIGCSE Technical Symposium on Computer Science Education).

          "A Balanced Approach to First-Year Computer Science," SIGCSE Bulletin, March 1992 (Proceedings of Twenty-Third SIGCSE Technical Symposium on Computer Science Education).

          "A Course in Computer Law," SIGCSE Bulletin, March 1992 (Proceedings of Twenty-Third SIGCSE Technical Symposium on Computer Science Education).

          "User Environments for Student Programmers," in M.Griffiths and D.Tagg, The Role of Programming in Teaching Informatics, (North-Holland, 1985), invited paper at IFIP Working Group on Teaching of Programming, Paris, France, May 1985.

          Programming for People/Pascal, Mayfield Publishing, Palo Alto, 1985.

           

          Presentations (partial list)

          "Best Practices for Responding to Student E-mail," poster presentation, UC 21st Century systemwide workshop on teaching, learning, and technology (Davis, CA, June 2008).

          "Teaching Machine Language Basics to Intro Students in Just One Week," conference workshop, Thirty-Eighth SIGCSE Technical Symposium on Computer Science Education (Covington, KY, March 2007).

          "Intellectual Property Law Basics for Software Engineering Educators," tutorial presentation, Nineteenth Conference on Software Engineering Education and Training, (North Shore, Oahu, HI, April 2006).

          D.G.Kay, A. van der Hoek, D. J. Richardson, "Extending Undergraduate CS Programs with Informatics: Emphasizing Software and System Design in Context," Tenth Annual Conference on Innovation and Technology in Computer Science Education (Monte de Caparica, Portugal, June 2005). [PDF]

          "Innovation in Undergraduate Computer Science Education," panel presentation, Tenth Annual Consortium for Computing Sciences in Colleges, Northeastern Conference (Providence, RI, April 2005). [Slides]

          "Intellectual Property Law Basics for Computer Science Instructors," conference workshop, Thirty-Sixth SIGCSE Technical Symposium on Computer Science Education, February 2005.

          "Intellectual Property Law Basics for Computer Science Instructors," conference workshop, Thirty-Fifth SIGCSE Technical Symposium on Computer Science Education, March 2004.

          "Intellectual Property Law Basics for Computer Science Instructors," conference workshop, Thirty-Fourth SIGCSE Technical Symposium on Computer Science Education, February 2003.

          "Intellectual Property Law Basics for Computer Science Instructors," conference workshop, Thirty-Second SIGCSE Technical Symposium on Computer Science Education, February 2001.

          "Collaboration vs. Plagiarism In Computer Science Programming Courses," panel presentation with position paper in SIGCSE Bulletin, February 2001 (Proceedings of Thirty-Second SIGCSE Technical Symposium on Computer Science Education)

          "Computer Law Basics for Software Engineering Educators," invited tutorial presentation, Fourteenth Conference on Software Engineering Education and Training, February 2001.

          "Intellectual Property Law Basics for Computer Science Instructors," technical seminar, Thirty-First SIGCSE Technical Symposium on Computer Science Education, March 2000

          "Teaching Advice and Support for New and Adjunct Faculty: Experiences, Policies, and Strategies" panel chair and presentation with position paper in SIGCSE Bulletin--Inroads, March 2000 (Proceedings of Thirty-First SIGCSE Technical Symposium on Computer Science Education). [PostScript] [PDF]

          "Intellectual Property Law Basics for Computer Science Instructors," SIGCSE Bulletin--Inroads, March 2000 (Proceedings of Thirty-First SIGCSE Technical Symposium on Computer Science Education). [Slides in PostScript] [Slides in PDF]

          "Large Introductory Courses in Research Computer Science Departments," panel chair and presentation with position paper in SIGCSE Bulletin, February 1998 (Proceedings of Twenty-Ninth SIGCSE Technical Symposium on Computer Science Education). [PostScript] [PDF]

          "Presentation Technologies for the Classroom," panel presentation, National Educational Computing Conference, July 1997.

          "Managing Large Introductory Courses," panel chair and presentation with position paper in SIGCSE Bulletin, February 1997 (Proceedings of Twenty-Eighth SIGCSE Technical Symposium on Computer Science Education).

          "Intellectual Property Protection for Software," invited talk, San Fernando Valley Chapter of the IEEE, April 1996.

          "The First Year: Beyond Language Issues," panel presentation, Twenty-Seventh SIGCSE Technical Symposium on Computer Science Education, February 1996.

          "Intellectual Property Protection for Software," invited talk, joint meeting of the Los Angeles Chapters of the ACM and IEEE, June 1995.

          "Changes in the AP Computer Science Exam: Programming Language," Rocky Mountain Small College Computing Conference, October 1995.

          "Teaching Advanced Placement Computer Science," week-long workshop at International Advanced Placement Institute, August 1995.

          "Changes in the Advanced Placement Computer Science Examination," panel presentation, Twenty-Sixth SIGCSE Technical Symposium on Computer Science Education, March 1995.

          "Automated Grading Assistance for Student Programs," panel chair and presentation with position paper in SIGCSE Bulletin, February 1994 (Proceedings of Twenty-Fifth SIGCSE Technical Symposium on Computer Science Education).

          "But What We Do is Different," panel presentation, Fourth National Conference on the Training and Preparation of Teaching Assistants, Chicago, November 1993.

          "Inter-Institutional Approaches to Training Computer Science Teaching Assistants," panel chair and presentation, Fourth National Conference on the Training and Preparation of Teaching Assistants, Chicago, November 1993.

          "Training Computer Science Teaching Assistants: A Seminar for New TAs," Fourth National Conference on the Training and Preparation of Teaching Assistants, November 1993.

          "Automated Grading Assistant for Student Programs," panel presentation, Rocky Mountain Symposium on Computing in Small Colleges, Denver, October 1993.


          http://www.ics.uci.edu/~kay/labtutors/193.w16.html ICS 193 Course Reference

          Winter Quarter 2016 — Information and Computer Sciences — UC Irvine

          Lab Tutor Seminar (ICS 193)

          COURSE REFERENCE

          Instructors: David G. Kay (kay@uci.edu), 5056 Donald Bren Hall; Rich Pattis (pattis@uci.edu), 4062 Donald Bren Hall

          Tutor coordinators: Andrew Yang (akyang1@uci.edu) and German Krikorian (gkrikori@uci.edu). Jointly we're reachable at tutors@ics.uci.edu.

          Meeting time and place: Tuesdays 2:00 to 3:20 in 5011 DBH. We expect to skip the meetings in weeks 5, 7, and 9; stay tuned to Email and Piazza for the final determination.

          Course requirements: It's simple: Everyone can get an A who performs professionally. Unprofessional things (like missing scheduled lab sessions or missing the ICS 193 meetings or not preparing and editing some teaching materials [see below]) will lower the grade and pretty quickly result in a tutor being asked not to be a tutor any more. Of course real emergencies come up (but normal academic obligations like midterms and projects aren't real emergencies; you can plan around them); in a real emergency, contact the coordinators and the TA of your section as soon as you possibly can. 

          To reiterate: Every tutor is expected to show up to each assigned lab session, to attend each scheduled meeting of ICS 193 [or make alternative arrangement with the coordinators in case of a class conflict], and to prepare and submit and edit some teaching materials as described below.

          Topics to be covered in ICS 193 meetings: Primarily we will discuss issues that have come up or may come up in your tutoring. We will talk some about computer science curricula and teaching in general, and we will also cover relevant aspects of Python and other computer science principles. Refer frequently to the Lab Tutor Guide, http://www.ics.uci.edu/~kay/labtutor.html

          Resources for tutors: This syllabus (with all its hyperlinks) is available on line at http://www.ics.uci.edu/~kay/labtutors/193.w16.html. There are other useful resources on David Kay's Teaching in ICS page.

          We're making heavy use of forums at Piazza.com this quarter (for tutors, as well as for the students). This is a site where questions can be posted and answers can be created collaboratively, wiki-style, both by other students in the class and by instructors.

          • Some or all of ICS 31/32/33/45C/45J/46/51 will have Piazza groups. You should go ahead and add yourself as students to the course(s) you'll be tutoring for, and as time permits, feel free to answer students' postings. Just remember three things: (a) It's better to say nothing than to post something wrong; recovering from incorrect postings is like putting toothpaste back in the tube. (b) Hundreds of students will read what you post, so you want to be sure you don't confuse more people than you help. If the questioner has done something an unusual way, preface your response with something like, "There are other ways to do this; nobody should adopt this approach if they have another approach that's working for them." (c) TAs have a little leeway to clarify assignment requirements for their own sections. So if a student asks, "Do we have to handle the X case?", the answer for that student may not be the same as it was for you when you took the course. If the answer isn't clearly written in the problem specification you should tell them to check with their own TA. Finally, be careful about posting code that actually solves the lab problems; give examples of similar usage or something, but don't write all of the students' code for them. [This is more of an issue after the first couple of weeks; early in the course, it's appropriate to be more concrete.]
          • ICS 193 also has a Piazza group where you can post questions and observations about tutoring. You can use it to ask things you'd otherwise have to postpone to the next Tuesday's ICS 193 class meeting. There's just one thing: If you're tutoring for ICS 32, realize that some of the ICS 31 tutors are students in ICS 32 this quarter. It's not appropriate to post solutions or approaches to the ICS 32 lab problems (or identifiable references to ICS 32 students) on the main ICS 193 Piazza group; it would deprive the ICS 32 students who are ICS 31 tutors of the opportunity to work it out for themselves (and especially since they're the strongest students, they should have that opportunity). The same goes for ICS 33 tutors not posting to the main ICS 193 group information that ICS 33 students who are ICS 31 or 32 tutors shouldn't see. But there's a solution: ICS 32 tutors have been added to the "ICS 32 Section" of the ICS 193 class on Piazza and likewise for ICS 33 tutors and an "ICS 33 Section". General tutoring issues, everyone should post and answer in the main ICS 193 Piazza group. But ICS 32 tutors who want to discuss sensitive ICS 32 issues should have those discussions in the ICS 32 Section, which is visible only to members (and likewise for ICS 33, 45C, and 45J).

          Resources by tutors: As you know, ICS 193 must exist to give tutors units for tutoring; moreover, ICS 193 has to be enough like an academic course so that when the program is eventually reviewed, nobody will object that we're giving units away for nonacademic purposes. At the same time, we'd like ICS 193 to be valuable and stimulating for you as tutors and helpful in making you more effective tutors, thereby improving the students' experience. 

          This quarter, instead of just having a weekly seminar meeting, we'll cancel the meeting some weeks and instead ask each tutor to design some teaching materials (that will help students with skills and concepts you've observed they need help with). This is all experimental, so any of the "rules" below can be altered if there's a good reason. 

          Basic requirement: During the quarter, each tutor will produce one set of "teaching materials" and will review and edit at least two sets of materials written by other tutors (so that each item is reviewed at least twice). First-time tutors may skip the production part and just edit two or three sets of other materials. 

          Quality control: We do two reviews of each item because eventually we will post them in the "Resources" section of the tutors.ics.uci.edu site. That means they have to be clear and grammatical, using correct CS terminology, with fully tested code, following good programming style—in short, as close to perfect as possible. Eventually we'll have a rich resource we can direct students to. Another upside is that you get the glory of seeing your name as the author of the published items. 

          Teaching materials can be exercises, programming problems, focused explanations of specific topics, illustrations, animations, summary tables, videos, games, or anything else that might help a student learn a topic in the course you're tutoring for. We're thinking mostly of text-based programming exercises, but if you think a more unconventional form will help you get your point across, go right ahead. 

          Stay tuned for logistical and administrative details. 


          David G. Kay, kay@uci.edu 

          Monday, March 30, 2015 5:04 PM 

          http://www.ics.uci.edu/~pattis/ICS-90/index.html ICS 90: New Student Seminar

          ICS 90: New Student Seminar

          Fall 2015


          Checkin Link (see Recording Attendance below)

          Click the Checkin link when you are told the appropriate word in class.
          If you cannot check in, see Rich, right after class.

          Instructors

          David G. Kay (kay@uci.edu)
          Rich Pattis (pattis@ics.uci.edu)

          Course Goals and Plans

          The three main goals of ICS 90 are to help new students make a successful
          transition to UCI, to build a sense of community in ICS, and to introduce
          new undergrads to the school, some of the faculty, the majors we offer,
          and the work we do.

          In a typical class session, two faculty members will speak for half an hour
          each, about their work and background; David and Rich will then also spend
          a half hour addressing more general issues about college life, the field of
          computing, and strategies for success in ICS.

          Student Expectations

          ICS 90 is a one-unit course graded Pass/Not Pass. We have designed it
          not to require much attention outside of the class meetings, but we do
          expect every student to attend every class. If you cannot attend class
          for any reason, send an email explanation to Rich Pattis as soon as possible.

          It's in class where the learning happens, so you should be present. Before
          the first class we will send an email to all our students describing how we
          will record attendance electronically.

          For issues that come up between class meetings, we have set up a forum at
          piazza.com. Sign yourself up and then participate by posting questions or
          supplying answers. We may also have opportunities to use Piazza in class
          for polls and surveys.

          You may bring your phone, tablet, or laptop for attendance-taking and for
          using Piazza, but do not use it for other purposes, unless asked by the speaker.
          After you check in, please put your devices away.

          Seating: Two Classrooms

          ICS 90 meets in two rooms at the same time: BS3 1200 and HSLH 100A.
          You are officially enrolled in one section or the other, but ignore which
          room you're enrolled in
          and instead attend in the following room:

          * If your class level is FR or SO, come to BS3 1200.
          * If your class level is JR or SR, come to HSLH 100A.

          This is your official "class level" as shown on WebReg at UCI.

          Most weeks, one faculty member will speak starting at 5:00 and another
          will speak starting at 5:30. All students are required to be present for
          these talks. The third half-hour of each week's class will be devoted to
          broader information about college-level work and more. This material
          is aimed primarily at new students and is required for those whose class
          level is FR or SO. We will have these discussions in BS3 1200 only.
          Upper division students (from HSLH 100A) are welcome to attend this
          third half-hour (the room is just three minutes away), but they may also
          choose to skip it.

          Recording Attendance

          Bring a phone, tablet, laptop, smart device to class that allows web browsing.

          During the lecture we will ask you to navigate to this ICS-90 page and
          record that you are attending class by following the Checkin link at the
          top of this web page. There you will see a message with instructions about
          how to continue. You will eventually enter a word that we will announce
          in class.

          It is an Academic Integrity violation to run this App and then leave
          class early, unless you have an emergency or have arranged permission
          beforehand with Rich Pattis. Likewise, you cannot check in for other
          students, but you may allow present students to use your device to run
          the App.


          Tentative Schedule of Faculty Speakers

          Date Speaker Topic
          9/30 5:00-5:30: David and Rich
          5:30-6:00: David and Rich
          Course Introduction and Attendance Taking Procedures
          Content (David's HS vs. College, David's College Advice, and Rich's Talk) and Questions; Majors(Rich); Majors(David)
          10/7 5:00-5:30: Ramesh Jain
          5:30-6:00: Rick Lathrop
          Weaving the Visual Web (Talk as web pptx and Talk as local pptx)
          Intelligent Systems and Molecular Biology (Talk as pptx and Talk as pdf)
          10/14 5:00-5:30: Pierre Baldi
          5:30-6:00: Padhraic Smyth
          Machine Learning in Scientific Discovery ( Talk as local pdf)
          Machine Learning : Adventures in Big Data
          10/21 5:00-5:30: Wayne Hayes
          5:30-6:00: Geoffrey Bowker
          Computational Science: using computers to advance scientific knowledge from butterflies to galaxies ( Talk as local ppt)
          The Sociology of Information Infrastructure
          10/28 5:00-5:30: Gary Olson
          5:30-6:00: Michael Carey
          Information Visualization
          It's Really All About The Data!
          11/4 5:00-5:30: Sandy Irani
          5:30-6:00: Alex Ihler
          Introduction to Quantum Computation (Talk as local powerpoint)
          Artificial Intelligence and Machine Learning (Talk as local pdf)
          11/11 No Class: Veteran's Day
          11/18 5:00-5:30: Mimi Ito
          5:30-6:00: Joshua Tanenbaum
          Anthropology of Internet Culture (Talk as local powerpoint)
          Transformative Play and Digital Games Research (Talk as local powerpoint)
          11/25 No Class: Pre-Thanksgiving
          12/2 5:00-5:30: Bill Tomlinson
          5:30-6:00: Ian Harris
          The Informatics Major
          The Computer Science Major (Talk as local powerpoint)
          http://www.ics.uci.edu/~kay/recommendations.html Letters of Recommendation

          Letters of Recommendation

          I will gladly write letters of recommendation for people I've worked with: students (past or present), TAs, tutors, or other colleagues.

          I may not be your best choice as a recommender, though. You shouldn't pick me just because you think I'm a nice guy and approachable enough to ask. Especially for CS graduate school (and especially for Ph.D. programs in departments that are "better" than ours), you want recommenders who know your work well—preferably advanced, independent, or research-oriented work. If we've done that together, great. If you've taken a class or two with me and gotten As, that's great, too, and I'll sell that as hard as I can, and if I'm your third recommender it might be enough. But in general those Ph.D. program admissions committees want recommenders who can say you've worked on research or other projects and carried them through to completion (brilliantlly, if possible). If your recommendation is going to be based on classwork, it's best that it be upper division classwork in an area you're interested in pursuing in grad school.

          Since I've taught large classes for many years, I receive many recommendation requests. A few guidelines will help me satisfy yours most effectively:

          • Give me enough lead time. If you do have a last-minute need for a letter, I'll try to accommodate it, but I'd prefer to have at least two weeks' advance notice. Also, it will make both of us feel more secure if you drop me an Email message three days before the letter is due, just to make sure it got sent out.

          • Give me the vital statistics. At a minimum, remind me of the details of our association: Which course(s) in which quarter(s) did you take, tutor, or TA? This helps me find your records and those of your class. It's really important, especially during the season when many recommendations are due, that I have this information in writing, right along with your recommendation forms. (Don't just tell me in person; write down each course and each quarter and send it to me in one message with everything you're sending.)

          • Give me details. The more information I have, the better. Copies of your resume or statement of purpose are helpful. Better still would be to help me recall any particularly good questions you asked in class, any conversations we had outside of class, any particularly interesting projects you've done, any teaching evaluations you have, or any other details that I may have forgotten—these will help flesh out your recommendation. This is important for people I haven't seen in a while, but it's also useful if I know you well and see you often; I still may not remember specific details of our early association.

          • Give me everything in one Email message. When the time comes for me to write your letters (and everyone else's), it's just too hard to search around for paper or multiple Email messages. I need one message, clearly labeled as coming from you, that contains whatever information you're providing me in text or attachments.

          • Don't spam the universe. Every school you apply to takes the recommender's time. Even though the body of the recommendation will probably be the same, each school has its own form and its own process. A student who applies to ten schools is asking the recommender to spend the better part of an hour just filling out forms on line. In most cases you should narrow your choices to the handful of schools whose programs fit you best (and vice versa). Application-spamming a dozen or more schools indicates that you haven't done enough research.

          • Let's talk. Try to arrange a time to sit down with me for a brief chat, to let me know what you've been doing and what you'd like to do. I may even be able to give you some useful advice for your application process and choice of schools.

          References Without Letters

          I'm also glad to entertain requests to serve as an employment reference or other reference that doesn't require that I write a letter. Most of the same guidelines apply: When you get in touch with me to ask, give me as much information as you can.

          Good luck!


          David G. Kay, kay@uci.edu http://www.ics.uci.edu/~kay/courses/us3/w16.html US 3 Course Reference
          Winter 2016 — Informatics Department — Bren School of ICS — UC Irvine

          Freshman Seminar (University Studies 3)

          Linguistics for Fun and Profit

          Course Reference

          Instructor: David G. Kay, 5056 Donald Bren Hall (kay@uci.edu).

          Quick links: Assignments Piazza Q&A Email archive Resources

          Course goals and learning outcomes: Every person in the world, except a few with severe disease or disability, speaks some language (and learns how to do it pretty much without conscious effort by the age of five or six). Yet few people have much understanding of "how language works" or of the basic principles of language that linguists have discovered, and even the linguists have a lot still to figure out (which is why you can't trust the green lines in Microsoft Word). Learning some of this can help you in areas ranging from child-rearing to persuasive writing to effective communication with diverse people. (That's the "profit" in the title; we won't explicitly address making actual cash from one's knowledge of language.)

          This course is also a Freshman Seminar, so it has the additional goal of letting you interact at a human scale with a faculty member, to see how we work and how we think. That makes it appropriate that we spend a bit of time talking more broadly about the university, why it's here, how it works, and how to get the most from your time here. You should feel free to bring up issues like this any time.

          Meeting place and times: Class meets Thursdays from 1:00 to 1:50 in Humanities Hall 232, with no meeting on March 3.

          Office hours: You are welcome to drop by my office at any time. If I'm there and not immersed in something else, I'll be glad to chat about the course or other topics. I will definitely be in or near my office on Tuesdays at 11:00 and Thursdays at at 9:30, when course-related matters will have first priority. Of course emergencies may come up, but I will try to give advance notice of any change. I'll also be happy to make arrangements for other times during the week; "making an appointment" is no big deal (but if you make one, don't skip it without getting in touch). The quickest and most effective way to reach me is by electronic mail.

          Questions and announcements: You can get a response to your course-related questions most quickly by posting them at Piazza.com (see below). If you need to reach me privately, send electronic mail to kay@uci.edu. I will never intentionally ignore a message, so if you don't receive a response, write again; sometimes overactive spam filters snag a legitmate message. Using course-specific subject lines and your UCInet Email address will help your messages get noticed.

          I will also send course announcements by Email to the official course mailing list, so you should check your Email regularly. Note that this mailing list goes to the Email address that the registrar has for you (your UCInet ID). If you prefer to read your Email on another account, you should set your UCInet account to forward your Email to your preferred account (see how in the Advice section below). Don't let this slide, in this class or any class; if you miss official announcements, your grade could suffer.

          This course has a home page at http://www.ics.uci.edu/~kay/courses/us3/; there's an archive of official course Email at http://e3.uci.edu/16w/w3m3/87569.

          Readings and course materials: Our main reference is the Linguistics Wikibook at http://en.wikibooks.org/wiki/Linguistics. We will assign readings each week, along with exercises or assignments.

          Course structure: Your course grade will be determined 50% from participation—showing up every Tuesday and being engaged with the topic—and 50% from the assigned written exercises. If you anticipate missing a class, please get in touch with me. There will be no exams. Since this is a one-unit course, you should expect to devote to it approximately 25% of the time and effort you'd devote to a standard four-unit course.

          We're required to say that in unusual circumstances, these criteria could change, but we do not expect that to happen.

          Special needs: Any student who feels he or she may need an accommodation due to a disability should contact the UCI Disability Services Center at (949) 824-7494 as soon as possible to explore the possible range of accommodations. We encourage all students having difficulty, whether or not due to a disability, to consult privately with the instructor at any time.

          What you must do right now to get started in US 3
          — If you do not have a UCInet ID, get one. See https://www.nacs.uci.edu/ucinetid/
          — If you prefer to read your electronic mail on an account other than your UCInet account (which is a good idea, since if your UCInet account's mailbox fills up, you stop receiving mail), redirect your mail at http://www.oit.uci.edu/email/deliverypoint.html
          — Go to www.piazza.com, and follow the steps to add yourself as a student in this course. Piazza.com provides a discussion forum for this class.
          — Complete the US 3 Questionnaire at eee.uci.edu (by the end of the first week).

          Good advice and helpful hints (for this class and every class):

          Read the syllabus (this sheet) with care. It gives you the "ground rules" for the course.

          Check your electronic mail regularly; this is an official channel for course announcements. When sending course-related mail, start the subject line with "US 3 or "Linguistics Course".

           

          http://www.ics.uci.edu/~kay/college-advice/
          Surviving—Better Yet, Thriving—in College
          * This is not meant to be comprehensive. There are plenty of college survival guides out there. This guide covers some points I find particularly important from my long experience working with first-year college students. (Click on the triangles to collapse points.)
          * College is a whole different ballgame from high school. See http://www.ics.uci.edu/~kay/college.html
          V Setting your goals: If you don't know where you're going, any path will take you there.
          * This is hard: You may not have a path planned out (and that's probably a good thing)
          * But in that case, one of your goals should be flexibility and breadth of opportunities available.
          V Some possible goals
          V Learning a lot
          * This covers many things: Learning how the world works, learning how to think like a scholar, learning how to do independent research (solve new problems), learning how to get along with diverse people, learning how to live (semi-)independently, learning the fundamentals of some academic field
          V Preparing for the future
          * Getting job skills (or the foundations for job skills that you will enhance in graduate or professional school, or actually on the job)
          * A college's internship programs, career planning/placement office, and network of alumni all help with this.
          V Making friends and having a good time
          * This is an important part of college, but not the most important. You might also accomplish this by running away to join the circus, for example.
          V Pursuing your goals
          V Academically, don't play it safe
          V Take a wide variety of courses
          * Take courses in areas you don't think you'll like and don't think you're good at. Your first year in college is not a time to be limiting your horizons to what's comfortable. But do some advance research to find the very best instructors.
          V If you succeed at everything you try, you're not trying enough things
          * Trying for a 4.0 is one of the stupidest strategies in college. If you end up with one, that's great, but never choose a course because you're afraid you'll get a low grade. Pass-fail courses can help in these situations.
          V You can be good at something even if you find it hard work.
          * Smart people expect the things they're good at to come easily, without a lot of effort to understand it or to produce good work. If they find something difficult or slow to acquire, they may think, "I'm not good at this." This is a huge mistake. There are depths to any topic, and getting to the front edge of a field will always require some serious work. Nobody in any field just knocks off a masterpiece (or even just decent, professional work) in a morning's time.
          V Keep an eye out for lectures, talks, seminars that aren't connected to a class.
          * Colleges often have visitors come through and they often make presentations. This is a low-commitment way to hear about a lot of new things.
          * Keep an eye out for performances and exhibits of all kinds, and go to every one you can. After you leave college it won't be this easy again until you retire.
          V Other perspectives
          * http://www.cs.duke.edu/~dr/happiness.html
          * http://www.cs.ucr.edu/~vahid/college_time.html
          V Stay safe personally
          V Cars
          * Automobile accidents kill more college students than anything else.
          * You know who drinks or does drugs. Don't drive with them.
          * Even at the risk of killing the party vibe, don't drive (as driver or passenger) with more people in the car than it was designed to hold, don't drive without a seatbelt, don't let two people share one seatbelt (you'll get squished), don't drive too fast, don't drive to show off, don't drive when you're angry or upset.
          * If the climate isn't what you're used to driving in (e.g., ice on the roads), get some practice before venturing out.
          * Do all the rock-and-roll you want (just keep the volume below hearing-damage levels)
          V Drugs and alcohol
          * Mostly, it's illegal, and in some states the penalties are draconian (often much harsher than when your parents smoked dope in the 60s). Your dorm may be dreary, but it beats a jail cell; the food's better, too. Work to change the laws if you want, but be careful about risking your future, and be careful of people who say, "Oh, come on; nothing's going to happen; they don't really care; I've never been hassled before; everybody does it; they don't bust college students."
          * Nobody really knows the long-term health effects of casual, recreational drug use. There are no pharmaceutical companies funding closely controlled studies of coke or speed or even marijuana.
          * If you plan to experiment anyway, do it on your own terms where it's totally safe. You don't know just how you'll react. Have a trusted friend nearby. Make sure you have a way to get home.
          V Sex
          * Sex is your personal choice. Do it only when you, yourself are ready, willing, and able (and then, only when your partner is the same). If that's not during your college years, that's fine, no matter what anyone else has done or is doing. Anyone who tries to encourage you after you have declined is not a friend.
          * Always practice safe sex. Every single time, no matter how passionate the moment. Be careful of anybody who encourages you to do otherwise, no matter how much you like them, no matter what they say.
          V Parties
          V College parties are part of the experience.
          * Avoid the punch. Heaven knows what's in it.
          * Head home when the rowdiness level gets too high. Always carry money for a cab.
          * Head home if you see illegal drugs. A bust doesn't discriminate between users and bystanders.
          * You can also meet people in class, in campus activities and organizations, in community activities. You can have real conversations in most of those places, too.
          V Finance
          * If you have a credit card, keep track of everything you charge and how much your current balance is. Your monthly bill should never be a surprise. Pay your bill in full every month without fail. This builds a credit rating and saves you from the voracious jaws of the credit card companies, who want nothing more than for you to build up a huge balance at 29% interest.
          V Health
          * Look out for the "freshman 15" pounds you might gain because food is readily available. Don't subsist totally on junk food. Try to get some regular exercise (which is a good stress reliever, too).
          * During cold and flu season especially, wash your hands frequently, avoid contact with sniffling people, try not to touch your face after touching door handles. It's no fun to miss class because you're sick in bed.
          * Eat right. Dress warm. Your mother was right.
          V You can try to do it all, but don't do it all at once.
          * You have a lot of options and a lot of freedom, but don't dive right into the deep end. Don't start skipping class just because you can. Don't stuff yourself just because your favorite foods are available 24/7. Don't stay up all night just because nobody says lights-out. A certain amount of experimentation is good; you don't want to be exactly the same person you were in high school. But try one thing at a time, more or less. A certain amount of testing your limits is also good: How many classes can you take in one term? How good a grade can you get if you write the paper in one all-nighter? But don't expect any significant short-cuts.
          V The occasional bad day or rough patch is inevitable. The question is how you deal with it.
          * No matter how independent and adventurous you are, there will come a time when the whole experience seems overwhelming. Partly it may be homesickness; partly it may be that everything is new—new place, new people, new activities, new expectations. Two or three things may hit you at once: You have a cold, you get an unexpectedly low grade; someone says something thoughtless or mean. Recognize that this will happen and expect it; it's inevitable and it's normal. Just scale back a little, maybe call home, recenter yourself.
          * Don't let your reverses define or defeat you. If you get a B on a paper or in a class, don't just say, "I'm only a B student" or "This isn't the field for me." Find out what merits an A and try to achieve it. If a classmate or instructor says something that hurts your feelings, examine it carefully before taking it too much to heart; everyone occasionally has bad days, misunderstands, chooses the wrong words.
          V When the going gets tough, the tough get help.
          * If it's class-related, start with your instructor, TA, or tutor. They're there for you.Tutoring, academic advising, personal counseling, health care; the college will provide services in all these areas.
          * The college typically provides many free or near-free services: Tutoring, academic advising, personal counseling, health care.
          * Don't wait until a situation becomes critical. A stitch in time saves nine.
          V Warning signs—If you hear yourself (or someone else) saying this, step back and think again:
          * "I'm not going to class; I haven't done the reading so I wouldn't understand anyway." Of course you should do the reading when it's assigned, but even if you haven't, more happens in class than just a recap of the reading. There could be announcements, activities, information that will help you with the reading. Your class is a learning community; you need to be present to participate.
          * "I don't want to bother the instructor (or slow down the class) by asking a question." Questions are expected and it's likely that other students will benefit from the answer to your question. If an instructor prefers you to hold questions until the end or until invited, that's fine, but otherwise, just ask.
          * "I have so much to do on this project that it won't matter if I delay starting until tomorrow." This is a common procrastinator's trap; before you know it, it'll be due the next morning and you won't have started. When you first get an assignment, at least read it over immediately; that will prime you to notice relevant information, even if you don't start serious work immediately. And it's much better to spend half an hour every day than to save it up (again, because you'll have the issues in the back of your mind in between sessions; it also gives you an opportunity to ask clarifying questions and to use the Writing Center).
          * For a surprising number of people, college was the best time in their life. Aspire to more than that, but enjoy it while you're there.
          * Copyright © 2009 by David G. Kay. All rights reserved.
          http://www.ics.uci.edu/~kay/courses/previous.html Previous Quarters' Courses

          Previous Quarters' Courses

        • Summer 2015: Informatics 131 (Human-Computer Interaction)
        • Summer 2015: ICS 10 (How Computers Work)
        • Spring 2015: ICS 10 (How Computers Work)
        • Spring 2015: University Studies 3—Freshman Seminar (Linguistics for Fun and Profit)
        • Spring 2015: ICS 193 (Tutoring in ICS)
        • Spring 2015: University Studies 197C (Uteach Practicum)
        • Spring 2015: ICS 398B (Advanced TA Seminar)
        • Winter 2015: ICS 31 (Introduction to Programming)
        • Winter 2015: Informatics 141 / CS 121 (Information Retrieval):
        • Winter 2015: ICS 193 (Tutoring in ICS)
        • Winter 2015: University Studies 197B (Uteach Theory and Practice)
        • Fall 2014: ICS 31 (Introduction to Programming)
        • Fall 2014: ICS 90 (New Student Seminar)
        • Fall 2014: ICS 193 (Tutoring in ICS)
        • Fall 2014: ICS 398A (Teaching Assistant Seminar)
        • Summer 2014: ICS 10 (How Computers Work)
        • Summer 2014: Informatics 131 (Human-Computer Interaction)
        • Spring 2014: ICS 10 (How Computers Work)
        • Spring 2014: ICS 193 (Tutoring in ICS)
        • Spring 2014: University Studies 197C (Uteach Practicum)
        • Winter 2014: ICS 31 (Introduction to Programming)
        • Winter 2014: Informatics 269 (Computer Law)
        • Winter 2014: ICS 90 (New Student Seminar)
        • Winter 2014: University Studies 3—Freshman Seminar (Linguistics for Fun and Profit)
        • Winter 2014: ICS 193 (Tutoring in ICS)
        • Winter 2014: University Studies 197B (Uteach Theory and Practice)
        • Fall 2013: ICS 31 (Introduction to Programming)
        • Fall 2013: ICS 90 (New Student Seminar)
        • Fall 2013: University Studies 4—Transfer Student Seminar (Beyond Java and C++: Functional Programming in Scheme)
        • Fall 2013: ICS 193 (Tutoring in ICS)
        • Fall 2013: ICS 398A (Teaching Assistant Seminar)
        • Summer 2013: ICS 10 (How Computers Work)
        • Summer 2013: Informatics 131 (Human-Computer Interaction)
        • Spring 2013: ICS 139W (Critical Writing on Information Technology)
        • Spring 2013: ICS 193 (Tutoring in ICS)
        • Spring 2013: University Studies 197C (Uteach Practicum)
        • Winter 2013: ICS 4 (Human Factors for the Web)
        • Winter 2013: ICS 31 (Introduction to Programming)
        • Winter 2013: ICS 193 (Tutoring in ICS)
        • Winter 2013: University Studies 197B (Uteach: Theory and Practice)
        • Fall 2012: ICS 31 (Introduction to Programming)
        • Fall 2012: ICS 90 (New Student Seminar)
        • Fall 2012: University Studies 4—Transfer Student Seminar (Beyond Java and C++: Functional Programming in Scheme)
        • Fall 2012: ICS 193 (Tutoring in ICS)
        • Fall 2012: ICS 398A (Teaching Assistant Seminar)
        • Summer 2012: ICS 10 (How Computers Work)
        • Summer 2012: Informatics 131 (Human-Computer Interaction)
        • Spring 2012: Informatics 269 (Computer Law)
        • Spring 2012: ICS 398B (Advanced TA Seminar)
        • Spring 2012: University Studies 197C (UTeach Practicum)
        • Winter 2012: Informatics 42 (Informatics Core Course II)
        • Winter 2012: ICS 4: Human Factors for the Web
        • Winter 2012: University Studies 197B (UTeach: Teaching Theory and Practice)
        • Fall 2011: Informatics 41 (Informatics Core Course I)
        • Fall 2011: ICS 90 (ICS First-Year Seminar)
        • Fall 2011: University Studies 4—Transfer Student Seminar (Beyond Java and C++: Functional Programming in Scheme)
        • Fall 2011: ICS 398A (Teaching Assistant Seminar)
        • Summer 2011:Informatics 131 (Human-Computer Interaction)
        • Spring 2011: ICS 4: Human Factors for the Web
        • Spring 2011: ICS 398B (Advanced TA Seminar)
        • Spring 2011: University Studies 197C (UTeach Practicum)
        • Winter 2011: ICS 10 (How Computers Work)
        • Winter 2011: ICS 398A (Teaching Assistant Seminar)
        • Winter 2011: University Studies 197B (UTeach: Teaching Theory and Practice)
        • Fall 2010: Informatics 41 (Informatics Core Course I)
        • Fall 2010: ICS 90 (ICS First-Year Seminar)
        • Fall 2010: ICS 398A (Teaching Assistant Seminar)
        • Summer 2010: Informatics 131 (Human-Computer Interaction)
        • Spring 2010: Informatics 269 (Computer Law)
        • Spring 2010: ICS 398B (Advanced TA Seminar)
        • Spring 2010: University Studies 197C (UTeach Practicum)
        • Winter 2010: ICS 4 (Design and Usability for the Web)
        • Winter 2010: ICS 398A (Teaching Assistant Seminar)
        • Winter 2010: University Studies 197B (UTeach: Teaching Theory and Practice)
        • Fall 2009: Informatics 41 (Informatics Core Course I)
        • Fall 2009: ICS H21 (Introduction to Computer Science I—Honors)
        • Fall 2009: ICS 90 (ICS First-Year Seminar)
        • Fall 2009: ICS 398A (Teaching Assistant Seminar)
        • Spring 2009: ICS 398B (Advanced Teaching Assistant Seminar)
        • Spring 2009: University Studies 197C (UTeach Practicum)
        • Winter 2009: ICS 4 (Design and Usability for the Web)
        • Winter 2009: ICS 398A (Teaching Assistant Seminar)
        • Winter 2009: University Studies 197B (UTeach: Teaching Theory and Practice)
        • Fall 2008: Informatics 41 (Informatics Core Course I)
        • Fall 2008: ICS H21 (Introduction to Computer Science I—Honors)
        • Fall 2008: ICS 90 (ICS Orientation Seminar)
        • Fall 2008: ICS 398A (Teaching Assistant Seminar)
        • Summer 2008: Informatics 131 (Human-Computer Interaction)
        • Spring 2008: Informatics 269 (Computer Law)
        • Spring 2008: ICS 398B (Advanced TA Seminar)
        • Spring 2008: University Studies 197C (UTeach Practicum)
        • Winter 2008: Informatics 42 (Informatics Core Course II)
        • Winter 2008: University Studies 197B (UTeach: Teaching Theory and Practice)
        • Winter 2008: ICS 398A (Teaching Assistant Seminar)
        • Fall 2007: Informatics 41 (Informatics Core Course I)
        • Fall 2007: ICS H21 (Introduction to Computer Science I—Honors)
        • Fall 2007: ICS 398A (Teaching Assistant Seminar)
        • Summer 2007: Informatics 131 (Human-Computer Interaction)
        • Summer 2007: ICS 139W (Communications Skills for Computer Scientists)
        • Spring 2007: ICS 398B (Advanced Teaching Assistant Seminar)
        • Winter 2007: Informatics 42 (Informatics Core Course II)
        • Winter 2007: ICS 398A (Teaching Assistant Seminar)
        • Fall 2006: Informatics 41 (Informatics Core Course I)
        • Fall 2006: ICS H21 (Introduction to Computer Science I—Honors)
        • Fall 2006: ICS 398A (Teaching Assistant Seminar)
        • Spring 2006: ICS 398B (Advanced Teaching Assistant Seminar)
        • Winter 2006: Informatics 42 (Informatics Core Course II)
        • Winter 2006: ICS 398A (Teaching Assistant Seminar)
        • Fall 2005: Informatics 41 (Informatics Core Course I)
        • Fall 2005: ICS H21 (Introduction to Computer Science I--Honors)
        • Fall 2005: ICS 398A (Teaching Assistant Seminar)
        • Spring 2005: Informatics 264 (Computer Law)
        • Spring 2005: ICS 398B (Advanced Teaching Assistant Seminar)
        • Winter 2005: Informatics 42 (Informatics Core Course II)
        • Winter 2005: ICS 398A (Teaching Assistant Seminar)
        • Fall 2004: Informatics 41 (Informatics Core Course I)
        • Fall 2004: ICS H21 (Introduction to Computer Science I--Honors)
        • Fall 2004: ICS 398A (Teaching Assistant Seminar)
        • Spring 2004: ICS 131 (Social Analysis of Computerization)
        • Winter 2004: ICS 104 (Human-Computer Interaction)
        • Winter 2004: ICS 398B (Advanced Teaching Assistant Seminar)
        • Fall 2003: ICS H21 (Introduction to Computer Science I--Honors)
        • Fall 2003: ICS 10A (Information: Presentation and Representation)
        • Fall 2003: ICS 398A (Teaching Assistant Seminar)
        • Summer 2003: ICS 131 (Social Analysis of Computerization)
        • Summer 2003: ICS 139W (Communications Skills for Computer Scientists)
        • Spring 2003: ICS 280 (Computer Law Seminar)
        • Winter 2003: ICS H22 (Introduction to Computer Science II--Honors)
        • Winter 2003: ICS 398B (Advanced Teaching Assistant Seminar)
        • Fall 2002: ICS H21 (Introduction to Computer Science I--Honors)
        • Fall 2002: ICS 131 (Social Analysis of Computerization)
        • Fall 2002: ICS 398A (Teaching Assistant Seminar)
        • Summer 2002: ICS 131 (Social Analysis of Computerization)
        • Summer 2002: ICS 139W (Communications Skills for Computer Scientists)
        • Spring 2002: ICS 131 (Social Analysis of Computerization)
        • Winter 2002: ICS 131 (Social Analysis of Computerization)
        • Winter 2002: ICS 398A (Teaching Assistant Seminar)
        • Fall 2001: ICS H21 (Introduction to Computer Science I--Honors)
        • Fall 2001: ICS 10A (Information: Presentation and Representation)
        • Fall 2001: ICS 398AB (Teaching Assistant Seminar)
        • Summer 2001: ICS 131 (Social Analysis of Computerization)
        • Summer 2001: ICS 139W (Communications Skills for Computer Scientists)
        • Spring 2001: ICS 280 (Computer Law Seminar)
        • Winter 2001: ICS H22 (Introduction to Computer Science II -- Honors)
        • Winter 2001: ICS 131 (Social Analysis of Computerization)
        • Winter 2001: ICS 398A (Teaching Assistant Seminar)
        • Fall 2000: ICS 10A (Information: Presentation and Representation)
        • Fall 2000: ICS 398AB (Teaching Assistant Seminar)
        • Spring 2000: ICS 139W (Communications Skills for Computer Scientists)
        • Winter 2000: ICS H22 (Introduction to Computer Science II--Honors)
        • Winter 2000: ICS 141 (Programming Languages)
        • Fall 1999: ICS 10A (Information: Presentation and Representation)
        • Fall 1999: ICS 141 (Programming Languages)
        • Fall 1999: ICS 398AB (Teaching Assistant Seminar)
        • Summer 1999: ICS 1P (Intro to Computing)
        • Spring 1999: ICS 22 (Intro to CS II)
        • Spring 1999: ICS 280 (Computer Law Seminar)
        • Winter 1999: ICS 22 (Intro to CS II)
        • Winter 1999: ICS H22 (Intro to CS II---Honors)
        • Fall 1998: ICS 22 (Intro to CS II)
        • Fall 1998: ICS 398AB (Teaching Assistant Seminar)
        • Spring 1998: ICS 22 (Intro to CS II)
        • Spring 1998: ICS 280 (Computer Law Seminar)
        • Winter 1998: ICS 22 (Intro to CS II)
        • Winter 1998: ICS 139W (Communications Skills for Computer Scientists)
        • Fall 1997: ICS H21 (Intro to CS I---Honors)
        • Fall 1997: ICS 398AB (Teaching Assistant Seminar)
        • Spring 1997: ICS 22 (Intro to CS II)
        • Winter 1997: ICS 22 (Intro to CS II)
        • Winter 1997: ICS 139W (Communications Skills for Computer Scientists)
        • Fall 1996: ICS H21 (Intro to CS I---Honors)
        • Fall 1996: ICS 1B (Patterns of Problem Solving)
        • Fall 1996: ICS 398A (Teaching Assistant Seminar)
        • Spring 1996: ICS 1P (Intro to Computing)
        • Spring 1996: ICS 22 (Intro to CS II)
        • Winter 1996: ICS 22 (Intro to CS II)
        • Winter 1996: ICS 280 (Computer Law Seminar)
        • Fall 1995: ICS 1A (Intro to Computing)
        • Fall 1995: ICS 1B (Patterns of Problem Solving)
        • Fall 1995: ICS 398 (TA Training Seminar)

        • David G. Kay, kay@uci.edu
          Monday, January 4, 2016 1:02 PM
          http://www.ics.uci.edu/~kay/college.html How College is Different from High School

          College isn't just high school without your parents around

          The university isn't just another school; it's fundamentally different from what you've been used to. The sooner you recognize the differences and learn how to take advantage of them, the more successful you'll be.

          We know that no high school has every characteristic we describe here, nor will every aspect of college match this description. Still, we find that most of these comparisons apply to most students' experiences, so we hope they'll be valuable.

          In high school ...
          At the university ...
          There were students who weren't headed for college; college-bound students were an elite group.

          Everyone's already in college. The level (of discourse, of work, of independence we expect) is higher.

          As a successful student, you expected to "get" everything right away.

          You should expect to find many topics challenging and not immediately clear. Your instructors expect this, too: A good student is one who asks interested, insightful questions, not one who thinks he or she already knows everything.

          The things you were good at were things that came easily to you. You may feel that if something is tough for you, you can't be good at it and you should study something else. That's wrong. If you're interested in a topic, stick with it, even if learning it is tough. Most experts in any field work very hard at what they do. "Natural talent" is at best one component of expertise, and not the most important one. Curiosity and tenacity also play a big part.
          If you didn't understand a topic right away, you could study it and figure it out yourself. There will be some topics that you can't figure out by yourself. This can be an unfamiliar and uncomfortable feeling, but expect it in many classes and be prepared: Don't worry that something's wrong with you or that you can't handle the work. An important part of college is the opportunity of working with your classmates and instructors; that's why you don't just buy the textbooks and read them by yourself. In each class, know what avenues are available to you (office hours, discussion sections, e-mail questions, …).
          Only the weaker students needed to ask questions.

          We expect everyone, especially the stronger students, to ask questions (often out loud or on line; sometimes of yourself). It's part of engaging the subject matter and being an active learner; it shows that you're thinking about the topic and it helps you learn.

          When someone asked a question, your reaction was sometimes, "Oh, isn't that obvious? That questioner isn't so smart." There may still be some students in some classes who react that way. But those people should probably be in the next class. Most students should have questions, and should not let a classmate's imagined reaction stop them from asking. (Instructors don't think questions are annoying; nearly always, they expect and welcome questions. What's annoying is a student who "doesn't want to bother" the instructor with questions and then has trouble with the work. Some instructors prefer questions during lecture, some at the end, some by e-mail or in office hours. What's your instructor's preference? Ask!)
          When someone asked a question, you felt you could tune out; if you understood what the teacher said, you didn't need to pay attention to further questions about it. You should listen to your classmates' questions. Sometimes they will just be for clarification, but maybe they'll clarify something beyond what you already understood. And sometimes, they will bring out new aspects or implications or concepts. Many instructors introduce new material in response to students' questions; you'll miss things if you think you can ignore other students' questions and the instructor's responses.
          You spent six hours a day in class, plus homework. You spend fewer hours in class, but you should expect to work more hours overall.
          You could ease off for a couple of days, even a week, and catch back up. A week is 10% of the quarter, a significant chunk. Readings and assignments keep coming. You need to try your hardest to keep up in every class all quarter, because working double-time to catch up is really hard.
          If you didn't show up to class, someone would notice and you'd get in trouble. Most classes don't take attendance. Instructors don't want to take time with bookkeeping or with hearing excuses; they usually presume that students are mature enough to take their education seriously. But "not required" does not mean "not important." The textbook, a classmate's notes, even an audio or video recording don't capture everything that goes on in class.
          Everybody had to be in school. For twelve years, everyone was resigned to it. It was, "I have to do this," not "I get to do this." Your presence is voluntary. You're in college because you want to be. You should get your money's worth. Be glad when you're given more reading, more assignments, more class time!
          Sometimes you felt as if you were assigned work just to fill up the time. But when you finished an assignment, your work was done. Instructors think carefully about how each assignment will help you learn the material; "busywork" is almost nonexistent. Instructors and TAs dislike grading more than you dislike doing the work; if anything, they're likely to assign less work than you may need to master the material. Be prepared to quiz yourself to see if you need to do more; your work isn't done until you can actually do what the instructor expects.
          You could finish your work more quickly than most students; teachers' advice about how to work effectively didn't really apply to you. Follow your instructors' advice about how to work effectively; they gear that advice towards college students, not towards the high school average. In most cases, they want to convey the way professionals in their field operate. If your instructor suggests that you read certain material or try certain problems, do those things, even if there's not specifc course credit for it. Don't think, "In high school, I never needed to do these supplementary things."
          A lot of the learning involved memorizing facts and spitting them back. Getting "the answer" was the goal. There are still facts to learn, but that's only the first step. Most courses expect you to apply the facts to solve problems. The dates of historical events help you determine the reasons for a war; the characteristics of certain components help you design a system to meet a specific need. Learning the process of solving new problems is the key.
          The only important material was the material that appeared on the exams. Learning what you need so you can do well on exams is still important, but it's not the only important thing. Instructors often provide additional material in lectures or the reading, material that may not be tested directly but that will help you in your later classes or in your career. That's a major part of the value of your college education, so don't skip class because you think the topic won't be on the test. This is not "just school"; it's preparation for real life.
          Teachers sent home "progress reports" and intervened when students got off track. You need to monitor your own progress, based on the feedback and scores you get on assignments and tests. The syllabus of each course should describe the quality of work that's expected. If you're concerned about your performance, speak with your instructor or TA; they won't come to you.
          Doing well mainly required putting in the effort to complete the assignments. You have to put in effort and complete assignments, but effort alone doesn't guarantee success. Some topics will be difficult for you (and if none are, then you're not taking the right courses and you're not getting your money's worth). When that happens, just thinking about it harder may not be enough; form a study group, speak with the TA or instructor, use LARC. When the going gets tough, the tough get help.
          Your parents were around to help keep you fed, clothed, and organized. You're on your own. If you're over 18, the university isn't allowed to tell your parents anything about you without your permission. That's not because keeping things from your parents is necessarily good; it just means that now you have primary responsibility for your own health, welfare, and education. The university provides a lot of support—food services, health services, counseling services, tutorial services—but you have to take the first step to seek it out and you shouldn't wait until a problem gets out of control.
          You had student clubs, athletics, service opportunities. You have more student clubs, athletics (participatory and spectator), and service opportunities. You also have concerts, plays, and art exhibits. You have academic talks by visiting scholars and distinguished speakers. You have a variety of social events. You have conversations with your dorm-mates lasting late into the night. You should take advantage of these opportunities; they're part of the college experience. But you can't do everything that looks interesting; you have to leave enough time for your academic work, because that's your first priority.

          If you like, read more advice about how to thrive in college.

          Your time in college can be the best time in your life (so far); make the most out of it!

          (Written by David G. Kay with suggestions from Shannon Tauro, Angelo Pioli, and Gabriela Marcu. If you have suggestions or comments, send them to kay@uci.edu. Copyright © 2006–2015 by David G. Kay. All rights reserved.)

          http://www.ics.uci.edu/~feldman/ICS131F00.html ICS131 ICS 131-Fall 2000-Start Page
          1. Syllabus
          2. Schedule
          3. List of Projects
          4. Lecture Two
          5. Lecture Three
          6. Lecture Four
          7. Lecture Five
          8. Lecture Six
          9. Lecture Seven
          10. Lecture Eight
          11. Lecture Nine
          12. Lecture Ten
          13. Lecture Eleven
          14. Lecture Twelve
          15. Lecture Thirteen
          16. Lecture Fourteen
          17. Lecture Fifteen
          18. Lecture Sixteen
          19. Lecture Seventeen
          20. Lecture Eighteen
          21. Lecture Nineteen
          http://www.ics.uci.edu/~feldman/ICS131.htm ICS131 ICS 131-Winter 2000-Start Page
          1. Syllabus
          2. Schedule and Reading List
          3. List of Projects
          4. Lecture Two
          5. Lecture Four
          6. Lecture Five
          7. Lecture Six
          8. Lecture Seven
          9. Lecture Eight
          10. Lecture Nine
          11. Lecture Ten
          12. Lecture Eleven
          13. Lecture Twelve
          14. Lecture Fourteen
          15. Lecture Fifteen
          16. Lecture Sixteen
          17. Lecture Seventeen
          18. Lecture Eighteen
          19. Lecture Nineteen
          http://www.ics.uci.edu/~eppstein/contact.html David Eppstein

          David Eppstein

          • About
          • Contact
          • Research
          • Students
          • Classes
          • Software
          • Math Fun
          Me in Limerick, by EM
          David Eppstein
          Computer Science Department
          Donald Bren School of Information & Computer Sciences
          University of California, Irvine
          Irvine, CA 92697-3435
          USA

          eppstein@ics.uci.edu
          david.eppstein@gmail.com

          Please only send to one of these two email addresses, not both. UCI-confidential information (such as anything involving course scores or grades) should go only to the UCI address.

          Office: Bren Hall, room 4082

          Phone: 949-824-6384
          Fax: 949-824-4056
          About | Contact | Research | Students | Classes | Software | Math Fun
          http://www.ics.uci.edu/~eppstein/eppstein.html Eppstein Relatives and Namesakes

          Eppstein Relatives and Namesakes on the Net


          California/New Zealand Eppsteins:

          • I am David Arthur Eppstein <eppstein@ics.uci.edu>. My first and middle names come from my late grandparents, David Nathan Eppstein (1904-1937) and Arthur Sambrook Dinsdale. Diana Rebecca Eppstein <deppstein@cox.net>, my wife, is an expert in user-interfaces and a programmer for Dr. Harvey Eisenberg. Her web site is mostly filled with pictures of our children Sara Caitlin and Timothy Dylan; there are even more pictures on my own site. We live in Irvine, California.
          • Anthony David Eppstein <tony@ramonahouse.com>, my father, is a photographer, web designer, and retired disk drive engineer. He and my mother, Maureen Valerie Eppstein <maureen@ramonahouse.com>, a poet, writer, editor, and publisher, now live in Mendocino, California.
          • Simon Anthony Eppstein, Cynthia Lynn Eppstein, Logan Stanley Eppstein, and Aaron Christopher Eppstein <Eppslo@aol.com> are my brother, sister-in-law, and two nephews respectively. Simon is an industrial designer; Cindy is a tech writer for a dental equipment firm. They live in Oakley, California.

          Kalamazoo Eppsteins:

          • Deborah Anne Eppstein is a VP at Theratech in Salt Lake CIty, Utah. She is a pharmaceuticals researcher with a Ph.D. from U. Arkansas in 1975 and publications including Cancer Immunology Immunotherapy 36 (1993) 171-176, Science 246 (1989) 1293-1297, J. Cell Phys. 141 (1989) 420-430, and many US patents including two with her brother Jonathan.
          • Elizabeth Ellen Eppstein Downs <betsymike@home.com> the oldest daughter of Samuel and Dorothy, is a partner with OWP&P Architects Inc., specializing in health care architecture, and is also a tournament bridge player.
          • Jonathan Arthur Eppstein, son of Samuel and Dorothy, is vice president of research and development for SpectRx, an Atlanta laser company, and has apparently written a technical paper or two on police Lidar speed detectors.
          • Laurel Katherine Eppstein <eppstein@wmich.edu>, daughter of Samuel and Dorothy, is a database designer for the National Council of the Pulp and Paper Industry for Air and Stream Improvement.
          • Margaret Jean Eppstein <eppstein@emba.uvm.edu>, daughter of Samuel and Dorothy, is a lecturer in computer science at U. Vermont. Fortunately her interests are in scientific computation rather than (say) discrete algorithms.
          • Samuel Hillel Eppstein and his wife Dorothy Jean Dodd Eppstein (parents of Betsy, Deborah, Jonathan, Laurel, and Margaret) built a Usonian Frank Lloyd Wright house near Kalamazoo. According to daughter Laurel, Samuel and his brother Victor changed their name to Eppstein after learning that one of their P's had been lost when the family immigrated to the U.S. This seems to be the same Samuel H. Eppstein who wrote a thesis on "The Parenteral Utilization of Amino Acids" for UIUC in 1936 and authored US patents 3275516 and 3651231 for Upjohn in Kalamazoo.

          California/Mississippi Eppsteins:

          • April J'Lene Eppstein <Aprilshan@aol.com>, daughter of James Charles, lives in Mississippi.
          • Christopher Mark Eppstein <ceppstein@terraspring.com> went to Caltech (where he co-authored a Phys. Rev. Lett. paper on "Parity Violation in Elastic Electron-Proton Scattering and the Proton's Strange Magnetic Form Factor"). He now works as a software engineer for ITM software, and runs websites whatsbetter.com and eppsteins.net.
          • James Charles Eppstein <eppsteij@aol.com>, son of Michael F., lives in Ocean Springs, Mississippi.
          • Jennifer Adele Eppstein <jefiner7@hotmail.com>, daughter of Mark William and Leslie, majored in business administration at CSU Fresno and currently works in pharmaceutical sales.
          • Julia Armstrong-Mazur Eppstein married Chris in December 2003, and attends Mill College.
          • Katie Joyce Eppstein <kookies7@yahoo.com> is the youngest daughter of Mark William and Leslie, soon heading to college after her high school graduation in June 2004.
          • Mark William Eppstein <epps5@mediaone.net>, son of Michael F., and his wife Leslie Lauren Eppstein live in Cambria, California. They have three children: Christopher Mark, Jennifer Adele, and Katie Joyce.
          • Michael F. Eppstein <eppsteim@ptw.com> passed away in March of 2002. in Los Angeles, California. His father Jerry was born in the New York area to a Max Eppstein who fled Russia due to Bolshevik persecution in the 1880's; the two-p spelling may have been acquired by Max at Ellis Island. Jerry married Madeline (née Plumado) in 1928, who was born in Wisconsin in the 1880's and moved with her family to Placerville, California in the early 1900s. The Plumado house, where Madeline grew up, is now a historical landmark in Placerville. Jerry and Madeline had three children: Judy Thornley (Passed away in 1999), Michael F., and Esther McDonald (living in Woodland Hills, California). A fourth daughter, Helen Bolton was born to Madeline from a previous marriage. Michael F. has five sons: Michael Dana, James Charles, Mark William, Matthew Allen (mysteriously vanished, last seen in Twenty-Nine Palms California in 2000, although rumors he had been found surfaced again in 2004), and Thomas Lee.
          • Thomas Lee Eppstein <teppsn3@se-iowa.net>, son of Michael F., runs several web sites: http://www.knoxvilleiowa.com, http://www.knoxvilleraceway.com, http://www.IowaRacing.com, http://www.RichardRealty-Auction.com, and http://www.edwardspublications.com.

          Arizona Eppsteins:

          • Viggo M. Fosse's Eppstein relatives: Robert Eppstein (1923-1994) and his children Steven W. Eppstein (1950-1999), Robert E. Eppstein, Laurel A. Eppstein, Richard I. Eppstein, and Christopher J. Eppstein (1961-1990).
          • Bob Eppstein is a maintenance tech. at the Arizona-Sonora Desert Museum in Tucson.
          • Davis & Eppstein P.C. is or was a law firm in Tucson, Arizona, at which Judge Kenneth Lee worked.
          • Robert W. Eppstein is apparently another genealogist: he has written an article "My great-grandfather's journal" for Stammbaum (The Journal of German-Jewish Genealogical Research).
          • Mrs. Robert W. Eppstein is a donor to KUAT (U. Ariz. tv/video station).
          • Roz Eppstein is an abstractionist artist reviewed in the Tucson Weekly.
          • Steven Wolfner Eppstein (1950-1999) was survived by children Matthew Eppstein and Meghann Eppstein; mother Audrie (m. Don Johnson), stepmother Roz Eppstein, brother and sister-in-law Bob Eppstein and Olga Eppstein, sister Laurie Eppstein Montano, and brother and sister-in-law Richard and Pam Eppstein.

          Missouri/Illinois Eppsteins:

          • Eppstein/Eppstine family of Mombach, Germany, 1800-1850.
          • Joseph A. Eppstein was alternate delegate to the Republican National Convention from Missouri, 1868 and owned a slave in Boonville, Missouri, in 1860.
          • H. F. Eppstein was listed (with family) as a 34-year-old farmer in the 1860 census in Greenville, Bond County, Illinois.

          Missouri/Texas/Colorado/Oregon Eppsteins:

          • Barbara Carpenter is the granddaughter of Carrie Eppstein Livingston. She writes: "Three Eppstein Brothers, Max, Elias and Leopold emigrated to the U.S. from Germany in 1854 (approximately). Two sisters, Charlotte and Rosalie, remained behind. Rosalie married a man named Simon. Their grandson became the managing director of Rosenthal China. Charlotte married Heinrich Guttman and had at least one son , Otto. Their son, Harvey Guttman lives in the U.S. The brothers settled in Saint Joseph, Missouri and owned a soap factory. The factory burned down and Leopold and Elias moved to Texas (perhaps Sherman, Texas). Max (4/1825-4/1903 moved to Denver and he and his wife, Bertha Herzberg (10/1837-2/1912) had seven children: Seraphene, Julia, Helen, Carrie, Arthur, and twin girls, Lily and May. Arthur, the only son married Viola Strauss. They had two children, Helen and Lily and, thus the Max Eppstein branch name was lost. ... I would love to find out about the families of Leopold and Elias, but have been unsuccessful to date."
          • The Arthur Eppstein Insurance Achievement Award was likely named after Arthur (above), who owned Oregon Automobile and Causality Company in Portland.
          • Lily Eppstein Morris, Arthur's daughter, graduated from Barnard in 1929 and endowed the Morris-Eppstein Scholarship Fund there in 1996.

          Ohio Eppsteins:

          I haven't sorted out all the connections here, but a lot of these people seem to be related to each other.
          • Alexander Richard Eppstein <eppstein@sas.upenn.edu>, son of Richard T., is now a student at U. Penn.
          • Andrew C. Eppstein <acepps@yahoo.com> son of Richard T., is a resident at the Indiana University School of Medicine, from Hong Kong by way of Toledo (where he published two papers on vascular flow), Yale, and CWRU.
          • Connie Eppstein, Richard T.'s sister-in-law, is a geriatric social worker at the Medical College of Ohio. Her husband Ed Eppstein passed away in August 2001.
          • David M. Eppstein <88930036%taonode@vmcms.csuohio.edu> is or was somehow associated with Cleveland State Univ. and passed the July 1996 Ohio bar exam
          • Edward J. (Ebby) Eppstein, Richard T's late uncle, founded the Webstrand Corp. and helped plan the Arrowhead business park in Maumee, Ohio.
          • Jean and Stanley Eppstein of Youngstown, Ohio helped found the Tuckerman Family Breast Cancer Research Endowment Fund at OSU. Apparently these are the same Jean and Stanley who were parents of Steven David.
          • Joseph Eppstein of Toledo, Ohio received a letter from Harry S. Truman dated 14 Nov 1944 on Senate Committee on Military Affars stationary.
          • Julia Eppstein (1848-1878) is buried in Cedar Hill Cemetery, Piqua, Ohio.
          • Laura Eppstein of Reading, Ohio placed 25th in the 1995 Junior Olympics Midget Girls' High Jump.
          • Michael Alan Eppstein <eppstein.3@osu.edu>, a student at Ohio State U., studied horticulture in spring 1996.
          • Richard T. Eppstein <eppstein@sylvania.sev.org> is the father of Alexander Richard and Andrew C., and president of the Toledo Better Business Bureau.
          • Steven David Eppstein is the son of Jean Leopold Eppstein and the late Stanley M. Eppstein, who was the son of Meyer Eppstein of Toledo Ohio. As of 2006, Steven and his two sons Sppencer Meyer (b.1991) and Austin Mark-Paul (b.1993) live in Youngstown, Ohio.

          Swiss Eppsteins:

          • Esther Eppstein <messagesalon@bluewin.ch>, b. 1967, daughter of Paul and sister of Golda, is a Zurich-based contemporary artist.
          • Golda Eppstein, b. 1969, daughter of Paul and sister of Esther, is an actress, mainly working in children's theater. She and her husband Roman Bernhard Eppstein have two children Max and Pina.
          • Paul Eppstein (16 Dec 1917 - 19 Nov 2001) was the father of Esther and Golda. According to Esther, he grow up in Zurich as a son of Jewish Polish immigrants, originaly called Epztain, but his father was called Bernhard Epstein (spelling changed to be more German). During World War II, Paul lost his Polish citizenship but avoided being deported and after the war became a Swiss citizen, somehow gaining another P in his name (perhaps to be more Swiss). He was well known in Zurich as a sportsman, friend of many artists and actors, art collecter, and entertainer.

          Eppstein non-relatives:

          Well, I'm sure they're related to someone, but I don't know to whom.
          • Richard Aronoff's Chicago Eppstein relatives: Charles Louis Eppstein (1836-1900) and his children Lena Eppstein, Benjamin Eppstein, Bertha Eppstein Loewy (1873-1941), Ida Eppstein Simmons, Nellie Eppstein, and Hattie Eppstein.
          • "Miss Eppstein" married Jacob August in 1855 in New York.
          • Arnold, Gustav, and Wilhelm Israel Eppstein are listed on this page apparently having something to do with the holocaust (but written in Czech).
          • Ben Eppstein was one of the discoverers of the body of Joe Simpson, a mining town businessman who was the last person lynched in California, in 1908.
          • Bob Eppstein is staff liaison to the Community Development Board of Appeals, Salem, Oregon.
          • Brian Eppstein was honored as an outstanding high school scholar-athlete by the Brady Co. of Milwaukee in Oct. 1996 and has a USCF chess tournament rating of 789. Is this the same Brian Eppstein included in this list of lazy or unwilling Shakespearean students for his now-inaccessible query on Hamlet's father's ghost? The same or another Brian Cran Eppstein is somehow associated with The U. Wisconsin Computer Aided Engineering facility and is apparently majoring in chem. eng., biochem., and math. at U. Wisc. He has family members named Gala Eppstein, Jessica Eppstein, and Sam Eppstein. Yet another Brian Eppstein was part of Digital Instruments' Tip Exchange / Tip Compensation Team.
          • Bryan Eppstein is a political consultant and campaign strategist for Fort Worth mayor Kay Granger.
          • Count Eberhard II von Eppstein built the Marksburg castle on the Rhine around the beginning of the 13th century.
          • D. Eppstein of Vipont Research Labs, Saskatoon wrote about production of sanguinarine in J. Fermentation and Bioengr. 74 (1992) 292-296.
          • David Eppstein <david@masco2.harvard.edu> is a staffer at the Medical Academic and Scientific Community Organization, Inc. of Boston, and contact person for their mercury wastewater discharge project.
          • Dori Eppstein <tpedlow@sunstroke.sdsu.edu> is a psychology master's student at San Diego State U.
          • Rabbi Elias Eppstein wrote a "Kansas city diary" in the early 1880's, which Howard Sachs wrote about in an article catalogued here. He also wrote the Confirmant's Guide to the Mosaic Religion. According to information provided to me by Philip Braverman from the book Roots in a Moving Stream: The Centennial History of Congregation B'nai Jehudah of Kansas City 1870-1970 (Frank J. Adler, 1972), Elias Eppsteinwas born in 1831, the son of an Alsatian rabbi, came to the USA in 1852, and served congregations in Syracuse NY, New York City, Jackson MI, Detroit MI, Milwaukee, Kansas City, Philadelphia, and finally Quincy, IL, where he was buried after his death in 1906. He is apparently not the same as the Elias Eppstein of Saint Joseph, MO, listed above under Missori/Texas/Colorado/Oregon Eppsteins despite at one point living within 50 miles of each other.
          • Edward Julius Eppstein (1923-1987) is buried in Nat. Mem. Cemetery of the Pacific, Oahu.
          • Eitan Eppstein, Reggi Eppstein, and Sarah Marder-Eppstein were present at the 17 Sep 1996 meeting of the Evanston IL library board
          • Emma and W. H. Eppstein are listed in the 1900 St. Louis Obituary Index.
          • Evelyn Eppstein Hauser d. May 1996.
          • Florence Levinson, widow of Robert, lives in Southfield MI, and has children Landra Eppstein Rosenthal (a legal worker in Berkeley, California) and Richard Eppstein (married to Joann Eppstein) in Windsor, Ontario.
          • Hans Eppstein is a musicologist (at Uppsala?), some of whose publications are listed in the J. S. Bach bibliography and some of which are published by Barenreiter. In 1997 he became a member of honour of the International Joseph Martin Kraus Soc..
          • Helene Eppstein (b. 1592) is included in this genealogy.
          • Henry Eppstein was past president of the Eureka Benevolent Soc.
          • Jim Eppstein, of the Irvine(!) police dept., was Named officer of the year for 1998 by the Orange County Auto Theft Advisory Committee.
          • The same or another Jim Eppstein served in the US Navy 1972-1976.
          • Joe Eppstein of Ft. Thomas, Kentucky, has email <121646@msn.com>.
          • Joe Eppstein won a trophy from the Inland Lake Yachting Assoc. in 1976.
          • John Eppstein wrote "Has the Catholic Church Gone Mad?" (no.943 in this catalog). Is this the same Eppstein cited here for "The Historicity of the Gospel Account of the Cleansing of the Temple"?
          • John Eppstein of Caversham Reading, England, has email address <100014.375@compuserve.com>
          • Joseph M. Eppstein (d. 1941) is buried in Ft. Leavenworth Nat. Cemetery, Kansas.
          • Joseph S. Eppstein of Evanston, Illinois, has email address <74134.3424@compuserve.com>.
          • Julius Eppstein's Plaza Florist (0418 Sutter St.) was included in this collection of photographs of San Francisco in the 1950s
          • Julius Leslie Eppstein (1897-1946) is buried in Los Angeles Nat. Cemetery, California.
          • Kate Eppstein <Kateeppstein@tesco.net> is interested in Sussex genealogy.
          • Katya Eppstein is apparently a musician or composer, responsible for Der Jägersmann. Perhaps this is a typo for Katya Ebstein?
          • Ken Eppstein is a fine artist and graphic designer for The Lenox Group (multimedia design studio). Their host www.lenox.com no longer seems to exist (Sep 1996). He also provided the graphics for the Stellar Crisis game.
          • L. Eppstein worked with Joseph Shiloach at the NIH on carbohydrate metabolism in insect cell cultures.
          • Lee Bernhardt Eppstein received a Ph.D. from Texas Christian U. in 1971 and is chief scientist at New Brunswick Sci. Corp. with patents and publications including J. Phys. Chem. 97 (1993) 3885-3889 (cited by this paper), and J. Biotech. 23 (1992) 291-301.
          • Leonard Eppstein of Texas went to Notre Dame (1884/85)
          • Lori Eppstein is a journalist in Northern California, with stories in the Pt. Reyes Light, Golden Gater, Jewish Bulletin of Northern Calif., and Coastal Traveler.
          • M. Eppstein is co-author of "A Cultural Resources Survey of the Red River Waterway from Shreveport, Louisiana, to the Mississippi River", U.S. Army Corps of Engineers, 1981, cited by a National Park Service environmental assessment of the Cane River area.
          • Marty Eppstein was a 1999 drummerboy leatherman contestant from Northern California.
          • Maurice J. Eppstein of Redwood City, Calif. is a veteran of the US Army Air Corps / Air Force 345th Bomb Group.
          • Max Eppstein was a liquor dealer listed in the 1900 Census for Denver, at 438 29th Street 67-68, with family consisting of himself (b. Apr 1855, imm. 1869), wife Rosa (b. Aug 1862, imm. 1872), son Zarie (b. Apr 1880), dau Fannie (b. Oct 1882), dau Ethel (b. Jan 1884), son Julius J. (b. Jan 1886), son Harry J. (b. Jul 1886), dau Lillie (b. Jan 1890), and dau Sadie (10 months old in June 1990).
          • Mike Eppstein is listed as chair for a meeting on technical analysis of stock markets. The same or another Mike Eppstein <mikeeppstein@webtv.net> is a Milla Jovovich fan.
          • Mike Eppstein is the stage name for pro wrestler Mike Drews.
          • Moses Eppstein married Amelia O'Brien, 1 Dec 1873 in Galesburg, Illinois.
          • Nell S. Eppstein Allen (1919-2000), dau. of Ernest E. and Elizabeth Wesley Eppstein.
          • Paul Eppstein (1901-1944) was a Jewish leader in Nazi Germany, sent to Theresienstadt.
          • Pascal Eppstein of Gavle, Sweden, has email address <nosferato@hotmail.com>
          • Renaud Eppstein <eppstein@univ-mlv.fr> is a combinatorialist at the University of Marne la Vallée.
          • Rick Eppstein <RichardE@otg.com works in sales for OTG software in Bethesda, Maryland.
          • Rudolph M. Eppstein is mentioned in a 1997 genealogy query which includes references to Eppsteins in the 1880 census for Champaign, Illinois, and the 1900 census for Chicago. The spelling seems uncertain, though, since Rudolph's son is listed with only one P.
          • S. Eppstein married Sarah Rosenfeld, San Francisco, 1880, and had a son in 1881.
          • Sam Eppstein is senior partner of Eppstein Uhen Architects Inc., a firm originally founded by an associate of Frank Lloyd Wright. (According to Betsy Downs, he is not known to be related to Samuel Hillel Eppstein, despite their shared architectural interests.)
          • Serge Eppstein <serge.eppstein@mailbox.swipnet.se> is a Procol Harum fan in Sweden.
          • Sigfrid and Widerat von Eppstein are listed as "Regenten" (for the years 1058-1060 and 1060-1075 respectively) in this chronical of the city of Fulda (Germany).
          • Sivan Eppstein is a 6th-grader in Sunnyvale, Calif.
          • Solvieg M. Eppstein, widow of Victor Eppstein died Oct 2000 in Maine, leaving survivors including a son John H. D. Eppstein of San Francisco. Is either this Victor or the Rabbi Victor Eppstein responsible for quote 488 the same as the Victor Eppstein who was brother of Samuel Hillel Eppstein?
          • Sylvia Rose Eppstein
          • married Benjamin Capin in 1928.
          • Sylviane Robardey-Eppstein is somehow associated with Groupe Hugo in France.
          • Tobias Eppstein sailed on the S.S. Silesia from Hambirg to New York, Jan. 1882.
          • Ury Eppstein of the Hebrew Univ. of Jerusalem (maybe in their Dept. of East Asian Studies?) studies Japanese performing arts and reviews musical performances for the Jerusalem Post and for the Israel Review of Arts and Letters.

          Eppstein non-people:

          • The Palais Eppstein is a building in Vienna, Austria, used for parliamentarian purposes.
          • Eppstein is also the name of a town near Frankfurt, Germany.
          • The Bob Eppstein Scholarship award is given annually by the Oregon Building Officials Assoc.
          • Le Chateau D'Eppstein is a novel by Alexandre Dumas père, about an ancient aristocratic family who live in a crumbling castle in the Taunus mountains north of Frankfurt.
          • Eppstein Uhen Architects Inc.
          • Herbst, Eppstein, Keller & Chadek, Inc., 210 E. Michigan St., Ste. 100, Milwaukee, is for some reason included in this address list.
          • Eppstein Group Inc. is a central Texas advertising agency.

          non-Eppstein relatives:

          • Lindy Banks <lindybanks@adelphia.net>, my wife's grandmother, is a bridge life master.
          • Chris Cassel <chris@garment-district.com, Ynniks@ix.netcom.com, Ynniks@aol.com>, my brother-in-law, runs Plumb Records, an indie label in Boston, as well as managing The Garment District, a vintage/alternative clothing store there.
          • David Cassel <dgc@lns62.lns.cornell.edu>, my wife's uncle, is a physics professor at Cornell U. Don't ask me what his home page is doing in Texas, it probably has something to do with the information supercollider.
          • Erik Cassel <erik_cassel@pacbell.net>, my wife's cousin, now works for Outhink. His wife Rebecca <smith_cassel@pacbell.net> is involved with a science education project at UCSF.
          • Monika Cassel <micassel@umich.edu>, my wife's cousin, has graduated from U. Mich and taken a position in Santa Fe.
          • Phyllis Cassel <phycassel@aol.com>, my mother-in-law, is a nurse and a member of the Palo Alto city planning commission.
          • Graeme Dinsdale <graeme_dinsdale@telus.net>, my uncle, is a contracter and former Islands Trust commissioner in Bowen Island, British Columbia.
          • Earl Hart <ehart@xtra.co.nz> is a distant cousin in New Zealand.
          • Alison Heckler <alison@anzat.com>, my aunt, is a travel agent specializing in New Zealand and Australia. She lives with her husband Derek (my uncle) in Petaluma, California.
          • Angela Heckler <angela@cp.net>, my cousin, works for Critical Path, a Silicon Valley company that handles email for large ISPs.
          • James Heckler <jamesandlindaheckler@yahoo.co.nz>, my cousin, was (last I heard) working in high-tech sail design after getting a degree in operations research or something similar.
          • Julia Heckler <juliaheckler@yahoo.com> is yet another of my cousins.
          • Lara Hoskins <jeff.lara@xtra.co.nz> is Angela's, James's, and Julia's sister and another of my cousins.
          • Bronwyn Hughes <bron_hughes@yahoo.co.uk>, my cousin, is doing something or other in London.
          • Pat Hughes <Pat@lbllaw.co.nz is my aunt.
          • Geoff Lealand <lealand@waikato.ac.nz>, my uncle, is in the Dept. of Film and Television Studies, Waikato U., NZ.
          • Don Stokes <don@daedalus.co.nz>, my cousin, runs a networking consulting firm in Wellington, NZ.
          • Evelyn M. Stokes <geogsec2@waikato.ac.nz>, my aunt, is a professor in the Geography dept., Waikato U., NZ, Dame Companion of the New Zealand Order of Merit, and a member of the Waitangi Tribunal (NZ commission for Maori land claims and other grievances).
          • Fil Stokes <drfilesq@hotmail.com>, my cousin, is an alternative comix author and alternative musix player.
          • David Epsteins with the wrong number of P's:

            • David Epstein, a businessman, donated money for a building in the U. Miami school of Business Administration.
            • Three people named "David Epstein" have UCSF chess ratings: one rated 1395 in New Jersey, one rated 2045 in California, and one rated 1196 in New York.
            • David Epstein <daveep@atlas.co.uk> posts to rec.video.satellite.europe, comp.sys.ibm.pc.hardware.cd-rom, and comp.publish.cdrom.software.
            • David Epstein <102136.660@CompuServe.COM> posts to rec.travel.misc.
            • David Epstein <depstein@worldnet.att.net> posts to comp.sys.mac.apps.
            • David Epstein <de11@cc.columbia.edu> is an assistant professor of political science at Columbia U. who sometimes posts to rec.games.chess.*. This is especially confusing as I also went to Columbia and have occasionally posted to the same groups...
            • David Epstein of Metuchen NJ runs a sports collectibles business.
            • Prof. David Epstein directs the MIT Symphony Orchestra.
            • David Epstein wrote the screenplay for Palookaville.
            • David Epstein <david@imagine1.com> knows something about conditional compilation in Fortran, attended Supercomputing '96, and helped invent the F programming language.
            • David A. Epstein is a holistic dentist.
            • Another David A. Epstein sells the IBM PVS visualization system.
            • David B. A. Epstein of Warwick U. <dbae@maths.warwick.ac.uk> is a topologist, one of the inventors of automatic group theory, and (very confusingly) associated with the U. Minn. Geometry Center. Mike Paterson has been trying to get us to co-author something but it hasn't happened yet.
            • David G. Epstein is somehow involved in litigation against Piper Aircraft.
            • David G. Epstein <david@dgelaw.com> is a construction failure lawyer, not the same as the Piper Aircraft lawyer (!), has a Columbia Univ. Ph.D. (!!), and works in Irvine (!!!). He has many Epstein cousins, fortunately not all also named David G.
            • David L. Epstein is a media and research assistant at Rutgers U.
            • David L. Epstein, M.D. is a professor of opthalmology at Duke U.
            • David M. Epstein <fund@netmedia.net.il> is also interested in genealogy, and has asked on soc.genealogy.jewish about his ancestor, Aryeh Lieb Epstein HaLevi of Königsburg.
            • David N. Epstein has an obituary in the Nov/Dec 2000 issue of Cornell magazine.
            • David Stanley Epstein wrote The Gem Merchant: How to be one - how to deal with one.
            • David Zvi Epstein, age 8, wrote a story about an elephant and a spaceship.

            David Eppstein, Information & Computer Science, UC Irvine
            http://www.ics.uci.edu/~eppstein/software.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            David Eppstein at the Balboa pier, December 2009
            Closest pairs experimental testbed of algorithms for greedy matching, approximate traveling salesman tours, and hierarchical clustering. C++ source code.

            Cryptogram Helper generates and solves substitution ciphers. Web applet and Java source.

            Fanorona ancient board game from Madagascar. Web applet and Java source.

            Filter keyword-controlled versionization of text files, used for several of my web pages. C source.

            Glider programs for finding and listing glider patterns in cellular automata. C source.

            LaTeX Unicodifier. OS X application to convert between LaTeX source code and unicode accented latin character strings. Requires OS X version 10.5 (or a working installation of Python and PyObjC).

            Lombardi spirograph software to draw graphs with circular-arc edges.

            Lookup uses Knuth-Morris-Pratt algorithm to find paragraphs containing a text string. C++ source.

            Mail to HTML converter. C++ source code.

            Number-theoretic hacks. C, C++, and Mathematica source code.

            Python algorithms and data structures.

            Tabulizer. OS X table editor. Open source with precompiled executable.

            wordsquare software for solving word square puzzles.

            webimg Mac program for making my photo web pages. Python source code.

            And finally, a blast from the past...
            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~eppstein/students.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            button eyes
            Current students:
            • William E. Devanny
            Former students:
            • Jeff Erickson (M.S. 1992)
            • David Hart (Ph.D. 2002)
            • Joseph Wang (Ph.D. 2003)
            • Josiah Carlson (Ph.D. 2007)
            • Kevin Wortman (Ph.D. 2009)
            • Darren Strash (Ph.D. 2011, coadvised with Mike Goodrich)
            • Joe Simons (Ph.D. 2014, coadvised with Mike Goodrich)
            • Zhanpeng (Jack) Cheng (M.S. 2014)
            • Michael Bannister (Ph.D. 2015)
            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~eppstein/pix/ Photo Galleries

            Photo Galleries

            I have collected here a few favorite pictures by subject; click on any thumbnail to reach a gallery of similar pictures.
            You can also view more photos grouped chronologically or see see and comment on some of my recent photos on Flickr.
            Action Portraits Art and Architecture
            Modernist Fauna Flora
            Beaches Landscape Sunsets

            David Eppstein, ICS, UC Irvine

            http://www.ics.uci.edu/~eppstein/pubs/selected.html David Eppstein - Publications

            David Eppstein - Publications


            Selected publications

            • Sparse dynamic programming.
              D. Eppstein, Z. Galil, R. Giancarlo, and G.F. Italiano.
              1st ACM-SIAM Symp. Discrete Algorithms, San Francisco, 1990, pp. 513–522.
              "Sparse dynamic programming I: linear cost functions", J. ACM 39: 519–545, 1992.
              "Sparse dynamic programming II: convex and concave cost functions", J. ACM 39: 546–567, 1992.

              Considers sequence alignment and RNA structure problems in which the solution is constructed by piecing together some initial set of fragments (e.g. short sequences that match exactly). The method is to consider a planar point set formed by the fragment positions in the two input sequences, and use plane sweep to construct a cellular decomposition of the plane similar to the rectilinear Voronoi diagram.

              (BibTeX -- Citations to conference version -- Citations to SDP I -- Citations to SDP II)

            • Provably good mesh generation.
              M. Bern, D. Eppstein, and J. Gilbert.
              31st IEEE Symp. Foundations of Comp. Sci., St. Louis, Missouri, 1990, pp. 231–241.
              J. Comp. Sys. Sci. 48: 384–409, 1994 (special issue for 31st FOCS).

              In this paper, we construct triangulations of point sets and polygons by using quadtrees to add extra vertices to the input. As a result we can guarantee that all triangles have angles bounded away from zero, using a number of triangles within a constant of optimal; this was the first paper to provide simultaneous bounds on mesh element quality and mesh complexity of this form, and therefore the first to provide finite element mesh generation algorithms that guarantee both the robustness of the algorithm against unexpected input geometries and the quality of its output.

              In the same paper we also use quadtrees to triangulate planar point sets so that all angles are non-obtuse, using linearly many triangles, and to triangulate higher dimensional point sets with no small solid angles and a number of simplices within a constant of optimal. Also, we can augment any higher dimensional point set so the Delaunay triangulation has linear complexity.

              In later follow-up work, I showed that the same technique can also be used to find a triangulation whose edges have total length within a constant factor of optimal. Bern, Mitchell, and Ruppert showed that alternative methods can be used to triangulate any polygon without obtuse angles; see "Faster circle packing with application to nonobtuse triangulation" for an algorithmic improvement to their technique. Additionally, with Bern, Chew, and Ruppert, we showed that any point set in higher dimensions can be triangulated with nonobtuse simplices. Bern and I surveyed these and related results in our paper "Mesh generation and optimal triangulation".

              (BibTeX -- Citations -- Preliminary copy of journal version -- MIT hypertext bibliography -- CiteSeer -- ACM DL (JCSS))

            • Sparsification--A technique for speeding up dynamic graph algorithms.
              D. Eppstein, Z. Galil, G.F. Italiano, and A. Nissenzweig.
              33rd IEEE Symp. Foundations of Comp. Sci., Pittsburgh, 1992, pp. 60–69.
              Tech. Rep. RC 19272 (83907), IBM, 1993.
              Tech. Rep. CS96-11, Univ. Ca' Foscari di Venezia, Oct. 1996.
              J. ACM 44 (5): 669–696, 1997.

              Uses a divide and conquer on the edge set of a graph, together with the idea of replacing subgraphs by sparser certificates, to make various dynamic algorithms as fast on dense graphs as they are on sparse graphs. Applications include random generation of spanning trees as well as finding the k minimum weight spanning trees for a given parameter k.

              (BibTeX -- Citations -- MIT hypertext bibliography -- ACM DL)

            • Finding the k shortest paths.
              D. Eppstein.
              35th IEEE Symp. Foundations of Comp. Sci., Santa Fe, 1994, pp. 154–165.
              Tech. Rep. 94-26, ICS, UCI, 1994.
              SIAM J. Computing 28 (2): 652–673, 1998.

              This paper presents an algorithm that finds multiple short paths connecting two terminals in a graph (allowing repeated vertices and edges in the paths) in constant time per path after a preprocessing stage dominated by a single-source shortest path computation. The paths it finds are the k shortest in the graph, where k is a parameter given as input to the algorithm.

              The k shortest paths problem has many important applications for finding alternative solutions to geographic path planning problems, network routing, hypothesis generation in computational linguistics, and sequence alignment and metabolic pathway finding in bioinformatics. Although there have been many papers on the k shortest paths problem before and after this one, it has become frequently cited in those application areas. Additionally, it marks a boundary in the theoretical study of the problem: prior theoretical work largely concerned how quickly the problem could be solved, a line of research that was closed off by the optimal time bounds of this paper. Subsequent work has focused instead on devising efficient algorithms for more complex alternative formulations of the problem that avoid the repeated vertices and other shortcomings of the alternative paths produced by this formulation.

              (BibTeX -- Full paper -- Citations -- Graehl implementation -- Jiménez-Marzal implementations -- Shibuya implementation -- Martins implementation -- CiteSeer: TR '94, SJC '98 -- ACM DL)

            • The crust and the beta-skeleton: combinatorial curve reconstruction.
              N. Amenta, M. Bern, and D. Eppstein.
              Graphical Models & Image Processing 60/2 (2): 125–135, 1998.

              We consider the problem of "connect the dots": if we have an unknown smooth curve from which sample points have been selected, we would like to find a curve through the sample points that approximates the unknown curve. We show that if the local sample density is sufficiently high, a simple algorithm suffices: form the Delaunay triangulation of the sample points together with their Voronoi vertices, and keep only those Delaunay edges connecting original sample points. There have been many follow-up papers suggesting alternative methods, generalizing the problem to the reconstruction of curves with sharp corners or to curves and surfaces in higher dimensions, etc.

              (BibTeX -- Citations -- CiteSeer -- ACM DL)

              pointy-haired anti-crust

            • Diameter and treewidth in minor-closed graph families.
              D. Eppstein.
              arXiv:math.CO/9907126.
              Algorithmica 27: 275–291, 2000 (special issue on treewidth, graph minors, and algorithms).

              This paper introduces the diameter-treewidth property (later known as bounded local treewidth): a functional relationship between the diameter of its graph and its treewidth. Previously known results imply that planar graphs have bounded local treewidth; we characterize the other minor-closed families with this property. Specifically, minor-closed family F has bounded local treewidth if and only if there exists an apex graph G that is not in F; an apex graph is a graph that can be made planar by removing a single vertex. The minor-free families that exclude an apex graph (and therefore have bounded local treewidth) include the bounded-genus graphs (for which, as with planar graphs, we show a linear bound for the treewidth as a function of the diameter) and K3,a-free graphs. As a consequence, subgraph isomorphism for subgraphs of bounded size and approximations to several NP-hard optimization problems can be computed efficiently on these graphs, extending previous results for planar graphs.

              Some of these results were announced in the conference version of "subgraph isomorphism for planar graphs and related problems" but not included in the journal version. Since its publication, there have been many more works on local treewidth. The class of problems that could be solved quickly on graphs of bounded local treewidth was extended and classified by Frick and Grohe, "Deciding first-order properties of locally tree-decomposable structures", J. ACM 48: 1184–1206, 2001; the proof that bounded local treewidth is equivalent to having an excluded apex minor was simplified, and the dependence of the treewidth on diameter improved, by a subsequent paper of Demaine and Hajiaghayi, "Diameter and treewidth in minor-closed graph families, revisited", Algorithmica 40: 211–215, 2004. The concept of local treewidth is the basis for the theory of bidimensionality, a general framework for fixed-parameter-tractable algorithms and approximation algorithms in minor-closed graph families; for a survey, see Demaine and Hajiaghayi, "The bidimensionality theory and its algorithmic applications", The Computer J. 51: 292–302, 2008.

              (BibTeX -- Citations)

            • Dynamic generators of topologically embedded graphs.
              D. Eppstein.
              arXiv:cs.DS/0207082.
              14th ACM-SIAM Symp. Discrete Algorithms, Baltimore, 2003, pp. 599–608.

              We describe a decomposition of graphs embedded on 2-dimensional manifolds into three subgraphs: a spanning tree, a dual spanning tree, and a set of leftover edges with cardinality determined by the genus of the manifold. This tree-cotree decomposition allows us to find efficient data structures for dynamic graphs (allowing updates that change the surface), better constants in bounded-genus graph separators, and efficient algorithms for tree-decomposition of bounded-genus bounded-diameter graphs.

              (BibTeX -- SODA talk slides -- Citations)

            • Quasiconvex analysis of backtracking algorithms.
              D. Eppstein.
              arXiv:cs.DS/0304018.
              15th ACM-SIAM Symp. Discrete Algorithms, New Orleans, 2004, pp. 781–790.
              ACM Trans. Algorithms 2 (4): 492–509 (special issue for SODA 2004), 2006.

              We consider a class of multivariate recurrences frequently arising in the worst case analysis of Davis-Putnam-style exponential time backtracking algorithms for NP-hard problems. We describe a technique for proving asymptotic upper bounds on these recurrences, by using a suitable weight function to reduce the problem to that of solving univariate linear recurrences; show how to use quasiconvex programming to determine the weight function yielding the smallest upper bound; and prove that the resulting upper bounds are within a polynomial factor of the true asymptotics of the recurrence. We develop and implement a multiple-gradient descent algorithm for the resulting quasiconvex programs, using a real-number arithmetic package for guaranteed accuracy of the computed worst case time bounds.

              The journal version uses the longer title "Quasiconvex analysis of multivariate recurrence equations for backtracking algorithms".

              (BibTeX -- SODA talk slides -- Citations)

            • Steinitz theorems for orthogonal polyhedra.
              D. Eppstein and E. Mumford.
              arXiv:0912.0537.
              26th Eur. Worksh. Comp. Geom., Dortmund, Germany, 2010.
              26th ACM Symp. Comp. Geom., Snowbird, Utah, 2010, pp. 429–438.
              J. Computational Geometry 5 (1): 179–244, 2014.

              We provide a graph-theoretic characterization of three classes of nonconvex polyhedra with axis-parallel sides, analogous to Steinitz's theorem characterizing the graphs of convex polyhedra.

              The journal version has the slightly different title "Steinitz theorems for simple orthogonal polyhedra".

              (Slides)

            • A Möbius-invariant power diagram and its applications to soap bubbles and planar Lombardi drawing.
              D. Eppstein.
              Invited talk at EuroGIGA Midterm Conference, Prague, Czech Republic, 2012.
              Discrete Comput. Geom. 52 (3): 515–550, 2014 (Special issue for SoCG 2013).

              This talk and journal paper combines the results from "Planar Lombardi drawings for subcubic graphs" and "The graphs of planar soap bubbles". It uses three-dimensional hyperbolic geometry to define a partition of the plane into cells with circular-arc boundaries, given an input consisting of (possibly overlapping) circular disks and disk complements, which remains invariant under Möbius transformations of the input. We use this construction as a tool to construct planar Lombardi drawings of all 3-regular planar graphs; these are graph drawings in which the edges are represented by circular arcs meeting at equal angles at each vertex. We also use it to characterize the graphs of two-dimensional soap bubble clusters as being exactly the 2-vertex-connected 3-regular planar graphs.


            Publications -- David Eppstein -- Theory Group -- Inf. & Comp. Sci. -- UC Irvine

            Semi-automatically filtered from a common source file. http://www.ics.uci.edu/~eppstein/index.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            self-portrait in mirror
            I am a Chancellor's Professor in the Computer Science Department of the University of California, Irvine.

            self-portrait in mirror

            My research (see selected publications) has covered many topics in computational geometry and graph algorithms, including:

            • Graph drawing and information visualization
            • Dynamic graph algorithms and dynamic closest pair data structures
            • Mesh generation and optimal triangulation
            • K-shortest paths and related combinatorial enumeration algorithms
            • Subgraph isomorphism and network statistics
            • Data depth and robust statistics
            • Exponential-time algorithms for NP-hard problems
            • Distance-preserving embeddings of graphs and metric spaces

            I am also an avid photographer and have many photos in my web photo gallery.

            This site is quite static; if you want more frequent updates (or to find out what's changed here) go to my livejournal or Google+ accounts.

            My name is not uncommon (although the spelling is atypical); see my page of Eppsteins on the net if you think you've reached the wrong me.

            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~eppstein/recmath.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            math is delicious
            Like many, I was inspired early on by Martin Gardner's Mathematical Games column, which included a mixture of puzzles, silliness, curious/useless math, and quite serious (but not overly technical) math. In that spirit, I've collected many pages and links of recreational math web sites.
            • My recreational math publications.
            • Number Theory. Implementations of some simple number-theoretic algorithms along with pointers to other number theory web pages.
            • Combinatorial Game Theory. Mathematical strategies for games like chess, go, and nim.
            • The Geometry Junkyard. Many links to recreational geometry web pages, open problem lists, lecture notes, usenet postings, and brief blurbs from my own papers. For more serious geometry links, see my Geometry in Action pages.
            • Gliders in "Life"-like cellular automaton rules.
            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~eppstein/research.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            With Somos-4 T-shirt, by Sara
            Publications. Bibliographic information, short summaries of papers, and pointers to full text of some papers. Sorted by subject, date, conference, or co-author.

            Curriculum vitae. If you really need a compact and formal listing of my achievements.

            UCI faculty profile. An official summary of highlights from the vita.

            Research projects and ideas for future projects (not very up-to-date).

            Collections of bibliographic references on topics of interest to me, and BibTeX styles to use with them.

            Citation database. Who else is referring to my research?

            Geometry in Action, a collection of links to applications of computational geometry, as well as some more general geometric pointers.

            The ICS Theory Group to which I belong.
            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~eppstein/teach.html David Eppstein

            David Eppstein

            • About
            • Contact
            • Research
            • Students
            • Classes
            • Software
            • Math Fun
            Portrait by Laurel Hungerford, 2003
            This quarter (Winter 2016), I am teaching
            • CS 261, graduate data structures
            • CS 269, theory seminar

            Courses I have offered in other quarters:

            • ICS 1F, computability (last offered W98)
            • US 3, cellular automata (last offered W07)
            • ICS 161, design and analysis of algorithms (last offered F15)
            • ICS 162, formal languages and automata (last offered F03)
            • CS 163 / CS 265, graph algorithms (last offered W15)
            • CS 164, computational geometry (last offered S14)
            • ICS 180, game programming project (last offered W99)
            • ICS 260, fundamentals of algorithms (last offered F02)
            • ICS 261, graduate data structures (last offered W11)
            • CS 263, analysis of algorithms. (last offered W15)
            • CS 266, computational geometry, (last offered S14)
            In addition I have occasionally offered graduate seminars, under the ICS 280 and CS 295 course numbers, but with a different topic each time; past topics include mesh generation (S97), computational statistics (S99), exponential algorithms (F00), and geometric graph algorithms (W07 and S08).
            About | Contact | Research | Students | Classes | Software | Math Fun
            http://www.ics.uci.edu/~irani/center.html


            Sandy Irani

            Professor

            Computer Science Department
            University of California, Irvine
            Irvine, CA 92697
            Tel: (949) 824-6346
            Fax: (949) 824-4056

            Email: last name at ics dot uci dot edu

            Reasearch Interests

            • Quantum Information and Computation

            • Online algorithms

            • Algorithms with applications to computer systems and resource allocation.

            http://www.ics.uci.edu/~irani/options.html

            Home

            Current teaching

            • ICS 6B: Boolean Algebra and Logic

            Past teaching

            Papers

          http://www.ics.uci.edu/computing/quarterlyAnnouncement/announcements-winter-2016.php Announcements Winter 2016

          • » Account
            • » New User Guide
            • » Activation
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            • » Specify Delivery Point
            • » Webmail
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            • » Thunderbird
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            • » Email Servers Information
            • » Checking Group Account Email
          • » Network
            • » UCInet Mobile
            • » VPN
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            • » UCI Weather Report
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          • » Linux
            • » ICS hosts
            • » Changing shell
            • » Using modules
            • » Security
            • » Group account access (gsu)
            • » Sun Grid Engine
          • » Other Services
            • » Labs
            • » Printing
            • » Activate MS Office
            • » Sophos
              • » Windows
              • » Mac
            • » Microsoft DreamSpark
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              • » Self-restore snapshot
              • » Restore request
            • » Quarterly announcements
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            • » Personal Webpage
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            • » Ethics Summary
          • » Contact
            • » Helpdesk
            • » Support Staff
            • » Who To Contact
          Announcements Winter 2016
          Winter 2016 Announements

          ICS Computing Support  Winter 2016 Announcements

          Table of Contents

          • New School File Server
          • Annual Mandatory UCI Password Change
          • Licenses for Microsoft Office and Adobe Acrobat
          • Check with us before making Technology Purchases
          • Research Groups: We Want to Touch Base
          • UCI GitHub
          • Certificates for ICS Hosts
          • Front Door Software Security
          • Take Attendance with CheckIn
          • Keep Instructional Labs Clean
          • Contacting ICS Computing Support Helpdesk
          • ICS.System Announcements

          For future reference, this document will also be available at the following URL:

          • http://www.ics.uci.edu/computing/quarterlyAnnouncement

          New School File Server

          A NetApp was was purchased to replace the aging Hitachi system. The new system's name is cybertron.  Quotas will increase significantly for faculty, staff, and students on the new system.

          More information @ https://swiki.ics.uci.edu/doku.php/hardware:storage:cybertron
          Login with ICS Credentials required.


          Annual Mandatory UCI Password Change

          Starting in January 2016, OIT is will be sending annual password change reminders to faculty, staff, students, and sponsored UCInetID holders who have not changed their UCInetID password in over a year.

          The process will be to send out notifications in batches over 30-day periods.  They will send a first reminder after 14 days and a final reminder 14 days after that.   After the final reminder, anyone in that batch who has not changed their password will have their UCInetID reset for re-activation.   OIT will be posting announcements on Zot!Portal and the OIT web sites.   There will be links to the password change page prominently displayed.

          For more information, please see http://www.oit.uci.edu/ucinetid/password-policy.  You may also contact the OIT Help Desk (oit@uci.edu, 949-824-2222) for additional questions.

          You can change your ICS Password @ https://support.ics.uci.edu/ltb/


          Licenses for Microsoft Office and Adobe Acrobat

          Annually, ICS purchases licenses for Microsoft Office, Microsoft Windows, and Adobe Acrobat.  These can be installed on systems purchased with UCI funds.  They require periodic connectivity to the UCI network to keep the license activated.  Contact helpdesk@ics.uci.edu for more information.

          Check with us before making Technology Purchases

          Please check with Computing Support before making Technology Purchases.  We can help clarify solutions, identify resources that are already available to you, and put you in touch with vendors that offer reduced pricing on some software and hardware.


          Research Groups: We Want to Touch Base

          We would like to meet with each research group.  It would be helpful to us to  learn  how you are using ICS resources and give us a chance to show you what services Computing Support offers.  Please contact helpdesk to arrange for us to attend one of your group meetings in Winter or Spring.


          UCI GitHub Enterprise

          UCI GitHub Enterprise is available to all Campus Faculty, Student, and Staff.  It is only available on campus.  For more information see  http://www.oit.uci.edu/uci-github.


          Certificates for ICS Hosts

          UC Irvine is participating in the InCommon Certificate program, which allows delegated administrators in campus departments to issue and renew digital certificates used for such purposes as securing web servers run on behalf of their department.  Through the InCommon Certificate program, UC Irvine pays a site fee (sponsored by OIT) and is then entitled to issue unlimited digital certificates through Comodo, a well-established commercial Certificate Authority.  More information about this program is available at http://www.incommonfederation.org/cert.  Contact helpdesk@ics.uci.edu for more information.


          Front Door Software Security

          OIT has licensed 'Lo-Jack,' software which you may want to consider using to facilitate recovery of lost or stolen mobile devices, such as laptops, tablets, and cell phones.  This service, by a company called Front Door Software, is available to all UC Irvine students, faculty, and staff at no cost.  It is already in use by approximately 1200 people on campus.  There are several features built into the software that can help aid in the recovery of a lost or stolen device. Please see http://www.oit.uci.edu/frontdoor for more information.


          Take Attendance with CheckIn

          If you need to take attendance for classes or meetings, you can use our web-based tool which is available at https://checkin.ics.uci.edu.  Checking in can be done via cell phone or laptop.  For more information, visit https://checkin.ics.uci.edu/index.php?r=site/about


          Keep Instructional Labs Clean

          Instructors, please urge your TAs to enforce the "NO FOOD or DRINKS" rule in the Instructional Labs.  The carpet was replaced, and the walls were painted last summer.  We have already had food spills and writing on the walls.  The care for the labs is everyone's responsibility.


          Contacting ICS Computing Support Helpdesk

          The ICS Computing Support Group should be contacted by email at helpdesk@ics.uci.edu and by phone at (949) 824-4222.  Normal business hours for the group are from 8 am to noon and 1 pm to 5 pm.  However, both the Helpdesk email and phone are monitored around the clock. Using these contact methods helps us provide a quicker response to trouble tickets and requests.

          More information about Bren:ICS Computing Support is available at the following URL:

          http://www.ics.uci.edu/computing/contact/helpdesk.php

          Please note:  If you believe that there is a problem with email delivery or that an email message to Helpdesk may not be processed by the ICS email servers, please call (949) 824-4222.


          ICS.SYSTEM Announcements (RSS feed available)

          Please remember to check the ics.system website frequently for postings regarding machine down times, software availability, and general computing announcements.  The site is available as both a URL and RSS feed. The URL for the site is:

          https://support.ics.uci.edu/ics.system/

          The RSS feed is available at:

          https://support.ics.uci.edu/ics.system/feed.xml


          UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page
          http://www.ics.uci.edu/computing/quarterlyAnnouncement/announcements-fall-2014.php Announcements Fall 2014

          • » Account
            • » New User Guide
            • » Activation
            • » Password Change/Reset
            • » Quota
            • » Renewal
            • » Mapping Network Drive
              • » Windows
              • » Mac
            • » FAQs
          • » E-mail
            • » ICS Google Mail
            • » Specify Delivery Point
            • » Webmail
            • » Thunderbird for ICS Gmail
            • » Thunderbird
            • » Mailing Lists
            • » Forwarding/Vacation/Spam Settings
            • » Email Servers Information
            • » Checking Group Account Email
          • » Network
            • » UCInet Mobile
            • » VPN
            • » ICS Netreg
            • » UCI Weather Report
            • » Open Port Request
          • » Linux
            • » ICS hosts
            • » Changing shell
            • » Using modules
            • » Security
            • » Group account access (gsu)
            • » Sun Grid Engine
          • » Other Services
            • » Labs
            • » Printing
            • » Activate MS Office
            • » Sophos
              • » Windows
              • » Mac
            • » Microsoft DreamSpark
            • » File Restore
              • » Self-restore snapshot
              • » Restore request
            • » Quarterly announcements
          • » Web
            • » Personal Webpage
            • » General Information
          • » Policies
            • » Ethics
            • » Ethics Summary
          • » Contact
            • » Helpdesk
            • » Support Staff
            • » Who To Contact
          Announcements Fall 2014
          quarterly-announcements-20130907

          ICS Computing Support  Fall 2014 Announcements

          Table of Contents

          • Copyright Infringement Policy
          • Email Delivery to @ics.uci.edu
          • New Mailman Lists
          • Password Reset / Security questions
          • Wireless in ICS 1st & 3rd Floors
          • New Software Packages
          • SGE 8.1.6 (Son of Grid Engine)
          • Drupal 7 had serious vulnerability
          • Contacting ICS Computing Support Helpdesk
          • Changes to your ICS Email Account
          • ICS.System Announcements

          For future reference, this document will also be available at the following URL:
          http://www.ics.uci.edu/computing/quarterlyAnnouncement/


          Copyright Infringement Policy

          It is illegal to distribute copyrighted materials, programs, movies, music, and other digital media, without consent of the copyright holder.

          If a complaint of copyright infringement is reported involving a computer or IP address assigned to you, your account will be locked until we have a chance to investigate the allegations. If copyrighted materials are found, your adviser (or Graduate Associate Dean) will be notified and you will be counseled regarding the issue. If a second complaint is filed you will be referred to the Dean of Students and the incident will be recorded in your permanent file. A third complaint will result in a recommendation that you be removed from the program.

          Additional information regarding campus copyright policy can be found here: http://www.oit.uci.edu/policy/copyright/


          Email Delivery to @ics.uci.edu

          ICS faculty, staff and graduate students that have not already specified an alternate email delivery point are encouraged to do so now. ICS Computing Support will eliminate local email delivery and IMAP access to new email. Mail sent to your @ics.uci.edu will be resent to the alternate email delivery point. Spam and virus scanning, space management and client access to email will be provided by your alternative email provider. ICS Computing Support will continue to provide you access to old email via the school’s IMAP servers.

          Computing Support recommends that you use one of the campus provided alternate email services and is available to help with the transition.  ICS provides ICS Google Apps, UCI provides several options here: http://www.oit.uci.edu/email-calendar.  .

          Follow the directions at: http://www.ics.uci.edu/computing/email/delivery_point.php to set your new email delivery point. If you will be using a 3rd party provider, choose“other” and specify the email address where your ICS e-mail will be delivered.


          ICS Mailman lists

          ICS Computing Support provides faculty or staff sponsored mailman based mailing lists related to education, research and administrative functions.

          New ad hoc mail lists can be created via OIT's mailman service. More information on the OIT mail lists is available here: http://www.oit.uci.edu/mailing-lists/mailman/


          Password Reset via Security questions

          Automated password recovery for ICS accounts is available at https://support.ics.uci.edu/ltb (available on campus or via vpn only).

          You may now use recovery email addresses and security questions when recovering a forgotten password. For new ICS accounts the information will be collected during the account creation process. If you have an existing account you can setup recovery email address and a security question here: https://support.ics.uci.edu/ltb

          After the above values have been recorded for your account,  you will be able to change the password without coming to our office.  You will be able to change your password once you provide the response to your security question. You can also request a password reset link sent to your recovery email address.  From that link you will be able to change your password.


          Wireless in ICS 1st & 3rd Floors

          Over the summer, OIT upgraded the wireless access outside CS1 1st & 3rd floor labs following complaints of students not being able to use the wireless networks due to over subscription.
          Please inform the helpdesk of issues with the wireless access points.


          New Software Packages

          Several updated packages were installed in the ICS Software library for 64 bit CentOS 5 and 6.  Each package can be added your Linux shell environment using the ICS module command.  The ICS Software library is available on all ICS Linux hosts under /pkg.

          • R/3.1.0
          • mathematica/9.01.
          • openssh-6.3p1
          • hpnssh-5.9p1
          • OpenCV/2.4.9
          • glog/0.3.3
          • lmdb/20140908
          • boost/1.55
          • python/2.7.8
          • caffe/20140909
          • mysql/5.6.21
          • jdk/8.0_25
          • jdk/7.0_71

          Requests for additional software installs should be sent to <helpdesk@ics.uci.edu>


          SGE 8.1.6 (Son of Grid Engine)

          SGE is provided to schedule and manage compute jobs on the the ICS clusters. SGE 8.1.6 (Son of Grid Engine)has replaced Sun Grid Engine 6.2u5.   Version 8.1.6 provides improved shadow master support for high availability.

          Information regarding grid computing at ICS is available here: http://www.ics.uci.edu/computing/linux/sge.php

          More information on the Sun of Grid Engine project is available here: https://arc.liv.ac.uk/trac/SGE

          All users have been migrated off of the older version and are using 8.1.6. Version 6.2u5 will be turned off permamently at the end of Fall 2014.


          Drupal 7 had serious vulnerability

          Please be aware that a very serious vulnerability came to light recently regarding Drupal 7.  If you run Drupal 7 you should restore your site from the 15th of October immediately.

          Drupal warns unpatched users: Assume your site was hacked.  "Attackers may have copied all data out of your site and could use it maliciously. There may be no trace of the attack." "Simply updating to Drupal 7.32 will not remove backdoors."

          Please see Drupals' initial announcment at https://www.drupal.org/SA-CORE-2014-005 and follow up here: https://www.drupal.org/PSA-2014-003.

          The full story is available at http://www.computerworld.com/article/2841320


          Contacting ICS Computing Support Helpdesk

          The ICS Computing Support Group should be contacted by email at  <helpdesk@ics.uci.edu> and by phone at (949)824-4222.  Normal business hours for the group are from 8am-noon and 1pm-5pm.    Both Helpdesk email and  phone are monitored around the clock and using them will help us provide quicker response to trouble tickets and requests. 

          More information about Bren:ICS Computing Support is available at the following URL:

          http://www.ics.uci.edu/computing/contact/helpdesk.php

          Please Note:  If you believe that there is a problem with email or that an email message to Helpdesk may not be processed by the ICS email servers, please call the help desk at (949)824-4222.


          Changes to your ICS Email Account

          ICS Google Apps for Education and Gmail accounts are available for all ICS faculty, staff and grads.  Computing Support recommends switching to the ICS Gmail service.

          Please visit the following link for more information about the ICS Google Apps program and instructions for setting up your account:

          http://www.ics.uci.edu/computing/email/google_apps.php


          ICS.SYSTEM Announcements (RSS feed available)

          Please remember to check the ics.system web site frequently for postings regarding machine down times, software availability and general computing announcements.  The site is available as both a URL and RSS feed. The URL for the site is:

          https://support.ics.uci.edu/ics.system/


          The RSS feed is available at:

          https://support.ics.uci.edu/ics.system/feed.xml


          UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page
          http://www.ics.uci.edu/computing/quarterlyAnnouncement/announcements-fall-2015.php Announcements Fall 2015

          • » Account
            • » New User Guide
            • » Activation
            • » Password Change/Reset
            • » Quota
            • » Renewal
            • » Mapping Network Drive
              • » Windows
              • » Mac
            • » FAQs
          • » E-mail
            • » ICS Google Mail
            • » Specify Delivery Point
            • » Webmail
            • » Thunderbird for ICS Gmail
            • » Thunderbird
            • » Mailing Lists
            • » Forwarding/Vacation/Spam Settings
            • » Email Servers Information
            • » Checking Group Account Email
          • » Network
            • » UCInet Mobile
            • » VPN
            • » ICS Netreg
            • » UCI Weather Report
            • » Open Port Request
          • » Linux
            • » ICS hosts
            • » Changing shell
            • » Using modules
            • » Security
            • » Group account access (gsu)
            • » Sun Grid Engine
          • » Other Services
            • » Labs
            • » Printing
            • » Activate MS Office
            • » Sophos
              • » Windows
              • » Mac
            • » Microsoft DreamSpark
            • » File Restore
              • » Self-restore snapshot
              • » Restore request
            • » Quarterly announcements
          • » Web
            • » Personal Webpage
            • » General Information
          • » Policies
            • » Ethics
            • » Ethics Summary
          • » Contact
            • » Helpdesk
            • » Support Staff
            • » Who To Contact
          Announcements Fall 2015
          Fall 2015 Announements

          ICS Computing Support  Fall 2015 Announcements

          Table of Contents

          • Copyright Infringement Policy
          • Email Delivery to @ics.uci.edu
          • Mailman Lists
          • Password Reset / Security Questions / Change It Often
          • Microsoft Office 365 ProPlus for Personal Use
          • ICS Google Accounts receive 10TB of Space
          • GitLab
          • Campus Cyber Security Initiative
          • SGE  (Son of Grid Engine)
          • Service Monitoring
          • MSDNAA becomes DreamSpark
          • MacMail Google Tricks
          • MatLab for UCI Students / Octave on ICS Lab Computers
          • Contacting ICS Computing Support Helpdesk
          • ICS.System Announcements


          For future reference, this document will also be available at the following URL:

          • http://www.ics.uci.edu/computing/quarterlyAnnouncement/

          Copyright Infringement Policy

          It is illegal to distribute copyrighted materials, programs, movies, music, and other digital media, without consent of the copyright holder.

          If a complaint of copyright infringement is reported involving a computer or IP address assigned to you, your account will be locked until we have a chance to investigate the allegations. If copyrighted materials are found, your adviser (or Graduate Associate Dean) will be notified and you will be counseled regarding the issue. If a second complaint is filed you will be referred to the Dean of Students, and the incident will be recorded in your file permanently. A third complaint will result in a recommendation that you be removed from the program.

          Additional information regarding campus copyright policy can be found here: http://www.oit.uci.edu/policy/copyright/


          Email Delivery to @ics.uci.edu

          ICS faculty, staff, and graduate students who have not already specified an alternate email delivery point should do so now. ICS Computing Support will eliminate local email delivery and IMAP access to new email. Mail sent to your @ics.uci.edu will be resent to the alternate email delivery point. Spam and virus scanning, space management, and client access to email will be provided by your alternative email provider. ICS Computing Support will continue to provide access to old email via the school's IMAP servers.

          Computing Support strongly recommends that you use one of the campus-provided alternate email services and is available to help with the transition. ICS provides ICS Google Apps while UCI provides several options here: http://www.oit.uci.edu/email-calendar.

          Follow the directions at http://www.ics.uci.edu/computing/email/delivery_point.php to set your new email delivery point. If you will be using a 3rd-party provider, choose "other" and specify the email address where your ICS e-mail will be delivered.


          Mailman lists

          New ad hoc mail lists can be created via OIT's mailman service. More information on the OIT mail lists is available here: http://www.oit.uci.edu/mailing-lists/mailman


          Password Reset via Security Questions / Change It Often

          Password recovery for ICS accounts is available at https://support.ics.uci.edu/ltb

          You may now use recovery email addresses and security questions when resetting a forgotten password. For new ICS accounts, the information will be collected during the account creation process. If you have an existing ICS account, you can specify a recovery email address and a security question here: https://support.ics.uci.edu/ltb

          After the above values have been recorded for your account, you will be able to change the password without coming to our office. You will be able to change your password once you provide the response to your security question. You can also request a password reset link sent to your recovery email address. You will be able to change your password using that link.

          Account credentials are being stolen at a very high rate. Some of the reasons are phishing, brute force attacks, malware, and weak passwords. It is good practice to change your passwords regularly.  Changing your password every 6 months is a good rule of thumb. It is also important to have a strong password:  8+ characters with letters, numbers, change of case, and symbols.  Campus is considering whether to force us to change our UCINetID Passwords once or twice a year.


           ICS Google accounts receive 10TB space


          All ICS faculty, staff, and graduate students are encouraged to use ICS Google Apps, a suite of applications which includes:         

          • ICS Gmail - Send and receive email with powerful search options, spam filtering, and chat
          • ICS Google Docs - Publish and collaborate in real-time on documents, spreadsheets, and presentations
          • Google Drive - Includes 10TB of storage

          The suite of ICS Google Apps is hosted online, and the applications are accessible via a web browser from any computer and most mobile devices.  Apps available for Windows, Mac OS X, iOS, and Android platforms.

          Please visit the following link for more information about the ICS Google Apps program and instructions for setting up your account:
          http://www.ics.uci.edu/computing/email/google_apps.php


          Microsoft Office 365 ProPlus for Personal Use

          Microsoft has begun providing Microsoft Office 365 ProPlus to UC Irvine students at no cost thanks to a staff campus agreement program (MCCA). ICS Faculty and staff are also eligible.  Students, faculty, and staff should follow these instructions: http://www.oit.uci.edu/microsoft-office-365-pro-plus

          Since the School of Information and Computer Sciences is participating in the Microsoft UC-MCCA software leasing program, you are now eligible to participate in Microsoft's Software
          Assurance Home Use Program (HUP). This program enables you to get a licensed copy of Microsoft Office 2013 (Windows) or 2011 (Mac) to install and use on your personally-owned home computer for only $9.95.   Contact helpdesk@ics.uci.edu for more information.


          GitLab

          ICS Computing Support is running a Gitlab test site. Research groups interested in having a managed Gitlab site are encouraged to contact helpdesk@ics.uci.edu.

          Gitlab is a web interface to Git, a version control management application similar to SVN.

          • Users can login using their ICS username and password
          • Accounts may be requested for non-ICS users
          • Users may start projects and add other ICS users to them as needed

          For more information on Gitlab, please visit https://gitlab.com/gitlab-org/gitlab-ce/blob/master/README.md


          Campus Cyber Security Initiative

          http://security.uci.edu/plan-controls.php

          Controlled Use of Administrative Privileges

          Protect and validate administrative accounts on desktops, laptops, and servers to prevent two common types of attack: (1) enticing users to open a malicious e-mail message, attachment, or file, or to visit a malicious website; and (2) cracking an administrative password and thereby gaining access to a target machine. Use robust passwords.

          Two Factor Authentication


          Require two-factor authentication for system administration.

          Test systems open at the firewall

          All systems available from off campus will be scanned for vulnerabilities. If you have a system which is accessible from off-campus locations, please make sure that patches are applied and unneeded services are turned off.


          SGE  (Son of Grid Engine)

          Information regarding grid computing at ICS is available here: http://www.ics.uci.edu/computing/linux/sge.php

          More information on the Sun of Grid Engine project is available here: https://arc.liv.ac.uk/trac/SGE


          Service Monitoring

          Computing Support runs Nagios and Ganglia to monitor ICS-hosted servers and services. The Nagios and Ganglia portals are available at the following URLs using your ICS credentials:
          https://nagios.ics.uci.edu
          http://ganglia.ics.uci.edu

          Nagios monitors the availability of servers and services.  Notifications are sent to system administrators when unscheduled outages occur.  Use your ICS login and password when prompted.

          Ganglia provides a history of resource utilization over time.
          Please send an email message to helpdesk@ics.uci.edu if you need help with these systems.


          MSDNAA becomes Microsoft DreamSpark

          DreamSpark, formerly MSDN Academic Alliance (MSDNAA), is a Microsoft program available and provided by ICS, through which ICS students and faculty can acquire licensed copies of Microsoft software such as Microsoft Windows, Visual Studio, and other products. The software can be used in coursework and personal non-commercial projects. Faculty and students may continue to use these products for non-commercial purposes, even after departing/graduating from the school. All free updates to the software, such as security updates and service packs, that are available to everyone through Windows Update, continue for the life of the product. The product keys, however, will not be reissued once the user's affiliation with the school is terminated.

          More details can be found at:  http://www.ics.uci.edu/computing/services/microsoft_dreamspark.php


          MacMail Google Setup

          To add new Google Apps account from domains like @ics.uci.edu or @uci.edu via Mail App, please follow the directions here:  https://support.google.com/mail/troubleshooter/1668960?hl=en

          1. To start, choose Enable IMAP.
          2. Once you have enabled IMAP, you should go back to the directions page and follow the instructions for setting up your client, in this case, Apple Mail.
            Note: 
            If you are using Yosemite, do not choose "Google" for the account type when adding your mail account.  Instead, choose "Other" to enter your account manually.  Please enter your username@ics.uci.edu address every time you are prompted for your username.
          3. Then verify your connection settings as indicated in the directions page.


          Once your account is setup, you should open the activity window to make sure that the system is syncing your mail from Google.


          MatLab for UCI Students / Octave on Lab Computers


          Matlab version R2014a is available for students to install on personally-owned laptops at no extra cost. The campus student Matlab license was paid by funds made available through UCI student E-Tech fees. For more details, please visit: http://laptops.eng.uci.edu/news/matlabstudentversionavailable-nocosttoucistudents

          Due to drive space limitations and the availability to install on personal systems, MatLab is no longer available on ICS lab computers. Over the past spring and summer, Octave was chosen as a suitable alternative as it provides similar functionality to MatLab and is available for free. More details about Octave can be found at the GNU Octave website:
          http://www.gnu.org/software/octave/about.html


          Contacting ICS Computing Support Helpdesk

          The ICS Computing Support Group should be contacted by email at helpdesk@ics.uci.edu and by phone at (949) 824-4222. Normal business hours for the group are from 8 am to noon and 1 pm to 5 pm. However, both the Helpdesk email and phone are monitored around the clock. Using them helps us provide a quicker response to trouble tickets and requests.

          More information about Bren:ICS Computing Support is available at the following URL:

          http://www.ics.uci.edu/computing/contact/helpdesk.php

          Please note:  If you believe that there is a problem with email or that an email message to Helpdesk may not be processed by the ICS email servers, please call (949) 824-4222.


          ICS.SYSTEM Announcements (RSS feed available)

          Please remember to check the ics.system website frequently for postings regarding machine down times, software availability, and general computing announcements. The site is available as both a URL and RSS feed. The URL for the site is:

          https://support.ics.uci.edu/ics.system/


          The RSS feed is available at:

          https://support.ics.uci.edu/ics.system/feed.xml
          UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page
          http://www.ics.uci.edu/computing/quarterlyAnnouncement/announcements-winter-2014.php Announcements Winter 2014

          • » Account
            • » New User Guide
            • » Activation
            • » Password Change/Reset
            • » Quota
            • » Renewal
            • » Mapping Network Drive
              • » Windows
              • » Mac
            • » FAQs
          • » E-mail
            • » ICS Google Mail
            • » Specify Delivery Point
            • » Webmail
            • » Thunderbird for ICS Gmail
            • » Thunderbird
            • » Mailing Lists
            • » Forwarding/Vacation/Spam Settings
            • » Email Servers Information
            • » Checking Group Account Email
          • » Network
            • » UCInet Mobile
            • » VPN
            • » ICS Netreg
            • » UCI Weather Report
            • » Open Port Request
          • » Linux
            • » ICS hosts
            • » Changing shell
            • » Using modules
            • » Security
            • » Group account access (gsu)
            • » Sun Grid Engine
          • » Other Services
            • » Labs
            • » Printing
            • » Activate MS Office
            • » Sophos
              • » Windows
              • » Mac
            • » Microsoft DreamSpark
            • » File Restore
              • » Self-restore snapshot
              • » Restore request
            • » Quarterly announcements
          • » Web
            • » Personal Webpage
            • » General Information
          • » Policies
            • » Ethics
            • » Ethics Summary
          • » Contact
            • » Helpdesk
            • » Support Staff
            • » Who To Contact
          Announcements Winter 2014
          quarterly-announcements-20130907

          ICS Computing Support  Winter 2014  Announcements

          Table of Contents

          • Gitlab Beta Testing
          • Campus Power Outage
          • New Software Packages
          • Contacting ICS Computing Support Helpdesk
          • Changes to your ICS Email Account
          • ICS.System Announcements
          For future reference, this document will also be available at the following URL:
            http://www.ics.uci.edu/computing/quarterlyAnnouncement/

          Gitlab Beta Testing

          ICS Computing Support is running a Gitlab beta test site.  Research groups interested in having a managed Gitlab site are encouraged to contact <helpdesk@ics.uci.edu>.

          Gitlab is a web interface to Git, a version control management application similar to SVN.

          - Users can login using their ICS user name/password
          - Accounts may be requested for non-ICS users
          - Users may start projects and add other ICS users to them as needed

          For more information on Gitlab, please visit https://gitlab.com/gitlab-org/gitlab-ce/blob/master/README.md




          Campus Wide Electrical Power Outage & Central Plant Shutdown

          We would like to remind everybody that Facilities Management has scheduled two campus wide electrical power outage in February.
          • Saturday, Feb. 8, 5 a.m.: Transfer to generator power, 30-minute outage
          • Monday, Feb. 10, 5 a.m.: Transfer back to normal power, 30-minute outage
          ICS Computing support plans to power down any  non-essential computing in the data center that is not on UPS and backup generator.  This will include most compute clusters and some research storage (/extra storage).   Systems will be powered down beginning on Friday, Feb 7 at noon.  Non critical systems will be brought back up on Monday morning, Feb 10th.

          Critical services and services that have UPS and backup generator will remain available during these power outages and throughout the weekend.  Services that will remain available during the weekend include most ICS storage (home directories), web service, database and  mail service.

          Additional information is available at:  http://snap.uci.edu/viewXmlFile.jsp?cmsUri=public/FacilitiesManagement/General/CampusWidePowerOutage.xml


          New Software Packages

          Several updated  packages were installed in the ICS Software library for 64 bit CentOS 5 and 6.  Each package can be added your Linux shell environment using the ICS module command.  The ICS Software library is available on all ICS Linux hosts under /pkg.
          • R/3.0.2
          • bison/3.0
          • cmake/2.8.12.1
          • gcc/4.8.2
          • java/1.7.0_45
          • flex/2.5.37
          • git/1.8.5.1
          • maven/3.1.1
          • maven/3.0.5
          • openssl/1.0.1
          • python/2.7.6 (in process)
          • python/3.3.3 (in process)
          • ruby/2.0.0
          • ruby/2.1.0
          Requests for additional software installs should be sent to <helpdesk@ics.uci.edu>

          Contacting ICS Computing Support Helpdesk

          The ICS Computing Support Group should  be contacted by email at  <helpdesk@ics.uci.edu> and by phone at (949)824-4222.  Normal business hours for the group are from 8am-noon and 1pm-5pm.     Both  Helpdesk email and  phone are monitored around the clock and using them will help us provide quicker response to trouble tickets and requests.  

          More information about Bren:ICS Computing Support is available at the following URL:

          http://www.ics.uci.edu/computing/contact/helpdesk.php
          Please Note:  If you believe that there is a problem with email or that an email message to Helpdesk may  not be processed by the ICS email servers, please call the help desk at (949)824-4222. 


          Changes to your ICS Email Account 

          ICS Google Apps for Education and Gmail accounts are available for all ICS faculty, staff and grads.  Computing Support recommends switching to the  ICS Gmail service.

          Please visit the following link for more information about the ICS Google Apps program and instructions for setting up your account:

          http://www.ics.uci.edu/computing/email/google_apps.php




          ICS.SYSTEM Announcements (RSS feed available)

          Please remember to check the ics.system web site frequently for postings regarding machine down times, software availability and general computing announcements.  The site is available as both a URL and RSS feed.  The URL for the site is:

          https://support.ics.uci.edu/ics.system/

          The RSS feed is available at:

          https://support.ics.uci.edu/ics.system/feed.xml


          UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page
          http://www.ics.uci.edu/computing/quarterlyAnnouncement/quarterly-announcement-20130907.php fall 2013 quarterly accouncements @ the bren school of information and computer sciences

          • » Account
            • » New User Guide
            • » Activation
            • » Password Change/Reset
            • » Quota
            • » Renewal
            • » Mapping Network Drive
              • » Windows
              • » Mac
            • » FAQs
          • » E-mail
            • » ICS Google Mail
            • » Specify Delivery Point
            • » Webmail
            • » Thunderbird for ICS Gmail
            • » Thunderbird
            • » Mailing Lists
            • » Forwarding/Vacation/Spam Settings
            • » Email Servers Information
            • » Checking Group Account Email
          • » Network
            • » UCInet Mobile
            • » VPN
            • » ICS Netreg
            • » UCI Weather Report
            • » Open Port Request
          • » Linux
            • » ICS hosts
            • » Changing shell
            • » Using modules
            • » Security
            • » Group account access (gsu)
            • » Sun Grid Engine
          • » Other Services
            • » Labs
            • » Printing
            • » Activate MS Office
            • » Sophos
              • » Windows
              • » Mac
            • » Microsoft DreamSpark
            • » File Restore
              • » Self-restore snapshot
              • » Restore request
            • » Quarterly announcements
          • » Web
            • » Personal Webpage
            • » General Information
          • » Policies
            • » Ethics
            • » Ethics Summary
          • » Contact
            • » Helpdesk
            • » Support Staff
            • » Who To Contact
          Fall 2013 Quarterly Announcements
          quarterly-announcements-20130907

          Table of Contents

          • Contacting ICS Computing Support Helpdesk
          • Changes to your ICS Email Account
          • ICS.System Announcements
          • Storage and Backups
          • Service Monitoring
          • Public Grid Computing
          • Dokuwiki and WordPress Farms
          • Data Center and Centralized Services
          • GitLab and Subversion
          For future reference, this document will also be available at the following URL:
            http://www.ics.uci.edu/computing/quarterlyAnnouncement/

          Contacting ICS Computing Support Helpdesk

          The ICS Computing Support Group should be contacted by email at helpdesk@ics.uci.edu and by phone at (949) 824-4222.  Normal business hours for the group are from 8am-noon and 1pm-5pm.  Both Helpdesk email and phone are monitored around the clock and using them will help us provide quicker response to help requests.  

          More information about Bren:ICS Computing Support is available at the following URL:

          http://www.ics.uci.edu/computing/contact/helpdesk.php

          Please Note:  If you believe that there is a problem with email or that an email message to Helpdesk may not be processed by the ICS email servers, please call the helpdesk at (949) 824-4222. 


          Changes to your ICS Email Account 

          There are two significant changes coming to the ICS email services.  First and foremost, ICS Google Apps for Education and Gmail accounts are available for all ICS faculty, staff, and grads.  Computing Support will be emphasizing ICS Gmail service in favor of the internal ICS IMAP email system.

          ICS email users will also be able to opt out of virus and spam email scanning.  If you setup an ICS Google Apps account or otherwise forward email to an address outside of ICS, then your email should be scanned by your mail provider instead of ICS mail scanners.  (Note:  Users forwarding via procmail will still be subject to ICS email virus and spam scanning).

          How to Setup Your ICS Google Apps and Gmail Account

          There are two steps to activating your ICS Google Apps account.

          Navigate to the https://elnino.ics.uci.edu page.  When prompted, login with your ICS account and password (the same one you use to login when you read your ICS email).  This website is available on campus or through the VPN.

          1.  Change your ICS password.  Click on the "Change Password" button at the above link. 

          2.  Under forwarding address, select "ICS Gmail."  Leave the textbox empty as demonstrated below.

          See directions online at http://www.ics.uci.edu/computing/email/icsgmail.html

          How to change your ICS email delivery point to a Third Party Email Provider

          These directions are for users that would like their mail forwarded to non-ICS or non-UCI addresses.

          Before you begin, it is important to note that email delivery to third party email accounts is not protected by UC contracts.

          Navigate to https://elnino.ics.uci.edu with your web browser.  

          When prompted, login with your ICS account and password (the same one you use to login when you read your ICS email).  

          Click on the "other" radio button and fill in the "Forwarding Address:" textbox with your third-party email address.

          Additional Email Resources


          ICS Email Resources
          ICS Google Apps Login  http://mail.google.com/a/ics.uci.edu
          ICS Google Apps Accounts Setup, Information and FAQ http://www.ics.uci.edu/computing/email/google_apps.html
          ICS Email Overview http://www.ics.uci.edu/computing/email
          Google Support http://support.google.com/?hl=en
          Using Thunderbird to read Gmail  https://support.google.com/mail/troubleshooter/1668960?hl=en

          Other Campus Email Services
          On campus OIT email account http://oit.uci.edu/email/
          UCI Google Apps Account http://www.google.uci.edu/
          UCI Email Delivery and Forwarding   http://oit.uci.edu/email/deliverypoint.html




          ICS.SYSTEM Announcements (RSS feed available)

          Please remember to check the ics.system website frequently for postings regarding machine down times, software availability, and general computing announcements.  The site is available as both a URL and RSS feed.  The URL for the site is:

          https://support.ics.uci.edu/ics.system/

          The RSS feed is available at:

          https://support.ics.uci.edu/ics.system/feed.xml


          Storage and Backups

          Researchers and Computing Support added over 100TB of spinning disk this summer.  Like most storage, this space is network-accessible from any ICS-supported host, comes with user-accessible snapshots, and is backed up to tape.

          If you are a researcher interested in purchasing a portion of this space, in terabyte increments, or storage hardware, please contact helpdesk@ics.uci.edu for more information on this and Dell discounts.

          Computing Support has also expanded backup capacity to keep up with growing storage demand.  We now run two tape libraries and multiple disk-to-disk backup pools.  Quarterly backups will be performed at the end of each quarter and stored for two years.

          Additional notes about ICS Backups

          Snapshot Backups

          File system snapshots are provided on most NFS-mounted directories (i.e. /home and /extra) as a user-accessible backup.   Snapshotted directories offer two to four weeks of snapshot backups. Contact helpdesk to find out the specifics on your file system.

          File system snapshots are a copy of a file system at a specific point in time, even while the original file system continues to be updated and used normally.  File system snapshots are typically available to the directory owner.

          Information on accessing home directory snapshots is available at http://www.ics.uci.edu/computing/services/snapshot.php

          Tape Backups

          Tape backups of /home and /extra directories are made regularly.  Tape backups are intended for disaster recovery after critical hardware failure.  Please keep in mind that directories named "scratch" are not backed up (case insensitive).

          Local File Systems

          The local file system is NOT part of regular backups.  This space is volatile, impermanent, and users should perform their own backups of any information saved on a local file system.

          Data may be stored on the local file system in the /scratch and /tmp directories.  Files are deleted from the /tmp directory at boot, after they have not been accessed for 10 days, or during routine system administration.

          Files from /scratch will be left intact except when the file systems are reformatted during installation, hardware failure, or emergency system administration.




          Service Monitoring

          Computing Support runs Nagios and Ganglia to monitor hosted ICS servers and services.  

          The Nagios and Ganglia portals are available at the following URLs.  Use an ICS login if prompted.

          https://nagios.ics.uci.edu
          http://ganglia.ics.uci.edu

          Nagios monitors the availability of servers and services.  Notifications are sent to system administrators when unscheduled outages occur.  Use your ICS login and password when prompted.

          Ganglia reports provides a history of resource utilization over time.

          Please send an email message to helpdesk if you need help with these systems.



          Public Grid Computing


          ICS Computing Support manages several community and research grid compute clusters.

          Grid computing is managed using Sun Grid Engine (SGE) 6.2u5.  Information about using SGE tools is available here:

          https://www.ics.uci.edu/computing/linux/sge.php

          Please use check pointing for better fault tolerance in general, but more so on these public clusters.

          The ICS public clusters are available to any member of the ICS community.   

          Public Queue Name Operating System Slots Runtime Limits
          12hour.q CentOS6 x86 64-bit 24 hard limit 12 hours, soft limit 10 hours
          15day.q CentOS6 x86 64-bit 36 hard limit 15 days, soft limit 14 days


          Dokuwiki and WordPress Farms

          For any research group that wishes to have a wiki for their website, Computing Support offers Dokuwiki as an option.  Dokuwiki stores all the data in plain text files and no database is required.  Custom themes and plug-ins are available upon request.  Currently, five research groups are using Dokuwiki to get their message out on the web.  The Computing Support group is also using it internally to document their procedures and work.  

          Computing Support has created a Wordpress Farm which is available to any faculty or research group.  Currently, two professors and four research groups are making use of Computer Support's managed WordPress farm to create a website or blog.  Each faculty and research group can download and install the theme and plug-ins of their choice to customize the website.  Security updates are installed by Computing Support as necessary to keep the WordPress installation secure.  

          Any faculty or research groups interested in creating a Dokuwiki or Wordpress site, just needs to send a message to helpdesk@ics.uci.edu.


          Data Center and Centralized Services

          Computing Support offers the following data center and centralized services.  In many cases, these services are available to both managed and unmanaged hosts.  Please send any inquiries about availability of these services (or anything else you can think of) to helpdesk.
          • Public Access Cycle servers
          • Rack Hosting:  Self Managed and ICS Managed
          • Data Storage and Management
          • Server and Service Monitoring
          • Load Balancing/High Availability
          • Subversion Repository Hosting
          • Git Repository Hosting (coming soon)
          • Wiki farm
          • Request Tracker Ticketing System
          • Conference Registration Services
          • Web Application Hosting


          GitLab and Subversion

          ICS Computing Support currently runs a research and instructional Subversion server that uses the if.SVNAdmin package to provide self service through a convenient GUI.  Computing Support also intends to provide GitLab service beginning Winter quarter.

          Both the GitLab and if.SVNAdmin allow repository owners to modify their own subversion authorization file, create logins, and assign permissions and roles through a web interface.

          ICS community members who are running Git or Subversion repositories or are interested in using a revision control system are encouraged to contact helpdesk.


          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 06 2016
          http://www.ics.uci.edu/~lab/policies/ uc irvine::bren school::lab - student Skip over navigation

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          • ABOUT
            • About the School
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          Bren school home > labs home > policies
          Lab guidelines »

          These are the policies that ICS students, faculty and staff must adhere to. These include general policies as well as specific course policies. You should be notified by your instructor if there is a specific policy for your course.

          ICS Instructional Lab Rules/Policies

          This section covers the UCI, as well as, the ICS specific computing guidelines that apply to anyone using the ICS Instructional Labs for computing.

          General Lab Policy

          Project ICS Course Policy

          Project ICS Door Code Request

          Please fill out the provided request sheet and have all of your team members sign it. Bring the completed form to the Lab Manager in CS346L. You will then receive the door code.

          *** All team members must be present to obtain door code ***

           

          More labs»
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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 09 2011
          http://www.ics.uci.edu/ugrad/policies/index.php undergraduate student policies @ the bren school of information and computer sciences
          • ABOUT
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          Bren school home > Undergraduate > policies
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          * Other policies important for students to know include the Non-Discrimination Policy Statements, Americans with Disabilities Act, and Jeanne Clery Act. It is recommended that students be familiar with the rules and regulations that govern students at UCI as outlined in the UCI General Catalogue.

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          http://www.ics.uci.edu/computing/services/microsoft_dreamspark_error.php microsoft dreamspark error codes @ the bren school of information and computer sciences

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          Microsoft DreamSpark Error Codes

          The following are the most common error codes and possible solutions.  If this page does not help you complete your download, visit CS 364 and ask a Lab Assistant for help.

          SDC 00100

          Unable to find the source CD (expecting to find a SDC file on a local CD).

          SDC 00200

          A general error that is typically caused by:
          (a) insufficient disk space to unpack, or
          (b) a disk error

          SDC 00300

          Unable to read from media, which is typically caused by the PC being:
          (a) unable to connect to network resource (for a Network-Based install), or
          (b) unable to read from CD (for a secure CD-based install)

          Please note that this is a network connection error only.

          SDC 00400

          Unable to complete the download, which is typically caused by:
          (a) an error in the configuration of the download package, usually due to a bad hosting location
          (b) no Internet connection found
          (c) Internet connection lost
          (d) a firewall or proxy server between the client station and hosting location, or
          (e) insufficient disk space to download

          SDC 00500

          CRC check failed, which can mean that the package is corrupt and unusable. This is typically caused by:
          (a) an incomplete transfer
          (b) a corruption during transfer, or
          (c) a corrupt package on the host server

          Error 70072

          Install limit has been reached, which is typically caused by:
          (a) the customer having attempted to download a product too many times to different folders (it is possible that no downloads have been successful), or
          (b) the customer having already successfully downloaded/installed the software, and now wanting to download again

          Error 70077

          Parameter authorization failure, which typically means that:
          (a) no hosting location has been provided on the system for the product, or
          (b) the delivery client is corrupt (delete and download again).
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
          http://www.ics.uci.edu/%7elab/students/printing.php uc irvine::bren school::lab - student Skip over navigation

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          Bren school home > labs home > student information
          Printing in the labs »

          There is one printer available in CS 364 for student use. ICS Labs use a pay-per-sheet printing system that is provided by OIT. Each page costs 10 cents to print and you must have a valid print card.

          • Where to get a print card
          • How to send and print documents
          • Getting Refunds

          How to send and print documents

          Windows:

          • Click on print from the application.
          • Make sure that the printer name is PayPrint CS364.
          • Go to the printer and follow the direction on the print station
          • On the screen, find your print job and then hit Print.
          • You can continue to print other jobs or click Logout when you are done.
          • The station will display how much value is left on the card.

            PDF Files - if you are having problems printing a PDF file, please make sure that the postscript level is set to 1. To change the level, do the following:

            • Click on print --> Properties... --> Advanced...
            • Under Document Options, find PostScript Options
            • Change PostScript Language Level to 1

            If you find the print job is coming out slowly, try printing a couple of pages at a time. Some PDF files are quite large and the printer cannot process it all at once.

          UNIX:

          • ASCII (text) files: mp asciifile.txt | lpr -Pppcs364
          • PostScript files: lpr -Ppcs364 postscript.ps
          • TEX DVI files: dvisp -Pppcs364 file.dvi

          For more information, please refer to the ICS Support UNIX Printing Guide.

          Getting Refunds

          If your print job does not print out properly (ie. streaks, paper jam) you can get a refund by contacting the Lab Manager in CS346L. If the Lab Manager is not available, please contact OIT.

          If the print card dispenser malfunctioned and you did not get a card or it did not add value to your card, please contact OIT.

           

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 09 2011
          http://www.ics.uci.edu/computing/email/google_setup.php ICS Google Account Setup @ the bren school of information and computer sciences

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          ICS Google Account Setup

          Creation

          An ICS Google Apps account will be automatically created for all ICS faculty, staff, and graduate students.  

          If you are a new user then you need take no additional action.  Your ICS Google Apps account was setup, and your forwarding address was set.  You may login here and access your email.

          Please continue on to Setup if you have an existing ICS account.

          Setup

          If you are an existing ICS user, then you will need to login to https://elnino.ics.uci.edu and make two additional changes.  Note: from off-campus locations, you must use the UCI VPN client.

          Change your password by clicking on the Change Password button. 

          Under forwarding address, select ICS Gmail.   Leave the textbox empty as demonstrated below.

          Elnino Password and Forwarding Address Setup

           

          Note:  Faculty may be presented with the following information when logging into elnino.com.  Click on the My Account button, as indicated below, in order to bring up the above page.

           

          ICS Faculty Elnino Presentation

          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: February 28 2014
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          Bren school home > labs home >
          Lab hours »

          Winter Quarter:

          Finals Week Schedule

          Holidays: All labs closed

          • Martin Luther King Jr. Holiday (Monday, January 18th)
          • Presidents' Day Holiday (Monday, February 15)

          CS 364b Open Lab and 364a Laptop Instructional Lab

          Located on the third floor of the Computer Science Building.

          Monday - Thursday 8:00AM to 10:00PM
          Friday 8:00AM to 8:00PM
          Saturday - Sunday 12:00PM to 6:00PM

          CS183, CS189, CS192

          Located on the first floor of the Computer Science Building.

          Please click on the room number above to view the reservation schedule for the current quarter. Lab reservation schedules are also posted outside each lab door.

          Monday - Friday 8:00AM to 8:00PM
          Saturday - SundayClosed

          CS193 Reserved for Project Class in Software System Design

          Located on the first floor of the Computer Science Building. To get access to this lab, you must be enrolled in Informatics 191. Please read the class policy.

           

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          Bren school home > labs home > lab assistants
          Lab assistants »

          To login to the Labbie page, click here.

          Need to fill out your timesheet? Click here.

           
          Do you want to be a Lab Assistant?

          This is probably one of the best job that you will ever have. Being a lab assistant gives flexible hours and the job is right on campus!

          We are currently not hiring. Please check back around the 8th week of next quarter.


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          Bren school home > labs home >
          Project ICS Computing Policy »

          Purpose:

          The purpose of this document is to communicate ICS policy for computers provided to the students enrolled in Informatics 191. The policy also contains specific details as to the level of service provided to students of the course. This document also details the responsibilities of students using ICS provided computing resources.

          Informatics 191:

          Project in System Design, covers the following areas of study: Specification, design, construction, testing, and documentation of a complete software system. This course has special emphasis on the need for and use of teamwork, careful planning, and other techniques in working with large systems.

          Facilities:

          Informatics 191: Students in this course are provided use of ICS computing resources in CS 193. There are 24PCs and each team can request one system be assigned to them. No food or drinks are allowed in the lab at any time.

          Computing Resources:

          Computing resources for the class include the following:

          • Windows 7 Computer Workstation
          • Administrator rights on the machine
          • UNIX group (gsu) account
          • Ethernet connection to the ICS campus network
          • ICS Standard Computing Environment

          NOTE: Printers are available at the OIT and ICS Labs. Students may send their print jobs to these locations.

          ICS Support Policy:

          ICS Computing Support Staff will support the computing resources made available to the students of Project ICS courses under the policy set forth in this section.

          • Computer System Support between 8AM – 5PM during business hours.
          • ICS does not take responsibility for any data placed by a student on a machine.
          • No data backup is performed on the computers.
          • ICS staff will not install additional software.
          • Computers that become corrupted or unusable will be remedied by reinstalling the standard operating system environment.
          • ICS will reformat the computer hard drives at the end of each academic year.

          Students Responsibility:

          This section details the responsibilities of the students enrolled in Project ICS.

          • Students must backup their own data to their home directory, group account directory, or portable medium such as a USB key.
          • Students are responsible to install software on the computer they are assigned.
          • Students are to have all data they wish to keep, removed by 5:00pm on the Friday of finals week of the quarter.
          • Students are still required to know and adhere to the UCI computer and network use policy, and the ICS Ethical Use of Computing rules.

          Additional Software:

          Any software not provided by ICS with the computer is the responsibility of the student and instructor to requisition, purchase and install. These guidelines are the responsibility of the instructor or customer and should be presented by the instructor to the students at the beginning of the quarter.

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          Bren school home > labs home >
          Lab hours »

          Winter Quarter Finals Schedule

          CS183, CS189, CS192 - Closed

          CS364 - Open Lab

          Located on the third floor of the Computer Science Building.

          March 12 - 13 12:00 PM to 6:00 PM
          March 14 - 17 9:00 AM to 8:00 PM
          March 18 9:00 AM to 5:00 PM
          March 19 - 20 Labs closed

           

          Spring Break (March 21st - March 25th)

          All labs closed.

           

          Spring Quarter

          Regular lab schedule resume Monday, March 28th.

           

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          Bren school home > labs home
          Labs »

          The ICS Labs are located in the Computer Science Building. Students enrolled in ICS classes can obtain computing accounts for use in the labs. Here, students can put into action what they've learned in their lectures.

          There are four labs with over two hundread machines, running Windows and Linux. Scheduled labs for classes are held in the first floor labs: CS183, CS189, and CS192. The main lab is located in CS364 and it is the only ICS Lab that is opened on the weekend. Please see the Lab Hours page for details on hours of operation.

           
          NEW in 2013: Kay Family Foundation Innovation Lab

          Thanks to a generous gift from the Kay Family Foundation, the Donald Bren School of Information and Computer Sciences now features a mobile technology lab designed to foster student collaboration. more

           

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          Bren school home > labs home > policies
          Lab guidelines »

          These are the policies that ICS students, faculty and staff must adhere to. These include general policies as well as specific course policies. You should be notified by your instructor if there is a specific policy for your course.

          ICS Instructional Lab Rules/Policies

          This section covers the UCI, as well as, the ICS specific computing guidelines that apply to anyone using the ICS Instructional Labs for computing.

          General Lab Policy

          Project ICS Course Policy

          Project ICS Door Code Request

          Please fill out the provided request sheet and have all of your team members sign it. Bring the completed form to the Lab Manager in CS346L. You will then receive the door code.

          *** All team members must be present to obtain door code ***

           

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 09 2011
          http://www.ics.uci.edu/%7elab/faculties/index.php uc irvine::bren school::lab - faculty Skip over navigation

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          • ABOUT
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          Bren school home > labs home > faculty information
          Faculty »

          Welcome to the ICS Computing Lab. This section will help provide information that you may need to provide to your students. If you have an comments or questions, please contact the Lab Manager.

          Lab Guidelines

          This section lists the rules that are enforced in the lab. Instructions and teaching assistants should also follow the same guideline to set a good example.

          Labs Access

          The labs are opened seven days a week except on holidays and during Summer Sessions. Please refer to the Lab Schedule for more details. If you would like to schedule a special lab time for retakes or for extra sessions, please contact the Lab Manager.

          Project ICS: Students taking this course may use the computers in CS193, which is reserved specifically for this class.

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 09 2011
          http://www.ics.uci.edu/%7elab/proj_ics/index.php uc irvine::bren school::lab software Skip over navigation

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          • ABOUT
            • About the School
            • Dean's Welcome
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            • Visit the Bren School
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          • DEPARTMENTS
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          Bren school home > labs home >
          Computing Support for Project ICS Students »

          Course Policy

          Group Account Request

          Accessing Group Account

          Apache/MySql/Subversion


          Group Account Request

          Each team can request a group account, where all members of the team can have access and store data. Please send an email to helpdesk@ics.uci.edu. The email should include:

          • The proffessor's name
          • Team number
          • ICS username of every members of the team

          The quota for a group account is 40MB. If you need more space, please have your proffessor send us a request.

          Apache/MySql/Subversion

          If you need to use any of these services, please send an email to helpdesk@ics.uci.edu along with the group account name that you requested previously.

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: June 25 2012
          http://www.ics.uci.edu/computing/email/google_vacation.php vacation message @ the bren school of information and computer sciences

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          vacation message

          If you have multiple Google accounts, please make sure that you log off any non-ICS Google accounts and then follow the steps below.

          1. Open a browser and go to www.gmail.com.  Use username@ics.uci.edu as your username and your ICS password to login.
          2. On the upper right corner, click on the gear symbol.
          3. Select Settings.
          4. Under General, scroll down to Vacation responder.
          5. Turn it on, set the date range, subject, and type in your vacation message. 
          6. Then scroll down and click on Save Changes.

            group email
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 26 2015
          http://www.ics.uci.edu/computing/email/google_faq.php ICS Google Account FAQs @ the bren school of information and computer sciences

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          ICS Google Account FAQs

          Q.  What is an ICS Google Apps Account?

          A.  ICS Google Apps is an installation of Google Apps for Education, a package of online applications that makes communicating and collaborating at school easier and more efficient. The cornerstone of Google Apps is Gmail.

          Q.  Will my ICS email address change?

          A.  Your ICS email address will still be username@ics.uci.edu.

          Q.  Will my personal information be shared with anyone outside of UC Irvine?

          A.  Your personal information will not be shared with anyone.

          Q.  How do I setup my email client on my computer, phone or tablet?

          A. Use Gmail's instructions for setting up your email client.  If you use Thunderbird, please follow our setup guide.

          Q.  Will my old email be transferred to my new ICS Google Apps account?

          A.  Your old mail will not be transferred automatically and will remain on the ICS email server accessible via any IMAP client (including Gmail). There are no immediate plans to end ICS IMAP email service and email stored on the ICS IMAP servers will remain available. 

          Q.  How do I copy my old mail to my ICS Google Account?

          A.  You can move your email from the ICS server to the ICS Google account manually.

          • Set your email client to connect to the ICS IMAP service (e.g. Thunderbird) using the ICS email setup instructions.
          • Enable IMAP on your ICS Google Apps account and set same email client to connect to the ICS Google Apps IMAP server: follow our setup guide.
            • Select I want to enable IMAP and follow the steps.
            • Choose Thunderbird as your client and follow the steps.
            • Select Ok, confirm my settings and use the given configuration.
          • Email can be dragged and dropped between email accounts once your email client is setup.

          Q.  What if I had already signed up for a Google Account using my ICS email address?

          A.  The next time you login to Google Apps with your ICS email address you will be instructed to merge accounts. Follow the directions. Send an email message to helpdesk@ics.uci.edu if you have any questions.

          Q.  What if I am already forwarding my UC Irvine email to a personal Gmail account?

          A.  ICS Google Apps is a separate service from a standard Gmail account. If you switch your forwarding address to your ICS Google Apps account, email messages addressed to username@ics.uci.edu will go to the ICS Google Apps account.

          Q.  Where can I find more answers about my ICS Google Apps account?

          A.  Please visit the Gmail page at the Google Apps Learning Center.

          Q.  How do I reset my ICS Google Apps password?

          A.  Like with any ICS account, contact support to have your password reset.

          Q. Can I sign on to multiple accounts in the same browser?

          A. Gmail will allow you to have multiple accounts open. So you can read your personal Gmail account and the uci/ics account in the same browser. 
          For more information see https://support.google.com/accounts/answer/1721977?hl=en

          Q. How do I enable a vacation message on my Google Account?

          A. You may turn on the Vacation responder for a specified using the web interface. Follow the directions here. 

          Q. What is the storage capacity for ICS Gmail?

          A. The storage capacity of a UCI Gmail account is currently 25GB.

          Q. Can I send and receive email from other accounts within my ICS Gmail account?

          A. Yes, to change the From: address of your messages. Follow the instructions here.

          Q. How do I set up my signature?

          A. Login to UCI Gmail. Click on the Gear icon at the upper right of the page. Click Settings. Verify that you are in the General tab. Find the Signature section and enter your signature in the box. Now press Save Changes at the bottom of the page.

          See also:  http://www.google.uci.edu/faq.html

          Q. Why can I not find a message when I search for it?

          A. Gmail does not search the Spam and Trash folders by default.  You need to add the clause in:anywhere to you search.  Follow the instructions here.

          Q. Why can I not see emails I sent in my Inbox?

          A. Check your "Sent Mail."  All messages that you send will be stored there, including the ones that you send to yourself.

          Q. How to keep messages from people I know out of the Spam folder?

          A. Add their email addresses to your Contacts.  Gmail will not place messages in the Spam folder if you have the email addresses in your Contact list.  You could also create filters for placing mail in your inbox or other boxes.  See the directions here.

          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: January 26 2015
          http://www.ics.uci.edu/computing/linux/access.php linux configurations @ the bren school of information and computer sciences

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          Inappropriate Linux Configurations

          In many cases, vendor-supplied files have inappropriate configurations which should be changed to prevent others from obtaining unwanted access.

          • /etc/hosts.equiv and $HOME/.rhosts
            • Decide if the file /etc/hosts.equiv is really required on your system.  If you are running commands such as rsh, rlogin, etc., this file allows other hosts to be trusted by your system.  Rlogin will then allow someone with the same account name to login to your machine from a listed host without supplying a password. This is a way that hackers can easily leap-frog from machine to machine.
            • Verify that the permissions on these files are set to 600.
            • Verify that the owner of $HOME/.rhosts  is the same as the account's owner and that the owner of /etc/hosts.equiv is root.
            • Make sure that the file does NOT contain the symbol "+" on any line as this allows anyone access to this account or system.
            • Verify that the usage of netgroups within .rhosts does not allow unintended access to this account. Make sure that only the accounts and hosts you want to provide access to have access.
            • Do not use '!' or '#' in these files.  There are no comment characters for these files.
            • Only trust hosts which are within your domain or under your direct managment.
            • Only use fully qualified hostnames (i.e., hostname.ics.uci.edu).
          •  

          • /usr/lib/X11/xdm/Xsession
          • Check this file for an xhost command with a '+'.  Remove that line, since it allows anyone on the network (or possibly on the Internet) to talk to your X server, insert commands into windows, and read your console keystrokes.

             

          • /etc/ttys and /etc/ttytab
          • The only terminal that should be set to secure should be the console.

             

          • /etc/aliases (or /usr/lib/aliases)
          • Check this mail alias file for inappropriate entries. When shipped, some alias files include an alias named uudecode or just decode.  This can almost always be commented out.

             

          • System files
            • Check the permissions and ownership of system files  and directories, especially the / (root) and /etc directories, and all system and network configuration files.
            • Examine file and directory protections before and after installing software. These procedures can cause file and directory protections to change without you being aware of it.
          •  

          • Setuid shell scripts are always potential security problems and can not be made secure. Do not create or allow setuid shell scripts, especially the setuid root ones.
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 04 2013
          http://www.ics.uci.edu/computing/linux/tools.php useful linux security tools @ the bren school of information and computer sciences

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          Useful Linux Security Tools

          Many programs are available which can help prevent break-ins and minimize the damage caused.  Since these programs are used to protect the security of your system, make sure you get any such tools from trusted websites.  Be careful with pre-compiled binaries, especially if you run these programs as root.

           

          • Install ssh and SSL FTP and use them instead of telnet, rlogin, rsh, and ftp to prevent unencrypted passwords from being sniffed from off the network.
          •  

          • Install TCP Wrappers to prevent access from untrusted sites or to limit access only to specific sites.
          •  

          • Use shadow passwords so that the system file containing the actual encrypted passwords is not accessible to others.  Redhat's default configuration does not use shadow passwords, but this can be easily changed by using the pwconv tool.
          •  

          • Use Tripwire to be notified when system files have been modified or when possible trojan horses have been inserted into your system.
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 05 2013
          http://www.ics.uci.edu/computing/linux/file-security.php File Security

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          File Security
          Understanding and Setting UNIX File Permissions

          Understanding and Setting UNIX File Permissions

          1. Introduction

          This guide gives a brief introduction to UNIX file permissions, provides instructions on how to perform simple configuration of UNIX file permissions, and shows how to find files that may have more permissive settings than intended.

          2. Theory of Operation

          UNIX assigns file permissions to three broad categories:

          1. The user that owns the file
          2. The group that owns the file
          3. Everyone else (a.k.a 'world' or 'other')

          Each permission category can be assigned three permissions:

          1. (r)ead
          2. (w)rite
          3. e(x)ecute

          UNIX permissions can be represented as symbols and as octal digits. Fig. 1 shows a table of possible values:

          Octal Symbol Permission
          0 --- No Permissions
          1 --x Execute
          2 -w- Write
          3 -wx Write and Execute
          4 r-- Read
          5 r-x Read and Execute
          6 rw- Read and Write
          7 rwx Read, Write, and Execute
          Fig. 1: Table of Permission Combinations

          The effect of these permissions varies by file type. There are numerous file types, but this guide will only deal with the following types:

          1. '-' — Regular file
          2. 'd' — Directory

          When applied to a regular file, UNIX permissions have the following effect:

          1. Read permission allows the file to be opened
          2. Write permission allows the file to be modified
          3. Execute permission allows the file to be executed directly from the shell, if it is a script or binary file

          When applied to a directory, UNIX permissions have the following effect:

          1. Read permission allows the directory contents to be listed
          2. Write permission allows files or directories to be created or deleted within the directory
          3. Execute permission allows the directory contents to be accessed

          UNIX permissions closest to the user take precedence. User permissions take priority over group permissions for the user who owns a file or directory, and both user and group permissions take priority over the 'other' permissions for the user and group who owns the file.

          3. Setting UNIX File Permissions

          There are three utilities used for managing UNIX permissions:

          1. 'ls -l' — Displays a detailed list of directory contents
          2. 'chmod' — Changes permissions
          3. 'chgrp' — Changes group owners

          3.1. 'ls -l'

          The ls commands outputs a list of files and directories within a given directory, and the '-l' option provides a detailed list of the directory contents (including permissions).

          Fig. 2 shows the output of 'ls -l' on a test file, with each section labeled. The portions of the output related to UNIX file system permissions are colored red:

          Permissions
          Fig. 2: Labeled output of 'ls -l'

          The output of Fig. 2 tells us the following:

          1. The '-' file type indicates that this is a regular file
          2. 'rwx' permissions for user indicates that the user who owns this file has read, write, and execute permissions for the file
          3. 'r-x' permissions for group indicates that the group who owns this file has read and execute permissions for the file
          4. '---' permissions for 'other' (i.e., everyone else) means that everyone other than the user or group who owns the file has no access to the file.
          5. The file is owned by user 'walbert' and group 'support'

           

          3.2. 'chmod'

          The chmod command changes the permission on a given file or directory.

          chmod sets permissions in two ways.

          1. Using symbols
          2. Using octal values

          This guide uses primarily octal values because they can be entered quickly. For more information on using symbols, refer to the chmod man page.

          When using octal values, a series of three digits defines the permissions for user, group, and other in that order. For example, a three-digit octal value of 640 would translate to read and write permissions for the user, read permission for the group, and no permission for others. When using octal values to define permissions, the new octal value entered will replace the existing permissions on the file or directory.

          (There is an optional leading digit which sets the setuid, setgid, and sticky bits. Usage of these permissions bits are outside the scope of this guide.)

          The following examples show common uses of chmod. After the chmod command, the output of the ls -l command is shown to demonstrate how the file permissions should look. Refer to the table in Fig. 1 for explanations of the octal values.

          3.2.1. Access for Only Yourself

          $ chmod 600 test_file
          $ ls -l test_file
          -rw------- 1 walbert ugrad 0 Nov  5 15:58 test_file

          3.2.2. Write Access for Yourself and Read Access for Your Group

          $ chmod 640 test_file
          $ ls -l test_file
          -rw-r----- 1 walbert ugrad 0 Nov  5 15:58 test_file

          3.2.3. Write Access for Yourself and Read Access for Everyone Else

          $ chmod 644 test_file
          $ ls -l test_file
          -rw-r--r-- 1 walbert ugrad 0 Nov  5 15:58 test_file

          3.2.4. Write and Execute Access for Yourself Only

          $ chmod 700 test_file
          $ ls -l test_file
          -rwx------ 1 walbert ugrad 0 Nov  5 15:58 test_file

          3.2.5. Write and Execute Access for Yourself, and Execute Access for Everyone

          $ chmod 755 test_file
          $ ls -l test_file
          -rwxr-xr-x 1 walbert ugrad 0 Nov  5 15:58 test_file

          3.2.6. Write Access to a Directory for Yourself, and Read Access for Your Group

          $ chmod 750 test_directory
          $ ls -l -d test_directory
          drwxr-x--- 2 walbert ugrad 2.0K Nov  5 15:59 test_directory/

          3.2.7. Remove All Non-User Access to a Directory and its Sub-directories

          $ chmod -R go-rwx test_directory
          $ ls -l -R test_directory
          test_directory/:
          total 4.0K
          drwx------ 3 walbert ugrad 2.0K Nov  5 16:53 foo/
          -rw------- 1 walbert ugrad    0 Nov  5 16:20 test_file

          test_directory/foo:
          total 4.0K
          drwx------ 3 walbert ugrad 2.0K Nov  5 16:53 bar/
          -rw------- 1 walbert ugrad    0 Nov  5 16:53 test1

          test_directory/foo/bar:
          total 4.0K
          drwx------ 2 walbert ugrad 2.0K Nov  5 16:53 baz/
          -rw------- 1 walbert ugrad    0 Nov  5 16:53 test2

          test_directory/foo/bar/baz:
          total 0
          -rw------- 1 walbert ugrad 0 Nov  5 16:53 test3

          In this case, symbolic permission assignment was used, as octal permission assignments usually do not produce the intended result when applied recursively. The symbolic permissions used in this example, 'go-rwx', can be translated as “for group and other—remove read, write, and execute permissions.”

          3.3. 'chgrp'

          The chgrp command changes the group ownership on a given file or directory.

          Changing group ownership may be necessary when sharing files and directories with other members of your research group.

          3.3.1. Changing the Group Ownership of a File

          $ chgrp support test_file
          $ ls -l test_file
          -rw-rw---- 1 walbert support 0 Nov  5 15:58 test_file

          3.3.2. Changing the Group Ownership of a Directory and its Contents

          $ chgrp -R support test_directory
          $ ls -l -R test_directory
          test_directory:
          total 4.0K
          drwx------ 3 walbert support 2.0K Nov  5 16:53 foo/
          -rw------- 1 walbert support    0 Nov  5 16:20 test_file

          test_directory/foo:
          total 4.0K
          drwx------ 3 walbert support 2.0K Nov  5 16:53 bar/
          -rw------- 1 walbert support    0 Nov  5 16:53 test1

          test_directory/foo/bar:
          total 4.0K
          drwx------ 2 walbert support 2.0K Nov  5 16:53 baz/
          -rw------- 1 walbert support    0 Nov  5 16:53 test2

          test_directory/foo/bar/baz:
          total 0
          -rw------- 1 walbert support 0 Nov  5 16:53 test3

          4. Finding Files with Unwanted Permissions

          This section provides information on the use of the find command to find and display files and directories with permissions that may be undesirable. All examples in this section will output results in the format typically provided by ls -l.

          All examples will use the following format:

          find /dir/name -perm /005 -type f -print0 | xargs -0 ls -l

          Explanation of these options:
          /dir/name — Directory being searched
          -perm — Instructs find to match files based on given permissions
          -type f — Instructs find to match only regular files
          -print0 — Sends output to stdout using the null character
          xargs — Command that builds another command to execute based on the content of stdin
          -0 — Use the null character as the item delimiter

          4.1. Find All Files Accessible by 'Other' in Any Way

          $ find ~ -perm /007 -type f -print0 | xargs -0 ls -l

          This will display all files within your home directory that have read, write, or execute permissions for 'other.' This is useful for getting a general idea of what others can access in your home directory.

          4.2. Find All Files Readable by 'Other'

          $ find . -perm /004 -type f -print0 | xargs -0 ls -l

          This will display all files within the current working directory that are world-readable. Many of these files will be 'common' files (such as shared libraries, icons, etc.) located in hidden directories, which are generally harmless. Any private files that appear on this list (e.g., private SSH keys or files on non-hidden directories) should be investigated.

          4.3. Find All Files Writable by 'Other'

          $ find /extra/research0 -perm /002 -type f -print0 | xargs -0 ls -l

          This will display all files in the /extra/research0 directory that are writable by 'other.' Any files that appear in this list should be investigated, and in most cases, permissions should be changed to something more restrictive.

          4.4. Find All Files Executable by User or Group, and Writable by 'Other'

          $ find ~ -perm -102 -type f -print0 | xargs -0 ls -l  # User
          $ find ~ -perm -012 -type f -print0 | xargs -0 ls -l  # Group


          This will display all files in your home directory that are both world-writable and executable by either the user or the group that owns the file. Files that are user-executable and world-writable are a major security vulnerability and should be fixed immediately unless you know exactly what you are doing.

          4.5. Find All Files Owned by a Specific Group

          $ find ~ -group NameOfGroup -type f -print0 | xargs -0 ls -l

          This will display all files in your home directory that are owned by the specified group.

          4.6. Find All Files Not Owned by a Specific Group

          $ find . -not -group NameOfGroup -type f -print0 | xargs -0 ls -l

          This will display all files in the current working directory that are not owned by the specified group.

          5. Further Resources

          For detailed documentation on the commands used in this guide, see the man pages for the following commands

          • ls
          • chmod
          • chgrp
          • find
          • xargs

          For other resources on the commands used in this guide (and UNIX file system permissions in general):

          • Wikipedia — File system permissions
          • Wikipedia — chmod
          • Wikipedia — chgrp
          • IBM — Learn Linux, 101: Manage file permissions and ownership
          • TLDP — 3.4.1. Access rights: Linux's first line of defense
          UC copyright | UCI directory | Intranet | Site Map | icswebmaster@ics.uci.edu | link::youtube page   link::facebook page | Updated: November 19 2013
          http://www.ics.uci.edu/computing/linux/services.php unneeded linux services @ the bren school of information and computer sciences

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          Unneeded Linux Services

          The more services running on your machine, the greater the chance that one or more will have a known security hole.  Therefore, you should turn off any unneeded services and ensure any services you run are the latest version and are properly configured.

          • Services in /etc/inetd.conf
            • Inetd is the daemon that manages many system services.  These are listed in its configuration file, /etc/inetd.conf, which can easily be modified to prevent inetd from starting unwanted services.  If you are the only user of your workstation, you can have almost everything commented out and will most likely not notice their absence.
            • Services which are often unneeded include: echo, daytime, chargen, time, login, shell, and exec.  To turn these off, follow the instructions at Turning off an inetd-launched service.
            • Services you probably want to keep include: telnet, ftp, identd.
          • Services started from system start-up scripts
          • Many other services are started in system start-up scripts. You should review your start-up scripts and comment out ones which are not appropriate for your workstation's purpose. For example, rpc.mountd and rpc.nfsd are probably unneeded on a single user workstation. kill -9 any unwanted services and then comment them out in the appropriate system start-up file.

          • Other services
            • To determine what other services you are running, use the command:
                        netstat -a | more
            • Use your package manager to list the packages installed (dpkg for Debian and rpm for RedHat).
            • Unless your machine is acting as a mail server, you should turn off sendmail since it is very large and is one of the most common targets for attacks because of its numerous security holes. Note: sendmail is not necessary if you are using pop to read your mail.
            • If you are not actively sharing files or printers from your machine with machines running a Microsoft operating system, you should turn off samba.
          • Do not run NIS or NIS+ if you do not need them. A standalone machine will never need these.
            • Define each netgroup to contain only usernames or only hostnames.  All utilities parse /etc/netgroup for either hosts or usernames, but never both.  Using separate netgroups makes it easier to remember the function of each netgroup. The added time required to administer these extra netgroups is a small cost in ensuring that strange permission combinations will not leave your machine in an insecure state.
          • Web servers: If you are running a web server, make sure you have the latest available release.  The latest version of Apache can be obtained from the official Apache Distribution site.
          • Anonymous FTP
            • Make sure that you are running the most recent version of ftpd. There are several well-known security problems with the ones that ship on distribution media.
            • Check your anonymous FTP configuration. It is important to follow the instructions provided with the operating system to properly configure the files and directories available through anonymous FTP (for example, file and directory permissions, ownership and group).
            • You should not use your system's standard password file or group file as the password file or group file for FTP.
            • The anonymous FTP root directory and its two subdirectories, etc and bin, should not be owned by the FTP user.
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 05 2013
          http://www.ics.uci.edu/grad/qa/index.php graduate student faq @ the bren school of information and computer sciences
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          Bren school home > Graduate > Questions & answers
          Graduate student faq

          Don't see your question here? Please e-mail it to us at gcounsel@ics.uci.edu.

          For Prospective Graduate Students

          • How long does it take for a decision to be reached on my application?

            Decisions are ongoing, therefore, you may hear as early as March or as late as May/June.

            You will be notified of the final decision via email and the online Tracker. We cannot provide you with the final decision over the phone.

          • When will I be notified of my funding offer?

            If funding has been awarded, notification will be included with your admissions offer.

          • How will I know if my file is missing any documents or whether my file is complete?

            Applicants may login to the online Tracker to check for application materials received.

          • Do I need to take the GRE General Exam?

            Yes, the general GRE exam is required of all applicants. GRE scores are good for five years, after which time the exam must be repeated. We cannot accept GMA scores in lieu of GRE scores.

            Only the General exam is required; we do not require the subject GRE exam, although it is recommended for applicants without a background in Computer Science or a related area.

          • If my undergraduate degree is not in the same area as the graduate degree in which I am interested, can I still apply?

            Yes, but it is helpful if you have taken courses in computer science and math, and/or have some related work experience.

          • Do I have to submit online letters of recommendation?

            You may elect to submit online or hard copy letters of recommendation.

          • May I submit more than three letters of recommendation?

            You may submit as many recommendation letters as you wish.

          • Can I attend part time or take evening classes?

            M.S. students may petition to attend part time, but Ph.D. students are required to attend full time.

            The Bren School does not offer evening or weekend classes at this time; all classes are offered during the day.

          • Can I be waived out of the TOEFL requirement?

            UCI will only waive the TOEFL requirement for applicants who have completed ALL requirements for a B.S., M.S. or Ph.D. degree in the U.S. prior to submitting their application.

          • Can the application fee be waived or deferred?

            No. In order for your application to be processed, the application fee must be received.

          • Is on-campus housing guaranteed?

            No, on-campus housing is not guaranteed.

            Admitted students may apply for housing after they submit their Statement of Intent to Register (SIR).

          • Can I send photocopies of my transcripts, GRE scores and TOEFL scores?

            Only official transcripts are acceptable in order to complete your application. Use institutional code 4859 to have official GRE and TOEFL scores sent from the testing agency to UCI.

          • Do I need to send bank certification or financial verification at the time of application?

            No. Once admitted, all international students who accept the admission offer are required to submit a financial questionnaire and financial verification documents.

          • If I am re-applying to the program, do I need to re-submit all my documents?

            You will need to submit an online application and pay the application fee in order to re-apply to the program.

            If you are re-applying within a one-year period, you may re-use the original documents; however, we encourage you to update your file with a new statement of purpose and letters of recommendation.

            GRE and TOEFL scores must be current at the time of re-application.
            Please send an email to gcounsel@ics.uci.edu letting us know which documents you would like to re-use.

            If you are re-applying after one year, you will need to re-submit all new documents.

          • Do you admit for the Winter or Spring quarter?

            No. The Bren School only admits for the Fall quarter.

          • Who do I contact if I’m having trouble with the online application?

            Send an email to UCI Graduate Division: ogs@uci.edu. Please note that you may not make changes to your application after it has been submitted.

          For Current Graduate Students

          • How do I advance to candidacy for the Master's degree?

            Complete and submit the MS Advancement to Candidacy form one quarter prior to when you expect your M.S. degree to be conferred. To access the form click here.

            Please check with the ICS Graduate Counselors for quarterly deadlines.

          • How often can I go on a Leave of Absence?

            Up to three quarters total.

          • When is the Leave of Absence deadline?

            The campus deadline is Friday of the third week of classes. However, ICS recommends that students submit the Leave of Absence petition by the registration deadline each quarter. To access the leave of absence form click here.

          • When is the deadline to apply for Part-Time status?

            The campus deadline is noon on Wednesday of the third week of classes. However, ICS recommends that students submit the Part-Time petition by the registration deadline each quarter.

          • Can a Ph.D. student apply for Part-Time status?

            No, only Master’s students are eligible to apply for part-time status. To access the part-time petition, titled Reduced Fee Part-Time Study Program, click here.

          • What is the composition of the Ph.D. advancement committee?

            The Ph.D. advancement committee is a five member committee of senate faculty. The Ph.D. advancement committee must be composed of the Chair, three general members and one outside member. The Chair of the candidacy committee must hold either a primary or joint appointment in the student's department. At least two members in addition to the Chair must hold either a primary or joint appointment in the student's department. The majority of the committee must be from the student's department. The outside member must be a UC Irvine faculty member and may not be affiliated with the student's department. Consult with the ICS graduate counselors for more information.

          • What is the composition of the Ph.D. final defense committee?

            The Ph.D. final defense committee is a three member committee of senate faculty drawn from your advancement committee. The majority of the committee MUST be from your department. Consult with the ICS graduate counselors if changes to the composition of your defense committee occur.

          • Who exactly are "senate faculty?"

            Senate faculty have the following titles: Assistant Professor, Associate Professor, Professor, and Lecturer/Senior Lecturer with Security of Employment (SOE).

          • Can I combine my Ph.D. advancement and my topic defense?

            Yes, with the consent of your advisor. To access the topic defense form click here.

          • Do I have to complete my courses before I can advance to candidacy for the PhD?

            Yes, you must complete all required course work before you can advance to candidacy for the PhD.

          • Do research units count toward course requirements?

            No. Course numbers 290, 298 and 299 do not count toward course requirements.

          • My degree requires a "publication-quality" research paper. Does that mean it has to be published before I can submit it to my advisor/committee?

            No, the paper does not have to be published. Your advisor/committee will assess the quality of your paper.

          For Current & Prospective TAs/Readers

          • Who do I see regarding TA/Reader assignments?

            The Department Managers.

          • Do I need to submit a TA/Reader application for each quarter?

            Yes.  You'll need to submit a TA/Reader application each quarter you wish to serve as a TA/Reader.

          • When will I be informed of the TA/Reader appointments?

            You will be notified via email as soon as you are assigned.

          • Do I need to fill out a TA/Reader application if I'm on a TA/Reader fellowship?

            Yes.

          • If I've been assigned as a TA/Reader, do I get academic credit?

            Yes, each quarter you are assigned as a TA/Reader, you will receive four units of academic credit by signing up for ICS 399. Note: ICS 399 does not count toward degree requirements.

          More Graduate »
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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster
          http://www.ics.uci.edu/grad/sao/index.php contact a counselor @ the bren school of information and computer sciences
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          Bren school home > Graduate > Academic advising
          Contact a counselor

          The primary focus of the Student Affairs Office is to assist students and faculty with University policies, procedures, and requirements related to the Bren ICS academic programs. The graduate staff coordinates the graduate admissions process, fellowships, and the yearly graduate student review. It also handles the various forms and administrative functions relating to graduate students.

          • Prospective students, please visit or contact the Bren School Graduate Office at gcounsel@ics.uci.edu for specific questions regarding admission procedures, required exams, fellowships, and degree requirements that were not answered on the Admissions page.
          • Current students, please visit or contact the Bren School graduate counselors during walk in hours, which are 1 p.m. to 3:30 p.m. daily. If you cannot make these times, please send an email to set up an appointment. Counselors are also available via email for specific questions regarding your academic planning, course scheduling, and progress on degree requirements.

          LOCATION

          The Bren School Student Affairs Office is located in the Information and Computer Science Building, Suite 352 (building #302 on campus map).



          OFFICE HOURS

          Monday - Friday

          9:00am-12:00pm and 1:00pm-4:00pm

          Walk-In Hours: 1:00pm-3:30pm



          CONTACT INFORMATION

          Telephone (949) 824-5156
          Fax (949) 824-4163
          E-mail gcounsel@ics.uci.edu
          Mailing Address ICS Graduate Office
          Donald Bren School of Information and Computer Sciences
          352 Information & Computer Science Bldg
          University of California, Irvine
          Irvine, CA 92697-3430



          GRADUATE COUNSELORS

          Kris Bolcer Director, ICS Student Affairs
          Karina Bocanegra

          Graduate Counselor

          Julie Kennedy

          Graduate Counselor

          Walk-in counseling is available from 1pm to 3:30 pm daily.



          ASSOCIATE DEAN FOR STUDENT AFFAIRS

          Please contact the Associate Dean for Student Affairs at adsa@ics.uci.edu to discuss concerns (not related to specific academic advising), or share your thoughts and recommendations about any aspect of the Bren School's graduate programs.

          The Associate Dean holds weekly office hours in the Bren School Student Affairs Office (SAO). Please call the SAO's front desk at (949) 824-5156 for quarterly hours of availability.

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          http://www.ics.uci.edu/ugrad/degrees/degree_cse_electives.php science electives for cse major @ the bren school of information and computer sciences
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          Bren school home > Undergraduate > Degrees
          Science electives for CSE major

          The science elective must be taken at UCI if the student is currently enrolled at UCI.

          Below are the approved courses that satisfy the science elective requirement for CSE majors. Please note there are two science elective restrictions students must keep in mind when selecting an elective course.

          1.) The material covered in the course cannot substantially repeat the material covered in the Computer Science and Engineering core courses (e.g., a computing for physics majors course from the Department of Physics and Astronomy would not be approved.)

          2.) Courses should primarily focus on understanding or applying scientific principles (e.g., a writing course offered by the School of Biological Sciences whose main focus was on writing would not be approved.)

          Pre-approved Technical Electives

          Biological Sciences
          » All Biological Sciences courses except those numbered 1-93, 142, and 189-199 are pre-approved science electives.

          Chemistry
          » All Chemistry courses except those numbered 1P, 5, H90 and 190-199 are pre-approved science electives.

          Earth System Science
          » All Earth System Science courses except those numbered 1, H90 and 190-199 are pre-approved science electives.

          Physics
          »

          All Physics courses except those numbered 2, 3A-B-C, 15-21, 50, 53, H90, 120, 125A-B, 129 and 190-199 are pre-approved science electives.

          Remedial courses such as Physics 2, "Introduction to Mathematical Methods for Physics", and Chemistry 1P, "Preparation for General Chemistry", will not count toward the basic science requirement for the Computer Science and Engineering (CSE) degree.

          Students may petition for alternate courses, and individual and group study (198, 199) will be reviewed on a case-by-case basis with the submission of an Individual Study Proposal.

          Assignment
          Faculty and advisors are encouraged to report suspect courses to the CSE Steering Committee, as content changes and course introductions may affect the list above.

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          http://www.ics.uci.edu/prospective/en/admissions/transfer/  Transfer « Admissions « Bren School of Information and Computer Sciences « University of California Irvine ?>
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          Details about requirements, application deadlines, and tips for enhancing your application

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          Transfer

          The Bren School has articulation agreements with California community colleges that allow you to take courses that not only meet our admissions criteria, but can also be applied to our degree requirements. If you have not done so already, check out www.assist.org for the latest articulation agreement specifically between your community college and UC Irvine. If you plan it right, most of our majors support you in starting with your upper-division major requirements in your first quarter as a Bren School and UCI student.

          Some community colleges such as Orange Coast College, Long Beach City College, and Fullerton College, have a more direct and specialized articulation program called Smart-ICS. For more information about this agreement, please visit the CS department at one of these community colleges.

          The campus offers a wealth of information for prospective freshmen, as well as details on all aspects of the admissions process. We particularly encourage you to explore the following:

          Admissions: http://www.admissions.uci.edu/

          Online publications: http://www.admissions.uci.edu/publications/online_publications.html

          and do not forget our mascot, Peter the Anteater: http://www.uci.edu/peter/

          Currently, undergraduate enrollment in the Bren School is about 2,000 students, out of a total of almost 23,500 for UC Irvine as a whole.

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          http://sconce.ics.uci.edu/203-W15/ CS 203 / NETSYS 240 Class Page

          ICS 203 / NetSys 240
          Network and Distributed Systems Security -- Winter 2015

          Instructor: Gene Tsudik


           

          Anouncements:

          Logistics:

          • Lecture: Tu/Th 9:30am-10:50pm, DBH 1300.
          • Professor's office hours: Monday 11:00am-12:00pm in DBH 3228 (Bren Hall). Alternatively, via email or by appointment.
          • Midterm: TBD, during class.
          • Final: Thur, Mar 19, 8:00am-10:00am.
          • Course Reader:
            • Cesar Ghali:
            • Office hours: Wednesday 3:00pm-4:00pm in ICS1 468A (Computer Science 1).

          Book Pointers:

          • Stallings' book  
          • FREE HAC book  

          Current Lecture Material (in PDF):

          1. 01/06: Introduction
          2. 01/08: Overview of Network Security
          3. 01/13: User Authentication
          4. 01/20: The Password That Never Was
          5. 01/22: Web Security
          6. 01/29: Named Data Networking
          7. 02/03: Spam
          8. 02/05: Phishing
          9. 02/10: Network Threats/Attacks
          10. 02/17: Fighting Insider Threats with Eye Movement Biometrics
          11. 02/24: Firewalls
          12. 02/26: Privacy and Anonymity
          13. 03/03: Privacy
          14. 03/05: E-Cash
          15. 03/10: Public Key Distribution, Certification and Revocation

          Talks:

          Related Links:

          • CS 134 -- Undergraduate Security Course (Winter 2015)
          • UCI Secure Computing and Networking Center (SCONCE)
          • UCI Security and Privacy Research Outfit (SPROUT)
          • 2014/2015 Computer Science Department Seminar Series

          Academic Honesty:

          • Academic honesty is always an uncomfortable topic and hopefully this will be an unnecessary reminder but here it is. Academic honesty will be strictly enforced, and misconduct dealt with according to the official policy for Academic misconduct at UC Irvine.
           


          http://sconce.ics.uci.edu/134-W15/ CS 134 Class Page

           

           

          CS 134

          Computer & Network Security: WINTER 2015

          Instructor: Gene Tsudik

           

          Announcements:

          • 01/22: The first homework assignment has been issued, due Jan 29 at Noon.
          • 01/22: The second homework assignment has been issued, due Feb 28 at Noon.
          • 01/22: The third homework assignment has been issued, due Mar 13 at Noon.

          Administrative

          • Course overview (informal)
          • Course Book
          • Lectures: TuTh 11:00-12:20 DBH 1500
          • Discussion Sessions: F 9:00-9:50 PCSB 120
          • Office Hours: Monday 11:00 am - 12:00 pm; DBH 3228 (Bren Hall)
          • Course TA:
            • Tyler Kaczmarek (tkaczmar at uci dot edu)
            • Office Hours: Wed 2:00 pm - 3:00 pm; ICS1 468A (Computer Science I building)

          Lecture Materials:

          • Week 1:  01/06: Introduction and Background    01/08: Cryptography; History, Simple Methods, and Primitives
          • Week 2:  01/13: Encryption   01/15: More Encryption  
          • Week 3:  01/20: Hash Functions   01/22: Some Fun Math  
          • Week 4:  01/27: Public Key Cryptography   01/29: Public Key Cryptography: Encryption and Signatures  
          • Week 5:  02/03: Public Key Cryptography: Identification   02/05: Authentication  
          • Week 6:  02/10: Biometrics   02/12: Midterm  
          • Week 7:  02/17: Certification and Protocols 
          • Week 8:  02/24: Public Key Certification and Revocation
          • Week 9:  03/3: Guest Lecture: Secure Design of Password Storage   03/05: Access Control
          • Week 10:  03/10: Privacy and Anonymity

          Miscellaneous:

          • Ciphers by Ritter (useful collection of stuff)
          • Handbook of Applied Cryptography (FREE!)
          • Cryptography and Networks Security Book by Stallings (lots of good pointers)

          Academic Honesty:

          • Academic honesty is always an uncomfortable topic and hopefully this will be an unnecessary reminder but here it is. Academic honesty will be strictly enforced, and misconduct dealt with according to the official policy for Academic misconduct at UC Irvine.

           

           

           


          http://ironwood.ics.uci.edu/ Welcome to the CS theory wiki! - CS Theory Wiki
          Table of Contents
          • How to edit this wiki
          • Our stuff
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          • How to be a graduate student at UCI

          Welcome to the CS theory wiki!

          How to edit this wiki

          • Crashcourse on wiki editing
          • Local DokuWiki documentation
          • The official DokuWiki manual

          To create a new page titled My new page

          1. add a link from this page (click the internal link button when editing, fill in Link to field)
            • for private pages, precede the title with private: instead of the default of wiki:
          2. click the link you created
          3. click Create
          4. the first line of the newly created page should be the page title like this:
            ====== My new page ======
          5. headers: level 1 is reserved for the title. Use level 2 to 6 for the sections of the article.
            • capitalization of title and headers: first letter and proper nouns only

          To delete a page, edit that page and delete all its contents.

          Our stuff

          • Student reading group
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          • A Twitter Feed of Algorithm Problems

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          How to be a graduate student at UCI

          • Conference travel tips
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          Trace: » Welcome to the CS theory wiki!

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          http://www.ics.uci.edu/~guptasid/ Siddharth Gupta
          Siddharth Gupta

          Siddharth Gupta

          Graduate Student

          • DBH 4032
          • siddharth.gupta@uci.edu
          • Curriculum vitae

          About

          I am a Computer Science PhD student in the Center for Algorithms and Theory of Computation at University of California, Irvine. My research interest are in the field of Algorithms and Graph Theory.

          Before joining UCI, I completed my M.Sc.(Hons.) Mathematics and B.E.(Hons.) Computer Science from BITS-Pilani, Goa Campus in 2014.

          Teaching

          Winter 2016

          • ICS 6D : Discrete Maths for CS(TA)

          Fall 2015

          • CS/CSE161: Design and Analysis of Algorithms(Reader)

          Publications

          A New Parallel Algorithm for Two-Pass Connected Component Labeling

          • Siddharth Gupta
          • Diana Palsetia
          • Md. Mostofa Ali Patwary
          • Ankit Agrawal
          • Alok Choudhary
          • Presented at MTAAP 2014.
          http://www.ics.uci.edu/%7etheory/doctorates.html Theory Doctorates
          Doctorates

          Former doctoral students

          Year Student Advisor Thesis title
          1980 Dov Harel G. Lueker Efficient Algorithms with Threaded Balanced Trees
          1985 Martin Katz D. Volper Geometric Retrieval: Data Structures and Computational Complexity
          Francis Murgolo G. Lueker Approximation Algorithms for Combinatorial Optimization Problems
          1986 Lawrence L. Larmore D. Hirschberg Methods of Solving Breakpoint Problems
          1988 James H. Hester D. Hirschberg Probabilistically Faster Search Structures
          Kadri Krause G. Lueker Efficient Parallel Algorithms for Recognition and Analysis of TSSP Graphs
          1990 Mariko Molodowitch G. Lueker Analysis and Design of Algorithms: Double Hashing and Parallel Graph Searching
          Cheng F. Ng D. Hirschberg Computational Complexity of Stable Matching Problems
          1991 Debra A. Lelewer Brum D. Hirschberg Data Compression on Machines with Limited Memory
          1993 Hari Asuri G. Lueker Parallel Algorithms for Sparse Graphs
          1994 Lynn M. Stauffer D. Hirschberg Parallel and High-Speed Data Compression
          1997 Vitus Leung S. Irani Scheduling with Conflicts and Applications to Traffic Signal Control
          Steven S. Seiden D. Hirschberg / S. Irani Randomization in Online Computation
          1998 Jonathan Kent Martin D. Hirschberg Machine Learning of Classifications via Generalized Linear Models: Theoretical and Practical Considerations
          2002 David Hart D. Eppstein Algorithms for Geometric Shortest Paths along Routes
          2003 Joseph Wang D. Eppstein Graph Algorithms for Complex Networks
          2006 John Augustine S. Irani Near-Optimal Solutions for Powering-Down Problems and Scheduling Jobs in FPGAs
          Yu (Jeremy) Meng M. Goodrich Confluent Graph Drawing
          Zheng (Jonathan) Sun M. Goodrich Algorithms for Hierarchical Structures, with Applications to Security and Geometry
          2008 Josiah Carlson D. Eppstein Solving Some Combinatorial Problems Embedded in Trees
          2009 Kevin Wortman D. Eppstein Minimum Dilation Stars
          Nodari Sitchinava M. Goodrich Parallel External Memory Model and Algorithms for Multicore Architectures
          2011 Darren Strash D. Eppstein / M. Goodrich   Algorithms for Geometric Graphs and Social Networks
          2013 Lowell Trott M. Goodrich Geometric Algorithms for Social Network Analysis
          2014 Joe Simons D. Eppstein / M. Goodrich New Dynamics in Geometric Data Structures
          Paweł Pszona M. Goodrich Practical Algorithms for Sparse Graphs
          2015 Michael Bannister D. Eppstein Lower Bounds and Fixed-Parameter Tractability of Drawing Graphs
          Jenny Lam S. Irani Cache Optimization for the Modern Web

          Department of Computer Science
          University of California, Irvine, CA 92697-3435
          http://www.ics.uci.edu/%7etheory/research.html Center for Algorithms and Theory of Computation ICS Theory Group

          Research in Algorithms and Theory of Computation at UC-Irvine

          The goal of research in theoretical computer science is to produce results, supported by rigorous proof, about problems dealing with computers and their applications. The questions to be investigated are often motivated by practical problems, but the goal of understanding the fundamental structure of the problem is often as important as producing a solution of immediate applicability. Despite this emphasis, it turns out that results that first might appear to be only of theoretical value are sometimes of profound relevance to practical problems.

          In particular, one of the major subareas of theoretical computer science, and the one pursued by the faculty and graduate students at UCI, is concrete complexity: We look at specific problems and try to determine the complexity (i.e., the amount of resources required) for obtaining a solution. Our work falls into three main areas: design of algorithms and data structures; analysis; problem complexity.

          Design of Algorithms and Data Structures

          Given a problem, we try to find efficient solution methods. A data structure is a way of organizing information; sometimes the design of an appropriate data structure can be the foundation for an efficient algorithm, and we have made a number of significant contributions to the field of data structures. In addition, one of our members has written a widely used and respected text on data structures, and is presently completing a second more introductory text.

          In addition to the design of new data structures, we are also interested in efficient algorithms for problems arising in a variety of fields. Often such problems can be represented in terms of trees, graphs, or strings, and we are interested in the design of efficient solutions for such problems.

          The field of computational geometry investigates the complexity of problems involving two-dimensional (or higher) spaces. This is an active research area which has not only theoretical depth but also practical applications in areas such as pattern recognition, VLSI layout, statistics, and image processing. One major area of our work is the investigation of certain properties of geometric constructs which can be modeled by graphs. We have also explored how solutions to geometric problems such as linear programming or the minimum spanning tree can be made dynamic, i.e., how we can efficiently maintain the solution when the input data are subject to change.

          Also of interest is the compression of data. For example, we have reduced the complexity of algorithms for compressing strings, and have also investigated the compression of structures such as quadtrees which are used for storing spatial data.

          Current work in genetics provides an exciting application area for algorithms. Some work done long ago by our present faculty, on longest common subsequences and on PQ-trees, has turned out to be valuable in solving problems that arise in genetics. More recently, one of our faculty has introduced sophisticated new methods for speeding the solution of problems such as DNA sequence comparison.

          Much of our work has dealt with the fast solution of problems by a single processor. The combination of declining cost of processors and the desire for fast solutions to problems has led to a great deal of interest in the use of parallelism to speed up computation. One natural question is thus: how long does it take to solve a given problem with a limited number of parallel processors? Some of us have been especially interested in solving problems on graphs very quickly without using an excessive number of processors.

          Analysis

          Once a solution method has been proposed, we seek to find a rigorous statement about its efficiency; analysis of algorithms can go hand-in-hand with their design, or can be applied to known algorithms. Some of this work is motivated in part by the theory of NP-completeness, which strongly suggests that certain problems are just too hard to solve exactly and efficiently all of the time. It may be, though, that the difficult cases are relatively rare, so we attempt to investigate the behavior of problems and algorithms under assumptions about the distribution of inputs.

          Our group at UCI has made major contributions in the area of probabilistic analysis. We have done work in algorithms for problems such as packing, partitioning, marking algorithms, and hashing. In particular, we have obtained a surprising result about the behavior of a well known marking algorithm, and an elegant analysis of double hashing.

          Probability can provide a powerful tool even when we do not assume a probability distribution of inputs. In an approach called randomization, one can introduce randomness into the algorithm itself so that even on on worst-case input it works well with high probability. For example, for the classical List Update Problem, which involves organizing data so that we can perform searches efficiently, one of our faculty has shown how to use randomization to beat the inherent limit of a deterministic approach.

          An area of considerable recent interest is on-line algorithms. Here we investigate the performance of algorithms which must provide answers based on part of the input before the remainder is available. A good example is memory paging---when swapping, the system must decide which memory pages to keep in its cache before it sees which ones will actually be used later. Earlier analysis of this problem had not been fully successful in explaining why a common heuristic performs so well. One of our faculty developed a new approach which formally models locality of reference, and thus can better explain the performance of paging algorithms.

          Problem complexity

          When efficient solutions appear difficult, negative results can sometimes provide very helpful guidance. Two major types of results are possible here.

          • In some cases one can actually prove that, under some model, the problem does not admit solution without a certain level of resources.
          • For many problems, good bounds of the above type are not available, but the problem can be shown to be equivalent in complexity to some well-known class of problems. For example, if a problem is NP-complete it cannot be solved in polynomial time unless P=NP, which is a major open question.

          Such results can save wasted effort by researchers, and in some cases might also suggest that algorithms from a different model should be considered.


          Department of Computer Science
          University of California, Irvine, CA 92697-3425
          http://vision.ics.uci.edu/people.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          Current Members:
          Raúl Díaz's picture
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          Golnaz Ghiasi's picture
          Golnaz Ghiasi
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          Maryam Khademi
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          Bailey Kong
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          Shu Kong's picture
          Shu Kong
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          Minhaeng Lee's picture
          Minhaeng Lee
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          Phuc Nguyen's picture
          Phuc Nguyen
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          James Supančič's picture
          James Supančič
          PhD Student
          Charless Fowlkes's picture
          Charless Fowlkes
          Associate Professor
          Affiliates:
          Ramesh Jain's picture
          Ramesh Jain
          Professor
          Aditi Majumder's picture
          Aditi Majumder
          Associate Professor
          Past Members:
          Hyeoungho Bae's picture
          Hyeoungho Bae
          PhD Student
          (2008-2013)
          Evgeniy Bart's picture
          Evgeniy Bart
          Postdoc
          (2005-2007)
          Yihang Bo's picture
          Yihang Bo
          Visiting PhD Student
          (2010-2010)
          Levi Boyles's picture
          Levi Boyles
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          (2009-2014)
          Chaitanya Desai's picture
          Chaitanya Desai
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          (2008-2012)
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          Sam Hallman
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          (2010-2015)
          Mohsen Hejrati's picture
          Mohsen Hejrati
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          (2010-2015)
          Sangeeta Jha's picture
          Sangeeta Jha
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          (2009-2010)
          Ragib Morshed's picture
          Ragib Morshed
          PhD Student
          (2010-2011)
          Dennis Park's picture
          Dennis Park
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          (2009-2014)
          Goutham Patnaik's picture
          Goutham Patnaik
          MS Student
          (2008-2009)
          Hamed Pirsiavash's picture
          Hamed Pirsiavash
          PhD Student
          (2007-2012)
          Ian Porteous's picture
          Ian Porteous
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          (2005-2010)
          Deva Ramanan's picture
          Deva Ramanan
          Associate Professor
          (2007-2015)
          Grégory Rogez's picture
          Grégory Rogez
          Postdoc
          (2013-2015)
          Tony Tran's picture
          Tony Tran
          MS Student
          (2008-2009)
          Carl Vondrick's picture
          Carl Vondrick
          Undergraduate
          (2008-2011)
          Shaofei Wang's picture
          Shaofei Wang
          MS Student
          (2011-2013)
          Max Welling's picture
          Max Welling
          Professor
          (2007-2015)
          Songfan Yang's picture
          Songfan Yang
          Postdoc
          (2014-2014)
          Yi Yang's picture
          Yi Yang
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          (2008-2013)
          Julian Yarkony's picture
          Julian Yarkony
          PhD Student
          (2007-2012)
          Xiangxin Zhu's picture
          Xiangxin Zhu
          PhD Student
          (2010-2014)
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/projects.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          icon Biological Image Analysis
          Charless Fowlkes, Sam Hallman, Julian Yarkony
          icon Ecological Statistics
          Charless Fowlkes, Max Welling
          icon Grouping and Mid-level Vision
          Evgeniy Bart, Yihang Bo, Charless Fowlkes, Golnaz Ghiasi, Sam Hallman, Ragib Morshed, Ian Porteous, Deva Ramanan, Max Welling, Yi Yang, Julian Yarkony
          icon Object Recognition
          Chaitanya Desai, Charless Fowlkes, Golnaz Ghiasi, Sam Hallman, Minhaeng Lee, Dennis Park, Hamed Pirsiavash, Deva Ramanan, Carl Vondrick, Shaofei Wang, Max Welling, Songfan Yang, Yi Yang, Xiangxin Zhu
          icon People and Activities
          Yihang Bo, Chaitanya Desai, Charless Fowlkes, Golnaz Ghiasi, Maryam Khademi, Hamed Pirsiavash, Deva Ramanan, Grégory Rogez, James Supančič, Carl Vondrick, Yi Yang
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/courses.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          • CS 116 - Intro to Computer Vision and Computational Photography
          • CS 117 - Project in Computer Vision
          • CS 216 - Image Understanding
          • CS 217 - Geometry and Light
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/links.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

          • home
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          Related research in ICS:
          • Center for Machine Learning
          • Computer Graphics Lab
          Vision research in other departments at UCI:
          • Computer Vision Lab (ECE)
          • Movement and Vision Lab (CogSci)
          • Visual Perception and Neuroimaging Lab (CogSci)
          Nearby Colleagues:
          • Computational Vision at Caltech
          • UCSD Computer Vision Laboratory
          • Statistical visual computing laboratory at UCSD
          • UCLA Vision Lab
          • UCLA Center for Image and Vision Science
          • UCSB Vision Research Lab
          • UC Berkeley Computer Vision Group
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/datasets/index.html Computational Vision | ICS | UC Irvine

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          Ground-truth and algorithm results

          • Xiangxin Zhu, Carl Vondrick, Deva Ramanan, Charless Fowlkes. Do we need more training data or better models for object detection?, BMVC, 2012.

            PASCAL VOC 10x training data (~1.9GB) [tar]

          • Yihang Bo and Charless Fowlkes. Shape-based Pedestrian Parsing. CVPR, 2011.

            Pedestrian parsing dataset [tar.gz]

            HumanEVA segment annotations[tar.gz]

          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/index.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          Welcome to the vision group in ICS at UC Irvine.

          Our lab studies computational vision, seeking to understand both the information processing capabilities of biological visual systems and develop computer vision systems. We are interested theoretical questions as well as practical applications ranging from motion capture to biological image analysis.



          Prospective Students:

          We are actively recruiting qualified undergradute and graduate students who are interested in pursuing research in computer vision. Current students are encouraged to sit in on our weekly meetings. Applicants interested in pursuing a graduate degree should apply via ICS Graduate Admissions and clearly indicate their research interests. The yearly deadline is December 15th.




          Group dinner, spicy Chinese food!


          Golnaz and Yi presenting at CVPR 2014


          Yi and Sam presenting at CVPR 2010


          Shopping for hats in San Fransisco


          Face Detection


          Pose Estimation


          Face Recognition + Augmented Reality?

          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/events.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

          • home
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          Weekly meetings: TBA

          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/contact.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          We are located in Donald Bren Hall on the UC Irvine Campus.

          Directions
          • » From LAX (42 miles from UCI)
          • » From John Wayne (5 miles from UCI)
          • » From major freeways (405, 73, 55, 5)
          Campus Map
          • » Campus map of UCI (PDF)
          • The Information and Computer Science building is number 314.
          Parking
          • » UCI guest parking information
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://vision.ics.uci.edu/publications.html Computational Vision | ICS | UC Irvine

          Computational Vision at UC Irvine  small eye

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          2015
          icon
          Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images
          Michael Chiang, Sam Hallman, Amanda Cinquin, Nabora Mochel, Adrian Paz, Shimako Kawauchi, Anne Calof, Ken Cho, Charless Fowlkes, Oliver Cinquin
          BMC Bioinformatics, 2015, 16:397.
          [pdf]
          icon
          Depth-based hand pose estimation: data, methods, and challenges
          James Supančič, Grégory Rogez, Yi Yang, Jamie Shotton, Deva Ramanan
          IEEE International Conference on Computer Vision, 2015.
          [pdf]
          icon
          Occlusion Coherence: Detecting and Localizing Occluded Faces
          Golnaz Ghiasi, Charless Fowlkes
          arXiv:, 2015, abs/1506.08347.
          [pdf]
          icon
          Whole-body tissue stabilization and selective extractions via tissue-hydrogel hybrids for high-resolution intact circuit mapping and phenotyping
          Jennifer Treweek, Ken Chan, Nicholas Flytzanis, Bin Yang, Benjamin Deverman, Alon Greenbaum, Antti Lignell, Cheng Xiao, Long Cai, Mark Ladinsky, Pamela Bjorkman, Charless Fowlkes, Viviana Gradinaru
          Nature Protocols, 2015, 10, 11, 1860--1896.
          [pdf]
          icon
          Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks
          Chunshui Cao, Xianming Liu, Yi Yang, Yinan Yu, Jiang Wang, Zilei Wang, Yongzhen Huang, Liang Wang, Chang Huang, Wei Xu, Deva Ramanan, Thomas Huang
          IEEE International Conference on Computer Vision, 2015.
          [pdf]
          icon
          Do We Need More Training Data?
          Xiangxin Zhu, Carl Vondrick, Charless Fowlkes, Deva Ramanan
          International Journal of Computer Vision, 2015, 1--17.
          [pdf]
          icon
          First-Person Pose Recognition using Egocentric Workspaces
          Grégory Rogez, James Supančič, Deva Ramanan
          CVPR, 2015.
          [pdf]
          icon
          Planar Ultrametrics for Image Segmentation
          Julian Yarkony, Charless Fowlkes
          Neural Information Processing Systems (NIPS), 2015.
          [pdf]
          icon
          Oriented Edge Forests for Boundary Detection
          Sam Hallman, Charless Fowlkes
          CVPR, 2015.
          [pdf]
          icon
          Using segmentation to predict the absence of occluded parts
          Golnaz Ghiasi, Charless Fowlkes
          British Machine Vision Conference (BMVC), 2015.
          [pdf]
          icon
          Hierarchical Planar Correlation Clustering for Cell Segmentation
          Julian Yarkony, Chong Zhang, Charless Fowlkes
          Energy Minimization Methods in Computer Vision and Pattern Recognition, 2015, 492--504.
          [pdf]
          icon
          Understanding Everyday Hands in Action from RGB-D Images
          Grégory Rogez, James Supančič, Deva Ramanan
          IEEE International Conference on Computer Vision, 2015.
          [pdf]
          icon
          Multi-scale recognition with DAG-CNNs
          Songfan Yang, Deva Ramanan
          IEEE International Conference on Computer Vision, 2015.
          [pdf]
          icon
          Articulated Pose Estimation With Tiny Synthetic Videos
          Dennis Park, Deva Ramanan
          The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015.
          [pdf]
          icon
          Lifting {GIS} Maps into Strong Geometric Context
          Raul Diaz, Minhaeng Lee, Jochen Schubert, Charless Fowlkes
          arXiv:, 2015, abs/1507.03698.
          [pdf]
          icon
          A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate
          Max Staller, Charless Fowlkes, Meghan Bragdon, Zeba Wunderlich, Javier Estrada, Angela DePace
          Development, 2015, 142, 3, 587--596.
          [pdf]
          icon
          Learning Optimal Parameters for Multi-target Tracking
          Shaofei Wang, Charless Fowlkes
          British Machine Vision Conference (BMVC), 2015.
          [pdf]
          2014
          icon
          Capturing long-tail distributions of object subcategories
          Xiangxin Zhu, Dragomir Anguelov, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, 915--922.
          [pdf]
          icon
          Parsing videos of actions with segmental grammars
          Hamed Pirsiavash, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, 612--619.
          [pdf]
          icon
          Fast Convolutional Sparse Coding (FCSC)
          Bailey Kong, Charless Fowlkes
          UC Irvine, 2014.
          [pdf]
          icon
          Analysis by synthesis: 3d object recognition by object reconstruction
          Mohsen Hejrati, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, 2014, 2449--2456.
          [pdf]
          icon
          Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model
          Golnaz Ghiasi, Charless Fowlkes
          CVPR, 2014.
          [pdf]
          icon
          3D Hand Pose Detection in Egocentric {RGB-D} Images
          Gr{\'{e}}gory Rogez, James III, Maryam Khademi, Jos{\'{e}} Montiel, Deva Ramanan
          CoRR, 2014, abs/1412.0065.
          [pdf]
          icon
          Parsing Occluded People
          Golnaz Ghiasi, Yi Yang, Deva Ramanan, Charless Fowlkes
          CVPR, 2014.
          [pdf]
          icon
          Microsoft COCO: Common objects in context
          Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C Zitnick
          Computer Vision--ECCV 2014, 2014, 740--755.
          [pdf]
          icon
          Learning Multi-target Tracking with Quadratic Object Interactions
          Shaofei Wang, Charless Fowlkes
          arXiv:, 2014, abs/1412.2066.
          [pdf]
          2013
          icon
          Articulated pose estimation with flexible mixtures-of-parts
          Yi Yang, Deva Ramanan
          IEEE TPAMI, 2013.
          [pdf]
          icon
          Egovision4Health - Assessing Activities of Daily Living from a Wearable RGB-D Camera for In-Home Health Care Applications
          Grégory Rogez, Deva Ramanan, J.M.M. Montiel
          ERCIM News, 2013, 2013, 95, 18-19.
          [pdf]
          icon
          Detecting Dynamic Objects with Multi-View Background Subtraction
          Raúl Díaz, Sam Hallman, Charless Fowlkes
          ICCV, 2013.
          [pdf]
          icon
          Efficiently Scaling up Crowdsourced Video Annotation - A Set of Best Practices for High Quality, Economical Video Labeling
          Carl Vondrick, Donald Patterson, Deva Ramanan
          International Journal of Computer Vision, 2013, 101, 1, 184-204.
          [pdf]
          icon
          Accurate Motion Deblurring using Camera Motion Tracking and Scene Depth
          Hyeoungho Bae, Charless Fowlkes, Pai Chou
          IEEE Workshop on Applications of Computer Vision (WACV), 2013.
          [pdf]
          icon
          Self-paced learning for long-term tracking
          James Supančič, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 2013, 2379--2386.
          [pdf]
          icon
          Monocular 3-D Gait Tracking in Surveillance Scenes
          Grégory Rogez, Jonathan Rihan, J.J. Guerrero, Carlos Orrite
          Cybernetics, IEEE Transactions on, 2013, PP, 99.
          [pdf]
          2012
          icon
          Detecting Actions, Poses, and Objects with Relational Phraselets
          Chaitanya Desai, Deva Ramanan
          ECCV (4), 2012, 158-172.
          [pdf]
          icon
          Face detection, pose estimation and landmark estimation in the wild
          Xiangxin Zhu, Deva Ramanan
          IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
          [pdf]
          icon
          Recognizing proxemics in personal photos
          Yi Yang, Simon Baker, Anitha Kannan, Deva Ramanan
          CVPR, 2012, 3522-3529.
          [pdf]
          icon
          Steerable Part Models
          Hamed Pirsiavash, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012.
          [pdf]
          icon
          Discriminative Decorrelation for Clustering and Classification
          Bharath Hariharan, Jitendra Malik, Deva Ramanan
          ECCV (4), 2012, 459-472.
          [pdf]
          icon
          Quantitative Analysis of Axonal Transport by Using Compartmentalized and Surface Micropatterned Culture of Neurons
          Hyung Kim, Jeong Park, Jae Byun, Wayne Poon, Carl Cotman, Charless Fowlkes, Noo Jeon
          ACS Chemical Neuroscience, 2012, 3, 6, 433--438.
          [pdf]
          icon
          Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification
          David Keator, James Fallon, Anita Lakatos, Charless Fowlkes, Steven Potkin, Alexander Ihler
          Human Brain Mapping, 2012.
          [pdf]
          icon
          Detecting Activities of Daily Living in First-person Camera Views
          Hamed Pirsiavash, Deva Ramanan
          Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012.
          [pdf]
          icon
          Analyzing 3D Objects in Cluttered Images
          Mohsen Hejrati, Deva Ramanan
          Advances in Neural Information Processing Systems 25, 2012, 602--610.
          [pdf]
          icon
          Patch Mosaic for Fast Motion Deblurring
          Hyeoungho Bae, Charless Fowlkes, Pai Chou
          Asian Conference on Computer Vision (ACCV), 2012.
          [pdf]
          icon
          Fast Human Pose Detection Using Randomized Hierarchical Cascades of Rejectors
          Grégory Rogez, Jonathan Rihan, Carlos Orrite, Philip Torr
          International Journal of Computer Vision, 2012, 99, 1, 25-52.
          [pdf]
          icon
          Fast Planar Correlation Clustering for Image Segmentation
          Julian Yarkony, Alexander Ihler, Charless Fowlkes
          ECCV (6), 2012, 568-581.
          [pdf]
          icon
          Do we need more training data or better models for object detection?
          Xiangxin Zhu, Carl Vondrick, Deva Ramanan, Charless Fowlkes
          British Machine Vision Conference (BMVC), 2012.
          [pdf]
          2011
          icon
          Layered Object Models for Image Segmentation
          Yi Yang, Sam Hallman, Deva Ramanan, Charless Fowlkes
          IEEE TPAMI, 2011.
          [pdf]
          icon
          Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
          Deva Ramanan, Simon Baker
          IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2011.
          [pdf]
          icon
          A Large-scale Benchmark Dataset for Event Recognition in Surveillance Video
          Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, C.-C. Chen, Jong Lee, Saurajit Mukherjee, J. Aggarwal, Hyungtae Lee, Larry Davis, Eran Swears, Xioyang Wang, Qiang Ji, Kishore Reddy, Mubarak Shah, Carl Vondrick, Hamed Pirsiavash, Deva Ramanan, Jenny Yuen, Antonio Torralba, Bi Song, Anesco Fong, Amit Roy-Chowdhury, Mita Desai
          CVPR, 2011.
          [pdf]
          icon
          Video Annotation and Tracking with Active Learning
          Carl Vondrick, Deva Ramanan
          NIPS, 2011, 28-36.
          [pdf]
          icon
          Contour Detection and Hierarchical Image Segmentation
          Pablo Arbelaez, Michael Maire, Charless Fowlkes, Jitendra Malik
          IEEE PAMI, 2011, 33(5).
          [pdf]
          icon
          N-best maximal decoders for part models
          Dennis Park, Deva Ramanan
          ICCV, 2011, 2627-2634.
          [pdf]
          icon
          Shirnk-Film Configurable Multiscale Wrinkles for Functional Alignment of Human Embryonic Stem Cells and their Cardiac Derivatives
          Aaron Chen, Deborah Lieu, Lauren Freshauf, Valerie Lew, Himanshu Sharma, Jianxian Wang, Diep Nguyen, Ioannis Karakikes, Roger Hajjar, Ajay Gopinathan, Elliot Botvinick, Charless Fowlkes, Ronald Li, Michelle Khine
          Advanced Materials, 2011, 23, 5785-5791.
          [pdf]
          icon
          Multi-scale Biomimetic Topography for the Alignment of Neonatal and Embryonic Stem Cell-derived Heart Cells
          Jesus Luna, Jesus Ciriza, Marcos Ojeda-Garcia, Marco Kong, Anthony Herren, Deborah Lieu, Ronald Li, Charless Fowlkes, Michelle Khine, Kara McCloskey
          Tissue Engineering: Part C, 2011, 17(5).
          [pdf]
          icon
          Tightening MRF Relaxations with Planar Subproblems
          Julian Yarkony, Ragib Morshed, Alex Ihler, Charless Fowlkes
          UAI, 2011.
          [pdf]
          icon
          Shape-based Pedestrian Parsing
          Yihang Bo, Charless Fowlkes
          CVPR, 2011.
          [pdf]
          icon
          Analysis of Gap Gene Reguation in a 3D Organism-Scale Model of the Drosophila melanogaster Embryo
          James Hengenius, Michael Gribskov, Ann Rundell, Charless Fowlkes, David Umulis
          PLoS ONE, 2011, 6, e26797.
          [pdf]
          icon
          Discriminative models for multi-class object layout
          Chaitanya Desai, Deva Ramanan, Charless Fowlkes
          International Journal of Computer Vision, 2011.
          [pdf]
          icon
          A Conserved Developmental Patterning Network Produces Quantitatively Different Output in Multiple Species of Drosophila
          Charless Fowlkes, Kelly Eckenrode, Meghan Bragdon, Miriah Meyer, Zeba Wunderlich, Lisa Simirenko, Cris Luengo, Soile Keränen, Clara, Henriquez, David Knowles, Mark Biggin, Michael Eisen, Angela DePace
          PLoS Genetics, 2011, 7, e1002346.
          [pdf]
          icon
          Articulated pose estimation with flexible mixtures-of-parts
          Yi Yang, Deva Ramanan
          CVPR, 2011.
          [pdf]
          icon
          Planar Cycle Covering Graphs
          Julian Yarkony, Alex Ihler, Charless Fowlkes
          UAI, 2011.
          [pdf]
          icon
          Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation
          Yutian Chen, Andrew Gelfand, Charless Fowlkes, Max Welling
          Proc. of the International Confernece on Computer Vision, 2011.
          [pdf]
          icon
          Statistical Tests for Optimization Efficiency
          Levi Boyles, Anoop Balan, Deva Ramanan, Max Welling
          NIPS, 2011, 2196-2204.
          [pdf]
          icon
          Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects
          Hamed Pirsiavash, Deva Ramanan, Charless Fowlkes
          IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2011.
          [pdf]
          icon
          Where's Waldo: Matching People in Images of Crowds
          Rahul Garg, Deva Ramanan, Steve Seitz, Noah Snavely
          IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
          [pdf]
          2010
          icon
          Layered Object Detection for Multi-Class Segmentation
          Yi Yang, Sam Hallman, Deva Ramanan, Charless Fowlkes
          CVPR, 2010.
          [pdf]
          icon
          Discriminative models for static human-object interactions
          Chaitanya Desai, Deva Ramanan, Charless Fowlkes
          IEEE Conference on Computer Vision and Pattern Recognition, Workshop on Structured Prediction, 2010.
          [pdf]
          icon
          Nonparametric identification of regulatory interactions from spatial and temporal gene expression data
          Anil Aswani, Soile Keräen, James Brown, Charless Fowlkes, David Knowles, Mark Biggin, Peter Bickel, Claire Tomlin
          BMC Bioinformatics, 2010, 11:413.
          [pdf]
          icon
          SEMARTCam Scheduler - Semantics Driven Real-Time Data Collection from Indoor Camera Networks to Maximize Event Detection
          Ronen Vaisenberg, Sharad Mehrotra, Deva Ramanan
          Journal of Real-Time Image Processing (JRTIP), 2010.
          [pdf]
          icon
          Covering Trees and Lower-bounds on Quadratic Assignment
          Julian Yarkony, Charless Fowlkes, Alex Ihler
          CVPR, 2010.
          [pdf]
          icon
          Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces
          Carl Vondrick, Deva Ramanan, Donald Patterson
          Proc. of the European Conference on Computer Vision, 2010.
          [pdf]
          icon
          Robust Tracking of the Upper Limb for Functional Stroke Assessment
          Sonya Allin, Nancy Baker, Emily Eckel, Deva Ramanan
          IEEE Transactions on Neural Systems and Rehabilitation Engineering (NSRE), 2010.
          [pdf]
          icon
          Natural-scene statistics predict how the figure-ground cue of convexity affects human depth perception
          Johannes Burge, Charless Fowlkes, Martin Banks
          Jounral of Neuroscience, 2010, 30(21), 7269-7280.
          [pdf]
          icon
          Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
          Oliver Rübel, Gunther Weber, Min-Yu Huang, E. Bethel, Mark Biggin, Charless Fowlkes, Cris Hendriks, Soile Keränen, Michael Eisen, David Knowles, Jitendra Malik, Hans Hagen, Bernd Hamann
          IEEE Transactions on Computational Biology and Bioinformatics, 2010, 7(1), 64-79.
          [pdf]
          icon
          Multiresolution models for object detection
          Dennis Park, Deva Ramanan, Charless Fowlkes
          ECCV, 2010.
          [pdf]
          2009
          icon
          From Contours to Regions: An Empirical Evaluation
          Pablo Arbelaez, Michael Maire, Charless Fowlkes, Jitendra Malik
          CVPR, 2009.
          [pdf]
          icon
          Bilinear classifiers for visual recognition
          Hamed Pirsiavash, Deva Ramanan, Charless Fowlkes
          Neural Info. Proc. Systems (NIPS), 2009.
          [pdf]
          icon
          Discriminative models for multi-class object layout
          Chaitanya Desai, Deva Ramanan, Charless Fowlkes
          IEEE International Conference on Computer Vision, 2009.
          [pdf]
          icon
          Local Distance Functions: A Taxonomy, New Algorithms, and an Evaluation
          Deva Ramanan, Simon Baker
          International Conference on Computer Vision (ICCV), 2009.
          [pdf]
          icon
          Exploiting Semantics for Scheduling Data Collection From Sensors in Real-Time to Maximimize Event Detection
          Ronen Vaisenberg, Sharad Mehrotra, Deva Ramanan
          Multimedia and Computer Networks (MMCN), 2009.
          [pdf]
          icon
          Visual Exploration of Three-dimensional Gene Expression Using Physical Views and Linked Abstract Views
          Gunther Weber, Oliber Rubel, Min-Yu Huang, Angela DePace, Charless Fowlkes, Soile Keranen, Cris Hendriks, Hans Hagen, David Knowles, Jitendra Malik, Mark Biggin, Bernd Hamann
          IEEE Transactions on Computational Biology and Bioinformatics, 2009, 6(2), 296-309.
          [pdf]
          icon
          Object Detection with Discriminatively Trained Part-Based Models
          Pedro Felzenszwalb, Ross Girshick, David McAllester, Deva Ramanan
          IEEE Pattern Analysis and Machine Intelligence (PAMI), 2009.
          [pdf]
          2008
          icon
          Increasing the density of active appearance models
          Kannan Ramnath, Simon Baker, Ian Matthews, Deva Ramanan
          IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
          [pdf]
          icon
          Using Contours to Detect and Localize Junctions in Natural Images
          Michael Maire, Pablo Arbelaez, Charless Fowlkes, Jitendra Malik
          CVPR, 2008.
          [pdf]
          icon
          Unsupervised Learning of Visual Taxonomies
          Evgeniy Bart, Ian Porteous, Pietro Perona, Max Welling
          CVPR, 2008.
          [pdf]
          icon
          Learning Probabilistic Models for Contour Completion.
          Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
          IJCV, 2008, 77, 47-63.
          [pdf]
          icon
          A Quantitative Spatiotemporal Atlas of Gene Expression in the Drosohpila Blastoderm
          Charless Fowlkes, Cris Luengo, Soile Keränen, Gunther Weber, Oliver Rübel, Min-Yu, Huang, Sohail Chatoor, Lisa Simirenko, Angela DePace, Clara, Henriquez, Amy Beaton, Richard Weiszmann, Susan Celniker, Bernd Hamann, David Knowles, Mark Biggin, Michael Eisen, Jitendra Malik
          Cell, 2008, 133, 364-374.
          [pdf]
          icon
          A Discriminatively Trained, Multiscale, Deformable Part Model
          Pedro Felzenszwalb, David McAllester, Deva Ramanan
          IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
          [pdf]
          2007
          icon
          Hybrid Generative-Discriminative Object Recognition
          Alex Holub, Max Welling, Pietro Perona
          Int. J. Computer Vision, 2007.
          [pdf]
          icon
          Assessment of Post Stroke Functioning Using Machine Vision
          Sonya Allin, Deva Ramanan
          IAPR Conference on Machine Vision Applications (MVA), 2007.
          [pdf]
          icon
          Local figure-ground cues are valid for natural images
          Charless Fowlkes, David Martin, Jitendra Malik
          Journal of Vision, 2007, 7, 8, 1--9.
          [pdf]
          icon
          Leveraging Archival Video for Building Face Datasets
          Deva Ramanan, Simon Baker, Sham Kakade
          ICCV, 2007, 141.
          [pdf]
          icon
          Tracking People by Learning Their Appearance
          Deva Ramanan, D.A. Forsyth, Andrew Zisserman
          PAMI, 2007, 29, 1, 65-81.
          [pdf]
          icon
          Learning to parse images of articulated bodies
          Deva Ramanan
          Advances in Neural Information Processing Systems 19, 2007, 1129--1136.
          [pdf]
          icon
          Using Segmentation to Verify Object Hypotheses
          Deva Ramanan
          CVPR, 2007, 1-8.
          [pdf]
          2006
          icon
          Building Models of Animals from Video
          Deva Ramanan, D.A. Forsyth, Kobus Barnard
          PAMI, 2006, 28, 8, 1319-1334.
          [pdf]
          icon
          Topographic Product Models Applied to Natural Scene Statistics
          Simon Osindero, Max Welling, Geoffrey Hinton
          Neural Comput., 2006, 18, 2, 381--414.
          [pdf]
          icon
          Training Deformable Models for Localization
          Deva Ramanan, Cristian Sminchisescu
          CVPR, 2006, I: 206-213.
          [pdf]
          icon
          3D Morphology and Gene Expression in the Drosophila Blastoderm at Cellular Resolution I: Data Acquisition Pipeline
          Cris Hendriks, Soile Keränen, Charless Fowlkes, Lisa Simirenko, Gunther Weber, Clara Henriquez, David Kaszuba, Bernd Hamann, Michael Eisen, Jitendra Malik, Damir Sudar, Mark Biggin, David Knowles
          Genome Biology, 2006, 7:R123.
          [pdf]
          icon
          Inferring nuclear movements from fixed material
          Charless Fowlkes, Jitendra Malik
          UC Berkeley, 2006, UCB//EECS-06-142.
          [pdf]
          icon
          Cue Integration for Figure/Ground Labeling
          Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
          Advances in Neural Information Processing Systems 18, 2006, 1121--1128.
          [pdf]
          icon
          3D Morphology and Gene Expression in the Drosophila Blastoderm at Cellular Resolution II: Dynamics
          Soile Keränen, Charless Fowlkes, Cris Hendriks, Damir Sudar, David Knowles, Jitendra Malik, Mark Biggin
          Genome Biology, 2006, 7:R124.
          [pdf]
          icon
          PointCloudXplore: Visual Analysis of 3D Gene Expression Data Using Physical Views and Parallel Coordinates
          O. Rubel, G.H. Weber, S.V.E. Keranen, Charless Fowlkes, C.L. Hendriks, L. Simirenko, N.Y. Shah, M.B. Eisen, M.D. Biggin, H. Hagen, D. Sudar, J. Malik, D.W. Knowles, B. Hamann
          Eurographics/IEEE VGTC Symposium on Visualization, 2006, 203--210.
          [pdf]
          icon
          The Rate Adapting Poisson Model for Information Retrieval and Object Recognition
          P. Gehler, A. Holub, Max Welling
          Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 2006, 337-344.
          [pdf]
          icon
          Figure/Ground Assignment in Natural Images
          Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
          ECCV, 2006, II: 614-627.
          [pdf]
          2005
          icon
          Combining Generative Models and Fisher Kernels for Object Recognition
          Alex Holub, Max Welling, Pietro Perona
          ICCV, 2005, I: 136-143.
          [pdf]
          icon
          Mid-level cues improve boundary detection
          Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
          UC Berkeley, 2005, UCB//CSD-05-1382.
          [pdf]
          icon
          Strike a Pose: Tracking People by Finding Stylized Poses
          Deva Ramanan, D.A. Forsyth, Andrew Zisserman
          CVPR, 2005, I: 271-278.
          [pdf]
          icon
          Detecting, Localizing and Recovering Kinematics of Textured Animals
          Deva Ramanan, D.A. Forsyth, Kobus Barnard
          CVPR, 2005, II: 635-642.
          [pdf]
          icon
          Tracking People and Recognizing Their Activities
          Deva Ramanan, D.A. Forsyth, Andrew Zisserman
          CVPR, 2005, II: 1194.
          [pdf]
          icon
          Computational studies of human motion: part 1, tracking and motion synthesis
          David Forsyth, Okan Arikan, Leslie Ikemoto, James O'Brien, Deva Ramanan
          Found. Trends. Comput. Graph. Vis., 2005, 1, 2-3, 77--254.
          [pdf]
          icon
          Scale-Invariant Contour Completion Using Conditional Random Fields
          Xiaofeng Ren, Charless Fowlkes, Jitendra Malik
          ICCV, 2005, II: 1214-1221.
          [pdf]
          icon
          Registering Drosophila Embryos at Cellular Resolution to Build a Quantitative 3D Atlas of Gene Expression Patterns and Morphology
          Charless Fowlkes, Cris Hendriks, Soile Keränen, Mark Biggin, David Knowles, Damir Sudar, Jitendra Malik
          CSB 2005 Workshop on BioImage Data Minning and Informatics, 2005.
          [pdf]
          2004
          icon
          Automatic Annotation of Everyday Movements
          Deva Ramanan, David Forsyth
          Advances in Neural Information Processing Systems 16, 2004.
          [pdf]
          icon
          Learning to detect natural image boundaries using local brightness, color, and texture cues
          David Martin, Charless Fowlkes, Jitendra Malik
          IEEE PAMI, 2004, 26, 5, 530-549.
          [pdf]
          icon
          Spectral Grouping Using the Nyström Method
          Charless Fowlkes, Serge Belongie, Fan Chung, Jitendra Malik
          IEEE PAMI, 2004, 26, 2, 214-225.
          [pdf]
          icon
          How Much Does Globalization Help Segmentation?
          Charless Fowlkes, Jitendra Malik
          UC Berkeley, 2004, UCB//CSD-04-1340.
          [pdf]
          2003
          icon
          Learning Affinity Functions for Image Segmentation: Combining Patch-Based and Gradient-Based Approaches
          Charless Fowlkes, David Martin, Jitendra Malik
          CVPR, 2003, II: 54-61.
          [pdf]
          icon
          Finding and tracking people from the bottom up
          Deva Ramanan, D.A. Forsyth
          CVPR, 2003, II: 467-474.
          [pdf]
          icon
          Using temporal coherence to build models of animals
          Deva Ramanan, D.A. Forsyth
          ICCV, 2003, 338-345.
          [pdf]
          2002
          icon
          Learning to Detect Natural Image Boundaries Using Brightness and Texture
          David, Martin, Charless, Fowlkes, Jitendra Malik
          Advances in Neural Information Processing Systems, 2002.
          [pdf]
          icon
          Spectral Partitioning with Indefinite Kernels Using the Nyström Extension
          Serge Belongie, Charless Fowlkes, Fan Chung, Jitendra Malik
          ECCV, 2002, III: 531 ff..
          [pdf]
          icon
          Extracting Global Structure from Gene Expression Profiles
          Charless Fowlkes, Qun Shan, Serge Belongie, Jitendra Malik
          Methods of Microarray Data Analysis II, 2002.
          [pdf]
          2001
          icon
          Efficient Spatiotemporal Grouping Using the Nyström Method
          Charless Fowlkes, Serge Belongie, Jitendra Malik
          CVPR, 2001, I:231-238.
          [pdf]
          icon
          A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics
          David Martin, Charless Fowlkes, Doron Tal, Jitendra Malik
          ICCV, 2001, II: 416-423.
          [pdf]
          2000
          icon
          Nonlinear Image Interpolation Through Extended Permutation Filters
          Deva Ramanan, Kenneth Barner
          ICIP, 2000, Vol I: 912-915.
          [pdf]
          icon
          Towards Automatic Discovery of Object Categories
          Markus Weber, Max Welling, Pietro Perona
          CVPR, 2000, II: 101-108.
          [pdf]
          icon
          Unsupervised Learning of Models for Recognition
          Markus Weber, Max Welling, Pietro Perona
          ECCV, 2000, I: 18-32.
          [pdf]
          icon
          Viewpoint-Invariant Learning and Detection of Human Heads
          Markus Weber, Wolfgang Einhaeuser, Max Welling, Pietro Perona
          AFGR, 2000, 20-27.
          [pdf]
          1999
          icon
          Unsupervised Learning of Models for Recognition
          Markus Weber, Max Welling, Pietro Perona
          JNSC, 1999.
          [pdf]
          Computational Vision | School of Information and Computer Sciences | UC Irvine
          © 2007-2015 UC Irvine
          http://graphics.ics.uci.edu/ iGravi

          iGravi

          Interactive Graphics & Visualization

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          News

          • Aug 2015
            Best Paper Award at 3DTV 2015

            The Paper "High-Resolution Lighting of 3D Reliefs Using a Network of Projectors and Cameras" got the best paper award in 3DTV 2015.  (Link)

             Authors: Behzad Sajadi, Mahdi Abbaspour Tehrani, Mehdi Rahimzadeh, Aditi Majumder.

          • May 2015
            EG paper presented in May 2015 on multi-spectral display

            Content-Independent Multi-Spectral Display Using Superimposed Projections
            Yuqi Li, Aditi Majumder, Dongming Lu, M. Gopi
            EUROGRAPHICS, 2015

          • Mar 2015
            VR Demo on multi-projector displays of arbitrary shape, resolution and scale becomes a big success in VR 2015
          • March 2015
            Paper presented in IEEE VR
          • Mar 2015
            Paper accepted to EUROGRAPHICS 2015

            Content-Independent Multi-Spectral Display Using Superimposed Projections
            Yuqi Li, Aditi Majumder, Dongming Lu, M. Gopi
            EUROGRAPHICS, 2015

          • Oct 2014
            Paper accepted to Pacific Graphics 2014

            Uddipan's paper titled "Finding Feature Similarites Between Geometric Trees" have been accepted to Pacific Graphics 2014.

          • Sep 2013
            Paper accepted in GD/SPM13

            The paper by Shanaz Mistry, Niranjan U N and M. Gopi on fitting 2D polygons using a data structure representing the cavities and the protrusions of a polygon is to be presented at the SIAM Conference on Geometric and Physical Modeling (GD/SPM13).

          • Dec 2012
            Uddipan and Gopi's paper won the sole best paper award at ICVGIP 2012

            We are ending the year with a big bang! Uddipan and Gopi's paper titled, Tweening Boundary Curves Of Non-Simple Immersions Of A Disk won the sole best paper award at ICVGIP 2012. A big congratulations to both

          • Sep 2012
            UCI projection based display is in the logo for IEEE VR 2013

            It is our great pride to report that the planar multi-projection display, which is currently the attention of a significant amount of research conducted in the iGravi lab, is part of the logo for the top international conference in virtual reality, IEEE Virtual Reality 2013. IEEE VR 2012 was hosted by the iGravi lab in March 2012. It was the most attended conference in the history of IEEE VR. As part of IEEE VR 2012, we had a very successful Open House at Calit2 showcasing all the research projects at iGravi where we also demonstrated this planar multi-projector display. Almost 450 people from around the globe attended the Open House.

          • Sep 2012
            UCI gets unprecedented attention in Siggraph 2012

            We had two papers in Siggraph 2012 from UCI. This is the first time we have had papers in Siggraph from UCI. Our paper on new enhanced resolution projection based displays was one of the four papers in the sole session on Computational Displays -- a very new emerging area in computer graphics and visualilzation community. The other papers were from MIT, Stanford and UC-Berkeley. We are very proud to wrestle a spot amongst such well-know players. The second paper was a joint work with Purdue University and was in the Appearance section. The other papers were from Yale University, MIT, Microsoft Research and HP Labs.

          • Apr 2012
            Paper accepted in Siggraph 2012
             Aditi Majumder

            Aditi
            Majumder

             Gopi Meenakshisundaram

            Gopi
            M.

             Behzad Sajadi

            Behzad
            Sajadi

            Computer Science Professor Aditi Majumder and graduate student Behzad Sajadi's paper is accepted to ACM Siggraph 2012.

            The paper, "Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing," proposes a technique for achieving higher resolution displays using lower resolution projectors.

            ACM Siggraph 2012 is the top international venue for computer graphics researchers and practitioners.

          • Apr 2012
            Paper accepted in TOG and to be presented in Siggraph 2012
             Aditi Majumder

            Aditi
            Majumder

             Behzad Sajadi

            Behzad
            Sajadi

            Computer Science Professor Aditi Majumder and graduate student Behzad Sajadi paper is accepted to TOG 2012 and will be presented at ACM Siggraph 2012.

            The paper, entitled "Fast High-Resolution Appearance Editing Using Superimposed Projections," proposes a technique that superimposes multiple projections onto an object of arbitrary shape and color to produce high resolution appearance changes.

            The full paper can be accessed here: http://www.ics.uci.edu/~bsajadi/files/tog12.pdf.

            ACM Siggraph 2012 is the top international venue for computer graphics researchers and practitioners.

          • Apr 2012
            Paper to be presented in IEEE Visualization 2012
             Aditi Majumder

            Aditi
            Majumder

             Behzad Sajadi

            Behzad
            Sajadi

            Computer Science Professor Aditi Majumder and graduate student Behzad Sajadi will be presenting at the IEEE Visualization 2012 Conference.

            The paper, entitled "Using Patterns to Encode Color Information in Dichromats," explores ways to use patterns to represent color.

            This paper proposes a technique for using patterns to encode color information for individuals with CVD, in particular for dichromats.  Further, since overlaying patterns does not compromise the underlying original colors, it does not hamper the perception of normal trichromats.

            The full paper can be accessed here: http://www.ics.uci.edu/~bsajadi/files/CVD-ColorEnconding.pdf.

          • Mar 2012
            IEEE VR and ACM I3D is hosted by UCI in Orange County

          • Oct 2011
            Prof. Majumder's research appears in UCI main page feature

             Aditi Majumder

            Aditi
            Majumder

            Prof. Majumder's research appears in UCI main page feature

            Prof. Majumder's research was highlighted in the second week of October via a campus level feature on the UCI website (http://www.uci.edu). The feature titled "Developing the Display Technologies of the Future" focused on Prof. Majumder's work on ubiquitous displays which has the potential to be used just about everywhere, from huge domes to small cell phones, from amusement parks to doctors’ exam rooms. This research is funded by a $0.65M 5-year NSF CAREER grant.

          • Oct 2011
            Prof. Majumder becomes nVidia Academic Partner

             Aditi Majumder

            Aditi
            Majumder

            Prof. Majumder becomes nVidia Academic Partner

            Prof. Majumder was awarded the nVidia academic partnership grant. As part of this grant she received high-end GPUs from nVidia worth around $20,000. There are around 70 nVidia academic partners around the world  of whom around 45 are from the United States including Marc Levoy from Stanford University, Markus Gross from ETH Zurich, Gabriel Taubin from Brown University, Ravi Ramamoorthi from UC-Berkeley and David Ebert from Purdue University.

          • Oct 2011
            Prof. Aditi Majumder and Prof. M. Gopi brings in nVidia grants to make UCI a CUDA Teaching Center
             Aditi Majumder

            Aditi
            Majumder

             Gopi Meenakshisundaram

            Gopi
            Meenakshisundaram

            Prof. Aditi Majumder and Prof. M. Gopi brings in nVidia grants to make UCI a CUDA Teaching Center 

            Prof. Majumder and Prof. Gopi's efforts were instrumental in winning an nVidia grant awarding UCI the status of CUDA Teaching Center (CTC). The grant consisted of equipment grant of 46 GTX580 GPUs and C2070 Tesla board which will be used to improve and augment ICS instructional facility and 50% instructional support funding for two quarters to help students in the lab with CUDA programming training. There are several such CTCs around the world including Purdue University, Rochester Institute of Technology, George Mason University, University of Wisconsin Madison, University of Texas Austin, UC-Los Angeles, and UNC Charlotte.

          • Sep 2011
            M. Gopi is the Conference Co-chair of I3D and Aditi Majumder is the General Co-chair of VR

            IEEE VR 2012 is the premier international conference and exhibition on virtual reality.

            You will find the brightest minds, the most innovative research, the leading companies and the most stimulation discussions in the fields of virtual environments, augmented reality, 3D user interfaces, all gathered in Orange County, California during March 4-8 (Sun-Thu), 2012. We invite you to submit your work, show your products and join us for a fascinating week of presentations, exhibits, workshops, tutorials and special events.

            Once again, IEEE VR 2012 is pleased to be co-located with IEEE Symposium on 3D User Interfaces (March 4-5). In addition, this time we are co-locating ACM SIGGRAPH Symposium on Interactive 3D Graphics, 2012 with IEEE VR. ACM I3D will happen just following IEEE VR on March 9-11, 2012 at the same venue. We hope that the co-location of these two conferences with large overlapping domains and interest will bring forth new ways to support cross-fertilization of ideas.

            IEEE VR, hosted by University of California, Irvine, will be held at the Westin South Coast Plaza, Costa Mesa in Orange County, CA. Orange County belongs to the greater Los Angeles (LA) area which is one of the biggest academic and IT centers of USA. UC-Irvine, UC-Los Angeles, University of Southern California, and Cal-State Fullerton are the main academic centers of the area. Los Angeles, Irvine, Westminister and other such towns of the area are the home of thousands of IT companies including gaming (e.g. Blizzard), entertainment (e.g. Disney, Dreamworks, Pixar, Institute for Creative Technologies), and devices (e.g. BenQ, Canon). With the co-location of IEEE 3DUI and ACM I3D, we will provide ample opportunities for cross-fertilization of ideas and technology across different synergistic communities centered around virtual reality.

          • Dec 2010
            Majumder Delivers ISVC Keynote

             Aditi Majumder

            Aditi
            Majumder

            Aditi Majumder, Computer Science Associate Professor, was a keynote speaker at the International Symposium on Visual Computing 2010, a prestigious graphics and vision conference that brings together renowned researchers from all over the world.

            Majumder’s talk, “Ubiquitous Displays: A Distributed Network of Active Displays,” presents her team’s work-in-progress on developing a new display paradigm in which displays are not mere carriers of information, but active members of the workspace — interacting with data, user, environment and other displays. The goal is to integrate such active displays seamlessly with the environment, making them ubiquitous to multiple users and data.

            Majumder’s research aims to make multi-projector displays truly commodity products and easily accessible to the common man. Her significant research contributions include photometric and color registration across multi-projector displays, enabling use of imperfect projectors in tiled displays, and more recently a distributed framework for tiled displays via a distributed network of projector-camera pairs. A 2009 recipient of the NSF CAREER award, she has played a key role in developing the first curved screen multi-projector display being marketed by NEC/Alienware and is an advisor at Disney Imagineering for advances in their projection-based theme park rides.

          • Sep 2010
            Majumder receives Best Paper Award at IEEE CVPR Workshop on PROCAMS
             Aditi Majumder

            Aditi
            Majumder

            Computer science professor Aditi Majumder has been awarded a Best Paper Award at the IEEE Workshop on Projector and Camera Systems (PROCAMS) held at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) in San Francisco. Her paper entitled, "Display Gamut Reshaping for Color Emulation and Balancing" was coauthored by researchers at Ostendo Technologies Ltd.

            Majumder et al present a hardware-assisted 3D gamut reshaping method that handles a gamut expansion in LED based DLP displays in emerging mobile digital light projectors (known commonly as pico-projectors). These projectors use multiple LED/laser sources instead of a singular white lamp providing a larger color gamut. The full abstract and paper can be found here [PDF link].

            The IEEE CVPR Workshop on PROCAMS is one of the top international venues for projector-camera systems researchers and practitioners.

            Majumder's research addresses novel projection based displays and methodologies to register and interact with them - an important problem to both the scientific and entertainment fields. Majumder has developed a suite of mathematical models, methods and software to register and interact with large tiled projection based displays.

          • Apr 2010
            Majumder and Sajadi win Best Paper award at IEEE Virtual Reality 2010
             Aditi Majumder

            Aditi
            Majumder

             Behzad Sajadi

            Behzad
            Sajadi

            Computer Science Professor Aditi Majumder and graduate student Behzad Sajadi have received the Best Paper Award at the IEEE Virtual Reality 2010 conference held in Boston.

            The paper, entitled "Auto-Calibration of Cylindrical Multi-Projector Systems," explores registering multiple projectors on vertically extruded a cylindrical display, which previously was only possible with a calibrated stereo camera.

            This papers shows that using some simple priors, one can achieve multiple projector registration on a cylindrical display using a single uncalibrated camera without any markers on the display. More importantly, the new method enables use of multiple overlapped projectors across corners of a vertically extruded surface with sharp edges. This is of tremendous benefit to virtual reality display systems like CAVEs, that avoided mounting projectors across the corners until today.

            The full paper can be accessed here: http://www.ics.uci.edu/~majumder/docs/VR10.pdf.

            IEEE Virtual Reality 2010 is the top international venue for virtual reality researchers and practitioners.

            Professor Majumder's research addresses how to produce a seamless image on a large-scale tiled display - an important problem to both the scientific and entertainment fields. Majumder has developed a suite of mathematical models, methods and software to correct the geometric, chromatic and luminescent variations that arise when tiling multiple projection displays.

          • Oct 2009
            Majumder, Sajadi receive paper award at IEEE Visualization

             Aditi Majumder

            Aditi
            Majumder

            A paper entitled, "Markerless View-Independent Registration of Multiple Distorted Projectors on Extruded Surfaces Using an Uncalibrated Camera" by Computer Science professor Aditi Majumder and graduate student Behzad Sajadi has won the runner up in the Best Paper Award at the IEEE Visualization 2009 conference held in Atlantic City this month.

            The paper presents the first algorithm to geometrically register multiple projectors in a view-independent manner on a common type of curved surface, vertically extruded surface, using an uncalibrated camera without attaching any obtrusive markers to the display screen. This simple markerless registration has the potential to have a large impact on easy set-up and maintenance of large curved multi-projector displays, common for visualization, edutainment, training and simulation applications.

            IEEE Visualization is the premier annual forum for visualization advances for academia, government, and industry. This event brings together researchers and practitioners with a shared interest in visualization tools, techniques, and technology.

            Majumder's research addresses how to produce a seamless image on a large-scale tiled display - an important problem to both the scientific and entertainment fields. She has developed a suite of mathematical models, methods and software to correct the geometric and color variations that arise when tiling multiple projection displays.

          http://www.ics.uci.edu/community/events/extreme/index.php hitec competition @ the bren school of information and computer sciences
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          Bren school home > Community > Events > hITEC
          hITEC competition

          hitec octane logo

          ABOUT hITEC

          The ICS Technology and Entrepreneurship Competition (hITEC) is the cornerstone of the Bren School entrepreneurship program.

          The competition is designed to foster a spirit of entrepreneurship among Bren School and UC Irvine students, and fuel the development of new technologies that have the potential to positively impact the marketplace.

          Benefits

          Gain experience using your skills to turn an idea into a potentially viable product.
          Compete for $9,000 in prizes.

          Notice

          Teams wishing to move on to the Stradling Yocca Carlson & Rauth Business Plan Competition (BPC) must register in both competitions. Registration in the BPC requires at least one team member be a student of the Merage School of Business.

          h.ITEC:The Student Experience
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          LATEST NEWS

          » Congratulations 2009 hITEC winners
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          The Bren Shcool would like to thank the 2009 hITEC sponsors:

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          http://www.ics.uci.edu/~ronen/Site/Home.html Home
           
           
           

          Ronen Vaisenberg

                                Home        Research        Teaching        Links        MiscResearch.htmlTeaching.htmlLinks.htmlMisc.htmlshapeimage_2_link_0shapeimage_2_link_1shapeimage_2_link_2shapeimage_2_link_3shapeimage_2_link_4
           

          I am currently perusing a Ph.D at the University of California, Irvine in the Donald Bren School of Information & Computer Sciences. My advisor is Prof. Sharad Mehrotra.


          My focus is on problems that relate to the management, extraction and fusion of information from multiple media sources. This area relates the the following established fields: databases and data management, time series data management, statistical databases, model building and classification applied in the context of media (text,image,video) processing.


          The Ph.D dissertation deals with the issues related to the data management support for sentient systems, for various first responding and life preserving applications funded by NSF’s ITR-Rescue (RESponding to Crisis and Unexpected Events) and DHS’s Safire (Situational Awareness for Firefighters).


          A unique approach where hidden semantics, such as statistical dependencies between events, are exploited to enhance the ability to accurately perform event detection from sensors (see MMCN’08 recalibration paper) or scheduling data collection from a large sensor network for the purpose of event detection (see MMCN’09 scheduling paper).

          The work is motivated by real-world emergency-response application needs and will contribute significantly to the emergency-responder’s ability to react to crisis and unexpected events.


          Before I joined Prof. Sharad Mehrotra's research group, I had worked with Prof. Ehud Gudes and Dr. Yuval Elovici for 2 years, the main research topic is secure data management, specifically: database encryption. My previous research experience includes Private and Secure data management, efficient service oriented processing on untrusted co-processors (see master's Thesis and Patent).




          Graduate Student Researcher (Ph.D Student)

          Department of Computer Science

          University of California, Irvine

          2069 Donald Bren Hall

          Irvine CA 92697-3425


          Bio:

           
           
          http://www.ics.uci.edu/community/news/notes/honors_2009.php student honors 2008-09 @ the bren school of information and computer sciences
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          Bren school home > Community > News > Noteworthy achievements
          Student honors 2008-2009

          Bren School Honors/School Awards
          2008-09 Academic Year

          Name Award
          Frederick Adi Cum Laude
          Phi Beta Kappa
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          Outstanding Contribution to Research
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          Phi Beta Kappa
          Dennis Bystritsky Cum Laude
          Campuswide Honors
          Phi Beta Kappa
          Mitchell Dempsey Outstanding Contribution to Research
          Paul Furiani Cum Laude
          Jeffrey Gee Phi Beta Kappa
          Sarah Gibas Magna Cum Laude
          Phi Beta Kappa
          Michael Gould Magna Cum Laude
          Campuswide Honors
          Phi Beta Kappa
          Jan Ignacio Outstanding Service by an Undergraduate
          Andrew Hatfield Cum Laude
          Phi Beta Kappa
          Paul Ngoc Huynh Cum Laude
          Hon San Ivan Ho Outstanding Contribution to Research
          Sean Kocol Magna Cum Laude
          Campuswide Honors
          Timothy Lam Outstanding Contribution to Research
          Rick Kuan Lee Cum Laude
          Cameron Lewis Magna Cum Laude
          Campuswide Honors
          Outstanding Contribution to Research
          Phi Beta Kappa
          Matthew Light Cum Laude
          Leslie Liu Chancellor's Award for Excellence in Undergraduate Research
          Daniel Maas Phi Beta Kappa
          Deeksha Malhotra Outstanding Service by an Undergraduate
          Gabriela Marcu Campuswide Honors
          Outstanding Service by an Undergraduate
          Phi Beta Kappa
          Liane Nakamura Cum Laude
          Campuswide Honors
          Phi Beta Kappa
          David Nguyen Cum Laude
          Akufayerem Nwede Outstanding Service by an Undergraduate
          Robert Olson Outstanding Service by an Undergraduate
          Tana Ouitavon Cum Laude
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          Angelo Pioli III Campuswide Honors
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          David Purpura Cum Laude
          Campuswide Honors
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          Phi Beta Kappa
          Jordan Sinclair Magna Cum Laude
          Outstanding Contribution to Research
          Phi Beta Kappa
          Ilya Sukharnikov Summa Cum Laude
          Edward Wong Cum Laude
          Phi Beta Kappa
          Yaoxiang Zhou Cum Laude
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          http://www.ics.uci.edu/~duboisc/ Christopher DuBois
          • about
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          Christopher DuBois

          Data scientist at Dato

          I am a data scientist at Dato, Inc. I obtained a Ph.D. in Statistics from the Department of Statistics at University of California, Irvine, working with Padhraic Smyth and the DataLab.

          My research focuses on probabilistic models for relational events, e.g. communication or interaction within a social network.

          In 2012 I co-organized a workshop on algorithmic and statistical approaches for large social networks at NIPS 2012.

          In 2012 I was an intern at Microsoft Research in the Machine Learning group working under Chris Meek. Much of my research throughout grad school has been supported by a National Defense Science and Engineering Graduate Fellowship.

          For more about me, you can check out my profiles on Kaggle, Stackoverflow, a blog from when I raced bicycles in Spain, or photos and a blog from some travels.

          Contact:[firstname] [at] dato.com

          http://www.ics.uci.edu/community/news/press/ press releases @ the bren school of information and computer sciences
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          Bren school home > Community > News > Press releases
          Press releases

          December 9, 2015
          Two UCI professors named 2015 ACM Fellows


          November 16, 2015
          36-Hour Hackathon takes over eSports Arena Nov. 20-22


          September 28, 2015
          40 ICS Students Attending 2015 Grace Hopper Celebration of Women in Computing Conference


          May 27, 2015
          UCI Launches First Undergraduate Data Science Program in the UC System


          April 9, 2015
          UC Irvine and the National Center for Women & Information Technology to recognize 52 high school women for computing achievements


          March 23, 2015
          International gathering of information schools and scholars at this week's iConference


          March 12, 2015
          SCSIM, UC Irvine partner to strengthen research and collaboration among IT industry


          February 27, 2015
          Innovative game developer Brianna Wu to discuss leading an all-women game studio Friday


          January 23, 2015
          Media Advisory: UC Irvine Holds Global Game Jam


          January 9, 2015
          Two ICS professors honored as ACM Fellows


           

           

           

           

           

           

           

           

           

           

           

           

           

           

           

           

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          http://www.ics.uci.edu/%7erares/ Rares Vernica, PhD - University of California, Irvine

          Rares Vernica

          [ra'-resh]
          PhD
          Rares Vernica photo
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          UCI Logo

          Overview

          PhD received in Summer 2011

          Disertation: Efficient Processing of Set-Similarity Joins on Large Clusters
          Advisers: Prof. Michael J. Carey and Prof. Chen Li

          My research area is large-scale data management and data-intensive computing. Since September 2011 I am a Research Scientist at HP Labs in Palo Alto, CA.

          Curriculum Vitae (CV)

          Projects

          ASTERIX
          Developing a highly scalable parallel platform for semi-structured data management and analysis;
          • Source code for Parallel Set-Similarity Joins Using MapReduce;
          Family Reunification
          Developing data integration, indexing, and search techniques to help people find their loved ones during or after a disaster;
          • Haiti and Chile Earthquake, online services and data for finding missing people;
          • UCI article featuring the Haiti Earthquake service;
          Flamingo
          Developing data cleaning techniques to deal with errors and inconsistencies in information systems;
          • Flamingo Package, open source library for approximate string matching;
          • Flamingo Toolkit, open source UDF library for approximate string matching for MySQL database;
          • PSearch, UC Irvine online directory search service;
          • UCI article featuring the PSearch service.

          Publications

          1. Adaptive MapReduce using Situation-Aware Mappers.
            Rares Vernica, Andrey Balmin, Kevin S. Beyer, Vuk Ercegovac.
            EDBT 2012
            paper slides
          2. Efficient Processing of Set-Similarity Joins on Large Clusters.
            Rares Vernica.
            Ph.D. Thesis, University of California, Irvine, 2011.
            Advisers: Prof. Michael J. Carey and Prof. Chen Li
            paper
          3. CIRCUMFLEX: A Scheduling Optimizer for MapReduce Workloads Involving Shared Scans.
            Joel Wolf, Deepak Rajan, Kirsten Hildrum, Rohit Khandekar, Sujay Parekh, Kun-Lung Wu, Andrey Balmin, Rares Vernica.
            LADIS 2011. (Workshop on Large Scale Distributed Systems and Middleware, collocated with VLDB 2011)
            paper doi
          4. ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving World Models.
            Alexander Behm, Vinayak R. Borkar, Michael J. Carey, Chen Li, Nicola Onose, Rares Vernica, Alin Deutsch, Yannis Papakonstantinou, Vassilis J. Tsotras
            Journal of Distributed and Parallel Databases, Special Issue on Cloud Computing, 2011
            paper doi
          5. Hyracks: A Flexible and Extensible Foundation for Data-Intensive Computing
            Vinayak R. Borkar, Michael J. Carey, Raman Grover, Nicola Onose, Rares Vernica
            ICDE 2011
            paper (long version)   doi
          6. AKYRA: Efficient Keyword-Query Cleaning in Relational Databases.
            Rares Vernica, Chen Li
            Technical Report, University of California, Irvine, 2011
            paper
          7. Efficient Parallel Set-Similarity Joins Using MapReduce.
            Rares Vernica, Michael J. Carey, Chen Li
            SIGMOD 2010
            paper (long version)   doi   slides (long version)   poster    source code
          8. Efficient Top-k Algorithms for Fuzzy Search in String Collections.
            Rares Vernica, Chen Li
            KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)
            paper   doi   slides
          9. Entity Categorization Over Large Document Collections.
            Venkatesh Ganti, Arnd Christian König, Rares Vernica
            KDD 2008: 274-282.
            paper   doi   slides
          10. SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases.
            Liang Jin, Chen Li, Rares Vernica
            VLDB J. 17(5): 1213-1229 (2008).
            paper   doi   slides   source code
          11. Relaxing Join and Selection Queries.
            Nick Koudas, Chen Li, Anthony K. H. Tung, Rares Vernica
            VLDB 2006: 199-210.
            paper   slides   source code

          Awards

          2010
          Yahoo! Key Scientific Challenges Winner in the Web Information Management area - UCI article
          2009
          Microsoft Student Travel Award for KEYS 2009, Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009
          2005-2006
          Chair's Fellowship, Department of Computer Science, University of California, Irvine
          2005
          California Institute for Telecommunications and Information Technology (Calit2) Fellowship
          2005
          Second place, Pentalog programming contest, Brasov, Romania
          1999 - 2004
          Merit-Based Scholarship, Politehnica University of Bucharest, Romania

          Hobby

          Kendo
          • Member All United States Kendo Federation (AUSKF), Southern California Kendo Federation (SCKF), Costa Mesa Kendo Dojo;
          • Rank 2 Dan;
          Rares Vernica - kendo
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          http://www.ics.uci.edu/community/news/spotlight/spotlight_uci_constructors_acm.php Finals bound @ the bren school of information and computer sciences
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          Bren school home > Community > News > Spotlights
          Finals bound

          UCI team takes second place in regional ACM competition, invited to finals in Russia next June

          The UCI Constructors team, consisting of students Michael Cappe, Nick Ajalat and Alan Castro,
          From front to back: Nick Alajat, Alan Castro and Michael Cappe

          Three undergraduate students from the Donald Bren School of Information and Computer Sciences have earned a spot in the finals of the Association for Computing Machinery’s International Collegiate Programming Contest.

          The UCI Constructors team, consisting of students Michael Cappe, Nick Ajalat and Alan Castro, and sponsored by computer science senior lecturer Richard Pattis, took second place in the ICPC’s Southern California Regional on Nov. 9. They competed in a field of 91 teams from 31 colleges and universities from Southern California and Las Vegas to earn the honor. They are among only 120 teams invited to compete at the ICPC World Finals, which will be held in Ekaterinburg, Russia, in June 2014. 

          “For the first time in my memory, UCI's teams started seriously studying and practicing for this contest, so I expected our standings to improve, but I was bowled over by a second-place finish,” Pattis said. “These students did an extraordinary job.”

          Now in its 38th year, the ICPC attracts computer science students from around the globe. This time, nearly 30,000 contestants from more than 2,300 universities in 91 countries competed in regional competitions at some 300 sites worldwide. According to the ICPC world finals fact sheet, "The contest fosters creativity, teamwork, and innovation in building new software programs, and enables students to test their ability to perform under pressure. Quite simply, it is the oldest, largest, and most prestigious programming contest in the world."

          — Story by Ted B. Kissell
          — Photo courtesy of ACM UC Irvine Chapter
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          http://www.ics.uci.edu/%7emjcarey/ Home : index

          Michael J. Carey

          Department of Computer Science

          University of California, Irvine

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          Professional Interests

          Database management systems, data-intensive computing, information integration, middleware, distributed systems, and computer system performance evaluation.

          Education

          Ph.D. in Computer Science, December 1983.
          University of California at Berkeley

          M.S. in Electrical Engineering (Computer Engineering), May 1981.
          Carnegie-Mellon University

          B.S. (University Honors) in Electrical Engineering and Mathematics, May 1979.
          Carnegie-Mellon University

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          http://www.ics.uci.edu/community/news/spotlight/spotlight_gamejam_winter_2012.php Game Jam Winter 2012 @ the bren school of information and computer sciences
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          Bren school home > Community > News > Spotlights
          Game Jam Winter 2012

          Creating games from scratch in 7 days

          Massteroid
          Computer games science major Jason Ames Lee, producer for the winning team, said his group was “concerned with the feeling of the game,” so they incorporated such features as screen-shaking to enhance the experience of gameplay.


          There’s an asteroid in space, falling towards Earth. Your mission: Help the asteroid achieve maximum destructive impact by having it collect debris as it travels through space. Depending on your ability to collect debris as you steer the asteroid, you could decimate an entire continent or wipe out only one person. 

          Such is the premise of Massteroid, winner of this quarter’s Game Jam, a "build a video game in a week" tournament organized by the Video Game Development Club (VGDC) at UCI.


          Tournament participants share their thoughts about Game Jam at the Unveiling Event.

          Competitors were randomly assigned to five groups at the start of the competition; each team of about 15 members was given one week to design a fully functional game that incorporates the theme of “growth.” An unveiling event was held 10 days later, with teams demonstrating their work and discussing their successes and challenges. (Click here to download and play the games.)

          Judges included Bren School faculty and representatives from the game industry. Scores were based on design, aesthetics, “fun factor” and topic integration.

          “Game Jam participants are essentially given a theme and a team, and are pushed out into the wilderness on their own,” incoming VGDC president and computer game science (CGS) major Justin Britch. “Creating a game from scratch in such a small amount of time leaves no room for slacking, and the level of competition is high — but so is the payoff.

          “After an intense week, students have a completed game that will help them get a job in the video game industry. At the conclusion of this most recent Game Jam, everyone agreed that we are making better, richer games with less experienced people, and that is a testament to our new freshman members.”

          Current VGDC president Chris Lang, an informatics major, said the games — particularly the tournament champion — received high praise from the judges.

          "Massteroid set the bar for what we expect out of VGDC projects,” said Lang. “The presentation was simple and crisp, but appropriate to the theme. The controls were intuitive and easy to jump into. And most importantly, the scope was small enough to ensure player success but expandable enough to allow for further development. Massteroid was universally praised by all of our industry  judges, some of who continue to play it in their offices.

          “Even with the large influx this year of young and inexperienced game developers, mostly from the Bren School’s CGS degree program, the VGDC is still improving and is producing higher quality projects than ever before. If this is the quality of work that can be accomplished in a week, I have high hopes for our yearlong projects."

          Four other games rounded out this quarter’s competition:

          Qbitz

          2nd place: The creators of Q-Bitz, in which the player shrinks and grows platforms in order to repair a city, said they were inspired by the retro feel of games from the 70s and 80s.

          Vine Rider

          3rd place: Vine Rider’s objective is to have the player escape a flooding cave by growing platforms in vertical and horizontal directions that take the player to safety. 

          Qbitz

          4th place: Satelles, the first game in Game Jam history to include an online high score database, was a mix of asteroids and tower defense in which the player must defend a space station from increasing amounts of debris.

          Qbitz

          5th place: Flora is a three-part mini game that involves level progression, where the player switches through screens to collect water, extinguish fire and build tree people.

          — Reported by Courtney Hamilton
          — Photos by Peter Huynh
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          http://www.ics.uci.edu/community/news/features/ Features archive @ The Bren School of Information and Computer Sciences
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          Bren school home > Community > News > Features
          Features archive
          • Unlocking the potential of deep learning
            February 18, 2016

            Unlocking the potential of deep learning

            Pierre Baldi leads ICS’s machine learning research, which assists particle physics experiments at CERN.

          • Applying data to real-world challenges
            January 27, 2016

            Applying data to real-world challenges

            Computer Science Ph.D. student Dimitrios Kotzias wins Yelp Dataset Challenge with novel machine learning algorithm.

          • Hayes receives Jacobs Foundation Advanced Research Fellowship
            January 22, 2016

            Hayes receives Jacobs Foundation Advanced Research Fellowship

            The $400,000 award will allow the informatics professor to continue her work on “Inclusive and Evidence-Based Technologies for Child and Youth Development.”

          • Banking on data
            January 8, 2016

            Banking on data

            ICS alumnus Pedro Domingos, Ph.D. 1997, discusses the push toward a master algorithm in machine learning.

          • Building the ‘perfect’ app
            December 16, 2015

            Building the ‘perfect’ app

            UCI Emergency Services Manager Anne Widney collaborates with Hadar Ziv’s capstone project class to expand the successful, cost-effective ZOTFinder app to Android users.

          • Mehrotra set to lead two NSF-funded research projects
            December 14, 2015

            Mehrotra set to lead two NSF-funded research projects

            Computer Science Professor Sharad Mehrotra receives $725,000 in NSF funding to head research projects in big data and disaster response cyber physical systems.

          • Two ICS professors named 2015 ACM Fellows
            December 9, 2015

            Two ICS professors named 2015 ACM Fellows

            Informatics Professor Paul Dourish and Computer Science Professor Michael Franz honored for their outstanding accomplishments in computing and IT

          • Research for the public good
            December 4, 2015

            Research for the public good

            Informatics Professor Crista Lopes’ research project evolves into a UC-wide library data-sharing portal, the DASH system.

          • Harnessing the power of new technology
            December 3, 2015

            Harnessing the power of new technology

            Informatics alumnus Nick Jonas '09 is reinventing everyday objects, starting with a smart umbrella stand called Raincheck.

          • zyBooks Touts 'Less Text, More Action'
            November 25, 2015

            zyBooks Touts 'Less Text, More Action'

            ICS alumni Smita Bakshi and Frank Vahid have found success with their interactive online textbook replacement platform zyBooks.

          • Connected learning through Minecraft
            November 13, 2015

            Connected learning through Minecraft

            Mimi Ito’s Connected Camps uses Minecraft as an educational platform to connect youth engagement with learning.

          • Creating a global social system inspired by UCI students
            October 27, 2015

            Creating a global social system inspired by UCI students

            ICS alumnus Sajjad Mustehsan '04 is hoping to tap a diverse mix of talented UCI grads to help him expand his location-based networking app Locye.

          • ICS, Engineering Alumni Celebrate 50th; Kickoff Hall of Fame
            October 16, 2015

            ICS, Engineering Alumni Celebrate 50th; Kickoff Hall of Fame

            ICS honors 20 alumni who have made a significant impact in their profession.

          • Student Spotlight: Homer Strong
            October 16, 2015

            Student Spotlight: Homer Strong

            Q&A with graduate statistics student Howard Strong who has received the Robert L. Newcomb Memorial Endowed Graduate Student Award.

          • A game changer
            October 9, 2015

            A game changer

            ICS research scientist Walt Scacchi testifies before California Assembly on the future and potential of games in education and the workforce.

          • Inquiry and equity in computer science education
            September 10, 2015

            Inquiry and equity in computer science education

            ICS hosts innovative Code.org professional development workshops during the summer in an effort to help facilitate more robust computer science instruction in K-12 schools.

          • Planting the seeds for women in technology
            August 24, 2015

            Planting the seeds for women in technology

            With grants from Google and NCWIT, ICS women's group facilitated computer science workshops for 30 young women from Westminster’s La Quinta High School.

          • Building an e-commerce powerhouse
            July 27, 2015

            Building an e-commerce powerhouse

            As CEO of EYEMAGINE, alumnus Andy Etemadi continues to utilize what he learned as an ICS student in the ’90s to remain at the forefront of the e-commerce industry.

          • Teens tackle world dilemmas in UCI’s summer APPcamp
            July 27, 2015

            Teens tackle world dilemmas in UCI’s summer APPcamp

            Middle school students develop mobile apps to address challenges posed by National Academy of Engineering

          • UCI to host CSULA students as part of $1.25 million NASA grant
            July 10, 2015

            UCI to host CSULA students as part of $1.25 million NASA grant

            The grant will directly support 60 undergrad and grad CSULA students in visiting UCI, and indirectly support another 120 students who will participate in the program. more

          • Autism AppJam highlights academia’s growing impact on the autism community
            July 2, 2015

            Autism AppJam highlights academia’s growing impact on the autism community

            The third annual competition expands collaboration while continuing to facilitate the discussion about technological interventions to aid those affected by autism.

          • ICS Day 2015 keeps students involved and connected
            June 8, 2015

            ICS Day 2015 keeps students involved and connected

            This year’s annual student-run tech carnival was filled with swag and selfies.

          • Developing an ANTrepreneurial Spirit
            May 19, 2015

            Developing an ANTrepreneurial Spirit

            Computer science freshman Alec Kriebel created and released an iOS app development course with the help of UCI’s Blackstone LaunchPad.

          • ICSSC presents Google-themed hackathon
            April 14, 2015

            ICSSC presents Google-themed hackathon

            Google Web Hacks provided nearly 50 students with 24 hours to build any web application using Google technologies.

          • Taking on the telecom industry
            April 3, 2015

            Taking on the telecom industry

            Alumnus Bayan Towfiq, CEO of Flowroute Inc., discusses bootstrapping a successful startup with a team of ICS alumni.

          • ICS grad students to present Racial Violence Archive at iConference
            March 6, 2015

            ICS grad students to present Racial Violence Archive at iConference

            Microsoft Research awarded the team of seven students $3,000 to present their collaborative class project at iConference 2015 in March.

          • Global Game Jam Spurs Inspiration and Connectivity
            February 26, 2015

            Global Game Jam Spurs Inspiration and Connectivity

            Informatics professor Joshua Tanenbaum brings the world’s largest game- creation event to UCI, producing seven games in 48 hours.

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          http://www.ics.uci.edu/community/news/articles/ news articles @ the bren school of information and computer sciences
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          Bren school home > Community > News > Articles
          News articles

          February 11, 2016
          Iowa State statistician organizes symposium to discuss stronger science behind forensics
          EurekAlert
          Re: Hal Stern


          February 11, 2016
          Dean Stern to speak at AAAS symposium on stronger science behind forensics


          February 11, 2016
          What Your Facebook Habit Means For Your Sleep
          Time
          Quoted: Gloria Mark


          February 10, 2016
          Check Facebook a lot? You might be sleep deprived
          KPCC Southern California Public Radio
          Quoted: Gloria Mark


          February 10, 2016
          2016 TLT Symposium announces keynote speakers
          Penn State News
          Re: Mimi Ito


          February 9, 2016
          ICS alum Steve Trimberger Elected to National Academy of Engineering


          February 4, 2016
          UCI researchers link compulsive Facebook checking to lack of sleep
          UCI News
          Quoted: Gloria Mark


          January 25, 2016
          Get A Grip On Your Information Overload With 'Infomagical'
          NPR
          Re: Gloria Mark


          January 19, 2016
          Hi, I'm a digital junkie, and I suffer from infomania
          Los Angeles Times
          Quoted: Gloria Mark


          January 14, 2016
          Private practices
          UCI News
          Quoted: Sharad Mehrotra


          January 6, 2016
          The triumph of email
          The Atlantic
          Re: Gloria Mark


          January 6, 2016
          From computer consultant to comic
          UCI News
          Quoted: Sanjay Manaktala '05


          January 5, 2016
          Put the cellphone away! Fragmented baby care can affect brain development
          UCI News
          Re: Dean Hal Stern mentioned


          December 23, 2015
          IEEE awards Fowlkes the Helmholtz Prize for paper with enduring impact
          ICS News
          Re: Charless Fowlkes


          December 21, 2015
          Statistics Ph.D. student receives ENAR Distinguished Student Paper Award
          ICS News
          Re: Statistics Ph.D. student Duy Ngo


          December 14, 2015
          University of California pressured to count computer science toward high school math requirement
          Contra Costa Times
          Quoted: Debra Richardson


          December 2, 2015
          Death by Flaming Water Ski, and Other Misfortunes
          The New Yorker
          Quoted: Geoffrey Bowker


          December 1, 2015
          Artificial intelligence called in to tackle LHC data deluge
          Nature
          Re: Pierre Baldi


          November 25, 2015
          Code.org partners with Microsoft in attempt to make coding fun
          The Student Newspaper
          Quoted: Mimi Ito


          November 25, 2015
          UCI hackathon has tech junkies working overtime
          Daily Pilot
          Re: HackUCI


          November 12, 2015
          Company Bans Email for 1 Week, Employee Stress Levels Plummet
          Time
          Quoted: Gloria Mark


          November 12, 2015
          Is Email Evil?
          The Atlantic
          Quoted: Gloria Mark


          October 29, 2015
          The next Silicon Beach? Orange County wants to build its tech community
          Los Angeles Times


          October 29, 2015
          Social Media Quizzes Could Give Hackers Access
          NBC Los Angeles
          Quoted: Gene Tsudik


          October 20, 2015
          Researchers aim to make privacy second nature for software developers
          EurekAlert
          Re: Hadar Ziv


          October 7, 2015
          Want to lose weight? Freeze the fat off with an ice vest, UCI researcher says
          Orange County Register
          Quoted: Wayne Hayes


          October 5, 2015
          Statisticians wanted: The lesser-known, but also hot tech field
          Seattle Times
          Quoted: Jessica Utts


          September 30, 2015
          Facebook seeks to conquer the workplace
          San Jose Mercury News
          Quoted: Gloria Mark


          September 29, 2015
          UCI to celebrate 50th with Festival of Discovery
          Orange County Register
          Re: exhibit exploring human-powered aircraft


          September 15, 2015
          Political campaigns fav donations via Twitter
          Marketplace Public Radio
          Quoted: Mimi Ito


          September 5, 2015
          After-School STEM Camps Emphasize Minecraft, Coding Skills
          Benzinga
          Quoted: Mimi Ito


          September 1, 2015
          Digital Entrepreneurs Over 50 In the App World
          AARP
          Quoted: Ramesh Jain


          August 21, 2015
          UCI grad named a Microsoft YouthSpark Challenge for Change winner
          Orange County Register
          Quoted: Nithin Jilla


          More community »
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          http://asterixdb.ics.uci.edu/ AsterixDB
          • Overview
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          Overview

          Welcome to the new home of the AsterixDB Big Data Management System (BDMS). The AsterixDB BDMS is the result of about 3.5 years of R&D involving researchers at UC Irvine, UC Riverside, and UC San Diego. The AsterixDB code base now consists of roughly 250K lines of Java code that has been co-developed at UC Irvine and UC Riverside.

          Initiated in 2009, the NSF-sponsored ASTERIX project has been developing new technologies for ingesting, storing, managing, indexing, querying, and analyzing vast quantities of semi-structured information. The project has been combining ideas from three distinct areas—semi-structured data, parallel databases, and data-intensive computing (a.k.a. today’s Big Data platforms)—in order to create a next-generation, open-source software platform that scales by running on large, shared-nothing commodity computing clusters.

          The ASTERIX effort has been targeting a wide range of semi-structured information, ranging from “data” use cases—where information is well-typed and highly regular—to “content” use cases—where data tends to be irregular, much of each datum may be textual, and the ultimate schema for the various data types involved may be hard to anticipate up front. The ASTERIX project has been addressing technical issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging time-tested parallel database techniques with modern data-intensive computing techniques to support performant yet declarative solutions to the problem of storing and analyzing semi-structured information effectively.

          The first fruits of this labor have been captured in the AsterixDB system that is now being released in preliminary or “Beta” release form. We are hoping that the arrival of AsterixDB will mark the beginning of the “BDMS era”, and we hope that both the Big Data community and the database community will find the AsterixDB system to be interesting and useful for a much broader class of problems than can be addressed with any one of today’s current Big Data platforms and related technologies (e.g., Hadoop, Pig, Hive, HBase, MongoDB, and so on). One of our project mottos has been “one size fits a bunch”—at least that has been our aim.

          © asterixdb.ics.uci.edu 2015.
          Design by Free CSS Templates

          http://www.ics.uci.edu/ugrad/internship/ Summer Undergraduate Research Internship in Computer Science @ the bren school of information and computer sciences
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          Bren school home > Undergraduate > Summer internship
          Internship program
          Summer Undergraduate Research Internship
          in Computer Science
          @ the University of California, Irvine

          We welcome applications for a newly launched summer undergraduate research internship program at the UC Irvine Donald Bren School of Information and Computer Sciences. Selected students will get an opportunity to visit UCI during the summer of 2015 and spend roughly 8-10 weeks working with faculty on exciting research projects. Projects are expected to cover a wide variety of topics including artificial intelligence, data management, embedded systems and architecture, machine learning, networking, systems and software, secure computing, and theoretical computer science.

          Who should apply?
          The internships are open to both domestic and international students enrolled in an undergraduate Computer Science program (or related field) who are currently in their junior or senior year. (In exceptional cases, recent graduates also will be considered). The internships are suitable for highly motivated undergraduate students, potentially interested in exploring doctoral studies, who wish to gain valuable research experience. It is especially well suited for students considering UCI as a possible destination for their doctoral study. The internship provides an opportunity for students to visit UCI, discover the charm of Southern California, get to know our faculty, and participate in research projects that are changing the computing landscape.

          How many internships are available?
          We expect to have 8-10 internship positions. The program will cover travel expenses to/from Irvine, and interns will have the opportunity of earning up to $450 a week to cover living expenses.

          What types of research projects will I work on?
          Click here for examples of research projects available through this internship program.

          • Offered by Bren Professor Michael Carey:

            AsterixDB is an open source BDMS (Big Data Management System) developed over a 4-year period at UC Irvine and UC Riverside. It has a rich feature set, managing as well as querying/analyzing data, that distinguishes it from the other Big Data platforms available in the open source world today. Its feature set makes it well-suited to modern uses such as web data warehousing or social data storage and analysis. AsterixDB has a semistructured NoSQL style data model (ADM), a declarative query language (AQL), and is designed to run on large "shared nothing" clusters like those powering Google, Facebook, and other big web companies. It has a novel storage architecture that supports fast data ingestion as well as efficient parallel queries, and has a number of other interesting features as well. The AsterixDB project has 1-2 positions for interns wishing to work with open source Big Data technologies. Possible projects include the development of flashy graphical administration and query monitoring tools (for which browser-based application development experience is needed), development of showcase applications (where the intern could perhaps provide their own idea for an application that utilize AsterixDB's spatial, temporal, and textual data support), or the development of a new component or policy manager for the system in cooperation with one of the team's graduate students (for which comfort with reading open source Java code, in a large code base, would be important). Interested interns are encouraged to download and test drive the system on their favorite Mac or Linux laptop in order to get a taste of what AsterixDB is all about.

          • Offered by Professor Michael Franz:

            We are developing a new way to automatically harden computer systems against cyber-attacks. Our techniques fundamentally improve the odds of defenders by equipping all computing systems with unique software. Like biological diversity curbs the spread of diseases, our artificial software diversity makes cyber-attacks costly and ineffective. Concretely, we are building a new compiler (on top of the industry-leading LLVM infrastructure) that does not just try to produce the fastest binary; instead it randomly transforms each program as it is produced. The resulting set of programs isn’t vulnerable to any one exploit. Our work has many implications at the OS and hardware level and is not just theoretical. You can go ahead and protect open source software simply by recompiling it.

            We don't expect you to have a lot of prior experience. You'll be working in team of half a dozen graduate students and post docs who offer lots of advice---all we ask is that you are motivated, disciplined, and know how to code.

            Project 1: Next-generation Cyber Defenses
            We are working to bring our compiler to new platforms, contribute our work back to the LLVM project, and develop new ways to randomize code. For example, you could help measure the security and performance on ARM systems or implement randomized function parameter shuffling. We are also working on new Control Flow Integrity (CFI) techniques that prevent attackers' code from executing. Specific projects are determined by the interns particular interests and skill sets.

            Project 2: Thinking Like an Attacker

            If you like to learn how to hack, reverse engineer or exploit vulnerabilities, this project is for you. We cannot evaluate the security of our research without trying to attack it---so we do exactly that. We build same kinds of attacks that are used in the wild today including return-oriented programming, JIT-spraying and side-channel attacks. Don't worry if you haven't developed exploits before, we'll help you find a project that matches your experience.

          • Offered by Chancellor's Professor Michael Goodrich:

            Algorithms are central to computing, and the Center for Algorithms and Theory of Computation is sponsoring summer internships to study paradigms and principles for the design and implementation of correct and efficient data structures and algorithms. Specific topics of interest include graph algorithms, randomized algorithms, geometric algorithms, and algorithms for computer security and privacy. We are looking for students with experience in algorithm design and analysis, ideally including proficiency with probability theory and combinatorics.

          • Offered by Professor Wayne Hayes:

            Professor Wayne Hayes works in the area of Computational Science, which means using computers to do science in the physical, biological, mathematical, and social sciences. Currently his projects include: the analysis of images of spiral galaxies; graph theory applied to biological networks; prediction of sea level rise due to global warming (in collaboration with NASA/JPL); applied numerical mathematics; all of which use parallel computation. Candidates will need to pass a test before being considered. More details can be found here.

          • Offered by Professor Sharad Mehrotra:

            With the proliferation of cloud computing, organizations and individuals are increasingly outsourcing their data management needs, leading to the problem of “loss of control” over one’s data. The Radicle project at UCI is taking a novel risk-based approach to secure data management in the cloud. In particular, we are developing revolutionary middleware solutions that selectively expose data based on balancing the benefit of such data exposure with the associated risks. An example of such a middleware is the CloudProtect system that sits between a user and web applications such as Picasa, DropBox, Google Drive, etc. The middleware intercepts the user’s interactions with the service, appropriately encrypts data when possible to support user’s security policies, and strikes a balance between service usability and security. The Radicle team is looking for 1 to 2 summer interns who are interested in contributing to the development of solutions for secure data management. This work could include building core component of CloudProtect, extending the nature of policies supported, hardening the software for broader release, incorporating new cloud-based services, conducting user studies using the software, etc.

          Will the entire internship program focus on research?
          While the program emphasizes research experience, it will include social activities so interns get to know one another and experience some of what Southern California has to offer.

          How do I apply?
          You may apply online at https://recruit.ap.uci.edu/apply/JPF02710; you must create an account to access the application and submit the required information, which includes your transcript(s), two letters of recommendation and a short personal statement. We will begin looking at applicants December 15th. Depending upon the level of interest and the number of qualified students we can find, we may have a second round selection on February 15th. If you miss the first deadline, we still encourage you to apply for the second deadline.

          Will housing be provided?
          Students must arrange their own housing. Information about off-campus housing options is available here.

          Can you tell me more about UCI?
          Located in coastal Orange County, near a thriving employment hub in one of the nation’s safest cities, UC Irvine was founded in 1965. One of only 62 members of the Association of American Universities, it’s ranked first among U.S. universities under 50 years old by the London-based Times Higher Education. The campus has produced three Nobel laureates and is known for its academic achievement, premier research, innovation and anteater mascot. UC Irvine has more than 28,000 students and 1,100 faculty and offers 192 degree programs. It’s Orange County’s second-largest employer, contributing $4.3 billion annually to the local economy. The university is about 5 miles from the Pacific Ocean, 45 miles from Los Angeles and 80 miles from San Diego. The beach cities of Orange County that neighbor Irvine are among the top tourist destinations in the United States.

          Can you tell me more about UCI’s computer science program?
          The Donald Bren School of Information and Computer Sciences is the only computing-focused school in the University of California system. UCI holds the No. 28 spot in the most recent U.S. News and World Report ranking of computer science programs, and Microsoft’s Academic Search website ranks the Bren School faculty as the 21st most influential group in the United States. Students graduating with a Ph.D. from the Bren School at UCI have flourished in positions in industry, industrial research labs, government and academia. For more information about our school and alumni, visit: http://www.ics.uci.edu/about/about_factsfigures.php

          Who do I contact for more information?
          For questions about the UCI summer undergraduate internship program in computer science, please email summerinternships@ics.uci.edu.

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          http://www.ics.uci.edu/about/annualreport/ ICS Annual Reports @ the Donald Bren School of Information and Computer Sciences
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          Bren school home > About > Annual report
          Annual Reports

          Fall 2015 Annual Report

          ICS Fall 2015 Annual Report
          » PDF version


          2013-2014 Annual Report

          ICS 2014 Annual Report
          » Download a PDF version


          2006-2007 Annual Report

          photo:: 2005-06 annual report cover

          »  Web version
          » PDF version


          2005-2006 Annual Report

          photo:: 2005-06 annual report cover
          » 
          Web version
          » PDF version

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          http://www.ics.uci.edu/about/annualreport/2006-07/index.php 2006-07 annual report @ the bren school of information and computer sciences

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          university of california, irvine
          donald bren school of information and computer sciences
          photo: donald bren hall dedication
          Donald Bren Hall Dedication. June 20, 2007. (L to R) Vice Chancellor Tom Mitchell, Brenda Drake, Chancellor Michael V. Drake, Donald Bren, and Dean Debra J. Richardson

          Dear Bren School Community,

          2006-07 was an exciting academic year at UC Irvine’s Donald Bren School of Information and Computer Sciences.

          We had many successes and firsts as we moved into the new Donald Bren Hall and welcomed many new graduate and undergraduate students.

          Today, we are an academic community of more than 1,500 students, over 100 full-time faculty and staff, and approximately 7,000 alumni worldwide.

          In teaching and scholarship, we continue to be among the top in information and computer sciences.

          To add to our list of accolades, our Networked Systems
          program was just rated number one by Academic Analytics, in addition to being ranked third in Information Systems.

          At the Bren School, we have a unique perspective on computing and information technology, stimulating society daily.

          Our vibrant community, comprised of researchers and educators as well as industry-leading scholars, explores innovative topics ranging from building complete computer systems on chips smaller than a finger nail to developing user interface systems that allow engineers on opposite sides of the world to collaborate effectively.

          I invite you stay in touch with us throughout the year by subscribing to our RSS feed or visiting our Web site regularly.

          Many thanks for your continued support of our vision.

          debra j. richardson's signature

          Debra J. Richardson
          The Ted and Janice Smith Family Foundation Dean

          Table of Contents

          1. Bren School Faculty
          2. Research Areas
          3. Department of Computer  Science
          4. Department of Informatics
          5. Department of Statistics
          6. In the News
          7. Centers of Excellence
          8. Bren School Events
          9. Student Affairs
          10. Bren School Alumni
          11. By the Numbers
          12. Bren School Development
          13. Honor Roll of Donors
          14. PDF Version photo: pdf logo

          | University of California copyright | communications@ics.uci.edu | Content last modified: June 05 2015
          http://www.ics.uci.edu/about/annualreport/2005-06/index.php annual report 2005-06 @ the bren school of information and computer sciences

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          university of california, irvine
          donald bren school of information and computer sciences
          photo:: dean debra j. richardson
          Dear Bren School Community,

          It is my pleasure to introduce the 2005-06 Donald Bren School of Information and Computer Sciences Annual Report. Through our annual report we hope to keep you – our close friends and colleagues – abreast of our accomplishments, as well as challenges.

          Today, we are an academic community of more than 1,500 students, over 100 full-time faculty and staff, and approximately 6,500 alumni worldwide. In teaching and scholarship, we continue to be among the top in information and computer sciences. The Chronicle of Higher Education ranked us third in Information Sciences research.

          I invite you stay in touch with us throughout the year by subscribing to our RSS feed or visiting our Web site regularly.

          Many thanks for your continued support of our vision as well as our research and education.

          debra j. richardson's signature

          Debra J. Richardson
          The Ted and Janice Smith Family Foundation Dean

          Table of Contents

          1. Bren School Faculty
          2. Research Areas
          3. Department of Computer  Science
          4. Department of Informatics
          5. Department of Statistics
          6. In the News
          7. Centers of Excellence
          8. Bren School Events
          9. Student Affairs
          10. Bren School Alumni
          11. By the Numbers
          12. Bren School Development
          13. Honor Roll of Donors
          14. PDF Version photo: pdf logo
          | University of California copyright | communications@ics.uci.edu | Content last modified: June 05 2015
          http://www.ics.uci.edu/community/news/brenbits/brenbits-spring2014 Brenbits Spring 2014

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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          Bren school home > Community > News >
          Brenbits Spring 2014
          Donald Bren School of Information < Computer Sciences

          Spring 2014

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          Spotlight

          Commencement 2014

           

          The students who received the 30 doctorates, 102 master's degrees and 284 bachelor's degrees from ICS this year got an unforgettable sendoff as President Barack Obama addressed UC Irvine's 2014 Commencement at Angel Stadium of Anaheim. And let's not forget the most important part: The President of the United States of America now knows how to Zot! Read more

          Features

          Hayes honored

          Associate professor Gillian Hayes was named the first holder of the Robert A. and Barbara L. Kleist Chair in Informatics. Read more

          Ready to play

          The first class of computer game science graduates finished their senior projects with a little help from Blizzard. Read more

          Making connections

          In its inaugural year, the ICS/Engineering Undergraduate Mentorship program helped women forge ties with industry professionals. Read more

          ICS at the White House

          Computer science professor Nalini Venkatasubramanian represented UCI and ICS at the SmartAmerica Expo in Washington, D.C. Read more

          Great people, great ideas

          ICS alumnus and startup entrepreneur Art Hitomi talks about what the Bren School has given him — and how he gives back. Read more

          Apps with an impact

          The second annual Autism AppJam engaged the Orange County community through apps that help families affected by autism. Read more

          Creative students

          The Butterworth and Beall product development contests elicited outstanding work from ICS and Engineering students. Read more

          A breath of fresh air

          When professor Tony Givargis and his team set out to build a better lung simulator, they discovered that software wasn’t enough. Read more

          Dean’s Message

          President Barack Obama’s historic commencement speech was a fitting coda to a truly special 2013-14 academic year, both for UC Irvine as a whole and for the Donald Bren School of Information and Computer Sciences. Read more

          Featured Videos

          Student Project Showcase

          More than 50 undergrads showed off their innovative work, including apps and interactive games.

           

          Ingenuity 2014 Keynote Address: Jeff Margolis of Welltok, Inc.

           

          ICS Day 2014

          Bren School community celebrates at seventh ICS Day.

          News

          Kobsa receives Intel grant for user-privacy research

           

          Tomlinson, Nardi and Patterson to design UC-wide online course

           

          Utts elected president of American Statistical Association

           

          Computer science undergrads win Facebook SoCal hackathon

           

          Tsudik receives research grant from Verisign

           

          Human-computer interaction conference honors several ICS papers

          Giving

          Bren School students shine in the classroom and in the community. Here, a group of undergraduates demonstrate their app at the Down Syndrome Foundation of Orange County. Your support of the Dean's Excellence Fund enables us to attract such dedicated scholars.

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

          Brenbits archive:

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          Bren school home > Community > News >
          Brenbits Summer 2013
          Donald Bren School of Information < Computer Sciences
          Brenbits Summer 2013

          Summer 2013

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          Spotlight

          Grad students get leg up from Google

          Doctoral students and Google Ph.D. Fellows Bart Knijnenburg and Yingyi Bu

           

          Doctoral students Bart Knijnenburg and Yingyi Bu received two of only 39 Google Ph.D. Fellowships awarded around the globe this year. They join “a select group recognized by Google researchers and their institutions as some of the most promising young academics in the world.” Read More

          Features

          Pomp & circumstance

          Bren School commencement

          The Bren School conferred 27 doctoral, 110 master’s and 196 bachelor’s degrees during the 2012-13 academic year.
          Read More / View Videos

          Ingenuity 2013

          Janice and Ted Smith

          Long-time supporters Ted and Janice Smith were honored at the inaugural UCI student technology showcase.
          Read More / View Videos

          Fostering inclusivity

          Kevin Mori

          Mori '13 received a Chancellor’s Award of Distinction for academic excellence and service to the campus.
          Read More

          Commercially viable

          Butterworth Competition winners

          Creators of 'Bluetooth Assisted Tracking' won the 10th annual Butterworth Product Development Competition.
          Read More

          Autism AppJam

          Autism AppJam

          Interdisciplinary student teams developed mobile apps designed to improve the lives of those with autism.
          Read More / View Video

          Tech Support

          Associate Professor Gillian Hayes

          Hayes inspires UCI's tech-savvy students to utilize their talents to make life better for those in need.
          Read More

          Wired for health

          Bren Professor Ramesh Jain

          Jain and his research team aim to integrate health data and make it globally available — by phone.
          Read More

          Announcing AsterixDB

          AsterixDB

          Researchers unveiled a next-generation platform for storing, managing and making use of Big Data.
          Read More

          Celebrating teachers

          Associate Professor Don Patterson and Ph.D. students Eric Hennigan and Jed Brubaker

          Ph.D. students and faculty were honored for their contributions to undergraduate instruction. 
          Read More

          Gifted guide

          Bren Professor Judy Olson

          The UCI Emeriti Association recognized Olson with an Outstanding Mentor Award.
          Read More

          Dean’s Message

           

          UC Irvine once again was named the No. 1 U.S. university under 50 years of age, and one of the top five young universities in the world, by Times Higher Education. Ranking universities is not an easy task, and the results can be controversial. There is, however, no disputing the rapid rise of UC Irvine as a top international research university. UCI, the youngest institution in the prestigious ... Read More

          Featured Videos

          CONFESSIONS OF A VC

          Fred Wilson, managing partner at Union Square Ventures, shared his insights as part of the Top Trends in Tech Speaker Series.

          ICS Day Student Festival

          Toy Hacking Workshop

          Student Project Showcase

          News

           

          Ihler receives NSF CAREER Award

          Stern helps lead $10 million NIH study

          Mazmanian receives Intel early career grant

          Student Council recognized for launching Med AppJam

          ICS women launch init(together) conference

          Brock awarded Data Science for Social Good fellowship

          Mehrotra and team win best paper award

          Roher receives NSF Graduate Research Fellowship

          Microsoft, Google, UC Mexus recognize Hayes

          Harris, student garner Best Paper Cyber Security Award

          Giving

           

          DEAN'S EXCELLENCE FUND

          Did you know UCI receives just 8.6% of its funding from the State? With 83% of our undergraduate students demonstrating financial need, your gift to the Bren School of ICS allows us to cultivate the next generation of inventors, leaders and problem-solvers. Every gift, no matter the amount, makes a difference. Please consider making a contribution today.

           

          Make a gift

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          Bren school home > Community > News >
          Brenbits Spring 2012
          Graduating students
          table

          Spring 2012

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          Spotlight

          ICS Day 2012

          table

           

          Organized by student leaders, the daylong event features coding competitions, a whiteboard challenge, dance crews, carnival games and the ever-popular dunk tank. 
          View Video

          Features

          Lauds & Laurels

          Walt Scacchi

           

          Senior research scientist Walt Scacchi (PhD '81) receives the Bren School's Distinguished Alumnus Award.
          Read More

           

          Budding entrepreneurs

          Butterworth Competition

           

          A team of undergraduates takes home the 9th Annual Butterworth Product Development Competition $5,000 grand prize.
          Read More

           

          AppJam champs

          AppJam

           

          It's all smiles for Team Socially Awkward Anteaters, winners of the latest "build an app in a week" tournament.
          Read More

           

          Franz wins IEEE award

          Michael Franz

           

          IEEE Computer Society honors professor Michael Franz with a Technical Achievement Award for his pioneering contributions.
          Read More

           

          Email = stress

          Gloria Mark

           

          Findings by professor Gloria Mark and scientist Stephen Voida could boost on-the-job productivity.
          Read More

           

          Projects galore

          Student Project Showcase

           

          Annual event highlights student innovations, from virtual learning tools to smartphone-enabled home automation.
          Read More

           

          Dean’s Message

           

          Our just-concluded spring quarter has been a season of recognition for faculty, students and alumni of the Bren School of ICS. You may have read or heard news coverage of Professor Gloria Mark’s study of email in the workplace. Gloria and her collaborators found that email “vacations” decreased stress and boosted productivity... Read More
           

          Save the Date

          ALUMNI EVENT 

          Google Irvine

          July 19, 2012
          Google Irvine


          ICS ALUMNI: Details will be sent via email. Please be on the lookout for the invitation!

          News

           

          UCI team earns prize for best educational game

          Faculty, alum win SIGMOD Test-of-Time Award

          Student Receives Yahoo! Labs award

          Professor Olson delivers 2012 Athena Lecture

          Two students receive prestigious NSF fellowship

          Bren School hosts annual AP Stats project competition
           

          Giving

          Student scholar

           

          Support Student Scholarships

          Did you know Bren School undergraduates traditionally boast some of the highest mean SAT scores on campus? With your help, we can continue to attract the best and brightest to UCI. Please consider making a gift to the ICS Annual Giving fund today. Your contribution, in any amount, will benefit deserving students.

          Make a Gift

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          • ABOUT
            • About the School
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          • DEPARTMENTS
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          Bren school home > Community > News >
          Brenbits Winter 2012
          image
          image

          Winter 2012

          Facebook Twitter Flickr YouTube

          Spotlight

          Making IT more PC

          image

           

          Values in design pioneer and new informatics professor Geoffrey Bowker advocates responsible technological innovation. Read More

          Features

          There’s an app for that

          image

           

          A group of undergrads led by a master’s student won the inaugural AppJam with "TimeToGo," a location-sensitive event reminder.
          Read More

           

          Statistically significant

          image

           

          As new head of the UCI Center for Statistical Consulting, Vinh Nguyen (MS ’07, PhD ’11) aims to help researchers across campus and Orange County.
          Read More

           

          Visualizing the future

          image

           

          Associate professor Aditi Majumder's lab team develops simpler, cheaper display technologies.
          Read More

           

          Game Jam update

          image

           

          Latest build-a-video-game-in-a-week competition features better, richer games.
          Read More

           

          DIY "but geekier"

          image

           

          New club provides a student-run physical space where members of the UCI community can work together on creative projects.
          Read More

           

          In the news

          image

           

          Postdoc Garnet Hertz and his drivable OutRun game cabinet are featured in the February issue of Popular Science.
          Read More

           

          Dean’s Message

           

          We are pleased to present the first “new look” Brenbits newsletter, a publication that features updates on faculty, students and alumni. This is a busy time of year for the school, with students from all over the world applying to enroll as undergraduate or graduate students at UC Irvine and ICS... Read More

          Save the Date

          Student Showcase
          March 22, 2012
          Donald Bren Hall


          The UCI campus community is invited to the Bren School's annual student showcase. This event highlights a range of student innovations, including mobile apps, interactive games and more.

          RSVP by March 20

          News

           

          ACM recognizes four faculty

          Baldi named IEEE Fellow

          Department of Statistics welcomes new associate professor

          Three new faculty join Department of Informatics

          Tomlinson and team receive "Energy to Educate" grant

          NSF awards $500K to Franz for virtual machines work

          Giving

          image

           

          To honor Bob Newcomb’s contributions as founding director of the UCI Center for Statistical Consulting, we invite you to join us in establishing The Robert L. Newcomb Graduate Fellowship Fund to support graduate students working in the center.

           

          image

          Brenbits is published by the UC Irvine Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          Bren school home > Community > News >
          Brenbits Winter 2013
          Donald Bren School of Information < Computer Sciences
          AppJam+ Mentors

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          Spotlight

          Dreaming big

          Computer science major Nithin Jilla

           

          After starting a campus club to assist Kenya schools, computer science major Nithin Jilla now aims to improve access to education on a global scale.
          Read More

          Features

          Innovation Lab launch

          Kay Family Foundation Innovation Lab

          Kay Family Foundation gift creates mobile technology lab designed to foster student collaboration.
          Read More

          Cancer breakthrough

          UCI team finds new target for treating wide spectrum of cancers

          UCI biologists, chemists and computer scientists find new target for treating wide spectrum of cancers.
          Read More

          From India to Ghana

          Nithya Sambasivan received her Ph.D. in 2012

          Alumna joins Google.org, continues research on technology use in developing countries.
          Read More

          Apps for health care

          First-ever Med AppJam a resounding succes

          100 Bren School and 30 medical students from UCI team up in first-ever Med AppJam tournament.
          Read More

          App design 101

          College prep via app design

          Coached by Bren School students, middle school teams learn STEM skills by creating educational smartphone games.
          Read More

          Intel research center

          ISTC for Social Computing

          UC Irvine anchors new $12.5 million Intel Science & Technology Center for Social Computing.
          Read More

          Gene genie

          Professor Gene Tsudik and team developed Genodroid

          Smartphone app developed by Bren School researchers permits secure storage and testing of DNA data.
          Read More

          Gender & tech

          Maria Klawe with members of UCI Women in Computer Sciences

          Harvey Mudd College president Maria Klawe kicks off Top Trends in Tech Speaker Series with "Gender and Technology" talk.
          View Video

          Year in review

          Participants in the 2012 A Mobile Application Showcase Event

          A look at some of the events that helped make 2012 another exceptional year for the Bren School.
          View Video

          Homecoming 2013

          Thousands took part in the UCI Homecoming Street Festival

          Bren and Samueli schools team up to welcome ICS and engineering alumni at annual UCI festival.
          View Photos

          Dean’s Message

           

          UC Irvine continues to thrive, receiving a record high 60,619 freshman applications for fall 2013. And with strong prospects for employment in the technology sector — the Bureau of Labor Statistics predicts the tech boom will create more than 1 million jobs by 2020 — ICS continues to be a popular destination within UCI. In fact, applications to our undergraduate programs are up 30 percent ... Read More

          Save the Date

          TOP TRENDS IN TECH

          Top Trends in Tech speaker Fred Wilson

          April 1, Monday, 5 to 6 p.m.
          Calit2 Auditorium, UCI


          Fred Wilson, managing partner at Union Square Ventures, will present "Confessions of a VC."

          RSVP NOW

          News

           

          Fowlkes receives NSF CAREER Award
           

          Hayes directs technology research for new autism center
           

          Nardi, Grinter (MS '94, PhD '96) named to CHI Academy
           

          Tsudik named IEEE Fellow
           

          Baldi named ACM Fellow
           

          Hancock joins statistics faculty
           

          Ramanan named to Brilliant 10
           

          Franz awarded U.S. Patent on software diversity
           

          Student receives prestigious diversity fellowship
           

          Peters Fellowship awarded to PhD student



          * View Noteworthy Achievements for more faculty and student news

          Giving

           

          Bren School Annual Fund

          Your annual gift to the Donald Bren School of Information and Computer Sciences allows us to provide the best programs for our students and to support these up-and-coming innovators as they create new and better technology to meet the challenges of our digital age. Every gift, no matter the amount, makes a difference. Consider making a contribution today. Together we can empower ICS students with the skills they need to serve future generations.
           

          Make a gift

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          http://www.ics.uci.edu/community/news/brenbits/brenbits-fall2014 Brenbits Fall 2014

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          Bren school home > Community > News >
          Brenbits Fall 2014
          Donald Bren School of Information < Computer Sciences

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          Spotlight

          VIDEO: 2014 Event Highlights

           

          Three words can sum up 2014 at ICS: more, bigger and better. As enrollment at the Bren School continues to grow, our world-class faculty, students, alumni and partners have produced not only groundbreaking research, but also an ever-increasing number of inspiring (and fun) events. Enjoy this look back at our 2014 events, have a happy holiday season, and join us in looking forward to a great 2015! Watch the video

          Features

          Jordan joins FCC

          ICS computer science professor Scott Jordan was named chief technology officer for the commission. Read more

          Welcome Week 2014

          A rollicking scavenger hunt helped introduce the latest wave of new Bren School students to campus. Read more

          A virtual meeting

          Informatics professor Crista Lopes balances academia with an active open-source development career. Read more

          New AAAS Fellow

          Computer science professor Eric Mjolsness was honored by the American Association for the Advancement of Science. Read more

          Getting deep

          Computer science professor Pierre Baldi helped develop "deep learning" techniques to hunt for Higgs bosons. Read more

          Lost? Try ZOTfinder

          ICS students have designed an app to help you find your way around the UCI campus — safely. Read more

          Dean’s Message

           

          It has been a busy Fall quarter for the faculty, students and staff of the Donald Bren School of Information and Computer Sciences. We welcomed more than 480 new freshman undergraduate majors (a record). Read more

          Save The Date: Spring Alumni Event

          ICS is coming to Silicon Valley! The Spring Alumni Event is scheduled for March 3 in the Bay Area. Watch your email box for more details.

          News

          UCI ranks in top 20 nationally for theoretical computer science

           

          Kobsa receives NSF grant to research user privacy decision support

           

          Nicolau named IEEE Fellow

           

          Jain co-authors textbook on multimedia computing

           

          Van der Hoek and LaToza receive $1.4 million from NSF to study 'crowdprogramming'

           

          Statistics student places second in prestigious biometrics competition

           

          In Memoriam: Robert L. Newcomb

          Giving

           

          Bren School students shine in the classroom and in the community. Here, Ph.D. student Kristin Roher mentors high schoolers at the Girls Inc. Summer Robotics Camp. Your support of the Dean's Excellence Fund enables us to attract such dedicated scholars.

           

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

          Brenbits archive:

          Spring 2014   |   Winter 2014   |   Fall 2013  

            Summer 2013   |   Winter 2013   |   Spring 2013   |   Winter 2012

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          http://www.ics.uci.edu/community/news/brenbits/brenbits-fall2013 Brenbits Fall 2013

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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          Bren school home > Community > News >
          Brenbits Fall 2013
          Donald Bren School of Information < Computer Sciences

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          Spotlight

          VIDEO: 2013 Event Highlights

          Holiday Anteater

           

          DEAN'S MESSAGE

          What a year! This fall, the Bren School of ICS welcomed a freshman class that comprises nearly 470 students — a 50-percent-plus increase from 2012 and more than triple the number that enrolled just five years ago. Indeed, 2013 has given us many reasons to celebrate: student and alumni accomplishments, faculty accolades, exciting new programs, and support from friends who generously give their time and resources. Thank you for your part in making the Bren School an exceptional community. I wish you a wonderful holiday season and a joyous 2014.

          — Hal Stern
          Ted and Janice Smith Family Foundation Dean

          Features

          Trio honored by ACM

          Padhraic Smyth

          Computer science professors Padhraic Smyth (pictured) and Rina Dechter named ACM Fellows; informatics professor André van der Hoek named ACM Distinguished Scientist.
          Read More

          Über-expert

          Dan Russell

          Google senior research scientist and ICS Distinguished Alumnus Dan Russell examines how people use the Internet to conduct searches and organize information.
          Read More

          Social thinkers

          Ellie Harmon

          The Intel Science & Technology Center for Social Computing welcomes an interdisciplinary class of fellows.
          Read More

          Russia-bound

          Nick Alajat, Alan Castro and Michael Cappe

          Undergrads earn a spot in the ACM International Collegiate Programming Contest World Finals.
          Read More

          Record numbers

          New students

          The Bren School kicked off fall 2013 with nearly 800 new faces, including its biggest-ever freshman class.
          Read More / View Video 

          What's next

          Henry Samueli

          Broadcom co-founder, chairman and CTO Henry Samueli headlines latest Top Trends in Tech event.
          Read More / View Photos

          To be or not to be

          Jed Brubaker

          Ph.D. candidate Jed Brubaker studies the postmortem persistence of digital identity.
          Read More

          Open for business

          OpenSimulator Community Conference

          Computer science professor Magda El Zarki directs new Institute for Virtual Environments and Computer Games.
          Read More / View Photos

          "Computing a brighter future"

          In a column published by the Orange County Register, Dean Hal Stern discusses the Bren School's record-setting freshman class, the earning potential of ICS alumni, community outreach programs led by students, and influential faculty research that draw generous support from federal agencies and corporations. 
          Read More

          Save the date

          Eric Baker

          The next Top Trends in Tech speaker event will take place Feb. 20, 2014, featuring Eric Baker, founder and CEO of viagogo, and co-founder and former president of StubHub.

          To join the invitation list, please send your request to khuerth@ics.uci.edu.

          News

          AppJam+ is back to inspire young computer scientists

          Transportation magazine features Regan's article on vehicular communications networks

          Duo get NSF grant to research maker movement

          Tech and medical students team up for Med AppJam

          Bowker and team receive EarthCube award from NSF

          Ph.D. candidate wins best presentation award at graph drawing symposium

          Papers by informatics faculty, students lauded at ubiquitous computing conference

          Tsudik delivers keynote at IEEE security conference

          Giving

           

          The Bren School is proud to foster a community of students who shine in the classroom and beyond — like Kha Tran, seen here mentoring a middle schooler as part of our AppJam+ program. Your support of the Dean’s Excellence Fund enables us to attract smart, talented scholars dedicated to their studies and to giving back to the community. Consider making a year-end contribution today. Your gift, in any amount, will make a big difference.

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          Bren school home > Community > News >
          Brenbits Winter 2014
          Donald Bren School of Information < Computer Sciences

          Winter 2014

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          Spotlight

          VIDEO: ICS student named Homecoming King

           

          David Conley (right), a senior in the computer game science program, earned his homecoming crown by besting eight other students in contests of talent, improvisational skills, campus knowledge and physical feats throughout the week leading up to the Homecoming men's basketball game in January. He also created a YouTube campaign video that featured a parody of the tune "I Just Can't Wait to Be King" from The Lion King. In this video profile, Conley talks about how he can't imagine having gone anywhere else but UCI. Watch the Video

          Features

          On cyberguard

          Gene Tsudik

          Gene Tsudik of UCI's Secure Computing & Networking Center explores ways to beef up Internet security. Read More

          Medical marvels

          Med AppJam

          ICS and School of Medicine students teamed up for the second annual Med AppJam. Read More

          Master mentor

          Dick Taylor

          Professor Emeritus Dick Taylor and the 30 Ph.D. students he has supervised have transformed the field of ICS. Read More

          The STEM tide

          Debra Richardson

          Professor Debra Richardson and other faculty discuss UCI's commitment to gender equity in science and technology fields. Read More

          A new classic

          Walt Scacchi

          Research scientist Walt Scacchi redesigned the kids' website of the San Francisco Symphony as virtual environment. Read More

          Book it

          Rina Dechter

          Professor Rina Dechter has published a new book based upon her reasearch into artificial intelligence and machine learning. Read More

          Dean’s Message

          The University of California prides itself on the importance of its mission to excel at both teaching and research. The research carried out by faculty and students at the Bren School helps to address critical societal issues — and also ensures that the education received by our students reflects the state of the art.. Read More

          Save the dates

          The Computer Science Department's Distinguished Lecture Series welcomes David A. Patterson of UC Berkeley on Friday, April 4, 2 p.m., at Calit2 Auditorium.

           

          Also, the ICS Spring Alumni Event will be April 29, 6-9:30 p.m., at the Sony Pictures Studios in Culver City.

          News

          Hayes receives award to explore use of Google Glass to help individuals with autism

           

          Mehrotra and team receive 10-year best paper award from key database conference

           

          Gary and Judith Olson receive NSF grant for distributed-work study

           

          Carey and Li receive $1.1 million in funding for big data management system research

           

          ICS grad students take second place in international data-mining competition

          Giving

           

          Bren School students like Angela Li — shown here helping a Brownie earn a Computer Expert badge — shine in the classroom and beyond. Your support of the Dean's Excellence Fund enables us to attract such smart, dedicated scholars. 

          Make a Gift

          Brenbits is published by the UCI Donald Bren School of Information and Computer Sciences. To contact the editor, please email communications@ics.uci.edu.

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          http://sherlock.ics.uci.edu/news.html Project SHERLOCK @ UCI: News.
          http://sherlock.ics.uci.edu/qgd.html Query and Goal Driven Entity Resolution Framework
          University of California
          Main Page
          News
          People
          Publications
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          SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

          News

          • Best Paper Award at ACM ICMR 2013 Conference for our paper titled "A Unified Framework for Context Assisted Face Clustering" (4/18/2013)
          • SHERLOCK @ UCI project has been funded by the NSF and DARPA, 08/01/11 - 07/30/14
          • CS295 Information Quality and Entity Resolution course has been offered by the CS Dept. at UCI.
          • SHERLOCK @ UCI project has received a $50,000 award from Google to work on "Graph based Disambiguation Framework for Web People Search" (11/17/2009).




          © 2013 SHERLOCK @ UCI. All Rights Reserved.
          http://sherlock.ics.uci.edu/people.html Project SHERLOCK @ UCI: People.
          University of California
          Main Page

          Query and Goal Driven Entity Resolution Framework

          Overview

          The significance of data quality research is motivated by the observation that the effectiveness of data-driven technologies such as decision support tools, data exploration, analysis, and scientific discovery tools is closely tied to the quality of data on which such techniques are applied. It is well recognized that the outcome of the analysis is only as good as the data on which the analysis is performed. That is why today organizations spend a tangible percent of their budgets on cleaning tasks such as removing duplicates, correcting errors, filling missing values, to improve data quality prior to pushing data through the analysis pipeline.

          Given the critical importance of the problem, many efforts, in both industry and academia, have explored systematic approaches to addressing the cleaning challenges. The work of our group focuses primarily on the entity resolution challenge that arises because objects in the real world are referred to using references or descriptions that are not always unique identifiers of the objects, leading to ambiguity.

          The traditional approach for entity resolution uses features associated with a reference (or a record) to find references that co-refer. In our project we are exploring which other sources and types of information could be used, in addition to features, to better disambiguate among references. This information could be present in that data being cleaned itself or can be obtained from external data sources, including ontologies, encyclopedias, and the Web. We are also looking into ways to guide and fine-tune the data cleaning process based on the type of analysis that will be done on the data being cleaned for it to reach higher disambiagution quality as well as efficiency.

          Faculty

          • Prof. Dmitri V. Kalashnikov
          • Prof. Sharad Mehrotra

          Current Students

          • Yasser Altowim, PhD student
          • Hotham Altwaijry, PhD student
          • Liyan Zhang, PhD student

          Alumni

          • Zhaoqi Chen (Stella), PhD, 2008 (first employment: Microsoft)
          • Virag Kothari, MS, 2011 (first employment: Yahoo!)
          • Rabia Nuray-Turan, PhD, 2011 (first employment: Metavana, Inc)
          • Kartik Udupa, MS, 2011 (first employment: PayPal)
          • Jie Xu (Jeffrey), PhD, 2014 (first employment: Facebook)

          Publications

          1. Progressive Approach to Relational Entity Resolution.
            Yasser Altowim, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In PVLDB, 7(11) Sep 1-5, 2014.
            [Download Paper] [Download Slides]

          2. Context Assisted Face Clustering Framework with Human-in-the-Loop.
            Liyan Zhnag, Dmitri V. Kalashnikov, Sharad Mehrotra
            In International Journal of Multimedia Information Retrieval (IJMIR), Springer, 2014
            [Download Paper]

          3. Efficient Summarization Framework for Multi-Attribute Uncertain Data.
            Jie Xu, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), June 22-27, 2014.
            [Download Paper] [Download Slides]

          4. Query Aware Determinization of Uncertain Objects.
            Jie Xu, Dmitri V. Kalashnikov, Sharad Mehrotra
            In IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2014
            [Download Paper]

          5. Query-driven approach to entity resolution.
            Hotham Altwaijry, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of International Conference on Very Large Data Bases (VLDB 2013), Aug 26-30, 2013.
            [Download Paper] [Download Slides]

          6. Context-based Person Identification Framework for Smart Video Surveillance.
            Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra, Ronen Vaisenberg
            In Machine Vision and Applications (MVA), 2013
            [Download Paper]

          7. A Unified Framework for Context Assisted Face Clustering.
            Liyan Zhang, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In ACM International Conference on Multimedia Retrieval (ACM ICMR 2013), Apr 16-19, 2013.
            (Best Paper Award)
            [Download Paper] [Download Slides]

          8. Adaptive connection strength models for relationship-based entity resolution.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra
            In ACM Journal of Data and Information Quality (ACM JDIQ), 2013
            [Download Paper]

          9. Exploiting web querying for web people search.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra
            In ACM Transactions on Database Systems (ACM TODS), 37(1), February 2012
            [Download Paper]

          10. Attribute and Object Selection Queries on Objects with Probabilistic Attributes.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, Sharad Mehrotra, and Yaming Yu.
            In ACM Transactions on Database Systems (ACM TODS), 37(1), February 2012
            [Download Paper]

          11. Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance.
            Liyan Zhang, Ronen Vaisenberg, Sharad Mehrotra, and Dmitri V. Kalashnikov.
            In Workshop on Information Quality and Quality of Service for Pervasive Computing (IQ2S 2011) in Conjunction with IEEE PERCOM 2011, invited paper, Mar 21-25, 2011.
            [Download Paper]

          12. Exploiting Context Analysis for Combining Multiple Entity Resolution Systems.
            Zhaoqi Stella Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), June 29-July 2, 2009.
            [Download Paper]

          13. Exploiting Web querying for Web People Search in WePS2.
            Rabia Nuray-Turan, Zhaoqi Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In 2nd Web People Search Evaluation Workshop (WePS 2009), 18th WWW Conference, April, 2009.
            [Download Paper]

          14. WEST: Modern Technologies for Web People Search.
            Dmitri V. Kalashnikov, Zhaoqi Chen, Rabia Nuray-Turan, Sharad Mehrotra, and Zheng Zhang.
            In Proc. of IEEE International Conference on Data Engineering (IEEE ICDE), demo publication, March 29 - April 4, 2009.
            [Download Paper]

          15. Web people search via connection analysis.
            Dmitri V. Kalashnikov, Zhaoqi Chen, Rabia Nuray-Turan, and Sharad Mehrotra.
            In IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 20(11), November 2008
            [Download Paper]

          16. Towards breaking the quality curse. A web-querying approach to Web People Search.
            Dmitri V. Kalashnikov, Rabia Nuray-Turan, and Sharad Mehrotra.
            In Annual International ACM SIGIR Conference, July 20-24, 2008.
            [Download Paper]

          17. Adaptive Graphical Approach to Entity Resolution.
            Stella Chen, Dmitri V. Kalashnikov, Sharad Mehrotra.
            In Proc. of ACM IEEE Joint Conference on Digital Libraries (ACM IEEE JCDL), June 17-23, 2007.
            [Download Paper]

          18. Self-tuning in Graph-based Reference Disambiguation.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of Int'l Conf. on Database Systems for Advanced Applications (DASFAA), Apr 9-12, 2007.
            [Download Paper]

          19. Disambiguation Algorithm for People Search on the Web.
            Dmitri V. Kalashnikov, Stella Chen, Rabia Nuray, Sharad Mehrotra, and Naveen Ashish.
            In Proc. of IEEE International Conference on Data Engineering (IEEE ICDE), short publication, April 16-20, 2007.
            [Download Paper]

          20. Domain-independent data cleaning via analysis of entity-relationship graph.
            Dmitri V. Kalashnikov and Sharad Mehrotra
            In ACM Transactions on Database Systems (ACM TODS), June 2006
            [Download Paper] [Code]

          21. Exploiting relationships for object consolidation.
            Zhaoqi Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of International ACM SIGMOD Workshop on Information Quality in Information Systems (ACM IQIS), June 13-17, 2005.
            [Download Paper]

          22. Exploiting relationships for domain-independent data cleaning.
            Dmitri V. Kalashnikov, Sharad Mehrotra, and Zhaoqi Chen.
            In Proc. of SIAM International Conference on Data Mining (SIAM Data Mining), April 21--23, 2005.
            [Download Paper] [Code]

          Software


        • RelDC - code for relationship-based entity resolution
        • Acknowledgement

          This material is based upon work supported by the National Science Foundation under Grant No. 1118114. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


          © 2013 SHERLOCK @ UCI. All Rights Reserved.
          http://sherlock.ics.uci.edu/data.html Project SHERLOCK @ UCI: Data Cleaning Datasets, Entity Resolution Datasets.
          University of California
          Main Page
          News
          People
          Publications
          Datasets
          Software
          Internal
          SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

          Faculty

          • Prof. Dmitri V. Kalashnikov
          • Prof. Sharad Mehrotra

          Graduate Students

          • Yasser Altowim, PhD student
          • Hotham Altwaijry, PhD student
          • Liyan Zhang, PhD student

          Alumni

          • Zhaoqi Chen (Stella), PhD, 2008 (first employment: Microsoft)
          • Virag Kothari, MS, 2011 (first employment: Yahoo!)
          • Rabia Nuray-Turan, PhD, 2011 (first employment: Metavana, Inc)
          • Kartik Udupa, MS, 2011 (first employment: PayPal)
          • Jie Xu (Jeffrey), PhD, 2014 (first employment: Facebook)




          © 2013 SHERLOCK @ UCI. All Rights Reserved.
          http://sherlock.ics.uci.edu/code.html Project SHERLOCK @ UCI: Data Cleaning Datasets, Entity Resolution Datasets.
          University of California
          Main Page
          News
          People
          Publications
          Datasets
          Software
          Internal
          SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

          Useful Data Cleaning Data Sets and Entity Resolution Data Sets


        • arXive hep-th: KDD Cup 2003 publication dataset: hep-th portion of arXive. Fully labeled, 29.5K unique papers, 13K unique authors
        • CiteSeer: a collection of research publications
        • Cora: a citation dataset from RIDDLE data repository
        • Cora: a citation dataset from Andrew McCallum's data repository
        • DBLP: a collection of bibliographic entries
        • DMOZ ontology: a large downloadable ontology
        • Enron Email Dataset: a dataset of Enron emails
        • FEBRL Database: Freely Extensible Biomedical Record Linkage
        • Freedb CD Dataset: Info on various CDs
        • IMDb: a collection of movie-related entries
        • Leipzig DB Group Datasets: publication data and product table data
        • PubMed/MEDLINE: over 20 Million bibliographic entries for biomedical literature see PubMed online for detail. Need to license it from the NIH. Texts of some publications are here: PMC.
        • RIDDLE Repository: various data cleaning-related datasets
        • SPOKE Challenge: a collection of labeled webpages for SPOKE Challenge
        • Stanford Movie Dataset: a collection of movie-related entries
        • UC Irvine Machine Learning Repository: a collection of various ML datasets
        • UIS Database Generator: generates synthetic names/addresses by adding errors into records
        • U.S. Census Names: frequently occurring first names and surnames from the 1990 Census
        • Web Disambiguation: a collection of labeled webpages used by McCallum et al. in WWW'05
        • WEPS Corpus: a collection of labeled webpages used by Artiles et al. in SIGIR'05
        • Wikilinks: Google's dataset of 11 Million webpages for cross-document entity resolution: info.
        • Wiktionary: a downloadable free-content multilingual dictionary



        • © 2013 SHERLOCK @ UCI. All Rights Reserved.
          http://sherlock.ics.uci.edu/pub.html Project SHERLOCK @ UCI: Data Cleaning Publications, Entity Resolution Publications.
          University of California
          Main Page
          News
          People
          Publications
          Datasets
          Software
          Internal
          SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

          Software


        • RelDC - code for relationship-based entity resolution



        • © 2013 SHERLOCK @ UCI. All Rights Reserved.
          http://www.ics.uci.edu/%7esysarch/projects/oldprojects.html Projects

          WebRTC

          WebRTC is an industry and standards effort to provide real-time communication capabilities into all browsers and make these capabilities accessible to software developers via standard HTML5 and Javascript APIs. WebRTC fills a critical gap in web technologies by allowing (a) the browser to access native devices (e.g., microphone, webcam) through a Javascript API and (b) to share the captured streams through using browser-to-browser Real-Time Communication. WebRTC also provides data sharing.
          We are investigating issues in WebRTC behavior and performance. To this end we have developed a benchmark suite, WebRTCBench. The goal of WebRTCBench is to provide a quantitative comparison of WebRTC implementations across browsers and devices (i.e., hardware platforms). WebRTC accomplishes three main tasks: Acquiring audio and video; Communicating Audio and Video; Communicating Arbitrary Data. These tasks are mapped one to one to three main Javascript APIs. These are as follows: MeadiaStream (i.e., getUserMedia); RTCPeerConnection; RTCDataChannel. Hence, a quantitative assessment of WebRTC implementations across browser and devices is performed via collecting performance of MediaStream, RTCPeerConnection, and RTCDataChannel. Because a MediaStream contains one or more media stream tracks (e.g., Webcam and Microphone), WebRTCBenc allows to define MediaStreams composed of Video, Audio, Data and any combination thereof. Likewise single peer connection with media server and multiple peer connections between browsers are supported in a WebRTC triangle.
          The current version of the benchmark can be used via http://core7.uci.edu

          Supported by the Intel Corp.

          Compiler and performance optimization using similarity analysis

          Maintaining and improving program performance in the multi-core era requires a large engineering effort (e.g., a large number of time consuming trials & tests). It involves finding, as efficiently as possible, a combination of attributes that characterize (i.e., expose similarity in) algorithmic optimizations, compiler optimizations, execution environment settings and hardware configurations to reach certain performance goals. This problem currently is not solved in its entirety because the number of attributes involved is very large. Thus programs are optimized based on a very limited number of such attributes at a time.
          This project investigates how to construct empirical performance models that provide program performance prediction across system configurations, where the term system includes the development environment, e.g., compilers, libraries and their settings, and execution environment, e.g., operating system, run-time environment, hardware and their settings. Predictions are required to be sufficiently accurate to reduce engineering effort (e.g., by replacing trials & tests with predictions).
          Specifically, the following issues are investigated: (i) the definition of two types of signatures, feature-aware and feature-agnostic respectively, to characterize programs and/or systems; (ii) techniques to expose and analyze the structure of similarity induced by a given type of signature on programs and systems; and (iii) techniques that leverage (learn from) such a structure of similarity and suggest ways for optimizing both serial and parallel programs.
          Feature-aware program signatures are constructed from a subset of hardware performance counters. The counters are tied to a specific performance task to accomplish, e.g., system evaluation, selection of compiler heuristics. Feature-agnostic program signatures are constructed from a collection of completion times for some combinations of (program, system). For this type of signature, the characterization of program and system is combined. Techniques that leverage this type of performance modeling can be applied to characterizing and comparing hardware configurations, compilers, run-time environments and even all the above combined.

          Supported by the National Science Foundation

          Improving single core performance via compiler-assisted out-of-order commit

          The growth in uniprocessor (single core) performance resulting from improvements in semiconductor technology has recently slowed down significantly. Sequential applications or sequential portions of parallel applications require further advances to improve their performance. Today's OOO processors complete instructions in their program order, which is a major performance bottleneck because any long-latency instruction, such as access to memory, delays the completion of all subsequent instructions. This project aims to achieve higher single core performance by defining a new, compiler assisted mechanism for out of order instruction completion. It investigates how the use of compile-time program knowledge can be passed to the hardware and be used to simplify the architectural checks required for such out of order completion. The architecture of a standard processor will be fully preserved and legacy software can execute without modification.

          Supported by the National Science Foundation

          Cache-Aware Synchronization and Scheduling of Data-Parallel Programs for Multi-Core Processors

          Multi-core (parallel) processors have become ubiquitous. The use of such systems is key to science, engineering, finance, and other major areas of the economy. However, increased applications performance on such systems can only be achieved with advances in mapping such applications to multi-core machines. This task is made more difficult by the presence of complex memory organizations which is perhaps the key bottleneck to efficient execution, and which has not been addressed effectively. This research involves making the mapping of the program to the machine aware of the complexities of the memory-hierarchy in all phases of the compilation process. This will ensure a good fit between the application code and the actual machine and thereby guarantee much more effective utilization of the hardware (and thus efficient/fast execution) than was previously possible.
          Multi-cores can benefit from new cache-hierarchy-aware compilation and runtime system (i.e., including compilation, scheduling, and static/dynamic processor mapping of parallel programs). These tasks have one thing in common: they all need accurate estimates of data element (iteration, task) computation and memory access times which are currently beyond the (cache-oblivious) state-of-the-art. This research thus develops new techniques for iteration space partitioning, scheduling, and synchronization which capture the variability due to cache, memory, and conditional statement behavior and their interaction.

          Supported by the National Science Foundation

          Acceleration of neural simulations

          We are collaborating with scientists who study and model how human brain performs certain tasks, e.g. vision. Computer simulation of such models is extremely compute-bound. We are looking at parallel, application-specific or custom architectures to accelerate such computations. Preliminary experience with FPGA-based, Cell, GPU, and parallel architectures is very encouraging.

          Reducing Power Consumption in Processors and Systems

          Power dissipation is a major issue in designing new processors and systems. In particular, CMOS technology scaling has significantly increased the leakage power dissipation so that it accounts for an increasingly large share of processor power dissipation. One of the main issue is how to achieve power savings without loss of performance.
          Much of our work in this area has focused on cache power dissipation. We addressed issues in L1 I- and D-cache dynamic as well as static power consumption. This included way caching to save static and dynamic power in high-associativity caches (as an alternative to way prediction), cached load-store queue as a low-cost alternative to L0 cache, using branch prediction information to save power in instruction caches. We addressed L2 power consumption, in particular leakage power in L2 peripheral circuits. The results of this research are applicable in both embedded and high-performance processors.
          Another aspect of this research is low-power instruction queue design for out-of-order processors. CAM-based instruction queues are not scalable and consume significant amount of power due to wide issue and CAM search on each cycle. One approach we proposed used a banked queue, thus dividing a CAM into smaller banks with faster search. A pointer table indicates which bank an instruction belongs to. A more complex approach disposed of CAM-based queue altogether and used instruction dependence pointers and RAM-based queue for "direct" wakeup. It solved the problem of how to achieve fast branch misprediction recovery when using pointers while using dependent pointers.
          We have investigated the problem of power consumption in the register file. Content-aware register file utilized knowledge of instruction operand and effective address width to reduce the number of bits read from the RF and to speed up TLB access using an "L0 TLB". This type of register file was also shown to enable a new type of clustered processor with improved performance and reduced power.
          Leakage in peripheral circuits of SRAM-based units is a major contributor to overall power dissipation as well as temperature increases. We have developed a number of circuit techniques using sleep transistors to reduce this leakage as well as architectural techniques to control the application of leakage reduction techniques.
          Finally, we studied power consumption in the main memory (DRAM) of embedded systems. For certain types of embedded systems and applications this is as important a component of overall power as the processor itself. We proposed ways to reduce power consumption in the DRAMs by utiizing buffering, delayed writes, and prefetching techniques.

          Supported by the National Science Foundation and DARPA

          Past Projects

          Speeding up Mobile Code Execution on Resource-Constrained Embedded Processors
          Supported by the National Science Foundation

          Compiler-Controlled Continuous Power-Performance Management
          Supported by DARPA

          Adaptive Memory Reconfiguration & Management
          Supported by DARPA

          http://www.ics.uci.edu/%7esysarch/projects/OOOcommit.html OOOcommit The growth in uniprocessor (single core) performance resulting from improvements in semiconductor technology has recently slowed down significantly. Sequential applications or sequential portions of parallel applications require further advances to improve their performance. Today's OOO processors complete instructions in their program order, which is a major performance bottleneck because any long-latency instruction, such as access to memory, delays the completion of all subsequent instructions. This project aims to achieve higher single core performance by defining a new, compiler assisted mechanism for out of order instruction completion. It investigates how the use of compile-time program knowledge can be passed to the hardware and be used to simplify the architectural checks required for such out of order completion. The architecture of a standard processor will be fully preserved and legacy software can execute without modification.

          Supported by the National Science Foundation
          http://www.ics.uci.edu/%7esysarch/people.html SysArch Group

          Faculty

          Prof. Alex Veidenbaum

          Prof. Alex Nicolau

          Graduate Students

          Dali Zhao

          Taesu Kim

          Nam Duong

          Laleh Aghababaie Beni

          PhD Alums

          Weiyu Tang 2004

          Anna Azevedo 2005

          Dan Nicolaescu 2006

          Arun Kejariwal, 2008

          Jelena Trajkovic, 2009

          Carmen Badea 2010

          Houman Homayoun 2010

          Rosario Cammarota, 2013

          http://www.ics.uci.edu/~alexv/pubs.html Recent Publications

          Selected Publications

          2014

          "Multiple stream tracker: a new hardware stride prefetcher."

            Taesu Kim, Dali Zhao, Alexander V. Veidenbaum.

          Conf. Computing Frontiers, 2014. p.34

           

          2013

          " Optimizing Program Performance via Similarity, Using a Feature-Agnostic Approach"
          Rosario Cammarota
          , Laleh Aghababaie Beni, Alexandru Nicolau, Alexander V. Veidenbaum.
          Intl. Conference on Advanced Parallel Processing Technology (APPT). Aug. 2013. LNCS series, vol. 8299, pp. 199-213.

           

          "On the Determination of Inlining Vectors for Program Optimization."
          Rosario Cammarota, Alexandru Nicolau, Alexander V. Veidenbaum, Arun Kejariwal, Debora Donato, Mukund Madhugiri.
          Compiler Construction (CC), pp. 164-183

          "Temperature aware thread migration in 3D architecture with stacked DRAM."
          Dali Zhao, Houman Homayoun, Alexander V. Veidenbaum.
          Intl. Symposium on Quality Electronic Design (ISQED), pp. 80-87

           

          2012

          "Compiler-Assisted, Selective Out-Of-Order Commit".
          Nam Duong and Alexander V. Veidenbaum.
          Computer Architecture Letters.

          "Improving Cache Management Policies Using Dynamic Reuse Distances".
          Nam Duong, Dali Zhao, Taesu Kim, Rosario Cammarota, Alexander V. Veidenbaum, and Mateo Valero.
          Intl. Symposium on Microarchitecture (Micro-45).

          "Revisiting level-0 caches in embedded processors."
          Nam Duong, Taesu Kim, Dali Zhao, Alexander V. Veidenbaum. Compiler, Architectures, and Synthesis for Embedded Systems (CASES). pp. 171-180

           

          2011

          "Pruning hardware evaluation space via correlation-driven application similarity analysis,"
          Rosario Cammarota, Arun Kejariwal, Paolo D'Alberto, Sapan Panigrahi, Alexander V. Veidenbaum, Alexandru Nicolau
          ACM Intl. Conf. on Computing Frontiers 2011

           

          2010

          "RELOCATE: Register File Local Access Pattern Redistribution Mechanism for Power and Thermal Management in Out-of-Order Embedded Processor,"
          Houman Homayoun, Aseem Gupta, Alexander V. Veidenbaum, Avesta Sasan, Fadi J. Kurdahi, Nikil Dutt,
          HiPEAC 2010: 216-231

          "Post-synthesis sleep transistor insertion for leakage power optimization in clock tree networks,"
          Houman Homayoun, Shahin Golshan, Eli Bozorgzadeh, Alexander V. Veidenbaum, Fadi J. Kurdahi
          ISQED 2010: 499-507

          "On the efficacy of call graph-level thread-level speculation,"
          Arun Kejariwal, Milind Girkar, Xinmin Tian, Hideki Saito, Alexandru Nicolau, Alexander V. Veidenbaum, Utpal Banerjee, Constantine D. Polychronopoulos.
          WOSP/SIPEW 2010: 247-248

          "Multiple sleep modes leakage control in peripheral circuits of a all major SRAM-based processor units,"
          Houman Homayoun, Avesta Sasan, Aseem Gupta, Alexander V. Veidenbaum, Fadi J. Kurdahi, Nikil Dutt.
          ACM Intl. Conf. Computing Frontiers 2010.

           

          2009

          "Synchronization optimizations for efficient execution on multi-cores,"
          Alexandru Nicolau, Guangqiang Li, Alexander V. Veidenbaum, Arun Kejariwal
          Proc. of the 23th ACM International Conference on Supercomputing (ICS09), June 2009, pp. 169-180

          "Power-aware load balancing of large scale MPI applications,"
          Maja Etinski, Julita Corbalan, Jesus Labarta, Mateo Valero, Alexander V. Veidenbaum
          IEEE International Symposium on Parallel&Distributed Processing (IPDPS 2009) pp. 1-8

          "Performance Characterization of Itanium 2-Based Montecito Processor,"
          Darshan Desai, Gerolf Hoflehner, Arun Kejariwal, Daniel M. Lavery, Alexandru Nicolau, Alexander V. Veidenbaum, Cameron McNairy
          SPEC Benchmark Workshop 2009, Springer LNCS Volume 5419/2009, pp. 36-56

          "Efficient Scheduling of Nested Parallel Loops on Multi-Core Systems,"
          Arun Kejariwal, Alexandru Nicolau, Utpal Banerjee, Alexander V. Veidenbaum, Constantine D. Polychronopoulos
          The 38th International Conference On Parallel Processing (ICPP-2009), pp.74-83

          "Brain Derived Vision Algorithm on High Performance Architectures,"
          Jayram Moorkanikara Nageswaran , Andrew Felch , Ashok Chandrasekhar , Nikil Dutt , Richard Granger , Alex Nicolau and Alex Veidenbaum
          International Jounral of Parallel Programming, Volume 37, Number 4 / August, 2009, pp.345-369

          "A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors,"
          Jayram Moorkanikara Nageswaran, Nikil D. Dutt, Jeffrey L. Krichmar, Alex Nicolau, Alexander V. Veidenbaum
          Neural Networks 22(5-6): 791-800 (2009)

          "On the exploitation of loop-level parallelism in embedded applications,"
          Arun Kejariwal, Alexander V. Veidenbaum, Alexandru Nicolau, Milind Girkar, Xinmin Tian, Hideki Saito
          ACM Trans. Embedded Computer Syst. 8(2) 2009

           

          2008

          "A Distributed Processor State Management Architecture for Large-Window Processors,"
          Isidro Gonzalez, Marco Galluzzi, Alex Veidenbaum, Marco A. Ramrirez, Adrian Cristal, Mateo Valero
          Intl. Symposium on Microarchitecture (Micro-41).

          "Multiple sleep mode leakage control for cache peripheral circuits in embedded processors,"
          Houman Homayoun, Mohammad A. Makhzan, Alexander V. Veidenbaum.

          ACM Intl Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES) 2008: 197-206

          "Adaptive techniques for leakage power management in L2 cache peripheral circuits,"
          Houman Homayoun, Alexander V. Veidenbaum, Jean-Luc Gaudiot.
          IEEE Intl Conference Computer Design (ICCD) 2008: 563-569

          "ZZ-HVS: Zig-zag horizontal and vertical sleep transistor sharing to reduce leakage power in on-chip SRAM peripheral circuits,"
          Houman Homayoun, Mohammad A. Makhzan, Alexander V. Veidenbaum. ICCD 2008: 699-706

          "A Two-Level Load/Store Queue based on Execution Locality,"
          Miquel Pericas, Adrian Cristal, Francisco J. Cazorla, Ruden Gonzalez,
          Alex Veidenbaum, Daniel A. Jimenez, and Mateo Valero. Proc. 35th ACM International Symposium on Computer Architecture (ISCA) June 2008

          "Impact of JVM superoperators on energy consumption in resource-constrained embedded systems,"
          Carmen Badea, Alexandru Nicolau, and Alexander V. Veidenbaum.

          Proc. of the ACM SIGPLAN-SIGBED conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), 2008.

          "Dynamic register file resizing and frequency scaling to improve embedded processor performance and energy-delay efficiency,"
          Houman Homayoun, Sudeep Pasricha, Mohammad A. Makhzan, and Alexander V. Veidenbaum.
          Proc. of the ACM/IEEE Design Automation Cinference (DAC) 2008.

          "Improving SDRAM access energy efficiency for low-power embedded systems,"
          Jelena Trajkovic, Alexander V. Veidenbaum, and Arun Kejariwal.
          ACM Transactions on Embedded Computer Systems, Vol. 7, No.3, 2008

          "Cache-aware iteration space partitioning,"
          Arun Kejariwal, Alexandru Nicolau, Utpal Banerjee, Alexander V. Veidenbaum, Constantine D. Polychronopoulos.
          Proc. of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPOPP) 2008.

           

          2007

          "A Simplified Java Bytecode Compilation System for Resource-Constrained Embedded Processors,"
          Carmen Badea, Alexandru Nicolau, Alexander V. Veidenbaum.

          Proc. of the ACM Intl. Conference on Compilers, Architecture, and Synthesis for Embedded Systems, Salzburg, Austria, Oct. 2007

          "Reducing Power Consumption in Peripheral Circuits of L2 caches,"
          Houman Homayoun and Alexander V. Veidenbaum. Proc. IEEE Intl. Conference on Computer Design, Lake Tahoe, Oct. 2007

          "Tight analysis of the performance potential of thread speculation using spec CPU 2006,"
          Arun Kejariwal, Xinmin Tian, Milind Girkar, Wei Li, Sergey Kozhukhov, Utpal Banerjee, Alexander Nicolau, Alexander V. Veidenbaum, Constantine D. Polychronopoulos,
          Proc. of the 12th ACM SIGPLAN Symposium on Principles and practice of parallel programming, Pages: 215 - 225, March 2007

           

          2006

          "Challenges in Exploitation of Loop Parallelism in Embedded Applications,"
          Arun Kejariwal, Alex Veidenbaum, Alex Nicolau, Milind Girkar, Xinmin Tian, and Hideki Saito.
          Proc. IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, October 2006

          "Fast Speculative Address Generation and Way Caching for Reducing L1 Data Cache Energy,"
          Dan Nicolaescu, Babak Salamat, Alexander Veidenbaum, and Mateo Valero.

          Proceedings of IEEE International Conference on Computer Design (ICCD'06), Oct. 2006

          "Probablistic Self-Scheduling: A Novel Scheduling Approach for Multiprogrammed Environments,"
          Arun Kejariwal, Milind Girkar, Hideki Saito, Xinmin Tian, Alexandru Nicolau, Alexander Veidenbaum, Constantine Polychronopoulos.
          Proceedings of Europar'06, August 2006

          "On the Performance Potential of Different Types of Speculative Thread-Level Parallelism,"
          Arun Kejariwal, Xinmin Tian, Wei Li, Milind Girkar, Sergey Kozhukhov, Hideki Saito, Utpal Banerjee, Alexandru Nicolau, Alexander V. Veidenbaum, Constantine D. Polychronopoulos.
          Proc. of the 20th ACM International Conference on Supercomputing (ICS06), June 2006

           

          2005

          "A New Pointer-based Instruction Queue Design and Its Power-Performance Evaluation, "
          Marco A. Ramirez, Adrian Cristal, Alexander V. Veidenbaum, Luis Villa, Mateo Valero.
          Proc. of the IEEE Int'l Conference on Computer Design (ICCD-2005), San Jose, Oct. 2005

          "High-Performance Annotation-Aware JVM for Java Cards,"
          Ana Azevedo, Arun Kejariwal, Alex Viedenbaum, Alexander Nicolau
          Proc. of the 5th ACM International Conference on Embedded software (EMSOFT05), Sept. 2005.

          "An Asymmetric Clustered Processor based on Value Content, "
          R. Gonzalez, A. Cristal, A. Veidenbaum, and M. Valero.
          Proc. of the 19th ACM International Conference on Supercomputing (ICS05), Boston, June 2005.

           

          2004

          "Low Energy, Highly-Associative Cache Design for Embedded Processors,"
          Alex Veidenbaum and Dan Nicolaescu,
          Int'l Symposium on Computer Design (ICCD-2004), San Jose, Oct. 2004

          "A Content Aware Register File Organization",
          R. Gonzalez, A. Cristal, A. Veidenbaum, and M. Valero,
          Proc. 31st International Symposium on Computer Architecture (ISCA04), Munich, Germany, June 2004.

          "Energy-Efficient Design for Highly Associative Instruction Caches in Next-Generation Embedded Processors,"
          J. L. Aragon, Dan Nicolaescu, Alex Veidenbaum, Ana-Maria Badulescu,
          Design Automation and Test Europe (DATE04): 1374-1375, March 2004

          "Direct Instruction Wakeup for Out-Of-Order Processors,"
          M. Ramirez, A. Cristal, A. Veidenbaum, L. Villa, and M. Valero,
          Int'l Workshop on Innovative Archtecture (IWIA'04), Jan. 2004

           

          2003

          "A Simple Low-Energy Instruction Wakeup Mechanism"
          M. Ramirez, A. Cristal, A. Veidenbaum, L. Villa, and M. Valero,
          5th Int'l Symposium on High-Perfromance Computing (ISHPC-V), Tokyo, Japan, Oct. 2003

          "Improving Branch Prediction Accuracy in Embedded Processors in the Presence of Context Switches" Sudeep Parisha and Alex Veidenbaum, Int'l Symposium on Computer Design (ICCD-2003), San Jose, Oct. 2003

          "Reducing Data Cache Energy Consumption via Cached Load/Store Queue," Dan Nicolaescu, Alex Veidenbaum, Alex Nicolau. International Symposium on Low Power Electronics and Design (ISLPED'03), Seoul, Aug. 2003

          "Energy aware register file implementation through instruction predecode," Ayala, J.L.; Lopez-Vallejo, M.; Veidenbaum, A.; Lopez, C.A. Proceedings IEEE International Conference On Application-specific Systems, Architectures, and Processors (ASIP03). Page(s): 81- 91 24-26 June 2003

          "Reducing Power Consumption for High-Associativity Data Caches in Embedded Processors," Dan Nicolaescu, Alex Veidenbaum, Alex Nicolau Design Automation and Test Europe (DATE'03), March 2003

          "Dynamically Adaptive Fetch Size Prediction for Data Caches"
          Weiyu Tang, A. Veidenbaum, Alex Nicolau.
          Int'l Workshop on Innovative Architecture (IWIA03), January 2003

           

          2002

          "Profile-based dynamic voltage scheduling using program checkpoints in the COPPER framework."
          A. Azevedo, I. Issenin, R. Cornea, R. Gupta, N. Dutt, A. Veidenbaum, and A. Nicolau. In Proceedings of Design, Automation and Test in Europe Conference (DATE'02), March 2002.

          "Power-Efficient Instruction Fetch Architecturte for Superscalar Processors"
          Anna-Maria Badulescu and Alex Veidenbaum, Proc. Parallel and Distributed Processign Techniques and Architecures (PDPTA02), June 25-27 2002


          "Integrated I-cache Way Predictor and Branch Target Buffer to Reduce Energy Consumption"
          Weiyu Tang, A. Veidenbaum, Alex Nicolau, and Rajesh Gupta, 4th Int'l Symposium on High-Perfromance Computing (ISHPC-IV), Nara, Japan, May 2002

           

          2001 and prior

          "Energy Efficient Instruction Cache for Wide-issue Processors" A Badulescu, A. Veidenbaum, Int'l Workshop on Innovative Architecture for Future Generation High-Performance Processors and Systems (IWIA), Jan 2001

          "Adapting Cache Line Size to Application Behavior" Alexander V. Veidenbaum , Weiyu Tang, Rajesh Gupta, Alexandru Nicolau, and Xiaomei Ji. , Proc. 1999 Int'l Conference on Supercomputing (ICS99), pp. 145-154, June 1999

          "Non-sequential Instruction Cache Prefetching for Multiple-Issue Processors", Alex Veidenbaum, Qinbo Zhao, and Abduhl Shameer, International Journal of High-Speed Computing, pp.115-140, Vol.10, No. 1., 1999

          "Interconnection Network Organization and its Impact on Performance and Cost of Shared Memory Multiprocessors", Sunil Kim and Alex Veidenbaum, PARALLEL COMPUTING Journal, vol. 25, 1999, pp. 283-309.

          "An Integrated Hardware/Software Approach to Data Prefetching for Shared-Memory Multiprocessors", Edward H. Gornish and Alex Veidenbaum, International Journal on Parallel Programming, pp. 323--332, volume 27(1), 1999.

          "On Interaction between Interconnection Network Design and Latency Hiding Techniques in Multiprocessors", Sunil Kim and Alex Veidenbaum. Accepted for publication in The Journal of Supercomputing, 1998

          "Decoupled Access DRAM Archiecture", Alex Veidenbaum and Kyle Gallivan, in Innovative Architecture for Future-Generation Processors and Systems, pp. 94-105, IEEE Computer Society Press, 1998

          "Instruction Cache Prefetching Using Multi-Level Branch Prediction", Alex Veidenbaum, Proc. Intnl. Symposium on High-Performance Computing, Springer-Verlag Lecture Notes in Computer Science, pp. 51-71, Nov. 1997

          "The Effect of Limited Network Bandwidth and its Utilization by Latency Hiding Techniques in Large-Scale Shared Memory Systems", Sunil Kim and Alex Veidenbaum, Proc.of International Conference on Parallel Architectures and Compilation Techniques (PACT'97), pp. 40-51, Nov. 1997

          "Stride-directed Prefetching for Secondary Caches", Sunil Kim and Alex Veidenbaum, Proc.1997 International Conference on Parallel Processing, pp. 314-321, Aug. 1997

          "On Shortest Path Routing in Single-Stage Shuffle-Exchange Networks", Sunil Kim and Alex Veidenbaum, Proc. 7th ACM Symposium on Parallel Algorithms and Architectures, July 1995

          "Scalability of the Cedar system", Stephen Turner and Alex Veidenbaum, Proceedings of Supercomputing'94, Nov. 1994.

          "An Integrated Hardware/Software Data Prefetching Scheme for Shared-Memory Multiprocessors", Edward H. Gornish and Alex Veidenbaum, Proc. 1994 Int'l Conference on Parallel Processing, Aug. 1994.

          "The Cedar System and an Initial Performance Study", David J. Kuck et al, Proc. 20th International Symposium on Computer Architecture, May 1993.

          "Performance Evaluation of Memory Caches in Multiprocessors", Y.-C. Chen and Alex Veidenbaum, Proc. 1993 Int'l Conference on Parallel Processing, Aug. 1993.

          "An Effective Write Policy for Software Coherence Schemes", Y.-C. Chen and Alex Veidenbaum, Proceedings of Supercomputing'92, pp. 661-672, Nov. 1992.

          "Detecting Redundant Accesses to Array Data", Elana Granston and Alex Veidenbaum, Proc. Supercomputing'91, pp. 854-865, Nov. 1991.

          "Comparison and Analysis of Software and Directory Coherence Schemes", Y.-C. Chen and Alex Veidenbaum, Proc. Supercomputing'91, pp. 818-829, Nov. 1991.

          "The Organization of the Cedar System", David J. Kuck et al, Proc. 1991 Int'l Conference on Parallel Processing, Vol. I, pp. 49-56, Aug. 1991.

          "Preliminary Performance Analysis of the Cedar Multiprocessor Memory System", K. Gallivan, W. Jalby, S. Turner, Alex Veidenbaum, and H. Wijshoff, Proc. 1991 Int'l Conference on Parallel Processing, Vol. I, pp. 71-75, Aug. 1991.

          "An Integrated Hardware/Software Solution for Effective Management of Local Storage in High- Performance Systems", Elana Granston and Alex Veidenbaum, Proc. 1991 Int'l Conference on Parallel Processing, Vol. II, pp. 83-90, Aug. 1991.

          "A Software Coherence Scheme with the Assistance of Directories", Y.-C. Chen and Alex Veidenbaum, Proc. 1991 Int'l Conference on Supercomputing, pp. 284-294, June 1991.

          http://www.ics.uci.edu/%7esysarch/projects/WebRTC.html WebRTC

          WebRTC is an industry and standards effort to provide real-time communication capabilities into all browsers and make these capabilities accessible to software developers via standard HTML5 and Javascript APIs. WebRTC fills a critical gap in web technologies by allowing (a) the browser to access native devices (e.g., microphone, webcam) through a Javascript API and (b) to share the captured streams through using browser-to-browser Real-Time Communication. WebRTC also provides data sharing.

          We are investigating issues in WebRTC behavior and performance. To this end we have developed a benchmark suite, WebRTCBench. The goal of WebRTCBench is to provide a quantitative comparison of WebRTC implementations across browsers and devices (i.e., hardware platforms). WebRTC accomplishes three main tasks: Acquiring audio and video; Communicating Audio and Video; Communicating Arbitrary Data. These tasks are mapped one to one to three main Javascript APIs. These are as follows: MeadiaStream (i.e., getUserMedia); RTCPeerConnection; RTCDataChannel. Hence, a quantitative assessment of WebRTC implementations across browser and devices is performed via collecting performance of MediaStream, RTCPeerConnection, and RTCDataChannel. Because a MediaStream contains one or more media stream tracks (e.g., Webcam and Microphone), WebRTCBenc allows to define MediaStreams composed of Video, Audio, Data and any combination thereof. Likewise single peer connection with media server and multiple peer connections between browsers are supported in a WebRTC triangle.

          The current version of the benchmark can be found here

          Supported by the Intel Corp.
          http://asterix.ics.uci.edu/index.html AsterixDB
          University of California
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          SHERLOCK @ UCI:  Entity Resolution and Data Quality Project at UC Irvine.

          Publications

          1. Progressive Approach to Relational Entity Resolution.
            Yasser Altowim, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In PVLDB, 7(11) Sep 1-5, 2014.
            [Download Paper] [Download Slides]

          2. Context Assisted Face Clustering Framework with Human-in-the-Loop.
            Liyan Zhnag, Dmitri V. Kalashnikov, Sharad Mehrotra
            In International Journal of Multimedia Information Retrieval (IJMIR), Springer, 2014
            [Download Paper]

          3. Efficient Summarization Framework for Multi-Attribute Uncertain Data.
            Jie Xu, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), June 22-27, 2014.
            [Download Paper] [Download Slides]

          4. Query Aware Determinization of Uncertain Objects.
            Jie Xu, Dmitri V. Kalashnikov, Sharad Mehrotra
            In IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2014
            [Download Paper]

          5. Query-driven approach to entity resolution.
            Hotham Altwaijry, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of International Conference on Very Large Data Bases (VLDB 2013), Aug 26-30, 2013.
            [Download Paper] [Download Slides]

          6. Context-based Person Identification Framework for Smart Video Surveillance.
            Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra, Ronen Vaisenberg
            In Machine Vision and Applications (MVA), 2013
            [Download Paper]

          7. Super-EGO: Fast Multi-Dimensional Similarity Join.
            Dmitri V. Kalashnikov
            In The International Journal on Very Large Data Bases (VLDB Journal), 4(2):561–585, 2013
            [Download Paper] [Code]

          8. A Unified Framework for Context Assisted Face Clustering.
            Liyan Zhang, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In ACM International Conference on Multimedia Retrieval (ACM ICMR 2013), Apr 16-19, 2013.
            (Best Paper Award)
            [Download Paper] [Download Slides]

          9. Adaptive connection strength models for relationship-based entity resolution.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra
            In ACM Journal of Data and Information Quality (ACM JDIQ), 2013
            [Download Paper]

          10. Exploiting web querying for web people search.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra
            In ACM Transactions on Database Systems (ACM TODS), 37(1), February 2012
            [Download Paper]

          11. Attribute and Object Selection Queries on Objects with Probabilistic Attributes.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, Sharad Mehrotra, and Yaming Yu.
            In ACM Transactions on Database Systems (ACM TODS), 37(1), February 2012
            [Download Paper]

          12. Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance.
            Liyan Zhang, Ronen Vaisenberg, Sharad Mehrotra, and Dmitri V. Kalashnikov.
            In Workshop on Information Quality and Quality of Service for Pervasive Computing (IQ2S 2011) in Conjunction with IEEE PERCOM 2011, invited paper, Mar 21-25, 2011.
            [Download Paper]

          13. Exploiting Context Analysis for Combining Multiple Entity Resolution Systems.
            Zhaoqi Stella Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of ACM SIGMOD Int'l Conf. on Management of Data (ACM SIGMOD), June 29-July 2, 2009.
            [Download Paper]

          14. Exploiting Web querying for Web People Search in WePS2.
            Rabia Nuray-Turan, Zhaoqi Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In 2nd Web People Search Evaluation Workshop (WePS 2009), 18th WWW Conference, April, 2009.
            [Download Paper]

          15. WEST: Modern Technologies for Web People Search.
            Dmitri V. Kalashnikov, Zhaoqi Chen, Rabia Nuray-Turan, Sharad Mehrotra, and Zheng Zhang.
            In Proc. of IEEE International Conference on Data Engineering (IEEE ICDE), demo publication, March 29 - April 4, 2009.
            [Download Paper]

          16. Web people search via connection analysis.
            Dmitri V. Kalashnikov, Zhaoqi Chen, Rabia Nuray-Turan, and Sharad Mehrotra.
            In IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 20(11), November 2008
            [Download Paper]

          17. Towards breaking the quality curse. A web-querying approach to Web People Search.
            Dmitri V. Kalashnikov, Rabia Nuray-Turan, and Sharad Mehrotra.
            In Annual International ACM SIGIR Conference, July 20-24, 2008.
            [Download Paper]

          18. Adaptive Graphical Approach to Entity Resolution.
            Stella Chen, Dmitri V. Kalashnikov, Sharad Mehrotra.
            In Proc. of ACM IEEE Joint Conference on Digital Libraries (ACM IEEE JCDL), June 17-23, 2007.
            [Download Paper]

          19. Self-tuning in Graph-based Reference Disambiguation.
            Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of Int'l Conf. on Database Systems for Advanced Applications (DASFAA), Apr 9-12, 2007.
            [Download Paper]

          20. Disambiguation Algorithm for People Search on the Web.
            Dmitri V. Kalashnikov, Stella Chen, Rabia Nuray, Sharad Mehrotra, and Naveen Ashish.
            In Proc. of IEEE International Conference on Data Engineering (IEEE ICDE), short publication, April 16-20, 2007.
            [Download Paper]

          21. Domain-independent data cleaning via analysis of entity-relationship graph.
            Dmitri V. Kalashnikov and Sharad Mehrotra
            In ACM Transactions on Database Systems (ACM TODS), June 2006
            [Download Paper] [Code]

          22. Exploiting relationships for object consolidation.
            Zhaoqi Chen, Dmitri V. Kalashnikov, and Sharad Mehrotra.
            In Proc. of International ACM SIGMOD Workshop on Information Quality in Information Systems (ACM IQIS), June 13-17, 2005.
            [Download Paper]

          23. Exploiting relationships for domain-independent data cleaning.
            Dmitri V. Kalashnikov, Sharad Mehrotra, and Zhaoqi Chen.
            In Proc. of SIAM International Conference on Data Mining (SIAM Data Mining), April 21--23, 2005.
            [Download Paper] [Code]

          24. Efficient Record Linkage in Large Data Sets.
            Liang Jin, Chen Li, and Sharad Mehtrotra.
            In Proc. of DASFAA Conference, 2003.
            (DASFAA 10-year Best Paper Award)



          © 2013 SHERLOCK @ UCI. All Rights Reserved.
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          Overview

          Welcome to the new home of the AsterixDB Big Data Management System (BDMS). The AsterixDB BDMS is the result of about 3.5 years of R&D involving researchers at UC Irvine, UC Riverside, and UC San Diego. The AsterixDB code base now consists of roughly 250K lines of Java code that has been co-developed at UC Irvine and UC Riverside.

          Initiated in 2009, the NSF-sponsored ASTERIX project has been developing new technologies for ingesting, storing, managing, indexing, querying, and analyzing vast quantities of semi-structured information. The project has been combining ideas from three distinct areas—semi-structured data, parallel databases, and data-intensive computing (a.k.a. today’s Big Data platforms)—in order to create a next-generation, open-source software platform that scales by running on large, shared-nothing commodity computing clusters.

          The ASTERIX effort has been targeting a wide range of semi-structured information, ranging from “data” use cases—where information is well-typed and highly regular—to “content” use cases—where data tends to be irregular, much of each datum may be textual, and the ultimate schema for the various data types involved may be hard to anticipate up front. The ASTERIX project has been addressing technical issues including highly scalable data storage and indexing, semi-structured query processing on very large clusters, and merging time-tested parallel database techniques with modern data-intensive computing techniques to support performant yet declarative solutions to the problem of storing and analyzing semi-structured information effectively.

          The first fruits of this labor have been captured in the AsterixDB system that is now being released in preliminary or “Beta” release form. We are hoping that the arrival of AsterixDB will mark the beginning of the “BDMS era”, and we hope that both the Big Data community and the database community will find the AsterixDB system to be interesting and useful for a much broader class of problems than can be addressed with any one of today’s current Big Data platforms and related technologies (e.g., Hadoop, Pig, Hive, HBase, MongoDB, and so on). One of our project mottos has been “one size fits a bunch”—at least that has been our aim.

          © asterixdb.ics.uci.edu 2015.
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          http://asterix.ics.uci.edu/documentation/index.html AsterixDB -
          AsterixDB

          • Last Published: 2014-07-14
          • Version: 0.8.6
          • |
          • Documentation Home
          • Documentation
          • Installing and Managing AsterixDB using Managix
          • AsterixDB 101: An ADM and AQL Primer
          • AsterixDB Javascript SDK
          • Asterix Data Model (ADM)
          • Asterix Query Language (AQL)
          • AQL Functions
          • AQL Allen's Relations Functions
          • AQL Support of Similarity Queries
          • Accessing External Data
          • REST API to AsterixDB

          Hyracks

          AsterixDB: A Big Data Management System

          Table of Contents

          • What Is AsterixDB?
          • Getting and Using AsterixDB

          What Is AsterixDB? [Back to TOC]

          In a nutshell, AsterixDB is a full-function BDMS (Big Data Management System) with a rich feature set that distinguishes it from pretty much any other Big Data platform that’s out and available today. We believe that its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. AsterixDB has:

          • A semistructured NoSQL style data model (ADM) resulting from extending JSON with object database ideas
          • An expressive and declarative query language (AQL) that supports a broad range of queries and analysis over semistructured data
          • A parallel runtime query execution engine, Hyracks, that has been scale-tested on up to 1000+ cores and 500+ disks
          • Partitioned LSM-based data storage and indexing to support efficient ingestion and management of semistructured data
          • Support for query access to externally stored data (e.g., data in HDFS) as well as to data stored natively by AsterixDB
          • A rich set of primitive data types, including spatial and temporal data in addition to integer, floating point, and textual data
          • Secondary indexing options that include B+ trees, R trees, and inverted keyword (exact and fuzzy) index types
          • Support for fuzzy and spatial queries as well as for more traditional parametric queries
          • Basic transactional (concurrency and recovery) capabilities akin to those of a NoSQL store

          Getting and Using AsterixDB [Back to TOC]

          You are most likely here because you are interested in getting your hands on AsterixDB—so you would like to know how to get it, how to set it up, and how to use it. The following is a list of the supporting documents that we have available today:

          1. Installing AsterixDB using Managix : This is our installation guide, and it is where you should start. This document will tell you how to obtain, install, and manage instances of AsterixDB, including both single-machine setup (for developers) as well as cluster installations (for deployment in its intended form).

          2. AsterixDB 101: An ADM and AQL Primer : This is a first-timers introduction to the user model of the AsterixDB BDMS, by which we mean the view of AsterixDB as seen from the perspective of an “average user” or Big Data application developer. The AsterixDB user model consists of its data modeling features (ADM) and its query capabilities (AQL). This document presents a tiny “social data warehousing” example and uses it as a backdrop for describing, by example, the key features of AsterixDB. By working through this document, you will learn how to define the artifacts needed to manage data in AsterixDB, how to load data into the system, how to use most of the basic features of its query language, and how to insert and delete data dynamically.

          3. Asterix Data Model (ADM), Asterix Functions, Asterix functions for Allen’s Relations, and Asterix Query Language (AQL) : These are reference documents that catalog the primitive data types and built-in functions available in AQL and the reference manual for AQL itself.

          4. REST API to AsterixDB : Access to data in an AsterixDB instance is provided via a REST-based API. This is a short document that describes the REST API entry points and their URL syntax.

          To all who have now come this far: Thanks for your interest in AsterixDB, and for kicking its tires in its Beta form. In addition to getting the system and trying it out, please sign up as a member of the AsterixDB user mailing list (asterixdb-users (at) googlegroups.com) so that you can contact us easily with your questions, issues, and other feedback. We want AsterixDB to be a “big hit” some day, and we are anxious to see what users do with it and to learn from that feedback what we should be working on most urgently in the next phase of the project.


          Copyright © 2014. All Rights Reserved.
          http://asterix.ics.uci.edu/download.html Asterix Download
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          Downloads

          Main Releases


          • You can download an AsterixDB release and its checksums from here:
            • AsterixDB Beta Release 0.8.6   MD5   SHA1   Release Notes
            • AsterixDB Beta Release 0.8.5   MD5   SHA1   Release Notes
            • AsterixDB Beta Release 0.8.3   MD5   SHA1   Release Notes
            • AsterixDB Beta Release 0.8.0   MD5   SHA1
          • To get started please follow the installation instructions.
          • AsterixDB is an open-source project. You can check out the source code in our Google Code repository.

          APIs and Language Bindings


          • AsterixDB Javascript Interface

          Demos and SDKs


          • TweetBook Demo
          • ADM & AQL 101 Demo

          Snapshot Releases


          • You can download a preview version of the latest changes from our master branch below (use at your own risk!):
            • AsterixDB 0.8.7-SNAPSHOT   SHA1

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          http://asterix.ics.uci.edu/contact.html ASTERIX - Contact Us
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          Contact Us

          Asterix is an open source project. We welcome comments, suggestions, use cases, and interesting and exciting colloabration opportunities.

          • AsterixDB user group
          • Hyracks user group

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          Asterix Talks

          • AsterixDB: A New Platform for Real-Time Big Data BI,
            Keynote Speech in BIRTE 2013 Workshop, Riva del Garda, Italy (co-located with VLDB 2013).
            Slides

          • Introducing AsterixDB (A Next-Generation Big Data Management System),
            The Orange County Hadoop User Group (OC-HUG) meetup, Irvine, CA
            Slides Video

          • Election and ASTERIX,
            ACM GIS BigSpatial 2012, Redondo Beach, CA.
            Slides

          • Inside "Big Data Management": Ogres, Onions, or Parfaits?,
            Keynote Speech in EDBT 2012.
            Video

          • ASTERIX project vision talks at IBM Research, Yahoo! Research, and Microsoft.
            Slides Video

          • Videos and slides from our Scalable Data Management lecture series.

          • Efficient Parallel Set-Similarity Joins Using Hadoop,
            Yahoo Hadoop Summit 2010.
            Slides

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          AsterixDB Team

          Faculty


          Michael Carey

          Chen Li

          Vassilis Tsotras

          Heri Ramampiaro

          Staff


          Vinayak Borkar

          Till Westmann

          PhD Students


          Yingyi Bu

          Inci Cetindil

          Eugenia Gabrielova

          Zachary Heilbron

          Young-Seok Kim

          Keren Ouaknine

          Pouria Pirzadeh

          Steven Jacobs

          Ildar Absalyamov

          Masters Students


          Abdullah Abdulrahman Alamoudi

          Ji Mahn Ok

          Alumni

          • Sattam Alsubaiee (Ph.D., now at KACST Saudi Arabia)
          • Raman Grover (Ph.D., now at Microsoft Research)
          • Jarod (Jian) Wen (Ph.D., now at Oracle Labs)
          • Alexander Behm (Ph.D., now at Cloudera)
          • Nicola Onose (Postdoc, now at Google)
          • Rares Vernica (Ph.D., now at HP Labs)
          • Vandana Ayyalasomayajula (MS, now at Yahoo!)
          • Madhusudan C.S. (MS, now at Google)
          • Khurram Faraaz (MS, now at IBM)
          • Diego Giorgini (Exchange Student, now MS student at University of Padua)
          • Manish Honnatti (MS, now at Zappos)
          • Ching-wei Huang (MS, now at MedInformatix)
          • Guangqiang Li (MS, now at MarkLogic)
          • Ling Ling (MS, now at MarkLogic)
          • Xiaoyu Ma (MS, now at QuantCast)
          • Siripen Pongpaichet (MS, now Ph.D. student at UCI)
          • Lin Shao (MS, now at Teradata/AsterData)
          • Dustin Lakin (BS, Entrepreneur)
          • Roman Vorobyov (BS, now at Orangebus)

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          Asterix News

          02/2015 "Data Ingestion in AsterixDB" was accepted to EDBT 2015!
          10/2014 We have four new publications, detailing many aspects of AsterixDB's features, design and use.
          07/2014 AsterixDB Beta Release 0.8.6 is now available to download! You can find the list of improvements over the previous release in the release notes.
          04/2014 AsterixDB Beta Release 0.8.5 is now available to download! You can find the list of improvements over the previous release in the release notes.
          01/2014 AsterixDB Beta Release 0.8.3 is now available to download! You can find the list of improvements over the previous release in the release notes.
          09/2013 Keynote: Mike Carey gave a keynote talk entitled "AsterixDB: A New Platform for Real-Time Big Data BI" at the BIRTE Workshop affiliated with the VLDB 2013 Conference in Riva del Garda, Italy: Slides
          06/2013 Press coverage of AsterixDB after beta release:
          • WSJ
          • Scientific Computing
          06/2013 Mike gave a talk about AsterixDB in the The Orange County Hadoop User Group (OC-HUG) meetup: Video Slides
          06/2013 AsterixDB Beta Release 0.8.0 is now available to download!

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          Asterix Publications

          • AsterixDB: A Scalable, Open Source BDMS,
            Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak Borkar, Yingyi Bu, Michael Carey, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann, Inci Cetindil, Madhusudan Cheelangi
            Proceedings of the VLDB Endowment, Vol. 7, No. 14

          • Pregelix: Big(ger) Graph Analytics on A Dataflow Engine,
            Yingyi Bu, Vinayak Borkar, Jianfeng Jia, Michael J. Carey, Tyson Condie
            Proceedings of the VLDB Endowment, Vol. 8, No. 2

          • Scalable Fault-Tolerant Data Feeds in AsterixDB,
            Raman Grover, Michael J. Carey.
            In CoRR abs, 1405.1705, 2014.

          • Storage Management in AsterixDB,
            Sattam Alsubaiee, Alexander Behm, Vinayak Borkar, Zachary Heilbron, Young-Seok Kim, Michael J. Carey, Markus Dreseler, Chen Li.
            VLDB 2014.

          • A Bloat-Aware Design for Big Data Applications,
            Yingyi Bu, Vinayak Borkar, Guoqing Xu, and Michael J. Carey.
            In Proceedings of the 2013 ACM SIGPLAN International Symposium on Memory Management (ISMM 2013). Seattle, WA, June 20-21, 2013.

          • Declarative Systems for Large-Scale Machine Learning,
            Vinayak Borkar, Yingyi Bu, Michael J. Carey, Joshua Rosen, Neoklis Polyzotis, Tyson Condie, Markus Weimer, Raghu Ramakrishnan.
            IEEE Data Engineering Bulletin. Volume 35, Number 2, June 2012.

          • BDMS Performance Evaluation: Practices, Pitfalls, and Possibilities,
            Michael J. Carey.
            In Selected Topics in Performance Evaluation and Benchmarking - 4th TPC Technology Conference, TPCTC 2012, Istanbul, Turkey, August 27, 2012

          • ASTERIX: An Open Source System for "Big Data" Management and Analysis,
            Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Nicola Onose, Pouria Pirzadeh, Rares Vernica, Jian Wen.
            PVLDB 2012 (demo).

          • Big data platforms: what's next?,
            Vinayak R. Borkar, Michael J. Carey, Chen Li.
            ACM Crossroads 19(1): 44-49, 2012.

          • ASTERIX: Scalable Warehouse-Style Web Data Integration,
            Sattam Alsubaiee, Alexander Behm, Raman Grover, Rares Vernica, Vinayak Borkar, Michael J. Carey, Chen Li.
            IIWeb 2012 (co-located with SIGMOD 2012).

          • Inside “Big Data Management”: Ogres, Onions, or Parfaits?,
            Vinayak R. Borkar, Michael J. Carey, Chen Li.
            EDBT 2012 (Keynote Talk).

          • Extending Map-Reduce for Efficient Predicate-Based Sampling,
            Raman Grover, Michael Carey.
            ICDE 2012. slides poster

          • ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models,
            Alexander Behm, Vinayak R. Borkar, Michael J. Carey, Raman Grover, Chen Li, Nicola Onose, Rares Vernica, Alin Deutsch, Yannis Papakonstantinou, and Vassilis J. Tsotras.
            Distrib. Parallel Databases 29, 3 (June 2011), 185-216.

          • Online Aggregation for Large MapReduce Jobs,
            Niketan Pansare, Vinayak R. Borkar, Chris Jermaine, Tyson Condie.
            VLDB 2011. source code

          • Map-reduce extensions and recursive queries,
            Foto N. Afrati, Vinayak R. Borkar, Michael J. Carey, Neoklis Polyzotis, Jeffrey D. Ullman.
            EDBT 2011 (Keynote Talk).

          • Answering Approximate String Queries on Large Data Sets Using External Memory,
            Alexander Behm, Chen Li, Michael J. Carey.
            ICDE 2011. source code

          • Hyracks: A Flexible and Extensible Foundation for Data-Intensive Computing,
            Vinayak Borkar, Michael J. Carey, Raman Grover, Nicola Onose, Rares Vernica.
            ICDE 2011. long version source code

          • Efficient Parallel Set-Similarity Joins Using MapReduce,
            Rares Vernica, Michael J. Carey, Chen Li.
            SIGMOD 2010. long version source code

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          Username: University of California Irvine User Book: Hadoop: The Definitive Guide, 3rd Edition. No part of any chapter or book may be reproduced or transmitted in any form by any means without the prior written permission for reprints and excerpts from the publisher of the book or chapter. Redistribution or other use that violates the fair use privilege under U.S. copyright laws (see 17 USC107) or that otherwise violates these Terms of Service is strictly prohibited. Violators will be prosecuted to the full extent of U.S. Federal and Massachusetts laws.

          YARN Configuration

          YARN is the next-generation architecture for running MapReduce (and is described in YARN (MapReduce 2)). It has a different set of daemons and configuration options than classic MapReduce (also called MapReduce 1), and in this section we look at these differences and discuss how to run MapReduce on YARN.

          Under YARN, you no longer run a jobtracker or tasktrackers. Instead, there is a single resource manager running on the same machine as the HDFS namenode (for small clusters) or on a dedicated machine, and node managers running on each worker node in the cluster.

          The YARN start-yarn.sh script (in the sbin directory) starts the YARN daemons in the cluster. This script will start a resource manager (on the machine the script is run on) and a node manager on each machine listed in the slaves file.

          YARN also has a job history server daemon that provides users with details of past job runs, and a web app proxy server for providing a secure way for users to access the UI provided by YARN applications. In the case of MapReduce, the web UI served by the proxy provides information about the current job you are running, similar to the one described in The MapReduce Web UI. By default, the web app proxy server runs in the same process as the resource manager, but it may be configured to run as a standalone daemon.

          YARN has its own set of configuration files, listed in Table 9-8; these are used in addition to those in Table 9-1.

          Table 9-8. YARN configuration files

          FilenameFormatDescription
          yarn-env.shBash scriptEnvironment variables that are used in the scripts to run YARN
          yarn-site.xmlHadoop configuration XMLConfiguration settings for YARN daemons: the resource manager, the job history server, the webapp proxy server, and the node managers

          Important YARN Daemon Properties

          When running MapReduce on YARN, the mapred-site.xml file is still used for general MapReduce properties, although the jobtracker- and tasktracker-related properties are not used. None of the properties in Table 9-4 are applicable to YARN, except for mapred.child.java.opts (and the related properties mapreduce.map.java.opts and mapreduce.reduce.java.opts, which apply only to map or reduce tasks, respectively). The JVM options specified in this way are used to launch the YARN child process that runs map or reduce tasks.

          The configuration files in Example 9-4 show some of the important configuration properties for running MapReduce on YARN.

          Example 9-4. An example set of site configuration files for running MapReduce on YARN

          <?xml version="1.0"?>
          <!-- mapred-site.xml -->
          <configuration>
            <property>
              <name>mapred.child.java.opts</name>
              <value>-Xmx400m</value>
              <!-- Not marked as final so jobs can include JVM debugging options -->
            </property>
          </configuration>
          <?xml version="1.0"?>
          <!-- yarn-site.xml -->
          <configuration>
            <property>
              <name>yarn.resourcemanager.address</name>
              <value>resourcemanager:8032</value>
            </property>
            <property>
              <name>yarn.nodemanager.local-dirs</name>
              <value>/disk1/nm-local-dir,/disk2/nm-local-dir</value>
              <final>true</final>
            </property>
            <property>
              <name>yarn.nodemanager.aux-services</name>
              <value>mapreduce.shuffle</value>
            </property>
            <property>
              <name>yarn.nodemanager.resource.memory-mb</name>
              <value>8192</value>
            </property>
          </configuration>

          The YARN resource manager address is controlled via yarn.resourcemanager.address, which takes the form of a host-port pair. In a client configuration, this property is used to connect to the resource manager (using RPC), and in addition, the mapreduce.framework.name property must be set to yarn for the client to use YARN rather than the local job runner.

          Although YARN does not honor mapred.local.dir, it has an equivalent property called yarn.nodemanager.local-dirs, which allows you to specify the local disks to store intermediate data on. It is specified by a comma-separated list of local directory paths, which are used in a round-robin fashion.

          YARN doesn’t have tasktrackers to serve map outputs to reduce tasks, so for this function it relies on shuffle handlers, which are long-running auxiliary services running in node managers. Because YARN is a general-purpose service, the MapReduce shuffle handlers need to be enabled explicitly in yarn-site.xml by setting the yarn.nodemanager.aux-services property to mapreduce.shuffle.

          Table 9-9 summarizes the important configuration properties for YARN.

          Table 9-9. Important YARN daemon properties

          Property nameTypeDefault valueDescription
          yarn.resourcemanager.addressHostname and port0.0.0.0:8032The hostname and port that the resource manager’s RPC server runs on.
          yarn.nodemanager.local-dirsComma-separated directory names/tmp/nm-local-dirA list of directories where node managers allow containers to store intermediate data. The data is cleared out when the application ends.
          yarn.nodemanager.aux-servicesComma-separated service names A list of auxiliary services run by the node manager. A service is implemented by the class defined by the property yarn.nodemanager.aux-services.service-name.class. By default, no auxiliary services are specified.
          yarn.nodemanager.resource.memory-mbint8192The amount of physical memory (in MB) that may be allocated to containers being run by the node manager.
          yarn.nodemanager.vmem-pmem-ratiofloat2.1The ratio of virtual to physical memory for containers. Virtual memory usage may exceed the allocation by this amount.

          Memory

          YARN treats memory in a more fine-grained manner than the slot-based model used in the classic implementation of MapReduce. Rather than specifying a fixed maximum number of map and reduce slots that may run on a tasktracker node at once, YARN allows applications to request an arbitrary amount of memory (within limits) for a task. In the YARN model, node managers allocate memory from a pool, so the number of tasks that are running on a particular node depends on the sum of their memory requirements, and not simply on a fixed number of slots.

          The slot-based model can lead to cluster underutilization, since the proportion of map slots to reduce slots is fixed as a cluster-wide configuration. However, the number of map versus reduce slots that are in demand changes over time: at the beginning of a job only map slots are needed, whereas at the end of the job only reduce slots are needed. On larger clusters with many concurrent jobs, the variation in demand for a particular type of slot may be less pronounced, but there is still wastage. YARN avoids this problem by not distinguishing between the two types of slots.

          The considerations for how much memory to dedicate to a node manager for running containers are similar to the those discussed in Memory. Each Hadoop daemon uses 1,000 MB, so for a datanode and a node manager, the total is 2,000 MB. Set aside enough for other processes that are running on the machine, and the remainder can be dedicated to the node manager’s containers by setting the configuration property yarn.nodemanager.resource.memory-mb to the total allocation in MB. (The default is 8,192 MB.)

          The next step is to determine how to set memory options for individual jobs. There are two controls: mapred.child.java.opts, which allows you to set the JVM heap size of the map or reduce task; an d mapreduce.map.memory.mb (or mapreduce.reduce.memory.mb), which is used to specify how much memory you need for map (or reduce) task containers. The latter setting is used by the application master when negotiating for resources in the cluster, and also by the node manager, which runs and monitors the task containers.

          For example, suppose that mapred.child.java.opts is set to -Xmx800m and mapreduce.map.memory.mb is left at its default value of 1,024 MB. When a map task is run, the node manager will allocate a 1,024 MB container (decreasing the size of its pool by that amount for the duration of the task) and will launch the task JVM configured with an 800 MB maximum heap size. Note that the JVM process will have a larger memory footprint than the heap size, and the overhead will depend on such things as the native libraries that are in use, the size of the permanent generation space, and so on. The important thing is that the physical memory used by the JVM process, including any processes that it spawns, such as Streaming or Pipes processes, does not exceed its allocation (1,024 MB). If a container uses more memory than it has been allocated, then it may be terminated by the node manager and marked as failed.

          Schedulers may impose a minimum or maximum on memory allocations. For example, for the Capacity Scheduler, the default minimum is 1024 MB (set by yarn.scheduler.capacity.minimum-allocation-mb), and the default maximum is 10240 MB (set by yarn.scheduler.capacity.maximum-allocation-mb).

          There are also virtual memory constraints that a container must meet. If a container’s virtual memory usage exceeds a given multiple of the allocated physical memory, the node manager may terminate the process. The multiple is expressed by the yarn.nodemanager.vmem-pmem-ratio property, which defaults to 2.1. In the example used earlier, the virtual memory threshold above which the task may be terminated is 2,150 MB, which is 2.1 × 1,024 MB.

          When configuring memory parameters it’s very useful to be able to monitor a task’s actual memory usage during a job run, and this is possible via MapReduce task counters. The counters PHYSICAL_MEMORY_BYTES, VIRTUAL_MEMORY_BYTES, and COMMITTED_HEAP_BYTES (described in Table 8-2) provide snapshot values of memory usage and are therefore suitable for observation during the course of a task attempt.

          YARN Daemon Addresses and Ports

          YARN daemons run one or more RPC and HTTP servers, details of which are covered in Table 9-10 and Table 9-11.

          Table 9-10. YARN RPC server properties

          Property nameDefault valueDescription
          yarn.resourcemanager.address0.0.0.0:8032The resource manager’s RPC server address and port. This is used by the client (typically outside the cluster) to communicate with the resource manager.
          yarn.resourcemanager.admin.address0.0.0.0:8033The resource manager’s admin RPC server address and port. This is used by the admin client (invoked with yarn rmadmin, typically run outside the cluster) to communicate with the resource manager.
          yarn.resourcemanager.scheduler.address0.0.0.0:8030The resource manager scheduler’s RPC server address and port. This is used by (in-cluster) application masters to communicate with the resource manager.
          yarn.resourcemanager.resource-tracker.address0.0.0.0:8031The resource manager resource tracker’s RPC server address and port. This is used by the (in-cluster) node managers to communicate with the resource manager.
          yarn.nodemanager.address0.0.0.0:0The node manager’s RPC server address and port. This is used by (in-cluster) application masters to communicate with node managers.
          yarn.nodemanager.localizer.address0.0.0.0:8040The node manager localizer’s RPC server address and port.
          mapreduce.jobhistory.address0.0.0.0:10020The job history server’s RPC server address and port. This is used by the client (typically outside the cluster) to query job history. This property is set in mapred-site.xml.

          Table 9-11. YARN HTTP server properties

          Property nameDefault valueDescription
          yarn.resourcemanager.webapp.address0.0.0.0:8088The resource manager’s HTTP server address and port.
          yarn.nodemanager.webapp.address0.0.0.0:8042The node manager’s HTTP server address and port.
          yarn.web-proxy.address The web app proxy server’s HTTP server address and port. If not set (the default), then the web app proxy server will run in the resource manager process.
          mapreduce.jobhistory.webapp.address0.0.0.0:19888The job history server’s HTTP server address and port. This property is set in mapred-site.xml.
          mapreduce.shuffle.port8080The shuffle handler’s HTTP port number. This is used for serving map outputs, and is not a user-accessible web UI. This property is set in mapred-site.xml.
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          Cluster Setup and Installation

          Your hardware has arrived. The next steps are to get it racked up and install the software needed to run Hadoop.

          There are various ways to install and configure Hadoop. This chapter describes how to do it from scratch using the Apache Hadoop distribution and provides background information on the things you need to think about when setting up Hadoop. Alternatively, if you would like to use RPMs or Debian packages for managing your Hadoop installation, then you might want to start with Cloudera’s Distribution, described in Appendix B.

          To ease the burden of installing and maintaining the same software on each node, it is normal to use an automated installation method such as Red Hat Linux’s Kickstart or Debian’s Fully Automatic Installation. These tools allow you to automate the operating system installation by recording the answers to questions that are asked during the installation process (such as the disk partition layout), as well as which packages to install. Crucially, they also provide hooks to run scripts at the end of the process, which are invaluable for doing the final system tweaks and customization that are not covered by the standard installer.

          The following sections describe the customizations that are needed to run Hadoop. These should all be added to the installation script.

          Installing Java

          Java 6 or later is required to run Hadoop. The latest stable Sun JDK is the preferred option, although Java distributions from other vendors may work, too. The following command confirms that Java was installed correctly:

          % java -version
          java version "1.6.0_12"
          Java(TM) SE Runtime Environment (build 1.6.0_12-b04)
          Java HotSpot(TM) 64-Bit Server VM (build 11.2-b01, mixed mode)

          Creating a Hadoop User

          It’s good practice to create a dedicated Hadoop user account to separate the Hadoop installation from other services running on the same machine.

          For small clusters, some administrators choose to make this user’s home directory an NFS-mounted drive, to aid with SSH key distribution (see the following discussion). The NFS server is typically outside the Hadoop cluster. If you use NFS, it is worth considering autofs, which allows you to mount the NFS filesystem on demand when the system accesses it. Autofs provides some protection against the NFS server failing and allows you to use replicated filesystems for failover. There are other NFS gotchas to watch out for, such as synchronizing UIDs and GIDs. For help setting up NFS on Linux, refer to the HOWTO at http://nfs.sourceforge.net/nfs-howto/index.html.

          Installing Hadoop

          Download Hadoop from the Apache Hadoop releases page (http://hadoop.apache.org/core/releases.html), and unpack the contents of the distribution in a sensible location, such as /usr/local (/opt is another standard choice). Note that Hadoop is not installed in the hadoop user’s home directory, as that may be an NFS-mounted directory:

          % cd /usr/local
          % sudo tar xzf hadoop-x.y.z.tar.gz

          We also need to change the owner of the Hadoop files to be the hadoop user and group:

          % sudo chown -R hadoop:hadoop hadoop-x.y.z

          Note

          Some administrators like to install HDFS and MapReduce in separate locations on the same system. At the time of this writing, only HDFS and MapReduce from the same Hadoop release are compatible with one another; however, in future releases, the compatibility requirements will be loosened. When this happens, having independent installations makes sense, as it gives more upgrade options (for more, see Upgrades). For example, it is convenient to be able to upgrade MapReduce—perhaps to patch a bug—while leaving HDFS running.

          Note that separate installations of HDFS and MapReduce can still share configuration by using the --config option (when starting daemons) to refer to a common configuration directory. They can also log to the same directory because the logfiles they produce are named in such a way as to avoid clashes.

          Testing the Installation

          Once you’ve created an installation script, you are ready to test it by installing it on the machines in your cluster. This will probably take a few iterations as you discover kinks in the install. When it’s working, you can proceed to configure Hadoop and give it a test run. This process is documented in the following sections.

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          Cluster Specification

          Hadoop is designed to run on commodity hardware. That means that you are not tied to expensive, proprietary offerings from a single vendor; rather, you can choose standardized, commonly available hardware from any of a large range of vendors to build your cluster.

          “Commodity” does not mean “low-end.” Low-end machines often have cheap components, which have higher failure rates than more expensive (but still commodity-class) machines. When you are operating tens, hundreds, or thousands of machines, cheap components turn out to be a false economy, as the higher failure rate incurs a greater maintenance cost. On the other hand, large database-class machines are not recommended either, since they don’t score well on the price/performance curve. And even though you would need fewer of them to build a cluster of comparable performance than one built of mid-range commodity hardware, when one did fail, it would have a bigger impact on the cluster because a larger proportion of the cluster hardware would be unavailable.

          Hardware specifications rapidly become obsolete, but for the sake of illustration, a typical choice of machine for running a Hadoop datanode and tasktracker in mid-2010 would have the following specifications:

          Processor

          Two quad-core 2-2.5 GHz CPUs

          Memory

          16-24 GB ECC RAM[75]

          Storage

          Four 1 TB SATA disks

          Network

          Gigabit Ethernet

          Although the hardware specification for your cluster will assuredly be different, Hadoop is designed to use multiple cores and disks, so it will be able to take full advantage of more powerful hardware.

          Why Not Use RAID?

          HDFS clusters do not benefit from using RAID (Redundant Array of Independent Disks) for datanode storage (although RAID is recommended for the namenode’s disks, to protect against corruption of its metadata). The redundancy that RAID provides is not needed, since HDFS handles it by replication between nodes.

          Furthermore, RAID striping (RAID 0), which is commonly used to increase performance, turns out to be slower than the JBOD (Just a Bunch Of Disks) configuration used by HDFS, which round-robins HDFS blocks between all disks. This is because RAID 0 read and write operations are limited by the speed of the slowest disk in the RAID array. In JBOD, disk operations are independent, so the average speed of operations is greater than that of the slowest disk. Disk performance often shows considerable variation in practice, even for disks of the same model. In some benchmarking carried out on a Yahoo! cluster (http://markmail.org/message/xmzc45zi25htr7ry), JBOD performed 10% faster than RAID 0 in one test (Gridmix) and 30% better in another (HDFS write throughput).

          Finally, if a disk fails in a JBOD configuration, HDFS can continue to operate without the failed disk, whereas with RAID, failure of a single disk causes the whole array (and hence the node) to become unavailable.

          The bulk of Hadoop is written in Java and can therefore run on any platform with a JVM, although there are enough parts that harbor Unix assumptions (the control scripts, for example) to make it unwise to run on a non-Unix platform in production. In fact, Windows operating systems are not supported production platforms (although they can be used with Cygwin as a development platform; see Appendix A).

          How large should your cluster be? There isn’t an exact answer to this question, but the beauty of Hadoop is that you can start with a small cluster (say, 10 nodes) and grow it as your storage and computational needs grow. In many ways, a better question is this: how fast does my cluster need to grow? You can get a good feel for this by considering storage capacity.

          For example, if your data grows by 1 TB a week and you have three-way HDFS replication, you need an additional 3 TB of raw storage per week. Allow some room for intermediate files and logfiles (around 30%, say), and this works out at about one (2010-vintage) machine per week, on average. In practice, you wouldn’t buy a new machine each week and add it to the cluster. The value of doing a back-of-the-envelope calculation like this is that it gives you a feel for how big your cluster should be. In this example, a cluster that holds two years of data needs 100 machines.

          For a small cluster (on the order of 10 nodes), it is usually acceptable to run the namenode and the jobtracker on a single master machine (as long as at least one copy of the namenode’s metadata is stored on a remote filesystem). As the cluster and the number of files stored in HDFS grow, the namenode needs more memory, so the namenode and jobtracker should be moved onto separate machines.

          The secondary namenode can be run on the same machine as the namenode, but again for reasons of memory usage (the secondary has the same memory requirements as the primary), it is best to run it on a separate piece of hardware, especially for larger clusters. (This topic is discussed in more detail in Master node scenarios.) Machines running the namenodes should typically run on 64-bit hardware to avoid the 3 GB limit on Java heap size in 32-bit architectures.[76]

          Network Topology

          A common Hadoop cluster architecture consists of a two-level network topology, as illustrated in Figure 9-1. Typically there are 30 to 40 servers per rack, with a 1 GB switch for the rack (only three are shown in the diagram) and an uplink to a core switch or router (which is normally 1 GB or better). The salient point is that the aggregate bandwidth between nodes on the same rack is much greater than that between nodes on different racks.

          Typical two-level network architecture for a Hadoop cluster

          Figure 9-1. Typical two-level network architecture for a Hadoop cluster

          Rack awareness

          To get maximum performance out of Hadoop, it is important to configure Hadoop so that it knows the topology of your network. If your cluster runs on a single rack, then there is nothing more to do, since this is the default. However, for multirack clusters, you need to map nodes to racks. By doing this, Hadoop will prefer within-rack transfers (where there is more bandwidth available) to off-rack transfers when placing MapReduce tasks on nodes. HDFS will be able to place replicas more intelligently to trade off performance and resilience.

          Network locations such as nodes and racks are represented in a tree, which reflects the network “distance” between locations. The namenode uses the network location when determining where to place block replicas (see Network Topology and Hadoop); the MapReduce scheduler uses network location to determine where the closest replica is as input to a map task.

          For the network in Figure 9-1, the rack topology is described by two network locations, say, /switch1/rack1 and /switch1/rack2. Because there is only one top-level switch in this cluster, the locations can be simplified to /rack1 and /rack2.

          The Hadoop configuration must specify a map between node addresses and network locations. The map is described by a Java interface, DNSToSwitchMapping, whose signature is:

          public interface DNSToSwitchMapping {
            public List<String> resolve(List<String> names);
          }

          The names parameter is a list of IP addresses, and the return value is a list of corresponding network location strings. The topology.node.switch.mapping.impl configuration property defines an implementation of the DNSToSwitchMapping interface that the namenode and the jobtracker use to resolve worker node network locations.

          For the network in our example, we would map node1, node2, and node3 to /rack1, and node4, node5, and node6 to /rack2.

          Most installations don’t need to implement the interface themselves, however, since the default implementation is ScriptBasedMapping, which runs a user-defined script to determine the mapping. The script’s location is controlled by the property topology.script.file.name. The script must accept a variable number of arguments that are the hostnames or IP addresses to be mapped, and it must emit the corresponding network locations to standard output, separated by whitespace. The Hadoop wiki has an example at http://wiki.apache.org/hadoop/topology_rack_awareness_scripts.

          If no script location is specified, the default behavior is to map all nodes to a single network location, called /default-rack.

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          Benchmarking a Hadoop Cluster

          Is the cluster set up correctly? The best way to answer this question is empirically: run some jobs and confirm that you get the expected results. Benchmarks make good tests because you also get numbers that you can compare with other clusters as a sanity check on whether your new cluster is performing roughly as expected. And you can tune a cluster using benchmark results to squeeze the best performance out of it. This is often done with monitoring systems in place (see Monitoring), so you can see how resources are being used across the cluster.

          To get the best results, you should run benchmarks on a cluster that is not being used by others. In practice, this is just before it is put into service and users start relying on it. Once users have scheduled periodic jobs on a cluster, it is generally impossible to find a time when the cluster is not being used (unless you arrange downtime with users), so you should run benchmarks to your satisfaction before this happens.

          Experience has shown that most hardware failures for new systems are hard drive failures. By running I/O-intensive benchmarks—such as the ones described next—you can “burn in” the cluster before it goes live.

          Hadoop Benchmarks

          Hadoop comes with several benchmarks that you can run very easily with minimal setup cost. Benchmarks are packaged in the test JAR file, and you can get a list of them, with descriptions, by invoking the JAR file with no arguments:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar

          Most of the benchmarks show usage instructions when invoked with no arguments. For example:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO
          TestFDSIO.0.0.4
          Usage: TestFDSIO -read | -write | -clean [-nrFiles N] [-fileSize MB] [-resFile 
          resultFileName] [-bufferSize Bytes]

          Benchmarking HDFS with TestDFSIO

          TestDFSIO tests the I/O performance of HDFS. It does this by using a MapReduce job as a convenient way to read or write files in parallel. Each file is read or written in a separate map task, and the output of the map is used for collecting statistics related to the file just processed. The statistics are accumulated in the reduce to produce a summary.

          The following command writes 10 files of 1,000 MB each:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -write -nrFiles 10
          -fileSize 1000

          At the end of the run, the results are written to the console and also recorded in a local file (which is appended to, so you can rerun the benchmark and not lose old results):

          % cat TestDFSIO_results.log
          ----- TestDFSIO ----- : write
                     Date & time: Sun Apr 12 07:14:09 EDT 2009
                 Number of files: 10
          Total MBytes processed: 10000
               Throughput mb/sec: 7.796340865378244
          Average IO rate mb/sec: 7.8862199783325195
           IO rate std deviation: 0.9101254683525547
              Test exec time sec: 163.387

          The files are written under the /benchmarks/TestDFSIO directory by default (this can be changed by setting the test.build.data system property), in a directory called io_data.

          To run a read benchmark, use the -read argument. Note that these files must already exist (having been written by TestDFSIO -write):

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -read -nrFiles 10 
          -fileSize 1000

          Here are the results for a real run:

          ----- TestDFSIO ----- : read
                     Date & time: Sun Apr 12 07:24:28 EDT 2009
                 Number of files: 10
          Total MBytes processed: 10000
               Throughput mb/sec: 80.25553361904304
          Average IO rate mb/sec: 98.6801528930664
           IO rate std deviation: 36.63507598174921
              Test exec time sec: 47.624

          When you’ve finished benchmarking, you can delete all the generated files from HDFS using the -clean argument:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar TestDFSIO -clean

          Benchmarking MapReduce with Sort

          Hadoop comes with a MapReduce program that does a partial sort of its input. It is very useful for benchmarking the whole MapReduce system, as the full input dataset is transferred through the shuffle. The three steps are: generate some random data, perform the sort, then validate the results.

          First, we generate some random data using RandomWriter. It runs a MapReduce job with 10 maps per node, and each map generates (approximately) 1 GB of random binary data, with keys and values of various sizes. You can change these values if you like by setting the properties test.randomwriter.maps_per_host and test.randomwrite.bytes_per_map. There are also settings for the size ranges of the keys and values; see RandomWriter for details.

          Here’s how to invoke RandomWriter (found in the example JAR file, not the test one) to write its output to a directory called random-data:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-examples.jar randomwriter random-data

          Next, we can run the Sort program:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-examples.jar sort random-data sorted-data

          The overall execution time of the sort is the metric we are interested in, but it’s instructive to watch the job’s progress via the web UI (http://jobtracker-host:50030/), where you can get a feel for how long each phase of the job takes. Adjusting the parameters mentioned in Tuning a Job is a useful exercise, too.

          As a final sanity check, we validate that the data in sorted-data is, in fact, correctly sorted:

          % hadoop jar $HADOOP_INSTALL/hadoop-*-test.jar testmapredsort -sortInput random-data \
            -sortOutput sorted-data

          This command runs the SortValidator program, which performs a series of checks on the unsorted and sorted data to check whether the sort is accurate. It reports the outcome to the console at the end of its run:

          SUCCESS! Validated the MapReduce framework's 'sort' successfully.

          Other benchmarks

          There are many more Hadoop benchmarks, but the following are widely used:

          • MRBench (invoked with mrbench) runs a small job a number of times. It acts as a good counterpoint to sort, as it checks whether small job runs are responsive.

          • NNBench (invoked with nnbench) is useful for load-testing namenode hardware.

          • Gridmix is a suite of benchmarks designed to model a realistic cluster workload by mimicking a variety of data-access patterns seen in practice. See the documentation in the distribution for how to run Gridmix, and the blog post at http://developer.yahoo.net/blogs/hadoop/2010/04/gridmix3_emulating_production.html for more background.[83]

          User Jobs

          For tuning, it is best to include a few jobs that are representative of the jobs that your users run, so your cluster is tuned for these and not just for the standard benchmarks. If this is your first Hadoop cluster and you don’t have any user jobs yet, then Gridmix is a good substitute.

          When running your own jobs as benchmarks, you should select a dataset for your user jobs and use it each time you run the benchmarks to allow comparisons between runs. When you set up a new cluster or upgrade a cluster, you will be able to use the same dataset to compare the performance with previous runs.

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          Chapter 9. Setting Up a Hadoop Cluster

          This chapter explains how to set up Hadoop to run on a cluster of machines. Running HDFS and MapReduce on a single machine is great for learning about these systems, but to do useful work they need to run on multiple nodes.

          There are a few options when it comes to getting a Hadoop cluster, from building your own, to running on rented hardware or using an offering that provides Hadoop as a service in the cloud. This chapter and the next give you enough information to set up and operate your own cluster, but even if you are using a Hadoop service in which a lot of the routine maintenance is done for you, these chapters still offer valuable information about how Hadoop works from an operations point of view.

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          SSH Configuration

          The Hadoop control scripts (but not the daemons) rely on SSH to perform cluster-wide operations. For example, there is a script for stopping and starting all the daemons in the cluster. Note that the control scripts are optional—cluster-wide operations can be performed by other mechanisms, too (such as a distributed shell).

          To work seamlessly, SSH needs to be set up to allow password-less login for the hadoop user from machines in the cluster. The simplest way to achieve this is to generate a public/private key pair and place it in an NFS location that is shared across the cluster.

          First, generate an RSA key pair by typing the following in the hadoop user account:

          % ssh-keygen -t rsa -f ~/.ssh/id_rsa

          Even though we want password-less logins, keys without passphrases are not considered good practice (it’s OK to have an empty passphrase when running a local pseudo-distributed cluster, as described in Appendix A), so we specify a passphrase when prompted for one. We use ssh-agent to avoid the need to enter a password for each connection.

          The private key is in the file specified by the -f option, ~/.ssh/id_rsa, and the public key is stored in a file with the same name but with .pub appended, ~/.ssh/id_rsa.pub.

          Next we need to make sure that the public key is in the ~/.ssh/authorized_keys file on all the machines in the cluster that we want to connect to. If the hadoop user’s home directory is an NFS filesystem, as described earlier, the keys can be shared across the cluster by typing:

          % cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys

          If the home directory is not shared using NFS, the public keys will need to be shared by some other means (such as ssh-copy-id).

          Test that you can SSH from the master to a worker machine by making sure ssh-agent is running,[77] and then run ssh-add to store your passphrase. You should be able to ssh to a worker without entering the passphrase again.

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          Hadoop Configuration

          There are a handful of files for controlling the configuration of a Hadoop installation; the most important ones are listed in Table 9-1. This section covers MapReduce 1, which employs the jobtracker and tasktracker daemons. Running MapReduce 2 is substantially different and is covered in YARN Configuration.

          Table 9-1. Hadoop configuration files

          FilenameFormatDescription
          hadoop-env.shBash scriptEnvironment variables that are used in the scripts to run Hadoop
          core-site.xmlHadoop configuration XMLConfiguration settings for Hadoop Core, such as I/O settings that are common to HDFS and MapReduce
          hdfs-site.xmlHadoop configuration XMLConfiguration settings for HDFS daemons: the namenode, the secondary namenode, and the datanodes
          mapred-site.xmlHadoop configuration XMLConfiguration settings for MapReduce daemons: the jobtracker, and the tasktrackers
          mastersPlain textA list of machines (one per line) that each run a secondary namenode
          slavesPlain textA list of machines (one per line) that each run a datanode and a tasktracker
          hadoop-metrics .propertiesJava PropertiesProperties for controlling how metrics are published in Hadoop (see Metrics)
          log4j.propertiesJava PropertiesProperties for system logfiles, the namenode audit log, and the task log for the tasktracker child process (Hadoop Logs)

          These files are all found in the conf directory of the Hadoop distribution. The configuration directory can be relocated to another part of the filesystem (outside the Hadoop installation, which makes upgrades marginally easier) as long as daemons are started with the --config option specifying the location of this directory on the local filesystem.

          Configuration Management

          Hadoop does not have a single, global location for configuration information. Instead, each Hadoop node in the cluster has its own set of configuration files, and it is up to administrators to ensure that they are kept in sync across the system. Hadoop provides a rudimentary facility for synchronizing configuration using rsync (see the upcoming discussion); alternatively, there are parallel shell tools that can help do this, such as dsh or pdsh.

          Hadoop is designed so that it is possible to have a single set of configuration files that are used for all master and worker machines. The great advantage of this is simplicity, both conceptually (since there is only one configuration to deal with) and operationally (as the Hadoop scripts are sufficient to manage a single configuration setup).

          For some clusters, the one-size-fits-all configuration model breaks down. For example, if you expand the cluster with new machines that have a different hardware specification from the existing ones, you need a different configuration for the new machines to take advantage of their extra resources.

          In these cases, you need to have the concept of a class of machine and maintain a separate configuration for each class. Hadoop doesn’t provide tools to do this, but there are several excellent tools for doing precisely this type of configuration management, such as Chef, Puppet, cfengine, and bcfg2.

          For a cluster of any size, it can be a challenge to keep all of the machines in sync: consider what happens if the machine is unavailable when you push out an update. Who ensures it gets the update when it becomes available? This is a big problem and can lead to divergent installations, so even if you use the Hadoop control scripts for managing Hadoop, it may be a good idea to use configuration management tools for maintaining the cluster. These tools are also excellent for doing regular maintenance, such as patching security holes and updating system packages.

          Control scripts

          Hadoop comes with scripts for running commands and starting and stopping daemons across the whole cluster. To use these scripts (which can be found in the bin directory), you need to tell Hadoop which machines are in the cluster. There are two files for this purpose, called masters and slaves, each of which contains a list of the machine hostnames or IP addresses, one per line. The masters file is actually a misleading name, in that it determines which machine or machines should run a secondary namenode. The slaves file lists the machines that the datanodes and tasktrackers should run on. Both masters and slaves files reside in the configuration directory, although the slaves file may be placed elsewhere (and given another name) by changing the HADOOP_SLAVES setting in hadoop-env.sh. Also, these files do not need to be distributed to worker nodes, since they are used only by the control scripts running on the namenode or jobtracker.

          You don’t need to specify which machine (or machines) the namenode and jobtracker run on in the masters file, as this is determined by the machine the scripts are run on. (In fact, specifying these in the masters file would cause a secondary namenode to run there, which isn’t always what you want.) For example, the start-dfs.sh script, which starts all the HDFS daemons in the cluster, runs the namenode on the machine that the script is run on. In slightly more detail, it:

          1. Starts a namenode on the local machine (the machine that the script is run on)

          2. Starts a datanode on each machine listed in the slaves file

          3. Starts a secondary namenode on each machine listed in the masters file

          There is a similar script called start-mapred.sh, which starts all the MapReduce daemons in the cluster. More specifically, it:

          1. Starts a jobtracker on the local machine

          2. Starts a tasktracker on each machine listed in the slaves file

          Note that masters is not used by the MapReduce control scripts.

          Also provided are stop-dfs.sh and stop-mapred.sh scripts to stop the daemons started by the corresponding start script.

          These scripts start and stop Hadoop daemons using the hadoop-daemon.sh script. If you use the aforementioned scripts, you shouldn’t call hadoop-daemon.sh directly. But if you need to control Hadoop daemons from another system or from your own scripts, the hadoop-daemon.sh script is a good integration point. Likewise, hadoop-daemons.sh (with an “s”) is handy for starting the same daemon on a set of hosts.

          Master node scenarios

          Depending on the size of the cluster, there are various configurations for running the master daemons: the namenode, secondary namenode, and jobtracker. On a small cluster (a few tens of nodes), it is convenient to put them on a single machine; however, as the cluster gets larger, there are good reasons to separate them.

          The namenode has high memory requirements, as it holds file and block metadata for the entire namespace in memory. The secondary namenode, although idle most of the time, has a comparable memory footprint to the primary when it creates a checkpoint. (This is explained in detail in The filesystem image and edit log.) For filesystems with a large number of files, there may not be enough physical memory on one machine to run both the primary and secondary namenode.

          The secondary namenode keeps a copy of the latest checkpoint of the filesystem metadata that it creates. Keeping this (stale) backup on a different node from the namenode allows recovery in the event of loss (or corruption) of all the namenode’s metadata files. (This is discussed further in Chapter 10.)

          On a busy cluster running lots of MapReduce jobs, the jobtracker uses considerable memory and CPU resources, so it should run on a dedicated node.

          Whether the master daemons run on one or more nodes, the following instructions apply:

          • Run the HDFS control scripts from the namenode machine. The masters file should contain the address of the secondary namenode.

          • Run the MapReduce control scripts from the jobtracker machine.

          When the namenode and jobtracker are on separate nodes, their slaves files need to be kept in sync, since each node in the cluster should run a datanode and a tasktracker.

          Environment Settings

          In this section, we consider how to set the variables in hadoop-env.sh.

          Memory

          By default, Hadoop allocates 1,000 MB (1 GB) of memory to each daemon it runs. This is controlled by the HADOOP_HEAPSIZE setting in hadoop-env.sh. In addition, the task tracker launches separate child JVMs to run map and reduce tasks in, so we need to factor these into the total memory footprint of a worker machine.

          The maximum number of map tasks that can run on a tasktracker at one time is controlled by the mapred.tasktracker.map.tasks.maximum property, which defaults to two tasks. There is a corresponding property for reduce tasks, mapred.tasktracker.reduce.tasks.maximum, which also defaults to two tasks. The tasktracker is said to have two map slots and two reduce slots.

          The memory given to each child JVM running a task can be changed by setting the mapred.child.java.opts property. The default setting is -Xmx200m, which gives each task 200 MB of memory. (Incidentally, you can provide extra JVM options here, too. For example, you might enable verbose GC logging to debug GC.) The default configuration therefore uses 2,800 MB of memory for a worker machine (see Table 9-2).

          Table 9-2. Worker node memory calculation

          JVMDefault memory used (MB)Memory used for eight processors, 400 MB per child (MB)
          Datanode1,0001,000
          Tasktracker1,0001,000
          Tasktracker child map task2 × 2007 × 400
          Tasktracker child reduce task2 × 2007 × 400
          Total2,8007,600

          The number of tasks that can be run simultaneously on a tasktracker is related to the number of processors available on the machine. Because MapReduce jobs are normally I/O-bound, it makes sense to have more tasks than processors to get better utilization. The amount of oversubscription depends on the CPU utilization of jobs you run, but a good rule of thumb is to have a factor of between one and two more tasks (counting both map and reduce tasks) than processors.

          For example, if you had eight processors and you wanted to run two processes on each processor, you could set both mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum to 7 (not 8, because the datanode and the tasktracker each take one slot). If you also increased the memory available to each child task to 400 MB, the total memory usage would be 7,600 MB (see Table 9-2).

          Whether this Java memory allocation will fit into 8 GB of physical memory depends on the other processes that are running on the machine. If you are running Streaming or Pipes programs, this allocation will probably be inappropriate (and the memory allocated to the child should be dialed down), since it doesn’t allow enough memory for users’ (Streaming or Pipes) processes to run. The thing to avoid is processes being swapped out, as this leads to severe performance degradation. The precise memory settings are necessarily very cluster-dependent and can be optimized over time with experience gained from monitoring the memory usage across the cluster. Tools such as Ganglia (GangliaContext) are good for gathering this information. See Task memory limits for more on how to enforce task memory limits.

          Hadoop also provides settings to control how much memory is used for MapReduce operations. These can be set on a per-job basis and are covered in the section on Shuffle and Sort.

          For the master nodes, each of the namenode, secondary namenode, and jobtracker daemons uses 1,000 MB by default, for a total of 3,000 MB.

          How Much Memory Does a Namenode Need?

          A namenode can eat up memory, since a reference to every block of every file is maintained in memory. It’s difficult to give a precise formula because memory usage depends on the number of blocks per file, the filename length, and the number of directories in the filesystem; plus, it can change from one Hadoop release to another.

          The default of 1,000 MB of namenode memory is normally enough for a few million files, but as a rule of thumb for sizing purposes, you can conservatively allow 1,000 MB per million blocks of storage.

          For example, a 200-node cluster with 4 TB of disk space per node, a block size of 128 MB, and a replication factor of 3 has room for about 2 million blocks (or more): 200 × 4,000,000 MB ⁄ (128 MB × 3). So in this case, setting the namenode memory to 2,000 MB would be a good starting point.

          You can increase the namenode’s memory without changing the memory allocated to other Hadoop daemons by setting HADOOP_NAMENODE_OPTS in hadoop-env.sh to include a JVM option for setting the memory size. HADOOP_NAMENODE_OPTS allows you to pass extra options to the namenode’s JVM. So, for example, if you were using a Sun JVM, -Xmx2000m would specify that 2,000 MB of memory should be allocated to the namenode.

          If you change the namenode’s memory allocation, don’t forget to do the same for the secondary namenode (using the HADOOP_SECONDARYNAMENODE_OPTS variable), since its memory requirements are comparable to the primary namenode’s. You probably also want to run the secondary namenode on a different machine in this case.

          There are corresponding environment variables for the other Hadoop daemons, so you can customize their memory allocations, if desired. See hadoop-env.sh for details.

          Java

          The location of the Java implementation to use is determined by the JAVA_HOME setting in hadoop-env.sh or from the JAVA_HOME shell environment variable, if not set in hadoop-env.sh. It’s a good idea to set the value in hadoop-env.sh, so that it is clearly defined in one place and to ensure that the whole cluster is using the same version of Java.

          System logfiles

          System logfiles produced by Hadoop are stored in $HADOOP_INSTALL/logs by default. This can be changed using the HADOOP_LOG_DIR setting in hadoop-env.sh. It’s a good idea to change this so that logfiles are kept out of the directory that Hadoop is installed in. Changing this keeps logfiles in one place, even after the installation directory changes due to an upgrade. A common choice is /var/log/hadoop, set by including the following line in hadoop-env.sh:

          export HADOOP_LOG_DIR=/var/log/hadoop

          The log director will be created if it doesn’t already exist. (If it does not exist, confirm that the Hadoop user has permission to create it.) Each Hadoop daemon running on a machine produces two logfiles. The first is the log output written via log4j. This file, which ends in .log, should be the first port of call when diagnosing problems because most application log messages are written here. The standard Hadoop log4j configuration uses a Daily Rolling File Appender to rotate logfiles. Old logfiles are never deleted, so you should arrange for them to be periodically deleted or archived, so as to not run out of disk space on the local node.

          The second logfile is the combined standard output and standard error log. This logfile, which ends in .out, usually contains little or no output, since Hadoop uses log4j for logging. It is rotated only when the daemon is restarted, and only the last five logs are retained. Old logfiles are suffixed with a number between 1 and 5, with 5 being the oldest file.

          Logfile names (of both types) are a combination of the name of the user running the daemon, the daemon name, and the machine hostname. For example, hadoop-tom-datanode-sturges.local.log.2008-07-04 is the name of a logfile after it has been rotated. This naming structure makes it possible to archive logs from all machines in the cluster in a single directory, if needed, since the filenames are unique.

          The username in the logfile name is actually the default for the HADOOP_IDENT_STRING setting in hadoop-env.sh. If you wish to give the Hadoop instance a different identity for the purposes of naming the logfiles, change HADOOP_IDENT_STRING to be the identifier you want.

          SSH settings

          The control scripts allow you to run commands on (remote) worker nodes from the master node using SSH. It can be useful to customize the SSH settings, for various reasons. For example, you may want to reduce the connection timeout (using the ConnectTimeout option) so the control scripts don’t hang around waiting to see whether a dead node is going to respond. Obviously, this can be taken too far. If the timeout is too low, then busy nodes will be skipped, which is bad.

          Another useful SSH setting is StrictHostKeyChecking, which can be set to no to automatically add new host keys to the known hosts files. The default, ask, prompts the user to confirm that he has verified the key fingerprint, which is not a suitable setting in a large cluster environment.[78]

          To pass extra options to SSH, define the HADOOP_SSH_OPTS environment variable in hadoop-env.sh. See the ssh and ssh_config manual pages for more SSH settings.

          The Hadoop control scripts can distribute configuration files to all nodes of the cluster using rsync. This is not enabled by default, but by defining the HADOOP_MASTER setting in hadoop-env.sh, worker daemons will rsync the tree rooted at HADOOP_MASTER to the local node’s HADOOP_INSTALL whenever the daemon starts up.

          What if you have two masters—a namenode and a jobtracker—on separate machines? You can pick one as the source and the other can rsync from it, along with all the workers. In fact, you could use any machine, even one outside the Hadoop cluster, to rsync from.

          Because HADOOP_MASTER is unset by default, there is a bootstrapping problem: how do we make sure hadoop-env.sh with HADOOP_MASTER set is present on worker nodes? For small clusters, it is easy to write a small script to copy hadoop-env.sh from the master to all of the worker nodes. For larger clusters, tools such as dsh can do the copies in parallel. Alternatively, a suitable hadoop-env.sh can be created as a part of the automated installation script (such as Kickstart).

          When starting a large cluster with rsyncing enabled, the worker nodes start at around the same time and can overwhelm the master node with rsync requests. To avoid this, set the HADOOP_SLAVE_SLEEP setting to a small number of seconds, such as 0.1 for one-tenth of a second. When running commands on all nodes of the cluster, the master will sleep for this period between invoking the command on each worker machine in turn.

          Important Hadoop Daemon Properties

          Hadoop has a bewildering number of configuration properties. In this section, we address the ones that you need to define (or at least understand why the default is appropriate) for any real-world working cluster. These properties are set in the Hadoop site files: core-site.xml, hdfs-site.xml, and mapred-site.xml. Typical examples of these files are shown in Example 9-1, Example 9-2, and Example 9-3. Notice that most properties are marked as final in order to prevent them from being overridden by job configurations. You can learn more about how to write Hadoop’s configuration files in The Configuration API.

          Example 9-1. A typical core-site.xml configuration file

          <?xml version="1.0"?>
          <!-- core-site.xml -->
          <configuration>
            <property>
              <name>fs.default.name</name>
              <value>hdfs://namenode/</value>
              <final>true</final>
            </property>
          </configuration>

          Example 9-2. A typical hdfs-site.xml configuration file

          <?xml version="1.0"?>
          <!-- hdfs-site.xml -->
          <configuration>
            <property>
              <name>dfs.name.dir</name>
              <value>/disk1/hdfs/name,/remote/hdfs/name</value>
              <final>true</final>
            </property>
          
            <property>
              <name>dfs.data.dir</name>
              <value>/disk1/hdfs/data,/disk2/hdfs/data</value>
              <final>true</final>
            </property>
            
            <property>
              <name>fs.checkpoint.dir</name>
              <value>/disk1/hdfs/namesecondary,/disk2/hdfs/namesecondary</value>
              <final>true</final>
            </property>
          </configuration>

          Example 9-3. A typical mapred-site.xml configuration file

          <?xml version="1.0"?>
          <!-- mapred-site.xml -->
          <configuration>
            <property>
              <name>mapred.job.tracker</name>
              <value>jobtracker:8021</value>
              <final>true</final>
            </property>
            
            <property>
              <name>mapred.local.dir</name>
              <value>/disk1/mapred/local,/disk2/mapred/local</value>
              <final>true</final>
            </property>
            
            <property>
              <name>mapred.system.dir</name>
              <value>/tmp/hadoop/mapred/system</value>
              <final>true</final>
            </property>
            
            <property>
              <name>mapred.tasktracker.map.tasks.maximum</name>
              <value>7</value>
              <final>true</final>
            </property>
            
            <property>
              <name>mapred.tasktracker.reduce.tasks.maximum</name>
              <value>7</value>
              <final>true</final>
            </property>
            
            <property>
              <name>mapred.child.java.opts</name>
              <value>-Xmx400m</value>
              <!-- Not marked as final so jobs can include JVM debugging options -->
            </property>
          </configuration>

          HDFS

          To run HDFS, you need to designate one machine as a namenode. In this case, the property fs.default.name is an HDFS filesystem URI whose host is the namenode’s hostname or IP address and whose port is the port that the namenode will listen on for RPCs. If no port is specified, the default of 8020 is used.

          Note

          The masters file that is used by the control scripts is not used by the HDFS (or MapReduce) daemons to determine hostnames. In fact, because the masters file is used only by the scripts, you can ignore it if you don’t use them.

          The fs.default.name property also doubles as specifying the default filesystem. The default filesystem is used to resolve relative paths, which are handy to use because they save typing (and avoid hardcoding knowledge of a particular namenode’s address). For example, with the default filesystem defined in Example 9-1, the relative URI /a/b is resolved to hdfs://namenode/a/b.

          Note

          If you are running HDFS, the fact that fs.default.name is used to specify both the HDFS namenode and the default filesystem means HDFS has to be the default filesystem in the server configuration. Bear in mind, however, that it is possible to specify a different filesystem as the default in the client configuration, for convenience.

          For example, if you use both HDFS and S3 filesystems, then you have a choice of specifying either as the default in the client configuration, which allows you to refer to the default with a relative URI and the other with an absolute URI.

          There are a few other configuration properties you should set for HDFS: those that set the storage directories for the namenode and for datanodes. The property dfs.name.dir specifies a list of directories where the namenode stores persistent filesystem metadata (the edit log and the filesystem image). A copy of each metadata file is stored in each directory for redundancy. It’s common to configure dfs.name.dir so that the namenode metadata is written to one or two local disks, as well as a remote disk, such as an NFS-mounted directory. Such a setup guards against failure of a local disk and failure of the entire namenode, since in both cases the files can be recovered and used to start a new namenode. (The secondary namenode takes only periodic checkpoints of the namenode, so it does not provide an up-to-date backup of the namenode.)

          You should also set the dfs.data.dir property, which specifies a list of directories for a datanode to store its blocks. Unlike the namenode, which uses multiple directories for redundancy, a datanode round-robins writes between its storage directories, so for performance you should specify a storage directory for each local disk. Read performance also benefits from having multiple disks for storage, because blocks will be spread across them and concurrent reads for distinct blocks will be correspondingly spread across disks.

          Tip

          For maximum performance, you should mount storage disks with the noatime option. This setting means that last-accessed time information is not written on file reads, which gives significant performance gains.

          Finally, you should configure where the secondary namenode stores its checkpoints of the filesystem. The fs.checkpoint.dir property specifies a list of directories where the checkpoints are kept. Like the storage directories for the namenode, which keep redundant copies of the namenode metadata, the checkpointed filesystem image is stored in each checkpoint directory for redundancy.

          Table 9-3 summarizes the important configuration properties for HDFS.

          Table 9-3. Important HDFS daemon properties

          Property nameTypeDefault valueDescription
          fs.default.nameURIfile:///The default filesystem. The URI defines the hostname and port that the namenode’s RPC server runs on. The default port is 8020. This property is set in core-site.xml.
          dfs.name.dirComma-separated directory names${hadoop.tmp.dir}/dfs/nameThe list of directories where the namenode stores its persistent metadata. The namenode stores a copy of the metadata in each directory in the list.
          dfs.data.dirComma-separated directory names${hadoop.tmp.dir}/dfs/dataA list of directories where the datanode stores blocks. Each block is stored in only one of these directories.
          fs.checkpoint.dir Comma-separated directory names${hadoop.tmp.dir}/dfs/namesecondaryA list of directories where the secondary namenode stores checkpoints. It stores a copy of the checkpoint in each directory in the list.

          Warning

          Note that the storage directories for HDFS are under Hadoop’s temporary directory by default (the hadoop.tmp.dir property, whose default is /tmp/hadoop-${user.name}). Therefore, it is critical that these properties are set so that data is not lost by the system when it clears out temporary directories.

          MapReduce

          To run MapReduce, you need to designate one machine as a jobtracker, which on small clusters may be the same machine as the namenode. To do this, set the mapred.job.tracker property to the hostname or IP address and port that the jobtracker will listen on. Note that this property is not a URI, but instead a host-port pair, separated by a colon. The port number 8021 is a common choice.

          During a MapReduce job, intermediate data and working files are written to temporary local files. Because this data includes the potentially very large output of map tasks, you need to ensure that the mapred.local.dir property, which controls the location of local temporary storage, is configured to use disk partitions that are large enough. The mapred.local.dir property takes a comma-separated list of directory names, and you should use all available local disks to spread disk I/O. Typically, you will use the same disks and partitions (but different directories) for MapReduce temporary data as you use for datanode block storage, as governed by the dfs.data.dir property, which was discussed earlier.

          MapReduce uses a distributed filesystem to share files (such as the job JAR file) with the tasktrackers that run the MapReduce tasks. The mapred.system.dir property is used to specify a directory where these files can be stored. This directory is resolved relative to the default filesystem (configured in fs.default.name), which is usually HDFS.

          Finally, you should set the mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum properties to reflect the number of available cores on the tasktracker machines and mapred.child.java.opts to reflect the amount of memory available for the tasktracker child JVMs. See the discussion in Memory.

          Table 9-4 summarizes the important configuration properties for MapReduce.

          Table 9-4. Important MapReduce daemon properties

          Property nameTypeDefault valueDescription
          mapred.job.trackerHostname and portlocalThe hostname and port that the jobtracker’s RPC server runs on. If set to the default value of local, the jobtracker is run in-process on demand when you run a MapReduce job (you don’t need to start the jobtracker in this case, and in fact you will get an error if you try to start it in this mode).
          mapred.local.dirComma-separated directory names${hadoop.tmp.dir}/mapred/localA list of directories where MapReduce stores intermediate data for jobs. The data is cleared out when the job ends.
          mapred.system.dirURI${hadoop.tmp.dir}/mapred/systemThe directory relative to fs.default.name where shared files are stored during a job run.
          mapred.tasktracker.map.tasks.maximumint2The number of map tasks that may be run on a tasktracker at any one time.
          mapred.tasktracker.reduce.tasks.maximumint2The number of reduce tasks that may be run on a tasktracker at any one time.
          mapred.child.java.optsString-Xmx200mThe JVM options used to launch the tasktracker child process that runs map and reduce tasks. This property can be set on a per-job basis, which can be useful for setting JVM properties for debugging, for example.
          mapreduce.map.java.optsString-Xmx200mThe JVM options used for the child process that runs map tasks. (Not available in 1.x.)
          mapreduce.reduce.java.optsString-Xmx200mThe JVM options used for the child process that runs reduce tasks. (Not available in 1.x.)

          Hadoop Daemon Addresses and Ports

          Hadoop daemons generally run both an RPC server (Table 9-5) for communication between daemons and an HTTP server to provide web pages for human consumption (Table 9-6). Each server is configured by setting the network address and port number to listen on. By specifying the network address as 0.0.0.0, Hadoop will bind to all addresses on the machine. Alternatively, you can specify a single address to bind to. A port number of 0 instructs the server to start on a free port, but this is generally discouraged because it is incompatible with setting cluster-wide firewall policies.

          Table 9-5. RPC server properties

          Property nameDefault valueDescription
          fs.default.namefile:///When set to an HDFS URI, this property determines the namenode’s RPC server address and port. The default port is 8020 if not specified.
          dfs.datanode.ipc.address0.0.0.0:50020The datanode’s RPC server address and port.
          mapred.job.trackerlocalWhen set to a hostname and port, this property specifies the jobtracker’s RPC server address and port. A commonly used port is 8021.
          mapred.task.tracker.report.address127.0.0.1:0The tasktracker’s RPC server address and port. This is used by the tasktracker’s child JVM to communicate with the tasktracker. Using any free port is acceptable in this case, as the server only binds to the loopback address. You should change this setting only if the machine has no loopback address.

          In addition to an RPC server, datanodes run a TCP/IP server for block transfers. The server address and port is set by the dfs.datanode.address property and has a default value of 0.0.0.0:50010.

          Table 9-6. HTTP server properties

          Property nameDefault valueDescription
          mapred.job.tracker.http.address0.0.0.0:50030The jobtracker’s HTTP server address and port
          mapred.task.tracker.http.address0.0.0.0:50060The tasktracker’s HTTP server address and port
          dfs.http.address0.0.0.0:50070The namenode’s HTTP server address and port
          dfs.datanode.http.address0.0.0.0:50075The datanode’s HTTP server address and port
          dfs.secondary.http.address0.0.0.0:50090The secondary namenode’s HTTP server address and port

          There are also settings for controlling which network interfaces the datanodes and tasktrackers report as their IP addresses (for HTTP and RPC servers). The relevant properties are dfs.datanode.dns.interface and mapred.tasktracker.dns.interface, both of which are set to default, which will use the default network interface. You can set this explicitly to report the address of a particular interface (eth0, for example).

          Other Hadoop Properties

          This section discusses some other properties that you might consider setting.

          Cluster membership

          To aid the addition and removal of nodes in the future, you can specify a file containing a list of authorized machines that may join the cluster as datanodes or tasktrackers. The file is specified using the dfs.hosts and mapred.hosts properties (for datanodes and tasktrackers, respectively), as well as the corresponding dfs.hosts.exclude and mapred.hosts.exclude files used for decommissioning. See Commissioning and Decommissioning Nodes for further discussion.

          Buffer size

          Hadoop uses a buffer size of 4 KB (4,096 bytes) for its I/O operations. This is a conservative setting, and with modern hardware and operating systems, you will likely see performance benefits by increasing it; 128 KB (131,072 bytes) is a common choice. Set this using the io.file.buffer.size property in core-site.xml.

          HDFS block size

          The HDFS block size is 64 MB by default, but many clusters use 128 MB (134,217,728 bytes) or even 256 MB (268,435,456 bytes) to ease memory pressure on the namenode and to give mappers more data to work on. Set this using the dfs.block.size property in hdfs-site.xml.

          Reserved storage space

          By default, datanodes will try to use all of the space available in their storage directories. If you want to reserve some space on the storage volumes for non-HDFS use, you can set dfs.datanode.du.reserved to the amount, in bytes, of space to reserve.

          Trash

          Hadoop filesystems have a trash facility, in which deleted files are not actually deleted, but rather are moved to a trash folder, where they remain for a minimum period before being permanently deleted by the system. The minimum period in minutes that a file will remain in the trash is set using the fs.trash.interval configuration property in core-site.xml. By default, the trash interval is zero, which disables trash.

          Like in many operating systems, Hadoop’s trash facility is a user-level feature, meaning that only files that are deleted using the filesystem shell are put in the trash. Files deleted programmatically are deleted immediately. It is possible to use the trash programmatically, however, by constructing a Trash instance, then calling its moveToTrash() method with the Path of the file intended for deletion. The method returns a value indicating success; a value of false means either that trash is not enabled or that the file is already in the trash.

          When trash is enabled, each user has her own trash directory called .Trash in her home directory. File recovery is simple: you look for the file in a subdirectory of .Trash and move it out of the trash subtree.

          HDFS will automatically delete files in trash folders, but other filesystems will not, so you have to arrange for this to be done periodically. You can expunge the trash, which will delete files that have been in the trash longer than their minimum period, using the filesystem shell:

          % hadoop fs -expunge

          The Trash class exposes an expunge() method that has the same effect.

          Job scheduler

          Particularly in a multiuser MapReduce setting, consider changing the default FIFO job scheduler to one of the more fully featured alternatives. See Job Scheduling.

          Reduce slow start

          By default, schedulers wait until 5% of the map tasks in a job have completed before scheduling reduce tasks for the same job. For large jobs this can cause problems with cluster utilization, since they take up reduce slots while waiting for the map tasks to complete. Setting mapred.reduce.slowstart.completed.maps to a higher value, such as 0.80 (80%), can help improve throughput.

          Task memory limits

          On a shared cluster, it shouldn’t be possible for one user’s errant MapReduce program to bring down nodes in the cluster. This can happen if the map or reduce task has a memory leak, for example, because the machine on which the tasktracker is running will run out of memory and may affect the other running processes.

          Or consider the case where a user sets mapred.child.java.opts to a large value and causes memory pressure on other running tasks, causing them to swap. Marking this property as final on the cluster would prevent it from being changed by users in their jobs, but there are legitimate reasons to allow some jobs to use more memory, so this is not always an acceptable solution. Furthermore, even locking down mapred.child.java.opts does not solve the problem, because tasks can spawn new processes that are not constrained in their memory usage. Streaming and Pipes jobs do exactly that, for example.

          To prevent cases like these, some way of enforcing a limit on a task’s memory usage is needed. Hadoop provides two mechanisms for this. The simplest is via the Linux ulimit command, which can be done at the operating-system level (in the limits.conf file, typically found in /etc/security) or by setting mapred.child.ulimit in the Hadoop configuration. The value is specified in kilobytes, and should be comfortably larger than the memory of the JVM set by mapred.child.java.opts; otherwise, the child JVM might not start.

          The second mechanism is Hadoop’s task memory monitoring feature.[79] The idea is that an administrator sets a range of allowed virtual memory limits for tasks on the cluster, and users specify the maximum memory requirements for their jobs in the job configuration. If a user doesn’t set memory requirements for his job, then the defaults are used (mapred.job.map.memory.mb and mapred.job.reduce.memory.mb).

          This approach has a couple of advantages over the ulimit approach. First, it enforces the memory usage of the whole task process tree, including spawned processes. Second, it enables memory-aware scheduling, where tasks are scheduled on tasktrackers that have enough free memory to run them. The Capacity Scheduler, for example, will account for slot usage based on the memory settings, so if a job’s mapred.job.map.memory.mb setting exceeds mapred.cluster.map.memory.mb, the scheduler will allocate more than one slot on a tasktracker to run each map task for that job.

          To enable task memory monitoring, you need to set all six of the properties in Table 9-7. The default values are all -1, which means the feature is disabled.

          Table 9-7. MapReduce task memory monitoring properties

          Property nameTypeDefault valueDescription
          mapred.cluster.map.memory.mbint-1The amount of virtual memory, in MB, that defines a map slot. Map tasks that require more than this amount of memory will use more than one map slot.
          mapred.cluster.reduce.memory.mbint-1The amount of virtual memory, in MB, that defines a reduce slot. Reduce tasks that require more than this amount of memory will use more than one reduce slot.
          mapred.job.map.memory.mbint-1The amount of virtual memory, in MB, that a map task requires to run. If a map task exceeds this limit, it may be terminated and marked as failed.
          mapred.job.reduce.memory.mbint-1The amount of virtual memory, in MB, that a reduce task requires to run. If a reduce task exceeds this limit, it may be terminated and marked as failed.
          mapred.cluster.max.map.memory.mbint-1The maximum limit that users can set mapred.job.map.memory.mb to.
          mapred.cluster.max.reduce.memory.mbint-1The maximum limit that users can set mapred.job.reduce.memory.mb to.

          User Account Creation

          Once you have a Hadoop cluster up and running, you need to give users access to it. This involves creating a home directory for each user and setting ownership permissions on it:

          % hadoop fs -mkdir /user/username
          % hadoop fs -chown username:username /user/username

          This is a good time to set space limits on the directory. The following sets a 1 TB limit on the given user directory:

          % hadoop dfsadmin -setSpaceQuota 1t /user/username
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          Username: University of California Irvine User Book: Hadoop: The Definitive Guide, 3rd Edition. No part of any chapter or book may be reproduced or transmitted in any form by any means without the prior written permission for reprints and excerpts from the publisher of the book or chapter. Redistribution or other use that violates the fair use privilege under U.S. copyright laws (see 17 USC107) or that otherwise violates these Terms of Service is strictly prohibited. Violators will be prosecuted to the full extent of U.S. Federal and Massachusetts laws.

          Security

          Early versions of Hadoop assumed that HDFS and MapReduce clusters would be used by a group of cooperating users within a secure environment. The measures for restricting access were designed to prevent accidental data loss, rather than to prevent unauthorized access to data. For example, the file permissions system in HDFS prevents one user from accidentally wiping out the whole filesystem from a bug in a program, or by mistakenly typing hadoop fs -rmr /, but it doesn’t prevent a malicious user from assuming root’s identity (see Setting User Identity) to access or delete any data in the cluster.

          In security parlance, what was missing was a secure authentication mechanism to assure Hadoop that the user seeking to perform an operation on the cluster is who she claims to be and therefore can be trusted. HDFS file permissions provide only a mechanism for authorization, which controls what a particular user can do to a particular file. For example, a file may be readable only by a certain group of users, so anyone not in that group is not authorized to read it. However, authorization is not enough by itself, because the system is still open to abuse via spoofing by a malicious user who can gain network access to the cluster.

          It’s common to restrict access to data that contains personally identifiable information (such as an end user’s full name or IP address) to a small set of users (of the cluster) within the organization who are authorized to access such information. Less sensitive (or anonymized) data may be made available to a larger set of users. It is convenient to host a mix of datasets with different security levels on the same cluster (not least because it means the datasets with lower security levels can be shared). However, to meet regulatory requirements for data protection, secure authentication must be in place for shared clusters.

          This is the situation that Yahoo! faced in 2009, which led a team of engineers there to implement secure authentication for Hadoop. In their design, Hadoop itself does not manage user credentials; instead, it relies on Kerberos, a mature open-source network authentication protocol, to authenticate the user. In turn, Kerberos doesn’t manage permissions. Kerberos says that a user is who he says he is; it’s Hadoop’s job to determine whether that user has permission to perform a given action. There’s a lot to Kerberos, so here we only cover enough to use it in the context of Hadoop, referring readers who want more background to Kerberos: The Definitive Guide by Jason Garman (O’Reilly, 2003).

          Which Versions of Hadoop Support Kerberos Authentication?

          Kerberos for authentication was first added in the 0.20.20x series of Apache Hadoop releases. See Table 1-2 for which recent release series support this feature.

          Kerberos and Hadoop

          At a high level, there are three steps that a client must take to access a service when using Kerberos, each of which involves a message exchange with a server:

          1. Authentication. The client authenticates itself to the Authentication Server and receives a timestamped Ticket-Granting Ticket (TGT).

          2. Authorization. The client uses the TGT to request a service ticket from the Ticket Granting Server.

          3. Service request. The client uses the service ticket to authenticate itself to the server that is providing the service the client is using. In the case of Hadoop, this might be the namenode or the jobtracker.

          Together, the Authentication Server and the Ticket Granting Server form the Key Distribution Center (KDC). The process is shown graphically in Figure 9-2.

          The three-step Kerberos ticket exchange protocol

          Figure 9-2. The three-step Kerberos ticket exchange protocol

          The authorization and service request steps are not user-level actions; the client performs these steps on the user’s behalf. The authentication step, however, is normally carried out explicitly by the user using the kinit command, which will prompt for a password. However, this doesn’t mean you need to enter your password every time you run a job or access HDFS, since TGTs last for 10 hours by default (and can be renewed for up to a week). It’s common to automate authentication at operating system login time, thereby providing single sign-on to Hadoop.

          In cases where you don’t want to be prompted for a password (for running an unattended MapReduce job, for example), you can create a Kerberos keytab file using the ktutil command. A keytab is a file that stores passwords and may be supplied to kinit with the -t option.

          An example

          Let’s look at an example of the process in action. The first step is to enable Kerberos authentication by setting the hadoop.security.authentication property in core-site.xml to kerberos.[80] The default setting is simple, which signifies that the old backwards-compatible (but insecure) behavior of using the operating system user name to determine identity should be employed.

          We also need to enable service-level authorization by setting hadoop.security.authorization to true in the same file. You may configure Access Control Lists (ACLs) in the hadoop-policy.xml configuration file to control which users and groups have permission to connect to each Hadoop service. Services are defined at the protocol level, so there are ones for MapReduce job submission, namenode communication, and so on. By default, all ACLs are set to *, which means that all users have permission to access each service, but on a real cluster you should lock the ACLs down to only those users and groups that should have access.

          The format for an ACL is a comma-separated list of usernames, followed by whitespace, followed by a comma-separated list of group names. For example, the ACL preston,howard directors,inventors would authorize access to users named preston or howard, or in groups directors or inventors.

          With Kerberos authentication turned on, let’s see what happens when we try to copy a local file to HDFS:

          % hadoop fs -put quangle.txt .
          10/07/03 15:44:58 WARN ipc.Client: Exception encountered while connecting to the
          server: javax.security.sasl.SaslException: GSS initiate failed [Caused by GSSEx
          ception: No valid credentials provided (Mechanism level: Failed to find any Ker
          beros tgt)]
          Bad connection to FS. command aborted. exception: Call to localhost/127.0.0.1:80
          20 failed on local exception: java.io.IOException: javax.security.sasl.SaslExcep
          tion: GSS initiate failed [Caused by GSSException: No valid credentials provided
          (Mechanism level: Failed to find any Kerberos tgt)]

          The operation fails because we don’t have a Kerberos ticket. We can get one by authenticating to the KDC, using kinit:

          % kinit
          Password for hadoop-user@LOCALDOMAIN: password
          % hadoop fs -put quangle.txt .
          % hadoop fs -stat %n quangle.txt
          quangle.txt

          And we see that the file is successfully written to HDFS. Notice that even though we carried out two filesystem commands, we only needed to call kinit once, since the Kerberos ticket is valid for 10 hours (use the klist command to see the expiry time of your tickets and kdestroy to invalidate your tickets). After we get a ticket, everything works just as it normally would.

          Delegation Tokens

          In a distributed system such as HDFS or MapReduce, there are many client-server interactions, each of which must be authenticated. For example, an HDFS read operation will involve multiple calls to the namenode and calls to one or more datanodes. Instead of using the three-step Kerberos ticket exchange protocol to authenticate each call, which would present a high load on the KDC on a busy cluster, Hadoop uses delegation tokens to allow later authenticated access without having to contact the KDC again. Delegation tokens are created and used transparently by Hadoop on behalf of users, so there’s no action you need to take as a user beyond using kinit to sign in, but it’s useful to have a basic idea of how they are used.

          A delegation token is generated by the server (the namenode in this case) and can be thought of as a shared secret between the client and the server. On the first RPC call to the namenode, the client has no delegation token, so it uses Kerberos to authenticate, and as a part of the response it gets a delegation token from the namenode. In subsequent calls, it presents the delegation token, which the namenode can verify (since it generated it using a secret key), and hence the client is authenticated to the server.

          When it wants to perform operations on HDFS blocks, the client uses a special kind of delegation token, called a block access token, that the namenode passes to the client in response to a metadata request. The client uses the block access token to authenticate itself to datanodes. This is possible only because the namenode shares its secret key used to generate the block access token with datanodes (which it sends in heartbeat messages), so that they can verify block access tokens. Thus, an HDFS block may be accessed only by a client with a valid block access token from a namenode. This closes the security hole in unsecured Hadoop where only the block ID was needed to gain access to a block. This property is enabled by setting dfs.block.access.token.enable to true.

          In MapReduce, job resources and metadata (such as JAR files, input splits, and configuration files) are shared in HDFS for the jobtracker to access, and user code runs on the tasktrackers and accesses files on HDFS (the process is explained in Anatomy of a MapReduce Job Run). Delegation tokens are used by the jobtracker and tasktrackers to access HDFS during the course of the job. When the job has finished, the delegation tokens are invalidated.

          Delegation tokens are automatically obtained for the default HDFS instance, but if your job needs to access other HDFS clusters, you can load the delegation tokens for these by setting the mapreduce.job.hdfs-servers job property to a comma-separated list of HDFS URIs.

          Other Security Enhancements

          Security has been tightened throughout HDFS and MapReduce to protect against unauthorized access to resources.[81] The more notable changes are listed here:

          • Tasks can be run using the operating system account for the user who submitted the job, rather than the user running the tasktracker. This means that the operating system is used to isolate running tasks, so they can’t send signals to each other (to kill another user’s tasks, for example) and so local information, such as task data, is kept private via local filesystem permissions.

            This feature is enabled by setting mapred.task.tracker.task-controller to org.apache.hadoop.mapred.LinuxTaskController.[82] In addition, administrators need to ensure that each user is given an account on every node in the cluster (typically using LDAP).

          • When tasks are run as the user who submitted the job, the distributed cache (Distributed Cache) is secure. Files that are world-readable are put in a shared cache (the insecure default); otherwise, they go in a private cache, readable only by the owner.

          • Users can view and modify only their own jobs, not others. This is enabled by setting mapred.acls.enabled to true. There are two job configuration properties, mapreduce.job.acl-view-job and mapreduce.job.acl-modify-job, which may be set to a comma-separated list of users to control who may view or modify a particular job.

          • The shuffle is secure, preventing a malicious user from requesting another user’s map outputs. However, the shuffle is not encrypted, so it is subject to malicious sniffing.

          • When appropriately configured, it’s no longer possible for a malicious user to run a rogue secondary namenode, datanode, or tasktracker that can join the cluster and potentially compromise data stored in the cluster. This is enforced by requiring daemons to authenticate with the master node they are connecting to.

            To enable this feature, you first need to configure Hadoop to use a keytab previously generated with the ktutil command. For a datanode, for example, you would set the dfs.datanode.keytab.file property to the keytab filename and dfs.datanode.kerberos.principal to the username to use for the datanode. Finally, the ACL for the DataNodeProtocol (which is used by datanodes to communicate with the namenode) must be set in hadoop-policy.xml, by restricting security.datanode.protocol.acl to the datanode’s username.

          • A datanode may be run on a privileged port (one lower than 1024), so a client may be reasonably sure that it was started securely.

          • A task may communicate only with its parent tasktracker, thus preventing an attacker from obtaining MapReduce data from another user’s job.

          One area that hasn’t yet been addressed in the security work is encryption: neither RPC nor block transfers are encrypted. HDFS blocks are not stored in an encrypted form either. These features are planned for a future release, and in fact, encrypting the data stored in HDFS could be carried out in existing versions of Hadoop by the application itself (by writing an encryption CompressionCodec, for example).

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          http://www.ics.uci.edu/~lmartie/education/ Processing Examples

          Processing Examples

          These are code samples to illustrate different concepts in Processing. I will try to update these on a weekly basis until around mid June 2013.
          • Random Walker
            • zip file
          • Balloons and Wind
            • zip file
          • Transformations, Force, and Vectors
            • zip file
          • Friction
            • zip file
          • Mutual Attraction
            • note:: Applies one force at a time to each pebble on each step.
            • zip file
          • Mutual Attraction with Trails
            • note:: Applies one force at a time to each pebble on each step.
            • zip file
          • Mutual Attraction Sum with Trails
            • note: Applies total sum of forces to each pebble on each step.
            • zip file
          • Mutual Attraction, Trails, and Mouse
            • note: Applies total sum of forces to each pebble on each step.
              Pebbles are also attracted to mouse.
            • zip file
          • Springs
            • zip file
          • Box Collision and Elastic Collision
            • note: Determines if 2D boxes collide and updates velocities using elastic collision.
              Some boxes crash hard and get stuck.
            • zip file
          • Particle System and Water
            • zip file
          • Binary Cellular Autmata
          • Binary Cellular Automata Generating Numbers
            • zip file
          • Recursion and Fractals
            • zip file
          • Recursion, Patterns, and Animation
            • zip file
          • Sprites with arrays
            • note: Use arrow keys.
            • zip file
          • Sprite Sheets
            • note: Use Left/Right arrow keys. Sprite Sheet
            • zip file
          • Sprite Sheets and Moving Background
            • note: Use Left/Right arrow keys. Sprite Sheet
            • zip file
          http://www.ics.uci.edu/~yubok/cv.html Yubo Kou CV

          Yubo Kou

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          Skill

          Programming: Java, Python, PHP

          Software: Office, R, MySQL

          Education

          PH.D., Informatics, University of California at Irvine June 2016

          M.S., Computer Science, Renmin University of China, China June 2010

          B.A., Computational and Applied Linguistics, Peking University, China June 2007

          Work Experience

          Research Intern, Keio-NUS CUTE Center, Singapore July 2013 - September 2013

          Research Intern, NEC Laboratories, China April 2011 - July 2011

          Graduate Research Assistant, WAMDM Lab, Renmin University of China September 2007 - June 2010

          Teaching

          TA of ICS 60 "Computer Games and Society" Fall 2012

          Reader of ICS 4 "Human Factors for the Web" Winter 2013

          Reader of ICS 62 "Game Technology and Interactive Media" Spring 2013

          TA of Informatics 161 "Social Analysis of Computing" Fall 2013

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          Me, My Wife and Our Cat

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          Conference Papers

          Yubo Kou, and Bonnie Nardi. Governance in League of Legends: A Hybrid System, Foundation of Digital Games, 2014, Fort Lauderdale, FL.Exemplary Paper. download

          Yubo Kou, and Bonnie Nardi. Regulating Anti-Social Behavior on the Internet: The Example of League of Legends, iConference, 2013, Fort Worth, Texas.

          Yubo Kou, Yukun Li and Xiaofeng Meng. DSI: A Method for Indexing Large Graphs Using Distance Set. In proceedings of the 11th International Conference on Web-Age Information Management (WAIM2010), July 15-17, 2010, Jiuzhaigou, China.

          Yukun Li, Xiaofeng Meng and Yubo Kou. An Efficient Method for Constructing Personal DataSpace. In proceedings of the 6th Web Information Systems and Applications Conference (WISA2009), September 18-20, 2009, Xuzhou, China.Best Paper.

          Yubo Kou, Yukun Li, Xiaofeng Meng, Xiangyu Zhang, and Jing Zhao. A Strategy for Task Mining in Personal Dataspace Management. Journal of Computer Research and Development,Vol.46 Suppl.:446-452, 2009.10. (in Chinese).

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          http://www.ics.uci.edu/~yubok/index.html Yubo Kou

          Yubo Kou

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          I'm a third year Ph.D. student in Department of Informatics, UCIrvine. I'm interested in regulation and governance in online communities, particularly in online games. I'm currently working with Prof. Bonnie Nardi. I use ethnographic methods to study people's interaction with technologies. My email address is yubok(at)uci.edu.

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          Regulation in Online Games

          Regulating people's behavior has been a challenging problem on the Internet,although cyber libertarians would argue against the regulation of the Internet. Lessig categorizes regulatory forces into norms, law, market and code. He believes that these four means can work together to reach an optimal mix in terms of regulatory efficiency.Yet nowadays new, hybrid regulatory regimes emerge, which do not belong to any single Lessigian category. For example, the Tribunal in League of Legends involves the comminity in policing itself, eBay court leverages crowdsourcing to resolute online dispute.How do these new regimes impact people's social life?

          Spatial Gesture for Public Large Displays

          Motion sensing devices such as Leapmotion and Kinect allow people to control large interactive displays at a distance.How do people interact with them? Which gestures do people consider natural and intuitive to interact with these devices?

          Occupational Identity

          I study how ICT facilitates new workers' learning.

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          http://www.ics.uci.edu/prospective/ko/opportunities/applied-learning/  응용 학습 « 기회 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
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          기회

          Bren:ICS의 교육은 단순한 교육과정 그 이상을 선사합니다.

          • 응용 학습
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          응용 학습

          사람들로 가득찬 프로젝트 발표 현장

          본교의 전공은 체계적이므로, 학생들은 교육의 일환으로 배운 내용을 실제로 적용할 수 있습니다. 모든 전공에는 재학 기간 내내 실시하는 수많은 종합 프로젝트 과정이 포함됩니다.

          프로젝트 과정에는 선수과목인 강좌가 여러 개 있으므로, 학생들은 이미 해당 주제의 기초를 알고 있는 상태에서 들어가 경험이 풍부한 강사의 지도 아래 고차원의 문제에 집중할 수 있습니다.

          실제 현장에서 일어나는 문제를 다룸으로써 Bren:ICS 종합 프로젝트 과정은 발생 가능한 광범위한 영역에 접근합니다. 본교를 거쳐간 학생들은 다음과 같은 영역에서 소프트웨어나 하드웨어로 이루어진 컴퓨터 기반 시스템을 지정, 설계, 개발하는 법을 배울 기회가 있었습니다.

          • 항공우주
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          • 비영리
          • 약제학

          광범위한 네트워크를 이루고 있는 본교의 수많은 협력 기업들이 프로젝트 과정을 듣는 학생 팀들의 현장 주관 기관으로 참여합니다. 이러한 산학간 협력을 통해 졸업생들은 풍부한 인턴십과 경력의 기회를 얻곤 합니다. 졸업생들이 자주 말하는 이야기에 따르면, 종합 과정은 UCI 재학 시절의 하이라이트였으며 첫 직장에 필요한 기본 능력을 준비하는 데 가장 결정적인 차별점을 선물해 주었다고 합니다.

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          http://www.ics.uci.edu/prospective/vi/opportunities/applied-learning/  Nghiên cứu Ứng dụng « CƠ HỘI « BREN: Khoa học Thông tin và Điện toán « Đại học California Irvine ?>
          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          CƠ HỘI

          Chương trình đào tạo của Bren:ICS đem đến cho bạn không chỉ những kiến thức trên giảng đường

          • Nghiên cứu Ứng dụng
          • Chương trình danh dự
          • Nghiên cứu
          • Quản trị doanh nghiệp

          Nghiên cứu Ứng dụng

          Chật kín chỗ ngồi trong phòng nghe thuyết trình về dự án

          Các chuyên ngành của chúng tôi được tổ chức chặt chẽ do vậy bạn thực sự được học những gì bạn bạn mong muốn khi đăng ký theo học. Mỗi chuyên ngành đều có nhiều chương trình đồ án thực tế được tổ chức trong suốt chương trình học.

          Các chương trình đồ án có nhiều chuỗi bài giảng được coi là điều kiện tiên quyết, vì thế bạn sẽ nắm được những kiến thức căn bản về chủ đề nghiên cứu và có thể tập trung vào những vấn đề cấp cao dưới sự hướng dẫn của một giảng viên giàu kinh nghiệm.

          Các chương trình đồ án thực tế Bren:ICS đề cập đến nhiều lĩnh vực đa dạng dựa theo những vấn đề trên thế giới thực tế thường xuyên diễn ra. Những sinh viên đạt yêu cầu có cơ hội nghiên cứu để xác định, thiết kế và phát triển các hệ thống chạy trên mạng lưới điện toán kết hợp giữa phần mềm và/hoặc phần cứng trong những lĩnh vực như:

          • hàng không vũ trụ
          • tri thức nhân tạo
          • y sinh học
          • tài chính kinh doanh
          • giáo dục
          • giải trí
          • môi trường
          • trò chơi
          • y tế
          • internet
          • luật
          • quản lý
          • sản xuất
          • phi lợi nhuận
          • dược lý học

          Mạng lưới rộng lớn các công ty liên kết với chúng tôi tham gia với tư cách là các điểm đăng cai cho các nhóm sinh viên khi thực hiện các đồ án thực tế. Hình thức hợp tác trường-ngành này thường đem đến cho các sinh viên cơ hội thực tập đa dạng và những cơ hội nghề nghiệp phong phú khi tốt nghiệp, nhất là những sinh viên thường xuyên tạo nên những điểm nhấn ở UCI trong quá trình thực hiện các chương trình đồ án thực tế và những sinh viên đã tạo nên sự khác biệt lớn trong quá trình nỗ lực trau dồi kỹ năng.

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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          http://www.ics.uci.edu/prospective/en/opportunities/applied-learning/contact/student-affairs/​  Applied Learning « Opportunities « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Opportunities

          A Bren School education provides you with more than just course work

          • Applied Learning
          • Honors
          • Research
          • Entrepreneurship

          Applied Learning

          Standing room only at project presentations

          Our majors are structured so you actually apply what you learn as part of the educational experience. Every major includes a number of capstone project courses throughout the years.

          Project courses have several lecture courses as prerequisites, so you enter knowing the basics of the subject matter and can concentrate on advanced issues under the guidance of an experienced instructor.

          By virtue of often addressing real world problems, Bren School capstone project courses touch upon a wide spectrum of possible domains. Past students have had the opportunity to learn to specify, design, and develop computer-based systems comprised of software and/or hardware in such domains as:

          • aerospace
          • artificial intelligence
          • biomedical
          • business finance
          • education
          • entertainment
          • environment
          • games
          • health care
          • internet
          • law
          • management
          • manufacturing
          • non-profit
          • pharmacology

          Many of our extensive network of affiliated companies participate as host sites for student teams in project courses. These school-industry partnerships often provide rich internship and career opportunities for graduates, who frequently report that capstone courses were the highlights of their time at UCI and made the critical difference in preparing them with a skill set that got them their first jobs.

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          http://www.ics.uci.edu/prospective/zh-tw/opportunities/applied-learning/  應用所學 « 機會 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
          • 機會
          • 職場
          • 生活
          • 入學
          • 聯絡方式

          機會

          貝林資科院的教育不只是唸書

          • 應用所學
          • 榮譽學生
          • 研究
          • 創業精神

          應用所學

          專案報告座無虛席

          各系課程在精心安排之下,能讓學生一邊受教一邊應用所學。 各系都會在各年級安排幾個期末專題課程。

          專題課程都有設下條件,要求必須先上過幾門課才能選讀,目的是讓選課的學生先有基本了解,以便在經驗豐富的老師帶領下,專心研究高深課題。

          貝林資科院的期末專題,經常都是處理現實社會中發生的問題,所以接觸到的領域非常廣泛。 過去的學生就曾經針對以下領域,學習如何選擇、設計、開發由軟硬體構成的電腦系統:

          • 航太
          • 人工智慧
          • 生物醫學
          • 企業財務
          • 教育
          • 娛樂
          • 環境
          • 遊戲
          • 醫療
          • 網際網路
          • 法律
          • 管理
          • 製造
          • 非營利
          • 藥理

          本校和眾多企業都有合作關係,其中多家公司參與這項計畫,讓本校期末專題課程的學生團隊到場研究。 這種建教合作關係通常帶來豐富的實習經驗,畢業生也能藉此尋找工作。很多校友都表示,期末專題是他們求學生涯的高峰。他們在這段期間學到的東西,就是日後找到第一份工作的關鍵。

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          問題與意見 | 隱私權和法律聲明 | 著作權查詢 | © 2016 UC Regents
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          http://www.ics.uci.edu/prospective/en/careers/alumni/  Alumni « Careers « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Careers

          What alumni accomplish in life with a Bren School degree continues to surprise even us

          • Breadth
          • Job Opportunities
          • Internships
          • Graduate School
          • Alumni

          Alumni

          A Bren School education prepares you for a broad range of careers, and not just in computing. Our alumni work at companies as diverse as Pixar, IBM, Yahoo, NASA, Microsoft, Google, Deloitte and Touche, Unisys, Accenture, Merrill Lynch, Blizzard, Broadcom, and many other world-renowned organizations, and have job titles such as Computer Forensic, Business Systems Architect, Software Architect, Development Manager, Digital Advertising Strategist, Attorney, Spiritual Counselor, Chief Technology Officer, IT Director, Product Manager, Software Engineer, Programmer/Analyst, and Consultant.

          Our courses provide a strong foundation for a career of lifelong learning, preparing you with core knowledge and skills upon which you will continue to expand your abilities throughout your career.

          Many of our students combine a Bren School major with a minor from across the campus (e.g., English, Psychology, Philosophy, Educational Studies, Economics, Chemistry, Studio Art, Digital Arts, Civic and Community Engagement, to name a few) to uniquely position them for the career of their choice.

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          http://www.ics.uci.edu/prospective/en/careers/job-opportunities/  Job Opportunities « Careers « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Careers

          What alumni accomplish in life with a Bren School degree continues to surprise even us

          • Breadth
          • Job Opportunities
          • Internships
          • Graduate School
          • Alumni

          Job Opportunities

          The job market is very strong for graduates of the Bren School. Computing careers are in high demand, and the various degrees that we offer prepare our students for the kinds of computing careers they desire. Frequently, it is the unique degree and its associated set of courses that interest an employer in the first place.

          Numerous sources point to a very healthy job market:

          • The U.S. Bureau of Labor Statistics predicts almost two million new jobs in computing, with five of the top ten fastest growing jobs (and rising salaries) across all possible careers being in information technology.
          • According to CNBC, three of the top ten highest paying bachelor’s degrees in 2010 involve computing.
          • The National Center for Women in Information Technology has prepared an interesting report on job outlook and number of graduates by state. Click “CA” on the map and then choose “Download statewide data” to see a report that compares projected engineering jobs versus projected computer science jobs.
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          http://www.ics.uci.edu/prospective/en/careers/breadth/contact/student-affairs/​  Breadth « Careers « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Careers

          What alumni accomplish in life with a Bren School degree continues to surprise even us

          • Breadth
          • Job Opportunities
          • Internships
          • Graduate School
          • Alumni

          Breadth

          The Bren School offers over one hundred courses covering a broad range of topics. You are sure to find multiple courses in your area(s) of interest, courses that not only introduce you to fundamentals but also explore topics in depth. This means undergraduates have the opportunity to study advanced computer science topics usually only offered in graduate programs elsewhere.

          Why are breadth and depth of the curriculum important for you to consider? Some students start college with only a vague idea of what they would like to study – they should be able to sample the offerings and find their niche. Others enter with a stronger sense of interest in a major – they should be able to delve in and continue to be challenged. Yet others find that their interests change or become more defined as they move through their major-required courses – they should not be prohibited from following their dreams.

          Our broad portfolio of courses ensures that regardless of interest, aptitude and talent, or desired career path, you will find what you need.

          Here is a sample of areas in which we have significant threads of undergraduate courses:

          • Networking
          • Distributed Computing
          • Advanced Computer Networks
          • Computer and Network Security
          • Software Architectures, Interoperability, and Distributed Systems
          • Social Impacts of Computing
          • Human Computer Interaction
          • Social Analysis of Computerization
          • Organizational Information Systems
          • Technology and Literacy
          • Software Engineering
          • Software Design I and II
          • Requirements Analysis and Engineering
          • Project in Software System Design
          • Software Tools and Methods
          • Theory of Computer Science
          • Fundamental Data Structures
          • Design and Analysis of Algorithms
          • Formal Languages and Automatas
          • Graph Algorithms
          • Computer Graphics
          • Digital Image Processing
          • Project in Advanced 3D Computer Graphics
          • Computational Geometry
          • Information Visualization
          • Computer Game Science
          • Computer Games and Society
          • Game Technologies and Interactive Media
          • Mobile and Ubiquitous Games
          • Multiplayer Game Systems
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          http://www.ics.uci.edu/prospective/zh-tw/careers/breadth/  廣度 « 職場 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
          • 機會
          • 職場
          • 生活
          • 入學
          • 聯絡方式

          職場

          貝林資科院畢業校友的成就,屢屢讓我們刮目相看

          • 廣度
          • 工作機會
          • 實習
          • 研究所
          • 校友

          廣度

          貝林資科院提供的課程超過百種,涵蓋各類主題, 讓你絕對能找到感興趣的科目。這些課程除了帶領學生一窺學術殿堂,還可深入探討特定主題。 所以就算是大學部的學生,也能選到別校只在研究所開設的高等資訊工程學科。

          為什麼必須審慎考慮課程安排的深度和廣度呢? 因為有的學生剛進大學時,對自己要唸的東西還懵懵懂懂。這時應該讓他們儘量嘗試,找出自己擅長的科目。 有的學生剛進校門就已經確定要主修什麼,這時就應該讓他們專攻特定主題,持續挑戰自我。 但是有的人中途改變興趣,或是在唸主修科目時更確定自己的志向所在,這時就不該讓他們的夢想受限。

          我們的課程取材極為廣泛。無論學生的興趣、性向、才華或未來有何生涯規劃,都能各取所需。

          以下列出大學部課程的幾個主要領域為例:

          • 網路
          • 分散式運算
          • 高等計算機網路
          • 電腦與網路安全
          • 軟體架構、互通和分散式系統
          • 電腦對社會的影響
          • 人機互動
          • 電腦化的社會分析
          • 組織資訊系統
          • 科技與素養
          • 軟體工程
          • 軟體設計上、下
          • 需求分析與工程
          • 軟體系統設計專案
          • 軟體工具與方法
          • 計算機理論
          • 資料結構導論
          • 演算法設計與分析
          • 正規語言和自動機
          • 圖形演算法
          • 電腦繪圖
          • 數位影像處理
          • 高等3D電腦繪圖專案
          • 計算幾何
          • 資訊視覺化
          • 電腦遊戲學
          • 電腦遊戲與社會
          • 遊戲技術和互動媒體
          • 手機遊戲與跨媒材遊戲(Ubiquitous Game)
          • 多人遊戲系統
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          http://www.ics.uci.edu/prospective/en/careers/graduate-school/  Graduate School « Careers « Bren School of Information and Computer Sciences « University of California Irvine ?>
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          Careers

          What alumni accomplish in life with a Bren School degree continues to surprise even us

          • Breadth
          • Job Opportunities
          • Internships
          • Graduate School
          • Alumni

          Graduate School

          Some students aspire to attend graduate school to pursue an advanced degree after completion of their B.S. program. They may wish to enroll in a M.S. program to take more in-depth classes to further hone their skills in certain topics, or enter a Ph.D. program to prepare themselves for a future career in academic or industrial research. Some students, too, enroll at a professional school to earn a MBA or Law degree.

          Bren School students have an excellent record in this regard, with our alumni succeeding at such prestigious institutions as Stanford, UCLA, UC Berkeley, UC San Diego, Carnegie Mellon University, Georgia Tech, Columbia, Harvard, MIT, and others. Our honors program and the many research opportunities that are available have proven invaluable to successfully transition to graduate school.

          how to get into graduate school How to get into graduate school

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          http://www.ics.uci.edu/prospective/ko/careers/breadth/  폭넓은 교육 « 진로 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
          • 학위
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          • 진로
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          • 입학
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          진로

          Bren:ICS의 동문들이 자신들의 영역에서 놀라운 일들을 해내고 있는 모습을 살펴 보십시오.

          • 폭넓은 교육
          • 취업 기회
          • 인턴십
          • 대학원
          • 동문

          폭넓은 교육

          Bren:ICS는 매우 다양한 주제로 100여 개 이상의 과정을 제공하고 있습니다. 그 가운데서 여러분은 관심 있는 분야의 과정과 기본 원칙을 배울 뿐 아니라, 주제를 깊게 파헤치는 과정을 다양하게 발견할 수 있을 것입니다. 그래서 다른 곳에서는 보통 대학원 과정에서나 접할 수 있는 상급 수준의 컴퓨터 과학 주제를 본교에서는 학부생 단계에서 공부할 수 있습니다.

          왜 폭넓고 깊이 있는 교육 과정을 고려하는 것이 중요할까요? 어떤 학생들은 공부하고 싶은 것에 대한 막연한 생각만으로 대학 생활을 시작합니다. 이러한 학생들은 제공된 교육을 체험한 후 자신에게 딱 맞는 자리를 찾아낼 수 있어야 합니다. 또 어떤 학생들은 전공에 대한 강력한 관심을 품고 대학에 들어옵니다. 이들은 깊이 탐구하고 과제를 계속 극복할 수 있어야 합니다. 하지만 또 다른 학생들은 전공에 필요한 과정들을 이수하면서 차차 자신의 관심이 바뀌거나 더욱 명확해진다고 생각합니다. 이들은 자신의 꿈을 마음껏 쫓아갈 수 있어야 합니다.

          본교의 폭넓은 과정은 여러분이 관심분야, 적성, 재능, 희망 진로 등에 따라 자신에게 필요한 것을 찾을 수 있도록 도와줍니다.

          본교에서 제공 중인 학부 과정에서 중요한 분야 몇 가지를 골라 소개합니다.

          • 네트워킹
          • 분산 컴퓨팅
          • 첨단 컴퓨터 네트워크
          • 컴퓨터와 네트워크 보안
          • 소프트웨어 아키텍처, 상호운용성, 분산 시스템
          • 컴퓨터의 사회적 영향
          • 인간과 컴퓨터의 상호작용
          • 전산화에 대한 사회적 분석
          • 조직 정보 시스템
          • 기술과 활용 능력
          • 소프트웨어 공학
          • 소프트웨어 설계 I 및 II
          • 요구사항 분석과 엔지니어링
          • 소프트웨어 시스템 설계 프로젝트
          • 소프트웨어 도구와 방법
          • 컴퓨터 과학 이론
          • 기본 데이터 구조
          • 알고리즘의 설계와 분석
          • 형식 언어와 오토마타
          • 그래프 알고리즘
          • 컴퓨터 그래픽
          • 디지털 영상 처리
          • 첨단 3D 컴퓨터 그래픽 프로젝트
          • 계산 기하학
          • 정보 시각화
          • 컴퓨터 게임 과학
          • 컴퓨터 게임과 사회
          • 게임 기술과 쌍방향 미디어
          • 모바일 게임과 유비쿼터스 게임
          • 멀티플레이어 게임 시스템
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          NGHỀ NGHIỆP

          Những việc mà các cựu sinh viên hoàn thành với tấm bằng từ trường Bren:ICS vẫn tiếp tục làm ngay cả chúng tôi không hết bất ngờ

          • Sự đa dạng
          • Cơ hội việc làm
          • Thực tập
          • Trường cao học
          • Cựu sinh viên

          Sự đa dạng

          Bren:ICS cung cấp hơn một trăm khóa học với nhiều chủ đề đa dạng. Chắc chắn bạn sẽ tìm thấy nhiều khóa học thuộc (những) lĩnh vực quan tâm của mình, những khóa học không chỉ trang bị cho bạn những kiến thức chủ yếu mà còn giúp bạn đào sâu nghiên cứu các chủ đề. Điều này có nghĩa là những sinh viên đại học có cơ hội nghiên cứu những chủ đề khoa học điện toán cao cấp thường chỉ được đưa vào trong các chương trình cao học ở những tổ chức giảng dạy khác.

          Tại sao sự đa dạng và độ sâu của chương trình giảng dạy lại quan trọng để bạn cân nhắc? Một số sinh viên bước chân vào giảng đường đại học với một ý niệm mơ hồ về những gì mà họ muốn học – họ nên có cơ hội định hướng cho mình những chương trình học và tìm cho mình một chương trình phù hợp nhất. Những sinh viên khác bắt đầu cuộc đời sinh viên với một cảm nhận rõ ràng hơn về chuyên ngành học mà họ lựa chọn – họ nên được tạo cơ hội đào sâu và liên tục thử sức mình. Còn những sinh viên khác lại thấy rằng những mối quan tâm của họ thay đổi hay ngày càng định hướng rõ ràng hơn khi trải qua các khóa học bắt buộc của chuyên ngành – họ không nên bị ngăn cản khi theo đuổi những ước mơ.

          Danh sách những khóa học phong phú của chúng tôi đảm bảo rằng dù bạn có nhiều mối quan tâm, năng khiếu và tài năng hay định hướng con đường sự nghiệp đến như thế nào đi chăng nữa, bạn sẽ tìm thấy được chương trình học bạn cần.

          Sau đây là ví dụ về những lĩnh vực chúng tôi có các chương trình đào tạo đại học chủ chốt:

          • Mạng
          • Điện toán phân bổ (Distributed Computing)
          • Mạng máy tính cao cấp (Advanced Computer Networks)
          • Anh ninh Máy tính và Mạng (Computer and Network Security)
          • Cấu trúc Phần mềm, Tính tương kết và các Hệ phân tán (Software Architectures, Interoperability, and Distributed Systems)
          • Tác động xã hội của ngành điện toán (Social Impacts of Computing )
          • Tương tác Máy tính và Con người (Human Computer Interaction)
          • Phân tích Xã hội của Tin học hóa (Social Analysis of Computerization)
          • Hệ thống Thông tin Tổ chức (Organizational Information Systems)
          • Công nghệ và Kỹ năng (Technology and Literacy)
          • Kỹ thuật Phần mềm (Software Engineering)
          • Thiết kế Phân mềm I và II
          • Phân tích và Thiết kế Yêu cầu (Requirements Analysis and Engineering)
          • Dự án Thiết kế Hệ thống Phần mềm
          • Công cụ và Phương pháp Sản xuất Phần mềm (Software Tools and Methods)
          • Lý thuyết Khoa học Điện toán (Theory of Computer Science)
          • Cấu trúc Dữ liệu Căn bản (Fundamental Data Structures)
          • Thiết kế và Phân tích Thuật toán (Design and Analysis of Algorithms)
          • Ngôn ngữ Hình thức và Automát (Formal Languages and Automatas)
          • Thuật toán Biểu đồ (Graph Algorithms)
          • Đồ họa Máy tính (Computer Graphics)
          • Xử lý Hình ảnh Kỹ thuật số (Digital Image Processing)
          • Dự án Đồ họa Máy tính 3D cao cấp
          • Hình học Điện toán (Computational Geometry)
          • Trực quan hóa Thông tin (Information Visualization)
          • Khoa học Trò chơi Điện toán (Computer Game Science)
          • Trò chơi Điện toán và Xã hội (Computer Games and Society)
          • Công nghệ Trò chơi và Truyền thông Tương tác (Game Technologies and Interactive Media)
          • Trò chơi trên điện thoại di động và trò chơi phổ dụng (Mobile and Ubiquitous Games)
          • Hệ thống trò chơi đa người chơi (Multiplayer Game Systems)
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          Đại học California, Irvine - Irvine, CA 92697 : 949-824-5011

          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          Careers

          What alumni accomplish in life with a Bren School degree continues to surprise even us

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          Internships

          Internships are a key element of your preparation for entering the workforce. They provide you with valuable project experience in the real world, give you a chance to taste what your future career might be like, and help build your resume. The majority of Bren School juniors and seniors spend their summer at an internship, and it is not uncommon for our sophomore and even freshmen students to also do so.

          Given the high-tech environment surrounding UC Irvine, numerous local internships are available, though quite a few of our students venture out to other parts of the country to pursue their interests.

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          機會

          貝林資科院的教育不只是唸書

          • 應用所學
          • 榮譽學生
          • 研究
          • 創業精神

          創業精神

          Adball.com:hITEC大賽得獎人

          貝林學院的學生不只會唸書, 還會創造自己的未來、改造這個社會。 他們的創業精神展現在許多方面。 每年都有學生組隊報名hITEC比賽。hITEC是校內舉辦的產品開發競賽,請來產業達人擔任指導老師,獎金超過9,000美元。 過去兩年得獎的產品概念,都已經成為真正的新創公司。

          • Clarity Labs透過標籤置放程式,利用電視觀眾的消費行為,讓觀眾只要點選電視節目中出現的商品,就能立刻上網購物。
          • Olepta讓醫療行為的所有流程現代化,徹底改革醫師管理客戶關係的作法。 這套軟體為醫療單位、病患、保險公司、病患家屬,以及醫療過程的其他關係人,開創全新的互動管道。

          在hITEC比賽獲得前三名的產品開發團隊,可自動晉級Paul Merage School of Business商學院每年舉辦的Stradling Yocca Carlson & Rauth營運計畫比賽,總獎金高達三萬美元。

          Clarity Labs和Olepta這兩個例子證明,本校每年都有學生開設新創公司,把創新的精神應用到網路、遊戲、醫療資訊系統、網際網路技術或其他領域。 想參加產品開發競賽的學生,選課時可登記貝林學院教授Ramesh Jain開設的創業精神課程。教授本人已經成立三家業績亮眼的公司。

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          • 학위
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          기회

          Bren:ICS의 교육은 단순한 교육과정 그 이상을 선사합니다.

          • 응용 학습
          • 우등생
          • 연구
          • 창업

          창업

          Adball.com – hITEC 대회 우승자

          브렌 스쿨의 학생들은 단순한 학생이 아닙니다. 이들은 자신의 경력과 사회를 만들고 싶어합니다. 이들의 모험 정신은 다양한 방식으로 나타납니다. 매년 학생들은 팀을 꾸려서 업계 멘토가 참여하고 9,000달러가 넘는 상금이 걸리는 교내 제품 개발 대회인 hITEC에 출전합니다. 지난 2년 동안 우승한 제품 아이디어들은 발전하여 실제 창업에까지 이르렀습니다.

          • Clarity Labs는 편리한 온라인 쇼핑을 위해 특정 TV 쇼에서 마음에 드는 제품을 클릭할 수 있는 태그 배치 어플리케이션을 통해 시청자의 소비자 행동을 활용하고 있습니다.
          • Olepta는 처음부터 마지막까지 실제 작업 흐름을 현대화하여 개업의들의 관계 관리에 일대 혁신을 일으키고 있습니다. Olepta의 소프트웨어는 의료 제공자, 환자, 보험 제공자, 가족 구성원, 그리고 환자의 병력에 관계된 기타 모든 당사자들 간에 새로운 창구를 열어 줍니다.

          hITEC의 제품 개발 팀 중 1등부터 3등까지는 폴 머라지 비즈니스 스쿨에서 매년 개최하는 Stradling Yocca Carlson & Rauth 사업 계획 대회에 자동 출전할 수 있는 자격을 얻습니다. 이 대회에서는 총 30,000달러의 현금이 수여됩니다.

          Clarity Labs와 Olepta로 예시한 바와 같이, 본교 출신 학생들은 네트워킹, 게임, 의학 정보 시스템, 인터넷 공학 등의 분야에서 자신들의 혁신적인 정신을 실현하기 위해 매년 창업의 문을 열고 있습니다. 이같은 제품 개발 대회에 참여하는 데 관심이 있는 학생은 Ramesh Jain 교수가 담당하는 창업 과정에 등록하면 됩니다. Ramesh Jain 교수는 기업 3개를 직접 설립하여 큰 성공을 거둔 바 있습니다.

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          CƠ HỘI

          Chương trình đào tạo của Bren:ICS đem đến cho bạn không chỉ những kiến thức trên giảng đường

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          Quản trị doanh nghiệp

          Adball.com – những người thắng cuộc của cuộc thi hITEC

          Sinh viên Đại học Bren không đơn thuần chỉ là những cô, cậu sinh viên chỉ biết có học. Họ muốn định hình nghề nghiệp và xã hội. Tinh thần quản trị doanh nghiệp của họ thể hiện theo nhiều cách thức. Hàng năm các đội sinh viên đua tranh trong cuộc thi hITEC, một cuộc thi phát triển sản phẩm thu hút sự tham gia của các cố vấn ngành và có các cơ cấu giải thưởng lên tới hơn $9.000 đô-la. Những ý tưởng về sản phẩm giành giải thưởng của hai năm vừa qua đã hình thành nên những bước khởi nghiệp thực tế:

          • Clarity Labs nâng cao hành vi tiêu dùng của người xem truyền hình bằng ứng dụng đặt thẻ cho phép người xem nhấp chuột vào những sản phẩm ưa thích của họ trong bất cứ chương trình TV nào để mua sắm tiện lợi trên mạng.
          • Olepta làm cuộc cách mạng hóa công việc quản lý đầu mối quan hệ dành cho các bác sĩ y khoa bằng cách hiện đại hóa quy trình khám chữa bệnh từ bắt đầu đến kết thúc. Phần mềm của hãng mở ra các kênh quan hệ mới giữa nhà cung cấp dịch vụ y khoa, bệnh nhân, công ty bảo hiểm, người thân, và tất cả các bên khác liên quan trong lịch sử điều trị y khoa của bệnh nhân.

          Ba đội phát triển sản phẩm dẫn đầu trong cuộc thi hITEC sẽ được tham gia Cuộc thi Kế hoạch Kinh doanh Stradling Yocca Carlson & Rauth hàng năm của trường Đại học Kinh doanh Paul Merage School với tổng số giải thưởng tiền mặt lên tới $30.000 đô-la.

          Ngoài hai ví dụ điển hình Clarity Labs và Olepta, hàng năm trong số các sinh viên tốt nghiệp từ trường chúng tôi, có những sinh viên lập công ty khởi nghiệp kinh doanh để áp dụng tinh thần đổi mới của họ vào các hệ thống mạng, trò chơi điện toán, thông tin y khoa, công nghệ internet hay một số lĩnh vực khác. Sinh viên có mong muốn tham gia vào một trong những cuộc thi phát triển sản phẩm này có thể ghi danh vào khóa học Quản trị doanh nghiệp do Giáo sư trường Bren là Ramesh Jain giảng dạy, đây là vị giáo sư tự thân sáng lập ba công ty và đạt được nhiều thành công vang dội.

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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          A Bren School education provides you with more than just course work

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          Entrepreneurship

          Adball.com – hITEC competition winners

          Bren School students are not just students. They want to shape their careers and society. Their entrepreneurial spirit shows up in many ways. Every year students form teams to compete in hITEC, our product development competition that involves industrial mentors and over $9,000 in prizes. The past two years' winning product ideas have gone on to form actual startups:

          • Clarity Labs leverages TV audiences' consumer behavior through a tag placement application that allows viewers to click on their favorite products in any given TV show for convenient online shopping.
          • Olepta revolutionizes relationship management for medical practitioners by modernizing the start-to-finish practice workflow. Its software opens new channels between the medical provider, the patient, insurance provider, family members, and all other parties involved in a patient's medical history.

          The top three product development teams in hITEC automatically qualify for The Paul Merage School of Business's annual Stradling Yocca Carlson & Rauth Business Plan Competition with cash awards totaling $30,000.

          As exemplified by Clarity Labs and Olepta, every year some of our of students launch new start-up companies to apply their innovative spirit to networking, gaming, medical information systems, internet technology, or some other area. A student who is interested in participating in one of these product development competitions may enroll in the Entrepreneurship course taught by Bren Professor Ramesh Jain, who has founded three highly successful companies himself.

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          http://www.ics.uci.edu/prospective/vi/degrees/computer-game-science/  Khoa học Trò chơi Điện toán « BẰNG CẤP « BREN: Khoa học Thông tin và Điện toán « Đại học California Irvine ?>
          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Khoa học Trò chơi Điện toán

          Chuyên ngành này là kết hợp chuyên ngành khoa học điện toán với chương trình đào tạo chuyên sâu về thiết kế, xây dựng và nắm vững những trò chơi điện toán và những dạng thức khác của môi trường tương tác. Sinh viên sẽ được trang bị kiến thức nền tảng vững chắc về khoa học điện toán, cộng thêm những khóa học về phim ảnh và những nghiên cứu đa phương tiện, toán học, vật lý và công nghệ trò chơi.

          Chương trình đào tạo của chuyên ngành tập trung mạnh mẽ vào mảng thiết kế, khả năng làm việc theo nhóm, và sự hiểu biết về các trò chơi điện toán và những công nghệ liên quan cùng phương tiện truyền thông trong bối cảnh xã hội và văn hóa. Những sinh viên quan tâm nghiên cứu về những khái niệm và công cụ nền tảng của những trò chơi điện toán và ứng dụng những khái niệm và công cụ đó để tạo nên những trò chơi điện toán của riêng mình nên nộp đơn theo học chuyên ngành này.

          Không gian trao đổi về Khoa học Trò chơi Điện toán Không gian trao đổi về Khoa học Trò chơi Điện toán

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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          http://www.ics.uci.edu/prospective/en/degrees/computer-game-science/contact/student-affairs/​  Computer Game Science « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
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          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Computer Game Science

          The Computer Game Science (CGS) major combines a solid foundation in computer science with a focus on designing, building, and understanding computer games and other forms of interactive media. The fundamentals of information and computer science — along with coursework in mathematics, statistics, physics, and film and media studies — provide students with the concepts and tools to study a wide scope of computer game technologies.

          CGS emphasizes design, teamwork, and the understanding of computer games and related technologies and media in a social and cultural context. The term “computer game” includes games that run on cell phones, mobile devices, PCs, consoles, Macs, web pages and even inside automobiles. CGS majors design and create games for entertainment, and also for education, training and social change.

          The study of computer games is an emerging field driven by advancing computer hardware and software technology, the widespread popularity of video games as an entertainment medium, and by the interest of artists, economists, educators, scientists and many others to use game technologies for communication, visualization, computation and learning.

          IS COMPUTER GAME SCIENCE FOR ME?

          CGS is ideal for anyone interested in learning the technical components of creating games — computer programming, graphics, network design, database management, artificial intelligence and much more — and working in teams to design and implement exciting new games. If you are primarily interested in the art or management sides of creating games, the CGS major may not be the best fit for you.

          Students who major in Computer Game Science will:

          • acquire a solid foundation in computer science and software development;
          • learn how to create interactive and human-centered computer game designs;
          • employ an interdisciplinary approach to computer game design and development, drawing on coursework in modeling and design, graphics, software engineering, hardware architectures, AI, algorithms, distributed systems, human interfaces and aesthetics;
          • be able to analyze and discuss computer game systems as communication, teaching and entertainment media that can be a force for education, social change and activism;
          • graduate with an extensive portfolio of implemented games.

          WHY STUDY AT UC IRVINE?

          Several factors contribute to the strength of UC Irvine’s Computer Game Science program, including:

          • Overall excellence. Ranked 28th nationally by U.S. News and World Report, computer science education at UCI is broad, deep and cutting-edge.
          • Location. Irvine and Orange County are home to a remarkable concentration of game development studios large and small, including industry giant Blizzard Entertainment. We consulted with these companies (many of which employ or were founded by UCI alumni) while planning the CGS major, and they look forward to offering internships and jobs to our students.
          • Collaboration. We partner with nearby Laguna College of Art and Design, which offers a Game Art major. Their student artists work with our CGS students to develop innovative, visually engaging games.

          WHAT COURSES DO I TAKE?

          The CGS major combines the fundamentals of computer science with about a dozen game-focused courses. Current requirements for this major can be found in the General Catalogue.

          WHAT CAN I DO WITH A DEGREE IN CGS?

          A wide variety of careers and graduate programs are open to Computer Game Science graduates. The video game industry is comparable in size to the film and music industries, and job growth projections are excellent for people with strong technical backgrounds. Many other fields, including mobile software development, interactive entertainment, and training and education software, have demand for similar skill sets and knowledge. CGS graduates are well trained in computer science, and can thus pursue graduate programs or any career that involves designing, implementing, evaluating or interacting with computer-based systems.

          Computer Game Science Open House Associate Professor Crista Lopes at the Computer Game Science Open House demonstrates her 3D simulation of a podcar system.

          In the News

          UCI students build games in a week
          The Orange County Register features Game Jam, a popular competition sponsored by the UCI Video Game Development Club. Visit the Bren School YouTube channel to view a wrap-up of the spring 2011 contest.
          UC Irvine’s new computer games major gets its game on
          The OC Weekly publishes a six-page spread about the Bren School’s newest undergraduate major.
          UC Irvine takes video games to the next level
          The Los Angeles Times previews CGS before its launch in Fall 2010.
          Meet the CGS mascot (mentioned in the LA Times article above), a character from the game Colossal Crisis, developed by UCI undergraduates James Dalby, Fritzie Mercado, Edward Fleischman and Quin Kennedy. The city is under attack by Godzilla, and the professor assigns multiple clones of our hero to collect equipment needed to fight back. Catch the demo video on YouTube.
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          http://www.ics.uci.edu/prospective/zh-tw/degrees/computer-game-science/  電腦遊戲學 « 學位 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
          • 機會
          • 職場
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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          電腦遊戲學

          本系除了資訊科學外,也側重設計、建築等課程,並需了解電腦遊戲和其他形式的互動媒體。 學生能在資訊科學領域打下良好基礎,並補充電影、媒體研究、數學、物理學和遊戲技術等課程。

          本系非常強調設計和團隊合作,另外也要了解電腦遊戲、相關技術,以及社會和文化脈絡下的媒體意義。 學生如果很想了解電腦遊戲的概念和工具,以及如何應用這些概念和工具自創遊戲,建議選擇這個科系。

          電腦遊戲學說明會 電腦遊戲學說明會

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          http://www.ics.uci.edu/prospective/ko/degrees/computer-game-science/  컴퓨터 게임 과학 « 학위 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
          • 학위
          • 기회
          • 진로
          • 대학 생활
          • 입학
          • 문의

          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
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          • ICS 전공미정 교양과정
          • 부전공

          컴퓨터 게임 과학

          이 전공은 컴퓨터 게임의 설계, 제작 및 이해에 초점을 맞춘 컴퓨터 과학과 기타 형태의 쌍방향 미디어를 결합하고 있습니다. 학생들은 컴퓨터 과학의 기본 원칙에 대한 탄탄한 기초 교육을 받으며, 영화와 미디어 연구, 수학, 물리학, 게임 공학 같은 과정을 통해 심화 학습을 합니다.

          이 전공은 컴퓨터 게임 및 관련 기술의 이해, 설계, 팀워크뿐 아니라 사회적, 문화적 맥락에서의 미디어를 집중적으로 배웁니다. 컴퓨터 게임의 근간이 되는 개념과 도구를 배우고 이러한 개념과 도구를 적용하여 나만의 것을 만드는 데 관심이 있는 학생들은 이 전공을 탐구하는 것이 좋습니다.

          컴퓨터 게임 과학 오픈 하우스 컴퓨터 게임 과학 오픈 하우스

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          http://www.ics.uci.edu/prospective/en/degrees/software-engineering/contact/student-affairs/​  Software Engineering (SE) « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
          • Opportunities
          • Careers
          • Student Life
          • Admissions
          • Contact

          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Software Engineering (SE)

          The Software Engineering major prepares students to be productive members of software engineering teams in a variety of application domains, with focus on the domains of major importance within each decade. It combines a solid foundation in computer science with knowledge of how to engineer modern software systems, and how to function within teams.

          Coursework in mathematics and statistics provide students the basis for rigorous thinking; coursework in the foundations of computer science provide students the basis for computational thinking; coursework in topics of software engineering prepares students for the production of software; project courses prepare students for the practice of software development. The major emphasizes the design and implementation of large software systems.

          IS SOFTWARE ENGINEERING FOR ME?

          Students who major in Software Engineering will:

          • Acquire a strong foundation in software engineering as well as a solid basis in computer science
          • Have the ability to become a productive member of software engineering teams in a variety of application domains including, but not restricted to, Web and mobile applications
          • Be inspired by technical knowledge and have an appreciation for life-long learning
          • Be capable of placing software in the social context in which is it developed and create novel applications that have the potential to bring social change

          WHAT COURSES DO I TAKE?

          Coursework involves mathematics and statistics, foundations of computer science, topics of software engineering and project courses. Current requirements for this major can be found in the General Catalogue.

          WHAT CAN I DO WITH A DEGREE IN SE?

          A wide variety of careers and graduate programs are open to Software Engineering graduates. The Web and mobile applications industry is a multi-billion dollar industry, and job growth projections are the strongest for people with strong technical backgrounds. Many other application domains, including interactive entertainment, medical information systems, and training and education software have demand for similar skill sets and knowledge. Graduate school in either computer science or software engineering or a related IT field is also a possible career path.

          Of interest

          Software Engineering Careers Continue to Boom
          According to IEEE publication Today's Engineer, software engineering is a hot industry, "with more demand for talented professionals than ever." In fact, the U.S. Department of Labor’s Bureau of Labor Statistics predicts a 30 percent growth rate for software engineering jobs through 2020 — much higher than the 14 percent average growth rate for all other occupations.
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          http://www.ics.uci.edu/prospective/zh-tw/admissions/freshmen/  新生 « 入學 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
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          入學

          入學申請要求、申請期限,以及申請書撰寫秘訣

          • 新生
          • 轉學
          • 住宿
          • 獎助學金
          • 申請手續提示

          新生

          準新鮮人可在校內獲得豐富的資訊,以及所有入學程序的詳細說明。 我們特別鼓勵你詳讀以下網頁:

          準學生: http://www.uci.edu/prospective.php

          入學方式: http://www.admissions.uci.edu/

          常見問題: http://www.admissions.uci.edu/resources/faq.html

          線上文件: http://www.admissions.uci.edu/publications/online_publications.html

          也別忘了本校的吉祥物食蟻獸Peter: http://www.uci.edu/peter/

          目前貝林資科院大學部共有850名學生,加大爾灣分校學生總數則將近22,000人。 2010年入學新生GPA平均分數是3.89,SAT平均分數是1822分。

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          http://www.ics.uci.edu/prospective/zh-tw/degrees/overview/  總覽 « 學位 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
          • 機會
          • 職場
          • 生活
          • 入學
          • 聯絡方式

          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          總覽

          資訊科學瞬息萬變,新技術、新應用不斷出現。 學生受的教育必須跟上時代,為他們做好準備,才能面對充滿刺激的相關工作。 資訊科學的影響力持續擴散,已然涵蓋所有產業,光是一個學位並不足以應付。 貝林資科院長久以來,一直以創新的教育課程享譽國際。現在我們堅持這項傳統,提供七個課程簡化的科系供學生選擇。

          所有科系都有共同的紮實基礎,再從這個基礎衍生出其他科目,提供量身訂作的現代化課表,以不斷增加的課程作為基礎。無論哪個課程,都經過本校精心設計、不斷更新,走在時代的最前端。

          我們的輔導老師能幫你選擇正確科系。 如果你到校後興趣改變,由於所有科系的核心理念相同,因此能在大一或大二剛開始時轉系,不需另外修課。

          如果需要各科系對照表, 請按這裡。

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          http://www.ics.uci.edu/prospective/zh-tw/opportunities/honors/  榮譽學生 « 機會 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
          • 機會
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          機會

          貝林資科院的教育不只是唸書

          • 應用所學
          • 榮譽學生
          • 研究
          • 創業精神

          榮譽學生

          加大爾灣分校的榮譽學生計畫非常發達(全校榮譽學生計畫,CHP),貝林資科院的所有學生都可參加。 這項計畫提供精選活動、同儕指導,還有讓榮譽學生切磋、合作、交流的校內「專區」,另外設有榮譽學生宿舍和特別研究機會。 貝林資科院學生在全校榮譽學生計畫內的比例一向很高。

          貝林資科院也有自己的研究榮譽方案。 大三、大四生可藉此機會了解研究程序,並在指導老師的帶領之下,鑽研更高深的學問。 入選這項計畫的學生要參加榮譽學生研討會,指導老師會教學生從事獨立研究(至少兩季),最後撰寫研究報告,由指導老師和榮譽學生計畫主任審閱。

          全校榮譽學生計畫

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          http://www.ics.uci.edu/prospective/zh-tw/student-life/diversity/  多元樣貌 « 生活 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
          • 學位
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          生活

          你的生活!

          • 多元樣貌
          • 參與校務
          • Bren School Organizations

          多元樣貌

          加大爾灣分校的學生組成份子非常多元,因此製造大量機會,讓來自四面八方的學生互相觀摩。他們的背景和經驗,對你在多元文化社會中求學、生活和工作的想法,起了輔助、互補、挑戰、增進,或擴展的作用。

          就算是人數較少的貝林資科院學生社群,他們的背景和興趣也非常多元。這點可從他們參加的課外活動看出,例如舞團、運動、劇場、社區服務、儲備軍官、兄弟/姊妹會、族群/文化聯誼會,以及擔任社團和學生組織幹部。

          Girls Inc.拜訪加大爾灣分校 Girls Inc.拜訪加大爾灣分校

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          機會

          貝林資科院的教育不只是唸書

          • 應用所學
          • 榮譽學生
          • 研究
          • 創業精神

          研究

          貝林資科院每季都有幾十名大學生(包括大一、大二生)和教授共同做研究。 研究課程沒有擋修制度。 只要你和教授都同意,就能一起做研究,直到你沒有意願為止。

          直接和教授合作不但能見識前衛研究的運作方式,也能一窺研究所的樣貌。 許多大學生不但與教授搭配研究,也和教授聘請的博士生和碩士生密切合作。

          這時你就成為團隊的一份子。 因此大學生研究員有時也能在科學刊物中列為共同作者。 某些學生更能主導研究,名列主要作者。

          擔任大學生研究員的好處,在於能學會解決尚未釐清的複雜問題,並且了解研究專題必須具備的成功要件。 參加大學生研究活動,對於申請研究所或專業學院(企管碩士、法律……等等)也有加分效果。 本校校友在史丹佛、UCLA、加大柏克萊分校、加大聖地牙哥分校、卡內基美隆、喬治亞理工、哥倫比亞、哈佛、麻省理工……等校都有傑出表現。

          加大爾灣分校的HIPerWall可顯示25600x8000個像素

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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          輔系

          學生如果想補強貝林資科院的學位,或加大爾灣分校的學士文憑,無論是藝術、生物科學還是其他領域,以下輔系都能為自己的大學教育錦上添花。

          • 生物醫學資訊: 本輔系讓學生了解如何用資訊科技,處理生物醫學領域的問題與資料,同時讓他們從資訊科學的角度,接觸生命科學的基本知識。
          • 數位資訊系統: 本輔系的目的是讓學生了解資訊系統、電腦運算、數位通訊等領域,而不需成為電腦程式設計師。
          • 資訊學: 本輔系特別要求學生了解人與電腦之間的關係,以及要如何以資訊和軟體設計的方式處理這種關係。
          • 資訊與資訊科學: 本輔系讓學生習得運算技術和程式寫作功力,另外也讓他們了解資訊科學的基本知識。
          • 統計學: 本輔系的目的是讓學生接觸統計理論與實務。
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          聯絡方式

          如有任何問題,歡迎聯絡我們

          • 學務處

          學務處

          貝林資科院課業諮詢老師能為校內所有主修學生提供協助。學生從入學到畢業都能獲得完整服務。 校內課業輔導從暑期新生訓練就已經開始。諮詢老師會協助新生規劃課程,教導他們上網選擇秋季課程。 學生在畢業前都能請貝林資科院諮詢老師幫忙設定學術和就業目標、找出拿到學歷的最佳捷徑,並且定時核對進度,朝畢業的方向邁進。 諮詢老師也會舉辦貝林資科院學生研討會,主題則與就業方向、實習工作、尋找教師導師,以及準備研究所有關。 貝林資科院課業諮詢老師以個別約談、不預約隨時諮商和電子郵件等方式提供協助。

          資科院1館352號室(請在校園地圖上尋找302號大樓 http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          諮詢時間: 週一至週五上午9點~12:00,下午1:00到4:00

          歡迎有意申請的學生撥打專線:(949) 824-5156,與課業諮詢老師約時間了解本校提供的文憑種類,以及最適合申請人課業和就業需求的學歷。

          學務處 學務處

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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          資訊學

          本系研究如何設計、開發、評估軟體應用程式,課程側重軟體的實際應用方式與應用場合。

          由於全盤採用實例和個案研究,因此學生能在軟體工程、軟體設計、人機互動、電腦輔助群組合作、資訊視覺化、資訊技術對企業與社會的影響等科目,打下良好的基礎。

          現代生活充滿數位科技,我們也一直與軟體進行互動、受其驅策。 喜歡設計軟體、很想了解如何設計實用軟體系統的學生,建議選擇這個科系。

          2008年資訊學第一屆畢業生

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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          資訊科學

          資訊科學系強調現代社會不可或缺的電腦運作原則,提供基礎紮實的教育內涵,讓學生面對五花八門的資訊就業市場時,能做好充分準備。

          學生獲得的知識,能在低階架構、低階系統、中階架構(例如程式語言、資料庫和網路),以及高階科目(例如人工智慧、電腦繪圖和網路安全)等各方面齊頭並重。 後續課程則讓學生專攻一個以上的方向。

          資訊科學 資訊科學

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          http://www.ics.uci.edu/prospective/zh-tw/uci/campus/  校園環境 « UC Irvine « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
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          UC Irvine

          加大爾灣分校族群豐富多元

          • 貝林學院
          • 校園環境
          • 爾灣市區
          • 學生服務

          校園環境

          加州大學爾灣分校走在研究、發現、學術的最前線,不但能改善加州居民的生活,也能造福世界每個角落。 本校畢業生在藝術、科學、商業和教育界都能獨當一面,各行各業都有人才。 其中有三位普立茲獎得主,還有通行全球的「HTTP/1.1」網路協定創造者。

          加大爾灣分校是大學教育的第一選擇。學生會發現本校不但師資陣容堅強,而且無論研究還是輔導,老師都樂意伸出援手。這裡有一流的醫科、法律、商業、教育和藝文專業學院,位於市郊的校園景色怡人、學生宿舍獲獎連連、全年都有精彩的校園活動,而且對課業和領導才能的培養無人能比,讓學生在現今唇齒相依的世界闖出一片天。

          加大爾灣分校詳細介紹: http://www.uci.edu/prospective.php。

          加大爾灣分校畢業典禮 加大爾灣分校畢業典禮

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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          資訊科學工程

          本系和Henry Samueli工學院共同開課,將資訊科學和資訊工程合而為一。 本系非常側重硬體,課程包括電路設計、網路設計、數位訊號處理和超大型積體電路設計,但也教授硬體運作時必須的軟體技術,例如作業系統和嵌入式系統。

          嵌入式設備是消費性商品和各種產品創新的幕後功臣,而且通常會大量採用,以構成感測網路,負責監控氣候、環境、建築結構……等等。 學生如果很想知道如何設計、製造這類設備,建議選擇這個科系。

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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          企業資訊管理

          本系和Paul Merage商學院共同開課,將資訊科學、資訊技術和企業管理合而為一。 學生能在數學、統計學、軟體工程、資料庫、經濟學、商業會計、管理學和資訊技術各方面打下良好基礎。

          現代企業完全靠資訊技術向前推進,尤其是資料。 學生如果很想知道如何應用計算機處理法和電腦工具,才能達到策略經營分析和決策目標,建議選擇這個科系。

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          http://www.ics.uci.edu/prospective/zh-tw/degrees/ics-undeclared-pre-major/  申報主修科系前課程 « 學位 « 貝林: 資訊電腦科學 « 加州大學爾灣分校 ?>
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          學位

          本校主修科目極為多樣,要當專家還是通才任君挑選

          • 總覽
          • 企業資訊管理
          • 電腦遊戲學
          • 資訊科學
          • 資訊科學工程
          • Data Science
          • 資訊學
          • Software Engineering
          • 資科院申報主修科系前課程
          • 輔系

          申報主修科系前課程

          新鮮人如果在決定主修科目前,想先了解自己對資訊科學和資訊技術的哪方面有興趣,可申請資科院無主修(ICS Undeclared)身分。

          這個選項讓新鮮人擁有貝林資科院學生的所有權益,包括能請課業輔導老師協助安排第一年的課程,涵蓋所有主修學生必修的初級資訊科學和數學課程,同時讓你了解貝林資科院的課程安排方式。

          請注意,對資訊科學工程系有興趣的學生,最好在入學時就決定主修這個科系,因為本系課程安排非常嚴謹。

          小常識

          你知道嗎?
          根據財經媒體CNBC權威報導,2010年薪資排行前10名的學士學位中,貝林資科院就有三個科系上榜! 完整排行榜請見 這裡。

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          聯絡方式

          如有任何問題,歡迎聯絡我們

          • 學務處

          學務處

          貝林資科院課業諮詢老師能為校內所有主修學生提供協助。學生從入學到畢業都能獲得完整服務。 校內課業輔導從暑期新生訓練就已經開始。諮詢老師會協助新生規劃課程,教導他們上網選擇秋季課程。 學生在畢業前都能請貝林資科院諮詢老師幫忙設定學術和就業目標、找出拿到學歷的最佳捷徑,並且定時核對進度,朝畢業的方向邁進。 諮詢老師也會舉辦貝林資科院學生研討會,主題則與就業方向、實習工作、尋找教師導師,以及準備研究所有關。 貝林資科院課業諮詢老師以個別約談、不預約隨時諮商和電子郵件等方式提供協助。

          資科院1館352號室(請在校園地圖上尋找302號大樓 http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          諮詢時間: 週一至週五上午9點~12:00,下午1:00到4:00

          歡迎有意申請的學生撥打專線:(949) 824-5156,與課業諮詢老師約時間了解本校提供的文憑種類,以及最適合申請人課業和就業需求的學歷。

          學務處 學務處

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          http://www.ics.uci.edu/prospective/en/degrees/overview/contact/student-affairs/​  Overview « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Overview

          The field of computer science continues to rapidly change as new technical possibilities and new application areas constantly arise. Your education must keep pace and position you for an exciting and relevant career. A single degree program can no longer do this in the face of how broad and all-encompassing the field has become. The Bren School is known worldwide for its long history of innovative educational programs, and continues this tradition today with six streamlined majors for you to choose from.

          All majors share a strong foundation, each branching from there to provide you with a tailored, modern curriculum that builds upon our ever-expanding portfolio of courses; courses that we carefully and continuously update to stay current.

          Our counselors can assist you in choosing the right major. Because our majors share a core philosophy, should your interests change after arrival you will be able to change majors in your freshman or early in your sophomore year without having to take additional coursework.

          For student perspectives on each of the majors, download the fliers below:


          Business
          Information
          Management

          Computer
          Game
          Science

          Computer
          Science

          Computer
          Science &
          Engineering

          Data
          Science

          Informatics

          Software
          Engineering

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          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Tổng Quát

          Lĩnh vực khoa học điện toán tiếp tục phát triển nhanh chóng vì những khả năng kỹ thuật và những lĩnh vực ứng dụng mới luôn luôn xuất hiện. Học vấn của bạn phải bắt kịp và đưa bạn đến một nghề nghiệp thú vị và thích đáng. Chương trình học chỉ cấp một bằng có thể không có khả năng đáp ứng được nhu cầu của thực tế là lĩnh vực ứng dụng đang ngày càng trở nên rộng lớn và đa dạng. Bren:ICS được cả thế giới biết đến với lịch sử lâu dài của các chương trình giáo dục cải tiến, và vẫn tiếp tục phát huy truyền thống này cho tới ngày hôm nay với bảy ngành học được sắp xếp hợp lý để cho bạn lựa chọn theo học.

          Tất cả các ngành học đều có chung một nền móng vững chắc mà từ đó từng phân ngành học cung cấp cho bạn một chương trình giảng dạy hiện đại, đáp ứng nhu cầu với hàng loạt các khóa học ngày càng được mở rộng; nội dung các khóa học liên tục được sàng lọc kỹ lưỡng và cập nhật để truyền tải những kiến thức mới nhất cho sinh viên.

          Các cố vấn học tập của chúng tôi có thể hỗ trợ bạn chọn lựa chuyên ngành phù hợp. Vì các chuyên ngành của chúng tôi được xây dựng trên một phương châm cốt lõi nên bạn có thể thay đổi các chuyên ngành trong năm đầu hoặc đầu năm thứ hai mà không phải học thêm bất cứ lớp học chuyển tiếp nào.

          Để so sánh trực tiếp nhiều chuyên ngành khác nhau: Hãy nhấp chuột vào đây.

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          • 학위
          • 기회
          • 진로
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          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          개요

          컴퓨터 과학은 새로운 기술의 가능성과 새로운 응용 분야가 끊임없이 나타나면서 계속 급변하는 분야입니다. 교육은 이런 변화를 따라가야 하며, 흥미롭고 적절한 진로에 맞춰 학생들의 포지션을 정해야 합니다. 컴퓨터 과학 분야가 폭넓어지고 모든 것을 망라하는 방향으로 바뀌는 지금, 학위 프로그램 하나만으로는 변화를 감당할 수 없습니다. Bren:ICS는 오랜 역사를 자랑하는 혁신적인 교육 프로그램으로 전 세계에 명성을 떨치고 있으며, 오늘날에도 자유롭게 선택할 수 있는 일곱 가지 현대적인 전공으로 이 전통을 계속 이어가고 있습니다.

          모든 전공들은 탄탄한 기반을 공유하며, 각 전공은 여기서 갈라져 나와 현대적인 맞춤식 커리큘럼을 제공합니다. 이 커리큘럼은 끊임없이 확장하는 포트폴리오의 과정, 본교가 시류를 반영하며 심혈을 기울여 지속적으로 최신화한 과정을 토대로 합니다.

          본교 카운슬러들은 학생들이 알맞은 전공을 선택할 수 있게 도와줍니다. 입학 후 관심사가 바뀌는 경우에도, 핵심 철학을 공유하는 본교 전공들 덕분에 추가적인 수업 활동 없이 신입생 또는 2학년 초에 전공을 바꿀 수 있습니다.

          다양한 전공을 나란히 비교해 보려면 여기를 클릭하십시오.

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          http://www.ics.uci.edu/prospective/en/degrees/minors/contact/student-affairs/​  Minors « Degrees « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
          • Opportunities
          • Careers
          • Student Life
          • Admissions
          • Contact

          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Minors

          Students interested in supplementing another Bren School degree or any UCI bachelor’s degree, whether it be in the arts or biological sciences or any other discipline, will find these minors complement their undergraduate education.

          • Bioinformatics: The minor provides a focused study of bioinformatics to supplement a student’s major program of study and prepares students for a profession, career, or academic pursuit in which biomedical computing is an integral part but not the primary focus.
          • Digital Information Systems: The minor is designed for students who want to learn about information systems, computation, and digital communication without preparing to be computer programmers.
          • Health Informatics: The minor in Health Informatics prepares students to understand the expanding role of information technology (IT) in health care and to participate in creating IT solutions to health care issues.
          • Informatics: The minor particularly centers on understanding the relationships among computers and people, and how these relationships must be addressed in information and software design.
          • Information and Computer Science: The minor contributes to students' competence in computing technology and programming proficiency, and in addition, exposes them to the fundamentals of computer science.
          • Statistics: The minor is designed to provide students with exposure to both statistical theory and practice.
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          http://www.ics.uci.edu/prospective/ko/degrees/minors/  부전공 « 학위 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
          • 학위
          • 기회
          • 진로
          • 대학 생활
          • 입학
          • 문의

          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          부전공

          또 하나의 Bren:ICS 학위를 추가하거나, 예술이든 생물학이든 어떤 다른 학문이든 UCI 학사 학위를 추가하는 데 관심이 있는 학생들은 이 부전공으로 학부 교육을 보완할 수 있을 것입니다.

          • 생체의학 컴퓨팅: 이 부전공은 생체의학 문제와 데이터에 적용되는 컴퓨팅에 대한 학생들의 능력에 도움을 줄 뿐 아니라, 학생들에게 컴퓨터 과학 관점에서 생명 과학의 기본 원칙을 알려줍니다.
          • 디지털 정보 시스템: 이 부전공은 컴퓨터 프로그래머가 될 준비를 하지 않은 상태로 정보 시스템, 계산학, 디지털 커뮤니케이션에 대해 배우기를 원하는 학생들을 위해 마련한 분야입니다.
          • 정보학: 이 부전공은 컴퓨터와 인간의 관계, 그리고 이러한 관계를 정보와 소프트웨어 설계에서 어떻게 다룰지 이해하는 데 특히 중점을 둡니다.
          • 정보와 컴퓨터 과학: 이 부전공은 컴퓨팅 기술과 프로그래밍 기량에 대한 학생들의 능력을 키우는 데 도움이 되며, 아울러 학생들에게 컴퓨터 과학의 기초 원칙도 알려줍니다.
          • 통계학: 이 부전공은 학생들에게 통계학의 이론과 실제를 모두 알려주기 위해 마련한 분야입니다.
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          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Chuyên ngành phụ

          Những sinh viên có mong muốn bổ sung một bằng cấp Bren:ICS hay bất cứ bằng cử nhân UCI nào, dù là về nghệ thuật hay khoa học sinh vật học hay bất cứ môn học nào khác, sẽ thấy chương trình chuyên ngành phụ này củng cố thêm học vấn đại học của họ.

          • Điện toán Y sinh học:Chuyên ngành phụ này củng cố thêm năng lực của sinh viên về lĩnh vực điện toán áp dụng vào những bài toán sinh vật học và dữ liệu, cũng như trang bị cho họ những kiến thức nền tảng về khoa học sự sống từ quan điểm khoa học điện toán.
          • Hệ thống Thông tin Kỹ thuật số:Chuyên ngành phụ này được thiết kế dành cho những sinh viên muốn tìm hiểu thêm về các hệ thống thông tin, thao tác điện toán, và giao tiếp kỹ thuật số nhưng không hẳn sẽ trở thành các nhà lập trình viên máy tính.
          • Tin học: Chuyên ngành phụ này đặc biệt chú trọng vào tìm hiểu mối quan hệ giữa hệ thống máy tính và con người, và cách thức những mối quan hệ này phải được thiết lập trong cấu trúc thông tin và thiết kế phần mềm.
          • Khoa học Thông tin và Điện toán:Chuyên ngành phụ này củng cố thêm khả năng của sinh viên về lĩnh vực công nghệ điện toán và kỹ năng lập trình, đồng thời trang bị cho họ những kiến thức nền tảng về khoa học điện toán.
          • Thống kê học: Chuyên ngành phụ này được thiết kế nhằm đưa các sinh viên tiếp cận tới cả lý thuyết và thực tiễn thống kê.
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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
          Site By Crisp
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          • 학위
          • 기회
          • 진로
          • 대학 생활
          • 입학
          • 문의

          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          비즈니스 정보 관리

          폴 머라지 비즈니스 스쿨(Paul Merage School of Business)과 공동으로 제공하는 이 전공은 컴퓨터 과학과 IT, 그리고 경영학의 교차점에 자리하고 있습니다. 학생들은 수학, 통계학, 소프트웨어 공학, 데이터베이스, 경제학과 기업회계, 경영 과학, 정보기술(IT)에 대한 탄탄한 기초 교육을 받습니다.

          현대의 기업들은 전적으로 IT와 데이터를 중심으로 돌아갑니다. 전략적인 비즈니스 분석과 의사결정 목표를 달성하기 위한 전산 기법과 도구의 적용 방법을 배우는 데 관심이 있는 학생들은 이 전공을 탐구하는 것이 좋습니다.

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          http://www.ics.uci.edu/prospective/vi/degrees/business-information-management/  Quản trị Thông tin Kinh doanh « BẰNG CẤP « BREN: Khoa học Thông tin và Điện toán « Đại học California Irvine ?>
          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Quản trị Thông tin Kinh doanh

          Là một chương trình đào tạo hợp tác với Đại học Kinh doanh Paul Merage, chuyên ngành này là sự hòa trộn giữa chuyên ngành khoa học điện toán và công nghệ thông tin với chuyên ngành quản trị kinh doanh. Sinh viên sẽ được trang bị kiến thức vững chắc về toán học, thống kê học, công nghệ phần mềm, cơ sở dữ liệu, kinh tế học và kế toán kinh doanh, khoa học quản lý và công nghệ thông tin.

          Các mô hình kinh doanh hiện đại đều dựa trên nền tảng công nghệ thông tin và quản lý theo hệ thống dữ liệu. Những sinh viên quan tâm tìm hiểu phương thức áp dụng những phương pháp và công cụ điện toán để đạt được các mục tiêu phân tích kinh doanh và quyết định chiến lược nên khám phá biển kiến thức của chuyên ngành này.

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          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
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          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Business Information Management

          As the business environment becomes increasingly global and information-centric, the need has increased for graduates who understand and can use technology that gathers and provides information, who are able to distill and recognize patterns in that information, and who can apply those analyses to achieve business objectives.

          Administered by the Donald Bren School of Information and Computer Sciences, Business Information Management (BIM) is a collaborative, interdisciplinary degree program between the Bren School and The Paul Merage School of Business.

          BIM majors receive a firm grounding in mathematics, statistics, software engineering, databases, economics and business accounting, management science and information technology. Students interested in learning how to apply computational methods and tools for achieving strategic business analysis and decision-making goals are encouraged to explore this degree program.

          IS BUSINESS INFORMATION MANAGEMENT FOR ME?

          Graduates of the program will:

          • learn the fundamentals of information and computer science, including the rudiments of software design and construction with an emphasis on data management;
          • grasp business fundamentals, covering all the functional areas in the business school;
          • understand the background and context in which information and its analysis will be applied.

          WHAT COURSES DO I TAKE?

          The curriculum is presented across three general academic areas:

          • Computing (computer science, informatics and software)
          • Business Foundations (accounting, finance, marketing, strategy and operations)
          • Analytical Methods (mathematics, statistics, economics, management science and decision analysis)

          Current requirements for the BIM major can be found in the General Catalogue .

          WHAT CAN I DO WITH A DEGREE IN BIM?

          This degree program prepares students for a wide variety of careers and life experiences. Business Information Management majors can pursue careers in the for-profit and not-for-profit sectors or can proceed to graduate school in several disciplines, including information systems, computing, economics, business and law.

          Potential careers for BIM majors include:

          • working at a consulting firm, auditing other companies’ technology policies for business efficiency.
          • becoming a business risk analyst, identifying ways to reduce a client’s dependency on seasonal e-commerce traffic.
          • serving as a program manager, leading a team in creating incentive and loyalty programs, so companies can get better business data.
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          • BẰNG CẤP
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          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Khoa học & Kỹ thuật Điện toán

          Là một chương trình đào tạo hợp tác với Đại học Kỹ nghệ Henry Samueli, chuyên ngành này là sự hòa trộn giữa khoa học điện toán và kỹ thuật điện toán. Chuyên ngành có một sự định hướng cụ thể tới phần cứng bằng những khóa học ví dụ như thiết kế mạch điện, thiết kế mạng, xử lý tín hiệu kỹ thuật số và thiết kế VLSI, nhưng cũng đề cập đến những kỹ thuật phần mềm cần thiết để vận hành phần cứng bằng những khóa học như hệ điều hành và hệ thống nhúng.

          Các thiết bị nhúng đang ngày càng thúc đẩy sự cải tiến đối với những hàng hóa và sản phẩm tiêu dùng, và chúng thường được sử dụng với số lượng lớn để hình thành một mạng lưới cảm biến theo dõi thời tiết, môi trường, cấu trúc vật lý, v.v... Những sinh viên có mong muốn tìm hiểu những thiết bị đó được thiết kế và xây dựng như thế nào nên khám phá chuyên ngành này.

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          • 학위
          • 기회
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          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          컴퓨터 과학 및 공학

          헨리 사무엘리 공과대학(Henry Samueli School of Engineering)과 공동으로 제공하는 이 전공은 컴퓨터 과학과 컴퓨터 공학을 결합하고 있습니다. 이 전공의 회로 설계, 네트워크 설계, 디지털 신호 처리, VLSI 설계 등의 과정은 하드웨어적인 성향이 강하지만, 운영 체제 및 내장형 시스템 같은 과정에서는 하드웨어를 사용할 수 있게 만드는 소프트웨어 기법도 다룹니다.

          내장형 장치는 소비재에서 점점 더 혁신을 주도하고 있으며, 날씨, 환경, 물리 구조를 모니터링하는 센서 네트워크를 형성하는 데 종종 다량으로 사용됩니다. 이러한 장치의 설계 및 구조화 방법을 배우는 데 관심이 있는 학생들은 이 전공을 탐구하는 것이 것이 좋습니다.

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          • Degrees
          • Opportunities
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          • Student Life
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Computer Science & Engineering

          Offered jointly with The Henry Samueli School of Engineering, the Computer Science and Engineering (CSE) major provides a unique educational opportunity for students interested in learning about both the hardware and software aspects of computers, and the application of computers to real-world problems. CSE includes methods of organizing and manipulating information (computer science), as well as the design of computers and their components (computer engineering). As such, CSE integrates interesting topics in computing devices, networks, software and systems engineering.

          CSE graduates are well-positioned for career opportunities in both academia and industry. Our alumni are employed at leading companies or continue their studies toward M.S. or Ph.D. degrees at prestigious universities.

          IS COMPUTER SCIENCE AND ENGINEERING FOR ME?

          Embedded devices increasingly drive innovation in consumer goods and products, and often are used in large quantities to form sensor networks that monitor the weather, environment, or physical structures. Students interested in learning how such devices are designed and constructed are encouraged to explore the CSE major.

          Graduates of the program will:

          • demonstrate broad knowledge of computer science and engineering;
          • design, describe and use state-of-the-art hardware/software systems;
          • maintain awareness of contemporary issues in computer science and engineering in a global and societal context, and an understanding of the professional and ethical responsibilities of their profession;
          • demonstrate effective oral and written communication.

          For more information on CSE goals and objectives see http://plaza.eng.uci.edu/degree-program/cse/mission.

          Annual student enrollment and graduation data can be found at: http://www.oir.uci.edu/student-data.html. Please note that annual student enrollment and graduation data for CSE is the sum of the data under both Engineering and Information and Computer Science.

          WHAT COURSES DO I TAKE?

          Current requirements for the CSE major can be found in the General Catalogue.

          WHAT CAN I DO WITH A DEGREE IN CSE?

          Computer Science and Engineering majors are involved in building hardware infrastructure — computers, networks and embedded devices — as well as operating systems, compilers and networking software. The focus is on cooperation between hardware and software to yield the highest performance.

          Graduates of the CSE program at UCI can pursue careers that involve traffic management, flight control, earthquake monitoring, automotive control and building smart homes. Many students also go on to graduate school, continuing their studies, conducting research and earning advanced degrees in computer engineering, computer science, information science, management or law.

          CSE senior design project students CSE students Niraj Desai, Patrick Murtha, Christopher Escobedo and Michael Sevilla with their senior design project, the Automated Labyrinth.

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          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Data Science

          As the only undergraduate Data Science degree in the UC system, the major will prepare students for careers in data analysis by combining foundational statistical concepts with computational principles from computer science. UCI’s new Data Science major has a dual emphasis on the principles of both statistics and computer science, with foundational training in statistical and mathematical aspects of data analysis, as well as in the broad principles of computer science (including algorithms, data structures, data management and machine learning). The program teaches students how to utilize their knowledge of statistical and computing principles to analyze and solve real-world data analysis problems. The major is suitable for students interested in either a career in industry or who wish to pursue more specialized graduate study.

          WHAT WILL YOU LEARN?

          As a Data Science major, you have the unique opportunity to take an interdisciplinary course of study that includes classes from all three ICS departments: statistics, computer science and informatics. Classes you will have the opportunity to take as a Data Science student include:

          Programming in C/C++ as a Second Language (ICS)
          An introduction to the lexical, syntactic, semantic and pragmatic characteristics of the C/C++ languages, with an emphasis on object-oriented programming, using standard libraries and programming with manual garbage collection.

          Machine Learning and Data-Mining (Computer Science)
          Learn the principles of machine learning and data mining applied to real-world data sets, with typical applications including spam filtering, object recognition and credit scoring.

          Statistical Methods for Data Analysis (Statistics)
          An exploration of statistical methods for analyzing data from surveys, experiments and cohort studies.

          Information Visualization (Informatics)
          An introduction to interactive visual interfaces for large data sets, including a foundation on the principles of human visual perception and human computer interaction that inform their design.

          POSSIBLE CAREERS

          • With a degree in Data Science from UCI, you might get a job working in the web and technology industries for companies such as Google, Facebook, Twitter, Microsoft or Samsung.
          • You might find a career in finance, working on Wall Street or for a banking or insurance company.
          • You might delve into engineering, working for a company like Boeing.
          • Or you might use your degree to get a job in the medical or public health field.

          DEMAND FOR DATA SCIENCE

          The demand for graduates with skills in both statistics and computer science currently outpaces supply. According to a 2011 McKinsey Global Institute study on big data, demand for individuals with data analysis skills will grow to almost 500,000 individuals by 2018, with a projected shortfall of about 200,000 individuals with deep analytical skills, as well as a shortage of 1.5 million managers and analysts to analyze big data and make decisions.

          UCI DATA SCIENCE INITIATIVE

          UC Irvine’s Data Science Initiative is a broad initiative with a focus on coordinating and linking the activities of researchers and students across campus involved in various aspects of data science. This initiative was founded in 2014 by the Office of the Provost and Executive Vice Chancellor and is supported through the Office of Academic Initiatives. One of the goals of the Data Science Initiative is to support the formation of the new Data Science major. For more information, visit datascience.uci.edu

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          Admissions

          Details about requirements, application deadlines, and tips for enhancing your application

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          Financial Aid

          Paying for the cost of a UCI education requires a partnership among parents, students, and the University. Each partner has responsibilities to fulfill in meeting that cost. The Financial Aid website for undergraduate students offers the most comprehensive resources about paying for your education.

          Important dates to keep in mind include:

          January 1st
          First day to submit FAFSA and supporting documentation.

          March 2nd
          Priority deadline to complete FAFSA.

          March 2nd
          Deadline to mail Cal Grant GPA Verification Form to the California Student Aid Commission.

          September 15th
          Registration Fees for fall quarter must be paid by this date.

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          Admissions

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          • Freshmen
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          Application Tips

          Your application matters and you should take great care in representing yourself, your accomplishments, and your aspirations. A good source with tips on how to approach your application and particularly the personal statement is the following University of California web site:

          http://www.universityofcalifornia.edu/admissions/how-to-apply/personal-statement/index.html

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          http://www.ics.uci.edu/prospective/ko/admissions/freshmen/  신입생 « 입학 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
          • 학위
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          입학

          자세한 지원 요건, 지원 마감일, 좋은 지원서를 만드는 요령

          • 신입생
          • 편입
          • 기숙사
          • 학자금 지원
          • 지원 요령

          신입생

          본교는 예비 신입생들을 위한 풍부한 정보는 물론 입학 절차의 모든 측면에 관한 상세한 정보를 제공하고 있습니다. 특히 다음 부분을 살펴보실 것을 권장합니다.

          예비 학생: http://www.uci.edu/prospective.php

          입학: http://www.admissions.uci.edu/

          자주 묻는 질문(FAQ): http://www.admissions.uci.edu/resources/faq.html

          온라인 출간물: http://www.admissions.uci.edu/publications/online_publications.html

          본교 마스코트인 개미핥기 피터(Peter the Anteater)도 잊지 마세요. http://www.uci.edu/peter/

          현재, Bren:ICS의 학부생 입학자 수는 850명이며, UC 어바인 전체로 하면 신입생이 총 22,000명 가까이 됩니다. 2010년에 입학한 신입생들의 GPA 평균은 3.89였고, SAT 평균 점수는 1,822점이었습니다.

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          http://www.ics.uci.edu/prospective/vi/admissions/freshmen/  Sinh viên năm nhất « TUYỂN SINH « BREN: Khoa học Thông tin và Điện toán « Đại học California Irvine ?>
          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          TUYỂN SINH

          Thông tin chi tiết về yêu cầu tuyển sinh, hạn chót nộp đơn và chỉ dẫn làm cho đơn xin nhập học phong phú

          • Sinh viên năm nhất
          • Chuyển tiếp
          • Nhà ở
          • Hỗ trợ Tài chính
          • Chỉ dẫn nộp đơn

          Sinh viên năm nhất

          Nhà trường cung cấp nhiều nguồn thông tin đa dạng dành cho sinh viên năm nhất cùng những thông tin chi tiết về mọi phương diện của quy trình tuyển sinh. Chúng tôi đặc biệt khuyến khích các bạn hãy cùng khám phá qua những kênh thông tin sau đây:

          Chuyên trang dành cho những ứng viên triển vọng: http://www.uci.edu/prospective.php

          Tuyển sinh: http://www.admissions.uci.edu/

          Những câu hỏi thường gặp: http://www.admissions.uci.edu/resources/faq.html

          Xuất bản phẩm trên mạng: http://www.admissions.uci.edu/publications/online_publications.html

          và đừng quên hai con vật lấy khước của chúng tôi, Peter và Anteater: http://www.uci.edu/peter/

          Hiện tại, số lượng sinh viên theo học hệ đại học ở Bren:ICS là 850 sinh viên trong tổng số gần 22.000 sinh viên UC Irvine. GPA bình quân đối với sinh viên năm nhất năm 2010 là 3,89, với điểm số SAT trung bình là 1822.

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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          Admissions

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          Housing

          UCI guarantees two years of housing to all freshmen who are single, under the age of 25, enrolling in fall quarter, and who meet the housing application and contract-return deadlines. Many UCI students choose to spend their entire time at UCI in student housing.

          UCI’s freshman housing communities provide Internet and cable TV connections in each student room, a hall/house living room, study rooms, laundry facilities, recreational facilities, computer lab, and custodial service for public areas and bathrooms.

          For details on UCI’s housing facilities and application timeline: UCI Housing Prospective Student website.

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          Admissions

          Details about requirements, application deadlines, and tips for enhancing your application

          • Freshmen
          • Transfer
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          • Financial Aid
          • Application Tips

          Freshmen

          The campus offers a wealth of information for prospective freshmen, as well as details on all aspects of the admissions process. We particularly encourage you to explore the following:

          Admissions: http://www.admissions.uci.edu/

          Online publications: http://www.admissions.uci.edu/publications/online_publications.html

          and do not forget our mascot, Peter the Anteater: http://www.uci.edu/peter/

          Currently, undergraduate enrollment in the Bren School is about 2,000 students, out of a total of almost 23,500 for UC Irvine as a whole.

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          • 학위
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          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          컴퓨터 과학

          컴퓨터 과학 전공은 현대 세계의 기반을 이루는 컴퓨터 사용의 원리를 강조하고, 학생들이 컴퓨터 분야의 다양한 진로를 준비할 수 있도록 탄탄한 기초 교육을 제공합니다.

          학생들은 낮은 단계의 아키텍처와 시스템부터 프로그래밍 언어, 데이터베이스, 네트워크 등 중간 단계의 인프라, 그리고 인공지능 같은 높은 단계의 주제들을 통해 지식의 균형을 얻습니다. 또한 후속 과정을 통해 이러한 방향(하나 또는 둘 이상)에 집중합니다.

          컴퓨터 과학 컴퓨터 과학

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          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
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          • ICS Undeclared Pre-Major
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          Computer Science

          The Computer Science major emphasizes the principles of computing that underlie our modern world and provides a strong foundational education to prepare students for a broad spectrum of possible careers in computing.

          Students receive a balance of knowledge in low-level computer architecture and systems, middle-level infrastructure like programming languages, databases and networks, and high-level topics such as artificial intelligence, computer graphics and network security. Students also learn about the mathematical and algorithmic foundations that form the basis of modern computing. Subsequent courses allow students to concentrate in one or more such directions.

          IS COMPUTER SCIENCE FOR ME?

          The CS major balances theoretical underpinnings with a strong emphasis on designing, programming and implementing large-scale systems. The curriculum covers the infrastructure of computers and networks, as well as the software that runs them. Students receive a broad view of the field through a comprehensive set of core classes and, through upper-division elective courses, are given the opportunity to specialize in topics such as machine learning, data mining, distributed and embedded systems, and security. The major places a heavy emphasis on “learning by doing” through a wide selection of classes in which students design and build in-depth projects over the course of a 10-week quarter. In addition, students receive a solid background in computer algorithms, which form the basis of modern computing. The major provides many practical skills that can be immediately put to use in the workplace while also giving students a conceptual foundation that will serve them well for the long term in a rapidly evolving field.

          WHAT COURSES DO I TAKE?

          Current Requirements for the CS major can be found in the General Catalogue.

          WHAT CAN I DO WITH A DEGREE IN CS?

          Graduates of the Computer Science program at UCI will be in a position to pursue a variety of careers in high-demand areas such as embedded systems, cloud infrastructure, web services, computer security, networking and data mining. They can be principal designers or involved in implementation, typically at companies that design, implement and sell or manage products or services in such areas. They may find themselves in charge of large-scale deployments and/or customizations at the organizations that use them.

          The strong scientific preparation also allows students to become involved in such areas as high-performance computing, computational biology and neuroscience — whether in industry or graduate school. In fact, many students choose to continue their studies, conducting research and earning graduate degrees in fields such as computer science, software engineering or information science. A background in computer science is also excellent preparation for careers in management, law, finance or consulting.

          Chen Li, associate professor of computer science, and his students created a Web search tool to reunite people after the quake that devastated Haiti. Chen Li, associate professor of computer science, and his students created a Web search tool to reunite people after the quake that devastated Haiti.

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          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Khoa học Điện toán

          Chuyên ngành khoa học điện toán tập trung vào những nguyên tắc điện toán hình thành nên thế giới hiện đại của chúng ta ngày nay và cung cấp những tri thức nền tảng vững chắc giúp cho sinh viên sẵn sàng đảm nhận các yêu cầu nghề nghiệp đa dạng trong lĩnh vực điện toán.

          Sinh viên được lĩnh hội một sự cân bằng kiến thức về cấu trúc và các hệ thống ở tầm thấp, cơ sở hạ tầng ở tầm trung như ngôn ngữ lập trình, cơ sở dữ liệu và mạng với những chủ đề ở tầm cao như tri thức nhân tạo, đồ họa máy tính và an ninh mạng. Những khóa học kế tiếp sẽ giúp các sinh viên có thể tập trung vào một hoặc nhiều chủ đề định hướng như vậy.

          Khoa học Điện toán Khoa học Điện toán

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          문의

          궁금한 점이 있으면 부담없이 질문해 주십시오.

          • 학생 업무

          학생 업무

          Bren:ICS의 지도 교수들은 모든 Bren:ICS 전공 학생들이 학업 진로를 시작해서 끝날 때까지 도와줍니다. 학교 중심의 학업 상담은 여름 오리엔테이션과 함께 시작합니다. 이 때 카운슬러는 신입생들이 학업 계획을 세울 수 있도록 도와주고, 이들에게 가을 학기 수업 온라인 등록 방법을 알려줍니다. 남은 재학 기간 동안 Bren:ICS 상담자들은 학업 및 진로 목표 수립을 정의 및 지원하고, 학위 요건을 충족하는 가장 효율적인 길을 찾고, 졸업까지의 시의적절한 진행을 살피며 도움을 줍니다. 상담 담당자는 진로 탐구, 인턴십, 교수 멘토 찾기, 대학원 준비와 같은 주제와 관련하여 Bren:ICS 학생들을 위한 워크샵도 후원합니다. Bren:ICS 학업 상담 담당자는 개인별 약속, 간이 상담, 이메일을 통해 도움을 줍니다.

          ICS 1, Suite 352(캠퍼스 지도의 302번 건물: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          시간: 월~금요일 오전 9시~정오, 오후 1시~4시

          다양한 학위 과정과 어떤 과정이 자신의 학업 및 진로 관심사에 적합할지 알아보려는 지원 희망자는 (949) 824-5156으로 자유롭게 전화하여 지도 교수와 상담 약속을 정할 수 있습니다.

          학생 업무 학생 업무

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          • BẰNG CẤP
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          LIÊN HỆ

          Nếu bạn có bất cứ thắc mắc nào, đừng ngần ngại hãy liên hệ với chúng tôi

          • Ban Sự vụ Sinh viên

          Ban Sự vụ Sinh viên

          Các chuyên gia tư vấn học tập của Bren:ICS hỗ trợ sinh viên thuộc tất cả các chuyên ngành học ở Bren:ICS, từ lúc bắt đầu bước chân vào trường cho đến khi kết thúc chương trình học. Hoạt động tư vấn giáo dục tại trường bắt đầu bằng chương trình định hướng mùa hè, khi đó các cố vấn sẽ giúp đỡ những sinh viên mới trực quan ra kế hoạch học tập của mình và hướng dẫn họ hoàn thành quá trình đăng ký trực tuyến để tham gia các lớp học của học kỳ mùa thu. Thông qua danh sách lựa chọn nghề nghiệp của sinh viên, các tư vấn viên của Bren:ICS sẽ hỗ trợ sinh viên định rõ và thiết lập mục tiêu học tập và nghề nghiệp, xác định những đường hướng hiệu quả nhất để đạt được những yêu cầu của chương trình cấp bằng, và định kỳ kiểm tra sự tiến bộ trong quá học tập. Đội ngũ chuyên gia tư vấn cũng tài trợ các cuộc hội thảo để các sinh viên Bren:ICS tìm hiểu về các chủ đề như khám phá nghề nghiệp, chương trình thực tập, tìm một giảng viên hướng dẫn riêng, và chuẩn bị đáp ứng các tiêu chí học cao học. Đội ngũ chuyên gia tư vấn học tập của Bren:ICS hỗ trợ qua các cuộc hẹn gặp riêng, tư vấn cho những sinh viên đến trực tiếp tại văn phòng và qua email.

          ICS 1, Suite 352 (tìm tòa nhà số 302 trên bản đồ khuôn viên trường tại đây: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          Giờ làm việc: Từ Thứ Hai đến Thứ Sáu, từ 9 giờ sángđến 12 giờ trưa và từ 1 giờ chiều đến 4 giờ chiều.

          Những ứng của viên tiềm năng cũng có thể gọi điện theo số (949) 824-5156 để đặt lịch hẹn với chuyên gia tư vấn học tập để tìm hiểu thêm về những tùy chọn về chương trình cấp bằng khác nhau và chương trình nào có thể phù hợp nhất với mong muốn về học vấn và nghề nghiệp của họ.

          Ban Sự vụ Sinh viên Ban Sự vụ Sinh viên

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          Contact

          Feel free to contact us with any questions you may have

          • Student Affairs

          Student Affairs

          Bren School academic advisors assist all Bren School majors, from start to finish of their academic career. School-based academic advising begins with summer orientation, during which counselors help new students map out their academic plan and guide them through on-line registration for fall quarter classes. Through the remainder of a student’s career, Bren School advisors are available to help define and support academic and career goal-setting, identify the most efficient routes for meeting degree requirements, and check for timely progress toward graduation. The counseling staff also sponsor workshops for Bren School students about such topics as career exploration, internships, finding a faculty mentor, and graduate school preparation. The Bren School academic advising staff offers assistance via individual appointments, drop-in counseling, and email .

          ICS 1, Suite 352 (find building # 302 on campus map here: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          Hours: Monday through Friday, 9 a.m. – 12:00 p.m. and 1:00 – 4:00 p.m.

          Prospective applicants are welcome to call (949) 824-5156 to schedule an appointment with an academic advisor to learn more about the various degree options and which might best fit their academic and career interests.

          Student Affairs Student Affairs

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          Student Life

          Your life!

          • Diversity
          • Campus Involvement
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          Diversity

          UC Irvine’s student body is very diverse, and offers a wealth of opportunities to learn with and from students whose backgrounds and experiences support, complement, challenge, enrich, or expand your understanding of how to learn, live, and work in a multicultural society.

          Even within the smaller Bren School student community, our students’ backgrounds and interests are richly diverse – as represented by their participation in extracurricular activities that include dance troupes, athletics, theater, community service, ROTC, greek-letter organizations, ethnic/cultural group affiliations, and leadership in student clubs and organizations.

          Girls Inc. visiting UC Irvine Girls Inc. visiting UC Irvine

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          Student Life

          Your life!

          • Diversity
          • Campus Involvement
          • Bren School Organizations

          Campus Involvement

          When you first visit the UCI campus, you will see that it is a pretty large place. Most students will tell you that a good way to get connected and make the campus feel smaller is to join a student club. Spend a couple minutes searching the Dean of Student’s database of over 300 registered student organizations to get a sense of the kinds of involvements and activities that might be of interest to you while here at UCI: http://www.campusorgs.uci.edu/.

          Many opportunities exist for learning about and practicing your leadership skills, both in the UCI community (e.g., Student Government, Housing) and Bren School student-run organizations.

          It will be up to you to decide what types of involvement, teamwork, and leadership experiences you want to acquire as you find your place in the UCI community.

          Campus Involvement Campus Involvement

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          • BẰNG CẤP
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          ĐỜI SỐNG SINH VIÊN

          Cuộc sống của chính bạn!

          • Tính đa dạng
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          Tính đa dạng

          Tập thể sinh viên UC Irvine rất đa dạng, vì thế bạn sẽ có nhiều cơ hội học tập, giao lưu với những sinh viên đến từ nhiều nền văn hóa và có những trải nghiệm khác nhau để hỗ trợ, bổ sung, thử thách, nâng cao và mở rộng sự hiểu biết của bạn về phương pháp học tập, cách sống và làm việc trong một xã hội đa văn hóa.

          Ngay cả trong cộng đồng sinh viên Bren:ICS ở quy mô nhỏ hơn thì sự đa dạng cũng thể hiện rõ qua các nền văn hóa và những mối quan tâm – được minh chứng qua sự tham gia của họ vào các hoạt động ngoại khóa bao gồm những hoạt động ca múa, điền kinh, nghệ thuật sân khấu, dịch vụ cộng đồng, ROTC, các tổ chức ký tự Hy Lạp, hội nhóm sắc tộc/văn hóa, và vai trò lãnh đạo trong các câu lạc bộ và tổ chức sinh viên.

          Những thiếu nữ đến thăm trường UC Irvine Những thiếu nữ đến thăm trường UC Irvine

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          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          Bren School Organizations

          Bren School students form, lead, and participate in student organizations that are of particular interest to those in a computing or technology-focused major, such as:

          • Bren School Student Council
          • Informatics Student Association
          • International Game Developers Association
          • Management Information Student Society
          • Society of Women Engineers
          • Video Game Developers Club
          • Women in Information and Computer Sciences

          Student clubs plan and host events that include socials, company info sessions, recruitment events, gaming competitions, and community service efforts.

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          • BẰNG CẤP
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          LIÊN HỆ

          Nếu bạn có bất cứ thắc mắc nào, đừng ngần ngại hãy liên hệ với chúng tôi

          • Ban Sự vụ Sinh viên

          Ban Sự vụ Sinh viên

          Các chuyên gia tư vấn học tập của Bren:ICS hỗ trợ sinh viên thuộc tất cả các chuyên ngành học ở Bren:ICS, từ lúc bắt đầu bước chân vào trường cho đến khi kết thúc chương trình học. Hoạt động tư vấn giáo dục tại trường bắt đầu bằng chương trình định hướng mùa hè, khi đó các cố vấn sẽ giúp đỡ những sinh viên mới trực quan ra kế hoạch học tập của mình và hướng dẫn họ hoàn thành quá trình đăng ký trực tuyến để tham gia các lớp học của học kỳ mùa thu. Thông qua danh sách lựa chọn nghề nghiệp của sinh viên, các tư vấn viên của Bren:ICS sẽ hỗ trợ sinh viên định rõ và thiết lập mục tiêu học tập và nghề nghiệp, xác định những đường hướng hiệu quả nhất để đạt được những yêu cầu của chương trình cấp bằng, và định kỳ kiểm tra sự tiến bộ trong quá học tập. Đội ngũ chuyên gia tư vấn cũng tài trợ các cuộc hội thảo để các sinh viên Bren:ICS tìm hiểu về các chủ đề như khám phá nghề nghiệp, chương trình thực tập, tìm một giảng viên hướng dẫn riêng, và chuẩn bị đáp ứng các tiêu chí học cao học. Đội ngũ chuyên gia tư vấn học tập của Bren:ICS hỗ trợ qua các cuộc hẹn gặp riêng, tư vấn cho những sinh viên đến trực tiếp tại văn phòng và qua email.

          ICS 1, Suite 352 (tìm tòa nhà số 302 trên bản đồ khuôn viên trường tại đây: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          Giờ làm việc: Từ Thứ Hai đến Thứ Sáu, từ 9 giờ sángđến 12 giờ trưa và từ 1 giờ chiều đến 4 giờ chiều.

          Những ứng của viên tiềm năng cũng có thể gọi điện theo số (949) 824-5156 để đặt lịch hẹn với chuyên gia tư vấn học tập để tìm hiểu thêm về những tùy chọn về chương trình cấp bằng khác nhau và chương trình nào có thể phù hợp nhất với mong muốn về học vấn và nghề nghiệp của họ.

          Ban Sự vụ Sinh viên Ban Sự vụ Sinh viên

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          Đại học California, Irvine - Irvine, CA 92697 : 949-824-5011

          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          문의

          궁금한 점이 있으면 부담없이 질문해 주십시오.

          • 학생 업무

          학생 업무

          Bren:ICS의 지도 교수들은 모든 Bren:ICS 전공 학생들이 학업 진로를 시작해서 끝날 때까지 도와줍니다. 학교 중심의 학업 상담은 여름 오리엔테이션과 함께 시작합니다. 이 때 카운슬러는 신입생들이 학업 계획을 세울 수 있도록 도와주고, 이들에게 가을 학기 수업 온라인 등록 방법을 알려줍니다. 남은 재학 기간 동안 Bren:ICS 상담자들은 학업 및 진로 목표 수립을 정의 및 지원하고, 학위 요건을 충족하는 가장 효율적인 길을 찾고, 졸업까지의 시의적절한 진행을 살피며 도움을 줍니다. 상담 담당자는 진로 탐구, 인턴십, 교수 멘토 찾기, 대학원 준비와 같은 주제와 관련하여 Bren:ICS 학생들을 위한 워크샵도 후원합니다. Bren:ICS 학업 상담 담당자는 개인별 약속, 간이 상담, 이메일을 통해 도움을 줍니다.

          ICS 1, Suite 352(캠퍼스 지도의 302번 건물: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          시간: 월~금요일 오전 9시~정오, 오후 1시~4시

          다양한 학위 과정과 어떤 과정이 자신의 학업 및 진로 관심사에 적합할지 알아보려는 지원 희망자는 (949) 824-5156으로 자유롭게 전화하여 지도 교수와 상담 약속을 정할 수 있습니다.

          학생 업무 학생 업무

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          • 學務處

          學務處

          貝林資科院課業諮詢老師能為校內所有主修學生提供協助。學生從入學到畢業都能獲得完整服務。 校內課業輔導從暑期新生訓練就已經開始。諮詢老師會協助新生規劃課程,教導他們上網選擇秋季課程。 學生在畢業前都能請貝林資科院諮詢老師幫忙設定學術和就業目標、找出拿到學歷的最佳捷徑,並且定時核對進度,朝畢業的方向邁進。 諮詢老師也會舉辦貝林資科院學生研討會,主題則與就業方向、實習工作、尋找教師導師,以及準備研究所有關。 貝林資科院課業諮詢老師以個別約談、不預約隨時諮商和電子郵件等方式提供協助。

          資科院1館352號室(請在校園地圖上尋找302號大樓 http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          諮詢時間: 週一至週五上午9點~12:00,下午1:00到4:00

          歡迎有意申請的學生撥打專線:(949) 824-5156,與課業諮詢老師約時間了解本校提供的文憑種類,以及最適合申請人課業和就業需求的學歷。

          學務處 學務處

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          Feel free to contact us with any questions you may have

          • Student Affairs

          Student Affairs

          Bren School academic advisors assist all Bren School majors, from start to finish of their academic career. School-based academic advising begins with summer orientation, during which counselors help new students map out their academic plan and guide them through on-line registration for fall quarter classes. Through the remainder of a student’s career, Bren School advisors are available to help define and support academic and career goal-setting, identify the most efficient routes for meeting degree requirements, and check for timely progress toward graduation. The counseling staff also sponsor workshops for Bren School students about such topics as career exploration, internships, finding a faculty mentor, and graduate school preparation. The Bren School academic advising staff offers assistance via individual appointments, drop-in counseling, and email .

          ICS 1, Suite 352 (find building # 302 on campus map here: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          Hours: Monday through Friday, 9 a.m. – 12:00 p.m. and 1:00 – 4:00 p.m.

          Prospective applicants are welcome to call (949) 824-5156 to schedule an appointment with an academic advisor to learn more about the various degree options and which might best fit their academic and career interests.

          Student Affairs Student Affairs

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          Comments & Questions | Privacy & Legal Notice | Copyright Inquiries | © 2016 UC Regents
          Site By Crisp
          http://www.ics.uci.edu/prospective/ko/degrees/ics-undeclared-pre-major/  전공미정 교양과정 « 학위 « BREN: 정보와 컴퓨터 과학(ICS) « 캘리포니아 대학교 어바인 ?>
          • 학위
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          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          전공미정 교양과정

          특정 전공에 전념하기에 앞서 컴퓨터 과학과 IT에 대한 관심을 우선 파헤치고 싶은 신입생은 ICS 전공미정 상태로 지원할 수 있습니다.

          이 옵션을 선택한 신입생은 학업 카운슬러 이용을 비롯하여 Bren:ICS 학생으로서의 혜택을 모두 누릴 수 있습니다. 참고로, 학업 카운슬러는 전공에 공통으로 들어가는 교양과정의 필수 컴퓨터 과학과 수학 과정을 포함시켜 1학년 학업 계획을 세울 수 있게 도와주고 Bren:ICS의 학업 진로가 어떨지 알려줍니다.

          컴퓨터 과학 및 공학 전공에 관심이 있는 학생들은 UCI에 입학할 때 대단히 체계적인 이 전공을 선택하는 것이 정말 좋습니다.

          잠깐 정보

          알고 계시나요?
          CNBC에 따르면, 2010년에 가장 높은 임금을 받은 학사 학위 10개 중 3개를 Bren:ICS에서 제공한다고 합니다. 전체 목록은 여기에서 확인하십시오.

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          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
          • Business Information Management
          • Computer Game Science
          • Computer Science
          • Computer Science & Engineering
          • Data Science
          • Informatics
          • Software Engineering
          • ICS Undeclared Pre-Major
          • Minors

          Undeclared Pre-Major

          New freshmen who want to explore their interests in computer science and information technology before committing to a specific major may apply as ICS Undeclared.

          This option gives freshmen all the benefits of being a Bren School student. An academic counselor will help you structure a first-year course plan that meets your interests and includes a core set of lower-division computer science and math courses common to all the majors. You'll learn what the Bren School majors are like and will transfer into a specific major before the start of your second year.

          Note that students interested in the Computer Science and Engineering degree program are strongly encouraged to start as a CSE major at the time they enter UCI, as the major is highly structured.

          Quick Facts

          Did you know?
          According to CNBC, of the top 10 highest paying bachelor’s degrees in 2010, the Bren School offers three of them? See the complete list here.

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          • BẰNG CẤP
          • CƠ HỘI
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          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Ngành học tiền chính thức

          Những sinh viên năm nhất có những mối quan tâm khác nhau trong lĩnh vực khoa học điện toán và công nghệ thông tin và muốn khám phá trước khi quyết tâm theo đuổi một chuyên ngành cụ thể thì có thể nộp đơn xin theo học Ngành học tiền chính thức được gọi là ICS Undeclared.

          Tùy chọn này cho phép các sinh viên năm nhất tận dụng mọi lợi ích của một sinh viên Bren:ICS, như được tiếp cận tới cố vấn học tập để được giúp đỡ xây dựng kế hoạch học tập năm đầu, trong đó bao gồm một loạt chương trình học về khoa học điện toán phân ban thấp hơn và những khóa học về toán học thông dụng đối với mọi chuyên ngành và mang đến cho bạn một ý niệm về chuyên ngành được giảng dạy ở Bren:ICS.

          Xin lưu ý rằng những sinh viên quan tâm đến chuyên ngành Khoa học và Kỹ thuật Điện toán nên bắt đầu đăng ký theo học chuyên ngành này khi nhập học vào trường UCI vì đây là chuyên ngành có cấu trúc bài giảng kiến thức chuyên sâu.

          Những số liệu nhanh

          Bạn có biết?
          Theo thống kê của CNBC, trong số 10 bằng cử nhân được trả lương cao nhất thì trong số đó Bren:ICS cấp ba bằng? Hãy xem danh sách đầy đủ tại đây.

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          Đại học California, Irvine - Irvine, CA 92697 : 949-824-5011

          Góp ý & Thắc mắc | Chính sách về Quyền Riêng tư & Thông báo Pháp lý | Những thắc mắc về bản quyền | © 2016 UC Regents
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          Opportunities

          A Bren School education provides you with more than just course work

          • Applied Learning
          • Honors
          • Research
          • Entrepreneurship

          Research

          Every quarter in the Bren School, scores of undergraduate students (including first- and second-year students) work on research projects with faculty. There is no prerequisite. If you and a professor agree to work together, you can, for as long as you want to.

          By working directly with professors, you not only get a taste of what cutting-edge research is like, you also get a preview of what graduate school might be like. Many undergraduates work not only with the faculty member, but also closely with the Ph.D. and M.S. students that the faculty member may employ.

          You become part of a team. As a result, it is not uncommon for undergraduate researchers to be co-authors on scientific publications. Some, indeed, have led these efforts and are first authors.

          As an undergraduate researcher, you will sharpen your skills on complex problems that are still in the process of being defined, and understand what contributes to a research project’s success. Participation in undergraduate research also positions you well to attend graduate or professional (MBA, Law, etc.) schools. Our alumni succeed at Stanford, UCLA, UC Berkeley, UC San Diego, Carnegie Mellon University, Georgia Tech, Columbia, Harvard, MIT, and others.

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          • 학위
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          기회

          Bren:ICS의 교육은 단순한 교육과정 그 이상을 선사합니다.

          • 응용 학습
          • 우등생
          • 연구
          • 창업

          연구

          Bren:ICS에서는 매 학기 수십 명의 학부생들이 교수진과 함께 연구 프로젝트를 수행합니다. 전제 조건은 없습니다. 교수와의 공동 연구에 동의하면, 원하는 동안 연구를 계속할 수 있습니다.

          교수들과 함께 직접 연구함으로써, 여러분은 최첨단 연구가 어떤 것인지 미리 맛볼 수 있을 뿐 아니라 대학원이 어떨지 대략적으로나마 체험할 수 있습니다. 많은 학부생들이 교수와 함께 연구를 할 뿐 아니라, 이들의 석박사급 학생들과도 긴밀히 연구하고 있습니다.

          여러분이 바로 팀의 일원이 되는 것입니다. 그래서, 학부생 연구원들도 과학 출판물을 공동 집필하는 기회가 드물지 않습니다. 실제로 일부는 노력해서 제 1 저자에 오르기도 합니다.

          학부생 연구원이 되면, 아직 정의되는 과정에 있는 복잡한 문제에 대한 기술을 연마하고, 연구 프로젝트의 성공에 기여하는 것이 무엇인지 이해할 수 있습니다. 또한 학부생 연구에 참여하면 대학원이나 전문(MBA, 법률 등) 대학원에 들어가는 데도 도움이 됩니다. 본교 졸업생들은 스탠퍼드, UCLA, UC 버클리, UC 샌디에이고, 카네기 멜론 대학, 조지아 공과대학, 컬럼비아, 하버드, MIT 등과 같은 명문대에 진학합니다.

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          • BẰNG CẤP
          • CƠ HỘI
          • NGHỀ NGHIỆP
          • ĐỜI SỐNG SINH VIÊN
          • TUYỂN SINH
          • LIÊN HỆ

          CƠ HỘI

          Chương trình đào tạo của Bren:ICS đem đến cho bạn không chỉ những kiến thức trên giảng đường

          • Nghiên cứu Ứng dụng
          • Chương trình danh dự
          • Nghiên cứu
          • Quản trị doanh nghiệp

          Nghiên cứu

          Mỗi học kỳ ở Bren:ICS, nhiều sinh viên theo học hệ đại học (kể cả sinh viên năm nhất và năm thứ hai) tham gia thực hiện các dự án nghiên cứu với đội ngũ giảng viên. Không có yêu cầu tiên quyết. Một khi bạn và một vị giáo sư đồng ý làm việc cùng nhau, bạn có thể làm việc với vị giáo sư này bao lâu cũng được.

          Khi làm việc trực tiếp với các giáo sư, bạn không chỉ cảm nhận được công việc nghiên cứu như thế nào mà còn hình dung được nghiên cứu bậc cao học sẽ như thế nào. Nhiều sinh viên đại học không chỉ làm việc trực tiếp với giảng viên trong đội ngũ cán bộ giảng dạy, mà còn làm việc chặt chẽ với những nghiên cứu sinh đang theo học Tiến sĩ và Thạc sĩ được giảng viên trong đội ngũ cán bộ giảng dạy tuyển dụng.

          Bạn trở thành một phần của đội ngũ những nghiên cứu sinh đó. Những nhà nghiên cứu đang còn đang là sinh viên đại học được đứng tên đồng tác giả trên các xuất bản phẩm khoa học là điều ít có. Nhưng trên thực tế đã có một số sinh viên với sự nỗ lực hết sức mình và đã trở thành các tác giả.

          Với tư cách là nhà nghiên cứu đang còn là sinh viên đại học, bạn sẽ có cơ hội nâng cao các kỹ năng xử lý những vấn đề phức tạp hiện vẫn còn đang được đưa ra tranh luận, và bạn hiểu được những gì đóng góp nên sự thành công của dự án nghiên cứu. Tham gia vào chương trình nghiên cứu hệ đại học cũng giúp rộng mở con đường theo học hệ cao học hoặc đào tạo chuyên nghiệp (Quản trị kinh doanh, Luật.vv...). Các cựu sinh viên của chúng tôi đạt được thành tích cao tại các trường Đại học Stanford, UCLA, UC Berkeley, UC San Diego, Carnegie Mellon University, Georgia Tech, Columbia, Harvard, MIT, và nhiều trường khác.

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          • 학위
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          기회

          Bren:ICS의 교육은 단순한 교육과정 그 이상을 선사합니다.

          • 응용 학습
          • 우등생
          • 연구
          • 창업

          우등생

          UCI는 Bren:ICS 학생이라면 신청할 수 있는 강력한 우등생 프로그램(CHP, Campuswide Honors Program)을 갖추고 있습니다. 이 프로그램은 엄선된 이벤트, 동료 멘토링, 우등생들끼리 모여서 공부하고 친목을 다지는 캠퍼스 "스터디 모임", 우등생 기숙사, 특별 연구 기회를 제공합니다. Bren:ICS는 전통적으로 CHIP 프로그램으로 강력한 두각을 나타내고 있습니다.

          Bren:ICS는 자체 연구 우등생 프로그램도 갖추고 있습니다. 3학년과 4학년은 연구 과정에 대해 배우고 상담 교수와 함께 심화 작업에 참여할 기회를 갖습니다. 이 프로그램에 들어온 학생들은 우등생 세미나에 참여하고, 상담 교수의 지도 아래 (최소 2학기 동안) 독립 연구를 실시하며, 연구 논문을 써서 해당 학생 담당 상담 교수와 우등생 프로그램 책임 교수의 검토를 받습니다.

          교내 우등생 프로그램

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          • 학위
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          문의

          궁금한 점이 있으면 부담없이 질문해 주십시오.

          • 학생 업무

          학생 업무

          Bren:ICS의 지도 교수들은 모든 Bren:ICS 전공 학생들이 학업 진로를 시작해서 끝날 때까지 도와줍니다. 학교 중심의 학업 상담은 여름 오리엔테이션과 함께 시작합니다. 이 때 카운슬러는 신입생들이 학업 계획을 세울 수 있도록 도와주고, 이들에게 가을 학기 수업 온라인 등록 방법을 알려줍니다. 남은 재학 기간 동안 Bren:ICS 상담자들은 학업 및 진로 목표 수립을 정의 및 지원하고, 학위 요건을 충족하는 가장 효율적인 길을 찾고, 졸업까지의 시의적절한 진행을 살피며 도움을 줍니다. 상담 담당자는 진로 탐구, 인턴십, 교수 멘토 찾기, 대학원 준비와 같은 주제와 관련하여 Bren:ICS 학생들을 위한 워크샵도 후원합니다. Bren:ICS 학업 상담 담당자는 개인별 약속, 간이 상담, 이메일을 통해 도움을 줍니다.

          ICS 1, Suite 352(캠퍼스 지도의 302번 건물: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          시간: 월~금요일 오전 9시~정오, 오후 1시~4시

          다양한 학위 과정과 어떤 과정이 자신의 학업 및 진로 관심사에 적합할지 알아보려는 지원 희망자는 (949) 824-5156으로 자유롭게 전화하여 지도 교수와 상담 약속을 정할 수 있습니다.

          학생 업무 학생 업무

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          • 학위
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          학위

          다양한 전공을 통해 원하는 분야의 필수적이며 특화된 능력을 갖출 수 있습니다.

          • 개요
          • 비즈니스 정보 관리
          • 컴퓨터 게임 과학
          • 컴퓨터 과학
          • 컴퓨터 과학 및 공학
          • Data Science
          • 정보학
          • Software Engineering
          • ICS 전공미정 교양과정
          • 부전공

          정보학

          이 전공은 실제 현장에서 소프트웨어 어플리케이션이 어떤 부분에, 어떻게 사용되는지에 초점을 맞춰 소프트웨어 어플리케이션을 설계, 개발하고 평가하는 방법을 연구합니다.

          실제 본보기와 사례 연구를 통해 학생들은 공학과 설계, 인간과 컴퓨터 간의 상호작용, 컴퓨터 지원 협업, 정보 시각화, IT가 조직과 사회에 미치는 영향에 대한 탄탄한 기초 교육을 받습니다.

          우리는 끊임없이 소프트웨어와 상호작용하고 소프트웨어에 의해 주도되는 디지털 사회에 살고 있습니다. 효과적이며 사용 가능한 소프트웨어 시스템의 설계 방법을 배우는 데 관심이 있는 학생들은 이 전공을 탐구하는 것이 좋습니다.

          정보학 첫 졸업반(2008년)

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          • 학위
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          UC Irvine

          UC 어바인은 풍부하고 다양한 공동체의 고향입니다.

          • 브렌 스쿨
          • 캠퍼스
          • 어바인 시
          • 학생 지원 서비스

          캠퍼스

          캘리포니아 대학교 어바인은 캘리포니아 전역의 생활을 개선하고 지구 곳곳의 공동체에 도움을 주는 연구, 발견, 학문 활동의 선봉에 서있습니다. 본교 졸업생들은 예술, 과학, 비즈니스, 교육 등 각계각층의 지도자로 활동하고 있습니다. 그 중에는 퓰리처상 수상자 3명과 전 세계에서 활용 중인 “HTTP/1.1” 인터넷 프로토콜의 설계자도 있습니다.

          학부 교육을 위한 최상의 선택으로 꼽히는 UCI의 재학생들은 연구와 멘토링을 통해 쉽게 만날 수 있는 인기 교수, 의학, 법률, 비즈니스, 교육 및 예술 분야의 훌륭한 전문 대학원, 아름다운 교외 캠퍼스, 수상 경력에 빛나는 학생 기숙사, 일년 내내 열리는 흥미로운 캠퍼스 이벤트, 오늘날 같은 상호의존적 세계에서 성공할 수 있도록 도와주는 최고의 학업 및 리더십 준비과정을 체험할 수 있습니다.

          UCI에 관한 자세한 내용은 http://www.uci.edu/prospective.php를 참조하십시오.

          UCI 졸업식 UCI 졸업식

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          http://www.ics.uci.edu/prospective/en/uci/student-services/  Student Services « UC Irvine « Bren School of Information and Computer Sciences « University of California Irvine ?>
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          UC Irvine

          UC Irvine is home to a rich and diverse community

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          Student Services

          The campus offers students a comprehensive range of programs and services designed to help students maximize their learning experiences while at UCI by:

          • strengthening student success skills;
          • facilitating student participation and leadership; and
          • protecting and enhancing the physical, social and mental well-being of students.

          Learn more about these services, available free of charge to all registered students: http://www.admissions.uci.edu/campus_life/student_services.html

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          http://www.ics.uci.edu/prospective/en/uci/city-of-irvine/  City of Irvine « UC Irvine « Bren School of Information and Computer Sciences « University of California Irvine ?>
          • Degrees
          • Opportunities
          • Careers
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          • Admissions
          • Contact

          UC Irvine

          UC Irvine is home to a rich and diverse community

          • Bren School
          • Campus
          • City of Irvine
          • Student Services

          City of Irvine

          The city of Irvine, consistently ranked as one of the safest large cities in America, enjoys a highly educated and diverse population so those new to the city feel right at home. Known as America’s “Most Successful Planned Community,” Irvine encompasses more than 65 square miles and enjoys a Mediterranean climate in a very clean and family-oriented environment in the heart of Orange County. And, Orange County offers wildlife sanctuaries, ecological reserves, nearby desert and mountain resorts, and 42 miles of Pacific Ocean coastline for residents to enjoy.

          With over 14,000 U.S. and foreign companies of all sizes, Irvine offers abundant career opportunities. International businesses in Irvine represent the industries of banking and finance, electronics, manufacturing, biotechnology/medicine, and design.

          Learn more about Irvine: http://www.cityofirvine.org/.

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          UC Irvine

          UC Irvine is home to a rich and diverse community

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          Campus

          The University of California, Irvine is at the forefront of research, discovery, and scholarly efforts that improve lives throughout California and benefit communities in every corner of the globe. Our graduates include leaders in the arts, sciences, business, and education – all walks of life. Among them are three Pulitzer Prize winners and the architect of the “HTTP/1.1” Internet protocol used worldwide.

          A top choice for undergraduate education, students who attend UCI discover stellar faculty who are easily accessible for research and mentoring; excellent professional schools in the fields of medicine, law, business, education, and the arts; a beautiful suburban campus; award-winning student housing; exciting campus events throughout the year; and unparalleled academic and leadership preparation to succeed in today’s interdependent world.

          Learn more about UCI: http://www.uci.edu/prospective.php.

          Graduation at UCI Graduation at UCI

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          Bren School

          By establishing the University of California's first computer science school in 2002, UC Irvine made an investment in the future that reflects its historical commitment to raising the bar of excellence.

          The Bren School began as a department in 1968. More than 35 years later, it was formally recognized as a school – and in June 2004, we were renamed the Donald Bren School of Information and Computer Sciences in recognition of our benefactor’s generous contribution and for his visionary leadership.

          As an independent school focused solely on the computer and information sciences, the Bren School has a unique perspective on the information technology disciplines, allowing us a broad foundation from which to build educational programs and research initiatives that explore the many applications of the computing discipline; from circuits and systems to software engineering and human aspects of computing.

          By blending research with education in multiple disciplines, the Bren School is leading interdisciplinary efforts in order to meet the challenges of the future.

          Download the Bren:10, the top 10 reasons to choose a Bren:ICS education: Click Here.

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          UC Irvine

          UC Irvine là nơi hội tụ của một cộng đồng phong phú và đa dạng

          • Đại học Bren
          • Khuôn viên trường
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          Khuôn viên trường

          Đại học California, Irvine dẫn đầu về hoạt động nghiên cứu, khám phá, và nỗ lực hết sức mình về công tác giảng dạy giúp nâng cao đời sống toàn bang California và đem lại ích lợi cho các cộng đồng trên khắp thế giới. Những sinh viên tốt nghiệp từ trường chúng tôi có những người làm lãnh đạo trong các lĩnh vực nghệ thuật, khoa học và giáo dục - tất cả mọi phương diện của đời sống. Trong số đó có ba người giành Giải Pulitzer và một người là kiến trúc sư của giao thức mạng “HTTP/1.1” đang được sử dụng trên toàn thế giới.

          Thêm vào đó là một sự lựa chọn đỉnh cao cho chương trình giáo dục hệ đại học là những sinh viên đến theo học tại UCI nhận thấy rằng đội ngũ giảng viên ưu tú luôn sẵn sàng giúp đỡ và dìu dắt các sinh viên trong học tập và nghiên cứu; những trường đại học chuyên nghiệp ưu việt thuộc các lĩnh vực y khoa, luật, kinh doanh, sư phạm và nghệ thuật; khuôn viên đại học ngoại ô tươi đẹp; khu nhà ở dành cho sinh viên được đánh giá cao; những sự kiện thú vị được tổ chức quanh năm; và chương trình giảng dạy trang bị kiến thức học thuật và khả năng lãnh đạo vô song giúp các sinh viên đạt được thành công trong thế giới tương thuộc ngày nay.

          Tìm hiểu thêm về UCI: http://www.uci.edu/prospective.php.

          Lễ trao bằng tốt nghiệp tại UCI Lễ trao bằng tốt nghiệp tại UCI

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          LIÊN HỆ

          Nếu bạn có bất cứ thắc mắc nào, đừng ngần ngại hãy liên hệ với chúng tôi

          • Ban Sự vụ Sinh viên

          Ban Sự vụ Sinh viên

          Các chuyên gia tư vấn học tập của Bren:ICS hỗ trợ sinh viên thuộc tất cả các chuyên ngành học ở Bren:ICS, từ lúc bắt đầu bước chân vào trường cho đến khi kết thúc chương trình học. Hoạt động tư vấn giáo dục tại trường bắt đầu bằng chương trình định hướng mùa hè, khi đó các cố vấn sẽ giúp đỡ những sinh viên mới trực quan ra kế hoạch học tập của mình và hướng dẫn họ hoàn thành quá trình đăng ký trực tuyến để tham gia các lớp học của học kỳ mùa thu. Thông qua danh sách lựa chọn nghề nghiệp của sinh viên, các tư vấn viên của Bren:ICS sẽ hỗ trợ sinh viên định rõ và thiết lập mục tiêu học tập và nghề nghiệp, xác định những đường hướng hiệu quả nhất để đạt được những yêu cầu của chương trình cấp bằng, và định kỳ kiểm tra sự tiến bộ trong quá học tập. Đội ngũ chuyên gia tư vấn cũng tài trợ các cuộc hội thảo để các sinh viên Bren:ICS tìm hiểu về các chủ đề như khám phá nghề nghiệp, chương trình thực tập, tìm một giảng viên hướng dẫn riêng, và chuẩn bị đáp ứng các tiêu chí học cao học. Đội ngũ chuyên gia tư vấn học tập của Bren:ICS hỗ trợ qua các cuộc hẹn gặp riêng, tư vấn cho những sinh viên đến trực tiếp tại văn phòng và qua email.

          ICS 1, Suite 352 (tìm tòa nhà số 302 trên bản đồ khuôn viên trường tại đây: http://www.uci.edu/campusmap/
          (949) 824-5156

          ucounsel@uci.edu

          Giờ làm việc: Từ Thứ Hai đến Thứ Sáu, từ 9 giờ sángđến 12 giờ trưa và từ 1 giờ chiều đến 4 giờ chiều.

          Những ứng của viên tiềm năng cũng có thể gọi điện theo số (949) 824-5156 để đặt lịch hẹn với chuyên gia tư vấn học tập để tìm hiểu thêm về những tùy chọn về chương trình cấp bằng khác nhau và chương trình nào có thể phù hợp nhất với mong muốn về học vấn và nghề nghiệp của họ.

          Ban Sự vụ Sinh viên Ban Sự vụ Sinh viên

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          CƠ HỘI

          Chương trình đào tạo của Bren:ICS đem đến cho bạn không chỉ những kiến thức trên giảng đường

          • Nghiên cứu Ứng dụng
          • Chương trình danh dự
          • Nghiên cứu
          • Quản trị doanh nghiệp

          Chương trình danh dự

          UCI cung cấp một chương trình danh dự sôi nổi (CHP, Chương trình Danh dự Liên trường - Campuswide Honors Program), trong đó các sinh viên trường Bren:ICS hội đủ điều kiện tham gia. Chương trình này tổ chức các sự kiện có chọn lọc, dịch vụ gia sư đồng môn, khuôn viên “tụ tập” nơi bạn và bạn học, những sinh viên tham gia chương trình danh dự cùng nhau tụ họp, làm việc và giao tiếp với nhau, chia sẻ nhà ở theo chương trình danh dự và những cơ hội nghiên cứu đặc biệt. Bren:ICS đã gây dựng được hình ảnh nổi bật trong chương trình CHP.

          Bren:ICS cũng cung cấp loạt chương trình danh dự của riêng mình. Những sinh viên năm áp chót và các sinh viên năm cuối có cơ hội tìm hiểu về quy trình nghiên cứu và tham gia vào các công việc đòi hỏi kiến thức và kỹ năng cao cấp với một cố vấn của khoa. Những sinh viên được nhận vào chương trình đều tham gia vào các cuộc hội thảo danh dự, thực hiện nghiên cứu độc lập dưới sự hướng dẫn của một cố vấn khoa (tối thiểu là hai học kỳ), và viết bài luận để cố vấn khoa và Giám đốc Khoa phụ trách Chương trình Danh dự duyệt xét.

          Chương trình Danh dự Liên trường

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          BẰNG CẤP

          Các chuyên ngành đa dạng của chúng tôi đáp ứng mọi nhu cầu học tập của bạn

          • Tổng Quát
          • Quản trị Thông tin Kinh doanh
          • Khoa học Trò chơi Điện toán
          • Khoa học Điện toán
          • Khoa học & Kỹ thuật Điện toán
          • Data Science
          • Tin học
          • Software Engineering
          • Ngành học tiền chính thức ICS
          • Chuyên ngành phụ

          Tin học

          Chuyên ngành này giảng dạy phương thức thiết kế, phát triển và đánh giá các ứng dụng phần mềm tập trung vào cách thức và địa điểm triển khai phần mềm trong thế giới thực.

          Trải nghiệm qua những ví dụ ở thế giới thực và các nghiên cứu theo từng trường hợp trong suốt khóa học, các sinh viên được trang bị kiến thức nền tảng vững chắc về công nghệ và thiết kế phần mềm, giao tiếp con người-máy tính, làm việc theo nhóm có sự hỗ trợ của máy tính, trực quan hóa thông tin, và ảnh hưởng của công nghệ thông tin đối với các tổ chức và xã hội.

          Chúng ta sống trong một xã hội kỹ thuật số và như thế chúng ta liên tục tương tác với phần mềm và phần mềm chi phối những xu hướng vận động của chúng ta. Những sinh viên ham thích thiết kế và quan tâm nghiên cứu cách thiết kế những hệ thống phần mềm hiệu quả và hữu dụng nên đăng ký theo học chuyên ngành này.

          Lớp đầu tiên tốt nghiệp chuyên ngành Tin học, 2008

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          • Degrees
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          Degrees

          Our broad selection of majors lets you be as specialized or general as you like

          • Overview
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          • Computer Game Science
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          • ICS Undeclared Pre-Major
          • Minors

          Informatics

          Informatics at UC Irvine brings together software engineering, human-computer interaction and the study of information technology in organizations into a single degree program.

          Traditional computer science programs concentrate on analyzing and designing computers and computer systems, but the success of those systems depends not only on their intrinsic features but also on human users and their requirements, characteristics and organizations. Informatics focuses on understanding the effect of information technology on people, studying computer systems in their real-world context, and determining how those systems can work effectively.

          Students with an affinity for design and an interest in learning how to develop effective and usable software systems are encouraged to explore this major.

          IS INFORMATICS FOR ME?

          The Informatics degree program offers a contemporary curriculum with an emphasis on group work (starting with the first course), studio-oriented design courses and a yearlong senior project.

          You may want to consider this major if you:

          • enjoy solving problems using all the tools you have available;
          • can work not only with technical details but also with “big-picture” issues;
          • have strong reading and writing skills and can think freely, creatively and systematically.

          Previous experience in computer programming is not required to start the Informatics major. Programming is just one aspect of Informatics, and the major introduces all the necessary skills at a manageable pace. Students who already have some programming experience will also find new concepts, even in the very first course.

          The first year of the program provides students with a hands-on introduction to the broad field of Informatics, centering on the Informatics Core Course. This yearlong course develops students’ basic understanding of software: how to design and construct programs, and how the programs operate as part of information technology systems.

          The second year builds up a portfolio of foundational concepts and techniques that establish the discipline of Informatics; these contribute to the “toolbox” students will use in subsequent years to solve large-scale information and software design problems. As sophomores, students begin to take more advanced courses that support their specialization in either software engineering, human-computer interaction, or the study of organizations and information technology. These may involve courses in Management, Psychology, Computer Science or Engineering.

          In the third year, all students study the design process, project management and the impacts of technology on the real world. Students continue to take electives in their specialized area of study.

          The fourth year is built around a yearlong capstone project in which groups of students tackle a significant assignment, typically from an outside client.

          WHAT COURSES DO I TAKE?

          Current requirements for this major can be found in the General Catalogue.

          WHAT CAN I DO WITH A DEGREE IN INFORMATICS?

          A degree in Informatics provides excellent preparation for a career at the forefront of the computing industry.

          Our graduates work in many industrial settings — ranging from start-up companies and small software houses to consulting firms and multinational corporations — in various roles, including:

          • Software Engineer
          • Human-Computer Interface Designer
          • Information Architect
          • Game Designer
          • Usability Engineer
          • Mobile Computing Systems Designer

          Many also go on to graduate school to pursue an advanced degree in computer engineering, computer science, information science, management or law.

          Informatics students demonstrate a prototype of their senior project — an interactive comic book app that teaches English to Japanese school-age children. Informatics students demonstrate a prototype of their senior project — an interactive comic book app that teaches English to Japanese school-age children.

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          Opportunities

          A Bren School education provides you with more than just course work

          • Applied Learning
          • Honors
          • Research
          • Entrepreneurship

          Honors

          UCI offers a vibrant honors program (CHP, the Campuswide Honors Program), for which Bren School students are eligible. This program offers select events, peer mentorship, a campus “hang out” where you and your fellow honors students convene, work, and socialize, honors housing, and special research opportunities. The Bren School traditionally has a strong representation in the CHP program.

          The Bren School also offers its own research honor sequence. Juniors and seniors have the opportunity to learn about the research process and engage in advanced work with a faculty advisor. Students admitted to the program participate in an honors seminar, conduct independent research under the guidance of a faculty advisor (for a minimum of two quarters), and write a research paper for review by their faculty advisor and the Honors Program Faculty Director.

          Campuswide Honors Program

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          http://www.ics.uci.edu/~cs122b/ http://www.ics.uci.edu/~pattis/ICS-33/ ICS-33 <A HREF="frameindex.html"> no frames link to programs </A>
          Richard Pattis
          http://pregelix.ics.uci.edu/ Pregelix
          • Overview
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          What is Pregelix?

          Pregelix is an open-source implementation of the bulk-synchronous vertex-oriented programming model (Pregel API) for large-scale graph analytics. Pregelix is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads efficiently. Pregelix supports a variety of physical runtime choice which can fit different sorts of graph algorithms, datasets, and clusters. Our motto is "Big Graph Analytics Anywhere!" --- from a single laptop to large enterprise clusters.

          Quick example:

            public class PageRankVertex extends Vertex<VLongWritable, DoubleWritable, FloatWritable, 
          		DoubleWritable> {
              ........
              @Override
              public void compute(Iterator<DoubleWritable> msgIterator) {
                  .......
                  sum = 0;
                  while (msgIterator.hasNext()) {
                    sum += msgIterator.next().get();
                  }
                  setVertexValue((0.15 / getNumVertices()) + 0.85 * sum);
                  sendMsgToAllNeighbors(vertexValue / getEdges().size());
                  ....
              }
            }
          

          The above code is the PageRank implementation on Pregelix.

          Performance:

          We have compared Pregelix with several popular Big Graph Analytics platforms, including Giraph, Hama, GraphLab, and GraphX. The figures below demonstrate the performance (i.e., end-to-end execution time and average iteration time) of the single source shortest paths algorithm (SSSP) on a 32-machine cluster using different platforms. Here contains more details.

          Pregelix can perform comparably to Giraph for memory-resident message-intensive workloads (like PageRank), can outperform Giraph by 15x for memory-resident message-sparse workloads (like the single source shortest paths algorithm), can scale to out-of-core workloads, can sustain multi-user workloads, and can outerpform GraphLab, GraphX, and Hama by more than an order of magnitude for various workloads. Checkout more details here.

          © pregelix.ics.uci.edu 2014.
          Design by Free CSS Templates

          http://www.ics.uci.edu/~thornton/ics45c/ ICS 45C Fall 2015

          ICS 45C Fall 2015 | News | Course Reference | Schedule | Project Guide | Notes and Examples | About Alex


          ICS 45C Fall 2015
          Course News


          Check back here every day or so. I will generally post important coursewide announcements here. They will be listed in reverse-chronological order (i.e., newest items first).

          Date Added News Item
          Su 12/13 The final course grades are now available.
          M 12/7 The last set of Notes and Examples is now available.
          W 11/25 Two new sets of Notes and Examples are now available.
          Tu 11/24 A new set of Notes and Examples is now available.
          F 11/21 Project #4 is now available, along with a Schedule update and one new set of Notes and Examples.
          Tu 11/17 A Schedule update, including a few missing readings from the Savitch textbook, for those of you using it alongside my Notes and Examples.
          Su 11/15 The Project #3 due date is postponed until Friday, November 20.
          W 11/11 New Notes and Examples sections are now available.
          Tu 11/3 Today's remaining Notes and Examples section, about linked data structures, are now available. Note that we aren't covering all of this material in lecture, since portions of it are prerequisite knowledge from courses you will have taken previously, but I've included a complete summary of the topic for those who haven't seen it recently.
          Sa 10/31 An additional Schedule update is available, plus the postponement of the due date for Project #3 until Wednesday, November 18, to give us more lecture time to be prepared for the fourth project.
          Th 10/29 A Schedule update is now available, along with two of next week's Notes and Examples sections.
          Th 10/29 I made a small tweak to the Pointers and the Heap notes. The code snippet in the section titled Dynamically allocating an object had a minor issue that I've fixed.
          Tu 10/27 Project #3 is now available.
          M 10/26 Notes and Examples through the end of this week are now available.
          Th 10/22 Two new sets of Notes and Examples are now available.
          F 10/16 Project #2 is now available, along with a Schedule update.
          F 10/16 An additional two sets of Notes and Examples are now available.
          Th 10/15 Two new sets of Notes and Examples are now available.
          W 10/14 The Schedule has been updated, along with one missing batch of Notes and Examples. More notes to come.
          Tu 10/13 The schedule of labs in the Course Reference has been updated, with a few of the hours each week removed, because they conflict with the labs for ICS 46.
          M 10/12 Multiple new sets of Notes and Examples are available, along with a Schedule update. Additionally, I retitled the notes previously titled Behind the Scenes 1; this section is now titled Behind the Scenes.
          Th 10/8 One batch of notes for today's lecture is now available; the rest will follow this weekend.
          Tu 10/6 One more tweak has been made to the lab schedule in the Course Reference — removing one of the Friday hours, which was a course conflict for the student who was scheduled to tutor it.
          M 10/5 A slight adjustment has been made to the lab schedule in the Course Reference. In particular, we'll no longer have a lab on Mondays or Wednesdays from 10:00am-11:00am, but all other hours we had before remain covered.
          F 10/2 Lab meetings will begin on Monday, October 5. A complete schedule of times, along with more details about what the lab meetings are, etc., is available in the Course Reference.
          Th 10/1 A Schedule update, along with some of next week's Notes and Examples, are now available.
          W 9/30 The Project #0 write-up has been updated to include details about enabling virtualization (VT-x/AMD-v) support and how to ensure that you're host machine can run a 64-bit VM. These details are based on things I've already sent out via email, but I've now included them with the rest of Project #0 for clarity.
          W 9/30 A new set of notes is now available in the Notes and Examples section.
          Tu 9/29 My office hours have been scheduled into a room. I'll be in ICS 209 on Tuesdays and Thursdays from 8:20-9:40pm, beginning tonight.
          M 9/28 The C++ Basics notes are available in the Notes and Examples section.
          Th 9/24 Added the first batch of Notes and Examples, which are also now listed in the Schedule alongside the lectures that correspond to them.
          M 9/21

          Welcome! Please note a couple of things as we get started this quarter:

          • The first lecture will meet on Thursday, September 24. There are no scheduled lab sections, but we will have informal lab meetings, with times and dates to be announced early in the quarter, but likely to begin sometime during Week 1. For more information about meeting times of lab meetings, see the Course Reference. For information about lecture material and readings, see the Schedule.
          • I encourage you to spend some time reading through the material on the course web site. It will be updated periodically throughout the quarter, and there will always be an announcement here describing each update. For now, notice the set of links at the top of this (and every) page, leading you to the Course Reference, the Schedule, and the Project Guide, as well as a set of Notes and Examples — mostly emanating from the lectures — that will be posted throughout the quarter.

          http://www.ics.uci.edu/~khanhtn1/vita/ VITA Project

          What is VITA program?

          • VITA is a free, IRS-sponsored tax preparation service available to qualifying taxpayers.
          • Through the IRS's VITA program, taxpayers can take advantage of traditional face-to-face tax preparation with an IRS-certified volunteer or taxpayers can use the facilitated self-assistance (FSA) service where taxpayers prepare and file own tax return with access to an IRS-certified volunteer if taxpayers have questions.
          • For both services, the VITA Program generally offers this free tax help to people who make $60,000 or less and need assistance in preparing their own tax returns. IRS-certified volunteers provide free basic income tax return preparation and can inform taxpayers about special tax credits for which they may qualify, such as Earned Income Tax Credit and Child Tax Credit.
          • VITA sites are generally located at community and neighborhood centers, libraries, schools, shopping malls, and other convenient locations. Most locations also offer free electronic filing.
          • To find a nearby VITA location that offers self-assistance tax preparation or traditional face-to-face tax preparation, go to here.
          • For general information about the VITA program, visit here.
          • Locally, VITA program is run by with many partners. Please refer to OC United Way page for more details here

          Due to the lack of human resources, the development has been stopped since Summer 2015.
          If you are an UCI student who are interested in working in this project, please contact me directly.

          Team Members

          We are building an online screening and appointment booking as well as volunteer management system to support OC United Way in its VITA operations.

          • Manager: Khanh Nguyen

          Past Members:

          • Tu Phu Hoang Le
          • Tien Quang Nguyen (Tim)
          • Vu Tran Nguyen
          • Xueyi Fan (Eddie) (Winter 2015)
          • Mudassir Idriss Mayet (Winter 2015)
          • Tsz Hang Ng (Kevin) (Winter 2015)
          • Pen Han Chang (Nick) (Winter 2015)
          • Luan Gia Lam (Jason) (Winter 2015)
          • Joshua Jang (Fall 2013)
          • Jordan Villanueva (Fall 2013)
          • Wendy Wei (Summer 2013)


          UCI
          http://www.ics.uci.edu/~kroher/Kristin_Roher/Home.html Kristin Roher
           
           
           

          Kristin Roher

           
           

          About Me


          1. I received my B.S. in Computer Engineering from the University of Pittsburgh. As an undergraduate, I leveraged iOS powered devices to calculate life-cycle assessment (LCA) scenarios using grid computing principles. Specifically, I wrote an application to receive data from a server (by allowing the server to ping the iOS device when data was available), compute a section of an LCA calculation, and send the results back to the server. I am currently conducting research with Professor Debra Richardson at the University of California, Irvine in pursuit of a Ph.D. in Informatics. My research involves introducing sustainability into the software engineering process as well as the web application described below.


          Research


          1. My current research My current research is within the field of sustainable HCI (human computer interaction). My work aims to leverage existing theories and techniques on social influence to design ICT systems that increase behaviors that contribute to sustainability. Research within social influence suggests that an individual’s sustainable behaviors will increase if they are made aware of the sustainable behaviors of others, especially others who are similar to them (e.g. friends or those in the same community). Information technology can be used to create this awareness through the use of social networks. Current approaches to behavior change within sustainable HCI have often focused on the individual as a rational actor. My work provides a more holistic view of individual behavior by considering the social factors that influence behavior.


          Teaching


          1. Over the past two summers I worked on a project with Girls Inc of Orange County to provide week-long summer camps to girls aged 13-18. The camps not only provided low-income girls with an opportunity to learn to program robots to dance, it also aimed to empower girls with a desire to learn more about STEM majors and careers. On the first day of the camp we asked how many were interested in computer science and only about 2 out of 20 girls raised their hands each week, however, by the end of the week we had more than 3/4 of the girls raising their hands when asked the same question. The camps taught the girls how fun computer science can be and about the many opportunities provided by knowing computer science skills. My role at the camp was to provide instruction, develop the curriculum, provide technical support for the robots and associated software, and assist in mentorship of the girls. By the end of the week the girls were able to code the robot to dance, tell jokes, hold conversations, and respond to commands.

          2. Check out the videos made about the camp:

          3. 1. Girls Inc. Summer Robotics Camp at UCI

          4. 2. How Kevin and GiGi Became Friends --The script for this one was written by one of the girls who attended the camp

          Email:

          kroher at uci dot edu


          Office:

          Department of Informatics

          Donald Bren School of Information and Computer Science

          University of California, Irvine

          5243 Donald Bren

          Irvine, CA 92697-3440


          CV: PDF

          USER 2013 Paper: PDF

          RePa 2013 Paper: PDF

           
           
          http://mondego.ics.uci.edu/projects/yelp/ The Yelp dataset challenge - Multilabel Classification of Yelp reviews into relevant categories

          Yelp Dataset Challenge

          The Team
          Hitesh Sajnani, Vaibhav Saini, Kusum Kumar , Eugenia Gabrielova , Pramit Choudary, Cristina Lopes

          Classifying Yelp reviews into relevant categories

          Yelp users give ratings and write reviews about businesses and services on Yelp. These reviews and ratings help other Yelp users to evaluate a business or a service and make a choice. While ratings are useful to convey the overall experience, they do not convey the context which led a reviewer to that experience. For example, consider a yelp review about a restaurant which has 4 stars:
          "They have the best happy hours, the food is good, and service is even better. When it is winter we become regulars".

          If we look at only the rating, it is difficult to guess why the user rated the restaurant as 4 stars. However, after reading the review, it is not difficult to identify that the review talks about good "food", "service" and "deals/discounts" (happy hours).

          A quick inspection of few hundred reviews helped us to decide important categories that are frequent in the reviews. We found 5 categories which include “Food”, “Service”, “Ambience”, “Deals/Discounts”, and “Worthiness”. "Food" and "Service" categories are easy to interpret. "Ambience" category relates to the décor, and look and feel of the place. "Deals and Discounts" category correspond to offers during happy hours, or specials run by the venue. “Worthiness” category can be summarized as value for money. Users often express the sentiment whether the overall experience was worth the money. It is important to note that "Worthiness" category is different from the “Price” attribute already provided by Yelp. "Price" captures whether the venue is “inexpensive”, “expensive” or “very expensive”.

          This high level categorization of reviews into relevant categories can help user to understand why the reviewer rated the restaurant as “high” or “low”. This information can help other yelpers to make a personalized choice, especially when one does not have much time to spend on reading the reviews. Moreover, such categorization can also be used to rank restaurants according to these categories.

          We formulted the task of classifying a review into relevant categories as a learning problem. However, since a review is inexclusively associated with multiple categories at the same time, it is not a simple binary classification or a multi-class classification. It is rather a multi-label classification problem.
          Here is a short video describing how (and why?) Yelp can build some cool features using this categorization:



          Corpus

          The Yelp dataset released for the academic challenge contains information for 11,537 businesses. This dataset has 8,282 check-in sets, 43,873 users, 229,907 reviews for these businesses. For our study, since we are only interested in the restaurant data, we have considered out only those business that are categorized as food or restaurants. This reduced the number of business to around 5,000.

          We selected all the reviews for these restaurants that had atleast one useful vote. From this pool of useful reviews, we randomly chose 10,000 reviews. A labeling codebook describing what categories to include was developed through an initial open coding of a random sample of 400 reviews. The codebook was validated and refined based on a second random sample of 200 reviews. This exercise helped us to fix 5 categories which include food, ambience, service, deals, and worthiness. Once we identified these 5 categories, the 10,000 reviews were divided into 5 bins with repitition in each bin. 6 Graduate student researchers from our group then read and annotated each of these reviews in the identified categories. It took us approx. 225 man hours to annotate all the reviews. We identified the conflicts in the annotation of reviews among different annotators. We removed all the reviews from the analysis where there were discrepancies among the annotators. This left us with 9019 reviews. We split these annotated reviews into 80% train and 20% test data.

          The review annotation process was very challenging and time consuming. We believe that it is one of the major contributions of this work. We plan to release the annotated data for researchers to extend the work.

          Feature Extraction and Normalization

          We extracted two types of features: (i) star ratings and (ii) textual features consisting of unigrams, bigrams and trigrams.
          For star ratings we created three binary features representing rating 1-2 stars, 3 stars, and 4-5 stars respectively.
          For extracting textual features, we first normalized the review text by converting it to lower–case and removing the special characters. We did not remove the stop words as they play important role to understand user sentiments. The cleaned text is then tokenized to collect unigrams (individual words) and calculate their frequencies across the entire corpus. This results in 54,121 unique unigrams. We condense this feature set by only considering unigrams with a frequency greater than 300, which results in 375 unigram features. Similarly we extract 208 bigrams and 120 trigrams.

          The arff files for the the features extracted for Train and Test data can be downloaded from here: Train.arff and Test.arff

          Here is a short video describing the corpus and the feature engineering



          Classification

          In this section, we will describe the various approaches we took to build a classifier. We will reason about our choices based on the advantage and disadvantage of each approach. You can also have a look at the video presentation, to get a quick overall idea


          The problem of classifying a review into multiple categories is a not a simple binary classification problem. Since a reviewer can talk about various things in his or her review, each review can be classified into multiple categories.

          One of the most popular and perhaps the simplest way to deal with multiple categories is to create a binary classifier for each category. So in our case, we create 5 binary classifiers for food, service, ambience, deals, and worthiness category. In order to do this, we need to transform the dataset into 5 different datasets where each dataset has information only about one category.

          To understand consider a scaled down version of our dataset which has only 4 categories (food, ambience, service, and deals) As shown in Figure 1., we create four different dataset from this original dataset such that each dataset is only associated with a specific category. For example, The new dataset created for "food" category will have only one label. The label will be '1' for all the datapoints which had '1' for "food" in the original dataset. Similarly the dataset created for "service" will have label as '1' for all the datapoints that had service as '1' in the original dataset. This is done for all the categories present in the dataset. Given a new review, binary classifier for each category predicts if the review belongs to a category. The final prediction is the union of all the binary predictors.

          Figure 1.

          Once the dataset is transformed in 4 different datasets, any binary classifier like nearest neighbour, SVM, decision trees, etc. can be used with this approach. Although this approach is pretty simple and treats each category independently. However, as a consequence, it ignores correlation among categories. This assumption may not hold true, especially if the categories share some aspects with each other. For example, in our case, mostly when people get deals, they feel the restaurant is worthy to visit. This means that "deals" and "worthiness" categories are correlated. Similarly we saw correlation between "service" and "ambience" category. Sometimes the correlation may be high, and sometimes it may be very low. However, in our case, we thought it was worth accounting for.

          In order to account for correlation among categories, we considered each different subset of L as a single category. Here L is the set of all the categories, a.k.a targets. So L = {Food, Service, Ambience, Deals}. For example, as shown in Figure 2, we transform the target for the Review 1, {Food, Deals} as a single target with value "1001". Similary for Review 2, {Ambience, Deals}, is tranformed into "0011". Here the pattern is formed by creating a vector which has fixed indices for each category. We then learn a multi-class classifier h : X --> P(L), where X is the review, P(L) is the powerset of L, containing all possible category subsets. This approach takes into account correlations between categories, but also suffers from the large number of category subsets. E.g., if we have 5 categories, this approach will generate 25 possible targets to predict; most of which will have only few datapoints to learn. This approach might work well if there is a large training dataset which covers all (at least most of) the possible targets to predict.

          Figure 2.

          We wanted to get best of both worlds i.e., consider correlation among categories and at the same time not get hit by the large number of subsets generated by the previous approach. Hence, we decided to use ensemble of classifiers where each classifier is trained using a different small subset (k) of categories. For example, let's say there are 4 categories {Food, Service, Ambience, Deals}. We choose subset size = 2. Hence, we build a total of 4C2 = 6 classifiers for the following combination of categories: {(Food,Service), (Food,Ambience), (Food, Deals), (Service, Ambience), (Service, Deals), (Ambience, Deals)}. See first part of Figure 3. For prediction, as shown in second part of Figure 3, we consider prediction of all the six classifiers and then take a majority vote.
          This approach considers correlations among categories and at the same time does not generate very large number of targets by considering only a small subset of categories for each classifier.

          Figure 3.

          Experiments

          Evaluation metrics

          We use Precision and Recall to measure the performance of a classifier.
          To understand, what precision and recall means in our context, consider (x,Y) to be a datapoint where x is the review text and Y is the set of true categories. Y ⊆ L, where L = {Food, Service, Ambience, Deals, Worthiness}.
          Let h be a classifier
          Let Z = h(x) be the set of categories predicted by h for the datapoint(x, Y). Then,
          Precision = |Y ∩ Z|/|Z| (Out of the categories predicted, how many of the them are true categories)
          Recall = |Y ∩ Z|/|Y| (Out of the total true categories, how many of them were predicted)

          Results

          We experimented with all the three approaches discussed above. We used Precision and Recall as our evaluation metrics. For each review, dThe comprehensive set of experiment configurations (different approaches, different classifiers, different feature sets, paramter settings) can be found in this result sheet.

          In the first approach of using L binary classifiers, where L is the total number of categories, we used Naive Bayes, k-Nearest Neighbour, Support Vector Machines (SMO implementation), decision trees, and Neural Networks. In the figure below we report results for Naive Bayes and K-NN for this approach as only they were competitive.
          In the second approach, where we consider label correlations and predict the powerset of labels. Decision trees performed the best in this category. We also experimented with ensemble of classifiers approach using decision trees that gave us the best results overall.


          Conclusion

          Yelp reviews and ratings are important source of information to make informed decisions about a venue. We conjecture that further classification of yelp reviews into relevant categories can help users to make an informed decision based on their personal preferences for categories. Moreover, this aspect is especially useful when users do not have time to read many reviews to infer the popularity of venues across these categories. In this paper, we demonstrated how reviews for restaurants can be automatically classified into five relevant categories with precision and recall of 0.72 and 0.71 respectively. We found that an ensemble of two multi-label classification technique (Binary Relevance and Label Powerset) performed better than the techniques individually. Moreover, there is no significant difference in performance when using a combination of bigrams, unigrams and trigrams instead of only unigrams. We also showed how the results of this study can be incorporated into Yelp’s existing website.

          Technical Report

          Multilabel Calssification of reviews in Yelp data

          Download Presentation

          Multilabel Calssification of reviews in Yelp data

          comments powered by Disqus http://www.ics.uci.edu/~djp3/classes/2010_09_INF133/ INF 133 Fall 2010-2011: User Interaction Software

          Informatics 133: User Interaction Software

          Fall 2010-2011

          Department of Informatics

          Donald Bren School of Information and Computer Sciences

          Home | Administrative Policies | Course Structure | Materials | Assignment Schedule

          From the catalog:

           

          "Introduction to human-computer interaction programming. Emphasis on current tools, standards, methodologies for implementing effective interaction designs. Widget toolkits, Web interface programming, geo-spatial and map interfaces, mobile phone interfaces. Strategies for evaluation of user interfaces." (catalog)

           

          Lecture: MWF 12:00 - 12:50

          Classroom: DBH 1300

           

          Discussion Section: N/A

          Classroom: N/A

           

          Instructor: Professor Don Patterson

          Email: djp3@ics.uci.edu

          Office Hours: F 1:00pm - 2:00pm DBH 5084

          Teaching Assistant:None

          Email: N/A

          Office Hours/Discussion Section: N/A

          Reader:Hitesh Sajnani

          Email: hsajnani@uci.edu

          Office Hours: N/A

          EEE Class Mailing List:

          Archive

          http://mondego.ics.uci.edu/projects/SourcererCC/ Code Clone Detection

          SourcererCC: Scaling Type-3 Clone Detection to Large Software Repositories

          Team @UC Irvine: Hitesh Sajnani, Vaibhav Saini, Cristina Lopes
          Team @University of Saskatchewan: Jeff Svejlanko, Chanchal Roy

          Project Description

          Given the availability of large-scale source-code repositories, there have been a large number of applications for clone detection. Unfortunately, despite a decade of active research, there is a marked lack in clone detectors that scale to large software repositories. In particular for detecting near-miss clones where significant editing activities may take place in the cloned code.
          We present SourcererCC, a token-based clone detector that targets the first three clone types, and exploits an index to achieve scalability to large inter-project repositories using a standard workstation. SourcererCC uses an optimized inverted-index to quickly query the potential clones of a given code block. Filtering heuristics based on token ordering are used to significantly reduce the size of the index, the number of code-block comparisons needed to detect the clones, as well as the number of required token-comparisons needed to judge a potential clone.
          We evaluate the scalability, execution time, recall and precision of SourcererCC, and compare it to four publicly available and state-of-the-art tools. To measure recall, we use two recent benchmarks, (1) an exhaustive benchmark of real clones, BigCloneBench, and (2) a Mutation/Injection-based framework of thousands of fine-grained artificial clones. We find SourcererCC has both high recall and precision, and is able to scale to a large inter-project repository (250MLOC) using a standard workstation.

          Tool Download and Usage

          In order to run the tool please follow the steps below:

          A. Generating the input file of the project for which you want to detect clones
          1. Click here to download input generator for the code clone detector (ast.zip).
          2. Unzip ast.zip and import the project ast in your eclipse workspace.
          3. Run it as an "Eclipse Application". This should open another eclipse instance where you will import the projects for which you want to generate the input file.
          4. After importing the project in the workspace of the new eclipse instance, click on the "Sample Menu" in the top menu bar and then click on the "Sample command" to run. This should generate the output (desired input file) in the path specified by variable "outputdirPath".
          5. Please note that you will have to change the location of output directory on line 61 of SampleHandler.java.this.outputdirPath = "/Users/vaibhavsaini/Documents/codetime/repo/ast/output/"; to your desired output directory.
          6. The generated input file name will be of the format: <ProjectName>-clone-INPUT.txt. For example, if your project name is jython, then the generated input file name should be jython-clone-INPUT.txt

          B. Running the clone detection tool on the generated input file
          1. Click here to download the CloneDetector (tool.zip).
          2. Unzip tool.zip and navigate to tool/ using terminal
          3. Copy the input file generated above (<ProjectName>-clone-INPUT.txt) into input/dataset directory.
          4. Open cd.sh, and assign <ProjectName> as value to the variable arrayname (line #5). For example, If your generated input file is jython-clone-INPUT.txt, line #5 should be arrayname=(jython)
          5. Execute the command ./cd.sh

          C. Generated output
          1. The generated output will be in the ./output folder.
          2. Files with extension .txt will have the computed clones and the files with .csv extension will have the time taken to detect clones
          D. Source Code
          The source code of SourcererCC can be found here on github.

          E. SourcererCC-I
          SourcererCC-I is an interactive version of the tool integrated with Eclipse IDE to help developers instantly find clones during software development and maintenance.
          A short video of Sourcerer-I in action can be found here and link to install the Eclipse plug-in is available here.

          Precision data as reported in the paper

          We randomly selected 390 of clone pairs detected by SourcererCC for manual inspection. This is a statistically significant sample with a 95% confidence level and a +/- 5% confidence interval. We split the validation efforts across three clone experts. This prevents any one judge's personal subjectivity from influencing the entire measurement. The judges found 355 to be true positives, and 35 to be false positives, for a precision of 91%.
          Reviewer True Positives False Positives
          Judge 1 TP-1 FP-1
          Judge 2 TP-2 FP-2
          Judge 3 TP-3 FP-3

          Effectiveness of Filtering Heuristics (Figure 1 in paper)

          The effectiveness of the filtering heuristics to eliminate candidate comparisons is demonstrated on 35 open source Apache Java projects. These projects are of varied size and span across various domains including search and database systems, server systems, distributed systems, machine learning and natural language processing libraries, network systems, etc. Most of these subject systems are highly popular in their respective domain. Such subject systems exhibiting variety in size and domain help counter a potential bias of our study towards any specific kind of software system The details of the projects including project name, size and the number of methods is reported in Table II below. Column 3 ( # Methods) shows total number of methods (total), number of methods after removing methods with size < 25 tokens (>25 tokens), and methods that are not exact duplicates (unique). Column 5 (Time Taken), Column 6 (# Candidates) and Column 7 (Terms Compared) show time taken to detect clones, number of candidates compared and total number of tokens compared for:
          (i) Naive - No filtering heuristics;
          (ii) Prefix - Sub-block filtering heuristic; and
          (iii) Pos - Both Sub-block and Token Position filtering heuristics together

          The tabulated data is also charted below. The horizontal axis shows the 35 subject systems sorted by the number of methods they contain (smallest on the left) . The vertical axis shows the performance metric value. The black circles, the red triangles, and the green plus marks show the performance metric values of when no filtering is applied, only sub-block filtering is applied, and sub-block and token position filtering applied respectively.
          http://www.ics.uci.edu/~enalisni/ShakespeareExplorer.html Shakespeare Sentiment Explorer

          Shakespeare Sentiment Explorer (Eric Nalisnick, 2013)

          http://www.ics.uci.edu/~dakuow1/index.html Dakuo Wang

          Dakuo Wang

          A HCI Researcher and Engineer

          • Home
          • CV
          • Projects
          • Publications
          • About Me
          • Contact Me

          Welcome.

          Hello there. I am Dakuo Wang. I am a Ph.D Candidate in the Department of Informatics at the University of California, Irvine.

          I study Human-Computer Interaction and Computer Supported Cooperative Work. I also design useful data visualizations, user interfaces, analytic tools and other digital stuff.

          Learn more about me or get in touch if you want to collaborate.

          Back to Top

          Recent Projects.

          • How People Write Together Now, and DocuViz Project

          • Chinese Internet User Experience Research

          • Sentiment Analysis on Twitter and Blog Following the U.S. 2012 Presidential Election

          Back to Top

          Publications

          • Wang, D., Olson, J. S., Zhang, J., Nguyen, T., & Olson, G. M. (2015). How Students Collaboratively Write using Google Docs. iConference 2015 Proceedings.

          • Wang, D., Olson, J. S., Zhang, J., Nguyen, T., & Olson, G. M. (2015). DocuViz: Visualizing Collaborative Writing. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM, New York, NY, USA, 1865-1874.

          • Wang, D., Mark, G. (2015). Internet Censorship in China: Examining User Awareness and Attitudes. ACM Trans. Computer-Human Interaction. under review.

          Back to Top

          About me.

          Education

          • University of California Irvine, California, U.S.A.
            From Sep 2012

            - Ph.D. in Informatics, Interested in Social Computing and Computer Support Cooperative Work

          • University of California Irvine, California, U.S.A.
            Sep 2011 -- Sep 2012

            - Master in Electrical Engineering and Computer Science (EECS) concentration of Computer Network and Distributed Computing

          • Ecole Centrale Electronique, Paris, France
            Sep 2008 -- Oct 2010

            - M.E. in Information System Engineering, Specialization in Enterprise Information System

          • Beijing University of Technology, Beijing, China
            Sep 2005 -- Sep 2008

            - B.S. Computer Science

          Employment

          • Research assistant, University of California Irvine, California, U.S.A.
            From Apr 2012

            - Working on Google Doc Visualization Project with Judith Olson
            - Working on Chinese Microblog Users’ Perception Towards Information Regulation Project with Gary Olson and Gloria Mark
            - Working on Sentiment Analysis of Twitter & Blogsphere during 2012 Presidential Election Project with Gloria Mark

          • Information System Design Engineer, France Telecom, Paris, France
            Apr 2009 – Sep 2010

            - Served as Information System Design Engineer in xDSL/FTTH PEMS Paris
            - Designed and implemented a knowledge base for DSLAM, using Linux/Apache/MYSQL/PHP

          • Information System Technician, Ecole Centrale Electronique, Paris, France
            Aug 2010 – Sep 2010

            - Designed and implemented a student internship information system for ECE Paris, using PHP Yii Framework for server side and JAVA Android for Android tablet app side
            - Worked with 3 other teammates following SCRUM develop methodology

          • Vice President of Student Union, Beijing University of Technology, Beijing, China
            Oct 2005 -- Oct 2006

            - Led 40 members, organize social practice activities (including internship, part-time job, social research and social survey) for whole university 30,000 students,
            - Took charge for the 2006 Summer Social Practice (more than 1000 students joined in and the final financial support achieved 30K euro).

          Awards

          • University of California Irvine Graduate Research/Teaching Fellowship
            Sep 2012 – Sep 2014

          Professional Associations

          • Member of Institute of Electrical and Electronics Engineers (IEEE)

          • Member of Association for Computing Machinery (ACM), ACM special interest group SIGCHI

          My Advisors

          • thumbnail

            Judith S. Olson

            the Bren Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine
            & with courtesy appointments in the School of Social Ecology and the Merage School of Business

          • thumbnail

            Gary Olson

            Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine
            & Professor Emeritus at the University of Michigan

          • thumbnail

            Gloria Mark

            Professor in Donald Bren School of Information and Computer Sciences in the University of California, Irvine

          Skills

          • Proficient in C, Java, J2EE, JSP and familiar with C++, C#, .NET, ARM assembly

          • Proficient in MySQL Database and familiar with Oracle (PL/SQL)

          • Proficient in HTML, CSS, JavaScript (ExtJS), PHP (Zend and Yii), CMS (Drupal, Symphony and Magento)

          • Comfortable with Linux, Windows and Mac OS

          Languages

          • Proficient in Chinese – native speaker

          • Proficient in English

          • Conversational in French

          Interest

          • Sport : Swimming, Table Tennis, Chess

          • Hobbies : Traveling, Reading

          More about me

          Download Curriculum vitae
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          Get in touch.

          Leave your comment.

          Send me a message



          Contact Information

          Department of Informatics
          5211 Donald Bren Hall
          University of California, Irvine
          Irvine, CA, 92697

          Phone: +1 949 864 9778
          Email: dakuo.wang [@] uci.edu

          Follow Me

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          • About
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          Copyright 2013 Dakuo Wang. - Last Updated: January, 2013     Designed by Styleshout

          http://www.ics.uci.edu/~pjsadows/personal.html Peter J Sadowski | Personal

          Peter J Sadowski

          UC Irvine, Department of Computer Science

          UCI Seal
          • Personal
          • Research
          • Home
          • Contact



          © 2014 • CE
          http://www.ics.uci.edu/~pjsadows/research.html Peter J Sadowski | Research

          Peter J Sadowski

          UC Irvine, Department of Computer Science

          UCI Seal
          • Personal
          • Research
          • Home

          Journal Publications:


          The Ebb and Flow of Deep Learning: a Theory of Local Learning
          P. Baldi and P. Sadowski
          (submitted)
          Enhanced Higgs Boson to Tau Tau Search with Deep Learning
          P. Baldi, P. Sadowski, and D. Whiteson.
          Physical Review Letters, 2015.
          Searching for Exotic Particles in High-energy Physics with Deep Learning
          P. Baldi, P. Sadowski, and D. Whiteson.
          Nature Communications, 2014.
          The Dropout Learning Algorithm
          P. Baldi and P. Sadowski.
          Artificial Intelligence, 2014.
          Inter-species Prediction of Protein Phosphorylation in the sbv IMPROVER Species Translation Challenge
          M. Biehl, P. Sadowski, G. Bhanot, A. Dayarian, P. Meyer, R. Norel, K. Rhrissorrakrai, H. Sahand, and M. Zeller.
          Bioinformatics, 2014.
          Small-Molecule 3D Structure Prediction Using Open Crystallography Data
          P. Sadowski and P. Baldi.
          Journal of Chemical Information and Modeling, 2013.

          Conference Papers:


          Learning Activation Functions to Improve Deep Neural Networks
          F. Agostinelli, M. Hoffman, P. Sadowski, P. Baldi
          (submitted)
          Deep Learning, Dark Knowledge, and Dark Matter
          P. Sadowski, J. Collado, D. Whiteson, P. Baldi
          NIPS 2014 Workshop on High-energy Physics and Machine Learning, 2014
          Searching for Higgs Boson Decay Modes with Deep Learning
          P. Sadowski, D. Whiteson, P. Baldi
          Advances in Neural Information Processing Systems, 2014.
          Deep Autoencoder Neural Networks for Gene Ontology Annotation Predictions
          D. Chicco, P. Sadowski, and P. Baldi.
          ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, 2014.
          Understanding Dropout
          P. Baldi and P. Sadowski.
          Advances in Neural Information Processing Systems, 2013.
          Bayesian and Pairwise Local Similarity Discriminant Analysis
          P. Sadowski, L. Cazzanti, and M. Gupta.
          Proc. IEEE Conf. on Cognitive Information Processing, 2010.
          • Contact



          © 2014 • CE
          http://www.ics.uci.edu/~pjsadows/contact.html Peter J Sadowski | Contact

          Peter J Sadowski

          UC Irvine, Department of Computer Science

          UCI Seal
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          • Research
          • Home

          Department of Computer Science
          247 ICS2
          Irvine, CA 92697

          • Contact



          © 2014 • CE
          http://www.ics.uci.edu/~pjsadows/index.html Peter J Sadowski

          Peter J Sadowski

          UC Irvine, Department of Computer Science

          UCI Seal
          • Personal
          • Research
          • Home
          • PhD Student
            Department of Computer Science
            Institute for Genomics and Bioinformatics
            Advisor: Pierre Baldi

            I study machine learning, particularly deep learning with artificial neural networks. I am interested in the development of novel algorithms and model architectures for learning useful data representations.

            Artificial neural networks can learn a broad set of functions from data. At UCI, I am working on a number of applications to the natural sciences, including the prediction of chemical reactions and the detection of exotic particles in high-energy physics.

          • Peter Sadowski
          • Contact



          © 2015 • CE
          http://www.ics.uci.edu/~jesmaeln/education.html Mahdi Abbaspour Tehrani

          Jamshid Esmaelnezhad

          Ph.D. Candidate
          University of California - Irvine

          • Home
          • Academic
          • Publication
          • Research
          • Teaching

          Sharif University of Technology
          Tehran, Iran
          B.S. in Computer Engineering - Software
          2007 - 2011

          Shahid Dastqeib High School (NODET)
          Tehran, Iran
          Diploma in Physics and Mathematics Discipline
          2003 - 2007

           
           

          Last Update January 2014

          http://www.ics.uci.edu/~jesmaeln/index.html Jamshid Esmaelnezhad

          Jamshid Esmaelnezhad

          Ph.D. Candidate
          University of California - Irvine

          • Home
          • Academic
          • Publication
          • Research
          • Teaching

          Welcome to My HomePage

          My name is Jamshid Esmaelnezhad which is written as جمشید اسماعیل نژاد in persian.

          I was born on July 16th,1989 in Shiraz, Iran.

          I'm doing a PhD and I'm working on databases and data management at Donald Bren School of Information and Computer Sciences of University of California - Irvine.

          My advisor is Prof. Chen Li

          And here is my CV.

           
          • Research Interest

            • Information Retrieval
            • Databases and Data Management
            • Machine Learning: Data Mining
            • Social Networks
            • Image Processing
          • Contact

            • Emails
            • jesmaeln[at]uci.edu
           

          Last Update January 2014

          http://www.ics.uci.edu/~jesmaeln/publication.html Mahdi Abbaspour Tehrani

          Jamshid Esmaelnezhad

          Ph.D. Candidate
          University of California - Irvine

          • Home
          • Academic
          • Publication
          • Research
          • Teaching

          Publications

          Inci Cetindil, Jamshid Esmaelnejad , Taewoo Kim, Chen Li, David Newman Efficient Instant-­Fuzzy Search with Proximity Ranking , ICDE 2014 (to appear).

          Inci Cetindil, Jamshid Esmaelnejad Chen Li, David Newman Efficient Instant­Fuzzy Search with Proximity Ranking , WebDB 2012, pp. 7­12.

          Jamshid Esmaelnejad Soheil Hassas Yeganeh, Jafar Habibi A novel method to find appropriate epsilon for DBSCAN , ACIIDS 2010 Part I, pp.93­102.

          Soheil Hassas Yeganeh, Jafar Habibi, Hassan Abolhassani, Mahdi Abbaspour Tehrani, Jamshid Esmaelnezhad, An approximation algorithm for finding skeletal points for density based clustering approaches, Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM, part of the IEEE Symposium Series on Computational Intelligence, pp. 403-410, Nashville, TN, USA, 2009.

           
           

          Last Update January 2014

          http://www.ics.uci.edu/~jesmaeln/research.html Mahdi Abbaspour Tehrani

          Jamshid Esmaelnezhad

          Ph.D. Candidate
          University of California - Irvine

          • Home
          • Academic
          • Publication
          • Research
          • Teaching

          Research Experiences

          • Databases and Information Systems / Information Retrieval : Project: iPubMed, instant, error tolerant search on MEDLINE: Enabling TopK framework to perform phrase search. Work with Prof. Chen Li, Inci Cetindil, September 2012-March 2013

          • Databases and Information Systems / Information Retrieval : Project: iPubMed, instant, error tolerant search on MEDLINE : Instant Search Query Log Analysis. Work with Prof. Chen Li, Inci Cetindil, September 2011­ September 2012

          • Data Mining / Clustering : Designing and implementing an algorithm to approximate the necessary parameters for DBSCAN 2; published in the second addressed paper. Work with S. Hassas Yeganeh, July 2008­-June 2009.

          • Data Mining / Clustering : Implementing several clustering algorithms and adding them to Weka 1 in order to be able to compare them with the algorithm introduced in the first addressed paper. Work with S. Hassas Yeganeh, April 2007­-July 2008.

          • Artificial Intelligence / Planning : An investigation on State­Space and Plan­Space planning algorithms. August­September 2010.

          • Image Processing / Face Detection : Designing and implementing an algorithm to reduce the dimension of image space in order to ease its characterization. Work with H. Mohseni, July 2009­-January 2010.
           
           

          Last Update January 2014

          http://www.ics.uci.edu/~jesmaeln/teaching.html Mahdi Abbaspour Tehrani

          Jamshid Esmaelnezhad

          Ph.D. Candidate
          University of California - Irvine

          • Home
          • Academic
          • Publication
          • Research
          • Teaching

          Present

          • CS122B: Project in Databases and Web Applications Website

          Teaching and Work Experience

          • Instructor, CS122B: Project in Databases and Web Applications, Winter 2014
          • Software Engineering Internship at SRCH2, Summer and Fall 2013
          • Teaching Assistant, Spring 2012, Computer Networks
          • Teaching Assistant, Winter 2012, Project in Databases and Web Applications
          • Teaching Assistant, Fall 2012, Boolean Algebra and Logic
          • Reader, Spring 2011, Project in Databases and Web Applications
          • Reader, Winter 2011, Discrete Mathematics
          • Reader, Fall 2011, Discrete Mathematics
          • Member of maintenance team of Global Tesol website, August 2010­ August 2011.
          • Teaching Assistant, Object Oriented Programming (OOP)
          • Teaching Assistant, Advanced Concepts in Programming
          • Teaching Assistant, C/C++ Programming
           
           

          Last Update January 2014

          http://www.ics.uci.edu/~aregan/ Index of /~aregan

          Index of /~aregan

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          http://www.ics.uci.edu/~jnnorton/polyculturewebsite/ Sustainable Polycultures at UCI
          UCI Polycultures
          • About
          • Database
          • Composer
          • Contact

          Sustainable
          Polycultures

          Growing resources sustainably and building community.
          A UCI Informatics Project.

          About the project

          The Sustainable Polyculture project at UCI aims to help people in Southern California design sustainable polycultures for personal use. A sustainable polyculture is a mutually dependent group of perennial and self-seeding annual plants designed to thrive with little or no external inputs and provide significant amounts of human resources.

          We are working with local community members, including Eco-apprentices of The Ecology Center in San Juan Capistrano, and Agroecology researchers to identify what novice growers need to design and grow sustainable polycultures for personal use. In this pursuit, we have engaged community members in building a sustainable polyculture at our demonstration site at the UCI Arboretum.

          We have found that support of Information Technology would help novices understand, organize and visualize the complex relationships among plants that must be formed to create a sustainable polyculture. We are building a comprehensive database of plants suitable for this application in Southern California and a design tool called the Sustainable Polyculture Composer.

          This work is managed by UCI Ph.D. Student Juliet Norton and Professor Bill Tomlinson in partnership with Professor Don Patterson at Westmont College and Professor Sarah Taylor Lovell at University of Illinois Urbana-Champagne. This research is funded by the National Science Foundation Award # 14442749.

          Southern California Sustainable Polyculture Database

          The database is currently under development and is expected to be available Summer 2016. If you are interested in participating in the population of the database please contact us at polycultures@ics.uci.edu

          Sustainable Polyculture Composer

          The composer is currently under development and is expected to be available end of 2016. If you are interested in participating in piloting early prototypes please contact us at polycultures@ics.uci.edu

          Contact us

          polycultures@ics.uci.edu

          Copyright © Sustainable Polycultures at UCI 2016

          http://www.ics.uci.edu/~jnnorton/homepage/ Grounded Technologist
          Grounded Technologist
          • Home
          • About
          • Projects
            • Permaculture and Gardening
            • UCF Arboretum
            • Simple Living Institute
            • Academic Projects
            • Graduate Research @ UCI
            • Plant Guild Composer
            • Graduate Research @ UCF
            • Water's Journey Through the Everglades
            • Full Body Navigation Interfaces
            • Lunar Quest
            • Minds of Chimera
            • Undergraduate Research @ UCF
            • Lunar Lunge
            • M4: MOUT
            • MR Warehouse
            • MR Restaurant
            • Smash Me
          • Contact

          Juliet Norton

          This is the informational website of a grounded technologist.

          About

          Juliet is a grounded technologist. Learn about the journey that brought her to UC, Irvine as a doctoral student in Informatics researching local food resilience.

          Projects

          This grounded technologist uses her computing knowledge to make a positive environmental and social impact. Read about her activities both in the soil and on the computer.

          Contact

          email: jnnorton [at] uci [dot] edu

          Juliet Headshot

          About

          Juliet is a grounded technologist. In the Spring of 2013, a wise man asked Juliet why she was so motivated to live sustainably. Sure, her exposure to the natural wonders of North America as a youth helped, but she realized it is her deep sense of responsibility for her actions upon the earth and to her kin. It is because of this intrinsic interest in sustainability and wellbeing that she calls herself a grounded technologist.

          Juliet grew up in Florida and attended college at the University of Central Florida where she received a B.A. in Digital Media and later a M.S. in Computer Science. In her spare time she was found in the Arboretum on campus gardening, until one day she decided to make that her research. Around that time Juliet integrated into the Central Florida permaculture community and transformed her lifestyle in a soul satisfying way. Not only was she able to satisfy her desire to live sustainably, but she was a part of a collective of people who inspired her to reach out and "give back" to the world with the knowledge she had gained.

          Today Juliet lives in Irvine and attends the University of California in effort to address critical issues that affect the long term wellbeing of earth and humanity. As a student of Bill Tomlinson, a member of the GreenIT lab, and a participant in the on-campus Sustainability Initiative, Juliet is surrounded by inspiring and supportive people to help her achive her goal of making the world a happier, healthier place to live now and for future generations.

          Projects

          Permaculture and Gardening

          • UCF Arboretum
          • Simple Living Institute

          Graduate Research @ UCI

          • Plant Guild Composer

          Graduate Research @ UCF

          • Water's Journey Through the Everglades
          • Full Body Navigation Interfaces
          • Lunar Quest
          • Minds of Chimera

          Undergraduate Research @ UCF

          • Lunar Lunge
          • M4: MOUT
          • MR Warehouse
          • MR Restaurant
          • Smash Me

          © Juliet Norton 2013

          http://www.ics.uci.edu/~wdevanny/ics46/ ICS/CSE 46

          ICS/CSE 46: Data Structures Implementation and Analysis (Summer 2015)


          Instructors and Office Hours

          Instructor: William Devanny
          Office: DBH 4032
          Email: wdevanny@uci.edu
          Office Hours: Thursday 10:00am-12:00pm

          Teaching assistant: Sridevi Maharaj
          Office: DBH 4044
          Email: sridevi.m@uci.edu
          Office Hours: Tuesday 2:00-3:00pm

          Lectures, Labs, and Exams

          Lecture: M/W/F 1:00-1:50pm in PSCB 120
          Lab: M 2:00-5:00pm in ICS 364A
          Midterm: July 24th
          Final: August 28th 1:00-2:50pm in PSCB 140

          Course Resources

          There is no required course textbook for this class. The following are optional textbooks:

          • Data Structures and Algorithms in C++ by Goodrich, Tamassia, Mount
            Published through Wiley
          • Data Structures and Algorithm Analysis in C++ by Weiss
            Published through Prentice Hall
          • The Algorithm Design Manual by Skiena
            Available free through Springer
          • Algorithms and Data Structures: The Basic Toolbox by Mehlhorn, Sanders
            Available free through Springer

          Questions about course material are best asked in person either in class, in lab, or in office hours. You can also ask questions by emailing myself or the TA (preferably both). Please include "ICS 46" or "CSE 46" somewhere in the subject line of your email.

          Grading Policy

          Grades will be computed by a weighted combination of your programming projects, quizzes, midterm, and final:

          • Project 0 (2%)
          • Project 1-5 (28%)
          • Quizzes (10%)
          • Midterm (25%)
          • Final (35%)

          The letter grade conversions will be decided at the end of the course.

          Tentative Course Schedule

          Week 1 (June 22nd)
          • Course overview
          • Templates
          • Arrays
          • O-Notation
          • Basic search
          • Insertion sort
          • Merge sort
          Week 2 (June 29th)
          • Omega and theta notation
          • Comparison sorting
          • Sorting lowerbound and an alternate proof
          • Amortized analysis
          • Linked lists
          • Array-based (dynamic) lists
          Week 3 (July 6th)
          • Hash tables, maps, and sets
          • O(n) sorting
          • Bucket and radix sort
          • Binary trees
          • Traversing binary trees
          Week 4 (July 13th)
          • Binary search trees
          • AVL trees
          • Stacks
          • Queues
          Week 5 (July 20th)
          • Skip lists
          • Midterm exam
          Week 6 (July 27th)
          • Priority queues
          • Binary heaps
          • Heapsort
          • Graphs
          • Adjacent lists
          • Adjacency matrices
          • Multidimensional arrays in a single array
          • DFS
          • BFS
          Week 7 (August 3rd)
          • Dijksta's algorithm
          • Bellman-Ford
          Week 8 (August 10th)
          • P vs. NP
          • Traveling salesperson problem
          • Minimum spanning tree
          • Kruskal's Algorithm
          • Union find
          Week 9 (August 17th)
          • Digraphs
          • Strong connectivity
          • Tarjan's algorithm
          • DAGs
          • Topological sorting
          Week 10 (August 24th)
          • Bloom filters (not on final)
          • Final exam (Friday August 28th)

          Solutions

          • Quiz 1
          • Quiz 2
          • Quiz 3
          • Quiz 4
          • Midterm (blank)
          • Midterm
          • Quiz 5
          • Quiz 6
          • Quiz 7
          • Quiz 8

          Projects

          As the course title indicates this course is about both implementation and analysis. Most of lecture time will be devoted to the analysis of data structures and algorithms, while the projects will require you to work on implementing the data structures.

          We will have a total of six projects. The first project (Project 0) will be much shorter and cover setting up our environment.

          • Project 0: VirtualBox and Templates due on Friday June 26th at 11:59pm
          • Project 1: Sorting Lab due on Friday July 3rd at 11:59pm
          • Project 2: Following Wikipedia Links due on Friday July 17th at 11:59pm
          • Project 3: An AVL Tree due on Sunday August 2nd Friday July 31st at 11:59pm
          • Project 4: Finding Shortest Paths due on Friday August 14th at 11:59pm
          • Project 5: Approximating TSP due on Wednesday August 26th at 11:59pm

          Projects will be graded on a 40 point scale with approximately 30 points for correctness and 10 points for code style. Correctness covers did you met the project specifications and did your code compile and run without error. Coding style covers did you write readable, well documented code. We expect that you will be working in the virtual machine you set up in Project 0 for all of your projects. If your code does not compile and run with the provided build script on the provided platform, then it does not compile and run for the purposes of grading.

          Projects will all be due at 11:59pm on their due date. You will be allowed one 48 hour extension on a project. We will apply this policy to the first project you submit late. No other late work will be accepted. Projects can be submitted by first following the instructions in Project 0 to create a .tar.gz file and then handing it in to the appropriate EEE dropbox. Submissions by email will not be accepted. Please make sure you submit the correct version of your project. Accidentally submitting the wrong code is not an acceptable reason for a regrade.

          Academic Honesty

          The ICS policy on academic honesty can be found here.

          All of the work you complete in this class should be your own. Discussing course material at a high level is encouraged, however at no point should you share code with other students in any form. Plagarism detection software will be run on the submissions for each project. Additionally, answers on exams and quizzes should be entirely your own work. Exams will be closed-note and closed-book.

          Anyone who commits an academic honesty violation will be reported accordingly and subject to the penalties described in the above policy.

          Accommodations for Disabilities

          If you feel you need any accommodations based on a disability, please contact me and the Disabilities Services Center as soon as possibile to ensure the apporpriate accommodations can me made.

          Acknowledgements

          Portions of this course are based on similar courses by Michael Bannister, Richard Pattis, and Alex Thornton

          http://www.ics.uci.edu/~lalehj/projects.html Laleh Jalali
          • Home
          • Asthma Management
          • Objective Self
          • Social Life Networks

          Qualitative Causal Modeling

          Aim: Developing frameworks for qualitative causal modeling to discover potential causal patterns of the underlying phenomena for the purpose of understanding and planning using qualitative events rather than quantitative variables. dashboard

          We designed and developed a computational framework for causal modeling by combining top-down and bottom-up analyses on large-scale heterogeneous data. Our framework abstracts signals to conceptual events for a unified and qualitative data representation and it provides a powerful methodology for qualitative causal inference using a high-level declarative language. This language allows for formulation of complex patterns from the combination of a unique set of well-defined operators. Bottom-up operators bring hidden patterns to the surface and help analysts gain insight into the data. Top-down operators enable analysts to incorporate their knowledge as causal assumptions and evaluate their plausibility.

          Asthma Risk Factor Recognition

          A great body of research in medical science applies correlation detection techniques and regression models to conclude a positive or negative correlation between asthma attacks and individual risk factors. However, results are in form of correlation coefficients and p-values, which are not expressive enough beyond assessing strong or weak positive/negative correlations and certainly not adequate for designing complex predictive models. We show the applicability of our causal modeling framework in extracting expressive causal rules in the context of a healthcare application for asthma management. To elaborate the main idea behind our work, consider Figure below as an example of causal relation between multiple data streams in the asthma management application. The last data stream is measured asthma outbreak in Osaka city and other streams are measured by physical sensors in the same location containing air pressure, humidity, rain, solar radiation, sunshine, wind, PM2.5 (Particulate Matter 2.5), and temperature. These values are discretized and qualitative events are extracted. Once an asthma outbreak is detected, physical sensory data within a preceding Tw time window is analyzed to find a set of complex causal patterns that might have resulted in the burst.

          streams

          Objective Self : Beyond Quantified Self

          The quantified self movement is an important step in introducing a scientific framework to help understand an individual based on continuously collected data. However, a question constantly arises: how to use all the information beyond viewing the aggregated data in the form of graphs and then using them for self and societal insight? We introduce a human-centric data fusion technique using events. Life events encode activities of daily living and environmental events encode states and state transitions in environmental variables. Utilizing qualitative causality framework, we can build effective user models by harvesting significant patterns as sequential and parallel relations among events. We call such user model an Objective self. Objective self is the process of objectively measure physical, physiological, and mental activities of human being and understanding the causal relations between these activities. A high level architecture of the system is shown in the figure below.

          streams

          Social Life Networks

          By using the enormous reach of mobile phones equipped with myriads of sensors, combined with Internet of Things, and current powerful computing infrastructure, the next generation of social networks can be designed not only to connect people with other people, but to connect people with other people and essential life resources. This work builds towards a bigger ambitious umbrella of 'Social Life Networks'. More details is available in the papers below.

          streams
        • Ramesh Jain, Laleh Jalali, Siripen Pongpaichet, and Amarnath Gupta. "Building Social Life Networks", IEEE Data Engineering Bulletin, 2013. [PDF][BibTeX]
        • Ramesh Jain, David Sonnen. "Social Life Networks", IT Professional Magazine, 2011. [PDF][BibTeX]
        • Ramesh Jain, Vivek Singh, and Mingyan Gao. "Social Life Networks for Middle of Pyramid", International Conference on Advances in ICT for Emerging Regions, 2011.
        • http://www.ics.uci.edu/~lalehj/publications/ Index of /~lalehj/publications

          Index of /~lalehj/publications

          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  
          [   ]Complex Asthma Risk Factor Recognition.pdf09-Feb-2016 22:40 1.0M 
          [   ]IJCAI16-Jalali.pdf09-Feb-2016 22:40 2.5M 
          [   ]Jalalipdm09.pdf09-Feb-2016 22:40 1.0M 
          [   ]ObjectiveSelf-mm-21-4-Vision.pdf09-Feb-2016 22:40 4.0M 
          [   ]PDA@IoT2014.pdf09-Feb-2016 22:40 2.1M 
          [   ]W141-jalali.pdf09-Feb-2016 22:40 1.0M 
          [   ]acmmm15bni.pdf09-Feb-2016 22:40 1.2M 
          [   ]bigdata15.pdf09-Feb-2016 22:40 1.8M 
          [   ]bni03-jalali-longVersion.pdf09-Feb-2016 22:40 1.3M 
          [   ]bni03-jalaliATS.pdf09-Feb-2016 22:40 1.2M 
          [   ]co-occurrence-jalali.pdf09-Feb-2016 22:40 593K 
          [   ]p91-SLN.pdf09-Feb-2016 22:40 842K 
          [   ]p201-Dao.pdf09-Feb-2016 22:40 1.8M 
          [   ]p1329-jalali.pdf09-Feb-2016 22:40 874K 
          [   ]sln-jain.pdf09-Feb-2016 22:40 1.2M 
          [   ]smartnoti.pdf09-Feb-2016 22:40 777K 

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          http://luci.ics.uci.edu/LUCIinterface.html LUCI: The Laboratory for Ubiquitous Computing and Interaction

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          http://www.ics.uci.edu/~hstrong/2015/04/03/hello/ Good day · Homer Strong

          Homer Strong

          Statistics PhD student at University of California, Irvine

          Home About GitHub Currently v0.0.2

          © 2015. All rights reserved.

          Good day

          03 Apr 2015

          I'm pretty busy at the moment but if you'd like to get in touch then you can reach me via hstrong at uci.edu

          Currently I'm affiliated with the following groups and projects.

          • Professor Padhraic Smyth's DataLab
          • Cosmic RAys Found In Smartphones, CRAYFIS
          • The UCI Data Science Initiative
          • Data Team at Yieldbot, Inc

          Related Posts

          http://www.ics.uci.edu/~hstrong/about/ About · Homer Strong

          Homer Strong

          Statistics PhD student at University of California, Irvine

          Home About GitHub Currently v0.0.2

          © 2015. All rights reserved.

          About

          My interests include statistics, machine learning, distributed systems, information visualization, and functional programming.

          I hail from western Colorado. My undergraduate degree in Mathematics is from Reed College in Portland, Oregon. During college I studied abroad through the Budapest Semesters in Mathematics program. Between schools I worked in the private sector, notably co-founding a natural language analytics startup, Lucky Sort.

          http://www.ics.uci.edu/~dramanan/ Please see my new page at http://www.cs.cmu.edu/~deva/ http://www.ics.uci.edu/~akhavans/mySite/Welcome.html Sepehr Akhavan
           

          CV

           
           

          Sepehr Akhavan

           
           

          I am a statistician specializing in Biostatistics and Data Visualization techniques.I got my masters degree in Statistics from University of California Irvine in 2011.

          I have the honor of doing research with Dr.Gillen. My current research focuses on developing a flexible Bayesian survival model with the time varying effect of time-dependent variables.  

          I also collaborate with department of Epidemiology at UC Irvine. I had the honor of working with Dr. Anton-Culver and Dr. Ziogas. My research in Epidemiology was primarily focused on:


          1. ✓ Developing a Bayesian model to discover significant pathways in Breast Cancer. Our method was based on Baurely et all’s method. (to be submitted)


          1. ✓Working on Genome-Wide Association Studies (GWAS) and methods for          investigating gene-environment interaction. Particularly, we were interested to investigate the association of 238 SNPs from 23 Interleukin with Breast Cancer risk.


          I am also part of the data analysis group at MIND Research Institute. My job is to investigate the efficacy of the supplemental math software developed at MIND. Currently, more than 320,000 students are using our software. Everyone of those 320,000 students can be followed through our database providing a wealth of information to be used to investigate the efficacy of the software. I’m also responsible for using/creating data visualizing techniques to visualize the effect of the software on students’ math scores.

           

          Learn Data Analysis

          I am co-founder and CEO at LearnDataAnalysis. LearnDataAnalysis is a phenomenal place for everyone to share content related to the field of data analysis from various different perspectives and from anywhere on the globe. LDA gives everyone a phenomenal opportunity to share her/his ideas on the web and to get feedback from those who are expert and interested in this field.

           
           
          http://www.ics.uci.edu/~willmlam/teaching/ Teaching
          • Home
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          William Lam

          Information

          PhD Candidate
          Computer Science
          UC Irvine
          Office: DBH 4099
          will...@ics.uci.edu
          PGP Key

          TA Appointments

          ICS/CSE 21 Introduction to Computer Science I Spring 2012
          CS 175 Project in Artificial Intelligence Winter 2012
          ICS/CSE 21 Introduction to Computer Science I Fall 2011
          CS 175 Project in Artificial Intelligence Spring 2011
          ICS/CSE 22 Introduction to Computer Science II Winter 2011
          ICS/CSE 21 Introduction to Computer Science I Fall 2010

          © 2015 William Lam

          http://www.ics.uci.edu/~willmlam/schedule/ Schedule
          • Home
          • Courses
          • Teaching
          • Schedule

          William Lam

          Information

          PhD Candidate
          Computer Science
          UC Irvine
          Office: DBH 4099
          will...@ics.uci.edu
          PGP Key

          © 2015 William Lam

          http://www.ics.uci.edu/~willmlam/courses/ Courses
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          • Courses
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          William Lam

          Information

          PhD Candidate
          Computer Science
          UC Irvine
          Office: DBH 4099
          will...@ics.uci.edu
          PGP Key

          Coursework Taken

          CS 211A Visual Computing
          CS 216 Image Understanding
          CS 230 Distributed Computer Systems
          CS 260 Fundamentals of the Design and Analysis of Algorithms
          CS 271 Artificial Intelligence
          CS 273A Machine Learning
          CS 274A Probabilistic Learning: Theory and Algorithms
          CS 274B Learning in Graphical Models
          CS 274C Neural Networks and Deep Learning
          CS 275 Constraint Networks
          CS 276 Belief Networks
          CS 295 Reasoning and Modeling with Graphical Models
          ICS 200 Seminar in Research in ICS
          ICS 398A Teaching Assistant Seminar

          © 2015 William Lam

          http://www.ics.uci.edu/~vpalepu/archives.html Archives - Vijay Krishna Palepu
          Research Projects News Notes CV
          / Archives
          Spider SENSE: Software-Engineering, Networked, System Evaluation | 07 Oct 2015
          publication


          paper
          Spider SENSE
          Revealing Runtime Features and Constituent Behaviors within Software | 07 Oct 2015
          publication


          paper
          Cerebro
          Attended VISSOFT 2015 | 01 Oct 2015
          news VISSOFT15 conference Software Visualizations Research

          VISSFOT 2015 was held in Bremen, Germany this year and it turned out to be an interesting time there for me! I presented two works around novel visualizations and software research, and got some great appreciation and feedback for it! Despite being a young community, I found the conference engaging and I am evermore enthusiastic about the role of visualizations in the area of software engineeri...

          Summer Internship at Microsoft | 15 Sep 2015
          news Microsoft internship engineering

          I spent a set of 12 amazing weeks this summer at Redmond, Washington, where I worked as an engineering intern with the Word engineering team at Microsoft. Aside from the challenging work, it was nice to work and have fun with a great set of people! And it was not just work, but I also had a time of my life while enjoying great Washington weather, a Marron 5 rock concert, a hike upto 6,800 ft...

          Part of Winning Team @ UCI Data Science Hackathon | 11 Jan 2015
          news hackathon data science
          PL241-Compiler | 11 Dec 2014
          project compiler SSA optimization code-generation program-analysis

          The PL241-Compiler is an SSA-based optimizing compiler that supports copy propagation, common subexpression elimination, constant folding, register allocation and code generation for the DLX (pronounced ‘Deluxe’) RISC processor architecture. I wrote the compiler as a part of a graduate course at UC Irvine. It was both a challenging and extremely useful course and compiler to work on. In fact, ...

          Advanced To Candidacy | 11 Dec 2014
          news PhD exam Software Engineering

          I am happy to report that I have passed the Advancement to Candidacy exam, the second milestone towards my PhD. Still a long way to go, but there is now one less thing to worry about! Also it seems, that now I get to call my self a Ph.D. Candidate at UC Irvine. The Advancement to Candidacy exam is the second milestone for a Ph.D. student in the Software Engineering Program at UC Irvine. It req...

          New Ideas Talk at ASE 2014 | 06 Oct 2014
          news ASE14 conference Software Engineering Research
          New Ideas Paper accepted at ASE 2014 | 02 Jul 2014
          news ASE14 dynamic analysis paper acceptance SpiderLab

          New Ideas Paper accepted at ASE 2014

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Discriminating Influences among Instructions in a Dynamic Slice | 02 Jul 2014
          publication


          paper
          Discriminating Dynamic Influences
          Talk at AIT, UoP | 30 Dec 2013
          news talk AIT Software Engineering PhD
          Software Engineering Phase 2 Exam Cleared | 19 Dec 2013
          news PhD exam Software Engineering

          The Phase 2 exam is the first milestone for a Ph.D. student in the Software Engineering Program at UC Irvine. It entails the reading of 62 research papers from all fields of software engineering and finally a 6 hour long essay-based exam that tests the examinees about the different aspects of software engineering as a field of practice and research, how it has progressed over the years, the ma...

          Technical Research Talk at ASE 2013 | 14 Nov 2013
          news ASE13 conference Software Engineering Research

          Technical Research Talk at ASE 2013

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Latex Random Conjunctions | 31 Aug 2013
          project latex conjunctions

          {Distracted by thinking of alternate ways of saying things that like “Moreover” and “Therefore”, in your scientific writing? Don’t be! You can now focus on the stuff that really matters and leave the burden of figuring out the new ways to say some common place words with this latex package. Code: https://github.com/VijayKrishna/latex-random-conjunctions

          Analyzing F.R.I.E.N.D.S. | 31 Aug 2013
          project text-processing F.R.I.E.N.D.S. html-processing

          This is a product of being a big fan of F.R.I.E.N.D.S. and a software engineer. :) I want to see if i can find something interesting by running some basic text analysis and statistics on the transcripts of the episodes. questions that i am trying to answer: - Who speaks the most? - Summaries of individual episodes? - Summaries of what each F.R.I.E.N.D.S. said? Code: https://github.com/Vijay...

          Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries | 23 Jul 2013
          publication


          paper
          Dynamic Dependence Summaries
          Dynamic Summaries paper accepted at ASE 2013 | 23 Jul 2013
          news ASE13 dynamic analysis paper acceptance SpiderLab

          My paper with Prof. Xu and Prof Jones, titled: “Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries”, was accepted at the technical research track at ASE 2013! This work describes a novel technique to summarize the API method behavior in terms of the dynamic dependence relations observed therein.

          Visualizing Constituent Behaviors within Executions | 19 Jul 2013
          publication


          paper
          The Brain
          The Brain accepted at VISSOFT NIER 2013 | 19 Jul 2013
          news VISSOFT13 Visualization paper acceptance SpiderLab

          Our paper titled: “Visualizing Constituent Behaviors within Executions”, that describes the visulization behind the early prototype that we created called THE BRAIN, was accepted at the NIER paper track at VISSOFT 2013! A draft version of the paper is available here.

          Johnny Takes on Stats | 10 Jun 2013
          project statistics Class Project Quantitative Methods hypothesis testing t-Test

          Live demo: http://www.ics.uci.edu/~vpalepu/205project The purpose of this interactive narrative-based tutorial on statistical methods is to establish a simplified way for students to learn statistical concepts (specifically Hypothesis testing with t-tests) that are grounded in real-life examples. In addition to a narrative, an interface in presented that allows students to interact with data s...

          The Brain - Prototype | 31 May 2013
          project SpiderLab ISR2013 Program-Execution Visualization
          Attending the ISR Forum 2013 | 31 May 2013
          news Software Engineering Research ISR2013 UCI

          Attending the ISR Forum 2013

          — Vijay Krishna Palepu (@vkrishnapalepu)

          Attending the ISR Forum 2013

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Attending ICSE 2013 | 23 May 2013
          news ICSE13 conference Software Engineering Research

          Attending ICSE 2013

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Trendy bugs: Topic trends in the Android bug reports | 03 Jun 2012
          publication


          paper
          Trendy Bugs
          Attending ICSE and MSR 2012 | 02 Jun 2012
          news ICSE '12 conference MSR '12 event

          Attending ICSE and MSR 2012

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Attending the ISR Forum 2012 | 17 May 2012
          news event ISR '12

          Attending the ISR Forum 2012

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Lambda Calculus Interpreter in Scheme | 16 Apr 2012
          project scheme lambda-calculus interpreter

          So this was a cool class project that I worked on where we were to implement a lambda calculus interpreter in any language of our choice. It just seemed too natural to do this in Scheme. :) It attempts to implement both alpha and beta reductions. The following code for the interpreter is also available as a gist, with test cases: https://gist.github.com/VijayKrishna/5180292.js ;;UCI Class P...

          Trendy Bugs: Topic Trends in the Android Bug Reports | 31 Mar 2012
          project Class Project Information Retrieval and Classification MSR '12

          Studying vast volumes of bug and issue discussions can give an understanding of what the community has been most concerned about, however the magnitude of documents can overload the analyst. We present an approach to analyze the development of the Android open source project by observing trends in the bug discussions in the Android open source project public issue tracker. This informs us of th...

          Plug & Play Models for Video Game Systems | 31 Mar 2012
          project Class Project Requirements Engineering for Games

          Developed a set of Plug & Play Models for Video Game systems. These models were developed with the aim of assisting game designers and engineers to identify the baseline requirements in their games which they can then modify to the specific requirements of the game that they are developing. The fact that there exists a common feature set across all games that are or may exist, is leveraged ...

          Dynamic Program Analyzer for Java Bytecode | 31 Mar 2012
          project Class Project Dynamic Program Analysis

          Developed a framework for dynamic program analysis for Java byte code using ASM. The framework instruments the bytecode for a given Java system/project and performs analysis to detect the data and control dependencies between statements of source code.

          Trendy Bugs accepted at MSR Challenge 2012 | 16 Mar 2012
          news MSR '12 MSR Challenge '12 paper acceptance

          Trendy Bugs accepted at MSR Challenge 2012

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Accepted at UCI for the Software Engineering Ph.D. | 16 Mar 2012
          news UCI acceptance phd

          Accepted at UCI for the Software Engineering Ph.D.

          — Vijay Krishna Palepu (@vkrishnapalepu)
          Web User Interface Wireframe of U.S. Presidential Election Board Game | 31 Dec 2011
          project Class Project HCI

          Developed wireframe of a web based user interface of an existing board game based on the U.S. Presidential Elections as a part of a 6 man team. Conducted Cognitive Walkthroughs and Heuristic Evaluations for the wireframes developed. The scope of the wireframes was limited to the first two phases, i.e. the Primaries and Conventions. The objective of the project was to take the first steps in de...

          Java Code to Flow Diagram Converter | 31 Mar 2010
          project Undergrad Final Year Project Static Proram Analysis

          Developed an Eclipse Plug-in to statically reverse engineer a selected Java code snippet, to corresponding Sequence Diagram. Recursive parsing of Abstract Syntax Tree of code was done to derive chronological ordering of method invocations, thus accurately depicting Program Flow.

          Mobile And Online Voting System | 30 Apr 2009
          project Imagine Cup '09

          Developed the software design, with a working prototype, for electoral voting via a seamless integration of both web and cellular technologies. Objective was to solve the toughest problems of mankind using technology. Involved intensive requirement analysis and research of target users, to identify a system required to meet that objective. Interviewed user groups from urban and rural background...

          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/index.html Vijay Krishna Palepu
          Research Projects News Notes CV
          Vijay Krishna Palepu • vpalepu [at] uci [dot] edu • 5243 Bren Hall, Spider Lab, University of California, Irvine, CA 92697-3440
          Vijay Krishna

          I am a Ph.D. Candidate in Software Engineering, at the Department of Informatics, University of California, Irvine. I graduated, my undergrad Bachelor of Engineering (B.E.) program, with a First Class with Distinction in Computer Engineering from the University of Pune, India.

          As a student of Software Engineering Research, I am interested in building, visualizing and studying reusable models of the runtime behavior of software components that can be leveraged for a number of software development, maintenance, and testing tasks. My current focus, however, is to facilitate software debugging which is a complex and ubiquitous activity within software engineering.

          My advisor is Prof. James A. Jones at the Spider Lab. James' research is focused towards software testing, analysis (compile-time and run-time) and debugging.

          @bitbucket.com • @Linkedin • @Instagram • @medium • @stackoverflow • @gist.github.com
          Background Photo: Mt. Ranier, © Vijay Krishna Palepu, 2015
          Latest
          • 01 Oct 2015 » Attended VISSOFT 2015 VISSOFT15 conference Software
          • 15 Sep 2015 » Summer Internship at Microsoft Microsoft internship engineering
          • 11 Jan 2015 » Part of Winning Team @ UCI Data Science Hackathon hackathon data science
          CEREBRO
          Hosted at: http://spideruci.github.io/cerebro
          Cerebro, formerly known as "The Brain", reveals clusters of source code that co-execute to produce behavioral features of the program throughout and within executions. We created a clustered visualization of source-code that is informed by dynamic control flow of multiple executions; each cluster represents commonly interacting logic that composes software features. In addition, we render individual executions atop the clustered multiple-execution visualization as user-controlled animations to reveal characteristics of specific executions—these animations may provide exemplars for the clustered features and provide chronology for those behavioral features, or they may reveal anomalous behaviors that do not fit with the overall operational profile of most executions. Both the clustered multiple-execution view and the animated individual-execution view provide insights for the constituent behaviors within executions that compose behaviors of whole executions. Inspired by neural imaging of human brains of people who were subjected to various external stimuli, we designed and implemented Cerebro to reveal program activity during execution. The result has revealed the principal behaviors of execution. Those behaviors were revealed to be (in some cases) cohesive, modular source-code structures and (in other cases) cross-cutting, emergent behaviors involving multiple modules.
          Research Publications ↵
          • Reddy, Nishaanth H.; Kim, Junghun; Palepu, Vijay Krishna and Jones, James, "Spider SENSE: Software-Engineering, Networked, System Evaluation," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-5, 27-28 September 2015. [paper] [website]
          • Palepu, Vijay Krishna and Jones, James, "Revealing Runtime Features and Constituent Behaviors within Software," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-10, 27-28 September 2015. [paper] [website]
          • Palepu, Vijay Krishna and Jones, James, "Discriminating Influences among Instructions in a Dynamic Slice," , 2014 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), to appear, 15-19 September 2014. [paper] [slides]
          • Palepu, Vijay Krishna; Xu, Guoqing and Jones, James, "Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries," , 2013 28th IEEE International Conference on Automated Software Engineering (ASE), pp.59-69, 11-15 November 2013. [paper] [slides]
          • Palepu, Vijay Krishna and Jones, James, "Visualizing Constituent Behaviors within Executions," , 2013 1st IEEE International Working Conference on Software Visualization (VISSOFT), pp.1-4, 27-28 September 2013. [paper] [vimeo]
          • Martie, Lee; Palepu, Vijay Krishna; Sajnani, Hitesh and Lopes, Cristina, "Trendy bugs: Topic trends in the Android bug reports," , 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp.120-123, 2-3 June 2012. [paper] [slides]
          Teaching
          • Reader, Senior Design Project (UCI, Spring 2012)
          • Reader, Concepts in Programming Languages II (UCI, Spring 2012)
          • Teaching Assistant, Senior Design Project (UCI, Fall 2012, Winter 2013, Spring 2013)
          • Guest Speaker on Testing Software Behavior, Graduate course on Software Testing and Analysis (UCI, Spring 2014)
          • Guest Speaker on QA and Testing, Introduction to Software Engineering (UCI, Summer 2013, Summer 2014)
          • Guest Speaker on Software Testing, Project in Software Engineering (UCI, Winter 2015)
          Talks
          • "Testing Software", Guest Lecture, Undergraduate course on Project in Software Engineering (UCI, Winter 2015) [slides]
          • "Discriminating Influences among Instructions in a Dynamic Slice", New Ideas Talk, Automated Software Engineering (Sweden, Summer 2014) [slides] [vimeo]
          • "QA and Software Testing", Guest Lecture, Undergraduate course on Introduction to Software Engineering (UCI, Summer 2014)
          • "Testing and Verifying Software Behavior", Guest Lecture, Graduate course on Software Testing and Analysis (UCI, Spring 2014) [notes]
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/2015/10/01/Attended-VISSOFT-2015.html Attended VISSOFT 2015
          Research Projects News Notes CV
          / News / Attended VISSOFT 2015
          Attended VISSOFT 2015
          Date: 01 Oct 2015

          VISSFOT 2015 was held in Bremen, Germany this year and it turned out to be an interesting time there for me! I presented two works around novel visualizations and software research, and got some great appreciation and feedback for it! Despite being a young community, I found the conference engaging and I am evermore enthusiastic about the role of visualizations in the area of software engineering and research.

          The two papers on Cerebro and Spider SENSE, can be found here and here respectively.

          As I participated (far right) in a panel discussion on visualizing software runtime after my talk on Cerebro: VISSOFT

          And like with every conference, I do like to take a walk around a foreign town. :)

          The architecture in Bremen’s old town is beautiful: bremen-2

          And the Cathedral in Bremen, Germany … is breathtaking: bremen


          Tags: VISSOFT15 conference Software Visualizations Research

          Related Posts

          • 07 Oct 2015 » Spider SENSE: Software-Engineering, Networked, System Evaluation
          • 07 Oct 2015 » Revealing Runtime Features and Constituent Behaviors within Software
          • 15 Sep 2015 » Summer Internship at Microsoft
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/cv.html CV - Vijay Krishna Palepu
          Research Projects News Notes CV
          / Vijay Krishna Palepu • vpalepu [at] uci [dot] edu • 5243 Bren Hall, Spider Lab, University of California, Irvine, CA 92697-3440 CV Resume
          EDUCATION
          • University of California, Irvine, USA, September 2012 - Current
            Ph.D., Software Engineering, Current GPA: 3.99
          • University of Pune, India, August 2006 - July 2010
            B.Engg., Computer Engineering, First Class with Distinction
          EXPERIENCE
          • Microsoft, June 2015 - September 2015, Redmond, WA, USA,
            Software Engineering Intern.
            • Worked on the Microsoft Word application.
            • Languages and Tools: C++.
          • Spider Lab, University of California, Irvine, June 2012 - Current, Irvine, CA, USA,
            Graduate Student Researcher.
            • Working on novel approaches to visualize, analyze and model program executions.
            • Developed software infrastructure for the instrumentation, analysis and exploration of Java program executions.
            • Languages and Tools: Java, ASM Bytecode Manipulation Library (asm.ow2.org), Javascript, D3.js.
          • University of California, Irvine, January 2012 - March 2012, Irvine, CA, USA,
            Graduate Student Researcher.
            • Worked on a design project for an internet game based on the U.S. Presidential Elections.
            • Languages and Tools: HotGloo Wireframing tool.
          • Indigo Architects, August 2010 - June 2011, Pune, MH, India,
            Software Developer.
            • Part of the Operations team of Zeus Travel Office Product Team. Built applications to monitor product behavior.
            • Languages and Tools: C#, XAML, Silverlight, WCF.
          • Persistent Systems, October 2009 - March 2010, Pune, MH, India,
            Project Intern.
            • Developed an Eclipse Plug-in to statically reverse engineer a Sequence Diagram for Java Projects to aid code comprehension. Final Year Project at University of Pune, Army Institute of Technology.
            • Languages and Tools: Java, Eclipse Standard Widget Toolkit, Eclipse Java Development Tooling (JDT).
          RESEARCH PUBLICATIONS ↵
          • Reddy, Nishaanth H.; Kim, Junghun; Palepu, Vijay Krishna and Jones, James, "Spider SENSE: Software-Engineering, Networked, System Evaluation," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-5, 27-28 September 2015. [paper] [website]
          • Palepu, Vijay Krishna and Jones, James, "Revealing Runtime Features and Constituent Behaviors within Software," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-10, 27-28 September 2015. [paper] [website]
          • Palepu, Vijay Krishna and Jones, James, "Discriminating Influences among Instructions in a Dynamic Slice," , 2014 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), to appear, 15-19 September 2014. [paper] [slides]
          • Palepu, Vijay Krishna; Xu, Guoqing and Jones, James, "Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries," , 2013 28th IEEE International Conference on Automated Software Engineering (ASE), pp.59-69, 11-15 November 2013. [paper] [slides]
          • Palepu, Vijay Krishna and Jones, James, "Visualizing Constituent Behaviors within Executions," , 2013 1st IEEE International Working Conference on Software Visualization (VISSOFT), pp.1-4, 27-28 September 2013. [paper] [vimeo]
          • Martie, Lee; Palepu, Vijay Krishna; Sajnani, Hitesh and Lopes, Cristina, "Trendy bugs: Topic trends in the Android bug reports," , 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp.120-123, 2-3 June 2012. [paper] [slides]
          PROJECTS ↵
          • The Brain - Prototype An online HTML5 application that can be used to visualize the execution of a single run of a sample Java program.
          • Trendy Bugs: Topic Trends in the Android Bug Reports Modeling discussions between developers in the Android Open Source Project's Public Issue tracker.
          • Dynamic Program Analyzer for Java Bytecode Runtime program analysis framework via Java byte code instrumentation using ASM (asm.ow2.org).
          • PL241-Compiler SSA-based optimizing compiler that supports copy propagation, common subexpression elimination.
          • Johnny Takes on Stats Interactive, narrative-based, tutorial for students to learn statistical concepts (specifically Hypothesis testing with t-Tests).
          • Lambda Calculus Interpreter in Scheme Lamba Calculus interpreter in Scheme.
          • Plug & Play Models for Video Game Systems Start up kit for carrying out Requirements engineering for Video Games.
          • Web User Interface Wireframe of U.S. Presidential Election Board Game UI wireframe for an online game based on the U.S. Presidential Elections.
          TEACHING
          • Reader, Senior Design Project (UCI, Spring 2012)
          • Reader, Concepts in Programming Languages II (UCI, Spring 2012)
          • Teaching Assistant, Senior Design Project (UCI, Fall 2012, Winter 2013, Spring 2013)
          • Guest Speaker on Testing Software Behavior, Graduate course on Software Testing and Analysis (UCI, Spring 2014)
          • Guest Speaker on QA and Testing, Introduction to Software Engineering (UCI, Summer 2013, Summer 2014)
          • Guest Speaker on Software Testing, Project in Software Engineering (UCI, Winter 2015)
          TALKS
          • "Testing Software", Guest Lecture, Undergraduate course on Project in Software Engineering (UCI, Winter 2015) [slides]
          • "Discriminating Influences among Instructions in a Dynamic Slice", New Ideas Talk, Automated Software Engineering (Sweden, Summer 2014) [slides] [vimeo]
          • "QA and Software Testing", Guest Lecture, Undergraduate course on Introduction to Software Engineering (UCI, Summer 2014)
          • "Testing and Verifying Software Behavior", Guest Lecture, Graduate course on Software Testing and Analysis (UCI, Spring 2014) [notes]
          • "Quality-Driven Development: Interacting with TDD and BDD", Invited Talk with Dr. Hadar Ziv (Southern California Quality Assurance Association, Orange County, Winter 2014) [slides]
          • "Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries", Technical Research Talk, Automated Software Engineering (Palo Alto, USA, Fall 2013) [slides]
          • "Interviews in User Centered Software Engineering", Lecture, Senior Design Project Class (UCI, Spring 2013) [presentation] [slides]
          • "Code Demo on Test Driven Development", Lecture, Senior Design Project Class (UCI, Fall 2012) [git]
          • "Goals in Requirements Engineering", Guest Lecture, Senior Design Project Class (UCI, Spring 2012) [slides]
          AWARDS
          • SIGSOFT Travel Grant, ACM, 2014.
          • Informatics Fellowship, Department of Informatics, University of California, Irvine, 2013.
          • AGS Travel Grant, University of California, Irvine, 2013.
          • Chair's Award, Department of Informatics, University of California, Irvine, 2012.
          • Bronze Medal and Scholarship Award, Department of Computer Sciences, Army Institute of Technology, University of Pune, 2011.
          PROFESSIONAL AFFILIATIONS
          • Student member of the Association for Computing Machinery (ACM) and the Special Interest Group on Software Engineering (ACM SIGSOFT).
          • Student member of the Institute for Software Research (ISR), University of California, Irvine.
          GRADUATE COURSE WORK

          Software Engineering; User Interface Design and Evaluation; Research Methods in Infromatics; Requirements Engineering; Information Retrieval; Software Performance and Reliability; Analysis of Programming Languages; Machine Learning; Software Architecture; Software Testing and Analysis; Quantitative Methods; Advanced Compiler Construction.

          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/news.html News - Vijay Krishna Palepu
          Research Projects News Notes CV
          / News
          • 01 Oct 2015 » Attended VISSOFT 2015
          • 15 Sep 2015 » Summer Internship at Microsoft
          • 11 Jan 2015 » Part of Winning Team @ UCI Data Science Hackathon
          • 11 Dec 2014 » Advanced To Candidacy
          • 06 Oct 2014 » New Ideas Talk at ASE 2014
          • 02 Jul 2014 » New Ideas Paper accepted at ASE 2014
          • 30 Dec 2013 » Talk at AIT, UoP
          • 19 Dec 2013 » Software Engineering Phase 2 Exam Cleared
          • 14 Nov 2013 » Technical Research Talk at ASE 2013
          • 23 Jul 2013 » Dynamic Summaries paper accepted at ASE 2013
          • 19 Jul 2013 » The Brain accepted at VISSOFT NIER 2013
          • 31 May 2013 » Attending the ISR Forum 2013
          • 23 May 2013 » Attending ICSE 2013
          • 26 Feb 2013 » Judging 6th Grade Science Projects at the IUSD Science Fair 2013
          • 02 Jun 2012 » Attending ICSE and MSR 2012
          • 17 May 2012 » Attending the ISR Forum 2012
          • 16 Mar 2012 » Trendy Bugs accepted at MSR Challenge 2012
          • 16 Mar 2012 » Accepted at UCI for the Software Engineering Ph.D.
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/2015/09/15/Summer-Internship-Microsoft.html Summer Internship at Microsoft
          Research Projects News Notes CV
          / News / Summer Internship at Microsoft
          Summer Internship at Microsoft
          Date: 15 Sep 2015

          I spent a set of 12 amazing weeks this summer at Redmond, Washington, where I worked as an engineering intern with the Word engineering team at Microsoft.

          Microsoft

          Aside from the challenging work, it was nice to work and have fun with a great set of people! And it was not just work, but I also had a time of my life while enjoying great Washington weather, a Marron 5 rock concert, a hike upto 6,800 ft. along the slope of Mount Rainier, a visit to the original Starbucks store and numerous walks through the lovely streets of Seattle! It has been a time to remember.

          Mount Rainier Starbucks


          Tags: Microsoft internship engineering

          Related Posts

          • 07 Oct 2015 » Spider SENSE: Software-Engineering, Networked, System Evaluation
          • 07 Oct 2015 » Revealing Runtime Features and Constituent Behaviors within Software
          • 01 Oct 2015 » Attended VISSOFT 2015
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/research.html Research - Vijay Krishna Palepu
          Research Projects News Notes CV
          / Research
          Summary

          As a software engineer, I am principally interested in improving the methods and techniques for software debugging (i.e. finding and fixing software faults). I am particularly interested in developing the facilities and theory to enable software developers better understand, locate and fix software faults. My research currently looks at building, visualizing and studying models of runtime behavior of software components in order to facilitate software debugging which is a complex and ubiquitous activity within software engineering.

          Models for studying runtime behavior of software often suffer from information overload given that even moderately sized software programs, which typically range from 10–100KLOC, often encompass millions of runtime actions that need to be accounted for. My work looks at solutions that facilitate analysis and comprehension of software executions in ways that combat information overload by (a) abstracting or summarizing program behavior and (b) globally visualizing program executions.

          Following are brief descriptions of the projects I have worked on.

          Spider SENSE
          Spider SENSE Today, many of the research innovations in software visualization and comprehension are evaluated on small-scale programs in a way that avoids actual human evaluation, despite the fact that these techniques are designed to help programmers develop and understand large and complex software. The investments required to perform such human studies often outweigh the need to publish. As such, the goal of this work (and toolkit) is to enable the evaluation of software visualizations of real-life software systems by its actual developers, as well as to understand the factors that influence adoption. The approach is to directly assist practicing software developers with visualizations through open and online collaboration tools. The mechanism by which we accomplish this goal is an online service that is linked through the projects' revision-control and build systems. We are calling this system Spider SENSE, and it includes web-based visualizations for software exploration that is supported by tools for mirroring development activities, automatic building and testing, and automatic instrumentation to gather dynamic-analysis data. In the future, we envision the system and toolkit to become a framework on which further visualizations and analyses are developed. Spider SENSE is open-source and publicly available for download and collaborative development.

          Reddy, Nishaanth H.; Kim, Junghun; Palepu, Vijay Krishna and Jones, James, "Spider SENSE: Software-Engineering, Networked, System Evaluation," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-5, 27-28 September 2015. [paper] [website]
          Cerebro
          Cerebro Software engineers organize source code into a dominant hierarchy of components and modules that may emphasize various characteristics over runtime behavior. In this way, runtime features may involve cross-cutting aspects of code from multiple components, and some of these features may be emergent in nature, rather than designed. Although source-code modularization assists software engineers to organize and find components, identifying such cross-cutting feature sets can be more difficult. This work presents a visualization that includes a static (i.e., compile-time) representation of source code that gives prominence to clusters of cooperating source-code instructions to identify dynamic (i.e., runtime) features and constituent behaviors within executions of the software. In addition, the visualization animates software executions to reveal which feature clusters are executed and in what order. The result has revealed the principal behaviors of software executions, and those behaviors were revealed to be (in some cases) cohesive, modular source-code structures and (in other cases) cross-cutting, emergent behaviors that involve multiple modules. In this paper, we describe our system (Cerebro), envisage the uses to which it can be put, and evaluate its ability to reveal emergent runtime features and internal constituent behaviors of execution. We found that: (1) the visualization revealed emergent and commonly occuring functionalities that cross-cut the structural decomposition of the system; (2) four independent judges generally agreed in their interpretations of the code clusters, especially when informed only by our visualization; and (3) interacting with the external interface of an application while simultaneously observing the internal execution facilitated localization of code that implements the features and functionality evoked externally.

          Palepu, Vijay Krishna and Jones, James, "Revealing Runtime Features and Constituent Behaviors within Software," , 2015 3rd IEEE Working Conference on Software Visualization (VISSOFT), pp.1-10, 27-28 September 2015. [paper] [website]
          Discriminating Dynamic Influences
          Discriminating Dynamic Influences Dynamic slicing is an analysis that operates on program execution models (e.g., dynamic dependence graphs) to sup- port the interpreation of program-execution traces. Given an execution event of interest (i.e., the slicing criterion), it solves for all instruction-execution events that either affect or are affected by that slicing criterion, and thereby reduces the search space to find influences within execution traces. Unfortunately, the resulting dynamic slices are still often prohibitively large for many uses. Despite this reduction search space, the dynamic slices are often still prohibitively large for many uses, and moreover, are provided without guidance of which and to what degree those influences are exerted. In this work, we present a novel approach to quan- tify the relevance of each instruction-execution event within a dynamic slice by its degree of relative influence on the slicing criterion. As such, we augment the dynamic slice with dynamic-relevance measures for each event in the slice, which can be used to guide and prioritize inspection of the events in the slice.

          Palepu, Vijay Krishna and Jones, James, "Discriminating Influences among Instructions in a Dynamic Slice," , 2014 29th IEEE/ACM International Conference on Automated Software Engineering (ASE), to appear, 15-19 September 2014. [paper] [slides]
          Dynamic Dependence Summaries
          Dynamic Dependence Summaries Modern applications make heavy use of third-party libraries and components, which poses new challenges for efficient dynamic analysis. To perform such analyses, transitive dependent components at all layers of the call stack must be monitored and analyzed, and as such may be prohibitively expensive for systems with large libraries and components. As an approach to address such expenses, we record, summarize, and reuse dynamic dataflows between inputs and outputs of components, based on dynamic control and data traces. These summarized dataflows are computed at a fine-grained instruction level; the result of which, we call “dynamic dependence summaries.” Although static summaries have been proposed, to the best of our knowledge, this work presents the first technique for dynamic dependence summaries. The benefits to efficiency of such summarization may be afforded with losses of accuracy. As such, we evaluate the degree of accuracy loss and the degree of efficiency gain when using dynamic dependence summaries of library methods. On five large programs from the DaCapo benchmark (for which no existing whole-program dynamic dependence analyses have been shown to scale) and 21 versions of NANOXML, the summarized dependence analysis provided 90% accuracy and a speed-up of 100% (i.e., ×2), on average, when compared to traditional exhaustive dynamic dependence analysis.

          Palepu, Vijay Krishna; Xu, Guoqing and Jones, James, "Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries," , 2013 28th IEEE International Conference on Automated Software Engineering (ASE), pp.59-69, 11-15 November 2013. [paper] [slides]
          The Brain
          The Brain In this New Ideas and Emerging Results paper, we present a novel visualization, THE BRAIN, that reveals clusters of source code that co-execute to produce behavioral features of the program throughout and within executions. We created a clustered visualization of source-code that is informed by dynamic control flow of multiple executions; each cluster represents commonly interacting logic that composes software features. In addition, we render individual executions atop the clustered multiple-execution visualization as user-controlled animations to reveal characteristics of specific executions—these animations may provide exemplars for the clustered features and provide chronology for those behavioral features, or they may reveal anomalous behaviors that do not fit with the overall operational profile of most executions. Both the clustered multiple-execution view and the animated individual-execution view provide insights for the constituent behaviors within executions that compose behaviors of whole executions. Inspired by neural imaging of human brains of people who were subjected to various external stimuli, we designed and implemented THE BRAIN to reveal program activity during execution. The result has revealed the principal behaviors of execution, and those behaviors were revealed to be (in some cases) cohesive, modular source-code structures and (in other cases) cross-cutting, emergent behaviors that involve multiple modules. In this paper, we describe THE BRAIN and envisage the uses to which it can be put, and we provide two example usage scenarios to demonstrate its utility.

          Palepu, Vijay Krishna and Jones, James, "Visualizing Constituent Behaviors within Executions," , 2013 1st IEEE International Working Conference on Software Visualization (VISSOFT), pp.1-4, 27-28 September 2013. [paper] [vimeo]
          Trendy Bugs
          Trendy Bugs Studying vast volumes of bug and issue discussions can give an understanding of what the community has been most concerned about, however the magnitude of documents can overload the analyst. We present an approach to analyze the development of the Android open source project by observing trends in the bug discussions in the Android open source project public issue tracker. This informs us of the features or parts of the project that are more problematic at any given point of time. In turn, this can be used to aid resource allocation (such as time and man power) to parts or features. We support these ideas by presenting the results of issue topic distributions over time using statistical analysis of the bug descriptions and comments for the Android open source project. Furthermore, we show relationships between those time distributions and major development releases of the Android OS.

          Martie, Lee; Palepu, Vijay Krishna; Sajnani, Hitesh and Lopes, Cristina, "Trendy bugs: Topic trends in the Android bug reports," , 2012 9th IEEE Working Conference on Mining Software Repositories (MSR), pp.120-123, 2-3 June 2012. [paper] [slides]
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/blog.html Notes - Vijay Krishna Palepu
          Research Projects News Notes CV
          / Notes
          • 28 Dec 2014 » Having Fun with Java Bytecode
          • 03 Jun 2014 » Notes on Testing/Verifying Behavior
          • 24 Aug 2013 » Notes on Bash, Shell and other useful stuff
          • 30 Mar 2013 » Notes for my Software Architecture Final
          • 03 Mar 2013 » Plotting Box-plots in Groups for Vectors of Varying Lengths
          • 26 Feb 2013 » Judging 6th Grade Science Projects at the IUSD Science Fair 2013
          • 23 Feb 2013 » Git Notes
          • 20 Feb 2013 » Junit and JDepend Demo
          • 18 Mar 2012 » State Of Software Engineering Practice Today
          • 16 Oct 2010 » Lets Make an Exception
          @medium
          • 24 Dec 2014 » What is success in Software Research?
          • 29 Jan 2014 » The Program and The Brain.
          • 25 Oct 2013 » The Warewolf named Software.
          • 09 Aug 2013 » Ideas and Implementations make each other possible.
          • 14 Jul 2013 » Programming, Ideas and Software.
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/2014/06/03/Notes-Testing-Software-Behavior.html Notes on Testing/Verifying Behavior
          Research Projects News Notes CV
          / Notes / Notes on Testing/Verifying Behavior
          Notes on Testing/Verifying Behavior
          Date: 03 Jun 2014

          S(i|a)mple Application: NumberTranslator: https://bitbucket.org/vpalepu/191b

          Q. How do you test/verify behaviors?

          1. think about or in terms of behaviors.
          2. actually verify behaviors.

          Q. How do you think about behaviors?

          (what do i mean by that?)

          old school testing demo: https://bitbucket.org/vpalepu/191b

          “Introducing BDD” by Dan North.

          • Test method names should be sentences
            • An expressive test name is helpful when a test fails
          • “Behaviour” is a more useful word than “test”
            • Emphasize behavior over testing
            • Determine the next most important behavior
            • Think: “what should the system should do?”;
            • Not: “is the system right?”
          • It is all about templates
            • A simple sentence template keeps test methods focused
              • The class should do something
            • Story/Behavior Templates:

              As a [X] I want [Y] so that [Z]

              Given [some initial context (the givens)], When [an event occurs], then [ensure some outcomes].

          Q. So are we really verifying behaviors with BDD?

          (what do i mean by that?)

          State vs Interactions
          • Most testing that we do is state based or value based testing. The issue with state based testing is that sometimes you can arrive at the right state with the wrong steps or interactions.
          • Process over Results
          • The basic idea behind BDD is to think in terms of behaviors; you still might be doing the same old state or value based testing.
            • So, BDD does not force test to Behaviors in terms of the actual interactions.

          Mocking - testing interactions.

          • Mocks enable us to test interactions.
          • Mocks are not Stubs or Fakes or Dummies.

          Martin Fowler - “Mocks Aren’t Stubs”

          http://martinfowler.com/articles/mocksArentStubs.html

          • Dummy objects are passed around but never actually used. Usually they are just used to fill parameter lists.
          • Fake objects actually have working implementations, but usually take some shortcut which makes them not suitable for production (an in memory database is a good example).
          • Stubs provide canned answers to calls made during the test, usually not responding at all to anything outside what’s programmed in for the test. Stubs may also record information about calls, such as an email gateway stub that remembers the messages it ‘sent’, or maybe only how many messages it ‘sent’.
          • Mocks are what we are talking about here: objects pre-programmed with expectations which form a specification of the calls they are expected to receive.

          • “the calls they are expected to receive.”. Here calls refers to method calls.
          • In the context of interactive applications like GUIs or Games, these method calls often represent interactions.
          • Mocks keep track of all the method calls that are made/being made; check them against a specification, and flag errors if the specifications and reality do not match up.

          Fun Reads:

          • “Mock Roles, not Objects”, Freeman, Pryce, Mackinnon, Walnes, OOPSLA 2004.
          • “Mocks Aren’t Stubs”, Martin Fowler, martinfowler.com/articles/mocksArentStubs.html
          • “Introducing BDD”, Dan North, dannorth.net/introducing-bdd
          • “expect-run-verify… Goodbye!”, http://monkeyisland.pl/2008/02/01/deathwish/
          • Mockito Tutorials: http://docs.mockito.googlecode.com/hg/latest/org/mockito/Mockito.html

          Other Notes:

          • Mockito cannot mock final classes … String, Integer, Scanner
          • Last stub always wins
          • Mockito uses a “verify what you want” philosophy for mocking, unlike most other mocking frameworks that use the “expect-run-verify” philosophy for mocking. more: “expect-run-verify… Goodbye!”, http://monkeyisland.pl/2008/02/01/deathwish/.

          Tags: notes testing software-behavior

          Related Posts

          • 07 Oct 2015 » Spider SENSE: Software-Engineering, Networked, System Evaluation
          • 07 Oct 2015 » Revealing Runtime Features and Constituent Behaviors within Software
          • 01 Oct 2015 » Attended VISSOFT 2015
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~vpalepu/projects.html Projects - Vijay Krishna Palepu
          Research Projects News Notes CV
          / Projects
          @research
          • The Brain - Prototype An online HTML5 application that can be used to visualize the execution of a single run of a sample Java program.
          • Trendy Bugs: Topic Trends in the Android Bug Reports Modeling discussions between developers in the Android Open Source Project's Public Issue tracker.
          • Dynamic Program Analyzer for Java Bytecode Runtime program analysis framework via Java byte code instrumentation using ASM (asm.ow2.org).
          @graduate coursework
          • PL241-Compiler SSA-based optimizing compiler that supports copy propagation, common subexpression elimination.
          • Johnny Takes on Stats Interactive, narrative-based, tutorial for students to learn statistical concepts (specifically Hypothesis testing with t-Tests).
          • Lambda Calculus Interpreter in Scheme Lamba Calculus interpreter in Scheme.
          • Plug & Play Models for Video Game Systems Start up kit for carrying out Requirements engineering for Video Games.
          • Web User Interface Wireframe of U.S. Presidential Election Board Game UI wireframe for an online game based on the U.S. Presidential Elections.
          @undergrad
          • Java Code to Flow Diagram Converter An eclipse plug-in that can statically reverse engineer Java code snippets to Sequence Diagrams.
          • Mobile And Online Voting System An online application that can be used to carry out voting for a democratic electoral process.
          hacks
          • Latex Random Conjunctions LaTeX hack to produce random phrases for conjunctions like "Therefore" and "Moreover".
          • Analyzing F.R.I.E.N.D.S. Who speaks the most? Summaries of individual episodes or what each F.R.I.E.N.D. said.
          all projects
          • PL241-Compiler SSA-based optimizing compiler that supports copy propagation, common subexpression elimination.
          • Latex Random Conjunctions LaTeX hack to produce random phrases for conjunctions like "Therefore" and "Moreover".
          • Analyzing F.R.I.E.N.D.S. Who speaks the most? Summaries of individual episodes or what each F.R.I.E.N.D. said.
          • Johnny Takes on Stats Interactive, narrative-based, tutorial for students to learn statistical concepts (specifically Hypothesis testing with t-Tests).
          • The Brain - Prototype An online HTML5 application that can be used to visualize the execution of a single run of a sample Java program.
          • Lambda Calculus Interpreter in Scheme Lamba Calculus interpreter in Scheme.
          • Trendy Bugs: Topic Trends in the Android Bug Reports Modeling discussions between developers in the Android Open Source Project's Public Issue tracker.
          • Plug & Play Models for Video Game Systems Start up kit for carrying out Requirements engineering for Video Games.
          • Dynamic Program Analyzer for Java Bytecode Runtime program analysis framework via Java byte code instrumentation using ASM (asm.ow2.org).
          • Web User Interface Wireframe of U.S. Presidential Election Board Game UI wireframe for an online game based on the U.S. Presidential Elections.
          • Java Code to Flow Diagram Converter An eclipse plug-in that can statically reverse engineer Java code snippets to Sequence Diagrams.
          • Mobile And Online Voting System An online application that can be used to carry out voting for a democratic electoral process.
          home • bitbucket.com • Linkedin • Instagram • medium • stackoverflow • github.com • archives • Background: Mt. Ranier, © Vijay Krishna Palepu, 2015
          http://www.ics.uci.edu/~jajones/Home.html http://www.ics.uci.edu/~vpalepu/2015/01/11/Won-Data-Science-Hackathon.html Part of Winning Team @ UCI Data Science Hackathon
          Research Projects News Notes CV
          / News / Part of Winning Team @ UCI Data Science Hackathon
          Part of Winning Team @ UCI Data Science Hackathon
          Date: 11 Jan 2015

          Rianne Conijn, Christian Adriano and I took part and came in second place in the Data Science Hackathon conducted at UCI. This hackathon took place on the 10th of January (2015) and is a part of a new data science initiative at UCI, aimed at coordinating the works of scientists across the campus involved in various aspects of data science.

          I, along with my team, analyzed a data dump of Reddit posts (as did other teams), in an effort to discover something interesting about the posts, users, and communities within Reddit that we were analyzing. I will be enclosing the details of our findings in greater detail in a more elaborate blog post to follow. But, for now, it should be suffice to say that for the majority of the posts that we looked at, were created during what you would typically call work hours (8am to 6pm). However, the most successful posts, in terms of the of votes they received and the volume of comments they generated, seemed to have come between, 6pm at night to 8am the next morning, on any average day.

          In an effort to make our effort available to the public, we are in the process of releasing our scripts and code that we wrote to analyze the data. The data along with our code is available at the following git repository: https://github.com/VijayKrishna/sleep-work-relax. I am also enclosing below the presentation that we made to present our finding to the rest of the teams at the end of the day.

          Overall, it was a really great day, where we got to work as a good strong team and on something that was super cool!


          Tags: hackathon data science

          Related Posts

          • 07 Oct 2015 » Spider SENSE: Software-Engineering, Networked, System Evaluation
          • 07 Oct 2015 » Revealing Runtime Features and Constituent Behaviors within Software
          • 01 Oct 2015 » Attended VISSOFT 2015
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{mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} table.MsoTableGrid {mso-style-name:"Table Grid"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-unhide:no; border:solid windowtext 1.0pt; mso-border-alt:solid windowtext .5pt; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-border-insideh:.5pt solid windowtext; mso-border-insidev:.5pt solid windowtext; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1026"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body lang=EN-US link=blue vlink=blue style='tab-interval:.5in'> <div class=WordSection1> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u>Publications<o:p></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u><o:p><span style='text-decoration:none'>&nbsp;</span></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u>REFERRED JOURNAL<o:p></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J27. Content-Independent Multi-Spectral Display Using Superimposed Projections<o:p></o:p></b></p> <p class=MsoNormal>Yuqi Li, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder, </u></i>Dongming Lu, M. Gopi</p> <p class=MsoNormal>Computer Graphics Forum, 2015.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>Presented at Eurographics 2015.<o:p></o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J26. A Distributed Memory Hierarchy and Data Management for Interactive Scene Navigation and Modification on Tiled Display Walls<o:p></o:p></b></p> <p class=MsoNormal>Duy Qoc-Lai, Behzad Sajadi, Shan Jiang, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder, </u></i>M. Gopi</p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2014.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>Presented at IEEE Virtual Reality 2015.<o:p></o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J25. Immersive Full-Surround Multi-User System Design<o:p></o:p></b></p> <p class=MsoNormal>JoAnn Kuchera-Morina, Matthew Wrighta, Graham Wakefielda, Charles Robertsa, Dennis Addertona, Behzad Sajadi, Tobias H�llerer, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>Computer and Graphics, Vol. 40, pp. 10-21, May 2014.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J24. Mobile Collaborative Video<o:p></o:p></b></p> <p class=MsoNormal>Kiarash Amiri, Shih-Hsien Yang, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder, </u></i>Fadi Kurdahi, Magda El Zarki</p> <p class=MsoNormal>IEEE Transactions on Circuits and Systems for Video Technology, 2014</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J23. Advances in Large Area Displays: The Changing Face of Visualization<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Behzad Sajadi</p> <p class=MsoNormal>IEEE Computer, 2013.</p> <p class=MsoNormal><a href="docs/ieee13-paper.pdf">paper</a>, <a href="docs/ieee13-video.mp4">video</a></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J22. Using Patterns to Encode Color Information in Dichromats<o:p></o:p></b></p> <p class=MsoNormal><span lang=PT-BR style='mso-ansi-language:PT-BR'>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Ros�lia G. Schneider, Manuel Menezes de Oliveira Neto, Ramesh Raskar<o:p></o:p></span></p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2012 </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>Presented in IEEE Visweek 2012<o:p></o:p></b></p> <p class=MsoNormal><a href="http://www.ics.uci.edu/%7Ebsajadi/files/CVD-ColorEncoding.pdf">paper</a><span style='mso-spacerun:yes'>� </span><a href="http://www.ics.uci.edu/%7Ebsajadi/files/CVD-ColorEncoding.mp4">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J21. Edge Guided Resolution Enhancement in Projectors Using Optical Pixel Sharing</b> <b style='mso-bidi-font-weight:normal'><o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, M. Gopi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u><b style='mso-bidi-font-weight:normal'><o:p></o:p></b></i></p> <p class=MsoNormal><span lang=FR style='mso-ansi-language:FR'>ACM Transactions on Graphics, 2012. <o:p></o:p></span></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span lang=FR style='mso-ansi-language:FR'>Presented in ACM Siggraph 2012<o:p></o:p></span></b></p> <p class=MsoNormal><span style='color:red'><a href="docs/sig12.pdf">paper</a><span style='mso-spacerun:yes'>�� </span><a href="http://www.ics.uci.edu/%7Ebsajadi/files/PixelSharingProjector.mp4">video</a><o:p></o:p></span></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:red'><span style='mso-spacerun:yes'>� </span></span></b><span style='color:red'><o:p></o:p></span></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J20. Fast High-Resolution Appearance Editing Using Superimposed Projections <o:p></o:p></b></p> <p class=MsoNormal>Dan Aliaga, Yu Hong Yeung, Alvin J. Law, Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>ACM Transactions on Graphics, 2012. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>Presented in Siggraph 2012<o:p></o:p></b></p> <p class=MsoNormal><a href="http://www.ics.uci.edu/%7Ebsajadi/files/tog12.pdf">paper</a><span style='mso-spacerun:yes'>�� </span><a href="docs/tog12-video.mov">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J19. Automated Cell Classification and Visualization for Analyzing Remyelination Therapy<o:p></o:p></b></p> <p class=MsoNormal>Koel Das, Monica Siegenthaler, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, Hans Keirstead, M. Gopi</p> <p class=MsoNormal>The Visual Computer, 2011.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J18. Auto-Calibration of Multi-Projector CAVE-like Immersive Environments <o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <u>Aditi Majumder</u> </p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2011.</p> <p class=MsoNormal><span style='mso-spacerun:yes'>�</span><a href="docs/tvcg-vrstextn11.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J17. Perceptually Based Appearance Modification for Complaint Appearance Editing <o:p></o:p></b></p> <p class=MsoNormal>Alvin J. Law, Daniel Aliaga, Zygmut Pizlo, Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>Computer Graphics Forum, 2011. </p> <p class=MsoNormal><a href="docs/cgf11.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J16. Switchable Primaries Using Shiftable Layers of Color Filter Arrays <o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Kazuhiro Hiwada, Atsuto Maki, Ramesh Raskar </p> <p class=MsoNormal><span lang=FR style='mso-ansi-language:FR'>ACM Transactions on Graphics (SIGGRAPH), 2011.<span style='mso-spacerun:yes'>� </span><o:p></o:p></span></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at ACM Siggraph 2011.<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/sig11-paper.pdf">paper</a>, <a href="docs/sig11l-video.mp4">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J15. Automatic Registration of Multi-Projector Domes Using a Single Uncalibrated Camera<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi,&nbsp;<i style='mso-bidi-font-style:normal'><u>Aditi&nbsp;Majumder</u></i>&nbsp;</p> <p class=MsoNormal>Computer Graphics Forum, 2011</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE/Eurographics EUROVIS 2011<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/EuroVis11-Dome.pdf">paper</a>, <a href="docs/Eurovis11.wmv">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J14. Auto-Calibrating Projectors for Tiled Displays On Piecewise Smooth Vertically Extruded Surfaces<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2011. </p> <p class=MsoNormal><a href="docs/tvcg-vrextn11.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J13. A Scalable Distributed Paradigm for Multi-User Interaction with Tiled Rear Projection Display Walls <o:p></o:p></b></p> <p class=MsoNormal>Pablo Roman, Maxim Lazarov, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i> </p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2010. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE Visualization 2010<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/VIS10.pdf">paper</a> , <a href="docs/Vis10.avi">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J12. Projector Placement Planning for High Quality Visualizations on Real World Colored Objects </b></p> <p class=MsoNormal>Alvin J. Law, Daniel Aliaga,<i style='mso-bidi-font-style: normal'> <u>Aditi Majumder</u></i> </p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2010.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE Visualization 2010<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/vis10-purdue.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J11. Scalable Multi-View Registration for Multi-Projector Displays on Vertically Extruded Surfaces<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u><o:p></o:p></i></p> <p class=MsoNormal>Computer Graphics Forum, 2010. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE/Eurographics EUROVIS 2010<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/EVIS10.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J10. Markerless View Independent Geometric Registration of Multiple Distorted Projectors on Vertically Extruded Surfaces Using a Single Uncalibrated Camera<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u> <o:p></o:p></i></p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2009. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE Visualization 2009</span></b><span style='color:black;mso-themecolor:text1'><o:p></o:p></span></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:red'>Runner-Up for Best Paper Award<o:p></o:p></span></b></p> <p class=MsoNormal><a href="http://www.ics.uci.edu/%7Emajumder/docs/VIS09CURVED.pdf">paper</a> , <a href="docs/vis09_curved.avi">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J9. Color Seamlessness in Multi-Projector Displays Using Constrained Gamut Morphing<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, Maxim Lazarov, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, M. Gopi. </p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2009. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE Visualization 2009<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/VIS09COLOR.pdf">paper</a> , <a href="docs/vis09_color.avi">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J8. Advances towards high-resolution pack-and-go displays: A survey<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Ezekiel Bhasker, Ray Juang </p> <p class=MsoNormal>Journal for the Society of Information Display (JSID), Special Issue for Selected papers from SID Symposium, 16(3), pp. 481-491, 2007.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J7. Perception Based Contrast Enhancement of Images<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, and Sandra Irani </p> <p class=MsoNormal>ACM Transactions on Applied Perception, 2007. </p> <p class=MsoNormal><a href="docs/TAP07.pdf">paper</a>, <a href="contrastcode/codecontrast.html">source code </a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J6. Registration Techniques for Using Imperfect and Partially Calibrated Devices in Planar Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal>Ezekiel Bhasker, Pinaki Sinha, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i> </p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2007. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented at IEEE Visualization 2007<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/VIS07.pdf">paper</a>.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J5. Asynchronous Distributed Calibration for Scalable Reconfigurable Multi-Projector Displays <o:p></o:p></b></p> <p class=MsoNormal>Ezekiel Bhasker, Pinaki Sinha, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i><span style='mso-spacerun:yes'>� </span></p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, 2006. </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:black;mso-themecolor:text1'>Presented in IEEE Visualization 2006<o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/vis06.pdf">paper</a>, <a href="docs/vizvideo.mov">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J4. Modeling Color Properties of Tiled Displays</b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, M. Gopi </p> <p class=MsoNormal>Computer Graphics Forum, June, 2005. </p> <p class=MsoNormal><a href="docs/CGF05.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J3. Camera Based Calibration Techniques for Seamless Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal>Ruigang Yang, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, and Michael Brown</p> <p class=MsoNormal><span style='mso-spacerun:yes'>�</span>IEEE Transactions on Visualization and Computer Graphics, Vol. 11, No. 2, March-April, 2005. </p> <p class=MsoNormal><a href="docs/TVCG05.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J2. Perceptual Photometric Seamlessness in Tiled Projection-Based Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u>,</i> Rick Stevens. </p> <p class=MsoNormal>ACM Transactions on Graphics, Vol. 24, No. 1, January 2005. </p> <p class=MsoNormal><a href="docs/TOG05.pdf">paper</a>, <a href="docs/results.wmv">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>J1. Color Non-Uniformity in Projection Based Displays: Analysis and Solutions<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Rick Stevens</p> <p class=MsoNormal>IEEE Transactions on Visualization and Computer Graphics, Vol. 10, No. 2, 2004. </p> <p class=MsoNormal><a href="docs/TVCG02.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u>REFERRED CONFERENCE<o:p></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u><o:p><span style='text-decoration:none'>&nbsp;</span></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C40. Seam Carving Based Aesthetics Enhancement for Photos<o:p></o:p></b></p> <p class=MsoNormal>Ke Li, Bo Yan, Jun Li, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>Signal Processing: Image Communications, 2015.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C39.High-Resolution Lighting of 3D Relief Maps Using a Network of Projectors and Cameras<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, Mahdi Abbaspour Tehrani, Mehdi Rehimzadeh, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>3D-TV conference on Immersive and Interactive 3D Media Experience Over Networks, Lisbon, July 2015.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:red'>Best Paper Award<o:p></o:p></span></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C38. Interactive Display Conglomeration on the Wall<o:p></o:p></b></p> <p class=MsoNormal>Duy Qoc-Lai and <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>IEEE VR Workshop on Everyday Virtual Reality: Rethinking Virtual Reality for Home and Office Environments, 2015</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C37. Real-time Mapping and Tracking of Optical Properties in Deep Tissue<o:p></o:p></b></p> <p class=MsoNormal>Kyle Cutler, Zachary DeStefano, Soroush M. Zarandi, Thomas D. O'Sullivan, Albert E. Cerussi, Gopi Meenakshisundaram, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, Seung-ha Lee, Bruce J. Tromberg.</p> <p class=MsoNormal>SPIE Photonics, Optical Tomography and Spectroscopy of Tissue XI, Feb 2015, paper 9319-58.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C36. Multi-scale Image Segmentation Using MSER<o:p></o:p></b></p> <p class=MsoNormal>Il-Seok Oh, Jinseon Lee, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>Computer Analysis of Images and Patterns (CAIP), August 2013.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C35. Image Enhancement in Projectors Via Optical Pixel Shift and Overlay<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, Duy-Quoc Lai, Alexander Iher, M. Gopi,<u> <i style='mso-bidi-font-style:normal'>Aditi Majumder</i></u>, </p> <p class=MsoNormal>International Conference on Computational Photography (ICCP), April, 2013.</p> <p class=MsoNormal><a href="docs/iccp13.pdf">paper</a></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C34. HD-GraphViz: Highly Distributed Graph Visualization on Tiled Displays<o:p></o:p></b></p> <p class=MsoNormal>Sangwon Chen, M. Gopi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i><u><o:p></o:p></u></p> <p class=MsoNormal>Indian Conference on Vision, Graphics and Image Processing (ICVGIP), December, 2012.</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><o:p>&nbsp;</o:p></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C33. Collaborative Video Playback on a Federation of Tiled Mobile Projectors enabled by Visual Feedback<o:p></o:p></b></p> <p class=MsoNormal>Kiarash Amiri, Shih-Hsien Yang, Fadi Kurdahi, Magda El Zarki, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>ACM Multimedia Systems, 2012</p> <p class=MsoNormal><a href="docs/papermmsys.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C32. Camera Based Video Synchronization for a Federation of <st1:place w:st="on">Mobile</st1:place> Projectors<o:p></o:p></b></p> <p class=MsoNormal>Kiarash Amiri, Shih-Hsien Yang, Fadi Kurdahi, Magda El-Zarki, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>IEEE/ACM Workshop on Projector Camera Systems, 2011. </p> <p class=MsoNormal><a href="docs/procams11.pdf">paper</a>, <a href="docs/PROCAMS2011-video.wmv">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C31. Automatic Analysis of Remyelination Therapy for Spinal Chord Injury<o:p></o:p></b></p> <p class=MsoNormal>Koel Das, Monica Siegenthaler, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, Hans Kierstead, M. Gopi </p> <p class=MsoNormal>International Conference on Computer Vision, Graphics and Processing, 2010. </p> <p class=MsoNormal><a href="http://www.ics.uci.edu/%7Egopi/PAPERS/ICVGIP10.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C30. Automatic Registration of Multiple Projectors on Swept Surfaces<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u> <o:p></o:p></i></p> <p class=MsoNormal>ACM Virtual Reality and Software Technology, 2010</p> <p class=MsoNormal><a href="docs/VRST10.pdf">paper</a>, <a href="docs/vrst10.wmv">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C29. ADICT: Accurate Direct and Inverse Color Transformation<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <u>Aditi Majumder</u></p> <p class=MsoNormal>European Conference on Computer Vision (ECCV), 2010. </p> <p class=MsoNormal><a href="docs/eccv2010.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C28. Display Gamut Reshaping for Color Emulation and Balancing<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Robert G. Brown, Hussein S. El-Ghoroury</p> <p class=MsoNormal>IEEE/ACM Workshop on Projector-Camera Systems, 2010 </p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:red'>Best Paper Award. <o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/procam10-BP.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C27. Device Independent Representation of Photometric Properties of a Camera <o:p></o:p></b></p> <p class=MsoNormal>Maxim Lazarov, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i> </p> <p class=MsoNormal>IEEE/ACM Workshop on Projector-Camera System, 2010. </p> <p class=MsoNormal><a href="docs/procam10represent.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C26. Auto-Calibration of Cylindrical Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i> </p> <p class=MsoNormal>Proceedings of IEEE Virtual Reality, 2010</p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><span style='color:red'>Best Paper Award. <o:p></o:p></span></b></p> <p class=MsoNormal><a href="docs/VR10.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C25. Data Handling Displays<o:p></o:p></b></p> <p class=MsoNormal>Maxim Lazarov, Hamed Pirsiavash, Behzad Sajadi, Uddipan Mukherjee, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u><o:p></o:p></i></p> <p class=MsoNormal>IEEE/ACM Workshop on Projector Camera Systems, 2009</p> <p class=MsoNormal><a href="docs/PROCAMS09.pdf">paper</a> , <a href="docs/front.avi">video</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C24. Maintaining Color Consistency Across Non-Linear Displays and Printers<o:p></o:p></b></p> <p class=MsoNormal>Behzad Sajadi, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i> </p> <p class=MsoNormal>SID Symposium Digest of Technical Papers, San Antonio, May 2009.</p> <p class=MsoNormal><a href="http://www.ics.uci.edu/%7Ebsajadi/files/P-35.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C23. Shared Visualization Spaces for Environment to Environment Communication<o:p></o:p></b></p> <p class=MsoNormal>Hamed Pirsiavash, Vivek Singh, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, Ramesh Jain </p> <p class=MsoNormal>Workshop on Media Arts, Science, and Technology (MAST),<st1:city w:st="on">Santa Barbara</st1:city>, <st1:state w:st="on">CA</st1:state>, Jan 2009.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C22. Advances Towards Next Generation Flexible Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal>Ezekiel Bhasker, Ray Juang<i style='mso-bidi-font-style: normal'>, <u>Aditi Majumder</u></i> </p> <p class=MsoNormal>ACM Siggraph Workshop on Emerging Display Technologies, <st1:city w:st="on">San Diego</st1:city>, August, 2007.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C21. Geometric Modeling and Calibration of Planar Multi-Projector Displays Using Rational Bezier Patches<o:p></o:p></b></p> <p class=MsoNormal>Ezekiel Bhasker, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u><o:p></o:p></i></p> <p class=MsoNormal><span style='mso-spacerun:yes'>�</span>IEEE CVPR Workshop on Projector Camera Systems, <st1:city w:st="on"><st1:place w:st="on">Minneapolis</st1:place></st1:city>, <st1:state w:st="on">Minnesota</st1:state>, June 2007.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C20. Photometric Self-Calibration of a Projector-Camera System<o:p></o:p></b></p> <p class=MsoNormal>Ray Juang, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u><o:p></o:p></i></p> <p class=MsoNormal>IEEE CVPR Workshop on Projector Camera Systems, <st1:city w:st="on"><st1:place w:st="on">Minneapolis</st1:place></st1:city>, <st1:state w:st="on">Minnesota</st1:state>, June 2007.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C19. Self Calibrating Tiled Displays<o:p></o:p></b></p> <p class=MsoNormal>Ezekiel Bhasker, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i></p> <p class=MsoNormal>SID Symposium Digest of Technical Papers, <st1:city w:st="on"><st1:place w:st="on">Long Beach</st1:place></st1:city>, May, 2007.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C18. Mobile Display Via Distributed Networked Projector-Camera Systems<o:p></o:p></b></p> <p class=MsoNormal>Pinaki Sinha, Ezekiel Bhasker, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u><o:p></o:p></i></p> <p class=MsoNormal>IEEE CVPR Workshop on Projector Camera Systems, <st1:state w:st="on"><st1:place w:st="on">New York</st1:place></st1:state>, June 2006.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C17. Contrast Enhancement of Images Using Human Contrast Sensitivity<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Sandra Irani, </p> <p class=MsoNormal>Symposium on Applied Perception in Graphics and Visualization, 2006. </p> <p class=MsoNormal><a href="docs/APGV06.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C16. Luminance Management for Seamless Multi-Projector Displays</b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder <o:p></o:p></u></i></p> <p class=MsoNormal>SID Symposium Digest of Technical Papers, <st1:city w:st="on"><st1:place w:st="on">Boston</st1:place></st1:city>, May 2005.</p> <p class=MsoNormal><br> <b style='mso-bidi-font-weight:normal'>C15. Greedy Algorithm for Local Contrast Enhancement of Images</b></p> <p class=MsoNormal>Kartic Subr<i style='mso-bidi-font-style:normal'>, <u>Aditi Majumder</u></i>, Sandra Irani</p> <p class=MsoNormal>International Conference on Image Analysis and Processing (ICIAP), <st1:city w:st="on">Cagliari</st1:city>, Italy, September, 2005.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C14. Contrast Enhancement of Multi-Displays Using Human Contrast Sensitivity<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder<o:p></o:p></u></i></p> <p class=MsoNormal>IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2005. </p> <p class=MsoNormal><a href="docs/CVPR05.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C13. Is Spatial Super-Resolution Possible with Multiple Overlapping Projectors? <o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder <o:p></o:p></u></i></p> <p class=MsoNormal>IEEE International Conference on Audio, Speech and Signal Processing (ICASSP), 2005. </p> <p class=MsoNormal><a href="docs/ICASSP05.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C12. Camera Based Evaluation of Photometric Compensation Methods on Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder <o:p></o:p></u></i></p> <p class=MsoNormal>Proceedings of IEEE International Conference on Image Processing, pp 3527-3530, Singapore, 2004.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C11. Camera Based Calibration Techniques for Seamless Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal>Michael S. Brown, <i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Ruigang Yang</p> <p class=MsoNormal>Applications of Computer Vision Workshop, European Conference of Computer Vision, 2004.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C10. Using a Camera to Capture and Correct Spatial Photometric Variation in Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, David Jones, Matthew McCrory, Michael E. Papka, Rick Stevens</p> <p class=MsoNormal>IEEE International Workshop on Projector Camera Systems, 2003. </p> <p class=MsoNormal><a href="docs/PROCAM03.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C9. PixelFlex2: A Comprehensive, Automatic, Casually-Aligned Multi-Projector Display<i style='mso-bidi-font-style:normal'><o:p></o:p></i></b></p> <p class=MsoNormal>Andrew Raij, Gennette Gill, <i style='mso-bidi-font-style: normal'><u>Aditi Majumder</u></i>, Herman Towles, Henry Fuchs </p> <p class=MsoNormal>IEEE International Workshop on Projector-Camera Systems, 2003.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C8. Photometrically Continuous Imagery in Reconfigurable Large Area Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Rick Stevens </p> <p class=MsoNormal>3rd Annual High Information Content Display System Symposium, 2003.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C7. LAM: Luminance Attenuation Map for Photometric Uniformity Across Projection Based Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Rick Stevens</p> <p class=MsoNormal>ACM Virtual Reality Software and Technology, 2002. </p> <p class=MsoNormal><a href="docs/VRST02.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C6. Properties of Color Variation Across Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder<span style='mso-spacerun:yes'>� </span><o:p></o:p></u></i></p> <p class=MsoNormal>SID Eurodisplay 2002. </p> <p class=MsoNormal><a href="docs/eurodisplay.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C5. Applying Geometry and Color Correction to Tiled Display Walls<o:p></o:p></b></p> <p class=MsoNormal>Justin Binns, Gennette Gill, Mark Hereld, David Jones, Ivan Judson, Ti Leggett,<i style='mso-bidi-font-style:normal'> <u>Aditi Majumder</u></i>, Matthew McCroy, Michael E. Papka Rick Stevens</p> <p class=MsoNormal><span style='mso-spacerun:yes'>�</span>IEEE Visualization, 2002. (Poster) </p> <p class=MsoNormal><a href="docs/Vizposter.pdf">paper</a> </p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C4. Hardware Accelarated Real Time Charcoal Rendering</b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, M. Gopi </p> <p class=MsoNormal>Symposium of Non-Photorealistic Amination and Rendering, 2002. </p> <p class=MsoNormal><a href="docs/NPAR02.pdf">paper</a>, <a href="docs/egrw01.mpg">video</a>.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C3. Computer Graphics Optique: Optical Superposition of Projected Computer Graphics<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Greg Welch </p> <p class=MsoNormal>Eurographics Workshop on Virtual Environment/ Immersive Projection Technology 2001. </p> <p class=MsoNormal><a href="docs/CGO01.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C2. Achieving Color Uniformity Across Multi-Projector Displays<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, Zue He, Herman Towles, Greg Welch</p> <p class=MsoNormal>IEEE Visualization 2000. </p> <p class=MsoNormal><a href="docs/VIS00.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>C1. Geometric Stitching for Real-Time Panoramic Image Generation Using Texture Maps<o:p></o:p></b></p> <p class=MsoNormal><i style='mso-bidi-font-style:normal'><u>Aditi Majumder</u></i>, M. Gopi, Brent W. Seales, Henry Fuchs</p> <p class=MsoNormal>ACM Multimedia, 1999. </p> <p class=MsoNormal><a href="docs/MM99.pdf">paper</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u>REFERRED COURSES/TUTORIALS<o:p></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'><u><o:p><span style='text-decoration:none'>&nbsp;</span></o:p></u></b></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>T5.</b> <u>Aditi Majumder</u> and Behzad Sajadi, Building your own Projection-Based VR Display System, Course Organizer, IEEE Virtual Reality, 2010. <a href="VRcourse.htm">(Course website)</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>T4.</b> <u>Aditi Majumder</u> and Michael S. Brown, Camera-Based Vision Techniques to Build Large-Area Multi-Projector Displays, Course Organizer, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2005.</p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>T3.</b> <u>Aditi Majumder</u> and Michael S. Brown, Camera-Based Techniques for Building Large-Area Multi-Projector Displays, Course Organizer, European Conference of Computer Vision (ECCV), 2004. </p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>T2.</b> <u>Aditi Majumder</u> and Michael S. Brown, Building Large Area Displays,Course Organizer, Eurographics 2003. <a href="docs/EG03_course.pdf">pdf</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><b style='mso-bidi-font-weight:normal'>T1.</b> <u>Aditi Majumder</u> and Michael S. Brown, Large-Scale Displays for the Masses: Techniques for Building Affordable and Flexible Multi-Projector Displays, Course Organizer, ACM SIGGRAPH 2003. <a href="docs/SIG03_course.pdf">pdf</a></p> <p class=MsoNormal><o:p>&nbsp;</o:p></p> <p class=MsoNormal><script type="text/javascript" src="http://s29.sitemeter.com/js/counter.js?site=s29adthome"> </script><a href="http://s29.sitemeter.com/stats.asp?site=s29adthome" target="_top"><span style='mso-no-proof:yes;text-decoration:none;text-underline: none'><img border=0 width=32 height=32 id="_x0000_i1026" src="http://s29.sitemeter.com/meter.asp?site=s29adthome" alt="Site Meter"></span></a></p> </div> </body> <!-- Site Meter --> </html> http://www.ics.uci.edu/~majumder/teach.html Aditi Majumder

          Teaching

          • CS 112: Introduction to Computer Graphics (Winter 2016) [Undergraduate]
          • CS 111: Digital Image Processing (Fall 2015) [Undergraduate]
          • CS 211A: Visual Computing (Fall 2015) [Graduate]
          • CS 213: Visual Perception (Spring 2013) [Graduate]
          • CS 299: Computational Photography (Winter 2012) [Graduate Seminar]
          • Computational Projectors and Cameras (Spring 2009) [Graduate]

          http://www.ics.uci.edu/~majumder/contrastcode/codecontrast.html Contrast Enhancement of Images using Human Contrast Sensitivity

          Contrast Enhancement of Images using Human Contrast Sensitivity

           


           

                      Left: Original image                                                                              Right: Enhanced image



          Human's perception follows Weber's Law. According to the law, brighter pixels require more enhancement than darker pixels so that we can perceive the change effectively. We effectively apply this fact to design a contrast-enhancement method for images that improves the local image contrast by controlling the local image gradient with a single parameter T.

          Following are the source code and the executables of this program. To run the code, use the following command.

          java  inputimage  outputimage  T  (e.g. java  input.jpg  output.jpg  3.0)

          T controls the amount of contrast enhancement achieved. Increasing T increases the amount of contrast enhancement.

          Image names must end with .jpg and T must be more than 1. The input image file must be in the same directory with executable codes.



          Executable files
          (zipped)

          Source code (zipped)

          http://www.ics.uci.edu/~majumder/students-N.html Aditi Majumder

          Current Graduate Students

             Duy Qoc-Lai

          Past Graduate Students

              PhD

              Behzad Sajadi (2012) Thesis: Automatic Registration of Large Non-Planar Multi-Projector Displays -  Quantitative Analyst, D.E. Shaw.

              Masters

          Pablo Roman (2010) Thesis: A Scalable Distributed Paradigm for Multi-User Interaction with Tiled Multi-Projector Displays - Doctoral candidate at Kyoto University, Japan

          Maxim Lazarov (2010) Thesis: A Novel Hyperspectral Camera - At Dreamworks

          Anna Diez (2009) Thesis: Edge Preserving Image Upsacling - At Telecommunications, Spain

          Mitsunubo Sugimoto (2009) Thesis: Connected Component Based Video Editing -At National Defense Academy Computer Science, Japan

          Ray Juang (2009) Thesis: Photometric Self-Calibration of a Projector-Camera Pair -At Google

          Ezekiel Bhasker (2008) - At Qualcomm

          Past UG Advisee

          Jason Kim – Bachelors in ICS, 2006, currently at Pixar.

          http://www.ics.uci.edu/~majumder/project.html Aditi Majumder

           

           

           

          Content-Adaptive User Controllable Camera

           

           

           

          iCVideo: Interactive Collaborative Video

           

           

           

           

           

          UBIQUITOUS DISPLAYS

           

           

           

           

          Tiled Projection-Based Display Walls

           

           

           

          Appearance Editing

           

           

           

          Perception Based Image Processing

           

           

           

          Non-Photorealistic Rendering

           

           

           

           

          http://www.ics.uci.edu/~stasio/Papers/cjkt04-abs.html

          A Robust Multisignature Scheme with Applications to Multicast Acknowledgement Aggregation
          Claude Castelluccia, Stanislaw Jarecki, Jihye Kim, Gene Tsudik

          ABSTRACT:

          The source of multicast communication needs to securely verify which multicast group members have received a multicast message, but verification of individually signed acknowledgments from each receiver imposes unnecessary computation and communication costs.  We propose a solution which allows the intermediate nodes along the multicast distribution tree to aggregate the authenticated acknowledgments sent by the multicast receivers to the source. 

          Our solution consists of a new multisignature scheme, secure under the discrete logarithm assumption in the random oracle model, which blends the well-known Schnorr signature scheme with the Merkle hash tree structure. The multisignature scheme we propose has a novel property of robustness, which allows for an efficient multisignature generation even in the presence of maliciously faulty nodes.

          http://www.ics.uci.edu/~stasio/fall04/ics268.html ICS 180: Introduction to Cryptography

          ICS 268: Cryptography and Communication Security

          Fall Quarter, 2004


          Instructor:       Stanislaw Jarecki

          • Class time:        Tu-Th, 11-12:20

          • Room:              ICF (Interim Classroom Facility, i.e. bldg 315), room 101

          • Extra day:         At the end of the quarter, there will be an extra day-long session with student presentations, either on Dec 2 (Thursday), Dec 3 (Friday), or Dec 4 (Saturday), to be decided in the first week.  Each presentation will be 20 minutes long.  You will be expected to listen to the talks of all the other students, but you will be excused from the part of the talks that conflicts with your other obligations on that day.

          • The class is open to upper-class undergraduates, with the permission of the instructor.  The requirements for the undergraduates are  reduced.  For example, no final presentation is required (see more below).

          • Class web site:  http://www.ics.uci.edu/~stasio/fall04/ics268.html

          • Prerequisites:    ICS 6A and ICS 161/261, also see below

          • Textbook (recommended but not required):   Douglas R. Stinson, "Cryptography: Theory and Practice (2nd edition)".

          • Main reading material:  Handouts, lecture notes, and other material available on-line (see a link below).

          • Office hours:     Monday 3-5, and otherwise by appointment.  I encourage you to use the office hours and also to communicate with me by email at stasio@ics.uci.edu

          Most important info:

          • Lecture summaries, lecture notes, problem sets, solutions, other handouts
          • Reference reading material

          Course Description:

          This course is an introduction to modern cryptography and security for graduates and advanced undergraduates.   The class will try to balance between the breadth of the coverage and an attempt to develop a general approach to the study of security issues.  The first aim of the class is to introduce students to various cryptographic tools like symmetric and public-key encryption schemes, signature schemes, message authentication schemes, identification protocols, and others.  The second and equally important aim of this class is to develop a "provable-security" paradigm of approaching any communication security problem.  This paradigm consists of (1) understanding the security *goal* of any protocol, i.e. understanding what properties a protocol needs to achieve to be considered secure, and (2) designing a protocol together with a *proof* that the protocol achieves these properties under some well-understood computational hardness assumptions, for example under the assumption that it is computationally hard to factor large composite numbers.

          The aim of the course is to introduce some fundamental cryptographic tools in such a way so that (1) you will be able to specify the security needs of the system you are designing and use existing cryptographic mechanisms in such a way so that your security needs are met, and (2) you will be able to develop new cryptographic mechanisms and protocols yourself. 

          To help further these goals, we'll end the class with conference-style presentations by the *graduate* students on some security/cryptography topic chosen by the student.

          What this class is not about:

          This class will not teach you all there is to know to make computers and networks secure.  Cryptography is only one layer in the stack of engineering issues that need to be solved to make computers and networks secure.  Computer security deals with lots of issues we will not touch on in the class, like buggy code, viruses, denial of service attacks, network monitoring techniques, preventing bad passwords, integrating various network services securely, and many more.  This class will  stay firmly on the layer of algorithms for the so-called "cryptographic primitives", i.e. the design of cryptographic tools like encryption, signatures, authentication.  While some of these tools will be probably very useful in solving any of the real-world security issues above, we will not be analyzing any such systems in this class.  On the other hand, we will often mention the real-world security issues like those listed above in motivating the security properties required of the cryptographic tools we will be designing.

          Another note of warning is that in this class we will not concentrate on techniques used to design and analyze block ciphers (like DES or AES) and hash functions (like MD5 and SHA), although the class will offer you some insight into security of such constructions.  We will focus instead on public key crypto, but we will spend a few lectures on private key algorithms too.

          Grading policy:

          Graduates:

          • 50% weekly problem sets
          • 25% take-home final
          • 25% a 30 minute conference-style presentation on a security/cryptography topic of your choice (the presentations will take place in a a special class depending on the number of students involved)

          Undergraduates:

          • 70% weekly problem sets
          • 30% take-home final

          Problem sets are due at the beginning of the class.  You are not allowed to work on the homework problems together with other students.  You are also not allowed to consult solutions from previous years or solutions available on-line.  You are allowed to consult other sources, such as textbooks, lecture notes, or research papers, but you must clearly mark any material you reference.

          Textbook and lecture notes:

          The (recommended) textbook is Douglas R. Stinson's "Cryptography: Theory and Practice", which is available through the UCI bookstore.  It is very good as a reference for a lot of the material we will be covering, but we will not follow it in great detail, and a lot of the lecture material is not covered by Stinson. 

          The primary source of the material will be the lecture notes and handouts which I will be posting on the web and distributing in class.

          Prerequisites:

          The formal prerequisites are ICS 6.A and ICS.161.  However, what you really need in general is this:

          • You cannot be �math-phobic� or �theory-phobic�.  The major focus of this class is on definitions and proofs, and we�ll use probability and number theory in the running time analysis and the security analysis of the algorithms we will study.

          More specifically, you need the following:

          • You should be comfortable with proofs, with elementary probability, and have the basic knowledge of discrete math used in computer science (e.g. ICS.6A).
          • It's highly recommended that you have some algorithms class (like ICS.161), and that you are familiar with asymptotic analysis of algorithm running time.
          • It'd be also easier for you if you took a computability/complexity class like ICS.162, so you are familiar with the notion of polynomial-time algorithms, and the notion of a reduction between computational problems.   However, this is material which we'll go over in this class to the extend that we'll use it, and I believe that you can pick it up easily.
          • You do not have to know modular arithmetic, because we will review everything we will need.  However, familiarity with it will be helpful.   If you want to learn modular arithmetic on a deeper level, consider the MATH.173A class taught every fall quarter by prof. Caryl Margulies in the math department (see also below).

          Other UCI classes on cryptography:

          This class is complimentary to the MATH.173A - 173B classes on number theory and cryptography which are taught by Prof. Margulies in the math department.  Having that class or any other preparation in number theory is a very good background for this class, but it is not necessary.  However, if you think you'd like to work on cryptography and security, and you do not have a strong background in number theory, I recommend that you take both classes this quarter.  Prof. Margulie's class is taught MWF 3-3:50 in ET 204, and the ICS students can either take it for a grade or pass/fail as a special topic course, ICS.299.

          Security Seminar Announcement:

          Students who want to learn more about crypto/security are encouraged to attend a weekly seminar of the SCONCE group, which takes place on Fridays 11:30-12:30, in ICS2 room 144.


          http://www.ics.uci.edu/~stasio/Papers/cjt04-abs.html Secret Handshakes from CA-oblivious Encryption
          Claude Castelluccia, Stanislaw Jarecki, Gene Tsudik

          ABSTRACT:

          Secret handshakes were recently introduced [BDSS+03] to allow members of the same group to authenticate each other secretly, in the sense that someone who is not a group member cannot tell, by engaging some party in the handshake protocol, whether that party is a member of this group. On the other hand, any two parties who are members of the same group will recognize each other as members. Thus, a secret handshake protocol can be used in any scenario where group members need to identify each other without revealing their group affiliations to outsiders.

          The work of [BDSS+03] constructed secret handshakes secure under the Bilinear Diffie-Hellman (BDH) assumption in the Random Oracle Model (ROM). We show how to build secret handshake protocols secure under a more standard cryptographic assumption of Computational Diffie Hellman (CDH), using a novel tool of CA-oblivious public key encryption, which is an encryption scheme s.t. neither the public key nor the ciphertext reveal any information about the Certification Authority (CA) which certified the public key. We construct such CA-oblivious encryption, and hence a handshake scheme, based on CDH (in ROM). The new scheme takes 3 communication rounds like the BDH-based scheme, but it is about twice cheaper computationally, and it relies on a weaker computational assumption.

          http://www.ics.uci.edu/~stasio/winter06/ics22H.html ICS H22, Winter 2005

          ICS H22, Winter 2006


          Course Reference

          Getting Started with Lab Assignments

          Lecture notes directory

          link to the archive for the class mailing list

          Handouts, assignments and topics (approximate, as thaught last quarter):

          Week Dates Lab
          Assignment
          Written
          Assignment
          Reading
          (from the GT textbook)
          Topics
          Week 1 Jan 9-13

          Lab 1
          (optional, due Wed, Jan 18)
          see also Lab1.doc

          Hw1
          (changed: due Tue, Jan 24, in class)

          Recursion: Sec.3.5
          Java basics: Ch.1
          Recursion (Lec1.ppt)
          Java Basics Review (Classes and Methods):  (Lec2.ppt)
          Friday discussion: Java Basics (Strings, Base Types) (Dis2.ppt)
          Week 2 Jan 16-20

          Lab 2
          (due Fri, Jan 27, 23:59pm)

          Example solution: Lab2.zip (this solution is good but not perfect: please read the TA's "comments.txt" note in the zip file!)

           

          Java basics: Ch.1 

          Java Basics Review (Assignment, Arithmetics, Casting, Control Flow): (Lec3.ppt)
          Wednesday discussion: Loops: (Dis3.ppt)
          Thursday: Methods, Parameter Passing, Variable Scoping: (Lec4.ppt)
          Friday discussion: Static/Dynamic methods and variables, Arrays: (Dis4.ppt)

          Week 3 Jan 23-27

          Lab 3
          (due Tue, Feb 7, 23:59pm)

          Three example solutions: Lab3 (see the TA's comments in each. One solution uses the simple Array, and the other two use the generic ArrayList.)

          Hw2.txt
          (due Tue, Jan 31, in class)

          Java inheritance and polymorphism: Ch.2 

          Class Hierarchy, Exception Handling: (Lec5.ppt)
          this/super, Abstract Classes (Dis5.ppt)
          Polymorphism, Dynamic Dispatch (Lec6 and Dis6.ppt)
          Directory with Examples in the Lecture & Discussion

          Week 4 Jan 30 -  Feb 3   Hw3.txt
          (due Thu, Feb 9, in class)

          Interfaces and generics in Ch.2

          Algorithm analysis: Ch.4

          Interfaces (Lec6.ppt)
          Example: Comparable Interface

          Analysis of Algorithms, O-notation, (Lec7.ppt)

          Week 5 Feb 6-10

          Lab 4
          (due Wed, Feb 15, 10am)

            Recurrence relations: Sec.11.1.5 

          Correctness and Running Time of Recursive Algorithms,
          Recurrence Relations

          (no powerpoint notes: all done on the blackboard...) 

          Week 6 Feb 13-17     Hw4 (due Wed, Feb 22)  Chapter 5 and Sections 3.2-3

          Stacks, Queues, Linked Lists
          (GTLec5) (GTLec6) (GTLec4)

          Week 7 Feb 20-24 Thursday Feb 23: Midterm Exam  

          Section 6.1.5

          Growing Arrays (Lec8.ppt)

          Week 8 Feb 27-March 3   Lab 5
          (due Fri, Mar 10, 11:59pm)

          Hw5.txt
          (due Tue, Mar 7, in class)

          Solutions: sol5.txt

          Chapter 6: Sections 6.1, 6.2, 6.3, 6.5 
          Chapter 8: Sections 8.1, 8.2

          Array Lists, Node Lists, Iterators (Lec9.ppt)

          Favorite Lists, Move-to-Front Heuristic (on blackboard...)
          Priority Queues (Lec10.ppt)

          Week 9 March 6-10     Hw6.txt
          (due Wed, Mar 15, in class)

          Chapter 7: Sections 7.1, 7.3,
          Chapter 8: Section 8.3

          Chapter 7:  Sections 7.1, 7.2 

          Binary Trees, Heaps (Lec11.ppt)

          General Trees, Tree Traversal Algorithms (Lec12.ppt)

           Week 10 March 13-17     

            Chapter 9: Sections 9.1, 9.3

          Chapter 9: Section 9.2

          Maps, Dictionaries (Lec13.ppt)

          Hash Functions and Hash Tables (Lec14.ppt)

          Finals Week March 20-24        FINAL EXAM

          http://www.ics.uci.edu/~stasio/Papers/dfjw04-abs.html

          Versatile Padding Schemes for Joint Signature and Encryption
          Yevgeni Dodis, Michael J. Freedman, Stanislaw Jarecki, Shabsi Walfish

          ABSTRACT:

          We build several highly-practical and optimized signcryption constructions directly from trapdoor permutations, in the random oracle model.  All our constructions share features such as simplicity, efficiency, generality, near-optimal exact security, flexible and ad-hoc key management, key reuse for sending/receiving data, optimally-low message expansion, "backward" use for plain signature/encryption, long message and associated data support, the stronger known qualitative security (so-called IND-CCA and sUF-CMA) and, finally, compatibility with the PKCS#1 infrastructure.  While some of these features are present in previous works to various extents, we believe that our schemes improve on earlier proposals in at least several dimensions.

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mso-level-text:��; mso-level-tab-stop:4.5in; mso-level-number-position:left; text-indent:-.25in; mso-ansi-font-size:10.0pt; font-family:Wingdings;} @list l3 {mso-list-id:1500778085; mso-list-type:hybrid; mso-list-template-ids:-738453186 67698689 67698691 67698693 67698689 67698691 67698693 67698689 67698691 67698693;} @list l3:level1 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:.25in; text-indent:-.25in; font-family:Symbol;} @list l3:level2 {mso-level-number-format:bullet; mso-level-text:o; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:.75in; text-indent:-.25in; font-family:"Courier New";} @list l3:level3 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:1.25in; text-indent:-.25in; font-family:Wingdings;} @list l3:level4 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; 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text-indent:-.25in; font-family:Wingdings;} @list l4 {mso-list-id:1535071840; mso-list-type:hybrid; mso-list-template-ids:-785879792 67698689 67698691 67698693 67698689 67698691 67698693 67698689 67698691 67698693;} @list l4:level1 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:.25in; text-indent:-.25in; font-family:Symbol;} @list l4:level2 {mso-level-number-format:bullet; mso-level-text:o; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:.75in; text-indent:-.25in; font-family:"Courier New";} @list l4:level3 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:1.25in; text-indent:-.25in; font-family:Wingdings;} @list l4:level4 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:1.75in; text-indent:-.25in; font-family:Symbol;} @list l4:level5 {mso-level-number-format:bullet; mso-level-text:o; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:2.25in; text-indent:-.25in; font-family:"Courier New";} @list l4:level6 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:2.75in; text-indent:-.25in; font-family:Wingdings;} @list l4:level7 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:3.25in; text-indent:-.25in; font-family:Symbol;} @list l4:level8 {mso-level-number-format:bullet; mso-level-text:o; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:3.75in; text-indent:-.25in; font-family:"Courier New";} @list l4:level9 {mso-level-number-format:bullet; mso-level-text:��; mso-level-tab-stop:none; mso-level-number-position:left; margin-left:4.25in; text-indent:-.25in; font-family:Wingdings;} ol {margin-bottom:0in;} ul {margin-bottom:0in;} --> </style> <!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} </style> <![endif]--><!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1026"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body lang=EN-US link=blue vlink=purple style='tab-interval:.5in'> <div class=WordSection1> <h2><span class=MsoBookTitle><span style='font-size:20.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif"'>CS.167:<span style='mso-spacerun:yes'>� </span>Applied Cryptography<o:p></o:p></span></span></h2> <h2 style='margin:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial; color:black'>Spri</span><span style='font-size:12.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black; font-weight:normal;mso-bidi-font-weight:bold'>n</span><span style='font-size: 12.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>g 2013<o:p></o:p></span></h2> <h2 style='margin:0in;margin-bottom:.0001pt'><span style='font-size:12.0pt; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial; color:black'><o:p>&nbsp;</o:p></span></h2> <p class=MsoNormal><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font: minor-latin;mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font: minor-latin;mso-bidi-font-family:Arial;color:black;mso-bidi-font-weight:bold'>This course explains the inner working of cryptographic tools, the security properties they are designed to achieve, how to reason about their security, and how to properly use them.</span><span style='font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'><span style='mso-spacerun:yes'>� </span></span><span style='font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family: Arial;color:black'>We will cover topics such as encryption (secret-key and public-key), message authentication, user authentication, digital signatures, key management, cryptographic hashing, network security protocols (SSL, IPsec), and public-key infrastructure.<span style='mso-spacerun:yes'>� </span>Towards the end of the class we will touch on a few advanced topics such as zero-knowledge proofs and secure computation.<o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>Prerequisites:</span></b><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>&nbsp;&nbsp;The course is intended for upper-level undergraduates, and we assume familiarity with algorithms (CS.161) and discrete math (ICS.6B / ICS.6D).<span style='mso-spacerun:yes'>� </span>Basic understanding of probability theory and modular arithmetic will be helpful, although we will review relevant concepts as we need them.<o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><o:p>&nbsp;</o:p></p> <div style='mso-element:para-border-div;border:none;border-top:solid #D0A760 1.5pt; padding:2.0pt 0in 0in 0in;background:white'> <h2 style='background:white;border:none;mso-border-top-alt:solid #D0A760 1.5pt; padding:0in;mso-padding-alt:2.0pt 0in 0in 0in'><span style='font-family:"Calibri","sans-serif"; color:#333333'>Syllabus, Lecture Notes, <span class=SpellE>Homeworks</span><o:p></o:p></span></h2> </div> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'>Course Syllabus and Lecture Notes<o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l4 level1 lfo2'><![if !supportLists]><span style='font-size:12.0pt;font-family:Symbol;mso-fareast-font-family:Symbol; mso-bidi-font-family:Symbol;color:black;font-weight:normal'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black; font-weight:normal'><a href="http://www.ics.uci.edu/~stasio/cs167/syllabus.html">Course Syllabus + lecture notes and readings</a><o:p></o:p></span></h2> <h2><span class=SpellE><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial; color:black'>Homeworks</span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>1.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_hmw1.pdf?op=getfile&amp;file=2693478"><span class=GramE>Homework #1 (<span class=SpellE>pdf</span>)</span></a><span class=GramE><span style='mso-tab-count:1'>�������� </span><span style='font-weight:normal;mso-bidi-font-weight:bold'>due</span> <span style='font-weight:normal;mso-bidi-font-weight:bold'>Monday, 4/22/2013, in class.</span></span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-spacerun:yes'>�� </span><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_sol1.pdf?op=getfile&amp;file=2710484">Solutions (<span class=SpellE>pdf</span>)</a><o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>2.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_hmw2.pdf?op=getfile&amp;file=2715656"><span class=GramE>Homework #2 (<span class=SpellE>pdf</span>)</span></a><span class=GramE><span style='mso-tab-count:1'>�������� </span><span style='font-weight:normal;mso-bidi-font-weight:bold'>due</span> <span style='font-weight:normal;mso-bidi-font-weight:bold'>Monday, 5/6/2013, in class.</span></span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-spacerun:yes'>�� </span><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_sol2.pdf?op=getfile&amp;file=2739446">Solutions (<span class=SpellE>pdf</span>)</a><o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>3.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_hmw3.pdf?op=getfile&amp;file=2742732"><span class=GramE>Homework #3 (<span class=SpellE>pdf</span>)</span></a><span class=GramE><span style='mso-tab-count:1'>�������� </span><span style='font-weight:normal;mso-bidi-font-weight:bold'>due</span> <span style='font-weight:normal;mso-bidi-font-weight:bold'>Monday, 5/20/2013, in class.</span></span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-spacerun:yes'>� </span><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_sol3.pdf?op=getfile&amp;file=2762446">Solutions (<span class=SpellE>pdf</span>)</a> <o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>4.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><a href="https://eee.uci.edu/toolbox/dropbox/download.php/sjarecki_hmw4.pdf?op=getfile&amp;file=2791257"><span class=GramE>Homework #4 (<span class=SpellE>pdf</span>)</span></a><span class=GramE><span style='mso-tab-count:1'>�������� </span><span style='font-weight:normal;mso-bidi-font-weight:bold'>due</span> <span style='font-weight:normal;mso-bidi-font-weight:bold'>Wednesday, 6/5/2013, in class.</span></span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'> <o:p></o:p></span></h2> <h2 style='margin-left:.25in'><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'><o:p>&nbsp;</o:p></span></h2> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black; font-weight:normal;mso-bidi-font-weight:bold'><o:p>&nbsp;</o:p></span></h2> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><o:p>&nbsp;</o:p></p> <div style='mso-element:para-border-div;border:none;border-top:solid #D0A760 1.5pt; padding:2.0pt 0in 0in 0in;background:white'> <h2 style='background:white;border:none;mso-border-top-alt:solid #D0A760 1.5pt; padding:0in;mso-padding-alt:2.0pt 0in 0in 0in'><span style='font-family:"Calibri","sans-serif"; color:#333333'>Course Information</span><span style='font-size:12.0pt; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial; color:black;font-weight:normal'><o:p></o:p></span></h2> </div> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'>Course Meeting Times<o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>5.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>Lectures:<span style='mso-tab-count: 2'>�������������� </span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'>Mondays, Wednesdays, Fridays, 9am-9:50am, <span class=SpellE>Steinhaus</span> Hall (bldg. 502), room 134<o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l0 level1 lfo4'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:Calibri;mso-fareast-theme-font: minor-latin;mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Calibri; mso-bidi-theme-font:minor-latin;color:black;font-weight:normal;mso-bidi-font-weight: bold'><span style='mso-list:Ignore'>6.<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>TA Discussion:<span style='mso-tab-count: 1'>����� </span></span><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt; font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin;mso-fareast-font-family: "Times New Roman";mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial; color:black;font-weight:normal;mso-bidi-font-weight:bold'>Thursdays, 11am-noon, Bren Hall (bldg. 314), room 4011, and Wednesdays, 2pm-3pm, Bren Hall, room 3011.<o:p></o:p></span></h2> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black; font-weight:normal;mso-bidi-font-weight:bold'>Attendance at the weekly discussion session led by the TA-led is optional, but highly recommended.<o:p></o:p></span></h2> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'>Course Staff and Office Hours:<o:p></o:p></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l3 level1 lfo6'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:black; font-weight:normal;mso-bidi-font-weight:bold'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>Instructor:<span style='mso-tab-count: 1'>����������� </span></span><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black;font-weight:normal;mso-bidi-font-weight: bold'>Prof. Stanislaw Jarecki, Donald Bren Hall (bldg. 314), <span class=GramE>room</span> 4026, email: <span class=SpellE><i style='mso-bidi-font-style:normal'>stasio</i></span> (at) <i style='mso-bidi-font-style:normal'>ics</i>.<i style='mso-bidi-font-style: normal'>uci</i>.<i style='mso-bidi-font-style:normal'>edu, o</i>ffice hours on <i style='mso-bidi-font-style:normal'>Wednesdays, 10am-11am and 2-3pm.<o:p></o:p></i></span></h2> <h2 style='margin-left:.25in;text-indent:-.25in;mso-list:l3 level1 lfo6'><![if !supportLists]><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:black; font-weight:normal;mso-bidi-font-weight:bold'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-size:12.0pt;mso-bidi-font-size: 18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>TA:</span><span style='font-size:12.0pt; mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif";mso-ascii-theme-font: minor-latin;mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font: minor-latin;mso-bidi-font-family:Arial;color:black;font-weight:normal; mso-bidi-font-weight:bold'><span style='mso-tab-count:3'>������������������������ </span><span class=SpellE>Quan</span> Nguyen, Computer Science (bldg. 302), <span class=GramE>room</span> 468, email: <i style='mso-bidi-font-style:normal'>quann1</i> (at) <i style='mso-bidi-font-style:normal'>uci.edu, </i>office<i style='mso-bidi-font-style:normal'> </i>hours on <i style='mso-bidi-font-style: normal'>Tuesdays, 11am-12pm and 2-3pm.<o:p></o:p></i></span></h2> <h2><span style='font-size:12.0pt;mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black; font-weight:normal;mso-bidi-font-weight:bold'>When you write an email to either the instructor or the TA concerning some topic related to this class, please prefix your email Subject line by  CS167: [& ] .<i style='mso-bidi-font-style: normal'><o:p></o:p></i></span></h2> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>Grading:</span></b><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'> <b><o:p></o:p></b></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'>70% <span class=SpellE>homeworks</span>, 30% final</span><span style='font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'><o:p>&nbsp;</o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><b><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman";mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial;color:black'>Text book:<o:p></o:p></span></b></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-family:"Calibri","sans-serif";mso-ascii-theme-font:minor-latin; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'>There is no required textbook in the class, but the following books can be used to supplement the lectures: </span><span style='font-family:"Calibri","sans-serif"; mso-ascii-theme-font:minor-latin;mso-fareast-font-family:"Times New Roman"; mso-hansi-theme-font:minor-latin;mso-bidi-font-family:Arial;color:black'><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo8; tab-stops:list .25in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; mso-fareast-font-family:"Times New Roman";color:#333333'>Optional:&nbsp;<i><a href="http://www.cs.umd.edu/~jkatz/imc.html"><span style='color:#336699'>Introduction to Modern Cryptography</span></a></i>&nbsp;by J. Katz and Y. <span class=SpellE>Lindell</span><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:.25in;text-indent:-.25in;mso-list:l1 level1 lfo8; tab-stops:list .25in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; mso-fareast-font-family:"Times New Roman";color:#333333'>Optional:&nbsp;<i><a href="http://www.cacr.math.uwaterloo.ca/hac/"><span style='color:#336699'>Handbook of Applied Cryptography</span></a>&nbsp;</i>by A. <span class=SpellE>Menezes</span>, P. Van <span class=SpellE>Oorschot</span>, S. Vanstone&nbsp;<a href="http://www.cacr.math.uwaterloo.ca/hac/"><span style='color:#336699'>(free!)</span></a><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-family:"Calibri","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#333333;background:white'>Note that the textbooks do not cover all the material discussed in class.<o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='font-family:"Calibri","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#333333;background:white'><o:p>&nbsp;</o:p></span></p> <div style='mso-element:para-border-div;border:none;border-top:solid #D0A760 1.5pt; padding:2.0pt 0in 0in 0in;background:white'> <h2 style='background:white;border:none;mso-border-top-alt:solid #D0A760 1.5pt; padding:0in;mso-padding-alt:2.0pt 0in 0in 0in'><span style='font-family:"Calibri","sans-serif"; color:#333333'>Homework Policy<o:p></o:p></span></h2> </div> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; color:#333333'>You may collaborate on the <span class=SpellE>homeworks</span>, but in groups whose size does not exceed two students.<o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; color:#333333'>Each student must write down their solution on their own <span class=SpellE>own</span>.&nbsp; If you collaborated with someone else on the homework, you must list the name of this person on your homework as your collaborator.<o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; color:#333333'>Similarly, if you consulted any other source while solving a homework problem, e.g. a book or on-line lecture notes, you must list this source clearly on your homework.<span style='mso-spacerun:yes'>� </span><b>It is a violation of an academic policy to consult any source on your homework without giving a proper credit to this source, whether it is another student you ask for help, a textbook, any material you find on-line, or any other source you consulted.</b><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; color:#333333'>Any extra credit question on the homework must be solved individually.<span style='mso-spacerun:yes'>� </span><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='font-family:"Calibri","sans-serif"; color:#333333'>Homework will be due on the due date in class.</span><span style='font-family:"Calibri","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#333333;background:white'><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;text-indent:-.25in;mso-list:l2 level1 lfo10; tab-stops:list .5in;background:white'><![if !supportLists]><span style='font-size:10.0pt;mso-bidi-font-size:12.0pt;font-family:Symbol; mso-fareast-font-family:Symbol;mso-bidi-font-family:Symbol;color:#333333'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><b><span style='font-family:"Calibri","sans-serif"; color:#333333'>Extensions</span></b><span style='font-family:"Calibri","sans-serif"; color:#333333'>:&nbsp; <span style='background:white'>Each student has a total of 72 extension hours throughout the quarter. This extension can be spent in units of 24 hours on any of the assignments and projects. The extension is granted automatically, but to take this extension you must<span class=apple-converted-space>&nbsp;</span><u>notify us</u><span class=apple-converted-space>&nbsp;</span>before the time the homework is due<span class=apple-converted-space>&nbsp;</span><u>by email to the TA</u>&nbsp;that you are taking an extension and you must specify how many 24 hour periods you are taking. &nbsp;Please deliver&nbsp;your submissions&nbsp;to the TA's office in ICS1 (<span class=SpellE>bldg</span> 302), room 458c,<span class=apple-converted-space>&nbsp;</span></span></span><span style='font-family: "Calibri","sans-serif";mso-bidi-font-family:Arial;color:#333333;background: white'><span style='orphans: auto;text-align:start;widows: auto;-webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;word-spacing:0px'>sliding it under his door if he's not there, and mark the time of the submission on the submission next to your name and class number. &nbsp;</span></span><span style='font-family:"Calibri","sans-serif"; color:#333333;background:white'><span style='orphans: auto;text-align:start; widows: auto;-webkit-text-size-adjust: auto;-webkit-text-stroke-width: 0px; word-spacing:0px'>There will be no additional extensions.</span></span><span style='font-family:"Calibri","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#333333;background:white'><o:p></o:p></span></p> <p class=MsoNormal style='mso-margin-top-alt:auto;margin-right:12.0pt; mso-margin-bottom-alt:auto;margin-left:24.0pt;background:white'><span style='font-family:"Calibri","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#333333;background:white'><o:p>&nbsp;</o:p></span></p> <div style='mso-element:para-border-div;border:none;border-top:solid #D0A760 1.5pt; padding:2.0pt 0in 0in 0in;background:white'> <h2 style='background:white;border:none;mso-border-top-alt:solid #D0A760 1.5pt; padding:0in;mso-padding-alt:2.0pt 0in 0in 0in'><span style='font-size:12.0pt; mso-bidi-font-size:18.0pt;font-family:"Calibri","sans-serif";color:#333333'>Last modified: April 24, 2013 <o:p></o:p></span></h2> </div> </div> </body> </html> http://www.ics.uci.edu/~stasio/spring04/ics180.html ICS 180: Introduction to Cryptography

          ICS 180: Introduction to Cryptography

          Spring Quarter, 2004


           Instructor:       Stanislaw Jarecki

          • Class time:        Tu-Th, 11-12:20
          • Room:              SSTR (social science trailer, bldg 203), room 100
          • Class number:   ICS 180, Section A
          • Prerequisites:    ICS 6A and ICS 161, but see below
          • Textbook:         None, but there�ll be lecture note handouts, and lots of material is available on-line (see more below)
          • Office hours:     Monday 3-5 (for now), and otherwise by appointment.  I encourage you to use the office hours and also to communicate with me by email.

          Most important info:

          • Lecture summaries, lecture notes, problem sets, solutions, other handouts       [shortcut to handout list]
          • Reference reading material

          Course Description:

          This course is an introduction to modern cryptography for advanced undergraduates.   In this class, rather than looking at many different cryptographic schemes and protocols, we will focus on understanding the *goals* of security protocols, i.e. on understanding what properties a protocol needs to achieve to be considered secure and truly useful for computer applications, and on showing how to *prove* that some protocol actually achieves these properties, and hence is indeed secure. We will see how to define, construct, and prove security of symmetric and public-key encryption schemes, signature schemes, authentication schemes, identification protocols, etc.  At the end of the course, the students will be able to understand cryptographic research and, if interested, pursue such research themselves.  The class should also be interesting and useful to anyone interested in general computer and network security.

          What this class is not about:

          This class will not teach you all there�s to know to make computers and networks secure.  Cryptography is only one tool, or if you will, one layer in the stack of engineering issues that need to be solved to make computers secure.  Computer security has to deal with lots of other issues we�ll not touch on in the class, like buggy code, viruses and other break-ins, denial of service attacks, the need to monitor a network, preventing bad passwords, integrating various network services securely, and many more.  We�ll stay firmly on the layer of algorithm design, although the real-world security issues like those listed above will motivate the security properties we�ll require of our cryptographic algorithms.

          In this class we�ll also not concentrate on techniques used to design and analyze block ciphers (like DES or AES) and hash functions (like MD5 and SHA).  The class will offer you some insight into security of such constructions, but designing and analyzing such constructions is mostly a separate art, and since there are no good techniques as of now to prove much about their security, we�ll not focus on these constructions in this class.

          Grading:

          All your grades will be based on weekly problem sets and a take-home final (which will be just a longer homework).  You are also encouraged to actively participate in the class.

          Lectures and Notes:

          Since there�s no textbook, it�s really important that you come to lectures and participate.  We�ll be distributing lecture notes and pointers to on-line information, but I�m still developing these and they might not always cover everything we do in class.

          Prerequisites:

          The formal prerequisites are ICS 6.A and ICS.161.  However, what you really need is this:

          • You cannot be �math-phobic� or �theory-phobic�!  This class will be about definitions and proofs, and we�ll use some probability and some number theory in analysis of our algorithms.

          More specifically, you need the following:

          • You should be comfortable with proofs, with elementary probability, and have the basic knowledge of discrete math used in computer science (e.g. ICS.6A).
          • It's highly recommended that you have some algorithms class (like ICS.161), and that you are familiar with asymptotic analysis of algorithm running time (e.g. that one algorithm has O(n2) running time and the other only O(n), and that the third algorithm is probabilistic and it has a probability of error O(1/n), etc.)
          • It'd be also easier for you if you took a computability/complexity class like ICS.162, so you are familiar with the notion of polynomial-time algorithms, and the notion of a reduction between computational problems.   However, this is material which we'll go over in this class to the extend that we'll use it, and I believe that you can pick it up easily.
          • You do not have to know modular arithmetic, although it will help if you were exposed to it before.  We�ll do a review of everything we will need.

          The last three topics listed above will be briefly reviewed in class. If you are missing some of this background, see the reading list link above for review material which is available on-line.

          This class is complementary to other UCI classes on security/cryptography:

          No previous experience in cryptography or security is necessary for this class (see the prerequisites above), but for those students who have taken or are thinking of taking other UCI classes on security/cryptography, here is a word of explanation why this class differs from and complements the related UCI classes:

          • ICS 168/268:   Students who took ICS 168/268 are very much encouraged to take this class.  The two classes cover different material and can be taken in any order.  The reason the two classes complement each other well is that ICS 168/268 focuses on breadth of cryptographic algorithms and secure applications, while this class focuses on depth, by laying out a systematic approach to the study of any secure algorithms and protocols.
          • Math 173A:   Similarly, students who took and liked "Intro to Cryptology", Math 173A, are encouraged to take this class too.  The material differs, but Math 173A is a very good introduction and background for this class.  Math 173A teaches number theoretical foundations of the two most famous public key algorithms, i.e. the RSA encryption/signature schemes based on the factoring assupmtion, and the encryption/signature schemes based on the discrete logarithm assumption.   This is a very useful and fun material, but from the point of view of crypto theory as taught in this class, what Math 173A shows is that a specific cryptographic object called a one way function can be efficiently built from particular number-theoretic assumptions (discrete log or factoring).  In this class we will take the assumption that one-way functions can be constructed as more or less our starting point (!), and we will see how to build more complex cryptographic objects on this and other assumptions (see further info in the tentative course outline below).


          http://www.ics.uci.edu/~stasio/Papers/js04-abs.html

          Handcuffing Big Brother: An Abuse-Resilient Transaction Escrow Scheme
          Stanislaw Jarecki and Vitaly Shmatikov

          ABSTRACT:

          We propose a practical abuse-resilient transaction escrow scheme with applications to privacy-preserving audit and monitoring of electronic transactions. Our scheme ensures correctness of escrows as long as at least one of the participating parties is honest, and it ensures privacy and anonymity of transactions even if the escrow agent is corrupt or malicious. The escrowed information is secret and anonymous, but the escrow agent can efficiently find transactions involving some user in response to a subpoena or a search warrant. Moreover, for applications such as abuse-resilient monitoring of unusually high levels of certain transactions, the escrow agent can identify escrows with particular common characteristics and automatically (i.e., without a subpoena) open them once their number has reached a pre-specified threshold. Our solution for transaction escrow is based on the use of Verifiable Random Functions. We show that by tagging the entries in the escrow database using VRFs indexed by users' private keys, we can protect users' anonymity while enabling efficient and, optionally, automatic de-escrow of these entries. We give a practical instantiation of a transaction escrow scheme utilizing a simple and efficient VRF family secure under the DDH assumption in the Random Oracle Model.

          http://www.ics.uci.edu/~stasio/winter04/ics280.html ICS 280: Introduction to the Theory of Cryptography

          ICS 280: Introduction to the Theory of Cryptography

          Winter Quarter, 2004


          tentative outline 

          list of reference readings

          handouts (and homeworks)


           Instructor:       Stanislaw Jarecki

          • Class times:     Tu-Th, 11-12:20
          • Room:             CS building, room 243
          • Class number:  ICS 280, Section C
          • Class code:     36783
          • Office hours:   Mon 3-5, and otherwise by appointment or just by stopping by
          • Textbook:       None, but lots of material is available on-line (see more below)

           

          Course Description:

          This course is an introduction to modern cryptography for graduate and advanced undergraduate students.   At the end of the course, the students will be able to understand current research in cryptography and, if interested, pursue such research themselves.  The cryptographic toolkit we will cover will be also useful for students interested in algorithms or in security.

          Modern cryptography provides tools for the design of provably secure protocols.  It shows that complex security requirements of modern computer systems can be satisfied by algorithms that are provably secure against adversarial attacks, assuming some well-defined computational complexity assumptions.  Existence of such proofs allows practitioners to build computer systems whose security rests on firm foundations.  However, the resulting computer systems are secure only to the degree that they are implemented correctly (an issue we will not cover in this class), and that the security requirements imposed on the cryptographic algorithm correctly represent the operation of the system, and hence the types of attacks that can be launched against it.  We will touch on this last point quite often in our class, whenever we discuss the security requirement of any cryptographic tool.

          This winter quarter course is intended as an introductory class, and so we will start with the fundamentals of modern cryptography and gradually move up to more complex cryptographic tools, which can then be applied to building secure protocols.  The primary focus of the class will be on:

          • Definitions:  We will see how to conceptualize the goals of security (e.g. "secure communication"), and by doing so we will define cryptographic objects (i.e. algorithms or protocols) of increasing complexity, like one-way functions, collision-resistant functions, pseudorandom functions, signature schemes, encryption schemes, and others.  We will define the needed properties of these objects by drawing examples from their practical applications.
          • Constructions, protocol design, composing systems from cryptographic objects, rigorous proofs of security:  We will see how to achieve various security goals and construct the corresponding cryptographic objects which provably meet the required properties under well-defined computational difficulty assumptions, e.g. the assumption that factoring is difficult.  In general, we will see how to construct more complex cryptographic objects from simpler ones, and how the resulting tools can be used to satisfy requirements of actual applications.

          The most important lesson of this course should be not any particular cryptographic construction, but the approach of modern cryptography:  (1) the  importance of defining the security requirements of the application at hand, and (2) knowing how to go about arguing if (and on what grounds) a proposed algorithm satisfies these requirements.

          Tentative Outline:

          See the tentative outline for the list of topics we will cover.

          Background Reading List:

          See the list of reference texts for the course

          Grading:

          There will be about 4-6 homework sets (counting 70%) and a take-home final (20%).  You will be expected to actively participate in the class.  Depending on the attendance level, 10% of the grade will be either for class participation or for scribing the lecture notes.

          Prerequisites:

          There are no formal prerequsites for this class.  However:

          • You should be comfortable with proofs, with elementary probability, and have the basic knowledge of discrete math used in computer science (e.g. ICS.6A).
          • It's recommended that you have some algorithms class (like ICS.161) and you are familiar with assymptotic analysis of algorithm running time.
          • It'd be also good if you took a computability/complexity class like ICS.162, so you are familiar with P/NP and the notion of a reduction between computational problems.
          • It will help to be familiar with basic algebraic concepts (groups, fields), and number theory concepts (e.g. primality), but you don't have to have a class in it.

          The last three topics listed above will be briefly reviewed in class. In fact, if you are missing some of this background, see the reading list link above for review material which is available on-line.  Even if you do not have all the background listed above, you will be able to pick it up from the class review, and then consult the listed textbooks when needed.

          This class is complementary to other UCI classes on security/cryptography:

          No previous experience in cryptography or security is necessary for this class (see the prerequisites above), but for those students who have taken or are thinking of taking other UCI classes on security/cryptography, here is a word of explanation why this class differs from and complements the related UCI classes:

          • ICS 168/268:   Students who took ICS 168/268 are very much encouraged to take this class.  The two classes cover different material and can be taken in any order.  The reason the two classes complement each other well is that ICS 168/268 focuses on breadth of cryptographic algorithms and secure applications, while this class focuses on depth, by laying out a systematic approach to the study of any secure algorithms and protocols.
          • Math 173A:   Similarly, students who took and liked "Intro to Cryptology", Math 173A, are encouraged to take this class too.  The material differs, but Math 173A is a very good introduction and background for this class.  Math 173A teaches number theoretical foundations of the two most famous public key algorithms, i.e. the RSA encryption/signature schemes based on the factoring assupmtion, and the encryption/signature schemes based on the discrete logarithm assumption.   This is a very useful and fun material, but from the point of view of crypto theory as taught in this class, what Math 173A shows is that a specific cryptographic object called a one way function can be efficiently built from particular number-theoretic assumptions (discrete log or factoring).  In this class we will take the assumption that one-way functions can be constructed as more or less our starting point (!), and we will see how to build more complex cryptographic objects on this and other assumptions (see further info in the tentative course outline below).


          Last modified: 07 Jan 2004
          http://www.ics.uci.edu/~stasio/ics6b-s10/index.html ICS 6B: Boolean Algebra & Logic - Spring 2010

          ICS 6B: Boolean Algebra & Logic - Spring 2010

          • Class meetings
            • Lecture: MWF 9:00-9:50am, in ICS 174
            • Discussion: MW 2:00-2:50pm, in ICS 174
          • Instructor
            • Professor: Stanislaw Jarecki
            • Office hours: MWF 10:00-10:45am, Donald Bren Hall, room 4026
            • Email: stasio (at) ics.uci.edu
          • Teaching assistant
            • Sholeh Forouzan
            • Office hours: Thursday, 3pm-5pm, ICS bldg, room 406B
            • Email: sforouza (at) ics.uci.edu
          • Grading
            Grading will be based on the following weights:
            • Quizzes (45%) will be given in lecture on Fridays at the start of the class. (Any exceptions will be announced in class.) The lowest quiz score will be dropped when computing your quiz average.
            • Homeworks (5%) will be due each Friday in lecture, gathered at the beginning of the class or together with quizzes. The lowest homework score will also be dropped when computing your homework average.
              (Homework grading policy: Homeworks are only graded on the scale 0-2, where 0 means no homework or clearly badly done one, 1 point is for most of the homework done seemingly correctly (this grade is assigned after a superficial examination of your solutions!), and 1 extra point is given for answering correctly a chosen exercise. Homework solutions will be released on-line on the day before the homework is due.)
              Click here for the homework assignments.
            • Miterm (20%) will take place on Monday, May 3.
            • Final (30%) will take place on Wed, Jun 9, 8am-10am.
          • Text book
            • [Rosen] Kenneth H. Rosen, Discrete Mathematics and Its Applications, 6th edition, McGraw Hill, 2007.
              This book is required, and it should be available at the UCI bookstore.
              Note: There is an online list of errata.
              You should read the sections of the book that pertain to the lectures and homeworks of the given week: The textbook will often cover the same material but with more detail, giving many more examples, and providing all sort of useful background material: You should always read the relevant sections of the book independently from attending the lectures and the discussion sessions!
            • For those who are ordering the textbook on-line and still have not received it, here are the xeroxed pages of Sections 1.1 and 1.2 which we are covering in the first week of class (they include solutions to odd-numbered exercises in these sections!).
          • Add/drop policy
            • During the first two weeks, all adds and drops will be handled using the electronic add drop (EAD) system.
              (In other words, I will not sign add cards or drop cards during the first two weeks.)
            • Authorization codes are available only from the ICS Student Affairs Office.
              I do not have any codes.
            • I will sign add cards during week 3 only if there are slots still available (which is most unlikely).
            • I will sign drop cards during weeks 4, 5, and 6.
          • Course announcements
            • Course announcements will be sent via email to the official UCI email address of all students enrolled in the class.
              (Send an email to the TA or the lecturer if you are not officially registered, e.g. because you are on the waiting list to register, and want to be on the email list.)
          • List of topics, by week. Numbers in parentheses are sections from [Rosen].
            Note that the following schedule is approximate.
            • Week 1: Logic (1.1), Propositional equivalences (1.2), Predicates and quantifiers (1.3)
            • Week 2: Nested quantifiers (1.4), Rules of inference (1.5), Introduction to proofs (1.6)
            • Week 3: Proof methods and strategy (1.7), Sets (2.1, 2.2), Functions (2.3)
            • Week 4: Relations and their properties (8.1), n-ary relations and their applications (8.2)
            • Week 5: Matrices (3.8), Representing relations (8.3), Closure of relations (8.4)
            • Week 6: Equivalence relations (8.5), Partial orderings (8.6)
            • Week 7: Boolean functions (11.1), Representing Boolean functions (11.2)
            • Week 8: Logic gates (11.3), Languages and grammars (12.1)
            • Week 9: Finite state machines (12.2, 12.3)
            • Week 10: Turing machines (12.5)

          Last modified: March 29, 2010 http://www.ics.uci.edu/~stasio/Papers/jsy04-abs.html

          An Attack on the Proactive RSA Signature Scheme in the URSA Ad Hoc Network Access Control Protocol
          Stanislaw Jarecki, Nitesh Saxena, Jeong Hyun Yi

          ABSTRACT:

          Recently, Luo, et al. in a series of papers [LL00, KZLLZ01, KLXL02, LZKLZ02, LKZLZ04] proposed a system called URSA for providing ubiquitous and robust access control in mobile ad hoc networks without relying on a centralized authority. The URSA system relies on the new proactive RSA signature scheme, which allows members in an ad hoc group to make access control decisions in a distributed manner. The proposed proactive RSA signature scheme is assumed secure as long as no more than an allowed threshold of participating members is simultaneously corrupted at any point in the lifetime of the scheme.

          In this paper we show an attack on this proposed proactive RSA scheme, in which an admissible threshold of malicious group members can completely recover the group RSA secret key in the course of the lifetime of this scheme. Our attack stems from the fact that the threshold signature protocol which is a part of this proactive RSA scheme leaks some seemingly innocuous information about the secret signature key. We show how the corrupted members can influence the execution of the scheme in such a way so that the slowly leaked information is used to reconstruct the entire shared secret.

          http://www.ics.uci.edu/~xhx/courses/CS284A/index.html CS284A: Representations & Algorithms for Molecular Biology / Introduction to Computational Biology and Bioinformaics

          CS284A: Representations & Algorithms for Molecular Biology

          Instructor: Xiaohui S. Xie
             Email: xhx at ics.uci.edu
             Office Location: 4058 Bren Hall

          Office hours: TT after class

          Meeting information: TT 3:30-4:50pm    Room: ICS 253

          Course code: 35370

          Lecture schedule

          Philosophy

          Prerequisites

          Subject requirements

          Textbooks


          Philosophy

          Recent breakthroughs in sequencing technology, genomics, gene expression profling, and compuational science have changed the face of modern biology, transforming it into a largely information-based science. This course introduces the state-of-the-art computational methods used in studying modern biological systems. We study the principles of algorithm design for biological datasets, analyze commonly used algorithms, and apply them to solve real-life problems. The course serves as an introductory course to the rapidly growing field of computational biology and bioinformatics. Students involved in active research in biology or bioinformatics are encouraged to bring their own research projects into the class, and develop them into course projects by teaming up with students from computer science.

          Prerequisites

          • familiarity with multivariate calculus and probability theory
          • knowledge of a programming language

          Subject requirements

          • no homework assignments, but a research project is required

          Textbooks

          • Recommended:  R. Durbin, S. Eddy, A. Krogh and G. Mitchison.   Biological Sequence Analysis
          • Recommended:  P. Baldi and S. Brunak,   2nd Edition 2001.   Bioinformatics: the Machine Learning Approach

            The two and other recommended textbooks will be placed on reserve at the UCI Science Library.
          • Supplemental texts, free online at the NCBI Bookshelf (click title to view):

            • Molecular Biology of the Cell, by Bruce Alberts, Alexander Johnson, Julian Lewis, Martin Raff, Keith Roberts, and Peter Walter. Garland Publishing, 2002.
            • Genomes, by T.A. Brown, BIOS Scientific Publishers, 2002
            • Perl Tutorial
            • http://aspn.activestate.com/ASPN/Python/Cookbook/
            • http://ivory.idyll.org/articles/advanced-swc/
            • http://www.diveintopython.org/
            • http://docs.python.org/lib/
            • http://docs.python.org/ref/ref.html
            • http://www.doughellmann.com/projects/PyMOTW/



          http://www.ics.uci.edu/~gopi/Resume.html Gopi Meenakshisundaram (M

          Gopi Meenakshisundaram (M. Gopi)

          Department of Computer Science

          University of California, Irvine

          gopi@ics.uci.edu

          http://www.ics.uci.edu/~gopi

          949 824 9498

          Education

          • Ph.D., University of North Carolina at Chapel Hill, 2001.
          • M.Sc (Engg.), Indian Institute of Science, 1995.
          • B.E. (Computer Science and Engg.), Thiagarajar College of Engineering, 1992.

           

          Work Experience

          • Professor, July 2013-, Department of Computer Science, UCI.
          • Associate Professor, July 2007- June 2013, Department of Computer Science, UCI.
          • Assistant Professor, July 2001- June 2007, Department of Computer Science, UCI.
          • Research/Teaching Assistant, 1995-2001, Dept. of Computer Science, UNC.
          • Summer Intern, AT&T Research Labs, Summer 1998 and Summer 1999.
          • Senior Software Engineer, Tata Elxsi, Bangalore India, Jan-July 1995.

           

          Journal Publications

           

          J29.      Jia Chen, Shan Jiang, Zachary Destefano, Sungeui Yoon, M. Gopi

          Optimally Redundant, Seek-Time Minimizing Data Layout for Interactive Rendering

          The Visual Computer, Nov 2015.

           

          J28.      Yuqi Li, Aditi Majumder, Dongming Lu, M. Gopi 

          Content-Independent Multi-Spectral Display Using Superimposed Projections

          Computer Graphics Forum, 34(2), 2015.

           

          J27.      Duy-Quoc Lai, Shan Jiang, Aditi Majumder, M. Gopi 

          A Distributed Memory Hierarchy and Data Management for Interactive Scene Navigation and Modification on Tiled Display Walls

          IEEE Transactions on Visualization and Computer Graphics, 21(6), 714-729, 2015.

           

          J26.      Jiang Shan, Behzad Sajadi, Alexander Ihler, M. Gopi 

          Optimizing Redundant-Data Clustering for Interactive Walkthrough Applications

          The Visual Computer 30(6-8): 637-647 (2014)

           

          J25.      Shanaz Mistry, U.N. Niranjan, M. Gopi 

          Puzzhull: Cavity and Protrusion Hierarchy to Fit Conformal Polygons

          Computer Aided Design, Nov 2013

           

          J24.      Yongwei Miao, Jonas Bosch, Renato Pajarola, M. Gopi 

          Feature sensitive re-sampling of point set surfaces with Gaussian spheres

          Science China, 55(9), pp 2075-2089, Aug 2012.

           

          J23.      Behzad Sajadi, M. Gopi, Aditi Majumder

          Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing

          ACM Transactions on Graphics, Aug 2012

           

          J22.      Koel Das, Monica Siegenthaler, Aditi Majumder, Hans Keirstead, M. Gopi
          Automated Cell Classification and Visualization for Analyzing Remyelination Therapy
          The Visual Computer, 2011

           

          J21.      M. Liu, A. Chakraborty, D. Singh, R. K. Yadav, M. Gopi, G. V. Reddy, A. Roy-Chowdhury
          Adaptive Cell Segmentation and Tracking for Volumetric Confocal Microscopy Images of A Developing Plant Meristem
          Molecular Plant Journal, 2011

           

          J20.      S. K. Suter, J. A. I. Guitian, F. Marton, M. Agus, A. Elsener, C.P.E. Zollikofer, M. Gopi, E. Gobbetti, R. Pajarola
          Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization
          IEEE Transactions on Visualization and Computer Graphics, 2011

           

          J19.      Jingliang Peng, Yan Huang, C.-C. Jay Kuo, Ilya Eckstein, M. Gopi

          Feature Oriented Progressive Lossless Mesh Coding

          Computer Graphics Forum, 2010

           

          J18.      Behzad Sajadi, Maxim Lazarov, M. Gopi, Aditi Majumder

          Color Seamlessness in Multi-Projector Displays using Constrained Gamut Morphing

          IEEE Transactions on Visualization and Computer Graphics, 15(9), pp 1317-1326, 2009.

           

          J17.      Pablo Diaz-Gutierrez, David Eppstein, M. Gopi

          Curvature Aware Fundamental Cycles

          Computer Graphics Forum, 28(7), pp 2015-2024, 2009.

           

          J16.      Pablo Diaz-Gutierrez, Jonas Bosch, Renato Pajarola, M. Gopi

          Streaming Surface Sampling Using Gaussian e-nets.

          The Visual Computer, 25(5-7), pp 411-422, 2009.

           

          J15.      Yan Huang, Jingliang Peng, C.-C Jay Kuo, M. Gopi

          A Generic Scheme for Progressive Point Cloud Coding

          IEEE Trans. on Visualization and Computer Graphics, pp 440-453, 14(2), Mar/Apr 2008.

           

          J14.      Don V. Black, M. Gopi, F. Kuester, F. Wessel, R. Pajarola

          Visualizing Flat Spacetime: Viewing Optical versus Special Relativistic Effects

          American Journal of Physics, 75(6), pp 540 - 545, June 2007.

           

          J13.      Pablo Diaz-Gutierrez, Anusheel Bhushan, M. Gopi, Renato Pajarola

          Single Strips for Fast Interactive Rendering

          The Visual Computer, 22(6), pp 372 - 386, June 2006.

           

          J12.      Pablo Diaz-Gutierrez, M. Gopi

          Quadrilateral and Tetrahedral Mesh Stripification Using 2-Factor Partitioning of the Dual Graph

          The Visual Computer, 21(8 -10), pp 689 - 697, Sep 2005.

           

          J11.      Pablo Diaz-Gutierrez, M. Gopi, Renato Pajarola

          Hierarchyless Simplification, Stripification and Compression of Triangulated Two-Manifolds

          Computer Graphics Forum, 24(3), pp 457-467, Sep 2005.

           

          J10.      Aditi Majumder, M. Gopi

          Modeling Color Properties of Tiled Displays

          Computer Graphics Forum, 24(2),pp 149-163, 2005.

           

          J9.      M. Gopi, David Eppstein

          Single-Strip Triangulation of Manifolds with Arbitrary Topology

          Computer Graphics Forum, 23(3), pp 371-379, 2004.

           

          J8.      S. Krishnan, D. Manocha, M. Gopi, T. Culver, J. Keyser

          BOOLE: A Boundary Evaluation System for Boolean Combinations of Sculptured Solids

          Int. Journal of Comp. Geometry and Applications, 11(1), 105-144, 2001.

           

          J7.      M. Gopi, S. Krishnan, C. T. Silva

          Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation

          Computer Graphics Forum, 19(3), pp C467-C478, 2000.

           

          J6.      M. Gopi, D. Manocha

          Simplifying Spline Models

          Comp. Geometry, Theory and Applications, 14, (1-3), 67-90, Nov. 1999.

           

          J5.      S. Krishnan, M. Gopi, M. Lin, D. Manocha, A. Pattekar

          Rapid Accurate Contact Determination between Spline Models using ShellTrees

          Computer Graphics Forum, 17(3), pp C315-C326, 1998.

           

          J4.      S.Krishnan, M.Gopi, D.Manocha, M.Mine

          Interactive Boundary Computation of Boolean Combinations of Sculptured Solids

          Computer Graphics Forum, 16(3), pp C67-C78,1997.

           

          J3.      M.Gopi, S.Manohar

          A Unified Architecture for the computation of B-Spline Curves and Surfaces

          IEEE Trans. on Parallel and Distributed Systems, 8(12), 1275-1287, 1997.

           

          J2.      M.Gopi, S.Manohar

          Parallel architecture for the computation of Uniform Rational B-Spline Patches

          Journal of Parallel and Distributed Computing, Nov. 1995.

           

          J1.      M.Gopi, S.Manohar

          A VLSI architecture for the computation of Uniform B-Spline curves

          Microprocessing and Microprogramming, EUROMICRO Journal, Nov. 1994.

           

          Conference/Workshop Publications

           

          C46.      Jia Chen, Shan Jiang, Zachary Destefano, Sungeui Yoon, M. Gopi 

          Performance Driven Redundancy Optimization of Data Layouts for Walkthrough Applications

          Computer Graphics International (CGI) 2015, Strasbourg, France.

           

          C45.      Yuqi Li, Aditi Majumder, Dongming Lu, M. Gopi 

          Content-Independent Multi-Spectral Display Using Superimposed Projections

          Eurographics 2015. (Same as J28)

           

          C44.      K. Cutler, Z. DeStefano, S. M. Zarandi, T. D. O'Sullivan, A. E. Cerussi, M. Gopi, A. Majumder, S-H. Lee, B. J. Tromberg

          Real-time Mapping and Tracking of Optical Properties in Deep Tissue

          SPIE Photonics, Optical Tomography and Spectroscopy of Tissue XI, Feb 2015, Paper 9319-59.

           

          C43.      Uddipan Mukherjee, M. Gopi 

          Finding Feature Similarities Between Geometric Trees

          Pacific Graphics, Oct 2014

           

          C42.      Jiang Shan, Behzad Sajadi, Alexander Ihler, M. Gopi 

          Optimizing Redundant-Data Clustering for Interactive Walkthrough Applications

          Computer Graphics International Conference, June 2014 (Same as J26)

           

          C41.      Shanaz Mistry, U.N. Niranjan, M. Gopi 

          Puzzhull: Cavity and Protrusion Hierarchy to Fit Conformal Polygons

          SIAM Conference on Geometric Design/Geometric and Physical Modeling, Nov 2013 (Same as J25)

           

          C40.      Shan Jiang, Behzad Sajadi, M. Gopi

          Single-Seek Data Layout for Walkthrough Applications

          SIBGRAPI Conference on Graphics, Patterns, and Images, Aug 2013.

           

          C39.      Behzad Sajadi, Duy-Quoc Lai, Alexander Iher, M. Gopi, Aditi Majumder

          Image Enhancement in Projectors Via Optical Pixel Shift and Overlay

          International Conference on Computational Photography (ICCP), April, 2013.

           

          C38.      Uddipan Mukherjee, M. Gopi 

          Tweening Boundary Curves of Non-simple Immersions of a Disk

          ICVGIP 2012. [Best Paper Award]

           

          C37.      Sangwon Chae,  Aditi Majumder, M. Gopi 

          HD-GraphViz: Highly Distributed Graph Visualization on Tiled Displays

          ICVGIP 2012.

           

          C36.      Behzad Sajadi, M. Gopi, Aditi Majumder

          Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing

          ACM Siggraph 2012. [Same as J23]

           

          C35.      Ishwar Kulkarni, Shanaz Y. Mistry, Brian Cummings, M. Gopi
          A Visual Navigation System for Querying Neural Stem Cell Imaging Data
          IEEE Visual Analytics Science and Technology (VAST), 2011

           

          C34.      K. Mkrtchyan, D. Singh, M. Liu, V. Reddy, A. Roy-Chowdhury, M. Gopi
          Efficient cell segmentation and tracking of developing plant meristem
          IEEE International Conference on Image Processing 2011.

           

          C33.      Uddipan Mukherjee, M. Gopi, Jarek Rossignac
          Immersion and Embedding of Self-Crossing Loops
          International Symposium on Sketch-Based Interfaces and Modeling, 2011

           

          C32.      Ishwar Kulkarni, Uddipan Mukherjee, Chris Sontag, Brian Cummings, M. Gopi
          Robust Segmentation and Tracking of Generic Shapes of Neuro-Stem Cells
          IEEE Conference on Healthcare Informatics, Imaging, and Systems Biology (HISB), 2011

           

          C31.      S. K. Suter, J. A. I. Guitian, F. Marton, M. Agus, A. Elsener, C.P.E. Zollikofer, M. Gopi, E. Gobbetti, R. Pajarola
          Interactive Multiscale Tensor Reconstruction for Multiresolution Volume Visualization
          IEEE Visualization 2011 [Same as J20]

           

          C30.      Behzad Sajadi, Shan Jiang, Jae-Pil Heo, Sung-Eui Yoon, M. Gopi.
          Data Management for SSDs for Large-Scale Interactive Graphics Applications

          ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D), 2011

           

          C29.      Koel Das, Monica Siegenthaler, Aditi Majumder, Hans Keirstead, M. Gopi

          Automated Analysis of Remyelination Therapy for Spinal Cord Injury

          ICVGIP 2010. [Oral Presentation - Top 10%, Invited for The Visual Computer Journal]

           

          C28.      Jingliang Peng, Yan Huang, C.-C. Jay Kuo, Ilya Eckstein, M. Gopi

          Feature Oriented Progressive Lossless Mesh Coding

          Pacific Graphics 2010. [Same as J19] [17%]

           

          C27.      Behzad Sajadi, Maxim Lazarov, Aditi Majumder, M. Gopi

          Color Seamlessness in Multi-Projector Displays using Constrained Gamut Morphing

          IEEE Visualization 2009. [Same as J18] [27%]

           

          C26.      Pablo Diaz-Gutierrez, David Eppstein, M. Gopi

          Curvature Aware Fundamental Cycles

          Pacific Graphics 2009. [Same as J17]. [18%]

           

          C25.      Yongwei Miao, Pablo Diaz-Gutierrez, Renato Pajarola, M. Gopi, Jieqing Feng.

          Shape Isophotic Error Metric Controllable Re-Sampling for Point-sampled Surfaces

          IEEE Intl. Conf. on Shape Modeling and Applications (SMI), June, 2009. pp.28-35. [26%]

           

          C24.      Pablo Diaz-Gutierrez, Jonas Bosch, Renato Pajarola, M. Gopi

          Streaming Surface Sampling Using Gaussian e-nets.

          Computer Graphics International, 2009. [Same as J16] [29%]

           

          C23.      Behzad Sajadi, Yan Huang, Pablo Diaz-Gutierrez, Sung-Eui Yoon, M. Gopi

          A Novel Page-Based Data Structure for Interactive Walkthroughs

          ACM Symposium on Interactive 3D Graphics and Games, Feb 2009.

           

          C22.      Pablo Diaz-Gutierrez, David Eppstein, M. Gopi

          Single Triangle Strip and Loop on Manifolds with Boundaries

          SIBGRAPI, [33%] October 2006.

           

          C21.      Masaki Kitago, M. Gopi

          Efficient and Prioritized Point Subsampling for CSRBF Compression

          EUROGRAPHICS Symposium on Point Based Graphics, July 2006.

           

          C20.      Yan Huang, Jingliang Peng, C.-C Jay Kuo, M. Gopi

          Octree-Based Progressive Geometry Coding of Point Clouds

          EUROGRAPHICS Symposium on Point Based Graphics, July 2006.

           

          C19.      Anusheel Bhushan, Oliver Le, Pablo Diaz-Gutierrez, M. Gopi

          Capturing and View-Dependent Rendering of Billboard Models

          International Symposium on Visual Computing, 2005.

           

          C18.      Koel Das, Pablo Diaz-Gutierrez, M. Gopi

          Sketching Free-form Surfaces Using Network of Curves

          EUROGRAPHICS Workshop on Sketch Based Interfaces and Modeling, 2005.

           

          C17.      Pablo Diaz-Gutierrez, M. Gopi

          Quadrilateral and Tetrahedral Mesh Stripification Using 2-Factor Partitioning of the Dual Graph

          Pacific Graphics 2005. [13.9%] [Same as J12]

           

          C16.      Pablo Diaz-Gutierrez, M. Gopi, Renato Pajarola

          Hierarchyless Simplification, Stripification and Compression of Triangulated Two-Manifolds

          EUROGRAPHICS 2005 [15%], 2nd Best Paper Award. [Same as J11]

           

          C15.      Pablo Diaz-Gutierrez, Anusheel Bhushan, M. Gopi, Renato Pajarola

          Constrained Strip Generation and Management for Efficient Interactive 3D Rendering

          Computer Graphics Int. Conference, 2005.[32%, Top 5% invited for journal].

           

          C14.      Pablo Diaz-Gutierrez, Anusheel Bhushan, Renato Pajarola, M. Gopi

          Weighted Strip Generation for Accelerated Rendering

          ACM SIGGRAPH Sym. on Interactive 3D Graphics and Games 2005. [Poster].

           

          C13.      Gautam Chaudhary, Koel Das, M. Gopi

          Curvature Minimizing Depth Interpolation for Intuitive and Interactive Space Curve Sketching

          Computer Graphics International Conference, 2005. [Poster]

           

          C12.      M. Gopi

          Controllable Single-Strip Generation for Triangulated Surfaces

          Pacific Graphics, pp 61-69, 2004. [25%]

           

          C11.      M. Gopi, David Eppstein

          Single-Strip Triangulation of Manifolds with Arbitrary Topology

          EUROGRAPHICS 2004 [18%], 2nd Best Paper Award. [Same as J9]

           

          C10.      O. Sen, C. Chemudugunta, M. Gopi

          Silhouette-Opaque Transparency Rendering

          IASTED Conf. on Computer Graphics and Imaging, 2003, pp 153-158.

           

          C9.      M. Gopi, S. Krishnan

          Fast and Efficient Projection Based Approach for Surface Reconstruction

          Brazilian Sym. on Computer Graphics and Image Processing, SIBGRAPI 2002.

           

          C8.      Aditi Majumder, M. Gopi

          Hardware Accelerated Real Time Charcoal Rendering

          SIGGRAPH/EUROGRAPHICS Non-Photorealistic Animation and Rendering, 2002, pp 59-66.

           

          C7.      M. Gopi, S. Krishnan, C. T. Silva

          Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation

          EUROGRAPHICS 2000 [37%], [Same as J7]

           

          C6.      Aditi Majumder, M. Gopi, B. Seales, H. Fuchs

          Geometric Stitching for Real-Time Panoramic Image Generation Using Texture Maps

          ACM Multimedia 1999, pp 169-178. [19%]

           

          C5.      L.Nyland, D.McAllister, V.Popescu, C.McCue, A.Lastra, P.Rademacher, M.Oliveira, G.Bishop, M.Gopi, M.Cutts, H.Fuchs .

          The Impact of Dense Range Data on Computer Graphics

          Proc. of Multi-View Modeling and Analysis Workshop(Part of CVPR), 1999.

           

          C4.      S. Krishnan, M. Gopi, M. Lin, D. Manocha, A. Pattekar

          Rapid Accurate Contact Determination between Spline Models using ShellTrees

          EUROGRAPHICS 1998. [Same as J5]

           

          C3.      S.Krishnan, M.Gopi, D.Manocha, M.Mine

          Interactive Boundary Computation of Boolean Combinations of Sculptured Solids

          EUROGRAPHICS 1997 [33%]. [Same as J4]

           

          C2.      M. Gopi, D. Manocha

          A Unified Approach for Simplifying Polygonal and Spline Models

          IEEE Visualization 1998, pp 271-278.

           

          C1.      M.Gopi, S.Manohar

          VLSI architecture for the computation of NURBS patches

          Proc. of International Conf. on VLSI Design, New Delhi, India, Jan 1995.

           

          Edited Books and Conference Proceedings

          B3.      M. Gopi, Sung-Eui Yoon, Marc Olano, Miguel Otaduy

          Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2013

          ACM Press 2013, ISBN 978-1-4503-1956-0.

           

          B2.      Michael Garland, Rui Wang, M. Gopi, Sung-Eui Yoon

          Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2012

          ACM Press 2012, ISBN: 978-1-4503-1194-6.

           

          B1.      G. Bebis, R. Boyle, B. Parvin, D. Koracin, P. Remagnino, A. V. Nefian, M. Gopi,

          V. Pascucci, J. Zara, J. Molineros, H. Theisel, T. Malzbender:

          Advances in Visual Computing, Second International Symposium.

          Lecture Notes in Computer Science 4291(Part I) and 4292 (Part II)

          Springer 2006, ISBN 3-540-48628-3.

           

          Thesis/Dissertations and Other Publications

          D2.      M. Gopi

          Theory and Practice of Sampling and Reconstruction of Manifolds with Boundaries

          Ph.D. Dissertation, Department of Computer Science, University of North Carolina at Chapel Hill, 2001.

          D1.      M. Gopi

          Special Purpose Architectures for B-Splines.

          M.S. Thesis, Supercomputer Education and Research Center, Indian Institute of Science, Bangalore, 1994.

          T2.      Pablo Diaz-Gutierrez, M. Gopi

          Gauss Sphere Sampling based Surface Approximation

          UCI-ICS Technical Report 07-08, 2007.

          T1.      M. Gopi

          On Sampling and Reconstructing Surfaces with Boundaries

          Canadian Conference on Computational Geometry, 2002.

           

          Video Proceedings

          V1.      David Eppstein, M. Gopi,

          Single-Strip Triangulation of Manifolds with Arbitrary Topology

          ACM Symposium of Computational Geometry, 2004. Video Proceedings.

           

          Patents

          P2.      Aditi Majumder, Behzad Sajadi, Gopi Meenakshisundaram, A Projector with Enhanced Resolution Via Optical Pixel Sharing, US Application # 20140035919 A1, (Applied: Aug 03, 2012.)

          P1.      Aditi Majumder, Gopi Meenakshisundaram, Behzad Sajadi, Color seamlessness across tiled multi-projector displays, US Patent # 9052584 B2, Jun 9, 2015. (Applied: Aug 28, 2009)

           

          Teaching/Research Awards

          A7.      Best Paper Award, ICVGIP, Mumbai, India, 2012.

          A6.      Service Award, Association for Computing Machinery, 2012.

          A5.      Second Best Paper Award, EUROGRAPHICS, Dublin, Ireland, 2005.

          A4.      Second Best Paper Award, EUROGRAPHICS, Grenoble, France, 2004.

          A3.      Excellence in Teaching Award, Instructional Resource Center/DUE, UCI, 2004.

          A2.      Link Foundation Fellow (1999-2000).

          A1.      Gold Medalist, Overall academic Performance, Thiagarajar College of Engineering, 1992.

           

          Funding Awards

          • PI, NSF "G&V: Compression Techniques for Direct Rendering", 2008-2012: $325,000.
          • PI, NSF "SGER: Modeling Memory Access Patterns of Geometry Processing Algorithms", 2007-2008 : $63,129.
          • Co-PI, NSF "RI: Integrating Illumination, Motion and Shape Models for Video Analysis", 2007-2010: $396,932.
          • Faculty Desktop Computing Initiative, 2005: $3500.
          • Research and Travel Grant, School of ICS, 2004-05: $1500.
          • Undergraduate Research Orientation Program, UCI, 2004-05 (Chris Welch).
          • Research and Travel Grant, ICS, 2003: $3000.
          • Research and Travel Grant, ICS, 2002: $4500.

           

          Invited Presentations

          • Keynote Speaker, International Symposium on Visual Computing, 2014
          • Institute for Informatics, University of Zurich, 2010
          • Department of Computer Science, UC-Davis, Sept. 2006.
          • Department of Computer Science, UNC, Chapel Hill, June 2006.
          • Department of Computer Science, SUNY, Stony Brook, June 2006.
          • Department of Computer Science, Univ. of Maryland, College Park, Feb 2006.
          • CalIT2 Layer Leader Meeting, June 2002.
          • Department of Information and Computer Science, UC, Irvine, 2001.
          • Department of Computer Science, University of Arizona, Tucson, 2001.

           

          Masters Research Advisor

          • Ishwar Kulkarni - nVidia
          • Shanaz Mistry - Siemens
          • Shan Jiang, (PhD UCI), Altair.
          • Yimin Li
          • Don Black.
          • Anusheel Bhushan
          • Damanpreet Singh

           

          Doctoral Graduate Advisor

          • Uddipan Mukherjee, CS, UCI - Graduated Dec 2013 - Intel Corp.
          • Shan Jiang, CS, UCI - Graduated Dec 2013 - Altair Engineering.
          • Sangwon Chae, EECS, UCI - Graduated March 2013 - Samsung-Korea.
          • Yan Huang, ICS, UCI. [6/05-01/09]- Graduated Jan 2009 - Associate Professor, Shandong University
          • Pablo Diaz-Gutierrez, ICS, UCI. (2003-08) - Graduated Dec 2008 - Entrepreneur (Appfluence).
          • Behzad Sajadi, CS, UCI [9/07-9/12] - Graduated - D.E. Shaw, NYC
          • Koel Das, EECS, UCI. [06/2004-06/2005] - Graduated - Assistant Professor, IISCER, Kolkata
          • Gautam Chaudhary, EECS, UCI [06/2004 - 08/2005]
          • Kartic Sankar Subr, ICS, UCI. [09/2002- 03/2004] - Associate Professor, Heriot Watt University, UK.
          • Ramaswamy Hariharan, ICS, UCI. [09/2002 09/2003] - Microsoft Corp.
          • Chaitanya Chemudugunta, ICS, UCI. [09/2002 09/2003] - Blizzard Entertainment
          • Osman Sen, ICS, UCI.[09/2002 06/2003]

           

          Current Doctoral Students

          • Mahdi Abbaspour Tehrani (Co-advisor with Aditi Majumder)
          • Nitin Agarwal
          • Jia Chen
          • Zahra Montazerri

           

          Undergraduate Research Advisor

          • Brian Charles, CS, UCI
          • Swati Bhonsle, CS, UCI
          • Jonathan Chuong, CS, UCI
          • Tana Ouitavo, CSE, UCI.
          • Aamir Shah, CS, UCI.
          • Danny Mardini, CS, UCI.
          • Ryan Barber, CS, UCI.
          • Devin Rosen, ICS, UCI.
          • Oliver Le, ICS, UCI.
          • Ian Byrd, ICS, UCI.
          • Frank Chen, ICS, UCI.
          • Benjamin Chen, ICS, UCI.
          • Barry Hon, ICS, UCI.
          • Chris Welch, ICS, UCI.

           

          Ph.D. Committee Member

          • Laleh Jalali
          • Siripen Pongpaichet
          • Vivek Singh, Assistant Professor, Rutgers University
          • Ben Compani [03/19/2012] - Google.
          • Jian Liang, (Dept. of Mathematics) [6/4/2012]
          • Pablo Diaz Gutierrez, Appfluence
          • Kartic Subr, Heriot Watt University.
          • Koel Das, EECS, UCI, Assistant Professor, IISCER, Kolkata.
          • Haitao Du, EECS, UCI.
          • Michael Shafae, ICS, UCI, Associate Prof., CSU, Fullerton.
          • Xiaohong Bao, ICS, UCI.
          • Miguel Sainz, EECS, UCI, nVidia, UK.

           

          Ph.D. Topic/Candidacy Committee Member

          • Yang Shi [EECS/ Candidacy: 11/03/2015]
          • Mengfan Tang [Candidacy: 06/17/2015]
          • Mahdi Abbaspour Tehrani [Candidacy: 06/09/2014]
          • U.N. Niranjan [Candidacy: 07/02/2013]
          • Siripen Pongpaichet [Candidacy: 07/03/2013]
          • Laleh Jalali [Candidacy: 06/26/2013]
          • Ish Rishabh [Topic defense: 10/17/2012]
          • Vivek Singh [Candidacy: 07/22/2009; Topic Defense: 06/06/2011; Thesis Defense: 08/20/2012]
          • Sangwon Chae [Candidacy: 09/14/2011; Thesis Defense: 03/12/2013]
          • Behzad Sajadi
          • Don Black
          • Jian Liang, Mathematics, UCI [12/17/2009]
          • Jie Feng, Mathematics, UCI [12/09/2010]
          • So Yamaoka, ICS, UCI. [11/29/06]
          • Kartik Chandra Muktinutalapati, EECS, UCI. [11/29/05]
          • Yan Huang, ICS, UCI. [8/24/05]
          • Mark Phair, EECS, UCI. [6/16/05]
          • Mohammad Ali Ghodrat, ICS, UCI. [3/25/05]
          • Pablo Diaz-Gutierrez, ICS, UCI. [3/24/05]
          • Radha Guha, EECS, UCI. [1/28/05]
          • Kiran Ramineni, ICS, UCI. [12/9/04]
          • Gautam Chaudhary, EECS, UCI. [12/03/04]
          • Koel Das, EECS, UCI. [12/03/04]
          • Michael Shafae, ICS, UCI. [9/18/03]
          • Xiaohong Bao, ICS, UCI. [9/18/03]
          • Haitao Du, EECS, UCI. [9/03]
          • Andre Nacul, ICS, UCI. [5/26/04]
          • Miguel Sainz, EECS, UCI. [6/13/02]

           

           

          UC-Systemwide/UCI/School/Department Services

          • University Committee on Faculty Welfare, 2010-2013.
          • UC-Systemwide Workgroup on Researcher Conflict-of-Interest Program Development 2012

           

          • Chair, Academic Program Review Board, 2015-2018
          • Senate Council on Planning and Budget, UCI 2013-2014
          • Chair, Senate Council on Faculty Welfare, Diversity and Academic Freedom, UCI 2011-2013
          • Senate Council on Faculty Welfare, Diversity, and Academic Freedom, UCI, 2010-2013
          • Chancellor`s Advisory Committee on Child Care, UCI, 2011-2013
          • Member, UCI Mental Health Initiative, 2012-
          • Senate Sub-committee on Undecided/Undeclared Students, UCI, 2008-10.

           

          • Steering Committee, Computer Game Science Major, Donald Bren School of ICS 2012-
          • Faculty Chair, Donald Bren School of Information and Computer Sciences, 2008-09.
          • Entrepreneurship and Leadership Committee, DBSICS, 2007-08.
          • Network Policy Committee, 2005-2006.
          • ICS Faculty Panel for Undecided/Undeclared students, 2005.
          • Web committee 2003
          • ICS Graduate Policy Committee, 2002-2003

           

          • Chair, CS Faculty Hiring Committee, 2015
          • Graduate Admissions Committee, 2011-14.
          • Graduate Admissions Committee, 2004-2005.
          • CS PhD Curriculum committee, 2004.
          • Graduate Admissions Committee, 2003-2004.
          • Graduate Admissions Committee, 2001-2002
          • Cryptography Faculty Search Committee, 2001-2002

           

          Professional Services

          • Guest Editor, IEEE Transactions on Visualization and Computer Graphics, 2013.
          • Papers Co-Chair, ACM Symposium on Interactive 3D Graphics and Games, 2013
          • Associate Editor, Journal of Graphical Models (GMOD), Elsevier Publication. 2010-
          • Conference Co-Chair, ACM Symposium on Interactive 3D Graphics and Games, 2012
          • Area Chair, Indian Conference on Vision, Graphics and Image Processing, 2012.
          • Area Chair, Indian Conference on Vision, Graphics and Image Processing, 2010.
          • Student Stipend Program Chair, ACM Symposium on Interactive 3D Graphics and Games, 2009.
          • Program Co-Chair, International Symposium on Visual Computing, 2006.
          • Program Chair, High Performance Visualization, ASTC Sym. on HPC 2004.

           

          • Program Committee, Conference on Geometric Modeling and Processing, 2014, 2015
          • Program Committee, ACM Interactive 3D Graphics and Games, 2008-2015
          • Program Committee, Expressive (Computational Aesthetics, Sketch-based Interfaces and Modeling, and Non-Photorealistic Rendering), 2013, 2014
          • Program Committee, CAD/Graphics 2011, 2013
          • Program Committee, Eurographics Workshop on Sketch-Based Interfaces and Modeling, 2005, 2006, 2008, 2011, 2012.
          • Program Committee, ACM Sym. on Solid and Physical Modeling, 2007-2010.
          • Program Committee, 3D Data Processing, Visualization, and Transmission, 2008.
          • Program Committee, Pacific Graphics, 2005, 2007.
          • Program Committee, SIBGRAPI, 2005-2009.
          • Program Committee, SIGGRAPH/Eurographics Sym. on Point Based Graphics, 2007, 2008.
          • Program Committee, International Symposium on Visual Computing, 2005-2013.
          • Session Chair, ASTC Symposium on HPC 2004.
          • Session Chair, IASTED Computer Graphics and Imaging, 2003.

           

          Reviewer

          • IEEE Visualization, IEEE TVCG, Siggraph Asia, Siggraph, Eurographics, Pacific Graphics.
          • Sym. Computational Geometry, Sym. Geometry Processing, ACM I3D.
          • Sym. Solid and Physical Modeling, SIBGRAPI, Sym. Visual Computing.
          • NSF Proposal Review Panel.
          • Proposal Reviewer for the Netherlands Organisation for Scientific Research (NOW).
          • American Society of Mechanical Engineers IDETC/CIE 2005.
          • Journal of Computer Aided Geometric Design (2005)
          • Eurographics Computer Graphics Forum.
          • Elsevier Graphical Models.
          • Symposium on Theoretical Aspects of Computer Science 2005.
          • Journal of Machine Vision and Applications.
          • IEEE Transactions on Computers
          http://www.ics.uci.edu/~bsajadi/ Behzad Sajadi's webpage
          • Latest news

            • Our paper "Edge-Guided Resolution Enhancement in Projectors via Optical Pixel Sharing" has been accepted for presentation in Siggraph 2012.

            • Our ToG paper "Perceptually-Based Appearance Modification for Compliant Appearance Editing" will be presented in Siggraph 2012.

            • Our TVCG paper "Using Patterns to Encode Color Information for Dichromats" will be presented in VisWeek 2012.

          • Related Links

            • Graphics/Visualization Lab
            • ICS Department
            • University of California, Irvine

          I am currently a senior Ph.D student in Computer Graphics and Visualization Lab. at University of California, Irvine. I'm interested in several domains mostly shared between computer graphics, visition and visualization. In particular, I've worked on several projects in the domains of Ubiquitous Displays, Computational Photography, and Large Scale Data Management and Rendering.

          If you are interested in my research, please visit my publications or project pages, or simply take a look at my resume. Also feel free to email me at [bsajadi at uci.edu] for further information.

           
          http://www.ics.uci.edu/~smyth/contactinfo.html Padhraic Smyth's Contact Information
          http://www.ics.uci.edu/~skong2/ Shu Kong (Aimery) - UC Irvine - Computer Vision

          Shu Kong (Aimery)

          I am a computer science PhD student at Donald Bren School of Information and Computer Sciences, UC Irvine. I work at with the Computational Vision Group where I am advised by Prof. Charless Fowlkes and working closely with Prof. Deva Ramanan. I am also collaborating with Prof. Olivier Cinquin and Prof. Surangi Punyasena. I am broadly interested in computer vision, machine learning and their applications.

          The best way to reach me is through email:

          • Email: aimerykong (at) gmail.com
          • Office: 4209 Donald Bren Hall (Oh, thanks to Sam, he drew a map to lead you here - map)
          Other links
          • CV, Github, Google Scholar, LinkedIn...

          Research Projects







          Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. http://www.ics.uci.edu/~fowlkes/publications.html charless c. fowlkes - uc irvine - computer vision

          mugshot

          charless c. fowlkes

          associate professor
          computer science
          uc irvine

          fowlkes@ics.uci
          4076 dbh
          949.824.6945

          uci : cs : vision group

          home
          publications
          presentations
          software



          papers and reports by year


          2016

          D. Tcheng, A. Nayak, C. Fowlkes, S. Punyasena, "Visual recognition software for binary classification and its application to spruce pollen identification" PLoS ONE, to appear

          R. Diaz, M. Lee, J. Schubert, C. Fowlkes, "Lifting GIS Maps into Strong Geometric Context" WACV 2016 arXiv:1507.03698 [pdf]

          2015

          C. McCusker, A. Athippozhy, C. Diaz-Castillo, C. Fowlkes, D. Gardiner, S. Voss, "Positional Plasticity in Regenerating Amybstoma mexicanum Limbs Is Associated With Cell Proliferation and Pathways of Cellular Differentiation", BMC Developmental Biology, 15:45, DOI 10.1186/s12861-015-0095-4 (2015). [pdf]

          M. Chiang, S. Hallman, A. Cinquin, N. Reyes de Mochel, A. Paz, S. Kawauchi, A. Calof, K. Cho, C. Fowlkes, O. Cinquin, "Analysis of in vivo single cell behavior by high throughput, human-in-the-loop segmentation of three-dimensional images", BMC Bioinformatics 2015, 16:397 doi:10.1186/s12859-015-0814-7. [pdf]

          J. Yarkony, C. Fowlkes, "Planar Ultrametrics for Image Segmentation", Proc. of , NIPS, Dec. 2015. arXiv:1507.02407 [pdf]

          J. B. Treweek, K. Chan, N. Flytzanis, B. Yang, B. Deverman, A. Greenbaum, A. Lignell, C. Xiao, L. Cai, M. Ladinsky, P. Bjorkman, C. Fowlkes, V. Gradinaru "Whole-Body Tissue Stabilization and Selective Extractions via Tissue-Hydrogel Hybrids for High Resolution Intact Circuit Mapping and Phenotyping", Nature Protocols, 10 (11), 1860-1896, (2015) DOI 10.1038/nprot.2015.122

          S. Wang, C. Fowlkes, "Learning Optimal Parameters for Multi-target Tracking", BMVC 2015 [pdf]

          G. Ghiasi, C. Fowlkes, "Using segmentation to predict the absence of occluded parts", BMVC 2015. [pdf] [data]

          R. Diaz, M. Lee, J. Schubert, C. Fowlkes, "Lifting GIS Maps into Strong Geometric Context" Technical Report, July 2015 arXiv:1507.03698 [pdf]

          G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces", Technical Report, June 2015 arXiv:1506.08347 [pdf] [code] [dataset]

          X. Zhu, C. Vondrick, C. Fowlkes, D. Ramanan, "Do we need more training data?", IJCV, DOI 10.1007/s11263-015-0812-2, March 2015 arXiv:1503.01508 [pdf]

          S. Hallman, C. Fowlkes, "Oriented Edge Forests for Boundary Detection", CVPR, Boston, MA, (June 2015).
          arXiv:1412.2066 [pdf] [code]

          M. Staller, C. Fowlkes, M. Bragdon, J. Estrada, Z. Wunderlich, A. DePace, "A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate", Development 142, p. 587-596, (2015) [pdf]

          2014

          S. Wang, C. Fowlkes, "Learning Multi-target Tracking with Quadratic Object Interactions", Technical Report, arXiv:1412.2066 (Dec. 2014) [pdf]

          J. Yarkony, C. Zhang, C. Fowlkes, "Hierarchical Planar Correlation Clustering for Cell Segmentation", EMMCVPR, Hong Kong, (Jan 2015). [pdf]

          G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Localizing Occluded Faces with a Hierarchcial Deformable Part Model", CVPR, Columbus, OH, (June 2014). [pdf]

          G. Ghiasi, Y. Yang, D. Ramanan, C. Fowlkes, "Parsing Occluded People", CVPR, Columbus, OH, (June 2014). [pdf]

          B. Kong, C. Fowlkes, "Fast Convolutional Sparse Coding (FCSC)", UCI Technical Report, (May 2014) [pdf]

          2013

          A. Chen, E. Lee, R. Tu, K. Santiago, A. Grosberg, C. Fowlkes, M. Khine, "Integrated Platform for Functional Monitoring of Biomimetic Heart Sheets Derived From Human Pluripotent Stem Cells", Biomaterials 35(2):675-683, [pdf]

          L. Mander, M. Li, W. Mio, C. Fowlkes, S. Punyasena, "Classification of grass pollen through the quantitative analysis of surface ornamentation and texture", Proc. R. Soc. B. 2013 280 (1770) [pdf]

          R. Díaz, S. Hallman, C. Fowlkes, "Detecting Dynamic Objects with Multi-View Background Subtraction", ICCV, Sydney, Australia (December 2013). [pdf]

          B. Andres, J. Yarkony, B.S. Manjunath, S. Kirchhoff, E. Turetken, C. Fowlkes, H. Pfister, "Segmenting Planar Superpixel Adjacency Graphs w.r.t. non-planar Superpixel Affinity Graphs", EMMCVPR, Lund, Sweden, (Aug. 2013). [pdf][sup]

          R. Díaz, S. Hallman, C. Fowlkes, "Multi-View Background Subtraction for Object Detection", Scene Understanding Workshop, Portland, OR, (June 2013).

          H. Bae, C. Fowlkes, P. Chou, "Accurate Motion Deblurring using Camera Motion Tracking and Scene Depth", WACV, Clearwater Beach, Florida, (Jan. 2013). [pdf]

          2012

          H. Bae, C. Fowlkes, P. Chou, "Patch Mosaic for Fast Motion Deblurring", ACCV, Daejeon, Korea, (Nov. 2012). [pdf]

          X. Zhu, C. Vondrick, D. Ramanan, C. Fowlkes, "Do we need more training data or better models for object detection?", BMVC, Surrey, UK (Sept. 2012). [pdf]

          J. Yarkony, A. Ihler, C. Fowlkes, "Fast Planar Correlation Clustering for Image Segmentation'', ECCV, Firenze, Italy (Oct. 2012). arXiv:1208.0378v1 [extended pdf]

          D. Keator, J. Fallon, A. Lakatos, C. Fowlkes, S. Potkin, A. Ihler, ``Feed-forward Hierarchical Model of the Ventral Visual Stream Applied to Functional Brain Image Classification'', Human Brain Mapping 35(1), p. 38–52, 2012. [pdf]

          H. Kim, J. Park, J. Byun, W. Poon, C. Cotman, C. Fowlkes, N. Jeon, "Quantitative analysis of axonal transport by using compartmentalized and surface micropatterned culture of neurons", ACS Chemical Neuroscience, 2012. [pdf]

          2011

          J. Hengenius, M. Gribskov, A. Rundell, C. Fowlkes, D. Umulis, "Analysis of Gap Gene Regulation in a 3D Organism-Scale Model of the Drosohpila melanogaster Embryo", PLoS ONE 6(11): e26797. doi:10.1371/journal.pone.0026797, 2011. [pdf][supplement]

          Y. Yang, S. Hallman, D. Ramanan, C. Fowlkes, "Layered Object Models for Image Segmentation", TPAMI, 34(9):1731-1743, 2011. [pdf]

          A. Chen, D. Lieu, L. Freschauf, V. Lew, H. Sharma, J. Wang, D. Nguyen, I. Karakikes, R. Hajjar, A. Gopinathan, E. Botvinick, C. Fowlkes, R. Li, M. Khine, "Shrink-Film Configurable Multiscale Wrinkles for Functional Alignment of Human Embryonic Stem Cells and their Cardiac Derivatives", 10.1002/adma.201103463, Advanced Materials, 2011. [pdf]

          C. Fowlkes, K. Eckenrode, M. Bragdon, M. Meyer, Z. Wunderlich, L. Simirenko, C. Hendriks, S. Keranen, C. Henreiquez, M. Biggin, M. Eisen, A. DePace, "A conserved developmental patterning network produces quantitatively different output in multiple species of Drosophila," PLoS Genetics, 7(10): e1002346, 2011. [pdf]

          Y. Chen, A. Gelfand, C. Fowlkes, M. Welling, "Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation", ICCV, Barcelona, Spain, (Nov. 2011) [pdf]

          J. Yarkony, R. Morshed, A. Ihler, C. Fowlkes. "Tightening MRF Relaxations with Planar Subproblems", UAI, Barcelona, Spain (July 2011). [pdf]    [NOTE: The original published version contained an erroroneous claim that was kindly pointed out by David Sontag. The one linked here is an updated version.]


          J. Yarkony, A. Ihler, C. Fowlkes. "Planar Cycle Covering Graphs", UAI, Barcelona, Spain (July 2011). [pdf]

          J. Yarkony, R. Morshed, A. Ihler, C. Fowlkes. "Planar Decompositions and Cycle Constraints", Workshop on Inference in Graphical Models with Structured Potentials, Colorado Springs, CO, (June 2011) [pdf]

          J. Yarkony, A. Ihler, C. Fowlkes. "Planar Cycle Covering Graphs", Technical Report, arXiv:1104.1204v1, 2011 [pdf]

          C. Desai, D. Ramanan, C. Fowlkes. "Discriminative models for multi-class object layout", 95(1), p. 1-12, International Journal of Computer Vision, 2011 [pdf]

          Y. Bo, C. Fowlkes. "Shape-based Pedestrian Parsing", CVPR, Colorado Springs, CO, (June 2011) [pdf]

          H. Pirsiavash, D. Ramanan, C. Fowlkes. "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects", CVPR, Colorado Springs, CO, (June 2011) [pdf]

          J. Luna, J. Ciriza, M. Ojeda-Garcia, M. Kong, A. Herren, D. Lieu, R. Li, C. Fowlkes, M. Khine, K. McCloskey, "Multi-scale Biomimetic Topography for the Alignment of Neonatal and Embryonic Stem Cell-derived Heart Cells", Tissue Engineering: Part C , 17(5), p. 579-588 , 2011 [pdf]

          P. Arbelaez, M. Maire, C. Fowlkes, J. Malik, "Contour Detection and Hierarchical Image Segmentation", TPAMI, 33(5), 2011 [pdf] [web page]

          2010

          S. Hallman, A. Skurikhin, C. Fowlkes. "Vehicle Detection on High-Resolution Commerical Satellite Imagery", Technical Report LA-UR-10-05877, Los Alamos National Laboratory, 2010

          A. Aswani, S. Keranen, J. Brown, C. Fowlkes, D. Knowles, M. Biggin, P. Bickel, C. Tomlin "Nonparametric identification of regulatory interactions from spatial and temporal gene expression data" BMC Bioinformatics,11:413, 2010. [pdf] *highly accessed*

          D. Park, D. Ramanan, C. Fowlkes "Multiresolution models for object detection" ECCV, Crete, Greece, (Sept. 2010) [pdf]

          J. Yarkony, C. Fowlkes, A. Ihler, "Covering Trees and Lower-bounds on Quadratic Assignment", CVPR, San Francisco, (June 2010) [pdf]

          Y. Yang, S. Hallman, D. Ramanan, C. Fowlkes, "Layered Object Detection for Multi-Class Segmentation" CVPR, San Francisco, (June 2010) [pdf]

          C. Desai, D. Ramanan, C. Fowlkes, "Discriminative models for static human-object interactions", CVPR Workshop on Structured Models in Computer Vision, San Fransisco, CA, (June 2010) [pdf]

          J. Burge, C. Fowlkes, M. Banks, ``Natural-scene statistics predict how the figure-ground cue of convexity affects human depth perception'', Journal of Neuroscience, 30(21):7269-7280 [pdf] [supplement]

          2009

          H. Pirsiavash, D. Ramanan, C. Fowlkes. "Bilinear classifiers for visual recognition", NIPS, Vancouver, Canada, (Dec. 2009). [pdf]

          C. Desai, D. Ramanan, C. Fowlkes. "Discriminative models for multi-class object layout", ICCV Kyoto, Japan, (Sept. 2009). [pdf] [code]     *awarded the Marr Prize for best paper*

          P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. "From Contours to Regions: An Empirical Evaluation", CVPR Miami Beach, FL, (June 2009). [pdf] [code]. A fast GPU implementation here!

          2008

          M. Maire, P. Arbelaez, C. Fowlkes and J. Malik. "Using Contours to Detect and Localize Junctions in Natural Images", CVPR Anchorage, AK, (June 2008). [pdf]

          O. Rübel, G. Weber, M-Y Huang, E. Bethel, M. Biggin, C. Fowlkes C. Leungo Hendriks, S. Keränen, M. Eisen, D. Knowles, J. Malik, H. Hagen, B. Hamann, "Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data" IEEE Transactions on Computational Biology and Bioinformatics, 7(1):64-79, 2010. [pdf]

          C. Fowlkes, C. Luengo Hendriks, S. Keränen, G. Weber, O. Rübel, M-Y Huang, S. Chatoor, L. Simirenko, A. DePace C. Henriquez, A. Beaton, R. Weiszmann, S. Celniker, B. Hamann, D. Knowles, M. Biggin, M. Eisen, J. Malik. "Constructing a quantitative spatio-temporal atlas of gene expression in the Drosophila blastoderm", Cell, 133(2), p. 364-374, 2008. [pdf] [supplement]
          Reviews of this paper also appeared in Developmental Cell and Nature Methods.
          The Drosophila blastoderm 3D gene expression atlas is online.

          X. Ren, C. Fowlkes, J. Malik. "Learning Probabilistic Models for Contour Completion in Natural Images", IJCV, 77:47-63, 2008. [pdf]

          2007

          G. Weber, O. Rübel, M-Y Huang, A. DePace, C. Fowlkes, S. Keränen, C. Luengo Hendriks, H. Hagen, D. Knowles, J. Malik, M. Biggin, and B. Hamann "Visual Exploration of Three-dimensional Gene Expression Using Physical Views and Linked Abstract Views", IEEE Transactions on Computational Biology and Bioinformatics, 6(2):296-309, 2009. [pdf]

          C. Fowlkes, D. Martin, J. Malik. "Local Figure/Ground Cues are Valid for Natural Images" Journal of Vision, 7(8):2, 1-9. [pdf] [dataset]

          2006

          C. Luengo-Hendriks, S. Keränen, C. Fowlkes, L. Simirenko, G. Weber, C. Henriquez, D. Kaszuba, B. Hamann, M. Eisen, J. Malik, D. Sudar, M. Biggin D. Knowles, "3D Morphology and Gene Expression in the Drosophila Blastoderm at Cellular Resolution I: Data Acquisition Pipeline", Genome Biology 2006, 7:R123 , [pdf] *highly accessed*

          S. Keränen, C. Fowlkes, C. Luengo Hendriks, D. Sudar, D. Knowles, J. Malik, M. Biggin, "3D Morphology and Gene Expression in the Drosophila Blastoderm at Cellular Resolution II: Dynamics", Genome Biology 2006, 7:R124, [pdf] *highly accessed*

          C. Fowlkes, J. Malik. "Inferring nuclear movements from fixed material", Technical Report EECS-2006-142, EECS Department, University of California, Berkeley, (November 2006). [pdf]

          X. Ren, C. Fowlkes, J. Malik. "Figure/Ground Assignment in Natural Images", ECCV, Graz, Austria, (May 2006). [pdf]

          O. Rübel, G. Weber, S. Keränen, C. Fowlkes, C. Luengo Hendriks, N. Shah, M. Biggin, H. Hagen, D. Knowles, J. Malik, D. Sudar and B. Hamann. "PointCloudXplore: Visual Analysis of 3D Gene Expression Data Using Physical Views and Parallel Coordinates", EuroVIS, Lisbon, Portugal, (May 2006). [pdf]

          2005

          X. Ren, C. Fowlkes, J. Malik. "Cue Integration for Figure/Ground Labeling", NIPS, Vancouver, Canada, (Dec. 2005). [pdf]

          X. Ren, C. Fowlkes, J. Malik. "Scale-Invariant Contour Completion using Conditional Random Fields", ICCV, Beijing, China, (Oct. 2005). [pdf]

          C. Fowlkes, C. Luengo Hendriks, S. Keränen, M. Biggin, D. Knowles, D. Sudar, J. Malik. "Registering Drosophila Embryos at Cellular Resolution to Build a Quantitative 3D Atlas of Gene Expression Patterns and Morphology", CSB 2005 Workshop on BioImage Data Minning and Informatics, Palo Alto, CA, (Aug. 2005). [pdf]

          X. Ren, C. Fowlkes, J. Malik. "Mid-level Cues Improve Boundary Detection", Technical Report CSD-05-1382, Division of Computer Science, University of California, Berkeley, (March 2005). [pdf]

          2004

          C. Fowlkes "A Note on Planar Factor Graphs", (Oct. 2004). [pdf]

          C. Fowlkes, J. Malik. "How Much Does Globalization Help Segmentation?", Technical Report CSD-04-1340, Division of Computer Science, University of California, Berkeley, (July 2004). [pdf]

          C. Fowlkes "Surveying Shape Spaces", survey article for Alan Weinstein's course on Reimannian Geometry [pdf]

          D. Martin, C. Fowlkes, J. Malik. "Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues", TPAMI 26 (5) p.530-549 [pdf]

          C. Fowlkes, S. Belongie, F. Chung, J. Malik. "Spectral Grouping Using The Nyström Method", TPAMI 26 (2) p.214-225 [pdf]

          2003

          C. Fowlkes, D. Martin, J. Malik. "Learning Affinity Functions for Image Segmentation: Combining Patch-based and Gradient-based Approaches", CVPR, Madison, WI, (June 2003). [pdf]

          2002

          D. Martin, C. Fowlkes, J. Malik. "Learning to Detect Natural Image Boundaries Using Brightness and Texture", NIPS , Vancouver, (Dec 2002). [pdf]

          S. Belongie, C. Fowlkes, F. Chung, J. Malik. "Spectral Partitioning with Indefinite Kernels using the Nyström Extension", ECCV , Copenhagen, (May 2002). [pdf]

          C. Fowlkes, Q. Shan, S. Belongie, J. Malik. "Extracting Global Structure from Gene Expression Profiles", in Methods of Microarray Data Analysis II, S. M. Lin and K. F. Johnson, editors. Kluwer Academic Publishers, 2002. [pdf]

          2001 and before

          C. Fowlkes, S. Belongie, J. Malik. "Efficient Spatiotemporal Grouping Using the Nyström Method", CVPR , Hawaii, (Dec. 2001). [pdf]

          D. Martin, C. Fowlkes, D. Tal, J. Malik. "A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics", ICCV, Vancouver, (July 2001). [pdf]     *awarded the Helmoltz Prize in 2015 for making a significant impact on the field of computer vision*

          M.C. Burl, C. Fowlkes, J. Roden, "Mining for Image Content", SCI-ISAS '99 Session on Intelligent Data Mining and Knowledge Discovery, Orlando, FL, (Aug 1999) [pdf]

          M.C. Burl, C. Fowlkes, J. Roden, A. Stechert, and S. Muukhtar, "Diamond Eye: A distributed Architecture for Image Data Mining", SPIE Conference on Data Minning and Knowledge Discovery, Orlando, FL (Apr 1999) [pdf]

          C. W. Fowlkes and C. C. Fowlkes "Passive Solar Contributions to Residential Ventilation", Conservation in Buildings: Northwest Perspective, Butte, MT (May 1985) [pdf]



          erdös = 2    



          http://vision.ics.uci.edu/pocv2012/index.html The Eighth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision

          Padhraic Smyth's contact information

          • Postal address:
            Department of Computer Science
            Bren School of Information and Computer Sciences
            University of California, Irvine
            CA 92697-3435

          • Office location: office 4216 in Bren Hall (4th floor), UCI main campus. Bren Hall is building 314 on the campus map.

          • Here's a campus map with my building marked in red, and parking in the APS structure marked in green. There is also metered visitor parking in lot 12B (to the left of my building on the map), which is a little closer.

          • E-mail: smyth at ics dot uci dot edu

          • Telephone: +1(949) 824 2558 (if all else fails :)

          • Fax: +1(949) 824 4056


          Administrative Assistant: Cindy Kennedy

          • Office 3028 in Bren Hall
          • E-mail: ckennedy at ics dot uci dot edu
          • Telephone: +1(949) 824 4479
          • Fax: +1(949) 824 4056
          http://www.ics.uci.edu/~smyth/professional.html Padhraic Smyth: Affiliations, etc

          Honors, Affiliations, Journals, Conferences, etc

          Honors

          • ACM Fellow 2013
          • AAAI Fellow, 2010
          • ACM SIGKDD Innovation Award, 2009
          • ACM SIGKDD Best Paper (and Runner-Up) Awards, 1997, 1998, 2000, and 2002
          • NSF CAREER Award, 1997

          Joint Faculty Appointments

          • Department of Statistics, UC Irvine
          • Department of Biomedical Engineering, UC Irvine

          Center Affiliations

          • Center for Machine Learning and Intelligent Systems, UC Irvine
          • Institute for Mathematical Behavioral Sciences (IMBS), UC Irvine.
          • Institute for Genomics and Bioinformatics (IGB), UC Irvine.
          • Center for Research on Information Technology and Organizations (CRITO), UC Irvine.

          Editorial and Advisory Boards

          • Bayesian Analysis (Advisory Board, 2006-2008)
          • Journal of Machine Learning Research (Editorial Board, 2000-present)
          • Journal of Data Mining and Knowledge Discovery (Editorial Board, 1999-present)

          Associate Editor

          • 2002-2004: Journal of the American Statistical Association
          • 2002-2004: IEEE Transactions on Knowledge and Data Engineering
          • 1998-2001: Machine Learning Journal

          Conference Organization

          • Program Chair, 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, San Diego, August 21-24, 2011.
          • Co-Chair, 2001 Symposium on the Interface of Computer Science and Statistics.
          • Co-Organizer, Mathematical Challenges in Scientific Data Mining, Institute for Pure and Applied Mathematics (IPAM), UCLA, January 14-18th 2002.
          • Co-Chair, Sixth International Workshop on AI and Statistics, 1997
          http://www.ics.uci.edu/~smyth/padhraic.html Padhraic
          The name Padhraic

          Padhraic is pronounced "Paw-rick". The letter "d" is silent since combinations of letters like adh act like a vowel sound ("aw" as in "raw") in Gaelic. This is a Gaelic version of the name Patrick which in turn came from the Latin/Roman name Patricius.

          Padhraic is a fairly common name in Ireland (but obviously less common in places like California). Somewhat confusingly there are multiple different spellings of the name in Ireland, such as Padraic and Padraig. To further confuse things, the pronounciation is also different in different parts of Ireland - in the very south of the country (like Cork) the name Padhraic might be pronounced something like "Pawd-rick", i.e., the "d" is pronounced and no longer silent. But I'm from the West of Ireland, where "Paw-rick" is the appropriate pronounciation.

          There are other idiosyncracies to Irish names. For example although my surname is Smyth (pronounced like the regular "Smith") the Gaelic version of this is Mac Gabhann which is pronounced "Ma-gow-an" (so "abh" acts like the vowel sound "ow", as in "owl"). Translated literally, Mac Gabhann means ``son of the blacksmith" (hence the Smith/Smyth translation into English).

          And many Irish people have the English version of their names on official documents like passports (including me) but use their Irish names for everything else (e.g., John/Sean, William/Liam, James/Seamus, and so on). Its a long story, but we tend to blame the English for all this confusion :) http://www.ics.uci.edu/~nalini/left.html Left hand menu column

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          http://www.ics.uci.edu/~nalini/home.html Nalini Venkatasubramanian, Professor of Information and Computer Science

           Nalini Venkatasubramanian

          • Professor, Information and Computer Science, University of California, Irvine
          • Electronic Mail: nalini@ics.uci.edu
          • Office: Donald Bren Hall 2086  or 4413 Calit2 Building
          • Office Phone: (949) 824-5898, (949)824-1622
          • Fax Phone: (949) 824-4056
          • Ph.D., University of Illinois

          Research Group

          ·         Distributed Systems Middleware Group

          ·         Information Systems Group (New)

          Affiliated Centers and Labs:

          • CERT- Center for Emergency Response Technologies
          • Co-Director, Networked Systems Program
          • The RESCUE (Responding to Crisis and Unexpected Events) Project,
          • The Responsphere Infrastructure,
          • Clustering, SAN and Fibre Channel Laboratory
          • Novell@ICS Lab
          • Laboratory for Ubiquitous Computing and Interaction
          • Center for Virtual Reality

          Prof. Venkatasubramanian's research focuses on enabling effective management and utilization of resources in the evolving global information infrastructure. Her areas of research include:

          • concurrent/distributed systems
          • reflective and adaptive middleware
          • multimedia systems and applications
          • middleware for mobile applications
          • formal reasoning of distributed systems.

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          http://www.ics.uci.edu/~nalini/top.html Nalini Venkatasubramanian Nalini Venkatasubramanian
          University of California, Irvine http://www.ics.uci.edu/~redmiles/students.html David Redmiles Students Page at UCI

          Description: ucirvine2

           

           

           

           

           

           

           

           

           

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          Present PhD Students (Principal Advisor)

          (Oliver) Yi Wang

          Benjamin Koehne

          Erik Trainer

           

          Other PhD Students (Committee Member)

          Patrick Shih

          and many others . . .

           

          Present Researchers / PostDocs / Visitors

          Ban Al-Ani, UCI

          Matthew Bietz, UCI

          Jovel Crisostomo, Aerospace

          Stewart Sutton, Aerospace

           

          Past PhD Graduates Supervised (Principal Advisor)

          Hiroko Wilensky, PhD 2011, Engineer, Boeing, Huntington Beach

          Wiwat Ruengmee, PhD 2010, Royal Thai Army, Bangkok

          Roberto Silveira Silva Filho, PhD 2009, Siemans, Princeton

          Cleidson Ronald Botelho de Souza, PhD 2005, IBM, San Paolo

          Michael Kantor, PhD 2001, Senior Member of Technical Staff, VMware, San Francisco

          Jason Robbins, PhD 1999, Software Engineer, Google, Irvine

          David Hilbert, PhD 1999, Senior Research Scientist, FX Pal, Palo Alto

           

          Past MS Graduates Supervised

          Jungmin Shin, MS 2008

          Stephen Quirk, MS 2007, Netflix

          Tom Herring, MS, SCE retired

          Santhoshi Dumpala Basaveswara, MS 2002

          Bharti Jhi, MS 2001

          Jaya Vaidyanathan, MS 1999

          Shilpa Shukla, MS 1997

          Anthony Kutscher, MS 1997

          Mikkel Brun, MS Exchange Student 1995

           

          Past BS Graduates Supervised

          Max Slabyak, BS 2003

           

          Past Visitors

          Christian Rathke, Hochschule der Medien, April - August 2006

          Cleidson Ronald Botelho de Souza, Federal University of Par�, October 2006

          Tobias Hildenbrand, Universitaet Mannheim, August � November 2006

          John Hosking, University of Auckland, November 2006

          Thorsten Hampel, Universitaet Paderborn, January 2007

           

          Past Post Docs

          Rogerio de Paula, Post Doc from August 2004-2005, now at Intel, San Paolo

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/schedule.html David Redmiles Schedule Page at UCI

           

           

           

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          Classes Winter 2014

          IN4MATX 233: Research in Human-Computer Interaction

           

          Classes Fall 2013

          IN4MATX 291S: Literature Survey in Software Engineering

           

          Classes Spring 2013

          IN4MATX 143: Information Visualization

          IN4MATX 233: Knowledge-Based User Interfaces

           

          Classes Winter 2013

          IN4MATX 131 LEC A: Human-Computer Interaction

          IN4MATX 295: SPECIAL TOPICS: The Human Aspects of Globally Distributed Software Development

           

          Sabbatical Academic Year 2011-2012

           

          Classes Spring 2011

          IN4MATX 143: Information Visualization

           

          Classes Spring 2010

          IN4MATX 143: Information Visualization

           

           

          Classes Spring 2009

          IN4MATX 143: Information Visualization

           

           

          Classes Fall 2008

           

          Classes Spring 2008

          IN4MATX 143: Information Visualization

          In4matx 209s Research Seminar

           

          Classes Fall Quarter 2007

           

          In4matx 233 Knowledge-Based User Interfaces

          In4matx 209s Research Seminar

           

          Classes Spring Quarter 2007

           

          In4matx 44 Seminar in Informatics Research Topics

           

          Classes Winter Quarter 2006

           

          104 Human Computer Interaction

          229 Seminar in Informatics

           

          Classes Fall Quarter 2005

           

          205 Human-Computer Interaction

          229 Seminar in Informatics (�Softalks�)

           

          Classes Spring Quarter 2005

          ICS 203B Ubiquitous Computing and Interaction

           

          Classes Fall Quarter 2004

          ICS 131 � Social Analysis of Computerization

           

          Classes Spring Quarter 2004

           

          ICS 125 Project in Software System Design

          ICS 227 Advanced User Interface Architecture

           

          Classes Fall Quarter 2003

           

          ICS 221 � Software Engineering

          ICS 131 � Social Analysis of Computerization

           

          Classes Spring Quarter 2003

           

          ICS229 - Seminar in Software ("Softalks")

           

          Sabbatical; Fall Quarter 2002 and Winter Quarter 2003

           

          Classes Winter Quarter 2002

           

          ICS280 WQ02 - Ubiquitous Computing in the Real World

           

          Classes Fall Quarter 2001

           

          ICS125 FQ01 - Project in System Design

          ICS227 FQ01 - User Interfaces and Software Engineering

           

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/links.html David Redmiles Links Page at UCI

           

           

           

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          New site for research group:

          http://cradl.ics.uci.edu/

           

          Former site for research group:

          http://awareness.ics.uci.edu:8080/ContinuousCoordination

           

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/research.html David Redmiles Research Page at UCI

           

           

           

           

           

           

           

           

           

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          A new research page is under construction here for current projects.

           

          Previous projects

           

          Continuous Coordination A brand new project for increasing awareness of social and technological information in collaborative software engineering environments.

           

          SWIRL Effective Security Awareness through Visualization, a project to research visualizations, mobile technology, and event monitoring to increase people�s awareness of interaction choices relevant to security (with support from NSF and Intel).

           

          Ariadne a Java-based plug-in to the Eclipse IDE that visualizes the social networks present in distributed software projects (with support from IBM).

           

          Yancees configurable and extensible software architectures to provide versatility to event notification services (with support from NSF).

           

          EDEM Expectation Driven Event Monitoring, a project and software infrastructure for JAVA to monitor usability events and package results in a way that software engineers could more easily integrate into a project lifecycle (with support from NSF and DARPA).

           

          ARGO a software design environment incorporating the idea of software critics, wizards, and a few other ideas based on cognitive theories of design (with support from NSF and DARPA).

           

          CASSIUS an event notification server for collaborative software, including group memory systems (with support from NSF and DARPA).

           

          Knowledge Depot a group memory system based on email (with support from NSF and DARPA).

           

          Explainer a software tool to support reuse through example-based explanation.

           

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/interests.html David Redmiles Interests Page at UCI

           

           

           

           

           

           

           

           

           

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          The Short Answer

           

          Software Engineering, Human Computer Interaction, Intelligent User Interfaces, Computer-Supported Cooperative Work, Human Design Activity, Activity Theory, Event Notification Services

           

          The Long Answer

           

          Graphical User Interfaces, Scientific Visualization, End User Studies, Field Studies, Activity Theory, Object-Oriented Programming, Knowledge Representation, Constraint-Based Systems, Agent-Based Systems, Event-Based Systems, Event Monitoring, Help Systems, Software Comprehension, Critic-Based Systems, Domain-Oriented Design Environments, Intelligent User Interfaces, Human-Computer Interaction, Usability Engineering, Computer-Supported Cooperative Work, and Software Engineering.

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/contact-info.html David Redmiles Detailed Contact Information Page at UCI

           

           

           

          David Redmiles

           

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          Telephone:

          voice +1 949 824 3823

          department manager +1 949 824 2901

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          redmiles at ics dot uci dot edu

          www.ics.uci.edu/~redmiles/

           

          Postal Mail:

          Dr. David F. Redmiles

          5038 Donald Bren Hall

          University of California, Irvine - Informatics

          Irvine, CA 92697-3440

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          Courier:

          Dr. David F. Redmiles

          UC Irvine/Informatics

          19172 Jamboree (5038 Donald Bren Hall)

          Irvine, CA 92697-3425

          +1 949 824 2901

           

           

           

           

           

          http://www.ics.uci.edu/~redmiles/publications.html David Redmiles Publications Page at UCI

           

           

           

           

           

           

           

           

           

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          CONFERENCE PUBLICATIONS

           

          (C.107) Al-Ani, B., Marczak, S., Prikladnicki, R., Redmiles, D. Revisiting the Factors that Engender Trust of Global Systems Engineers, The 8th IEEE International Conference on Global Software Engineering (ICGSE 2013, Bari, Italy), August 2013, pp. 31-40.

           

          (C. 106) Koehne, B., Redmiles, D. Identity Design in Virtual Worlds, The 4th International Symposium on End-User Development (IS-EUD 2013, Copenhagen, Denmark), Springer Lecture Notes in Computer Science, V. 7897, June 2013, pp. 56-71.

           

          (C.105) Wang, Yi, Redmiles, D. Understanding Cheap Talk and the Emergence of Trust in Global Software Engineering: An Evolutionary Game Theory Perspective, The 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2013), held in conjunction with the 35th International Conference on Software Engineering (ICSE 2013, San Francisco, California), May 25, 2013, published in ACM SIGSOFT Software Engineering Notes, V. 38, N. 5, September 2013, pp. 34-37.

           

          (C.104) Al-Ani, B., Bietz, M., Wang, Y., Trainer, E., Koehne, B., Marczak, S., Redmiles, D., Prikladnicki, R. Globally Distributed System Developers: Their Trust Expectations and Processes, The 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013, San Antonio, Texas), February 2013, pp. 563-573.

           

          (C.103) Marczak, S., Al-Ani, B., Redmiles, D., Prikladnicki, R. Designing Tools to Support Trust in Distributed Software Teams, Workshop held in conjunction with he 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013, San Antonio, Texas), Conference Companion, February 24, 2013, 4 pages, Workshop papers available online at http://collab.di.uniba.it/trusttheorytools/.

           

          (C.102) Al-Ani, G., Remiles, D., de Souza, C.R.B., Prikladnicki, R., Marczak, S., Lanubile, F., Calefato, F. Trust in Virtual Teams: Theory and Tools, Workshop Summary, Workshop held in conjunction with he 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013, San Antonio, Texas), Conference Companion, February 24, 2013, pp. 301-306.

           

          (C.101) Schumann, J., Shih, P., Redmiles, D., Horton, G. Supporting Initial Trust in Distributed Idea Generation and Evaluation, The 2012 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2012, Sanibel Island, FL), October 2012, pp. 199-208.

           

          (C.100) Al-Ani, B., Wang, Y., Marczak, S., Trainer, E., Redmiles, D. Distributed Developers and the Non-Use of Web 2.0 Technologies: A Proclivity Model, The 7th International Conference on Global Software Engineering (ICGSE 2012, Porto Alegre, Brazil), August 2012, pp. 104-113.

           

          (C.99) Koehne, B., Redmiles, D. Envisioning Distributed Usability Evaluation through a Virtual World Platform, The 2012 International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), held in conjunction with the 34th International Conference on Software Engineering (ICSE 2012, Zurich, Switzerland), June 2012, pp. 73-75.

           

          (C.98) Wang, Y., Trainer, E., Al-Ani, B., Redmiles, D., Marczak, S. Attitude and Usage of Collaboration Tools in GSE: A Practitioner Oriented Theory, The 2012 International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), held in conjunction with the 34th International Conference on Software Engineering (ICSE 2012, Zurich, Switzerland), June 2012, pp. 135-137.

           

          (C.97) Trainer, E.H., Redmiles, D.F. Foundations for the Design of Visualizations that Support Trust in Distributed Teams, International Working Conference on Advanced Visual Interfaces (AVI 2012, Capri Island, Italy), May 2012, pp. 34-41.

           

          (C.96) Al-Ani, B., Trainer, E., Redmiles, D., Simmons, E. Trust and Surprise in Distributed Teams: Towards an Understanding of Expectations and Adaptations, The 4th ACM International Conference on Intercultural Collaboration (ICIC 2012, Bengaluru, India), March 2012, pp. 97-106.

           

          (C.95) Wilensky, H., Redmiles, D. A Blog Considered from the Perspective of Social Practice Theory, the ACM Conference on Computer-Supported Cooperative Work (CSCW 2012, Seattle, WA), Conference Companion, February 2012, pp. 243-246.

           

          (C.94) Al-Ani, B., Marczak, S., Trainer, E., Redmiles, D., Prikladnicki, R. Distributed Developers� Perspectives of Web 2.0 Technologies in Supporting the Development of Trust, Workshop on The Future of Collaborative Software Development, held in conjunction with the ACM Conference on Computer-Supported Cooperative Work (CSCW 2012, Seattle, WA), February 2012, 3 pages, Workshop papers available online at http://research.microsoft.com/en-us/events/futurecsd/.

           

          (C.93) Al-Ani, B., Wilensky, H., Redmiles, D., Simmons, E. An Understanding of the Role of Trust in Knowledge Seeking and Acceptance Practices in Distributed Development Teams, The 6th International Conference on Global Software Engineering (ICGSE 2011, Helsinki, Finland), August 2011, pp. 25-34.

           

          (C.92) Koehne, B., Redmiles, D., Fischer, G. Extending the Meta-Design Theory: Engaging Participants as Active Contributors in Virtual Worlds, The Third International Symposium on End-User Development (IS-EUD 2011, Torre Canne, Italy), June 2011, pp. 264-269.

           

          (C.91) Trainer, E., Al-Ani, B., Redmiles, D. Impact of Collaborative Traces on Trustworthiness, The 2011 International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), held in conjunction with the 33rd International Conference on Software Engineering (ICSE 2011, Honolulu, Hawaii), May 2011, pp. 40-47.

           

          (C.90) Al-Ani, B. and Redmiles, D. In Strangers We Trust? Findings of an Empirical Study of Distributed Development, IEEE International Conference on Global Software Engineering (ICGSE, Limerick, Ireland), July 2009, pp. 121-130.

           

          (C.89) Al-Ani, B. and Redmiles, D. Investigating Decision Making Processes (DMPes) in Distributed Development Teams: Findings of a Comparative Empirical Study, International Conference on Global Software Engineering (ICGSE, Limerick, Ireland), July 2009, pp. 51-60.

           

          (C.88) Wilensky, H., Redmiles, D., Su, N. The Dissemination of Knowledge Management, The 2009 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2009, Sanibel Island, FL), May 2009, pp. 199-208.

           

          (C.87) Shih, P.C., Nguyen, D.H., Hirano, S.H., Redmiles, D.F., Hayes, G.R. GroupMind: Supporting Idea Generation through a Collaborative Mind-mapping Tool, The 2009 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2009, Sanibel Island, FL), May 2009, pp. 139-148.

           

          (C.86) Al-Ani, B., Redmiles, D. Challenges Encountered When Identifying Remote Stakeholders, Workshop on Human-centered Computing in International Development, held in conjunction with the 2009 Conference on Human Factors and Computing Systems (CHI 2009, Boston, MA), available online at http://www.ics.uci.edu/~nsambasi/hcc4id/, April 2008.

           

          (C.85) Trainer, E., Redmiles, D.F. Towards an Infrastructure for Software Visualization Research, First International Workshop on Infrastructure for Research in Collaborative Software Engineering (IReCoSE), held in conjunction with The 16th International Symposium on the Foundations of Software Engineering (FSE 2008, Atlanta, GA), November 2008, available online at http://home.segal.uvic.ca/~IRCoSE-2008/.

           

          (C.84) Al-Ani, B., Redmiles, D., van der Hoek, A. Sense-Making and Mindfulness of Interdependencies in Virtual Organizations, Workshop on Supporting Distributed Team Work, held in conjunction with the Conference on Computer-Supported Cooperative Work (CSCW 2008, San Diego, CA), November 2008, 4 pages, Workshop description available online at http://docs.google.com/View?docid=dhncd3jd_343cmcr7mcm.

           

          (C.83) Wilensky, H. and Redmiles, D. Adoption of Web 2.0 in the Enterprise: Technological Frames of KM Practitioners and Users, Workshop on What to expect from Enterprise 3.0: Adapting Web 2.0 to Corporate Reality, held in conjunction with the Conference on Computer-Supported Cooperative Work (CSCW 2008,� San Diego, CA), November 2008, 3 pages, Proceedings available online at http://swiki.cs.colorado.edu/CSCW2008-Web20.

           

          (C.82) Sarma, A., Redmiles, D., van der Hoek, A. Empirical Evidence of the Benefits of Workspace Awareness in Software Configuration Management, The 16th ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2008, Atlanta, Georgia), November 2008, pp. 113-123.

           

          (C.81) Trainer, E., Quirk, S., de Souza, C.R.B., and Redmiles, D.F. Analyzing a Socio-Technical Visualization Tool Using Usability Inspection Methods, The IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC, Herrsching am Ammersee, Germany), September 2008, pp. 78-81.

           

          (C.80) Ruegmee, W., Silva Filho, R. S.,Bajracharya, S. K., Lopes, C. V., Redmiles, D. F. XE (eXtreme Editor) � Bridging the Aspect-Oriented Programming Usability Gap, The 23rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2008, L'Aquila, Italy), September, 2008, pp. 435-438.

           

          (C.79) Wilensky, H., Su, N., Redmiles, D., Mark, G. A community of Knowledge Management Practitioners: Mirroring Power across Social Worlds, The 20th IFIP World Computer Congress, WG12.6 Conference on Knowledge Management in Action (KMIA 2008, Milano Italy), September 2008, pp. 195-207.

           

          (C.78) de Souza, C.R.B., Redmiles, D.F. An Empirical Study of Software Developers Management of Dependencies and Changes, The 30th International Conference on Software Engineering (ICSE 2008, Leipzig, Germany), May 2008, pp. 241-250.

           

          (C.77) Al-Ani, B., Trainer, E., Ripley, R., Sarma, A., van der Hoek, A., Redmiles, D.F. Continuous Coordination within the Context of Cooperative and Human Aspects of Software Engineering, The 2008 International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE), held in conjunction with the 30th International Conference on Software Engineering (ICSE 2008, Leipzig, Germany), May 2008, pp. 1-4.

           

          (C.76) Al-Ani, B., Redmiles, D. Forces that Influence Trust in Technology in the Middle East: Culture, Politics and History, Workshop on HCI for Community and International Development, held in conjunction with the 2008 Conference on Human Factors and Computing Systems (CHI 2008, Florence, Italy), April 2008.

           

          (C.75) Fischer, G., Redmiles, D. Transdisciplinary Education and Collaboration, Meeting of the Human Computer Interaction Consortium (HCIC 2008, Frasier, CO), February 2008, also available to member organizations at http://www.hcic.org/.

           

          (C.74) de Souza, Quirk, S., Trainer, E., Redmiles, D.F. Supporting Collaborative Software Development through the Visualization of Socio-Technical Dependencies, The 2007 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2007, Sanibel Island, FL), November 2007, pp 147-156.

           

          (C.73) Su, N.M., Wilensky, H., Redmiles, D., Mark, G. The Gospel of Knowledge Management in and out of a Professional Community, The 2007 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2007, Sanibel Island, FL), November 2007, pp. 197-206.

           

          (C.72) de Souza, C.R.B., Redmiles, D.F. The Awareness Network: Should I display my actions to whom? And, whose actions should I monitor?, The 10th European Conference on Computer Supported Co-operative Work (ECSCW 2007, Limerick, Ireland), September 2007, pp. 99-117.

           

          (C.71) Sarma, A., Redmiles, D., van der Hoek, A. A Comprehensive Evaluation of Workspace Awareness in Software Configuration Management Systems, IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2007, Coeur d'Al�ne, Idaho), September 2007, pp. 23-26.

           

          (C.70) Silva Filho, R.S. and Redmiles, D.F. Managing Feature Interaction by Documenting and Enforcing Dependencies in Software Product Lines, The 9th International Conference on Feature Interactions in Software and Communication Systems (ICFI 2007, Grenoble, France), September 2007, pp.33-48.

           

          (C.69) de Souza, C. R. B., Hildenbrand, T., Redmiles, D. Toward Visualization and Analysis of Traceability Relationships in Distributed and Offshore Software Development Projects, First International Conference on Software Engineering Approaches For Offshore and Outsourced Development (SEAFOOD 2006, Z�rich, Switzerland), February 2007, pp 182-199.

           

          (C.68) Redmiles, D., Cheng, L-T. Damian, D., Herbsleb, J., Kellogg, W. Panel: Collaborative Software Engineering � New and Emerging Trends, Supplemental Proceedings of the Conference on Computer-Supported Cooperative Work (CSCW 2006� Banff, Canada), November 4-8, 2006, pp. 237-239.

           

          (C.67) Al-Ani, B., Sarma, A., Bortis, G., Almeida, I., Trainer, E., van der Hoek, A., Redmiles, D. Continuous Coordination (CC): A New Collaboration Paradigm, Workshop on Supporting the Social Side of Large Scale Software Development, in conjunction with the Conference on Computer-Supported Cooperative Work (CSCW 2006� Banff, Canada), November 2006, pp. 69-72.

           

          (C.66) Fonseca, S.B., de Souza, C.R.B., Redmiles, D.F. Exploring the Relationship between Dependencies and Coordination to Support Global Software Development Projects, IEEE International Conference on Global Software Engineering (ICGSE 2006, Cost�o do Santinho, Florian�polis, Brazil), October 2006, p. 243-244.

           

          (C.65) Silva Filho R. S., Geyer, W., Brownholtz, B., Redmiles, D. F. Understanding the Trade-offs of Blending Collaboration Services in Support of Contextual Collaboration, The 12th International Workshop on Groupware (CRIWG 2006� Medina del Campo, Spain), September 2006, Lecture Notes in Computer Science, Vol. 4154, pp. 270-285.

           

          (C.64) Silva Filho, R. S., Redmiles, D. F. Towards the use of Dependencies to Manage Variability in Software Product Lines, Workshop on Managing Variability for Software Product Lines: Working with Variability Mechanisms, held in conjunction with the 10th International Software Product Line Conference (SPLC 2006, Baltimore, MD), August 2006, pp. 10-15.

           

          (C.63) Rode J., Johansson, C., DiGioia, P, Silva Filho, R. S., Nies, K., Nguyen D. H., Ren J., Dourish, P., Redmiles D. F. Seeing Further: Extending Visualization as a Basis for Usable Security, Symposium On Usable Privacy and Security (SOUPS 2006, Pittsburgh, PA), July 12-14, 2006, pp. 145-155.

           

          (C.62) Silva Filho R. S., Redmiles, D. F. Extending Desktop Applications with Pocket-size Devices, Symposium On Usable Privacy and Security (SOUPS 2006, Pittsburgh, PA), July 12-14, 2006, published online at http://cups.cs.cmu.edu/soups/2006/program.html.

           

          (C.61) Redmiles, D., de Paula, R., Wilensky, H., Kosaka, K. What Ideal End Users Teach Us About Collaborative Software, The 2005 International ACM SIGGROUP Conference on Supporting Group Work (GROUP 2005, Sanibel Island, FL), November 2005, pp. 260-263.

           

          (C.60) Trainer, E., Quirk, S., de Souza, C.R.B., Redmiles, D.F. Bridging the Gap between Technical and Social Dependencies with Ariadne, The Eclipse Technology eXchange (eTX) Workshop, held in conjunction with the 20th Object-oriented Programming, Systems, Languages and Applications (OOPSLA) Conference (San Diego, CA), October 2005, pp. 26-30.

           

          (C.59) Silva Filho R. S., Redmiles D. Striving for Versatility in Publish/Subscribe Infrastructures, The Fifth International Workshop on Software Engineering and Middleware (SEM 2005), held in conjunction with the Fourth Joint Meeting of the European Software Engineering Conference and ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2005. Lisbon, Portugal), September 2005, pp. 17-24.

           

          (C.58) de Paula, R., Ding, X., Dourish, P., Nies, K., Pillet, B., Redmiles, D., Ren, J., Rode, J., and Silva Filho, R. Two Experiences Designing for Effective Security, The 2005 Symposium On Usable Privacy and Security (SOUPS 2005, Pittsburgh, PA), July 6-8, 2005, pp. 25-34.

           

          (C.57) Ren, J., Taylor, R.N., Dourish, P., Redmiles, D. Towards An Architectural Treatment of Software Security: A Connector-Centric Approach, The Workshop on Software Engineering for Secure Systems, held in conjunction with the 27th International Conference on Software Engineering, (ICSE 2005, St. Louis, Missouri) May 15-16, 2005, pp. 1-7.

           

          (C.56) de Souza, C. R. B., Redmiles, D., Cheng, L.-T., Millen, D., Patterson, J. Sometimes You Need to See Through Walls�A Field Study of Application Programming Interfaces, Conference on Computer-Supported Cooperative Work (CSCW 2004�Chicago, IL), November 2004, pp. 63-71.

           

          (C.55) de Souza, C.R.B., Dourish, P., Redmiles, D., Quirk, S., Trainer, E. From Technical Dependencies to Social Dependencies, Workshop on Social Networks, held in conjunction with the Conference on Computer-Supported Cooperative Work (CSCW 2004�Chicago, IL), November 2004, available at http://www.ischool.washington.edu/mcdonald/cscw04/.

           

          (C.54) de Souza, C.R.B., Redmiles, D., Cheng, L.-T., Millen, D., Patterson, J. How a Good Software Practice thwarts Collaboration�The Multiple Roles of APIs in Software Development, the 12th International Symposium on Foundations of Software Engineering (FSE 2004�Newport Beach, CA), November 2004, pp. 221-230.

           

          (C.53) Redmiles, D., Nakakoji, K. Panel on Design: Supporting Reflective Practitioners, the 26th International Conference on Software Engineering (ICSE 2004�Edinburgh, Scotland), May 2004, pp. 688-690.

           

          (C.52) van der Hoek, A., Redmiles, D., Dourish, P., Sarma, A., Silva Filho, R., de Souza, C. Continuous Coordination: A New Paradigm for Collaborative Software Engineering Tools, Workshop on Directions in Software Engineering Environments (WoDiSEE 2004), held in conjunction with the 26th International Conference on Software Engineering (ICSE 2004�Edinburgh, Scotland), May 2004, to appear, also available at http://www.cs.auckland.ac.nz/%7Eherm/WoDiSEE2004/.

           

          (C.51) Naslavsky, L., Silva Filho, R.S., de Souza, C.R.B., Dias, M., Richardson, D., Redmiles, D. Distributed Expectation-Driven Residual Testing, Second International Workshop on Remote Analysis and Measurement of Software Systems (RAMSS 04), held in conjunction with the 26th International Conference on Software Engineering (ICSE 2004�Edinburgh, Scotland), May 2004, pp. 45-49.

           

          (C.50) Fischer, G., M�rch, A., Nakakoji, K., Redmiles, D. Designing for Reflective Practitioners: Sharing and Assessing Progress by Diverse Communities, Extended Abstracts of the 2004 Conference on Human Factors and Computing Systems (CHI 2004�Vienna, Austria), April 2004, pp. 1711-1712.

           

          (C.49) de Souza, C.R.B., Redmiles, D., Dourish, P., �Breaking the Code�, Moving between Private and Public Work in Collaborative Software Development, International Conference on Supporting Group Work (GROUP 2003, Sanibel Island, FL), November 2003, pp. 105-114.

           

          (C.48) de Souza, C.R.B., Redmiles, D.F. �Breaking the Code�, Private and Public Work in Collaborative Software Development, the 8th European Conference of Computer-Supported Cooperative Work (ECSCW 2003�Helsinki, Finland), September 2003, pp. 40-42.

           

          (C.47) de Souza, C.R.B., Redmiles, D.F., Opportunities for Extending Activity Theory for Studying Collaborative Software Development, Workshop on Applying Activity Theory to CSCW Research and Practice, in conjunction with the 8th European Conference of Computer-Supported Cooperative Work (ECSCW 2003�Helsinki, Finland), September 2003 , available at http://www.uku.fi/atkk/actad/ecscw2003-at/.

           

          (C.46) de Souza, C.R.B., Redmiles, D.F., Mark, G., Penix, J., Sierhuis, M. Management of Interdependencies in Collaborative Software Development, ACM-IEEE International Symposium on Empirical Software Engineering (ISESE 2003�Rome, Italy), September 2003, pp. 294-303.

           

          (C.45) de Souza, C.R.B., Oliveira, H.L.R., da Rocha, C.R.P., Gon�alves, K.M., Redmiles, D.F. Using Critiquing Systems for Inconsistency Detection in Software Engineering Models, The Fifteenth International Conference on Software Engineering and Knowledge Engineering (SEKE 2003�San Francisco, CA), July 2003, pp. 196-203.

           

          (C.44) Silva Filho, R. S., de Souza, C.R.B., Redmiles, D.F. The Design of a Configurable, Extensible and Dynamic Notification Service, Second International Workshop on Distributed Event-Based Systems (DEBS 2003), In conjunction with The ACM SIGMOD/PODS Conference (San Diego, CA), June 2003, available at� http://www.eecg.toronto.edu/debs03/papers/silva_filho_etal_debs03.pdf.

           

          (C.43) Silva Filho, R.S., Slabyak, M., Redmiles, D.F. A Web-based Infrastructure for Group Awareness based on Events, Workshop on Network Services for Groupware, ACM Conference on Computer Supported Cooperative Work (CSCW 2002�New Orleans, LA), November 2002, available at� http://awareness.ics.uci.edu/~rsilvafi/papers/Workshops/CSCW2002-workshop.pdf.

           

          (C.42) Kantor, M., Redmiles, D. CASSIUS: Designing Dynamic Subscription and Awareness Services, Workshop on Ad hoc Communications and Collaboration in Ubiquitous Computing Environments, ACM Conference on Computer Supported Cooperative Work (CSCW 2002� New Orleans, LA), November 2002, available at http://www.cs.uoregon.edu/research/wearables/cscw2002ws/papers/Kantor.pdf.

           

          (C.41) de Souza, C.R.B., Basaveswara, S.D., and Redmiles, D.F. Using Event Notification Servers to Support Application Awareness, IASTED International Conference on Software Engineering and Applications (Cambridge, MA), November 2002, pp. 691-697.

           

          (C.40) de Souza, C.R.B., Penix, J., Sierhuis, M., Redmiles, D. Analysis of Work Practices of a Collaborative Software Development Team, International Symposium on Empirical software Engineering (ISESP 2002, Nara, Japan), V. II, October 2002, pp. 3-4.

           

          (C.39) Ledru, Y., Redmiles, D. Report on the workshop on the State of the art in Automated software Engineering, The Seventeenth IEEE International Automated Software Engineering Conference (Edinburgh, UK), September 2002, pp. 307-308.

           

          (C.38) Dourish, P., Redmiles, D. An Approach to Usable Security Based on Event Monitoring and Visualization, New Security Paradigms Workshop (NSPW 2002,� Virginia Beach, VA), September 2002, pp. 84-88.

           

          (C.37) de Souza, C.R.B., Basaveswara, S.D., and Redmiles, D.F. Supporting Global Software Development with Event Notification Servers, International Workshop on Global Software Development, Twenty-fourth International Conference on Software Engineering (ICSE 2002, Orlando, FL), May 21, 2002, pp. 9-13.

           

          (C.36) Redmiles, D. Supporting the End Users� Views, Working Conference on Advanced Visual Interfaces (AVI 2002, Trento, Italy), May 2002, pp. 34-42.

           

          (C.35) de Souza, C.R.B., Basaveswara, S.D., and Redmiles, D. Lessons Learned Using Notification Servers to Support Application Awareness, Meeting of the Human Computer Interaction Consortium (HCIC 2002, Frasier, CO), February 2002, available to member organizations at http://www.hcic.org/ or as UCI Technical Report UCI-ICS-02-11.

           

          (C.34) Kantor, M., Redmiles, D. Creating an Infrastructure for Ubiquitous Awareness, Eight IFIP TC 13 Conference on Human-Computer Interaction (INTERACT 2001, Tokyo, Japan), July 2001, pp. 431-438.

           

          (C.33) Hilbert, D., Redmiles, D. Large-Scale Collection of Usage Data to Inform Design, Eight IFIP TC 13 Conference on Human-Computer Interaction (INTERACT 2001, Tokyo, Japan), July 2001, pp. 569-576.

           

          (C.32) Redmiles, D. Software Requirements for Supporting Collaboration through Categories, Workshop on Classification Schemes in Cooperative Work, ACM Conference on Computer Supported Cooperative Work (CSCW 2000�Philadelphia, PA), December 2000, published online at http://bscw.gmd.de/bscw/bscw.cgi.

           

          (C.31) Robbins, J., Redmiles, D. Cognitive Support, UML Adherence, and XMI Interchange in Argo/UML, The Conference on Construction of Software Engineering Tools (CoSET �99�Los Angeles, CA), May 1999, pp. 61-70.

           

          (C.30) Vaidyanathan, J., Robbins, J., Redmiles, D. Using HTML to Create Early Prototypes, Human Factors in Computing Systems CHI �99 Conference (Pittsburgh, PA), Late-Breaking Results, ACM, May 1999, pp. 232-233.

          �

          (C.29) Robbins, J., Kantor, M., Redmiles, D. Sweeping Away Disorder with the Broom Alignment Tool, Human Factors in Computing Systems CHI �99 Conference (Pittsburgh, PA), Late-Breaking Results, ACM, May 1999, pp. 250-251.

           

          (C.28) Hilbert, D., Redmiles, D. Separating the Wheat from the Chaff in Internet-Mediated User Feedback, The Workshop on Internet-based Groupware for User Participation in Product Development, ACM Conference on Computer Supported Cooperative Work (CSCW �98�Seattle, WA), November 1998, pp. 31-35.

           

          (C.27) Hilbert, D., Redmiles, D. Agents for Collecting Application Usage Data Over the Internet, The Second International Conference on Autonomous Agents (Agents �98, Minneapolis/St. Paul, MN), ACM, May 10-13, 1998, pp. 149-156.

           

          (C.26) Robbins, J., Medvidovic, N., Redmiles, D., Rosenblum, D. Integrating Architecture Description Languages with a Standard Design Method, The Twentieth International Conference on Software Engineering (ICSE �98, Kyoto, Japan), IEEE Computer Society Press, April 19-25, 1998, pp. 209-218.

           

          (C.25) Hilbert, D., Redmiles, D. An Approach to Large-Scale Collection of Application Usage Data Over the Internet, The Twentieth International Conference on Software Engineering (ICSE �98, Kyoto, Japan), IEEE Computer Society Press, April 19-25, 1998, pp. 136-145.

           

          (C.24) Shukla, S., Nardi, B., Redmiles, D. Hit Squads & Bug Meisters: Discovering New Artifacts for the Design of Software Supporting Collaborative Work, Human Factors in Computing Systems CHI �98 Conference Summary (Los Angeles, CA), ACM April 1998, pp. 363-364.

           

          (C.23) Robbins, J., Hilbert, D., Redmiles, D. Software Architecture Critics in Argo, 1998 International Conference on Intelligent User Interfaces (IUI �98, San Francisco, CA), ACM Press, New York, NY, January 6-9, 1998, pp. 141-144.

           

          (C.22) Hilbert, D., Robbins, J., Redmiles, D. EDEM: Intelligent Agents for Collecting Usage Data and Increasing User Involvement in Development, 1998 International Conference on Intelligent User Interfaces (IUI �98, San Francisco, CA), ACM Press, New York, NY, January 6-9, 1998, pp. 73-76.

           

          (C.21) Kantor, M., Zimmermann, B., Redmiles, D. From Group Memory to Project Awareness Through Use of the Knowledge Depot, The 1997 California Software Symposium (Irvine, CA), UCI Irvine Research Unit in Software, Irvine, CA, November 7, 1997, pp. 19-26.

           

          (C.20) Robbins, J., Redmiles, D., Rosenblum, D. Integrating C2 with the Unified Modeling Language, The 1997 California Software Symposium (Irvine, CA), UCI Irvine Research Unit in Software, Irvine, CA, November 7, 1997, pp. 11-18.

           

          (C.19) Robbins, J., Hilbert, D., Redmiles, D. Argo: A Design Environment for Evolving Software Architectures, Nineteenth International Conference on Software Engineering (Boston, MA), IEEE Computer Society Press, ACM Press, New York, NY, IEEE Computer Society Press, Los Alamitos, CA, May 1997, pp. 600-601.

           

          (C.18) Shukla, S., Redmiles, D. Collaborative Learning in a Bug-Tracking Scenario, Workshop on Approaches for Distributed Learning through Computer Supported Collaborative Learning (Boston, MA), held in conjunction with the Conference on Computer Supported Cooperative Work (CSCW 96), ACM, November 1996.

           

          (C.17) Robbins, J., Hilbert, D., Redmiles, D. Using Critics to Analyze Evolving Architectures, Second International Software Architecture Workshop (ISAW-2�San Francisco, CA), held in conjunction with SIGSOFT�96: the Fourth Symposium on the Foundations of Software Engineering (FSE4), ACM, October 1996, pp. 90-93.

           

          (C.16) Robbins, J., Hilbert, D., Redmiles, D. Extending Design Environments to Software Architecture Design, The 11th Annual Knowledge-Based Software Engineering (KBSE-96) Conference (Syracuse, NY), IEEE Computer Society, Los Alamitos, CA, September 1996, pp. 63-72�Best Paper Award.

           

          (C.15) Robbins, J., Morley, D., Redmiles, D., Filatov, V., Kononov, D. Visual Language Features Supporting Human-Human and Human-Computer Communication, The IEEE Symposium on Visual Languages (Boulder, CO), IEEE Computer Society, Los Alamitos, CA, September 1996, pp. 247-254.

           

          (C.14) Robbins, J., Redmiles, D. Software Design From the Perspective of Human Cognitive Needs, The 1996 California Software Symposium (Los Angeles, CA), UCI Irvine Research Unit in Software, Irvine, CA, April 1996, pp. 16-27.

           

          (C.13) Rathke, C., Redmiles, D. Improving the Explanatory Power of Examples by a Multiple Perspectives Representation, The 1994 East-West Conference on Computer Technologies in Education (EW-ED�94, Crimea, Ukraine), P. Busilovsky, S. Dikareva, J. Greer, V. Petrushin (eds.), September 1994, pp. 195-200 � Best Paper Award.

           

          (C.12) Girgensohn, A., Redmiles, D., Shipman, F. Agent-Based Support for Communication between Developers and Users in Software Design, The 9th Annual Knowledge-Based Software Engineering (KBSE-94) Conference (Monterey, CA), IEEE Computer Society Press, Los Alamitos, CA, September 1994, pp. 22-29.

           

          (C.11) Rathke, C., Redmiles, D. An Object-Oriented Representation Language to Support Multiple Perspective Explanations, The ECOOP Workshop on Artificial Intelligence for Object-Oriented Software Engineering, H. Kaindl, J. Laubsch, A. Schappert (eds.), July 1994.

           

          (C.10) Redmiles, D. Observations On Using Empirical Studies in Developing a Knowledge-Based Software Engineering Tool, The 8th Annual Knowledge-Based Software Engineering (KBSE-93) Conference (Chicago, IL), IEEE Computer Society Press, Los Alamitos, CA, September 1993, pp. 170-177.

           

          (C.9) Redmiles, D. Reducing the Variability of Programmers� Performance Through Explained Examples, The Conference on Human Factors in Computing Systems, (INTERCHI and CHI 93, Amsterdam, The Netherlands), ACM, April 1993, pp. 67-73.

           

          (C.8) Majidi, M., Redmiles, D. A Knowledge-Based Interface to Promote Software Understanding, The 6th Annual Knowledge-Based Software Engineering Conference (KBSE 91, Syracuse, NY), IEEE Computer Society Press, Los Alamitos, CA, September 1991, pp. 178-185.

           

          (C.7) Fischer, G., Henninger, S., Redmiles, D. Intertwining Query Construction and Relevance Evaluation, The Conference on Human Factors in Computing Systems, (CHI 91, New Orleans, LA), ACM, April 1991, pp. 55-62.

           

          (C.6) Fischer, G., Henninger, S., Redmiles, D. Cognitive Tools for Locating and Comprehending Software Objects for Reuse, Thirteenth International Conference on Software Engineering (Austin, TX), IEEE Computer Society Press, ACM, IEEE, Los Alamitos, CA, May 1991, pp. 318-328.

           

          (C.5) Redmiles, D. Explanation to Support Software Reuse, The Workshop on Explanation, held in conjunction with the 8th National Conference on Artificial Intelligence (AAAI 90, Boston, MA), July 1990, pp. 20-24.

           

          (C.4) Kerner, A., Redmiles, D., Kracker, M. Schritte zur Generierung graphischer Praesentationen von Retrieval-Ergebnissen (Steps Toward Generating Graphical Presentations of Retrieval Results), Graphik und KI (Graphics and A.I. Conference), Koenigswinter bei Bonn, F.R. Germany, April 1990, pp. 58-67.

           

          (C.3) Henninger, S., Ignatowski, A., Rathke, C., Redmiles, D. A Knowledge-Based Design Environment for Graphical Network Editors, The 22nd Annual Hawaii Conference on System Sciences (HICSS 89, Kailua-Kona, Hawaii), Vol. II: Software Track, IEEE Computer Society, January 1989, pp. 881-891.

           

          (C.2) Redmiles, D. K*: A FORTRAN-Based Code for Programming and Evaluating Interactive Software, Engineering Databases: Software for On-Line Applications, PVP-Vol. 96, Proceedings of the 1984 Pressure Vessels and Piping Conference and Exhibition (San Antonio, TX), J.T. Fong (ed.), ASME, New York, NY, June 1984, pp. 61-69.

           

          (C.1) Bhansali, K.J., Redmiles, D., Murray, J.L., Sims, J.S. Database Development under the ASM/NBS Program on Alloy Phase Diagrams, Proceedings of the 29th National SAMPE Symposium (Reno, NV), SAMPE, April 1984, pp. 1450-1463.

           

           

          JOURNAL PUBLICATIONS

           

          (J.19) Sarma, A., Redmiles, D., van der Hoek, A. Palant�r: Early Detection of Development Conflicts Arising from Parallel Code Changes, IEEE Transactions on Software Engineering, 2011, (to appear).

           

          (J.18) Su, N.M., Wilensky, H.N., Redmiles, D.F. Doing Business with Theory: Communities of Practice in Knowledge Management, Computer-supported Cooperative Work, 2011, (to appear).

           

          (J.17) de Souza, C.R.B., Redmiles, D.F. The Awareness Network, To Whom Should I Display My Actions? And, Whose Actions Should I Monitor?, IEEE Transactions on Software Engineering, V. 37, N. 3, May/June 2011, pp. 325-340.

           

          (J.16) Sarma, A., Redmiles, D., van der Hoek, A. Categorizing the Spectrum of Coordination Technology, IEEE Computer, V. 43, No. 6, June 2010, pp. 61-67.

           

          (J.15) de Souza, C.R.B., Redmiles, D. On The Roles of APIs in the Coordination of Collaborative Software Development, Computer Supported Cooperative Work, V. 18, Nos. 5-6, December, 2009, pp. 445-475.

           

          (J.14) Al-Ani, B., Redmiles, D. Supporting Trust in Distributed Teams through Continuous Coordination, IEEE Software, November / December 2009, pp. 35-40.

           

          (J.13) Al-Ani, B., Redmiles, D., van der Hoek, A., Alvim, M., da Silva, I., Mangano, N., Trainer, E., Sarma, A. Continuous Coordination within Software Engineering Teams: Concepts and Tool Support, Journal of Computer Science and Engineering in Arabic: Special Issue on Software Engineering, vol. 1, no 3, 2008, pp. 10-33.

           

          (J.12) Geyer, W., Silva Filho, R. S., Brownholtz, B., Redmiles, D. F. The Trade-Offs of Blending Synchronous and Asynchronous Communication Services to Support Contextual Collaboration, Journal of Universal Computer Science, Special issue on Groupware: Issues and Applications with a selection of papers presented at 12th International Workshop on Groupware, V. 14, No. 1, March 2008, pp. 4-26.

           

          (J.11) Redmiles, D., van der Hoek, A., Al-Ani, B., Quirk, S., Sarma, A., Silva Filho, R., de Souza, C., and Trainer, E. Continuous Coordination: A New Paradigm to Support Globally Distributed Software Development Projects, Wirtschaftsinformatik, V. 49, 2007, pp. S28-S38.

           

          (J.10) de Paula, R., Ding, X., Dourish, P., Nies, K., Pillet, B., Redmiles, D., Ren, J., Rode, J., and Silva Filho, R. In the Eye of the Beholder: A Visualization-based Approach to Information System Security, International Journal of Human-Computer Studies (IJHCS), Special Issue on HCI Research in Privacy and Security, v. 63, No. 1-2, July 2005, pp. 5-24.

           

          (J.9) Redmiles, D. Introduction to the Special Issue of CSCW on Activity Theory and the Practice of Design, Computer-supported Cooperative Work, Special Issue on Activity Theory and the Practice of Design, Vol. 11, No. 1-2, 2002, pp. 1-11.

           

          (J.8) Collins, P., Shukla, S., Redmiles, D. Activity Theory and System Design: A View from the Trenches, Computer-supported Cooperative Work, Special Issue on Activity Theory and the Practice of Design, Vol. 11, No. 1-2, 2002, pp. 55-80.

           

          (J.7) Medvidovic, N., Rosenblum, D., Redmiles, D., Robbins, J.� Modeling Software Architectures in the Unified Modeling Language, ACM Transactions on Software Engineering and Methodology, Vol. 11, No. 1, January 2002, pp. 2-57.

           

          (J.6) Hilbert, D., Redmiles, D. Extracting Usability Information from User Interface Events, ACM Computing Surveys, Vol. 32, No. 4, December 2000, pp. 384-421.

           

          (J.5) Robbins, J., Redmiles, D. Cognitive Support, UML Adherence, and XMI Interchange in Argo/UML, Information and Software Technology, Vol. 42, No.2, January 2000, pp.79-89.

           

          (J.4) Robbins, J., Redmiles, D. Software Architecture Critics in the Argo Design Environment, Knowledge-Based Systems, Vol. 11, No.1, September 1998, pp.47-60.

           

          (J.3) Robbins, J., Hilbert, D., Redmiles, D. Extending Design Environments to Software Architecture Design, Automated Software Engineering, Vol. 5, No. 3, July 1998, pp. 261-290.

           

          (J.2) Fischer, G., Redmiles, D., Williams, L., Puhr, G., Aoki, A., Nakakoji, K. Beyond Object-Oriented Technology: Where Current Approaches Fall Short, Human Computer Interaction, Vol. 10, No. 1, 1995, pp. 79-119.

           

          (J.1) Fischer, G., Girgensohn, A., Nakakoji, K., Redmiles, D. Supporting Software Designers with Integrated, Domain-Oriented Design Environments, IEEE Transactions on Software Engineering, Special Issue on Knowledge Representation and Reasoning in Software Engineering, Vol. 18, No. 6, 1992, pp. 511-522.

           

           

          BOOK CHAPTERS

           

          (B.4) Sarma, A., Al-Ani, B., Trainer, E., Silva Filho, R.S., da Silva, I., Redmiles, D., van der Hoek, A.� Continuous Coordination Tools and their Evaluation, in I. Mistr�k, I., J. Grundy, A. van der Hoek, J. Whitehead (eds.), Collaborative Software Engineering, Springer, Ch. 8, pp. 153-178.

           

          (B.3) de Souza, C.R.B., Redmiles D., On the Alignment of Organizational and Software Structure, in B. Whitworth and A. de Moor, (eds.), Handbook of Research on Socio-Technical Design and Social Networking Systems, Ch 7, IGI Global, 2009, pp. 94-104.

           

          (B.2) Fischer, G., Grudin, J., McCall, R., Ostwald, J., Redmiles, D., Reeves, B., Shipman, F. Seeding, Evolutionary Growth and Reseeding: The Incremental Development of Collaborative Design Environments, in G. Olson, T. Malone, J. Smith (eds.), Coordination Theory and Collaboration Technology, Lawrence Erlbaum Associates, 2001, pp. 447-472.

           

          (B.1) Sims, J.S., Redmiles, D.F., Clark, J.B. ASM/NBS Numerical and Graphical Database for Binary Alloy Phase Diagrams, in J.R. Cuthill, N.A. Gokcen, J.E. Morral (eds.), Computerized Metallurgical Databases, The Metallurgical Society, 1988, pp. 119-134.

           

           

           

          http://www.ics.uci.edu/~staceyah/research.html Stacey Hancock | Research

          Stacey Hancock

          Department of Statistics

          University of California, Irvine

          Donald Bren Hall 2204, Irvine, CA 92697-1250
          stacey.hancock@uci.edu | tel: 949/824-9795

          • Home
          • Teaching
          • Research
          • Curriculum Vitae
          • Department of Statistics

          Research

          "Far better an approximate answer to the right question, than the exact answer to the wrong question, which can always be made precise." - John Tukey

          My primary research interests lie in statistics education. Currently, we are exploring how students use metaphors and metonymies when learning statistical concepts related to sampling distributions and informal statistical inference. Additional research topics include time series analysis, specifically, change-point detection, and statistical applications in ecology.

          Recent Activities

          Slides from "Using the Guidelines to Develop a New Undergraduate Program" invited talk, Joint Statistical Meetings, Seattle, WA, August 8-13, 2015.

          Slides from "Metonymy as a Lens into Student Understanding of Sampling Distributions" invited talk, San Diego State University Statistics Seminar, April 9, 2015.

          Slides from invited panel discussion on teaching statistics, International Conference on Statistics and its Interactions with Other Disciplines, Ho Chih Minh City, Vietnam, June 5-7, 2013.

          Selected Publications

          "New Undergraduate Data Science Programs" article in Amstat News, 1 July 2015, interview about our new data science major at UCI.

          Davis, R. A., Hancock, S., and Yao, Y.-C. (2015). On consistency of minimum description length model selection for piecewise autoregressions. To appear in Journal of Econometrics.

          Noll, J. and Hancock, S. (2014). Proper and paradigmatic metonymy as a lens for characterizing student conceptions of distributions and sampling. Educational Studies in Mathematics. http://dx.doi.org/10.1007/s10649-014-9547-1

          http://www.ics.uci.edu/~staceyah/teaching.html Stacey Hancock | Teaching

          Stacey Hancock

          Department of Statistics

          University of California, Irvine

          Donald Bren Hall 2204, Irvine, CA 92697-1250
          stacey.hancock@uci.edu | tel: 949/824-9795

          • Home
          • Teaching
          • Research
          • Curriculum Vitae
          • Department of Statistics

          Teaching

          "The test of a good teacher is not how many questions she can ask her pupils that they will answer readily, but how many questions she inspires them to ask her which she finds it hard to answer." - Alice Wellington Rollins

          "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write." - Wilks, 1951, paraphrasing H. G. Wells

          Winter 2016 Office Hours

          Scheduled office hours are held in DBH 2013 unless otherwise specified. Office hours by appointment will be in my office, DBH 2204. Office hours will not be held on university holidays. In order to maximize office hour effectiveness, please come prepared with written questions.

          • Monday 2-3pm (Stats 7 students given preference)
          • Tuesday 10:00-11:30am (Stats 7 or Stats 111/202)
          • Wednesday 8-9am (Stats 111/202 students given preference)
          • Also available by appointment.

          Winter 2016 Courses...

          Stats 7: Basic Statistics
          Stats 111/202: Statistical Methods for Data Analysis II

          Fall 2015 Courses...

          Stats 7: Basic Statistics
          Stats 210: Statistical Methods I: Linear Models

          Spring 2015 Courses...

          Stats 112/203: Statistical Method for Data Analysis III

          Winter 2015 Courses...

          Stats 7: Basic Statistics
          Stats 111/202: Statistical Methods for Data Analysis II

          Fall 2014 Courses...

          Stats 7: Basic Statistics
          Stats 120A: Introduction to Probability and Statistics
          Stats 201: Statistical Methods for Data Analysis I

          Spring 2014 Courses...

          Stats 112/203: Statistical Methods for Data Analysis III

          Winter 2014 Courses...

          Stats 7: Basic Statistics
          Stats 111/202: Statistical Methods for Data Analysis II
          Stats 120B/Math 131B: Introduction to Probability and Statistics

          Fall 2013 Courses...

          Stats 7: Basic Statistics
          Stats 120A/Math 131A: Introduction to Probability and Statistics


          Statistics Resources

          Data sources...

          Some of my favorite statistics links...

          • R 
          • Andrew Gelman's Statistics Blog
          • Hans Rosling's  Joy of Stats Documentary

          Data Visualization...

          • ManyEyes, a previous project developed by the IBM Visualization and Behavior Group
          • FlowingData
          • We Feel Fine
          • Junk Charts
          • Information is Beautiful
          • Chart Porn
          • ...and link to 37 other "data-ish blogs you should know about"

          Other fun and interesting links...

          • Scholarpedia
          • TED: Ideas Worth Spreading

          http://www.ics.uci.edu/~staceyah/index.html Stacey Hancock

          Stacey Hancock

          Department of Statistics

          University of California, Irvine

          Donald Bren Hall 2204, Irvine, CA 92697-1250
          stacey.hancock@uci.edu | tel: 949/824-9795

          • Home
          • Teaching
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          • Curriculum Vitae
          • Department of Statistics

          Stacey Hancock

          "Reality is like a face reflected in the blade of a knife; its properties depend on the angle from which we view it." - Master Hsing Yun, Describing the Indescribable

          http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html BIDA Book
          Ch 10 Dog data Ch 10 Dog data (regression) Ch 10 IL-1 beta data Ch 10 Dental data Ch 10 Exercise 10.15 Ch 10 WinBUGS Models Ch 10 R code Ch 10 WinBUGS code Ch 10 WinBUGS code (Word)
          Compound symmetry Dog Dog mixed Dental
          Pancreatic cancer data Chapter WinBUGS code
          Appendix C FEV data Appendix C FEV data (Excel) Appendix C R code Appendix C WinBUGS model
          Ch 15 Proof 15.1.2 Ch 15 R code Ch 15 WinBUGS code
          DPM DPM (Word) MPT MPT (Word) Other code Other code (Word)
          Ch 14 WinBUGS code Ch 14 WinBUGS code (Word)
          Ch 13 Larynx cancer data Ch 13 Cow data Ch 13 Leukemia data Ch 13 Cow abortion data Ch 13 Ovarian cancer data Ch 13 Exercise 13.20 data Ch 13 Kidney data Ch 13 Lung cancer data Ch 13 Kidney output data Ch 13 SAS code Ch 13 R code Ch 13 WinBUGS code
          Leukemia Leukemia model Kidney
          Exercise 13.20 code Larynx cancer Larynx cancer (Word) Cow abortion Cow abortion (Word) Leukemia Leukemia (Word) Kidney Kidney (Word)
          Ch 12 Leukemia data Ch 12 WinBUGS model Ch 12 R code Ch 12 WinBUGS code Ch 12 WinBUGS code (Word)
          Ch 11 Fabric data Ch 11 Watkins data Ch 11 Grille defects data Ch 11 FMD data Ch 11 FMD data (Excel) Ch 11 Armadillo data section 1.5 Ch 11 SAS code Ch 11 R code Ch 11 WinBUGS code Ch 11 WinBUGS code (Word)
          Ch 9 Diasorin data Ch 9 Exercise 9.21 data Ch 9 Bank salary data Ch 9 FEV data Ch 9 Section 9.7 Models Ch 9 WinBUGS Models Ch 9 WinBUGS code Ch 9 R code Ch 9 R code Anova
          Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
          FEV 1 FEV 2 Diasorin Dugong
          FEV FEV (Word) Anova Anova (Word)
          Ch 8 Cow data Ch 8 Logodds data Ch 8 Logodds trauma data Ch 8 Oring data Ch 8 Posterior iterates Ch 8 Prior iterates Ch 8 Toenail data Ch 8 Trauma data Ch 8 Toenail code book Ch 8 R code Ch 8 WinBUGS code Ch 8 WinBUGS code (Word)
          Ch 7 FEV data Ch 7 R code Ch 7 WinBUGS code Ch 7 WinBUGS code (Word)
          Ch 6 WinBUGS code Ch 6 WinBUGS code (Word)
          Ch 5 R Code Ch 5 Diasorin model 1 Ch 5 Diasorin model 2 Ch 5 Sample size proportion Ch 5 WinBUGS code Ch 5 WinBUGS code (Word)
          Diasorin Sample size Other R code
          Ch 4 R code Ch 4 WinBUGS code Ch 4 WinBUGS code (Word) Lindley-Jeffreys Paradox
          Ch 3 R code Ch 3 WinBUGS code Ch 3 WinBUGS code (Word)
          Ch 2 No links
          Ch 1 Armadillo data section 1.3 Ch 1 Armadillo data section 1.5 Ch 1 Brass alloy zinc data Ch 1 Cow data Ch 1 Lung cancer data Ch 1 Survival data Ch 1 WinBUGS code Ch 1 WinBUGS code (Word)
          http://www.ics.uci.edu/~mmazmani/Site/Home.html Melissa Mazmanian
           
           
           
           

          Melissa Mazmanian

           
           

          I am an Associate Professor in Informatics in the School of Information and Computer Sciences at University of California, Irvine. At UCI I currently serve as the faculty director of the LUCI lab for ubiquitous computing and interaction, Co-Director for the Center for Organizational Research (COR), and member of the executive board for the UCI Center for Ethnography. I was alos provided the opportunity to be a faculty participant in the Intel Science & Technology Center for Social Computing.


          My interests revolve around the experience of communication technologies as used in-practice within organizational and personal contexts, specifically in relation to identity projection and the nature of time in the digital age. I have conducted a variety of ethnographic and qualitative research projects on the individual experience and social dynamics that emerge when people adapt to using wireless modes of communication and have a series of publications on these topics.


          Click here for a short bio.


          The Informatics department allows me the opportunity to teach a variety of interdisciplinary courses at both the undergraduate and graduate level and I have the pleasure of working with a variety of excellent doctoral students.


          I earned my PhD in Organization Studies from the MIT Sloan School of Management in 2009 where I worked with Wanda Orlikowski and JoAnne Yates. I received my MSI in Information Economics, Management and Policy from the University of Michigan School of Information in 2002. I also completed a BA in 1997 in English Literature and Creative Writing at Colorado College.

          m.mazmanian [at] uci [dot] edu

          T: 949.824.9284

          F: 949.824.4056

          5074 Donald Bren Hall

          Irvine, CA 92697


          Click here for directions to my office

          If you are interested in applying to study as a graduate student in our department click here.

          Announcements:


          I am thrilled to announce that my tenure case was approved and I was appointed an Associate Professor of Informatics.

          May 2015


          I am proud to announce that Kathleen Pine and myself were recently awarded a grant from the National Science Foundation: Creating a Data-Driven World: Situated Practices of Collecting, Curating, Manipulating, and Deploying Data in Healthcare. Click here for information about the grant.

          September, 2013


          I was recently named a 2013 early career faculty honor awardee from Intel Corporation. Thank you Intel.

          September, 2013


          My work was featured on the UCI home page. Click here to learn a bit more about my work and history

          April, 2011

          Copyright 2015: Melissa Mazmanian

           
           
          http://evoke.ics.uci.edu/newsite/wp-login.php EVOKE Lab and Studio › Log In

          EVOKE Lab and Studio

          Lost your password?

          ← Back to EVOKE Lab and Studio

          http://www.ics.uci.edu/~sharad/students.html Sharad Mehrotra Group Page

          List of Graduated Ph.D. Students

           

           

          PostDoctoral Students

           

          Yueting Zhang

           

          PhDs

           

          Yong Rui (UIUC)

          Michael Ortega Binderberger (UIUC)

          Kexiang Xu (UIUC)

          Kaushik Chakrabarti (UIUC)

          Kringkai Porkaew (UIUC)

          Hakan Hacigumus (UCI)

          Dawit Seid (UCI)

          Bijit Hore (UCI)

          Hojjat Jafarpour (UCI)

          Ram

          Stella Chen (UCI)

          Ronen Vaisenberg (UCI)

          Rabia Nuray (UCI)

           

           

          Masters

           

          Undergraduate Students

           

          Visiting Students

           

           

           

          Current Students

           

          PhD

          Ronen Vaisenberg

          Liyan Zhang

          Rabia Nuray

          Pouria Pirazadeh

           

          http://www.ics.uci.edu/~sharad/projects.html Sharad Mehrotra Group Page

          Multimedia Analysis & Retrieval (MARS)

          The MARS project designed and developed an integrated multimedia information retrieval and database management infrastructure, entitled Multimedia Analysis and Retrieval System (MARS), that supported multimedia information as first-class objects suited for storage and retrieval based on their content. Specifically, research in the MARS project focused on content representation, multimedia information retrieval, multimedia feature indexing, and multimedia data management. MARS pioneered the usage of relevance feedback mechanisms in multimedia retrieval. Furthermore, as part of MARS we developed amongst the most scalable techniques for high dimensional data indexing and retrieval.

          MARS was funded by NSF through the CAREER award for Prof. Mehrotra

          Quality Aware Sensor Infrastructure (QUASAR)

          The Quasar project investigated issues related to data management in sensor enriched environments.� Unlike conventional distributed database systems, a sensor data architecture must handle extremely high data generation rates from a large number of small autonomous components. And, unlike the emerging paradigm of data streams, it is infeasible to think that all this data can be streamed into the query processing site, due to severe bandwidth and energy constraints of battery-operated wireless sensors. Thus, sensing data architectures must become quality-aware, regulating the quality of data at all levels of the distributed system, and supporting user applications' quality requirements in the most efficient manner possible.

           

          QUASAR Project was funded by NSF through a medium ITR grant.

           

          Database as a Service (DAS)

          Advances in the networking technologies have triggered one of the key industry responses, the "software as a service" initiative, also referred to as the application service provider (ASP) model. To address the above-stated problem, the DAS project pioneered the concept of� "Database as a service" model that inherits all the advantages of the ASP model, indeed even more, given that a large number of organizations have their own DBMSs. The model allows organizations to leverage hardware and software solutions provided by the service providers, without having to develop them on their own, thereby freeing them to concentrate on their core businesses. The DAS project explored the� viability of database-as-a-service (DAS) model. The project made pioneering contributions to understanding and realizing challenge of data privacy in outsourcing.

          �

          The DAS project was funded by NSF through a small ITR grant.

           

           

          RESCUE Project

           

           

          Responsphere Project

           

          Funded by NSF through the infrastructure grant, this project created a campus level sensing testbed including cameras, acoustic sensors, sensor motes, cell phones, motion sensors, RFID, etc. to create a deeply sensed environment that supported research on various aspects of crisis response. It allowed us to capture campus level emergency drills, capture, store and analyze data from it. Various innovative solutions including a new architecture for sensor data processing entitled SATWARE, a Fire Incident Command Board (FICB), a Disaster Portal came out as a result of Responsphere. Responsphere was also integral to support variety of research in RESCUE.

           

           

          SAFIRE Project

           

          SATWARE Project

           

           

           

          http://i-sensorium.ics.uci.edu/ Irvine Sensorium
          Center of Emergency Response Technologies , Donald Bren School of Information and Computer Science , University of California, Irvine

          IRVINE SENSORIUM

          • Home
          • Research
          • People
          • Partnerships
          • Internal

          A shared experimental laboratory housing state-of-the-art sensing, actuation, networking and mobile computing devices

          to enable researchers to emulate sentient spaces and applications in their target domains of interest.
          read more ...

          PROJECT LINKS

          • I-Sensorium Sensor Status
          • Bren Hall Infrastructure Status
          • General Infrastructure Status
          • Surveillance Visualization Demo

          CONTACT US

          Department of Computer Science
          School of Information and Computer Sciences
          University of California, Irvine
          Irvine, CA 92697-3435

          10 / 2011

          Our researchers have initiated the first phase of the I-sensorium project. In this phase, we are capturing data in a format which is defined by a general data model. These data streams are created from a broad range of sensors including cameras, microphones, mobile phones.


          8 / 2011

          The I-Sensorium project has been funded by the National Science Foundation.

          This material is based upon work supported by the National Science Foundation under Award Number 1059436.
          Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. © 2011 The Regents of the University of California, All Rights Reserved

          http://www.ics.uci.edu/~sharad/pubs.html Sharad Mehrotra Group Page

          Publication List on ISG Web Site

          This is a relatively complete set of my recent publications

           

           

          Publication List on� Google Scholar

          Link to my Google Scholar Profile

           

          Publication List on DBLP

          Link to my publications on DBLP

           

          http://www.ics.uci.edu/~dsm/cypress/ CYPRESS
          Home | People | Publications | Events | Videos and demos | For Members
             





          CYPRESS: Dependability Techniques for Instrumented Cyber-Physical Spaces

          The CYPRESS (CYber Physical RESilliance and Sustainability) project explores techniques for dependability, resilience and sustainability in cyberphysical spaces. The project derives its name from the Cypress tree that represents durability and sustainability

          Advances in sensing, embedded computing, and communication technologies offer unprecedented opportunities to add “intelligence” into physical systems and enable the creation of Instrumented Cyber- Physical Spaces (ICPSs). Example critical infrastructures include airports, energy distribution networks, and organizations such as schools and hospitals. Example critical applications range from surveillance to security and situation-aware emergency response. For ICPSs to transform our lives through new functionality, robustness, and efficiency, they must provide dependable infrastructure components in the presence of failures and disruptions and generate dependable information in the face of errors in sensing, communications, and computations. Dynamic adaptability of large ICPSs that employ heterogeneous sensing and actuation technologies becomes the fundamental enabler for dependable ICPSs.

          Dependability, as defined by the IFIP 10.4 Working Group on Dependable Computing and Fault Tolerance, refers to the trustworthiness of computing systems that allows reliance to be justifiably placed on the services it delivers. Dependability constitutes a variety of non-functional requirements including availability, reliability, maintainability, safety, and integrity. In the context of ICPS, dependability can broadly be classified at two interdependent levels that, combined, can provide a trustworthy platform for building applications:

          • Infrastructure Dependability – how dependable are the underlying infrastructure components (e.g., sensors, networks, actuators, computing/storage elements, software environments) in the presence of diverse failures that may lead to disruptions, and

          • Information Dependability – how dependable is the information generated by the underlying infrastructure given errors/uncertainty in sensor readings and data analysis mechanisms.

          In this project, we are expoiting an “observe-analyze-adapt” (OAA) architecture in which an ICPS has a model of itself, its objectives, and its effects on the environment; the ICPS achieves dependability objectives through adaptation using runtime application of formal analysis methods. The proposed dependability techniques are cross-layer in nature and range from combining multiple networking and messaging technologies to adaptive sensing and information fusion.

          software architecture

          Figure 1 illustrates our approach to designing ICPS management software - using OAA approach where a self- observing, introspecting system will initiate a logical adaptation of its components to meet dependability needs. ICPS systems and devices (designed using a cross-layer architecture) supply dynamic streams of information that are used by application which in turn adapt the usage and execution of the infrastructure. Since ICPS systems are dynamic, observation and monitoring of a system and its evolution is critical to enabling dependability. At the heart of the system is the ICPS StateDB that implements the “observe” aspect of the OAA cycle. A formal modeling and reasoning component allows for concrete specification of the ICPS system and analyzes the current system state to reason about the dependability properties; providing a limited, but focused “analyze” component in the OAA cycle. The outcome of the analysis will help us generate adaptations that comply with dependability constraints. The adaptation component makes changes to deal with dynamics in the environment, implements human-driven changes by embedding human activities and human-in-the-loop decisions, and deploys the logical adaptations into the physical ICPS infrastructure. A key feature of our approach (see right side of Figure 2) is the ability to perform cross-layer analysis and adaptation, both vertically (i.e., across abstraction layers ranging from the application, to middleware, OS, and hardware) and horizontally, across geographically distributed components interconnected via multiple, heterogeneous networks. Thus OAA at the infrastructure level enables stability of the entire operational infrastructure. The proposed effort focuses on system-level techniques for dependable CPS operation – human interactions occur via applications that specify their dependability needs.

          cross layer adaption

          There are four main research tasks included in our project:
          1.Dependable, Cross-Layer Observation and State Management
          2.Formal Methods for CPS System Dependability Analysis
          3.Adaptations to Support Infrastructure Dependability
          4.Adaptations to Support Information Dependability

          Research Task 1 corresponds to the observe step in OAA. In this task we will generate a cross-layer specification of the underlying system, its abilities and application dependability needs using quantitative and qualitative analysis and design an ICPS state capture service. For the analyze step, we use lightweight formal methods to analyze the current state of the system, given an infrastructure and application to determine violations of dependability needs (Task 2). Finally, the adapt step is investigated in Research Tasks 3 and 4 where adaptations are designed to enhance infrastructure and information dependability.

          We aim to develop: (1) an array of specific cross-layer adaptation techniques to support infrastructure and information dependability in ICPSs using the OAA paradigm, (2) a formal modeling framework supporting executable formal models to maintain runtime system models and formal analysis techniques to guide the adaptation process, and (3) the incorporation of (1) and (2) into middleware services that provide dependability-aware ICPS state management and adaptation. Responsphere/I-sensorium, are real, NSF-funded ICPS infrastructure on the University of California at Irvine (UCI) campus will be used to identify research challenges, concretize our research, and test and validate our ideas with an emergency response application (situational awareness in firefighting). We will build on our experience and software developed in the DHS-funded project SAFIRE and prior NSF project RESCUE to use Responsphere in testing for emergency drills planned in collaboration with our first responder partners.



          CYPRESS 2011 Poster

          Home | People | Publications | Events | Videos and demos
          This page was last updated on Thursday, March 24, 2011
          Webmaster: Zhijing DOT Qin AT gmail DOT com



          This material is based upon work supported by the National Science Foundation under ward Numbers 1063596, 1059436. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
          http://vision.ics.uci.edu/sccv/ Computational Vision | ICS | UC Irvine

          4th Annual Southern California Computer Vision Meetup (Spring 2012)


          Date: May 21st, 2012

          Time: 10am-5pm

          Location: 6011 Bren Hall, UC Irvine

          Cost: Free



          It is our pleasure to invite you to attend the fourth annual Southern California Computer Vision Meetup on May 21st, 2012. We hope you will join us for a day of exciting talks and discussion on the latest computer vision research being carried out by the large scientific community in Southern California.


          See the list of registered attendees here.
          A compact schedule of speakers is here.
          Get driving directions here
          Parking map 1 map 2



          Schedule:

          10:00-10:30 - Arrival, Coffee and pastries in 6011 Donald Bren Hall
          10:30-12:00 - Talk session 1
          12:00-1:00 - Lunch and Break on 6th floor balcony
          1:00-2:30 - Talk session 2
          2:30-3:00 - Coffee Break
          3:00-4:30 - Talk session 3


          2010 meeting archive

          2009 meeting archive

          2008 meeting archive







          Sponsor:

          Evolution Robotics Retail
          Jobs at Evolution Robotics Retail






          For more info, contact Charless Fowlkes fowlkes@ics.uci.edu



          Computational Vision | School of Information and Computer Sciences | UC Irvine         © 2010 UC Irvine, last updated: 2011-1-1

          http://www.ics.uci.edu/~fowlkes/presentations.html charless c. fowlkes - uc irvine - computer vision

          mugshot

          charless c. fowlkes

          associate professor
          computer science
          uc irvine

          fowlkes@ics.uci
          4076 dbh
          949.824.6945

          uci : cs : vision group

          home
          publications
          presentations
          software



          some presentations


          C. Fowlkes, C. Luengo Hendriks, S. Keränen, A. DePace, G. Weber, O. Rübel, M-Y Huang, S. Chatoor, L. Simirenko, C. Henriquez, A. Beaton, R. Weiszmann, S. Celniker, B. Hamann, M. Eisen, D. Knowles, M. Biggin, J. Malik, "Building a Quantitative Spatio-temporal Atlas of Gene Expression in the Drosophila Bastoderm", ICSB07, Long Beach, CA, (Oct 2007). [extended abstract]

          J. Burge, C. Fowlkes, M. Banks, "Configural cues, disparity, and depth perception: internalization of natural scene statistics" ECVP, Arezzo, Italy, (Aug 2007). [Perception, 36 supp]

          C. Fowlkes, C. Luengo Hendriks, S. Keränen, A. DePace, G. Weber, O. Rübel, M-Y Huang, L. Simirenko, B. Hamann, M. Eisen, D. Sudar, D. Knowles, M. Biggin, J. Malik, "Complex Interactions Between D/V and A/P Patterning Systems Before Gastrulation Revealed by a 3-D Atlas of Gene Expression Patterns." 47th Drosophila Research Conference, Houston, TX, (April 2006). [pdf]

          X. Ren, C. Fowlkes, J. Malik. "Familiar configuration enables figure/ground assignment in natural scenes", VSS, Sarasota, FL, (May 2005). [Journal of Vision, 5(8) p.344] [pdf]

          C. Fowlkes, C. Luengo Hendriks, S. Keränen, M. Biggin, D. Knowles, D. Sudar, J. Malik. "Building Composite Maps of Gene Expression Patterns and Morphology: Registering 3D Representations of Drosophila Embryos", 46th Drosophila Research Conference, San Diego, CA, (Apr. 2005). [pdf]

          C. Fowlkes, "Perceptual Organization and Linear Algebra" slides for MSRI PREP Workshop tutorial on "The Mathematics of Images". MSRI, Berkeley, CA, (March 2005). [pdf]

          C. Fowlkes, "Learning the Ecological Statistics of Perceptual Organization", Qualifying Exam, Berkeley, CA, (December 2004). [pdf]

          C. Fowlkes, "Affinity Functions for Image Segmentation", slides for CVPR tutorial on "Graph Based Image Segmentation". Washington DC, (June 2004) [pdf]

          C. Fowlkes, D. Martin, J. Malik. "On Measuring the Ecological Validity of Local Figure-Ground Cues", ECVP, Paris, France, (Sept. 2003). [Perception, 32 supp, p. 171] [pdf]

          C. Fowlkes, D. Martin, J. Malik. "Ecological Statistics of Grouping by Similarity", VSS, Sarasota, FL, (May 2003). [Journal of Vision, 3(9) p.43] [pdf]

          C. Fowlkes, D. Martin, J. Malik. "Understanding Gestalt Cues and Ecological Statistics Using A Database of Human Segmented Images", Presented at POCV Workshop, Vancouver, (July 2001). [ppt]

          http://www.ics.uci.edu/~fowlkes/software.html charless c. fowlkes - uc irvine - computer vision

          mugshot

          charless c. fowlkes

          associate professor
          computer science
          uc irvine

          fowlkes@ics.uci
          4076 dbh
          949.824.6945

          uci : cs : vision group

          home
          publications
          presentations
          software



          misc source code


          • Make Kymograph ImageJ plugin

             An ImageJ plugin written by Sam Hallman for extracting kymographs from images of axons

          • the berkeley segmentation engine (BSE)

             C++ code for computing segmentation cues based on contour and texture.

          • heirarchical mixtures of experts

             heirarchical mixtures of experts for classification using k-way logistic functions.

          • neighborhood components analysis

             a quick matlab implementation of NCA (see Goldberger et al, NIPS04).

          Deep Understanding of Image Aesthetics

        • Pollen Grains Detection, Segmentation, and Categorization

        • C. Elegans Detection, Segmentation, and Counting

          Publications

          • Shu Kong, Zhuolin Jiang, Qiang Yang, "Modeling Neuron Selectivity over Simple Mid-Level Features for Image Classification", IEEE Trans. on Image Processing, 2015
            [paper]

          • Yuetan Lin, Shu Kong, Donghui Wang, Yueting Zhuang, "Saliency Detection within a Deep Convolutional Architecture", AAAI'14 Workshop on Cognitive Computing for Augmented Human Intelligence, 2014.
            [paper]

          • Donghui Wang*, Shu Kong*, "A Classification-Oriented Dictionary Learning Model: Explicitly Learning the Particularity and Commonality Across Categories", Pattern Recognition, 2014.
            [paper] [code]

          • Shu Kong, Donghui Wang, "Learning Exemplar-Represented Manifolds in Latent Space for Classification", ECML/PKDD, 2013.
            [paper] [code]

          • Donghui Wang, Xikui Wang, Shu Kong, "Integration of Multi-Feature Fusion and Dictionary Learning for Face Recognition", Image and Vision Computing (IVC), 2013.
            [paper] [code]

          • Shu Kong, Donghui Wang, "Learning Individual-Specific Dictionaries with Fused Multiple Features for Face Recognition", FG, 2013.
            [paper]

          • Shu Kong, Xikui Wang, Donghui Wang, "Multiple Feature Fusion for Face Recognition", FG, 2013.
            [paper] [code]

          • Shu Kong, Donghui Wang, "A Dictionary Learning Approach for Classification: Separating the Particularity and the commonality", ECCV, 2012.
            [paper] [code]

          • Shu Kong, Donghui Wang, "Transfer Heterogeneous Unlabeled Data for Unsupervised Clustering", ICPR, 2012.
            [paper] [code]

          • Shu Kong, Donghui Wang, "A Multi-task Learning Strategy for Unsupervised Clustering via Explicitly Separating the Commonality", ICPR, 2012.
            [paper]

          • Donghui Wang, Shu Kong, "Learning Class-Specific Dictionaries for Digit Recognition from Spherical Surface of a 3D Ball", Machine Vision and Applications (MVA), 2012.
            [paper] [SingleBall_dataset (288MB)] [MultiBall_dataset (121MB)]

          • Donghui Wang, Shu Kong, "Feature Selection from High-Order Tensorial Data via Sparse Decomposition", Pattern Recognition Letters, 2012.
            [paper] [code]

          Funding

          • NSF DBI-1262547 2015-
          • Multidisciplinary Design Program 2014-2015

          Casual Talks

          • "Deep Understanding Image Aesthetics", Vision Group, UCI, Sep. 30, 2015. [slides]

          • "Image Quality and Aesthetics Estimation", Adobe Systems Inc, Sep. 18, 2015.

          • "Automated Biological Image Analysis using Computer Vision and Machine Learning", Multi-Disciplinary Project Research Symposium, Calit2 Auditorium, May. 30, 2015.

          • "Beyond R-CNN detection: Learning to Merge Contextual Attribute", Vision Group, UCI, Jan. 29, 2015. [slides]

          • "A Story from Saliency to Objectness and Extension by Deep Neural Network with Perspective and Doubt", Vision Group, UCI, Nov. 6, 2014. [slides]

          Misc

          • I'm a co-founder of SEED -- a Registered Campus Organization to promote harmony and love within the campus, to bring critical thinking and loving attitude across cultures towards daily lives.








          http://www.ics.uci.edu/~fowlkes/bioshape/index.html charless c. fowlkes - uc irvine - computer vision

          mugshot

          charless c. fowlkes

          associate professor
          computer science
          uc irvine

          fowlkes@ics.uci
          4076 dbh
          949.824.6945

          uci : cs : vision group

          home
          publications
          presentations
          software




          The overarching goal of this project is to develop analytic methods that enable scientists to efficiently and automatcially discover relationships between shape and function in biological systems. This work has been supported by the NSF Emerging Frontiers and Biological Infrastructure Divisions in the form of a large collaborative effort on bioshape (bioshapes.org, DBI-1053036) and a collaborative project on bioimage analysis for understanding pollen morphology and texture (DBI-1262547)




            Pollen Texture-based Classification and Analysis (with Punyasena Lab at UIUC)

            The practice of identifying pollen has a large number of scientific applications and is used in fields as diverse as archaeology, biostratigraphy (the dating of rocks), and forensic science. Pollen and spores play a particularly important role in paleontology, because they form the most abundant and extensive record of plant diversity, dating back hundreds of millions of years. However, many critical hypotheses in plant ecology and evolution (e.g. the assembly of plant communities, speciation and extinction) cannot be fully tested with pollen data due to the extreme difficulty of recognizing species from pollen and spore material. We are developing methods for automatically classifying pollen based on texture and shape features. This approach has been validated in distinguishing visually similar SEM images of pollen from several different species based on fine scale textural differences.

            Publications:
            • S. Punyasena, L. Mander, M. Li, W. Mio, C. Fowlkes, Classifying grass pollen using high-resolution imaging and the quantitative analysis of surface texture, BSA symposium on Bioinformatic and Biometric Methods in Plant Morphology

            • L. Mander, M. Li, W. Mio, C. Fowlkes, S. Punyasena, "Classification of grass pollen through the quantitative analysis of surface ornamentation and texture", Proc. R. Soc. B. 2013 280 (1770) [pdf]

            • B. Kong, C. Fowlkes, "Fast Convolutional Sparse Coding", UCI Technical Report, May 2014 [pdf]

            • J. Yarkony, C. Fowlkes, "Planar Ultrametric Rounding for Image Segmentation", Technical Report, July 2015 arXiv:1507.02407 [pdf]

            Software:
            • Texture recognition using sparse coding histograms and nearest-neighbor classification. This code include a MATLAB demo for classifying grass pollen SEM images (dataset from Mander et al 2013) which achieves ~77 percent accuracy in species identification.

              [texture_coding_0.2.tar.gz]

            • Fast sparse convolutional sparse coding implementation from Bailey Kong

              [github]




            Gene expression and developmental patterning in Drosophila (with DePace Lab at Harvard)

            For a gene to function properly, it must be active in the right place, at the right time, and in the right amount. Changes in any of these features can lead to observable differences between individuals and species and in some cases can lead to disease. We do not currently understand how the position, timing, and amount of gene expression is encoded in DNA sequence. One approach to this problem is to compare how gene expression differs between species and to try to relate changes in DNA sequence to changes in gene expression. We are developing methods for measuring and comparing gene expression patterns at high spatial and temporal resolution between embryos of different species of Drosophila. The methods allow us to control for variation in the size, shape, and number of nuclei between embryos.

            Publications:

            • M. Staller, C. Fowlkes, M. Bragdon, J. Estrada, Z. Wunderlich, A. DePace, "A gene expression atlas of a bicoid-depleted Drosophila embryo reveals early canalization of cell fate", Development 142, p. 587-596, (2015) [pdf]

            • C. Fowlkes, K. Eckenrode, M. Bragdon, M. Meyer, Z. Wunderlich, L. Simirenko, C. Hendriks, S. Keranen, C. Henreiquez, M. Biggin, M. Eisen, A. DePace, "A conserved developmental patterning network produces quantitatively different output in multiple species of Drosophila," PLoS Genetics, 7(10): e1002346, 2011. [pdf]

            • C. Fowlkes, C. Luengo Hendriks, S. Keränen, G. Weber, O. Rübel, M-Y Huang, S. Chatoor, L. Simirenko, A. DePace C. Henriquez, A. Beaton, R. Weiszmann, S. Celniker, B. Hamann, D. Knowles, M. Biggin, M. Eisen, J. Malik. "Constructing a quantitative spatio-temporal atlas of gene expression in the Drosophila blastoderm", Cell, 133(2), p. 364-374, 2008. [pdf] [supplement]



            Software:
            • PointCloudAlign toolkit (v1.0.1) for registering gene expression patterns acquired using the BDTNP PointCloudToolbox image processing pipeline

              [pcalign.1.01.tar.gz]




            Adaptive software for automated detection, tracking and segmentation (with Cinquin Lab at UCI)

            While the particular uses of images as an experimental tool in biology vary widely, the common tasks of recognition, grouping and tracking are ubiquitous. At present, automated image analysis is typically limited to large scale projects that can justify the resources required for development of custom software. The resulting systems are often brittle and require redevelopment each time a slightly different dataset appears. We are working on generic tools that can be interactively retrained by the end user with the goal of expanding the role of quantitative imaging and modeling in biology by “mining the long tail” of under-analyzed experimental data.

            Publications:
            • A. Cinquin, M. Chiang, A. Paz, S. Hallman, O. Yuan, C. Fowlkes, O. Cinquin "Stochastic cycling balances stem cell self-renewal and senescence", [under review]

            • M. Chiang, S. Hallman,A. Cinquin, N. Mochel, A. Paz, S. Kawauchi, A. Calof, K. Cho, C. Fowlkes, O. Cinquin, "Analysis of in vivo single cell behavior by three-dimensional spatial cytometry", [under review]

            • X. Zhu, C. Vondrick, C. Fowlkes, D. Ramanan, "Do we need more training data?", IJCV, DOI 10.1007/s11263-015-0812-2, 2015 [arXiv:1503.01508] [pdf]

            • S. Wang, C. Fowlkes, "Learning Multi-target Tracking with Quadratic Object Interactions", Technical report, arXiv:1412.2066, (Dec. 2014) [pdf]

            • S. Hallman, C. Fowlkes, "Oriented Edge Forests for Boundary Detection", CVPR, Boston, MA, (June 2015). arXiv:1412.4181 [pdf] [code]


            Software:

            • MATLAB code for training template based detectors for detecting cell nuclei in fluorescence image stacks.

              [cell_detector_0.1.tar.gz]

            • MATLAB code for learning and inference in multi-target tracking with pairwise interactions between objects.

              [coming soon]








            Quantification of cellular and sub-cellular alignment (with Khine Lab at UCI)

            Nano- and microscale topographical cues play critical roles in the induction and maintenance of various cellular functions, including morphology, adhesion, gene regulation, and communication. Recent studies indicate that structure and function at the heart tissue level is exquisitely sensitive to mechanical cues at the nano-scale as well as at the microscale level. We have developed image analysis tools coupled with an inexpensive culture platform comprised of biomimetic wrinkles that simulate the heart’s complex anisotropic and multiscale architecture for facile and robust cardiac cell alignment. We demonstrate the cellular and subcellular alignment of both neonatal mouse cardiomyocytes as well as those derived from human embryonic stem cells. By mimicking the fibrillar network of the extracellular matrix, this system enables monitoring of protein localization in real time and therefore the high-resolution study of phenotypic and physiologic responses to in-vivo like topographical cues.


            Publications:
            • A. Chen, D. Lieu, L. Freschauf, V. Lew, H. Sharma, J. Wang, D. Nguyen, I. Karakikes, R. Hajjar, A. Gopinathan, E. Botvinick, C. Fowlkes, R. Li, M. Khine, "Shrink-Film Configurable Multiscale Wrinkles for Functional Alignment of Human Embryonic Stem Cells and their Cardiac Derivatives", 10.1002/adma.201103463, Advanced Materials, 2011. [pdf]

            • J. Luna, J. Ciriza, M. Ojeda-Garcia, M. Kong, A. Herren, D. Lieu, R. Li, C. Fowlkes, M. Khine, K. McCloskey, "Multi-scale Biomimetic Topography for the Alignment of Neonatal and Embryonic Stem Cell-derived Heart Cells", Tissue Engineering: Part C , 17(5), p. 579-588 , 2011 [pdf]

            • A. Chen, E. Lee, R. Tu, K. Santiago, A. Grosberg, C. Fowlkes, M. Khine, "Integrated Platform for Functional Monitoring of Biomimetic Heart Sheets Derived From Human Pluripotent Stem Cells", Biomaterials, 35(2):675-683. [pdf]


            Software:
            • [alignment quantification tools coming soon]









            Functionally derived shape metrics for analyzing Bat Biosonar (with Mueller Lab at VT)

            Statistical analysis of collections of shapes typically relies on some measure of distance between shapes which is specified a priori without consideration of the relevance of particular modes of variation to biological function. We are developing methods that leverage accurate simulation of acoustical propagation to understand the functional importance of aspects of bat ear and noseleaf shape. We exploit the adjoint method to efficiently perform a global analysis of the sensitivity of acoustical performance to shape features.

            Interactive WebGL visualization of ear geometry and beam pattern [link]










            Registration of chick embryo outflow tract dynamics with fluorescence microscopy-based protein expression data (with Rugyoni Lab at OHSU)

            It is known that changes in hemodynamic forces affect developmental growth of the heart but mechanisms underlying this process are not well understood. We are developing methods that combine dynamic shape data collected from the functioning outflow tract in chick embryo along with protein expression data (e.g. collagen) in order to try and discover correlations between the dynamic shape and underlying tissue development.

          POCV 2012
          The Eighth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision

          Providence, Rhode Island, USA
          June 16, 2012
          In Conjunction with IEEE CVPR 2012


          Home

          Technical Program

          Program Committee



          Workshop Chairs

          Iasonas Kokkinos,
          Ecole Centrale Paris

          Charless Fowlkes,
          University of California, Irvine


          NEWS
          The submission deadline is now April 9th.
          The electronic submission system is now online at: https://cmt.research.microsoft.com/POCV2012.

          Theme
          Perceptual Organization is the process of establishing a meaningful relational structure over raw visual data so as visual primitives arising from the common physical cause are grouped together. A driving motivation behind perceptual organization research in computer vision is to deliver compact representations and to reduce the space of hypotheses for higher-level visual tasks. Since early demonstrations in the 1980s underscored its usefulness in object recognition, the computer vision community has seen various applications of PO in artificial vision systems such as in figure-ground segmentation, contour completion, stereo matching, model indexing, change detection, activity recognition, and more. Recent progress in PO has encouraged more participation from experts in related areas such as object recognition, texture and motion analysis. Because of its wide applicability, the potential payoff from perceptual organization research is enormous. Among the objectives of POCV is to encourage presentation of new ideas and facilitate discussion on the future of PO. To this end, papers introducing novel concepts will be considered for acceptance even if they lack full experimental validation. The schedule will include longer periods for questions and answers after each talk than CVPR to enable dialogue and the exchange of ideas. This POCV workshop will place a special emphasis on works that use PO to build a bridge between low- and high-level vision.

          Invited Speakers
          Kristin Grauman, University of Texas, Austin
          Pedro Felzenszwalb, Brown University
          Ronen Basri, Weizmann Institute
          Jitendra Malik, University of California, Berkeley



          Scope
          Papers are solicited in all areas of perceptual organization, including but not limited to:
          • image segmentation
          • contour completion
          • spatiotemporal/motion segmentation
          • figure-ground discrimination
          • integration of top-down and bottom-up methods
          • perceptual organization for object or activity detection/recognition
          • unification of segmentation, detection and recognition
          • biologically-motivated methods
          • neural basis for perceptual organization
          • learning in perceptual organization
          • graphical methods
          • natural scene statistics
          • evaluation methods
          This POCV workshop will place a special emphasis on works that use PO to bridge the gap between low- and high-level vision.

          Paper Submission
          Submission is electronic, and must be in PDF format. Papers must not exceed 8 double-column pages. Submissions must follow standard IEEE CVPR 2-column format of single-spaced text in 10 point Times Roman, with 12 point interline space. All submissions must be anonymous. Please us the IEEE Computer Society CVPR format kit.

          All reviewing will be double blind, so the paper must not include any information which allows the authors to be identified. For example, this might require that some references to the authors' previous work be left blank or that authors refer to their previous work in the third person. This is not optional. Papers that provide obvious identifying information may be rejected without review.

          As per conference policy, camera ready versions of accepted papers will be submitted to IEEE and authors must submit the IEEE copyright transfer form. Each paper is allocated 6 pages for no additional cost. Additional pages, up to 8 total, are $100/page which must be paid with the author's registration. No paper may be more than 8 pages. One author of each paper must register for the conference by the camera-ready due date, or the paper will not be included on the conference DVD or in IEEE Explorer. There will be 1-day registration too, if anyone wants to just attend a workshop. Otherwise the regular CVPR registration will cover all workshops as usual.

          In submitting a paper to the POCV Workshop, authors acknowledge that no paper of substantially similar content has been or will be submitted to another conference or workshop during the POCV review period. Dual submission of accepted CVPR papers allowed as long as there is at least 30% new content



          Important Dates

          • Submission deadline: 11:59pm PST, April 9, 2012
          • Reviews Due: April 22, 2012
          • Notification: April 24, 2012
          • Camera ready versions due May 1, 2012
          Past POCV Workshops
          • 2010 CVPR (San Francisco, CA)
          • 2008 CVPR (Anchorage, AK)
          • 2006 CVPR (New York, NY)
          • 2004 CVPR (Washington, DC)
          • 2001 ICCV (Vancouver, Canada)
          • 1999 ICCV (Crete, Greece)
          • 1998 CVPR (Santa Barbara, CA)




          http://www.ics.uci.edu/~shallman/ Sam Hallman
          Photo of me

          Sam Hallman

          Ph.D. candidate   Computer vision   UCIrvine
          Email: shallman@uci.edu     Office: DBH 4209 (map)

          Resume   /   Google Scholar   /   LinkedIn

          Hi! I was a Ph.D. candidate in the computer vision group at UC Irvine, where I was advised by Charless Fowlkes. I graduated in September and will work at Amazon in Seattle. I received a B.Sc. in computer science from UC Irvine in 2009.

          Publications

          • Oriented Edge Forests for Boundary Detection
            S. Hallman, C. Fowlkes, CVPR, Boston (June 2015)
            [Paper]   [1-page summary]   [Code]   [Poster]

          • Detecting Dynamic Objects with Multi-View Background Subtraction
            R. Díaz*, S. Hallman*, C. Fowlkes, ICCV, Sydney, Austrailia (December 2013)
            [Paper]   [Supplementary material]   [Poster]

          • Multi-View Background Subtraction for Object Detection
            R. Díaz*, S. Hallman*, C. Fowlkes, SUNw, Portland, OR (June 2013)

          • Layered Object Models for Image Segmentation
            Y. Yang, S. Hallman, D. Ramanan, C. Fowlkes, TPAMI, 34(9):1731-1743, 2011.
            [Paper]   [Project page]

          • Layered Object Detection for Multi-Class Segmentation
            Y. Yang, S. Hallman, D. Ramanan, C. Fowlkes, CVPR, San Francisco, (June 2010)
            [Paper]   [Poster]   [Slides]   [Talk]

          • Vehicle Detection on High-Resolution Commerical Satellite Imagery
            S. Hallman, A. Skurikhin, C. Fowlkes. Technical Report LA-UR-10-05877, Los Alamos National Laboratory, 2010
          * shared first author

          Other Links

          • MATLAB File Exchange profile
          • Yi Yang's beautiful elephant drawing

          http://www.ics.uci.edu/~minhaenl/ MinHaeng, Lee @ UCI

          MinHaeng, Lee

          Ph.D Student
          UC, Irvine
          Computer Science
          C.V. (PDF)

          Research Interests

          My research field includes computer vision, robotics, and machine learning. I am more interested in middle and high level vision problems regarding to object detection/recognition, and focusing on robot as a target.

          Academic Projects

          Geometric Information System aided Scene understanding (2015, Spring, CS216,217)


          Application Projects

          Music Hub - synchronous music play (2015, Spring, CS244)


          Technical Report Code [project page]

          Publications

          Conferences

          Minhaeng Lee, Myungjin Choi, Yu-Wing Tai : Robust Pan-Sharpening via Color Samples Relocation and Edge Aware Interpolation. IEEE Internationl Conference on Image Processing (ICIP) 2014. [pdf][bibtex]

          Donghyeon Cho, Minhaeng Lee, Sunyeong Kim, Yu-Wing Tai : Modeling the calibration pipeline of the Lytro camera for high quality light-field image reconstruction.International Conference on Computer Vision (ICCV) 2013. [pdf][bibtex]

          Inchang Choi, Minhaeng Lee, Yu-Wing Tai : Video Matting Using Multi-Frame Nonlocal Matting Laplacain.European Conference on Computer Vision (ECCV) 2012. [pdf][bibtex]

          Implementations

          DIgital Refocusing using 4D Lightfield images [referenced paper][src][example Data]
          charless c. fowlkes - uc irvine - computer vision

          mugshot

          charless c. fowlkes

          associate professor
          computer science
          uc irvine

          fowlkes@ics.uci
          4076 dbh
          949.824.6945

          uci : cs : vision group

          home
          publications
          presentations
          software



          Combinatorial Inference and Learning for Fusing Recognition and Perceptual Grouping


          The overarching goal of this NSF funded project (IIS-1253538) is to develop integrated models for fusing recognition and perceptual grouping.

          When presented with a novel image, humans typically have little problem providing a consistent interpretation of the scene in terms of contours, surfaces, junctions, and the relations between them. This process of perceptual organization is closely coupled with recognition of familiar shapes and materials. Perceptual organization can aid recognition by reducing the complexity of a cluttered scene to a small number of candidate surfaces while recognition can help resolve ambiguities in grouping based on local image cues. This project is developing a computational framework that fuses top-down information provided by recognition with bottom-up perceptual organization in order to automatically produce a coherent scene interpretation. This research includes (1) identifying local image features that provide cues to grouping and figure-ground, (2) developing libraries of composable detectors that capture the appearance of objects, parts and their spatial relations, and (3) designing models and efficient inference routines that explicitly reason about occlusion and the binding of image regions and contours into object shapes.

          Integrated models of grouping and recognition have direct significance to expand the computer vision capabilities of robotics and assistive technologies that must operate in complex, cluttered environments. The framework being developed also has applications in automating biological image analysis where top-down shape information are useful in resolving noisy local measurements.




          Publications:


          J. Yarkony, C. Fowlkes, "Planar Ultrametric Rounding for Image Segmentation", Technical Report, July 2015 arXiv:1507.02407 [pdf]

          S. Wang, C. Fowlkes, "Learning Optimal Parameters for Multi-target Tracking", BMVC 2015 [pdf]

          G. Ghiasi, C. Fowlkes, "Using segmentation to predict the absence of occluded parts", BMVC 2015. [pdf] [data]

          R. Diaz, M. Lee, J. Schubert, C. Fowlkes, "Lifting GIS Maps into Strong Geometric Context" Technical Report, July 2015 arXiv:1507.03698 [pdf]

          G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Detecting and Localizing Occluded Faces", Technical Report, June 2015 arXiv:1506.08347 [pdf] [code] [dataset]

          X. Zhu, C. Vondrick, C. Fowlkes, D. Ramanan, "Do we need more training data?", IJCV, DOI 10.1007/s11263-015-0812-2, March 2015 arXiv:1503.01508 [pdf]

          S. Hallman, C. Fowlkes, "Oriented Edge Forests for Boundary Detection", CVPR, Boston, MA, (June 2015).
          arXiv:1412.2066 [pdf] [code]

          S. Wang, C. Fowlkes, "Learning Multi-target Tracking with Quadratic Object Interactions", Technical report, arXiv:1412.2066 (Dec. 2014) [pdf]

          G. Ghiasi, C. Fowlkes, "Occlusion Coherence: Localizing Occluded Faces with a Hierarchcial Deformable Part Model", CVPR, Columbus, OH, (June 2014). [pdf]

          G. Ghiasi, Y. Yang, D. Ramanan, C. Fowlkes, "Parsing Occluded People", CVPR, Columbus, OH, (June 2014). [pdf]

          R. Díaz, S. Hallman, C. Fowlkes, "Detecting Dynamic Objects with Multi-View Background Subtraction", ICCV, Sydney, Austrailia (December 2013). [pdf]

          R. Díaz, S. Hallman, C. Fowlkes, "Multi-View Background Subtraction for Object Detection", Scene Understanding Workshop, Portland, OR, (June 2013).



          Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

          http://www.ics.uci.edu/~jutts/Jobs.htm Jessica M Utts Jobs

          Return to Jessica Utts's Home Page

          Employment History

          Date Title Deparment and Institution
          2008 to present Professor
          Department of Statistics
          University of California, Irvine
          2001-2008 Director
          Davis Honors Challenge
          University of California, Davis
          1993-2008 Professor
          Department of Statistics
          University of California, Davis
          1996-1998 Associate Vice Provost, University Outreach
          University of California, Davis
          Winter 1994
          Visiting Senior Research Fellow
          Dept. of Psychology 
          University of Edinburgh (Scotland)
          1988-1993 Director
          Statistical Laboratory 
          University of California, Davis
          1987-1989 Visiting Scientist
          Cognitive Sciences Program
          SRI International, Menlo Park,CA
          Summer 1983, 1984-1985 Visiting Professor Department of Statistics
          Stanford University
          1979-1993 Assistant and Associate Professor Division of Statistics
          University of California, Davis
          1978-1979 Assistant Professor Department of Mathematics
          University of California, Davis
          1978  Instructor Department of Statistics
          Penn State University
          http://www.ics.uci.edu/~jutts/GAISE/index.html AMATYC/GAISE Handouts Strategic Initiatives Workshop
          November 8 and 9, 2005
          San Diego, CA
          Preparation of Two-Year College Mathematics Instructors to Teach Statistics with GAISE

          Organizer: Brian E. Smith
          Presenters: Bob delMas and Jessica Utts

          GAISE REPORT

          Power Point Presentation on GAISE Report (Session 1)

          Handouts from the Resource Folder:

          1. Coverpage
          2. Schedule
          3. ASA Expense Form
          4. Gaslamp map
          5. Trolley map
          6. Notes from GAISE overview power point presentation
          7. Recommendation 1 - Statistical Literacy Concepts from Seeing Through Statistics Activities Manual
          8. Recommendation 4 on Active Learning
          9. Recommendation 5 on Using Technology
          10. Recommendation 3 on Concepts - illustrated for confidence intervals
          11. Recommenation 2 on Using Real Data
          12. Links to Useful Resources for Teaching Statistics
          13. Recommendation 6 on Assessment

          http://www.ics.uci.edu/~jutts/activities.htm Jessica Utts SCIENTIFIC AWARDS AND RECOGNITIONS

          Return to Jessica Utts's Home Page

          SCIENTIFIC AWARDS AND RECOGNITIONS

          • Distinguished Service Award, National Institute of Statistical Sciences (2011)
          • Founders Award, American Statistical Association (2009)
          • Fellow, Association for Psychological Science (2007)
          • Outstanding Contribution Award, Parapsychological Association (2006)
          • Harry C. Carver Medal, Institute of Mathematical Statistics (2005)
          • Outstanding Faculty/Staff Award, Graduate Group in Epidemiology, UC Davis (2001)
          • Fellow, American Association for the Advancement of Science (1992)
          • Fellow, Institute of Mathematical Statistics (1991)
          • Fellow, American Statistical Association (1990)
          • Academic Senate Distinguished Teaching Award, UC Davis (1984)
          • Magnar Ronning Award for Teaching Excellence, UC Davis (1981)
          • Phi Beta Kappa, State University of New York at Binghamton (1973)

          PROFESSIONAL AFFILIATIONS AND OFFICES (alphabetical order)

          American Association for the Advancement of Science

          • Section U Executive Committee (Member at large) (2011-2016)
          • Member, Committee on Council Affairs (Executive Committee of the Council) (2006-08)
          • Council Member representing Secction U (2006-08)
          • Secretary of Section U (1995 – 2003)
          • Chair, Section U (Statistics) Nominating Committee (1994 – 1995)

          American Statistical Association

          • President-Elect (2015), President (2016), Past President (2017)
          • Member of the Board of Directors (2010-2012)
          • Chair-Elect (2006), Chair (2007), Past Chair (2008), Section on Statistical Education
          • Secretary/Treasurer, Section on Bayesian Statistical Science (2001-2003)
          • Publications Officer, Section on Statistical Education (1999-2001)
          • President, State College PA Chapter (1977 – 1978)

          International Biometric Society

          • Chair, Finance Committee (2000-2004)
          • President, Western North American Region (1986)
          • Regional Committee (1982 – 1984)
          • Program Chair (1983)

          Caucus for Women in Statistics

          • President (1988)

          Institute of Mathematical Statistics

          • Council Member (2000-2003)
          • Treasurer (1988 – 1994)
          • Assistant Program Secretary (1980 & 1989)

          International Statistical Institute

          • Elected Member
          • International Association for Statistical Education, Vice President (2011-13)

          Parapsychological Association:

          • Board Member (1995-2003, 2005-2007, 2009-2014)

          Phi Beta Kappa

          • President of UC Davis Chapter (1984 – 1985)

          Society for Scientific Exploration

          • Council Member (1987 – 1993)

          MAJOR CONSULTATIONS AND PANELS

          • Chief Reader, Advanced Placement Statistics Program, July 1, 2014 to present
          • College Board Advanced Placement Development Committee – Statistics (1997-2003, Chair 2001-2003)
          • Quantitative Literacy Design Team, National Council on Education and the Disciplines
          • National Science Foundation, various evaluation panels
          • National Institutes of Health, various evaluation panels
          • Panelist to evaluate US Government, “Stargate” Program
          • National Academy of Sciences, Panel on the Evaluation of AIDS Interventions
          • Congressional Office of Technology Assessment, Panel to Assess Defense Technologies
          • National Park Service, Statistics Short Courses for Resource Management Trainees
          • California Department of Health Services, Courses on Statistics for Groundwater
          • SRI International Cognitive Sciences Program, Consultant
          • California Public Utilities Commission, Consultant
          • Pacific Gas and Electric Company, Consultant
          • Hershey Medical Center, Sudden Infant Death Syndrome Study, Consultant
          • Television interviews with ABC News 20/20, ABC Nightline, Larry King Live, CNN Morning News, BBC and other local, national and international programs.

          EDITORIAL POSITIONS (Past and Current)

          • Associate Editor, The American Statistician
          • Associate Editor, Journal of the American Statistical Association (Theory and Methods)
          • Associate Editor, Journal of the American Statistical Association (Reviews)
          • Statistical Editor, Journal of the American Society for Psychical Research

          MAJOR ACADEMIC SENATE APPOINTMENTS

          UC Davis, UC Irvine and UC Systemwide
          • Chair, Assessment Committee, UC Irvine Academic Senate (Spring 2010 � Sept 2013)
          • Vice-Chair and Chair, University Committee on Committees (2002-2004)
          • Vice-Chair of the UC Davis Academic Senate (1994-1996)
          • Chair, Faculty of the College of Letters and Science (1992-1993)
          • Chair, Academic Senate Committee on Student-Faculty Relationships
          • Chair, Academic Senate Committee on Teaching
          • Chair, Academic Senate Committee on Distinguished Teaching Awards
          • Chair, Committee on Committees

          CURRENT and RECENT GRANT SUPPORT

          Epidemiology Of Primary Biliary Cirrhosis, P.I.: Merrill Gershwin

          • Funded by: National Institute Of Diabetes And Digestive And Kidney Diseases (DK056839)
          • Period covered: 9/2000 – 8/2005
          • Amount: $534,493

          Borage Oil And Ginkgo Biloba (Egb 761) In Asthma, P.I.: Merrill Gershwin

          • Funded by: National Center For Complementary & Alternative Medicine (AT000637)
          • Period covered: 9/2000 – 7/2005
          • Amount: $384,062

          Engineering Emerging Urban Systems: Competing Land Uses and the Effects on Built and Natural Environments (with D. Niemeier)

          • Funded by: National Science Foundation
          • Period covered: 7/1/99 – 6/30/01
          • Amount: $360,000

          Model Projects: Enhancing the Educational Environment and Opportunities for Women in Engineering, Math and Science (with D. Niemeier, A. Laub and P. Rock)

          • Funded by: Alfred P. Sloan Foundation
          • Period covered: 7/1/97 – 6/30/01
          • Amount: $525,000

          PH.D. STUDENTS SUPERVISED

          Matthew Wood, 1997
          Current Employment: Biostatistician, Stanford University
          Matthew Clark, 1988
          Current Employment: Yuba College, Woodland, CA

          http://www.ics.uci.edu/~jutts/admin.htm Jessica M. Utts Administrative Experience

          Return to Jessica Utts's Home Page

          ADMINISTRATIVE EXPERIENCE

          DIRECTOR, DAVIS HONORS CHALLENGE, UNIVERSITY HONORS PROGRAM
          University of California, Davis, 2003 to 2008
          Administrative responsibility for UC Davis's open application, campus-wide honors program. The Program included over 400 students at all undergraduate levels. Responsibilities also included faculty development for the faculty teaching in the program, and serving as campus representative for prestigious national and international scholarships (e.g. Rhodes and Marshall).

          ASSOCIATE VICE PROVOST, UNIVERSITY OUTREACH
          University of California, Davis, 1996 to 1998
          Provided campus leadership for the development and coordination of outreach activities, linking the campus community with business, government and other external constituents. Reporting units include University Extension, Summer Sessions and the Pro Femina Research Consortium.

          FACULTY ASSISTANT TO THE PROVOST
          University of California, Davis, 1995 to 1996
          Worked with Vice Provost M.R.C. Greenwood in the development of new outreach initiatives. Major accomplishments included establishing a GIS-based website illustrating UC Davis outreach projects geographically and by subject matter, and organizing a Science and Technology Summit for business, government and university leaders, with prominent speakers from these sectors including heads of government agencies, the California Lieutenant Governor and CEOs of some of the largest companies in the US.

          TREASURER AND CHIEF FINANCIAL OFFICER
          Institute of Mathematical Statistics, Hayward, CA, 1988 to 1994
          Administrative and financial responsibility for an international professional organization that publishes four premier scientific journals and has an annual budget of over $1 million. During my term of service I guided the organization through major staff transitions, the establishment of a new journal and the modernization of its business practices. I also established a gift membership program for colleagues in developing countries and a travel award program for new researchers.

          DIRECTOR, STATISTICAL LABORATORY
          University of California, Davis, 1988 to 1993
          Administrative responsibility for a campus service and recharge unit that provides statistical advice to students, faculty and external clients. Included supervision of three permanent employees as well as several part-time student consultants.

          CHAIR, WOMEN'S STUDIES PROGRAM
          University of California, Davis, 1985 to 1987
          Provided direction and oversight for an expanding interdisciplinary academic program, including course development, hiring of temporary faculty and supervision of staff.

          http://www.ics.uci.edu/~jutts/azpsi.html The Paranormal: the Evidence and its Implications for Consciousness - Jessica Utts and Brian D. Josephson

          Jessica Utts Home Page
          Brian D. Josephson Home Page


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          THE PARANORMAL: THE EVIDENCE AND ITS IMPLICATIONS FOR CONSCIOUSNESS

          .
          Jessica Utts and Brian D. Josephson

           

          A slightly shortened version of this article was published in the Times Higher Education Supplement's special section on Consciousness linked to the Tucson II conference "Toward a Science of Consciousness", Apr. 5th. 1996, page (v).

           

          Those who recognise that significant discoveries in science are very often prompted by observations that do not fit expectations will find a stimulating challenge in accumulating evidence that it is possible to elicit psychic functioning in experiments with ordinary volunteers acting as subjects. Even more convincing results occur with specially selected subjects.

          In one type of experiment, a "target" photograph or video segment is randomly chosen out of a set of four possibilities. A "sender" attempts to transmit it mentally and a "receiver" is then asked to provide an account either verbally or in writing of what she imagines it might be. She is then shown the four possibilities, and selects the one she thinks best matches her perception. By chance alone, a correct match is expected on average one time in four, whereas the experiments typically show the considerably higher success rate of around one in three.

          The recent declassification of the US government's psychical research programme (experiments on "remote viewing", similar to the type just described except that it used independent judges to assess the matches rather than having the subjects judge themselves) has permitted a comparison to be made of the results of this programme with those described in the open literature. Despite the different judging procedure, similar success rates were found. In addition, many of the governmental experiments used gifted subjects. The success rate was then even higher, typically over forty percent. The few experiments in the open literature that used gifted subjects found similar success rates.

          In the past, critics have attempted to discredit positive results in psychical research on grounds of lack of repeatability. But, as anyone with a training in statistics knows, even where an influence exists, an isolated experiment with an insufficient number of trials may not demonstrate a statistically significant effect. Accordingly, without a more sophisticated analysis, "failure to reproduce an effect" does not demonstrate its absence. Suppose, for example, psychic abilities, in line with the results already described, increase the chances of a successful match from 1/4 to 1/3. Then (according to the accepted statistical theories), an experiment with 30 trials, which has been typical of these experiments, would have less than a 17% chance of achieving a result of statistical significance. The more recent larger experiments still utilise only about 100 trials, and have only about a 57% chance of achieving statistical significance.

          Detailed analysis of the complete collection of experiments on this type of phenomenon shows that what holds, despite changes in equipment, experimenter, subjects, judges, targets and laboratories, is far greater consistency with the 1 in 3 success rate already mentioned than with the 1 in 4 chance expectation rate. Such consistency is the hallmark of a genuine effect, and this, together with the very low probability of the overall success rate observed occurring by chance, argues strongly for the phenomena being real and not artifactual.

          Reexamination of other types of psychical investigations reveals that they too achieved replicable effects, which went largely unappreciated because of a poor understanding of statistics. For instance, an analysis of experiments in precognitive card guessing and related "forced-choice" experiments, published by Honorton and Ferrari in the Journal of Parapsychology, found that gifted subjects were able to achieve consistently about a 27% success rate when 25% was expected by chance. Similar U.S. government experiments have been revealed to have achieved the same 27% success rate over thousands of trials. If chance alone were the explanation for these results, it would be truly remarkable to achieve a 27% success rate over thousands of trials, and it would be even more remarkable to see identical results in the government work. For further details about the recent evidence, including both a favourable and a skeptical assessment of the U.S. government experiments, consult the Journal of Scientific Exploration, Vol. 10(1), or http://www-stat.ucdavis.edu/users/utts/ on the Internet.

          Strong statistical results are of course meaningless if experiments are not properly conducted. Debunkers of parapsychology are fond of showcasing the very few experiments that have been found to have serious problems. But that ignores the fact that the vast majority of experiments were done using excellent protocols, paying close attention to potential subtle cues, using well-tested randomisation devices and so on. For the past decade the U.S. government experiments were overseen by a very high-level scientific committee, consisting of respected academics from a variety of disciplines, all of whom were required to critique and approve the protocols in advance. There have been no explanations forthcoming that allow an honest observer to dismiss the growing collection of consistent results.

          What are the implications for science of the fact that psychic functioning appears to be a real effect? These phenomena seem mysterious, but no more mysterious perhaps than strange phenomena of the past which science has now happily incorporated within its scope. What ideas might be relevant in the context of suitably extending science to take these phenomena into account? Two such concepts are those of the observer, and non-locality. The observer forces his way into modern science because the equations of quantum physics, if taken literally, imply a universe that is constantly splitting into separate branches, only one of which corresponds to our perceived reality. A process of "decoherence" has been invoked to stop two branches interfering with each other, but this still does not answer the question of why our experience is of one particular branch and not any other. Perhaps, despite the unpopularity of the idea, the experiencers of the reality are also the selectors.

          This idea perhaps makes sense in the light of theories that presuppose that quantum theory is not the ultimate theory of nature, but involves (in ways that in some versions of the idea can be made mathematically precise) the manifestations of a deeper "subquantum domain". In just the same way that a surf rider can make use of random waves to travel effortlessly along, a psychic may be able to direct random energy at the subquantum level for her own purposes. Some accounts of the subquantum level involve action at a distance, which fits in well with some purported psychic abilities.

          These proposals are extremely speculative. What needs to be done, in any event, is to integrate mental phenomena more thoroughly into the framework of science (including the quantum level) than is presently the case. The research of Lawrence LeShan (as described in his book The Medium, the Mystic and the Physicist), where interviews with psychics disclosed that they were aware of a "hierarchy of meaningful interconnections", perhaps provides a hint of what might be involved. Science has a poor handle on ideas such as meaningful interconnections since they are alien to its usual ways of thinking. Perhaps it will need to overcome its current abhorrence of such concepts in order to arrive at the truth.

          * * * * * * * * * *

          Jessica Utts is professor of statistics, University of California, Davis, and was one of two experts commissioned by the CIA to review the two-decade U.S. government psychic research programme in the Summer of 1995. She has recently published a book, Seeing Through Statistics, Duxbury Press, 1996, designed to improve understanding of statistical studies. Brian Josephson, Nobel Laureate, is professor of physics, University of Cambridge, and heads the Mind-Matter Unification Project at the Cavendish Laboratory, Cambridge.


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          The contents of this document are copyright �1996 by Times Supplements


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          Back to mceagle.com References        

          This document, for over two years, was hosted on the University of Oregon web server. In Autumn of 1998 it was no longer available through that source. This paper is part of a group of papers, all related to the same very public and very controversial report. It would be a form of bias to make the other papers available when this one is not. Since this document is no longer available via link from Dr. Hyman's university, we are providing a locally-hosted copy for review. -- Webmaster


          Evaluation of Program on Anomalous Mental Phenomena

          Ray Hyman

          University of Oregon

          Eugene, Oregon

          September 11, 1995

          INTRODUCTION

          Professor Jessica Utts and I were given the task of evaluating the program on "Anomalous Mental Phenomena" carried out at SRI International (formerly the Stanford Research Institute) from 1973 through 1989 and continued at SAIC (Science Applications International Corporation) from 1992 through 1994. We were asked to evaluate this research in terms of its scientific value. We were also asked to comment on its potential utility for intelligence applications.

          The investigators use the term Anomalous Mental Phenomena to refer to what the parapsychologists label as psi. Psi includes both extrasensory perception (called Anomalous Cognition by the present investigators) and psychokinesis (called Anomalous Perturbation by the present investigators). The experimenters claim that their results support the existence of Anomalous Cognition--especially clairvoyance (information transmission from a target without the intervention of a human sender) and precognition. They found no evidence for the existence of Anomalous Perturbation.

          Our evaluation will focus on the 10 experiments conducted at SAIC. These are the most recent in the program as well as the only ones for which we have adequate documentation. The earlier SRI research on remote viewing suffered from methodological inadequacies. Another reason for concentrating upon this more recent set of experiments is the limited time frame allotted for this evaluation.

          I will not ignore entirely the earlier SRI research. I will also consider some of the contemporary research in parapsychology at other laboratories. This is because a proper scientific evaluation of any research program has to place it in the context of the broader scientific community. In addition, some of this contemporary research was subcontracted by the SAIC investigators.

          Professor Utts has provided an historical overview of the SRI and SAIC programs as well as descriptions of the experiments under consideration. I will not duplicate what she has written on these topics. Instead, I will focus on her conclusions that:

          Using the standards applied to any other area of science, it is concluded that psychic functioning has been well established. [Utts, Sept. 1995, p 1]

          Arguments that these results could be due to methodological flaws in the experiments are soundly refuted. Effects of similar magnitude to those found in government-sponsored research at SRI and SAIC have been replicated at a number of laboratories across the world. Such consistency cannot be readily explained by claims of flaws or fraud. [Utts, Sept. 1995, p 1]

          Because my report will emphasize points of disagreement between Professor Utts and me, I want to state that we agree on many other points. We both agree that the SAIC experiments were free of the methodological weaknesses that plagued the early SRI research. We also agree that the SAIC experiments appear to be free of the more obvious and better known flaws that can invalidate the results of parapsychological investigations. We agree that the effect sizes reported in the SAIC experiments are too large and consistent to be dismissed as statistical flukes.

          I also believe that Jessica Utts and I agree on what the next steps should be.

          We disagree on key questions such as:

          1. Do these apparently non-chance effects justify concluding that the existence of anomalous cognition has been established?

          2. Has the possibility of methodological flaws been completely eliminated?

          3. Are the SAIC results consistent with the contemporary findings in other parapsychological laboratories on remote viewing and the ganzfeld phenomenon?

          The remainder of this report will try to justify why I believe the answer to these three questions is "no."

          SCIENTIFIC STATUS OF THE PROGRAM

          Science is basically a communal activity. For any developed field of inquiry, a community of experts exist. This community provides the disciplinary matrix which determines what questions are worth asking, which issues are relevant, what variables matter and which can be safely ignored, and the criteria for judging the adequacy of observational data. The community provides checks and balances through the referee system, open criticism, and independent replications. Only those relationships that are reasonably lawful and replicable across independent laboratories become part of the shared scientific store of "knowledge."

          An individual investigator or laboratory can contribute to this store. However, by itself, the output of a single investigator or laboratory does not constitute science. No matter how careful and competent the research, the findings of a single laboratory count for nothing unless they can be reliably replicated in other laboratories. This rule is true of ordinary claims. It holds true especially for claims that add something new or novel to the existing database. When an investigator, for example, announces the discovery of a new element, the claim is not accepted until the finding has been successfully replicated by several independent laboratories. Of course, this rule is enforced even more when the claim has revolutionary implications that challenge the fundamental principles underlying most sciences.

          GENERAL SCIENTIFIC HANDICAPS OF THE SAIC PROGRAM

          The brief characterization of scientific inquiry in the preceding section alerts us to serious problems in trying to assess the scientific status of the SAIC research. The secrecy under which the SRI and SAIC programs was conducted necessarily cut them off from the communal aspects of scientific inquiry. The checks and balances that come from being an open part of the disciplinary matrix were absent. With the exception of the past year or so, none of the reports went through the all-important peer-review system. Worse, promising findings did not have the opportunity of being replicated in other laboratories.

          The commendable improvements in protocols, methodology, and data-gathering have not profited from the general shake-down and debugging that comes mainly from other laboratories trying to use the same improvements. Although the research program that started in 1973 continued for over twenty years, the secrecy and other constraints have produced only ten adequate experiments for consideration. Unfortunately, ten experiments--especially from one laboratory (considering the SAIC program as a continuation of the SRI program)--is far too few to establish reliable relationships in almost any area of inquiry. In the traditionally elusive quest for psi, ten experiments from one laboratory promise very little in the way of useful conclusions.

          The ten SAIC experiments suffer another handicap in their quest for scientific status. The principal investigator was not free to run the program to maximize scientific payoff. Instead, he had to do experiments and add variables to suit the desires of his sponsors. The result was an attempt to explore too many questions with too few resources. In other words, the scientific inquiry was spread too thin. The 10 experiments were asked to provide too many sorts of information.

          For these reasons, even before we get to the details (and remember the devil is usually in the details), the scientific contribution of this set of studies will necessarily be limited.

          PARAPSYCHOLOGY'S STATUS AS A SCIENCE

          Parapsychology began its quest for scientific status in the mid-1800s. At that time it was known as psychical research. The Society for Psychical Research was founded in London in 1882. Since that time, many investigators--including at least four Nobel laureates--have tried to establish parapsychology as a legitimate science. Beginning in the early 1930s, J.B. Rhine initiated an impressive program to distance parapsychology from its tainted beginnings in spiritualistic seances and turn it into an experimental science. He pulled together various ideas of his predecessors in an attempt to make the study of ESP and PK a rigorous discipline based on careful controls and statistical analysis.

          His first major publication caught the attention of the scientific community. Many were impressed with this display of a huge database, gathered under controlled conditions, and analyzed with the most modern statistical tools. Critics quickly attacked the statistical basis of the research. However, Burton Camp, the president of the Institute of Mathematical Statistics, came to the parapsychologists' defense in 1937. He issued a statement that if the critics were going to fault parapsychological research they could not do so on statistical grounds. The critics then turned their attention to methodological weaknesses. Here they had more success.

          What really turned scientists against parapsychological claims, however, was the fact that several scientists failed to replicate Rhine's results. This problem of replicability has plagued parapsychology ever since. The few, but well-publicized, cheating scandals that were uncovered also worked against parapsychology's acceptance into the general scientific community.

          Parapsychology shares with other sciences a number of features. The database comes from experiments using controlled procedures, double-blind techniques where applicable, the latest and most sophisticated apparatus, and sophisticated statistical analysis. In addition, the findings are reported at annual meetings and in refereed journals.

          Unfortunately, as I have pointed out elsewhere, parapsychology has other characteristics that make its status as a normal science problematic. Here I will list only a few. These are worth mentioning because they impinge upon the assessment of the scientific status of the SAIC program. Probably the most frequently discussed problem is the issue of replicability. Both critics and parapsychologists have agreed that the lack of consistently replicable results has been a major reason for parapsychology's failure to achieve acceptance by the scientific establishment.

          Some parapsychologists have urged their colleagues to refrain from demanding such acceptance until they can put examples of replicable experiments before the scientific community. The late parapsychologist J.G. Pratt went further and argued that parapsychology would never develop a replicable experiment. He argued that psi was real but would forever elude deliberate control. More recently, the late Honorton claimed that the ganzfeld experiments had, indeed, achieved the status of a replicable paradigm. The title of the landmark paper in the January 1994 issue of the Psychological Bulletin by Bem and Honorton is "Does psi exist? Replicable evidence for an anomalous process of information transfer." In her position paper "Replication and meta-analysis in parapsychology" (Statistical Science, 1991, 6, pp. 363-403), Jessica Utts reviews the evidence from meta-analyses of parapsychological research to argue that replication has been demonstrated and that the overall evidence indicates that there is an anomalous effect in need of explanation.

          In evaluating the SAIC research, Utts points to the consistency of effect sizes produced by the expert viewers across experiments as well as the apparent consistency of average effect sizes of the SRI and SAIC experiments with those from other parapsychological laboratories. These consistencies in effect sizes across experiments and laboratories, in her opinion, justify the claim that anomalous mental phenomena can be reliably replicated with appropriately designed experiments. This is an important breakthrough for parapsychology, if it is true. However, to anticipate some of my later commentary, I wish to emphasize that simply replicating effect size is not the same thing as showing the repeated occurrence of anomalous mental phenomena. Effect size is nothing more than a standardized difference between an observed and an expected outcome hypothesized on the basis of an idealized probability model. An indefinite number of factors can cause departures from the idealized probability model. An investigator needs to go well beyond the mere demonstration that effect sizes are the same before he/she can legitimately claim that they are caused by the same underlying phenomenon.

          In my opinion, a more serious challenge to parapsychology's quest for scientific status is the lack of cumulativeness in its database. Only parapsychology, among the fields of inquiry claiming scientific status, lacks a cumulative database. Physics has changed dramatically since Newton conducted his famous experiment using prisms to show that white light contained all the colors of the spectrum. Yet, Newton's experiment is still valid and still yields the same results. Psychology has changed its ideas about the nature of memory since Ebbinghaus conducted his famous experiments on the curve of forgetting in the 1880s. We believe that memory is more dynamic and complicated than can be captured by Ebbinghaus' ideas about a passive, rote memory system. Nevertheless, his findings still can be replicated and they form an important part of our database on memory.

          Parapsychology, unlike the other sciences, has a shifting database. Experimental data that one generation puts forth as rock-solid evidence for psi is discarded by later generations in favor of new data. When the Society for Psychical Research was founded in 1882, its first president Henry Sidgwick, pointed to the experiments with the Creery sisters as the evidence that should convince even the most hardened skeptic of the reality of psi. Soon, he and the other members of the Society argued that the data from Smith-Blackburn experiments provided the fraud-proof case for the reality of telepathy. The next generation of psychical researchers, however, cast aside these cases as defective and we no longer hear about them. Instead, they turned to new data to argue their case.

          During the 1930s and 1940s, the results of Rhine's card guessing experiments were offered as the solid evidence for the reality of psi. The next generation dropped Rhine's data as being flawed and difficult to replicate and it hailed the Soal-Goldney experiments as the replicable and rock-solid basis for the existence of telepathy. Next came the Sheep-Goat experiments. Today, the Rhine data, the Sheep-Goats experiments, and the Soal-Goldney experiments no longer are used to argue the case for psi. Contemporary parapsychologists, instead, point to the ganzfeld experiments, the random-number generator experiments, and--with the declassifying of the SAIC experiments--the remote viewing experiments as their basis for insisting that psi exists.

          Professor Utts uses the ganzfeld data and the SAIC remote viewing results to assert that the existence of anomalous cognition has been proven. She does not completely discard earlier data. She cites meta-analyses of some of the earlier parapsychology experiments. Still, the cumulative database for anomalous mental phenomena does not exist. Most of the data accumulated by previous investigators has been discarded. In most cases the data have been discarded for good reasons. They were subsequently discovered to be seriously flawed in one or more ways that was not recognized by the original investigators. Yet, at the time they were part of the database, the parapsychologists were certain that they offered incontestable evidence for the reality of psi.

          How does this discussion relate to our present concerns with the scientific status of the SAIC program? This consideration of the shifting database of parapsychology offers a cautionary note to the use of contemporary research on the ganzfeld and remote viewing as solid evidence for anomalous mental phenomena. More than a century of parapsychological research teaches us that each generation of investigators was sure that it had found the `Holy Grail'--the indisputable evidence for psychic functioning. Each subsequent generation has abandoned their predecessors' evidence as defective in one way or another. Instead, the new generation had its own version of the holy grail.

          Today, the parapsychologists offer us the ganzfeld experiments and, along with Jessica Utts, will presumably will include the SAIC remote viewing experiments as today's reasons for concluding that anomalous cognition has been demonstrated. Maybe this generation is correct. Maybe, this time the" indisputable" evidence will remain indisputable for subsequent generations. However, it is too soon to tell. Only history will reveal the answer. As E.G. Boring once wrote, when writing about the Soal-Goldney experiments, you cannot hurry history.

          Meanwhile, as I will point out later in this report, there are hints and suggestions that history may repeat itself. Where Utts sees consistency and incontestable proof, I see inconsistency and hints that all is not as rock-solid as she implies.

          I can list other reasons to suggest that parapsychology's status as a science is shaky, at best. Some of these reasons will emerge as I discuss specific aspects of the SAIC results and their relation to other contemporary parapsychological research.

          THE CLAIM THAT ANOMALOUS COGNITION EXISTS

          Professor Utts concludes that "psychic functioning has been well established." She bases this conclusion on three other claims: 1) the statistical results of the SAIC and other parapsychological experiments "are far beyond what is expected by chance" ; 2) "arguments that these results could be due to methodological flaws are soundly refuted" ; and 3) "Effects of similar magnitude to those found in government-sponsored research at SRI and SAIC have been replicated at a number of laboratories across the world."

          Later, in this report, I will raise questions about her major conclusion and the three supporting claims. In this section, I want to unpack just what these claims entail. I will start with the statistical findings. Parapsychological is unique among the sciences in relying solely on significant departures from a chance baseline to establish the presence of its alleged phenomenon. In the other sciences the defining phenomena can be reliably observed and do not require indirect statistical measures to justify their existence. Indeed, each branch of science began with phenomena that could be observed directly. Gilbert began the study of magnetism by systematically studying a phenomenon that had been observed and was known to the ancients as well as his contemporaries. Modern physics began by becoming more systematic about moving objects and falling bodies. Psychology became a systematic science by looking for lawful relationships among sensory discriminations. Another starting point was the discovery of lawful relationships in the remembering and forgetting of verbal materials. Note that in none of these cases was the existence of the defining phenomena in question. No one required statistical tests and effect sizes to decide if magnetism was present or if a body had fallen. Psychophysicists did not need to reject a null hypothesis to decide if sensory processes were operating and memory researchers did not have to rely on reaching accepted levels of significance to know if recall or forgetting had occurred.

          Each of the major sciences began with phenomena whose presence was not in question. The existence of the primary phenomena was never in question. Each science began by finding systematic relationships among variations in the magnitudes of attributes of the central phenomena and the attributes of independent variables such as time, location, etc. The questions for the investigation of memory had to do with how best to describe the forgetting curve and what factors affected its parameters. No statistical tests or determination of effect sizes were required to decide if, in fact, forgetting was or was not present on any particular occasion.

          Only parapsychology claims to be a science on the basis of phenomena (or a phenomenon) whose presence can be detected only by rejecting a null hypothesis. To be fair, parapsychologists also talk about doing process research where the emphasis is on finding systematic relationships between attributes of psi and variations in some independent variable. One conclusion from the SRI/SAIC project, for example, is that there is no relationship between the distance of the target from the viewer and the magnitude of the effect size for anomalous cognition. However, it is still the case that the effect size, and even the question of whether anomalous cognition was present in any experiment, is still a matter of deciding if a departure from a chance base line is non-accidental.

          At this point I think it is worth emphasizing that the use of statistical inference to draw conclusions about the null hypothesis assumes that the underlying probability model adequately represents the distributions and variations in the real world situation. The underlying probability model is an idealization of the empirical situation for which it is being used. Whether or not the model is appropriate for any given application is an empirical matter and the adequacy of the model has to be justified for each new application. Empirical studies have shown that statistical models fit real world situations only approximately. The tails of real-world distributions, for example, almost always contain more cases than the standard statistics based on the normal curve assume. These departures from the idealized model do not have much practical import in many typical statistical applications because the statistical tests are robust. That is, the departures of the actual situation from the assumed probability model typically do not distort the outcome of the statistical test.

          However, when statistical tests are used in situations beyond their ordinary application, they can result in rejections of the null hypothesis for reasons other than a presumed departure from the expected chance value. Parapsychologists often complain that their results fail to replicate because of inadequate power. However, because the underlying probability models are only approximations, too much power can lead to rejections of the null hypothesis simply because the real world and the idealized statistical model are not exact matches. This discussion emphasizes that significant findings can arise for many reasons--including the simple fact that statistical inference is based on idealized models that mirror the real world only approximately.

          I agree with Jessica Utts that the effect sizes reported in the SAIC experiments and in the recent ganzfeld studies probably cannot be dismissed as due to chance. Nor do they appear to be accounted for by multiple testing, file-drawer distortions, inappropriate statistical testing or other misuse of statistical inference. I do not rule out the possibility that some of this apparent departure from the null hypothesis might simply reflect the failure of the underlying model to be a truly adequate model of the experimental situation. However, I am willing to assume that the effect sizes represent true effects beyond inadequacies in the underlying model. Statistical effects, by themselves, do not justify claiming that anomalous cognition has been demonstrated--or, for that matter, that an anomaly of any kind has occurred.

          So, I accept Professor Utts' assertion that the statistical results of the SAIC and other parapsychological experiments "are far beyond what is expected by chance." Parapsychologists, of course, realize that the truth of this claim does not constitute proof of anomalous cognition. Numerous factors can produce significant statistical results. Operationally, the presence of anomalous cognition is detected by the elimination of all other possibilities. This reliance on a negative definition of its central phenomenon is another liability that parapsychology brings with its attempt to become a recognized science. Essentially, anomalous cognition is claimed to be present whenever statistically significant departures from the null hypothesis are observed under conditions that preclude the operation of all mundane causes of these departures. As Boring once observed, every success in parapsychological research is a failure. By this he meant that when the investigator or the critics succeed in finding a scientifically acceptable explanation for the significant effect the claim for ESP or anomalous cognition has failed.

          Having accepted the existence of non-chance effects, the focus now is upon whether these effects have normal causes. Since the beginning of psychical research, each claim that psychic functioning had been demonstrated was countered by critics who suggested other reasons for the observed effects. Typical alternatives that have been suggested to account for the effects have been fraud, statistical errors, and methodological artifacts. In the present discussion I am not considering fraud or statistical errors. This leaves only methodological oversight as the source for a plausible alternative to psychic functioning. Utts has concluded that "arguments that these results could be due to methodological flaws are soundly refuted." If she is correct, then I would have to agree with her bottom line "that psychic functioning has been well established."

          Obviously I do not agree that all possibilities for alternative explanations of the non-chance results have been eliminated. The SAIC experiments are well-designed and the investigators have taken pains to eliminate the known weaknesses in previous parapsychological research. In addition, I cannot provide suitable candidates for what flaws, if any, might be present. Just the same, it is impossible in principle to say that any particular experiment or experimental series is completely free from possible flaws. An experimenter cannot control for every possibility--especially for potential flaws that have not yet been discovered.

          At this point, a parapsychologist might protest that such "in principle" arguments can always be raised against any findings, no matter how well conceived was the study from which they emerged. Such a response is understandable, but I believe my caution is reasonable in this particular case. Historically, many cases of evidence for psi were proffered on the grounds that they came from experiments of impeccable methodological design. Only subsequently, sometimes by fortunate accident, did the possibility of a serious flaw or alternative explanation of the results become available. The founders of the Society for Psychical Research believed that the Smith-Blackburn experiments afforded no alternative to the conclusion that telepathy was involved. They could conceive of no mundane explanation. Then Blackburn confessed and explained in detail just how he and Smith had tricked the investigators.

          The critics became suspicious of the Soal-Goldney findings not only because the results were too good, but also because Soal lost the original records under suspicious circumstances. Hansel, Scott, and Price each generated elaborate scenarios to explain how Soal might have cheated. Hansel and Scott reported finding peculiar patterns in the data. The scenarios, for accounting for these data, however, were extremely complicated and required the collusion of several individuals--some of whom were prominent statesmen and academics. The discovery of how Soal actually had cheated was made by the parapsychologist Betty Markwick. The finding came about through fortuitous circumstances. The method of cheating turned out to involve only one person and employed an ingenious, but simple, method that none of the critics had anticipated.

          During the first four years of the original ganzfeld-psi experiments, the investigators asserted that their findings demonstrated psi because the experimental design precluded any normal alternative. Only after I and a couple of parapsychologists independently pointed out how the use of a single set of targets could provide a mundane alternative to psychic communication did the ganzfeld experimenters realize the existence of this flaw. After careful and lengthy scrutiny of the ganzfeld database, I was able to generate a lengthy list of potential flaws.

          Honorton and his colleagues devised the autoganzfeld experiments. These experiments were deliberately designed to preclude the flaws that I and others had eventually discovered in the original ganzfeld database. When the statistically significant results emerged from these latter experiments, they were proclaimed to be proof of anomalous communication because all alternative mundane explanations had been eliminated. When I was first confronted with these findings, I had to admit that the investigators had eliminated all but one of the flaws that I had listed for the original database. For some reason, Honorton and his colleagues did not seem to consider seriously the necessity of insuring that their randomization procedures were optimal. However, putting this one oversight aside, I could find no obvious loopholes in the experiments as reported.

          When I was asked to comment on the paper that Daryl Bem and Charles Honorton wrote for the January 1994 issue of the Psychological Bulletin, I was able to get much of the raw data from Professor Bem. My analyses of that data revealed strong patterns that, to me, pointed to an artifact of some sort. One pattern, for example, was the finding that all the significant hitting above chance occurred only on the second or later occurrence of a target. All the first occurrences of a target yielded results consistent with chance. Although this was a post hoc finding, it was not the result of a fishing expedition. I deliberately looked for such a pattern as an indirect way of checking for the adequacy of the randomization procedures. The pattern was quite strong and persisted in every breakdown of the data that I tried--by separate investigator, by target type, by individual experiment, etc. The existence of this pattern by itself does not prove it is the result of an artifact. As expected, Professor Bem seized upon it as another peculiarity of psi. Subsequent to finding this pattern, I have learned about many other weaknesses in this experiment which could have compromised the results. Robert Morris and his colleagues at the University of Edinburgh took these flaws ,as well as some additional ones that they uncovered, into account when they designed the ganzfeld replication experiments.

          The point of this discussion is that it takes some time before we fully recognize the potential flaws in a newly designed experimental protocol. In some cases, the discovery of a serious flaw is the result of a fortuitous occurrence. In other cases, the uncovering of flaws came about only after the new protocol had been used for a while. Every new experimental design, as is the case for every new computer program, requires a shakedown period and debugging. The problems with any new method or design are not always apparent at first. Obvious flaws may be eliminated only to be replaced by more subtle ones.

          How does this apply to the SAIC experiments? These experiments were designed to eliminate the obvious flaws of the previous remote viewing experiments at SRI. Inspection of the protocol indicates that they succeeded in this respect. The new design and methodology, however, has not had a chance to be used in other laboratories or to be properly debugged. Many of the features that could be considered an asset also have possible down sides. I will return to this later in the report when I discuss the use of the same viewers and the same judge across the different experiments. For now, I just want to suggest some general grounds for caution in accepting the claim that all possible methodological flaws have been eliminated.

          The third warrant for Jessica Utts' conclusion that psi has been proven is that "Effects of similar magnitude to those found in government-sponsored research at SRI and SAIC have been replicated at a number of laboratories across the world." I will discuss this matter below. For now, I will point out that effects of similar magnitude can occur for several different reasons. Worse, the average effect size from different parapsychological research programs is typically a meaningless composite of arbitrary units. As such, these averages do not represent meaningful parameters in the real world. For example, Honorton claimed that the autoganzfeld experiments replicated the original ganzfeld experiments because the average effect size for both databases was approximately identical. This apparent similarity in average effect size is meaningless for many reasons. For one thing, the similarity in size depends upon which of many possible averages one considers. In the case under consideration the average effect size was obtained by adding up all the hits and trials for the 28 studies in the database. One experimenter contributed almost half to this total. Others contributed in greatly unequal numbers. The average will differ if each experimenter's contribution is given equal weight.

          In addition, the heterogeneity of effect sizes among separate investigators is huge. All the effect sizes, for example, of one the investigators were negative. Another investigator contributed mostly moderately large effect sizes. If the first investigator had contributed more trials to the total, then the average would obviously have been lower. Similar problems exist for the average from the autoganzfeld experiments. In these latter experiments, the static targets--which most closely resembled the overwhelming majority of targets in the original database--yielded an effect size of zero. The dynamic targets yielded a highly significant and moderate effect size. Is the correct average effect size for these experiments based on a composite of the results of the static and dynamic targets or should it be based only the dynamic targets?

          THE SAIC PROGRAM

          As I have indicated, the SAIC experiments are an improvement on both the preceding SRI experiments as well as previous parapsychological investigations. The investigators seem to have taken pains to insure that randomization of targets for presentation and for judging was done properly. They have eliminated the major flaw in original SRI remote viewing experiments of non-independence in trials for a given viewer. Some of the other features can be considered as improvements but also as possible problems. In this category I would list the use of the same experienced viewers in many experiments and the use of the same target set across experiments. The major limitations that I see in these studies derive from their newness and their having been conducted in secrecy. The newness simply means that we have not had sufficient time to debug and to grasp fully both the strengths and weaknesses of this protocol. The secrecy aggravated this limitation by preventing other investigators from reviewing and criticizing the experiments from the beginning, and by making it impossible for independent laboratories to replicate the findings. (1)

          The fact that these experiments were conducted in the same laboratory, with the same basic protocol, using the same viewers across experiments, the same targets across experiments, and the same investigators aggravates, rather than alleviates, the problem of independent replication. If subtle, as-yet-undetected bias and flaws exist is the protocol, the very consistency of elements such as targets, viewers, investigators, and procedures across experiments enhances the possibility that these flaws will be compounded.

          Making matters even worse is the use of the same judge across all experiments. The judging of viewer responses is a critical factor in free-response remote viewing experiments. Ed May, the principle investigator, as I understand it, has been the sole judge in all the free response experiments. May's rationale for this unusual procedure was that he is familiar with the response styles of the individual viewers. If a viewer, for example, talks about bridges, May--from his familiarity with this viewer--might realize that this viewer uses bridges to refer to any object that is on water. He could then interpret the response accordingly to make the appropriate match to a target. Whatever merit this rationale has, it results in a methodological feature that violates some key principles of scientific credibility. One might argue that the judge, for example, should be blind not only about the correct target but also about who the viewer is. More important, the scientific community at large will be reluctant to accept evidence that depends upon the ability of one specific individual. In this regard, the reliance on the same judge for all free-response experiments is like the experimenter effect. To the extent that the results depend upon a particular investigator the question of scientific objectivity arises. Scientific proof depends upon the ability to generate evidence that, in principle, any serious and competent investigator--regardless of his or her personality--can observe.

          The use of the same judge across experiments further compounds the problem of non-independence of the experiments. Here, both Professor Utts and I agree. We believe it is important that the remote viewing results be obtainable with different judges. Again, the concern here is that the various factors that are similar across experiments, count against their separate findings as independent evidence for anomalous cognition.

          HAS ANOMALOUS COGNITION BEEN PROVEN?

          Obviously, I do not believe that the contemporary findings of parapsychology, including those from the SRI/SAIC program, justify concluding that anomalous mental phenomena have been proven. Professor Utts and some parapsychologists believe otherwise. I admit that the latest findings should make them optimistic. The case for psychic functioning seems better than it ever has been. The contemporary findings along with the output of the SRI/SAIC program do seem to indicate that something beyond odd statistical hiccups is taking place. I also have to admit that I do not have a ready explanation for these observed effects. Inexplicable statistical departures from chance, however, are a far cry from compelling evidence for anomalous cognition.

          So what would be compelling evidence for the reality of anomalous cognition? Let's assume that the experimental results from the SAIC remote viewing experiments continue to hold up. Further assume that along with continued statistical significance no flaws or mundane alternative possibilities come to light. We would then want to ensure that similar results will occur with new viewers, new target pools, and several independent judges. Finally, to satisfy the normal standards of science, we would need to have the findings successfully replicated in independent laboratories by other parapsychologists as well as nonparapsychologists.

          If the parapsychologists could achieve this state of affairs, we are faced with a possible anomaly, but not necessarily anomalous cognition. As the parapsychologist John Palmer has recognized, parapsychologists will have to go beyond demonstrating the presence of a statistical anomaly before they can claim the presence of psychic functioning. This is because, among other things, the existence of a statistical anomaly is defined negatively. Something is occurring for which we have no obvious or ready explanation. This something may or may not turn out to be paranormal. According to Palmer, parapsychologists will have to devise a positive theory of the paranormal before they will be in a position to claim that the observed anomalies indicate paranormal functioning.

          Without such a positive theory, we have no way of specifying the boundary conditions for anomalous mental phenomena. Without such a theory we have no way of specifying when psi is present and when it is absent. Because psi or anomalous cognition is currently detected only by departures from a null hypothesis all kinds of problems beset the quest for the claim and pursuit of psychic functioning. For example, the decline effect, which was investigated in one of the SAIC experiments, was once used as an important sign for the presence of psi. J.B. Rhine discovered this effect not only in some of his data but in his re-analyses of data collected by earlier investigators. He attached great importance to his effect because it existed in data whose investigators neither knew of its existence nor had they been seeking it. In addition, the decline effect helped Rhine to explain how seemingly null results really contained evidence for psi. This is because the decline effect often showed up as an excess of hitting in the early half of the experiment and as a deficit of hitting in the second half of the experiment. These two halves, when pooled together over the entire experiment, yielded an overall hit rate consistent with chance.

          Although Rhine and other parapsychologists attached great importance to the decline effect as a reliable and often hidden sign of the presence of psychic functioning, the reliance on this indicator unwittingly emphasizes serious problems in the parapsychologist's quest. As the SAIC report on binary coding states, the decline effect is claimed for a bewildering variety of possibilities. Some investigators have found a decline effect going from the first quarter to the last quarter of each separate score sheet in their experiment. Other investigators have reported a decline effect as a decrease in hit rate from the first half to the second half of the total experiment. Still others find a decline effect across separate experiments. Indeed, almost any variation where the direction is from a higher hit rate to a lower hit rate has been offered as evidence for a decline effect. To confuse matters further, some investigators have claimed finding evidence for an incline effect.

          If the decline effect is a token for the presence of psi, what should one conclude when the data, as was the case in the SAIC experiment on binary coding, show a significant departure from the null hypothesis but no decline effect? We know what the parapsychologist's conclude. As long as they get a significant effect, they do not interpret the absence of the decline effect as the absence of psychic functioning. This state of affairs holds as well for several other effects that have been put forth as tokens or signs of anomalous mental functioning. Several such signs are listed in the Handbook of Parapsychology [1977, B.B. Wolman, Editor].

          Typically, such signs are sought when the attempt to reject the ordinary null hypothesis fails. Displacement effects are frequently invoked. When his attempts to replicate Rhine's results failed, Soal was persuaded to re-analyze his data in terms of displacement effects. His retrospective analysis uncovered two subjects whose guesses significantly correlated with the target one or two places ahead of the intended target. In his subsequent experiments with these two subjects, one kept hitting on the symbol that came after the intended target while the other produced significant outcomes only when her guesses were matched against the symbol that occurred just before the intended target. Negative hitting, increased variability, and other types of departures from the underlying theoretical probability model have all been used as hidden signs of the presence of psychic functioning.

          What makes this search for hidden tokens of psi problematic is lack of constraints. Any time the original null hypothesis cannot be rejected, the eager investigator can search through the data for one or more these markers. When one is found, the investigator has not hesitated in offering this as proof of the presence of psi. However, if the null hypothesis is rejected and none of these hidden signs of psi can be found in the data, the the investigator still claims the presence of psi. This creates the scientifically questionable situation where any significant departure from a probability model is used as proof of psi but the absence of these departures does not count as evidence against the presence of psi.

          So, acceptable evidence for the presence of anomalous cognition must be based on a positive theory that tells us when psi should and should not be present. Until we have such a theory, the claim that anomalous cognition has been demonstrated is empty. Without such a theory, we might just as well argue that what has been demonstrated is a set of effects--each one of which be the result of an entirely different cause.

          Professor Utts implicitly acknowledges some of the preceding argument by using consistency of findings with other laboratories as evidence that anomalous cognition has been demonstrated. I have already discussed why the apparent consistency in average effect size across experiments cannot be used as an argument for consistency of phenomena across these experiments. To be fair, parapsychologists who argue consistency of phenomena across experiments often go beyond simply pointing to consistency in effect sizes.

          One example is the claim that certain personality correlates replicate across experiments. May and his colleagues correctly point out, however, that these correlations tend to be low and inconsistent. Recently, parapsychologists have claimed that extroversion correlates positively with successful performance on anomalous cognition tasks. This was especially claimed to be true of the ganzfeld experiments. However, the apparently successful replication of the autoganzfeld experiments by the Edinburgh group [under subcontract to the SAIC program] found that the introverts, if anything, scored higher than the extroverts.

          The autoganzfeld experiments produced significant effects only for the dynamic targets. The static targets produced zero effect size. Yet the bulk of the targets in the original ganzfeld database were static and they produced an effect size that was significantly greater than the zero effect size of the autoganzfeld experiments [ I was able to demonstrate that there was adequate power to detect an effect size of the appropriate magnitude for the static targets in the autoganzfeld experiments]. Further indication of inconsistency is the SAIC experiment which found that the only the static targets produced a significant effect size, whereas the dynamic targets yielded a zero effect size. May and his colleagues speculated that the failure of the dynamic targets was due to a `bandwidth' that was too wide. When they apparently narrowed the bandwidth of the dynamic targets in a second experiment, both dynamic and static targets did equally well. It is unclear whether this should be taken as evidence for consistency or inconsistency. Note that the hypothesis and claim for the autoganzfeld experiments is that dynamic targets should be significantly better than static ones. As far as I can tell the original dynamic targets of the ganzfeld experiments are consistent with an unlimited bandwidth.

          Other important inconsistencies exist among the contemporary databases. The raison d'�tre for the ganzfeld experiments is the belief among some parapsychologists that an altered state facilitates picking up the psi signal because it lowers the noise-to-signal ratio from external sensory input. The touchstone of this protocol is the creation of an altered state in the receiver. This contrasts sharply with the remote viewing experiments in which the viewer is always in a normal state. More important is that the ganzfeld researchers believe that they get best results when each subject serves as his/her own judge. Those experiments in the ganzfeld database that employed both external judges and subjects as their own judges found that their results were more successful using subjects as their own judges. The reverse is true in the remote viewing experiments. The remote viewer experimenters believe that external judges provide much better hit rates than viewer-judges. This difference is even more extreme in the SAIC remote viewing where a single judge was used for all experiments. This judge, who was also the principal investigator, believed that he could achieve best results if he did the judging because of his familiarity with the response styles of the individual viewers.

          So even if the ganzfeld and the SAIC remote viewing experiments have achieved significant effects and average effect sizes of approximately the same magnitude, there is no compelling reason to assume they are dealing with the same phenomena or phenomenon. To make such a claim entails showing that the alleged effect shows the same pattern of relationships in each protocol. Almost certainly, a positive theory of anomalous mental phenomena that predicts lawful relationships of a recognizable type will be necessary before a serious claim can be made that the same phenomenon is present across different research laboratories and experiments. Such a positive theory will be necessary also to tell us when we are and when we are not in the presence of this alleged anomalous cognition.

          WHAT NEEDS TO BE EXPLAINED?

          Professor Utts and many parapsychologists argue that they have produced evidence of an anomaly that requires explanation. They assert that the statistical effects they have documented cannot be accounted for in terms of normal scientific principles or methodological artifact. After reviewing the results from the SAIC experiments in the context of other contemporary parapsychological research, Utts is confident that more than an anomaly has been demonstrated. She believes the evidence suffices to conclude that the anomaly establishes the existence of psychic functioning.

          This evidence for anomalous cognition, according to Utts and the parapsychologists, meets the standards employed by the other sciences. By this, I think Professor Utts means that in many areas of scientific inquiry the decision that a real effect has occurred is based on rules of statistical inference. Only if the null hypothesis of no difference between two or more treatments is rejected can the investigator claim that the differences are real in the sense that they are greater than might be expected on the basis of some baseline variability. According to this standard, it seems that the SAIC experiments as well as the recent ganzfeld experiments have yielded effects that cannot be dismissed as the result of normal variability.

          While the rejection of the null hypothesis is typically a necessary step for claiming that an hypothesized effect or relationship has occurred, it is never sufficient. Indeed, because the underlying probability model is only an approximation, everyone realizes that the null hypothesis is rarely, if ever, strictly true. In practice, the investigator hopes that the statistical test is sufficiently robust that it will reject the null hypothesis only for meaningful departures from the null hypothesis. With sufficient power, the null hypothesis will almost certainly be rejected in most realistic situations. This is because effect sizes will rarely be exactly zero. Even if the true effect size is zero in a particular instance, sufficient power can result in the rejection of the null hypothesis because the assumed statistical model will depart from the real-world situation in other ways. For most applications of statistical inference, then, too much power can result in mistaken inferences as well as too little power.

          Here we encounter another way in which parapsychological inquiry differs from typical scientific inquiry. In those sciences that rely on statistical inference, they do so as an aid to weeding out effects that could be the result of chance variability. When effect sizes are very small or if the experimenter needs to use many more cases than is typical for the field to obtain significance, the conclusions are often suspect. This is because we know that with enough cases an investigator will get a significant result, regardless of whether it is meaningful or not. Parapsychologists are unique in postulating a null hypothesis that entails a true effect size of zero if psi is not operating. Any significant outcome, then, becomes evidence for psi. My concern here is that small effects and other departures from the statistical model can be expected to occur in the absence of psi. The statistical model is only an approximation. When power is sufficient and when the statistical test is pushed too far, rejections of the null hypothesis are bound to occur. This is another important reason why claiming the existence of an anomaly based solely on evidence from statistical inference is problematic.

          This is one concern about claiming the existence of an anomaly on the basis of statistical evidence. In the context of this report, I see it as a minor concern. As I have indicated, I am willing to grant Professor Utts' claim that the rejection of the null hypothesis is probably warranted in connection with the SAIC and the ganzfeld databases. I have other concerns. Both have to do with the fact that no other science, so far as I know, would draw conclusions about the existence of phenomena solely on the basis of statistical findings. Although it is consistent with scientific practice to use statistical inference to reject the null hypothesis, it is not consistent with such practice to postulate the existence of phenomena on this basis alone. Much more is required. I will discuss at least two additional requirements.

          Thomas Kuhn's classic characterization of normal and revolutionary science has served as the catalyst for many discussions about the nature of scientific inquiry. He popularized the idea that normal scientific inquiry is guided by what he called a paradigm. Later, in the face of criticisms, he admitted that he had used the term paradigm to cover several distinct and sometimes contradictory features of the scientific process. One of his key uses of the term paradigm was to refer to the store of exemplars or textbook cases of standard experiments that every field of scientific inquiry possesses. These exemplars are what enable members of a scientific community to quickly learn and share common principles, procedures, methods, and standards. These exemplars are also the basis for initiating new members into the community. New research is conducted by adapting one or more of the patterns in existing exemplars as guidelines about what constitutes acceptable research in the field under consideration.

          Every field of inquiry, including parapsychology, has its stock of exemplars. In parapsychology these would include the classic card guessing experiments of J.B. Rhine, the Sheep-Goat experiments, etc. What is critical here is the striking difference between the role of exemplars in parapsychology as contrasted with their role in all other fields of scientific inquiry. These exemplars not only serve as models of proper procedure, but they also are teaching tools. Students in a particular field of inquiry can be assigned the task of replicating some of these classic experiments. The instructor can make this assignment with the confident expectation that each student will obtain results consistent with the original findings. The physics instructor, for example, can ask novice students to try Newton's experiments with colors or Gilbert's experiments with magnets. The students who do so will get the expected results. The psychology instructor can ask novice students to repeat Ebbinghaus' experiments on forgetting or Peterson and Peterson's classic experiment on short-term memory and know that they will observe the same relationships as reported by the original experimenters.

          Parapsychology is the only field of scientific inquiry that does not have even one exemplar that can be assigned to students with the expectation that they will observe the original results! In every domain of scientific inquiry, with the exception of parapsychology, many core exemplars or paradigms exist that will reliably produce the expected, lawful relationships. This is another way of saying that the other domains of inquiry are based upon robust, lawful phenomena whose conditions of occurrence can be specified in such a way that even novices will be able to observe and/or produce them. Parapsychologists do not possess even one exemplar for which they can confidently specify conditions that will enable anyone--let alone a novice--to reliably witness the phenomenon.

          The situation is worse than I have so far described. The phenomena that can be observed with the standard exemplars do not require sensitive statistical rejections of the null hypothesis based on many trials to announce their presence. The exemplar in which the student uses a prism to break white light into its component colors requires no statistics or complicated inference at all. The forgetting curve in the Ebbinghaus experiment, requires nothing more than plotting proportion recalled against trial number. Yet, to the extent that parapsychology is approaching the day when it will possess at least one exemplar of this sort, the "observation" of the "phenomenon" will presumably depend upon the indirect use of statistical inference to document its presence.

          In the standard domains of science, this problem of having not a single exemplar for reliably observing its alleged phenomenon, would be taken as a sign that the domain has no central phenomena. When Soviet scientists announced the discovery of mitogenetic radiation, some western scientists attempted to replicate the findings. Some reported success; others reported mixed results; and many failed entirely to observe the effect. Eventually scientists, including the Soviets, abandoned the quest for mitogenetic radiation. Because no one, including the original discover, could specify conditions under which the phenomenon--if there be one--could be observed, the scientific community decided that there was nothing to explain other than as-yet-undetected artifacts. The same story can be told about N-Rays, Polywater, and other candidate phenomena that could not be reliably observed or produced. We cannot explain something for which we do not have at least some conditions under which we can confidently say it occurs. Even this is not enough. The alleged phenomenon not only must reliably occur at least under some conditions but it also must reliably vary in magnitude or other attributes as a function of other variables. Without this minimal amount of lawfulness, the idea that there is something to explain is senseless. Yet, at best, parapsychology's current claim to having demonstrated a form of anomalous cognition rests on the possibility that it can generate significant differences from the null hypothesis under conditions that are still not reliably specified.

          I will suggest one more reason for my belief that it is premature to try to account for what the SAIC and the ganzfeld experiments have so far put before us. On the basis of these experiments, contemporary parapsychologists claim that they have demonstrated the existence of an "anomaly." I will grant them that they have apparently demonstrated that the SAIC and the ganzfeld experiments have generated significant effect sizes beyond what we should expect from chance variations. I will further admit that, at this writing, I cannot suggest obvious methodological flaws to account for these significant effects. As I have previously mentioned, this admission does not mean that these experiments are free from subtle biases and potential bugs. The experimental paradigms are too recent and insufficiently evaluated to know for sure. I can point to departures from optimality that might harbor potential flaws--such as the use of a single judge across the remote viewing experiments, the active coaching of viewers by the experimenter during judging procedures in the ganzfeld, my discovery of peculiar patterns of scoring in the ganzfeld experiments, etc. Having granted that significant effects do occur in these experiments, I hasten to add that without further evidence, I do not think we can conclude that these effects are all due to the same cause--let alone that they result from a single phenomenon that is paranormal in origin.

          The additional reason for concern is the difference in the use of `anomaly' in this context and how the term `anomaly' is used in other sciences. In the present context, the parapsychologists are using the term `anomaly' to refer to apparently inexplicable departures from the null hypothesis. These departures are considered inexplicable in the sense that apparently all normal reasons for such departures from the null hypothesis have been excluded. But these departures are not lawful in the sense that the effect sizes are consistent. The effect sizes differ among viewers and subjects; they also differ for different experimenters; they come and go in inexplicable ways within the same subject. Possibly some of these variations in effect size will be found to exhibit some lawfulness in the sense that they will correlate with other variables. The SAIC investigators, for example, hope they have found such correlates in the entropy and bandwidth of targets. At the moment this is just a hope.

          The term `anomaly' is used in a much more restricted sense in the other sciences. Typically an anomaly refers to a lawful and precise departure from a theoretical baseline. As such it is something the requires explaining. Astronomers were faced with a possible anomaly when discrepancies from Newtonian theory were reported in the orbit of Uranus. In the middle 1800s, Urban Leverrier decided to investigate this problem. He reviewed all the data on previous sightings of Uranus--both before and after it had been discovered as new planet. On the basis of the previous sightings, he laboriously recalculated the orbital path based on Newtonian theory and the reported coordinates. Sure enough, he found errors in the original calculations. When he corrected for these errors, the apparent discrepancy in Uranus' orbit was much reduced. But the newly revised orbit was still discrepant from where it should be on Newtonian theory. With this careful work, Leverrier had transformed a potential anomaly into an actual anomaly. Anomaly in this sense meant a precise and lawful departure from a well-defined theory. It was only after the precise nature, direction, and magnitude of this discrepancy was carefully specified did Leverrier and the scientific community decide that here was an anomaly that required explanation. What had to explain was quite precise. What was needed was an explanation that exactly accounted for this specific departure from the currently accepted theory.

          Leverrier's solution was to postulate a new planet beyond the orbit of Uranus. This was no easy task because it involved the relatively unconstrained and difficult problem of inverse perturbations. Leverrier had to decide on a size, orbit, location, and other attributes of a hitherto unknown body whose characteristics would be just those to produce the observed effects on Uranus without affecting the known orbit of Saturn. Leverrier's calculations resulted in his predicting the location of this hitherto unknown planet and the astronomer Galle located this new planet, Neptune, close to where Leverrier had said it would be.

          The point of this story is to emphasize the distinction between the parapsychologists' use of anomaly from that of other scientists'. Anomalies in most domains of scientific inquiry are carefully specified deviations from a formal theory. What needs to be explained or accounted for is precisely described. The anomalies that parapsychologists are currently talking about differ from this standard meaning in that the departures are from the general statistical model and are far from having the status of carefully specified and precise deviations from a theoretical baseline. In this latter case we do not know what it is that we are being asked to explain. Under what conditions can we reliably observe it? What theoretical baselines are the results a departure from? How much and in what direction and form do the departures exist? What specifically must our explanation account for?

          Finally, I should add that some parapsychologists, at least in the recent past, have agreed with my position that parapsychological results are not yet ready to be placed before the scientific community. Parapsychologists such as Beloff, Martin Johnson, Gardner Murphy, J.G. Pratt and others have complained that parapsychological data are volatile and messy. Some of these investigators have urged their colleagues to first get their house in order before they ask the scientific community at large to take them seriously. Martin Johnson, especially, has urged his colleagues to refrain from asking the scientific community to accept their findings until they can tame them and produce lawful results under specified conditions. Clearly, parapsychology has still not reached this desired state. At best, the results of the SAIC experiments combined with other contemporary findings offer hope that the parapsychologists may be getting closer to the day when they can put something before the scientific community and challenge it to provide an explanation.

          POTENTIALS FOR OPERATIONAL APPLICATIONS

          It may seem obvious that the utility of remote viewing for intelligence gathering should depend upon its scientific validity. If the scientific research cannot confirm the existence of a remote viewing ability, then it would seem to be pointless to try an use this non-existent ability for any practical application. However, the matter is not this simple. If the scientific research confirms the existence of anomalous cognition, this does not guarantee that this ability would have useful applications. Ed May, in his presentation to the evaluation panel, gave several reasons why remote viewing could be real and, yet, not helpful for intelligence gathering. In his opinion, approximately 20 percent of the information supplied by a viewer is accurate. Unfortunately, at the time the remote viewer is generating the information, we have no way of deciding which portion is likely to be the accurate one. Another problem is that the viewer's information could be accurate, yet not relevant for the intelligence analyst's purposes.

          This question is related to the problem of boundary conditions which I discussed earlier in this report. From both a scientific and an operational viewpoint the claim that anomalous cognition exists is not very credible until we have ways to specify when and when it is not present. So far, parapsychology seems to have concentrated only in finding ways to document the existence of anomalous cognition. The result is a patchwork quilt of markers that, when present, are offered as evidence for the presence of psi. These markers or indicators include the decline effect, negative hitting as well as positive hitting, displacement hitting, the incline effect, increased variability, decreased variability and just about any other way a discrepancy from a probability model can occur. A cynic will note that the absence of any or most of these markers is not used as evidence for the absence of psi. This lack of way to distinguish between the presence and absence of anomalous cognition creates many challenges for parapsychology, some of which I have already discussed.

          So, even if remote viewing is a real ability possessed by some individuals, its usefulness for intelligence gathering is questionable. If May is correct, then 80% of the all the information supplied by this talented viewer will be erroneous. Without any way to tell which statements of the views are reliable and which are not, the use of this information may make matters worse rather than better.

          Can remote viewing have utility for information gathering even if it cannot be scientifically validated? I can imagine some possibilities for remote viewing to be an asset to the intelligence analyst even when the viewer possesses no valid paranormal powers. The viewer might be a person of uncommonly good sense or have a background that enables him or her to provide helpful information even if it does not come from a paranormal source. Another possibility is that the viewer, even though lacking in any truly accurate intelligence information, might say things or open up new ways of dealing with the analyst's problem. In this latter scenario the remote viewer is a catalyst that may open up new ways of looking at an intelligence situation much like programs for problem solving and creative thinking stimulate new ways of looking at a situation. However, if the usefulness of the remote viewer reduces to a matter of injecting common sense or new perspectives into the situation, I believe that we can accomplish the same purpose in more efficient ways.

          In considering potential utility, I am most concerned about separation of the operational program in remote viewing from the research and development phase. By default, the assessment of the usefulness of the remote viewing in the operational arena is decided entirely by subjective validation or what May and Utts call prima facie evidence. Granted it is difficult to assess adequately the effectiveness of remote viewing in the operational domain. Nevertheless, better ways can be devised than have apparently been used up to now. In our current attempt to get an initial idea about the effectiveness of the current operational use of remote viewing, we have simply been asking individuals and agencies who have used the services of the remote viewers, if the information they received was accurate and useful. Whatever information we get from this survey is extremely limited for the purposes of judging the utility of remote viewing in the operational domain.

          Even psychologists who should know better underrate the power of subjective validation. Anyone who relies on prima facie evidence as a basis for affirming the validity of remote viewing should carefully read that portion of Marks and Kamman's The Psychology of the Psychic [1981] in which they discuss the SRI and their own experiments on remote viewing. In the early stages of their attempt to replicate the SRI remote viewing experiments, they were astonished at the high quality of their subject's protocols and the apparent accuracy of the viewing. After each session, the experimenters and the subject (viewer) would visit the target site and compare the verbal protocol with the actual site. The specific details of the viewers' responses appeared to match specific objects in the target site with uncanny accuracy. When they gave the verbal protocols to the judge, a distinguished professor, to blindly match against the actual target sites, he was astonished at how well what he considered the closest matching protocol for each site matched actual details of the target. He had no doubt that the viewers had demonstrated strong remote viewing abilities.

          So, both the viewers and the judge quickly became convinced of the reality of remote viewing on the basis of the uncanny matches between the verbal descriptions and the actual target sites. The experimenters received a rude awakening when they discovered that, despite the striking matches observed between target and verbal description, the judge had matched the verbal protocols to the wrong target sites. When all parties were given the results the subjects could not understand how the judge could have matched any but the actual target site to their descriptions. For them the match was so obvious that it would be impossible for the judge to have missed it. The judge, on the other hand, could not accept that any but the matches he made could be paired with the actual target sites.

          This phenomenon of subjective validation is pervasive, compelling and powerful. Psychologists have demonstrated it in a variety of settings. I have demonstrated it and written about in the context of the psychic reading. In the present context, subjective validation comes about when a person evaluates the similarity between a relatively rich verbal description and an actual target or situation. Inevitably, many matches will be found. Once the verbal description has been judged to be a good match to a given target, the description gets locked in and it becomes virtually impossible for the judge to see the description as fitting any but the original target.

          Unfortunately, all the so-called prima facie evidence put before us is tainted by subjective validation. We are told that the many details supplied by the viewers were indeed inaccurate. But some details were uncannily correct and even, in one case, hidden code words were correctly revealed. Such accounts do indeed seem compelling. They have to be put in the context, however, of all such operational attempts. We have to know the general background and expectations of the viewers, the questioners, etc. Obviously, the targets selected for the viewers in the operational setting will have military and intelligence relevance. If the viewer [some of the viewers have intelligence backgrounds] suspects the general nature of the target, then previous background knowledge might very well make the presence, say of a gantry, highly likely. In addition, the interactions and questioning of the viewers in these settings appear to be highly suggestive and leading.

          I can imagine that the preceding paragraph might strike a reader as being unreasonable. Even allowing for subjective validation, the possibility that a viewer might accurately come up with secret code words and a detailed description of particular gantry is quite remote on the basis of common sense and sophisticated guessing. I understand the complaint and I realize the reluctance to dismiss such evidence out of hand. However, I have had experience with similarly compelling prima facie evidence for more than a chance match between a description and a target. In the cases I have in mind, however, the double blind controls were used to pair descriptions with the true as well as with the wrong target sites. In all these test cases with which I am familiar, the unwitting subjects found the matches between their descriptions and the presumed target equally compelling regardless of whether the presumed target was the actual or the wrong one.

          What this says about operational effectiveness, is that, for evaluation purposes, half of the time the viewers and the judges should be mislead about the what was the actual target. In these cases, both the interrogator and the viewer, as well as the judge, have to be blind to the actual targets. Under such conditions, if the judges and the others find the matches between the verbal descriptions and the actual targets consistently better than the matches between the verbal descriptions and the decoy targets, then this would constitute some evidence for the effectiveness of remote viewing. I can confidently predict, regardless of the outcome of such an evaluation, that many of the verbal descriptions when matched with decoy targets will be judged to be uncanny matches.

          SUGGESTIONS: WHAT NEXT?

          I have played the devil's advocate in this report. I have argued that the case for the existence of anomalous cognition is still shaky, at best. On the other hand, I want to state that I believe that the SAIC experiments as well as the contemporary ganzfeld experiments display methodological and statistical sophistication well above previous parapsychological research. Despite better controls and careful use of statistical inference, the investigators seem to be getting significant results that do not appear to derive from the more obvious flaws of previous research. I have argued that this does not justify concluding that anomalous cognition has been demonstrated. However, it does suggest that it might be worthwhile to allocate some resources toward seeing whether these findings can be independently replicated. If so, then it will be time to reassess if it is worth pursuing the task of determining if these effects do indeed reflect the operation of anomalous cognition. This latter quest will involve finding lawful relationships between attributes of this hypothesized phenomenon and different independent variables. Both the scientific and operational value of such an alleged phenomenon will depend upon how well the conditions for its occurrence can be specified and how well its functioning can be brought under control.

          Both Professor Utts and I agree that the very first consideration is to see if the SAIC remote viewing results will still be significant when independent judges are used. I understand Ed May's desire to use a judge who is very familiar with the response styles of the experienced viewers. However, if remote viewing is real, then conscientious judges, who are blind to the actual targets, should still be able to match the verbal descriptions to the actual targets better than chance. If this cannot be done, the viability of the case for remote viewing becomes problematical. On the other hand, assuming that independent judges can match the descriptions to the correct targets reasonably well, then it becomes worthwhile to try to independently replicate the SAIC experiments.

          At this point we face some interesting questions. Should we try to replicate the remote viewing studies by using the same viewers, the same targets, and the same protocol? Perhaps change only the experimenters, the judge, and the laboratory? At some point we would also want to change the targets. For completeness, we would also want to search for new, talented viewers.

          If independent replications confirm the SAIC findings, we still have a long way to go. However, at this stage in the proceedings, the scientific community at large might be willing to acknowledge that an anomaly of some sort has been demonstrated. Before the scientific community will go beyond this acknowledgment, the parapsychologists will have to devise a positive theory of anomalous communication from which they can make testable predictions about relationships between anomalous communication and other variables.

          CONCLUSIONS

          The Scientific Status of the SAIC Research Program

          1. The SAIC experiments on anomalous mental phenomena are statistically and methodologically superior to the earlier SRI remote viewing research as well as to previous parapsychological studies. In particular, the experiments avoided the major flaw of non-independent trials for a given viewer. The investigators also made sure to avoid the problems of multiple statistical testing that was characteristic of much previous parapsychological research.

          2. From a scientific viewpoint, the SAIC program was hampered by its secrecy and the multiple demands placed upon it. The secrecy kept the program from benefiting from the checks and balances that comes from doing research in a public forum. Scrutiny by peers and replication in other laboratories would accelerated the scientific contributions from the program. The multiple demands placed on the program meant that too many things were being investigated with too few resources. As a result, no particular finding was followed up in sufficient detail to pin it down scientifically. Ten experiments, no matter how well conducted, are insufficient to fully resolve one important question, let alone the several that were posed to the SAIC investigators.

          3. Although, I cannot point to any obvious flaws in the experiments, the experimental program is too recent and insufficiently evaluated to be sure that flaws and biases have been eliminated. Historically, each new paradigm in parapsychology has appeared to its designers and contemporary critics as relatively flawless. Only subsequently did previously unrecognized drawbacks come to light. Just as new computer programs require a shakedown period before hidden bugs come to light, each new scientific program requires scrutiny over time in the public arena before its defects emerge. Some possible sources of problems for the SAIC program are its reliance on experienced viewers, and the use of the same judge--one who is familiar to the viewers, for all the remote viewing.

          4. The statistical departures from chance appear to be too large and consistent to attribute to statistical flukes of any sort. Although I cannot dismiss the possibility that these rejections of the null hypothesis might reflect limitations in the statistical model as an approximation of the experimental situation, I tend to agree with Professor Utts that real effects are occurring in these experiments. Something other than chance departures from the null hypothesis has occurred in these experiments.

          5. However, the occurrence of statistical effects does not warrant the conclusion that psychic functioning has been demonstrated. Significant departures from the null hypothesis can occur for several reasons. Without a positive theory of anomalous cognition, we cannot say that these effects are due to a single cause, let alone claim they reflect anomalous cognition. We do not yet know how replicable these results will be, especially in terms of showing consistent relations to other variables. The investigators report findings that they believe show that the degree of anomalous cognition varies with target entropy and the `bandwidth' of the target set. These findings are preliminary and only suggestive at this time. Parapsychologists, in the past, have reported finding other correlates of psychic functioning such as extroversion, sheep/goats, altered states only to find that later studies could not replicate them.

          6. Professor Utts and the investigators point to what they see as consistencies between the outcome of contemporary ganzfeld experiments and the SAIC results. The major consistency is similarity of average effect sizes across experiments. Such consistency is problematical because these average effect sizes, in each case, are the result of arbitrary combinations from different investigators and conditions. None of these averages can be justified as estimating a meaningful parameter. Effect size, by itself, says nothing about its origin. Where parapsychologists see consistency, I see inconsistency. The ganzfeld studies are premised on the idea that viewers must be in altered state for successful results. The remote viewing studies use viewers in a normal state. The ganzfeld experimenters believe that the viewers should judge the match between their ideation and the target for best results; the remote viewers believe that independent judges provide better evidence for psi than viewers judging their own responses. The recent autoganzfeld studies found successful hitting only with dynamic targets and only chance results with static targets. The SAIC investigators, in one study, found hitting with static targets and not with dynamic ones. In a subsequent study they found hitting for both types of targets. They suggest that they may have solution to this apparent inconsistency in terms of their concept of bandwidth. At this time, this is only suggestive.

          7. The challenge to parapsychology, if it hopes to convincingly claim the discovery of anomalous cognition, is to go beyond the demonstration of significant effects. The parapsychologists need to achieve the ability to specify conditions under which one can reliably witness their alleged phenomenon. They have to show that they can generate lawful relationships between attributes of this alleged phenomenon and independent variables. They have to be able to specify boundary conditions that will enable us to detect when anomalous cognition is and is not present.

          Suggestions for Future Research

          1. Both Professor Utts and I agree that the first step should be to have the SAIC protocols rejudged by independent judges who are blind to the actual target.

          2. Assuming that such independent judging confirms the extra-chance matchings, the findings should be replicated in independent laboratories. Replication could take several forms. Some of the original viewers from the SAIC experiments could be used. However, it seems desirable to use a new target set and several independent judges.

          Operational Implications

          1. The current default assessment of the operational effectiveness of remote viewing is fraught with hazards. Subjective validation is well known to generate compelling, but false, convictions that a description matches a target in striking ways. Better, double blind, ways of assessing operational effectiveness can be used. I suggest at least one way in the report.

          2. The ultimate assessment of the potential utility of remote viewing for intelligence gathering cannot be separated from the findings of laboratory research.

          ------------------

          (1) The SAIC did benefit from the input of a distinguished oversight committee. But this still falls far short of what could have taken place in an open forum.

          [End]


          Back to mceagle.com References

          http://www.ics.uci.edu/~jutts/response.html Response to Ray Hyman's Report (AIR)

          Jessica Utts Home Page


          Copyright Notice      Article References

           
          RESPONSE TO RAY HYMAN'S REPORT
          of September 11, 1995
          "Evaluation of Program on Anomalous Mental Phenomena
          "

          Professor Jessica Utts
          Division of Statistics
          University of California, Davis

          September 15, 1995

          Ray Hyman's report of September 11, 1995, written partially in response to my report of September 1, 1995 elucidates the issues on which he and I agree and disagree. I basically concur with his assessment of where we agree and disagree, but there are three issues he raises with regard to the scientific status of parapsychology to which I would like to respond.

          1. "Only parapsychology, among the fields of inquiry claiming scientific status, lacks a cumulative database (p. 6)."

          It is simply not true that parapsychology lacks a cumulative database. In fact, the accumulated database is truly impressive for a science that has had so few resources. While critics are fond of relating, as Professor Hyman does in his report, that there has been "more than a century of parapsychological research (p. 7)" psychologist Sybo Schouten (1993, p. 316) has noted that the total human and financial resources devoted to parapsychology since 1882 is at best equivalent to the expenditures devoted to fewer than two months of research in conventional psychology in the United States.

          On pages 4 and 5 of their September 29, 1994 SAIC final report, May, Luke and James summarize four reports that do precisely what Professor Hyman claims is not done in parapsychology; they put forth the accumulated evidence for anomalous cognition in a variety of formats. Rather than dismissing the former experiments, parapsychologists build on them. As in any area of science, it is of course the most recent experiments that receive the most attention, but that does not mean that the field would divorce itself from past work. Quite to the contrary, past experimental results and methodological weaknesses are used to design better and more efficient experiments.

          As an example of the normal progress of inquiry expected in any area of science, the autoganzfeld experiments currently conducted by parapsychologists did not simply spring out of thin air. The original ganzfeld experiments followed from Honorton's observation at Maimonides Medical Center, that anomalous cognition seemed to work well in dreams. He investigated ways in which a similar state could be achieved in normal waking hours, and found the ganzfeld regime in another area of psychology. The automated ganzfeld followed from a critical evaluation of the earlier ganzfeld experiments, and a set of conditions agreed upon by Honorton and Professor Hyman. The current use of dynamic targets in autoganzfeld experiments follows from the observation that they were more successful than static targets in the initial experiments. The investigation of entropy at SAIC follows from this observation as well. This is just one example of how current experiments are built from past results.

          2. "Only parapsychology claims to be a science on the basis of phenomena (or a phenomenon) whose presence can be detected only by rejecting a null hypothesis (p. 8)."

          While it is true that parapsychology has not figured out all the answers, it does not differ from normal science in this regard. It is the norm of scientific progress to make observations first, and then to attempt to explain them. Before quantum mechanics was developed there were a number of anomalies observed in physics that could not be explained. There are many observations in physics and in the social and medical sciences that can be observed, either statistically or deterministically, but which cannot be explained.

          As a more recent example, consider the impact of electromagnetic fields on health. An article in Science (Vol. 269, 18 August 1995, p. 911) reported that "After spending nearly a decade reviewing the literature on electromagnetic fields (EMFs), a panel of the National Council on Radiation Protection and Measurements (NCRP) has produced a draft report concluding that some health effects linked to EMFs such as cancer and immune deficiencies appear real and warrant steps to reduce EMF exposure... Biologists have failed to pinpoint a convincing mechanism of action." In other words, a statistical effect has been convincingly established and it is now the responsibility of science to attempt to establish its mechanism, just as in parapsychology.

          As yet another example, consider learning and memory, which have long been studied in psychology. We know they exist, but brain researchers are just beginning to understand how they work by using sophisticated brain imaging techniques. Psychologists do not understand these simple human capabilities, and they certainly do not understand other observable human phenomena such as what causes people to fall in love. Yet, no one would deny the existence of these phenomena just because we do not understand them.

          In any area involving the natural variability inherent in humans, science progresses by first observing a statistical difference and then attempting to explain it. At this stage, I believe parapsychology has convincingly demonstrated that an effect is present, and future research attempts should be directed at finding an explanation. In this regard parapsychology in on par with scientific questions like the impact of electromagnetic fields on health, or the cross-cultural differences in memory that have been observed by psychologists.

          3. "Parapsychology is the only field of scientific inquiry that does not have even one exemplar that can be assigned to students with the expectation that they will observe the original results (p. 18)."

          I disagree with this statement for two reasons. First, I can name other phenomena for which students could not be expected to do a simple experiment and observe a result, such as the connection between taking aspirin and preventing heart attacks or the connection between smoking and getting lung cancer. What differentiates these phenomena from simple experiments like splitting light with a prism is that the effects are statistical in nature and are not expected to occur every single time. Not everyone who smokes gets lung cancer, but we can predict the proportion who will. Not everyone who attempts anomalous cognition will be successful, but I think we can predict the proportion of time success should be achieved.

          Since I believe the probability of success has been established in the autoganzfeld experiments, I would offer them as the exemplar Professor Hyman requests. The problem is that to be relatively assured of a successful outcome requires several hundred trials, and no student has the resources to commit to this experiment. As I have repeatedly tried to explain to Professor Hyman and others, when dealing with a small to medium effect it takes hundreds or sometimes thousands of trials to establish "statistical significance." In fact, the Physicians Health Study that initially established the link between taking aspirin and reducing heart attacks studied over 22,000 men. Had it been conducted on only 2,200 men with the same reduction in heart attacks, it would not have achieved statistical significance. Should students be required to recruit 22,000 participants and conduct such an experiment before we believe the connection between aspirin and heart attacks is real?

          Despite Professor Hyman's continued protests about parapsychology lacking repeatability, I have never seen a skeptic attempt to perform an experiment with enough trials to even come close to insuring success. The parapsychologists who have recently been willing to take on this challenge have indeed found success in their experiments, as described in my original report.


          REFERENCE

          SCHOUTEN, SYBO (1993). "Are we making progress?" In Psi Research Methodology: A Re-examination, Proceedings of an International Conference, Oct 29-30, 1988, edited by L. Coly and J. McMahon, NY: Parapsychology Foundation, Inc., pgs. 295-322.


          COPYRIGHT NOTICE

          The contents of this document are copyright �1995 by Jessica Utts. All rights reserved.


          Jessica Utts Home Page

          http://www.ics.uci.edu/~alexv/webrtc.html WebRTC and WebRTCBench

          WebRTC and WebRTCBench

          WebRTC is an industry and standards effort to provide real-time communication capabilities into all browsers and make these capabilities accessible to software developers via standard HTML5 and Javascript APIs. WebRTC fills a critical gap in web technologies by allowing (a) browser access to native devices (e.g., microphone, webcam) through a Javascript API and (b) sharing captured streams using Real-Time browser-to-browser Communication. WebRTC also provides data sharing.

          WebRTC accomplishes three main tasks: Acquiring audio and video; Communicating Audio and Video; Communicating Arbitrary Data. These tasks map one to one to three main Javascript APIs: MeadiaStream (i.e., getUserMedia); RTCPeerConnection; RTCDataChannel. Various aspects of video, audio and data transmission can be evaluated once a connection is established.

          We are investigating issues in WebRTC behavior and performance. The latter is particularly important on mobile devices. We have developed a benchmark suite, WebRTCBench, to help identify perfromance issues and target them for improvement. The goal of WebRTCBench is to measure various aspects of connection establishment and streaming between WebRTC peers. This allows a quantitative comparison of WebRTC implementations across browsers and devices (i.e., hardware platforms). WebRTCBench allows definition and evaluation of MediaStreams composed of Video, Audio, Data or any subset of the three. It can establish a single peer connection with a media server and multiple peer connections between browsers in a WebRTC triangle.

          The current version of the benchmark can be downloded here

          Supported by the Intel Corp.

          http://www.ics.uci.edu/~alexv/131/ CompSci131

          CompSci131 Parallel and Distributed Systems

          Prof. A. Veidenbaum

           

          Winter 2016

           

           

          Welcome to the CompSci131 home page. Everything you need will be posted here. Check here frequently for the recently added information

           

           

          Course Syllabus

           

          Course Material

          Transparencies

          Assignments

          Discussion slides

           

          Midterm Review

          Final info

           

           

          New Information

          12/22/15 Installed

          1/19/16   Discussion 1 slides added

          1/19/16   Lab 1 posted

          1/25/16   Discussion 2 slides posted

          1/28/16   Homework 1 posted

          2/5/16   Midterm review posted

          2/7/16   Lab2 posted

          2/18/16   Lab3 posted

          http://www.ics.uci.edu/~alexv/low_pwr.html Low-Power processors

          Reducing power consumption in
          embedded and high-performance processors.

          Power dissipation is a major issue in processor design. In particular, CMOS technology scaling
          has significantly increased the leakage power dissipation so that it accounts for an increasingly large
          fraction of processor power dissipation. One of the main issue is how to achieve power savings without loss
          of performance.

          Much of our work in this area has focused on cache power dissipation. We addressed issues
          in L1 I- and D-cache dynamic as well as static power consumption. This included way caching to save
          static and dynamic power in high-associativity caches (as an alternative to way prediction),
          cached load-store queue as a low-cost alternative to L0 cache, using branch prediction information
          to save power in instruction caches. We addressed L2 power consumption, in particular leakage power
          in L2 peripheral circuits. The results of this research are applicable in both embedded and
          high-performance processors.

          Another aspect of this research is low-power instruction queue design for out-of-order processors.
          CAM-based instruction queues are not scalable and consume significant amount of power due to wide
          issue and CAM search on each cycle. One approach we proposed used a banked queue, thus dividing a
          CAM into smaller banks with faster search. A pointer table indicates which bank an instruction belongs to.
          A more complex approach disposed of CAM-based queue altogether and used instruction dependence pointers
          and RAM-based queue for "direct" wakeup. It solved the problem of how to achieve fast branch
          misprediction recovery when using pointers while using dependent pointers.

          Finally, we investigated the problem of power consumption in the register file. Content-aware register file
          utilized knowledge of instruction operand and effective address width to reduce the number
          of bits read from the RF and to speed up TLB access using an "L0 TLB". This type of register file was also
          shown to enable a new type of clustered processor with improved performance and reduced power.

          See recent publication list for papers with details of the above. http://www.ics.uci.edu/~alexv/250A/ Computer Science 250A

          CompSci250A Computer Systems Architecture

          Welcome to the Computer Science 250A home page.
          This is where all handouts can be accessed on line.
          Check here frequently for the recently added information

          Course Syllabus

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          New Information

          4/1/14 Installed
          http://www.ics.uci.edu/~alexv/152/ Welcome to CS152

          CS152 Computer Systems Architecture

          Welcome to the CS152 home page. This is where all handouts can be accessed on line. Check here frequently for the recently added information

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          1/3/11 Installed
          http://www.ics.uci.edu/~alexv/multicore.html Multi-core software

          Synchronization and Scheduling of Data-Parallel Programs for Multi-Core Processors

          Multi-core (parallel) processors have become ubiquitous. The use of such systems is key to science, engineering, finance, and other major areas of the economy. However, increased applications performance on such systems can only be achieved with advances in mapping such applications to multi-core machines. This task is made more difficult by the presence of complex memory organizations which is perhaps the key bottleneck to efficient execution. This research involves making the mapping of the program to the machine aware of the complexities of the memory-hierarchy in all phases of the compilation process to ensure a good fit between the application code and the actual machine and thereby guarantee much more effective utilization of the hardware (and thus efficient/fast execution) than was previously possible.

          This research develops a new cache-hierarchy-aware compilation and runtime system (i.e., including compilation, scheduling, and static/dynamic processor mapping of parallel programs). These tasks have one thing in common: they all need accurate estimates of data element (iteration, task) computation and memory access times which are currently beyond the (cache-oblivious) state-of-the-art. This research aims to develop new techniques for iteration space partitioning, scheduling, and synchronization which capture the variability due to cache, memory, and conditional statement behavior and their interaction.

          http://www.ics.uci.edu/~alexv/154/ Welcome to CS154

          CompSci154 Computer Design Lab

          Syllabus

          Lectures

          Labs

          Ashenden's "VHDL Cookbook"

          Stratix architecture (pdf)


          ModelSim tutorial (pdf)

          http://www.ics.uci.edu/~yunanc/Yunan_Chen/Welcome.html Welcome
           

          Yunan Chen

           
           
           

          Recent News

          August 2013: I will be co-chairing the iConference dissertation competition this year.

          January 2013: I will be co-chairing the Workshop on Interactive Systems in Healthcare (WISH 2013) to be held in Washington DC on November 16th, 2013.

          November 2012: I participated in three exciting panels on health informatics in ASIS&T, AMIA, and WISH conferences. 

          September 2012: I was awarded a NSF HCC grant to study “Patient-provider handoffs: collaboration challenge and technology design,” where I will explore the information flow and information management problems to support continuity of care.

          September 2012: Our proposal on “Quantifying Electronic Medical Record Usability to Improve Clinical Workflow” was funded by AHRQ. I am a co-investigator on this project.

          August 2012: We will be organizing a  workshop on “Beyond Formality: Informal Communication in Health Practice” at the CSCW 2013 conference.

          I am an assistant professor in the Department of Informatics at the Donald Bren School of Information and Computer Sciences (ICS), and the Institute for Clinical and Translational Science (ICTS) at the University of California, Irvine.

          My research interests lie in the intersection of Human-Computer Interaction (HCI), Computer Supported Cooperative Work (CSCW) and Health Informatics. In particular, I am interested in designing and evaluating interactive systems for  clinical collaboration, clinical documentation, patient-provider interaction and information management for chronic care. 

          I graduated with a Ph.D. from the iSchool at Drexel University in 2008, and a Medical Degree from China Medical University in 2003.

          Research Interests

          Working with me

          My research aims to provide empirical, conceptual, and theoretical understandings of information practices in healthcare through in situ examination of how end users (i.e., frontline clinicians and clerical staff) interact with computerized technology. In particular, I am interested in the complex interplay between individual, group, organization, and technology in clinical environments.

          My recent projects focus on sociotechnical design in Electronic Health Record (EHR) system. Drawing upon observations in my research and clinical practice, I firmly believe that as opposed to a record-keeping device, EHR system should be conceptualized, and therefore designed, as a ‘working space’ centered on fostering communication, coordination, and collaboration activities at work. I firmly believe that the success of EHRs ultimately lies in how well they support end users’ day-to-day job routines. Lastly, I believe EHRs will soon become an indispensable tool enabling unprecedented opportunities for consumers of healthcare (i.e., patients, families, and informal caregivers) to take full advantage of their own data in disease management and health maintenance.

           

          If you are interested in applying to graduate school in Human-Centered Computing, Information Science, and Informatics. Click Here.

          If you are already a student at UCI and are interested in Medical Informatics, please email me. In the past, I have worked with students in Informatics, Public Health and Nursing sciences.

           
           
          http://www.ics.uci.edu/~guoqingx/research/projects/analysis.html Program Analyses for Bloat Detection and Optimization

          Exploiting Systems Support for Highly Scalable Program Analyses


          About

           

          How to scale sophisticated program analyses to large codebase has been a key challenge in the program analysis research for at least a decade. The inability of scaling is the major factor that prevents analysis-based techniques (e.g., verification, model checking, and static bug detection) from being widely adopted in industry. Program analysis researchers tackle the problem typically by developing approximations, trading off analysis capability for scalability. However, approximations render analyses less useful and, even with approximations, most analyses still cannot scale to large software on the computing frontier, such as Hadoop, HDFS, or Spark. My systems-building experience enables me to think from a different angle: now that efficient systems can be built to process datasets as large as the whole internet or human genome, why don't we shift our focus from improving algorithms and making better analysis tools to leveraging decades of experience in the systems community to develop efficient Big Code analysis systems (not just tools)? We hope that with the help of a series of systems-level attempts, we can unleash the power of program analysis on big code, helping realize the dream of developing fully-verified, bug-free software.

           

           

          (1) Graspan: Graph system as an engine for scalable program analysis

          As the first step, we are working on a project that exploits out-of-core support to perform quick dynamic transitive closure computation on very large (program) graphs. This system is designed to analyze modern software with extremely large codebase in a precise and scalable way without needing to make any compromise on analysis capabilities. We are currently evaluating Graspan on various analyses for Linux kernel. More information about Graspan will be reported here.

           


          Papers

           

          To be added.

           


          People

           

          o   Kai Wang

          o   Aftab Hussain

          o   Zhiqiang Zuo

          o   Harry Xu


          Software

           

          To be added.


          Support

           

          This research is funded in part by NSF under grants CNS-1321179 and CCF-140982, and by ONR under grant N00014-14-1-0549.


          main page

          http://www.ics.uci.edu/~guoqingx/ppopp13/workshops.html PPoPP'13 Call for Workshops and Tutorials

          18th ACM SIGPLAN Symposium on
          Principles and Practice of Parallel Programming (PPoPP 2013)

          February 23-27, 2013, Shenzhen, China

          Accepted Workshops:

          Workshop on Leveraging Abstractions and Semantics in High-performance Computing (LASH-C)
          International Workshop on Programming Models and Applications for Multicores and Manycores (PMAM 2013)
          International Workshop on Parallel Programming Model for the Masses (PPMM 2013)

          Accepted Tutorials:

          MPI for Dummies (half-day tutorial)
          Advanced Parallel Programming with MPI-1, MPI-2 and MPI-3 (half-day tutorial)
          GPGPU for Real-Time Data Analytics (half-day tutorial)
          Challenges and Opportunities of Heterogeneous Computing (half-day tutorial)
          Extracting Maximum Parallel Performance from Intel® Xeon Processors and Intel® Xeon Phi™ Coprocessors: A Systematic Approach (half-day tutorial)

           

           

          http://www.ics.uci.edu/~guoqingx/research/publications.html Publications

          Publications back to homepage

          Copyright notice: The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.


          2016:

          TOCS

          Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, and Guoqing Xu,
          "
          Understanding and Combating Memory Bloat in Managed Data-Intensive Systems",
          ACM Transactions on Computer Systems
          (Accepted with minor revision)
          [PDF]

          2015:

          SOSP'15

          Lu Fang, Khanh Nguyen, Guoqing Xu, Brian Demsky, and Shan Lu,
          "
          Interruptible Tasks: Treating Memory Pressure as Interrupts for Highly Scalable Data-Parallel Programs",
          25th ACM Symposium on Operating Systems Principles, Monterey, CA,
          October 2015. (Acceptance rate: 30/186, 16.1%)
          [PDF][One-column PDF][Talk]

           

          USENIX ATC'15

          Kai Wang, Guoqing Xu, Zhendong Su, and Yu David Liu,
          "
          GraphQ: Graph Query Processing with Abstraction Refinement -- Programmable and Budget-Aware Analytical Queries over Very Large Graphs on a Single PC",
          2015 USENIX Annual Technical Conference, Santa Clara, CA,
          July 2015. (Acceptance rate: 35/221, 15.8%)
          [PDF][Talk][Download]

           

          ECOOP'15

          Lu Fang, Liang Dou, and Guoqing Xu,
          "
          PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification",
          European Conference on Object Oriented Programming, Prague, Czech Republic,
          July 2015. (Acceptance rate: 31/136, 22.8%)
          [PDF][Talk]

           

          ASPLOS'15

          Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Xu,
          "
          Facade: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications",
          20th International Conference on Architectural Support for Programming Languages and Operating Systems, Istanbul, Turkey,
          March 2015. (Acceptance rate: 48/287, 16.7%)
          [PDF][Talk]

          2014:

          TOSEM'14

          Guoqing Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, Edith Schonberg, and Gary Sevitsky,
          "
          Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing",
          ACM Transactions on Software Engineering and Methodology,
          Volume 23, Issue 3, Article No. 23, 2014.
          [Paper]

           

          CGO'14

          Dacong Yan, Guoqing Xu, Shengqian Yang, and Atanas Rountev,
          "
          LeakChecker: Practical Static Memory Leak Detection for Managed Languages",
          IEEE/ACM International Conference on Code Generation and Optimization,
          Orlando, FL, March 2014. (Acceptance rate: 29/103, 28%)
          [PDF][Talk]

           

          2013:

          ASE'13

          Vijay Krishna Palepu, Guoqing Xu, and James A. Jones,
          "
          Improving Efficiency of Dynamic Analysis with Dynamic Dependence Summaries",
          IEEE/ACM International Conference on Automated Software Engineering,
          Silicon Valley, CA , November 2013. (Acceptance rate: 43/253, 17%)
          This paper is an extended report of Vijay's course project in CS 295.
          [PDF][Talk]

           

          OOPSLA'13

          Guoqing Xu,
          "
          Resurrector: A Tunable Object Lifetime Profiling Technique for Optimizing Real-World Programs",
          ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and Applications,
          Indianapolis, IN , October 2013. (Acceptance rate: 50/204, 25%)
          [PDF][Talk][Implementation]

           

          FSE'13

          Khanh Nguyen and Guoqing Xu,
          "
          Cachetor: Detecting Cacheable Data to Remove Bloat",
          ACM SIGSOFT Symposium on the Foundations of Software Engineering,
          Saint Petersburg, Russia, August 2013. (Acceptance rate: 51/251, 20%)
          [PDF][Talk]

           

          ISSTA'13

           

           

           

          Mengchen Li, Yuanjun Chen, Linzhang Wang, and Guoqing Xu,
          "Dynamically Validating Static Memory Leak Warnings",
          ACM SIGSOFT International Symposium on Software Testing and Analysis, Lugano, Switzerland, July 2013. (Acceptance rate: 32/124, 25.8%)

          [PDF][Talk]

           

          ISMM'13

           

           

           

          Yingyi Bu, Vinayak Borkar, Guoqing Xu, and Michael J. Carey,
          "A Bloat-Aware Design for Big Data Applications",
          ACM SIGPLAN International Symposium on Memory Management, Seattle, WA, June 2013. (Acceptance rate: ?/?, ?%)

          [PDF][Talk][A Chinese Translation]

           

          TOSEM'13

          Guoqing Xu and Atanas Rountev,

          "Precise Memory Leak Detection for Java Software Using Container Profiling",
          ACM Transactions on Software Engineering and Methodology, 22(3), Article No. 17, July 2013

          This paper supercedes our ICSE'08 paper on memory leak detection.
          [PDF]

           

          ECOOP'13

          Guoqing Xu,
          "CoCo:
          Sound and Adaptive Replacement of Java Collections",
          European Conference on Object-Oriented Programming, Montpellier, France, July 2013. (Acceptance rate: 29/116, 25%)

          [PDF][Talk]

          2012:

          OOPSLA'12

          Guoqing Xu,
          "Finding Reusable Data Structures",
          ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, Tucson, AZ, October 2012. (Acceptance rate: 57/228, 25%)

          [PDF][Talk]

           

          TSE'12

           

          Raffi Khatchadourian, Phil Greenwood, Awais Rashid, and Guoqing Xu,
          "Pointcut Rejuvenation: Recovering Pointcut Expressions in Evolving Aspect-Oriented Software",
          IEEE Transactions on Software Engineering, vol. 38, no. 3, May-June 2012.

          [PDF]

           

          WODA'12

          Shengqian Yang, Dacong Yan, Guoqing Xu, and Atanas Rountev,

          "Dynamic Analysis of Inefficiently-Used Containers",

          International Workshop on Dynamic Analysis, Minneapolis, MN, USA, July 2012.

          [PDF]

           

          SOAP'12

          Dacong Yan, Guoqing Xu, and Atanas Rountev,

          "Rethinking Soot for Summary-Based Whole-Program Analysis",

          ACM SIGPLAN International Workshop on the State Of the Art in Java Program Analysis, Beijing, China, June 2012.

          [PDF]

           

          ECOOP'12

          Guoqing Xu, Dacong Yan, Atanas Rountev,

          "Static Detection of Loop-Invariant Data Structures",
          European Conference on Object-Oriented Programming, Beijing, China, June 2012. (Acceptance rate: 30/140, 21.4%)
          [PDF][Talk]

           

          ICSE'12

          Dacong Yan, Guoqing Xu, and Atanas Rountev,

          "Uncovering Performance Problems in Java Applications with Reference Propagation Profiling",
          ACM SIGSOFT/IEEE International Conference on Software Engineering, Zurich, Switzerland, June 2012. (Acceptance rate: 87/408, 21.3%)
          [PDF][Talk]

           

          Papers Between 2005 and 2011:

           

          PhD'11

           

          Guoqing Xu,
          "Analyzing Large-Scale Object-Oriented Software to Find and Remove Runtime Bloat",
          Doctoral Dissertation, Department of Computer Science and Engineering, Ohio State University, Aug. 2011.
          [PDF]

           

          ISSTA'11

          Dacong Yan, Guoqing Xu, and Atanas Rountev,
          "Demand-Driven Context-Sensitive Alias Analysis for Java",
          ACM SIGSOFT International Symposium on Software Testing and Analysis, Toronto, Canada, July 2011. (Acceptance rate: 35/121, 29%)
          [PDF][Talk]

           

          PLDI'11

          Guoqing Xu, Michael Bond, Feng Qin, and Atanas Rountev
          "LeakChaser: Helping Programmers Narrow Down Causes of Memory Leaks",
          ACM SIGPLAN Conference on Programming Language Design and Implementation, San Jose, CA, June 2011. (Acceptance rate: 55/236, 23%)
          [PDF][Implementation]
          [TalkTalk]

           

          FoSER'10

          Guoqing Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, and Gary Sevitsky,
          "Software Bloat Analysis: Finding, Removing, and Preventing Performance Problems in Modern Large-Scale Object-Oriented Applications",
          2010 ACM SIGSOFT FSE/SDP Working Conference on the Future of Software Engineering Research, Santa Fe, NM, November, 2010
          (a position paper that argues performance optimization becomes more of a software engineering problem, surveys the existing work on bloat analysis, and provides a roadmap for future work)
          [PDF][Talk]

           

          PLDI'10

          Guoqing Xu and Atanas Rountev,
          "Detecting Inefficiently-Used Containers to Avoid Bloat",
          ACM SIGPLAN Conference on Programming Language Design and Implementation, Toronto, Canada, June 2010. (Acceptance rate: 41/204, 20%)
          [PDF][Talk]

           

          PLDI'10

          Guoqing Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, Edith Schonberg, and Gary Sevitsky,
          "Finding Low-Utility Data Structures",
          ACM SIGPLAN Conference on Programming Language Design and Implementation, Toronto, Canada, June 2010. (Acceptance rate: 41/204, 20%)
          [PDF][Talk]

           

          ASE'09

          Raffi Khatchadourian, Phil Greenwood, Awais Rashid, and Guoqing Xu,
          "Pointcut Rejuvenation: Recovering Pointcut Expressions in Evolving Aspect-Oriented Software",
          Short paper, the 24th IEEE/ACM International Conference on Automated Software Engineering (ASE '09), Auckland, New Zealand, Nov 2009.

          [PDF]

           

          ECOOP'09

          Guoqing Xu, Atanas Rountev, Manu Sridharan,
          "Scaling CFL-Reachability-Based Points-to Analysis Using Context-Sensitive Must-Not-Alias Analysis",
          23rd European Conference on Object-Oriented Programming, LNCS 5653, Genova, Italy, July 2009. (Acceptance rate: 25/117, 21%)
          [PDF][Talk]

           

          PLDI'09

          Guoqing Xu, Matthew Arnold, Nick Mitchell, Atanas Rountev, Gary Sevitsky,
          "Go with the Flow: Profiling Copies to Find Runtime Bloat",
          ACM SIGPLAN Conference on Programming Language Design and Implementation, Dublin, Ireland, June 2009. (Acceptance rate: 41/196, 21%)
          [PDF][Talk]

           

          ISSTA'08

          Guoqing Xu, Atanas Rountev,
          "Merging Equivalent Contexts for Scalable Heap-cloning-based Context-sensitive Points-to Analysis",
          ACM SIGSOFT International Symposium on Software Testing and Analysis, Seattle, Washington, July 2008. (Acceptance rate: 26/100, 26%)
          [PDF][Talk]

           

          ICSE'08

          Guoqing Xu, Atanas Rountev,
          "Precise Memory Leak Detection for Java Software Using Container Profiling",
          ACM SIGSOFT/IEEE International Conference on Software Engineering, Leipzig, Germany, May 2008. (Acceptance rate: 56/371, 15%)
          Won an ACM SIGSOFT Distinguished Paper Award.
          Invited talk at ISEC'09
          [PDF][Talk]

           

          AOSD'08

          Guoqing Xu, Atanas Rountev,
          "AJANA: A General Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ Software",
          ACM SIGPLAN-SIGSOFT International Conference on Aspect-Oriented Software Development, Brussels, Belgium, March 2008. (Acceptance rate: 17/79, 22%)
          [PDF][Talk]

           

          CC'08

          Atanas Rountev, Mariana Sharp, Guoqing Xu,
          "IDE Dataflow Analysis in the Presence of Large Object-Oriented Libraries",
          International Conference on Compiler Construction, Budapest, Hungary, March 2008. (Acceptance rate: 18/71, 25%)
          [PDF]

           

          FSE'07

          Guoqing Xu, Atanas Rountev, Yan Tang, and Feng Qin,
          "Efficient Checkpointing of Java Software Using Context Sensitive Capture and Replay",
          ACM SIGSOFT Symposium on the Foundations of Software Engineering, Dubrovnik,Croatia, Sept 2007. (Acceptance rate: 43/251, 17%)
          [PDF][Talk]

           

          ICSE'07

          Guoqing Xu, Atanas Rountev,
          "Regression Test Selection for AspectJ Software",
          ACM SIGSOFT/IEEE International Conference on Software Engineering, Minneapols, MN, May 2007. (Acceptance rate: 50/334, 15%)
          Nominated for ACM SIGSOFT Distinguished Paper Award
          [PDF][Talk]

           

          Papers Before 2005:

          ISSTA-DS06

          Guoqing Xu,
          "Precisely Selecting Regression Tests for Aspect-Oriented Programs",
          Doctoral Symposium, International Symposium on Software Testing and Analysis, Portland, ME, July 2006.

           

          WTAOP'06

          Guoqing Xu,
          "A Regression Tests Selection Technique for Aspect-Oriented Programs",
          2nd International Workshop on Testing of Aspect-Oriented Programs, Portland, ME, July 2006.
          [PDF]

           

          SEN'04

          Guoqing Xu, Zongyuan Yang, Haitao Huang,
          "A Basic Model for Components Implementation of Software Architecture",
          ACM SigSoft Software Engineering Notes, Vol.29, No.5. pp. 1-11, Sep. 2004.
          [PDF]

           

          APSEC'04-a

          Guoqing Xu, Zongyuan Yang, Haitao Huang,
          "JCMP: Linking Architecture with Component Building",
          11th IEEE Asia-Pacific Software Engineering Conference, Pusan, South Korea, Nov. 2004.
          [PDF]

           

          APSEC'04-b

          Guoqing Xu, Zongyuan Yang, Haitao Huang, Qian Chen, Ling Chen, Fengbin Xu,
          "JAOUT: Automated Generation of Aspect-Oriented Unit Test",
          11th IEEE Asia-Pacific Software Engineering Conference, Pusan, South Korea, Nov. 2004.
          [PDF]

           

          FSE-poster'04

          Guoqing Xu,
          "JCMP: Linking Architecture with Component Building",
          ACM SigSoft International Symposium on Foundation of Software Engineering, Poster Session, Newport Beach, CA, Nov. 2004.
          [PDF][Poster]

           

          ISFST'04

          Guoqing Xu, Zongyuan Yang,
          "A Novel Approach to Unit Testing: The Aspect-Oriented Way",
          International Symposium on Future Software Technology, Xi'An, China, Oct. 2004.
          [PDF]

           

          NASAC'04

          Guoqing Xu, Zongyuan Yang,
          "Towards Automated Generation of Unit Test",
          China National Anual Software Application Conference, Beijing, China, Oct. 2004.
          [PDF (in Chinese)]

           

          FATES'03

          Guoqing Xu, Zongyuan Yang,
          "JMLAutoTest: A Novel Automated Testing Framework based on JML and JUnit",
          International Workshop on Formal Approaches to Testing of Software, Montreal, Canada, Oct. 2003.
          [PDF][Talk]

           

          Technical Reports:

          TR'08

          Raffi Khatchadourian, Phil Greenwood, Awais Rashid, Guoqing Xu,
          "Pointcut Rejuvenation: Recovering Pointcut Expressions in Evolving Aspect-Oriented Software",
          Technical Report COMP-001-2008, Computing Department, Lancaster University, August 2008.
          [PDF]

           

          TR'07

          Guoqing Xu, Atanas Rountev,
          "Data-flow and Control-flow Analysis of AspectJ Software for Program Slicing",
          Technical Report OSU-CISRC-5/07-TR46, CSE/OSU, May 2007.
          [PDF]

           

           

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mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman","serif";} </style> <![endif]--> <meta name=Title content="CompSci 142 / CSE 142 Spring 2014"> <meta name=Keywords content=""> <!--[if gte mso 9]><xml> <o:shapedefaults v:ext="edit" spidmax="1026"/> </xml><![endif]--><!--[if gte mso 9]><xml> <o:shapelayout v:ext="edit"> <o:idmap v:ext="edit" data="1"/> </o:shapelayout></xml><![endif]--> </head> <body bgcolor="#D0D0FF" lang=EN-US link="#0000A0" vlink="#004000" style='tab-interval:.5in'> <div class=WordSection1> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><span class=SpellE><span style='font-size:9.0pt; font-family:"Arial","sans-serif";mso-bidi-font-family:"Times New Roman"; color:#000040'>CompSci</span></span><span style='font-size:9.0pt;font-family: "Arial","sans-serif";mso-bidi-font-family:"Times New Roman";color:#000040'> 142 / CSE 142<span style='mso-spacerun:yes'>� </span>Winter 2016 | <a href="index.html">News</a> | <a href="CourseReference.html">Course Reference</a> | <a href="Schedule.html">Schedule</a> | <a href="ProjectGuide/index.htm">Project Guide</a> <br> This webpage was adapted from <a href="http://www.ics.uci.edu/~thornton/">Alex Thornton</a> s offering of CS 141<o:p></o:p></span></p> <div style='margin-top:3.75pt;margin-bottom:3.75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='font-size:11.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman";color:black'> <hr size=1 width="100%" noshade style='color:#000040' align=center> </span></div> </div> <div> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><span class=SpellE><b><span style='font-size:18.0pt; font-family:"Arial","sans-serif";mso-bidi-font-family:"Times New Roman"; color:#000040'>CompSci</span></b></span><b><span style='font-size:18.0pt; font-family:"Arial","sans-serif";mso-bidi-font-family:"Times New Roman"; color:#000040'> 142 / CSE 142 Winter 2016<br> Course News<o:p></o:p></span></b></p> </div> <div> <div style='margin-top:3.75pt;margin-bottom:3.75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='font-size:11.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman";mso-bidi-font-family:"Times New Roman";color:black'> <hr size=1 width="100%" noshade style='color:#000040' align=center> </span></div> </div> <p><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'>I will generally post important course wide announcements here. <o:p></o:p></span></p> <table class=MsoNormalTable border=1 cellspacing=0 cellpadding=0 style='border-collapse:collapse;border:none;mso-border-alt:solid black .75pt; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:19.8pt'> <td style='border:solid black 1.0pt;mso-border-alt:solid black .75pt; background:navy;padding:3.75pt 3.75pt 3.75pt 3.75pt;height:19.8pt'> <p class=MsoNormal><b><i><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:white'>Date&nbsp;Added<o:p></o:p></span></i></b></p> </td> <td style='border:solid black 1.0pt;border-left:none;mso-border-left-alt: solid black .75pt;mso-border-alt:solid black .75pt;background:navy; padding:3.75pt 3.75pt 3.75pt 3.75pt;height:19.8pt'> <p class=MsoNormal><b><i><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:white'>News Item<o:p></o:p></span></i></b></p> </td> </tr> <tr style='mso-yfti-irow:1'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:black'>Friday 12/11/15<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>Welcome! The webpage is up! A tentative <a href="Schedule.html">schedule</a> is available. <o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:2'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:black'>Monday 1/4/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>The first project is <a href="ProjectGuide/Project1/index.htm">out</a>. It is due Wednesday 1/13. <o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:3'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:black'>Wednesday 1/13/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>The second project is <a href="ProjectGuide/Project2/index.htm">out</a>. It is due Sunday 1/24.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:4'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:SimSun;mso-bidi-font-family:"Times New Roman"; color:black'>Monday 1/25/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>The </span><span style='mso-ascii-font-family: "Times New Roman";mso-ascii-theme-font:major-fareast;mso-fareast-font-family: SimSun;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast; color:black'>third</span><span style='mso-ascii-font-family:"Times New Roman"; mso-ascii-theme-font:major-fareast;mso-hansi-font-family:"Times New Roman"; mso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-fareast;color:black'> project is <a href="ProjectGuide/Project3/index.htm">out</a>. It is due </span><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast; mso-fareast-font-family:SimSun;mso-hansi-font-family:"Times New Roman"; mso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-fareast;color:black'>Wednesday</span><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast; mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast; color:black'> </span><span style='mso-ascii-font-family:"Times New Roman"; mso-ascii-theme-font:major-fareast;mso-fareast-font-family:SimSun;mso-hansi-font-family: "Times New Roman";mso-hansi-theme-font:major-fareast;mso-bidi-font-family: "Times New Roman";mso-bidi-theme-font:major-fareast;color:black'>2</span><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast; mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast; color:black'>/</span><span style='mso-ascii-font-family:"Times New Roman"; mso-ascii-theme-font:major-fareast;mso-fareast-font-family:SimSun;mso-hansi-font-family: "Times New Roman";mso-hansi-theme-font:major-fareast;mso-bidi-font-family: "Times New Roman";mso-bidi-theme-font:major-fareast;color:black'>3</span><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font:major-fareast; mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font:major-fareast; mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font:major-fareast; color:black'>.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:5'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:SimSun;mso-bidi-font-family:"Times New Roman"; color:black'>Tuesday 2/2/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>Our midterm is scheduled on Tuesday, Feb 9.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:6'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:SimSun;mso-bidi-font-family:"Times New Roman"; color:black'>Monday 2/8/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-hansi-font-family:"Times New Roman";mso-hansi-theme-font: major-fareast;mso-bidi-font-family:"Times New Roman";mso-bidi-theme-font: major-fareast;color:black'>The fourth project is <a href="ProjectGuide/Project4/index.htm">out</a>. It is due Wednesday 2/17.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:7;mso-yfti-lastrow:yes'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:SimSun;mso-bidi-font-family:"Times New Roman"; color:black'>Friday 2/19/16<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='mso-ascii-font-family:"Times New Roman";mso-ascii-theme-font: major-fareast;mso-fareast-font-family:SimSun;mso-hansi-font-family:"Times New Roman"; mso-hansi-theme-font:major-fareast;mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:major-fareast;color:black'>The <a href="ProjectGuide/Project5/index.htm">fifth </a>and <a href="ProjectGuide/Project6/index.htm">sixth </a>projects are out. The due dates are 2/25 and 3/13.<o:p></o:p></span></p> </td> </tr> </table> <p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"; color:black'><o:p>&nbsp;</o:p></span></p> </div> </div> </body> </html> http://www.ics.uci.edu/~guoqingx/courses/plseminar/2011fall.html UCI Programming Language Seminar


          CS 295: UCI Programming Languages Seminar

          Fall, 2011(Tuesday 5:00-7:00pm, CS 432)

          Co-instructors: Michael Franz (CS), Brian Demsky (EECS), and Harry Xu (CS)

          Brief Introduction:

          This is a paper reading class co-taught by the three programming languages professors at UCI. We welcome all students
          interested in doing research on programming languages and software systems to join the class. The schedule for this
          class is on a per-quarter-basis. For the current (Fall 2011) quarter, we will be reading and discussing some of the best
          papers in the 20 years of PLDI. Please email any one of us if you have a question.

          Announcement:

          Starting from 10/11, we will meet at CS 432 instead of Bren 1422.

          Note: ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) is a flagship
          conference in the area of programming languages and software systems, and is actually one of the best conferences
          in all computer science disciplines.


          Date

          Paper

          Presenter

          09/27

          Research Overview Michael, Brian, and Harry

          09/27

          Edith Schonberg: On-the-fly detection of access anomalies (retrospective), PLDI 1988 Jim
          10/04 Susan L. Graham, Peter B. Kessler,and Marshall K. McKusick: Gprof: A call graph execution profiler (retrospective), PLDI 1982 Andrei
          10/11 Gregory J. Chaitin: Register allocation and spilling via graph coloring (retrospective), PLDI 1982 Brian
          10/18 Jack W. Davidson and Christopher W. Fraser: Automatic generation of peephole optimizations (retrospective), PLDI 1984 Eric
          10/25

          Michael G. Burke and Ron Cytron: Interprocedural dependence analysis and parallelization (restropective) , PLDI 1986

          Weiwei
          11/1 Susan Horwitz, Thomas W. Reps, and David Binkley: Interprocedural slicing using dependence graphs (retrospective), PLDI 1988 Shree
          11/8 Janet Fabri and Frances E. Allen: Automatic storage optimization (retrospective). PLDI 1981 Garrett
          11/15 Monica S. Lam and Michael E. Wolf: A data locality optimizing algorithm (retrospective), PLDI 1991 Shamitha
          11/22 Jens Knoop, Oliver Ruthing, and Bernhard Stephen, Lazy code motion (retrospective) , PLDI 1992 Yonghun
          11/29 William Landi and Barbara G. Ryder: A safe approximate algorithm for interprocedural pointer aliasing (retrospective), PLDI 1992 Palaniappan and Aravind

           

          http://www.ics.uci.edu/~guoqingx/tools/ajana.html Software: AJANA

          AJANA: A General Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ Software

          Thanks to Aleksandra Seremina for providing a Romanian translation of this webpage.

          Aspect-oriented software presents new challenges for the designers of static analyses. Our work aims to establish systematic foundations for dataflow analysis of AspectJ software. We propose a control- and data-flow program representation for AspectJ programs, as basis for subsequent interprocedural dataflow analyses. The representation is built at the source code level and captures the semantic intricacies of various pointcut designators, multiple applicable advices per joint point, dynamic advices, and general flow of data to, from, and between advices.

          We also propose two dataflow analyses for AspectJ software: (1) a novel object effect analysis based on a flow- and context-sensitive must-alias analysis, and (2) a dependence analysis used for constructing the system dependence graph for slicing, refactoring, change impact analysis, etc. Both analyses are representative of a general category of dataflow analyses referred to as interprocedural distributed environment (IDE) problems. The two analyses are built on top of the proposed representation, and take into account the complex flow of control and data due to aspect-oriented features. We present a study of the proposed techniques on 37 program versions, using our AJANA analysis framework which is based on the abc AspectJ compiler. The results show that the representation can be built efficiently, that it is superior to an approach based on the woven bytecode, and that it enables analyses that are both faster and more precise. These findings strongly indicate that the proposed approach is a promising candidate for a foundation upon which various interprocedural analyses for AspectJ can be designed and built.


          Download (Version 0.1, Released on March 8, 2009)

          • ajana-0.1.jar - source and binary

          Publications

          • "AJANA: A General Framework for Source-Code-Level Interprocedural Dataflow Analysis of AspectJ Software"
            by Guoqing Xu and Atanas Rountev
            In Proc. International Conference on Aspect-Oriented Software Developement (AOSD'08), Brussels, Belgium, March, 2008. [PDF] [Talk]
          • "Regression Test Selection for AspectJ Software"
            by Guoqing Xu and Atanas Rountev
            In Proc. ACM SIGSOFT/IEEE International Conference on Software Engineering (ICSE'07), Minneapolis, MN, May, 2007. [PDF] [Talk]

          AJANA Usage:

          This section shows how to use AJANA framework.

          1. prerequisite:
            Before being able to use AJANA, you have to install the AspectBench compiler, which may in turn require the installation of the Soot program analysis framework.
          2. Where to start:
            The main entrance of the program is analysis.aspectj.Main, which takes five parameters:
            • arg[0] : the directory containing all benchmarks
            • arg[1]: the benchmark name
            • arg[2]: benchmark version -- this does not make sense now, though it used to work for test selection purpose
            • arg[3]: main class name
            • arg[4]: client analysis selector -- 0 for object effect analysis; 1 for slicing

          Example: if the bytecode of a project dcm is located at directory

          /home/gxu/projects/dcm/v1/

          ,the command line args that you need to give are the follows,

          /home/gxu/projects/dcm dcm v1 certrevsim.Simulator 0

          1. Usage details:
            Currently, the tool does not support the use of per pointcut. You can simply build your analysis on top of the AJIG representation constructed by AJANA, by instantiating class

          analysis.aspectj.ajig.AspectJInterModuleGraph

          , and then invoking method

          AspectJInterModuleGraph.build(SootMethod startMethod)

          . The AJIG control flow and data flow representations are described precisely in our ICSE'07 and AOSD'08 papers, respectively.

          1. Sample analysis:
            You can look at my effect analysis

          method build in analysis.aspectj.summary.SummaryAnalysis

          for the example of manipulating AJIG.


          Contributions

          You can contribute to AJANA by sending bug reports, code patches, and suggestions. Please send your inquiries to xug at cse dot ohio-state dot edu.


          People

          • Harry Xu
          • Nasko Rountev

          Publications that make use of AJANA

          • "Unweaving the impact of aspect changes in AspectJ"
            by Luca Cavallaro and Mattia Monga
            In Proc. the 2009 workshop on Foundations of Aspect-Oriented Languages (FAOL), Charlottesville, VA, March, 2009.

          Acknowledgement

          We thank all of the developers of the abc AspectJ compiler, without whom the research would not happen.


          Last updated: March 8, 2009

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<body bgcolor="#D0D0FF" lang=EN-US link="#0000A0" vlink="#004000" style='tab-interval:.5in'> <div class=WordSection1> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><span class=SpellE><span style='font-size:9.0pt; font-family:"Arial","sans-serif";color:#000040'>CompSci</span></span><span style='font-size:9.0pt;font-family:"Arial","sans-serif";color:#000040'> 141 / CSE 141 / Informatics 101 Winter 2014 | <a href="index.html">News</a> | <a href="CourseReference.html">Course Reference</a> | <a href="Schedule.html">Schedule</a> | <a href="ProjectGuide/index.htm">Project Guide</a> | <a href="CodeExamples/index.htm">Code Examples</a><br> <span class=GramE>This</span> webpage was adapted from <a href="http://www.ics.uci.edu/~thornton/">Alex Thornton</a> s offering of CS 141<o:p></o:p></span></p> <div style='margin-top:3.75pt;margin-bottom:3.75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='font-size:11.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman";color:black'> <hr size=1 width="100%" noshade style='color:#000040' align=center> </span></div> </div> <div> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><span class=SpellE><b><span style='font-size:18.0pt; font-family:"Arial","sans-serif";color:#000040'>CompSci</span></b></span><b><span style='font-size:18.0pt;font-family:"Arial","sans-serif";color:#000040'> 141 / CSE 141 / Informatics 101 <span class=GramE>Winter</span> 2014<br> Course News<o:p></o:p></span></b></p> </div> <div> <div style='margin-top:3.75pt;margin-bottom:3.75pt'> <div class=MsoNormal align=center style='text-align:center'><span style='font-size:11.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman";color:black'> <hr size=1 width="100%" noshade style='color:#000040' align=center> </span></div> </div> <p><span style='font-size:11.0pt;font-family:"Arial","sans-serif";color:black'>I will generally post important course wide announcements here. <o:p></o:p></span></p> <table class=MsoNormalTable border=1 cellspacing=0 cellpadding=0 style='border-collapse:collapse;border:none;mso-border-alt:solid black .75pt; mso-yfti-tbllook:1184;mso-padding-alt:0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td style='border:solid black 1.0pt;mso-border-alt:solid black .75pt; background:navy;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><b><i><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:white'>Date&nbsp;Added<o:p></o:p></span></i></b></p> </td> <td style='border:solid black 1.0pt;border-left:none;mso-border-left-alt: solid black .75pt;mso-border-alt:solid black .75pt;background:navy; padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><b><i><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:white'>News Item<o:p></o:p></span></i></b></p> </td> </tr> <tr style='mso-yfti-irow:1'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Mon, 3/3<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Project 5 is <a href="ProjectGuide/Project5/index.htm">online</a>. It is due 11:59pm Tuesday March 18.<o:p></o:p></span></p> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>The final exam is scheduled at PSCB 140, 4:00-6:00pm, Wednesday, March 19.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:2'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Mon, 2/24<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Project 4 is <a href="ProjectGuide/Project4/index.htm">online</a>. It is due 11:59pm Friday March 7.<span style='mso-spacerun:yes'>� </span><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:3'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Mon, 2/17<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Sample code for Project 3 and <span class=SpellE>Makefile</span> can be found <a href="ProjectGuide/Project3/sampleCode">here</a>. Please fill in the rest of the implement.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:4'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span class=SpellE><span style='font-size:10.0pt; font-family:"Arial","sans-serif";mso-fareast-font-family:"Times New Roman"; color:black'>Thur</span></span><span style='font-size:10.0pt;font-family: "Arial","sans-serif";mso-fareast-font-family:"Times New Roman";color:black'>, 2/13<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>The due date of Project 3 has been extended to 11:59pm Friday February 21.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:5'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Tue, 2/4<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Project 3 is <a href="ProjectGuide/Project3/index.htm">online</a>. Its due date is 11:59pm Tuesday February 18. We will use C for project 3. Here is an online <a href="http://www.physics.drexel.edu/courses/Comp_Phys/General/C_basics/">C tutorial</a>.<span style='mso-spacerun:yes'>� </span><o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:6'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Fri, 1/30<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Midterm is scheduled on Wednesday, Feb 12. We ll use the second half of the Monday (Feb 10) class to go over the materials covered in the midterm.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:7'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span class=SpellE><span style='font-size:10.0pt; font-family:"Arial","sans-serif";mso-fareast-font-family:"Times New Roman"; color:black'>Thur</span></span><span style='font-size:10.0pt;font-family: "Arial","sans-serif";mso-fareast-font-family:"Times New Roman";color:black'>, 1/23<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Project 2 is <a href="ProjectGuide/Project2/index.htm">online</a>. Its due date is 11:59pm Tuesday February 4.<span style='mso-spacerun:yes'>� </span>We will use Java for project 2. Here is an online <a href="http://docs.oracle.com/javase/tutorial/">Java tutorial</a>.<o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:8'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>Tue, 1/7<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Project 1 is <a href="ProjectGuide/Project1/index.htm">online</a>. Its due date is 11:59pm Tuesday January 21. We will use Python for project 1. If you don t have experience with Python, please check out this <a href="http://docs.python.org/2/tutorial/">online Python tutorial</a>. <o:p></o:p></span></p> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>A few other things:<o:p></o:p></span></p> <ul type=disc> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;mso-list:l0 level1 lfo3;tab-stops:list .5in'>The lab sections will meet for the first time on Thursday, January 9.</li> <li class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;mso-list:l0 level1 lfo3;tab-stops:list .5in'>I encourage you to spend some time reading through the material on this course web site. It will be updated periodically throughout the quarter, and there will always be an announcement here describing each update. For now, notice the set of links at the top of this (and every) page, leading you to the <a href="CourseReference.html">Course Reference</a>, the <a href="Schedule.html">Schedule</a>, and the <a href="ProjectGuide/index.htm">Project Guide</a>, as well as a set of commented <a href="CodeExamples/index.htm">Code Examples</a> that will be posted during the course of the quarter.</li> </ul> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'><o:p>&nbsp;</o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:9'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>M 12/6<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>A detailed class schedule is available. <o:p></o:p></span></p> </td> </tr> <tr style='mso-yfti-irow:10;mso-yfti-lastrow:yes'> <td style='border:solid black 1.0pt;border-top:none;mso-border-top-alt:solid black .75pt; mso-border-alt:solid black .75pt;background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p class=MsoNormal><span style='font-size:10.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'>M 11/26<o:p></o:p></span></p> </td> <td style='border-top:none;border-left:none;border-bottom:solid black 1.0pt; border-right:solid black 1.0pt;mso-border-top-alt:solid black .75pt; mso-border-left-alt:solid black .75pt;mso-border-alt:solid black .75pt; background:#E0E0FF;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <p><span style='font-size:10.0pt;font-family:"Arial","sans-serif";color:black'>Welcome! The webpage is up!<o:p></o:p></span></p> </td> </tr> </table> <p class=MsoNormal><span style='font-size:11.0pt;font-family:"Arial","sans-serif"; mso-fareast-font-family:"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></p> </div> </div> </body> </html> http://www.ics.uci.edu/~guoqingx/research/projects/bloat.html Program Analyses for Bloat Detection and Optimization

          Program Analyses for Bloat Detection and Optimization


          About

           

          Despite the employment of faster CPUs and larger memory systems, the levels of inefficiencies in real-world programs grow surprisingly fast and there is an ever-increasing demand for performance optimization in modern software. Performance and scalability issues are becoming increasingly critical partly due to the pervasive use of object-oriented programming languages. The inefficiencies inherent in the implementation of an object-oriented language as well as the commonly adopted design and implementation principles in the object-oriented community often combine to hurt performance. The community-wide recognition of the importance of abstraction and reuse results in increased emphasis on modular design, declaration of general interfaces, and use of models and patterns. Programmers are taught to focus first and foremost on them, taking it for granted that compilers and run-time systems can remove all the inefficiencies. In a large program that is typically built on top of many layers of frameworks and libraries, a small set of inefficiencies can multiply and quickly get magnified to slow down the system. When the call stack grows to be deep, the usefulness of the dataflow analyses in a dynamic compiler becomes limited and the optimizer can no longer remove these inefficiencies. As a result, many applications suffer from chronic run-time performance problems that significantly affect scalability and performance. This is a serious problem for real-world software systems used every day by thousands of businesses. The pressing need for new optimization techniques can be especially seen as object-orientation goes everywhere into systems of any size. The extensive use of object-oriented languages in the development of memory-constrained applications such as smartphone apps (e.g., Java used in Android and C# used in Windows phones) and data-intensive systems (e.g., Hadoop, Giraph, and Hyracks) introduces numerous research challenges-- these systems have small memory space but large amounts of data to process and inefficiencies in these systems can be significantly exacerbated. The burden of reducing unnecessary work should not be only on the shoulder of hardware designers, especially in the modern era when Moore's dividend becomes less obvious. It strongly calls for higher-level performance optimization techniques that can detect and remove inefficiencies for all categories of object-oriented applications. Our group has an established record on performance optimization for large-scale systems. Our recent efforts focus on the following projects:

           

          (1) Providing efficient infrastructures for detecting bloat

          A recent piece of work focuses on the development of a tunable object lifetime profiling technique, called Resurrector [OOPSLA13]. Many existing optimization techniques (such as object pooling and pretenuring) require precise identification of object lifetimes. However, it is particularly challenging to obtain object lifetimes both precisely and efficiently: precise profiling techniques such as Merlin introduce several hundred times slowdown even for small programs while efficient approximation techniques often sacrifice precision and produce less useful lifetime information. Resurrector solves the problem by exploring the middle ground between high precision and high efficiency to find the precision-scalability sweetspot for various optimization techniques. Resurrector's design is motivated by an important observation that the scalability bottleneck of a traditional OLP algorithm (such as Merlin) lies in the need to compute transitive closures on the dead objects (e.g., Merlin's backward pass). Resurrector improves efficiency by completely eliminating this need. Similarly to Merlin, Resurrector first identifies the root dead objects whose reference counts are zero. Instead of computing transitive closures from them, Resurrector exploits object caching and reusing to find dead objects (transitively reachable from the roots) that have non-zero reference counts.

           

          Another infrastructure we have built is a runtime framework that performs abstract dynamic slicing [PLDI10-a, TOSEM14] to identify performance problems that manifest themselves in dataflow activities. Abstract dynamic slicing, a technique that applies dynamic slicing over an abstract domain whose size is limited by bounds independent of the runtime execution. This technique is embedded in the general framework parameterized by the abstract domain. The output of this framework is an abstract dependence graph that contains abstractions of instructions, rather than their actual runtime instances. This new approach is motivated by the observation that a client of dynamic slicing often needs to access only a small portion of the complete execution trace collected by a regular slicing algorithm and thus tremendous effort is wasted on collecting information not used by the client. The runtime (space and time) overhead can be significantly reduced if the slicing algorithm is client aware, that is, it understands what information would be needed by its client and records only such information during the execution. Abstract dynamic slicing makes this possible by asking the analysis developer to provide an abstraction that specifies this knowledge.

           

          (2) Improved performance debugging and testing

          Performance problems in a large-scale application are extremely difficult to find. Traditional performance test oracles such as time/memory checks are coarse-grained and subjective; as a result, performance bugs often escape to production runs, hurting software reliability and user experience. We are in the process of developing a general technique, called PerfBlower, that can amplify the effects of a class of performance problems whose symptoms can be described by logical statements over a history of heap updates as well as provide precise diagnostic information. Amplification serves as an automated test oracle because it increases memory consumption significantly for tests that trigger performance problems while having a very small impact on bug-free runs. As a result, developers can easily divide tests into successful and failing runs, and focus their effort on failing tests. Please read our ECOOP'15 paper for details.

           

          Caching/resuing opportunities can often be found in large-scale applications. A big source of run-time performance problems in large-scale, object-oriented applications is the frequent creation of data structures whose lifetimes are disjoint, and whose shapes and data content are always the same. Constructing these data structures and computing the same data values many times is expensive; significant performance improvements can be achieved by reusing their instances, shapes, and/or data values rather than reconstructing them. We first classify caching/reusing opportunities into three categories: instance reusability, shape reusability, and data reusability [OOPSLA12]. We next develop scalable runtime techniques that can quickly detect these opportunities by exploiting cooperative compiler and runtime system support. For example, work from [OOPSLA12] is a technique that piggybacks on GC to find reuse opportunities while Cachetor [FSE13] relies on heavyweight dependence profiling to understand if data values are cacheable.

           

          (3) Adaptive selection of algorithms and data structures

          An important source of runtime bloat is the inefficient use of container implementations. Standard libraries of object-oriented languages such as Java and C# contain collection frameworks that provide with users, for each abstract data type (such as List), many different implementations (such as ArrayList and LinkedList), each of which features a different design choice suitable for a specific execution scenario. However, in real-world development, choosing the most appropriate container implementation is challenging. As a result, developers tend to keep using the implementations that are most general or well-known (e.g., HashSet for Set), regardless of whether or not they fit the usage context. We develop a novel container optimization technique, called CoCo, that is able to (1) determine at run time, for each container instance (e.g., a LinkedList object) used in the program, whether or not there exists another container implementation (e.g., ArrayList) that is more suitable for the execution; and (2) automatically and safely switch to this new container implementation (e.g., replace the old LinkedList object with a new ArrayList object online) for increased efficiency.While there exists work (such as Chameleon and Brainy) that could identify Java collection inefficiencies and report them to users for offline inspection, none of these techniques can change implementations online. Details about CoCo can found in the ECOOP'13 paper. In collaboration with the information system group, we are currently developing techniques that can automatically select table joining algorithms for message-based Big Data systems (such as graph processing systems). Details of this project will be reported later.

           

          (4) Static and dynamic detection of Java memory leaks

          In managed languages such as Java and C#, developers do not need to worry about memory correctness issues such as dangling pointers and double free errors. However, it remains challenging to avoid leaks. A memory leak in a managed language is caused by keeping unnecessary references to objects that are no longer used. These objects cannot be reclaimed by the garbage collector (GC), often leading to severe performance degradation and even program crashes. We have developed both static and dynamic techniques for memory leak detection. In particular, we propose LeakChaser [PLDI11], a specification-based leak detector, that exploits user insight (expressed in the form of liveness assertions) to narrow down causes of memory leaks. Another attractive direction is to perform static leak detection because it does not rely on any leak-triggering inputs, allowing compile-time tools to find leaks before software is released. A long-standing issue that prevents practical static memory leak detection for Java is that it can be very expensive to statically determine object liveness in large applications. We present a practical static leak detection technique, called LeakChecker [CGO14] that bypasses this problem by considering a common leak pattern. In many cases severe leaks occur in loops where, in each iteration, some objects created by the iteration are unnecessarily referenced by objects external to the loop. These unnecessary references are never used in later loop iterations. Based on this insight, we shift our focus from computing liveness, which is very difficult to achieve precisely and efficiently for large programs, to the easier goal of identifying objects that flow out of a loop but never flow back in.


          Papers

           

          o   PerfBlower: Quickly Detecting Memory-Related Performance Problems via Amplification,

          Lu Fang, Liang Dou, and Guoqing (Harry) Xu.

          ECOOP'15: European Conference on Object-Oriented Programming.

          [Slides]

           

          o   LeakChecker: Practical Static Memory Leak Detection for Managed Languages,

          Dacong Yan, Guoqing (Harry) Xu, Shengqian Yang, and Atanas Rountev.

          CGO'14: International Conference on Code Generation and Optimization.

          [Slides]

           

          o   Scalable Runtime Bloat Detection Using Abstract Dynamic Slicing,

          Guoqing (Harry) Xu, Nick Mitchell, Matthew Arnold, Atanas Rountev, Edith Schonberg, and Gary Sevitsky.

          TOSEM'14, ACM Transactions on Software Engineering and Methodology.

           

          o   Resurrector: A Tunable Object Lifetime Profiling Technique for Optimizing Real-World Programs,

          Guoqing (Harry) Xu.

          OOPSLA'13, ACM SIGPLAN Conference on Object-Oriented Programming Systems, Language, and Applications.

          [Slides]

           

          o   Cachetor: Detecting Cacheable Data to Remove Bloat,

          Khanh Nguyen and Guoqing (Harry) Xu.

          FSE'13, ACM SIGSOFT Symposium on the Foundations of Software Engineering.

          [Slides]

           

          o   CoCo: Sound and Adaptive Replacement of Java Collections,

          Guoqing (Harry) Xu.

          ECOOP'13, European Conference on Object-Oriented Programming.

          [Slides]

           

          o   Finding Reusable Data Structures,

          Guoqing (Harry) Xu.

          OOPSLA'12, ACM SIGPLAN Conference on Object-Oriented Programming Systems, Language, and Applications.

          [Slides]


          People

           

          o   Lu Fang

          o   Khanh Nguyen

          o   Harry Xu


          Software

           

          o   PerfBlower, a performance problem amplification tool

          o   LeakChaser, a specification-based memory leak detector for Java

          o   Resurrector: a tunable object lifetime profiling tool based on Jikes RVM

           


          Support

           

          This research is funded in part by NSF under grants CNS-1321179 and CCF-140982, and by ONR under grant N00014-14-1-0549.


          main page

          http://www.ics.uci.edu/~guoqingx/courses/295/fall13/ UCI Programming Language Seminar

          CS 295: UCI Programming Languages Seminar -- Memory Models

          Fall, 2013(Tuesday 5:00-6:40pm, DBH 1423 before Oct. 31; Thursday 5:00-6:40pm, DBH 3011, after Oct. 31)

          Instructor: Harry Xu (CS)

          Brief Introduction:

          This is a programming languages paper reading class. We welcome all students interested in doing research on programming languages and software systems to join the class. The schedule for this class is on a per-quarter-basis. For the current (Fall 2013) quarter, we will be reading and discussing papers on memory models. Please email me if you have a question.


          Date

          Paper

          Presenter

          10/01

          Class Overview slides

          Harry

          10/08

          Chapter 3 of the book ``A premier on memory consistency and cache coherence``

          Khanh

          10/15

          Chapter 4 of the book ``A premier on memory consistency and cache coherence``

          Chapter 5 of the book ``A premier on memory consistency and cache coherence``

          Jianfei

          David

          10/22

          Memory Models: A Case for Rethinking Parallel Languages and Hardware, Adve and Boem

          Conflict Exceptions: Simplifying Concurrent Language Semantics with Precise Hardware Exceptions for Data-Races, Lucia et al., ISCA 10

          Ruize

          Taewoo and Anbang

          10/29

          The Java Memory Model, Manson et al. POPL 05

          Java Memory Model Examples: Good, Bad and Ugly, Aspinall and Sevcik, VAMP 07

          Peizhao

          Ji Mahn

          11/07

          Foundations of the C++ Concurrency Memory Model, Boehm and Adve, PLDI 08

          Plan B: A Buffered Memory Model for Java, Demange et al., POPL 13

          Kai

          Jianfeng

          11/14

          Relaxed Memory Models: an Operational Approach, Boudol and Petri, POPL 09

          Adversarial Memory For Detecting Destructive Races, Flanagan and Freund, PLDI 10

          Zhidong

          Bharathram

          11/21

          DRFx: A Simple and Efficient Memory Model for Concurrent Programming Languages, Marino et al., PLDI 10

          Efficient Processor Support for DRFx, a Memory Model with Exceptions, Singh et al., ASPLOS 11

          Byron

          Jiacheng

          11/25

          End-to-End Sequential Consistency, Singh et al., ISCA 12

          MemSAT: Checking Axiomatic Specifications of Memory Models, Torlak et al., PLDI 10

          Yutao

          Prasanna

          12/05

          A Case for an SC-Preserving Compiler, Marino et al., PLDI 11

          Efficient Sequential Consistency via Conflict Ordering, Lin et al., ASPLOS 12

          Lu

          Nan

           

          http://www.ics.uci.edu/~guoqingx/courses/253/sp2013/index.html CS 253-Principles of Program Analysis

          CS 253/INF 212: Analysis of Programming Languages

          Spring 2013, Monday, Wednesday, Friday 8:00-8:50am, ICS 180

          Instructor: Harry Xu, Office hour: Thursday 2-4pm, DBH 3212, Credits: 4

          Reader: Taesu Kim, Office: 3068 DBH

          Schedule

          Project FAQ

          Course Overview

          In this offering the class, we focus on principles of program analysis techniques.

          Prerequisites

          Required: background on discrete math + experience with Java

          Optional: language and compiler background (e.g., covered in CS 141, 142(a), or 242)

          Course Organization

          The course has five separate components. We will spend two weeks on each component, covering both the concepts and the state-of-the-art research. For each component, homework assignments and a project will be given.

           

          Comp 1: Foundations + Dataflow analysis

          Comp 2: Abstract interpretation

          Comp 3: Constraint-based analysis

          Comp 4: Type and effect system

          Comp 5: Practical static analyses

          Grading

          Paper critiques, presentations, etc.(20%)

          Projects (40%)

          Take-home final (40%)

          References (no textbook required)

          Principles of Program Analysis, Flemming Nielson, Hanne R. Nielson, and Chris Hankin, Springer, 2005.

          Compilers: Principles, Techniques, and Tools, Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman, Pearson Education, Inc. (2nd Edition) ---- dragon book


           

          http://www.ics.uci.edu/~guoqingx/courses/295/winter11/ CS 295--Run-time Techniques for Efficient and Reliable Program Execution

          CS 295: Run-time Techniques for Efficient and Reliable Program Execution

          Winter, 2012, Mon Wed 5:00-6:50pm, DBH 1427

          Instructor: Harry Xu, Office hour: by appointment, Credits: 4

          Schedule

          Paper list

          Project

          Course Overview

          Performance and reliability are the two major concerns in the development of modern large-scale applications. The goal of this course is to explore dynamic analysis techniques and run-time systems that can help programmers find functional bugs and/or performance problems during the execution and remove these problems (automatically or manually).

          Prerequisites

          There are no formal prerequisites, but it will help to have some background in programming languages, compilers, runtime systems, and/or software engineering in general; and program analysis, software optimization, and debugging in particular..

          Course Organization

          The course will have the following two parts:

          (1) Lectures by the Instructor

          We'll spend the first few meetings covering background on dynamic program analysis and runtime systems.

           

          (2) Student presentations

           

          Each student will present two or three top-conference papers from the list. For each presentation, two students (other than the presenter) are responsible for giving feedback as to how to improve the presentation.

          (3) Projects

          Students will form groups to undertake a project that explores a new research idea in the related areas. Detailed project information can be found here.

          Grading

          The activities of the class include the following four parts:

           

          (1) Paper critiques (15%): There are two presentations scheduled for each class. Students are required to carefully read a number of papers on the same topic before the class and write critiques for the two papers that will be presented. I need your critiques the night before the class so that I can give them back to you in the class. For example, for the Monday class, your critiques are due 6pm on Sunday. Rules of thumb for writing good paper critiques can be found here.

           

          (2) Paper presentation (30%): Two papers presented in each class cover similar topics, so we will have an opportunity to read and compare a range of papers solving similar problems. The presenter is also the discussion leader, who is expected to prepare for a set of interesting questions that can provoke further thoughts and discussions. Although the number of papers each student needs to present will be determined by the number of students in the class, I expect each student to present at least two papers in the quarter.

           

          (3) In-class discussion (15%): We will have in-depth discussions not only on the papers presented, but also on related papers and creative ideas that may open up opportunities for future work.

           

          (4) Projects (40%): Research projects are conducted on a per-group-basis. Each group has two students who work together to develop a novel research idea. I will set up several meetings with each group to have in-depth discussions on the proposed project. Each group will report their projects twice in the class: a kickoff presentation to propose the project (typically in the third and/or fourth week) and a progress presentation at the end of the quarter. Each group is required to turn in a project writeup (like a research paper) that describes the idea, the implementation, and the experimental results. I hope some of the high-quality project reports can be turned into top-conference submissions.

           

          Acknowledgments

          Thanks to Mike Bond, Nasko Rountev, and Feng Qin for providing advice and webpage help with this course.


           

          http://www.ics.uci.edu/~guoqingx/oopsla-pc-workshop/index.html

          Schedule for Pre-PC-Meeting Workshop

          Wednesday, May 15, 2013

           

          Time

          Who

          Topic

          8:30

          Everybody in the hotel

          Shuttle picks you up at the hotel

          9:10

          Crista and Harry

          Introduction

          9:30

          Harry Xu

          Object-Orientation Meets Big Data: Language Techniques towards Highly-Efficient Data-Intensive Computing

          9:45

          Sam Tobin-Hochstadt

          From the Principle to Practice with Class in the First Year

          10:00

          Don Batory

          DxT: Design by Transformation

          10:15

          Emerson Murphy-Hill

          An Adoption Theory of Secure Development Tools

          10:30

          Danny Dig

          Interactive Program Transformation

          10:45

          Coffee Break

           

          11:15

          Gang Tan

          Towards Safe Language Interoperation

          11:30

          David Liu

          Types for Energy Management

          11:45

          Alex Summers

          Changing perspective can be useful: on relating alternative logics for automatic software verification

          12:00

          Christian Kastner

          Analyzing all 2^10000 Configurations of the Linux Kernel

          12:15

          Noam Rinetzky

          Verifying Highly Concurrent Memory Reclamation Algorithms with Grace

          12:30

          Lunch

           

          2:45

          Kiyokuni (KIYO) Kawachiya

          Programming Language X10 on Multiple JVMs

          3:00

          Hridesh Rajan

          Capsule-oriented Programming

          3:15

          Mircea Filip Lungu

          Program Comprehension Across Abstraction Levels

          3:30

          Coffee break

           

          4:00

          David Lorenz

          Language Oriented Modularity

          4:15

          Hidehiko Masuhara

          COP with Only Layers

          4:30

          Stefan Hanenberg

          Human-Centered Studies on Type Systems: An Experiment Series

          4:45

          Zhendong Su

          "Natural" Programming for TouchDevelop

           

           

           

           

           

           

           

           

           

          5:50

          Everybody

          Shuttle goes back to the hotel

           

           

           

           

           

           

           

           

          http://www.ics.uci.edu/~lfang3/ Biography of Lu Fang
          • Biography
          • Research
          • Publication
          • Others

          Biography

          Chinese of Lu Fang
          Lu Fang
          Ph.D. Candidate

          Donald Bren School of Information and Computer Sciences
          University of California, Irvine
          California, U.S.

          Email: Email of Lu Fang

          Now, I am a forth year Ph.D. student at Donald Bren School of Information and Computer Sciences, University of California, Irvine. My research focuses on program analysis and software systems. I am interested in building and optimizing Big Data systems. My advisor is Prof. Harry(Guoqing) Xu.

          • 2009-2012 School of Electronics Engineering and Computer Science, Peking University
            Degree: M.S. in Computer Science, received in July 2012
            Advisor: Associate Prof. Junfeng Zhao
            Master Thesis: Design and Implementation of Usage Example Recommendation System for Java API. Download PDF
          • 2005-2009 School of Electronics Engineering and Computer Science, Peking University
            Degree: B.S. in Computer Science, received in July 2009
            Bachelor Thesis: Design and Implementation of a Prediction Technique Based Web Service Selection Tools

          Lu Fang, Donald Bren School of Information and Computer Sciences, University of California, Irvine
          Last update: 11/03/2015
          http://www.ics.uci.edu/~guoqingx/research/projects/bigdata.html Support for Highly Scalable Big Data Systems

          Language, Compiler, and Runtime System Support for Highly Scalable and Adaptive Big Data Systems


          About

           

          Modern computing has entered the era of Big Data. Developing systems that can scale to massive amounts of data with relatively small amounts of resources is a key challenge faced by both researchers and practitioners. Conventional wisdom about scalability is that the performance of a system should increase proportionally with the increase of any kind of resource (e.g., CPU, memory, or network bandwidth). However, we believe this is not sufficient. An important yet ignored aspect of scalability is that if the amount of resource of some kind decreases, the performance of the system should not be reduced proportionally. In other words, the system should be designed in a way so that resources of other kinds can be exploited to remedy the performance reduction resulting from the lost resource. Driven by this insight, our group has made a number of attempts towards building highly-adaptive Big Data systems that can automatically adapt their behaviors to the amount of available resources.

           

          (1) Facade: A compiler and runtime system for (almost) object-bounded Big Data applications

          A managed Big Data application often suffers from large space overhead and GC cost due to extremely large numbers of objects and references in the heap. A key observation is that, in a scalable system, the number of heap objects representing data items cannot grow proportionally with the dataset cardinality. We develop Facade, a Java-based compiler and runtime, that can statically bound the number of heap objects that represent data items. Facade advocates to store data items in native memory and create objects as facades to represent data items. It uses a new execution model that dynamically establishes a many-to-one mapping between an unbounded set of data items in native memory and a statically bounded set of objects in the heap, thereby reducing significantly the number of objects, their associated space overhead (i.e., pointers and headers), as well as the GC cost. Please read our ASPLOS'15 paper for details.

           

          (2) Interruptible Task: Treating memory pressure as interrupts for highly scalable data parallel programs

          Real-world data-parallel programs commonly suffer from great memory pressure, especially when they are executed to process large datasets. Memory problems lead to excessive GC effort and out-of-memory errors, significantly hurting system performance and scalability. This paper proposes a systematic approach that can help data-parallel tasks survive memory pressure, improving their performance and scalability without needing any manual effort to tune system parameters. Our approach advocates interruptible task (ITask), a new type of data-parallel tasks that can be interrupted upon memory pressure---with part or all of their used memory reclaimed---and resumed when the pressure goes away. To support ITasks, we propose a novel programming model and a runtime system, and have instantiated them on two state-of-the-art platforms Hadoop and Hyracks. A thorough evaluation demonstrates the effectiveness of ITask: it has helped real-world Hadoop programs survive 13 out-of-memory problems reported on StackOverflow; a second set of experiments with 5 already well-tuned programs in Hyracks on datasets of different sizes shows that the ITask-based versions are 1.5--3x faster and scale to 3--24x larger datasets than their regular counterparts. Please read our SOSP'15 paper for details.

           

          (3) Semantics-aware graph simplification

          Real-world graphs (such as the Yahoo webgraph and the twitter graph) are extremely large and often need a cluster of machines to process. To support efficient test and debugging, it is important to generate, from real-world graphs, a small subset of vertices and edges that retain interesting properties in the original graphs. Existing techniques focused primarily on graph sampling that uses statistical methods to sample a large graph so that the generated graph follows the same distribution. However, all of these techniques are semantics-agnostic, meaning that they prune graph without considering what each application is interested in. For example, page rank is interested in edge density while maximal clique cares more about the sizes of the cliques in the graph. We are in the process of developing novel algorithms that intelligently prune a graph based on the user's specifications of interesting properties. More details will be reported here.

           

          (4) Speculative region-based memory management

          Most real-world Big Data systems are written in managed languages. These systems suffer from severe memory problems due to the massive volumes of objects created to process input data. Allocating and deallocating a sea of objects puts a severe strain on the garbage collector, leading to excessive GC efforts and/or out-of-memory crashes. Region-based memory management has been recently shown to be effective to reduce GC costs for Big Data systems. However, all existing region-based techniques require significant user annotations, resulting in limited usefulness and practicality. This paper reports an ongoing project, aiming to design and implement a novel speculative region-based technique that requires only minimum user involvement. In our system, objects are allocated speculatively into their respective regions and promoted into the heap if needed. We develop an object promotion algorithm that scans regions for only a small number of times, which will hopefully lead to significantly improved memory management efficiency. We are currently in the process of implementing this idea in OpenJDK.

           

          (5) I/O Efficient disk-based graph processing

          Disk-based graph processing systems often need to load a large amount of data repeatedly (in each computational iteration) although much of the (edge) data may not be necessary for vertex computation. To improve graph processing efficiency, our group has been working on two related projects: GraphQ and DynaGraph.

           

          GraphQ is a scalable querying framework for very large graphs, built upon a key insight that many interesting graph properties -- such as finding cliques of a certain size, or finding vertices with a certain page rank -- can be effectively computed by exploring only a small fraction of the graph, and traversing the complete graph is an overkill. The centerpiece of our framework is the novel idea of abstraction refinement, where the very large graph is represented as multiple levels of abstractions, and a query is processed through iterative refinement across graph abstraction levels. As a result, GraphQ enjoys several distinctive traits unseen in existing graph processing systems: query processing is naturally budget-aware, friendly for out-ofcore processing when ``Big Graphs`` cannot entirely fit into memory, and endowed with strong correctness properties on query answers. With GraphQ, a wide range of complex analytical queries over very large graphs can be answered with resources affordable to a single PC, which complies with the recent trend advocating single machine-based Big Data processing. Experiments show GraphQ can answer queries in graphs 4-6 times bigger than the memory capacity, only in several seconds to minutes. In contrast, GraphChi, a state-of-the-art graph processing system, takes hours to days to compute a whole-graph solution. An additional comparison with a modified version of GraphChi that terminates immediately when a query is answered shows that GraphQ is on average 1.6-13.4x faster due to its ability to process partial graphs. For details, please read our USENIX ATC'15 paper.

           

          DynaGraph is another attempt that tries to reduce I/O costs by using dynamic partitions. Existing disk-based graph systems use static partitions that are created before processing starts. These partitions have static layouts and are loaded entirely into memory in every single iteration despite that much of the edge data is not changed in many iterations and these unchanged edges have zero new impact on the computation of vertex values. We propose an optimization that targets this I/O inefficiency for a general class of disk-based graph algorithms whose computation functions are distributive over aggregation. Our optimization advocates dynamic partitions whose layouts are dynamically adjustable. A dynamic partition only contains edges that will make new contributions and is thus much smaller than its static counterpart. Loading dynamic partitions is much faster and has much lower I/O costs. To support dynamic partitions, we propose a novel accumulation-based programming/execution model that expresses computation in terms of contributions flowing through edges. As a proof of concept, we have implemented this optimization in GraphChi, a popular disk-based graph processing system. Our experiments show that dynamic partitions yield speedups of up to 2.8x (on average 1.8x) over static partitions on five large graphs. More details will be reported here.

           


          Papers

           

          o   A bloat-aware design for Big Data applications,

          Yingyi Bu, Vinayak Borkar, Guoqing (Harry) Xu, and Michael J. Carey.

          ISMM'14: ACM SIGPLAN International Symposium on Memory Management.

          [Slides]

           

          o   Facade: A compiler and runtime system for (almost) object-bounded Big Data applications,

          Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing (Harry) Xu.

          ASPLOS'15: 20th International Conference on Architectural Support for Programming Languages and Operating Systems,

          [Slides]

           

          o   GraphQ: Graph query processing with abstraction refinement --- scalable and programmable analytics over very large graphs on a single PC,

          Kai Wang, Guoqing (Harry) Xu, Zhendong Su, and Yu David Liu,

          ATC'15: 2015 USENIX Annual Technical Conference,

          [Slides]

           

          o   Interruptible tasks: Treating memory pressure as interrupts for highly scalable data-parallel programs,

          Lu Fang, Khanh Nguyen, Guoqing (Harry) Xu, Brian Demsky, and Shan Lu,

          SOSP'15: 25th ACM Symposium on Operating Systems Principles,

          [Slides]

           

          o   Speculative region-based memory management for Big Data systems,

          Khanh Nguyen, Lu Fang, Guoqing (Harry) Xu, and Brian Demsky,

          PLOS'15: 8th International Workshop on Programming Languages and Operating Systems,

          [Slides]

           


          People

           

          o   Lu Fang

          o   Khanh Nguyen

          o   Keval Vora

          o   Kai Wang

          o   Yingyi Bu

          o   Sanaz Alamian

          o   Harry Xu

           


          Support

           

          This research is funded in part by NSF under grant CNS-1321179 and by ONR under grant N00014-14-1-0549.


          main page

          http://www.ics.uci.edu/~guoqingx/tools/alias.htm Software: Demand-Driven Context-Sensitive May Alias Analysis

          Demand-Driven Context-Sensitive Alias Analysis for Java

          Software tools for program understanding, transformation, verification, and testing often require an efficient yet highly-precise alias analysis. Typically this is done by computing points-to information, from which alias queries can be answered. This paper presents a novel context-sensitive, demand-driven alias analysis for Java that achieves efficiencyby answering alias queries directly, instead of relying on an underlying points-to analysis. The analysis is formulated as a context-free-language (CFL) reachability problem over a language that models calling context sensitivity, and over another language that models field sensitivity (i.e., flow of reference values through fields of heap objects).

           

          To improve analysis scalability, we propose to compute procedural reachability summaries online, during the CFL-reachability computation. This cannot be done indiscriminately, as the benefits of using the summary information do not necessarily outweigh the cost of computing it. Our approach selects for summarization only a subset of heavily-used methods (i.e., methods having a large number of incoming edges in the static call graph). We have performed a variety of studies on the proposed analysis. The experimental results show that, within the same time budget, the precision of the analysis is higher than that of a state-of-the-art highly-precise points-to analysis. In addition, the use of method summaries canlead to significant improvements in analysis performance.

           


          Download (Version 0.1, Released on July 17, 2013)

          • demand-alias.zip - source and binary

          Publications

          • "Demand-Driven Context-Sensitive Alias Analysis for Java"
            by Dacong Yan, Guoqing Xu and Atanas Rountev
            In Proc. ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA'11), Toronto, ON, Canada, July, 2011. [PDF] [Talk]
          • "Scaling CFL-Reachability-Based Points-to Analysis Using Context-Sensitive Must-Not-Alias Analysis"
            by Guoqing Xu, Atanas Rountev, and Manu Sridharan
            In Proc. European Conference on Object-Oriented Programming (ECOOP'09), LNCS 5653, Genova, Italy, July, 2009. [PDF] [Talk]

          Tool Usage:

          This section shows how to use the demand-driven alias analysis.

          1) Install soot...

           

          2) See compile.sh for an example of how to compile the source code.

           

          3) The main entrance of the tool is client.datarace.DataraceMain. It queries our analysis for the aliasing relationship between the base objects of each pair of heap load and store (e.g., x and y in "x.f = ...; ... =y.f") to simulate the first step of a data race detector.

           

          Arguments to its main() method are:

          - args[0]: path to the JDK classes (rt.jar, jce.jar, jsse.jar, etc)

          - args[1]: the directory containing the class files of the program you want to analyze

          - args[2]: the main class of the program you want to analyze

           

          4) The parameters of the analysis are defined by a set of properties set using the "-D" VM options. Details can be found in alias.Util.

           

          The key property is MayAlias. When it is set to spa, it runs our alias analysis for the "data race" client. When it is set to spark, it runs an alias analysis by first performing a points-to analysis using Spark.

           

          5) Example:

           

          java -Xmx2G -DMayAlias=spa -classpath MayAlias/bin client.datarace.DataraceMain /path/to/rt.jar:/path/to/jce.jar:/path/to/jsse.jar

          /path/to/target_program/bin target.Main

           

           

          Since the analysis is based on a symbolic points-to graph, here are the detailed steps to construct such a graph:

           

          1) Setup

           

          * Configure soot to run spark. See an example in client.slicing.SlicerMain.main().

          * When you run it, remember to put the bin directory before soot.jar in the classpath, e.g., "-classpath MayAlias/bin:soot.jar:jasmin.jar:polyglot.jar".

           

          2) SPG construction

           

          edu.osu.cse.pa.Main m = edu.osu.cse.pa.Main.v();

          m.buildSPG();

           

          3) Use SPG

           

          The following statement should be used to obtain an Intra-procedural SPG for method mtd:

          SymbolicPointerGraph spg = SymbolicPointerGraph.v(mtd);

           

          Inter-procedural SPG: it's simply intra-procedural SPGs connected by entry/exit edges, so there's no explicit representation for it. See an example in the method at edu.osu.cse.pa.Main:1025.

           


          Contributions

          You can contribute to this project by sending bug reports, code patches, and suggestions. Please send your inquiries to yan@cse.ohio-state.edu or harry.g.xu@uci.edu.


          People

          • Tony Yan
          • Harry Xu
          • Nasko Rountev

          Publications that make use of the tool

          • "Fast Algorithms for Dyck-CFL Reachability with Applications to Alias Analysis"
            by Qirun Zhang, Michael R. Lyu, Hao Yuan, and Zhendong Su
            In Proc. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI'13), Seattle, WA, USA, June 2013.

          Acknowledgements

          We thank all of the developers of the Soot program analysis framework, without whom the research would not happen. This material is based upon work supported by an IBM Ph.D. Fellowship and the National Science Foundation under Grants Number CCF-0546040 and CCF-1017204. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


          Last updated: July 24, 2013

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3.75pt 3.75pt 3.75pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'> <td width=1103 style='width:827.5pt;padding:3.75pt 3.75pt 3.75pt 3.75pt'> <table class=MsoNormalTable border=0 cellpadding=0 width=1072 style='width:804.0pt;mso-cellspacing:1.5pt;mso-yfti-tbllook:1184;mso-padding-alt: 0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;height:19.0pt'> <td width=1068 colspan=2 style='width:801.0pt;background:#D1D7DC; padding:.75pt .75pt .75pt .75pt;height:19.0pt'> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><a href="http://2015.splashcon.org/"><b style='mso-bidi-font-weight:normal'><span style='font-size:8.5pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; color:#FFA34F;mso-no-proof:yes'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="Picture_x0020_1" o:spid="_x0000_i1025" type="#_x0000_t75" href="http://2015.splashcon.org/" style='width:843.75pt;height:114pt; visibility:visible;mso-wrap-style:square' o:button="t"> <v:imagedata src="index_files/image001.jpg" o:title=""/> </v:shape><![endif]--><![if !vml]><span style='mso-ignore:vglayout'><img border=0 width=1125 height=152 src="index_files/image002.jpg" v:shapes="Picture_x0020_1"></span><![endif]></span></b></a></p> </td> </tr> <tr style='mso-yfti-irow:1;mso-yfti-lastrow:yes;height:15.2pt;mso-row-margin-right: 27.5pt'> <td style='background:#EFEFEF;padding:.75pt .75pt .75pt .75pt;height:15.2pt'> <p class=MsoNormal align=center style='mso-margin-top-alt:auto;mso-margin-bottom-alt: auto;text-align:center'><span class=genmed1><b><span style='font-size:8.5pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'><a href="http://woda15.ics.uci.edu/" target="_blank">Home</a> &nbsp; | &nbsp; Schedule &nbsp; | <a href="http://2015.splashcon.org/" target="_blank">OOPSLA  15</a></span></b></span></p> </td> <td style='mso-cell-special:placeholder;border:none;padding:0in 0in 0in 0in' width=37><p class='MsoNormal'>&nbsp;</td> </tr> </table> </td> </tr> <tr style='mso-yfti-irow:1;height:58.5pt'> <td style='padding:3.75pt 3.75pt 3.75pt 3.75pt;height:58.5pt'><u9:p></u9:p> <table class=MsoNormalTable border=0 cellpadding=0 width="100%" style='width:100.0%;mso-cellspacing:1.5pt;mso-yfti-tbllook:1184;mso-padding-alt: 0in 5.4pt 0in 5.4pt'> <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes;mso-yfti-lastrow:yes'> <td style='padding:.75pt .75pt .75pt .75pt'> <h2 align=center style='text-align:center'><span style='font-size:13.5pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'>The 13th International Workshop on Dynamic Analysis (WODA '15)<u2:p></u2:p></span><o:p></o:p></h2> <u9:p></u9:p> <h2 align=center style='text-align:center'><span style='font-size:10.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"'>Co-located with SPLASH/OOPSLA 2015<br> Pittsburgh, PA<br> October 26 or 27, 2015<o:p></o:p></span></h2> </td> </tr> </table> <span style='font-size:10.0pt;font-family:"Times New Roman","serif"; mso-fareast-font-family:SimSun;mso-ansi-language:EN-US;mso-fareast-language: ZH-CN;mso-bidi-language:AR-SA'><u2:p></span></td> </tr> <tr style='mso-yfti-irow:2;mso-yfti-lastrow:yes;height:242.1pt'> <td style='padding:3.75pt 3.75pt 3.75pt 3.75pt;height:242.1pt'> <p><span style='font-size:9.0pt;font-family:"Verdana","sans-serif"; color:black'>Dynamic analysis is widely used in software development to understand various run-time properties of a program.<span style='mso-spacerun:yes'>� </span>Dynamic analysis includes both offline techniques, which operate on some captured representation of the program's behavior (e.g., a trace), and run-time techniques, which analyze the behavior on the fly, while the system is executing. Although inherently incomplete, dynamic analyses can be more precise than their static counterparts and show promise in aiding the understanding, development, and maintenance of robust and reliable large scale systems. Moreover, the data they provide enable statistical inferences to be made about program behavior.<span style='mso-spacerun:yes'>� </span>Dynamic analysis is playing a central role in the understanding of applications and systems as we grapple with emerging challenges such as systemic runtime bloat, high energy consumption, and the explosion of Big Data. The overall goal of WODA is to bring together researchers and practitioners working in all areas of dynamic analysis to discuss new issues, share results and ongoing work, and foster collaborations.&nbsp; <u2:p></u2:p></span></p> <p><span style='font-size:9.0pt;font-family:"Verdana","sans-serif"; color:black'>This workshop is a forum for researchers and practitioners interested in the intersection of compilers, programming languages, architecture, software engineering, systems, high-performance computing, performance engineering, machine learning, and data mining for addressing software and system performance. The workshop focuses on developing and studying analytic technologies (e.g., program analysis, statistical analysis, machine learning, data mining, visualization) applied on various software or system artifacts (e.g., production systems, tests, program traces, system logs) to address issues in software and system reliability, dependability, performance, and scalability. <span style='mso-spacerun:yes'>�</span><u2:p></u2:p><o:p></o:p></span></p> <p><span style='font-size:9.0pt;font-family:"Verdana","sans-serif"; color:black'>Please visit the official workshop website at <a href="http://2015.splashcon.org/track/WODA-2015-papers"><span style='color:windowtext'>http://2015.splashcon.org/track/WODA-2015-papers</span></a>.</span></p> <h2><strong><u><span style='font-size:10.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman"'>Previous WODAs</span></u></strong><strong><u><span style='font-size:10.0pt;font-family:"Verdana","sans-serif";mso-fareast-font-family: SimSun'><o:p></o:p></span></u></strong></h2> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span class=MsoHyperlink><span lang=EN-GB style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast'><a href="http://woda14.cs.rutgers.edu/" target="_blank"><u><span lang=EN-US style='font-family:"Verdana","sans-serif";color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'>WODA  14</span></u></a> </span></span><span style='mso-bidi-font-size:9.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast; color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'>(co-located with ISSTA)</span><span style='mso-bidi-font-size:9.0pt;mso-fareast-font-family: "Times New Roman";mso-fareast-theme-font:minor-fareast;color:black; mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span class=MsoHyperlink><span lang=EN-GB style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast'><a href="http://woda13.eecs.umich.edu/" target="_blank"><u><span lang=EN-US style='font-family:"Verdana","sans-serif";color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'>WODA  13</span></u></a></span></span><span style='mso-bidi-font-size:9.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast; color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'> (co-located with ASPLOS)<o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://woda2012.ec-spride.de/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  12</span></u></a> (co-located with ISSTA)<o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="https://www.cs.purdue.edu/woda11/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  11</span></u></a> (co-located with ISSTA)<o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://isr.uci.edu/woda10/Welcome.html" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  10</span></u></a> (co-located with ISSTA)<span style='mso-spacerun:yes'>���� </span><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span class=MsoHyperlink><span lang=EN-GB style='mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast'><a href="http://research.cs.wisc.edu/woda-2009/" target="_blank"><u><span lang=EN-US style='font-family:"Verdana","sans-serif";color:black;mso-ansi-language: EN-US;mso-fareast-language:ZH-CN'>WODA  09</span></u></a></span></span><span style='mso-bidi-font-size:9.0pt;font-family:"Verdana","sans-serif"; mso-fareast-font-family:"Times New Roman";mso-fareast-theme-font:minor-fareast; color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'> (co-located with ISSTA)<o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://research.cs.wisc.edu/woda-2008/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  08</span></u></a> (co-located with ISSTA) <o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="https://www.st.cs.uni-saarland.de/woda/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  07</span></u></a> (co-located with ICSE)<span style='mso-spacerun:yes'>���� </span><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://www.cs.arizona.edu/~ngupta/WODA06/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  06</span></u></a> (co-located with ICSE)<span style='mso-spacerun:yes'>���� </span><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://www.csd.uwo.ca/woda2005/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  05</span></u></a> (co-located with ICSE)<span style='mso-spacerun:yes'>���� </span><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://www.cs.virginia.edu/woda2004/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  04</span></u></a> (co-located with ICSE)<span style='mso-spacerun:yes'>� </span><span style='mso-spacerun:yes'>���</span><o:p></o:p></span></p> <p class=SIGPLANParagraph style='margin-left:.5in;text-indent:-.25in; mso-list:l1 level1 lfo4'><![if !supportLists]><span style='mso-bidi-font-size: 9.0pt;font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family: Symbol;color:black;mso-ansi-language:EN-US;mso-fareast-language:ZH-CN'><span style='mso-list:Ignore'>�<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span></span></span><![endif]><span style='mso-bidi-font-size:9.0pt; font-family:"Verdana","sans-serif";mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast;color:black;mso-ansi-language:EN-US; mso-fareast-language:ZH-CN'><a href="http://www.cs.nmsu.edu/~jcook/woda2003/" target="_blank"><u><span style='mso-bidi-font-size:10.0pt'>WODA  03</span></u></a> (co-located with ICSE)<span style='mso-spacerun:yes'>�� </span><o:p></o:p></span></p> </td> </tr> </table> </div> <p class=MsoNormal style='mso-margin-top-alt:auto;mso-margin-bottom-alt:auto'><span style='mso-fareast-font-family:"Times New Roman"'><u2:p>&nbsp;</u2:p></span></p> </div> </body> </html> http://www.ics.uci.edu/~guoqingx/courses/142b/winter13/index.html CS 295--Run-time Techniques for Efficient and Reliable Program Execution

          CS 142 (b): Compiler Construction Project

          Winter 2013, Tue Thurs 8:00-9:20am, ICS 180

          Instructor: Harry Xu, Office hour: by appointment, Credits: 4

          TA: Gulfem Savrun Yeniceri (gsavruny@uci.edu), Office: CS 444

          Schedule

          Test cases

          Project FAQ

          Useful resource (pointer tutorials, JVM specs, X86 instruction set, etc.)

          Course Overview

          This is a heavyweight project class that only programming geeks would love to take. The entire class is nothing but a project to build a Java compiler. The compiler takes .class files (bytecode) as input and generates X86 executable code. The course will give you hands-on experience with compiler construction and optimizations.

          Prerequisites

          CS 142 (a) and C/C++ programming experience.

          Course Organization

          The course has five separate phases, and each phase is constituted by a lecture (given by me or the TA) that introduces the goal of the phase as well as a number of lab sessions.

           

          Phase 1: Parsing .class files

          Phase 2: Building a Java interpreter

          Phase 3: Building SSA

          Phase 4: Developing SSA-based optimizations (e.g., register allocation and other dataflow-based optimizations)

          Phase 5: Generating X86 machine code

          Grading

          Each student needs to give a demo presentation on his/her project at the end of the quarter. I will provide a set of simple Java programs to test your compiler. You can get at least a B if your compiler can do phase 1, 2, and 3. If phase 4 and 5 are implemented and your compiler passes all my test cases, you will get an A. If your compiler does everything and has one additional dataflow optimization implemented, you will get an A+.

          Acknowledgments

          Thanks to Michael Franz, Shannon Alfaro, Mason Chang, and Eric Hennigan for providing advice and help with this course.


           

          http://cert.ics.uci.edu/board.html CERT - Center for Emergency Response Technologies
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          CERT Community Advisory Board

          The CERT Center stives to create a coalition of government, academic, and industry leaders to forward reserach within public safety. To assist us in that end, we have a dedicated group of Advisory Board members. Our Advisory Board consists of experts within their respective domains and help keep CERT at the forefront of emergency response technology research.

          Advisory Board Chair: Ellis Stanley, Dewberry

          Advisory Board Members:

          Satish Ajmani, County of Orange

          Lance Brooks, Department of Homeland Security

          Karen Butler, City of San Diego Police Department

          David Cardenas, Los Angeles County Department of Health Services

          Dawna Finley, City of Irvine

          Bob Garrott, Los Angeles County Office of Emergency Management

          Marc Gomez, UCI Environmental Health and Safety

          Jacob Green, City of Ontario

          Fred Halenar, City of Champaign

          William Maheu, City of San Diego Police Department

          Kathy McKeever, Governor's Office of Homeland Security

          Paulette Murphy, SPAWAR

          David Rose, UCSD Campus Police

          Eileen Salmon, City of Irvine

          Nancy L. Suski, Lawrence Livermore National Lab

          Dave Svenson, The Boeing Company

          Rich Toro, Orange County Fire Authority

          James Watkins, Governor's Office of Emergency Services

           



          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/people.html CERT - Center for Emergency Response Technologies
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          People

           

          List of Faculty, Staff, and Researchers participating in CERT:


          • Sharad Mehrotra,April 13, 2010 3:21 PM/www.ics.uci.edu/faculty/profiles/view_faculty.php?ucinetid=nalini">Nalini Venkatasubramanian, Professor, Computer Science

          • Gene Tsudik, Professor, Computer Science

          • Carter Butts , Assistant Professor, Sociology

          • Chen Li, Professor, Computer Science

          • Padhraic Smyth, Professor, Computer Science

          • Gloria Mark, Professor, Computer Science

          • Magda El Zarki, Professor, Computer Science

          • Ramesh Jain, Bren Professor, Computer Science

          • Dmitri V. Kalashnikov, Assistant Adjunct Professor, Computer Science

          • Naveen Ashish, Visiting Assistant Project Scientist

          • Chris Davison, IT Architect and Technology Manager/Project Scientist

          • Jay Lickfett, Programmer/Analyst, Rescue

          • Mirko Montanari, Programmer/Analyst, Rescue

          • Alessandro Ghigi, Programmer/Analyst, Rescue

          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/partnerships.html CERT - Center for Emergency Response Technologies
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          The Center for Emergency Response Technologies would like to thank all of these organizations for their generosity, time, efforts, and input:

           

          Academic Partners

          Bionet Research Project
          Brigham Young University
          Calit2 - California Institute for Telecommunications and Information Technology
          Responsphere
          Research Project
          University of California, Irvine
          University of California, Irvine Environmental Health and Safety
          University of California, San Diego
          University of California, San Diego
          Police Department
          University of Colorado
          at Boulder
          University of Colorado
          at Boulder
          Natural Hazards Center
          University of Illinois
          at Urbana-Champaign
          University of Maryland
          WIISARD Research Project - Wireless Internet Information System for Medical Response in Disasters

           

          Industry Partners

          5G Wireless
          Alliance of Foam Packaging Recyclers
          AMD
          Apani
          Asvaco
          Boeing
          Canon
          Convera
          Cox Communications
          D-Link
          Deltin Corp.
          Masimo
          Ether2
          IBM
          ImageCat, Inc.
          Microsoft
          Printronix
          The School Broadcasting Company
          Vital Data Technology
          Walker Wireless

           

          Government Partners

          City of Champaign
          City of Irvine
          City of Los Angeles
          City of Ontario
          Fire Department
          City of San Diego
          Los Angeles County
          Metropolitan Medical Strike Team
          Orange County Fire Authority
          National Science Foundation
          Newport Beach Fire Department

           

          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/EMWS09/index.html CERT - Center for Emergency Response Technologies
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          The United States Department of Homeland Security, Science and Technology Directorate is partnering with UCI's CERT Center to host the :

          Workshop on

          Emergency Management: Incident, Resource, and Supply Chain Management (EMWS09)

            November 5-6, 2009

          Center for Emergency Response Technologies, UC Irvine

          University of California , Irvine

          4100 Calit2 Building, Irvine , CA 92697-2800

          Workshop attendance is open subject to space availability, with October 26 as the cut-off date. Workshop registration is free. For registration details please visit:
          https://www.enstg.com/signup/passthru.cfm?ConferenceCode=WOR77476

          Further information on the program can be found here (draft workshop document).


          Important Dates
          • Extended Deadline for Position paper submission: October 19th
          • October 23, 2009- Author notification
          • November 5-6, 2009- Workshop

          Agenda


          Thursday November 5th, 2009 (Videos of Day 1 Presentations)
          7:30 – 8:00 am Registration / Breakfast (Calit2 Atrium)
          8:00 – 8:10 am Workshop Kickoff Nabil Adam, DHS S&T (Calit2 Auditorium)
          8:10 – 8:20 am Sharad Mehrotra, UC, Irvine: Welcome Address (Calit2 Auditorium)
          8:20 – 8:30 am Sue Bryant, UC, Irvine, Vice Chancellor (Calit2 Auditorium)
          8:30- 8:45 am Lawrence Skelly, DHS S&T (Calit2 Auditorium)
          8:45– 9:45 am UICDS Demo (Chip Mahoney. SAIC) (Calit2 Auditorium)
          9:45 - 9:55 am Coffee Break
          9:55 - 10:55 am Panel: Government Perspective on Emergency Management
          Chair: Lawrence Skelly (DHS S&T)
          Panel Members: Larry Collins (LA County), Mark Cooper (Governor’s Office, State of Louisiana), Charles Hagan (Logistics Office, State of Florida), Shawn Matz (Logistics Branch Chief, FEMA) (Calit2 Auditorium)
          11:00– 12:00 pm Panel: Industrial Perspective on Response Technologies
          Chair: Ron Eguchi (ImageCat Inc.) Panel Members: Chip Mahoney (SAIC, Inc.), Dale Svenson (Boeing), Scott Gregory (ESRI), John Ellenberger (SAP Research), Shawn Smith (Emergency Visions) (Calit2 Auditorium)
          12:00 -12:45 Lunch (Calit2 Atrium)
          12:45– 2:00 pm Position Papers
          2:00 – 3:00 pm Panel: Virtual Worlds and Homeland Security
          Chair: Mike Macedonia (Forterra Inc.) Panel Members: Michael Pack (U Maryland), Ron Tarr (U of Central Florida), Randy Hill (USC), Walt Scacchi (UCI) (Calit2 Auditorium)
          3:00 - 3:15 pm Coffee Break
          3:15– 4:40 pm Breakout Sessions
          Incident Level Response- Chair: Brent Woodworth Co-Chairs: Ellen Sogolow, Rufus Edwards
          Regional Level Response- Chair: Ellis Stanley Co-Chairs: Chuck Hagan, Carter Butts
          Community Response- Chair: Bruce Davis Co-Chairs: Nalini Venkatasubramanian, Nancy Suski
          4:45 – 5:30 pm Breakout Reports (Calit2 Auditorium)
          5:30 - 7:00 pm Reception / Poster Session (Calit2 Atrium)


          Friday November 6th, 2009 (Videos of Day 2 Presentations)
          7:30-8:00 am Breakfast (Calit2 Atrium)
          8:00-8:15 am Welcome Back Nabil Adam, DHS-Science and Technology Directorate
          (Calit2 Auditorium)
          8:15 – 9:30 am Panel: Emerging Cloud Computing Infrastructure and its Role for Emergency Management Chair: Mike Carey (UCI) Panel Members: Mike Olsen (Coudera), Raghu Ramakrishnan (Yahoo), Gus Hunt (CTO, CIA) Hakan Hacigumus (NEC) (Calit2 Auditorium)
          9:30 – 10:45 am Panel: Sensors and Emergency Management: What has been Achieved, Potential, and Related Challenges Chair: Mani Chandy (CalTech)
          Panel Members: Ron Cabrera (LA Fire), Daniel Cotter (CTO, DHS), Andrei Shkel (UCI/DARPA), Bruce H. Varner, (Fire Chief, Santa Rosa)
          10:45 – 11:00 am Coffee Break
          11:00 – 12:15 pm Panel: New Issues and Challenges in Emergency Management of Pharmaceutical/Healthcare Supply Chain Chains Chair: Lei Lei (Rutgers)
          Panel Members: Beth E. Ford (IFF), Ron Guido (J&J), Michael Pinedo (NYU),
          Max Shen (UC, Berkeley), Michael Trocchia (Norvartis)
          12:15 -12:50 pm Lunch / Poster Session (Calit2 Atrium)
          12:50 - 1:20 pm Position Papers
          1:20 - 1:30 pm Break
          1:30 – 2:45 pm
          Breakout Sessions
          Incident Level Response- Chair: Brent Woodworth Co-Chairs: Ellen Sogolow, Rufus Edwards
          Regional Level Response- Chair: Ellis Stanley Co-Chairs: Chuck Hagan, Carter Butts
          Community Response- Chair: Bruce Davis, Co-Chairs: Nalini Venkatasubramanian, Nancy Suski
          2:45 – 3:45 pm Breakout Reports (Calit2 Auditorium)
          3:45 – 4:00 pm Closing Remarks Nabil Adam, DHS-Science and Technology Directorate
          (Calit2 Auditorium)
          4:00 pm Adjourn

          Position Papers (Click here to download ALL the Papers (zip file)

          Thursday, November 5th


          Paper Session 1
          Calit2 Auditorium 05 NOV 09
          Software-Defined Ultra-wideband Radio Communications: A New RF Technology
          for Emergency Response Applications

          Authors: Faranak Nekoogar and Farid Dowla
          Public-Private Partnerships: Key Drivers of Disaster Supply Chains
          Authors: Ramesh Kolluru and Mark Smith
          Distributed Information Sharing for Disaster Response and Recovery:
          www.VirtualDisasterViewer.com
          Authors: John Bevington, Beverley Adams, Enrica Verrucci, Paul Amyx, Charles Huyck
          and Ronald Eguchi
          Agent-based Evacuation Modeling: Simulating the Los Angeles International Airport
          Authors: Jason Tsai, Emma Bowring, Shira Epstein, Natalie Fridman, Prakhar Garg,
          Gal Kaminka, Andrew Ogden, Milind Tambe and Matthew Taylor

          Paper Session 2
          Calit2 3008 05 NOV 09
          Emergency Incident Management
          Author: Paul Matheis
          Speech-Based Situational Awareness for Crisis Response
          Authors: Dmitri Kalashnikov, Dilek Hakkani-Tür, Gokhan Tur
          and Nalini Venkatasubramanian
          A Policy Driven Meta-Alert System for Crisis Communications
          Author: Nalini Venkatasubramanian
          Design and Optimization of Agent-Based Emergency Supply Chains
          Authors: Ozlem Ergun, Pinar Keskinocak and Julie Swann


          Paper Session 3
          Calit2 4301 05 NOV 09
          Challenges in Evacuation Route Planning for Incident Management
          Author: Shashi Shekhar
          Technologies for Emergency Response Logistics
          Author: Shawn Smith
          Design of a Temporal Geosocial Semantic Web for Emergency Management Operations
          Authors: Bhavani Thuraisingham, Latifur Khan, and Murat Kantarcioglu
          Rapid Information Integration for Emergency Response
          Author: Naveen Ashish

          Friday November 6th


          Paper Session 1
          Calit2 Auditorium 06 NOV 09
          Information Management as the Key to Emergency Management
          Author: Paul Scerri
          New Challenges to Emergency Management of Pharmaceutical/Healthcare
          Supply Chain Disruptions
          Authors: S. Graves, L. Lei, B. Melamed, M. Pinedo, L. Qi, Z.J. Shen and X. Xu
          Paper Session 2
          Calit2 1322 06 NOV 09
          Multiagent Systems to Support Coordinated Emergency Management
          Author: Edmund Durfee
          Integrated Resource and Logistics Management through Secure Information Sharing
          for Effective Emergency Response

          Authors: Vijay Atluri, Soon Ae Chun, John Ellenberger, Basit Shafiq and Jaideep Vaidya
          Paper Session 3
          Calit2 4301 06 NOV 09
          When Disaster Strikes: Lessons from NGOs and Industry Emergency Supply Chains
          Authors: Ozlem Ergun, Pinar Keskinocak and Julie Swann
          Intelligent Agents and UICDS
          Authors: Clare Grasso, Tim Finin, Michael Grasso, Yelena Yesha and Anupam Joshi

          Authors can submit their final versions of their papers through Easy Chair:

          http://www.easychair.org/conferences/?conf=emws09


          Workshop General Chair: Lawrence Skelly, DHS-S&T lawrence.skelly@dhs.gov

            Program Chair/Co-chair

          •  Nabil R. Adam, DHS-S&T nabil.adam@dhs.gov (Chair)

          •  Sharad Mehrotra, UC Irvine sharad@ics.uci.edu (Co-Chair)

           

          Program Committee

          •  Vijay Atluri, Rutgers U
          • Chaitanya Baru, UCSD
          • Ron Cabrera, Chief, LA County Fire
          • Mani Chandy, CALTECH
          • Soon Ae Chun, CUNY
          • Larry Collins, Captain, LA County Fire
          • Mark Cooper, GOHSEP, State of Louisiana
          • Jerry Couretas, Lockheed Martin
          • Bruce Davis, DHS-S&T
          • Edmund H. Durfee, U of Michigan
          • David Ebert, Purdue U.
          • Ron Eguchi, CEO, ImageCat Inc.
          • Ahmed K. Elmagarmid, Purdue U
          • Ramesh Kolluru, U of Louisiana
          • Lei Lei, Rutgers U
          • Brock Long, EMA, State of Alabama
          • Mike Macedonia, Forterra Inc.
          • Paul Matheis, Newport Beach Fire Dept
          • James W. Morentz, SAIC, Inc.
          • Steve Sellers, California Governor’s Office of Emergency Services
          • Basit Shafiq, Rutgers U
          • Michael B. Smith, DHS-S&T
          • Steve Stein, PPNL
          • Nancy Suski, LLNL
          • Bhavani Thuraisingham, UT Dallas
          • Kathleen Tierney, Hazards Center-UC Boulder
          • Jaideep Vaidhya, Rutgers U
          • Linda Vasta, DHS-S&T
          • Nalini Venkatasubramanian, UC Irvine
          • Marianne Winslett, UI, Urbana-Champaign
          • Brent Woodworth, SAHANA Software Foundation Board

          This page was last updated on: January 21, 2010 1:35 PM
          http://cert.ics.uci.edu/directions.html CERT - Center for Emergency Response Technologies
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          Directions

          Address:

          CERT Project
          University of California, Irvine
          4019 Bren Hall
          Irvine, CA 92697-3425
          (949) 824-9104

          CERT Director
          Sharad Mehrotra
          2082 Bren Hall (Comp Sci 3)
          Irvine, CA 92697-3435
          (949) 824-5975

          CERT co-PI
          Nalini Venkatasubramanian
          2086 Bren Hall (Comp Sci 3)
          Irvine, CA 92697-3435
          (949) 824-5898

          Maps:

          Campus maps are available. We strongly encourage you to print one and bring it with you. The directions below guide you to a parking lot. The map will help you navigate the campus once you park.

          Parking Permits:

          Permits are required at UCI. Please visit the UCI Parking website for information on obtaining a permit.

          Directions:

          The CERT project is housed on the 4th floor of the Calit2 Building at UCI.

          From 73 South: exit at Bison Ave. Turn LEFT. Make a RIGHT on E. Peltason Dr. Take the first LEFT hand turn into parking lot 12B. If you make it to the stop sign you have gone too far.

          From 73 North: exit at Bison Ave. Turn RIGHT. Make a RIGHT on E. Peltason Dr. Take the first LEFT hand turn into parking lot 12B. If you make it to the stop sign you have gone too far.

          From 5/405 South : take 55 South to the 73 south. exit at Bison Ave. Turn LEFT. Make a RIGHT on E. Peltason Dr. Take the first LEFT hand turn into parking lot 12B. If you make it to the stop sign you have gone too far.

          From 5/405 North: exit at Jeffrey/University, turning left (Jeffrey turns to University at the 405). Go to Culver Ave. and turn LEFT. Go to Campus Ave. and turn RIGHT. Go to E. Peltason Dr. and turn LEFT. You will pass 2 stop signs. After the 2nd stop sign, turn RIGHT into lot 12B. If you get to Bison Ave, you have gone too far.

          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/index.html CERT - Center for Emergency Response Technologies
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          During a crisis event, bringing the right information, at the right time, to the right person, can significantly impact the quality of decision making for First Responders as well as the general public. Effective decision making during a crisis can be directly attributed to saving lives and property. The research team assembled by the Center for Emergency Response Technologies (CERT) believes that information technologies can enhance decision making abilities.

          The goal of the center is new and innovative technology research. The focus will be on how technology will improve emergency response. At the system level: robust systems, resiliency in extreme situations. At the Information level: convert large-scale, multi-modal information into actionable information upon which decisions can be made. A layer of social science research provides the application context. Engineering research (e.g., transportation systems and alert systems) provide a specific lifeline for which the value of IT can be illustrated.

          CERT will radically transform the ability of responding organizations to gather, manage, use, and disseminate information within emergency response networks and to the general public. Depending upon the severity of the crisis, response may involve numerous organizations including multiple layers of government, public authorities, commercial entities, volunteer organizations, media organizations, and the public. These entities work together to save lives, preserve infrastructure and community resources, and to reestablish normalcy within the population. The efficacy of response is determined by the ability of decision-makers to understand the crisis at hand and the state of the available resources to make vital decisions. The quality of these decisions in turn depends upon the timeliness and accuracy of the information available to the responders.

          CERT will be an interdisciplinary effort that brings computer scientists, engineers, social scientists, and disaster science experts together to explore technological innovations in order to deliver the right information to the right people at the right time during crisis response. Joint research collaboration with Calit2 and the UCI School of Social Sciences is being sought in order to maximize the impact of the proposed ICS Center.

          Latest News: Cert News

          Upcoming Event: Southern California UICDS Consortium. In conjunction with DHS, First Responders, and other stakeholders, the CERT Team is planning a "kick-off" meeting to establish a UICDS Consortium and UICDS Core at UCI. Date and time TBA.

          In March (2010), Prof. Chen Li received an NSF award to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake .

          Distingushed Lecture (10 NOV 09): Yueting Zhuang Professor and Dean of the College of Computer Science, Zhejiang University: Digital Libraries and its potential in-depth applications. http://isg.ics.uci.edu/events.html

          Workshop November 5th and 6th: DHS S&T / UCI CERT: Workshop on
          Emergency Management: Incident, Resource, and Supply Chain Management
          (EMWS09).

          Distinguished Lecture (October 8, 2009: 11AM, Bren Hall: Room 4011): Nanda Kambhatla, Ph.D, Manager, Data Analytics, IBM India Research Lab, Bangalore India: Abstract of Talk and Nanda's Bio


          WPI Precision Personnel Locator Workshop: August 3-4, 2009. Dr. Chris Davison presented: SAFIRE – Technological Research and Solutions Impacting Situational Awareness for Firefighters

          Distinguished Lecturer: Ron Eguchi, CEO ImageCat Inc., May 15, 2009. Topic: Earthquakes, Hurricanes and other Disasters: A View from Space.

          Firefighter Forum: May 15, 2009. Special Topic: Wildland Fires. Location: Bren Hall, Room 4001, UCI

          Disaster Pictures

          This page was last updated on: April 14, 2010 8:28 AM
          http://cert.ics.uci.edu/initiatives.html CERT - Center for Emergency Response Technologies
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          Initiatives

          Public safety, public safety technologies, and research within public safety are a continually growing domain. CERT will serve as a vehicle to pursue funding within public safety from agencies such as NSF (e.g., an ERC), DARPA, and others.

          We are working in the following research areas: situational awareness, acoustic location sensing, robust networking, human bio-sensing, incident visualization, and disaster/disaster response simulation.


          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/seminar/Nanda/abstract.htm Utilizing New Technologies in Managing Hazards and Disasters

          We present a general framework for automatically extracting social networks and biographical facts from conversational speech. Our approach relies on fusing the output produced by multiple information extraction modules, including entity > recognition and detection, relation detection, and event detection modules. We describe the specific features and algorithmic refinements effective for conversational speech. These cumulatively increase the performance of social network extraction from 0.06 to 0.30 for the development set, and from 0.06 to 0.28 for the test set, as measured by f-measure on the ties within a network. The same framework can be applied to other genres of text � we have built an automatic biography generation system for general domain text using the same approach.

           

          http://isg.ics.uci.edu/events.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • About
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          • ISG Seminars
          • Invited Talks
            • Upcoming
            • Past

          ISG Seminars

          Regular ISG seminar

          Time: Every Fri afternoon, 2pm - 3:00pm; Location: Bren Hall 3011

          2009-2010 ISG Scalable Data Management Seminar Series [talks]

          Support for the ISG Seminar Series from Yahoo! is gratefully acknowledged.

          Invited Talks

          Upcoming Events

          Feb 12, 2016
          SPEAKER: Wenjun Zeng
          Human Centric Video Analytic
          Details
          Date and TimeFeb 12, 2016 2PM
          LocationDBH 3011


          SpeakerWenjun Zeng
          TitleHuman Centric Video Analytic
          AbstractVideo is the biggest big data that contains an enormous amount of information. At Microsoft Research Asia, we are leveraging computer vision and deep learning to develop a cloud-based intelligence engine that can turn raw video data into insights to facilitate various applications and services. In this talk, I will introduce our recent effort on human centric video analysis and present some latest technologies we have developed including online learning based face/human tracking/identification, skeleton-based human action recognition using regularized deep LSTM networks, and real-time human action detection and forecasting in streaming video, etc. I will also shed some light on the go-to-market aspect of this intelligent cloud effort.
          Speaker BioWenjun (Kevin) Zeng is a Principal Research Manager overseeing the Internet Media Group and the Media Computing Group at Microsoft Research Asia, while on leave from the Univ. of Missouri (MU). He had worked for PacketVideo Corp., Sharp Labs of America, Bell Labs, and Panasonic Technology prior to joining MU in 2003. Wenjun has contributed significantly to the development of international standards (ISO MPEG, JPEG2000, and OMA). He received his B.E., M.S., and Ph.D. degrees from Tsinghua Univ., the Univ. of Notre Dame, and Princeton Univ., respectively. His current research interest includes mobile-cloud media computing, computer vision, social network/media analysis, multimedia communications, and content/network security. He is an Associate Editor-in-Chief of IEEE Multimedia Magazine, an AE of IEEE Trans. on Circuits & Systems for Video Technology (TCSVT), and was an AE of IEEE Trans. on Info. Forensics & Security and IEEE Trans. on Multimedia (TMM). He is/was on the Steering Committee of IEEE Trans. on Mobile Computing (current) and IEEE TMM (2009-2012). He served as the Steering Committee Chair of IEEE ICME in 2010 and 2011, and has served as the TPC Chair of several IEEE conferences (e.g., ChinaSIP’15, WIFS’13, ICME’09, CCNC’07). He will be a general co-Chair of ICME2018. He is currently guest editing a TCSVT Special Issue on Visual Computing in the Cloud - Mobile Computing, and was a Special Issue Guest Editor for the Proceedings of the IEEE, IEEE TMM, and ACM TOMCCAP. He is a Fellow of the IEEE.

          Past Events

          Jan 22, 2016
          SPEAKER: Pat Helland (Salesforce.com)
          Subjective Consistency
          Details
          Date and TimeJan 22, 2016 2PM
          LocationDBH 3011


          SpeakerPat Helland (Salesforce.com)
          TitleSubjective Consistency
          AbstractGray and Reuter define consistency as: “A transaction is a correct transformation of the state. The actions taken as a group do not violate any of the integrity constraints associated with the state. This requires that the transaction be a correct program.” Loosely translated, you should be happy with yourself. In your own opinion, you should be sane. Through the years, this most ill defined property of the ACID transaction has been conflated with isolation and interpreted through the prism of read/write updates to an apparently centralized record oriented database. This suits systems developers that need a narrow definition to feel they can accomplish something. Unfortunately, it leaves many real world problems in the dust. In this talk, we will explore the models of consistency and the facades of reality used in practical Systems.
          Speaker BioPat Helland has 37 years experience implementing databases, transaction systems, application platforms, replication systems, fault tolerance, and distributed systems. From 1973-1976, Pat attended UC Irvine in the ICS department, loved his ICS undergrad and grad classes, and lived non-stop at the computing facility as an operator on the PDP-10 and Xerox Sigma-7. This was back in the day when a computer was the size of a room and the only way to program was to stay up all night accessing the timesharing system. During the 1980s, Pat was Chief Architect of the Tandem NonStop's TMF (Transaction Monitoring Facility), the transaction processing and recovery engine behind NonStop SQL. He started working at Microsoft in 1994 and drove the design and architecture for MTS (Microsoft Transaction Server), the N-tier transactional computing environment for Windows as well DTC, the Distributed Transaction Coordinator. A few years later, Pat led the development of SQL Service Broker, a high speed exactly once transactional messaging system. From 2005 to early 2007, Pat worked at Amazon on the Product Catalog. In 2007, he returned to Microsoft working on a number of projects including adding indexing and affinitized placement of data into Cosmos, the massively parallel computation and storage engine behind Bing (Big Data). Cosmos supports exabytes of data running on hundreds of thousands of computers. He was one of the original architects for the real-time event driven transactional engine for Cosmos. Since early 2012, Pat has worked at Salesforce.com on database management, storage, and data center issues. He was recently added to the UCI ICS Inaugural Hall of Fame.
          Dec 4th, 2015
          SPEAKER: Volker Mische (Couchbase)
          Indexing Data at Scale in Couchbase Server
          Details
          Date and TimeDec 4th, 2015 2PM
          LocationDBH 4011


          SpeakerVolker Mische (Couchbase)
          TitleIndexing Data at Scale in Couchbase Server
          AbstractThis talk will provide an overview of Couchbase Server with a focus on its new query and indexing capabilities. The talk will include a look under the hood at how NoSQL data is stored, scaled, indexed, and queried at scale in the Couchbase world.
          Speaker BioVolker Mische was the creator of GeoCouch, a geospatial extension for Apache CouchDB and Couchbase. A degree (diploma) in computer science with a minor in geography (University of Augsburg, Germany) and a one year intership at LISAsoft Pty Ltd, Australia in 2008 deepened his experience in the geospatial field. As a proponent of open source, Volker has contributed to various projects, including Apache CouchDB, MapQuery (project lead), OpenLayers, TileCache, GeoNetwork, Rockbox and WYMeditor.
          Nov 13th, 2015
          SPEAKER: Ahmed Eldawy (U. Minnesota)
          SpatialHadoop: A MapReduce Framework for Spatial Data
          Details
          Date and TimeNov 13th, 2015 2PM
          LocationDBH 4011


          SpeakerAhmed Eldawy (U. Minnesota)
          TitleSpatialHadoop: A MapReduce Framework for Spatial Data
          Abstract This talk describes SpatialHadoop, a full-fledged MapReduce framework which extends Hadoop to support spatial data processing efficiently. SpatialHadoop injects spatial data awareness inside the core of Hadoop to make it orders of magnitude more efficient than plain-vanilla Hadoop. It adapts traditional spatial indexes, such as R-tree, Quad tree and K-d tree, to HDFS and uses these indexes to support a wide range of spatial operations, including, range query, kNN, spatial join, and computational geometry operations. In addition, it provides a high level language, termed Pigeon, which makes the system easier for non-technical users. Furthermore, SpatialHadoop has an extensible and scalable visualization layer that is able to produce giga-pixel images for terabytes of data, which allows users to explore big spatial data. The open source nature of SpatialHadoop allows it to be used in a wide range of research projects and live applications, including, SHAHED, for querying and visualizing satellite data, TAREEG, a web-based extractor for OpenStreetMap data, and TAGHREED, a system for querying and analyzing Twitter data.
          Speaker BioAhmed Eldawy is a sixth year PhD candidate at the Computer Science and Engineering department in University of Minnesota. He is a member of the Data Management Lab under the supervision of Prof. Mohamed Mokbel. His broad area of research interest is in databases and data management. His main research topic for PhD purposes is spatial data management in distributed environments. He is the founder and creator of SpatialHadoop, one of the most widely used systems for processing spatial data using MapReduce. He will be visiting UCI to work with the AsterixDB team for one month in the Nov./Dec. 2015 timeframe.
          Nov 6th, 2015
          SPEAKER: Profs. Vassilis Tsotras (UC Riverside) & Sharad Mehrotra (UC Irvine)
          Dealing with BAD TIPPERS
          Details
          Date and TimeNov 6th, 2015 2PM
          LocationDBH 4011


          SpeakerProfs. Vassilis Tsotras (UC Riverside) & Sharad Mehrotra (UC Irvine)
          TitleDealing with BAD TIPPERS
          AbstractThe first full ISG Seminar of 2015-16 will be a sequence of two half-hour talks on new projects that are happening in the world of ISG. The first talk will describe the BAD project, where BAD is short for Big Active Data. BAD is a joint effort between UCR and UCI. First-generation Big Data management projects like AsterixDB have been passive in nature - queries, updates, and data analysis techniques have been scaled to handle very large volumes of data. In contrast, the BAD effort aims to develop techniques to continuously and reliably capture Big Data collections (arising from social, mobile, Web, and sensed data sources) and to provide timely delivery of the right information to the relevant end users. In short, BAD aims to provide a scalable foundation for moving from Big Passive Data to Big Active Data. Techniques are being developed to enable the accumulation and monitoring of petabytes of data of potential interest to millions of end users; when "interesting" new data appears, it should be delivered to end users in a time frame measured in (100's of) milliseconds. The NSF-sponsored BAD project has been underway for just one year, and it will eventually lead to a series of BAD results, BAD papers, and BAD open source software. The second talk will describe a brand new UCI project, sponsored by DARPA, called TIPPERS. To learn more about TIPPERS, you'll just have to come to the talk!
          Speaker BioVassilis Tsotras is a Professor of Computer Science at UC Riverside and co-PI of the AsterixDB project and now the BAD project, both of which span UCR and UCI. He is also the director of the newly formed Data Science Center at UCR. Sharad Mehrotra is a Professor of Computer Science at UC Irvine and co-PI of the TIPPERS project. He is also the Vice Chair for Graduate Studies here at UCI.
          Oct 9, 2015
          SPEAKER: David Lomet (Microsoft Research)
          Multi-Version Range Concurrency Control in Deuteronomy
          Details
          Date and TimeOct 9, 2015 2PM
          LocationDBH 4011


          SpeakerDavid Lomet (Microsoft Research)
          TitleMulti-Version Range Concurrency Control in Deuteronomy
          AbstractThe Deuteronomy transactional key value store executes millions of serializable transactions/second by exploiting multi-version timestamp order concurrency control. However, it has not supported range operations, only individual record operations (e.g., create, read, update, delete). In this paper, we enhance our multi-version timestamp order technique to handle range concurrency and prevent phantoms. Importantly, we maintain high performance while respecting the clean separation of duties required by Deuteronomy, where a transaction component performs purely logical concurrency control (including range support), while a data component performs data storage and management duties. Our range technique continues to manage concurrency information in a latch-free manner. With our range enhancement, Deuteronomy can reach scan speeds of nearly 250 million records/s (more than 27 GB/s) on modern hardware, while providing serializable isolation complete with phantom prevention.
          Speaker BioDavid Lomet founded and manages the Database Group at Microsoft Research Redmond. Earlier, he worked at DEC, IBM, and as professor at Wang Institute. He has a PhD from University of Pennsylvania. David has worked in architecture, languages, and distributed systems. His primary focus is database systems. He is an inventor of transactions while on sabbatical at University of Newcastle-on- Tyne in 1975. He has authored 100+ papers, including two SIGMOD best papers, and he holds over 50 patents. He and his group have made multiple contributions to Microsoft products. His recent Bw-tree is used in SQL Server's Hekaton main-memory dbms (http://research.microsoft.com/en-us/news/features/hekaton-122012.aspx). David has been ICDE PC co-chair and conference co-chair, TC on Data Engineering chair, ICDE Steering Committee member, and member of the Computer Society Board of Governors. David was awarded IEEE Meritorious Service and SIGMOD Contributions Awards for serving as editor in chief of the IEEE Data Engineering Bulletin for 20+ years (http://tab.computer.org/tcde/bull_about.html). He has served as VLDB PC co-chair, and on the VLDB Board, and been an editor of ACM TODS and VLDB Journal. He is a Fellow of the IEEE (and Golden Core Member), ACM, and AAAS.
          Sep 8, 2015
          SPEAKER: Themis Palpanas (Paris Descartes University)
          Data Series Management: The Road to Big Sequence Analytics
          Details
          Date and TimeSep 8, 2015 Noon
          LocationDBH 3011


          SpeakerThemis Palpanas (Paris Descartes University)
          TitleData Series Management: The Road to Big Sequence Analytics
          AbstractThere is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from the Inernet of Things, biology, astronomy, entomology, the web, and other domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. In this talk, we describe recent efforts in designing techniques for indexing and mining truly massive collections of data series that will enable scientists to easily analyze their data. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce solutions to this problem. Furthermore, we discuss novel techniques that adaptively create data series indexes, allowing users to correctly answer queries before the indexing task is finished. We also show how our methods allow mining on datasets that would otherwise be completely untenable, including the first published experiments using one billion data series. Finally, we present our vision for the future in big sequence management research.
          Speaker BioThemis Palpanas is a professor of computer science at the Paris Descartes University, France. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the University of Trento and the IBM T.J. Watson Research Center. He has also been a Visiting Professor at the National University of Singapore, worked for the University of California, Riverside, and visited Microsoft Research and the IBM Almaden Research Center. His research solutions have been implemented in world-leading commercial data management products and he is the author of eight US patents. He is the recipient of three Best Paper awards (including ICDE and PERCOM), and the IBM Shared University Research (SUR) Award in 2012, which represents a recognition of research excellence at worldwide level. He has been a member of the IBM Academy of Technology Study on Event Processing, and is a founding member of the Event Processing Technical Society. He has served as General Chair for VLDB 2013, the top international conference on databases.
          August 20, 2015
          SPEAKER: Yingyi Bu
          On Software Infrastructure for Scalable Graph Analytics
          Details
          Date and TimeAugust 20, 2015 1PM
          LocationDBH 3011 (TBD)


          SpeakerYingyi Bu
          TitleOn Software Infrastructure for Scalable Graph Analytics
          AbstractRecently, there is a growing need for distributed graph processing systems that are capable of gracefully scaling to very large datasets. In the meantime, in real-world applications, it is highly desirable to reduce the tedious, inefficient ETL (extraction, transformation, and loading) gap between tabular data processing systems and graph processing systems. Unfortunately, those challenges have not been easily met due to the intense memory pressure imposed by process-centric, message passing designs that many graph processing systems follow, as well as the separation of tabular data processing runtimes and graph processing runtimes. In this thesis, we first propose a bloat-aware design paradigm towards the development of efficient and scalable Big Data applications in object-oriented, GC enabled languages and demonstrate that programming under this paradigm does not incur significant programming burden but obtains remarkable performance gains (e.g., 2x). Based on the design paradigm, we then build Pregelix, an open source distributed graph processing system which is based on an iterative dataflow design that is better tuned to handle both in-memory and out-of-core workloads. As such, Pregelix offers improved performance characteristics and scaling properties over current open source systems (e.g., we have seen up to 15x speedup compared to Apache Giraph and up to 35x speedup compared to distributed GraphLab). Finally, we integrate Pregelix with the open source Big Data management system AsterixDB to offer users a mix of a vertex-oriented programming model and a declarative query language for richer forms of Big Graph analytics with reduced ETL pains.
          Speaker Bio Yingyi Bu is a PhD Candidate from University of California, Irvine
          Jun 5, 2015
          SPEAKER: Michael Carey (UCI)
          Details
          Date and TimeJun 5, 2015 3 pm
          LocationDBH 3011


          SpeakerMichael Carey (UCI)
          Title
          Abstract
          Speaker Bio
          May 29, 2015 (Special Time\Place)
          SPEAKER: Michael Franklin, UC Berkeley Computer Science
          Big Data, Data Science, and other Buzzwords that Really Matter
          Details
          Date and TimeMay 29, 2015 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerMichael Franklin, UC Berkeley Computer Science
          TitleBig Data, Data Science, and other Buzzwords that Really Matter
          AbstractData is all the rage across industry and across campuses. While it may be temping to dismiss the buzz as just another spin of the hype cycle, there are substantial shifts and realignments underway that are fundamentally changing how Computer Science, Statistics and virtually all subject areas will be taught, researched, and perceived as disciplines. In this talk I will give my personal perspectives on this new landscape based on experiences organizing a large, industry-engaged academic Computer Science research project (the AMPLab), in helping to establish a campus-wide Data Science research initiative (the Berkeley Institute for Data Science), and my participation on a campus task force charged with mapping out Data Science Education for all undergraduates at Berkeley.
          Speaker BioMichael Franklin is the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division of the EECS Department at UC Berkeley. He is director of the Berkeley AMPLab, a 70+ person effort fusing scalable computing, machine learning, and human computation to make sense of data at scale. AMPLab software including: Spark, Shark, and Mesos, plays a significant role in the emerging Big Data ecosystem. The lab is funded by an NSF CISE Expeditions Award, the Darpa XData program, and 26 companies including founding sponsors Amazon Web Services, Google, and SAP.
          May 15, 2015 (special place)
          SPEAKER: Yunyao Li (IBM Research - Almaden)
          SystemT: an Algebraic Approach to Declarative Information Extraction
          Details
          Date and TimeMay 15, 2015 (special place) 3 pm
          LocationDBH 4011


          SpeakerYunyao Li (IBM Research - Almaden)
          TitleSystemT: an Algebraic Approach to Declarative Information Extraction
          AbstractIn recent years, Information Extraction (IE) has become increasingly important to a wide array of enterprise applications, ranging from Business Intelligence and Semantic Search to Data-as-a-Service. Such applications drive four main requirements for IE systems: accuracy, embeddability, scalability, transparency, and usability. In this talk I will give an overview of SystemT, a declarative IE system designed to address these requirements. SystemT ships today with multiple products across 4 IBM Software Brands and is used in multiple ongoing research projects. SystemT is based on the basic principle underlying relational database technology: complete separation of specification from execution. SystemT uses a declarative rule language, AQL, and an optimizer that generates high-performance algebraic execution plans for AQL rules. We show that SystemT removes the expressivity and performance limitations of previous state-of-art rule-based systems based on cascading grammars, delivering comparable result quality, and an order of magnitude higher annotation throughput with much lower memory footprint. We also present the ongoing research and development efforts in making SystemT more usable for both technical and business users. If time permits, I will also briefly cover IBM technologies built based on similar principle to enable large-scale data analytics.
          Speaker BioYunyao Li is a Master Inventor, Research Staff Member and Research Manager with IBM Almaden Research Center, where she manages the Scalable Natural Language Processing group. Her expertise is in the interdisciplinary areas of databases, natural language processing, human-computer interaction, and information retrieval. She has published over 30 peer-reviewed, referred articles, and filed nearly 20 patents in these areas. Yunyao is particularly interested in designing, developing, and analyzing large scale systems that are usable by a wide spectrum of users. Towards this direction, her current focus is on text analytics. She is a founding member of SystemT, a state-of-the-art information extraction engine, and Gumshoe, a novel enterprise search engine that has been powering IBM intranet and ibm.com search since 2010. Her contributions in these projects have recognized by multiple prestigious IBM internal awards. She received her PhD degree in Computer Science and Engineering from the University of Michigan, Ann Arbor in 2007. Before that, she obtained dual-degrees of M.S.E in Computer Science & Engineering and M.S in Information from Computer Science and Engineering and School of Information respectively at the University of Michigan - Ann Arbor. She went to college at Tsinghua University, Beijing, China, and graduated with dual-degrees of B.E in Automation and B.S in Economics.
          May 8, 2015 (Informatics Seminar)
          SPEAKER: Sean Young (UCLA)
          Reading between the Tweets: Using Social Technology Data to Predict Real-World Outcomes
          Details
          Date and TimeMay 8, 2015 (Informatics Seminar) 3 pm
          LocationDBH 6011


          SpeakerSean Young (UCLA)
          TitleReading between the Tweets: Using Social Technology Data to Predict Real-World Outcomes
          AbstractSocial big data from technologies like social media, online search, and mobile apps are being used to better understand and predict real-world events and outcomes. For example, researchers in the health and medical space have been studying whether social data could be used to monitor and predict outbreaks of disease, susceptibility to disease, and patient response to treatment. The University of California Institute for Prediction Technology (UCIPT) was created to address questions like these. Funded by the UC Office of the President, the Institute brings together researchers across University of California campuses as well as community stakeholders to study whether and how social technology data can be used to predict events in areas like health/medicine, politics, and security. This talk with provide an overview of the field of Prediction Technology as well as research being addressed by the Institute, such as 1) how Twitter is being used to predict HIV risk behaviors and outbreaks, 2) how google search terms are being used to predict political events, and 3) social media users' perceptions of having their data used for research.
          Speaker BioSean Young, PhD, MS is the Director of the University of California Institute for Prediction Technology (UCIPT), the UCLA Center for Digital Behavior, and an Assistant Professor of Family Medicine. As the Director of UCIPT, Dr. Young seeks to bridge researchers across University of California campuses to study how data from social technologies like social media and wearable devices can be used to predict real-world events in areas like health/medicine, politics, and security. His research at the Center for Digital Behavior is focused on use of social media and mobile health technologies to change and predict behavior. He has studied how social media can address issues related to HIV and drug use prevention and treatment behavior in the U.S., Peru, and South Africa, and Iran, and among various at-risk populations around the world. He was the Primary Investigator of the Harnessing Online Peer Education (HOPE) UCLA, HOPE Peru, and HOPE Care studies, showing the effectiveness of! using social media to increase HIV testing. His team completed the first study to create methods of using observational big data (> 150 million tweets) from social media for drug and HIV-related surveillance. He teaches a course for UCLA undergraduates called Hacking Global Health on how to use social media and mobile technologies to quickly address global health needs.
          May 1, 2015
          SPEAKER: C. Mohan (IBM Research - Almaden)
          Modern Database Systems: Modernized Classic Systems, NewSQL and NoSQL
          Details
          Date and TimeMay 1, 2015 3 pm
          LocationDBH 3011


          SpeakerC. Mohan (IBM Research - Almaden)
          TitleModern Database Systems: Modernized Classic Systems, NewSQL and NoSQL
          AbstractThe last many years have seen the emergence of a new class of database systems which I am calling Modern Database Systems (MDS). They are classified broadly as NoSQL and NewSQL systems. Apart from the development of such systems, traditional DBMS vendors have also extended their decades old systems like DB2, Informix, Oracle and SQL Server with major architectural enhancements that address modern requirements and take advantage of hardware (processor, memory and storage) advances. This talk will do a broad brush analysis of MDS. It is targeted at a broad set of database systems and applications people. It is intended to let the audience better appreciate what is really behind the covers of many of these systems, going beyond the hype associated with these open source, commercial and research systems. The capabilities and limitations of such systems will be addressed.
          Speaker Bio
          May 1, 2015 (Special Time\Place)
          SPEAKER: C. Mohan (IBM Research - Almaden)
          Big Data: Hype and Reality
          Details
          Date and TimeMay 1, 2015 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerC. Mohan (IBM Research - Almaden)
          TitleBig Data: Hype and Reality
          AbstractBig Data has become a hot topic in the last few years in both industry and the research community. For the most part, these developments were initially triggered by the requirements of Web 2.0 companies. Both technical and non-technical issues have continued to fuel the rapid pace of developments in the Big Data space. Open source and non-traditional software entities have played key roles in the latter. As it always happens with any emerging technology, there is a fair amount of hype that accompanies the work being done in the name of Big Data. The set of clear-cut distinctions that were made initially between Big Data systems and traditional database management systems are being blurred as the needs of the broader set of (“real world”) users and developers have come into sharper focus in the last couple of years. In this talk, I will survey the developments in Big Data and try to distill reality from the hype!
          Speaker Bio
          Apr. 24, 2015
          SPEAKER: Dimitrios Gunopoulous
          Analysing spatiotemporal document collections
          Details
          Date and TimeApr. 24, 2015 3 pm
          LocationDBH 3011


          SpeakerDimitrios Gunopoulous
          TitleAnalysing spatiotemporal document collections
          AbstractWe consider the problem of searching in live, time-stamped and geolocated document collections. As the number and size of such data collections increase, the problem of efficiently indexing and searching becomes more important. We present novel approaches for keyword search and for event discovery in this space. We also describe applications of such techniques in the emergency response domain.
          Speaker BioDimitrios Gunopulos is a Professor in the Department of Informatics and Telecommunications, University of Athens. He got his PhD from Princeton University in 1995. He was a Postoctoral Fellow at the Max-Planck-Institut for Informatics, a Research Associate at the IBM Almaden Research Center, and an Assistant, Associate, and Full Professor at the Department of Computer Science and Engineering in the University of California Riverside. His research is in the areas of Data Mining, Knowledge Discovery in Databases, Data Science, Databases, Sensor Networks, Peer-to-Peer systems, and Algorithms. He has co-authored over a hundred journal and conference papers that have been widely cited. His research has been supported by NSF (including an NSF CAREER award), the DoD, the Institute of Museum and Library Services, the Tobacco Related Disease Research Program, the European Commission, the General Secretariat of Research and Technology, AT&T, and Nokia. He has served as a General co-Chair in IEEE ICDM 2010, as a PC co-Chair in ECML/PKDD 2011, in IEEE ICDM 2008, in ACM SIGKDD 2006, in SSDBM 2003, and in DMKD 2000.
          Apr. 17, 2015
          SPEAKER: Xifeng Yan (UCSB)
          Schemaless Graph Querying
          Details
          Date and TimeApr. 17, 2015 3 pm
          LocationDBH 3011


          SpeakerXifeng Yan (UCSB)
          TitleSchemaless Graph Querying
          AbstractQuerying complex graph databases such as knowledge graphs and social networks is a challenging task for non-expert users. Due to their complex schemas and variational information descriptions, it becomes very hard for users to formulate a query that can be properly processed by the existing systems. We argue that for a user-friendly graph query engine, it must support various kinds of transformations such as synonym, abbreviation, and ontology. Furthermore, the derived query results must be ranked in a principled manner. In this talk, we will review our recent efforts that address the usability and scalability issues arising in querying large, heterogeneous graph data from three perspectives: query formulation, query execution and result presentation. Our goal is to build an integrated query infrastructure that mitigates the complexity of accessing graph data. It provides simple and intuitive query interfaces, including a schema-less query engine for keyword, graph pattern, and natural language queries. It also helps users digest query output quickly through query result summarization and continuously refines query results online based on user feedback. We will conclude the talk with new directions in managing and accessing graph data.
          Speaker Bio Xifeng Yan is an associate professor at the University of California, Santa Barbara. He holds the Venkatesh Narayanamurti Chair of Computer Science. He has been working on modeling, managing, and mining graphs in information networks, computer systems, social media and bioinformatics. He received NSF CAREER Award, IBM Invention Achievement Award, ACM-SIGMOD Dissertation Runner-Up Award, and IEEE ICDM 10-year Highest Impact Paper Award. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2006 and was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008.
          Apr. 10, 2015
          SPEAKER: Liuba Shrira (Brandeis University)
          Modular and efficient past state protocols for transactional systems
          Details
          Date and TimeApr. 10, 2015 3 pm
          LocationDBH 3011


          SpeakerLiuba Shrira (Brandeis University)
          TitleModular and efficient past state protocols for transactional systems
          AbstractThe remarkable drop in storage costs makes it possible and attractive to capture past application states and store them for a long time. This opens the possibility that kinds of demanding analysis like forecasting, formerly dependent on data warehouses and temporal databases, can become available to everyday applications in off-the-shelf data stores. The challenge is how to organize past states so that they are ``not in the way'' and ``always there'' when needed. Our approach, called Retro, integrates a low-level consistent snapshot system into a data store storage manager, allowing to run unmodified data store programs against the snapshots, side by side with programs running against the current state. The approach is attractive for several reasons. An application can take snapshots efficiently with any frequency, keep them indefinitely, or garbage-collect them at low cost, a useful feature in long-lived systems. A principled methodology derives the snapshot protocols from the native data store storage manager mechanisms, allowing to implement the snapshot system in a modular way, without extensive modifications to the data store internals, making the approach suitable in off-the-shelf data stores. The talk will describe the new techniques that underly Retro and present preliminary performance results from a prototype we built in Berkeley DB, indicating Retro is efficient, imposing moderate performance penalty on the native data store, on expected common workloads.
          Speaker BioLiuba Shrira is a Professor in the Computer Science Department at Brandeis University, and is affiliated with the Computer Science and Artificial Intelligence Laboratory at MIT. She received her Ph.D. from Technion, Israeli Institute of Technology, and has been affiliated with Microsoft Research, Cambridge, UK, Microsoft Research Asia, Beijing, and Computer Science Department, in the Technion, Haifa. Her research interests span aspects of design and implementation of distributed systems and especially storage systems. This includes fault-tolerance, availability and performance issues. Her recent focus is on long-lived transactional storage, time travel (in storage), software upgrades, and support for collaborative access to long-lived objects.
          Apr. 3, 2015 (Special time/place)
          SPEAKER: Vijay Chidambaram (University of Wisconsin - Madison)
          Performance and Reliability in Modern Storage Systems
          Details
          Date and TimeApr. 3, 2015 (Special time/place) 1:30 pm
          LocationDBH 4011


          SpeakerVijay Chidambaram (University of Wisconsin - Madison)
          TitlePerformance and Reliability in Modern Storage Systems
          AbstractStorage services form the platform on which widely-used cloud services, mobile applications, data analytics engines, and transactional databases are built. Such services are trusted with irreplaceable personal and commercial information by users, companies, and even governments. The designers of storage services often have to choose between performance and reliability. If the developer makes the system reliable, performance is often significantly reduced. If the developer instead maximizes performance, a crash could lead to data loss and corruption. In this talk, I describe how to build systems that achieve both strong reliability and high performance. In many systems, reliability is maintained by carefully ordering updates to storage. The key insight is that the low-level mechanism used to enforce ordering is overloaded: it provides durability as well as ordering. I introduce a new primitive, osync(), that decouples ordering from durability of writes. I present Optimistic Crash Consistency, a new crash-recovery protocol that builds on osync() to provide strong reliability guarantees and high performance. I implement these techniques in the Optimistic File System (OptFS) and show that it provides 10X increased performance for some workloads. With researchers in Microsoft, I employ the principles of Optimistic Crash Consistency in a distributed storage system, resulting in 2-5X performance improvements.
          Speaker BioVijay Chidambaram is a Ph.D candidate in the Department of Computer Sciences at the University of Wisconsin-Madison. His current research focus is to ensure the reliability of applications in the rapidly changing landscape of storage and cloud computing. Specifically, he has contributed new reliability techniques in (local and distributed) storage systems, and built frameworks for finding reliability bugs in applications. His work has resulted in patent applications by Samsung and Microsoft. He was awarded the Microsoft Research Fellowship in 2014, and the University of Wisconsin-Madison Alumni Scholarship in 2009.
          Mar. 13, 2015
          SPEAKER: Lada Adamic, David Kempe, Mark Handcock, Carter Butts
          Networks, Algorithms, Statistics and Social Science (Data Science Initiative Sponsored Event)
          Details
          Date and TimeMar. 13, 2015 10am - 5pm
          LocationCalit2 Auditorium


          SpeakerLada Adamic, David Kempe, Mark Handcock, Carter Butts
          TitleNetworks, Algorithms, Statistics and Social Science (Data Science Initiative Sponsored Event)
          AbstractSee http://datascience.uci.edu/event-registration/?ee=14 for more details
          Speaker Bio
          Mar. 6, 2015
          SPEAKER: Raman Grover (AsterixDB)
          Scalable Fault-Tolerant Elastic Data Ingestion in AsterixDB
          Details
          Date and TimeMar. 6, 2015 3 pm
          LocationDBH 3011


          SpeakerRaman Grover (AsterixDB)
          TitleScalable Fault-Tolerant Elastic Data Ingestion in AsterixDB
          AbstractIn this dissertation, we develop the support for continuous data ingestion in AsterixDB, an open-source Big Data Management System (BDMS) that provides a platform for storage and analysis of large volumes of semi-structured data. Data feeds are a mechanism for having continuous data arrive into a BDMS from external sources and incrementally populate a persisted dataset and associated indexes. The need to persist and index "fast-flowing'' high-velocity data (and support ad hoc analytical queries) is ubiquitous. However, the state of the art today involves 'gluing' together different systems. AsterixDB is different in being a unified system with "native support'' for data ingestion. We discuss the challenges and present the design and implementation of the concepts involved in modeling and managing data feeds in AsterixDB. AsterixDB allows the runtime behavior, allocation of resources, and the offered degree of robustness to be customized (by associating an ingestion policy) to suit the application(s) that wish to consume the ingested data. Results from experiments that evaluate the scalability and fault-tolerance of the AsterixDB data feeds facility are reported. We include an evaluation of the built-in ingestion policies and study their effect as well on throughput and latency. An evaluation and comparison with a `glued' together system formed from popular engines - Storm (for streaming) and MongoDB (for persistence) - is also included.
          Speaker Bio
          Feb. 27, 2015
          SPEAKER: Fatma Ozcan (IBM Research - Almaden)
          SQL Comes Back, But This is not Your Father's DBMS!
          Details
          Date and TimeFeb. 27, 2015 3 pm
          LocationDBH 3011


          SpeakerFatma Ozcan (IBM Research - Almaden)
          TitleSQL Comes Back, But This is not Your Father's DBMS!
          AbstractRecent years have seen the resurgence of SQL; this time in the context of Big Data Platforms. SQL-on-Hadoop was one of the hottest topics in 2014, with many newcomers, and adaptations of old systems. There are significant differences between the new incarnation of SQL systems, and the traditional enterprise warehouses. First, semi-structured data is inherent in the Hadoop data as the data frequently comes from noSQL and web sources. Hence JSON data and complex types are more important than in traditional systems. Second, SQL is only one of the steps in the bigger analytical flows. This requires SQL to interact with other frameworks, including streaming, ETL, as well as advanced analytics and machine learning. Finally, we see more user-defined function in big data platforms, because a lot of business logic needs to be executed closer to the data. In this talk, I will first describe IBM Big SQL, an SQL-on-Hadoop offering that works on all Hadoop data formats. I will describe how we adapted IBM database technology to this new world. In the second part of the talk, I will describe a couple of research projects in IBM Almaden that focus on new aspects of the Hadoop SQL engines and the big data eco-system.
          Speaker BioFatma Özcan is a Research Staff Member and a manager at IBM Almaden Research Center. Her current research focuses on platforms and infra-structure for large-scale data analysis, Hadoop and database integration, and query optimization for semi-structured data. Dr Özcan got her PhD degree in computer science from University of Maryland, College Park. She has over 10 years of experience in semi-structured and structured data management, query processing and optimization, and has delivered core technologies into IBM DB2 and BigInsights products. She is the co-author of the book "Heterogeneous Agent Systems", and co-author of several conference papers and patents. She has chaired program committees for various conferences, and served on NSF (National Science Foundation) panels. She is a member of the ACM.
          Feb. 20, 2015
          SPEAKER: Karthik Ramasamy (Twitter)
          Real Time Analytics@Twitter
          Details
          Date and TimeFeb. 20, 2015 3 pm
          LocationDBH 3011


          SpeakerKarthik Ramasamy (Twitter)
          Title Real Time Analytics@Twitter
          AbstractReal time analytics seems to be a buzz word these days. Twitter identified the need for real time analytics early on and invested in a massive data pipeline that collects, aggregates, processes large volumes of data in real time. At the heart of the pipeline is Twitter Storm, a real-time stream processing engine widely used in Twitter. Storm is used for real-time data analytics, time series aggregation, and powering real-time features like trending topics. In this talk, we will give an overview of real time analytics, discuss the twitter real time data pipeline and how Storm is used for extracting analytics. We will also discuss the challenges we faced and lessons we have learned while building this infrastructure at Twitter.
          Speaker BioKarthik is the engineering manager and technical lead for Real Time Analytics at Twitter. He has two decades of experience working in parallel databases, big data infrastructure and networking. He cofounded Locomatix, a company that specializes in real timestreaming processing on Hadoop and Cassandra using SQL that was acquired by Twitter. Before Locomatix, he had a brief stint with Greenplum where he worked on parallel query scheduling. Greenplum was eventually acquired by EMC for more than $300M. Prior to Greenplum, Karthik was at Juniper Networks where he designed and delivered platforms, protocols, databases and high availability solutions for network routers that are widely deployed in the Internet. Before joining Juniper at University of Wisconsin, he worked extensively in parallel database systems, query processing, scale out technologies, storage engine and online analytical systems. Several of these research were spun as a company later acquired by Teradata. He is the author of several publications, patents and one of the best selling book "Network Routing: Algorithms, Protocols and Architectures." He has a Ph.D. in Computer Science from UW Madison with a focus on databases.
          Feb. 13, 2015
          SPEAKER: Yannis Papakonstantinou (UCSD)
          The SQL++ Query Language: Support for native JSON, while backwards-compatible with SQL
          Details
          Date and TimeFeb. 13, 2015 3 pm
          LocationDBH 3011


          SpeakerYannis Papakonstantinou (UCSD)
          TitleThe SQL++ Query Language: Support for native JSON, while backwards-compatible with SQL
          AbstractSQL-on-Hadoop, NewSQL and NoSQL databases provide semi-structured data models (typically JSON-based). They now drive towards declarative, SQL-alike query languages. However, their idiomatic, non-SQL language constructs, the many variations and the lack of formal syntax and semantics pose problems. Notably, database vendors end up with unclear semantics and complicated implementations, as they add one feature at-a-time. The presented SQL++ semi-structured data model bridges JSON and the SQL data model. The SQL++ query language is backwards compatible with SQL, while supporting native JSON. SQL++ includes configuration options that describe different options of language semantics and formally capture the variations of existing database languages. SQL++ is unifying: By appropriate choices of configuration options, the SQL++ semantics can morph into the semantics of any of eleven popular semistructured databases, which we surveyed, as the experimental validation shows. In this way, SQL++ allows a formal characterization of the capabilities of the emerging query languages. We briefly discuss the key role of SQL++ and SQL++ Incremental View Maintenance in the FORWARD application and visualization development platform. SQL++ also is the query language of the FORWARD middleware query processor. We briefly discuss issues and opportunities in federated queries over SQL and non-SQL database.
          Speaker BioYannis Papakonstantinou is a Professor of Computer Science and Engineering at the University of California, San Diego. His research is in the intersection of data management technologies and the web, where he has published over ninety research articles and received over 10,000 citations. He has given multiple tutorials and invited talks, has served on journal editorial boards and has chaired and participated in program committees for many international conferences and workshops. He also teaches for UCSD's Master of Advanced Studies in Data Science. Yannis enjoys to commercialize his research and to inform his research accordingly. He was the CEO and Chief Scientist of Enosys Software, which built and commercialized an early Enterprise Information Integration platform for structured and semistructured data, which became part of BEA's Aqualogic. His lab's recent FORWARD platform is in use by UCSD and commercial applications. He is in the technical advisory board of Brightscope Inc and GraphSQL Inc.
          Feb. 6, 2015
          SPEAKER: Ansgar Scherp
          Extraction and Analyses of Schema Information on the Linked Open Data Cloud
          Details
          Date and TimeFeb. 6, 2015 3 pm
          LocationDBH 3011


          SpeakerAnsgar Scherp
          TitleExtraction and Analyses of Schema Information on the Linked Open Data Cloud
          AbstractThe Linked Open Data (LOD) cloud interlinks information about entities from different data sources and across various domains using the Resource Description Framework (RDF). In contrast to traditional relational databases, the LOD cloud does not provide a fixed, pre-defined schema. Rather, RDF allows for flexibly modeling the data schema by attaching RDF types to the entities and by using domain-specific RDF properties to describe the entities. The talk presents recent developments on the extraction and analysis of schema information from the LOD cloud. For example, with SchemEX, we have developed an efficient approach and tool for a stream-based extraction and indexing of schema information from Linked Open Data (LOD) at web-scale. The schema index provided by SchemEX can be used to locate distributed data sources in the LOD cloud. The SchemEX approach is used in LODatio, a Google-inspired search engine designed for data engineers to find relevant sources of LOD. Further analysis of schema structures on the LOD cloud include investigating the redundancy between type and property information, use of vocabularies in pay-level domains, and change of schema information over weekly snapshots of a larger amount of LOD data. The talk will conclude with current developments and future work.
          Speaker BioAnsgar Scherp is a professor at the Leibniz Information Center for Economics and Kiel University, Kiel, Germany
          Jan. 30, 2015
          SPEAKER: Chris Jermaine (Rice University)
          Large-Scale Machine Learning with the SimSQL System
          Details
          Date and TimeJan. 30, 2015 3 pm
          LocationDBH 3011


          SpeakerChris Jermaine (Rice University)
          TitleLarge-Scale Machine Learning with the SimSQL System
          AbstractIn this talk, I'll describe the SimSQL system, which is a platform for writing and executing statistical codes over large data sets, particularly for machine learning applications. Codes that run on SimSQL can be written in a very high-level, declarative language called Buds. A Buds program looks a lot like a mathematical specification of an algorithm, and statistical codes written in Buds are often just a few lines long. At its heart, SimSQL is really a relational database system, and like other relational systems, SimSQL is designed to support data independence. That is, a single declarative code for a particular statistical inference problem can be used regardless of data set size, compute hardware, and physical data storage and distribution across machines. One concern is that a platform supporting data independence will not perform well. But we've done extensive experimentation, and have found that SimSQL performs as well as other competitive platforms that support writing and executing machine learning codes for large data sets.
          Speaker BioChris Jermaine is an associate professor of computer science at Rice University. He is the recipient of an Alfred P. Sloan Foundation Research Fellowship, a National Science Foundation CAREER award, and an ACM SIGMOD Best Paper Award. In his spare time, Chris enjoys outdoor activities such as hiking, climbing, and whitewater boating. In one particular exploit, Chris and his wife floated a whitewater raft (home-made from scratch using a sewing machine, glue, and plastic) over 100 miles down the Nizina River (and beyond) in Alaska.
          Jan. 16, 2015
          SPEAKER: Ryan Compton (Howard Hughes Research Laboratories)
          Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization
          Details
          Date and TimeJan. 16, 2015 3 pm
          LocationDBH 3011


          SpeakerRyan Compton (Howard Hughes Research Laboratories)
          TitleGeotagging One Hundred Million Twitter Accounts with Total Variation Minimization
          Abstract Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure, allowing us to discard poor location inferences and control the overall error of our approach. Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80% of public tweets. http://arxiv.org/abs/1404.7152
          Speaker Bio Ryan Compton is postdoc in the Information and System Sciences Laboratory at Howard Hughes Research Laboratories in Malibu, CA. His work focuses on social media data mining for early detection of newsworthy events. In 2012 Ryan finished a mathematics PhD at UCLA with a thesis on sparsity promoting optimization for quantum mechanical signal processing. His website is http://www.ryancompton.net.
          Dec. 12, 2014
          SPEAKER: Heri Ramampia (Norwegian University of Science and Technology)
          Boosting Event-Related Image Retrieval with Spatiotemporal Distribution of Tag Terms
          Details
          Date and TimeDec. 12, 2014 3 pm
          LocationDBH 3011


          SpeakerHeri Ramampia (Norwegian University of Science and Technology)
          TitleBoosting Event-Related Image Retrieval with Spatiotemporal Distribution of Tag Terms
          AbstractMedia sharing applications, such as Flickr and Panoramio, contain a large amount of pictures related to real life events. For this reason, although still being a challenging task, the development of effective methods to retrieve these pictures is important. Recognizing this importance, and to improve the retrieval effectiveness of tag-based event retrieval systems, we have proposed a new effective method to extract a set of geographical tag features from raw geo-spatial profiles of user tags. The main idea is to use these features to select the best expansion terms in a machine learning-based query expansion approach. Specifically, we apply rigorous statistical exploratory analysis of spatial point patterns to extract the geo-spatial features. Then, we used the features both to summarize the spatial characteristics of the spatial distribution of a single term, and to determine the similarity between the spatial profiles of two terms -- i.e., term-to-term spatial similarity. To further improve our image retrieval approach, we investigated the effect of combining our geo-spatial features with temporal features. In this presentation, I will try to give an overview of the methods we used (1) to extract the spatio-temporal featrues from image tags, and (2) how to use these features to improve the retrieval performance, focusing on retrieval of event-related images. Finally, I will discuss the results from our experiments, and show how our method has improved the state-of-the-art approach.
          Speaker BioHeri Ramampiaro is an Associate Professor at the Dept of Computer and Information Science, Norwegian University of Science and Technology (NTNU). He is Head of the Data and Information Management group. His main research interests include Information Retrieval, BigData, Information Extraction/Text Mining, Bioinformatics and Health Informatics. Ramampiaro is currently on a one-year research sabbatical, visiting the ISG group, UC Irvine.
          Dec. 5, 2014
          SPEAKER: Daniel Wood (DELL)
          Dell and Big Data Software: Data replication and Reorganization
          Details
          Date and TimeDec. 5, 2014 3 pm
          LocationDBH 3011


          SpeakerDaniel Wood (DELL)
          TitleDell and Big Data Software: Data replication and Reorganization
          AbstractThe role of an enterprise independent software vendor (ISV) is to develop tools and technologies that support the information systems of their customers. Customers cannot easily switch technologies or adapt them to their needs without causing disruptions to their business. This is where software vendors are able to help. I will discuss how an ISV like Quest managed to go from startup to acquisition and the details of two of the relevant enterprise technologies. The first technology, database replication, has become critical in scaling many of the household names we know. We will explore this technology and its evolution from homogeneous database replication to heterogeneous database replication that includes Oracle, SQL Server, and Hadoop systems. The second technology, data reorganization, is crucial to curbing storage hungry databases and improving database performance by defragmenting data.
          Speaker BioDaniel Wood is a manager of software development inside Dell Software Group (Formerly Quest Software) where he has worked for 13 years. Daniel focuses on database management tools and problems at scale. Daniel holds a BA in physics from University of California Santa Barbara.
          Nov. 21, 2014 (Special Time/Place)
          SPEAKER: Prof. Wei Wang (UCLA)
          Big Data Analytics in Science
          Details
          Date and TimeNov. 21, 2014 (Special Time/Place) 11 am
          LocationDBH 6011


          SpeakerProf. Wei Wang (UCLA)
          TitleBig Data Analytics in Science
          AbstractBig data analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information. Its revolutionary potential is now universally recognized. Data complexity, heterogeneity, scale, and timeliness make data analysis a clear bottleneck in many biomedical applications, due to the complexity of the patterns and lack of scalability of the underlying algorithms. Advanced machine learning and data mining algorithms are being developed to address one or more challenges listed above. It is typical that the complexity of potential patterns may grow exponentially with respect to the data complexity, and so is the size of the pattern space. To avoid an exhaustive search through the pattern space, machine learning and data mining algorithms usually employ a greedy approach to search for a local optimum in the solution space, or use a branch-and-bound approach to seek optimal solutions, and consequently, are often implemented as iterative or recursive procedures. To improve efficiency, these algorithms often exploit the dependencies between potential patterns to maximize in-memory computation and/or leverage special hardware for acceleration. In this talk, I will present some open challenges faced by data scientist in biomedical fields and our approaches to tackle these challenges through examples such as multi-locus QTL analysis and transcriptome quantification using RNAseq data.
          Speaker BioWei Wang is a professor in the Department of Computer Science at University of California at Los Angeles and the director of the Scalable Analytics Institute (ScAi). She is a member of the UCLA Jonsson Comprehensive Cancer Center. She received her PhD degree in Computer Science from the University of California at Los Angeles in 1999. Before she rejoined UCLA, she was a professor in Computer Science and a member of the Carolina Center for Genomic Sciences and Lineberger Comprehensive Cancer Center at the University of North Carolina at Chapel Hill from 2002 to 2012, and was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang's research interests include big data, data mining, bioinformatics and computational biology, and databases.
          Nov. 18, 2014 (Special Time/Place)
          SPEAKER: Hwanjo Yu, Associate Professor POSTECH (Pohang University of Science and Technology)
          Search and Mining for Big Data
          Details
          Date and TimeNov. 18, 2014 (Special Time/Place) 4 pm
          LocationDBH 3011


          SpeakerHwanjo Yu, Associate Professor POSTECH (Pohang University of Science and Technology)
          TitleSearch and Mining for Big Data
          AbstractBig data is recently defined (by Gartner) as high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization. In this talk, we first present key challenges in Big data programming, that are distinct from conventional parallel processing. After that, we introduce several research projects dealing with large volume of data in the data mining lab at POSTECH, that are, PubMed relevance feedback search engine, blackbox video search, novel recommendation, and timing when to recommend.
          Speaker Bio Hwanjo Yu received his PhD in Computer Science at the University of Illinois at Urbana-Champaign at June 2004 under the supervision of Prof. Jiawei Han. From July 2004 to January 2008, he had been an assistant professor at the University of Iowa. He is now an associate professor at POSTECH (Pohang University of Science and Technology). He developed influential algorithms and systems in the areas of data mining, database, and machine learning, including (1) algorithms for classifying without negative examples (PEBL, SVMC), (2) privacy-preserving SVM algorithms, (3) SVM-JAVA : an educational java open source for SVM, (4) RefMed : relevance feedback search engine for PubMed, (5) TurboGraph : a fast parallel graph engine handling billion-scale graphs in a single PC. His methods and algorithms were published in prestigious journals and conferences including ACM SIGMOD, ACM SIGKDD, IEEE ICDE, IEEE ICDM, ACM CIKM, etc., where he is also serving as a program committee.
          Nov. 14, 2014
          SPEAKER: Dr. Jiannan Wang (UC Berkeley)
          SampleClean: Fast and Accurate Query Processing on Dirty Data
          Details
          Date and TimeNov. 14, 2014 3 pm
          LocationDBH 3011


          SpeakerDr. Jiannan Wang (UC Berkeley)
          TitleSampleClean: Fast and Accurate Query Processing on Dirty Data
          AbstractThe vision of AMPLab is to integrate Algorithms (Machine Learning), Machines (Cloud Computing) and People (Crowdsourcing) to make sense of Big Data. In the past several years, the lab has developed a variety of open-source software (e.g., Spark and MLBase) to integrate the three resources. For the People part, one of our main focuses is on data cleaning. Real-world data is often “dirty”. Data cleaning is usually a tedious and time-consuming process which requires a lot of human work. In the AMPLab, we have exploited the use of crowdsourcing to reduce the human cost. While crowdsourcing makes data cleaning more scalable, it is still highly inefficient for large datasets. To overcome this limitation, we started the SampleClean project last year. The project aims to investigate how to obtain accurate query results from dirty data, by only cleaning a small sample of the data. We achieved this goal by marrying data cleaning with sampling-based approximate query processing, and addressing many challenging statistical issues. We build a new system that combines our work on crowdsourcing data cleaning and SampleClean query processing. An initial version of the system has shown that our system can help users to obtain very accurate query results on dirty data, at significantly reduced cleaning cost.
          Speaker BioJiannan Wang is a postdoc in the AMPLab at UC Berkeley, where he works with Prof. Michael Franklin and leads the SampleClean project. His research is focusing on developing algorithms and systems for extracting value from “dirty" data. He obtained his PhD from the Computer Science Department at Tsinghua University. During his PhD, he has been a visiting scholar at Chinese University of Hong Kong and UC Berkeley, and an intern at Qatar Computing Research Institute. His PhD research work was supported by Google PhD Fellowship, Boeing Scholarship, and “New PhD Researcher Award” by Chinese Ministry of Education. His PhD dissertation has won the China Computer Federation (CCF) Distinguished Dissertation Award. His similarity-join algorithm has won the first place of EDBT String Similarity Search/Join Competition.
          Nov. 7, 2014
          SPEAKER: Prof. Shahram Ghandeharizadeh (USC)
          BG: A Benchmark for Interactive Social Networking Actions
          Details
          Date and TimeNov. 7, 2014 3 pm
          LocationDBH 3011


          SpeakerProf. Shahram Ghandeharizadeh (USC)
          TitleBG: A Benchmark for Interactive Social Networking Actions
          AbstractBG is a benchmark for interactive social networking actions (also known as simple or small data operations). It is motivated by the flurry of novel data store designs ranging from SQL to NoSQL and NewSQL, Cache Augmented SQL, graph databases and others. More than 40 data stores have been introduced in the past decade including systems contributed by the social networking sites, e.g., Cassandra by Facebook and Voldemort by LinkedIn. Some systems sacrifice strict ACID (Atomicity, Consistency, Isolation, Durability) properties and opt for BASE (Basically Available, Soft-state, Eventual Consistency) to enhance performance. BG strives to compare these systems with one another quantitatively. This presentation details the design of BG and its SoAR metric to rate data stores. We describe how BG quantifies the amount of unpredictable (stale, erroneous, or inconsistent) data produced by a data store. We present ratings from an industrial strength relational database management system, a document store named MongoDB, a graph data store named Neo4j, and an extensible data store named HBase. We show the use of SoAR to evaluate both vertical and horizontal scalability of MongoDB and HBase. We also describe the use of BG to evaluate novel cache replacement algorithms such as CAMP and consistency frameworks such as IQ. We conclude with the use of BG to demonstrate a novel SQL middleware named KOSAR. BG is joint work with Sumita Barahmand. Visit http://bgbenchmark.org to download BG.
          Speaker Bio Shahram Ghandeharizadeh received his Ph.D. degree in Computer Science from the University of Wisconsin, Madison, in 1990. Since then, he has been on the faculty at the University of Southern California. In 1992, he received the National Science Foundation Young Investigator's Award for his research on the physical design of parallel database systems. In 1995, he received an award from the School of Engineering at USC in recognition of his research activities. He was a recipient of the ACM Software System Award 2008. His primary motivation for developing BG is today's proliferation of many data stores and a scarcity of benchmarks to substantiate their claims.
          Oct. 30, 2014 (Special Day/Time)
          SPEAKER: Dr. David Lomet (MSR)
          Achieving Ridiculously High TPS
          Details
          Date and TimeOct. 30, 2014 (Special Day/Time) 4-5 pm
          LocationDBH 3011


          SpeakerDr. David Lomet (MSR)
          TitleAchieving Ridiculously High TPS
          AbstractThe Deuteronomy architecture provides a clean separation of transaction functionality (performed in a transaction component, or TC) from data management functionality (performed in a data component, or DC). In prior work we implemented both a TC and DC that achieved modest performance. We recently built a high performance DC (the Bw-tree key value store) that achieves very high performance on modern hardware via latch-free and log structuring techniques and is currently shipping as an indexing and storage layer in Microsoft systems such as Hekaton and DocumentDB. The new DC executes operations more than 100x faster than the TC we previously implemented. This talk describes how we achieved two orders of magnitude speedup in TC performance and shows that a full Deuteronomy stack can achieve very high performance overall. We built the TC using techniques analogous to the Bw-tree (latch-free data structures, log-structuring). The TC uses multi-version concurrency control (MVCC) to improve concurrency and performance. Our new prototype TC scales to 32 cores on our 4 socket NUMA machine and commits more than a million of transactions per second for a variety of workloads.
          Speaker BioDavid Lomet has been a principal researcher and manager of the Database Group at Microsoft Research, Redmond since 1995. Before that, he was at Digital Equipment Corporation, mainly at Cambridge Research Lab. Earlier, he was a research staff member at IBM Research in Yorktown and subsequently a Professor at Wang Institute. Lomet spent a sabbatical at Newcastle University working with Brian Randell. He is best known for his work in database systems and is one of the inventors of the transaction concept. His database work has focused on access methods, concurrency control, and recovery. His recent Bw-tree work is part of Microsoft's Hekaton main memory database system. He has published over 100 papers, including two SIGMOD "best paper" awards, and has over 40 patents. Lomet has served on many PCs, including SIGMOD, VLDB, and ICDE. He has been ICDE'2000 PC co-chair, VLDB'2006 Core Track Chair, and ICDE'2001 conference co-chair. Lomet has been editor-in-chief of the Data Engineering Bulletin since 1992, and won the 2011 SIGMOD Contributions Award for this. He has also been an editor of ACM TODS and the VLDB Journal, has served on the VLDB Endowment Board and ICDE Steering Committee, and has been Chair of the IEEE TC on Data Engineering. Dr. Lomet is a Fellow of AAAS, ACM, and IEEE.
          Oct. 24, 2014 (Special Time/Place)
          SPEAKER: Prof. Padhraic Smyth and others
          UCI Data Science Kickoff Meeting
          Details
          Date and TimeOct. 24, 2014 (Special Time/Place) 1:30-5 PM (with reception to follow)
          LocationCalIIT2 Auditorium


          SpeakerProf. Padhraic Smyth and others
          TitleUCI Data Science Kickoff Meeting
          AbstractThis will be the official kickoff event for a new UCI campus-wide Data Sciences Initiative. Come hear about the Initiative as well as efforts related to Data Sciences from across the campus. The afternoon's program will be followed by a reception at 5 PM. This event is open to anyone/everyone who is interested. A detailed agenda for this afternoon event can be found hanging here: http://datascience.uci.edu/.
          Speaker Bio
          Oct. 17, 2014
          SPEAKER: Mark Callaghan (Facebook)
          Still Doing It Wrong
          Details
          Date and TimeOct. 17, 2014 3 pm
          LocationDBH 3011


          SpeakerMark Callaghan (Facebook)
          TitleStill Doing It Wrong
          AbstractFamous people have interesting things to say about my work and my fate. I hope to provide more context on doing "small data" (per request, e.g., OLTP) at scale. I will start with a short history of web-scale MySQL from 2005 until today and predict where it is heading in the next 5 years. My current work is to improve storage efficiency for small data (per request) workloads. Algorithms tend to be fixed in their behavior, while both workloads and storage device performance vary. There is thus an opportunity to improve efficiency by making algorithms more dynamic.
          Speaker BioMark Callaghan has worked with great teams to make MySQL better for scale-out deployments at Facebook and Google for 9+ years. His current focus at Facebook is the analysis and improvement of database algorithms and storage systems for small data (OLTP) workloads. He also works with WebScaleSQL and RocksDB to make MySQL and MongoDB better. Prior to his web-scale work Mark spent many years working on RDBMS internals at Oracle and Informix. He invented and implemented a very fast general purpose sort algorithm for the Oracle RDBMS. He has an MS in CS from UW-Madison.
          Oct. 10, 2014
          SPEAKER: Prof. Jimeng Sun (Georgia Institute of Technology)
          Do it Once, Do it Right - Building a Scalable Predictive Modeling Platform for Healthcare Applications
          Details
          Date and TimeOct. 10, 2014 3 pm
          LocationDBH 3011


          SpeakerProf. Jimeng Sun (Georgia Institute of Technology)
          TitleDo it Once, Do it Right - Building a Scalable Predictive Modeling Platform for Healthcare Applications
          Abstract Predictive models are designed to predict the likelihood of one or more outcomes and are playing an increasing important role in biomedical research. Thanks to the explosion of Electronic Heart Records (EHR), the interest in building predictive models based on EHR data has skyrocketed in recent years. There are some major challenges that remain to be addressed. In this talk I will explore two of them. Effective algorithms are lacking in dealing with high-dimensional, longitudinal, sparse, inaccurate and inconsistent EHR data. The methodologies to develop predictive models are still labor intensive and ad-hoc. These rudimentary approaches are hindering the quality and throughput of healthcare and biomedical research. In this talk, we promote a holistic approach that addresses both challenges by combining 1) algorithm development and 2) system building. We believe that a more robust and domain specific big-data platform could significantly speedup the development of robust and accurate predictive models for biomedical research. I will present different projects covering both aspects of such a platform: Algorithms: I will first describe our work on computational phenotyping from EHR data using sparse tensor factorization; then I will present a patient similarity method using supervised distance metric learning System: I will introduce a parallel predictive modeling platform using Hadoop for enabling large scale modeling and exploration of big healthcare data
          Speaker BioJimeng Sun is an Associate Professor of School of Computational Science and Engineering at College of Computing in Georgia Institute of Technology. Prior to joining Georgia Tech, he was a research staff member at IBM TJ Watson Research Center. His research focuses on health analytics using electronic health records and data mining, especially in designing novel tensor analysis and similarity learning methods and developing large-scale predictive modeling systems. Dr. Sun has worked on various healthcare applications such as computational phenotyping from electronic health records, heart failure onset prediction and hypertension control management. He has collaborated with many healthcare institutions including Vanderbilt university medical center, Children's healthcare of Atlanta, Center for Disease Control and Prevention (CDC), Geisinger Health System and Sutter Health. He has published over 70 papers, filed over 20 patents (5 granted). He has received ICDM best research paper award in 2008, SDM best research paper award in 2007, and KDD Dissertation runner-up award in 2008. Dr. Sun received his B.S. and M.Phil. in Computer Science from Hong Kong University of Science and Technology in 2002 and 2003, and a PhD in Computer Science from Carnegie Mellon University in 2007.
          Oct. 3, 2014
          SPEAKER: ISG Faculty
          2014-15 ISG Welcome (Back) Seminar
          Details
          Date and TimeOct. 3, 2014 3 pm
          LocationDBH 3011


          SpeakerISG Faculty
          Title2014-15 ISG Welcome (Back) Seminar
          Abstract
          Speaker Bio
          May. 30, 2014
          SPEAKER:
          No Seminar
          Details
          Date and TimeMay. 30, 2014 11 pm
          LocationDBH 6011


          Speaker
          TitleNo Seminar
          AbstractGo to CS Colloquium for Ed Lazowska's Big Data talk.
          Speaker Bio
          May. 23, 2014
          SPEAKER: Odej Kao (TU Berlin)
          Dynamic Scheduling and Resource Management for Big Data
          Details
          Date and TimeMay. 23, 2014 3 pm
          LocationDBH 3011


          SpeakerOdej Kao (TU Berlin)
          TitleDynamic Scheduling and Resource Management for Big Data
          Abstract
          Speaker Bio
          May. 16, 2014
          SPEAKER: Daniel Ford (Dell Research)
          The Unintended Consequences of The Internet of Things
          Details
          Date and TimeMay. 16, 2014 3 pm
          LocationDBH 3011


          SpeakerDaniel Ford (Dell Research)
          Title The Unintended Consequences of The Internet of Things
          Abstract Computer technology is subject to rapid change and evolution. Each new development seems to be subject to exuberant hyperbole. The latest round is focusing on embedded electronics and is called The Internet of Things, or just "The IoT." The main force powering this "hype machine" is the suggestion of vast new commercial opportunities, estimated by some sources to be in the range of $19 Trillion. This talk begins with the conclusion that the "hype" about The Internet of Things is underplayed, and that the commercial implications of The Internet of Things are the smallest parts of a bigger story. It compares The IoT to other historical technological developments, examining what is similar, and what is without historical precedent. In particular, it finds the impact of applications of The Internet of Things to be spectacularly unconstrained. All this leads to the idiom "Careful what you wish for." So, while commerce is "wishing for" a $19 Trillion market, our society, and our economy, are going to get something else. The talk concludes with an examination of some of the potential unintended consequences of The Internet of Things. It predicts that areas as diverse as Brand Management, Advertising, Propaganda, Healthcare, Law Enforcement, Insurance, Automobile ownership, Politics, and Warfare, to name just a few, will all be affected in ways few are considering.
          Speaker BioDr. Ford is Executive Director and Chief Scientist for Mobility and The Internet of Things for Dell Research in San Jose, California. Prior to joining Dell Research, Dr. Ford was CEO and co-founder of Paupt Labs LLC, in New York. He also was with IBM Research, in a variety of positions, for nineteen years before that. His immediate research interests are focused on The Internet of Things, including supporting software architectures, novel applications, and unintended consequences. Previous research interests have included Healthcare Informatics, Pandemic Modeling, Social Networking, Mobile Computing, Web Search, and High Performance Tertiary Storage Systems. Dr. Ford has twenty-eight issued US patents and dozens of peer reviewed publications. Dr. Ford earned his Ph.D. in Computer Science from the University of Waterloo in 1992.
          May. 9, 2014
          SPEAKER: Vinayak Borkar (UC Irvine)
          An Efficient Platform for Parallel Data Processing on Large Clusters
          Details
          Date and TimeMay. 9, 2014 3 pm
          LocationDBH 3011


          SpeakerVinayak Borkar (UC Irvine)
          TitleAn Efficient Platform for Parallel Data Processing on Large Clusters
          AbstractThe growth of user activity on the Internet and the rise of social networks has led to an exponential growth of data. Storage of this data and its subsequent analysis have posed significant challenges. Unlike the business data that drove research and development of relational databases for the past several decades, Web and Social data tend to have rich and varying structure, making traditional database systems a poor choice for their management. The astronomical size and semi-structured nature of this new data has forced companies in the business of managing it to look for other cost-effective solutions. In 2004 Google presented the MapReduce system as a way to harness the power of thousands of commodity machines to solve problems like building a search index over the entire World Wide Web in reasonable time at reasonable cost; MapReduce turned out to be a useful tool for performing other parallel computations over large amounts of data while presenting a simple programming model to users. Soon after the MapReduce paper, the open source community created the Hadoop platform to resemble Google's MapReduce system. Hadoop soon became a popular platform for processing large amounts of data using commodity computers. In an effort to boost user productivity, new declarative languages were designed and built to compile high-level declarative queries down to Hadoop MapReduce programs, making Hadoop the de-facto runtime layer for large-scale parallel data computation. While widely used and popular today, Hadoop was not intentionally designed to be a runtime layer for higher-level declarative languages. In this talk we explore an alternative to the Hadoop platform whose design is rooted in parallel database research from the 1980s and 1990s. Hyracks is an efficient runtime platform that accepts data-parallel jobs from users and from high-level language compilers and executes them on a cluster of commodity machines. We describe the design of Hyracks as well as salient aspects of its implementation. Using Hyracks we study the trade offs involved in building an extensible and reusable set of runtime components for large-scale data processing. We show experimentally that Hyracks is a highly configurable platform and well-suited for several different data processing tasks. We do so via three different use cases: executing queries expressed in high-level declarative languages, running actual Hadoop jobs using the Hadoop Compatibility Layer of Hyracks, and finally, running parallel graph computations using Pregelix, an open source graph analytics platform that uses Hyracks and emulates Google's Pregel programming model for analyzing large graphs in parallel. A number of new declarative parallel languages have been proposed for querying and analyzing very large data sets. To aid with the construction of declarative parallel query compilers, we propose an extensible algebraic framework called Algebricks. Algebricks is a model-agnostic compilation framework that provides the ability for a compiler to inject its own semantics in an extensible manner. Algebricks includes a reusable and extensible set of logical and physical operators and a large set of general purpose rewrite rules useful to most query compilers. We describe the implementation of three different language compilers (AsterixDB for AQL, Hivesterix for HiveQL, and VXQuery for XQuery) that use the Algebricks compiler framework to create parallel jobs to run on a Hyracks Cluster.
          Speaker Bio
          May. 2, 2014
          SPEAKER: Dick Bulterman (FXPAL)
          Authoring Support for Social Media Interaction: Understanding Compound Multimedia Dependencies
          Details
          Date and TimeMay. 2, 2014 3 pm
          LocationDBH 3011


          SpeakerDick Bulterman (FXPAL)
          TitleAuthoring Support for Social Media Interaction: Understanding Compound Multimedia Dependencies
          Abstract Creating compelling multimedia content is a difficult task. It involves not only the creative process of developing a compelling media-based story, but it also requires significant technical support for content editing and management. This process is made more complex by an increased desire for media personalization: the story you tell Mom about an event may be different than the version you’d like to share with your friends. It is also different from the version you’d like to tell your own children 15 years after the event had taken place. The makes media authoring a context- and time- sensitive problem. No wonder most researchers analyze media instead of create it! It is tempting to categorize multimedia authoring in terms of component areas: media encoding, media storage, media access, media transport, media rendering and overall presentation composition and control. Unfortunately, this partitioning blurs the dependencies that exist among these component areas that ultimately determine the success of an authoring system. Using the broad problem of social media interaction as an example, this talk will consider the composite effects of creating and accessing and transporting and presenting rich media objects for use by non- technical end users. The talk will survey several approaches to describe and manage media interactions. We will focus on the temporal modelling of context-sensitive personalized interactions of complex collections of independent media objects. Using the concepts of ‘togetherness’ being employed in the EU’s FP-7 project TA2: Together Anywhere, Together Anytime, we will follow the process of media capture, profiling, composition, sharing and end-user manipulation. We will consider the promise of using automated tools and contrast this with the reality of letting real users manipulation presentation semantics in real time. The talk will not present a closed form solution, but will present a series of topics and problems that can stimulate the development of a new generation of systems to stimulate social media interaction.
          Speaker Bio Dr. Dick Bulterman is President of the FX Palo Alto Laboratory (FXPAL) and professor of computer science at the VU University in Amsterdam. Before joining FXPAL in 2013, he was a senior researcher at CWI in Amsterdam, where he founded the Distributed Multimedia Languages and Interfaces group. In 1999, he started Oratrix Development BV, a CWI spin-off company that transferred the group's SMIL-based GRiNS software to many parts of the civilized world. Prior to joining CWI in 1988, he was on the faculty of the Division of Engineering at Brown University, where he was part of the Laboratory for Engineering Man/Machine Systems. Dr. Bulterman received a Ph.D. in computer science from Brown University (USA) in 1982. In 2013 he was awarded the ACM SIGMM Lifetime Technical Achievement Award. He is a member of Sigma Xi, the ACM and the IEEE.
          April. 18, 2014
          SPEAKER: Pekka Kostamaa (Teradata)
          Big Data at Teradata – Teradata Unified Data Architecture
          Details
          Date and TimeApril. 18, 2014 3 pm
          LocationDBH 3011


          SpeakerPekka Kostamaa (Teradata)
          TitleBig Data at Teradata – Teradata Unified Data Architecture
          Abstract Unified Data Architecture (UDA) is Teradata’s strategy and program for Big Data. UDA combines three platforms in a unified architecture: 1. Data Warehouse; 2. Discovery Platform; 3. Data Platform. This talk will describe the architecture and present real customer use cases.
          Speaker BioPekka Kostamaa is Senior Director of Product Management for the Teradata Database. His team is responsible for the strategy and definition of new releases of Teradata, from concept phase through development to delivery to customers. Previously, Pekka was the Vice President of Engineering and Big Data Lab for Teradata Aster and Director of Advanced Development and Enterprise Architecture for Teradata R and D. He has several publications, holds twenty patents with several pending, and presented the Keynote Speech at the ICDE 2011 conference and an Invited Talk at the 2012 DOLAP Workshop. He is a member of the UCLA Computer Science Advisory Boards.
          March. 14, 2014
          SPEAKER: Christoph Freytag (Humboldt U)
          When to say NO to protect Privacy when answering Queries
          Details
          Date and TimeMarch. 14, 2014 3 pm
          LocationDBH 3011


          SpeakerChristoph Freytag (Humboldt U)
          TitleWhen to say NO to protect Privacy when answering Queries
          AbstractThis talk presents privacy concepts that keep the balance between utility and privacy when returning answers to a sequence of queries. In particular we show how to model the (increasing) knowledge of an adversary resulting from the answers to queries by a sequence of bipartite graphs. Those provide the foundation for deciding when a privacy breach occurs (might occur) and how to balance the need for accurate responses versus the right for privacy. Examples demonstrate the intricacies of managing this trade-off.
          Speaker BioJohann-Christoph Freytag is currently full professor for Databases and Information Systems (DBIS) at the Computer Science Department of the Humboldt-Universität zu Berlin, Germany. Before joining the department in 1994, he was a research staff member at the IBM Almaden Research Center (1985-1987), a researcher at the European Computer-Industry-Research Centre (ECRC, in Munich, Germany, 1987-1989), and the head of Digital's Database Technology Center (also in Munich, 1990-1993). He holds a Ph.D. in Applied Mathematics/Computer Science from Harvard University, MA. Prof. Freytag's research interests include all aspects of query processing and query optimization in object-relational database systems, new developments in the database area (such as semi-structured data, data quality, databases and security), privacy in database systems, and applying database technology to applications such as GIS, genomics, and bioinformatics/life science. In the last years he received the IBM Faculty Award four times for collaborative work in the areas of databases, middleware, and bioinformatics/life science. He organized the VLDB conference in Berlin in 2003 and was a member of the VLDB Endowment (2001-2007) and in the head of the German database interest group of the GI (Fachbereich DBIS, Gesellschaft fur Informatik).
          March. 7, 2014
          SPEAKER: Michalis Petropoulos and Mohamed Soliman (Pivotal)
          Orca: A Modular Query Optimizer Architecture for Big Data
          Details
          Date and TimeMarch. 7, 2014 3 pm
          LocationDBH 3011


          SpeakerMichalis Petropoulos and Mohamed Soliman (Pivotal)
          TitleOrca: A Modular Query Optimizer Architecture for Big Data
          AbstractThe performance of analytical query processing in data management systems depends primarily on the capabilities of the system's query optimizer. Increased data volumes and heightened interest in processing complex analytical queries have prompted Pivotal to build a new query optimizer. In this talk we present the architecture of Orca, the new query optimizer for all Pivotal data management products, including Pivotal Greenplum Database and Pivotal HAWQ. Orca is a comprehensive development uniting state-of-the-art query optimization technology with own original research resulting in a modular and portable optimizer architecture. In addition to describing the overall architecture, we highlight several unique features and present performance comparisons against other systems.
          Speaker Bio Michalis Petropoulos is managing the query processing team at Pivotal Inc. His R and D team develops the query optimizer and executor for Pivotal’s massively parallel and distributed data management products, Pivotal Greenplum Database and Pivotal HAWQ. Before that, Michalis was an Assistant Professor in the Computer Science and Engineering Department at SUNY Buffalo from 2006 to 2010. He received his PhD in Computer Science from the University of California, San Diego in 2005. In 2006, Michalis co-authored a publication that was awarded an Honorable Mention as top-3 finalist in the SIGMOD 2006 Best Paper Award competition. In 2010, he co-authored a publication that received the Best Interdisciplinary Paper Award at the ACM Conference on Information and Knowledge Management (CIKM). Mohamed Soliman is a Staff 1 at Pivotal, where he works on building massively distributed database systems for efficient support of data warehousing and analytics. His work at Pivotal is mainly in the research and development of Orca, a next generation query optimizer for Big Data. Prior to that, Mohamed has conducted graduate studies at University of Waterloo, where he received his PhD in computer science in 2010 on the topic of rank-aware retrieval in probabilistic databases.
          Feb. 21, 2014
          SPEAKER: Inci Cetindil (UCI ISG)
          Details
          Date and TimeFeb. 21, 2014 3 pm
          LocationDBH 3011


          Speaker Inci Cetindil (UCI ISG)
          Title
          Abstract
          Speaker Bio
          Feb. 14, 2014
          SPEAKER: Siripen Pongpaichet (UCI ISG)
          EventShop
          Details
          Date and TimeFeb. 14, 2014 3 pm
          LocationDBH 3011


          SpeakerSiripen Pongpaichet (UCI ISG)
          Title EventShop
          Abstract EventShop is a computational framework that has the ability to integrate and process streaming data from heterogeneous data sources. Data from all data sources are first transformed into a Space/Time/Theme (STT) data model, with a hierarchical extension of STT being used to handle data coming from sources that have different resolutions in space and/or time. Various types of spatio-temporal operators can then be applied to recognize and predict actionable situations. Appropriate actions/recommendations can be sent to individuals based on their circumstances. This talk will provide an overview of the EventShop project and the sorts of use cases it is intending to address. (Here is the link to our website http://eventshop.ics.uci.edu:8080/sln/)
          Speaker Bio Siripen is a UCI CS Ph.D. student working in Ramesh Jain's EventShop group.
          Feb. 7, 2014
          SPEAKER: Inci Cetindil (UCI ISG)
          (Postponed due to illness)
          Details
          Date and TimeFeb. 7, 2014 3 pm
          LocationDBH 3011


          Speaker Inci Cetindil (UCI ISG)
          Title (Postponed due to illness)
          Abstract
          Speaker Bio
          Jan 31, 2014 (Special Location)
          SPEAKER: Padhraic Smyth (UCI)
          Statistical Machine Learning with Count Data (Informatics Seminar)
          Details
          Date and TimeJan 31, 2014 (Special Location) 3 pm
          LocationDBH 6011


          SpeakerPadhraic Smyth (UCI)
          TitleStatistical Machine Learning with Count Data (Informatics Seminar)
          Abstract (Regular ISG Seminar attendees are encouraged to attend this week's very interesting Informatics Seminar - we don't want to conflict with this talk!) Data represented in the form of sets of counts is easy to acquire and can be surprisingly useful in practice. For example, a simple way to represent a set of documents is as a "bag of words" where each document is represented just by the counts of words that occur in the document, a representation that has been the basis for many successful applications of machine learning to text data. In this talk we will review some important developments over the past 10 years in modeling data represented in the form of counts, combining ideas from statistics and machine learning. The talk will describe the general principles involved and then illustrate how these ideas can be applied to text documents, email communications, and social networks, including recent work in my research group. The talk will conclude with some speculative comments on future directions.
          Speaker BioPadhraic Smyth is a Professor in the Department of Computer Science (with a joint appointment in Statistics) and Director of the Center for Machine Learning and Intelligent Systems at the University of California, Irvine. His research interests include machine learning, data mining, pattern recognition, and applied statistics. He received a first class honors degree in Electronic Engineering from University College Galway (National University of Ireland) in 1984, and the MSEE and PhD degrees from the Electrical Engineering Department at the California Institute of Technology in 1985 and 1988 respectively. From 1988 to 1996 he was a Technical Group Leader at the Jet Propulsion Laboratory, Pasadena, and has been on the faculty at UC Irvine since 1996. Dr. Smyth is an ACM Fellow, a AAAI Fellow, and recieved the ACM SIGKDD Innovation Award in 2009. He is co-author of two well-known research texts in data mining: Modeling the Internet and the Web: Probabilistic Methods and Algorithms (with Pierre Baldi and Paolo Frasconi in 2003), and Principles of Data Mining, MIT Press, August 2001, co-authored with David Hand and Heikki Mannila. He has served in editorial positions for journals such as the Journal of the American Statistical Association, the IEEE Transactions on Knowledge and Data Engineering, and the Journal of Machine Learning Research. His research has been funded by a variety of government agencies such as NSF, NIH, ONR, DARPA and DOE, as well by companies such as Google, IBM, Microsoft, and Yahoo! In addition to his academic research he is also active in consulting, working with companies such as Samsung, Netflix, eBay, Oracle, Microsoft, Yahoo!, Nokia, and ATT.
          Jan. 24, 2014
          SPEAKER: Inna Giguere (Data Architect, Disney Interactive Media BI)
          Web Analytics at the happiest place on earth
          Details
          Date and TimeJan. 24, 2014 3 pm
          LocationDBH 3011


          SpeakerInna Giguere (Data Architect, Disney Interactive Media BI)
          TitleWeb Analytics at the happiest place on earth
          Abstract Business analytics requirements at Disney Interactive have pushed the limits of the Omniture reporting systems that has been used for the past decade into building an internal tracking and data warehouse solution. Consequently, we have built a data warehouse and enabled Video and Game Producers to fine-tune new content in near real-time, as well as provide an exhaustive platform for Data Scientists to build recommendation systems. The presentation will focus on current data pipeline architecture at Disney Interactive and cover specific steps and challenges. I will discuss how we (BI team) were able to leverage Hadoop’s map/reduce processing capabilities and Vertica MPP engine to load data continuously from multiple sources. However, one of our biggest challenges remains handling memory intensive hash joins in Vertica without sacrificing performance.
          Speaker BioInna Giguere is Data Architect at Disney Interactive Media Business Intelligence group. For the last 2 years she has been leading the architecture design and implementation of the Analytics Data Warehouse utilizing Hadoop, Vertica, and Scribe technology. Previously based out of San Francisco Bay Area and London, Inna has 16 years on industry experience creating scalable Data Warehouse solutions with focus on DB performance optimization in transactional and reporting systems. Her experience spans across technologies starting from COBOL/DB2 to Oracle (8i – 11g), to SQL Server 2005-2012, to Vertica 6.1 working on datasets ranging from hundreds of megabytes to hundreds of terabytes. She has earned MS in Statistics in 2010.
          Jan. 17, 2014
          SPEAKER: Phillip Sheu (Department of EECS)
          Semantic Computing and Applications
          Details
          Date and TimeJan. 17, 2014 3 pm
          LocationDBH 3011


          SpeakerPhillip Sheu (Department of EECS)
          TitleSemantic Computing and Applications
          Abstract Semantic Computing (SC) is an emerging field that addresses computing technologies which allow users to search, create, manipulate and connect computational resources (including data, documents, tools, people, agents, devices, etc.) based on semantics. Semantic Computing includes the computing technologies (e.g., artificial intelligence, natural language, software engineering, data and knowledge engineering, computer systems, signal processing, etc.), and their interactions, that may be used to extract or process computational content and descriptions. While some areas of Semantic Computing have appeared as pieces in different disciplines, Semantic Computing glues these pieces together into an integrated theme with synergetic interactions. It addresses not only the analysis and transformation of signals (e.g., pixels, words) into useful information, but also how such information can be accessed and used to synthesize new signals. The National Science Foundation has approved the planning of an Industry/University Cooperative Research Center (I/UCRC) for Semantic Computing currently involving UCI, UCSD and UCLA. The missions of the I/UCRC are to develop semantic technologies that may facilitate the transition of the Internet into its next generation, and develop new business models to stimulate, strengthen, and grow the economy. An important outcome of this I/UCRC is a Semantic Problem Solving Network (SPSN) which is a public consortium of resources from all domains including data, documents, devices, products, services, and people. The resources are interconnected and integrated with a service-oriented architecture and a semantic layer to help the public to solve general problems and professional users to solve domain specific problems (e.g., finance, IT, health, defense, entertainment, education, manufacturing). This talk will introduce Semantic Computing and its applications, the operations of the I/UCRC, the architecture of the SPSN, how companies and academic researchers can join or affiliate with the I/UCRC and SPSN, and how companies and academic researchers can benefit.
          Speaker Bio Phillip C.-Y. Sheu is a professor of EECS, Computer Science and Biomedical Engineering at the University of California, Irvine. He received his B.S. degree in EE from National Taiwan University, and MS and Ph.D degrees in EECS from the University of California at Berkeley. Dr. Sheu’s current research interests include semantic computing and complex biomedical systems. He is a fellow of IEEE, a founder of the IEEE Computer Society Technical Committee on Semantic Computing (TCSEM), IEEE International Conference on Semantic Computing (ICSC), International Journal of Semantic Computing (IJSC), the NSF I/UCRC (Industry University Cooperative Research Center) for Semantic Computing (ISC) being planned, and a main author of the book Semantic Computing (SC, eds. P. Sheu, H. Yu, C.V. Ramamoorthy, A. Joshi and L.A. Zadeh, IEEE and Wiley, 2010).
          Nov. 15, 2013
          SPEAKER: José A. Blakeley (Microsoft Corporation)
          Microsoft SQL Server Parallel Data Warehouse - Architecture Overview
          Details
          Date and TimeNov. 15, 2013 3 pm
          LocationDBH 3011


          SpeakerJosé A. Blakeley (Microsoft Corporation)
          TitleMicrosoft SQL Server Parallel Data Warehouse - Architecture Overview
          Abstract In this talk I will present an architectural overview of the SQL Server Parallel Data Warehouse DBMS system. PDW is a massively parallel processing, share nothing, scale-out version of SQL Server for data warehouse and big data workloads. The product is packaged as a database appliance built on industry standard hardware.
          Speaker BioJosé Blakeley is Partner Architect in the Modern Data Warehousing Unit of the Server and Tools Division at Microsoft where he contributes to the development of the SQL Server Parallel Data Warehouse (PDW) DBMS product. José joined Microsoft in 1994. Some of his contributions at Microsoft include the development of the OLE DB data access interfaces, the integration of the .NET runtime with SQL Server 2005, the extensibility features in SQL Server, and the creation of the ADO.NET Entity Framework in Visual Studio 2008. José has authored many conference papers, book chapters and journal articles on design aspects of relational and object database management systems, and data access. Jose has 20 patents awarded and 22 patents pending. He became an ACM Fellow in 2009. Before joining Microsoft, José was a member of the technical staff with Texas Instruments where he was co-principal investigator of the DARPA Open-OODB system. He received a B. Eng from ITESM, Monterrey, Mexico, and a Ph.D. in computer science from University of Waterloo, Canada.
          Oct. 25, 2013
          SPEAKER: David Lomet (joint work with Justin Levandoski and Sudipta Sengupta)(MSR)
          LLAMA: A Cache/Storage Subsystem for Modern Hardware
          Details
          Date and TimeOct. 25, 2013 3 pm
          LocationDBH 3011


          SpeakerDavid Lomet (joint work with Justin Levandoski and Sudipta Sengupta)(MSR)
          TitleLLAMA: A Cache/Storage Subsystem for Modern Hardware
          Abstract LLAMA is a subsystem designed for new hardware environments that supports an API for page-oriented access methods, providing both cache and storage management. Caching (CL) and storage (SL) layers use a common mapping table that separates a page’s logical and physical location. CL supports data updates and management updates (e.g., for index re-organization) via latch-free compare-and-swap atomic state changes on its mapping table. SL uses the same mapping table to cope with page location changes produced by log structuring on every page flush. To demonstrate LLAMA’s suitability, we tailored our latch-free Bw-tree implementation to use LLAMA. The Bw-tree is a B-tree style index. Layered on LLAMA, it has higher performance and scalability using real workloads compared with BerkeleyDB’s B-tree, which is known for good performance.
          Speaker Bio David Lomet (Ph.D from Penn) is a Principal Researcher and manager of the Database Group at Microsoft Research Redmond. Earlier, he was at Digital's CRL, Wang Institute, and IBM Research. Lomet has over 100 papers on databases, indexing, concurrency, and recovery, including two SIGMOD "best papers". He is an inventor of transactions. Lomet has served on SIGMOD, VLDB, and ICDE PCs, being co-chair of ICDE'2000 and VLDB'2006. He won SIGMOD's Contributions Award for his service as Data Engineering Bulletin Editor-in-Chief since 1992. He has been editor of ACM TODS, VLDB Journal, and DAPD. He has served on the VLDB Endowment and ICDE Steering Committee, has been IEEE TCDE Chair and is a Fellow of AAAS, ACM, and IEEE.
          Oct. 18, 2013
          SPEAKER: Tyson Condie (Microsoft and UCLA)
          Big Learning Systems
          Details
          Date and TimeOct. 18, 2013 3 pm
          LocationDBH 3011


          SpeakerTyson Condie (Microsoft and UCLA)
          TitleBig Learning Systems
          Abstract A new wave of systems is emerging in the space of Big Data Analytics that open the door to programming models beyond Hadoop MapReduce (HMR). It is well understood that HMR is not ideal for applications in the domain of machine learning and graph processing. This realization is fueling a number of new (Big Data) system efforts: Berkeley Spark, Google Pregel, GraphLab (CMU), and AsterixDB (UC Irvine), to name a few. Each of these add unique capabilities, but form islands around key functionalities: fault-tolerance, resource allocation, and data caching. In this talk, I will provide an overview of some Big Data systems starting with Google's MapReduce, which defined the foundational architecture for processing large data sets. I will then identify a key limitation in this architecture; namely, its inability to efficiently support iterative workflows. I will then describe real-world examples of systems that aim to fill this computational void. I will conclude with a description of my own work on a layering that unifies key runtime functionalities (fault-tolerance, resource allocation, data caching, and more) for workflows (both iterative and acyclic) that process large data sets.
          Speaker Bio Tyson Condie is a principal scientist with the Cloud and Information Services Lab at Microsoft and an Assistant Professor at UCLA. He received his Ph.D. from Berkeley. His research focuses on data analytics, distributed systems, Internet-scale query processing and optimization, and declarative language design and implementation. His current work involves building a system software stack for large-scale data processing tasks on resource managers like Apache YARN, Berkeley Mesos, Google Omega, and Facebook Corona.
          Oct. 11, 2013
          SPEAKER: Anhai Doan (University of Wisconsin and WalmartLabs)
          Toward Hands-Off Crowdsourcing: Crowdsourced Entity Matching for the Masses
          Details
          Date and TimeOct. 11, 2013 3 pm
          LocationDBH 3011


          SpeakerAnhai Doan (University of Wisconsin and WalmartLabs)
          TitleToward Hands-Off Crowdsourcing: Crowdsourced Entity Matching for the Masses
          AbstractEntity matching (EM) finds data records that refer to the same real-world entity. Recent work has applied crowdsourcing to EM, and has clearly established the promise of this approach. This work however is limited in that it crowdsources only parts of the EM workflow, requiring a developer who knows how to code to execute the remaining parts. Consequently, this work does not scale to the growing EM need at enterprises and crowdsourcing startups, and cannot handle scenarios where ordinary users (i.e., the masses) want to leverage crowdsourcing to match entities. To address these problems, we propose the notion of hands-off crowdsourcing (HOC), which crowdsources the entire workflow of a task, thus requiring no developers. We show how HOC can represent a next logical direction for crowdsourcing research, scale up EM at enterprises and crowdsourcing startups, and open up crowdsourcing for the masses. We describe Corleone, a HOC solution for EM. We show how Corleone uses the crowd to generate blocking rules, applies active learning to learn matchers, estimates accuracy given severe skew, and identifies difficult-to-match pairs to which Corleone can apply more complex matchers. Finally, we discuss the implications of our work to executing crowdsourced RDBMS joins, cleaning learning models, and soliciting complex information types from crowd workers.
          Speaker BioAnHai Doan is an Associate Professor in the database group at the University of Wisconsin, Madison. His current interests include crowdsourcing, knowledge bases, data integration, and information extraction. He received the ACM Doctoral Dissertation Award in 2003 and a Sloan fellowship in 2007. AnHai was Chief Scientist of Kosmix, a social media startup acquired by Walmart in 2011. Currently he also works as Chief Scientist of WalmartLabs, a research and development lab devoted to analyzing and integrating data for e-commerce. AnHai is a co-author of “Principles of Data Integration” (with Alon Halevy and Zack Ives), a textbook published by Morgan Kaufmann in 2012.
          Sept. 20, 2013
          SPEAKER: Li Xiong (Emory University)
          Real-Time Aggregate Monitoring with Differential Privacy
          Details
          Date and TimeSept. 20, 2013 2 pm
          LocationDBH 3011


          SpeakerLi Xiong (Emory University)
          TitleReal-Time Aggregate Monitoring with Differential Privacy
          Abstract While Big Data promises significant economic and social benefits, it also raises serious privacy concerns. Real-time aggregate statistics of data collected from individuals can be shared to enable many applications such as disease surveillance and traffic monitoring. However, it must be ensured that the privacy of individuals is not compromised. While differential privacy has emerged as a de facto standard for private data analysis, directly applying the differential privacy mechanisms on time-series has limited utility due to high correlations between data values. In this talk, I will present FAST, a novel Filtering and Adaptive Sampling based framework for monitoring aggregate Time-series under differential privacy. FAST adaptively samples long time-series according to detected data dynamics and simultaneously uses filtering techniques to dynamically predict and correct released data values. I will present experimental studies using real datasets demonstrating the feasibility and benefit of FAST and conclude with open questions.
          Speaker Bio Li Xiong is an Associate Professor in the Department of Mathematics and Computer Science and the Department of Biomedical Informatics at Emory University where she directs the Assured Information Management and Sharing (AIMS) research group. She holds a PhD from Georgia Institute of Technology, an MS from Johns Hopkins University, and a BS from University of Science and Technology of China, all in Computer Science. She also worked as a software engineer in IT industry for several years prior to pursuing her doctorate. Her areas of research are in data privacy and security, distributed data management, and biomedical informatics. She is a recent recipient of the Career Enhancement Fellowship by Woodrow Wilson Foundation, a Cisco Research Award, and an IBM Faculty Innovation Award. Her current research is supported by NSF and AFOSR.
          Sep. 13, 2013
          SPEAKER: Raman Grover (ISG PhD candidate)
          Scalable Fault-tolerant Elastic Data Feeds
          Details
          Date and TimeSep. 13, 2013 3 pm
          LocationDBH 3011


          SpeakerRaman Grover (ISG PhD candidate)
          TitleScalable Fault-tolerant Elastic Data Feeds
          AbstractIn this ISG talk / thesis proposal, I describe and study the support for data feed ingestion in AsterixDB, a Big Data Management System (BDMS) that provides a platform for the scalable storage, searching, and analysis of very large volumes of semi-structured data. Data feeds are a mechanism for having continuous data arrive into a database system from external sources that produce data continuously, and to have that data incrementally populate a persisted dataset and associated indexes. To my knowledge, this will be the first system to explore the challenges involved in building a data ingestion facility that deals with semi-structured data and employs partitioned parallelism to scale the facility and couple it with high-volume and/or parallel external data sources. I describe language-level support for modeling/defining a feed and present the methodology for providing tolerance to software/hardware failures. Mechanisms by which a feed can dynamically adapt to different workloads for optimum usage of resources are provided.
          May 17, 2013
          SPEAKER: Charles Boicey (UCI Irvine Health and Information Services)
          Apache Hadoop in the Healthcare Setting
          Details
          Date and TimeMay 17, 2013 3 pm
          LocationDBH 3011


          SpeakerCharles Boicey (UCI Irvine Health and Information Services)
          TitleApache Hadoop in the Healthcare Setting
          AbstractApache Hadoop is open source software that enables distributed processing of large data sets across clusters of computers. Hadoop can scale up to thousands of computers, each able to store and process data. Hadoop is capable of ingesting and storing the types of data found in healthcare, structured, unstructured, image and video. Hadoop also has an advantage for healthcare in its ability to interoperate with other open source software. This interoperability combined with scalability makes Hadoop an ideal platform for the development of a software ecosystem that fills in the gaps left by the Electronic Medical Record and Enterprise Data Warehouse.
          Speaker Bio Charles Boicey is the Informatics Solutions Architect for the UC Irvine Health. At UCI Charles is responsible for the development and implementation of the enterprise data warehouse, health information exchange, home health integration and UC Irvine Health’s “Big Data” initiative. Charles has 20 years of experience in the healthcare field.cope of clinical expertise encompasses trauma critical care nursing. Charles is Vice President of the American Nursing Informatics Association.
          May 10, 2013
          SPEAKER:
          No Seminar
          Details
          Date and TimeMay 10, 2013 3 pm
          LocationDBH 3011


          Speaker
          TitleNo Seminar
          Abstract No ISG seminar. Leaving this time free so that ISG affiliates can attend today's ICS Trends in Society and Information Technology talk (see www.ics.uci.edu/trends).
          Speaker Bio
          April 19, 2013
          SPEAKER: Michael J. Carey (with the AsterixDB dev team)
          Want To Kick My Asterix(DB)?
          Details
          Date and TimeApril 19, 2013 3 pm
          LocationDBH 3011


          SpeakerMichael J. Carey (with the AsterixDB dev team)
          TitleWant To Kick My Asterix(DB)?
          Abstract Due to several faculty traveling and thus being MIA this coming Friday, the planned ISG seminar by Teradata is being postponed until later in the quarter. Instead, this week's Friday ISG seminar slot will be used to invite ISG (and ICS) community participation in the forthcoming Beta Release of a new open source BDMS (Big Data Management System) that members of UCI's ASTERIX project have been working on for nearly four years. We want this new "product", to be called AsterixDB, to be very high quality - and we are hereby inviting interested helpers at UCI to come hear about it and then help us polish it by downloading it and playing with it - trying out its data model, query language, and API for apps - kicking its tires - this Friday! Our goal is to get a handful of "outside the team" folks to join us in using the system ahead of the Beta Release and then filing any issues using the GoogleCode issue tracking infrastructure - so that when we release this publically, it's well-polished and well shaken out. (To date we have only delivered an Alpha Release, and only very recently, to one of our industrial partners.) So - if you like database technology and would like to help us deliver "Big Data 2.0" to the world in a month or so - and you have time/interest in playing a bit in the very near term - PLEASE COME FRIDAY and we will show you what AsterixDB is all about! This will be an informal presentation, based on giving a tour of our Alpha documentation and the release info on the GoogleCode wiki, and then having the team give a demo and even help you get the system working in real time if you bring your favorite laptop when you come. We hope to see some of you there! (Refreshments will be provided, as usual for the ISG seminar, but we might upgrade the refreshments a little for this event; we'll see. We will also surely do something nice later for any outside folks who do end up significantly contributing in this manner to the quality of the release.) If you plan to come on Friday, please RSVP to the speaker (mjcarey@ics.uci.edu) so we know what to maybe expect in terms of potential turnout. Thx!
          Speaker Bio Mike Carey is a Professor in the ISG subgroup of the UCI CS department. His goal is to eventually change the Big Data management landscape forever through the great work that our AsterixDB team has done and is now preparing to share. :-)
          April 12, 2013
          SPEAKER: Shahram Ghandeharizadeh (USC)
          Details
          Date and TimeApril 12, 2013 3 pm
          LocationDBH 3011


          SpeakerShahram Ghandeharizadeh (USC)
          Title
          Abstract
          Speaker Bio
          March 1, 2013
          SPEAKER: Pat Helland (Salesforce)
          Immutability Changes Everything
          Details
          Date and TimeMarch 1, 2013 3 pm
          LocationDBH 3011


          SpeakerPat Helland (Salesforce)
          TitleImmutability Changes Everything
          AbstractFor a number of decades, I've been saying "Computing Is Like Hubble's Universe, Everything Is Getting Farther Away from Everything Else". It used to be that everything you cared about ran on a single database and the transaction system presented you the abstraction of a singularity; your transaction happened at a single point in space (the database) and a single point in time (it looked like it was before or after all other transactions). Now, we see a more complicated world. Across the Internet, we put up HTML documents or send SOAP calls and these are not in a transaction. Within a cluster, we typically write files in a file system and then read them later in a big map-reduce job that sucks up read-only files, crunches, and writes files as output. Even inside the emerging many-core systems, we see high-performance computation on shared memory but increasing cost to using semaphores. Indeed, it is clear that "Shared Memory Works Great as Long as You Don't Actually SHARE Memory". There are emerging solutions which are based on immutable data. It seems we need to look back to our grandparents and how they managed distributed work in the days before telephones. We realize that "Accountants Don't Use Erasers" but rather accumulate immutable knowledge and then offer interpretations of their understanding based on the limited knowledge presented to them. This talk will explore a number of the ways in which our new distributed systems leverage write-once and read-many immutable data.
          Speaker BioPat Helland has been working on databases, transaction processing, messaging, and distributed systems for 34 years. In the 1980s, he was chief architect of the Tandem NonStop's transaction system called TMF (Transaction Monitoring Facility). From 1991 to 1994, he worked at HaL Computers (a subsidiary of Fujitsu) and designed and architected a CC-NUMA (Cache Coherent Non-Uniform Memory Architecture) multiprocessor which Fujitsu shipped. Starting in 1994, Pat worked at Microsoft where he was the chief architect for MTS (Microsoft Transaction Server) and DTC (Distributed Transaction Coordinator). Later, he built SQL Service Broker which offers high performance (>100K msg/sec) transactional exactly-once messaging integrated with SQL Server. From 2005 to 2007, Pat work at Amazon on the product catalog and then returned in 2007 to Microsoft. By 2009, he was working on Cosmos, the multi-peta-byte storage and computational plumbing behind Bing. This year, Pat moved to San Francisco to be by the grandkids and joined Salesforce.com working on database and filesystem technology.
          Feb 22, 2013
          SPEAKER: Swaroop Jagadish and Kapil Surlaker (LinkedIn)
          On Brewing Fresh Espresso: LinkedIn’s Distributed Data Serving Platform
          Details
          Date and TimeFeb 22, 2013 3 pm
          LocationDBH 3011


          SpeakerSwaroop Jagadish and Kapil Surlaker (LinkedIn)
          TitleOn Brewing Fresh Espresso: LinkedIn’s Distributed Data Serving Platform
          AbstractAs LinkedIn has grown, our core data sets and request processing requirements have grown as well. The development of Espresso was motivated by our desire to migrate LinkedIn’s online serving infrastructure from monolithic, commercial, RDBMS systems running on high cost specialized hardware to elastic clusters of commodity servers running free software; and to improve agility by enabling rapid development by simplifying the programming model, separating scalability, routing, caching from business logic. Espresso is a document-oriented distributed data serving platform that has been built to address LinkedIn’s requirements for a scalable, performant, source-of-truth primary store. It provides a hierarchical document model, transactional support for modifications to related documents, real- time secondary indexing, on-the-fly schema evolution and provides a timeline consistent change capture stream. This talk describes the motivation and design principles involved in building Espresso, its architecture and presents a set of experimental results that characterize the performance of the system along various dimensions.
          Speaker Bio Swaroop Jagadish is a member of the Data Infrastructure team at Linkedin, where he works on distributed data systems such as Databus, Helix and Espresso. Prior to that, he worked at Yahoo where he built one of the first real-time bidding engines in the display-ads industry. He holds B.E. from BMS College of Engineering and M.S. from University of California, Santa Barbara. Kapil Surlaker is a member of the Data Infrastructure team at Linkedin, where he works on distributed data systems such as Databus, Helix and Espresso. Prior to that, he worked at Kickfire (acquired by Teradata) where he built high-performance Database systems. Earlier in his career, he worked on replication technology at Oracle where he was part of the team that built Oracle Streams. He holds B.Tech. (CS) from IIT Bombay and M.S. From University of Minnesota.
          Feb 8, 2013
          SPEAKER: Ronen Vaisenberg (Google)
          Practice Talk: Scheduling Sensors for Monitoring Sentient Spaces using an Approximate POMDP Policy (Percom2013)
          Details
          Date and TimeFeb 8, 2013 3 pm
          LocationDBH 3011


          SpeakerRonen Vaisenberg (Google)
          TitlePractice Talk: Scheduling Sensors for Monitoring Sentient Spaces using an Approximate POMDP Policy (Percom2013)
          AbstractWe present a framework for sensor actuation and control in sentient spaces, in which sensors are used to observe a physical phenomena. Our framework utilizes the spatio-temporal statistical properties of an observed phenomena, with the goal of maximizing an application specified reward. Specifically, we define an observation of a phenomena by assigning it a discrete value (state) and we model its semantics as the transition between these values (states). This semantic model is used to predict the future states in which the phenomena is likely to be at, based on partially observed past states. To accomplish real-time agility, we designed an approximate, adaptive-grid solution for POMDPs that yields practically good results, and in some cases, guarantees on the quality of the approximation. We instantiate the framework in a camera network and use it perform real- time actuation of large-scale sensor networks. To the best of our knowledge, we are the first to address the problem of actuating a large scale sensor network based on an approximated POMDP formulation. Our semantic model is simple enough to be implemented in real-time, yet powerful enough to capture meaningful semantics of typical behavior. Our action selection process is as fast as a table lookup in real-time.
          Speaker Bio
          Feb 15, 2013
          SPEAKER: Hongzhi Wang (ISG)
          Details
          Date and TimeFeb 15, 2013 3 pm
          LocationDBH 3011


          SpeakerHongzhi Wang (ISG)
          Title
          Abstract
          Speaker Bio
          Feb. 1, 2013
          SPEAKER: Marco Sanvido (Pure Storage)
          The Why and How of an All-Flash Enterprise Storage Array
          Details
          Date and TimeFeb. 1, 2013 3 pm
          LocationDBH 3011


          SpeakerMarco Sanvido (Pure Storage)
          TitleThe Why and How of an All-Flash Enterprise Storage Array
          AbstractEnterprise storage is an $30 billion a year industry dominated by spinning disks. Flash storage is poised to take a large chunk of the market, having grown significantly in capacity and production, driven by consumer electronics. Flash's technical advantages over disk promise storage arrays that are faster and easier to use while consuming less power and costing less. The downsides of flash (inc. large erase blocks, limited overwrites, and higher price) mean that using flash as a drop-in replacement for disk leads to increased price, volatile performance, and decreased reliability. In this talk, we describe the design of the Pure FlashArray, an enterprise storage array built around consumer flash storage. The array and its software, Purity, play to the advantages of flash while minimizing the downsides. Purity writes to flash in multiples of the erase block size and stores its metadata in a key-value store that minimizes overwrites and stores approximate answers, trading extra reads for fewer writes. And, Purity reduces data stored on flash through a range of techniques, including compression, deduplication, and thin provisioning. The net result is a flash array that deliver a sustained read-write workload of over 100,000 4kb I/O requests per second while maintaining sub-millisecond latency. With many customers seeing 4x or greater data reduction, the Pure FlashArray ends up being cheaper than disk too.
          Speaker Bio Dr. Marco Sanvido holds a Dipl.-Ing. degree (1996) and a Dr.techn. degree (2002) in Computer Science from the Swiss Federal Institute of Technology in Zürich, Switzerland (ETHZ). He was a co-founder of weControl, an ETHZ spin-off, where he developed low-power and real-time embedded systems for autonomous flying vehicles. He was a postdoctoral researcher in Computer Science at the University of California at Berkeley from 2002 to 2004, and thereafter he worked on virtualization at VMware. In 2005 he then became a researcher at Hitachi Global Storage Technologies, where he worked on hard disk drive and solid state drive architectures. Since 2010 Marco joined Pure Storage as a Principal Software Engineer.
          Jan. 25, 2013
          SPEAKER: Silvius Rus (Quantcast)
          Petabyte Scale Data Processing at Quantcast
          Details
          Date and TimeJan. 25, 2013 3 pm
          LocationDBH 3011


          SpeakerSilvius Rus (Quantcast)
          TitlePetabyte Scale Data Processing at Quantcast
          AbstractThe talk will present the big data storage, processing and query systems in production at Quantcast. We receive up to 50 TB of new data every day, respond to 500,000 events per second, process up to 30 PB per day and store tens of petabytes of data. We have implemented our own MapReduce software stack that scales better and has significantly lower resource requirements than Hadoop. The QFS file system is available open source at https://github.com/quantcast/qfs/wiki.
          Speaker BioSilvius Rus leads Big Data Platforms at Quantcast. He directs, manages and participates in the development of cluster language runtimes (SQL, Sawzall), petabyte scale map-reduce, interactive big data analytics, cluster resource management, distributed file systems and large scale realtime processing. Before Quantcast he was at Google working on Gmail load balancing across datacenters, parallel memory allocation performance, server performance and C++ compiler and library optimization. Silvius holds a PhD in computer science from Texas A and M University, where he worked on full program optimization based on hybrid (static and dynamic) analysis of memory reference patterns.
          Jan. 18, 2013 (Special Time)
          SPEAKER: Joe Hellerstein (UC Berkeley)
          Keep CALM and Query On
          Details
          Date and TimeJan. 18, 2013 (Special Time) 11 am
          LocationDBH 6011


          SpeakerJoe Hellerstein (UC Berkeley)
          TitleKeep CALM and Query On
          Abstract Any modern software system of note has two key characteristics: it is a distributed system, and it manages significant amounts of data. As a result, the topic of distributed data consistency has become a key problem in the engineering of modern software systems. Conventional distributed systems wisdom dictates that perfect consistency is too expensive to guarantee in general, and consistency mechanisms—if they are used at all—should be reserved for infrequent, small-scale, mission-critical tasks. Like most design maxims, these ideas are not so easy to translate into practice; all kinds of unavoidable tactical questions pop up. For example: • Exactly where in my multifaceted system is loose consistency “good enough” to meet application needs? • How do I know that my “mission-critical” software isn’t tainted by my “best effort” components? • How do I ensure that my design maxims are maintained as software and developer teams evolve? Until recently, answers to these questions have been more a matter of folklore than mathematics. In this talk, I will describe the CALM Theorem, which links Consistency And Logical Monotonicity, and discuss how it can inform distributed software development. I'll also describe Bloom, a "disorderly" distributed programming language developed in my group. Bloom admits a form of automated CALM analysis, which enables a compiler to answer questions like the ones above. Time permitting, I will also point out some additional results from my two main research projects: the BOOM project on large-scale system development, and the d^p project on human interaction in the data analysis lifecycle.
          Speaker Bio Joseph M. Hellerstein is a Chancellor's Professor of Computer Science at the University of California, Berkeley, whose research focuses on data-centric systems and the way they drive computing. A Fellow of the ACM, his work has been recognized via awards including an Alfred P. Sloan Research Fellowship, MIT Technology Review's TR10 and TR100 lists, Fortune Magazine's "Smartest in Tech" list, and two ACM-SIGMOD "Test of Time" awards. He has led a number of influential open source projects, including Bloom, MADlib, Telegraph, and TinyDB. In 2012, Joe co-founded Trifacta, Inc, which develops productivity software for data analysts.
          Dec. 14, 2012
          SPEAKER: Bijit Hore (UCI ISG)
          Hide-and-Seek in the cloud: How to securely store and query your data in untrusted environments
          Details
          Date and TimeDec. 14, 2012 3 pm
          LocationDBH 3011


          SpeakerBijit Hore (UCI ISG)
          TitleHide-and-Seek in the cloud: How to securely store and query your data in untrusted environments
          AbstractSecurity and privacy of data is a major concern for organizations (and many individuals) that use cloud-based services to cater to their IT needs. This is cited as the central reason why many federal, healthcare, and financial organizations have not embraced cloud computing in a major way in spite of its many benefits. In this talk we consider the central problem of "data confidentiality", that arises while storing sensitive data in the cloud. While data encryption is an obvious solution for ensuring confidentiality, standard algorithms like AES make the data unusable in the cloud. For example, keyword search or database queries cannot be evaluated against the encrypted data. Over the past decade, many new schemes have been developed, that admit a variety of computations directly on the encrypted representation. We give a brief overview of some of the important techniques proposed in this arena, specifically, for evaluating keyword-match and range queries. Finally, we describe our own contributions to this area and conclude with a discussion about open problems and future directions.
          Speaker Bio
          Dec. 7, 2012
          SPEAKER:
          No Seminar
          Details
          Date and TimeDec. 7, 2012 3 pm
          Location


          Speaker
          TitleNo Seminar
          Abstract No ISG seminar. Leaving this time free so that ISG affiliates can attend today's ICS Trends in Society and Information Technology talk (see www.ics.uci.edu/trends).
          Speaker Bio
          Nov. 30, 2012
          SPEAKER: Peter Bailis (UC Berkeley)
          Probabilistically Bounded Staleness for Practical Partial Quorums
          Details
          Date and TimeNov. 30, 2012 3 pm
          LocationDBH 3011


          SpeakerPeter Bailis (UC Berkeley)
          TitleProbabilistically Bounded Staleness for Practical Partial Quorums
          AbstractData store replication results in a fundamental trade-off between operation latency and data consistency. In this talk, we examine this trade-off in the context of quorum-replicated data stores. Under partial, or non-strict quorum replication, a data store waits for responses from a subset of replicas before answering a query, without guaranteeing that read and write replica sets intersect. As deployed in practice, these configurations provide only basic eventual consistency guarantees, with no limit to the recency of data returned. However, anecdotally, partial quorums are often “good enough” for practitioners given their latency benefits. We explain why partial quorums are regularly acceptable in practice, analyzing both the staleness of data they return and the latency benefits they offer. We introduce Probabilistically Bounded Staleness (PBS) consistency, which provides expected bounds on staleness with respect to both versions and wall clock time. We derive a closed-form solution for versioned staleness as well as model real-time staleness for representative Dynamo-style systems under internet-scale production workloads. Using PBS, we measure the latency-consistency trade-off for partial quorum systems. We quantitatively demonstrate how and why eventually consistent systems frequently return consistent data within tens of milliseconds while offering significant latency benefits. This is joint work with Shivaram Venkataraman, Mike Franklin, Joe Hellerstein, and Ion Stoica at UC Berkeley. An earlier version of this work appeared at VLDB 2012 (selected for "Best of VLDB 2012"), and an implementation of PBS is slated for release in Cassandra 1.2.0. Demo: http://pbs.cs.berkeley.edu/#demo
          Speaker BioPeter Bailis is a graduate student in Computer Science at UC Berkeley, where he works closely with Joe Hellerstein, Ion Stoica, and Ali Ghodsi. He currently studies distributed systems, with a particular focus on distributed consistency models. Peter received his A.B. from Harvard College in 2011, where he worked with Margo Seltzer and Matt Welsh and was a 2011 CRA Outstanding Undergraduate Researcher. He is the recipient of the NSF Graduate Research Fellowship and the Berkeley Fellowship for Graduate Study and is a co-founder of @TinyToCS, the premiere journal for Computer Science research of 140 characters or less.
          Nov. 9, 2012
          SPEAKER: Chaitan Baru (SDSC at UCSD)
          Data Initiatives at SDSC
          Details
          Date and TimeNov. 9, 2012 3 pm
          LocationDBH 3011


          SpeakerChaitan Baru (SDSC at UCSD)
          TitleData Initiatives at SDSC
          Abstract As a data-oriented supercomputer center, SDSC is engaged in a variety of activities that support data-intensive computing and big data, from research and development to fielding production systems, and enabling end applications. This talk will provide an overview of several data activities at SDSC including the Gordon supercomputer; data intensive applications on Gordon; the SDSC Cloud, with Globus Online interface for OpenStack; and new initiatives such as the Center for Large-scale Data Systems Research (CLDS). We will present two CLDS programs, one on establishing industry standards for Big Data Benchmarking and another on Data Growth and Data Value. A new initiative targeted at long-tail scientific data, motivated partly by needs identified by the NSF EarthCube initiative and by the challenges faced by a typical research university, will also be presented. There are many opportunities for joint collaborations and student projects across these initiatives.
          Speaker Bio Chaitan Baru, is Associate Director for Data Initiatives at the San Diego Supercomputer Center Director, UC San Diego, where he also directs the Center for Large-scale Data Systems research (CLDS). His technical interests are in the areas of scientific data management, large-scale data systems, data integration, data analytics, and parallel database systems. He has been involved in cyberinfrastructure projects across a range of science disciplines, e.g. earth sciences, ecological sciences, hydrology, earthquake engineering, biomedical sciences, and others. He is PI of the OpenTopography project; coordinator of the Data Discovery, Mining, and Access community group for the NSF EarthCube project; and Chair, Coordinating Committee for Big Data Benchmarking. Before joining SDSC 16 years ago, Baru led one of the development teams at IBM for DB2 Parallel Edition (a shared-nothing database engine). Prior to that, he was on the faculty of the EECS Dept, University of Michigan. Baru has a B.Tech. in Electronics Engineering from IIT Madras, and an ME and PhD in Electrical Engineering from the University of Florida, Gainesville.
          Nov. 2, 2012
          SPEAKER:
          No Seminar
          Details
          Date and TimeNov. 2, 2012 3 pm
          Location


          Speaker
          TitleNo Seminar
          Abstract No ISG seminar. Leaving this time free so that ISG affiliates can attend today's ICS Trends in Society and Information Technology talk (see www.ics.uci.edu/trends).
          Speaker Bio
          Oct. 26, 2012
          SPEAKER: Yingyi Bu (ISG PhD student)
          Pregelix: Think Like a Vertex, Scale like Spandex
          Details
          Date and TimeOct. 26, 2012 3 pm
          LocationDBH 3011


          SpeakerYingyi Bu (ISG PhD student)
          TitlePregelix: Think Like a Vertex, Scale like Spandex
          Abstract Recently, there are more and more demands for analyzing Big Graph Data. For example, the scale of the world wide web keeps expanding to billions of web pages and hyper-links, the key social network sites like Facebook, LinkedIn, Twitter all have a rapidly growing gigantic social graph, and the biology science people assemble genomes from huge de Bruijn graphs. To analyze such Big graphs requires a system which can not only scale out to hundreds or thousands of machines, but also do the computation very efficiently. In this talk, I will introduce the Pregelix system, which supports easy programming and scales to large commodity machine clusters. I will first illustrate the programming model -- application programmers need zero knowledge of the parallel/distributed system, but just "think like a vertex" and write a couple of functions that encapsulate the logic for what one graph vertex does. After that, I will detail the shining internals of Pregelix, including the system architecture, the scalable dataflow runtime, the execution strategies, and the out-of-core support. Then, I will walk through a few examples built on top of Pregelix, such as PageRank and connected components. Finally I will demonstrate our performance numbers and conclude the talk. (Truth in lending disclosure: the programming model and API were shamelessly borrowed from Google's Pregel graph analytics platform, hence the name:-))
          Speaker Bio Yingyi Bu is a PhD student in the ISG group of UC Irvine. He is working on the ASTERIX project that aims at an open source data-intensive computing platform, with new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing intensive semi-structured data. Within the project, Yingyi has been working on the data-model independent algebra/optimization layer, the ASTERIX query optimizer, and the Pregelix system.
          Oct. 19, 2012
          SPEAKER: David Lomet (Microsoft Research)
          The Bw-Tree: A B-tree for New Hardware Platforms
          Details
          Date and TimeOct. 19, 2012 3 pm
          LocationDBH 3011


          SpeakerDavid Lomet (Microsoft Research)
          TitleThe Bw-Tree: A B-tree for New Hardware Platforms
          AbstractThe emergence of new hardware and platforms has led to reconsideration of how data management systems are designed. However, certain basic functions such as key indexed access to records remain essential. While we exploit the common architectural layering of prior systems, we make radically new design decisions about each layer. Our new form of B-tree, called the Bw-tree achieves its very high performance via a latch-free approach that effectively exploits the processor caches of modern multi-core chips. Our storage manager uses a unique form of log structuring that blurs the distinction between a page and a record store and works well with flash storage. This paper describes the architecture and algorithms for the Bw-tree, focusing on the main memory aspects. The paper includes results of our experiments that demonstrate that this fresh approach produces outstanding performance.
          Speaker Bio David Lomet has been a principal researcher managing the Microsoft Research Database Group at Microsoft Research since 1995. Earlier, he spent seven and a half years at Digital Equipment Corporation. He has been at IBM Research in Yorktown and a Professor at Wang Institute. Dr. Lomet spent a sabbatical at University of Newcastle-upon-Tyne working with Brian Randell. He has a Computer Science Ph.D from the University of Pennsylvania. Dr. Lomet has done research and product development in architecture, programming languages, and distributed systems. His primary interest is database systems, focusing on access methods, concurrency control, and recovery. He is one of the inventors of the transaction concept and is an author of over 100 papers and 45 patents. Two papers won SIGMOD "best paper" awards. He received the 2010 SIGMOD Contributions Award for his work as editor-in-chief of the Data Engineering Bulletin since 1992. Dr. Lomet has served on program committees, including SIGMOD, PODS, VLDB, and ICDE. He was ICDE'2000 PC co-chair and VLDB 2006 PC core chair. He is a member of the ICDE Steering Committee and VLDB Board. He is a past editor of ACM TODS and the VLDB Journal. Dr. Lomet is IEEE Golden Core Member and has received IEEE Outstanding Contribution and Meritorious Service Awards. Dr. Lomet is a Fellow of the ACM, IEEE, and AAAS.
          Oct. 5, 2012
          SPEAKER: Thomas Bodner (TU Berlin)
          A Taxonomy of Platforms for Analytics on Big Data (Stratosphere talk series 5)
          Details
          Date and TimeOct. 5, 2012 4:30 pm - 5pm
          LocationDBH 3011


          SpeakerThomas Bodner (TU Berlin)
          TitleA Taxonomy of Platforms for Analytics on Big Data (Stratosphere talk series 5)
          Abstract Within the past few years, industrial and academic organizations designed a wealth of systems for data-intensive analytics including MapReduce, SCOPE/Dryad, ASTERIX, Stratosphere, Spark, and many others. These systems are being applied to new applications from diverse domains other than (traditional) relational OLAP, making it difficult to understand the tradeoffs between them and the workloads for which they were built. We present a taxonomy of existing system stacks based on their architectural components and the design choices made related to data processing and programmability to sort this space. We further demonstrate a web repository for sharing Big Data analytics platform information and use cases. The repository enables researchers and practitioners to store and retrieve data and queries for their use case, and to easily reproduce experiments from others on different platforms, simplifying comparisons.
          Speaker Bio Thomas Bodner is a second year Master's student in the computer science department at the Technische Universität Berlin working in the Database Systems and Information Management (DIMA) group on the Stratosphere project. He received his B.S. from the University of Cooperative Education at Stuttgart. In the course of his studies, Thomas Bodner studied abroad at University of California, Irvine and Royal Melbourne Institute of Technology. He worked as an intern at the IBM Almaden Research Center and the IBM Böblingen Laboratory. His research interests include benchmarking of and query optimization for Big Data analytics systems.
          Oct. 5, 2012
          SPEAKER: Alexander Alexandrov
          Generating a Myriad of Atoms in the Blink of an Eye (Stratosphere talk series 4)
          Details
          Date and TimeOct. 5, 2012 4 pm - 4:30pm
          LocationDBH 3011


          SpeakerAlexander Alexandrov
          TitleGenerating a Myriad of Atoms in the Blink of an Eye (Stratosphere talk series 4)
          Abstract Data from real-world applications is regarded as the golden standard for database systems evaluation. Unfortunately, finding appropriate real-world datasets is often hard due to various privacy-related constraints. To overcome this problem, we developed the Myriad Parallel Data Generator Toolkit - a generic toolkit for declarative specification of synthetic data generators that provides built-in parallelization support for the specified data generation programs. In this talk, I will motivate and present the main technical challenges solved by the highly-parallel execution model of the Myriad Toolkit. In addition, to demonstrate the usability of the toolkit, I will also give a brief overview of the supported data generator specification syntax and explain how different statistical constraints for the generated data can be implemented using the appropriate combination of specification routines.
          Speaker Bio Alexander Alexandrov is a research associate at the Database Systems and Information Management research group at the Technische Universität Berlin. Before moving to Berlin for a Master in Computer Science at TU Berlin, he received his Bachelor of Science in Software and Internet Technologies at the University of Mannheim. Alexander has been working on the Stratosphere project both as student and research assistant since 2009. His research interests include data generation, evaluation, and query optimization for large-scale parallel batch processing systems with partial operator semantics.
          Oct. 5, 2012
          SPEAKER: Stephan Ewen (TU Berlin)
          Spinning Fast Iterative Data Flows (Stratosphere talk series 3)
          Details
          Date and TimeOct. 5, 2012 3:30 pm - 4pm
          LocationDBH 3011


          SpeakerStephan Ewen (TU Berlin)
          TitleSpinning Fast Iterative Data Flows (Stratosphere talk series 3)
          Abstract Parallel data flow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk iterative algorithms are supported by novel data flow frameworks, these systems cannot exploit computational dependencies present in many algorithms, such as graph algorithms. As a result, these algorithms are inefficiently executed and have led to specialized systems based on other paradigms, such as message passing or shared memory. We propose a method to integrate "incremental iterations", a form of workset iterations, with parallel data flows. After showing how to integrate bulk iterations into a dataflow system and its optimizer, we present an extension to the programming model for incremental iterations. The extension alleviates for the lack of mutable state in dataflows and allows for exploiting the "sparse computational dependencies" inherent in many iterative algorithms. The evaluation of a prototypical implementation shows that those aspects lead to up to two orders of magnitude speedup in algorithm runtime, when exploited. In our experiments, the improved dataflow system is highly competitive with specialized systems while maintaining a transparent and unified data flow abstraction.
          Speaker Bio Stephan Ewen is a research associate at the department for Database Systems and Information Management (DIMA) at the Technische Universität Berlin. He is working on the Stratosphere Project that aims at creating a versatile and efficient analytics engine for deep analysis of Big Data on cloud platforms. Within the project, Stephan works on the system's data flow programming abstraction, the data flow optimization and the parallel runtime system. Prior to joining the DIMA group, Stephan completed the "Applied Computer Science" program at the University of Cooperative Education Stuttgart jointly with IBM Germany and got his Diploma from the University of Stuttgart. In the course of his studies, Stephan Ewen worked, among others, for the IBM Almaden Research Centre and the IBM Development Laboratory Böblingen.
          Oct. 5, 2012
          SPEAKER: Kostas Tzoumas (TU Berlin)
          Query Optimization with MapReduce Functions (Stratosphere talk series 2)
          Details
          Date and TimeOct. 5, 2012 3 pm - 3:30 pm
          LocationDBH 3011


          SpeakerKostas Tzoumas (TU Berlin)
          TitleQuery Optimization with MapReduce Functions (Stratosphere talk series 2)
          Abstract Many systems for big data analytics employ a data flow programming abstraction to define parallel data processing tasks. In this setting, custom operations expressed as user-defined functions are very common. We address the problem of performing data flow optimization at this level of abstraction, where the semantics of operators are not known. Traditionally, query optimization is applied to queries with known algebraic semantics. In this work, we find that a handful of properties, rather than a full algebraic specification, suffice to establish reordering conditions for data processing operators. We show that these properties can be accurately estimated for black box operators using a shallow static code analysis pass based on reverse data and control flow analysis over the general-purpose code of their user-defined functions. We design and implement an optimizer for parallel data flows that does not assume knowledge of semantics or algebraic properties of operators. Our evaluation confirms that the optimizer can apply common rewritings such as selection reordering, bushy join order enumeration, and limited forms of aggregation push-down, hence yielding similar rewriting power as modern relational DBMS optimizers. Moreover, it can optimize the operator order of non-relational data flows, a unique feature among today's systems.
          Speaker Bio Kostas Tzoumas is a postdoctoral researcher co-leading the Stratosphere research project at the Technische Universität Berlin. He received his PhD from Aalborg University in 2011 with a thesis on discovering and exploiting correlations for query optimization. He was a visiting researcher at the University of Maryland, College Park, and an intern at Microsoft Research. He received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens in 2007. His research interests are centered around systems for data analytics, including query processing and optimization in massively parallel environments.
          Oct. 5, 2012
          SPEAKER: Volker Markl (TU Berlin)
          The Current State of the Stratosphere (Stratosphere talk series 1)
          Details
          Date and TimeOct. 5, 2012 3 pm - 5pm
          LocationDBH 3011


          SpeakerVolker Markl (TU Berlin)
          TitleThe Current State of the Stratosphere (Stratosphere talk series 1)
          Abstract Introduction to the Stratosphere system.
          Speaker Bio Volker Markl is a Full Professor and Chair of the Database Systems and Information Management (DIMA) group at the Technische Universität Berlin (TU-Berlin). Prior to joining TU-Berlin, Dr. Markl lead a research group at FORWISS, the Bavarian Research Center for Knowledge-based Systems in Munich, Germany, and was a Research Staff member and Project Leader at the IBM Almaden Research Center in San Jose, California, USA. His research interests include: information as a service, new hardware architectures for information management, information integration, autonomic computing, query processing, query optimization, data warehousing, electronic commerce, and pervasive computing. Volker has presented over 100 invited talks in numerous industrial settings and at major conferences and research institutions worldwide. He has authored and published more than 50 research papers at world-class scientific venues. Volker regularly serves as member and chair for program committees of major international database conferences. He also is a member of the Board of Trustees of the VLDB Endowment. Volker has 5 patent awards, and he has submitted over 20 invention disclosures to date. Over the course of his career, he has garnered many prestigious awards, including the European Information Society and Technology Prize, an IBM Outstanding Technological Achievement Award, an IBM Shared University Research Grant, an HP Open Innovation Award, and the Pat Goldberg Memorial Best Paper Award.
          Jun. 8, 2012
          SPEAKER: Kerim Yasin Oktay and Bijit Hore (ISG)
          CloudProtecti and Risk-Aware Workload Distribution in Hybrid Clouds
          Details
          Date and TimeJun. 8, 2012 3 pm
          LocationDBH 3011


          Speaker Kerim Yasin Oktay and Bijit Hore (ISG)
          TitleCloudProtecti and Risk-Aware Workload Distribution in Hybrid Clouds
          Abstract In this talk, we describe the CloudProtect system from the recently accepted paper in IEEE Cloud 2012. The CloudProtect middleware empowers users to encrypt sensitive data stored within various cloud applications. However, most web applications require data in plaintext for implementing the various functionalities and in general, do not support encrypted data management. Therefore, CloudProtect strives to carry out the data transformations (encryption/decryption) in a manner that is transparent to the application, i.e., preserves all functionalities of the application, including those that require data to be in plaintext. Additionally, CloudProtect allows users flexibility in trading off performance for security in order to let them optimally balance their privacy needs and usage-experience. This paper explores an efficient and secure mechanism to partition computations across public and private machines in a hybrid cloud setting. We propose a principled framework for distributing data and processing in a hybrid cloud that meets the conflicting goals of performance, sensitive data disclosure risk and resource allocation costs. The proposed solution is implemented as an add-on tool for a Hadoop and Hive based cloud computing infrastructure. Our experiments demonstrate that the developed mechanism can lead to a major performance gain by exploiting both the hybrid cloud components without violating any pre-determined public cloud usage constraints.
          Jun. 1, 2012
          SPEAKER: Ken Slocum (UCSD)
          Scalable Lineage Capture for DISC
          Details
          Date and TimeJun. 1, 2012 3 pm
          LocationDBH 3011


          SpeakerKen Slocum (UCSD)
          TitleScalable Lineage Capture for DISC
          Abstract Scale-out data processing architectures enable sophisticated ``big data'' analytics, but understanding and debugging multi-step dataflows that ingest large volumes of data remains a fundamental challenge. We are building a system called Newt, a scalable architecture for capturing fine-grain, record-level provenance from these data-intensive scalable compute (DISC) systems in a generic manner. Developers leverage a unique API to instrument these systems, actively capturing fine-grain lineage across multi-step, perhaps non-relational, transformations. We report on our experiences instrumenting Hyracks and Hadoop, and find that Newt's capture incurs 16-26% time overheads for the PigMix benchmark and a 14% overhead on a complex 145-stage de novo genomic assembler.
          Speaker Bio Ken Yocum is an associate research scientist in the Department of Computer Science at UC San Diego where he runs the Synoptic Systems Lab. While he once worked on high-speed networking (briefly holding the land-speed record for gigabit TCP), he has since become enamored with the myriad systems challenges of "big data" processing and software-defined networks. He received his Ph.D. from Duke University, and his B.S from Stanford. When he's not working, he enjoys his children, cycling, and going to the race track.
          May. 29, 2012 (Special Time)
          SPEAKER: Murali Mani (University of Michigan, Flint)
          Algebraic Manipulation of Encrypted Databases
          Details
          Date and TimeMay. 29, 2012 (Special Time) 12 pm
          LocationDBH 3011


          SpeakerMurali Mani (University of Michigan, Flint)
          Title Algebraic Manipulation of Encrypted Databases
          Abstract Can we improve on the work that received the 10 year ACM SIGMOD test of time award? In this talk, we will outline our preliminary approach at doing the entire query processing on the server/cloud, while the client is involved only with encryption, and decryption. Our work is based on Craig Gentry's revolutionary recent work on fully homomorphic encryption (first such scheme was published in 2009). We utilize Craig Gentry's scheme for query processing, while maintaining the algebraic framework that is a key aspect of database systems. There are several avenues for future investigation: exploring physical implementations for algebraic operators beyond what we have investigated; exploring query optimization and utilization of indexes; exploring feasibility of Craig Gentry's fully homomorphic encryption in the context of databases as some aspects of his scheme are very time consuming.
          Speaker Bio Murali Mani finished his PhD in Computer Science from UCLA in 2003. Since then, he has worked at WPI, and is currently an assistant professor at University of Michigan, Flint. His areas of interest are database systems, and his significant projects have been on event stream processing, processing of XML streaming data, provenance metadata management, and data modeling using XML schemas. His research on XML stream processing and provenance have been supported by NSF.
          May. 25, 2012
          SPEAKER: Jimmy Lin (Twitter)
          Flexibility without Anarchy: Analytics Infrastructure at Twitter
          Details
          Date and TimeMay. 25, 2012 3 pm
          LocationDBH 3011


          SpeakerJimmy Lin (Twitter)
          TitleFlexibility without Anarchy: Analytics Infrastructure at Twitter
          AbstractThe data analytics infrastructure at Twitter supports a myriad of technologies: Hadoop, Pig (with Python and JRuby), Cascading/Scalding, HBase, MySQL, Vertica, and ZooKeeper. Our philosophy is to let developers and data scientists use whatever tools they are most comfortable with, while allowing individual components to be weaved together into complex analytic tapestries. Managing complex workflows that cross language boundaries (e.g. Java vs. Pig vs. Scala) as well as architectures with significant impedance mismatches (e.g., Hadoop vs. Vertica) has been and continues to remain a significant challenge. In this talk, I'll detail some of these issues and our present solutions.
          Speaker Bio Jimmy Lin is a visiting scientist at Twitter, currently on leave from the University of Maryland. His current research focuses on scalable algorithms for data analytics, particularly on text and graph data. At Twitter, he works on services designed to surface relevant content for users and the distributed infrastructure that supports mining relevance signals from massive amounts of data.
          May. 18, 2012
          SPEAKER: Raman Grover (ISG Ph.D. student)
          ASTERIX: Scalable Warehouse-Style Web Data Integration
          Details
          Date and TimeMay. 18, 2012 3 pm
          LocationDBH 3011


          SpeakerRaman Grover (ISG Ph.D. student)
          TitleASTERIX: Scalable Warehouse-Style Web Data Integration
          Abstract A growing wealth of digital information is being generated on a daily basis in social networks, blogs, online communities, etc. Organizations and researchers in a wide variety of domains recognize that there is tremendous value and insight to be gained by warehousing this emerging data and making it available for querying, analysis, and other purposes. This new breed of “Big Data” applications poses challenging requirements against data management platforms in terms of scalability, flexibility, manageability, and analysis capabilities. At UC Irvine, we are building a nextgeneration database system, called ASTERIX, in response to these trends. We present ongoing work that approaches the following questions: How does data get into the system? What primitives should we provide to better cope with dirty/noisy data? How can we support efficient data analysis on spatial data? Using real examples, we show the capabilities of ASTERIX for ingesting data via feeds, supporting set-similarity predicates for fuzzy matching, and answering spatial aggregation queries.
          Speaker Bio
          May. 11, 2012
          SPEAKER: Inci Cetindil (ISG Ph.D. student)
          Analysis of Instant Search Query Logs
          Details
          Date and TimeMay. 11, 2012 3 pm
          LocationDBH 3011


          SpeakerInci Cetindil (ISG Ph.D. student)
          TitleAnalysis of Instant Search Query Logs
          AbstractInstant search is a new search paradigm that shows results as a user types in a query. It has become increasingly popular in recent years due to its simplicity and power. In an instant- search system, every keystroke from a user triggers a new request to the server. Therefore, its log has a richer content than that of a traditional search system, and previous log analysis research is not applicable to this type of log. In this study, we present the problem of analyzing the query log of an instant-search system. We propose a classification scheme for user typing behaviors. We also compare the log of an instant-search system and that of a traditional search system on the same data. The results show that on a people directory search system, instant search can typically save 2 seconds per search, reduce the typing effort by showing the results with fewer characters entered, and increase the success rate.
          Speaker Bio
          April. 27, 2012
          SPEAKER: Afsin Akdogan (University of Southern California)
          Voronoi-based Geospatial Query Processing with MapReduce
          Details
          Date and TimeApril. 27, 2012 3 pm
          LocationDBH 3011


          SpeakerAfsin Akdogan (University of Southern California)
          TitleVoronoi-based Geospatial Query Processing with MapReduce
          Abstract Geospatial queries (GQ) have been used in a wide variety of applications such as decision support systems, profile-based marketing, bioinformatics and GIS. Most of the existing query-answering approaches assume non parallel processing on a single machine although GQs are intrinsically parallelizable. There are some approaches that have been designed for parallel databases and cluster systems; however, these only apply to the systems with limited parallel processing capability, far from that of cloud-based platforms. In this study, I present the problem of parallel geospatial query processing with MapReduce programming model. Our approach creates a spatial index, Voronoi diagram, for given data points in 2D space and enables efficient processing of GQs. We evaluated the performance of our proposed techniques and correspondingly compared them with their closest related work while varying the number of employed nodes.
          Speaker BioAfsin Akdogan received his master’s degree in computer science from Cornell University in 2009. He received a best paper award in IEEE Cloud Computing Technology and Science conference in 2010. He has also interned at Yahoo. He is currently working towards his Ph.D. degree in computer science at the University of Southern California and his research focuses on cloud computing, parallel data processing languages and geo-spatial databases.
          April. 20, 2012
          SPEAKER: Leila Jalali (Ph.D. student in ISG)
          A Reflective Approach to Synchronization for Consistent Multisimulations
          Details
          Date and TimeApril. 20, 2012 3 pm
          LocationDBH 3011


          SpeakerLeila Jalali (Ph.D. student in ISG)
          TitleA Reflective Approach to Synchronization for Consistent Multisimulations
          AbstractIn this talk, I consider the challenge of designing a framework that supports the integration of multiple existing autonomous simulation models into an integrated simulation environment (multisimulation). In particular, I focus on solutions for synchronization problem in multisimulation to orchestrate consistent information flow through multiple simulator: (1) a transaction-based approach to modeling the synchronization problem in multisimulations by mapping it to a problem similar to multidatabase concurrency; we express multisimulation synchronization as a scheduling problem where the goal is to generate “correct schedules” for time advancement and data exchange across simulators that meets the dependencies without loss of concurrency, (2) a hybrid scheduling strategy which adapts itself to the “right” level of pessimism/optimism based on the state of the execution and underlying dependencies, and (3) relaxation model for dependencies which guarantee bounded violation of consistency to support higher levels of concurrency. We also develop two key optimizations: (a) efficient checkpointing/rollback techniques, and (b) relaxation model for dependencies which guarantee bounded violation of consistency to support higher levels of concurrency. We evaluate our proposed techniques via a detailed case study from the emergency response domain by integrating three disparate simulators – a fire simulator (CFAST), an evacuation simulator (Drillsim) and a communication simulator (LTEsim).
          April. 13, 2012 (Special Time\Place)
          SPEAKER: Jennifer Widom (Stanford)
          Data-Centric Human Computation + From 100 Students to 100,000
          Details
          Date and TimeApril. 13, 2012 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerJennifer Widom (Stanford)
          TitleData-Centric Human Computation + From 100 Students to 100,000
          AbstractThis talk will have two completely independent parts -- one related to research and the other to education. In the first part of the talk, I'll describe our ongoing research in leveraging human computation for tasks related to data. Human computation ("crowdsourcing") augments traditional computation with the use of human abilities to solve sub-problems that are difficult for computers, e.g., object or image comparisons, information extraction, relevance judgements, and data gathering. We are addressing two different types of data-centric human computation: (1) Fundamental algorithms, such as sorting, clustering, and data cleaning, in which the basic operations (e.g., compare, filter) are performed by humans. (2) A database-system like platform in which declarative queries are posed by users, and the system orchestrates a combination of stored and crowdsourced data to answer them. Common to both areas is the need to formalize and optimize new tradeoffs among latency (humans are much slower than computers), cost (humans require real money to perform tasks), and quality (humans are inaccurate and inconsistent). In the second part of the talk, I'll describe my recent experience teaching introductory databases to 60,000 students. Admittedly only 25,000 of them submitted their homework, and a mere 6500 achieved a strong final score. But even with 6500 students, I more than quadrupled the total number of students I've taught in my entire 18-year academic career. I began by "flipping" the way I teach my Stanford course and, as a side-effect, making all components of the course freely available online. But the big inflection point came when I offered the online course in a structured fashion with a schedule, automatically-graded assignments and exams, and most importantly a worldwide community of students. I'll cover a variety of topics related to the massive online course, both logistical and social, while avoiding speculation on the future of higher education.
          Speaker BioJennifer Widom is the Fletcher Jones Professor and Chair of the Computer Science Department at Stanford University. She received her Bachelor's degree from the Indiana University School of Music in 1982 and her Computer Science Ph.D. from Cornell University in 1987. She was a Research Staff Member at the IBM Almaden Research Center before joining the Stanford faculty in 1993. Her research interests span many aspects of nontraditional data management. She is an ACM Fellow and a member of the National Academy of Engineering and the American Academy of Arts and Sciences; she received the ACM SIGMOD Edgar F. Codd Innovations Award in 2007 and was a Guggenheim Fellow in 2000; she has served on a variety of program committees, advisory boards, and editorial boards.
          March. 16, 2012 (Special Time\Place)
          SPEAKER: Cyrus Shahabi (USC)
          TransDec: A Data-Driven Framework for Decision-Making in Transportation Systems
          Details
          Date and TimeMarch. 16, 2012 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerCyrus Shahabi (USC)
          TitleTransDec: A Data-Driven Framework for Decision-Making in Transportation Systems
          AbstractThe vast amounts of transportation datasets (traffic flow, incidents, etc.) collected by various federal and state agencies are extremely valuable in 1) real-time decision-making, planning, and management of the transportation systems, and 2) conducting research to develop new policies to enhance the efficacy of the transportation systems. In this talk, I will present our data-driven framework, dubbed TransDec (short for Transportation Decision-Making), which enables real-time integration, visualization, querying, and analysis of dynamic and archived transportation data. I will show that considering the large size of the transportation data, variety of the data (different modalities and resolutions), and frequent changes of the data, implementation of such a scalable system that allows for effective querying and analysis of both archived and real-time data is an intrinsically challenging data management task. Subsequently, I will focus on a route-planning problem where the weights on the road-network edges vary as a function of time due to the variability of traffic congestion. I will show that naïve approaches to address this problem are either inaccurate or slow, motivating the need for new solutions. Consequently, I will discuss our initial approach to this problem and demonstrate its implementation within the TransDec framework.
          Speaker BioCyrus Shahabi is a Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also the Director of the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California. He is also the CTO and co-founder of a USC spin-off, Geosemble Technologies. He received his B.S. in Computer Engineering from Sharif University of Technology in 1989 and then his M.S. and Ph.D. Degrees in Computer Science from the University of Southern California in May 1993 and August 1996, respectively. He authored two books and more than hundred- fifty research papers in the areas of databases, GIS and multimedia. Dr. Shahabi has received funding from several agencies such as NIJ, NSF, NASA, NIH, DARPA, AFRL, and DHS as well as several industries such as Google, Microsoft, NCR, NGC, and Chevron. He was an Associate Editor of IEEE Transactions on Parallel and Distributed Systems (TPDS) from 2004 to 2009. He is currently on the editorial board of the VLDB Journal, IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Computers in Entertainment and Journal of Spatial Information Science. He is the founding chair of IEEE NetDB workshop and also the general co-chair of ACM GIS 2007, 2008 and 2009. He chaired the nomination committee of ACM SIGSPATIAL for the 2011-2014 terms. He regularly serves on the program committee of major conferences such as VLDB, ACM SIGMOD, IEEE ICDE, ACM SIGKDD, and ACM Multimedia. Dr. Shahabi is a recipient of the ACM Distinguished Scientist award in 2009, the 2003 U.S. Presidential Early Career Awards for Scientists and Engineers (PECASE), the NSF CAREER award in 2002, and the 2001 Okawa Foundation Research Grant for Information and Telecommunications. He was the recipient of US Vietnam Education Foundation (VEF) faculty fellowship award in 2011, an organizer of the 2011 National Academy of Engineering “Japan- America Frontiers of Engineering” program, an invited speaker in the 2010 National Research Council (of the National Academies) Committee on New Research Directions for the National Geospatial- Intelligence Agency, and a participant in the 2005 National Academy of Engineering “Frontiers of Engineering” program.
          March. 9, 2012
          SPEAKER: Nga Dang (Ph.D. student in ISG)
          QuARES: A Quality-Aware Renewable Energy-driven Sensing Framework
          Details
          Date and TimeMarch. 9, 2012 3 pm
          LocationDBH 3011


          SpeakerNga Dang (Ph.D. student in ISG)
          TitleQuARES: A Quality-Aware Renewable Energy-driven Sensing Framework
          Abstract Mobile devices, such as smartphones and tablets, are getting increasingly popular, and continue to generate record-high amount of mobile data traffic. For example a recent Cisco report indicates that mobile data traffic will increase 39 times by 2015, while 66% of such boost is due to video traffic. Network capacity issue may be partially coped by deploying more cellular base stations, installing dedicated broadcast networks, or upgrading the cellular base stations to support 4G. However, these approaches all result in additional costs on new network infrastructure, and might not be fully compatible with existing obile devices. Also, according to the report, the network capacity provided by cellular network providers is predicted to be only 10 time increasing by 2015, which implies that the above methods do not still meet the requirement for increasing mobile traffic. A better way is moving data to other networks to reduce heavy traffic in cellular networks. In our research, we study motivations and methods to offload part of mobile traffic from cellular networks to other networks such as WiFi or Ad Hoc, which are available in most modern smartphones. Such these methods are cheap, practical, and easily implemented.
          March. 1, 2012 (Special Time\Place)
          SPEAKER: Archan Misra (Singapore Management University)
          Real-time Mobile Sensing/Analytics and the LiveLabs Experimentation Platform
          Details
          Date and TimeMarch. 1, 2012 (Special Time\Place) 11 am
          LocationDBH 4011


          SpeakerArchan Misra (Singapore Management University)
          TitleReal-time Mobile Sensing/Analytics and the LiveLabs Experimentation Platform
          Abstract This talk explores the ongoing transformation of the mobile device into a combined “sensing and analytics” platform, distinguished by two key features: a) efficient localized processing of sensor data streams and b) localized coordination and distributed computation among a set of proximal mobile nodes. I will first introduce the LiveLabs Experimentation Platform, a unique “urban behavioral testbed” that combines innovations in wireless networks, mobile sensing and App deployment to enable an ecosystem of industry partners to test next-generation context-based applications on approx. 30,000 real- life users in urban environments, such as the SMU campus, 2 major shopping malls and a resort theme park. I will then describe ongoing research on offline and near-real time energy-efficient, continuous smartphone-based human context estimation or “activity mining”, with a special focus on how such analytics can utilize proximity-driven social interactions. I will then briefly cover two ongoing projects that exploit such context-sensing to: a) optimize the delivery of mobile advertising and b) perform real- time adaptation of femtocellular indoor networks.
          Feb. 17, 2012
          SPEAKER: Russell Sears (Yahoo! Research)
          A general purpose Log Structured Merge Tree
          Details
          Date and TimeFeb. 17, 2012 3 pm
          LocationDBH 3011


          SpeakerRussell Sears (Yahoo! Research)
          TitleA general purpose Log Structured Merge Tree
          Abstract Data management workloads are increasingly write-intensive and subject to strict latency SLAs. This presents a dilemma: Traditional update in place systems have unmatched latency properties but poor write throughput. In contrast, existing log structured techniques significantly improve write throughput but generally sacrifice read performance and exhibit unacceptable latency spikes. We begin by presenting a new performance metric: read fanout, and argue that, along with read amplification and write amplification, it better characterizes the real-world performance of index algorithms than existing approaches such as asymptotic analysis and price/performance. We then present a Log Structured Merge (LSM) tree implementation that combines the best properties of B-Trees and log structured approaches: (1) Unlike existing log structured trees, our implementation has near-optimal read and scan performance, and (2) we present merge algorithms that bound write latencies without impacting write throughput or allowing merges to block application writes for extended periods of time. We do this by introducing a new ``spring and gear'' scheduler that ensures merges at each level of the tree make steady progress. This allows us to avoid blocking application writes without resorting to techniques that degrade read performance. We use Bloom filters to improve index performance, and find that a number of subtleties arise. First, it is important to ensure that reads can safely stop after finding the first version of a record. Otherwise, frequently written items will incur multiple disk lookups. Second, many applications and data management architectures check for preexisting values at insertion time. Avoiding the disk seek performed by the check is crucial for such applications. This work will appear in Sigmod 2012.
          Feb. 10, 2012 (Special Time\Place)
          SPEAKER: Anhai Doan (U. Wisconsin and Walmart Labs - ex Kosmix)
          Social Media, Data Integration, and Human Computation
          Details
          Date and TimeFeb. 10, 2012 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerAnhai Doan (U. Wisconsin and Walmart Labs - ex Kosmix)
          TitleSocial Media, Data Integration, and Human Computation
          AbstractSocial media has emerged as a major frontier on the World-Wide Web, with applications ranging from helping teenagers track Justin Bieber to e-commerce to fostering revolutions. In this talk I will discuss our work in this area, as carried out at Wisconsin, Kosmix, and @WalmartLabs. I describe how we integrate data from 'traditional' Web sources to build a global taxonomy, greatly expand it with social-media data, then leverage it to build consumer-facing applications. Example applications include building topic pages, detecting Twitter events, and monitoring these events. I discuss the critical role of data integration and human computation in processing social media. Finally, I discuss how all of these can help the emerging area of social commerce, and why Walmart recently acquired Kosmix to make inroads into this new and exciting area.
          Speaker BioAnHai Doan is an Associate Professor at the University of Wisconsin-Madison. His interests cover databases, AI, and Web, with a current focus on data integration, large-scale knowledge bases, social media, crowdsourcing, human computation, and information extraction. He received the ACM Doctoral Dissertation Award in 2003, a CAREER Award in 2004, and a Sloan Fellowship in 2007. AnHai was Chief Scientist of Kosmix, a social media startup acquired by Walmart in 2011. Currently he also works as Chief Scientist of @WalmartLabs, a research and development lab devoted to integrating social and mobile data for e-commerce.
          Feb. 3, 2012 (Special Time\Place)
          SPEAKER: Yannis Papakonstantinou (UCSD)
          Declarative, optimizable data-driven specifications of web and mobile applications
          Details
          Date and TimeFeb. 3, 2012 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerYannis Papakonstantinou (UCSD)
          TitleDeclarative, optimizable data-driven specifications of web and mobile applications
          AbstractDevelopers of web and mobile application development write too much low level "plumbing" code to efficiently access, integrate and coordinate application state that resides on multiple sub-systems of the architecture, and is accessed using different languages: SQL at the database server; HTML and Javascript at the browser, which in HTML5 includes its own database state; Java or other programming languages at the application server. The FORWARD project replaces such low level code with declarative specifications. Its cornerstones are (i) the unified application state virtual database, which enables modeling and manipulating the entire application state in an extension of SQL, named SQL++ (ii) specification of Ajax pages as essentially rendered views over the unified application state. Consequently the following three problems are resolved by appropriate reduction to data management problems, where prior database research literature is leveraged and extended. 1. The partial change of Ajax pages, in response to application state changes, is reduced to an incremental view maintenance problem. Id's that retain the provenance of the page data play an instrumental efficiency role. 2. Efficient data access is reduced to semistructured query processing over an integrated view that involves large database(s) and small main memory-based sources. 3. The inherent location transparency of the specifications is exploited in order to perform computation at the appropriate location (browser vs server). More broadly, the talk discusses ongoing and future work in utilizing the increased abilities of HTML5 clients towards achieving low latency mobile web applications applications, while location transparency of the specifications is retained.
          Speaker Bio Yannis Papakonstantinou is a Professor of Computer Science and Engineering at the University of California, San Diego. His research is in the intersection of data management technologies and the web, where he has published over eighty research articles. He has given multiple tutorials and invited talks, has served on journal editorial boards and has chaired and participated in program committees for many international conferences and workshops. Yannis was the CEO and Chief Scientist of Enosys Software, which built and commercialized an early XML-based Enterprise Information Integration platform. Enosys Software was acquired in 2003 by BEA Systems. He was the CEO and is the Chief Scientist of app2you, which has commercialized UCSD R and D on rapid development of web applications for data-driven analytics and business process management. He is the Chief Computer Scientist of a pharmaceutical spin-off startup in the area of data analytics for the pharmaceutical industry. He has been in the technical advisory board of multiple startups, currently including Brightscope Inc. Yannis holds a Diploma of Electrical Engineering from the National Technical University of Athens, MS and Ph.D. in Computer Science from Stanford University (1997) and an NSF CAREER award for his work on data integration.
          Jan. 27, 2012
          SPEAKER: Kurt Brown (EMC/Greenplum)
          The Future of Big Data Analytics
          Details
          Date and TimeJan. 27, 2012 3 pm
          LocationDBH 3011


          SpeakerKurt Brown (EMC/Greenplum)
          TitleThe Future of Big Data Analytics
          Abstract"Big Data" and analytics have both existed in some form for as long as computing itself, but only now has technology advanced to the point that, together, they are starting to qualitatively change the way organizations and individuals perceive, understand, and predict the world around them. In this talk, I'll set Big Data Analytics in a historical context to help sort out what aspects of current technologies (hardware, software, and programming models) are simply transient artifacts or long-term trends, and to project where Big Data Analytics is possibly headed (from the perspective of Greenplum and EMC).
          Speaker BioKurt Brown is currently Director of Advanced R and D at Greenplum/EMC. Prior to EMC, he co-directed Intel's Berkeley Research Lab, spent 13 years with IBM in operating systems and database R and D on the East and West coasts, and co-founded three startups in database middleware, small business marketing services, and residential energy management. He received his PhD in 1995 from the University of Wisconsin for work in automated database performance tuning.
          Jan. 13, 2011
          SPEAKER: Thomas Bodner
          The Stratosphere Parallel Analysis Framework, Present and Future
          Details
          Date and TimeJan. 13, 2011 3:00 pm
          LocationDBH 3011


          SpeakerThomas Bodner
          TitleThe Stratosphere Parallel Analysis Framework, Present and Future
          AbstractData-intensive computing is a much investigated topic in current research. Next to parallel databases, new flavors of data processors have established themselves - most prominently the MapReduce programming and execution model. The new systems provide key features that current parallel databases lack, such as flexibility in the data models, the ability to parallelize custom functions, and fault tolerance that enables them to scale out to thousands of machines. This talk presents the current state of Stratosphere system, a cloud data and query processor that has been released as open-source in spring 2011. The system consists of the parallel data programming model PACT, an extension of the MapReduce programming model for the specification of complex data-intensive tasks in the cloud, and the elastic, massively parallel execution engine Nephele, a Dryad-like parallel data processor. Furthermore, I give a demo of the most recent Stratosphere release. And finally, I report on future enhancements for Stratosphere, particularly, for the compilation, optimization and parallel execution of data-intensive operations in the system.
          Speaker Bio Since October 2010, Thomas Bodner is a Master's student at the department for Database Systems and Information Management (DIMA) at the Technical University of Berlin. Between 2007 and 2010, Thomas Bodner completed the Applied Computer Science program at the University of Cooperative Education, Stuttgart, jointly with IBM Germany as partner. In the course of his undergraduate studies, he studied abroad for one semester at the Royal Melbourne Institute of Technology, Australia and worked as an intern at the IBM Almaden Research Center, California, USA and the IBM Böblingen Laboratory in Germany, exploring query optimization and in-memory technologies for database management systems. His research interests include architectures for information management, query processing and optimization, benchmarking and machine learning.
          Dec. 9, 2011 (Special Time\Place)
          SPEAKER: Pat Helland (Microsoft)
          If You Have Too Much Data, then "Good Enough" Is Good Enough
          Details
          Date and TimeDec. 9, 2011 (Special Time\Place) 11 am
          LocationDBH 6011


          SpeakerPat Helland (Microsoft)
          TitleIf You Have Too Much Data, then "Good Enough" Is Good Enough
          AbstractClassic database systems offer crisp answers for a relatively small amount of data. These systems hold their data in one or a relatively small number of computers. With a tightly defined schema and transactional consistency, the results returned from queries are crisp and accurate. New systems have humongous amounts of data content, change rates, and querying rates and take lots of computers to hold and process. The data quality and meaning are fuzzy. The schema, if present, is likely to vary across the data. The origin of the data may be suspect, and its staleness may vary. Today's data systems coalesce data from many sources. The Internet, B2B, and enterprise application integration (EAI) combine data from different places. No computer is an island. This large amount of interconnectivity and interdependency has led to a relaxation of many database principles. In this talk, consider the some of the ways in which today's answers differ from what we used to expect.
          Speaker Bio Pat Helland has been working in distributed systems, transaction processing, databases, and similar areas since 1978. For most of the 1980s, he was the chief architect of Tandem Computers' TMF (Transaction Monitoring Facility), which provided distributed transactions for the NonStop System. With the exception of a two-year stint at Amazon, Helland has worked at Microsoft Corporation since 1994 where he was the architect for Microsoft Transaction Server and SQL Service Broker. Until September, 2011, he was working on Cosmos, a distributed computation and storage system that provides back-end support for Bing. Pat recently relocated to San Francisco with his wife to be close to the grandchildren and to explore new opportunities in "Big Data" and/or "Cloud Computing".
          Nov. 18, 2011
          SPEAKER: Yi Pan and Masood Mortazavi (Yahoo!)
          Scalability and Programming Model in Serving Storage Systems
          Details
          Date and TimeNov. 18, 2011 3pm
          LocationDBH 3011


          SpeakerYi Pan and Masood Mortazavi (Yahoo!)
          TitleScalability and Programming Model in Serving Storage Systems
          AbstractWe will review some of the storage technologies Yahoo applications use in Yahoo's cloud platform. These serving storage systems can scale to extremely large numbers of records. After discussing overall architecture of these scalable storage systems, we will focus on Sherpa (PNUTS). Sherpa is a multi-tenant, distributed, highly elastic key-value store with a well-defined transaction semantics that serves data for 100s of Yahoo applications. To exemplify the type of scalability challenges we face, we will describe how we're evolving Sherpa along various dimensions. We will then focus on the programmability dimension and explain how we have implemented a highly scalable, eventually consistent indexing system for Sherpa. Design decisions we have made to balance concerns related to consistency and availability will be discussed, and we hope to elucidate the basic questions that come up, repeatedly, when evolving such massively scalable systems while they are in operation.
          Speaker BioDr. Masood Mortazavi works as a senior principal architect at Yahoo's serving storage systems group. His interests include distributed systems, scalability, multi-tenancy and cloud serving systems. Masood has also worked for Huawei Technologies, Sun Microsystems, Tecknowledge and Hughes Aircrafts. Masood's LinkedIn profile can be found here: http://www.linkedin.com/in/mortazavi . . . At Yahoo, he helps advance cloud platform and storage technologies. Dr. Yi Pan graduated with a Ph.D. degree in computer science from University of California at Irvine. He got his B.S. and M.S. Degree from Fudan University in Shanghai, China. His main interests expand across many areas in large scale distributed computer networks and applications. Currently, he works as a principal software engineer in Yahoo!’s Cloud Platform Group. His main goal is to push forward Yahoo!’s state-of-art cloud storage systems with innovative features.
          Nov. 4, 2011
          SPEAKER: Thomas Bodner
          Myriad - Parallel Data Generation on Shared-Nothing Architectures
          Details
          Date and TimeNov. 4, 2011 3:30 pm
          LocationDBH 3011


          SpeakerThomas Bodner
          TitleMyriad - Parallel Data Generation on Shared-Nothing Architectures
          Abstract The need for efficient data generation for the purposes of testing and benchmarking newly developed data-intensive computing systems has increased with the emergence of big data problems. As synthetic data model specifications evolve over time the data generator programs implementing these models have to be continuously adapted – a task that might become complex as the set of model constraints grows. This talk presents Myriad - a new parallel data generation toolkit. Data generators created with the toolkit can produce very large datasets by exploiting a completely parallel execution model, while at the same time maintain cross-partition dependencies, correlations and distributions in the generated data. In addition, I report on our efforts towards a benchmark suite for large-scale parallel analysis systems that uses Myriad for the generation of large social network graphs and OLAP-style relational datasets.
          Speaker Bio Since October 2010, Thomas Bodner is a Master's student at the department for Database Systems and Information Management (DIMA) at the Technical University of Berlin. Between 2007 and 2010, Thomas Bodner completed the Applied Computer Science program at the University of Cooperative Education, Stuttgart, jointly with IBM Germany as partner. In the course of his undergraduate studies, he studied abroad for one semester at the Royal Melbourne Institute of Technology, Australia and worked as an intern at the IBM Almaden Research Center, California, USA and the IBM Böblingen Laboratory in Germany, exploring query optimization and in-memory technologies for database management systems. His research interests include architectures for information management, query processing and optimization, benchmarking and machine learning.
          Oct. 21, 2011
          SPEAKER: David Lomet (Microsoft Research)
          Deuteronomy: Transaction Support for Cloud Data
          Details
          Date and TimeOct. 21, 2011 3pm
          LocationDBH 3011


          SpeakerDavid Lomet (Microsoft Research)
          TitleDeuteronomy: Transaction Support for Cloud Data
          AbstractThe Deuteronomy system supports efficient and scalable ACID transactions in the cloud by decomposing the storage engine into: (a) a transactional component (TC) that manages transactions and their ``logical" concurrency control and undo/redo recovery, and (b) a data component (DC) that knows about the access methods and supports a record-oriented interface with atomic operations, but knows nothing about transactions. The Deuteronomy TC can be applied to data anywhere, in the cloud, local, etc. with a variety of deployments for both the TC and DC components. In this talk, we first describe the architecture of our TC, and the considerations that led to it. We next describe the contract between TC and DC, how we changed the operation protocol to simplify it and make it more efficient. We have implemented both TC and multiple DCs, and will describe our TC implementation in detail. We will end a few words about observed performance and scalability.
          Speaker Bio David Lomet is a principal researcher managing the Microsoft Research Database Group. Earlier, he worked at Digital, IBM Research, and Wang Institute. He has a CS Ph.D from the University of Pennsylvania. He is author of over 100 papers (two SIGMOD "best papers") and has 45 patents. He has served on program committees (SIGMOD, PODS, VLDB, ICDE...), was ICDE'2000 PC co-chair, VLDB'2006 PC core chair, and is on the ICDE Steering Committee, the VLDB Board, is TCDE Chair and has been an editor for TODS, VLDBJ, and JDPD. He is the Data Engineering Bulletin EIC, for which he received the SIGMOD Contributions Award. He received IEEE Golden Core, Outstanding, and Meritorious Service Awards and is a Fellow of IEEE, ACM, and AAAS.
          Oct. 21, 2011 (Special Time\Place)
          SPEAKER: Danny Sullivan (Editor In Chief, Search Engine Land)
          From Search 1.0 to Search 4.0
          Details
          Date and TimeOct. 21, 2011 (Special Time\Place) 11am
          LocationDBH 6011


          SpeakerDanny Sullivan (Editor In Chief, Search Engine Land)
          TitleFrom Search 1.0 to Search 4.0
          AbstractWhen search engines first began, they focused on crawling web pages and "words on the page" ranking analysis. That system quickly failed, being far too easy to game. Search 2.0 gave us ranking where links were used as votes; Search 3.0, a third generational system, introduced blending vertical search results with web matches. Currently underway, the fourth generational trend of Search 4.0 taps into human signals, from social networks and personalization, to refine search results. The "how and why" of this evolution has unfolded.
          Speaker BioWidely considered a leading "search engine guru," Danny Sullivan has been helping webmasters, marketers and everyday web users understand how search engines work for over a decade. Danny's expertise about search engines is often sought by the media, and he has been quoted in places like The Wall St. Journal, USA Today, The Los Angeles Times, Forbes, The New Yorker and Newsweek and ABC's Nightline. Danny began covering search engines in late 1995, when he undertook a study of how they indexed web pages. The results were published online as "A Webmaster's Guide To Search Engines," a pioneering effort to answer the many questions site designers and Internet publicists had about search engines. Danny currently heads up Search Engine Land as editor-in-chief, which covers all aspects of search marketing and search engine news. Danny also serves as Third Door Media's chief content officer, which owns Search Engine Land and the SMX: Search Marketing Expo conference series. Danny also maintains a personal blog called Daggle and microblogs on Twitter: @dannysullivan.
          Oct. 14, 2011
          SPEAKER: Tyson Condie (Yahoo! Research)
          Scal(a)ing up Machine Learning and Graph-based Analytics
          Details
          Date and TimeOct. 14, 2011 3pm
          LocationDBH 3011


          SpeakerTyson Condie (Yahoo! Research)
          TitleScal(a)ing up Machine Learning and Graph-based Analytics
          Abstract Machine learning practitioners are increasingly interested in applying their algorithms to Big Data. Unfortunately, current high-level languages for data analytics (e.g., Hive, Pig, Sawzall, Scope) do not fully cover this domain. One key missing ingredient is the means to efficiently support iteration over the data. Zaharia et al., were the first to answer this call from a systems perspective with Spark. Spark adds the notion of a working set to data-parallel workflows and has published speed-ups of 30x over Hadoop MapReduce for many machine learning and graph algorithms. Unfortunately, Spark does cover the whole pipeline of Big Data analytics; at Yahoo!, it is common to compose Pig, MPI and direct MapReduce program modules into workflows. This fractioning of individual processing steps can be a major pain e.g., for optimization, debugging, and code readability. Our prescription to this dilemma is a new DSL for data analytics called ScalOps. Like Pig, ScalOps combines the declarative style of SQL and the low-level procedural style of MapReduce. Like Spark, ScalOps can optimize its runtime—the Hyracks parallel-database engine—for repeated access to data collections. ScalOps is part of a broader research agenda to explore new abstractions for machine learning and graph-based analytics. In this talk, I will present example workflows from the machine learning domain expressed in ScalOps and their translation to Hyracks recursive query plans.
          Sept. 30, 2011
          SPEAKER: Grad. students
          System Demo
          Details
          Date and TimeSept. 30, 2011 3pm
          LocationDBH 3011


          SpeakerGrad. students
          TitleSystem Demo
          Sept. 23, 2011
          SPEAKER: ISG memebers
          ISG Gathering
          Details
          Date and TimeSept. 23, 2011 3pm
          LocationDBH 3011


          SpeakerISG memebers
          TitleISG Gathering
          June 3, 2011
          SPEAKER: Donald Kossman
          Predictable Performance for Unpredictable Workloads
          Details
          Date and TimeJune 3, 2011 2pm
          LocationDBH 3011


          SpeakerDonald Kossman
          TitlePredictable Performance for Unpredictable Workloads
          AbstractThis talk presents the design of SwissBox. SwissBox is a database appliance designed to process thousands of concurrent queries and updates with bounded query response times and strict data freshness guarantees. The system was designed to aggressively share operations between concurrent queries and updates. This talk shows the design of the storage manager (called Crescando) and the design of the query processor (called SharedDB). Furthermore, the talk presents the results of performance experiments with workloads from an airline reservation system.
          Speaker BioDonald Kossmann is a professor for Computer Science at ETH Zurich (Switzerland). He received his MS from the University of Karlsruhe and completed his PhD at the University of Aachen. After that, he held positions at the University of Maryland, the IBM Almaden Research Center, the University of Passau, the University of Munich, and the University of Heidelberg. He is an ACM fellow, member of the board of trustees of the VLDB endowment, and was the program committee chair of the ACM SIGMOD Conf., 2009. He is a co-founder of i-TV-T (1998), XQRL Inc. (acquired by BEA in 2002), and 28msec Inc. (2007). His research interests lie in the area of databases and information systems.
          May 20, 2011
          SPEAKER: Ronen Vaisenberg
          Scheduling and Actuating Camera Networks to Maximize Event Detection
          Details
          Date and TimeMay 20, 2011 2pm
          LocationDBH 3011


          SpeakerRonen Vaisenberg
          TitleScheduling and Actuating Camera Networks to Maximize Event Detection
          AbstractA distributed camera network allows for many compelling applications, such as large-scale tracking, face recognition, occupancy monitoring or event detection. In most practical systems, resources are either constrained or mutually exclusive. Constraints arise from network bandwidth restrictions, I/O and disk usage from writing images, and CPU usage needed to extract features from the images. Detecting events in real time requires dynamically choosing a subset of the available sensors for processing at any given time. Furthermore, certain camera configurations are not feasible. For example, a camera cannot zoom into two different regions in its field of view. Zooming into a specific area in the field of view of a camera would generate a high resolution image of the region in the expense of a wider field of view. Thus, the field of view needs to be changed dynamically to get a higher resolution images of certain regions of the space at the expanse other regions. In order to illustrate the complexity of this problem, consider a face recognition application, which is only interested in high resolution (by means of optical zoom) facial images. If we always zoom into a region to look for a high res face, we might miss presence of a person in different region and hence opportunity for zooming later to get the face in next time step. In this talk we examine the problem of scheduling sensors for data collection and actuating them on real time to maximize some user-specified objective - e.g., detecting as much motion as possible or collect as many high resolution facial images. The main idea behind our approach is the use of sensor semantics to guide the scheduling process. We learn a dynamic probabilistic model of motion correlations between cameras, and use the model to guide resource allocation for our sensor network. Although previous work has leveraged probabilistic models for sensor-scheduling, our work is distinct in its focus on real-time building-monitoring using a camera network. We validate our approach using a sensor network of a dozen cameras spread throughout a university building, recording measurements of unscripted human activity over a two week period. We automatically learn a semantic model of typical behaviors, and show that one can significantly improve efficiency of resource allocation and actuation by exploiting this model.
          May 13, 2011 (Special)
          SPEAKER: Prof. John Ousterhout (Stanford)
          RAMCloud: Scalable High-Performance Storage Entirely in DRAM
          Details
          Date and TimeMay 13, 2011 (Special) 11am
          LocationDBH 6011


          SpeakerProf. John Ousterhout (Stanford)
          TitleRAMCloud: Scalable High-Performance Storage Entirely in DRAM
          AbstractDisk-oriented approaches to online storage are becoming increasingly problematic: they do not scale gracefully to meet the needs of new large-scale Web applications, and improvements in disk capacity have out-stripped improvements in access speed. In this talk I will describe a new approach to datacenter storage called RAMCloud, where information is kept entirely in DRAM and large-scale systems are created by aggregating the main memories of thousands of commodity servers. A RAMCloud can provide durable and available storage with 100-1000x the throughput of disk-based systems and 100-1000x lower access latency. By combining low latency and large scale, RAMClouds will enable a new class of applications that manipulate large datasets more intensively than has ever been possible.
          Speaker BioJohn Ousterhout is Professor (Research) of Computer Science at Stanford University. His current research focuses on infrastructure for Web applications and cloud computing. Ousterhout's prior positions include 14 years in industry where he founded two companies (Scriptics and Electric Cloud), preceded by 14 years as Professor of Computer Science at U.C. Berkeley. He is the creator of the Tcl scripting language and is also well known for his work in distributed operating systems and file systems. Ousterhout received a BS degree in Physics from Yale University and a PhD in Computer Science from Carnegie Mellon University. He is a member of the National Academy of Engineering and has received numerous awards, including the ACM Software System Award, the ACM Grace Murray Hopper Award, the National Science Foundation Presidential Young Investigator Award, and the U.C. Berkeley Distinguished Teaching Award.
          May 9, 2011 (Special)
          SPEAKER: Prof. Barton P. Miller
          Scaling Up to Large (Really Large) Systems
          Details
          Date and TimeMay 9, 2011 (Special) 11am
          LocationDBH 3011


          SpeakerProf. Barton P. Miller
          TitleScaling Up to Large (Really Large) Systems
          AbstractI will discuss the problem of developing tools and middleware for large scale parallel environments. We are especially interested in systems, both leadership class parallel computers and clusters that have 100,000's or even millions of processors. The infrastructure that we have developed to address this problem is called MRNet, the Multicast/Reduction Network. MRNet's approach to scale is to structure control and data flow in a tree-based overlay network (TBON) that allows for efficient request distribution and flexible data reductions. I will then present an overview of the MRNet design, architecture, and computational model and then discuss several of the applications of MRNet. The applications include scalable automated performance analysis, a vision clustering application and, most recently, an effort to develop our first petascale debugging tool, STAT, a scalable stack trace analyzer running currently on 100,000's of processors on both the Cray XT and IBM BlueGene.
          Speaker BioProf. Barton Miller is a Professor of Computer Sciences at the University of Wisconsin. Bart is a product of the UC System: he received his BA degree from UC San Diego in 1977 and his MS and PhD in Computer Science from UC Berkeley in 1980 and 1984, respectively. His research interests include distributed and parallel program performance and tools, binary code analysis and instrumentation, computer security, scalable systems, operating systems, and software testing. Bart is a Fellow of the ACM.
          May 6, 2011
          SPEAKER: Matthias Nicola, IBM
          A Matter of Time: Temporal Data Management in DB2 for z/OS
          Details
          Date and TimeMay 6, 2011 2pm
          LocationDBH 3011


          SpeakerMatthias Nicola, IBM
          Title A Matter of Time: Temporal Data Management in DB2 for z/OS
          AbstractTime is a critical dimension in data management. For many enterprises it is useful or even required to have the ability to go back in time and look at a past state of the database. Many applications also need to manage time in their business records, such as contract start and end dates, expiration dates, or "effective dates" to indicate that information is valid for a certain period in the past, presence, or future. This presentation describes typical use cases for temporal data management and describes the temporal capabilities in DB2, including system time, business time, and bitemporal support.
          Speaker BioMatthias Nicola is a senior software engineer at IBM's Silicon Valley Lab, in San Jose, CA, USA. He focuses on DB2 performance and benchmarking, XML, temporal data management, in-database analytics, and other emerging technologies. Matthias also works closely with customers and business partners to help them design, optimize and implement DB2 solutions. Previously Matthias worked on data warehouse performance at Informix Software. Matthias received his PhD in computer science from the Technical University of Aachen, Germany.
          April 25, 2011 (Special)
          SPEAKER: Prof. Christos Faloutsos, CMU
          Mining Billion-node Graphs
          Details
          Date and TimeApril 25, 2011 (Special) 11am
          LocationDBH 6011


          SpeakerProf. Christos Faloutsos, CMU
          TitleMining Billion-node Graphs
          AbstractWhat do graphs look like? How do they evolve over time? How to handle a graph with a billion nodes? We present a comprehensive list of static and temporal laws, and some recent observations on real graphs (like, e.g., ``eigenSpokes''). We present tools, and specifically ``oddBall'' for discovering anomalies and patterns, as well as fast algorithms for immunization. Finally, we present an overview of the PEGASUS system which is designed to handle billion-node graphs, running on top of the "hadoop" system.
          Speaker BioChristos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), seventeen ``best paper'' awards, (including two ``test of time'') and four teaching awards. He has served as a member of the executive committee of SIGKDD; he is an ACM Fellow; he has published over 200 refereed articles, 11 book chapters and one monograph. He holds five patents and he has given over 30 tutorials and over 10 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data.
          April 22, 2011
          SPEAKER: Jerome Simeon, IBM Research T.J. Watson
          Algebraic Comprehensions (Database Optimization for Web 2.0 Queries)
          Details
          Date and TimeApril 22, 2011 2pm
          LocationDBH 3011


          SpeakerJerome Simeon, IBM Research T.J. Watson
          TitleAlgebraic Comprehensions (Database Optimization for Web 2.0 Queries)
          AbstractDirect support for querying is becoming a "must have" for programming languages targeting Web 2.0 and Cloud development. Most of those languages (Microsoft's Linq, University of Edinburgh's Links, EPFL's Scala, Yahoo!'s Pig Latin, IBM's Thorn, etc) rely on the classic notion of comprehensions over collections. At the language level, comprehensions are a perfect choice, being well understood programming constructs, and capturing the expressive power of SQL iterators. At the compiler level, however, they are at odds with database optimizers which mostly rely on relational (or nested-relational) algebras. That mismatch was clearly on display during the design of XQuery, whose semantics is based on comprehensions, and for which most implementations target relational backends. We propose a alternative functional semantic formulation of XQuery to the one proposed by W3C, which is also based on comprehensions but has the benefit of corresponding precisely to compilation into a typed algebra that supports traditional database optimizations. First, this provides a formal foundation for XQuery implementations that want to ensure semantics integrity with the standard, along with modern database optimization techniques. Also, it provides key insights into the nature of database compilers that we believe is essential for the integration of database and programming languages technology. We notably discover that type systems for database algebras require an original solution to the old problem of subtyping with record concatenation, and that such a type system can eliminate the need for complex side conditions used in query language optimization.
          Speaker BioJerome Simeon is a Researcher for the Scalable XML Infrastructure Group at IBM T.J. Watson. He holds a degree in Engineering from EcolePolytechnique, and a Ph.D. from Universite d'Orsay. Previously, Jerome worked at INRIA from 1995 to 1999, and Bell Laboratories from 1999 to 2004. His research interests include databases, programming languages, compilers, and semantics, with a focus on Web development. He has put his work into practice in areas ranging from telecommunication infrastructure, to music. He is a co-editor for five of the W3C XML Query specifications, and has published more than 50 papers in scientific journals and international conferences. He is also a project lead for the Galax open-source XQuery implementation, and a co-author of "XQuery from the Experts" (Addison Wesley, 2004).
          April 15, 2011
          SPEAKER: Tyson Condie, Yahoo! Research
          RubySky: Exploring Big Data with Transparency and Adjustability
          Details
          Date and TimeApril 15, 2011 2pm
          LocationDBH 3011


          SpeakerTyson Condie, Yahoo! Research
          TitleRubySky: Exploring Big Data with Transparency and Adjustability
          AbstractIn this talk, I will introduce a new scripting language for ad-hoc exploration of large data sets, called RubySky. As with several prior efforts, RubySky scripts execute either in a local environment or in the cloud (Hadoop). Typically, cloud-based execution is highly opaque and hands-off, rendering debugging and iterative code development very difficult. RubySky, on the other hand, aims for a more transparent and adjustable paradigm. It includes the ability to ``peek into'' intermediate cloud execution pathways, integrated as a first-class language construct. Also integrated into the language is a way for the user to make last-minute code revisions, at any point at which troublesome data is encountered in the cloud. Combined, these features aim to improve usability for users who develop and run single-use scripts that explore new data sets. This is joint work with Christopher Olston at Yahoo! Research.
          April 1, 2011
          SPEAKER: Doug Terry, Microsoft Research
          Replicated Data Consistency Explained through Baseball
          Details
          Date and TimeApril 1, 2011 2pm
          LocationDBH 3011


          SpeakerDoug Terry, Microsoft Research
          TitleReplicated Data Consistency Explained through Baseball
          Abstract A variety of relaxed consistency models for replicated data have been proposed and studied as an alternative to one-copy serializability, and some of these are being used in cloud storage systems. The designers of such systems particularly avoid two-phase commit for updates to geo-replicated data that spans multiple data centers on different continents. Instead, many cloud services, including systems from Amazon, Yahoo, and Microsoft, have adopted techniques that provide eventual consistency. This talk explores the hows and whys of different consistency models. The discussion will be driven by a simple example: maintaining the score of a baseball game. We'll see that people with various roles in the game can tolerate and benefit from different types of consistency when accessing the score.
          Speaker BioDoug Terry is a Principal Researcher in the Microsoft Research Silicon Valley lab. His research focuses on the design and implementation of novel distributed systems including mobile and cloud services. He currently is serving as Chair of ACM's Special Interest Group on Operating Systems (SIGOPS) and as a member of the ACM Council. Prior to joining Microsoft, Doug was the co-founder and CTO of a start-up company named Cogenia, Chief Scientist of the Computer Science Laboratory at Xerox PARC, and an Adjunct Professor in the Computer Science Division at U. C. Berkeley, where he still occasionally teaches a graduate course on distributed systems. Doug has a Ph.D. in Computer Science from U.C. Berkeley and is an ACM Fellow.
          Mar 31, 2011
          SPEAKER: Doug Terry, Microsoft Research
          Cimbiosys: Content-based Replication for Mobile Devices and the Cloud
          Details
          Date and TimeMar 31, 2011 11am
          LocationDBH 6011


          SpeakerDoug Terry, Microsoft Research
          TitleCimbiosys: Content-based Replication for Mobile Devices and the Cloud
          Abstract As people increasingly use mobile devices and cloud services to share large data collections, exploiting communication proximity and selectively replicating content is essential. Cimbiosys is a replicated storage platform that permits each device to define its own content-based filtering criteria and to exchange data directly with other devices. This talk focuses on the key challenge of ensuring eventual consistency in the face of fluid network connectivity, redefinable content filters, and arbitrary updates. Notably, Cimbiosys guarantees that each device eventually stores precisely those items whose latest version matches its custom filter and represents its replication-specific metadata in a compact form, resulting in low data synchronization overhead. This permits ad hoc replication between newly encountered devices and frequent synchronization between established partners, even over low bandwidth wireless networks or across geo-distributed data centers. (This talk will be a Ted and Janice Smith Distinguished lecture, and not at the normal time or place for ISG Seminars.)
          Speaker BioDoug Terry is a Principal Researcher in the Microsoft Research Silicon Valley lab. His research focuses on the design and implementation of novel distributed systems including mobile and cloud services. He currently is serving as Chair of ACM's Special Interest Group on Operating Systems (SIGOPS) and as a member of the ACM Council. Prior to joining Microsoft, Doug was the co-founder and CTO of a start-up company named Cogenia, Chief Scientist of the Computer Science Laboratory at Xerox PARC, and an Adjunct Professor in the Computer Science Division at U. C. Berkeley, where he still occasionally teaches a graduate course on distributed systems. Doug has a Ph.D. in Computer Science from U.C. Berkeley and is an ACM Fellow.
          Mar 25, 2011
          SPEAKER: Alexander Behm, UCI PhD student
          Answering Approximate String Queries on Large Data Sets Using External Memory
          Details
          Date and TimeMar 25, 2011 2pm
          LocationDBH 3011


          SpeakerAlexander Behm, UCI PhD student
          TitleAnswering Approximate String Queries on Large Data Sets Using External Memory
          AbstractAn approximate string query is to find from a collection of strings those that are similar to a given query string. Answering such queries is important in many applications such as data cleaning and record linkage, where errors could occur in queries as well as the data. Many existing algorithms have focused on in-memory indexes. In this paper we investigate how to efficiently answer such queries in a disk-based setting, by systematically studying the effects of storing data and indexes on disk. We devise a novel physical layout for an inverted index to answer queries and we study how to construct it with limited buffer space. To answer queries, we develop a cost-based, adaptive algorithm that balances the I/O costs of retrieving candidate matches and accessing inverted lists. Experiments on large, real datasets verify that simply adapting existing algorithms to a disk-based setting does not work well and that our new techniques answer queries efficiently. Further, our solutions significantly outperform a recent tree-based index, BED-tree. This talk is a ICDE practice talk.
          Mar 18, 2011
          SPEAKER: Pinaki Sinha
          Summarization of Personal Photo Collections
          Details
          Date and TimeMar 18, 2011 2pm
          LocationDBH 3011


          SpeakerPinaki Sinha
          TitleSummarization of Personal Photo Collections
          AbstractThe volume of personal photos hosted on photo archives and social sharing platforms has been increasing exponentially. According to recent estimates, 6 Billion photos are uploaded on Facebook per month. It is difficult to get an overview of a large collection of personal photos without browsing though the entire database manually. In this talk, I will discuss a framework to generate representative subset summaries from photo collections present on personal archives or social networks. I will define salient properties of an effective photo summary and model summarization as an optimization of these properties, given the size constraints. Computer vision, and IR based techniques will be used to generate summaries that "look good" as well as are informative. I will also introduce information theory based metrics for evaluating photo summaries based on their information content and the ability to satisfy user's information needs. I will also discuss the manual evaluation experiments that were done to evaluate summaries.
          Mar 11, 2011
          SPEAKER: Dmitri V. Kalashniknov
          Entity resolution
          Details
          Date and TimeMar 11, 2011 2pm
          LocationDBH 3011


          Speaker Dmitri V. Kalashniknov
          TitleEntity resolution
          Mar 4, 2011
          SPEAKER: Rares Vernica
          Efficient Processing of Set-Similarity Joins on Large Clusters
          Details
          Date and TimeMar 4, 2011 2pm
          LocationDBH 3011


          SpeakerRares Vernica
          Title Efficient Processing of Set-Similarity Joins on Large Clusters
          Feb 23, 2011
          SPEAKER: Dr. Terence Sim
          Getting More From Fisher
          Details
          Date and TimeFeb 23, 2011 3pm
          LocationDBH 3011


          Speaker Dr. Terence Sim
          TitleGetting More From Fisher
          AbstractThe Fisher Linear Discriminant (FLD) is commonly used in classification to find a subspace that maximally separates class patterns according to the Fisher Criterion. It was previously proven that a pre-whitening step can be used to truly optimize the Fisher Criterion. In this talk, we show that more insight and more applications may be derived from this classical technique. First, we explore the subspaces induced by this whitened FLD. In particular, we show how the Identity Space and Variation Space are useful for decomposing and representing data. We give sufficient conditions for these spaces to exist. Through experiments we also show how these spaces may be used for classification and image synthesis. Second, we further extend classical Fisher to handle data exhibiting multiple factors (modes), e.g. face images that exhibit personal identity, illumination, and pose. We call our method Multimodal Discriminant Analysis (MMDA), which is useful for decomposing a dataset into independent modes. For face images, MMDA effectively separates identity, illumination and pose into mutually orthogonal subspaces. MMDA is based on maximizing the Fisher Criterion on all modes simultaneously, and is therefore well-suited for multimodal and mode-invariant pattern recognition. We also show that MMDA may be used for dimension reduction, and for synthesizing face images under novel illumination, and even novel personal identity.
          Speaker BioTerence Sim is an Asst. Prof. at the School of Computing, National University of Singapore. He teaches an undergraduate course in computer vision, as well as a graduate course in multimedia fundamentals. For research, he works primarily in these areas: face recognition, biometrics, and computational photography. He is also interested in computer vision problems in general, such as shape-from-shading, photometric stereo, object recognition. On the side, he dabbles with some aspects of music processing, such as polyphonic music transcription. Dr. Sim serves as Vice-Chairman of the Biometrics Technical Committee (BTC), Singapore, and Chairman of the Cross-Jurisdictional and Societal Aspects Working Group (WG6) within the BTC. The interesting issues here are the legal and privacy aspects of using biometrics. He also serves as Vice-President of the Pattern Recognition and Machine Intelligence Association (PREMIA), a national professional body for pattern recognition. Dr. Sim obtained his PhD from Carnegie Mellon in 2002, his MSc from Stanford University in 1991, and his SB from MIT in 1990.
          Feb 16, 2011
          SPEAKER: Laura Haas (IBM)
          New Principles for Information Integration
          Details
          Date and TimeFeb 16, 2011 11am
          LocationDBH 4011


          Speaker Laura Haas (IBM)
          TitleNew Principles for Information Integration
          Abstract Ten years ago, Clio introduced nonprocedural schema mappings to describe the relationship between data in heterogeneous schemas. This enabled powerful tools for mapping discovery and integration code generation, greatly simplifying the integration process. However, further progress is needed. We see an opportunity to raise the level of abstraction further, and propose two new principles that the next generation of integration systems should embody. Holistic information integration supports iteration across the various integration tasks, leveraging information about both schema and data to improve the integrated result. Integration independence allows applications to be independent of how, when, and where information integration takes place, making materialization and the timing of transformations an optimization decision that is transparent to applications. This talk introduces these principles and describes some promising recent work in these directions.
          Speaker Bio Laura Haas is an IBM Fellow and has been director of computer science at IBM Almaden Research Center since 2005. Previously, Dr. Haas was responsible for Information Integration Solutions (IIS) architecture in IBM's Software Group after leading the IIS development team through its first two years. She joined the development team in 2001 as manager of DB2 UDB Query Compiler development. Before that, Dr. Haas was a research staff member and manager at the Almaden lab for nearly twenty years. In IBM Research, she worked on and managed a number of exploratory projects in distributed database systems. Dr. Haas is best known for her work on the Starburst query processor (from which DB2 UDB was developed); on Garlic, a system which allowed federation of heterogeneous data sources; and on Clio, the first semi-automatic tool for heterogeneous schema mapping. Garlic technology, married with DB2 UDB query processing, is the basis for the IBM WebSphere Information Server's federation capabilities, while Clio capabilities are a core differentiator in IBM’s Rational Data Architect. Dr. Haas has received several IBM awards for Outstanding Technical Achievement and Outstanding Innovation, and an IBM Corporate Award for her work on federated database technology. In 2010 she was recognized with the Anita Borg Institute Technical Leadership Award. She is a member of the National Academy of Engineering and the IBM Academy of Technology, an ACM Fellow, and Vice Chair of the board of the Computing Research Association. Dr. Haas received her PhD from the University of Texas at Austin, and her bachelor degree from Harvard University.
          Feb 4, 2011 (POSTPONED)
          SPEAKER: Amy Voida
          Homebrew Databases
          Details
          Date and TimeFeb 4, 2011 (POSTPONED) 2pm
          LocationDBH 3011


          Speaker Amy Voida
          TitleHomebrew Databases

          For more information on CS distinguished lectures, please visit Computer Science Department Seminar Series.

          ^ top

          Last Updated on January 07, 2011

          http://cert.ics.uci.edu/seminarseries.html CERT - Center for Emergency Response Technologies
          • Home
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          Upcoming Events

          Upcoming Workshop November 5th and 6th: DHS S&T / UCI CERT: Workshop on
          Emergency Management: Incident, Resource, and Supply Chain Management
          (EMWS09).

          Past Events

          May 15, 2009. Topic: Earthquakes, Hurricanes and other Disasters: A View from Space.

          Firefighter Forum: May 15, 2009. Special Topic: Wildland Fires. Location: Bren Hall, Room 4001, UCI

          CERT Workshop Series: Emergency Information Dissemination in Schools Friday, September 26, 2008

          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/SAFIRE/ SAFIRE: Situational Awareness for Firefighters
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          Our Goals:

          Emergency responders in all roles must process information, make critical decisions, take appropriate actions, and communicate effectively with others around them in order to preserve the safety of firefighters. These activities must be accomplished under dynamic and dangerous conditions where time is of the essence, and rarely with complete situational awareness. When a high level of situational awareness is achieved, more informed decisions can be made, resulting in much more effective actions and better management of risks. The quality of situational awareness that exists throughout all levels of the emergency response hierarchy has a direct impact on the safety of individual firefighters.

          Achieving situational awareness requires acquisition of knowledge of past events, an in-context understanding of present circumstances, and anticipation of future events. It also requires a high degree of communication and coordination up-and-down the chain of command, as well as between peers, whether the peers are individual firefighters, incident commanders, or cities within an operational area.

          The key deliverable is a Fire Incident Command Board (FICB) through which the user can establish and maintain situational awareness utilizing a wide range of sensor and data streams from the field as well as existing centralized information systems such as CAD and GIS. The FICB prototype that will be delivered as a result of the project will be specifically targeted towards the needs of an incident commander. We anticipate that future efforts could expand upon the FICB prototype created during this project in order to provide FICB variants specific to needs of other users, e.g. planning and intelligence analysts in EOCs.


          © SAFIRE
          http://cert.ics.uci.edu/motivation.html CERT - Center for Emergency Response Technologies
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          Motivation

          Public safety, public safety technologies, and research within public safety are a continually growing domain. CERT will serve as a vehicle to pursue funding within public safety from agencies such as NSF (e.g., an ERC), DARPA, and others.

          CERT will be a high-visibility center for ICS and UCI. Already, the ICS Center has industry support in the form of seed money from the SB Corporation. Current industrial partners (leveraged from Rescue) include market leaders such as Boeing, IBM, and Motorola. A high-visibility center is attractive to other industrial partners and is beneficial to UCI.

          Another motivation for establishing CERT is the continuity of the Rescue project. Rescue is in the fourth year of a five year NSF funding cycle. Rescue has established many research areas within the emergency response domain as well as cultivated many industry, academic, and government strategic partners. The CERT ICS center will leverage the established Rescue project and expand upon proven successes.

          The proposed center would be a multi-disciplinary is endeavor as evidenced by the eclectic list of faculty participants. These prospective faculty members include UCI sociologists, engineers, and a number of ICS faculty members.

          This page was last updated on: April 14, 2010 8:24 AM
          http://cert.ics.uci.edu/seminar/Nanda/bio.htm Utilizing New Technologies in Managing Hazards and Disasters

          � Nanda Kambhatla has nearly 17 years of research experience in the

          ��areas of Natural Language Processing (NLP), text mining, information

          ��extraction, dialog systems, and machine learning. He holds 6 U.S

          ��patents and has authored over 30 publications in books, journals, and

          ��conferences in these areas. Nanda holds a B.Tech in Computer Science

          ��and Engineering from the Institute of Technology, Benaras Hindu

          ��University, India, and a Ph.D in Computer Science and Engineering from

          ��the Oregon Graduate Institute of Science & Technology, Oregon, USA.

          �

          ��Currently, Nanda is the manager of the Data Analytics Group at IBM's

          ��India Research Lab (IRL), Bangalore. The group is focused on research

          ��on machine translation, Natural Language Processing, text analysis and

          ��machine learning techniques for developing analytics solutions to help

          ��IBM's services divisions. Most recently, Nanda was the manager of the

          ��Statistical Text Analytics Group at IBM's T.J. Watson Research Center,

          ��the Watson co-chair of the Natural Language Processing PIC, and the

          ��task PI for the Language� Exploitation Environment (LEE) subtask for

          ��the DARPA GALE project. He has been leading the development of

          ��information extraction tools/products and his team has achieved top

          ��tier results in successive Automatic Content Extraction

          ��(ACE) evaluations conducted

          ��by NIST for extracting entities, events and relations from text from

          ��multiple sources, in multiple languages and genres.

          �

          ��Earlier in his career, Nanda has worked on natural language web-based

          ��and spoken dialog systems at IBM. Before joining IBM, he has worked on

          ��information retrieval and filtering algorithms as a senior research

          ��scientist at WiseWire Corporation, Pittsburgh and on image compression

          ��algorithms while working as a postdoctoral fellow under Prof. Simon

          ��Haykin at McMaster University, Canada.

          �

          ��Nanda's research interests are focused on NLP and technology solutions

          ��for creating, storing, searching, and processing large volumes of

          ��unstructured data (text, audio, video,

          ��etc.) and specifically on applications of statistical learning

          ��algorithms to these tasks.

          http://www.ics.uci.edu/~pkammer/ Peter Kammer

          Peter J. Kammer

          Peter Kammer at Cataract Falls Ph.D., Information and Computer Science, UC Irvine, June 2004
          Advisor: Professor Richard N. Taylor
          E-Mail: pkammer (at-sign) ics.uci.edu

          Currently:
          Senior Software Engineer
          Google, Inc.
          1600 Amphitheatre Pkwy
          Mountain View, CA 94043
          Working on Google For Nonprofits.
          Previously on Public Data Explorer.


          Full CV (PDF)


          Dissertation Title: A Distributed Architectural Approach to Supporting Work Practice

          Dissertation Abstract: This dissertation presents an architectural style to directly support the formal and informal relationships that underlie work. Rather than supporting work with the formal definition of work processes or by enhancing the capabilities of users in small groups, as have many previous approaches, the focus of this work is on the structure of coordination within the organization.

          The work is guided by three specific goals. The first is to provide scoped information and communication spaces surrounding work activities occurring within the system. This is done at the level of task, user, group, and larger organizational structures. The second goal is to provide decentralized ownership and control of work product and process, allowing them to be defined and maintained within their context of creation and use. Finally, the style seeks to support work at varying levels of definition, integrating structured models of work with ad-hoc work activity.

          The foundation of this style is a peer-to-peer architecture, each peer providing an independent addressable location associated with a particular user. The peer's workspace is subdivided into task spaces to provide locations to associate resources for individual tasks. These task spaces serve not just to organize the user's work but also to provide independent points of connection with larger organizational structures (and the related tasks of other users). Independent connectors, with their own addressable identities, define the relationships between task spaces and also provide the mechanism for defining larger, more complex, organizational relationships. The model of authority is a compositional one. Rather than authority to access and manage resources being devolved from a central server, users maintain control of their individual peers and enter into trust relationships with other peers or groups.

          The style is demonstrated and validated using two mechanisms. First, it is applied to a range of coordination processes and organizational structures taken from prior literature. Second, a prototype implementation is described along with its application to an example work process.

          Dissertation Committee: Professor Richard N. Taylor (chair), Professor Gloria Mark, Professor David S. Rosenblum

          Full Text of Dissertation (PDF, 1.5 meg)


          Publications and Presentations

          Refereed Journal
          P. J. Kammer, G. A. Bolcer, R. N. Taylor, A. S. Hitomi, M. Bergman, "Techniques for supporting dynamic and adaptive workflow," Computer Supported Cooperative Work, vol. 9, no. 3-4, 269-92, August 2000


          Refereed Conference
          P. J. Kammer, R. N. Taylor, "An Architectural Style for Supporting Work Practice: Coping with the Complex Structure of Coordination Relationships ," 2005 International Symposium on Collaborative Technologies and Systems, 218-227, St. Louis, MO, May, 2005
          P. J. Kammer, "Supporting dynamic distributed work processes with a component and event based approach," 22nd International Conf. on Software Engineering (2000), 710-712, Limerick, Ireland, June, 2000 (Doctoral Colloquium)
          P. J. Kammer, G. A. Bolcer, R. N. Taylor, and Arthur S. Hitomi. "Supporting distributed workflow using HTTP". In Proceedings of the Fifth International Conference on the Software Process, pages 83-94, Lisle, IL, June 1998.


          Weakly/Non-refereed
          P. J. Kammer, "Distributed Groupware and Web Services", CSCW 2002 Workshop: Network Services for Groupware, New Orleans, LA, November, 2002.
          P. J. Kammer, "Building the Process: Component Based Workflow Architectures in a Distributed World", CSCW 2000 Workshop: Beyond Workflow Management: Supporting Dynamic Organizational Process, Philadelphia, PA, December, 2000.
          G. A. Bolcer, M. Gorlick, A. S. Hitomi, P. J. Kammer, B. Morrow, P. Oreizy, R. N. Taylor. Peer-to-Peer Architectures and the Magi Open-Source Infrastructure. Whitepaper, Endeavors Technology Inc., December, 2000.
          P. J. Kammer and D. W. McDonald. Putting Words to Work: Integrating Conversation with Workflow Modeling. Tech. Report, UCI-ICS-99-30, Information and Computer Science, Univ. of California, Irvine, August 1, 1999.
          P. J. Kammer, G. A. Bolcer, M. Bergman. "Adaptive workflow on the world wide web," CSCW 1998 Workshop; Towards Adaptive Workflow Systems, Seattle, WA, November 1998.
          A. S. Hitomi, P. J. Kammer, G. A. Bolcer, R. N. Taylor. "Distributed workflow using HTTP: An example using software prerequirements." 1998 International Conf. on Software Engineering, Kyoto, Japan, Apr. 1998 (Formal Demo).


          Other Presentations
          Panelist: Impact of Peer-to-Peer Networking, 9th International Conference on Network Protocols (ICNP 2001), Riverside, CA, November 14, 2001
          "Endeavors Process Support System" Presentation to LA Java Users Group, December 3rd, 1996.










          http://www.ics.uci.edu/~jie/ Jie Ren
          Jie Ren Jie Ren (任杰)
          AreaSoftware
          AdvisorRichard N. Taylor
          E-mailjie@ics.uci.edu

           Biography    Research      Teach        Work       Service   Publication   Contact  

          From September 1999 to January 2006, I was a Ph.D. student in the Department of Informatics of the Donald Bren School of Information and Computer Sciences. My research area was Software. My advisor is Richard N. Taylor. I was also affiliated with Institute for Software Research.

          Before coming to UCI, I studied software engineering at Software Engineering Lab of Fudan University and worked there as a lecturer.

          After graduating from UCI, I started working for Google at its Santa Monica office.

          My resume is here. My curriculum vitae is here.

          Last Modified: November 5, 2006

          Valid CSS! Valid XHTML 1.0!

          http://www.ics.uci.edu/~dan/puz/

          Puzzles

          • Dr. Matrix by Wei-Hwa Huang from G4G8
          • G4G8 tiling problem
          • Problems of geometric combinatorics
          • PEGS - a simple game -- can you achieve optimality?
          http://www.ics.uci.edu/~dan/genealogy/ Dan's Genealogy Page

          Dan's Genealogy Page

          • Family genealogy pages
            • Miller -- updated Nov 2013
          • Search for names
            • Soc.Sec.DeathIndex: ( ancestry ), GenealogyBank,    Fold3,    http:ssdmf.info
            • Soc.Sec (family tree maker)
            • MyHeritage.com, search
            • Find-a-grave,     Veteran grave-locator,     JewishData.com
            • FamilyTreeNow
            • mooseroots:
              • births incl. AZ 1855-1937, CA 1905-95, ID 1861-1912, KY 1990-2012, LA?, MO?, OH 1908-2011, OR?, SD?, TN?, UT?, WV?
              • marr+div incl. AB, ID, IN, KY, ME, MN, OH, TN, TX,WV
              • death incl. ID 1890-1962, ME 1960+, MO 1910-63, NYC 1957-63, OH 1960+, TN 1908-12,14-33
            • radaris     people-finders     full name directory
            • (1)Holocaust Memorial Museum:     (2)Holocaust Memorial Museum:     Krakow IDs 1940     Bedzin 1939
            • 19th century Poland,    
                    enter surname (req):
                    enter town (optional):
            • CastleGarden immigration center 1855-1890     CastleGarden search by Steve Morse
            • Ellis Island 1892-1957
            • Ellis Island 1892-1924:
            • Other immigration databases First Name       Surname      
            • Steve Morse pages (Ellis Island)
            • Ellis Island Search
            • genealogy place
            • US census: search  
              • FamSrch: 1880,   1900,   1910,   1920,   1930,     1940,     Ftnote: 1930 ANCESTRY: 1940
              • images: 1910,   1920,   1930
            • NYC   B 1878-1909 *     D 1868-1948 * -49     Grooms 1864-1937 *     Brides 1866-1937 *     M bklyn Jan-Jun 1957     Alien Statemts     Early: B D M     M: NassauSuffolk *
            • NYT birth announcements: Jan 1929 Feb 1929
            • NY state deaths 1957-63
            • NY census 1905-15-25
            • old NY newspapers
            • FamSrch NY: B -1962,   M -1980,   D -1952
            • FamilySearch, ***, LDS, U.S. collections
            • many states     misc B     misc M     misc D
            • FamilyTreeLegends BMD Record Search
            • 2012-13 Voters: CT   FL   OK   RI   UT         CO   DE   OH  

            • CA   b:1905-95     CA   d:1940-97   CA   Brides 1986 [newguest+enjoy]
              CA SF
            • FL   d:1877-1916 (vitalsrch.com: [newguest+enjoy])
            • IL   m:1763-1900,     IL d:-1915, 1916-50,     IL Chicago   B 1872,1916-35,  M 1872-1960,  D 1872-1990
            • IN Jefferson Cty (Madison) delayed B M:1851-1965 D:1838-50
            • LA -- New Orleans:   B 1790-1905   M 1800-1927   D 1804-1938
            • MO   bd:-1910,     MO d:1910-63,     MO county marrs: Jackson, Greene, McDonald     MO newspapers 1852-1922
            • OH   d:1913-44,     OH Cleveland b:1849, 1871-1908
            • OK   marrs,     OK counties-- Ok: m:1889-1951,     OK Creek: m:1907-21
            • TN   d:1914-32,     TN   D 1949-2009, M 1980-2009,   Memphis B 1874-1906, D 1848-1960, M 1820-1989
            • TX   b:1903-10,26-29,     TX d:1890-1976,     TX d:1964-98     TX (m:1966-2008) (div:)
            • western states marriages
            • marriages sorted by name
            • England
              • England 1837-1946
              • England Search:   B   M   D
              • Search England probate notices
              • UK
            • Argentina
            • Australia: collection search     Search BMD NewSouthWales     cemetery
            • JewishGen Hungary, UK, USA, AustriaCzech
            • indexes: [also try b/m, d records] AZ (b:1855-1935,d:1844-1960), BC (bmd:1872-1903,33,89), GA (d:1919-27), IA (census:1885,1925, m:1836-1925), KY (d:1911-92,m:1973-93), ME (d:1960-97), ME (m:1892-1967,1977-2009), MI (d:1897-1920), NJ (m:1848-78), NJ (d:1878-86), UT (d:1904-58), W.VA (b:-1910, d:-1969, m:-1970)
            • PolandGenWeb archives
            • people search
            • on-line resources, deathindexes
            • Search Engine for Online Historical Directories
            • Jewish researchers ( gedcom, password required ) tree
            • Galicia Given Names Database
            • Budapest Civil Registration Marriages explained here
            • Czestochowa-Radomska Area Research Group Holocaust Records
            • Database of Jews deported from Bohemian lands and Terezin
            • Poles in Soviet Union 1939-59 -- in Polish
            • Polish Red Cross research for Polish relatives lost in WW2 -- in Polish
            • converted Jews -- in Polish
            • Krakow survivors
            • Dutch Jewry search
            • JRI-PL
            • 1929 Krakow businesses
            • Vienna cemeteries
            • Netherlands + Belgium, also worldwide digital resource index
            • Yad Vashem database
            • Oshpitzin Yizkor database
            • GENDEX
            • WWW (familytreemaker)
            • Kindred Konnections surnames or general search
          • Resources -- general
            • Gregorian-Hebrew Calendar converter (from ShtetLinks)
            • soundex
            • finding family from passenger lists
            • requesting forms from NARA
            • The Genealogy Home Page at genhomepage
            • LifeLines genealogical program
            • genealogy on the web
            • Archives Portal Europe
            • Immigrant Ships Transcribers Guild
            • US GenWeb
            • East Europe, JewishGen info/links
            • hot links
          • Resources -- Jewish
            • Jewish Genealogy
            • locate Shtetls: by name, by approx location
            • Antwerp diamond trade
            • web sites for Jewish genealogy
            • Eastern Europe FAQ
            • Gaon of Vilna
            • Austrian-Jewish genealogy
          • Poland -- see also "Krakow: A Guide to Jewish Genealogy" by Geoffrey Weisgard
            • Jewish Records Indexing - Poland
            • Galicia record database
            • Vital Records in Poland
            • PolandGenWeb
            • Polish Jewish LDS microfilm: list (text), locator
            • Polish occupation definitions, Polish terms
            • Jewish-Polish heritage
            • Galicia
            • Carpatho-Rusyn Knowledge Base
            • Slovak + Carpatho-Rusyn Genealogy Research pages
          • maps
            • Poland (50-54o N, 19-22o E), Galicia (48-50o N, 20-23o E), Silesia (50-52o N, 15-19o E)
            • lots of other Polish maps
            • generate your own
          • other
            • soundex converter, calendar converter
          _

          Dan Hirschberg
          dan at ics.uci.edu
          http://www.ics.uci.edu/~kibler/ics171/Lectures/ Index of /~kibler/ics171/Lectures

          Index of /~kibler/ics171/Lectures

          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  
          [   ]Lec1-AIIntro.ppt28-Jun-2004 08:22 127K 
          [   ]Lec2-StateSpace.ppt07-Jul-2004 11:05 97K 
          [   ]Lec3-UninformedSearch.ppt08-Jul-2004 10:52 109K 
          [   ]Lec4-InformedSearch.ppt12-Jul-2004 08:43 52K 
          [   ]Lec6-GamePlaying.ppt26-Jul-2004 08:42 413K 
          [   ]Lec7-PropLogic.ppt22-Jul-2004 08:26 142K 
          [   ]Lec8-PredicateLogic.ppt30-Jul-2004 13:14 127K 
          [   ]Lec10-ConstraintSat.ppt02-Aug-2004 16:05 63K 
          [   ]Lec11- ExpertSystems.ppt05-Aug-2004 08:55 72K 
          [   ]Lec12-Probability.ppt13-Aug-2004 07:58 146K 
          [   ]Lec13-BayesNet.ppt16-Aug-2004 09:13 53K 
          [   ]Lec14-LearningOverview.ppt20-Aug-2004 09:13 56K 
          [   ]Lec16-IBL.ppt25-Aug-2004 21:39 61K 
          [   ]Lec17-DecisionTrees.ppt27-Aug-2004 10:14 61K 
          [   ]Lec18-Perceptron.ppt30-Aug-2004 09:27 32K 
          [   ]Lec19-NeuralNets.ppt31-Aug-2004 13:52 40K 
          [   ]Lect15-Weka.ppt08-Aug-2004 08:39 40K 
          [   ]LogicAssignment.doc20-Jul-2004 10:15 24K 
          [   ]Prolog.ppt29-Oct-2003 11:29 35K 
          [   ]SearchQuizAns.doc19-Jul-2004 08:56 35K 
          [   ]TSPHwkLocal.ppt09-Jul-2004 08:07 39K 

          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~bic/os/ Home Page

          Operating Systems Principles

          Home Lecture Notes Solutions Figures Errata Additional Exercises Ordering Information Prentice-Hall

          RESOURCES WEB SITE

          Table of Contents

          Lubomir F. Bic
          University of California, Irvine

          Alan C. Shaw
          University of Washington

          PRENTICE-HALL/PEARSON EDUCATION

           

          YOUR FEEDBACK IS IMPORTANT:

          We intend to keep this page up to date and to revise the book periodically.

          Please contact us whenever you find any errors or have questions/suggestions.
          Let us know which parts of the book you like and which you dislike.
          If you develop new exercises and would like to share them with others, please email them to us and we will post them on this web page.

          Contact:  bic@ics.uci.edu or  shaw@cs.washington.edu

           

          http://www.ics.uci.edu/~bic/courses/51/ Lubomir Bic

           

           
          Lubomir Bic

          • email: bic@ics.uci.edu
          • office: ICS3 (Bren Hall), Room 3224
          • phone/fax: 949-824-5248

          This course page will open approximately one week prior to instruction begin

           

          School of Information and Computer Sciences,


          University of California, Irvine CA 92717-3425
           

          http://www.ics.uci.edu/~dechter/talks/lifted-minischool-2014/ UCI Lifted Algorithms Mini-School

          UCI Lifted Algorithms Mini-School (November 3-6)
           

           

          In the week of November 3rd, we will have a mini-school in Lifted Algorithms for Probabilistic Programming.

          Location: DBH 4011.
          Two experts in this area will visit us and present tutorials:
        • Rodrigo de Salvo Braz
        • Vibhav Gogate

          Schedule

          November 3: Rodrigo
        • AI/ML Semniar: 1:00p - 2:00p, DBH 4011 (Slides)
          November 4: Rodrigo
        • Tutorial: 10:00a - 12:00p, DBH 3011 (Slides, [also used on the next day])
        • Discussion: 2:00p, DBH 4013
          November 5: Rodrigo, Vibhav
        • Tutorial: 10:00a - 12:00p, DBH 4011 (Slides)
        • Discussion: 2:30p, DBH 4011
          November 6: Vibhav
        • Tutorial: 10:00a - 12:00p, DBH 4011 (Slides) (Slides)


          Please send any questions, concerns or comments to Rina Dechter.

          http://www.ics.uci.edu/~arcadia/atUCI.html Arcadia at UCI Note: The Arcadia project ended in 1997. This web site is for archival purposes; we can no longer guarantee liveness of links.

          Arcadia at UCI


          The University of California, Irvine's Department of Information and Computer Science is a member of the Arcadia Consortium for research in Software Engineering Environments, as part of the overall Arcadia Project.

          The Arcadia Group at UCI

            * People
            • Principal Investigators
            • Graduate Students
            • Staff
            • Part-time Staff and Undergraduates
            • Visiting Researchers

          Arcadia Projects and Software

          UCI Arcadia projects are divided into the following areas:

            * Analysis and Testing
            • ProDAG - Program Dependence Analysis Graph System
            • TAOS - Testing with Analysis and Oracle Support

            * Metrics and Evaluation
            • Amadeus Measurement-Driven Analysis and Feedback System

            * User Interface
            • Chiron User Interface System
            • Chiron-2 Architectural Style for GUI Software
            • GLAD Generic LAyout for Directed Graphs

            * Process Support
            • Endeavors Process Support System
            • Teamware Process Support System

            * Software Architecture
            • The C2 Architectural Style
            • Argo Design Environment

            * Hyperware
            • Chimera Heterogeneous Hypermedia System
            • WebSoft: Software for Webmasters

            * Language Processing
            • Aflex/Aeyacc: a lexical scanner and parser generator for Ada
            • Ada Makefile Generator (adamakegen)
            • Plumber: Memory leak detector for Ada

          Many of the above systems are available via anonymous ftp from liege.ics.uci.edu. Visit the individual project pages for direct links.


          Conferences

          • ICSE18, Berlin
          • ISSTA96, San Diego
          • First Workshop on Formal Methods in Software Practice, San Diego
          • Annual California Software Symposium (Formerly ISS), UC Irvine

          Industry Alliances

          IRUS, the Irvine Research Unit in Software, is an alliance of applied research and technology partnerships between academia and industry focused on advancing the state-of-the-art and state-of-the-practice in software production.


          Arcadia Sponsors

          The UCI Arcadia research group would like to thank the following sponsors for their support. In all cases, the results and publication of our research does not necessarily reflect the position or the policy of the sponsors, and no official endorsement should be inferred.
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          The Arcadia Project <arcadia-www@ics.uci.edu>
          Department of Information and Computer Science,
          University of California, Irvine, CA 92717-3425
          Last modified: Fri Dec 9 17:26:59 1994 http://www.ics.uci.edu/~gbowker/classification/ WORKING INFRASTRUCTURES:  
           

          At this site, we present the introduction, first two chapters and concluding chapters of our book on classification systems published by MIT Press in 1999.   Related work can be found at my publications site. Here is a real audio interview with Geof Bowker about classification systems.
           
           








          SORTING THINGS OUT:

          CLASSIFICATION AND ITS CONSEQUENCES
           
           
           
           
           
           
           
           
           
           

          Geoffrey C. Bowker

          Susan Leigh Star
           
           
           
           
           
           

          University of California, San Diego







          TABLE OF CONTENTS

          Acknowledgements

          Introduction: To Classify is Human

          Chapter One: Some Tricks of the Trade in Analyzing Classification
           
           

          Part One: Classification and Large Scale Infrastructures

          Chapter Two: The Kindness of Strangers - Kinds and Politics in Classification Systems

          Chapter Three: The ICD as Information Infrastructure

          Chapter Four: Classification, Coding and Coordination
           
           

          Part Two: Classification and Biography, or System and Suffering

          Chapter Five: Of Tuberculosis and Trajectories

          Chapter Six: The Case of Race Classification and Reclassification under Apartheid
           
           

          Part Three: Classification and Work Practice

          Chapter Seven: What a Difference a Name Makes - The Classification of Nursing Work

          Chapter Eight: Organizational Forgetting, Nursing Knowledge, and Classification
           
           

          Part Four: The Theory and Practice of Classifications

          Chapter Nine: Categorical Work and Boundary Infrastructures; Enriching Theories of Classification

          Chapter Ten: Why Classifications Matter
           
           

          Bibliography

          Illustrations

                          Figure 1. Map of cholera epidemics. Source: A. Proust, 1892.

                          Figure 2. French Bill of Health. Source: A. Proust, 1892.

          Figure 3. Mortality Table, England in Seventeenth Century. Source: J. Graunt, 1662. [glossy to come]

          Figure 4. Star and Ruhleder�s Definition of Infrastructure. [in text]

          Figure 5. Heart Failure in ICD-10. Source: ICD 10, Volume 1: 494.

          Figure 6. Pigeonholing the Classification � the ICD Family. Source: Adapted from ICD-10, 1. [in text]

          Figure 7. A Standard International Death Certificate. Source: Adapted from Fagot-Largeault, 1986. [in text]

          Figure 8. Senility. Source: Department of Commerce, Bureau of the Census, Manual of the International List of Causes of Death, Department of Commerce, US Bureau of the Census, Washington, DC: Government Printing Office, 1913: 131.

          Figure 9. Sacred ablutions in the zemzem, or fountain, at Mecca. (Proust, 1892)

          Figure 10. Monitoring the Classification: the Recovery Room. Source: Healthcare Financial Management, December 1996.

          Figure 11. Some Conflicting Needs of ICD Users. [in text]

          Figures 12(a) and 12 (b). Photographs (2)of Doc Holliday�s Grave. Source: Photograph by Susan Leigh Star, Glenwood Springs, Colorado, August, 1994.

          Figure 13. TB Testing at the University of Illinois. Source: http://www.uiuc.edu. [in text]

          Figure 14. The Strauss-Corbin Model of Body-Biography Trajectory.

          Figure 15. Multiple Identity Trajectories along the Body-Biography Trajectory (Timmermans).

          Figure 16. Star and Bowker: The Topology-Typology Twist.

          Figure 17. Charges And Convictions Under The Immorality Act During The Year Ending June 1967. [in text]

          Figure 18. South African Passbook.

                          Figure 19. Vic Wilkinson and his family Figure 20. Numbers Of Objections To Racial Classifications Under The Population Registration Act, 1968. [in text]

          Figure 21. A scale for comparing the color of skin, 1958

          Figure 22a. Sandra Laing and her mother (1).

          Figure 22b. Sandra Laing�s father.

          Figure 22c. Sandra Laing and her mother (2)

          Figure 23. Airway Management, NIC.

          Figure 24. Spiritual Support, NIC.

          Figure 25. Humor, NIC.

          Figure 26. Indirect Care � Emergency Cart Checking, NIC. Source: NIC. 2nd Edition.

          Figure 27. Culture Brokerage, NIC. Source: NIC. 2nd Edition.
           
           

          Introduction: To Classify Is Human

          Our lives are henged round with systems of classification, limned by standard formats, prescriptions, and objects. Enter a modern home and you are surrounded by standards and categories spanning the color of paint on the walls and in the fabric of the furniture, the types of wires strung to appliances, the codes in the building permits allowing the kitchen sink to be properly plumbed and the walls to be adequately fireproofed. Ignore these forms at your peril � as a building owner, be sued by irate tenants; as an inspector, risk malpractice suits denying your proper application of the ideal to the case at hand; as a parent, risk toxic paint threatening your children.

          To classify is human. Not all classifications take formal shape or are standardized in commercial and bureaucratic products. We all spend large parts of our days doing classification work, often tacitly, and make up and use a range of ad hoc classifications in order to do so. We sort dirty dishes from clean, white laundry from colorfast, important email to be answered from e-junk. We match the size and type of our car tires to the amount of pressure they should accept. Our desktops are a mute testimony to a kind of muddled folk classification: papers which must read by yesterday, but which have been there since last year; old professional journals which really should be read and even in fact may someday be, and which have been there since last year; assorted grant applications, tax forms, various work-related surveys and forms waiting to be filled out for everything from parking spaces to immunizations. These surfaces may be piled with sentimental cards which are already read but which can�t yet be thrown out alongside reminder notes to send similar cards to parents, sweethearts, or to friends for their birthdays, all piled on top of last year�s calendar (which who knows, may be useful at tax time). Any part of the home, school or workplace reveals some such system of classification: medications classed as not for children occupy a higher shelf than safer ones; books for reference are shelved close to where we do the Sunday crossword puzzle; door keys are color-coded and stored according to frequency of use.

          What sorts of things order these piles, locations, and implicit labels? We have certain knowledge of these intimate spaces, classifications that seem to live partly in our hands � definitely not just in the head or in any formal algorithm. The knowledge about which thing will be useful at any given moment is embodied in a flow of mundane tasks and practices and many varied social roles (child, boss, friend, employee). When we need to put our hands on something, it is there.

          Our computer desktops are no less cluttered. Here the electronic equivalent of "not yet ready to throw out" is also well represented. A quick scan of one of the author�s desktops reveals seven residual categories represented in the various folders of email and papers: "fun" "take back to office" "remember to look up" "misc." "misc. correspondence" "general web information" "teaching stuff to do" and "to do." We doubt if this is an unusual degree of disarray or an overly prolific use of the "none of the above" category so common to standardized tests and surveys.

          These standards and classifications, however imbricated in our lives, are ordinarily invisible. The formal, bureaucratic ones trail behind them the entourage of permits, forms, numerals, and the sometimes-visible work of people who adjust them to make organizations run smoothly. In that sense, they may become more visible, especially when they break down, or become objects of contention. But what are these categories? Who makes them, and who may change them? When and why do they become visible? How do they spread? What, for instance, is the relationship between locally generated categories, tailored to the particular space of a bathroom cabinet, and the commodified, elaborate, expensive ones generated by medical diagnoses, government regulatory bodies, and pharmaceutical firms?

          Remarkably for such a central part of our lives, we stand for the most part in formal ignorance of the social and moral order created by these invisible, potent entities. Their impact is indisputable, and as Foucault reminds us, inescapable. Try the simple experiment of ignoring your gender classification and use instead whichever toilets are the nearest; try to locate a library book shelved under the wrong Library of Congress catalogue number; stand in the immigration queue at a busy foreign airport without the right passport or arrive without the transformer and the adaptor that translates between electrical standards. The material force of categories appears always and instantly.

          At the level of public policy, classifications such as those of regions, activities, and natural resources play an equally important role. Whether or not a region is classified as ecologically important; whether another is zoned industrial or residential come to bear significantly on future economic decisions. The substrate of decision-making in this area, while often hotly argued across political camps, is only intermittently visible. Changing such categories, once designated, is usually a cumbersome, bureaucratically fraught process.

          For all this importance, classifications and standards occupy a peculiar place in studies of social order. Anthropologists have studied classification as a device for understanding the cultures of others � categories such as the raw and the cooked have been clues to the core organizing principles for colonial Western understandings of "primitive" culture. Some economists have looked at the effects of adopting a standard in those markets where networks and compatibility are crucial. For example, videotape recorders, refrigerators and personal computer software embody arguably inferior technical standards, but standards that benefited from the timing of their historical entry into the marketplace. Some historians have examined the explosion of natural history and medical classifications in the late nineteenth century, both as a political force and as an organizing rubric for complex bureaucracies. A few sociologists have done detailed studies of individual categories linked with social movements, such as the diagnosis of homosexuality as an illness and its demedicalization in the wake of gay and lesbian civil rights. Information scientists work every day on the design, delegation and choice of classification systems and standards, yet few see them as artifacts embodying moral and aesthetic choices that in turn craft people�s identities, aspirations and dignity. Philosophers and statisticians have produced highly formal discussions of classification theory, but few empirical studies of use or impact.

          Both within and outside the academy, single categories or classes of categories may also become objects of contention and study. The above-mentioned demedicalization of the category homosexual in the American Psychiatric Association�s Diagnostic and Statistical Manual 3 (the DSM, a handbook of psychiatric classification) followed direct and vigorous lobbying of the APA by gay and lesbian advocates (Kirk and Kutchins, 1992). During this same era, feminists were split on the subject of whether the categories of premenstrual syndrome and post partum depression would be good or bad for women as they became included in the DSM. Many feminist psychotherapists were engaged in a bitter argument about whether to include these categories. As Ann Figert (1996) relates, they even felt their own identities and professional judgements to be on the line. (Allan Young (1995) makes the complicating observation that psychiatrists increasingly use the language of the DSM to communicate with each other and their accounting departments, although they frequently don�t believe in the categories they are using).

          More recently, as discussed in Chapter 6, the option to choose multiple racial categories was introduced as part of the US Government routine data collection mission, following Statistical Directive 15 in October, 1997. The Office of Management and Budget issued the directive, and conservatively, its implementation will cost several million dollars. One direct consequence is the addition of this option to the US Census, an addition that was fraught with political passion. A march on Washington concerning the category took the traditional ultimate avenue of mass protest for American activists. The march was conducted by people who identified themselves as multi-racial, and their families and advocates. At the same time, it was vigorously opposed by many African-American and Hispanic civil rights groups (among several others), who saw the option as a "whitewash" against which important ethnic and policy-related distinctions would be lost (Robbin, 1998).

          However, despite the contentiousness of some categories, none of the above-named disciplines or social movements has systematically addressed the pragmatics of the invisible forces of categories and standards in the modern built world, especially the modern information technology world. Foucault�s (1970; 1982) work comes the closest to a thoroughgoing examination in his arguments that an archaeological dig is necessary in order to find the origins and consequences of a range of social categories and practices. He focussed on the concept of order, and its implementation in categorical discourse. The ubiquity described by Foucault appears as an iron cage of bureaucratic discipline against a broad historical landscape. But there is much more to be done, both empirically and theoretically. No one, including Foucault, has systematically tackled the question of how these properties inform social and moral order via the new technological and electronic infrastructures. Few have looked at the creation and maintenance of complex classifications as a kind of work practice, with its attendant financial, skill and moral dimensions. These are the tasks of this book.

          We take Foucault�s practical archaeology as a point of departure for examining several cases of classification, some of which have become formal or standardized, and some of which have not. We have several concerns in this exploration, growing both from the consideration of classification work, and its attendant moral dimensions. First, we seek to understand the role of invisibility in the work that classification does in ordering human interaction. We want to understand how these categories are made and kept invisible, and in some cases, we want to challenge the silences surrounding them. In this sense, our job here is to find tools for seeing the invisible, much as Émile Durkheim passionately sought to convince his audience of the material force of the social fact � to see that society was not just an idea � over one hundred years ago (Durkheim, 1982).

          We also explore systems of classification as part of the built information environment. Much as a city planner or urban historian would leaf back through highway permits and zoning decisions to tell a city�s story, we delve the dusty archives of classification design in order better to understand how wide-scale classification decisions have been made.

          We have a moral and ethical agenda in our querying of these systems. Each standard and each category valorizes some point of view and silences another. This is not inherently a bad thing � indeed it is inescapable. But it is an ethical choice, and as such it is dangerous � not bad, but dangerous. For example, the decision of the US Immigration and Naturalization Service to classify some races and classes as desirable for US residents, and others as not, resulted in a quota system which valued affluent people from Northern and Western Europe over those (especially the poor) from Africa or South America. The decision to classify students by their standardized achievement and aptitude tests valorizes some kinds of knowledge skills and renders other kinds invisible. Other types of decisions with serious material force may not immediately appear as morally problematic. The collective standardization in the United States on VHS videotapes over Betamax, for instance, may seem ethically neutral. The classification and standardization of types of seed for farming is not obviously fraught with moral weight. But as Busch (1995) and Addelson (1994) argue, such long-term, collective forms of choice are also morally fraught. We are used to viewing moral choices as individual, as dilemmas, and as rational choices. We have an impoverished vocabulary for collective moral passages, to use Addelson�s terminology. For any individual, group or situation, classifications and standards give advantage or they give suffering. Jobs are made and lost; some regions benefit at the expense of others. How these choices are made, and how we may think about that invisible matching process is at the core of the ethical project of this work.

          Working Infrastructures

          Sorting Things Out stands at the crossroads of sociology of knowledge and technology, history and information science. The categories represented on our desktops and in our medicine cabinets are fairly ad hoc and individual, not even legitimate anthropological folk or ethno classifications. They are not often investigated by information scientists (but see Kwasnik, 1988, 1991; Beghtol, 1995; Star, 1998). But everyone uses and creates them in some form, and they are (increasingly) important in organizing computer-based work. They often have old and deep historical roots. True, Personal Information Managers are designed precisely to make this process transparent, but even with their aid, the problem continues: we still must design or select categories, still enter data, still struggle with things that don�t fit. At the same time, we rub these ad hoc classifications against an increasingly elaborate large-scale system of formal categories and standards. Users of the Internet alone navigate, now fairly seamlessly, more than two hundred formally elected Internet standards for information transmission each time we send an email message. If we are to understand larger-scale classifications, we also need to understand how desktop classifications link up with those that are formal, standardized, and widespread.

          Every link in hypertext creates a category. That is, it reflects some judgment about two or more objects: they are the same, or alike, or functionally linked, or linked as part of an unfolding series. The rummage sale of information on the World Wide Web is overwhelming, and we all agree that finding information is much less of a problem than assessing its quality -- the nature of its categorical associations, and by whom they are made (Bates, in press). The historical cultural model of social classification research in this book, from desktop to wide-scale infrastructure, is a good one through which to view problems of indexing, tracking, and even compiling bibliographies on the web. In its cultural and workplace dimensions, it offers insights into the problematics of design of classification systems, and a lens for examining their impact. It looks at these processes as a sort of crafting of treaties. In this, a cross-disciplinary approach is crucial. Any information systems design that neglects use and user semantics is bound for trouble down the line � it will become either oppressive or irrelevant. Information systems mix up the conventional and the formal, the hard technical problems of storage and retrieval with the hard interactional problems of querying and organizing.

          Information systems are undergoing rapid change. There is an explosion of information on the World Wide Web and associated technologies, and fast moving changes in how information may converge across previously disparate families of technology � for instance, using one�s television to retrieve email and browse the web, using one�s Internet connections to make telephone calls. Whatever we write here about the latest electronic developments will be outdated by the time this book sees print, a medium many would argue is itself anachronistic.

          Conventions of use, and understandings of the impact of these changes on social organization are slower to come. The following example illustrates the intermingling of the conventional and the local in the types of classificatory links formed by hypertext. A few years ago, our university was in the enviable position of having several job openings in Library and Information Science. Both the authors were on the search committee. During the process of sifting through applications and finding out more about candidates, the need arose to query something on the candidate�s resume. We used the Alta Vista search engine to find the candidate�s email address. (Of course, the first thing one really does with Alta Vista is egosurfing - checking one�s own name to see how many times it appears on the web - but we had already done that.) His email address and formal institutional home page appeared in about 15 seconds on our desktop -- but so did his contributions to a discussion on world peace, a feminist bulletin board, and one of the more arcane alt.rec Usenet groups. We found ourselves unable to stop our eyes from roving through the quoted Usenet posts � category boundaries surely never meant to be crossed by a job search committee. Fortunately for us as committee members, we interpreted what we found on the web as evidence that the applicant was a more well rounded person than his formal CV had conveyed. He became a more interesting candidate.

          But of course, it might have gone badly for him. In less than a minute we had accessed information about him that crossed a social boundary of de facto privacy, access, and awareness context (Glaser and Strauss, 1965). The risk of random readership had been there in some sense when he posted to a public space -- but who on a search committee in the old days of a couple of years ago could possibly be bothered searching ftp archives? Who would have time? There are many ethical and etiquette-related questions here, of course, with the right to privacy not least among them. The incident also points to the fact that as a culture we have not yet developed conventions of classification for the web that bear much moral or habitual conviction in daily practice. The label alt.rec does not yet have the reflex power that the label private does on a desk drawer or notebook cover. We would never open someone�s desk drawer or diary. We are not normally known to be a rude people -- but we haven�t yet developed or absorbed routine similar politeness for things such as powerful web search engines. We were thus somewhat embarrassed and confused about the morality of mentioning the alt.rec postings to the committee.

          As we evolve the classifications of habit -- grow common fingertips with respect to linkages and networks -- we will be faced with some choices. How standardized will our indexes become? What forms of freedom of association (between people, between texts and people, between texts) do we want to preserve, and which are no longer useful? Who will decide these matters?

          Investigating Infrastructure

          People do many things today that a few hundred years ago would have looked like magic. We all know versions of this banal assertion - we�ve probably all made it in one form or another or ourselves at some point. And if we don�t understand a given technology it looks like magic: for example, we are perpetually surprised by the mellifluous tones read off our favorite CDs by, we believe, a laser. Most of us have no notion of the decades of negotiation that inform agreement on, inter alia, standard disc size, speed, electronic setting, and amplification standards. It is not dissimilar to the experience of magic one enjoys at a fine restaurant or an absorbing play. A common description of a good waiter or butler (one thinks of Jeeves in the Wodehouse stories) is that she clears a table and smoothes the unfolding of events �as if by magic.� In a compelling play, the hours of rehearsal and missteps are disappeared from center stage, behind a seamless front stage presentation. Is the magic of the CD different from the magic of the waiter or the theater ensemble? Are these two kinds of magic or one -- or none?

          This book is an attempt to answer this question, which can be posed more prosaically as:

          • What work do classifications and standards do? Again, we want to look at what goes into making things work like magic: making them fit together so that we can buy a radio built by someone we have never met in Japan, plug it into a wall in Champaign, Illinois and hear the world news from the BBC.
          • Who does that work? We explore the fact that all this magic involves much work: there is a lot of hard labor in effortless ease. Such invisible work is often not only underpaid - it is severely underrepresented in theoretical literature (Star and Strauss, 1999). We will discuss where all the �missing work� that makes things look magical goes.
          • What happens to the cases that don�t fit? We want to draw attention to cases that don�t fit easily into our magical created world of standards and classifications: the left handers in the world of right-handed magic, chronic disease sufferers in the acute world of allopathic medicine, the vegetarian in MacDonald�s (Star, 1991b) and so forth.
          These are issues of great import. It is easy to get lost in Baudrillard�s (1990) cool memories of simulacra. He argues that it is impossible to sort out media representations from �what really happens.� We are unable to stand outside representation or separate simulations from nature. At the same time, he pays no attention to the work of constructing the simulations, or the infrastructural considerations that underwrite the images/events (and we agree that separating them ontologically is a hopeless task). The hype of our postmodern times is that we don�t need to think about this sort of work any more. The real issues are scientific and technological, stripped of the conditions of production - in artificial life, thinking machines, nanotechnology, and genetic manipulation.... Clearly each of these is important. But there is more at stake - epistemologically, politically and ethically - in the day to day work of building classification systems and producing and maintaining standards than in abstract arguments about representation. Their pyrotechnics may hold our fascinated gaze; they cannot provide any path to answering our moral questions.
           
           
           
           

          What Are You?

          I grew up in Rhode Island, a New England state which is largely Italian-American and French-Canadian, known chiefly for its small stature. When I was a kid in our neighborhood, the first thing you would ask on encountering a newcomer was "what�s your name?" The second was "what are you?" "What are you" was an invitation to recite your ethnic composition in a kind of singsong voice. 90% of the kids would say "Italian with a little bit of French," or "half-Portuguese, one-quarter Italian and one-quarter Armenian." When I would chime in with "half Jewish, one quarter Scottish and one quarter English," the range of responses went from very puzzled looks to "does that mean you�re not Catholic?" Wherein, I guess, began my fascination with classification, and especially with the problem of residual categories, or, the Other, or not elsewhere classified.

          --Leigh Star
           
           
           
           

          Two Definitions : Classification and Standards

          Up to this point, we have been using the terms classification and standardization without formal definition. Let us clarify the terms now.

          Classification

          A classification is a spatial, temporal or spatio-temporal segmentation of the world. A �classification system� is a set of boxes (metaphorical or literal) into which things can be put in order to then do some kind of work - bureaucratic or knowledge production. In an abstract, ideal sense, a classification system exhibits the following properties:

          1. There are consistent, unique classificatory principles in operation. One common sort of system here is the genetic principle of ordering. This refers not to DNA analysis, but an older and simpler sense of the word: classifying things by their origin and descent (Tort, 1989). A genealogical map of a family�s history of marriage, birth and death is genetic in this sense (even for adopted children and in-laws). So is a flow chart showing a hierarchy of tasks deriving from one another over time. There are many other types of classificatory principles � sorting correspondence by date received (temporal order), for example, or recipes by those most frequently used (functional order).
          2. The categories are mutually exclusive. In an ideal world, categories are clearly demarcated bins, into which any object addressed by the system will neatly and uniquely fit. So in the family genealogy, one mother and one father give birth to a child, forever and uniquely attributed to them as parents - there are no surrogate mothers, or issues of shared custody or of retrospective DNA testing. A rose is a rose, not a rose sometimes and a daisy other times.
          3. The system is complete. With respect to the items, actions or areas under its consideration, the ideal classification system provides total coverage of the world it describes. So, for example, a botanical classifier would not simply ignore a newly discovered plant, but would always strive to name it. A physician using a diagnostic classification must enter something in the patient�s record where a category is called for; where unknown, the possibility exists of a medical discovery, to be absorbed into the complete system of classifying.
          No real-world working classification system that we have looked at meets these �simple� requirements and we doubt that any ever could. In the case of unique classificatory systems, people disagree about their nature; they ignore or misunderstand them; or they routinely mix together different and contradictory principles. A library, for example, may have a consistent Library of Congress system in place, but supplement it in an ad hoc way. Best sellers to be rented out to patrons may find themselves on a separate shelf; very rare, pornographic or expensive books may be locked away from general viewing at the discretion of the local librarian. Thus, the books are moved, without being formally reclassified, yet carry an additional functional system in their physical placement.

          For the second point, mutual exclusivity may be impossible in practice, as when there is disagreement or ambivalence about the membership of an object in a category. Medicine is replete with such examples, especially when the disease entity is controversial or socially stigmatized. In terms of the third point, completeness, there may be good reasons to ignore data that would make a system more comprehensive. The discovery of a new species on an economically important development site may be silenced for monetary considerations. An anomaly may be acknowledged, but be too expensive � politically or bureaucratically -- to introduce into a system of record keeping. In Chapter 2, we demonstrate ways of reading classification systems so as to be simultaneously sensitive to these conceptual, organizational and political dimensions.

          Consider the International Classification of Diseases (ICD), which will be one of our major examples throughout this book. The full title of the current (10th) edition of the ICD, is: "ICD-10 - International Statistical Classification of Diseases and Related Health Problems; Tenth Revision." Note that it is designated a �statistical� classification. By this is meant that only diseases which are statistically significant are to be entered in (it is not an attempt to classify all disease).

          The ICD calls itself a �classification,� even though many have said that it is a �nomenclature� since it has no single classificatory principle (it has at least four; which are not mutually exclusive (this point is developed in Chapter 4)). A nomenclature simply means an agreed-upon naming scheme, and need not follow any classificatory principles. The nomenclature of streets in Paris, for example, includes those named after intellectual figures, plants and trees, battles, and politicians, as well as those inherited from former governments, such as Rue de Lutèce (Lutèce was the ancient Roman name for Paris). There is no classificatory system. Nomenclature and classification are frequently confused, however, since often attempts are made to model nomenclature on a single, stable system of classification principles - as for example with botany (Bowker, in press) or anatomy. In the case of the ICD, diagnostic nomenclature and the terms in the ICD itself were conflated in the American system of diagnosis-related groups (DRGs), much to the dismay of some medical researchers. In many cases the ICD represents a compromise between conflicting schemes:

          The terms used in categories C82-C85 for non-Hodgkin�s lymphomas are those of the Working Formulation, which attempted to find common ground among several major classification systems. The terms used in these schemes are not given in the Tabular List but appear in the Alphabetical Index; exact equivalence with the terms appearing in the Tabular List is not always possible. (ICD-10, 1: 215). However, the ICD presents itself clearly as a classification scheme and not a nomenclature. Since 1970, there has been an effort underway by the World Health Organization to build a distinct International Nomenclature of Diseases (IND), whose main purpose will be to provide: "a single recommended name for every disease entity" (ICD-10, 1: 25).

          For the purposes of this book, we take a broad enough definition so that anything that is consistently called a classification system and treated as such can be included in the term. This is a classic Pragmatist turn � things perceived as real are real in their consequences (Thomas and Thomas, 1917). If we took a purist or formalist view, the ICD would be a (somewhat confused) nomenclature and who knows what the IND would represent. With a broad, Pragmatic definition we can look at the work that is involved in building and maintaining a family of entities that people call classification systems - rather than attempt the Herculean, Sisyphian task of purifying the (un)stable systems in place. Howard Becker makes a cognate point here:

          Epistemology has been a ... negative discipline, mostly devoted to saying what you shouldn�t do if you want your activity to merit the title of science, and to keeping unworthy pretenders from successfully appropriating it. The sociology of science, the empirical descendant of epistemology, gives up trying to decide what should and shouldn�t count as science, and tells what people who claim to be doing science do. (Becker, 1996: 54-55). The work of making, maintaining, and analyzing classification systems is richly textured. It is one of the central kinds of work of modernity, including science and medicine. It is, we argue, central to social life.

          Standards

          Classifications and standards are closely related, but not identical. While this book focuses on classification, standards are crucial components of the larger argument. The systems we discuss often do become standardized; in addition, a standard is in part a way of classifying the world. What then are standards? The term as we use it in the book has several dimensions:

          1. A �standard� is any set of agreed-upon rules for the production of (textual or material) objects.
          2. A standard spans more than one community of practice (or site of activity). It has temporal reach as well, in that it persists over time.
          3. Standards are deployed in making things work together over distance and heterogeneous metrics. For example, computer protocols for Internet communication involve a cascade of standards (cf. Abbate and Kahin, 1995) which need to work together well in order for the average user to gain seamless access to the web of information. There are standards for the components to link from your computer to the phone network, for coding and decoding binary streams as sound, for sending messages from one network to another, for attaching documents to messages, and so forth.
          4. Legal bodies often enforce standards - be these professional organizations, manufacturers� organizations or the State. We might say tomorrow that volapük (a universal language that boasted some 23 journals in 1889 (Proust, 1989: 580)) or its successor Esperanto shall henceforth be the standard language for international diplomacy. Without a mechanism of enforcement, or a grassroots movement, we shall fail.
          5. There is no natural law that the best standard shall win - QWERTY, Lotus 123, DOS and VHS are often cited in this context. The standards that do win may do so for a variety of other reasons: they build on an installed base, they had better marketing at the outset, and they were used by a community of gatekeepers who favored their use. Sometimes standards win due to an outright conspiracy, as in the case of the gas refrigerator documented by Cowan (1985).
          6. Standards have significant inertia, and can be very difficult and expensive to change.
          It was possible to build a cathedral like Chartres without standard representations (blueprints) and standard building materials (regular sizes for stones, tools etc.) (Turnbull, 1993). People invented an amazing array of analog measuring devices (such as string lengths). Each cathedral town posted the local analog metric (a length of metal) at its gates, so that peripatetic master builders could calibrate their work to it when they arrived in the town. They did not have a wide-scale measurement system such as our modern metric or decimal systems. (Whether as a result of this local improvisation or not, Turnbull notes, many cathedrals did fall down!)

          It is no longer possible to build a complex collective project without standardized measurements. Consider a modern housing development; too much needs to come together from distant and proximate sources - electricity, gas, sewer, timber sizes, screws, nails and so on. The control of standards is a central, often underanalyzed feature of economic life (but see the work of Paul David - for example David and Rothwell, 1994 - for a rich treatment). They are key to knowledge production as well - Latour (1987) speculates that far more economic resources are spent creating and maintaining standards than in producing �pure� science. There are a number of histories of standards which point to the development and maintenance of standards as being critical to industrial production.

          At the same time, just as with classifications, these dimensions of standards are in some sense idealized. They embody goals of practice and production that are never perfectly realized � like Plato�s triangles. The process of building to a standardized code, for example, usually includes a face-to-face negotiation between builder(s) and inspector(s), which itself includes a history of relations between those people. Small deviations are routinely overlooked, unless the inspector is making a political point. The idiom "good enough for government use" embodies the common sense accommodations of the slip between the ideal standard and the contingencies of practice.

          In this and in many other ways, then, classifications and standards are two sides of the same coin. Classifications may or may not become standardized. If they do not, they are ad hoc, limited to an individual or a local community, and/or of limited duration. At the same time, every successful standard imposes a classification system, at the very least between good and bad ways of organizing actions or things. And the workarounds involved in the practical use of standards frequently entail the use of ad hoc non-standard categories. For example, a patient may respond to a standardized protocol for the management of chronic back pain by approximating the directions and supplementing them with an idiosyncratic or alternative medical classification scheme. If the protocol requires a number of exercises done three times a day, the patient may distinguish good days from bad days, vacation days from working days, and only do the exercises when they deem them necessary.

          Classifications and standards are related in another sense, which concerns the use of a classification by more than one social world or community of practice, and the impact that use has on questions of membership and the taken-for-grantedness of objects. Throughout this book, we speak of classifications as objects for cooperation across social worlds, or as boundary objects (Star and Griesemer, 1989). Drawing from earlier studies of interdisciplinary scientific cooperation, we define boundary objects as those objects that both inhabit several communities of practice and satisfy the informational requirements of each of them. In working practice, they are objects that are able both to travel across borders and maintain some sort of constant identity. They can be tailored to meet the needs of any one community (they are plastic in this sense, or customizable). At the same time, they have common identities across settings. This is achieved by allowing the objects to be weakly structured in common use, imposing stronger structures in the individual-site tailored use. They are thus both ambiguous and constant; they may be abstract or concrete. In Chapter 9, we explore in detail the abstract ramifications of the use of classifications by more than one community and the connection with the emergence of standards.

          The Structure of this Book

          In order to explore these questions, we have written a first chapter detailing some key themes of the work to follow. We have then divided the middle of the book into three parts, which look at several classification systems. We have structured these studies around three issues in turn: classification and large-scale infrastructures (Part 1); classification and biography (Part 2) and classification and work practice (Part 3). Weaving these three themes in combination, we can explore the texture of the space within which infrastructures work and classification systems from different worlds meet, adjust, fracture or merge. In two concluding chapters, we elaborate some theoretical conclusions from these studies.

          Part 1: Classification and Large-Scale Infrastructures

          Classification systems are integral to any working infrastructure. In Part 1 (Chapters 2-4) we examine how a global medical classification system was developed to serve the conflicting needs of multiple local, national and international information systems.

          Our investigation here begins in the late nineteenth century, with another kind of information explosion � the development of myriad systems of classification and standardization of modern industrial and scientific institutions.

          In the nineteenth century people learned to look at themselves as surrounded by tiny, invisible things which have the power of life or death: microbes and bacteria. They learned to teach their children to wash their hands of germs before eating, and later, to apply antiseptic salve to a cat scratch or an inflamed fingernail. Company washrooms sprouted signs admonishing employees to wash hands before returning to work, especially if they worked with food served to others. In this period, people also learned how to perform surgery that would not usually be fatal and how to link gum disease with bacteria between the teeth.

          At the same time as they learned these practices with respect to germs, another ubiquitous set of tiny, invisible things were being negotiated and sewn into the social fabric. These were formal, commodified classifications and standards, both scientific and commercial. People classified, measured and standardized just about everything -- animals, human races, books, pharmaceutical products, taxes, jobs, and diseases. The categories so produced lived in industry, medicine, science, education and government. They ranged from the measurement of machine tools to the measurement of people�s forearms and foreheads. The standards were sometimes physically tiny measures -- how big should be a standard size second of time, or an eyeglass screw or an electrical pulse rate? At other times, they were larger: what size should a railroad car be, or a city street � or a corporation? Government agencies, industrial consortia, and scientific committees created the standards and category systems. So did mail order firms, machine tool manufacturers, and animal breeders, and thousands of other actors. Most of these activities became silently embodied in the built environment and in notions of good practice. The decisions taken in the course of their construction are forever lost to the historical record. In fact, their history is considered by most to be boring, trivial, and unworthy of investigation.

          There are some striking similarities to our own late 20th century historical moment in that faced by Europeans at the end of the 19th century. A new international information-sharing and gathering movement was starting, thanks to the advent of wide-scale international travel, international quasi-governmental governance structures, and a growing awareness that many phenomena (like epidemics and markets) would not be confined to one country. In the 19th century, people faced for the first time large numbers of bodies and their microbes moving rapidly across national borders and between large bureaucracies -- and at an unprecedented rate. Especially in the case of epidemics, international public health became an urgent necessity. Attempts to control these passengers represent one of the first large-scale Western medical classification schemes: ships who had called at ports on the way back from Mecca had to follow a period of quarantine during which anyone infected would become symptomatic -- thus emulating the slower timeline of horse or camel travel.

          Figure 1. Map of Cholera Epidemics. Source: A. Proust, 1892.
           
           

          After quarantine, one was given a �clean bill of health� and allowed freedom of transport. This was a costly delay for the ships. And so, of course, a black market in clean bills of health appeared shortly thereafter ... . The problem of tracking who was dying of what, where on earth became a permanent feature of international bureaucracy.

          Figure 2. French Bill of Health. Source: A. Proust, 1892.
           
           
















          Constructing such a list may seem to us like a comparatively straightforward task, once the mechanisms for reporting were in place. However, for over one hundred years there has never been consensus about disease categories or about the process of collecting data. So one culture sees spirit possession as a valid cause of death, another ridicules this as superstition; one medical specialty sees cancer as a localized phenomenon to be cut out and stopped from spreading, another sees it as a disorder of the whole immune system which merely manifests in one location or another. The implications for both treatment and classification differ. Trying to encode both causes results in serious information retrieval problems.

          In addition, classifications shift historically. In Britain in 1650 we find that 696 people died of being �aged�; 31 succumbed to wolves 9 to grief and 19 to �King�s Evil�. "Mother" claimed 2 in 1647 but none in 1650, but in that year 2 were �smothered and stifled.� Seven starved in 1650 (Graunt, 1662), but by 1930 the World Health Organization would make a distinction: if an adult starved to death it was a misfortune; if a child starved, it was homicide. Death by wolf alone becomes impossible by 1948, where death from animals is divided between venomous and non-venomous, and only dogs and rats are singled out for categories of their own (ICD 5, 1948, p. 267).

          Insert Figure 3. Mortality Table, England in Seventeenth Century. Source: J. Graunt, 1662.
           
           

          The first part of this book is dedicated to understanding the construction of the International Classification of Diseases (ICD): a classification scheme with its origins in the late 19th century but still present today � indeed it is ubiquitous in medical bureaucracy and medical information systems. The ICD constitutes an impressive attempt to coordinate information and resources about mortality and morbidity globally. For the background research for understanding international processes of classification, we went to Geneva and studied the archives of the World Health Organization and its predecessors such as the League of Nations and the Office Internationale d�Hygiène Publique. Roughly every ten years since the 1890s, the ICD has been revised. The UN and the WHO have kept some records of the process of revision; others are to be found in the file cabinets of individuals involved in the revision process.

          What we found was not a record of gradually increasing consensus, but a panoply of tangled and crisscrossing classification schemes, held together by an increasingly harassed and sprawling international public health bureaucracy. Spirit possession and superstition never do reconcile, but in order that some data be entered on the Western-oriented death certificate, it becomes possible from the WHO point of view for a death to be assigned the category �non-existent disease�.

          One of the other major influences on keeping medical records has been insurance companies, as we discuss in Chapter 4. As the working lives of individuals became more closely tied up with the state and its occupational health concerns, the classification of work-related diseases (including industrial accidents) became very important. Life expectancy measures were equally important, both in terms of estimating the available labor force and for basic planning measures. Of course, occupational and non-work related medical classifications did not always line up: companies might have been reluctant to take responsibility for unsafe working conditions, latency in conditions such as asbestosis makes data hard to come by; there may have been moral conflicts about the cause of such illnesses.

          In similar fashion, any classification that touched on religious or ethical questions (and surprisingly many do so) would be disputed. If life begins at the moment of conception, abortion is murder and a fetus dead at 3 months is a stillbirth, encoded as a live infant death. Contemporary abortion wars in the U.S. and Western Europe attest to the enduring and irreconcilable ontologies involved in these codifications.

          For a bureaucracy to establish a smooth data collection effort, a means must be found to detour around such higher-order issues. The statistical committee discussed in Chapter 4, assigned with determining the exact moment of the beginning of life by number of attempted breaths and weight of fetus or infant, cuts a Solomon-like figure against such a disputed landscape. At the same time, there is an element of reductionist absurdity here � how many breaths equals �life�? If not specified, another source of quality control for data is lost; if specified, it seems to make common sense ironic. This is an issue we will revisit as well in the discussion of nursing interventions, Chapter 7.

          Algorithms for codification do not resolve the moral questions involved, although they may obscure them. For decades, priests, feminists and medical ethicists on both sides have debated the question of when a human life begins. The moral questions involved in encoding such information � and the politics of certainty and of voice involved � are much more obscure.

          Forms like the death certificate, when aggregated, form a case of what Kirk and Kutchins call "the substitution of precision for validity" (1992; see also Star, 1989b). That is, when a seemingly neutral data collection mechanism is substituted for ethical conflict about the contents of the forms, the moral debate is partially erased. One may get ever more precise knowledge, without having resolved deeper questions, and indeed, by burying them.

          There is no simple pluralistic answer to how such questions may be resolved democratically or with due process. Making all knowledge retrievable, and thus re-debatable, is an appealing solution in a sense from a purely information scientific point of view. However, from a practical organizational viewpoint this fails. For example, in 1927, a manual describing simultaneous causes of death listed some 8,300 terms, which represented 34 million possible combinations that might appear on the face of a death certificate. A complete user manual for filling out the certificate would involve 61 volumes of 1,000 pages each. This is clearly not a pragmatic choice for conducting a task which most physicians also find boring, low-status, and clinically unimportant.

          As we know from studies of work of all sorts, people do not do the ideal job, but the doable job. When faced with too many alternatives and too much information, they satisfice (March and Simon, 1958). As an indicator of this, studies of the validity of codes on death certificates repeatedly show that doctors have favorite categories; these are regionally biased; and autopsies (which are rarely done anyway) have a low rate of agreement with the code on the form (Fagot-Largeault, 1989).

          Even when there is relatively simple consensus about the cause of death, the act of assigning a classification can be socially or ethically charged. Thus, in some countries the death certificate has two faces: a public certificate which is handed to the funeral director in order that arrangements be made quickly and discreetly, and a statistical cause which is filed anonymously with the public health department. In this case, the doctor is not faced with telling the family of a socially unacceptable form of death: syphilis can become heart failure, or suicide can become a stroke. For example, as we discuss in Chapter 4, the process of moving to an anonymous statistical record may reveal hidden biases in the reporting of death. Where the death certificate is public, stigma and the desire to protect the feelings of the family may reign over scientific accuracy.

          Over the years, those designing the list of causes of death and disease have struggled with all of these problems. One of the simple but important rules of thumb to try to control for this degree of uncertainty is to distribute the residual categories. "Not elsewhere classified" appears throughout the entire ICD, but nowhere as a top-level category. So since uncertainty is inevitable, and its scope and scale essentially unknowable, at least its impact will not hit a single disease or location disproportionately. Its effects will remain as local as possible; the quest for certainty is not lost, but postponed, diluted, and abridged.

          With the rise of very-large scale information systems, the Internet, the World Wide Web, and digital libraries, we find that the sorts of uncertainties faced by the WHO are themselves endemic in our own lives. When we use email filters, for example, we risk losing the information that doesn�t fit the sender�s category: junk email is very hard to sort out automatically in a reliable way. If we have too many detailed filters, we lose the efficiency sought from the filter in the first place. As we move into desktop use of hyperlinked digital libraries, we fracture the traditional bibliographic categories across media, versions, genres, and author. The freedom entailed is that we can customize our own library spaces; but as Jo Freeman (1972) pointed out in her classic article, �The Tyranny of Structurelessness,� this is also so much more work that we may fall into a lowest-level convenience classification rather than a high-level semantic one. In one of our digital library projects at Illinois, for example, several undergraduates we interviewed in focus groups stated that they would just get five references for a term paper -- any five -- since that�s what the professor wanted, and they�d better be ones that are listed electronically and available without walking across campus.

          The ICD classification is in many ways an ideal mirror of how people designing global information schemes struggle with uncertainty, ambiguity, standardization, and the practicalities of data quality. Digging into the archives, and reading the ICD closely through its changes, reveals some of the upstream, design-oriented decisions informing the negotiated order achieved by the vast system of forms, boxes, software, and death certificates. At the same time, we have been constantly aware of the human suffering often occasioned by the apparently bloodless apparatus of paperwork through which these data are collected.

          Part 2: Classification and Biography

          Our second section of this book looks at two cases where the lives of individuals are broken, twisted, and torqued by their encounters with classification systems. This often-invisible anguish informs another level of ethical inquiry. Once having been made, the classification systems are applied to individual cases � sometimes resulting in a kind of surreal bureaucratic landscape. Sociologist Max Weber spoke of the "iron cage of bureaucracy" hemming in the lives of modern workers and families. The cage formed by classification systems can be constraining in just this way � although cage might be too impoverished a metaphor to describe its variations and occasional stretches. In Chapters 5 and 6, we look at biography and classification. We chose two examples where classification has become a direct tool mediating human suffering. Our first case concerns tuberculosis patients, and the impact of disease classification on their lives. We use historical data to discuss the experience of the disease within the tuberculosis asylum.

          Tuberculosis patients, like many with chronic illness, live under a confusing regime of categories and metrics (see also Ziporyn, 1992). Many people were incarcerated for years � some for decades � waiting for the disease to run its course, to achieve a cure at high altitudes, or to die there. They were subjected to a constant battery of measurements: lung capacity, auscultation, body temperature and pulse rate, X-rays, and, as they were developed, laboratory tests of blood and other bodily fluids. The results of the tests determined the degree of freedom from the sanatorium regime, as well as, ultimately, the date of release.

          As will be no surprise to medical sociologists, the interpretation and negotiations of the tests between doctor and patient were fraught with questions of the social value of the patient (middle class patients being thought more compliant and reliable when on furlough from the asylumthan those from lower classes); with gender stereotypes; and with the gradual adaptation of the patient�s biographical expectations to the period of incarceration. Thomas Mann�s The Magic Mountain and Julius Roth�s Timetables are full of stories of classification and metrication. We examine how different timelines, and expectations about those timelines, unfold in the two remarkable volumes. Biography, career, the state of the medical art with respect to the disease, and the public health adjudication of tuberculosis are all intertwined against the landscape of the sanatorium.

          Life in the sanatorium has a surreal, almost nightmarish quality, as detailed by Mann, Roth and many other writers throughout the twentieth century. This sense comes precisely from the misalignment between a patient�s life expectations, the uncertainties of the disease and of the treatment, and the negotiations laden with other sorts of interactional burdens. It is one thing to be ill and in the hospital, with an indefinite release date. It is another when the date of release includes one�s ability to negotiate well with the physicians, their interpretation of the latest research, and the exigencies of public health forms and red tape. We call this agglomeration torque, a twisting of timelines that pull at each other, and bend or twist both patient biography and the process of metrication. When all are aligned, there is no sense of torque or stress; when they pull against each other over a long period, a nightmare texture emerges.

          A similar torque is found in our second case in this section, that of race classification and reclassification under apartheid in South Africa. Between 1950 and the fall of apartheid forty years later, South Africans were ruled under an extremely rigid, comprehensive system of race classification. Divided into four main �racial� groups (White/European, Bantu (Black), Asian and Colored (Mixed Race)), people�s lives were rigidly segregated. The segregation extended from so-called petty apartheid (separate bus stops, water fountains and toilets) to rights of work, residency, education and freedom of movement. This system became the target of worldwide protest, and eventually came to a formal end. These facts are common knowledge. What has been less well documented or publicized are the actual techniques used to classify people by race. In Chapter 6, we examine in detail some cases of mixed-race people who applied to be re-classified after their initial racial designation by the state. These borderline cases serve to illuminate the underlying architecture of apartheid. This was a mixture of brute power, confused eugenics and appropriations of anthropological theories of race. The scientific reason given for apartheid by the white supremacist Nationalist Party was �separate development� � the idea that in order to develop naturally, the races must develop separately.

          In pursuing this ideology, of course, people and families that crossed the color barrier were problematic. If a natural scientific explanation was given for apartheid, systematic means should be available to winnow white from black, colored from black and so on. As the chapter delineates, this attempt was fraught with inconsistencies and local work-arounds, as people never easily fit any categories. Over 100,000 people made formal appeals concerning their race classification; most were denied.

          Although it lies at a political extreme, these cases form a continuum with the classification of people at different stages of tuberculosis. In both cases, biographies and categories fall along often-conflicting trajectories. Lives are twisted, even torn, in the attempt to force the one into the other. These torques may be petty or grand, but they are a way of understanding the co-construction of lives and their categories.

          Part 3: Classification and Work Practice

          In Part 3, Chapters 7 and 8, we will look at how classification systems organize and are organized by work practice. We will look at the effort of a group of nursing scientists based at the University of Iowa and led by Joanne McCloskey and Gloria Bulechek to produce a classification of nursing interventions. Their Nursing Intervention Classification (NIC) aims to depict the range of activities that nurses carry out in their daily routines. Their original system consisted of a list of some 336 interventions; each comprised of a label, a definition, a set of activities, and a short list of background readings. Each of those interventions is in turn classified within a taxonomy of six domains and 26 classes. For example, one of the tasks nurses commonly perform is preparing and monitoring intravenous medication. The nursing intervention "Epidural Analgesia Administration" is defined as: "preparation and delivery of narcotic analgesics into the epidural space"; another common one, "Cough Enhancement", groups activities designed to help respiration.

          The Iowa NIC researchers built up their system of nursing interventions inductively. They created a preliminary list that distinguished between nursing interventions and activities, then nurtured a large grassroots network of nursing researchers. This group narrowed the preliminary list of interventions to the original 336 published in Nursing Interventions Classification, and further validated them via surveys and focus groups. Different interventions were reviewed for clinical relevance, and a coding scheme was developed. The classification system grew through a cooperative process, with nurses in field sites trying out categories, and suggesting new ones in a series of regional and specialist meetings. Since 1992, the nurses have added over 50 interventions to their original list. We attended a number of these meetings, and interviewed many of the nurses involved.

          Caring work such as calming and educating patients, usually done by nurses, often cuts across specific medical diagnostic categories. The NIC investigators use their list of interventions in order to make visible and legitimate the work that nurses do. The idea is that it will be used to compare work across hospitals, specialties and geographical areas, and to build objective research measures for the outcomes. NIC, although still relatively young, promises to be a major rallying point for nurses in the decades to come. Before NIC, much nursing work was invisible to the medical record. As one nurse poignantly said, "we were just thrown in with the cost of the room." Another said, "I am not a bed!" The traditional, quintessential nurse would be ever present, caregiving, and helpful -- but not a part of the formal patient-doctor information structure. Of course, this invisibility is bound up with traditional gender roles, as with librarians, social workers and primary school teachers.

          But as with the ICD, classifying events is difficult. In the case of NIC, the politics move from a politics of certainty to a politics of ambiguity. The essence of this politics is walking a tightrope between increased visibility and increased surveillance; between over-specifying what a nurse should do and taking away discretion from the individual practitioner.

          When discretion and the tacit knowledge that is part of every occupation meet the medical bureaucracy which would account for every pill and every moment of health care workers� time, contradictions ensue. This is especially true in the "softer" areas of care. Social-psychological caregiving is also one of the areas where this dilemma is prominent. For example, NIC lists as nursing interventions "anticipatory guidance" and "mood management" -- preparation for grief, or surgery. Difficult though these are to capture in a classification scheme, one much more difficult is "humor." How can one capture humor as a deliberate nursing intervention? Does sarcasm, irony, or laughter count as a nursing intervention? When do you stop? How to reimburse humor, how to measure this kind of care? No one would dispute its importance, but it is by its nature a situated and subjective action. A grey area of common sense remains for the individual staff nurse to define whether some of the nursing interventions are worth classifying.

          There are continuing tensions within NIC between just this kind of common sense, and abstracting away from the local in order to standardize and compare, while at the same time rendering invisible work visible. Nurses� work is often invisible for a combination of good and bad reasons. Nurses have to ask mundane questions, rearrange bedcovers, move a patient�s hand so that it is closer to a button, and sympathize about the suffering involved in illness. Bringing this work out into the open and differentiating its components can mean belaboring the obvious or risking being too vague.

          One of the battlefields where comparability and control appear as opposing factors is in linking NIC to costing. NIC researchers assert that the classification of nursing interventions will allow a determination of the costs of services provided by nurses and planning for resources needed in nursing practice. As the nurse above says, nursing treatments are usually bundled in with the room price. NIC is used in the development of nursing health care systems and may provide a planning vehicle for previously untracked costs. As we shall see, NIC can also be problematic for nurses. Like any other classification scheme which renders work visible, it can also render surveillance easier � and could in the end lead to a Tayloristic dissection of the tasks of nursing (as the NIC designers are well aware). So-called unskilled tasks may be taken out of their hands and the profession as a whole may suffer a loss of autonomy and the substitution of rigid procedure for common sense.

          As in the case of the ICD, there are many layers of meaning involved in developing and implementing nursing classification. NIC might look like a straightforward organizational tool: it is in fact much more than that. It merges science, practice, bureaucracy and information systems. NIC coordinates bodies, impairments, charts, reimbursement systems, vocabularies, patients, and health care professionals. Ultimately, it provides a manifesto for nursing as an organized occupation, a basis for a scientific domain and a tool for organizing work practices.

          Why it is important to study classification systems

          The sheer density of the collisions of classification schemes in our lives calls for a new kind of science, a new set of metaphors, linking traditional social science and computer and information science. We need a topography of things such as the distribution of ambiguity; the fluid dynamics of how classification systems meet up � a plate tectonics rather than static geology. This new science will draw on the best empirical studies of workarounds, information use, and mundane tools such as desktop folders and file cabinets (perhaps peering backwards out from the web and into the practices). It will also use the best of object-oriented programming and other areas of computer science to describe this territory. It will build on years of valuable research on classification in library and information science.

          Let us finish this introduction with a future scenario that symbolizes this abstract endeavor. Imagine that you�re walking through a forest of inter-articulated branches. Some are covered with ice or snow, and the sun melts their touching tips to reveal space between. Some are so thickly brambled they seem solid. Others are oddly angular in nature, like esplanaded trees.

          Some of the trees are wild, some have been cultivated. Some are old and gnarled, and some are tiny shoots; some of the old ones are nearly dead, others show green leaves. The forest is still wild, but there are some parks, and some protocols for finding one�s way along, at least on the known paths. Helicopters flying overhead can quickly tell you how many types of each tree, even each leaf, there are in the world, but they cannot yet give you a guidebook for birdwatching or forestry management. There is a lot of underbrush and a complex ecology of soil bacteria, flora and fauna.

          Now imagine that the forest is a huge information space, and each of the trees and bushes are classification systems. Those who make them up and use them are the animals and plants, and the soil is a mix of the Internet, the paper world, and other communication infrastructures.

          Your job is to describe this forest. You may write a basic manual of forestry, or paint a landscape, compose an opera, or improve the maps throughout. What will your product look like? Who will use it?

          In this book, we show from our studies of medical, scientific and race classification that, like a good forest, some areas will be left wild, or in darkness, or even unmapped (that is, some ambiguity will remain). We will show that abstract schema that do not take use into account -- say, maps that leave out landmarks or altitude or how readers use maps -- will simply fail. (That is, common sense will be seen as the precious resource that it is.) We intuit that a mixture of scientific, poetic, and artistic talents, such as that represented in the hypertextual world, will be crucial to this task. We will demonstrate the value of a mixture of formal and folk classifications, used sensibly in the context of people�s lives.

          Chapter One: Some Tricks of the Trade in Analyzing Classification

          My guess is that we have a folk theory of categorization itself. It says that things come in well-defined kinds, that the kinds are characterized by shared properties, and that there is one right taxonomy of the kinds.

          It is easier to show what is wrong with a scientific theory than with a folk theory. A folk theory defines common sense itself. When the folk theory and the technical theory converge, it gets even tougher to see where that theory gets in the way � or even that it is a theory at all (Lakoff, 1987: 121).

          Information infrastructure is a tricky thing to analyze.  Really good, usable systems disappear, almost by definition. The easier they are to use, the harder they are to see. As well, most of the time, the bigger they are, the harder they are to see. Unless we are electricians or building inspectors, we rarely think about the myriad of databases, standards, and instruction manuals subtending our reading lamps, much less about the politics of the electric grid that they tap into. And so on, as many layers of technology accrue and expand over space and time. Systems of classification (and of standardization) form a juncture of social organization, moral order, and layers of technical integration.  Each sub-system inherits, increasingly as it scales up, the inertia of the installed base of systems that have come before.

          Infrastructures are never transparent for everyone, and their workability as they scale up becomes increasingly complex.  Through due methodological attention to the architecture and use of these systems, we can achieve a deeper understanding of how it is that individuals (and communities) meet infrastructure.  We know that this means, at the least, an understanding of infrastructure that includes:

          • a historical process of development of many tools, arranged for a wide variety of users, and made to work in concert;
          • a practical match between routines of work practice, technology, and wider-scale organizational and technical resources;
          • a rich set of negotiated compromises ranging from epistemology to data entry which are both available and transparent to communities of users;
          • a negotiated order in which all of the above, recursively, can function together.
          The following shows a more elaborate definition of infrastructure, following Star and Ruhleder (1996), who emphasize that one person�s infrastructure may be another�s barrier:
           
           

          Figure 4 Star and Ruhleder�s Definition of Infrastructure

            • Embeddedness. Infrastructure is sunk into, inside of, other structures, social arrangements and technologies;
            • Transparency. Infrastructure is transparent to use, in the sense that it does not have to be reinvented each time or assembled for each task, but invisibly supports those tasks;
            • Reach or scope. This may be either spatial or temporal -- infrastructure has reach beyond a single event or one-site practice;
            • Learned as part of membership. The taken-for-grantedness of artifacts and organizational arrangements is a sine qua non of membership in a community of practice (Lave and Wenger, 1991; Star, 1996). Strangers and outsiders encounter infrastructure as a target object to be learned about. New participants acquire a naturalized familiarity with its objects as they become members;
            • Links with conventions of practice. Infrastructure both shapes and is shaped by the conventions of a community of practice, e.g. the ways that cycles of day-night work are affected by and affect electrical power rates and needs. Generations of typists have learned the QWERTY keyboard; its limitations are inherited by the computer keyboard and thence by the design of today�s computer furniture (Becker, 1982);
            • Embodiment of standards. Modified by scope and often by conflicting conventions, infrastructure takes on transparency by plugging into other infrastructures and tools in a standardized fashion.
            • Built on an installed base. Infrastructure does not grow de novo; it wrestles with the inertia of the installed base and inherits strengths and limitations from that base. Optical fibers run along old railroad lines; new systems are designed for backward-compatibility; and failing to account for these constraints may be fatal or distorting to new development processes (Monteiro and Hanseth, 1996).
            • Becomes visible upon breakdown. The normally invisible quality of working infrastructure becomes visible when it breaks: the server is down, the bridge washes out, there is a power blackout. Even when there are back-up mechanisms or procedures, their existence further highlights the now-visible infrastructure.
            • Is fixed in modular increments, not all at once or globally. Because infrastructure is big, layered and complex, and because it means different things locally, it is never changed from above. Changes take time and negotiation, and adjustment with other aspects of the systems involved.
          This chapter offers four themes, methodological points of departure for the analysis of these complex relationships. Each theme operates as a gestalt switch - it comes in the form of an infrastructural inversion (Bowker, 1994). This inversion is a struggle against the tendency of infrastructure to disappear (except when breaking down). It means learning to look closely at technologies and arrangements which, by design and by habit, tend to fade into the woodwork (sometimes literally!).

          Infrastructural inversion means recognizing the depths of interdependence of technical networks and standards, on the one hand, and the real work of politics and knowledge production on the other. It foregrounds these normally invisible Lilliputian threads, and furthermore gives them causal prominence in many areas normally attributed to heroic actors, social movements, or cultural mores. The inversion is similar to the argument made by Becker (1982) in his book Art Worlds. Most history and social analysis of art has neglected the details of infrastructure within which communities of artistic practice emerge. Becker�s inversion examines the conventions and constraints of the material artistic infrastructure, and its ramifications. For example, the convention of musical concerts lasting about three hours ramifies throughout the producing organization. Parking attendants, unions, ticket takers, and theater rentals are arranged in cascading dependence on this interval of time. An eight-hour musical piece, which is occasionally written, means rearranging all of these expectations � which in turn is so expensive that such productions are rare. Or paintings are about the size, usually, that will hang comfortably on a wall. They are also the size that fits rolls of canvas, the skills of framers, and the very doorways of museums and galleries. These constraints are mutable only at great cost, and artists must always consider them before violating them.

          Scientific inversions of infrastructure were the theme of a pathbreaking edited volume, The Right Tools for the Job: At Work in Twentieth-Century Life Sciences (Clarke and Fujimura, 1992). The purpose of this volume was to tell the history of biology in a new way � from the point of view of the materials that constrain and enable biological researchers. Rats, petri dishes, taxidermy, planaria, drosophila and test tubes take center stage in this narrative. The standardization of genetic research on a few specially-bred organisms (notably drosophila) has constrained the pacing of research, and the ways the questions may be framed, and has given biological supply houses an important, invisible role in research horizons. While elephants or whales might answer different kinds of biological questions, they are obviously unwieldy lab animals. While pregnant cow�s urine played a critical role in the discovery and isolation of reproductive hormones, no historian of biology had thought it important to describe the task of obtaining gallons of it on a regular basis. Adele Clarke (1998) puckishly relates her discovery, in the memoirs of a biologist, of the technique required to do so: tickle the cow�s labia in order to make her urinate. A starkly different view of the tasks of laboratory biology emerges from this image. It must be added to the processes of stabling, feeding, impregnating, and caring for the cows involved. The supply chain, techniques and animal handling methods had to be invented along with biology�s conceptual frame; they are not accidental, but constitutive.

          Our infrastructural inversion with respect to information technologies and their attendant classification systems follows this line of analysis. Like the cow�s urine or the 8-hour concert, we have found many examples of counterintuitive, often humorous struggles with constraints and conventions in the crafting of classifications. For instance, as we shall see in Chapter 5, in analyzing the experience of tuberculosis patients in Mann�s The Magic Mountain, we found the story of one woman who had been incarcerated so long in the sanatorium that leaving it became unthinkable. She recovered from the disease, but tried to subvert the diagnosis of wellness. When the doctors took her temperature, she would surreptitiously dip the thermometer in hot water to make it seem that she still had a fever. On discovering this, the doctors created a thermometer without markings, so that she could not tell what the mercury column indicated. They called this "the silent sister." The silent sister immediately becomes itself a telling indicator of the entangled infrastructure, medical politics, and the use of metrics in classifying tubercular patients. It tells a rich metaphorical story, and may become a concept useful beyond the rarified walls of the fictional Swiss asylum. What other silent sisters will we encounter in our infrastructural inversion � what surveillance, deception, caring, struggling, or negotiating?

          In the sections below, we present four themes that require the special double vision implied in the anecdotes above. They frame the new way of seeing which brings to life large-scale, bureaucratic classifications and standards. Without this map, excursions into this aspect of information infrastructure can be stiflingly boring. Many classifications appear as nothing more than lists of numbers with labels attached, buried in software menus, users� manuals, or other references. As we discuss in Chapter 2, new eyes are needed for reading classification systems, for restoring the deleted and desiccated narratives to these peculiar cultural/technical/scientific artifacts.

          Methodological Themes for Infrastructural Inversion

          1. Ubiquity

          The first major theme is the ubiquity of classifying and standardizing. Classification schemes and standards literally saturate our environment. In the built world we inhabit, thousands and thousands of standards are used everywhere, from setting up the plumbing in a house to assembling a car engine to transferring a file from one computer to another. Consider the canonically simple act of writing a letter longhand, putting it in an envelope and mailing it. There are standards for (inter alia): paper size, the distance between lines in lined paper, envelope size, the glue on the envelope, the size of stamps, their glue, the ink in your pen, the sharpness of its nib, the composition of the paper (which in turn can be broken down to the nature of the watermark, if any; the degree of recycled material used in its production, the definition of what counts as recycling). And so forth.

          Similarly, in any bureaucracy, classifications abound -- consider the simple but increasingly common classifications that are used when you dial an airline for information ("if you are traveling domestically, press 1"; "if you want information about flight arrivals and departures...."). And once the airline has hold of you, you are classified by them as a frequent flyer (normal, gold or platinum); corporate or individual; tourist or business class; short haul or long haul (different fare rates and scheduling apply).

          Howard Becker relates a delightful anecdote concerning his classification by an airline. A relative working for one of the airlines told him how desk clerks handle customer complaints. The strategy is first to try to solve the problem. If the customer remains unsatisfied, and becomes very angry in the process, the clerk dubs him or her "an irate". The clerk then calls the supervisor, "I have an irate on the line," shorthand for the category of very irritated passenger.

          One day Becker was having a difficult interaction with the same airline. He called the airline desk, and in a calm tone of voice, said, "Hello, my name is Howard Becker and I�m an irate. Can you help me with this ticket?" The clerk began to sputter, "How did you know that word?!!" Becker had succeeded in unearthing a little of the hidden classificatory apparatus behind the scenes at the airline. He notes that the interaction after this speeded up and went particularly smoothly.
           
           

          This categorical saturation furthermore forms a complex web. While it is possible to pull out a single classification scheme or standard for reference purposes, in reality none of them stand alone. So a subproperty of ubiquity is interdependence, and frequently, integration. A systems approach might see the proliferation of both standards and classifications as purely a matter of integration -- almost like a gigantic web of interoperability. Yet the sheer density of these phenomena go beyond questions of interoperability. They are layered, tangled, textured; they interact to form an ecology as well as a flat set of compatibilities. That is to say, they facilitate the co-ordination of heterogeneous �dispositifs techniques� (Foucault, 1975). They are lodged in different communities of practice - such as laboratories, records offices, insurance companies and so forth. There are spaces between (unclassified, non-standard areas), of course, and these are equally important to the analysis. It seems that increasingly these spaces are marked as unclassified and non-standard.

          It is a struggle to step back from this complexity and think about the issue of ubiquity, rather than try to trace the myriad connections in any one case. The ubiquity of classifications and standards is curiously difficult to see � as we are quite schooled in ignoring both, for a variety of interesting reasons. We also need concepts for understanding movements, textures, and shifts that will grasp patterns within the ubiquitous larger phenomenon. The distribution of residual categories (�not elsewhere classified� or �other�) is one such concept. �Others� are everywhere, structuring social order. Another such concept might be what Strauss et al. (1985) call a �cumulative mess trajectory.� In medicine, this occurs when one has an illness, is given a medicine to cure the illness, but incurs a serious side effect, which then needs to be treated with another medicine, etc. If the trajectory becomes so tangled that you cannot turn back, and the interactions multiply, �cumulative mess� results. We see this phenomenon in the interaction of categories and standards all the time -- ecological examples are particularly rich places to look.

          Materiality and Texture

          The second methodological departure point is that classifications and standards are material, as well as symbolic. How do we perceive this densely saturated classified and textured world? Under the sway of cognitive idealism, it is easy to see classifications as properties of mind and standards as ideal numbers or floating cultural inheritances. But they have material force in the world. They are built into and embedded in every feature of the built environment (and many of the nature/culture borderlands, such as with engineered genetic organisms).

          All classification and standardization schemes are a mixture of physical entities such as paper forms, plugs, or software instructions encoded in silicon, and conventional arrangements such as speed and rhythm, dimension, and how specifications are implemented. Perhaps because of this mixture, the web of intertwined schemes can be difficult to see. In general, the trick is to question every apparently natural easiness in the world around us and look for the work involved in making it easy. Within a project or on a desktop, the seeing consists in seamlessly moving between the physical and the conventional. So when a computer programmer writes some lines of Java code, she moves within conventional constraints and makes innovations based on them; at the same time, she strikes plastic keys, shifts notes around on a desktop, and consults manuals for various standards and other information. If we were to try to list out all the classifications and standards involved in writing a program, the list could run to pages. Classifications include types of objects, types of hardware, matches between requirements categories and code categories, and meta-categories such as the goodness of fit of the piece of code with the larger system under development. Standards range from the precise integration of the underlying hardware to the 60Hz power coming out of the wall through a standard size plug.

          Merely reducing the description to the physical aspect such as the plugs does not get us anywhere interesting in terms of the actual mixture of physical and conventional or symbolic. A good operations researcher could describe how and whether things would work together, often purposefully blurring the physical/conventional boundaries in making the analysis. But what is missing is a sense of the landscape of work as experienced by those within it. It gives no sense of something as important as the texture of an organization: it is smooth or rough? Bare or knotty? What is needed is a sense of the topography of all of the arrangements -- are they colliding? co-extensive? gappy? orthogonal? One way to get at these questions is to take quite literally the kinds of metaphors that people use when describing their experience of organizations, bureaucracies, and information systems, discussed in more detail in Chapter 9.

          When we think of classifications and standards as both material and symbolic, we adapt a set of tools not usually applied to them. There are tools for analysing built structures, such as structural integrity, enclosures and confinements, permeability, and durability, among many others. Structures have texture, and depth. The textural way of speaking of classifications and standards is common in organizations and groups. Metaphors of tautness, knots, fabrics and networks pervade modern language (Lakoff and Johnson, 1980).

          The Indeterminacy of the Past: Multiple times, multiple voices

          The third methodological theme concerns the past as indeterminate. We are constantly revising our knowledge of the past in light of new developments in the present. This is not a new idea to historiography, or to biography. We change our resumes as we acquire new skills to seem like smooth, planned paths of development, even if the change had been unexpected or undesired. When we become members of new social worlds, we often retell our life stories in new terminology. A common example of this is religious conversion, where the past is retold as exemplifying errors, sinning and repentance (Strauss, 1959). Or when coming out as gay or lesbian, childhood behaviors and teenage crushes become indicators of early inklings of sexual choice (Wolfe and Stanley, 1980).

          At wider levels of scale, these revisions also mean the introduction of new voices � many possible kinds of interpretations of categories, texts, and artifacts. Multiple voices and silences are represented in any scheme that attempts to sort out the world. No one classification organizes reality for everyone -- e.g. the red light-yellow light-green light traffic light distinctions don�t work for blind people (who need sound coding). In looking to classification schemes as ways of ordering the past, it is easy to forget those who have been overlooked in this way. Thus, the indeterminacy of the past implies recovering multi-vocality; it also means understanding how standard narratives that seem universal have been constructed (Star, 1991a).

          There is no way of ever getting access to the past except through classification systems of one sort or another - formal or informal, hierarchical or not ... . Take the apparently unproblematic statement: "In 1640, the English Revolution occurred; this led to a twenty year period in which the English had no monarchy". The classifications involved here, all problematic, include:

          • the current segmentation of time into days, months and years. Accounts of the English revolution generally use the Gregorian calendar, which was adopted some hundred years later - so causing translation problems with contemporary documents;
          • the classification of �peoples� into English, Irish, Scots, French and so on. These designations were by no means so clear at the time - the whole discourse of �national genius� or character really only arose in the nineteenth century;
          • the classification of events into revolutions, reforms, revolts, rebellions and so forth (cf. Furet, 1978 on thinking the French revolution). There really was no concept of �revolution� at the time; our current conception is marked by the historiographical work of Karl Marx.
          • what do we classify as being a �monarchy�? There is a strong historiographical tradition that says that Oliver Cromwell was a monarch - he walked, talked and acted like one after all. Under this view, there is no hiatus at all in this English institution; rather a usurper took the throne.
          There are two major historiographic schools of thought with respect to using classification systems on the past. One maintains that we should only use classifications available to actors at the time, much as an ethnographer tries faithfully to mirror the categories of their respondents. Authors in this tradition warn against the dangers of anachronism. Hacking (1995) on child abuse is a sophisticated version which we discuss in Chapter 7. If a category did not exist contemporaneously, it should not be retroactively applied.

          The other school of thought holds that we should use the real classifications that progress in the arts and sciences has uncovered. Often, history informed by current sociology will take this path. For example, Tort�s (1989) work on �genetic� classification systems (which were not so called at the time, but which are of vital interest to the Foucaldian problematic) imposes a post hoc order on nineteenth century classification schemes in a variety of sciences. Even though those schemes were perceived by their creators as responding solely to the specific needs of the discipline they were dealing with (etymology, say, or mineralogy), he demonstrates that there was a link between many different schemes (both direct in terms of people shifting disciplines and conceptual in terms of their organization) that allows us to perceive an order nowhere apparent to contemporaries.

          From a Pragmatist point of view, both aspects are important in analyzing the consequences of modern systems of classification and standardization. We seek always to understand classification systems according to the work that they are doing, and the networks within which they are embedded. That entails both an understanding of the categories of those designing and using the systems, and a set of analytic questions derived from our own concerns as analysts.
           
           

          When is it a Harley?

          One of the ways the past becomes indeterminate is through gradual shifts in what it means to "really be" something -- the essence of it.

          Sitting in a tattoo parlor, surrounded by people I don�t usually hang out with. Young men in black leather vests and sun-bleached hair. I turn to the waiting room reading material, which in this case is the monthly Thunder Press, a newsletter for motorbike aficionados. The lead article asks the question: "Is It Still a Harley" if you have customized your bike yourself. The Oregon Department of Motor Vehicles makes the definitive call:

          "Anything that is not totally factory-built will make it a reconstructed motorcycle, and it will be called �Assembled� on the title." (p. 69)

          A major activity in the Harley social world is customizing features of one�s motorcycle, and there are important symbolic and affiliative signs attached to the customizing process. Deleting the name Harley from the registration form is perceived as an insult to the owner, and this insult is stitched together in the article with others that come from the government toward bikers (restricting meeting places, insisting on helmet-wearing, being overly enthusiastic in enforcing traffic violations by bikers).

          This is a pure example of the politics of essence, of identity politics. It is echoed in many areas of life, for example, in James Davis� (1991) classic study, Who Is Black? where the question of the one-drop rule in the United States, and the rejection of mixed-race people as a legitimate category is an old and a cruel story. The central process here is the distillation of the sine qua non out from the messy and crenellated surrounds -- the rejection of marginality in favor of purity.

          When this occurs, the suffering of the marginal becomes privatized and distributed, creating the conditions for pluralistic ignorance ("I�m the only one"). Meeting the purity criteria of the essentialized category also becomes bureaucratized, and again the onus is shifted to the individual alone. Only when the category is joined with a social movement can the black box of essence be re-opened, as for example with the recent uprisings and demonstrations of mixed race Hispanic people toward the US Census and its rigid categories. The problem becomes clear if one is both Black and Hispanic, a common combination in the Caribbean. Which shall be the master trait through which the government perceives you?
           
           

          --Leigh Star





          References: Anonymous, "Is it Still a Harley," Thunder Press 5:4 (July, 1996: 1 and 69).
           
           

          When we ask historical questions about the deeply and heterogeneously structured space of classification systems and standards, we are dealing with a 4-dimensional archaeology. The systems move in space, time and process. Some of the archaeological structures we uncover are stable, some in motion; some evolving, some decaying. They are not consistent. An institutional memory, about, for example, an epidemic, can be held simultaneously and with internal contradictions (sometimes piecemeal or distributed and sometimes with entirely different stories at different locations) across a given institutional space.

          In the case of AIDS, classifications have shifted significantly over the last 20 years, including the invention of the category in the 1980s (from Gay-Related Immune Disorder (GRID) through a chain of other monikers to the now-accepted Acquired Immune Deficiency Syndrome). It is now to some extent possible to look backwards at cases which might previously have been AIDS (Grmek, 1990) before we had the category (a problematic gaze to be sure, as Bruno Latour (forthcoming) has written about tuberculosis). There are epidemiological stories about trying to collect information about a shameful disease; there is a wealth of personal and public narratives about living with it. There is a public health story and a virology story, which use different category systems. There are the standardized forms of insurance companies and the categories and standards of the census bureau. When an attempt was made to combine these data in the 1980s to disenfranchise young men living in San Francisco from health insurance, the resultant political challenge stopped the combination of this data from being so used. At the same time, the San Francisco blood banks refused for years to employ HIV screening, thus denying the admission of another category to their blood labeling -- as Shilts (1987) tells us, with many casualties as a result. Whose story has categorical ascendancy here? That question is forever morally moot � all of the stories are important, and all of the categories tell a different one.

          Practical Politics

          The fourth major theme is uncovering the practical politics of classifying and standardizing. This is the design end of the spectrum of investigating categories and standards as technologies. There are two processes associated with these politics: arriving at categories and standards, and, along the way, deciding what will be visible or invisible within the system.

          It follows from the indeterminacy discussed above that the spread or enforcement of categories and standards involves negotiation or force. Whatever appears as universal or, indeed, standard, is the result of negotiations, organizational processes, and conflict. How do these negotiations take place? Who determines the final outcome in preparing a formal classification? Visibility issues arise as one decides where to make the cuts in the system, for example, down to what level of detail one specifies a description of work, of an illness, of a setting. Because there are always advantages and disadvantages to being visible, this becomes crucial in the workability of the schema. As well, ordinary biases of what should be visible, or legitimated, within a particular scheme, are always in action. The tradeoffs involved in this sort of politics are discussed in Chapters 5 (on tuberculosis) and 7 (on nursing work).

          Someone, somewhere, often a body of people in the proverbial gray suits and smoke-filled rooms, must decide and argue over the minutiae of classifying and standardizing. The negotiations themselves form the basis for a fascinating practical ontology -- our favorite example is when is someone really alive? Is it breathing, attempts at breathing, movement....? And how long must each of those last? Whose voice will determine the outcome is sometimes an exercise of pure power: we, the holders of Western medicine and scions of colonial regimes, will decide what a disease is, and simply obviate systems such as acupuncture or Aryuvedic medicine. Sometimes the negotiations are more subtle, involving questions such as the disparate viewpoints of an immunologist and a surgeon, or a public health official (interested in even ONE case of the plague) and a statistician (for whom one case is not relevant).
           
           
           
           

          There�s no such thing as a rodent

          An article in the San Jose Mercury News by Rick Weiss declares: "Researchers say there�s no such thing as a rodent." He quotes an article from Nature, which argues thatthe 2,000 species of animals ordinarily considered rodents, including rats, mice, and guinea pigs -- did not evolve from a common ancestor. The finding is deeply controversial. Weiss says, "On one side are researchers who have spent their careers hunched over fossils or skeletal remains to determine which animals evolved from which." On the other, the article continues, are those who would use DNA analysis to make the determination. The fossil studiers say that DNA is not yet accurate enough. The classification of species has always been deeply controversial. Biologists speak of a rough cut among their ranks: lumpers (those who see fewer categories and more commonalties) vs. splitters (those who would name a new species with fewer kinds of difference cited). There are always practical consequences for these names. Splitters, for example, often included people who wanted a new species named after them, and the more species there are, the more likely is an eponymous label. The deliberately provocative headline of this article demands a response: "well, don�t tell that to my cat." We often refer implicitly in this fashion to the power of naming -- blurring the name of the category with its members. (San Jose Mercury News. ( June 13, 1996: 5A - by Rick Weiss)
           
           

          Once a system is in place, the practical politics of these decisions are often forgotten, literally buried in archives (when records are kept at all) or built into software or the sizes and compositions of things. In addition to our archaeological expeditions into the records of such negotiations, this book provides some observations of the negotiations in action.

          Finally, even where everyone agrees on how classifications or standards should be established, there are often practical difficulties about how to craft them. For example, a classification system with 20,000 bins on every form is practically unusable for data entry purposes. The constraints of technological record keeping come into play at every turn. For example, the original International Classification of Diseases had some 200 diseases not because of the nature of the human body and its problems but because this was the maximum number that would fit the large census sheets then in use.

          Sometimes the decision simply about how fine-grained to make the system has political consequences as well. For instance, describing and recording someone�s tasks, as in the case of nursing work, may mean controlling or surveilling their work as well, and may imply an attempt to take away discretion. After all, the loosest classification of work is accorded to those with the most power and discretion, who are able to set their own terms. There are financial stakes as well. In a study of a health insurance company�s system of classifying for doctor and patient reimbursement, Gerson and Star (1986), found that doctors wanted the most fine-grained of category systems, so that each procedure could be reimbursed separately, and thus most profitably. Data entry personnel and hospital administrators, among others, wanted broader, simpler and coarser-grained categories for reasons of efficiency. These conflicts were, however, invisible to the outside world, which received only the forms for reimbursement purposes and a copy of the codebook for reference. Both the content of the categories and the structure of the overall scheme are concerns for due process within organizations � whose voice will be heard, and when will enough data, of the right granularity, have been collected?

          Infrastructure and Method: Convergence

          These ubiquitous, textured classifications and standards help frame our representation of the past and the sequencing of events in the present. They can best be understood as doing the ever-local, ever-partial work of making it appear that science describes nature (and nature alone) and that politics is about social power (and social power alone). Consider the case of psychoanalysts discussed at length in Young (1995); Kirk and Kutchins (1992) and Kutchins and Kirk (1997). In order to receive reimbursement for their procedures, they now need to couch them in a biomedical language (the DSM). Theoretically, this rubric is anathema to them, systematically replacing the categories of psychoanalysis with the language of the pharmacopoeia and of the biochemistry of the brain. However, the DSM is the lingua franca of the medical insurance companies. Thus, psychoanalysts use the categories not only to obtain reimbursement, but as a shorthand to communicate with each other. There are local translation mechanisms that allow the DSM to continue to operate in this fashion, and at the same time, to become the sole legal, recognized representation of mental disorder. A �reverse engineering� of the DSM or the ICD reveals the multitude of local political and social struggles and compromises which go into the constitution of a �universal� classification.
           
           
           
           

          Fitting Categories to Circumstances

          An academic friend on the East Coast tells an anecdote of negotiation with her long-term psychoanalyst about how to fill out her insurance forms. She was able to receive several free sessions of therapy a year under her health insurance plan. Each year, she and her therapist would discuss how best to categorize her. It was important to represent the illness as serious and long-term. At the same time, they were worried that the information about the diagnosis might not always remain confidential. What could they label her that would be both serious and non-stigmatizing? Finally, they settled on the diagnosis of obsessive-compulsive. No academic would ever be penalized for being obsessive-compulsive, our friend concluded with a wry laugh! (Kirk and Kutchins (1992) document similar negotiations between psychiatrists and patients.)

          Standards, categories, technologies and phenomenology are increasingly converging in large-scale information infrastructure. As we have indicated in this chapter, this convergence poses both political and ethical questions. These questions are by no means obvious in ordinary moral discourse. For all the reasons given above, large-scale classification systems are often invisible, erased by their naturalization into the routines of life. Conflict and multiplicity are often buried beneath layers of obscure representation.

          Methodologically, we do not stand outside these systems, nor pronounce on their mapping to some otherworldly �real� or �constructed� nature. Rather, we are concerned with what they do, pragmatically speaking, as scaffolding in the conduct of modern life. Part of that analysis means understanding the co-construction of classification systems with the means for data collection and validation.

          In order to clarify our position here, let us take an analogy. In the early nineteenth century in England there were a huge number of capital crimes - starting from stealing a loaf of bread and going up. However, precisely because the penalties were so draconian, few juries would ever impose the maximum sentence; and indeed there was actually a drastic reduction in the number of executions even as the penal code was progressively strengthened. There are two ways of writing this history - one can either concentrate on the creation of the law; or one can concentrate on the way things worked out in practice. This is very similar to the position taken in Latour�s We Have Never Been Modern (1993). He argues that we can either look at what scientists say that they are doing (working within a purified realm of knowledge) or at what they actually are doing (manufacturing hybrids of nature-culture). We think both are important. We are advocating here a pragmatic methodological development -- pay more attention to the classification and standardization work that allows for hybrids to be manufactured and so more deeply explore the terrain of the politics of science in action.

          The point for us is that both words and deeds are valid kinds of account. Early sociology of science in the actor-network tradition concentrated on the ways in which it comes to seem that science gives an objective account of natural order: trials of strength, enrolling of allies, cascades of inscriptions and the operation of immutable mobiles (Latour, 1987; 1988). It drew attention to the importance of the development of standards (though not to the linked development of classification systems); but did not look at these in detail. Sociologists of science invited us to look at the process of producing something which looked like what the positivists alleged science to be. We got to see the Janus face of science, as both constructed and realist. In so doing we followed the actors, often ethnographically. We shared their insights (allies must be enrolled, translation mechanisms must be set in train so that, in the canonical case, Pasteur�s laboratory work can be seen as a direct translation of the quest for French honor after defeat in the battlefield - Latour, 1988).

          However, by the very nature of the method, we also shared the actors� blindness. The actors being followed did not themselves see what was excluded: they constructed a world in which that exclusion could occur. Thus if we just follow the doctors who create the International Classification of Diseases at the World Health Organization in Geneva, we will not see the variety of representation systems that other cultures have for classifying diseases of the body and spirit; and we will not see the fragile networks these classification systems subtend. Rather, we will see only those who are strong enough, and shaped in such a fashion as to impact allopathic medicine. We will see the blind leading the blind.

          This blindness occurs by changing the world such that the system�s description of reality becomes true. Thus, for example, consider the case where all diseases are classified purely physiologically. Systems of medical observation and treatment are set up such that physical manifestations are the only manifestations recorded. Physical treatments are the only treatments available. Under these conditions, then, logically schizophrenia may only result purely and simply from a chemical imbalance in the brain. It will be impossible to think or act otherwise. We have called this the principle of convergence (Star and Bowker, 1994; Star, Bowker and Neumann, in press).

          Resistance

          Reality is �that which resists,� according to Latour�s (1987) pragmatist-inspired definition. The resistances that designers and users encounter will change the ubiquitous networks of classifications and standards. While convergence may seem at times to create an inescapable cycle of feedback and verification, the very multiplicity of people, things and processes involved mean that they are never locked in for all time.

          The methods in this chapter offer an approach to resistance as a reading of where and how political work is done in the world of classifications and standards; and how such artifacts can be problematized and challenged. Donald MacKenzie�s (1990) wonderful study of �missile accuracy� furnishes the best example of this approach. In a concluding chapter to his book, he discusses the possibility of �uninventing the bomb�, by which he means changing society and technology in such a way that the atomic bomb becomes an impossibility. Such change, he suggests, can be carried out in part at the overt level of political organizations. However, and crucially for our purposes, he also sensitizes the reader to the site of the development and maintenance of technical standards as a site of political decisions and struggle. Standards and classifications, however dry and formal on the surfaces are suffused with traces of political and social work. Whether we wish to uninvent any particular aspect of complex information infrastructure is properly a political and a public issue. Because it has rarely been cast in that light, tyrannies of various sorts flourish. Some are the tyrannies of inertia � red tape � rather than explicit public policies. Others are the quiet victories of infrastructure builders inscribing their politics into the systems. Still other are almost accidental � systems that become so complex that no one person and no organization can really predict or administer good policy.

          The magic of modern technoscience is a lot of hard work, smoke-filled rooms, and boring lists of numbers and settings. Tyranny or democracy, its import on our lives cannot be denied. This chapter has offered a number of points of departure for evaluation, resistance, and better analysis of one of its least-understood aspects.
           
           
























          PART ONE
           
           
           
           
           
           
           
           

          CLASSIFICATION AND LARGE SCALE INFRASTRUCTURES
































          In the following three chapters, which analyze the International Classification of Diseases (ICD) we shall look at the operation of classification systems in supporting large-scale infrastructural arrangements. In Chapter 2 we concentrate on the text of the ICD itself, producing a reading of this classificationwhich has over the past century ingrained itself in a multiplicity of forms, work arrangements and laws worldwide. We examine how its internal structure affords the prosecution of multiple agendas. In Chapter 3, we will discuss the history of the ICD, showing how it has changed over time in step with changing information technology and changing organizational needs. In Chapter 4, we draw general design implications from the study of this highly effective, long-term and wide-scale classification scheme.
           
           






           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           

          PART TWO
           
           
           
           
           
           
           
           

          CLASSIFICATION AND BIOGRAPHY; OR SYSTEM AND SUFFERING






























          In the last three chapters we have looked at classification and wide-scale coordination between multiple organizations. In the next two chapters, we shall examine the relationship between classification and biography. How do classification systems that intimately interpenetrate our lives � shaping and being shaped by them � affect our experience? We shall look in Chapter 5 at the intimate classification systems developed by sufferers of tuberculosis and their doctors. So doing, we shall develop the themes of trajectory (the movement through time of lives, diseases and institutions) and torque (the twisting of that biography in the framework of a classification system). In Chapter 6 we shall develop these themes further through an analysis of race classification schemes in South Africa under apartheid. Through this extreme example we shall explore how difficult it is to operate a simple dichotomous classification scheme: and how the lives of those caught in its interstices are torqued.
           
           















           
           
           
           
           
           
           
           
           
           
           

          PART THREE
           
           
           
           
           
           
           
           

          CLASSIFICATION AND WORK PRACTICE


































          In these next two empirical chapters we shall look at classification and work practice. Taking the example of the design of a system for classifying nursing work (NIC), we shall examine how classification systems which represent work embody multiple tensions � notably in this case between control and autonomy (Chapter 7) and the representation of current work practice and learning from previous generations of practice (Chapter 8). Such tensions are integral to the operation of work classifications. Due attention should be paid to their occurrence in order to evaluate the political and ethical implications of the introduction of new classificatory infrastructures.
           
           







           
           
           
           
           
           
           
           
           
           
           
           
           
           
           

          PART FOUR
           
           
           
           
           
           
           
           

          THE THEORY AND PRACTICE OF CLASSIFICATIONS


































          In this final part of the book, we attempt to weave the threads from each of the chapters into a broader theoretical fabric. Throughout the book we have demonstrated that categories are tied to the things that people do; to the worlds to which they belong. In large-scale systems, those worlds often come into conflict. The conflicts are resulted a variety of ways. Sometimes boundary objects are created which allow for cooperation across the borders. At other times, such as in the case of apartheid, voices are stifled, and violence obtains.

          Chapter 9 discusses an abstract model of the several processes involved in both the development of boundary objects, or any other alternatives. The key concept in this chapter is multiplicity; the multiplicity both of people's memberships, and of the ways in which objects are naturalized simultaneously in more than one world. People become members of many communities of practice. They do so it different rates, and with different degrees of completeness. Some communities are all encompassing, others occupy very little of one's life space. Some things in shared quite locally; others become standardized across thousands of social worlds. While it is impossible, and will always be impossible, fully to map the myriad of relationships even a simple situation contains, it is possible to get at least a gestalt sense of the issues involved.

          The chapter discusses the multiple trajectories of membership and naturalization. It discusses the consequences of some memberships being silenced, ignored, or devalued. It examines the notion of "cyborg," as a term for discussing the relationships between memberships and the naturalization of objects. The categorical exile of people and objects creates a monstrous landscape, such as those seen in chapters 5 and 6.

          The chapter concludes with recognition of the language that people often use in describing the complexities of people, things and their relationships. We often turn to metaphors of texture to describe our sense we moral, emotional and organizational feeling of these relationships. A situation can be knotty and tangled; it can be handled smoothly and without ruffles. Following Lakoff and Johnson (1980), we take these "metaphors we live by" as more than poetic but imprecise means of expression. The metaphors are more systematic and more telling than that. Indeed, they are a key to understanding and perhaps to modeling some of the more complex phenomena facing us in what Ina Wagner (personal communication) has called "the classification society."

          The book concludes with a discussion of the ways in which research on classification and standards can inform a larger program of research into the building and maintaining of infrastructure -- and its simultaneous material/cultural nature.
           
           

          Chapter Nine: Categorical Work and Boundary Infrastructures: Enriching Theories of Classification

          Where do categories come from? How do they span the boundaries of the communities that use them? How can we see and analyze something so ubiquitous and infrastructural -- something so "in between" a thing and an action? These questions have been at the heart of much of social science over the past one hundred years. Sociology and history are both concerned with relationships -- which are invisible except through indicators such as the actions people perform. One cannot directly see relations such as membership, learning, ignoring or categorizing. They are names we give to patterns and indicators. If someone is comfortable with the things and language used by a group of others, we say that he or she is a member of that group. In this sense, categories -- our own and those of others -- come from action, and in turn from relationships. They are, as sociologists like Aaron Cicourel remind us, continually re-made and refreshed, with a lot of skilled work (1964). The cases in this book are framed in dialogue with an extensive literature on language, group membership and classification.

          In this chapter, we make several aspects of that dialogue more explicit. However, our goal here is much more modest than a thoroughgoing analysis of categorization and language. We examine classification systems as historical and political artifacts, very much as part of modern Western bureaucracy. Assigning things, people or their actions to categories is a ubiquitous part of work in the modern, bureaucratic state. Categories in this sense arise from work and from other kinds of organized activity, including the conflicts over meaning that occur when multiple groups fight over the nature of a classification system and its categories.

          In this chapter, we pick up the theoretical strands of the cases in this volume, to begin to develop a more general notion of classification systems. So doing, we take a step bacnk and look at how the various kinds of classifcation we have discussed knit together to form the texture of a social space. We move from classifying and boundary objects to categorical work and boundary infrastructures, weaving along the way the many strands that our cases have presented. As we noted in Chapter 1, maintaining a vision that allows us to see the relationships between people, things, moral order, categories and standards is difficult. It requires a good map and a working compass � which we shall try to provide below.

          The journey begins by clearing away some of the theoretical brush surrounding the very notions of categories and classification. Many scholars have seen categories as coming from an abstract sense of "mind," little anchored in the exigencies of work or politics. The work of attaching things to categories, and the ways in which those categories are ordered into systems, is often overlooked (except by theorists of language such as Harvey Sacks, 1975; 1992).

          We present classification systems in modern organizations as tools that are both material and symbolic. As information technologies used to communicate across the boundaries of disparate communities, they have some unique properties. Next, we present some basic propositions about large-scale information systems. We examine how they are used to communicate across contexts. These systems are always heterogeneous. Their ecology encompasses the formal and the informal, and the arrangements that are made to meet the needs of heterogeneous communities � some cooperative, and some coercive.

          The third part of the journey involves understanding two sets of relationships: first, and analytically, between people and membership; and between things and their naturalization by communities of practice.

          The fourth step moves away from the analytical device of single person-single membership and single object � single naturalization, to describing a more complex set of multiple relationships. Everyone is part of multiple communities of practice. Things may be naturalized in more than one social world -- sometimes differently, sometimes in the same fashion. Both people�s memberships and the naturalization of objects are multiple � and these processes are, furthermore, intimately intertwined.

          The fifth part of the chapter introduces the idea of categorical work � the work that people do to juggle both these multiple memberships and the multiple naturalizations of objects. In this work is the genius of what Sacks called "doing being ordinary" (1975) or what Strauss pointed to in his "continual permutations of action" (1993). In the simplest-seeming action, such as picking an article of clothing to wear, is embedded complex knowledge of situations (Where will I go today? What should I look like for the variety of activities in which I will participate?) involving multiple memberships and how objects are used differently across communities (will this shirt "do" for a meeting with the dean, lunch with a prospective lover, and an appointment with the doctor at the end of the day?). Many of these choices become standardized and built into the environment around us -- for example, the range of clothing we select is institutionalized by the retail stores to which we have access, traditions of costuming, and so forth. To think of this formally, the institutionalization of categorical work across multiple communities of practice, over time, produces the structures of our lives, from clothing to houses. The parts that are sunk into the built environment we call here boundary infrastructures -- objects that cross larger levels of scale than boundary objects.

          Finally, we conclude the chapter with a discussion of future directions for research into classifications, standards and their complex relationships with memberships in communities of practice. This includes ways we might visualize and model these intricate relationships.

          The overall goal of the chapter is to trace theoretically what we have shown empirically and methodologically throughout the volume: that categories are historically situated artifacts, and like all artifacts, are learned as part of membership in communities of practice. We want as well to talk about this insight beyond the individual "mind, " task, or the small scale -- classifications as technologies are powerful artifacts that may link thousands of communities, and span highly complex boundaries.

          1. What sort of thing is a category?

          In so far as the coding scheme establishes an orientation toward the world, it constitutes a structure of intentionality whose proper locus is not the isolated, Cartesian mind, but a much larger organizational system, one that is characteristically mediated through mundane bureaucratic documents such as forms. (Goodwin, 1996: 65)

          Classification is a core topic within anthropology, especially cognitive anthropology, and within computer science, as the quote from Goodwin attests. Recently, as we discuss in Chapter 2, there has been a move to understand the practical, work-related aspects of classification, as part of a larger project of revisioning cognition (e.g. Suchman, 1988; Hutchins, 1996; Keller and Keller, 1996; Lave, 1986).

          Revisioning Cognition

          Within anthropology, psychology and the sociology of science, the last two decades have seen a resurgence of the struggle to understand the material, social and ecological aspects of cognition. The work in this book has been deeply informed by that intellectual movement. In brief, the research in this tradition seeks to ground activities previously seen as individual, mental and non-social as situated, collective and historically specific. On this view, for example, solving a mathematical problem is not a matter of mentally using an algorithm and coming up with the correct answer, in a fashion that exists outside of time or culture. Rather, it is a process of assembling materials close to hand, and using them with others in specific contexts. Jean Lave, for example, studied mathematical problem-solving in everyday life, and contrasted it with formal testing situations (1986; 1988). She followed adults shopping for the best buy in a supermarket, people in Weight Watchers weighing cottage cheese in order to get the correct unit for the diet's specifications, and a variety of other mundane activities. She observed people performing highly abstract, creative mathematical problem-solving in these circumstances: creating new units of analysis transposed against given ones in order to measure units, literally cutting up the cottage cheese and moving these material units around, or holding one can against another. These tasks were performed successfully by people who tested badly in a traditional math test. There was, she argued, no way to separate the material circumstances of the problem-solving from the mathematical challenges. Those who seem to solve mathematics problems without such outside help are not working in a putative realm of pure number. Rather, they and their observers have so naturalized the structures within which they are operating that they have become invisible. Lucy Suchman makes a similar argument for the process of planning as material resource; Ed Hutchins for navigation problems (1993); Janet and Charles Keller for designing and measuring in doing iron blacksmithing work (1996). In this book, we join their effort at reforming the notion of categorizing and classifying -- so often seen as purely mental.

          The problem of conceptualizing classifications is also akin to Cole�s (1996) search for the nature of artifacts in mediated action. Cole notes that:

          An artifact is an aspect of the material world that has been modified over the history of its incorporation into goal-directed human action. By virtue of the changes wrought in the process of their creation and use, artifacts are simultaneously ideal (conceptual) and material. They are ideal in that their material form has been shaped by their participation in the interactions of which they were previously a part and which they mediate in the present (Cole, 1996: 117). The materiality of categories, like that of other things associated with the purely cognitive, has been difficult to analyze. The Janus-faced conceptual/material notion of artifacts suggested by Cole, combined with the attention to the use in practice of categories is a good way to begin. Classifications are both conceptual (in the sense of persistent patterns of change and action, resources for organizing abstractions) and material (in the sense of being inscribed, transported, and affixed to stuff).

          Cole�s intent is to emphasize the conceptual and symbolic sides of things often taken as only materials, tools and other artifacts. It is similarly felicitous to emphasize the brute material force of that which has been considered ideal, such as categories.

          1.1 The Pragmatist Turn

          The most radical turn taken by Pragmatist philosophers such as Dewey and Bentley, and closely followed by Chicago School sociologists such as Thomas and Hughes, is perhaps the least understood. It is related, both historically and conceptually, to the cognitive reforms detailed above. Consequences, asserted Dewey against a rising tide of analytic philosophy, are the thing to look at in any argument -- not ideal logical antecedents. What matters about an argument is who, under what conditions, takes it to be true. Carried over into sociology, W. I. And Dorothy Thomas used it (as Howard Becker would some decades later) to argue against essentialism in examining so-called deviants or problem children (Thomas and Thomas, 1917; Becker, 1964). If social scientist do not understand people�s definition of a situation, they do not understand it at all. That definition -- whether it is the label of deviant or the performance of a religious ritual -- is what people will shape their behavior towards.

          This is a much more profound cut on social construction than the mere notion that people construct their own realities. It makes no comment on where the definition of the situation may come from -- human or nonhuman, structure or process, group or individual. It powerfully draws attention to the fact that the materiality of anything (action, idea, definition, hammer, gun or school grade) is drawn from the consequences of its situation.

          The Pragmatist turn, like the activity theoretical turn taken by Cole and others, emphasizes the ways in which things perceived as real may mediate action (Star, 1996). If someone is taken to be a witch, and elaborate technical apparatus with which to diagnose her or him as such is developed, then the reality of witchcraft obtains in the consequences -- perhaps death at the stake. Classification systems are one form of technology, used in the sense Cole employs, linked together in elaborate informatic systems and enjoining deep consequences for those touched by them.

          The following section discusses the problems of scaling up, from boundary objects and classifications systems on the one hand, to a notion of boundary infrastructure. This draws together the notions of multiplicity and the symbolic/material aspects of categories as artifacts discussed above.

          2. Information Systems across Contexts

          At its most abstract, the design and use of information systems involves linking experience gained in one time and place with that gained in another, via representations of some sort. Even seemingly simple replication and transmission of information from one place to another involves encoding and decoding as time and place shift. Thus the context of information shifts in spite of its continuities; and this shift in context imparts heterogeneity to the information itself. Classifications are a very common sort of representation used for this purpose. Formal classification systems are, in part, an attempt to regularize the movement of information from one context to another; to provide a means of access to information across time and space. The ICD, for example, moves information across the globe, over decades, and across multiple conflicting medical belief and practice systems.

          One of the interesting features of communication is that, broadly speaking, in order to be perceived, information must reside in more than one context. We know what something is by contrast with what it is not. Silence makes musical notes perceivable; conversation is understood as a contrast of contexts, speaker and hearer, words, breaks and breaths. In turn, in order to be meaningful, these contexts of information must be re-linked through some sort of judgment of equivalence or comparability. This occurs at all levels of scale, and we all do it routinely as part of everyday life.

          None of this is new in theories of information and communication: we have long had models of signals and targets, background, noise and filters, signals and quality controls. We are moving this insight to the level of social interaction. That is, people often cannot see what they take for granted until they encounter someone who does not take it for granted.

          A radical statement of this would be that information is only information when there are multiple interpretations. One person�s noise may be another�s signal, or two people may agree to attend to something -- but it is the tension between contexts that actually creates representation. What becomes problematic under these circumstances is the relationships between people and things, or objects, the relationships that create representations, not just noise. The ecological approach we have taken in this volume adds people as active interpreters of information, who themselves inhabit multiple contexts of use and practice (Star 1991). This multiplicity is primary, not accidental nor incidental.

          Consider, for example, the design of a computer system to support collaborative writing. Eevi Beck (1995: 53) studied the evolution of one such system: "how two authors, who were in different places, wrote an academic publication together making use of computers. . .The work they were doing and the way in which they did it was inseparable from their immediate environment and the culture which it was part of". To make the whole system work, they had to juggle time zones, spouses� schedules, and sensitivities about parts of work practice such as finishing each other�s sentences as well as manipulating the technical aspects of the writing software and hardware. They had to build a shared context in which to make sense of the information. Beck is arguing against a long tradition of de-contextualized design where only the technical, or narrowly construed considerations about work hold sway.

          We lack good relational language here. There is a permanent tension between the formal and the empirical, the local/situated and attempts to represent information across localities. It is this tension itself which is under-explored and under-theorized. It is not just a set of interesting metaphysical observations. It can also become a pragmatic unit of analysis. How can something be simultaneously concrete and abstract? The same and yet different? People are not (yet, we hope) used to thinking in this fashion in science or technology. As information systems grow in scale and scope, however, the need for such complex analyses grows as well. In opposition to the old hierarchical databases, where relations between classes had to be decided once for all at the time of original creation, many databases today incorporate object-oriented views of data whereby different attributes can be selected and combined on the fly for different purposes. The tailorability of software applications similarly becomes very important for customizing use in different settings (Trigg and Bødker, 1994).

          If we look at these activities in the context of practice, we see what Suchman and Trigg (1993) call the �artful integration� of local constraints, received standardized applications, and the re-representation of information. The tension between locales remains. This tension it is not something to be avoided or deleted. When the sort of "artful integration" discussed by Suchman and Trigg becomes a) an ongoing, stable relationship between different social worlds, and b) shared objects are built across community boundaries, then boundary objects arise.

          Boundary objects are one way that the tension between divergent viewpoints may be managed. There are of course many other ways. All of them involve accommodations, workarounds, and in some sense, a higher level of artful integration. It too is managed by people�s artful juggling, gestalt switching, and on the spot translating.

          Too often, this sort of work remains invisible to traditional science and technology, or to rational analyses of process. This tension is itself collective, historical, and partially institutionalized. The medium of an information system is not just wires and plugs, bits and bytes, but also conventions of representation, information both formal and empirical. A system becomes a system in design and use, not the one without the other. The medium is the message, certainly, and it is also the case that both are political creations (Taylor and Van Every, 1993). In Donna Haraway�s words:

          No layer of the onion of practice that is technoscience is outside the reach of technology of critical interpretation and critical inquiry about positioning and location; that is the condition of embodiment and mortality. The technical and the political are like the abstract and the concrete, the foreground and the background, the text and the context, the subject and the object. (Haraway, 1993: 10) A fully developed method of multiplicity/heterogeneity for information systems must draw on many sources and make many unexpected alliances (Star 1989a: Chapter 1; Star 1989b; Hewitt 1986; Goguen 1997). If both people and information objects inhabit multiple contexts, and a central goal of information systems is to transmit information across contexts, then a representation is a kind of pathway that includes everything populating those contexts. This includes people, things/objects, previous representations, and information about its own structure. The major requirements for such an ecological understanding of the path of re-representation are thus:

          1. How objects can inhabit multiple contexts at once, and have both local and shared meaning;

          2. How may people who live in one community, and draw their meanings from people and objects situated there, communicate with those inhabiting another?

          3. How relationships form between 1) and 2) above � that is, how can we model the information ecology of people and things, across multiple communities?

          4. What range of solutions to these three questions is possible, and what moral and political consequences attend each of them -- cui bono?

          Standardization has been one of the common solutions to this class of problems: if interfaces and formats are standard across contexts, then at least the first three questions become clear, and the fourth seems to become moot. But we know from a long and gory history of attempts to standardize information systems that standards don�t remain standard for very long, and that one person�s standard is another�s confusion and mess (Gasser, 1986; Star, 1991b). We need a richer vocabulary than that of standardization or formalization with which to characterize the heterogeneity and the processual nature of information ecologies.

          3. Boundary Objects and Communities of Practice

          The class of questions posed by the slippage between classifications and standards on the one hand, and the contingencies of practice on the other, form core problematics in both sociology of science and in studies of use and design in information science. A rich body of work has grown up in both fields which documents the clever ways people organize and re-organize when the local circumstances of their activities do not match the prescribed categories or standards (see e.g. Gasser, 1986; Kling and Scacchi, 1982; Lave, 1988; Sacks, 1975; Star, 1983). Making or using any kind of representation is a complex accomplishment, a balance of improvisation and accommodation to constraint.

          People learn how to do this everyday, impossible action as they become members of what Lave and Wenger (1991) call communities of practice, or what Strauss (1978) calls social worlds. A community of practice (or social world) is a unit of analysis that cuts across formal organizations, institutions like family and church, and other forms of association such as social movements. It is, put simply, a set of relations between people doing things together (Becker, 1986). The activities, with their stuff, their routines and exceptions, are what constitute the community structure. Newcomers to the community learn by becoming "sort of" members, what Lave and Wenger (1991) call the process of �legitimate peripheral participation.� They have investigated how this membership process unfolds, and how it is constitutive of learning.

          We are all in this sense members of various social worlds � communities of practice -- that conduct activities together. Membership in such groups is a complex process, varying in speed and ease, with how optional it is, and how permanent it may be. One is not born a violinist, as we all know, but gradually becomes a member of the violin-playing community of practice through a long period of lessons, shared conversations, technical exercises, and participation in a range of other related activities.

          People live, with respect to a community of practice, along a trajectory (or continuum) of membership, which has elements of both ambiguity and duration. They may move from legitimate peripheral participation to full membership in the community of practice, and it is extremely useful in many ways to conceive of learning this way.

          How does this include categories?

          Learning the ropes and rules of practice in any given community entails a series of encounters with the objects involved in the practice: tools, furniture, texts, and symbols, among others. It also means managing encounters with other people, and with classes of action. Membership in a community of practice has as its sine qua non an increasing familiarity with the categories that apply to all of these. As the familiarity deepens, so does one�s perception of the object as strange or of the category itself as something new and different. Anthropologists call this the naturalization of categories or objects. The more at home you are in a community of practice, the more you forget the strange and contingent nature of its categories seen from the outside.

          Illegitimacy, then, is seeing those objects as would a stranger -- either as a naïf or by comparison with another frame of reference in which they exist. And this is not to be equated with an idealized notion of skill, but with membership. One does not have to be Isaac Stern to know fully and naturally what to do with a violin, where it belongs, and how to act around violins and violinists. But if you use a Stradivarius to swat a fly (but not as part of an artistic Futurist event!) you have clearly defined yourself as an outsider, in a way that a schoolchild practicing scales has not.

          Membership can thus be described individually as the experience of encountering objects, and increasingly being in a naturalized relationship with them. (Think of the experience of being at home, and how one settles down and relaxes when surrounded by utterly familiar objects; think of how demented one feels in the process of moving house.)

          From the point of view of learning-as-membership and participation, then, the illegitimate stranger is a source of learning. Someone�s illegitimacy appears as a series of interruptions to experience (Dewey 1916; Dewey 1929), or a lack of a naturalization trajectory. In a way, then, individual membership processes are about the resolution of interruptions (anomalies) posed by the tension between the ambiguous (outsider, naive, strange) and the naturalized (at home, taken-for-granted) categories for objects. Collectively, membership can be described as the processes of managing the tension between naturalized categories on the one hand, and the degree of openness to immigration on the other. Harvey Sacks, in his extensive investigations into language and social life, notes that categories of membership form the basis of many of our judgments about ordinary action. "You can easily enough come to see that for any population of persons present there are available alternative sets of categories that can be used on them. That then poses for us an utterly central task in our descriptions; to have some way of providing which set of categories operate in some scene -- in the reporting of that scene or in its treatment as it is occurring" (1991a: 116). Sacks draws attention to the ways in which being ordinary are not pre-given but are in fact a kind of job -- a job which asserts the nature of membership:

          Whatever we may think about what it is to be an ordinary person in the world, an initial shift is not to think of an "ordinary person" as some person, but as somebody having as their job, as their constant preoccupation, doing "being ordinary." It's not that somebody is ordinary, it's perhaps that that's what their business is. And it takes work, as any other business does. And if you just extend the analogy of what you obviously think of as work -- as whatever it is that takes analytic, intellectual, emotional energy -- then you can come to see that all sorts of normalized things -- personal characteristics and the like -- are jobs which are done, which took some kind of effort, training, etc.. So I'm not going to be talking about to an "ordinary person" as this or that person, or as some average, i.e., a non--exceptional person on some statistical basis, but as something that is the way somebody constitutes themselves, and, in effect, a job that they do on themselves. Fate and the people around and may be coordinatively engaged in assuring that each of them are ordinary persons, and that can then be a job that they undertake together, to achieve that each of them, together, are ordinary persons (1992b: 216) The performance of this job includes the ability to choose the proper categories under which to operate, to perform this ordinariness. The power of Sack's work, like that of John Dewey (e.g., 1929), is that he draws attention to the ways in which the ordinary -- and the interruption to the expected experience -- are delicate constructions, made and re-made every day.
           
           

          3.1 Boundary Objects

          Science and technology are good places to study the rich mix of people and things brought to bear on complex problem solving questions, although the points made here are more generally applicable as well. Categories and their boundaries are centrally important in science, and scientists are especially good at documenting and publicly arguing about the boundaries of categories. Thus, in turn, science is a good place to understand more about membership in communities. This point of departure has led us to try to understand people and things ecologically, both with respect to membership and with respect to the things they live with, with a focus on scientists (Star, 1995a). One of the observations is that scientists routinely cooperate across many communities of practice. They thus bring different naturalized categories with them into the partnerships.

          In studying scientific problem solving, we have been concerned for a number of years to understand how scientists could cooperate without agreeing about the classification of objects or actions. Scientific work is always composed of members of different communities of practice (we know of no science that is not interdisciplinary in this way, especially if - as we do - you include laboratory technicians and janitors). Thus, memberships (and divergent viewpoints, or perspectives) present a pressing problem for modeling truth, the putative job of scientists. In developing models for this work, Star coined the term �boundary objects� to talk about how scientists balance different categories and meanings (Star and Griesemer, 1989; Star, 1989b). Again, the term is not exclusive to science, but science is an interesting place to study such objects because the push to make problem-solving explicit gives one an unusually detailed amount of information about the arrangements.

          Boundary objects are those objects that both inhabit several communities of practice and satisfy the informational requirements of each of them. Boundary objects are thus both plastic enough to adapt to local needs and constraints of the several parties employing them, yet robust enough to maintain a common identity across sites. They are weakly structured in common use, and become strongly structured in individual-site use. These objects may be abstract or concrete. Star and Griesemer (1989) first noticed the phenomenon in studying a museum, where the specimens of dead birds had very different meanings to amateur bird-watchers and professional biologists, but "the same" bird was used by each group. Such objects have different meanings in different social worlds but their structure is common enough to more than one world to make them recognizable, a means of translation. The creation and management of boundary objects is a key process in developing and maintaining coherence across intersecting communities.

          Another way of talking about boundary objects is to consider them with respect to the processes of naturalization and categorization we discussed above. Boundary objects arise over time from durable cooperation between communities of practice. They are working arrangements that resolve anomalies of naturalization without imposing a naturalization of categories from one community or from an outside source of standardization. (They are therefore most useful in analyzing cooperative and relatively equal situations; issues of imperialist imposition of standards, force and deception have a somewhat different structure.) In terms of this book, sets of boundary objects arise directly from the problematics created when two or more differently naturalized classification systems collide. Thus nursing administrators create classification systems which serve hospital administrators and nursing scientists; soil scientists create classifications of soil to satisfy geologists and botanists (Chatelin, 1979). Other outcomes of these meetings are explored as well � the dominance of one over another; how claims of authority may be manipulated to higher claims of naturalness.

          The processes by which communities of practice manage divergent and conflicting classification systems are complex, the more so as people are all members in fact of many communities of practice, with varying levels of commitment and consequence. Under those conditions, how are boundary objects established and maintained? Does the concept scale up? What is the role of technical infrastructure? Is a standard ever a boundary object? How do classification systems, as artifacts, play a role?

          4. Membership and Naturalization: People and Things

          As Engeström (1990b) and other activity theorists note so well, tools and material arrangements always mediate activity. People never act in a vacuum or some sort of hypothetical pure universe of doing, but always with respect to arrangements, tools, and material objects. Strauss has recently made a similar point, emphasizing the continuity and permeability of such arrangements -- action never really starts from scratch, from a tabula rasa (1993). Both Engeström and Strauss go to great lengths to demonstrate that an idea, or something that has been learned, can also be considered as having material/objective force in its consequences and mediations.

          "Object" includes all of this: stuff and things, tools, artifacts and techniques, and ideas, stories, and memories -- those objects which are treated as consequential by community members (Clarke and Fujimura 1992a; 1992b). They are used in the service of an action, and mediate it in some way. Something actually becomes an object only in the context of action and use -- it then becomes as well something that has force to mediate subsequent action. It is easier to see this from historical examples than it may be to look to contemporary ones. For instance, the category of hysteria was naturalized in medicine and in popular culture at the end of the 19th-century. As with the example of witchcraft discussed above, people used the diagnosis of hysteria for purposes of social control as well as for medical treatment. It became a category through which physicians, social theorists, and novelists discussed pain and anxiety and, arguably, the changing social status of women. The point is not who believed what when, but rather that the category itself became an object existing in both communities. It was a medium of communication, whatever else it may also have been.

          A community of practice is defined in large part according to the co-use of such objects, since all practice is so mediated. The relationship of the newcomer to the community largely revolves around the nature of the relationship with the objects -- and not, counterintuitively, directly with the people. As noted above with respect to action, this sort of directness only exists hypothetically -- there is always mediation by some sort of object. Acceptance or legitimacy derives from the familiarity of action mediated by member objects.

          But familiarity is a fairly sloppy word. We mean it here not instrumentally, as in proficiency, but relationally, as a measure of taken-for-grantedness. (An inept programmer can still be a member of the community of practice of computer specialists, albeit a low status one in that she takes for granted the objects to be used.) A better way to describe the trajectory of an object in a community is as one of naturalization. Naturalization means stripping away the contingencies of an object�s creation and its situated nature. A naturalized object has lost its anthropological strangeness. It is in that narrow sense de-situated -- members have forgotten the local nature of the object�s meaning, or the actions that go into maintaining and recreating its meaning We no longer think much about the miracle of plugging a light into a socket and obtaining illumination, and must make an effort of anthropological imagination to remind ourselves of contexts in which it is still not naturalized.

          Objects become natural in a particular community of practice over a long period of time (see Latour�s (1987) arguments in Science in Action for a good discussion of this). Objects exist, with respect to a community, along a trajectory of naturalization. This trajectory has elements of both ambiguity and duration. It is not pre-determined whether an object will ever become naturalized, or how long it will remain so -- rather, practice/activity is required to make it so and keep it so. The more naturalized an object becomes, the more unquestioning the relationship of the community to it; the more invisible the contingent and historical circumstances of its birth; the more it sinks into the community�s routinely forgotten memory. Light switches, for instance, are ordinary parts of modern life. Almost all people living in the industrialized world know about light bulbs and electricity, even if they live without it, and switches and plugs are naturalized objects in most communities of practice. People don�t think twice about their nature, only about whether or not they can find them when needed.

          Commodity and infrastructure technologies are often naturalized in this way. In a sense, they become a form of collective forgetting, or naturalization, of the contingent, messy work they replace. We wrote this chapter on Macintosh and IBM computers; cutting and pasting with it are no longer phenomenologically novel operations, although we can remember when they once were. We have naturalized the mouse, the operation of selecting text, and the anachronistic "cut and paste" metaphor.

          Multiplicity

          In this chapter so far, there are, then, analytically two sets of relationships: between people and membership, on the one hand; and objects and naturalization on the other hand. In any given instance, both membership and naturalization are relations along a trajectory. In saying this, we do not want to re-create a great divide between people and objects, reifying an objectless human or wild child. Ironically in a way, social science has spent incredible resources on precisely this sort of search. There is something compelling about the idea of a person without "a society," naked even of touch or language. The sad case of "Genie," a child kept captive by her parents for many years (Rymer, 1993; Star, 1995d), or the "wild child of Aveyron" which amazed 18th century philosophers, are emblematic of this propensity. They have been seen as holding the key to language, or in a way to what it is to be human.

          However, exactly the opposite is true. People-and-things, which are the same as people-and-society, cannot be separated in any meaningful practical sense. At the same time, it is possible for analytical purposes to think of two trajectories travelling in tandem, membership and naturalization. Just as it is not practically possible to separate a disease from a sick patient, yet it is possible to speak of the trajectories of disease and biography operating and pulling at one another, as we have done in Chapter 5, in the case of tuberculosis.

          Residual categories, marginal people, and monsters

          People often seen multiplicity and heterogeneity as accidents or exceptions. The marginal person, who is for example of mixed race, is portrayed as the troubled outsider; just as the thing that doesn�t fit into one bin or another gets put into a "residual" category. This habit of purity has old and complicated origins in western scientific and political culture (e.g. as explicated by Dewey, 1916). The habit perpetuates a cruel pluralistic ignorance. No one is pure. No one is even average. And all things inhabit someone�s residual category in some category system. The myriad of classifications and standards that surround and support the modern world, however, often blind people to the importance of the "other" category as constitutive of the whole social architecture (Derrida, 1980).

          Communities vary in their tastes for openness, and in their tolerance for this ambiguity. Cults, for example, are one sort of collective which is low on the openness dimension and correspondingly high on the naturalization/positivism dimension -- us vs. them.

          In recent years social theorists have been working toward enriching an understanding of multiplicity and misfit, and decentering the idea of an unproblematic mainstream. The schools of thought grappling with this include feminist research (e.g. Haraway, 1997), multi-cultural or race-critical theory (e.g. Ferguson, et al., 1990), symbolic interactionism, and activity theory (e.g., Cole, 1996; Wertsch, 1991; 1998). During the same period, such issues have become increasingly of concern to some information scientists. As the information systems of the world expand and flow into each other, and more kinds of people use them for more different things, it becomes harder to hold to pure or universal ideas about representation or information.

          Some of these problems are taken up in the intellectual common territory sometimes called cyborg. Cyborg, as used for example by Donna Haraway (1991) and Adele Clarke (1993; 1998), means the intermingling of people, things (including information technologies), representations and politics in a way that challenges both the romance of essentialism and the hype about what is technologically possible. It acknowledges the interdependence of people and things, and just how blurry the boundaries between them have become. The notion cyborg has clearly touched a nerve across a broad spectrum of intellectual endeavors. The American Anthropological Association has hosted sessions on cyborg anthropology for the past several years; the weighty Cyborg Handbook was published a few years ago (Gray, 1995).

          Through looking at ubiquitous classification systems and standards, it is possible to move towards an understanding of the stuff that makes up the networks that shape much of modern daily life in cyborg fashion. We draw attention here to the places where the work gets done of assuring that these networks will stick together: to the places where human and non-human are constructed to be operationally and analytically equivalent. So doing, we explore the political and ethical dimensions of classification theory.

          Why should computer scientists read African-American poets? What does information science have to do with race critical or feminist methods and metaphysics? The collective wisdom in those domains is one of the richest places for which to understand these core problems in information systems design: how to preserve the integrity of information without a priori standardization and its often-attendant violence. In turn, if those lessons can be taken seriously within the emerging cyber world, there may yet be a chance to strengthen its democratic ethical aspects. It is easy to be ethnocentric in virtual space; more difficult to avoid stereotypes. The lessons of those who have lived with such stereotypes are important, perhaps now more than ever.

          Borderlands and Monsters

          People who belong to more than one central community are also important sources for understanding more about the links between moral order and categorization. Such "marginal" people have long been interest to social scientists and novelists alike. Marginality as a technical term in sociology refers to human membership in more than one community of practice; here we are emphasizing those people who belong to communities that are different in key, life-absorbing ways, such as racial groups (as per our discussion in Chapter 6). A good example of a marginal person is someone who belongs to more than one race, i.e. half white and half Asian. Again, we are not using marginality here in the sense of center/margin or center/periphery (e.g., not �in the margins�), but rather in the old-fashioned sense of Robert Park�s marginal man, the one who has a double vision by virtue of having more than one identity to negotiate (Park 1952; Stonequist 1937; Simmel 1950 {1908}; Schütz, 1944). Strangers are those who come and stay a while, long enough so that membership becomes a troublesome issue -- they are not just nomads passing through, but people who sort of belong and sort of don�t.

          Marginality is an interesting paradoxical concept for people and things. On the one hand, membership means the naturalization of objects, which mediate action. On the other, everyone is a member of multiple communities of practice. Yet since different communities generally have differently naturalized objects in their ecology, how can someone maintain multiple membership without becoming simply schizophrenic? How can they naturalize the same object differently, since naturalization by definition demands forgetting about other worlds?

          There are as well some well-known processes in social psychology for managing these tensions and conflicts: passing, or making one community the shadow for the other; splitting, or having some form of multiple personality/chameleon; fragmenting or segmenting the self into compartments; becoming a nomad, intellectually and spiritually if not geographically (Larsen (1986) covers many of these issues in her exquisite fiction).

          One dissatisfaction we have with these descriptions is that they all paint each community of practice as ethnocentric, as endlessly hungry and unwilling collectively to accommodate internal contradictions. There is also an implicit idea of a sort of imperialist über-social world (the mainstream) that is pressing processes of assimilation on the individual (e.g. Americanization processes in the early 20th century). Communities vary along this dimension of open/closedness, and it is equally important to find successful examples of the nurturing of marginality (although it is possible that by definition they exist anarchically and not institutionally/bureaucratically). Here again, feminism has some important lessons. An important theme in recent feminist theory is resistance to such imperializing rhetoric and the development of alternative visions of coherence without unconscious assumption of privilege. Much of it emphasizes a kind of double-vision, such as that taken up in the notion of borderlands by Anzaldúa (1987), or the qualities of partiality and modesty of Haraway�s cyborg (1991).

          Charlotte Linde�s book on the processes of coherence in someone�s life stories also provides some important clues. She especially emphasizes accidents and contingency in the weaving together of a coherent narrative (Linde 1993). The narratives she analyses are in one sense meant to reconcile the heterogeneity of multiply naturalized object relations in the person, where the objects in question are stories/depictions of life events. Linden (1993) and Strauss (1959) have made similar arguments about the uncertainty, plasticity, and collectivity of life narratives.

          In traditional sociology this model might have overtones of functionalism, in its emphasis on insiders/outsiders and their relations. But functionalists never considered the nature of objects or of multiple legitimate memberships. If we think in terms of a complex cluster of multiple trajectories simultaneously of both memberships and naturalizations, it is possible to think of a many-to-many relational mapping.

          The mapping suggested here pushes us further into the analysis of the cyborg. On the one hand, cyborgs as an image are somehow grotesque. Imagining the relationships between people and things such that they are truly inter-penetrated means rethinking human nature itself. It is reminiscent somehow of bad science fiction. Yet analytically, it is a crucial notion for understanding technoscience, and classifications as artifacts.

          How can we think of cyborgs in terms of the analysis presented in this chapter? The mapping between things, people, and membership provides a way in. Anzaldúa's work on borderlands rejects any notion of purity based on membership in a single, pristine racial, sexual, or even religious group (1987). Haraway's work pushes this analysis a bit further. In speaking of borderlands, both those concerning race and those concerning the boundaries between humans and things, she employs the term "monsters."

          A monster occurs when an object refuses to be naturalized (Haraway 1992); a borderland occurs when two communities of practice coexist in one person (Anzaldúa 1987). Borderlands are the naturalized home of those monsters known as cyborgs.

          If we read monsters as persistent resisters of transparency/naturalization within some community of practice, then the experience of encountering an anomaly (such as that routinely encountered by a newcomer to science, for instance a woman or man of color) may be keyed back into membership. A person realizes that they don�t belong when what seems like an anomaly to them seems natural for everyone else. Over time, collectively, such outsider experiences (the quintessential stranger) can become monstrous in the collective imagination. History and literature are full of the demonizing of the stranger. Here is what Haraway (1992) has called �the promise of monsters� and one of the reasons that for years they have captured the feminist imagination. Frankenstein peering in the warmly-lit living room window; Godzilla captured and shaking the bars of his cage are intuitions of exile and madness, and symbols of how women�s resistance and wildness has been imprisoned and reviled, kept just outside.

          In a more formal sense, monsters and freaks are also ways of speaking about the constraints of the classifying and (often) dichotomizing imagination. Ritvo (1997) writes of the proliferation of monsters in the 18th and 19th century, linking it to a simultaneous increase in public awareness of scientific classification and hunger for the exotic. As classification schemes proliferated, so did monsters:

          Monsters were understood, in the first instance, as exceptions to or violations of natural law. The deviations that characterized monsters, however, were both so various and in some cases, so subtle as significantly to complicate this stock account�. As a group, therefore, monsters were united not so much by physical deformity or eccentricity as by their common inability to fit or be fitted into the category of the ordinary � a category that was particularly liable to cultural and moral construction (Ritvo, 1997: 133-134). In a practical sense, this is a way to talk about what happens to any outsider. For example, it could refer to experience in the science classroom when someone comes in with no experience of formal science, or to the transgendered person who does not fit cultural gender dichotomies (Stone, 1991). It is not simply a matter of the strangeness, but of the politics of the mapping between the anomalies and the forms of strangeness/marginality.

          In accepting and understanding the monsters and the borderlands there may be an intuition of healing and power, as Gloria Anzaldúa (1987) shows us in her brilliant and compassionate writing. In her essay, "La conscientia de la mestiza," the doubleness and the ambiguity of the male/female, straight/gay, Mexican/American borderland becomes the cauldron for a creative approach to surviving, a rejection of simplistic purity and of essentialist categories (1987). At the same time, she constantly remembers the physical and political suffering involved in these borderlands, refusing a romanticized version of marginality that often plagued the early sociological writers on the topic.

          The path traced by Anzaldúa is not an easy healing and certainly not a magic bullet, but a complex and collective twisted journey, a challenge to easy categories and simple solutions. It is, in fact, a politics of ambiguity and multiplicity -- this is the real possibility of the cyborg. For scholars, this is necessarily an exploration that exists in interdisciplinary borderlands and crosses the traditional divisions between people, things, and technologies of representation.

          Engineered vs. Organic Boundary Objects

          Would be possible actually to design boundary objects? To engineer them in the service of creating a better society? On the surface, this idea is tempting. In some sense, this has been the goal of progressive education, multiculturalism in the universities, and, the goal of the design of information systems that may be accessed by people with very different points of view.

          Most schools now are lousy places to grow boundary objects, because they both strip away the ambiguity of the objects of learning, and impose or ignore membership categories (except artificial hierarchically assigned ones). In mass schooling and standardized testing, an attempt is made to insist on an engineered community of practice, where the practices are dictated, and the naturalization process is monitored and regulated while ignoring borderlands. They are virtual factories for monsters. In the 1970s and 1980s many attempts were made to include other communities in the formula via affirmative action and multicultural initiatives. But where these lacked the relational base between borderlands and the naturalization of objects, they ran aground on the idea of measuring progress in learning. This is partly a political problem and partly a representational one. As feminists learned so painfully over the years, a politics of identity based on essences can only perpetuate vicious dualisms. That is, if one as a white male science teacher were to bring in an African American woman as a (Platonic) representative of African American-ness and/or woman-ness, then attempt to match her essential identity to the objects in the science classroom (without attending much to how they are fully naturalized objects in another community of practice), costly and painful mismatches are inevitable. The teacher risks causing serious damage to her self-articulation (especially where she is alone), and her ability to survive (a look at the dismal retention statistics of women and minority men in many sciences and branches of engineering will underscore this point). Any mismatch becomes her personal failure, since the measurement yardstick remains unchanged although the membership criteria seem to have been stretched. Again, both borderlands and anomalous objects have been deleted. Kal Alston (1993), writing of her experience as an African-American Jewish feminist, has referred to herself as a unicorn -- a being at once mythical and unknowable, straddling multiple worlds.

          But all people belong to multiple communities of practice -- it�s just that in the case of the African-American woman in science, the visibility and pressure is higher, and her experience is especially dense in the skill of surviving multiplicity. Thus Patricia Hill Collins� title, "Learning from the Outsider Within" has many layers and many directions to be explored as we all struggle for rich ways of mapping that honors this experience and survival (1986). Karla Danette Scott (1995) has recently written about the interwoven languages of Black women coming to university, and how language becomes a resource for this lived complexity. They �talk black� and �talk white� in a seamless, context-driven web, articulating the tensions between those worlds as a collective identity. This is not just code switching, but braided identity, a borderland.

          Wildness

          Things and people are always multiple, although that multiplicity may be obfuscated by standardized inscriptions. In this sense, with the right angle of vision, things can be seen as heralds of other worlds, and of a wildness that can offset our naturalizations in liberatory ways. Holding firmly to a relational vision of people-things-technologies, in an ethical political framework, there is a chance to step off the infinite regress of measuring the consumption of an object naturalized in one centered world, such as the objects of Western science, against an infinitely-expanded set of essentially-defined members as consumers.

          By relational here we argue against misplaced concretism, or a scramble unthinkingly to assimilate the experiences of things to pre-given categories. We affirm the importance of process and ethical orientations. We also mean to take seriously the power of membership, its continual nature (i.e. we are never not members of some community of practice), and the inherent ambiguity of things. Boundary objects, however, are not just about this ambiguity, they are not just temporary solutions to disagreements about anomalies. Rather, they are durable arrangements between communities of practice. Boundary objects are the canonical forms of all objects in our built and natural environments. Forgetting this, as people routinely do, means empowering the self-proclaimed objective voice of purity that creates the suffering of monsters in borderlands. Due attention to boundary objects entails embracing the gentle and generous vision of mestiza consciousness offered by Anzaldúa.

          Casual vs. Committed Membership

          Another dimension to take into account here is the degree to which membership demands articulation at the higher level. Being a woman and African American and disabled are three sorts of membership that are non-optional, diffused throughout life, and embedded in almost every sort of practice and interaction. So it�s not equitable to talk about being a woman in the same breath as being a scuba diver -- although there are ways in which both can be seen under the rubric community of practice. But if we go to the framework presented above, there is a way to talk about it. To the extent that the joint objects are both multiply naturalized in conflicting ways and diffused through practices that belong to many communities they will pose a sticking point that defies casual treatment. So for scuba diving -- it is primarily naturalized in a leisure world, and not especially central to any others. Its practice is restricted and membership is contained, neither contagious nor diffuse. On the other hand, learning mathematics is multiply naturalized across several powerful communities of practice (mathematics and science teachers and practitioners); at the same time it is both strange and central to others (central in the sense of a barrier to further progress). It is also diffused through many kinds of practices, in various classrooms, disciplines and workplaces (Hall and Stevens, 1995). Some communities of practice expect it to be fully naturalized -- a background tool or a substrate/infrastructure -- in order to get on with the business of being, e.g. a scientist. (Lave 1988). However, there is no map or sense of the strangeness of the object across other memberships. Here, too, information technologies are both diffused and strange, with rising expectations of literacy across worlds.

          These relations define a space against which and into which information technologies of all sorts enter. These technologies of representation are entering into all sorts of communities of practice on a global scale, in design and in use. They are a medium of communication and broadcast, as well as of standardization. The toughest problems in information systems design are increasingly those concerned with modeling cooperation across heterogeneous worlds, of modeling articulation work and multiplicity. If we do not learn to do so, we face the risk of a franchised, dully standardized infrastructure (�500 channels and nothing on,� in Mitch Kapur�s words), or of an Orwellian nightmare of surveillance.

          Feminism and race-critical theory offer traditions of reflective denaturalization, of a politics of simultaneity and contradiction intuited by the term cyborg. Long ago feminists began with the maxim that the personal is political, and that each woman�s experience has a primacy that we must all learn to afford. Feminism went from reductionist identity politics to cyborg politics in less than twenty years. Much of this was due to the hard work and suffering of communities of practice who had been made monstrous or invisible, especially women of color and their articulation of the layered politics of insider/outsider and borderlands. One part of the methodological lesson from feminism read in this way is that experience/experiment incorporates an ethics of ambiguity, with both modesty and anger. This means that how we hear each other is a matter of listening forth from silence -- listening is active, not passive; it means stretching to affiliate with multiplicity. In Nell Morton�s words, this is "hearing to speech":

          • Not only a new speech but a new hearing.
          • Hearing to speech is political
          • Hearing to speech is never one-sided. Once a person is heard to speech she becomes a hearing person.
          • Speaking first to be heard is power over. Hearing to bring forth speech is empowering. (Morton, 1985: 210).
          Part of the moral vision of this book concerns how we may, through challenge and analysis of infrastructure, better hear each other to speech.

          5. Multiple Marginality, Multiple Naturalizations: Categorical Work

          The model proposed here takes the form of a many-to-many relational mapping, between multiple marginality of people (borderlands and monsters) and multiple naturalizations of objects (boundary objects and standards). Over time, the mapping is between the means by which individuals and collectives have managed the work of creating coherent selves in the borderlands on the one hand, and to create durable boundary objects on the other.

          It is also not just many-to-many relational, but meta-relational. By this we mean that the map must point simultaneously to the articulation of selves and the naturalization of objects. One of the things that is important here is honoring (we won�t say capturing) the work involved in borderlands and boundary objects. This work is almost necessarily invisible from the point of view of any single community of practice: as Collins (1986) asks, what white really sees the work of self-articulation of the black who is juggling multiple demands/audiences/contingencies? It is not just willful blindness (although it can be that), but much more akin to the blindness between different Kuhnian paradigms, a revolutionary difference. Yet the juggling is both tremendously costly and brilliantly artful. Every community of practice has its overhead: paying your dues, being regular, hangin�, being cool, being professional, people like us, conduct becoming, getting it, catching on ... . And the more communities of practice one participates in, the higher the overhead -- not just in a straightforwardly additive sense, but interactively. Triple jeopardy (i.e. being old, Black, and female) is not just three demographic variables or conditions added together, but a tremendously challenging situation of marginality requiring genius for survival. The overheads interact.

          From Articulation Work to Categorical Work

          What is the name for this work of managing the overheads and anomalies caused by multiple memberships on the one hand, and multiply naturalized objects on the other? Certainly, it is invisible. Most certainly, it is methodological, in the sense of reflecting on differences between methods and techniques. At first glance, it resembles articulation work, that is, work done in real time to manage contingencies; work that gets things back on track in the face of the unexpected, that modifies action to accommodate unanticipated contingencies. Within both symbolic interactionism and the field of computer-supported cooperative work, the term articulation work has been used to talk about some forms of this invisible juggling work (Schmidt and Bannon 1992; Gerson and Star 1986).

          Articulation work is richly found for instance in the work of head nurses, secretaries, homeless people, parents, and air traffic controllers, although of course all of us do articulation work in order to keep our work going. Modeling articulation work is one of the key challenges in the design of cooperative and complex computers and information systems. This is because real time contingencies, or in Suchman�s (1987) terms, situated actions, always change the use of any technology (for example, when the host of a talk forgets to order a computer projector, can one quickly print out and assemble a handout?)

          Other aspects of cooperative work concerns novelty and the ways in which one person�s routine may be another�s emergency or anomaly (Hughes, 1970), or in the words of Schmidt and Simone (1996), both the consequences and the division of labor of cooperative work. The act of cooperation is the interleaving of distributed tasks; articulation work manages the consequences of this distributed aspect of the work. Schmidt and Simone note the highly complex dynamic and recursive relationship between the two -- managing articulation work can itself become articulation work, and vice versa, ad infinitum.

          The consequences of the distribution of work, and its different meanings in different communities, must be managed for cooperation to occur. The juggling of meanings (memberships and naturalizations), is what we term categorical work. For example, what happens when one clerk, User A, entering data into a large database does not think of abortion as a medical matter, but as a crime; while another, User B, thinks of it as a routine medical procedure? Person A�s definition excludes abortion from the medical database, Person B�s includes it. The resulting data will be, at the least, incomparable, but in ways that may be completely invisible to User C, compiling statistics for a court case arguing for the legalization of abortion based on prevalence. When this aspect of the coordination of work is deleted and made invisible in this fashion, then voices are suppressed and we see the formation of master narratives and the myth of the mainstream universal (Star and Strauss, 1999).

          Thus, we can see categorical work as partly about managing the mismatches between memberships and naturalization. One way to think about this is through the management of anomalies -- as a tracer. Anomalies or interruptions, the cause of contingency, come when some person or object, from outside the world at hand, interrupts the flow of expectations. One reason that glass box technology or pure transparency is impossible is that anomalies always arise when multiple communities of practice come together, and useful technologies cannot be designed in all communities at once. Monsters arise when the legitimacy of that multiplicity is denied. Our residual categories in that case become clogged and bloated.

          Transparency is in theory the endpoint of the trajectory of naturalization, as complete legitimacy or centrality is the endpoint of the trajectory of membership in a community of practice. However, due to the multiplicity of membership of all people, and the persistence of newcomers and strangers, as well as the multiplicity of naturalization of objects, this is inherently nonexistent in the real world. For those brief historical moments where it seems to be the case, it is unstable.

          In place of transparency � and it is a good enough counterfeit to work most of the time as transparency � one encounters convergence: the mutual constitution of a person or object and their representation. People get put into categories, and learn from those categories how to behave. Thus there is the ironic observation that East Enders in London learn cockney (and how to be cockney) through watching the soap opera East Enders on television: "I am an East Ender therefore I must talk like this; and I must drink such and such a brand of beer � ". Aided by bureaucratic institutions, such cultural features take on a real social weight. If official documents force an Anglo-Australian, say, to choose one identity or the other; and if friends and colleagues encourage that person, for the convenience of small talk, to make a choice � then they are likely to become ever more Australian: suffering alongside his or her now fellow-countrypeople if new immigration measures are introduced in America or if �we� lose a cricket test. The same process occurs with objects � once a film has been thrown into the X-rated bin, then there is a strong incentive for the director to make it really X-rated; once a house has been posted as condemned, then people will feel free to trash it.

          Where the difference lies between transparency and convergence is that where transparency ideally just produces a reflection of the way things really are (and so, in Jullien�s (1995) beautiful phrase captures the �propensity of things� in any situation); convergence can radically break down � over time or across geographical borders. When categories do break down in this fashion they leave no continuous trace back to the previous regime. So, for instance, when the category of �hysteric� became medically unfashionable, then people with (what used to be called) hysteria were distributed into multiple widely scattered categories. At that juncture, there was no point in their seeing the same doctors, or learning from each other what hysteria was �

          Scaling Up: Generalization and Standards

          "Similarity is an institution." --Mary Douglas (1986: 55)

          In this whole complicated co-construction process, what are the things that make objects and statuses seem given, durable, real? For, as DesrosiÀ res (1990) reminds us, partly through classification work, large scale bureaucracies are very good at making objects, people and institutions hold together. Some objects are naturalized in more than one world. They are not then boundary objects, but rather they become standards within and across the multiple worlds in which they are naturalized. Much of mathematics, and in the West, much of medicine and physiology fits this bill. In the Middle Ages a lot of Christian doctrine fit this too. The hegemony of patriarchy arose from the naturalization of objects across a variety of communities of practice, with the exclusion of women from membership and the denial of our alternative interpretations of objects (Kramarae 1988; Merchant 1980; Croissant and Restivo, 1995).

          When an object becomes naturalized in more than one community of practice, its naturalization gains enormous power -- to the extent that a basis is formed for dissent to be viewed as madness or heresy. It is also where ideas like "laws of nature" get their power, because we are always looking to other communities of practice as sources of validity, and if as far as we can look we find naturalization the invisibility layers up and becomes doubly, triply invisible. Sherry Ortner�s (1974) classic essay on man:culture/woman:nature shows that this has held for the subjugation of women even where specific cultural circumstances vary widely, and her model of the phenomenon rests on the persistent misunderstanding of borderlands and ambiguity in many cultures. Before her, Simone de Beauvoir (1948) wrote of the ethics of ambiguity, showing the powerful negative consequences of settling for one naturalized mode of interaction. We still need an ethics of ambiguity, still more urgently with the pressure to globalize, and the integration of systems of representation through information technologies across the world.

          We have presented here a model of memberships, naturalizations, and the work we do in managing their multiplicity. Further analysis is needed here to examine different types of categorical work, and how they emerge under different circumstances. In the next section, we continue with a discussion of categorization and pragmatism.

          Boundary Infrastructure

          Any working infrastructure serves multiple communities of practice simultaneously � be these within a single organization or distributed across multiple organizations. A hospital information system, for example, has to respond to the separate as well as the combined agendas of nurses, records clerks, government agencies, doctors, epidemiologists, patients and so forth. In order to do so, it must bring into play stable regimes of boundary objects � such that any given community of practice can interface with the information system and pull out the kinds of information objects that it needs.

          Clearly boundary infrastructures are not perfect constructions. The chimera of a totally unified and universally applicable information system (still regrettably favored by many) should not be replaced by the chimera of a distributed, boundary-object driven information system fully respectful of the needs of the variety of communities it serves. To the contrary, as we saw in the case of NIC, nurses have needed to make a series of serious concessions about the nature and quality of their data before hoping to gain any kind of entry into hospital information systems. As we have seen, these difficulties generalize � though they are to some extent counterposed by processes of convergence.

          Boundary infrastructures by and large do the work that is required to keep things basically moving along. Because they deal in regimes and networks of boundary objects (and not of unitary, well-defined objects), boundary infrastructures have sufficient play to allow for local variation together with sufficient consistent structure to allow for the full array of bureaucratic tools (forms, statistics and so forth) to be applied. Even the most regimented infrastructure is ineluctably also local: if workarounds are needed they will be put into place. The ICD, for example, is frequently used to code cultural expectations (such as low heart attack rates in Japan) even though these are nowhere explicitly part of the classification system.

          What we gain with the concept of boundary infrastructure over the more traditional unitary vision of infrastructures is the explicit recognition of the differing constitution of information objects within the diverse communities of practice which share a given infrastructure.

          6. Future Directions: Texture and Modeling of Categorical Work and Boundary Infrastructures

          If you could say it, you would not need metaphor. If you could conceptualize it, it would not be metaphor. If you could explain it, you would not use metaphor. (Morton, 1985: 210)

          So far in this chapter we have given a series of analytic categories which we hope will prove useful in the analysis and design of information infrastructures. At the limit, as Nelle Morton points out, we arrive at the sets of metaphors which people use to describe information networks of all kinds. These metaphors we live by are powerful means of organizing work and intellectual practice. We will now look at one cluster of metaphors � centered on the concept of filiations � which we believe, offers promise for future analytical work.

          How are Categories Tied to People? : Filiations

          "The frequency with which metaphors of weaving, threads, ropes and the like appear in conjunction with contextual approaches to human thinking is quite striking." (Cole, 1996: 135)

          filiation (fIlI�eIS&schwa.n). Also 6 filiacion.

          1 Theol. The process of becoming, or the condition of being, a son.

          Many Dicts. have a sense �adoption as a son,� illustrated by the first of our quotes from Donne. The sense is etymologically justifiable, and may probably exist; but quot. 1628 seems to show that it was not intended by Donne.

          2 The designating (of a person) as a son; ascription of sonship.

          3 The fact of being the child of a specified parent. Also, a person�s parentage; �whose son one is.�

          4 The fact of being descended or derived, or of originating from; descent, transmission from.

          5 The relation of one thing to another from which it may be said to be descended or derived; position in a genealogical classification.

          6 Formation of branches or offshoots; chiefly concr., a branch or offshoot of a society or language.

          7 = AFFILIATION 3. lit. and fig. (Oxford English Dictionary, 2)

          Categories touch people in a variety of ways -- they are assigned, they become self-chosen labels, they may be statistical artifacts. They may be visible or invisible to any other group or individual. We use the term filiation here � related via Latin to the French "fil" for thread -- a thread that goes from a category to a person. This metaphor allows a rich examination of the architecture of the multiple categories that touch people�s lives. Threads carry a variety of textural qualities that are often applied to human interactions: tension, knottiness or smoothness, bundling, proximity, thickness. We select a small number here to focus on in the worked example below.

          Loosely coupled � tightly coupled

          A category (or system of categories) may be loosely or tightly coupled with a person. Gender and age are very tightly coupled with a person as categories. One of the interesting aspects of the investigation of virtual identities in MUDs and elsewhere on line is the loosening of these traditionally tightly coupled threads under highly constrained circumstances (e.g. Turkle, 1994). Loosely coupled categories may be those that are transient, such as the color one is wearing on a given day or one�s position in a waiting line. Somewhere in the middle are hair color, which may shift slowly over a lifetime or change in an afternoon, or marital status.

          Scope

          Categories� filiations have variable scope. Some are durable threads which cover many aspects of someone�s identity and which are accepted as such on a very wide or even global scale. (Noting for the record that none are absolute, none cover all aspects of someone�s identity, and there is no category, which is completely globally accepted.) The category alive or dead is quite thick and nearly global. So we can think of two dimensions of scope: thickness and scale. How thick is the individual strand: gossamer or thickest rope? With how many others is it shared?

          What is its ecology?

          Classifications have habitats. That is, the filiations between person and category may be characterized as inhabiting a space or terrain with some of the properties of any habitat. It may be crowded or sparse, peaceful or at war, fertile or arid. In order not to mix too many metaphors, two important questions about filiations and their ecology that may be visualized in threadlike terms are:

          How many ties are there? That is, how many other categories are tied to this person, and in what density?

          Do these threads contradict or complement (torque vs. boundary object of cooperation)? That is, are the threads tangled, or smoothly falling together?

          Who controls the filiation?

          The question of who controls any given filiation is vital to an ethical and political understanding of information systems whose categories attach to individuals. A first crude characterization concerns whether the filiation was chosen or imposed (an echo of the sociological standard, achieved or ascribed); whether it may be removed and by whom; and under whose control and access is the apparatus to do so. Questions of privacy are important here, as with medical information classifying someone with a social stigmatized condition. The nature of the measure for the filiation here is important loci of control as well. For example, an IQ test may be an important way to classify people. People at some remove from those who take the test developed it. The measure, IQ, is controlled from afar. Past criticisms of IQ tests charge that this control is racially biased and biased by gender, on these grounds.

          Is it reversible or irreversible?

          Finally, there is the important question of whether the filiation is reversible. The metaphor of branding someone is not accidental in this regard, branding meaning that a label is burned into the skin and completely irreversible. Some forms of filiation have this finality for the individual, regardless of how the judgment was later regarded (e.g. a charge of guilt for murder may mean permanent public guilt regardless of a jury�s verdict, as with the decades� long attempt of Sam Shepard�s son to prove his father�s innocence). Many are somewhere between, but knowing how reversible is the filiation is important for understanding its impact.

          The metaphor of filiation presented here could be used to characterize a texture of information systems where categories touch either individuals or things. The aesthetics of the weave and the degree to which the individual is bound up or supported by it are among the types of characterizations that could be made. There are brute renderings, such as having two thick, irreversible threads tying one person to conflicting categories, as with the examples above. More subtly, it is possible to think in terms of something like Granovetter's strength of weak ties, and characterize the thousand and one classifications which weakly tie people to information systems as binding or torquing in another way.

          To summarize. The metaphor of filiation is useful to the extent that it can be used to ask questions of working infrastructures in new and interesting ways. Two questions that rise directly out or our treatment of the metaphor for any individual or group filiation are:

          • What will be the ecology and distribution of suffering?
          • Who controls the ambiguity and visibility of categories?
          Conclusion

          We have in this chapter argued that there is more to be done in the analysis of classification systems than deconstructing universal master narratives. Certainly, such narratives should be challenged. However, we have attempted to show that there are ways of scaling up from the local to the social, via the concept of boundary infrastructures, and that we can in the process recognize our own hybrid natures without losing our individuality. The value of this approach is that it allows us to intervene in the construction of infrastructures - which surely exist and are powerful - as not only critics but also designers.

          Chapter Ten: Why Classifications Matter

          Classifications are powerful technologies. Embedded in working infrastructures they become relatively invisible without losing any of that power. In this book we demonstrate that classifications should be recognized as the significant site of political and ethical work that they are. They should be reclassified.

          In the past 100 years, people in all lines of work have jointly constructed an incredible, interlocking set of categories, standards, and means for inter-operating infrastructural technologies. We hardly know what we have built. No one is in control of infrastructure; no one has the power centrally to change it. To the extent that we live in, on, and around this new infrastructure, it helps form the shape of our moral, scientific and esthetic choices. Infrastructure is now the great inner space.

          Ethnomethodologists and phenomenologists have shown us that what is often the most invisible is right under our noses. Everyday categories are precisely those that have disappeared -- into infrastructure, into habit, into the taken for granted. These everyday categories are seamlessly inter-woven with formal, technical categories and specifications. As Cicourel notes:

          The decision procedures for characterizing social phenomena are buried in implicit common sense assumptions about the actor, concrete persons, and the observer's own views about everyday life. The procedures seem intuitively "right" or "reasonable" because they are rooted in everyday life. The researcher often begins his classifications with only broad dichotomies, which he expects his data to "fit," and then elaborates on these categories if apparently warranted by his "data." (1964:21). The hermeneutic circle is indeed all around us.

          There is no simple unraveling of the built information landscape, or, pace Zen practice, of unsettling our habits at every waking moment. Black boxes are necessary, and not necessarily evil. The moral questions arise when the categories of the powerful become the taken for granted; when policy decisions are layered into inaccessible technological structures; when one group's visibility comes at the expense of another's suffering.

          There are as well basic research questions implied by this navigation into infrastructural space. Information technology operates through a series of displacements, from action to representation, from the politics of conflict to the invisible politics of forms and bureaucracy. Decades ago, Max Weber wrote of the iron cage of bureaucracy. Modern humans, he posited, are constrained at every juncture from true freedom of action by a set of rules of our own making. Some of these rules are formal, most are not. Information infrastructure adds another level of depth to the iron cage. In its layers, and in its complex interdependencies, it is a gossamer web, with iron at its core.

          We have looked at several sets of classification schemes � the classifications of diseases, viruses, tuberculosis, race, and of nursing work. These are all examples of working classification systems: they are or have been maintained by organizations, governments and individuals. We have observed several dances between classifier and classified, but have nowhere seen either unambiguous entities waiting to be classified or unified agencies seeking to classify them. The act of classification is of its nature infrastructural, which means to say that it is both organizational and informational, always embedded in practice (Keller and Keller, 1996).

          In our interviews of public health officials, nurses or scientists, we have found that they recognize this about their own classification systems. At the same time, there is little inducement to share problems across domains. Because of the invisible work involved in local struggles with formal classification systems and standards, a great deal of what sociologists would call "pluralistic ignorance" obtains. This is the feeling that "I am the only one". People often have a picture that somehow their problems are unique: they believe that other 'real' sciences do not have the same set of makeshift compromises and workarounds.

          It is important in the development and implementation of classifications (and many related fields � such as the development and deployment of standards or archives) that we get out of the loop of trying to emulate a distant perfection that on closer analysis turns out to me just as messy as our own efforts. The importance lies in a fundamental rethinking of the nature of information systems. We need to recognize that all information systems are necessarily suffused with ethical and political values, modulated by local administrative procedures. These systems are active creators of categories in the world as well as simulators of existing categories. Remembering this, we keep open and can explore spaces for change and flexibility that are otherwise lost forever.

          Such politics are common to most systems employing formal representations. Rogers Hall, in his studies of algebra problem solving by both children and professional math teachers, talks about the shame that children feel about their unorthodox methods for arriving at solutions. Often using innovative techniques such as imaginary devices, but not traditional formulaic means, they achieved the right answer the wrong way. One child called this "the dirt way." A grown-up version of the dirt way is related by the example given earlier of the "good organizational reasons for bad organizational records" (Bitner and Garfinkel, 1967). There are good organizational reasons for working around formal systems; these adaptations are necessarily local. What is global is the need for them.

          We have in this book attempted to develop tools for maintaining these open spaces. Michel Serres has best expressed the fundamental ethical and political importance of this task. He has argued that the sciences are very good at what they do: the task of the philosopher is to keep open and explore the spaces that otherwise would be left dark and unvisited because of their very success, since new forms of knowledge might arise out of these spaces. Similarly, we need to consistently explore what is left dark by our current classifications ('other' categories) and design classification systems that do not foreclose on rearrangements suggested by new forms of social and natural knowledge.

          There are many barriers to this exploration. Not least among them is the barrier of boredom. Delving into someone else's infrastructure as we discussed in the introduction, has about the entertainment value of reading the white pages of the phone book. One does not encounter the dramatic stories of battle and victory, of mystery and discovery that make for a good read.

          In an introductory chapter, we laid the theoretical framework for the discussion of classification as an infrastructural practice, stressing the political and ethical texturing of classification schemes. In Part 1, we examined the International Classification of Diseases (ICD) as a large-scale long-term system ingrained in the work practices of multiple organizations and states. We argued that their organizational roots and operational exercise texture such systems. Such texture is an inescapable, appropriate feature of their constitution, and it is a feature that merits extended consideration in a discussion of the politics of infrastructure. In Part 2, we looked at the intersection between classification and individual biography in the case of the classification of tuberculosis and of race classification under apartheid in South Africa. Generalizing the arguments made in these chapters, we maintained that individuals in the modern State operate within multiple classification systems � from the small-scale, semi-negotiated system as with the informal classification of tuberculosis patients negotiated with doctors up to enforced universal systems such as race classification. We drew attention to the torquing of individual biographies as people encounter these reified classifications. Finally, we examined classification and work practice, taking the example of the classification of nursing work. We argued that multiple tensions between representation and autonomy, disability and discretion, forgetting the past and learning its lessons make such classifications a key site of political and professionalization work. We are all called upon to justify our productivity when we are embedded in complex modern organizations. The dilemma faced by nurses in accounting for their work is on the present in the modern organization. Even children are not exempt.

          We have seen throughout this book that people (and the information systems they build) routinely conflate formal and informal, prototypical and Aristotelian aspects of classification. There is no such thing as an unambiguous, uniform classification system (indeed the deeper one goes into the spaces of classification expertise � for example librarianship or botanical systematics � the more perfervid one finds the debates between rival classificatory schools). This in turn means that there is room in the constitution of any classification system with organizational and political consequences - and few schemes if any are without such dimensions � for technical decisions about the scheme to systematically reflect given organizational and political positions. Since, then, we are dealing with an agonistic field, there will be no pure reflection of a single position, but rather dynamic tensions between multiple positions. And finally, since the classification system is not a pure reflection of such positions (an impossible aim in its own right � no classification system can reflect either the social or the natural world fully accurately) but also integrally a tool for exploring the real world, there is no simple prediction from how a given set of alliances or tensions leads invariably to a given classification used in a given way.

          As sets of classification systems coalesce into working infrastructures they become integrated into information systems of all sorts. Thus we have argued throughout this book that information systems design should be informed by organizational and political analysis at this level. We are not offering this as an ex cathedra design principle. Rather, we have � along with many researchers in the field of social informatics - demonstrated empirically that invisible organizational structures influence the design and use of systems: the question is not whether or not this occurs but rather how to recognize, learn from and plan for the ineluctable presence of such features in working infrastructures. We have suggested one design aid here: long term and detailed ethnographic and historical studies of information systems in use, so that we can build up an analytic vocabulary appropriate to the task.

          Working infrastructures contain multiple classification systems, which are both invisible, in the senses above, and ubiquitous. The invisibility of infrastructure makes visualization or description difficult. The metaphors we reached for to describe infrastructure are ironic and somehow childish. We speak of "way down in the underwear," "underneath the system," or use up/down metaphors such as "runs under," or "runs on top of." Lakoff and Johnson (1980) write of metaphors we live by. Our infrastructural metaphors show how baffled we often are by these systems. They are like undergarments, or tunnel dwellers.

          Another set of metaphors often used in organizations speaks indirectly to the experience of infrastructure. These are the metaphors of texture, omnipresent in human relationships. Texture metaphors speak to the densely patterned interaction of infrastructures and the experience of living in the "classification society." Texture speaks to the way that classifications and standards link the individual with larger processes and structures. These links generate both enabling/constraining patterns over a set of systems (texture), and developmental patterns for an individual operating within a given set (trajectory). Thus we have used the metaphor of the texture of a classification system to explore the fact that any given classification provides surfaces of resistances (where the real resist its definition), blocks against certain agendas and smooth roads for others. Within this metaphorical landscape, the individual's trajectory � often, for all that, perceived as continuous and self-consistent � is at each moment twisted and torqued by classifications and vice versa.

          Therefore we have, through our analysis of various classification systems, attempted to provide a first approximation to an analytic language which recognizes that the architecture of classification schemes is simultaneously a moral and an informatic one. This book has brought to light as crucial to the design process the reading of classification schemes as political and cultural productions. We have stressed that any classification scheme can be read in this fashion. We initially deliberately eschewed cases like DSM-IV, where categories have often already become explicit objects of political contention, such as "homosexual" or "pre-menstrual tension." In the psychiatric case, there can in this sense often be a more direct read-off from political exigencies to disease categories. Although such readings are of course highly valuable in their own right (see Kirk and Kutchins, 1992; Kutchins and Kirk, 1997 and Figert, 1996), we first took the more muted cases posed by the ICD, where the politics were quieter. This we hoped would show the generalizability of the thesis that all category systems are moral and political entities. This was balanced later in the volume with an analysis of the much more obviously politically laden categories generated by the pro-apartheid government and its scientific apologists.

          This book has implications for both designers and users (and we are all increasingly both) of complex information spaces. It provides intellectual and methodological tools for recognizing and working with the ethical and political dimensions of classification systems. In particular we have underlined several design exigencies, that speak both to the architecture of information systems encoding classification systems and to their development and change:

          • Recognizing the balancing act of classifying. Classification schemes always represent multiple constituencies. They can do so most effectively through the incorporation of ambiguity � leaving a certain terms open for multiple definitions across different social worlds: they are in this sense boundary objects. Designers must recognize these zones of ambiguity, protecting them where necessary in order to leave free play for the schemes to do their organizational work.
          • Rendering voice retrievable. As classification systems get ever more deeply embedded into working infrastructures they risk getting black-boxed and thence made both potent and invisible. By keeping the voices of classifiers and their constituents present, the system can retain maximum political flexibility. This includes the key ability to be able to change with changing natural, organizational and political imperatives. A caveat here, drawn from Chapter 7's lesson about the invisibility of nursing work: we are not simply celebrating visibility, or naively proposing a populist agenda for the empire of classification. Visibility is not an unmitigated good. Rather, by retrievability, we are suggesting that under many circumstances, the "rule by no one" or the "iron cage of bureaucracy" is strengthened by its absence. When classification systems and standards acquire inertia because they are part of invisible infrastructure, the public is de facto excluded from policy participation.
          • Being sensitive to exclusions. We have in particular drawn attention here to the distribution of residual categories (who gets to determine what is other). Classification systems always have 'other' categories, to which actants (entities or people) who remain effectively invisible to the scheme are assigned. A detailed analysis of these others throws into relief the organizational structure of any scheme (Derrida, 1998). Residual categories have their own texture that operates like the silences in a symphony to pattern the visible categories and their boundaries.
          Stewart Brand's (1994) wonderful book, How Buildings Learn, gives many examples of how buildings get designed as they are used as much as on the architect's drawing-board. Thus a house with a balcony and numerous curlicues around the roof will become a battened-down square fortress block under the influence of a generation of storms from the northeast. Big single-family mansions become apartment buildings as a neighborhood's finances change. These criteria generalize to classification systems. Through these three design criteria we are drawing attention to the fact that architecture becomes archaeology over time. This in turn may become a cycle.

          Overall, we have argued that classifications are a key part of the standardization processes that are themselves the cornerstones of working infrastructures. People have always navigated sets of classification spaces. Mary Douglas (1984), among others, has drawn attention to this feature of all societies from the indigenous and tribal to the most 'industrialized. Today, with the emergence of new information infrastructures, these classification systems are becoming ever more densely interconnected. This integration began roughly in the 1850s, coming to maturity in the late 19th century with the flourishing of systems of standardization for international trade and epidemiology. Local classification schemes (of diseases, nursing work, viruses�) are now increasingly giving way to these standardized international schemes which themselves are being aligned with other large scale information systems. In this process, it is becoming easier for the individual to act and perceive him or her self as a completely naturalized part of the "classification society," since this thicket of classifications is both operative (defining the possibilities for action) and descriptive. As we are socialized to become that which can be measured by our increasingly sophisticated measurement tools, the classifications increasingly naturalize across wider scope. On a pessimistic view, we are taking a series of increasingly irreversible steps towards a given set of highly limited and problematic descriptions of what the world is and how we are in the world.

          For these reasons, we have argued in this book that it is politically and ethically crucial to recognize the vital role of infrastructure in the "built moral environment." Seemingly purely technical issues like how to name things and how to store data in fact constitutes much of human interaction, and much of what we come to know as natural. We have argued that a key for the future is to produce flexible classifications, whose users are aware of their political and organizational dimensions, and which explicitly retain traces of their construction. In the best of all possible worlds, at any given moment the past could be reordered to better reflect multiple constituencies now and then. Only thus we will be able to fully learn the lessons of the past. In this same optimal world, we could tune our classifications to reflect new insitutional arrangements or personal trajectories - reconfigure the world on the fly. The only good classification is a living classification.

          Envoi

          We would hate to have to assign a Dewey classification number to this book, which straddles sociology, anthropology, history and information systems, and design. Our modest hope is that it will not find its way onto the fantasy shelves�. http://www.ics.uci.edu/~lopes/opensim/ OpenSimulator

          OpenSimulator

          OpenSimulator is an open source application server and a framework for developing 3D virtual environments a-la Second Life. I became involved with this project back in 2008 during my first sabbatical; I was looking for an opportunity to reconnect with software development in the real world. I haven't stopped since then.

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          Alexander Ihler

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          Graphical Model Algorithms at UC Irvine

          Check out the REES package and our other Software.

          Welcome to GraphModAlg@UCI, the home page of Prof. Dechter's group in the Bren School of ICS at UC Irvine. Our research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing and probabilistic reasoning.

          Software

          This web page serves as a platform to make our algorithms and their implementations available to interested researchers. Feel free to have a look at the current list of available Software. Some of our algorithms have recently been evaluated in the UAI'08 probabilistic inference evaluation.

          We also provide a number of example problem instances in our Repository.

          Research overview

          The reasoning group's research headed by Prof. Dechter is in the field of automated reasoning in Artificial Intelligence, focusing on Graphical Models. Graph-based models (e.g., Bayesian and constraint networks, influence diagrams and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in Artificial Intelligence and general computer science.

          These models are used in numerous applications in industrial and engineering tasks, such as scheduling, planning, diagnosis and prediction, design, and hardware and software verification. These reasoning problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization and probabilistic inference.

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          News

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          News For 2006

          Sudeep Pasricha selected to receive the 2006 Joseph Fischer Memorial Fellowship Award for Outstanding Academic Achievement in Computer Science at UCI
          June, 2006

          ACES member and PhD candidate Sudeep Pasricha has been selected to receive the Joseph Fischer Memorial Fellowship Award for 2006. This award is given to an outstanding ICS PhD or MS student once every two years, in recognition of academic excellence.

          ACES bids farewell to Per Gunnar
          May
          , 2006

          Per Gunnar has just finished his one year sabbatical at ACES, and will be retuning to Norway this summer. Per is an Associate Professor at the Norwegian University of Science and Technology. We hope to see you again Per!

          ACES bids farewell to Partha Biswas and Aviral Shrivastava
          May
          , 2006

          It was time to say goodbye to our illustrious and beloved graduate students Partha Biswas and Aviral Shrivastava. Partha as joined Mathworks while Aviral is joining ASU as a faculty. We wish them both the very best!

          ACES at DATE 2006 conference, Munich, Germany
          March, 2006

          ACES had 4 papers at DATE this year. The details of the paper are:

          R. Cornea, A. Nicolau, N. Dutt, " Software Annotations for Power Optimization on Mobile Devices," DATE '06, March 2006

          S. Pasricha and N. Dutt, "COSMECA: Application Specific Co-Syntesis of Memory and Communication Architectures fo MPSoC," DATE '06, March 2006

          S. Park, A. Shrivastava, N. Dutt, E. Earlie, A. Nicolau, Y. Paek, " Automatic Generation of Operation Tables for Fast Exploration of Bypasses in Embedded Processesors," DATE '06, March 2006

          P. Biswas and N. Dutt, "Automatic Identification of Application-Specific Funtional Units wiht Architectturally Visible Storage," DATE '06, March 2006

          ACES bids farewell to Ameet Patil
          March
          , 2006

          Ameet Patil visited the ACES lab for 2 months to explore research opportunities in the group. Ameet returned to the University of York in UK where he is pursuing his Ph.D. in Computer Science. We hope to see you again sometime in the future Ameet!

          Sudeep Pasricha gets BEST PAPER AWARD at ASPDAC 2006 conference, Yokohama, Japan
          January, 2006

          Sudeep Pasricha's paper at the ASPDAC 2006 conference has been awarded with the Best Paper Award. In addition, ACES has 2 more papers at the ASPDAC conference. The details are:

          S. Pasricha, N. Dutt, M. Ben-Romdhane �Constraint-Driven Bus Matrix Synthesis for MPSoC,� Proceedings of ASPDAC-2006, January 2006 (BEST PAPER AWARD)

          H. Oh, N. Dutt and S. Ha, �Memory Optimal Single Appearance Schedule with Dynamic Loop Count for Synchronous Dataflow Graphs,� Proceedings of ASPDAC-2006, January 2006

          S. Banerjee, E. Bozorgzadeh, and N. Dutt, �PARLGRAN: Parallelism Granularity Selection for Scheduling Task Chains on Dynamically Reconfigurable Architectures,� Proceedings of ASPDAC-2006, January 2006

          Partha Biswas gets BEST PAPER NOMINATION at VLSI Design 2006 conference, Hyderabad, India
          January, 2006

          Partha Biswas's paper has been nominated for the Best Paper Award at the VLSI Design conference, 2006. We wish him the best of luck! The paper details are:

          P. Biswas, S. Banerjee, N. Dutt, L. Pozzi, and P. Ienne, �Performance and Energy Benefits of Instruction-Set Extensions in an FPGA Soft Core,� Proceedings of the 2006 International Conference on VLSI Design, Hyderabad, India, January, 2006

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          We are interested in software, hardware, and various combinations thereof that bring people together to engender novel forms of social interaction and possibly enact social change.

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          We believe in bringing people and technologies together in novel and engaging ways. The Social Code Group explores issues involved in creating interactive technology for a variety of audiences, from children to gamers to IT professionals.
          Informatics Department | Bren School of ICS | Calit2 | UC Irvine
          http://www.ics.uci.edu/~theory/workshop/ Workshop on Cloud Security

          ICS Theory Group

          Workshop on Cloud Security

          Wednesday, February 18, 2015
          Donald Bren Hall, Room 4011


          Agenda

          8:30-9:00am: Coffee
          9:00-9:15am: Welcome and Announcements
          9:15-10:00am: Azer Bestavros, Mechanisms for Efficient and Trustworthy Cloud Markets
          10:00-10:45am: Roberto Tamassia, Accountable Storage
          10:45-11:15am: Coffee break
          11:15am-noon: Nikos Triandopoulos, Falcon Codes: Fast, Authenticated LT-Codes
          noon-1:30pm: Lunch
          1:30-2:15pm: Dimitris Papadopoulos, Verifiable Queries on Outsourced Datasets: Cryptographic Tools and Constructions for Specific Functions
          2:15-3:00pm: Rodrigo Fonseca, Defending against Resource Depletion Attacks in Multi-tenant Distributed Systems
          3:00-3:30pm: Break
          3:30-4:15pm: Michael Goodrich, Zig-zag Sort: A Simple Deterministic Data-Oblivious Sorting Algorithm Running in O(n log n) Time
          4:15-5:00pm: Discussion
          5:00pm: Adjourn
          http://luci.ics.uci.edu/lightweight/ Light Weight: LUCI: The Laboratory for Ubiquitous Computing and Interaction at UCI
          LUCI logo

          Lightweight Version

          Interactive Flash Version

          OVERVIEW
          WE ARE LUCI
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          http://www.ics.uci.edu/~shz/ Shuang Zhao

          Redirecting to my homepage...

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          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~malek/ Page Redirection If you are not redirected automatically, follow the link Undergraduate Research Opportunities in Wayne Hayes's Group

          Primary projects:

          (1) Graph theory applied to biological network alignment: YouTube Video

          (2) Image analysis of spiral galaxies: YouTube Video

          (3)Predicting sea level rise by modeling melting ice caps: visit here.

          So.... you think you're interested in undergraduate research?

          I've supervised dozens of undergaduate research students over the years. It's fun and productive for all --- you, me, other students, and other scientific collaborators. And it's hard work, but rewarding.

          To work in my group, you need to demonstrate yourself to be significantly above average among your peers. To get into my research group, you need to undertake one of the following tests and submit the results to me. If they're not correct... no problem, I give second chances. You're also allowed to ask questions, but try to keep them to a minimum. The primary criteria by which you will be judged are how well you can perform these tasks independently, without much help.

          In all cases you need to submit a PDF write-up, with histograms or figures plotted. I don't need your code. I want to see your write-up including a description of you did, and why, and your results with commentary. THIS IS JUST AS MUCH A TEST OF YOUR COMMUNICATION SKILLS AS CODING. Doing research requires critical thinking and the ability to explain your rationale for what you did AND WHY. Without that your code or blind results are worthless.

          There are three projects I'm working on. They're described in the PowerPoint presentation here. You only need to do ONE of the three tests, depending upon which project you're interested in working on. You need to do the task, and then write it up nicely, with graphs or plots to illustrate your answer. You should be able to do the task within about a week at most, but the answer has to be GOOD. If you hand in a GOOD solution later, that's better than a crappy solution earlier. I want results both good and fast.

          1) If you want to do the biological network alignment project, you need to know what a graph is and how to work with them, especially how to code with them. Your task is the following: you're given a text file representing a network. The first line of the file is N, the number of nodes. You will name the nodes from 0 through N-1. The remaining lines will have two integers per line, representing an edge. You don't know in advance how many edges there are, you just keep reading until you reach end of file. When you are done, you are to compute the number of CONNECTED COMPONENTS in the graph, and output a single integer. I will provide some sample inputs and outputs in the next day or two. I don't care what language you use. In addition, in your write up, include a histogram of the distribution of DEGREES of nodes. That is, how many nodes have degree zero, degree 1, etc., up to the max degree. If you don't know any of these terms, look them up. The data for this project is here.

          2) If you want to work in the Galaxy Image Analysis project, then you should start by playing around with any galaxy images you find on the web and putting them into http://sparcfire.ics.uci.edu. Once you get the hang of it, you have two choices:

          (A) find an image of NGC5054, or take the one from my paper with Darren Davis (cited on the above web page), and try to find a set of SparcFire parameters that can find the "dim" arm on the right hand side of the image of that galaxy in the above paper.

          (B) Go get the following file: here Each row is some data about a galaxy, and the columns have names in the top row. You don't need to know what all of the columns mean, but pay attention to these ones: P_CS: the probability that this galaxy is a spiral. numDcoArcsGEXXX for various values of XXX: the number of arms in that spiral galaxy that are longer than XXX. Your task is to plot a histogram of the number of galaxies with N or more arms of length XXX, for each of the XXX values in the file. It would be best to plot all the histograms on one figure to be easily able to compare them to each other.

          3) If you want to work on the global warming project, then you should start by going to the website http://issm.jpl.nasa.gov and seeing what the project is all about. Then you have two choices:

          (A) If you have access to a Unix / Matlab environment (eg., at the ICS OpenLab), then download, compile, and install ISSM, and then pick a tutorial to go through. Plot at least one of the plots listed in the tutorial, and explain what it is in your own words. Submit a PDF write-up of your result.

          (B) If you do not have access or are not familiar with Unix / Matlab then look at this file: https://www.dropbox.com/s/tvieux4f72jlqmr/Greenland-Points.tsv.gz?dl=0 Then find the centroid of the points in the file, and then count how many points are within a radius of 500 miles of that point. The points are latitude and longitude. You'll need to be able to compute distances in miles given positions in latitude and longitude.

          (C) C++/JAVASCRIPT: Write a wrapper function in C++ around the built-in square root function. Your function could be called "mysqrt", take a double as argument, and return a double that is the square root of the input. Convert the C++ code into Javascript using Emscripten (there are other Emscripten mirror sites, choose one that works). Then embed the Javascript into a web page. The web page simply has a field into which the user will type a number (you need to do error checking, eg., only non-negative numbers are allowed to be input), and output the square root on the page. You MUST use Emscripten to perform the C++ to Javascript translation. The point here is that if you join the group, you will be converting LARGE C++ programs into Javascript, using Emscripten. Also, MATLAB experience is a plus because we will eventually be converting Matlab to Javascript as well, although we are not aware of any automated way to do this (yet!).

          Let me know if there are any ambiguities, but your job is to do this task with as little supervision from me as is possible.

          Please direct all questions to me at whayes@uci.edu. http://hombao.ics.uci.edu/hernando.html Hernando Ombao UCIrvine

          Hernando Ombao, Ph.D.
          Professor
          Department of Statistics
          University of California at Irvine
          Bren Hall, Room 2206
          Irvine, CA 92697 USA
          Phone: (949) 824-5679
          Email: hombao AT uci DOT edu
          CV

          RESEARCH AREAS

          • Time Series Analysis
          • Spatio-temporal modelling
          • Statistical Learning
          • Applications to Brain Science (fMRI, EEG, MEG, EROS)

          RESEARCH GROUPS

          • Space-Time Modeling at UC-Irvine
          • Computational Research in Neuroscience
          • Recent NSF-Funded Workshop on Statistics in NeuroImaging
            24 July to 26 July 2012 at San Diego, CA
            Co-orgznized with M. Lindquist (Johns Hopkins) and W. Thompson (UCSD)

          I Support up-and-coming artists

          http://hombao.ics.uci.edu/neurostatsw2012.html Stats-Neuro-Workshop2012

          Developing Novel Statistical Methods in NeuroImaging
          July 24-26, 2012 (immediately preceding the Joint Statistical Meeting 2012)
          University of California at San Diego


          The goal of the workshop is to identify open problems in statistical research that emerge from current challenges in neuroimaging.

          The analysis of brain data presents statistical challenges because of its massiveness, high dimensionality and complex spatio-temporal dependence structure. We expect to see open lines of statistical research especially in the areas of time series, spatial analysis, dimension reduction, statistical learning, functional data analysis, statistical computation and foundations of statistical inference. At the workshop, leaders in neuroimaging will deliver lectures on theoretical background in neuroscience and in the state-of-the-art statistical methods for the analysis of brain imaging data. The workshop topic is timely due to the increased role of late of mathematical and statistical methods in neuroimaging.


          FEES

          • Fee of the three-day workshop is US$650 (including lunch)
          • Optional housing is an additional $200
            • Shared on-campus apartment (2 rooms per apt; 2 persons per room)
            • Check-in on July 23 (starting 3pm); Checkout on July 26 by 6 pm
            • Meals: breakfast July 24 through lunch July 26 included


          REGISTRATION
          (web-based)
          Limited seats only. The deadline for application is extended to 15 May 2012. Note that registration details and scholarship applications done at CHECKOUT.


          SCHOLARSHIPS

          Note: Application for scholarships is closed effective 26 April 2012.

          We anticipate financial support from the National Science Foundation (DMS). Partial scholarships will be available to junior scholars (PhD students and recent PhDs). There will be travel support (up to $200) and workshop scholarship (up to $500).

          In the registration, indicate if you are applying for (1) travel support; and/or (2) partial workshop scholarship

          Applicants for travel support and/or housing will be notified of the decision by late May 2012.

          Minorities and women are especially encouraged to apply.


          WORKSHOP INSTRUCTORS

          Greg Brown (UC San Diego), Richard Buxton (UCSD), Anders Dale (UCSD), Mark Fiecas (UCSD), Martin Lindquist (Columbia University), Tom Liu (UCSD), Tim Mullen (UCSD), Hernando Ombao (UC Irvine), Wesley Thompson (UCSD)


          TENTATIVE PROGRAM


          July 24

          • Morning
            • Introduction and Workshop Registration
          • Afternoon
            • Physiological Basis of the BOLD (R. Buxton)
            • Reliability of fMRI (G. Brown)
            • Calibration of the BOLD Signal (T. Liu)
          • Evening
            • Socials
          July 25
          • Morning
            • Overview of Current Statistical Methods (Lindquist, Ombao, Thompson)
          • Afternoon
            • Basics of UNIX Programming (M Fiecas)
            • FSL - image viewing, pre-processing, batchmode processing (M Fiecas)
          July 26
          • Morning
            • Structural Imaging and Genetics (A. Dale)
            • EEG - physics, source localization (T. Mullen)
          • Afternoon
            • FSL – statistical modeling, visual displays (M. Fiecas)
            • Discussion of Emerging Mathematical and Statistical Research in NeuroImaging (M. Lindquist, H. Ombao and W. Thompson)


          ORGANIZING COMMITTEE

          Martin Lindquist (Columbia University)
          Hernando Ombao (Univ California at Irvine)
          Wesley Thompson (Univ California at San Diego)


          INQUIRIES

          Send to stats-neuro@ics.uci.edu


          ACKNOWLEDGEMENT

          This workshop is mainly supported by the US National Science Foundation (Division of Mathematical Sciences). We also acknowlege support by the UC Irvine Bren School of Information Sciences.

          http://www.ics.uci.edu/404.php error 404 - page not found

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: May 23 2014
          http://www.ics.uci.edu/~schark/ The Schark Research Group

          Welcome to the Schark Research Group

          at the University of California, Irvine





          Principal Investigator

          Isaac D. Scherson

          Research Interests

          -Parallel Computing Architectures
          -Operating Systems for Parallel Computers
          -Parallel Algorithms
          -Simulation Models
          -Performance Evaluation

          Research Associates

          Graduated Students

          PhD

          Elisha Caspari
          Sener Ilgan
          David Kramer
          Brian Alleyne
          Peter Corbett
          Raghu Subramanian
          Chi-Kai Chien
          Umesh Krishnaswamy
          Veronica Reis
          Luis Miguel Campos
          Vara Ramakrishnan
          Fabricio Silva
          David Wangerin
          Shean T. McMahon
          Daniel Valencia

          M.S.

          Sandeep Sen
          Yiming Ma
          Li-Wei Gary Chen
          John LastMinute Zapisek

          Current Students

          Enrique Cauich
          Richert Wang
          John Duselis
          Martin Pieters


          Research Review and Current Research

          Our group's research interests fall in the general areas of Parallel and Distributed computer architectures and concurrent computations and its applications. The research group is currently working on five main subjects: operating systems for parallel computers, interconnection networks, performance evaluation, parallel algorithms, simulation models, predictive parallelism, and resource management and discovery.

          Service Address Routing (SAR)

          The idea behind SAR is that nodes are not aware of each other, but of functionality there is for them to use. Nodes do not explicitly send messages to each other, but instead request instances of a particular function (service) to be used. In this manner, addresses do not identify nodes directly, but rather identify types of services nodes can provide. The intelligence related to finding and selecting which node will receive a service request (when there are multiple nodes capable of providing such service) is in the network. Although the network is envisioned as a single switching entity, a hierarchical, multi-module implementation is proposed for scalability purposed using the Least Common Ancestor Network (LCAN) as the interconnection design. The LCAN architecture inherently provides fast simultaneous service discovery and redundancy, which increases performance and fault tolerance within the system. The paradigm has been shown to be feasible using a combination of hardware/firmware fabric that can efficiently perform the service name search necessary for discovery. The approach does not use the conventional DNS service and performs the equivalent of an associative, location independent search

          The LCAN-embedded Hierarchical Service Directory is an embodiment of a SAR network. We have shown the feasibility of this idea and also that it can be efficiently mapped to the amorphous GRID with highly desirable properties such as scalability, fault tolerance, and fully distributed operation.

          Other advantages, obtained in tightly coupled implementations where the switching network contains the resource management functionality for the cluster, are also present in loosely-coupled systems with systems in either software and/or hardware.

          Transparent Remote Execution (TREx)


          TREx is an environment destined to use idle CPU cycles in a network to provide cost-effective, high-performance distributed computing. Typically, clerical stations are underutilized in an institutional network. Processes from overloaded nodes on the network might utilize this idle computing power. TREx is a mechanism to federate lightly used stations to perform useful calculations. Furthermore, TREx provides execution speedup, increases productivity, and can create a low-cost, high-performance cluster.

          TREx allows programs to execute locally or remotely without user intervention with a properly administered workstation. Process execution is transparent to the user and remote execution appears as if it was done locally. We refer to a set of participating computers as a Federated Cluster of Workstations. TREx consists of three components: the first one is a daemon that will subscribe itself to the federated cluster if the computer is underutilized and unsubscribe it otherwise; the second is a "server program" that organizes the nodes into hierarchical structures handling the static service discovery and static load distribution inside the federated cluster; the third component is a fully distributed resource manager that handles the number of federations defined in a single network, dynamic membership in the federations, and dynamic load distribution and migration within federations.

          This project involves two major challenges. The first one is how to dynamically organize the nodes and how to structure the network in order to provide efficient discovery and executions. The second involves maintaining a secure group membership in the federated cluster in order to avoid malicious behavior such as denial of service attacks or mal-intentioned code execution.

          Low-level Resource Management

          In high speed networks, individual resources, such as solid state memory, are being employed by a remote user individually, as opposed to having to utilize the whole remote system. This implementation is done at the driver level.

          Advanced Quality of Service (AQoS)

          Quality of Service is an important feature when dealing with real-time applications such as VOIP or Streaming Media. However, due to the internet's heterogeneous architecture, controlling the QoS is a challenging task if a receiver is located outside of a private network and must traverse other service providers' domains. Overlay networks are a solution to improve QoS when sending information outside a controlled network. The Advanced Quality of Service (AQoS) project extends TREx's resource manager by monitoring the network as a resource in order to use its local information to find better quality routes than the underlying internet backbone. This is a novel, purely distributed, adaptive, and scalable solution for improving routes regardless of a node's physical location on the internet using an overlay network.


          Research Interests

          Operating Systems

          The design goal of operating systems for parallel computers is to provide a level of support to the programmer similar to that provided by current uniprocessor operating systems. The programmer programs a virtual machine with as many virtual processors necessary to exploit the inherent parallelism of the application. The operating system emulates this virtual machine, making parallel programs portable. In this context various problems are being addressed: spatial and temporal scheduling of virtual processors, efficient synchronization techniques, virtual memory management and I/O issues.

          Interconnection Networks

          Our work on interconnection networks for massively parallel systems involves the development of cost-effective high performance networks capable of supporting thousands or millions of processing elements. Included in this study is the performance analysis of Expanded Delta Networks (EDNs) and Least Common Ancestor Networks (LCANs) under commonly occurring sets of processor to processor communication patterns. As a result of the effort, efficient off-line routing algorithms for EDNs were developed and applied to commercially available massively parallel computers.

          Performance Evaluation

          Current research in performance evaluation deals with the development of models and methodologies for a general supercomputer performance evaluation theory. Such methods are being developed bottom up by building on known computational models and benchmarks.

          Parallel Algorithms

          Algorithmic topics include parallel models of computation and algorithms. Additionally, based on the development of Shearsort, our research group is looking for a proper taxonomy of parallel sorting. Many similarities can be found among sorting techniques and a unified framework is needed to enable further advances in this important area of study.

          Simulation Models

          Current research focuses on the development of all areas of advanced simulation models and techniques. Topics of interest include general purpose discrete-events simulation, synthetic workload generation, performance evaluation, and analysis of scheduling and load balancing algorithms through simulation.

          Synthetic Workload Generation

          The NASA Remote Exploration and Experimentation Project (REE) is interested in supporting academic research projects in areas that are needed to facilitate the development of fault-tolerant, COTS-based, parallel processors for use in space. The Schark research group is investigating the development of synthetic workload models for use in characterizing REE system performance over a wide range of application-types and fault conditions.


          Software

          Load Balancing/Scheduling Simulator

          Selected Publications

          • Ph.D. Theses



            • Service Address Routing for Concurrent Computing
              Daniel Valencia Sanchez, 2007


            • Predictive Adaptive Parallelism
              David Wangerin, 2006


            • Suitability Measures for High Performance Computers
              Shean McMahon, 2004


            • The Convergence of Massively Parallel Processors and Multiprocessors
              Vara Ramakrishnan, 2000


            • Resource Management Techniques for Multiprogrammed Distributed Systems
              Luis Miguel Campos, 1999


            • Designing Virtual Memory Systems for Parallel and Distributed Computing
              Veronica Reis, 1996


            • Computer Evaluation Using Performance Vectors
              Umesh Krishnaswamy, 1995


            • A Framework for Parallel Job Scheduling
              Raghu Subramanian, 1995


          • Journal and Conference Papers



            • Isaac D. Scherson, Daniel Valencia,and Enrique Cauich
              Service Address Routing: A Network-Embedded Resource Management Layer for Cluster Computing
              accepted for publication in "Cluster Computing", Elsevier.


            • Richert Wang, Enrique Cauich, and Isaac D. Scherson
              Federated Clusters Using the Transparent Remote Execution (TREx) Environment
              The Thirteenth International Conference on Parallel and Distributed Systems (ICPDS) Hsinchu, Taiwan, December 5-7, 2007.


            • Isaac D. Scherson, Daniel Valencia, Enrique Cauich, John Duselis, and Richert Wang
              Federated Grid Clusters Using Service Address Routed Optical Networks
              "Future Generation Computing Systems" (FGCS): International Journal of Grid Computing: Theories, Methods, and Applications, Volume 23, Issue 8, Elsevier, November 2007.


            • Isaac D. Scherson, Daniel Valencia,and Enrique Cauich
              Service Discovery for Grid Computing Using LCAN Mapped Hrarchical Directories
              "Journal of Supercomputing", Special Issue on GRID Computing, Volume 42, Issue 1, Springer, USA, October 2007.


            • Aljundi A. Ch., Dekeyser J.-L., Kechadi M.-T., and Scherson I. D.
              A Universal Performance Factor for Multi-Criteria Evaluation of Multistage Interconnection Networks
              Special Issue on Future Generation Computer Systems, Volume 22, Issue 7, Elsevier, August 2006.


            • David Wangerin and Isaac D. Scherson
              Using Predictive Adaptive Parallelism to Address Portability and Irregularity
              Proceedings of the 2005 International Symposium on Parallel Architectures, Algorithms, and Networks, Las Vegas, NV, USA, December 7-9, 2005, pp 370-375.


            • S. Meftali, J-L Dekeyser, and Scherson I. D.
              Scalable Multistage Networks for Multiprocessor System-on-Chip Design
              Proceedings of the 2005 International Symposium on Parallel Architectures, Algorithms, and Networks, Las Vegas, NV, USA, December 7-9, 2005, pp 352-356.


            • Daniel Valencia and Isaac D. Scherson
              Service Address Routing:A Network Architecture for Tightly Coupled Distributed Computing Systems
              Proceedings of the 2005 International Symposium on Parallel Architectures, Algorithms, and Networks, Las Vegas, NV, USA, December 7-9, 2005, pp 296-303.


            • C. Morin, R. Lottiaux, G. Vall�e, P. Gallard, D. Margery, J.-Y. Berthou, I. Scherson
              Kerrighed and Data Parallelism: Cluster Computing on Single System Image Operating Systems
              Proceedings of the 2004 IEEE International Conference on Cluster Computing, San Diego, CA, USA, September 2004.


            • Aljundi A. Ch., Dekeyser J.-L., and Scherson I. D.
              An Interconnection Networks Comparative Performance Evaluation Methodology: Delta and Over-Sized Delta Networks
              16th International Conference on Parallel and Distributed Computing Systems, Reno, USA, August 2003.


            • David Wangerin and Isaac D. Scherson
              Automatic Resource Management using an Adaptive Parallel Environment
              Proceedings of IPDPS 2003 Workshop on Massively Parallel Processing, Nice, France, April 2003


            • Aljundi A. Ch., Dekeyser J.-L., Kechadi M.-T., and Scherson I. D.
              A Study of an Evaluation Methodology for Unbuffered Multistage Interconnection Networks
              Proceedings of the 17th International Parallel and Distributed Processing Symposium (IPDPS'03), Nice, France, April 2003


            • Gaetan Scotto di Apollonia, Isaac Scherson, Christophe Gransart, Jean-Marc Geib
              Simulation-Aided Deployment of Distributed Applications in Heterogeneous Systems
              Proceedings of the 2002 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, San Diego, California, July 2002, pp.278-287


            • Aljundi A. Ch., Dekeyser J.-L., Kechadi M.-T., Scherson I. D.
              Comparitive Simulations and Performance Evaluation of MCRB Networks Using Multidimensional Queue Management
              Proceedings of the 2002 International Symposium on Performance Evaluation of Computer and Telecommunication Systems, San Diego, California, July 2002, pp.288-296


            • Shean T. McMahon and Isaac D. Scherson
              A Statistical Mechanical Approach to a Framework for Modeling Irregular Programs on Distributed or Cluster Computers
              Proceedings of the 35th Annual Simulation Symposium 2002, San Diego, USA, April 2002


            • D. Wangerin, C. DeCoro, L.M. Campos, H. Coyote, and I.D. Scherson
              A Modular Client-Server Discrete Event Simulator for Networked Computers
              Proceedings of the 35th Annual Simulation Symposium 2002, San Diego, USA, April 2002


            • H.D. Karatza and I.D. Scherson
              "Scheduling Job Classes in a Distributed System".
              Proceedings of SPECTS'2001, 2001 SCS Symposium on Performance Evaluation of Computer and Telecommunication Systems, SCS, Orlando, Florida, July 2001, pp. 322-329.


            • Luis Miguel Campos et al.
              A General Purpose Discrete Event Simulator
              Symposium on Performance Evaluation of Computer and Telecommunication Systems, Orlando, USA , July 2001


            • Fabricio Silva and Isaac D. Scherson
              Efficient Parallel Job Scheduling Using Gang Service
              To appear, International Journal of Foundations of Computer Science (June 2001)


            • Fabricio Silva and Isaac D. Scherson
              Simulation-based Average Case Analysis for Parallel Job Scheduling
              Proceedings of the 34th Annual Simulation Symposium, Seattle, April 2001


            • Fabricio Silva and Isaac D. Scherson
              Improving Parallel Job Scheduling Using Runtime Measurements
              Proceedings of the 6th Workshop on Job Scheduling Strategies for Parallel Processing , Cancun, Mexico, May 2000.


            • Vara Ramakrishnan and Isaac D. Scherson
              Executing Communication-Intensive Irregular Programs Efficiently
              Proceedings of Irregular 2000, Cancun, Mexico, May2000.


            • Fabricio Silva and Isaac D. Scherson
              Improving Throughput and Utilization in Parallel Machines Through Concurrent Gang
              Proceedings of the IEEE International Parallel and Distributed Processing Symposium 2000, Cancun, Mexico, May 2000.


            • Luis Miguel Campos and Isaac D. Scherson
              Rate of Change Load Balancing in Distributed and Parallel Systems
              Parallel Computing Journal, Volume 26, pages 1213-1230.


            • Fabricio Silva and Isaac D. Scherson
              Towards Flexibility and Scalability in Parallel Job Scheduling
              11th IASTED International Conference on Parallel and Distributed Computing and Systems, Boston, USA, November 1999.


            • Fabricio Silva and Isaac D. Scherson
              Concurrent Gang: Towards a Flexible and Scalable Gang Scheduler
              11th Symposium on Computer Architecture and High Performance Computing, Natal, Brazil, September 1999.


            • Fabricio Silva and Isaac D. Scherson
              Bounds on Gang Scheduling Algorithms
              2nd International Conference on Parallel Computing Systems, Ensenada, Mexico, August 1999.


            • Vara Ramakrishnan, Isaac D. Scherson and Raghu Subramanian
              Efficient Techniques for Nested and Disjoint Barrier Synchronization
              Journal of Parallel and Distributed Computing, Special Issue on Compilation and Architectural Support for Parallel Applications, vol. 58, pp 333-356.


            • Fabricio Silva and Isaac D. Scherson
              Improvements in Parallel Job Scheduling Using Gang Service
              1999 International Symposium on Parallel Architectures, Algorithms and Networks, Freemantle, Australia, June 1999.


            • Luis Miguel Campos and Isaac D. Scherson
              Rate of Change Load Balancing in Distributed and Parallel Systems
              IPPS/SPDP, San Juan, Puerto Rico, April 1999, pp 701-707.


            • Isaac D. Scherson and Luis Miguel Campos
              Efficient Task Scheduling Heuristic for Multiprocessor Systems,
              Parallel and Distributed Computing and Systems, Las Vegas, USA, October 1998, pp 32-37.


            • Fabricio Silva, Luis Miguel Campos and Isaac D. Scherson
              Improvements in Gang Scheduling for Parallel Computers,
              Parallel Computing Workshop, Singapore, September 1998, pp P2-H1--P2-H5.


            • Isaac D. Scherson and Luis Miguel Campos
              A Distributed Dynamic Load Balancing Strategy Based on Rate of Change,
              Parallel Computing Workshop, Singapore, September 1998, pp P2-I1--P2-I8.


            • Fabricio Silva, Luis Miguel Campos and Isaac D. Scherson
              A Lower Bound for Dynamic Scheduling of Data Parallel Programs,
              Euro-Par 98, Southampton, UK, September 1998, pp 367-372.


            • Christophe Rapine, Isaac D. Scherson and Denis Trystram
              On Line Scheduling of Parallelizable Jobs,
              Euro-Par 98, Southampton, UK, September 1998, pp 322-327.


            • V. L. Reis and Isaac D. Scherson
              Impacts of Network Latency on Parallel Virtual Memory Management,
              Symposium on Performance Evaluation of Computer and Telecommunication Systems, Reno, USA, July 1998.


            • Luis Miguel Campos, V. L. Reis and Isaac D. Scherson
              Swap File Organizations for Parallel Virtual Memory,
              Parallel and Distributed Computing and Systems, Washington DC, USA,October 1997, pp 209-214.


            • V. L. Reis, Luis Miguel Campos and Isaac D. Scherson,
              Parallel Virtual Memory for Time Shared Environments,
              Eighth Brasilian Symposium on Computer Architectures and High Performance Processing, Recife, Brazil, August 1996, pp 223-233.


            • R. Subramanian, Isaac D. Scherson, V. L. Reis and Luis Miguel Campos,
              Scheduling Computationally Intensive Data Parallel Programs,
              Placement Dynamique et Repartition de Charge: application aux systemes paralleles et repartis, Paris, France, July 1996, pp 39-60.


            • U. Krishnaswamy and Isaac D. Scherson,
              Micro-architecture Evaluation Using Performance Vectors,
              SIGMETRICS'96, May 1996.


            • Luis Miguel Campos, V. L. Reis and Isaac D. Scherson,
              A distributed fault tolerant implementation of a stock market and brokerage office,
              First International Workshop on Distributed Systems, Salvador, Brazil, May 1996.


            • V. L. Reis and Isaac D. Scherson,
              A Virtual Memory Model for Parallel Supercomputers,
              International Parallel Processing Symposium, April 1996, pp 537-543.


            • F. Chen, V. L. Reis and Isaac D. Scherson,
              A Study of Parallel Input/Output Subsystems,
              International Workshop on Advanced Parallel Processing Technologies, Beijing, China, September 1995, pp 10-15.


            • V. Ramakrishnan, Isaac D. Scherson and R. Subramanian,
              Efficient Techniques for Fast Nested Barrier Synchronization,
              Symposium on Parallel Algorithms and Architectures, Santa Barbara, USA, July 1995, pp 157-164.


            • Isaac D. Scherson and C.-K. Chien,
              Least Common Ancestor Networks,
              VLSI Design, 1995, Vo. 2, No. 4, pp 353-364.


            • R. Subramanian and Isaac D. Scherson,
              An Analysis of Diffusive Load-Balancing,
              Symposium on Parallel Algorithms and Architectures, June 1994, pp 220-225.


            • Isaac D. Scherson and R. Subramanian,
              Efficient Off-line Routing of Permutations on Restricted Access Expanded Delta Networks,
              International Parallel Processing Symposium, April 1993, pp 248-290.


            • U. Krishnaswamy and Isaac D. Scherson,
              A Methodology for Performance Evaluation of Supercomputers,
              International Parallel Processing Symposium - Parallel Systems Fair, April 1993, pp 46-49.


            • Isaac D. Scherson, D. A. Kramer, and B. D. Alleyne,
              Bit-Parallel Arithmetic in a Massively Parallel Associative Processor,
              IEEE Transactions on Computers, Vol. 41, Number 10, October 1992, pp.1201-1210.


            • B. D. Alleyne and Isaac D. Scherson,
              Expanded Delta Networks for Very Large Parallel Computers,
              International Conference on Parallel Processing, August 1992, pp 127-131.


            • Isaac D. Scherson,
              Orthogonal Graphs for the Construction of a Class of Interconnection Networks,
              IEEE Transactions on Parallel and Distributed Systems, January 1991.


          School of Information and Computer Science,
          University of California, Irvine CA 92697-3425

          http://www.ics.uci.edu/~smyth/courses/cs175/ CS 175, Winter 2016

          CS 175, Project in Artificial Intelligence, Winter 2016

          Time:  Mondays and Wednesdays, 11:00 to 12:20
          Location: ICS 174      Course code: 34310

          Professor: Padhraic Smyth, Office Hours: Tuesdays, 10 to 11:30, DBH 4216 

          TAs:   
          Kevin Bache,  email: kbache at ics.uci.edu, Office Hours: Mondays 2 to 4pm, DBH 4059
          Jihyun Park,  email: jihyunp at uci.edu,  Office Hours: Wednesdays, 3 to 5pm, DBH 4013
          Discussion Section: Friday, 1:00 to 1:50, ICS 174 

          Course Description: students in this project class will work in small teams to develop artificial intelligence and machine learning algorithms and apply them to a range of different problems related to text analysis. These problems will include, for example, document classification and clustering, sentiment analysis, information extraction, word prediction, and text synthesis. Projects will make use of real-world data from sources such as Twitter, Wikipedia, news articles, product and movie reviews, emails, the US patent database, and more.   

          Piazza Class Website: for questions, online discussion, announcements.  The Piazza link for our class is:  https://piazza.com/uci/winter2016/compsci175/home. Please use the Piazza class forum as your first option for asking questions outside of class or office hours - the TA, or professor, or students in the class will typically be able to reply quickly. If you have a specific question about the grading of your assignment or report please contact the TA (who will either answer your question directly or pass it along to the professor). If you must use email (other than Piazza) for some reason (although we would prefer you don't), please put "[CS 175]" at the start of your subject line.

          Weekly schedule/syllabus

          Links to software (NLTK, Anaconda, etc)


          Assignments and deadlines
          Assignment 1   (submit to EEE by 10pm Monday January 11th)
          Assignment 2    (submit to EEE by 10pm Friday January 22nd))
          Project proposal  (submit to EEE (as a PDF file) by 10pm Friday January 29th)
          Project progress report  (submit to EEE (as a PDF file) by 10pm Monday February 22nd)
          Progress presentation  (due 
          TBD)
          Project final report  (due TBD)
              Instructions for final submissions
              Template for the final report
          (Note: assignments or reports submitted within a 24-hour window after the deadline will have their points reduced by 50%. Beyond the 24-hour late window the assignments or reports will not be graded.)

          Additional Information
              Slides from class lectures

              Background reading and links
                     
          Classification and supervised learning
                      Clustering, topic modeling, text embedding (unsupervised learning)
                      Examples of text data sets
                      Examples of demos, software, applications
                      Where to look for research papers

              
          Grading Policy
          Final grades will be a weighted combination of project scores as follows

          20% for Assignments 1 and 2 (10% each)
          20% for the Project Proposal
          20% for the Project Progress Report
          10% for the in-class Project Presentations (5% each)
          30% for the Final Report

          Academic Honesty (all students are required to read this page)
          http://www.ics.uci.edu/~smyth/courses/cs277/ CS 277: Main Page

          CS 277: Data Mining, Winter 2014


          • Time:  Mondays and Wednesdays, 5:00 to 6:20
          • Location: ICS 174 
          • Course code: 34980
          • Professor: Padhraic Smyth
            • Email: smyth at ics.uci.edu. Note: for any class-related emails make sure to put [CS277] at the start of the subject line of your email
            • Office Hours: Tuesdays 10 to 11:30am, DBH 4212 
          • Reader:  Maryam Khademi
            • Email: mkhademi at uci.edu. Please contact Maryam with any questions you have about the Assignments.
            • Office Hours:  Thursdays 12 noon to 1pm, Room DBH 4209

          • Please consult our course page on Piazza for course-related announcements and student posts and discussion

          • Syllabus and Slides

          • Project Guidelines
            • Please hand in project reports/proposals in class and also upload electronic copies to the appropriate folder on EEE
            • Project proposals due Monday January 27th in class
            • Progress Report 1 due Monday February 10th in class
            • Progress Report 2 due Wednesday February 26th in class
            • Final report due (electronic submission to EEE) by 8am Monday March 17th
          • Assignment 1 (due Wednesday January 15th)

          • Links to Software

          • Prerequisites for taking this Course: CS 273a or CS 274a or an equivalent class. In particular, you are expected to have working knowledge of basic concepts in machine learning, particularly classification and regression algorithms (e.g., material such as described in this CS 178/273a set of summary notes). 

          • Goals of this Course: This course will cover a variety of important application areas for data mining and machine learning, such as techniques for text classification and text mining, Web data analysis, recommender systems, data analysis of social network and social media data, anomaly and fraud detection, and credit scoring.  The course will build on your knowledge of basic concepts of machine learning/data mining algorithms (e.g., from CS 273a) to show how these algorithms can be applied in specific contexts. A significant portion of each student's grade will depend on a class project.

          • Reading and Textbooks: 
            • There is no required text for this class - but there are multiple introductory and more advanced texts that are available on the Web that you may find useful.
            • In addition we will be providing lists of specific research papers to read throughout the quarter.
          • Grading Policy
            Final grades will be a weighted combination of project scores as follows
          • 10% for  Assignment 1
          • 20% for each of the Progress Reports
          • 30% for the Final Report

          • Academic Honesty 
            Academic honesty is taken seriously. For homework problems or programming assignments you are allowed to discuss the problems or assignments verbally with other class members, but under no circumstances can you look at or copy anyone else's written solutions or code relating to homework problems or programming assignments. All problem solutions and code submitted must be material you have personally written during this quarter, except for any library or utility functions which we supply. Failure to adhere to this policy can result in a student receiving a failing grade in the class. It is the responsibility of each student to be familiar with UCI's academic honesty policies. 
             




          http://www.ics.uci.edu/~smyth/courses/cs274/ CS 274A: Main Page

          CS 274A: Probabilistic Learning: Theory and Algorithms, Winter 2016


           
          • Time:  Mondays and Wednesdays, 3:30 to 4:50
          • Location: ICS 180
          • Course code: 34980
          • Professor: Padhraic Smyth. Office Hours: Tuesdays, 10:00 to 11:30 , DBH 4216
          • Syllabus  (dates may shift during the quarter but content will remain broadly the same)
          • Background Notes: notes to accompany lectures, pointers to relevant sections of the text, and background reading 
          • Questions?
            • Please use the Piazza class Web site for class-related questions and discussion:  https://piazza.com/uci/winter2016/cs274a/home

          • Homeworks
          • Homework 1 [PDF] [LaTeX] [Matlab code] Hardcopy due in class on Wednesday January 13th
          • Homework 2 [PDF] [LaTeX] [Matlab code] Hardcopy due at the start of class on Monday January 25th
          • Homework 3 [PDF] [LaTeX] [Data set] Hardcopy due at the start of class on Wednesday January 17th

          • Textbooks (we will be referring to sections of these texts during the quarter: they are recommended but not required):
            • Machine Learning: A Probabilistic Perspective, by Kevin Murphy, MIT Press, 2012. This has a lot more material than we will have time to cover in this class, but if you intend to do research in machine learning (or related to machine learning) after you complete this class, then this text is an excellent investment..
            • Bayesian Reasoning and Machine Learning, by David Barber, Cambridge University Press (PDF version freely available online). 

          • Other Optional Reference Texts: 
            • Pattern Recognition and Machine Learning, by Chris Bishop, 2007, Springer. Widely used comprehensive text on statistical learning - the Murphy and Barber texts are a bit more up to date.
            • Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman,  Springer, 2009. Excellent text on machine learning from a statistical perspective. PDF version available freely online. See also An Introduction to Statistical Learning with Applications in R, 2013, which is a more introductory/applied version of the original book, and is also available freely online.
            • All of Statistics: A Concise Course in Statistical Inference, by Larry Wasserman, 2004, Springer. Written as a concise introduction to key ideas in statistics, written with computer scientists and machine learning students as a target audience. 
            • Note that this list is not intended to be exhaustive - there are several other excellent texts on machine learning that were published in recent years.

          • Course Goals: Students will develop a comprehensive understanding of probabilistic approaches to learning from data. Probabilistic learning is a key component in many areas within modern computer science, including artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, and so forth. The course will provide a tutorial introduction to the basic principles of probabilistic modeling and then demonstrate the application of these principles to the analysis, development, and practical use of machine learning algorithms.  Topics covered will include probabilistic modeling, defining likelihoods, parameter estimation using likelihood and Bayesian techniques, probabilistic approaches to classification, clustering, and regression, and related topics such as model selection and bias/variance tradeoffs. 
          • Prerequisites for taking this class: Knowledge of basic concepts in probability, multivariate calculus, and linear algebra are required for this course. Please note that a good understanding of probability in particular is important for this class.  

          • Grading Policy
            Final grades will be based on a combination of homework assignments and exams: 30% homeworks, 30% midterm, and 40% final. No credit for late homeworks - instead your lowest scoring homework will be dropped and not included in your score.

          • Academic Honesty 
            Academic honesty is taken seriously. For homework problems or programming assignments you are allowed to discuss the problems or assignments verbally with other class members, but under no circumstances can you look at or copy anyone else's written solutions or code relating to homework problems or programming assignments. All problem solutions and code submitted must be material you have personally written during this quarter, except for any library or utility functions which we supply. Failure to adhere to this policy can result in a student receiving a failing grade in the class. It is the responsibility of each student to be familiar with UCI's academic honesty policies. 

          • UCI Catalog Course Description: 
            Probabilistic Learning: Theory and Algorithms: A unified probabilistic framework for learning algorithms. Classical pattern recognition algorithms, probabilistic mixture models, kernel methods, hidden Markov models, among others. Multivariate data analysis concepts for classification and clustering. Methodologies such as cross-validation and bootstrap. Prerequisites: basic calculus and linear algebra.
             





          http://www.ics.uci.edu/~rdiazgar/ Raúl Díaz - Personal Homepage

          Raúl Díaz García

          e-mail: rdiazgar (at) uci.edu
          office: 4219 Donald Bren Hall

          I'm a PhD candidate at UC Irvine, where I'm advised by professor Charless Fowlkes at the Computer Vision group.

          My research is focused on improving object detection by finding geometric context cues. In particular, I investigate how structure from motion and multi-view stereo can help in the world of scene understanding.

          [resume] [Google Scholar] [LinkedIn]

          Publications

          R. Díaz, M. Lee, J. Schubert, C. Fowlkes "Lifting GIS Maps into Strong Geometric Context", Technical Report , July 2015.
          [paper]

          R. Díaz*, S. Hallman*, C. Fowlkes, "Detecting Dynamic Objects with Multi-View Background Subtraction", ICCV , Sydney, Australia, December 2013.
          [paper] [supplementary material]

          R. Díaz*, S. Hallman*, C. Fowlkes, "Multi-View Background Subtraction for Object Detection", Scene Understanding Workshop , Portland, OR, June 2013.
          [abstract] [poster]

          * shared first author

          Presentations

          R. Díaz, "Strong Geometric Context for Image Understanding", Invited seminar talk, Computer Vision Center, UAB, July 2014. [video]

          http://www.ics.uci.edu/~jutts/is8c/ Integrated Studies 8C

          Integrated Studies 8C - Spring 2005

          Department of Statistics

          University of California, Davis

          Welcome to the homepage for Integrated Studies 8C

          Class Time

          Mon/Wed 10:00-11:50, Miller Hall
          Contact Information:
          Professor Jessica Utts
          387 Kerr Hall, Department of Statistics
          752-6496, jmutts@ucdavis.edu
          Office Hours: By appointment

          Some sections of this website include .pdf files.
          Download Adobe Acrobat Reader to read these files

          Syllabus (pdf version)

          Syllabus (Word version)

        • Journal page explanation
        • Links to Resources (Researchers web pages, Journals, Online experiments, Laboratories, etc.)

          Ideas for Team Projects and Expectations for Projects and Presentations

        • Power calculator for experiments testing one proportion
        • Power calculator for experiments comparing two proportions
        • Ideas for Term Papers

          John Stenzel's "Grading Criteria" for term papers (useful advice for writing a good paper too)

          References for Papers in Reader

          Links to Required Reading Material NOT in the Reader:
          Parapsychology FAQ (Frequently Asked Questions)
              FAQ File 2  and FAQ File 3

          Zen and the Art of Debunkery

          Links to Some Online Reading Material (including some papers in the reader)

          Solutions to Statistics Exercises
          • Solutions to Chapter 8 exercises (binomial and normal)
          • Solutions to Chapter 11 exercises (hypothesis testing)
          • Solutions to confidence interval exercises (Ch 19 of STS)

          Email Archive

          Sample Statistics Quiz and Key to Sample Statistics Quiz

          Sample Final Exam (sorry, no answers)


           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           
           







          http://www.ics.uci.edu/~jutts/110/ Statistics 110

          Statistics 110 - Fall 2015

          Department of Statistics

          University of California, Irvine

          Welcome to the homepage for Statistics 110: Statistical Methods for Data Analysis I (Undergraduate Level)

          Class Time

          Lecture
          Discussion
          174 ICS
          Mon, Wed 12:30 - 1:50pm
          140 Social Science Lab
          Fri 11:00 - 11:50 and 12:00 - 12:50

          How, when and where to find us:


          Professor Jessica Utts TA: Brandon BermanReader: Micah Jackson
          2038 Donald Bren Hall 2032 Donald Bren Hall2013 Donald Bren Hall
          (949) 824-0649 no phoneno phone
          jutts_at_uci.edu [Not clickable to avoid spam] bermanb_at_uci.edu gmjackso_at_uci
          Mon, Wed 2:00 to 3:30pm, and by appointment
          In 2038 or 2032 DBH
          Special midterm office hours: Tues, Oct 27, 4:00 - 5:30; no office hours Wed, Oct 28
          Tues 2:30 to 4:00pm
          Fri 1:30 to 3:00pm
          Both in 2032 DBH
          Mon 4:00 to 5:00pm
          2013 DBH
          For questions on grading only

          Syllabus and other information

          • Syllabus
          • Look up your homework and exam ID code (listed by first 6 digits of student ID#)
          • Link to website to accompany the textbook, including data sets

          Information about getting an ICS Computing Account, and Installing and Using R and R Studio (Updated throughout the quarter)

          • How to get an account to use the ICS Labs
          • ICS Lab Hours
          • How to Install R and R Studio (pdf)
          • A Tutorial on installing and using R and R Studio
          • The R Project Homepage
          • Helpful website on learning and using R, from UCLA Academic Technology Services
          • R Session showing how to get results for the Sign reading by Age example, including confidence and prediction intervals

          Data Sets and Applets (Posted as needed)

          • Highway Sign data, with Age listed first, then Distance
          • Height and weight data on n = 43 males, used for examples in class
          • MedGPA data set (accompanying the book, used in class and in homework)
          • Applet for guessing and viewing the regression line when you input data
          • Applet that allows you to place points on scatter plot and view regression line (not supported in Google Chrome)
          • Confidence interval applet
          • PhysicalData.txt for Homework #5

          Practice Exams and Exam Keys

          • Review for Midterm (posted at least a week before the midterm)
          • Practice Midterm Exam
          • Practice Midterm Exam Key
          • Midterm Exam Key (Posted after the exam)


          • Final Exam Review (topics since the midterm only)
          • Practice Final
          • Practice Final Key
          • Final Exam Key (Posted after the exam)

          • Cover Sheet for points reconsideration

          Homework Solutions, posted after they are due

        • Assignment #1, Due Wed, Oct 7
        • Assignment #2, Due Wed, Oct 14
        • Assignment #3, Due Wed, Oct 21
        • Assignment #4, Due Mon, Oct 26
        • Assignment #5, Due Mon, Nov 9
        • Assignment #6, Due Wed, Nov 18
        • Assignment #7, Due Wed, Nov 25
        • Assignment #8, Due Wed, Dec 2

        •  

          Daily Schedule

           
          Date Sections covered and skipped; other topics covered Material from class lectures and discussion (posted when covered)Assignment and Date Due
          Mon Sept 28 Introduction and start Chapter 0 Lecture 1 slides (as a pdf file, 6 to a page)
          Wed Sept 30 Finish Chapter 0; Sections 1.1, 1.2 Lecture 2 slides or Compact version Homework assignment #1, due Wed, Oct 7
          MedGPA dataset
          Fri Oct 2 Disc Introduction to R and R Studio R Code from Oct 2 discussion and as a .txt file
          Mon Oct 5 Sections 1.3 to 1.5 Finishing Lecture 2 slides or Compact version
          Lecture 3 slides or Compact version
          Wed Oct 7 Review hypothesis testing, confidence intervals and distributions; Section 2.1Lecture 4 slides or Compact version
          R code and results for Highway sign-reading example
          Homework assignment #2, due Wed, Oct 14
          Q5 clarified Oct 11; see text in red.
          Fri Oct 9 Disc R for Regression (plots, linear models, etc) R Code from Oct 9 discussion
          Mon Oct 12 Sections 2.2 and 2.3 Lecture 5 slides or Compact version
          Wed Oct 14 Confidence and prediction intervals, Section 2.4Lecture 6 slides or Compact version
          Highway sign example showing CI and PI commands and results
          Homework assignment #3, due Wed, Oct 21
          Fri Oct 16 Disc More R for Regression; Question and Answer R Code from Oct 16 discussion
          Mon Oct 19 Sections 3.1 and 3.2 Lecture 7 slides or Compact version
          Wed Oct 21 Section 3.3 and Section 3.6 as applied to material in 3.3 Lecture 8 slides (in color) or Compact version (in black and white) Homework assignment #4, due Mon, Oct 26
          Fri Oct 23 Disc R for multiple regression; Midterm reviewR Code from Oct 23 discussion
          Review for Midterm
          Mon Oct 26 Section 3.5; More about ANOVA (not in book) No lecture slides today - on white board, and these 2 examples:
          Multicollinearity example and Example of why order matters
          Wed Oct 28 MIDTERM EXAM (covers through Fri, Oct 23) No homework this week
          Fri Oct 30 Disc More about R for multiple regression R Code from Oct 30 discussion
          Mon Nov 2 Section 3.4 and finish Section 3.6 Lecture 11 slides or Compact version Homework assignment #5, due Mon Nov 9 (covers 10/26 and 11/2 lectures)
          PhysicalData.txt for Homework #5
          Wed Nov 4 Section 4.2 (Skip 4.1) Lecture 12 slides or Compact version
          Best Subsets Real Estate Example
          Fri Nov 6 Disc R for creating and comparing models; variable selection methods in R R Code from Nov 6 discussion
          Mon Nov 9 Sections 1.5 and 4.3 Lecture on white board, then go over examples.
          UK regions example or 2 slides to a page
          Case diagnostics for the real estate example
          Case diagnostics in R
          Homework assignment #6, due Nov 18 (covers 11/4 and 11/9 lectures)
          StateSAT data
          Description of the State SAT data
          Wed Nov 11 Holiday, no class
          Fri Nov 13 Disc Case diagnostics in R; Regression hypotheses stated as models R Code from Nov 13 discussion
          Mon Nov 16 Start Chapter 5 Lecture on white board, then go over example.
          GPA and seat location example
          Wed Nov 18 Continue Chapter 5; Section 7.2 Lecture on white board, then go over example.
          Party Days and seat location example
          Homework assignment #7, due Wed, Nov 25
          Fri Nov 20 Sections 4.4 and 7.5; R for analysis of variance; comparing lm, anova and aov Notes for Nov 20 Discussion
          Mon Nov 23 Chapter 6 Lecture 16 slides or Compact version
          Two factor ANOVA Example
          Wed Nov 25 Other topics in analysis of variance (random effects, nested factors, repeated measures) Lecture 17 slides or Compact version Homework assignment #8, due Dec 2
          Fri Nov 27 Disc Thanksgiving holiday, no discussion
          Mon Nov 30 Analysis of covariance; Analysis of variance with more than two factors Lecture on white board
          Analysis of covariance example
          Wed Dec 2 Wrap up and review ANOVA scenarios for discussion and Answers (posted after class)
          Review for final exam
          Fri Dec 4 Disc Question/answer for final exam
          Mon Dec 7 Final Exam, 1:30 to 3:30pm

           
            http://www.ics.uci.edu/~jutts/st108/ Statistics 108

          Statistics 108 - Fall 2007

          Department of Statistics

          University of California, Davis

          Welcome to the homepage for Statistics 108: Regression Analysis

          Class Time

           
          Lecture
          Discussion
          Who's Likely to Be Giving It
          Professor Jessica Utts
          Irina Udaltsova
          Location and Time
          6 Olson Hall
          MWF 4:10 - 5:00
          Both discussion sections held on Tuesdays
          A01 11-11:50am 7 Wellman
          A02 4:10-5pm 261 Olson
          Students are responsible for what is covered.

          How, when and where to find us:

          Jessica Utts Irina Udaltsova
          4214 Math Sciences Bldg 1117 Math Sciences Bldg
          752-6496 no phone
          jmutts@ucdavis.edu isudaltsova-at-ucdavis.edu
          Tues and Fri 2:00 - 3:30 or by appointment
          SPECIAL OFFICE HOURS FOR FINAL EXAM:
          Monday, Dec 10, 10am-noon
          Mon 5:00 - 6:00 and Wed 1:00 - 2:00 or by appointment
          SPECIAL OFFICE HOURS FOR FINAL EXAM:
          Friday, Dec 7, 5-6

          Information about R - Thanks to Michael McAssey for these materials!!

          • Installing R
          • Getting Data into R
          • Making a Scatterplot in R
          • Simple Linear Regression in R
          • Regression Inferences in R
          • More Regression Inferences in R
          • Diagnostics in R
          • Transformations in R
          • Conducting a Lack of Fit Test in R
          • Matrices in R
          • Multiple Regression in R
          • Extra Sums of Squares in R
          • Polynomial and Interaction Regression Models in R
          • Model Selection in R

          Additional R Resources:
          100 page Introduction to R from the R website
          Practical Regression and Anova using R, by Julian Faraway

          Syllabus, resources and material from class lectures

          • Syllabus
          • Publisher's website for the textbook (contains same material as the book CD)
          • Powerpoint slides from Friday September 28 (as a pdf file)
          • Review of Stat 13 from Friday October 5
          • Height and Weight Example from October 10, with R output
          • Data file used in October 10 example
          • October 12 alcohol and tobacco example (illustrates outlier)
          • October 12 seafood prices example (illustrates unequal variance)
          • October 15 pictures of transformations on X
          • R Code for Discussion 3, October 16 (pdf)
          • R Code for Discussion 3, October 16 (Word)
          • R Code for Discussion 4, October 23 (pdf)
          • R Code for Discussion 4, October 23 (Word)
          • Real Estate Example from October 26
          • Transformed Real Estate Data Example from October 31 (Word format)
          • Real Estate Example comparing models, Nov 5
          • R Code for Discussion 6, November 6 (pdf)
          • R Code for Discussion 6, November 6 (Word)
          • Chug time example of quadratic model, Nov 9
          • R Code for Discussion 7, November 13 (pdf)
          • R Code for Discussion 7, November 13 (Word)
          • Predicting height from mother's height - example using indicator variables, Nov 14
          • Best subsets regression example, Nov 19
          • R Code for Discussion 8, November 20 (pdf)
          • R Code for Discussion 8, November 20 (Word)
          • R Code for Discussion 9, November 27 (pdf)
          • R Code for Discussion 9, November 27 (Word)
          • Case Diagnostics for Real Estate Example, short version
          • Case Diagnostics for Real Estate Example, all cases version
          • R Code for Discussion 10, December 4 (pdf)
          • R Code for Discussion 10, December 4 (Word)
          • List of topics for review on Friday, December 7

          Practice Exams and Exam Keys (posted after the exam) (Download Adobe Acrobat Reader to read these .pdf files)

          • Practice Midterm Exam
          • Practice Midterm Exam Key
            (An error was corrected in question 3d. It should have referred to the model in part b, not part c.)
          • Midterm Exam Key
          • Final Exam Sample Multiple Choice Questions
          • Final Exam Key

          Homework Solutions, posted after they are due (Download Adobe Acrobat Reader to read these .pdf files)

        • Assignment #1, Due Wed. October 10
        • R Code for HW1 (pdf)
        • R Code for HW1 (Word)
        • Assignment #2, Due Wed. October 17
        • Assignment #3, Due Wed. October 24
        • R Code for HW3 (pdf)
        • R Code for HW3 (Word)
        • Assignment #4, Due Wed. October 31
        • R Code and results for Exercise 5.24ab (pdf)
        • Assignment #5, Due Wed. November 14
        • R Code for HW5 (pdf)
        • R Code for HW5 (Word)
        • Assignment #6, Due Wed. November 21
        • R Code for HW6 (pdf)
        • R Code for HW6 (Word)
        • Assignment #7, Due Wed. November 28
        • R Code for HW7 (pdf)
        • R Code for HW7 (Word)
        • Assignment #8, Due Wed. December 5

        •  

          E-mail list archive (kerberos log-in and password required)

          Homework assignments:

           
          Date Assigned Date Due Assignment Sections covered and skipped
          Fri Sept 28 None None Overview of Regression
          Mon Oct 1 Wed. Oct 10 Ch. 1: #2, 8, 12, 30 Sections 1.1 to 1.5 (Read 1.4 on your own)
          Wed Oct 3 Wed. Oct 10 Ch. 1: #27; use computer, show work Sections 1.6, 1.7; Skip 1.8
          Fri Oct 5 Wed. Oct 10 Ch. 2: #27 Section 2.1, briefly 2.2, Skip 2.3
          Mon Oct 8 Wed. Oct 10 Ch. 2: #9, 10, 12 Sections 2.4, 2.5; Skip 2.6
          Wed. Oct 10 Wed. Oct 17 Ch. 2: #28ab, 29bde Sections 2.7 (except pgs 68-71) and 2.9; Do 2.8 later; Read 2.10; Skip 2.11
          Fri. Oct 12 Wed. Oct 17 Ch. 3: #2, 9 (draw plot by hand or computer) Sections 3.1 to 3.3; Skip 3.4 to 3.6
          Mon. Oct 15 Wed. Oct 17 Ch. 3: #19,20 Section 3.9; Read 3.8
          Also Mon. Oct 15 Wed. Oct 24 Ch. 3: #18 Already noted above
          Wed. Oct 17 Wed. Oct 24 Ch. 3: #23 AND
          (unrelated to 3.23) Conduct the test for lack of fit on the Height-weight data used in October 10 example.
          Useful handouts:
          Height and Weight Example from October 10, with R output
          Conducting a Lack of Fit Test in R
          Sections 2.8 and 3.7; Skip Chapter 4
          Fri. Oct 19 Wed. Oct 24 Ch. 5: #1, 3, 8ab (Do these by hand, not computer) Sections 5.1 to 5.6; Read 5.7 (go over in Tues discussion)
          Mon. Oct 22 Wed. Oct 24 Ch. 5: #5 (by hand or computer), 17ab, AND Use matrix algebra to show that the hat matrix H is idempotent. Sections 5.8 to part of 5.11
          Wed. Oct 24 Wed. Oct 31 Ch. 5: #24ab Sections 5.11 and 5.13; read 5.12
          Fri. Oct 26 Wed. Oct 31 Ch. 6: #1, 22ab,
          AND Interpret in your own words the coefficients (b0, b1, b2) for the Example in Section 6.9 (see pg 240 for coefficients).
          Section 6.1
          Mon. Oct 29 Wed. Oct 31 Ch. 6: No new homework today. Sections 6.2 to 6.5
          Wed. Oct 31 Wed. Nov 14 Ch. 6: #15bc, 16a, 17 Sections 6.6, 6.7
          Mon. Nov 5 Wed. Nov 14 Ch. 7: #5, 26 Sections 7.1 to 7.3; Skip 7.4, 7.5
          Wed. Nov 7 Wed. Nov 14 Ch. 7: #22 Finish Section 7.3 (HW is from Sect. 7.6, will cover Fri.)
          Fri. Nov 9 Wed. Nov 21 Ch. 8: #4abeg (parts a, b, e, g only) Section 7.6; Section 8.1
          Wed. Nov 14 Wed. Nov 21 Ch. 8: #16abc, 21
          Data for 8.16 and 8.20, with headers
          Sections 8.3, 8.4
          Fri. Nov 16 Wed. Nov 21 Ch. 8: #17, 18, 20 Sections 8.2, 8.5 (Read 8.6, 8.7)
          Mon. Nov 19 Wed. Nov 28 Ch. 9: #25bc AND Fit the best model from part c and create an appropriate residual plot. Write down the fitted model, show the plot, and comment on whether you think it's a good model.
          Rows 57-113 of Appendix C.1
          Sections 9.1 (partial), 9.3, 9.4 (Read 9.1, 9.2)
          Wed. Nov 21 No HW CLASS CANCELLED
          Mon. Nov 26 Wed. Nov 28 Ch. 9: #4, 5, 7 Finish Sections 9.3, 9.4 (Skip 9.5, 9.6 or read if interested)
          Wed. Nov 28
          Fri. Nov 30
          Mon. Dec 3
          Wed. Dec 5 Chapter 10 homework is in this file. There will be no further assignments.

          Chapter 10 homework data, if you need it.
          Sections 10.2, 10.3, 10.4

           
            http://www.ics.uci.edu/~jutts/8/ Statistics 8

          Statistics 8, Fall 2010

          Department of Statistics

          University of California, Irvine

          Welcome to the homepage for Statistics 8: Introduction to Biostatistics

          Class Time

          Lecture
          Discussion Sections
          DBH 1100
          Mon, Wed, Fri 12:00 - 12:50pm
          1)Mon 4:00 - 4:50pmICS 180Shandong Min
          2)Mon 4:00 - 4:50pmICS 174Jason Kramer
          3)Mon 5:00 - 5:50pmICS 174Jason Kramer
          4)Mon 6:00 - 6:50pmICS 174Shandong Min

          How, when and where to find us:

          Professor Jessica Utts TA (Sections 2 and 3): Jason Kramer TA (Sections 1 and 4): Shandong Min
          2212 Donald Bren Hall (DBH) 2032 Donald Bren Hall 2032 Donald Bren Hall
          (949) 824-0649no phoneno phone
          jutts_at_uci.edu jskramer_at_uci.edu shandonm_at_uci.edu
          Mon, Wed 3-4
          or by appointment
          Mon 1-3
          Fri 1-2
          Tues 3-4
          Thurs 3-5
          Office hours by day: Mon
          1-3 (Kramer)
          3-4 (Utts)
          Tues
          3-4 (Min)
          Wed
          3-4 (Utts)
          Thurs
          3-5 (Min)
          Fri
          1-2 (Kramer)
          Extra office hours for final exam:
          (Final exam is Monday, Dec 6, 1:30 - 3:30)
          Fri, Dec 3
          1-3 instead of 1-2 (Kramer)
          Weekend of Dec 4, 5
          Utts available by email most times
          Mon, Dec 6
          10-noon (Utts)

          Links to Resources (News stories about statistical studies, Surveys and polls, Interactive learning tools, Statistics jokes, etc.)

          Register your iclicker to receive credit for clicker questions! Even if you registered it last year, you need to do it again each fall. "Student ID" is your 8-digit UCI ID number.

          Instruction and Resources for R and R Commander: (These files will be posted as needed)

          • How to get an account to use the ICS Labs
          • ICS Lab Hours
          • How to Install and Use R and R Commander (Word)
          • Additional notes on how to install R commander, helpful for Mac users
          • The R Project Homepage
          • An R Commander website, by John Fox, who developed R Commander
          • How to use R Commander for assignment from Chapters 2 and 5 (Word)
          • How to use R Commander for Contingency Tables (Section 6.4)
          • Using R Commander to find binomial probabilities

          Data sets and Descriptions (These will be posted as needed)

          • Description of the data sets in Mind On Statistics, 3rd edition
          • UCDavis1.txt (for practice using R Commander)
          • rainfall.txt (if you want to use it for the assignment given on Monday, Sept 27))
          • oldfaithful.txt, for Exercise 5.76 (Note that a line such as " ,80" indicates that "duration" is missing. R Commander will know what to do.)
          • deprived.txt, for Exercise 11.78

          Announcements (also see first page of notes from each lecture for announcements)

          • Known typos in Mind On Statistics, 3rd edition

          Jump to:

          • Syllabus and Class Lectures
          • Homework Solutions
          • Sample Exams
          • Exam Keys

          Homework assignments and dates for Assignment Weeks #1 to #9 (but #9 is not due):

          Lecture# and Date Sections Covered
          (Tentative schedule, may be updated after each class)
          Date HW Due Assignment (or discussion topic)
          Assignment Week #1 (Quiz available 1pm Wed 9/29 to noon Fri 10/1)
          1. Fri. Sept 24 Chapter 1 and Sections 2.1 to 2.3; READ Chapter 1 Fri. Oct 11.10, 2.9, 2.27
          2. Mon. Sept 27 Sections 2.4 to 2.6 Fri. Oct 12.42ac, 2.51, 2.61, 2.75
          Mon. Sept 27 Disc. Hands on team project 1, participation points awarded Discussion: Relationships in two-way tables
          3. Wed. Sept 29 Section 2.7; Using R Commander Fri. Oct 1 2.81, 2.84, 2.99
          Assignment Week #2 (Quiz available 1pm Wed 10/6 to noon Fri 10/8)
          4. Fri. Oct 1 Sections 5.1 and 5.2 Fri. Oct 8 5.5a, 5.17, 5.18, 5.76 (Use R Commander) oldfaithful.txt
          5. Mon. Oct 4 Sections 5.3 to 5.5 Fri. Oct 8 5.29, 5.34, 5.43, 5.51
          Mon. Oct 4 Disc. Learn R Commander, no participation points Discussion: R Commander
          6. Wed. Oct 6 Sections 6.1 to 6.3; READ "Misleading Statistics...", pgs 201-202, on your own. Fri. Oct 86.23, 6.36
          Assignment Week #3 (Quiz available 1pm Wed 10/13 to noon Fri 10/15)
          7. Fri. Oct 8 Section 6.4 Fri. Oct 15 6.44*, 6.45*, 6.50bcde, 6.52 (*44 and 45 count together for one point)
          8. Mon. Oct 11 Chapter 3 Fri. Oct 153.13, 3.23, 3.63, 3.86
          Mon. Oct 11 Disc. Hands on team project 2, participation points awarded Discussion: Distracted behavior, relative risk and chi-square test
          9. Wed. Oct 13 Chapter 4 Fri. Oct 154.13, 4.21, 4.36
          Review and Midterm #1
          10. Fri. Oct 15 Catch up and Review No assignment
          11. Mon. Oct 18 First Midterm, on Chapters 1 to 6 No assignment
          Mon. Oct 18 Disc. Hands on team project 3, participation points awarded Discussion: Let's Make a Deal
          Assignment Week #4 (Quiz available 1pm Mon 10/25 to noon Wed 10/27)
          12. Wed. Oct 20 Sections 7.1 to 7.3 Wed. Oct 27 7.17, 7.27, 7.28
          13. Fri. Oct 22 Sections 7.4 and 7.5; Skip 7.6 Wed. Oct 27 7.35, 7.51, 7.53 (answer in back, show work), 7.55
          14. Mon. Oct 25 Sections 8.1 to 8.3 Wed. Oct 27 8.1, 8.14, 8.27
          Mon. Oct 25 Disc. Hands on team project 4, participation points awarded Discussion: Gambling and lotteries
          Assignment Week #5 (Quiz available 1pm Mon 11/1 to noon Wed 11/3)
          15. Wed. Oct 27 Section 7.7 and supplemental material on intuition and probability Wed. Nov 3 Homework Assignment
          16. Fri. Oct 29 Section 8.4 and extra material Wed. Nov 3 8.33, 8.39, 8.87 (will want to use computer)
          17. Mon. Nov 1 Sections 8.5 to 8.7; Skip 8.8 Wed. Nov 3 8.48a, 8.55c and 8.56 (count together as 1 problem), 8.67a
          Mon. Nov 1 Disc. Hands on team project 5, participation points awarded Discussion: Birthday problem and team problem solving; Problems and solutions
          Assignment Week #6 (Quiz available 1pm Mon 11/8 to noon Wed 11/10)
          18. Wed. Nov 3Sections 9.1 to 9.4Mon. Nov 8 9.15, 9.25, 9.37, 9.44
          19. Fri. Nov 5 Sections 9.5 and 9.9, Lessons 1 and 2 Mon. Nov 8 9.50 (each of the 6 parts counts as 1 point)
          20. Mon. Nov 8 Chapter 10 Not due 10.5, 10.22, 10.42
          Review and Midterm #2
          Mon. Nov 8 Disc. Review for Midterm 2, no participation points Discussion: Review for Midterm
          21. Wed. Nov 10 Second Midterm, on Chapters 7 to 10
          (except 7.6, 8.8, 9.6 to 9.8, 9.9 - Lesson 3)
          No assignment
          Assignment Week #7 (Quiz available 1pm Wed 11/17 to noon Fri 11/19
          22. Fri. Nov 12 Sections 12.1 to 12.3, except 12.2-Lesson 3 Fri. Nov 19 12.15b and 12.16b (count together as 1 pt), 12.19, 12.85 (worth 2 pts)
          23. Mon. Nov 15 12.4, 12.2-Lesson 3Fri. Nov 1912.62, 12.83, 12.101
          Mon. Nov 15 Disc. Discussion cancelled today. No discussion today.
          24. Wed. Nov 17 Sections 9.6 to 9.8, 9.9-Lesson 3 Fri. Nov 19 9.66, 9.90, 9.91
          Assignment Week #8 (Quiz available 1pm Wed 11/24 to noon Mon 11/29)
          25. Fri. Nov 19 Chapter 11 Mon. Nov 29 11.40, 11.53, 11.78 (Use R Commander,data under "DataSets" above; counts double)
          26. Mon. Nov 22 Sections 13.1 to 13.3 Mon. Nov 29 13.15, 13.24ac, 13.25 (partial answer in back, show work)
          Mon. Nov 22 Disc. Hands on team project 6, participation points awarded Discussion: Animal eyes- confidence intervals and test
          27. Wed. Nov 24 Section 15.3 Mon. Nov 29 15.6, 15.26bd, 15.35
          Week #9 (No Quiz)
          28. Mon. Nov 29 Sections 16.1, 16.2 Assignment not due 16.1, 16.7, 16.8, 16.16, 16.17
          Mon. Nov 29 Disc. Hands on team project 7, participation points awarded Discussion: Interpreting medical journal articles
          29. Wed. Dec 1 Sections 12.5, 13.5, 13.6, 13.8, parts of Chapter 17; Skip 13.7 Assignment not due 12.73, 12.80, 13.50, 13.51, 17.2, 17.14
          30. Fri. Dec 3 Review for Final Exam No assignment
          Final Exam
          Mon. Dec 6 Final Exam, 1:30pm to 3:30pm

          Syllabus and class lectures; these will be posted shortly before each lecture

          • Syllabus (pdf)
          • Lecture 1, Sept 24
          • Lecture 1, Compact version
          • Lecture 2, Sept 27
          • Lecture 2, Compact version
          • Lecture 3, Sept 29
          • Lecture 3, Compact version
          • Lecture 4, Oct 1 (No compact version)
          • Lecture 5, Oct 4
          • Lecture 5, Compact version
          • Lecture 6, Oct 6
          • Lecture 6, Compact version
          • Lecture 7, Oct 8
          • Lecture 8, Oct 11
          • Lecture 8, Compact version, but missing pages 8 and 9 of full version (Census pages)
          • Lecture 9, Oct 13
          • Lecture 9, Compact version
          • Review for first midterm, covered Fri Oct 15 (Review and Midterm count as lecture days 10 and 11)
          • Lecture 12, Oct 20, updated Oct 28 with answers to non-credit clicker questions
          • Lecture 12, Compact version
          • Lecture 13, Oct 22
          • Lecture 13, Compact version
          • Lecture 14, Oct 25
          • Lecture 14, Compact version
          • Lecture 15, Oct 27
          • Lecture 15, Compact version
          • Lecture 16, Oct 29
          • Lecture 16, Compact version
          • Lecture 16 power point slides on remote viewing
          • Lecture 17, Nov 1
          • Lecture 17, Compact version
          • Lecture 18, Nov 3
          • Lecture 18, Compact version
          • Lecture 19, Nov 5
          • Lecture 19, Compact version
          • Lecture 20, Nov 8
          • Lecture 20, Compact version (missing pages 3 and 26 from full version)
          • Review for second midterm, covered in Discussion, Nov 8
          • Lecture 22, Nov 12 (Class #21 is midterm exam)
          • Lecture 22, Compact version
          • Lecture 23, Nov 15
          • Lecture 23, Compact version
          • Lecture 24, Nov 17
          • Lecture 24, Compact version
          • Lecture 25, Nov 19
          • Lecture 25, Compact version
          • Lecture 26, Nov 22
          • Lecture 26, Compact version
          • Lecture 27, Nov 24
          • Lecture 27, Compact version
          • Lecture 28, Nov 29
          • Lecture 28, Compact version
          • Lecture 29, Dec 1
          • Lecture 29, Compact version
          • Review for the final exam, Dec 3
          • Lecture 30 with announcements, slide on error bars, and review for the final exam, Dec 3
          • Concepts from the quarter that seem to need extra review

          Homework Solutions (The solutions will be posted the day after homework is due.)

          • Assignment #1, Due Fri, October 1
          • Assignment #2, Due Fri, October 8
          • Assignment #3, Due Fri, October 15
          • Assignment #4, Due Wed, October 27
          • Assignment #5, Due Wed, November 3
          • Assignment #6, Due Mon, November 8
          • Assignment #7, Due Fri, November 19
          • Assignment #8, Due Mon, November 29
          • Solutions to problems assigned Mon 11/29 and Wed 12/1 (not due)

          Sample Exams

          • Midterm 1 will be a mix of free response and multiple choice questions. Here are samples of both types:
            Sample Midterm 1 Free Response Exam and Key
            Sample Midterm 1 Multiple Choice Questions and Key


          • Midterm 2 will be a mix of free response and multiple choice questions. Here are samples of both types:
            Sample Midterm 2 Free Response Exam and Key
            Sample Midterm 2 Multiple Choice Questions and Key


          • The final exam will include 30 to 40 points of free response questions on material covered since the 2nd midterm, and 30 to 35 multiple choice questions worth 2 points each from material covered anytime in the course. (Total of 100 points.) Here are samples of both types (these sample multiple choice questions are based on the new material only - see sample multiple choice for the midterms to see examples for the remainder of the course):
            Sample Final Exam Free Response Questions and Key
            Sample Final Exam Multiple Choice Questions and Key

          Practice Problems for Chapters 7 to 9 will be provided as each chapter is covered:

          • Chapter 7: 9, 15, 20, 36, 41, 47, 48, 49, 52, 54, 78; Solutions
          • Chapter 8: 3, 10, 20, 31, 34, 35, 40, 48b, 55ab, 57, 66, 67b; Solutions
          • Chapter 9: 2, 5, 9, 10, 13, 18, 24ab, 31, 36, 45, 48a, 85cd, 123, 124; Solutions

          Exam Keys (The keys will be posted shortly after each exam has been given.)

          • Midterm 1 Keys: Your exam may have had questions in a different order than these.
            Version A
            Version B
          • Midterm 2 Keys:
            Version A
            Version B
            Version C
            Version D
          • Final Exam Key:
            Final exam key (Your exam may have had questions in a different order.)

          http://www.ics.uci.edu/~jutts/st390/ Statistics 390

          Statistics 390 - Fall 2007

          Department of Statistics

          University of California, Davis

          Welcome to the homepage for Statistics 390

          Class Time

          Tuesdays, 12:10-2:00, 1143 MSB or 1139 MSB

          Contact Information:

          Professor Jessica Utts
          4214 MSB
          752-6496, jmutts@ucdavis.edu

          Yolanda Hager
          1222 MSB

          yhagar@wald.ucdavis.edu

          Email Archive

          Websites with tips for overcoming nervousness
          101 Things You Can Do in the First Three Weeks of Class

          Learning Style Test

          More about Learning Styles (Thanks, Samantha!)

          Schedule:

          Date and Activities:

          October 2: Introduction; TA Handbook; General principles and tips for TAs
          October 9: Computer Lab – meet in 1139 MSB (Web page design, computing facilities, Minitab)
          October 16: Videotaped presentations + discussion on grading exams
          October 23: Computer Lab – meet in 1139 MSB (SAS)
          October 30: Meet in 1147 (Seminar room) Presentation on JMP by JMP Staff, and PIZZA!!
          November 6: Videotaped presentations + discussion
          November 13: Computer Lab – meet in 1139 MSB (R)
          November 20: Videotaped presentations + discussion
          November 27: Computer Lab – meet in 1139 MSB (Latex)
          December 4: How to conduct in-class projects in statistics classes

          http://www.ics.uci.edu/~jutts/st13v-06/ Statistics 13

          Statistics 13V - Fall 2006

          Department of Statistics

          University of California, Davis

          Welcome to the homepage for Statistics 13V

          Mon 5:10-6:30pm, 234 Wellman Hall, CRN 41265

          NOTE: If your major or program requires Statistics 13, Statistics 13V fulfills the requirement.
          JUMP TO:
          WEEKLY NOTES
          HOMEWORK SOLUTIONS
          QUIZ KEYS
          FINAL EXAM KEY

          How to Contact Us:

          Professor Jessica Utts Teaching Assistant: Clayton Schupp
          4214 Math Sciences Bldg 1214 Math Sciences Bldg
          752-6496 cschupp@wald.ucdavis.edu
          jmutts@ucdavis.edu Office hours:Wed 12-1, Thurs 1-2, in 1214 MSB
          Office hours: Mon 3-4:30 (except 10/2, 11/6, 12/4, 1-2:30 instead), Wed 9:30-11, or by appointment Optional Discussion: Mondays, 12:10-1, 235 Wellman
          Office Hours and Discussion by Day of the Week: Mon Optional Discussion, 12:10-1 (Schupp, 235 Wellman), 3-4:30 (Utts, 4214 MSB), Wed 9:30-11 (Utts, 4214 MSB), 12-1 (Schupp, 1214 MSB); Thurs 1-2 (Schupp, 1214 MSB); and by appointment

          Links to Resources (News Stories, Surveys, Statistical Bloopers, etc.)

          How to register for CyberStats (Course Key is E-YNDJYQ78DQ6KJ)

          Link to ThomsonNow, including CyberStats

          E-mail list archive

          October 2 PowerPoint presentation

          The remaining sections of this website include .pdf files.
          Download Adobe Acrobat Reader to read these files

          Syllabus and Notes for each Week (includes assignments!!)

          • Syllabus (Word version)
            • pdf version

          NOTES WILL BE POSTED BY MONDAY FOR THE COMING WEEK:
          • Notes for the week of October 2 (due Oct 9)
            • pdf version
          • Notes for the week of October 9 (due Oct 16)
            •  pdf version
          • Notes for the week of October 16 (due Oct 23)
            • pdf version
          • Notes for the week of October 23 (due Oct 30) 
            • pdf version
          • Notes for the week of October 30 (due Nov 6)
            • pdf version
          • Notes for the week of November 6 (due Nov 13)
            • pdf version
          • Notes for the week of November 13 (due Nov 20)
            • pdf version
          • Notes for the week of November 20 (due Nov 27)
            • pdf version
          • Notes for the week of November 27 (due Dec 4)
            • pdf version
          • Notes for the week of December 4 (for the final exam)
            • pdf version
          Sample Quizzes:
          • Sample Quiz #1 (for Oct 9)
            • Sample Quiz #1 Key
          • Sample Quiz #2 (for Oct 16)
            • Sample Quiz #2 Key
          • Sample Quiz #3 (for Oct 23)
            • Sample Quiz #3 Key
          • Sample Quiz #4 (for Oct 30)
            • Sample Quiz #4 Key
          • Sample Quiz #5 (for Nov 6)
            • Sample Quiz #5 Key
          • Sample Quiz #6 (for Nov 13)
            • Sample Quiz #6 Key
          • Sample Quiz #7 (for Nov 20)
            • Sample Quiz #7 Key
          • Sample Quiz #8 (for Nov 27)
            • Sample Quiz #8 Key
          • Sample Quiz #9 (for Dec 4)
            • Sample Quiz #9 Key
          Sample Final Exams:
          • Sample Final Exam (2002)
            • Sample Final Exam Key (2002)
          • Sample Final Exam (2003)
            • Sample Final Exam Key (2003)

          Quiz Keys - will be posted after the exams are given

          • Quiz #1, October 9
          • Quiz #2, October 16
          • Quiz #3, October 23
          • Quiz #4, October 30
          • Quiz #5, November 6
          • Quiz #6, November 13
          • Quiz #7, November 20
          • Quiz #8, November 27
          • Quiz #9, December 4

          Homework Solutions (These will be available after they are due.)

          • Homework #1, Due Mon, Oct. 9
          • Homework #2, Due Mon, Oct. 16
          • Homework #3, Due Mon, Oct. 23
          • Homework #4, Due Mon, Oct. 30
          • Homework #5, Due Mon, Nov. 6
          • Homework #6, Due Mon, Nov. 13
          • Homework #7, Due Mon, Nov. 20
          • Homework #8, Due Mon, Nov. 27
          • Homework #9, Due Mon, Dec. 4

          Final Exam Key (Posted after the final exam)


          http://www.ics.uci.edu/~sysarch/ SysArch Group Home Page

            Parallel Architectures and Systems Lab

          The Parallel Architectures and Systems group in the Department of Computer Science at UCI conducts research on processor architecture and software for parallel, high-performance and distributed systems (including embedded systems). The best way to learn about our work is to look at our projects and publications.

          People (2013)



          Left to right: Ro, Alex V, Nam, Laleh, Dali, Taesu, and our summer intern Vivek
          Prof. Nicolau was travelling at the time this picture was taken...

          Projects

          WebRTC and WebRTCBench

          "WebRTC is perhaps the most disruptive of all HTML5 APIs. By making the audio and video communications programmable in the browser language, it is expected to have a profound impact on the way we communicate with each other. WebRTC will enable exciting new experiences on computing devices in individual communications, group meetings, e-commerce, customer support, remote collaborations, media, and online education among others. WebRTC has the potential of transforming the web to the main real-time interaction medium.

          Kudos to the UC Irvine research team of Prof. Nicolau and Prof. Veidenbaum for developing WebRTCBench, a ground breaking benchmarking suite for quantitative measurement of the system characteristics of fundamental operations of WebRTC. With the rapidly increasing importance of WebRTC in computing and communication industries, I have growing hope that WebRTCBench will play a major role in helping optimize WebRTC implementations and shape this exceptionally important emerging technology."

          Dr. Mohammad Reza Haghighat
          Senior Principal Engineer and leader of Intel HTML5 technical strategy

          Compiler and performance optimization using similarity analysis

          Improving single core performance via compiler-assisted out-of-order commit

          Some prior projects


          Publications


          http://www.ics.uci.edu/~alexv/250B/Spr13/ Welcome to CS250B

          CS250B Modern Microprocessors

            Lectures
            Reading List
            Project

            A sample memory component
          http://www.ics.uci.edu/~alexv/IWIA/ Welcome to IWIA Web pages

          International Workshop on Innovative Architecture for Future Generation Processors and Systems

          2014 We are back

          2012 The Big Island

          2011 The Big Island

          2010 The Big Island (Hawaii)

          2009 Maui

          2008 The Big Island

          2007 Maui

          2006 The Big Island (Hawaii)

          2005 Oahu

          2004 - back to Maui!

          2003

          2002

          2000

          1999

          1998

          1997

          All previous year proceedings are available from IEEE Computer Society Press (or CPS)

          Due to a shift in workshop date from October to January, there was no meeting in 2001. Instead, the next meeting took place in Jan. of 2002.

          http://www.ics.uci.edu/community/news/spotlight/index.php spotlights @ the bren school of information and computer sciences
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          Bren school home > Community > News > Spotlights
          Spotlights

          These spotlights illustrate only a partial list of the experiences faculty, alumni and students have during their time at the Bren School and beyond.

          To suggest someone to spotlight please contact Ted Kissell at communications@ics.uci.edu or (949) 824-6469.


           

          » BUTTERWORTH AND BEALL COMPETITIONS 2014
          Butterworth and Beall competitions reward the best in both hardware and software. more »

          » ICS GOES TO D.C.
          Nalini Venkatasubramanian presents SmartAmerica project at White House. more »

          » A BREATH OF FRESH AIR
          When Tony Givargis and his team set out to build a better lung simulator, they discovered that software wasn’t enough. more »

          » MORE THAN MEETS THE EYE
          Student council's annual ICS Day event showcases community. more »

          » APPS WITH AN IMPACT
          Second annual Autism AppJam engages Orange County community. more »

          » GREAT PEOPLE, GREAT IDEAS
          Alum Art Hitomi talks about what ICS has given him — and how he gives back. more »

          » BOOK IT
          Rina Dechter publishes new book on graphical models in artificial intelligence. more »

          » GAME DAYS
          Atari, Blizzard power up VGDC's Game Developers Week. more »

          » ÜBER EXPERT
          Google senior research scientist and 2013 ICS Distinguished Alumnus Dan Russell examines how people use the Internet to conduct searches and organize information. more »

          » WHAT'S NEXT
          Broadcom co-founder, chairman and CTO Henry Samueli headlined the latest Top Trends in Tech event, presented jointly by the Bren and Samueli schools. more »

          » FINALS BOUND
          UCI team takes second place in regional ACM competition, invited to finals in Russia next June. more »

          » SOCIAL THINKERS
          Intel Science & Technology Center for Social Computing welcomes an interdisciplinary class of fellows. more »

          » BIG DAY FOR ICS
          Dean Hal Stern welcomes a record-setting class of new students as the 2013-14 academic year kicks off with nearly 800 new faces. more »

          » GIFTED GUIDE
          UCI Emeriti Association recognizes Bren Professor Judith Olson with a 2013 Outstanding Mentor Award. more »

          » 10TH ANNUAL BUTTERWORTH COMPETITION
          Creators of Bluetooth Assisted Tracking, or BAT, win the 2013 Butterworth Product Development Competition, a contest that promotes the creation of commercially viable technology while fostering entrepreneurship among students. more »

          » 2013 CELEBRATION OF TEACHING
          Ph.D. students Jed Brubaker and Eric Hennigan and associate professor Don Patterson honored for their outstanding contributions to undergraduate instruction. more »

          » FOSTERING INCLUSIVITY
          Recent business information management graduate closes impressive UCI career with a 2013 Chancellor’s Award of Distinction. more »

          » ANTEATER AWARD WINNER
          ICS Student Council recognized by the office of Student Life & Leadership for launching first-ever Med AppJam competition. more »

          » AUTISM APPJAM
          UC Irvine students from various disciplines team up to create mobile applications that benefit persons with autism. more »

          » INIT(TOGETHER)
          Student group launches conference to facilitate growth of vibrant female tech community in Southern California. more »

          » A WHOLE NEW CLASS
          Association for Computing Machinery taps professor Gene Tsudik to spearhead major security classification overhaul. more »

          » CULTIVATING COLLABORATION
          Dean Stern unveils the Kay Family Foundation Innovation Lab, which features Apple TVs, a PolyVision smartboard, and MacBook Airs and iPads available for checkout by students. more »

          » FIRST-EVER MED APPJAM
          Students from UCI's Bren School and School of Medicine team up to create iOS-based applications useful to both physicians and patients. more »

          » 2012 ECR FELLOW
          First-year Ph.D. student Kristin Roher received a Eugene-Cota Robles Fellowship, considered the most prestigious diversity fellowship offered at UC Irvine. more »

          » ALUMNA JOINS GOOGLE.ORG
          Nithya Sambasivan (Ph.D. ’12), now a user experience researcher with the philanthropic division of Google, continues to study the role that digital technology plays in developing countries. more »

          » SCACCHI RECEIVES DISTINGUISHED ALUMNUS AWARD
          Walt Scacchi (Ph.D. ’81), senior research scientist and research faculty at the Institute for Software Research and research director for the Center for Computer Games and Virtual Worlds, received the Bren School’s 2012 Distinguished Alumnus Award. more »

          » PRODUCT DEVELOPMENT SUPERSTARS
          Ten teams of UCI students representing various disciplines — including computer science, business and the arts — competed for cash prizes totaling $10,000 in the final round of the 9th Annual Butterworth Product Development Competition. more »

          » PROJECTS GALORE
          Dozens of undergraduates showed off their innovations at the annual Student Project Showcase, including iPad and Android apps, web portals, and a home automation system. more »

          » LATEST APPJAM FOCUSES ON SELF-IMPROVEMENT
          A team of UCI undergraduates took home the latest AppJam Tournament grand prize with “AwkTalk,” which provides users with instant feedback on their social skills. more »

          » 2012 NSF FELLOWS
          Bren School Ph.D. students Kevin Bache and Nicholas DiGiuseppe join 15 other UC Irvine students as 2012 National Science Foundation Graduate Research Fellows. more »

          » CREATING GAMES FROM SCRATCH IN 7 DAYS
          Five groups, each with about 15 students, participated in the latest Game Jam, organized by the Video Game Development Club. Competitors were given one week to design a fully functional video game that incorporates the theme of "growth." more »

          » NEW STUDENT CLUB GETS CREATIVE
          With workshop topics ranging from Arduino hacking to espresso brewing, new student organization Design, Art, and Technology Hackerspace (DAT Space) is making good on its promise to provide a student-run physical space where members of the UCI community can meet and work together on creative projects. more »

          » STUDENTS BUILD APPS IN A WEEK
          Eight teams of Bren School students with varying levels of programming experience — ranging from proficient to none — created fully functional mobile applications in just one week, as part of the inaugural ICS Student Council AppJam Tournament. more »

          » CSEDWEEK 2011
          Dec. 4-10 marks the third annual Computer Science Education Week — a national movement that spotlights the critical role computer science education plays in preparing students for 21st Century careers. Debra Richardson, professor of informatics and founding dean of the Bren School, is chair of CSEdWeek. more »

          » CAREER FAIR SEASON
          Bren School students gathered recently at the Student Center to meet with potential employers. A Career Center-sponsored fair gave them opportunities to learn more about available jobs and internships, while the ICS Student Council Reverse Career Fair provided students a chance to showcase their projects to recruiters. more »

          » WELCOME WEEK 2011
          The Donald Bren School of Information and Computer Sciences this fall welcomes more than 500 new students, including its largest freshman class since 2003. more »

          » SOLVING MICROSOFT'S SEARCH CHALLENGE
          A group of Bren School students took third place in the Microsoft Speller Challenge, in which teams submitted programs designed to suggest plausible alternatives to misspellings in search queries. More than 100 teams submitted 435 programs, placing UCI in the top 1 percent of the competition. more »

          » WORKING HARD ON PLAYING GAMES
          A weeklong competition organized by UCI’s Video Game Development Club prompted Bren School students to publish games on the Xbox Live Indie Arcade. more »

          » NSF FELLOWSHIP WINNER
          Informatics graduate student Sen Hirano is one of 20 UCI students who received a prestigious 2011 National Science Foundation Graduate Research Fellowship. more »

          » BUDDING ENTREPRENEURS SHINE
          The top three teams in the 2011 Butterworth Product Development Competition were recognized in a June awards ceremony. more »

          » UCI QUARTER PLANNER
          A fourth-year Computer Science major is on a quest to perfect a tool that helps UCI students better visualize their quarter. more »

          » STUDENTS REAP CAMPUS AWARDS
          Three Bren:ICS student organizations and a freshman computer science major took home Anteater Awards, presented by the Office of the Dean of Students, for their achievements throughout the 2010-11 academic year. more »

          » SHOWCASE HIGHLIGHTS
          More than 60 undergraduates in 14 teams presented their projects at the 2011 Bren:ICS Student Showcase. The event highlighted a range of student innovations, from mobile apps, to search engines, interactive games and more. more »

          » HE GETS AROUND
          Postdoctoral Research Fellow Garnet Hertz has designed a video-game concept car that combines an arcade game cabinet with a real-world electric vehicle. As the driver navigates, a computer-vision system projects the surrounding environment as an 8-bit video game on its windshield. more »

          » LEADING BY EXAMPLE
          As the first in his family to attend college, Jose Romero-Mariona says pursuing a Ph.D. has proven to be an exciting journey. His undergraduate work in ICS at UC Irvine influenced his desire to make a difference in the field of science, while actively participating in student organizations early in his educational career served as a platform for his current involvement as a peer mentor. more »

          » SOCIALLY CONNECTED
          Social media is everywhere - Facebook, Twitter, YouTube, My Space, blogs. Making sense of this online networking universe is Donald Patterson, UC Irvine assistant professor of informatics. more »

          » PRESCRIPTION FOR SUCCESS
          David Cheng, always wanted to be a businessman. In 1997, he saw his opportunity and founded Accenx, a leading provider of health information exchange and interoperability solutions, with more than 150 customers and the largest dedicated team of integration specialists in the industry. more »

          » BLENDED PASSION
          Jeff Monroe didn’t see a difference between the creativity of the artist and the creativity of the software engineer, so he co-founded a company that melded the two and provided him an outlet for both of his passions. more »

          » PREDICTING PROPERTIES
          Doctoral student Chloe Azencott hopes her research into statistical machine learning and data mining can help reduce the time and cost of the drug development process. more »

          » HYBRID VIGOR
          Doctoral student Yong Ming Kow is studying modders, users that create functionality in a software product that is much more suited to their own needs. Kow wants to learn how modding culture can impact the future of software and product development. more »

          » ISCHOOLS INSIGHT
          More than 50 doctoral students from three UC campuses discussed their research and a variety of research related issues at the inagural UC iSchools Workshop held in January 2008. more »

          » STUDENT AMBASSADORS TO THE RESCUE
          The Bren School Student Ambassadors program is on a mission to reach out to potential computer science students through a near-peer outreach program that profiles student successes. more »

          » SUPERNATURAL CONCLUSIONS
          If members of the scientific community once debated the existence of psychic abilities, professor of statistics Jessica Utts' research has helped put the argument to rest. more »

          » STAYING CURRENT
          Marco Cesarano knows the field of computer science is constantly evolving and if he wanted to keep his skills current, he would have to take advantage of opportunities to improve his skills and knowledge. When he saw an advertisement at the National Council of Research in his hometown of Naples, Italy for UC Irvine’s Master’s in Information and Computer Science with a Concentration in Embedded Systems, he knew he found an avenue that would allow him to take his skills and knowledge to the next level. more »

          » ONCE IN A LIFETIME OPPORTUNITY
          Rosario Cammarota was looking for a Master’s program, but he wanted to stay in his hometown of Naples, Italy and continue to work full time at the local start-up he joined. Pursuing a Master's degree in Embedded Systems at UC Irvine provided him a 'once in a lifetime opportunity' to study in the United States for two summers while studying the rest of the time at the Istituto di Cibernetica Eduardo Caianiello in Naples, Italy. more »

          » A THIRST FOR KNOWLEDGE
          Working as an IT consultant was rewarding for Charu Srivastava, but she had a thirst for knowledge that could only be quenched by returning to school for an advanced degree. Pursuing a Master's degree in Embedded Systems at UC Irvine enabled her to do that and allowed her a unique opportunity to study for three quarters at the Istituto di Cibernetica Eduardo Caianiello in Naples, Italy. more »

          » UNDERSTANDING INFLUENCE
          Doctoral student Lilly Irani is studying how the experiences of gender and cultural background, such as national background, influence the way technologies get designed. more »

          » CALLFIRE ANSWERS THE CALL
          Integrating a 100 year-old medium – the telephone, with a 30-year old medium – the Internet, rekindled the friendship of four alumni and led to the creation of CallFire, a company that, with fewer than six full-time employees has already attained several million dollars in revenue. more »

          » OVERCOMING OVERLOAD
          Doctoral student Norman Makoto Su studies how people combine certain media types, and how they manage their interactions can reveal what strategies and policies organizations can employ to alleviate "communication" overload and design of systems to manage communications. more »

          » CRUISE CONTROL
          Jasmine Yau ’03 discovered that a career in information and computer sciences requires a lot more than strong technical knowledge. A business analyst at Kelley Blue Book, she works on the company web site, kbb.com, which requires her to rely on her interpersonal skills as much as her technical skill. more »

          » FUN AND GAMES
          Hector Parra didn't want to graduate without taking the computer game development class, and while it gave him an opportunity to spend time playing in a virtual world, it also taught him intangible skills he can use in the real world. more »

          » KING OF RISC
          Jeffrey Ma '03 is putting what he learned at the Bren School to full use at ARM, an intellectual property company specializing in mobile low-power, low-cost RISC processors. more »

          » THE PRODUCER
          As Executive Producer of UCI's Vagina Monologues, Jacob Knobel found himself spending a lot of time on repetitive tasks, so the senior Informatics major utilized his programming skills to create a stage management software program that allowed him to work more efficiently and focus on more important aspects of the production. more »

          » THE DILEMMA
          Trina Choontanom credits her two older sisters for sparking her interest in the field of computer science, however, she didn't know if she wanted to concentrate on computer software or hardware. Luckily UCI made the decision easy by offering a major that combined both areas. more »

          » STAYING CONNECTED
          Informatics Ph.D. student Bryan Semaan is studying how people use technology to continue their collaborative work despite living in a disrupted environment caused by a natural disaster, terrorist attack or conflict. more »

          » IN THE ZONE
          When Rebecca Maessen was considering where to attend college, she wanted a place that would allow her to fulfill her passion for basketball and computer science, even if it meant going half way around the world. more »

          » BEST OF BOTH WORLDS
          Michelle Ang, didn't know if she wanted to concentrate on computer software or hardware. Luckily UCI made the decision easy by offering a major that combined both areas. more »

          » SWEET MUSIC
          Sam Archer is accustomed to creating fluid rhythms as a member of the UCI Pep Band, a trait he has carried over to the class room. more »

          » ON THE JOB EXPERIENCE
          Heading into the end of his sophomore year David Purpura already has more job experience than most college graduates. more »

          » OUT OF THE CUBE
          Aurora Bedford was interested in the human aspect of computers, not just hardware stuff and that interest drew her to the Bren School's Informatics major. more »

          » STARTING EARLY
          Informatics major Bryant Hornick is one of the more than one-third of UCI undergraduates who don't wait until graduate school to do research with faculty. more »

          » A HELPING HAND
          Della Halim, president of the Women in Information and Computer Sciences student group, helps ensure the female students in ICS feel more at home in their often male dominated classes. more »

          » KING OF THE ANTHILL
          Jeff Fulkerson made Bren School history by becoming the school’s first student to run for and win UCI Homecoming King. He was coronated during halftime of the February 3rd men's basketball game. more »

          » THE COORDINATOR
          Ph.D. candidate Anita Sarma is researching ways to prevent breakdowns in the coordination of software development projects. Her novel approach warns developers of relevant parallel activities and changes that will lead to potential development conflicts. more »

          » RUNNING ON OCTANE
          During his freshmen year, Robert Olson's participation in the Bren School's hITEC OCTANe product development competition ignited his passion for entrepreneurship and earned him a $5,000 check. more »

          » FOREIGN EXCHANGE
          After receiving a reciprocity fellowship from UC’s Education Abroad Program (EAP) and spending one year at UCI, Ph.D. student Pablo Diaz-Gutierrez decided that graduate school at UCI was right for him. more »

          » CHANGING COURSE
          Ph.D. student Kristina Winbladh had her heart set on becoming a biomedical researcher, until she discovered that computers were a lot more logical than organic cells. more »

          » FAMILY TIES
          Blue and gold blood courses through Sarah Gibas' veins, both her parents attended UCI as ICS majors, but it wasn’t just the strong family ties that lead her to choose UCI. more »

          » DASHBOARD CONFESSIONAL
          Orange County native Gerald Bortis is working on Dashboard, a project that allows software developers to become more aware of the work that is going on in their environment and helps them to more effectively collaborate with their teammates. more »

          » A PERFECT FIT
          After completing his Bachelor degree at UCI, Ph.D. student Rex Chen saw no reason to leave the friendly and supportive confines of Irvine when he choose to pursue his Ph.D. more »

          » CUSTOMER CONFIDENTIAL
          After graduating with his bachelor degree, Einar Mykletun worked as a programmer, but that was not satisfying enough and he ultimately decided to pursue a Ph.D. in the area of security and cryptography. Now, Mykletun is working on ways to protect the confidentiality of customer data stored on vendor databases. more »

          » THE MODELER
          Ph.D. student Guy Yosiphon was looking for a challenge. After earning a bachelor degree he worked in the software industry for two and a half years as a programmer and project leader. The work was rewarding, but he missed the challenge of academic studies and the scientific research environment, two things he found at the Bren School. more »

          » BETTER, FASTER, SMALLER
          What Calvin Klein, Tommy Hilfiger and Donna Karan are to fashion, Ph.D. student Sudeep Pasricha is to small electronics we use on a regular basis. The next time you purchase a smaller, flatter cell phone or a digital camera that is the size of a credit card and weighs about as much, you'll have Pasricha's research efforts to thank. more »

          » TRAILBLAZER
          A member of the inaguaral class of the Informatics major and the founder and president of the Informatics Student Association, (INSA), Gabriela Marcu is blazing a trail for future Informatics students at UCI. more »

          » FUELING A PASSION
          Indecision about a career led Amber Israelsen to leave school and work for several years in a variety of industries. It was during these journeyman years, that she discovered her passion for computers. more »

          » THE SECOND TIME AROUND
          At 45, David Lamb is only months away from graduation and beginning his second career as a computer scientist. more »

          » THE APPRENTICE
          Although she's only 23, Bren School of ICS graduate Terica Kindred '03 has an impressive resume: Software engineer. Speaker. Author. more »

          » MAN OF THE HOUSE
          Michael Golightly
          , housing assistant for the ICS Theme House, helps his 24 residents adjust to life in college by providing mentorship and resources for their development and enrichment. more »

          » UNLOCKING THE CLUB HOUSE
          Jennifer Law, former president of UCI's Women in Computer Science (WICS) group, is working to help and encourage women to pursue a college degree and a successful career in computer science. more »

          » DYNAMIC DUO
          Victor Liu and Adam Bonner took a risk when graduating from the Bren School by foregoing the opportunity to join established companies. Instead they decided to branch out on their own. That gamble is starting to pay off. more »

          » FATHER OF CAD/CAM
          Success is somewhat commonplace for the trailblazing Patrick Hanratty. Anointed the 'Father of CAD/CAM' for his pioneering work in computer-aided design and manufacturing, industry analysts estimate that 70 percent of all 3-D mechanical CAD/CAM systems available today trace their roots back to Hanratty’s original code. more »

          » EXPERIENTIAL EXPERT
          Ramesh Jain, former professor of embedded and experiential systems at Georgia Institute of Technology, was recently named the first Donald Bren Professor of Information and Computer Sciences at UC Irvine. more »

          » GOOGLED
          Nhu Vuong '05 has come a long way since she began her career at UCI just over four years ago. Vuong, an information and computer science major, is now a software engineer at Google and working on building a company-internal web application. more »

          » CLIMBING HIGH
          From her vantage, Debra Richardson sees computer technology transforming society for the better. more »

          » YAHOO!
          Technical Yahoo probably wasn't a title that Shawn Shah '05 imagined having as an undergraduate major in information and computer science. more »

          » REVEALING RHTHYMS
          Pierre Baldi uses math - and music - to help transform reams of research data into biological breakthroughs. more »

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          Bren school home > Community > News > Spotlights
          Spotlights

          These spotlights illustrate only a partial list of the experiences faculty, alumni and students have during their time at the Bren School and beyond.

          To suggest someone to spotlight please contact Ted Kissell at communications@ics.uci.edu or (949) 824-6469.


           

          » BUTTERWORTH AND BEALL COMPETITIONS 2014
          Butterworth and Beall competitions reward the best in both hardware and software. more »

          » ICS GOES TO D.C.
          Nalini Venkatasubramanian presents SmartAmerica project at White House. more »

          » A BREATH OF FRESH AIR
          When Tony Givargis and his team set out to build a better lung simulator, they discovered that software wasn’t enough. more »

          » MORE THAN MEETS THE EYE
          Student council's annual ICS Day event showcases community. more »

          » APPS WITH AN IMPACT
          Second annual Autism AppJam engages Orange County community. more »

          » GREAT PEOPLE, GREAT IDEAS
          Alum Art Hitomi talks about what ICS has given him — and how he gives back. more »

          » BOOK IT
          Rina Dechter publishes new book on graphical models in artificial intelligence. more »

          » GAME DAYS
          Atari, Blizzard power up VGDC's Game Developers Week. more »

          » ÜBER EXPERT
          Google senior research scientist and 2013 ICS Distinguished Alumnus Dan Russell examines how people use the Internet to conduct searches and organize information. more »

          » WHAT'S NEXT
          Broadcom co-founder, chairman and CTO Henry Samueli headlined the latest Top Trends in Tech event, presented jointly by the Bren and Samueli schools. more »

          » FINALS BOUND
          UCI team takes second place in regional ACM competition, invited to finals in Russia next June. more »

          » SOCIAL THINKERS
          Intel Science & Technology Center for Social Computing welcomes an interdisciplinary class of fellows. more »

          » BIG DAY FOR ICS
          Dean Hal Stern welcomes a record-setting class of new students as the 2013-14 academic year kicks off with nearly 800 new faces. more »

          » GIFTED GUIDE
          UCI Emeriti Association recognizes Bren Professor Judith Olson with a 2013 Outstanding Mentor Award. more »

          » 10TH ANNUAL BUTTERWORTH COMPETITION
          Creators of Bluetooth Assisted Tracking, or BAT, win the 2013 Butterworth Product Development Competition, a contest that promotes the creation of commercially viable technology while fostering entrepreneurship among students. more »

          » 2013 CELEBRATION OF TEACHING
          Ph.D. students Jed Brubaker and Eric Hennigan and associate professor Don Patterson honored for their outstanding contributions to undergraduate instruction. more »

          » FOSTERING INCLUSIVITY
          Recent business information management graduate closes impressive UCI career with a 2013 Chancellor’s Award of Distinction. more »

          » ANTEATER AWARD WINNER
          ICS Student Council recognized by the office of Student Life & Leadership for launching first-ever Med AppJam competition. more »

          » AUTISM APPJAM
          UC Irvine students from various disciplines team up to create mobile applications that benefit persons with autism. more »

          » INIT(TOGETHER)
          Student group launches conference to facilitate growth of vibrant female tech community in Southern California. more »

          » A WHOLE NEW CLASS
          Association for Computing Machinery taps professor Gene Tsudik to spearhead major security classification overhaul. more »

          » CULTIVATING COLLABORATION
          Dean Stern unveils the Kay Family Foundation Innovation Lab, which features Apple TVs, a PolyVision smartboard, and MacBook Airs and iPads available for checkout by students. more »

          » FIRST-EVER MED APPJAM
          Students from UCI's Bren School and School of Medicine team up to create iOS-based applications useful to both physicians and patients. more »

          » 2012 ECR FELLOW
          First-year Ph.D. student Kristin Roher received a Eugene-Cota Robles Fellowship, considered the most prestigious diversity fellowship offered at UC Irvine. more »

          » ALUMNA JOINS GOOGLE.ORG
          Nithya Sambasivan (Ph.D. ’12), now a user experience researcher with the philanthropic division of Google, continues to study the role that digital technology plays in developing countries. more »

          » SCACCHI RECEIVES DISTINGUISHED ALUMNUS AWARD
          Walt Scacchi (Ph.D. ’81), senior research scientist and research faculty at the Institute for Software Research and research director for the Center for Computer Games and Virtual Worlds, received the Bren School’s 2012 Distinguished Alumnus Award. more »

          » PRODUCT DEVELOPMENT SUPERSTARS
          Ten teams of UCI students representing various disciplines — including computer science, business and the arts — competed for cash prizes totaling $10,000 in the final round of the 9th Annual Butterworth Product Development Competition. more »

          » PROJECTS GALORE
          Dozens of undergraduates showed off their innovations at the annual Student Project Showcase, including iPad and Android apps, web portals, and a home automation system. more »

          » LATEST APPJAM FOCUSES ON SELF-IMPROVEMENT
          A team of UCI undergraduates took home the latest AppJam Tournament grand prize with “AwkTalk,” which provides users with instant feedback on their social skills. more »

          » 2012 NSF FELLOWS
          Bren School Ph.D. students Kevin Bache and Nicholas DiGiuseppe join 15 other UC Irvine students as 2012 National Science Foundation Graduate Research Fellows. more »

          » CREATING GAMES FROM SCRATCH IN 7 DAYS
          Five groups, each with about 15 students, participated in the latest Game Jam, organized by the Video Game Development Club. Competitors were given one week to design a fully functional video game that incorporates the theme of "growth." more »

          » NEW STUDENT CLUB GETS CREATIVE
          With workshop topics ranging from Arduino hacking to espresso brewing, new student organization Design, Art, and Technology Hackerspace (DAT Space) is making good on its promise to provide a student-run physical space where members of the UCI community can meet and work together on creative projects. more »

          » STUDENTS BUILD APPS IN A WEEK
          Eight teams of Bren School students with varying levels of programming experience — ranging from proficient to none — created fully functional mobile applications in just one week, as part of the inaugural ICS Student Council AppJam Tournament. more »

          » CSEDWEEK 2011
          Dec. 4-10 marks the third annual Computer Science Education Week — a national movement that spotlights the critical role computer science education plays in preparing students for 21st Century careers. Debra Richardson, professor of informatics and founding dean of the Bren School, is chair of CSEdWeek. more »

          » CAREER FAIR SEASON
          Bren School students gathered recently at the Student Center to meet with potential employers. A Career Center-sponsored fair gave them opportunities to learn more about available jobs and internships, while the ICS Student Council Reverse Career Fair provided students a chance to showcase their projects to recruiters. more »

          » WELCOME WEEK 2011
          The Donald Bren School of Information and Computer Sciences this fall welcomes more than 500 new students, including its largest freshman class since 2003. more »

          » SOLVING MICROSOFT'S SEARCH CHALLENGE
          A group of Bren School students took third place in the Microsoft Speller Challenge, in which teams submitted programs designed to suggest plausible alternatives to misspellings in search queries. More than 100 teams submitted 435 programs, placing UCI in the top 1 percent of the competition. more »

          » WORKING HARD ON PLAYING GAMES
          A weeklong competition organized by UCI’s Video Game Development Club prompted Bren School students to publish games on the Xbox Live Indie Arcade. more »

          » NSF FELLOWSHIP WINNER
          Informatics graduate student Sen Hirano is one of 20 UCI students who received a prestigious 2011 National Science Foundation Graduate Research Fellowship. more »

          » BUDDING ENTREPRENEURS SHINE
          The top three teams in the 2011 Butterworth Product Development Competition were recognized in a June awards ceremony. more »

          » UCI QUARTER PLANNER
          A fourth-year Computer Science major is on a quest to perfect a tool that helps UCI students better visualize their quarter. more »

          » STUDENTS REAP CAMPUS AWARDS
          Three Bren:ICS student organizations and a freshman computer science major took home Anteater Awards, presented by the Office of the Dean of Students, for their achievements throughout the 2010-11 academic year. more »

          » SHOWCASE HIGHLIGHTS
          More than 60 undergraduates in 14 teams presented their projects at the 2011 Bren:ICS Student Showcase. The event highlighted a range of student innovations, from mobile apps, to search engines, interactive games and more. more »

          » HE GETS AROUND
          Postdoctoral Research Fellow Garnet Hertz has designed a video-game concept car that combines an arcade game cabinet with a real-world electric vehicle. As the driver navigates, a computer-vision system projects the surrounding environment as an 8-bit video game on its windshield. more »

          » LEADING BY EXAMPLE
          As the first in his family to attend college, Jose Romero-Mariona says pursuing a Ph.D. has proven to be an exciting journey. His undergraduate work in ICS at UC Irvine influenced his desire to make a difference in the field of science, while actively participating in student organizations early in his educational career served as a platform for his current involvement as a peer mentor. more »

          » SOCIALLY CONNECTED
          Social media is everywhere - Facebook, Twitter, YouTube, My Space, blogs. Making sense of this online networking universe is Donald Patterson, UC Irvine assistant professor of informatics. more »

          » PRESCRIPTION FOR SUCCESS
          David Cheng, always wanted to be a businessman. In 1997, he saw his opportunity and founded Accenx, a leading provider of health information exchange and interoperability solutions, with more than 150 customers and the largest dedicated team of integration specialists in the industry. more »

          » BLENDED PASSION
          Jeff Monroe didn’t see a difference between the creativity of the artist and the creativity of the software engineer, so he co-founded a company that melded the two and provided him an outlet for both of his passions. more »

          » PREDICTING PROPERTIES
          Doctoral student Chloe Azencott hopes her research into statistical machine learning and data mining can help reduce the time and cost of the drug development process. more »

          » HYBRID VIGOR
          Doctoral student Yong Ming Kow is studying modders, users that create functionality in a software product that is much more suited to their own needs. Kow wants to learn how modding culture can impact the future of software and product development. more »

          » ISCHOOLS INSIGHT
          More than 50 doctoral students from three UC campuses discussed their research and a variety of research related issues at the inagural UC iSchools Workshop held in January 2008. more »

          » STUDENT AMBASSADORS TO THE RESCUE
          The Bren School Student Ambassadors program is on a mission to reach out to potential computer science students through a near-peer outreach program that profiles student successes. more »

          » SUPERNATURAL CONCLUSIONS
          If members of the scientific community once debated the existence of psychic abilities, professor of statistics Jessica Utts' research has helped put the argument to rest. more »

          » STAYING CURRENT
          Marco Cesarano knows the field of computer science is constantly evolving and if he wanted to keep his skills current, he would have to take advantage of opportunities to improve his skills and knowledge. When he saw an advertisement at the National Council of Research in his hometown of Naples, Italy for UC Irvine’s Master’s in Information and Computer Science with a Concentration in Embedded Systems, he knew he found an avenue that would allow him to take his skills and knowledge to the next level. more »

          » ONCE IN A LIFETIME OPPORTUNITY
          Rosario Cammarota was looking for a Master’s program, but he wanted to stay in his hometown of Naples, Italy and continue to work full time at the local start-up he joined. Pursuing a Master's degree in Embedded Systems at UC Irvine provided him a 'once in a lifetime opportunity' to study in the United States for two summers while studying the rest of the time at the Istituto di Cibernetica Eduardo Caianiello in Naples, Italy. more »

          » A THIRST FOR KNOWLEDGE
          Working as an IT consultant was rewarding for Charu Srivastava, but she had a thirst for knowledge that could only be quenched by returning to school for an advanced degree. Pursuing a Master's degree in Embedded Systems at UC Irvine enabled her to do that and allowed her a unique opportunity to study for three quarters at the Istituto di Cibernetica Eduardo Caianiello in Naples, Italy. more »

          » UNDERSTANDING INFLUENCE
          Doctoral student Lilly Irani is studying how the experiences of gender and cultural background, such as national background, influence the way technologies get designed. more »

          » CALLFIRE ANSWERS THE CALL
          Integrating a 100 year-old medium – the telephone, with a 30-year old medium – the Internet, rekindled the friendship of four alumni and led to the creation of CallFire, a company that, with fewer than six full-time employees has already attained several million dollars in revenue. more »

          » OVERCOMING OVERLOAD
          Doctoral student Norman Makoto Su studies how people combine certain media types, and how they manage their interactions can reveal what strategies and policies organizations can employ to alleviate "communication" overload and design of systems to manage communications. more »

          » CRUISE CONTROL
          Jasmine Yau ’03 discovered that a career in information and computer sciences requires a lot more than strong technical knowledge. A business analyst at Kelley Blue Book, she works on the company web site, kbb.com, which requires her to rely on her interpersonal skills as much as her technical skill. more »

          » FUN AND GAMES
          Hector Parra didn't want to graduate without taking the computer game development class, and while it gave him an opportunity to spend time playing in a virtual world, it also taught him intangible skills he can use in the real world. more »

          » KING OF RISC
          Jeffrey Ma '03 is putting what he learned at the Bren School to full use at ARM, an intellectual property company specializing in mobile low-power, low-cost RISC processors. more »

          » THE PRODUCER
          As Executive Producer of UCI's Vagina Monologues, Jacob Knobel found himself spending a lot of time on repetitive tasks, so the senior Informatics major utilized his programming skills to create a stage management software program that allowed him to work more efficiently and focus on more important aspects of the production. more »

          » THE DILEMMA
          Trina Choontanom credits her two older sisters for sparking her interest in the field of computer science, however, she didn't know if she wanted to concentrate on computer software or hardware. Luckily UCI made the decision easy by offering a major that combined both areas. more »

          » STAYING CONNECTED
          Informatics Ph.D. student Bryan Semaan is studying how people use technology to continue their collaborative work despite living in a disrupted environment caused by a natural disaster, terrorist attack or conflict. more »

          » IN THE ZONE
          When Rebecca Maessen was considering where to attend college, she wanted a place that would allow her to fulfill her passion for basketball and computer science, even if it meant going half way around the world. more »

          » BEST OF BOTH WORLDS
          Michelle Ang, didn't know if she wanted to concentrate on computer software or hardware. Luckily UCI made the decision easy by offering a major that combined both areas. more »

          » SWEET MUSIC
          Sam Archer is accustomed to creating fluid rhythms as a member of the UCI Pep Band, a trait he has carried over to the class room. more »

          » ON THE JOB EXPERIENCE
          Heading into the end of his sophomore year David Purpura already has more job experience than most college graduates. more »

          » OUT OF THE CUBE
          Aurora Bedford was interested in the human aspect of computers, not just hardware stuff and that interest drew her to the Bren School's Informatics major. more »

          » STARTING EARLY
          Informatics major Bryant Hornick is one of the more than one-third of UCI undergraduates who don't wait until graduate school to do research with faculty. more »

          » A HELPING HAND
          Della Halim, president of the Women in Information and Computer Sciences student group, helps ensure the female students in ICS feel more at home in their often male dominated classes. more »

          » KING OF THE ANTHILL
          Jeff Fulkerson made Bren School history by becoming the school’s first student to run for and win UCI Homecoming King. He was coronated during halftime of the February 3rd men's basketball game. more »

          » THE COORDINATOR
          Ph.D. candidate Anita Sarma is researching ways to prevent breakdowns in the coordination of software development projects. Her novel approach warns developers of relevant parallel activities and changes that will lead to potential development conflicts. more »

          » RUNNING ON OCTANE
          During his freshmen year, Robert Olson's participation in the Bren School's hITEC OCTANe product development competition ignited his passion for entrepreneurship and earned him a $5,000 check. more »

          » FOREIGN EXCHANGE
          After receiving a reciprocity fellowship from UC’s Education Abroad Program (EAP) and spending one year at UCI, Ph.D. student Pablo Diaz-Gutierrez decided that graduate school at UCI was right for him. more »

          » CHANGING COURSE
          Ph.D. student Kristina Winbladh had her heart set on becoming a biomedical researcher, until she discovered that computers were a lot more logical than organic cells. more »

          » FAMILY TIES
          Blue and gold blood courses through Sarah Gibas' veins, both her parents attended UCI as ICS majors, but it wasn’t just the strong family ties that lead her to choose UCI. more »

          » DASHBOARD CONFESSIONAL
          Orange County native Gerald Bortis is working on Dashboard, a project that allows software developers to become more aware of the work that is going on in their environment and helps them to more effectively collaborate with their teammates. more »

          » A PERFECT FIT
          After completing his Bachelor degree at UCI, Ph.D. student Rex Chen saw no reason to leave the friendly and supportive confines of Irvine when he choose to pursue his Ph.D. more »

          » CUSTOMER CONFIDENTIAL
          After graduating with his bachelor degree, Einar Mykletun worked as a programmer, but that was not satisfying enough and he ultimately decided to pursue a Ph.D. in the area of security and cryptography. Now, Mykletun is working on ways to protect the confidentiality of customer data stored on vendor databases. more »

          » THE MODELER
          Ph.D. student Guy Yosiphon was looking for a challenge. After earning a bachelor degree he worked in the software industry for two and a half years as a programmer and project leader. The work was rewarding, but he missed the challenge of academic studies and the scientific research environment, two things he found at the Bren School. more »

          » BETTER, FASTER, SMALLER
          What Calvin Klein, Tommy Hilfiger and Donna Karan are to fashion, Ph.D. student Sudeep Pasricha is to small electronics we use on a regular basis. The next time you purchase a smaller, flatter cell phone or a digital camera that is the size of a credit card and weighs about as much, you'll have Pasricha's research efforts to thank. more »

          » TRAILBLAZER
          A member of the inaguaral class of the Informatics major and the founder and president of the Informatics Student Association, (INSA), Gabriela Marcu is blazing a trail for future Informatics students at UCI. more »

          » FUELING A PASSION
          Indecision about a career led Amber Israelsen to leave school and work for several years in a variety of industries. It was during these journeyman years, that she discovered her passion for computers. more »

          » THE SECOND TIME AROUND
          At 45, David Lamb is only months away from graduation and beginning his second career as a computer scientist. more »

          » THE APPRENTICE
          Although she's only 23, Bren School of ICS graduate Terica Kindred '03 has an impressive resume: Software engineer. Speaker. Author. more »

          » MAN OF THE HOUSE
          Michael Golightly
          , housing assistant for the ICS Theme House, helps his 24 residents adjust to life in college by providing mentorship and resources for their development and enrichment. more »

          » UNLOCKING THE CLUB HOUSE
          Jennifer Law, former president of UCI's Women in Computer Science (WICS) group, is working to help and encourage women to pursue a college degree and a successful career in computer science. more »

          » DYNAMIC DUO
          Victor Liu and Adam Bonner took a risk when graduating from the Bren School by foregoing the opportunity to join established companies. Instead they decided to branch out on their own. That gamble is starting to pay off. more »

          » FATHER OF CAD/CAM
          Success is somewhat commonplace for the trailblazing Patrick Hanratty. Anointed the 'Father of CAD/CAM' for his pioneering work in computer-aided design and manufacturing, industry analysts estimate that 70 percent of all 3-D mechanical CAD/CAM systems available today trace their roots back to Hanratty’s original code. more »

          » EXPERIENTIAL EXPERT
          Ramesh Jain, former professor of embedded and experiential systems at Georgia Institute of Technology, was recently named the first Donald Bren Professor of Information and Computer Sciences at UC Irvine. more »

          » GOOGLED
          Nhu Vuong '05 has come a long way since she began her career at UCI just over four years ago. Vuong, an information and computer science major, is now a software engineer at Google and working on building a company-internal web application. more »

          » CLIMBING HIGH
          From her vantage, Debra Richardson sees computer technology transforming society for the better. more »

          » YAHOO!
          Technical Yahoo probably wasn't a title that Shawn Shah '05 imagined having as an undergraduate major in information and computer science. more »

          » REVEALING RHTHYMS
          Pierre Baldi uses math - and music - to help transform reams of research data into biological breakthroughs. more »

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          http://www.ics.uci.edu/ugrad/DELETEadmissions/transfer/smart/index.php smart-ics credit transfer program @ the bren school of information and computer sciences
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          Bren school home > Undergraduate > > Transfer students > SMART-ICS program
          SMART-ICS credit transfer program

          SMART-ICS is a program that encourages partnership with area community colleges in an effort to provide student transfers to ICS subject credit for all lower division math and ICS courses required for the ICS degree.

          The Bren School currently has SMART-ICS agreements with:

          » Fullerton College
          » Long Beach Community College
          » Orange Coast College

          Students should contact the community college they are interested in attending to learn about its SMART program.

          FOR COMMUNITY COLLEGE ADMINISTRATORS

          SMART Reasoning
          The typical community college student transfers to UC Irvine's Donald Bren School of Information and Computer Sciences with a junior standing in terms of units completed.

          Yet, because of the difficulty of articulating community college computer science and discrete math courses to ICS' requirements, most of those transferring into the ICS major receive limited ICS course transfer credit.

          To complete the missing courses often requires a transfer student to spend more time (and money) to obtain an ICS degree than a student who entered UCI as a freshman ICS major.

          SMART Program
          SMART-ICS, Standardized Major Requirements to Transfer into Information and Computer Science, is a new articulation program that allows community college students to meet the lower-division computer science and mathematics course requirements of the ICS major by covering designated topics and meeting a programming proficiency requirement, rather than by a series of course-to-course articulations.

          Transfer students completing the SMART-ICS requirements come to the ICS major immediately prepared to begin upper-division courses in ICS. SMART-ICS does not affect ICS' course-to-course articulation options; they are still in place and available.

          SMART Steps
          Most community colleges will satisfy SMART-ICS by specifying a group of courses that a student will complete. Still, SMART-ICS is about topic coverage: ICS will accept any SMART program that reasonably ensures satisfaction of the SMART-ICS requirements. In particular, if you wish, your school can have multiple programs that meet the SMART requirements.

          To guide your community college in developing its SMART programs, please refer to the SMART Chart.

          The Chart lists each topic to be covered and, as a guide to the depth of coverage, the number of lecture hours to devote to it; it also spells out the programming proficiency requirement.

          Community colleges interested in partnering with ICS in its SMART-ICS program follow four simple steps:

          • Notify ICS of your interest in participating in SMART-ICS
          • Create your SMART programs, using the SMART Chart as your guide
          • Obtain ICS certification of your programs
          • Certify your transfer students when they complete one of your SMART programs

          SMART Resources
          Below is information to assist you in the creation of your SMART programs:

          • Core Knowledge and Skills documents
          • Current Computer Science and Math syllabi
          • One-on-one assistance available in creating your program
          • SMART Chart
          • SMART Textbooks*
            • ICS 21/CSE 21:
              Horstmann, Cay. Java Concepts, 5th edition. Wiley & Sons.
            • ICS 22/CSE 22 and ICS 23/CSE 23:
              Goodrich, Michael T. & Tamassia, Roberto. Data Structures & Algorithms in Java, 4th edition. Wiley & Sons.
            • ICS 51:  
              Tanenbaum, Andrew S. Structured Computer Organization, 4th edition. Prentice Hall.
            • ICS 52:
              Sommerville, Ian. Software Engineering, 6th edition. Addison-Wesley.
            • ICS 76/Math 67:
              Ross, Sheldon. A First Course in Probability, 6th edition. Prentice-Hall.
            • Math 6D/ICS 6D/Math 6B:
              Rosen, Kenneth H. Discrete Mathematics and its Applicaitons, 5th edition. McGraw-Hill.
            • Math 6G:
              Fraleigh, John B. and Beauregard. Raymond A. Linear Algebra, 3rd edition. Addison-Wesley.
            • Math 2A-B:
              Stewart. Calculus, 4th edition. Brooks/Cole. or
              Stewart. Single Variable Calculus, 4th edition. Brooks/Cole.
              (The latter text is just the first chapters of the former text.)
            • Math 2J:
              None: Notes by UCI Professor R. C. Reilly are used.
          *This list is subject to change, as each instructor determines the text(s) to use in her/his courses.
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          http://www.ics.uci.edu/~projects/SATware/ SATware
          Home | People | Publications | Press | Videos and demos | Intranet
          Featured Videos and demos

          SATware: a middleware for sentient spaces

          SATware is a multimodal sensor data stream querying, analysis, and transformation middleware that aims at realizing a sentient system. SATware provides applications with a semantically richer level of abstraction of the physical world compared to raw sensor streams, providing a flexible and powerful application development environment. It supports mechanisms for application builders to specify events of interest to the application, mechanisms to map such events to basic media events detectable directly over sensor streams, a powerful language to compose event streams, and a run-time for detection and transformation of events. SATware is being developed in the context of the Responsphere infrastructure at the UC Irvine campus.

          In contrast with classic pervasive middleware, SATware provides application developers a semantic view of the pervasive space. This semantic layer is at the same abstraction level that users reason at. This way, application developers need to worry about the semantics of an application, and not about the details of where sensors are and how data has to be collected from them. SATware provides users with a semantic layer that abstracts sensor data streams with raw sensed data into entity based streams. The user only needs to worry about entities (for example, person X, or room Y) and events regarding those entities (for example, person X is in room Y or room Y is empty).

          SATware semantic layers

          The basic architecture of SATware can be summarized in Figure 2. SATware is organized in a series of layers where each layer (SATRuntime-SATRepository, SATDeployer, SATLite, and SATQL) provides an extra level of abstraction of the sensing infrastructure. The lowest layer, the SATRuntime layer, is distributed along machines in a network (including sensors) and provides a runtime environment where operators can be injected and executed. The highest layer provides mechanisms for the users to query about the pervasive environment.

          Overview of SATware's architecture

          At the top level, applications/users write queries with SATQL. These queries are translated into a graph of operators where each operator performs a function on a certain stream of data and produces another stream of data. For example, Figure 3 represents a query to detect who leaves the coffee pot without coffee on the burner, and with the burner on. These graphs of operators are expressed in the SATLite language. The SATLite language provides syntax and semantics to describe graphs of operators. Namely, it provides primitives to describe operators and streams (operators are connected by streams).

          A query expressed as a graph of operators

          Once a graph of operators has been defined in SATLite, each operator is then assigned a machine where it will execute. The mapping of operator graphs to machines and the deployment of such operators and establishment of their connections is done by the SATDeployer. Different objectives need to be consider when injecting operators. These objectives include minimizing communication cost, latency, or operator computation cost. In addition, SATware will consider reusing existing operators in the network. This way, operator graphs can share operators, which minimizes cost. SATDeployer uses methods provided by SATRepository in order to deploy operators in the network.

          The SATRepository provides an API to deploy operators, to access a repository of operators, and to learn about the state of SATware. The state of SATware contained in the directory server includes which sensors are available and how to access them, which processing nodes are available and how much resources they are offering, the network topology and its state, and the current agent deployment.

          SATRuntime is a reflective distributed runtime installed in every SATware node but the central directory (Figure 4). Each operator is implemented as a mobile agent that can migrate to any of SATware's nodes. Figure 4 depicts SATRuntime's detailed architecture. The system nodes are of three types: a) sensor nodes, b) processing nodes, and c) the central directory. Sensor nodes correspond to the heterogeneous set of sensors in the pervasive infrastructure (e.g., Responsphere).

          The processing nodes are nodes where the SATRuntime is installed allowing agents to execute there. SATRuntime provides mobility support and message passing to SATware agents. When deploying a graph of operators SATRuntime nodes (not the agents) are explicitly connected according to the topology. This way, agents are programmed in a topology-agnostic manner. The central directory accepts queries in either SATQL format or SATLite format and deploys them.

          SATware's detailed architecture

          Home | People | Publications | Press | Videos and demos | Intranet
          This page was last updated on June, 2007

          SATware is part of Responsphere and Rescue.

          This material is based upon work supported by the National Science Foundation under ward Numbers 0331707, 0331690, and 0403433. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
          http://www.ics.uci.edu/~jsupanci/HANDS-2015/ IEEE Computer Society Workshop on Observing and understanding hands in action, CVPR 2015

          IEEE Computer Society Workshop on Observing and understanding hands in action (HANDS 2015)

          In conjunction with CVPR 2015 , June 12th, 2015

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          News:

          • Submission Deadline Extended to March 30th
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          http://www.ics.uci.edu/about/bren/gallery/ Untitled Document http://www.ics.uci.edu/community/events/openhouse/research06.html Lean Software Development

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          Lean Software Development »


          The Bren School enjoys an international reputation for its research on the human and social dimensions of computer system design and use. Whether your development team members are down the hall or in the land down under, Bren School researchers are working on ways to ensure the collaborative nature of software development continues to flow and bottlenecks are avoided.

          To learn more visit the Department of Informatics Web site.

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          http://www.ics.uci.edu/community/events/openhouse/research02.html Building the Genes of Tomorrow

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          Building the Genes of Tomorrow »


          Professors Rick Lathrop and G. Wesley Hatfield's success in the lab and customer feedback about their synthetics genes soon led to thoughts of a spinoff company. With the help of the Bren School, College of Medicine and UCI's Office of Technology Alliances, (Computationally Optimized DNA Assembly) Genomics Inc. was born.

          To learn more visit the CODA Genomics Web site, or watch this short highlight video.

           

           

          © 2007 The Donald Bren School of Information and Computer Sciences

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          Irvine, CA 92697-3425

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          http://www.ics.uci.edu/community/events/openhouse/research07.html Seamless Multi-screen Displays

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          Seamless Multi-screen Displays »


          Professor Aditi Majumder has created a way to display large images on multiple displays without seams or color variations. Her research has been a boon to medical and scientific researchers, providing them a way to view large digital picture files without scrolling up and down and working on multiple documents without wasting time switching between screens.

          To learn more read this article.

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          © 2007 The Donald Bren School of Information and Computer Sciences

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          6210 Donald Bren Hall

          Irvine, CA 92697-3425

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          http://www.ics.uci.edu/community/events/openhouse/research01.html Join us virtually

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          Creating Environmental Awareness »


          Whether it is utilizing the interactive Eco Raft game to teach children about conservation and social ecology, or allowing consumers to use their PDA's and cell phones to learn about the environmental impact of the products they buy while at the store or, tracking discarded monitors, Professor Tomlinson and his students are helping to ensure a greener tomorrow.

          To learn more visit the Eco Raft Web site, or watch this short highlight video.


          Eco Raft Highlight Video »


           

           

          © 2007 The Donald Bren School of Information and Computer Sciences

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          Irvine, CA 92697-3425

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          http://www.ics.uci.edu/community/events/openhouse/research09.html Testing Large-Scale Galaxy Simulations

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          Testing Large-Scale Galaxy Simulations »


          Researchers in the physical sciences such as astronomy and physics rely on computational power to create and test large-scale simulations. Professor Wayne Hayes collaborates across disciplines to test the reliability of large-scale physical simulations such as galaxy and cosmological n-body simulations as performed by astronomers.

          You must have the Flash plug-in before you can view the video. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

           

           

           

           

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          http://www.ics.uci.edu/community/events/openhouse/research12.html Cyber-Security and Privacy

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          Cyber-Security and Privacy »


          The Center for Cyber-Security and Privacy focuses on the importance of security and privacy in our increasingly computerized life. This importance of security and privacy is evident in the prevalence of major news stories about identity theft, privacy-eroding legislation and industry practices, spam, phishing, worms and viruses. The Center aims to develop feasible and effective remedies that are legally permissible and enforceable, and understandable and acceptable for computer users.

          To learn more visit the Center for Cyber-Security and Privacy Web site.

          You must have the Flash plug-in before you can view the video. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

           

           

           

           

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          http://www.ics.uci.edu/community/events/openhouse/research10.html Technology and Everyday Life

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          Technology and Everyday Life »


          The design, use, and impact of technologies are determined not solely by technical factors but also by how those technologies are shaped by social pressures and demands. Additionally, computing is migrating from the desktop computer into the everyday world, in the form of information appliances, digital devices, wireless networks, smart environments, and mobile, handheld and wearable devices.

          To learn more visit the Laboratory for Ubiquitous Computing and Interaction (LUCI) Web site.

          You must have the Flash plug-in before you can view the video. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

           

           

           

           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/research11.html Embedded Systems

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          Embedded Systems »


          With applications ranging from electronic wallets to information appliances, implanted adaptive insulin pumps, smart automobile air-bag systems, and wireless wrist communicators, embedded computer systems have immense potential to change our daily routines. Ian Harris’ research group explores cost-critical and life-critical applications, including automotive design and sensor-based medical devices.

          To learn more visit the System Test Laboratory Web site.

          You must have the Flash plug-in before you can view the video. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

           

           

           

           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/research05.html Experiential Environments

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          Experiential Environments »


          Information is used for implementing all decisions and is essential for communication and making systems work. On the other hand, decision-making requires insights resulting from observation and analysis of all relevant data and information from multiple sources. By using computers and users synergistically as a system, a very different type of computing environment, experiential environments, could be developed. Experiential environments allow a user to directly observe data and information of interest related to an event and to interact with the data based on his interests in the context of that event.

           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/research03.html Improving Emergency Response

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          Improving Emergency Response »


          September 11 and Hurricane Katrina are universally recognized as recent history’s most deadly U.S. disasters. The events share an important distinction: experts agree that communication systems failed miserably, increasing the death toll and level of destruction. UC Irvine researchers are working on ways to utilize technology to ensure these deadly problems can be avoided in the future.

          To learn more visit the Project ResCUE Web site, or watch this short highlight video.


          Project ResCUE Highlight Video »


           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/research04.html Ubiquitous Computing

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          Ubiquitous Computing »


          Researchers at the Laboratory for Ubiquitous Computing and Interaction (LUCI) are interested in the challenges of designing, using and understanding the elements of a ubiquitous computing world. Some of these different facets include computing in the face of mobile computers and mobile users, understanding and exploring new patterns of socio-technical behavior, and the design and construction of technology which supports ubiquitous computing.

          To learn more visit the LUCI Web site, or watch this short highlight video.


          Ubiquitous Computing Highlight Video »


           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/research08.html Keeping Score

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          Keeping Score »


          Excerpted from a PBS special and narrated by Danny Glover, Statistics Chair Hal Stern talks about statistical analysis in baseball. Sports are full of small 's' statistics, which are the field statistics that track a player's performance. The field of statistics – or the big 'S' statistics – is about drawing conclusions from data.

          To learn more visit the Department of Statistics Web site.

          You must have the Flash plug-in before you can view the video. If your system does not have the plug-in, it is available as a free download from Adobe's Web site.

           

           

           

           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

          http://www.ics.uci.edu/community/events/openhouse/secondlife.html Join us virtually

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          Join us virtually »


          photo:: bren school buildings

          Donald Bren Hall in Second Life.


          A virtual Donald Bren Hall (view a gallery of snap shots) has been created in Second Life by Crista Lopes' students to allow visitors to share in our open house no matter where in the physical world they are located.

          Once there you can tour the building and watch videos highlighting the life changing research done by our faculty and students.

          Follow the instructions below on how to create a character in Second Life and visit TechCoast, our virtual home.

          Second Life is a 3-D virtual world entirely built and owned by its residents. Since opening to the public in 2003, it has grown explosively and today is inhabited by a total of 5,905,638 people from around the globe.

          Several Bren School faculty are researching social interaction in the virtual world, as well as conducting classes and holding research group meetings.


          Create a character »


          To create a Second Life account, go to the Second Life web site, and complete the registration form


          Download the Second Life client »


          Once you have an account, you will need to follow the instructions for downloading and installing the Second Life client.

          The client is available for Windows, Mac OS X and Linux and provides the user interface you will use to participate in Second Life.


          Getting to Donald Bren Hall »


          The first time you login, you will be placed in Orientation Island, where you must pass 4 very simple
          tutorials before you can roam freely.

          After that, you can finally visit the Bren School island and Donald Bren Hall by pulling out the map and searching for TechCoast and then choosing "Teleport".

          Alternatively, you can open a web browser and type this in the address line: secondlife://TechCoast.


          Virtual Donald Bren Hall photo gallery »


          photo:: bren school buildings

          Under Construction Indefinitley, even in cyberspace.

           

          photo:: bren school buildings

          Donald Bren Hall on TechCoast Island in Second Life.

           

          photo:: bren school buildings

          TechCoast Island's amphitheater.

           

          photo:: bren school buildings

          Scenic TechCoast Island.

           

          © 2007 The Donald Bren School of Information and Computer Sciences

          University of California, Irvine

          6210 Donald Bren Hall

          Irvine, CA 92697-3425

          info@ics.uci.edu

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          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

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          Bren school home > labs home > lab specifications
          Lab software »

          The ICS Computing Labs currently have over two hundred Windows and UNIX machines in five labs.

          Lab Hours

          Software Specifications

          Hardware Specifications

           

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          Bren school home > labs home > lab specifications
          Lab hardware »

          CS364
          • Total of 80 of the following:
            • Dell Optiplex 745 - 2.13 GHz Core 2 Duo E6400, 4GB RAM, 160GB hard drive
            • Dell Optiplex 755 - 3 GHz Core 2 Duo E8400, 4GB RAM, 160GB hard drive
            • Dell Precision 490 - 3 GHz Xeon 5160, 4GB RAM, 160GB hard drive
          • (10) Networking Lab Stations*
          CS183
          • (45) Dell Optiplex 755 - 2.66 GHz Core 2 Duo E6750, 4GB RAM, 160GB hard drive
          CS189
          • (45) Dell Optiplex 755 - 2.66 GHz Core 2 Duo E6750, 4GB RAM, 160GB hard drive
          CS192
          • (40) Dell Optiplex 745 - 2.13 GHz Core 2 Duo E6400, 4GB RAM, 160GB hard drive
          CS193 (reserved for Informatics 117 and 191)
          • (24) Dell OptiPlex - 2.4GHz, 1GB RAM, 20GB HD, 17" Monitor
          CS195 (reserved for Informatics 117 and 191)
          • (5) Dell OptiPlex - 2.4GHz, 521MB RAM, 20GB HD, 17" Monitor


          Building CS2 Room 170 (reserved for courses involving the Computer Game Sciences major)*
          More information can be found here: http://www.ics.uci.edu/~lab/labs_specs/cgs_software.php
          • (20) Dell XPS 8300 - Core i5-2500 3.3GHz, 8GB RAM, 1.5TB hard drive, Dual 22" Monitors per station


          Building CS2 Room 162 (reserved for Embedded Systems related courses)*
          • (16) Dell Optiplex 745 - 2.13 GHz Core 2 Duo E6400, 4GB RAM, 160GB hard drive, 17" Monitor

          * must be enrolled in appropriate class to use

          More labs»
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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: August 27 2013
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          Bren school home > labs home > lab specifications
          Lab software »

          All ICS workstations are loaded with a standard system environment. This section lists the environment for our Windows 7 Labs, as well as how to obtain a display of software available on our Linux/Solaris hosts.

          For information regarding software on systems intended for the Computer Game Science major, please refer to this page:
          http://www.ics.uci.edu/~lab/labs_specs/cgs_software.php


          If there are any concerns or questions regarding specific versions of a program on this list, please let us know by emailing Helpdesk@ics.uci.edu

          CS183, 189, 192, 193 and 364 Windows 7 Labs

          Text editors:

          • TextPad 7

          PDF/PS Viewers:

          • Adobe Acrobat 11 Pro
          • GSview Ghostview 5
          • GNU Ghostscript 9

          Programming Environments:

          • Android Development Kit (Android SDK)
            • Eclipse + ADT plugin
          • Java:
            • Eclipse Standard 4.4
              • Subclipse
              • PyDev
            • JDK 8
            • Junit 4.11
            • Weka 3.6
          • Scheme:
            • Racket 6
          • Prolog:
            • SWI-Prolog 6.6
          • Python 3.4
            • IDLE (included as part of Python)
            • Python Imaging Library (PIL) --> replaced by Pillow - reference: http://www.lfd.uci.edu/~gohlke/pythonlibs/
            • Numpy - reference: http://www.lfd.uci.edu/~gohlke/pythonlibs/
          • Steel Bank Common Lisp
          • Other:
            • Microsoft Visual Studio 2013

          Internet Browsers:

          • Firefox
          • Google Chrome
          • Internet Explorer

          Other:

          • Altera 14
            • ModelSim
          • ArgoUML 0.34
          • djgpp
          • MinGW
          • Flash Player 16
          • KompoZer
          • Mathematica 9
          • Microsoft Office 2013
          • Pidgin
          • Putty 0.63
          • Quicktime 7
          • R 3.1.0
            • R Commander
          • RStudio
          • SAS 9.4 *
          • BYOB Scratch 4 (now web-based) available via internet browser at: http://snap.berkeley.edu/run
          • Scratch (now web-based) available via internet browser at: http://scratch.mit.edu
          • IrfanView
          • GIMP
          • Dev-C++ and Glut
          • Sophos
          • Stata 10
          • Wing IDE 101 5.0.0
          • VirtualBox

          Note: Every quarter, our Windows Support staff review the available software with faculty to make additions, deletions, and other changes as they become necessary.

          * Software marked are not installed on all machines. They are limited to only some machines in the CS364 Lab. Please see the *map* for details.

          • SAS (6)

          Linux and Solaris Environments

          The Modules application handles software in our Linux/Solaris environments. To get the latest list of available software modules, type the following command at a Unix prompt:

          module avail

          If you are unable to use the module command, please follow these instructions:

          http://www.ics.uci.edu/~lab/students/unixfiles.php

          For more details about the Linux/Solaris servers and software modules, please refer to the following page:

          http://www.ics.uci.edu/computing/linux/hosts.php

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          Windows

          Linux

          Solaris
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          Bren school home > labs home > student information
          Student FAQs »

          • How can I get a job working in the ICS Labs
          • Why isn't the Lab open 24 hours a day?
          • What do I do if a machine is broken?
          • What does a lab assistant do anyway?
          • Can I bring food into the lab?
          • What should I do if the people around me are making it hard to concentrate?
          • Can I print out non-ICS stuff if I'm an ICS major?
          • When can I use ICS equipment?
          • Can I play games on the Lab computers?
          • Do I really have to leave when the lab closes?
          • Why are these machines so slow?
          • What time do the Labs close?
          • How much disk space do I get for my class?
          • Can I borrow one of these cool mouse pads?
          • Where can I make a suggestion about Lab?

          Q: How can I get a job working in the ICS Labs?

          If you want to apply as a Lab Assistant (the people behind the desk in the Lab) fill out and turn in the Job Application at the appropriate time (Usually Week 7 of the quarter if we are hiring).

          Q: Why isn't the Lab open 24 hours a day?

          We believe having an Assistant on duty is a greater deterrent than a few cameras (although we reserve the right to supplement the Assistants with cameras later). Recent surveys show that students don't want to work the graveyard shift.

          Q: What do I do if a machine is broken?

          Contact a Lab Assistant at the help desk. Do not attempt to fix it yourself.

          Q: What does a Lab Assistant do anyway?

          Lab Assistants do not substitute for TAs or instructors. Lab Assistants do not know everything there is to know about computers. Lab Assistants do attempt to deal with Lab problems or report the problems to someone who can deal with them. Lab Assistants do activate ICS computer accounts and maintain printers. They also watch over the Labs and maintain a safe and comfortable instructional environment.

          Q: Can I bring food into the Lab?

          NO! It smells, creates a trash problem, attracts pests, and provides yet another danger to our precious computers.

          Q: What should I do if the people around me are making it hard to concentrate?

          You should notify the Lab Assistant on duty, who will take care of the problem.

          Q: Can I print out non-ICS stuff if I'm an ICS major?

          Computers are for ICS use primarily. If a computer is free or the Lab is not busy you may complete and printout non-ICS coursework.

          Q: When can I use ICS equipment?

          During your class Lab time when there is an instructor or TA present. There are also times when no classes are scheduled, and you may utilize the machines at those times as well. CS 364 serves as an Open Lab almost exclusively.

          Q: Can I play games on the Lab computers?

          This may not directly answer the question, but know these things: Instructional purposes always have priority. Do not put unlicensed software on any of the machines. Do not create a disturbance in the Lab which may disrupt students who are working on course material. What this means is that academic and instructional purposes always take precendence over games and other leisurely activities.

          Q: Do I really have to leave when the Lab closes?

          Yes. Even if you are very trustworthy, the motion sensors trust no one.

          Q: Why are these machines so slow?

          We have Pentium 400Mhz workstations. It must be you... No, seriously. If you are logging in to a Unix host to do course work you may be logged on with a hundred other people.

          Q: What time do the Labs close?

          Check out the Lab Schedule page.

          Q: How much disk space do I get for my class?

          Disk space varies by courseload. The default amount is 40 MB.

          Q: Can I borrow one of these cool mouse pads?

          Don't even think about it. In fact, do not remove any equipment from the Labs ever. If you're still thinking about it, perhaps you need some Philosophy courses.

          Q: Where can I make a suggestion about Lab?

          As always, send an email to ICS Lab. We need good ideas like plants need the sun.

           

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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 12 2011
          http://www.ics.uci.edu/computing/ computing support @ the bren school of information and computer sciences

          • » Account
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          Bren School Computing Support

          Supporting the Instructional, Research, and Administrative Missions of the Bren School.  

          Location:  346 Information and Computer Science (Bldg. 302)

          Hours: Monday - Friday 8 am to 12 pm, 1 pm to 5 pm (excluding University holidays)

          Phone: 949-824-4222 

          Computing Support operates a Helpdesk to ensure that requests are tracked and completed in a timely manner.  The Helpdesk should be consulted with problems regarding ICS accounts, accessing Bren School ICS systems, supplied/supported software, and when planning to purchase new systems.  For more information, please refer to the Helpdesk page.

          Please use the pull-down menu along the top to navigate our site.  We also suggest that you browse the system annoucements and quarterly announcements for the latest updates on what we have going on here at ICS.

          • Important System Announcements
          • Quarterly Announcements
          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: November 14 2013
          http://www.ics.uci.edu/%7elab/students/unixfiles.php uc irvine::bren school::lab - student Skip over navigation

          This site will look much better in a browser that supports web standards, but it is accessible to any browser or Internet device.

          • ABOUT
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          Bren school home > labs home > student information
          Replacing Unix .(dot) Files »

          If you are unable to use the module command or if your prompt looks unual, then you're probably missing "." files. Every account start off with basic statup files. These files may have been removed mistakenly if the user was not careful.

          The following files MUST be in your home directory (H-drive):

          • .login
          • .logout
          • Depending on which shell you're using, you will also have on of the following file:
            • .cshrc (If you've never changed your shell, this is the default one setup for all users.)
            • .bashrc
            • .tcshrc
            • .zshrc

          If one or all of the above files is missing, you will have problems logging onto the ICS hosts.

          You can add them by following these steps:

          1. SSH to openlab.ics.uci.edu from a Windows machine
            • If you can NOT SSH to openlab, have the lab assistant reset your password and try again.
            • If it still does not work, please ask the lab assistant to file an online report form.

          2. Type in the following command at the prompt:
            • %cd /opt/local/etc/skel/

          3. Copy all missing files ending with "@" to /home/"login_name"/."filename"
            Remember to put the dot before the destination file.
            • For example, if you are missing the .cshrc file you would type the following at the prompt:
              • %cp cshrc /home/"login_name"/.cshrc
            • You may also want to look at the other example.* files as some have more options than other that you may like to use.

          4. After copying all the files you are missing, return to your home directory by typing the following command at the prompt:
            • %cd

          5. Finally, you need to chmod all the files you copied. This basically means changing the permission on the file so that it is only changeable by you. Type the following command for each of the files you copied:
            • %chmod 744 ."filename"
            • For example, for the cshrc file, it would be:
              • %chmod 744 .cshrc

          6. You're DONE!

          If you have any questions regarding the above procedure, please contact the Lab Assistant.

          More labs»
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          http://www.ics.uci.edu/%7elab/policies/labguidelines.php uc irvine::bren school::lab - student Skip over navigation

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          Bren school home > labs home > policies
          Lab guidelines »

          The following are official ICS policies which every student using the ICS Labs is expected to follow. Students should read and understand these guidelines because they can and will be held accountable for them. If you do not agree with any of the rules or guidelines, feel free to contact us and share your thoughts. UCI has rules that apply to all Computing Labs. This page also contains specific rules that apply to ICS Labs.

          General Information about Ethics

          • Summary of Ethical Use of Computing Resources - A Summarized version of the Ethical Use of Computing Resources.
          • Ethical Use of Computing Resources - The formal ethics document which every student using the ICS labs and/or an ICS account must abide by.
          • Computer Use Policy - The University's computer ethics document.

          Lab Specific Guidelines

          ICS LABS ARE FOR ICS MAJORS AND STUDENTS ENROLLED IN ICS COURSES ONLY.
          • The funding for our labs is only for those students that are ICS majors and/or enrolled in ICS classes.
          • The privilege of using the ICS Labs does not extend to relatives, friends or roomates of ICS majors.
          • Don't feel homeless if you aren't an ICS major or enrolled in an ICS class. You can either change your major or take advantage of the campus-wide resources provided by OIT
          LAB WORKSTATIONS ARE FOR EDUCATIONAL PURPOSES
          • Do not do any commercial work, whether or not you are paid for it.
          • Do not use the lab's network connection to transfer, in or out, copyrighted material and other non-academic data, e.g., .mp3s.
          • Be aware that electronic sources(web pages, email, etc.) are meant to be viewed electronically and printing of these resources not only wastes paper but destroys the purpose of electronic resources.
          • Do not print coursework for non-ICS classes. You may make modest use of computing resources for non-ICS coursework if you are an ICS major and if the lab is not full. If the lab is full, please do the coursework elsewhere.
          NO FOOD OR DRINK IN ANY OF THE ICS LABS
          • You must get rid of food and/or drinks before you enter the lab. Once you pass the doorway entrance of the lab, you are in the lab.
          • Food and drinks can damage the computer equipment.
          • If you are found with food you will be asked to remove the food immediately from the Lab and be subject to a citation.
          DO NOT DISTURB THE OTHER PEOPLE AROUND YOU
          • Try to keep talking noise to a minimal level.
          • Don't lock your display and leave. Displays locked for more than 15 minutes can be unlocked by the lab attendant.
          • Respect personal space of others.
          DO NOT STEAL
          • This includes mouse balls, mouse pads, printer paper, monitors and computers.
          • Lab equipment is expensive and anything taken must be replaced. The need to replace stolen equipment may result in increased fees and/or decreased resources.
          • When caught you will be reported to the proper authorities.
          DO NOT COPY SOFTWARE OFF OF LAB WORKSTATIONS
          • Much of the software on the lab machines is commercial software. We have bought licences for this software and if you want a copy, you must go buy it. Fortunately, you are a student and are eligible for educational prices on software. Check out the UCI Computer Store for more information.
          DO NOT DOWNLOAD OR INSTALL SOFTWARE ONTO LAB WORKSTATIONS
          • It is illegal.
          • The software may be tampered in such a way that it has a virus or will cause some unexpected activity.

           

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          Bren school home > labs home > student information
          Student information »

          The ICS Computing Labs are provided for students who are enrolled in ICS classes. Before you can begin using the computers in the labs, you must first obtain an ICS account. This section will help you with everything pertaining to the lab.

          • Account Activation
          • Account FAQs
          • Lab Guidelines
          • MSDNAA Software
          • Password
          • Printing in the Labs
          • Student FAQs
          • Turning in Assignments
          • Unable to use the module command
          • Openlab Login Problems
          • Wireless Connection

          For more information on troubleshooting, please visit the ICS Support page.


          MSDNAA Software

          The MSDN Academic Alliance helps provide software to students at no cost. For more information, please visit the MSDNAA FAQs page. If you already have an account, you can proceed to the login page.

          Password

          If you've forgotten your password, please see the Lab Assistant in CS364. You will be asked for your username and a form of picture ID.

          If you need to change your password, please go here. The page is only available from on-campus. If you're off-campus, please make sure that you're first connected via the UCI VPN.

          Turning in Assignments

          Assignments are turned in one of two ways: masterhit dropbox or checkmate. Please consult with your syllabus on which procedures you must follow for the specific class.

          Openlab Login Problems

          When you login to an ICS host, if you login and get sent back to the login screen, then you are either missing system files or you are over quota. To check, login to a Windows machine and then use SecureCRT to connect to openlab.ics.uci.edu.

          • If you can login, then you are probably over quota. Check your quota and remove files as necessary.
          • If you cannot login or if you can login but the prompt looks unusual, then you are probably missing important files. Please follow the instructions on how to Replace UNIX files.

           

          More labs»
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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: February 05 2013
          http://www.ics.uci.edu/computing/services/msdnaa_faq.php msdnaa faq @ the bren school of information and computer sciences

          • » Account
            • » New User Guide
            • » Activation
            • » Password Change/Reset
            • » Quota
            • » Renewal
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              • » Windows
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          • » E-mail
            • » ICS Google Mail
            • » Specify Delivery Point
            • » Webmail
            • » Thunderbird for ICS Gmail
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            • » Email Servers Information
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            • » UCInet Mobile
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            • » Helpdesk
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          MSDNAA FAQ
          • What is the MSDNAA Program?
          • Am I eligible to download MSDNAA software?
          • What software is available for download?
          • What if I want a software that has not been made available?
          • How much does it cost to get the software?
          • What can the software being provided be used for?
          • Do I have to uninstall the software once I graduate or stop taking ICS courses?
          • How can I obtain the software?
          • Why is the size of the file I downloaded so small or unexecutable?
          • I am getting an error in the middle of my download, what does it mean?
          • How do I receive my login/password?
          • What is an SDC file and why can't I open or unpack it?
          • How much hard disk space do I need?
          • How can I reuse my product keys if I need to reinstall my computer?

          Q: Am I eligible to download MSDNAA software?

          If you are enrolled in any course under the Bren School of ICS course for credit, you are eligible to download the MSDNAA software.  Faculty, instructors, and TAs are eligible to receive the software if you have an active ICS account.

          Q: What software is available for download?

          Please login to the MSDNAA page and look under the Software section for an updated listing.

          Q: What if I want a software that has not been made available?

          If you need a particular software for a class or research project, please email helpdesk with an explanation of why you need it and we will look into its availability.  NOTE: Microsoft Office is not included in the MSDN Academic Alliance program.

          Q: How much do I need to pay for the software?

          In an agreement with Microsoft, the MSDN Academic Alliance program provides this software free of charge.  There is a small charge if you choose to have the installation CD mailed to you.

          Q: What can the software being provided be used for?

          The software is provided for instructional and non-profit research purposes.  It cannot be used for infrastructure or profit.

          Q: Do I have to uninstall the software once I graduate or once I am no longer a student of the Bren School of ICS?

          The license is perpetual.  This means that you do not need to uninstall the software.  The software and product keys you have received are yours to keep, but you will lose the ability to download the MSDNAA software once your ICS account expires.

          Q: How can I obtain the software?

          You may download the software from the MSDNAA website.

          Alternatively, the burner machine in CS 364 lab has the ISO images of the software in the "MSDNAA Software" folder.  You can burn your own CDs/DVDs to take home or bring a USB flash drive and transfer the ISO files you might need.  However, you will still need to login into MSDNAA site and "purchase" the software from their website to get the necessary product keys.

          Q: Why is the size of the file I downloaded so small or unexecutable?

          If you are downloading the MSDNAA software, the file that first gets downloaded may not have the .exe file extension.  This has occurred when Mozilla Firefox is used as the browser.  Add the .exe extension if necessary and double-click the file.  It is a downloader file of a very small size that is used to download an SDC file.  After the SDC file is downloaded, the downloader is required to extract the actual software from it.

          Q: I am getting an error in the middle of my download, what does it mean?

          In many cases, you should be able to figure out what is wrong by the content of the error code.  If it is not immediately obvious what you should do, check the Download Error Help Page.  If you still cannot complete your download, please send an email message to helpdesk.

          Q: How do I receive my login/password?

          Please user your ICS account username and password to login.

          Q: What is an SDC file and why can't I open or unpack it?

          When you double click the executable file that you downloaded from MSDNAA, it will ask you where to unpack the file.  There will be an SDC file inside the folder you choose to store the downloaded file.  This is a zip file that the Downloader executable will extract for you when the download is completed.  You need to use the Downloader to extract the install files from the SDC file.  The SDC file is safe to delete after the setup files are extracted from it.

          Q: How much hard disk space do I need?

          Some files required at least 2GB of space for the SDC file that is downloaded plus the files that are extracted from it.  If you do not have enough disk space for the SDC file and the extracted files, you will need to download the file again in order to unzip the SDC file.  Make sure you have enough disk space before proceeding to download.

          Q: How can I reuse product keys issued to me if I need to reinstall my computer?

          If you made a hardware change or got rid of your old computer and need to reinstall software that needs to be activated, such as Windows 7, Visio, and Project, the first step is to try telephone registration to speak to a Microsoft representative.  If you need to be reissued new product keys because the telephone registration did not work, email helpdesk and state your reasons for the request thoroughly.

          Copyright Inquiries | UCI Directory | Intranet | icswebmaster | Updated: September 28 2015
          http://www.ics.uci.edu/~eli/courses/seminar-s10/seminar-s10.html ICS 252-Introduction to Computer Design

          Spring 2010

          seminar on cyber physical systems (cps)

          (CS259s / CS295)

          cps-logo.jpg

          Instructors: Prof. Nikil Dutt and Prof. Eli Bozorgzadeh, Computer Science Department

          When: Thurs. 2:00-4:30 p.m.

          Location:  DBH 4011

          Brief Description:

          Cyber physical systems focus on integration of the system computation and physical components. Although embedded systems research targets generation of systems for highly constrained and tightly embedded applications, the contribution has been loosely coupled with sensors and physical elements of the systems. As a result, CPS aims at network of computational components and physical components as opposed to only (part of) computational components of the target system. With advances in CPS, the system development for applications such as automotives, health care, and transportation will be improved in term of reliability, integrity, and adaptivity.

          CPS is recognized as a key area of research in the US as well as in Europe and Asia. The term cyber physical system is very broad and encompasses various research topics from software and modeling to systems, networking, and control in computer science and engineering. In this weekly seminar, we first plan to introduce cyber physical systems, what CPS systems are and what differentiate them from embedded systems. Then, we will discuss several related papers. Please use the links below to get further information on CPS.

          Tentative Seminar Schedule

           

          Thursday April 1, Introduction to CPS

          Speakers: Nikil Dutt, Eli Bozorgzadeh, and Sung-Soo Lim

           

          Useful Links

          • Cyber Physical Systems Week (CPS WEEK) /For previous year proceedings check ACM library)
          • Cyber Physical System Summer School at Georgia tech
          • Cyber Physical System Forum
          • Cyber Physical System Lab at UCLA
          • Cartel at MIT: Example of CPS

           

           

          http://www.ics.uci.edu/~shendric/family.html Family

          Emily Anne Hendrickson
          Born:
          June 1, 2005, 10:31 am
          Weight: 7 pounds, 14 ounces
          Length: 20.5 inches


          Updated pictures of Emily


          September 21 , 2005: Emily has started rolling over, smiling, and playing! We are so happy to be parents! Click on an image for a larger picture.


          Emily taking a bath with grandma

          Hey, I know you.... you're my daddy!

          I like to smile and play

          Emily, her daddy, and her cousin Jacob

          ZZZzzzzzzz...... (again), This is Emily's swimsuit pose

          I'm loved!


          June 25, 2005: By popular demand, I've posted a few more pictures. Click on an image for a larger picture.


          Hi, I'm Emily. Do you want to play with me?

          C'mon I know you do.

          Put up your dukes!

          No, wait, I'm hungry!

          ZZZzzzzzzz......

          Yeah, pretty much I'm always in sombodys arms...
          And that's the way it should be.


          June 3, 2005: Wow! I'm a dad! Jeanette and I are so excited that our little girl is finally here! She's beautiful, of course! She has a similar hair color as mine, but with blond highlights. We can't really tell what color her eye's are, they're dark. But all that will likely change anyway. People say that she looks like me. My favorite thing about her is her nose! Every once in a while she'll look around and our eye's will meet. That is a wonderful experience! Click on an image for a larger picture.


          This is right after Emily was born. Jeanette, although tired, is happy to hold our little girl in her arms for the first time.

          Emily's first bath.

          What's this?... Fingers! Mmmmm...

          Yawwwwn... I'm tired now!

          Yep, time to take a nap after the hard job of being born.

          I think that she knows that she's cute.

          Jeanette leaving the hospital with our little girl.

          Our new family.

          Finally, the view from our room. If you look really hard, you can see the ocean.


          January 10, 2005: Here are some pictures from the ultrasound. Click on an image for a larger picture.


          Nose & Mouth




          Left arm

          Right arm

          One foot

          Other foot



          Hand





          http://www.ics.uci.edu/~fielding/pubs/dissertation/top.htm Architectural Styles and the Design of Network-based Software Architectures

          UNIVERSITY OF CALIFORNIA, IRVINE

          Architectural Styles and
          the Design of Network-based Software Architectures

          DISSERTATION

          submitted in partial satisfaction of the requirements for the degree of

          DOCTOR OF PHILOSOPHY

          in Information and Computer Science

          by

          Roy Thomas Fielding

          2000

           

          Dissertation Committee:
          Professor Richard N. Taylor, Chair
          Professor Mark S. Ackerman
          Professor David S. Rosenblum

          PDF Editions

          1-column for viewing online
          2-column for printing

          Table of Contents

          Dedication
          Acknowledgments
          Curriculum Vitae
          Abstract of the Dissertation
          Introduction
          CHAPTER 1: Software Architecture
          1.1 Run-time Abstraction
          1.2 Elements
          1.3 Configurations
          1.4 Properties
          1.5 Styles
          1.6 Patterns and Pattern Languages
          1.7 Views
          1.8 Related Work
          1.9 Summary
          CHAPTER 2: Network-based Application Architectures
          2.1 Scope
          2.2 Evaluating the Design of Application Architectures
          2.3 Architectural Properties of Key Interest
          2.4 Summary
          CHAPTER 3: Network-based Architectural Styles
          3.1 Classification Methodology
          3.2 Data-flow Styles
          3.3 Replication Styles
          3.4 Hierarchical Styles
          3.5 Mobile Code Styles
          3.6 Peer-to-Peer Styles
          3.7 Limitations
          3.8 Related Work
          3.9 Summary
          CHAPTER 4: Designing the Web Architecture: Problems and Insights
          4.1 WWW Application Domain Requirements
          4.2 Problem
          4.3 Approach
          4.4 Summary
          CHAPTER 5: Representational State Transfer (REST)
          5.1 Deriving REST
          5.2 REST Architectural Elements
          5.3 REST Architectural Views
          5.4 Related Work
          5.5 Summary
          CHAPTER 6: Experience and Evaluation
          6.1 Standardizing the Web
          6.2 REST Applied to URI
          6.3 REST Applied to HTTP
          6.4 Technology Transfer
          6.5 Architectural Lessons
          6.6 Summary
          Conclusions
          References

          List of Figures

          Figure 5-1. Null Style
          Figure 5-2. Client-Server
          Figure 5-3. Client-Stateless-Server
          Figure 5-4. Client-Cache-Stateless-Server
          Figure 5-5. Early WWW Architecture Diagram
          Figure 5-6. Uniform-Client-Cache-Stateless-Server
          Figure 5-7. Uniform-Layered-Client-Cache-Stateless-Server
          Figure 5-8. REST
          Figure 5-9. REST Derivation by Style Constraints
          Figure 5-10. Process View of a REST-based Architecture

          List of Tables

          Table 3-1. Evaluation of Data-flow Styles for Network-based Hypermedia
          Table 3-2. Evaluation of Replication Styles for Network-based Hypermedia
          Table 3-3. Evaluation of Hierarchical Styles for Network-based Hypermedia
          Table 3-4. Evaluation of Mobile Code Styles for Network-based Hypermedia
          Table 3-5. Evaluation of Peer-to-Peer Styles for Network-based Hypermedia
          Table 3-6. Evaluation Summary
          Table 5-1. REST Data Elements
          Table 5-2. REST Connectors
          Table 5-3. REST Components

          [Next] © Roy Thomas Fielding, 2000. All rights reserved. [How to reference this work.]
          http://www.ics.uci.edu/~sgirish/index.html Girish's Home Page

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Welcome to Girish's home page

           

          About me

          I am a Post-doctoral researcher in the Institute for Software Research at the University of California, Irvine. I am working on trust and reputation management in decentralized peer-to-peer architectures and applications with Professor Richard Taylor. I have a PhD and Masters degree in Information and Computer Science from UC Irvine and completed my undergraduate studies in Electrical and Electronics Engineering from the Birla Institute of Technology and Science, Pilani, India.


          What's New?

          I am serving on the Technical Program Committee for the International Conference on Software Engineering Advances (ICSEA 2006) to be held from Oct 29 - Nov 1, 2006, in Tahiti, French Polynesia.

          I recently also served as a Program Committee (PC) member for the Trust, Recommendations, Evidence and other Collaboration Know-how (TRECK) track of the 21st Annual ACM Symposium on Applied Computing (SAC) held in Dijon, France from April 23-27, 2006.


          Research Interests

          My research interests lie in the area of decentralized peer-to-peer (P2P) architectures and applications. Decentralized P2P architectures are characterized by the absence of a central authority or infrastructure that controls and coordinates the behavior of peers in the system. Instead peers have to rely upon information received from other peers and execute local decisions autonomously in order to achieve their individual goals. In the absence of a single centralized authority that can help regulate and coordinate a decentralized system, each peer must take steps to safeguard itself against malicious attacks. This results in a host of challenges such as: how can these attacks be countered, what measures can be adopted to detect and neutralize these attacks, how can a peer be designed so as to facilitate the incorporation of these measures, etc.

          My current work has two primary focus: architecture-based approach for building trust-enabled decentralized systems, and examining various types of reputation-based systems. You can read more about my research in the Research section.

             
                     
          http://www.ics.uci.edu/~sgirish/contact.html Contact Girish

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Contact Information

           

          • Mailing Address: ICS2-215, Department of Informatics, Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, CA - 92697-3440
          • Telephone Number: +1 (949) 824 - 9561
          • Fax : +1 (949) 824 - 1715
          • Email Address: sgirish[at the rate]ics[dot]uci[dot]edu
             
                     
          http://www.ics.uci.edu/~sgirish/links.html Research Links

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Research Links

           

          Java Links

          Sun's JDK 1.5

          Sun's Java Tutorial


          Decentralization

          Rohit Khare's dissertation


          Decentralized Trust Management

          Survey on Decentralized Trust Management


          Software Architecture Links

          UCI Software Architecture Research

          PACE

          ArchStudio 3.0

          xADL 2.0

          Architecture TradeOff Analysis

             
                     
          http://www.ics.uci.edu/~sgirish/cv.html

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Curriculum Vitae


           

          Download a pdf version of my December 2006 Curriculum Vitae.

          If you want to contact me, please send me email at - sgirish[at the rate]ics[dot]uci[dot]edu

             
                     
          http://www.ics.uci.edu/~sgirish/research.html

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Research

           

          My research interests are directed along two topics. The first is to investigate architecture-based approaches for developing trust-enabled decentralized applications. The second is to explore and compare different types of decentralized reputation-based trust systems with a view to better understand and model such systems.

          1. Software Architecture Approach - While there exist several trust and reputation models in the research literature, there has been little work directed at identifying how these models can be integrated into the architecture of a decentralized peer. Towards this end, along with other colleagues Justin Erenkrantz and Scott Hendrickson, I developed the PACE architectural style. PACE stands for the Practical Architectural approach for Composing Egocentric Trust. The PACE architectural style provides specific design principles that guide the incorporation of trust models into the architecture of a decentralized peer. More information on the PACE style can be obtained on the PACE project web page and in my Publications.

          2. Decentralized Reputation Models - My survey of decentralized trust and reputation models also revealed two other important shortcomings that have not been addressed by researchers.

          • The first is the lack of a common understanding of what a trust model is and what are the elements that constitute a trust model. Currently, there is no framework in the research literature that can help express and describe existing trust models and also serve as the basis for the creation of trust models in the future.
          • The second shortcoming stems from the fact that though there are a number of trust models proposed by researchers, there is unfortunately no framework that can help put these different models into perspective. As a result, application designers may be unable to decide what trust model to pick given certain application requirements. There is, thus, a compelling need for a comparison framework and infrastructure that will help compare these different models against their various capabilities.

          My recent efforts have been directed at addressing these two shortcomings. Specifically, I am investigating how different reputation-based trust models can be expressed, evaluated, and compared. My work so far has focused on -

          • The creation of a generic extensible framework, called the 4C framework, that facilitates the rapid specification of a trust model and the generation of a corresponding XML-based trust model description. Details of the 4C framework can be found here.
          • The creation of (i) a theoretical framework, called TREF, that compares the capabilities of reputation-based trust models in the face of threats, and (ii) a java-based simulation framework, called SIFT, that simulates the behavior of trust models under different application settings and threat conditions towards (a) assisting in the selection of an appropriate model for a given application setting, and (b) pointing at future refinements to those models.
             
                     
          http://www.ics.uci.edu/~sgirish/publications.html Publications

          Girish Suryanarayana's ICS Page
             
            Home     Research      Publications       Links      Contact     Curriculum Vitae    
           

          Page Last Updated - July 11th, 2007
           

           


          Publications

           

          Journals

          Architecting Trust-enabled Peer-to-Peer File-sharing Applications - Girish Suryanarayana, Mamadou H. Diallo, Justin R. Erenkrantz and Richard N. Taylor in ACM Crossroads, issue on Software Engineering, Vol. 12, No. 4, Summer 2006.

          An Architectural Approach for Decentralized Trust Management - Girish Suryanarayana, Justin R. Erenkrantz and Richard N. Taylor in IEEE Internet Computing special issue on Ad Hoc and P2P Security, Vol. 9, No. 6, pp. 16-23, November/December 2005.


          Conferences/Workshop

          "From Representations to Computations: The Evolution of Web Architectures" - Justin Erenkrantz, Michael Gorlick, Girish Suryanarayana and Richard N. Taylor - in Proceedings of the 6th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, Dubrovnik, Croatia, September 2007. [To appear]

          Architectural Support for Trust Models in Decentralized Applications - Girish Suryanarayana, Mamadou Diallo, Justin Erenkrantz and Richard N. Taylor - in Proceedings of the 28th International Conference on Software Engineering, Shanghai, China, May 2006.

          PACE: An Architectural Style for Trust Management in Decentralized Applications - Girish Suryanarayana, Justin Erenkrantz, Scott Hendrickson and Richard N. Taylor - in Proceedings of the Fourth Working IEEE/IFIP Conference on Software Architecture, June 2004.

          A Decentralized Algorithm for Coordinating Independent Peers: An Initial Examination - Girish Suryanarayana and Richard N. Taylor - in Proceedings of the Tenth International Conference on Cooperative Information Systems (CoopIS), October 2002.


          Technical Reports

          Tool Support for Incorporating Trust Models into Decentralized Applications - Mamadou H. Diallo, Girish Suryanarayana and Richard N. Taylor -ISR Technical Report UCI-ISR-06-4, April 2006.

          TREF: A Threat-centric Comparison Framework for Decentralized Reputation Models - Girish Suryanarayana and Richard N. Taylor -ISR Technical Report UCI-ISR-06-2, January 2006.

          A Survey of Trust Management and Resource Discovery Technologies in Peer-to-Peer Applications - Girish Suryanarayana and Richard N. Taylor -ISR Technical Report UCI-ISR-04-6, July 2004.

          PACE: An Architectural Style for Trust Management in Decentralized Applications - Girish Suryanarayana, Justin Erenkrantz, Scott Hendrickson and Richard N. Taylor -ISR Technical Report UCI-ISR-03-9, September 2003.

             
                     
          http://www.ics.uci.edu/~taylor/classes/123/syllabusSQ08.html IN4MATX 123 Spring 2008

          Informatics 123: Software Architectures, Distributed Systems, and Interoperability

          Spring Quarter 2008

          Last update: May 29, 2008

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 123" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 12:30-1:50 p.m, ICS180

          TA Rosalva Gallardo (rgallard at uci.edu). Office hours: Tuesday 10-10:50am. DBH 5051.
          Discussion M 1:00-1:50, ICS 180
          Web site: http://www.ics.uci.edu/~taylor/classes/123/syllabusSQ08.html

          What's New?

          • [May 29, 2008] More changes to lecture schedule.
          • [May 27, 2008] Minor update to lecture schedule.
          • [May 19, 2008] Discussion slides and assignment #3 posted.
          • [May 6, 2008] Lecture schedule shuffled.
          • [April 28, 2008] Discussion slides and assignment #2 posted.
          • [April 21, 2008] Slides from today's discussion posted. Class schedule tweaked.
          • [April 14, 2008] Slides from today's discussion posted.
          • [April 7, 2008] First assignment posted + slides from today's discussion section.
          • [March 31, 2008] Website open

          Description - Schedule - Grading - Readings - Policies


          Description

          Catalog description:

          Prepares students to engineer well-structured software systems. Students learn a wide range of software architectural styles, architectural platforms that provide standard services to applications, and formal architecture description languages. Prerequisites: Informatics 122 or the following: ICS 51 with a grade of C or better; Informatics 101/CS 141/CSE141 and Informatics 111/CSE121; Mathematics 2A-B and Statistics 67/Mathematics 67.


          Textbook and Assigned Readings

          The following textbook is required; readings will be assigned on a weekly basis.

          Software Architecture: Foundations, Theory, and Practice. Richard N. Taylor, Nenad Medvidovic, and Eric M. Dashofy. Copyright © 2009 John Wiley & Sons, Inc. (ISBN: 9780470167748)


          Schedule

          The schedule is subject to change. 
          Week Date Topic Readings Discussion/Assignments
          1

          A

          P

          R

          I

          L

           

          1 Tu The Big Idea Chapter 1  
          3 Th The Big Idea, continued
          2
          8 Tu Architectures in Context Chapter 2

          Slides from 4/7/08 Discussion.

          Assignment #1

          10 Th Basic Concepts and Definitions Chapter 3  
          3
          15 Tu Designing Architectures Chapter 4 Slides from 4/14/08 Discussion.
          17 Th Architectural Styles    
          4
          22 Tu Myx and ArchStudio   Slides from 4/21/08 Discussion.
          24 Th Styles and Greenfield Design Chapter 5  
          5
          29 Tu Midterm Exam  

          Slides from 4/28/08 Discussion.

          Assignment #2

          M

          A

          Y

          1 Th Connectors    
          6
          6 Tu Distributed Systems and Applied Architectures Chapter 11  
          8 Th Introduction to Modeling Chapter 6  
          7
          13 Tu Modeling and Notations    
          15 Th No class Chapter 9  
          8
          20  Tu Implementing Architectures  

          Slides from 5/19/08 Discussion.

          Assignment #3

          22  Th Implementation Techniques    
          9
          27 Tu Architecture-based Adapation and Robotics Chapter 11 (and 14 for that matter)  
          29 Th Architecture-based Adapation    
          10

          J

          U

          N

          E

          2 Applied Architectures, part II    
          5 Review    
          Exam 12 Th Final Exam, 10:30-12:30 p.m.    


          Grading and Evaluations

          Grading.

          • Midterm: 20%
          • Final: 25%
          • Assignments: 55%

          Project Matters

          • Development Environments:
            • Eclipse
            • ArchStudio4
          • Configuration Management System: SVN

          Policies

          Course Evalutions. The window for spring quarter online evaluations will open at <TBD> and close at <TBD>.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          © University of California, 2008.
          http://www.ics.uci.edu/~taylor/ics52_fq01/syllabus.html ICS 52 Syllabus Fall 2001

          Information and Computer Science 52:
          Introduction to Software Engineering

          Fall Quarter, 2001
          Location: ELH 100 -- as of October 1, 2001 (Need a map?)
          Monday and Wednesday, 3:30 — 4:50
          Course code: 36130
          Discussion location: SSPA 1100
          Monday, Wednesday, Friday, 1:00 -- 1:50
          Course code for discussion section: 36131
          (Last modified Sunday, November 25, 2001)

          WHAT'S NEW?

          [November 25, 2001] The final set of lecture slides are now on-line.

          [November 23, 2001] The final assignment is now available. No surprises, as compared to what I described in the lecture on Monday. I have postponed the due date, howver, until next Friday.

          [November 16, 2001] The STMP java files are available: SMTPComponent.java and SMTPInterface.java

          [November 8, 2001] A solution guide to the midterm is available. See the "midterm" in the schedule for the pointer. The midterms will be returned in the discussion section on Friday, November 9th.

          [November 5, 2001] The implementation assignment is now available. The due date for it has been pushed back to Monday, November 19th.

          [November 3, 2001] The slides for week 7 are now available (with page numbers this time!). I've also made some changes to the schedule and to the reading assignments. Assignment 2 is now due on 11/7.

          [October 31, 2001] The slides on user interface design are now available.

          [October 29, 2001 ] Girish has provided some examples of module descriptions, such as should be used in Assignment 2. Check them out. It is a MS Word document.

          [October 15, 2001] The slides for week 4 have been modified in a significant way. If you downloaded the slides yesterday evening please be sure to get the current copy!

          [October 7, 2001] The slides for week 3 are now available. I've also made available Professor van der Hoek's slides from last spring quarter on software engineering principles and on requirements engineering. They are "hanging off" the week 2 segment of the schedule, below.

          [September 28, 2001] Beginning Monday, October 1st, the lecture portion of ICS 52 will meet in ELH 100. Also: the discussion section will meet Monday, October 1, in its normal time and place.

          [September 26, 2001] Want to add the class? Here's what you MUST do before Friday at 1:00.

          • Send me email requesting that you be added.
          • Tell me that you have met the prerequisites. (If you have not me the prerequisites I will not add you. If you tell me that you have met the prereqs and I later discover that this isn't correct, I will drop you from the class.
          • Tell me whatever information you believe will help me correctly prioritize your request as compared to other students' requests, SHOULD the need arise. For example, graduating ICS majors get first priority. So: tell me your major, your year, and any extenuating circumstances.


          Overview | Textbooks | Schedule | Assignments | TAs | Keeping in Touch | Computing | Academic Dishonesty |

          Instructors

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Monday and Wednesday: 11-12:20. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          Introduction to the concepts, methods, and current practice of software engineering. The study of large-scale software production; software lifecycle models as an organizing structure; principles and techniques appropriate for each stage of production. Laboratory work involves a project illustrating these elements.

          Prerequisite: ICS 23 with a grade of C or better.

          In addition to the skills and concepts introduced in previous classes, students should have these computing skills when they enter the class (or learn them independently in the first week of the quarter):

          • The use of a text editor (Word, etc.) to create documents.
          • The use of a drawing package (PowerPoint, Visio, etc.) to create graphics for these documents.

          The instructional objectives for the course are as follows:

          • introduce you to the discipline of software engineering;
          • experientially acquaint you with one version of the software lifecycle;
          • provide working knowledge of at least one reasonable technique to be applied in each phase of the lifecycle;
          • provide particular insight into software architecture, design by information hiding, and the problems of software analysis and testing.

          Minimum Knowledge and Skills Expected of Students Who Receive Passing Grades

          Software Life Cycle

          • Mastery:
            • Knowing different life cycles and their appropriateness in different situations
            • Knowing basic principles of software engineering (such as separation of concerns, modularity, and abstraction) and knowing how they apply throughout the software life cycle
          • Proficiency:
            • Understanding tradeoffs and relationships among the various activities in the software life cycle
            • Understand the meaning and use of a set of basic software qualities

          Requirements

          • Mastery:
            • Interviewing a customer to elicit requirements
            • Writing a textual (non-formal) requirements document
          • Proficiency:
            • Understand the structure of a requirements document and know the appropriate kinds of information in such a document

          Architectural Design

          • Mastery:
            • Know the differences among interaction patterns of a set of basic architectural styles
            • Understand the difference between architecture and module design
          • Proficiency:
            • Choosing an appropriate architectural style for a particular problem

          Module Design

          • Mastery:
            • Using provided/exported and required/imported interfaces to define module boundaries
            • Identifying and defining modules in a design
            • Identifying and defining abstract data types in a design
          • Proficiency:
            • Applying coupling, cohesion, fan-in, and fan-out
            • Creating USES and COMPRISES diagrams
          • Exposure:
            • Creating a design for a nontrivial, sizable problem

          Programming

          • Mastery:
            • General rules of programming style and clarity (short rehash from earlier classes)
          • Proficiency:
            • Mapping a module design onto an implementation in source code
          • Exposure:
            • Using existing modules and libraries in an implementation
            • Coding under a heavy deadline (requiring tradeoffs between code quality and code functionality)

          Testing

          • Proficiency:
            • Testing a program for failures
            • Applying white-box testing on short pieces of code
            • Applying black-box testing on short pieces of code
          • Exposure:
            • Understanding the many dimensions of software quality assurance
            • Understanding the inspection and code walk-through process

           


          Textbooks

          Required:
          • Software Engineering, 6th edition, by Ian Sommerville. 2001. Addison-Wesley. $85.15 new at the bookstore.
          • The class notes will be available on the WWW after the class meeting in which they are used.

          Schedule (Subject to change)

          Week Week Date Date Lecture topic Lecture Topic Schedule items Discussion Topic Assignments Assignments Readings (all from Sommerville, unless noted otherwise)
          1 September 24 Introduction     Chapter 1. Skim 2. All of 3, but with more attention to the first 2 subsections. Skim 4
          -- September 26

          Process and Principles

          (continuation of the lecture notes associated with the first lecture, above)

               
          2 October 1

          Requirements Engineering

          and here's the slides I used for my keynote talk at STRAW '01

            Requirements assignment issued Chapters 5, 6 (lighter), and 7.
          -- October 3

          Requirements Engineering (URL here is to Sommerville's slides from his Chapter 7)

          See also Professor van der Hoek's excellent slides on software qualities and requirements, and then on requirements engineering

               
          3 October 8 Architectures     Chapters 10 (key), 11 (a little lighter), and 14.
          -- October 10 Architectures   Requirements assignment due  
          4 October 15 Design   Design assignment issued Chapter 15.
          -- October 17 Design, continued from Monday. (Regular lecture)      
          5 October 22 Design      
          -- October 24 Design, Review for mid-term      
          6 October 29 Mid-term Exam      
          -- October 31 UI Design      
          7 November 5 Integration testing and Implementation issues   Implementation assignment issued. Pages 452-457 provide good material on integration testing. This is part of chapter 20, which is on the schedule below.
          -- November 7 Continued   Arch and module interfaces due .  
          8 November 12 No class Veteran's Day   Chapters 19 and 20.
          -- November 14 Debugging & Quality assurance      
          9 November 19 Testing.  

          Implementation due

          Testing assignment issued.

          See especially section 20.1.2 (since that's the technique you'll be using on the testing assignment)
          -- November 21       .
          10 November 26 Maintenance and Configuration Mgmt.     Chapter 27 and 29
          -- November 28 Course Review   Testing assignment due on Friday November 30th.  
          Exams December 3 FINAL EXAM 4:00 — 6:00    

          Assignments and Assessment

          Aggregate grade for the project: 45%.
          Midterm: 20%.
          Final: 35%

          The project consists of four assignments. Their relative weighting (as a percentage of your final grade) is as follows:

          Assignment Weight
          Requirements 10
          Architecture and module interfaces NOTE that this is now a revised document! Use it, not the original!! Note also that the due date is now 11/7. 18
          Implementation 7
          Testing 10

          Specific assignments will be placed on the web, giving the assignment, the required format, and specific grading criteria.

          We do not grade on a curve, meaning that we will not assert in advance that half of the class will receive grades at or below a C+, and half above that. Grading is done based on mastery of the material as exhibited in the exams and the project. If everyone masters the material very well, then everyone will get an A. On the other hand, if no one were to master the material at all adequately, then everyone would receive an F.

          NO LATE ASSIGNMENTS WILL BE ACCEPTED, unless you have a legible excuse from a physician, an extreme family emergency, or unless you are willing to accept an extreme penalty with respect to your assignment's grade.


          Teaching Assistant and Readers

          • TA: Girish Sunyanarayana
          • Readers:
            • Jin Liang
            • Volkan Aginler
            • Xiao Zhang

          Keeping in Touch

          Check this website regularly. This is the definitive location for course information. Announcements concerning assignments will be made here. The course mailing list will also be used to make announcements, provide instructions, and so on. The course mailing list will be "read only" from the student's perspective. I.e. the instructor, TA, and readers can post messages to the list,but not students. If you ask one of us a question whose answer is relevant to the rest of the class, we'll post the appropriate material to the list.

          An important note about email

          Any email that you send in conjunction with this class must be sent from a UCI account. That is, if you want any response or action taken, then you must use your UCI account to send the email. Email from yahoo, hotmail, juno, @home, or any other non-UCI site will be ignored.


          Computing

          All computing will be done on the department's NT machines.

          You may use another computer to produce the documents you turn in. (No handwritten assignments allowed).

          All implementation work will be done in Java.

          Please use the computing equipment or instructional purposes only. Also watch out on the social subtleties of electronic mail.


          Academic Dishonesty

          Cheating in ICS 52 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. Don't do it.
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/softalks/fq00_schedule.html FQ00 ICS 229 Softalks Schedule

          FQ00 ICS 229 Softalks Schedule


          What's New

          • [9/26] For those of you unfamiliar with this class (most of you): ICS 229 is a seminar in which YOU make all the presentations.

            Students in past years have presented a wide variety of things: some students use the class as a forum in which to give a "dry run" of a presentation they'll be making at a conference. Some have used it as a way of getting ready for their Phase II oral exam by presenting their survey paper. First year grad students are usually in the most difficult spot: you're just starting your graduate student career, so what do you present? Probably a best answer is: present a technical paper that you believe to be both interesting and significant --- something you think everyone else would be interested in. If you need help in identifying an appropriate paper I can make several suggestions. A great place to start is the Phase II reading list: http://www.ics.uci.edu/IRUS/software/phaseII_reading_list.html

            Attendance in the seminar is mandatory (I will take attendance) and you MUST make a presentation sometime during the quarter.

            The purpose of the first meeting (tomorrow's) is simply to set up the schedule for the rest of the quarter. The class will be over by 2:25. So --- think about what you'd like to present, or at a minimum, WHEN you would like to make a presentation. We'll put names to dates and then we'll be done. You do not have to commit to a specific topic tomorrow, just a specific date.
          • [9/25] Web site opened.

          Schedule

          Softalks meets Wednesdays, from 2:00 to 3:50 in CS 243.

          1, 2, or even 3 speakers may be scheduled for each date, depending on the nature of the talks to be given.

          This quarters' speakers and topics (subject, as usual, to change):

          Date Date Speaker(s) Speakers Topic(s) Topics Notes
          September 27 Course organizational meeting    
          October 4 Jie Ren Using Visio as an architecture design tool ---
          October 11   ---  
          October 18 Roberto Silva Filho   ---
          October 25

          Roshan

          Eric Dashofy

          Santhoshi

          ---  
          November 1

          Girish Suryanarayana

          Adrita Bhor

          Siena

          P2P

          ---
          November 8 No meeting (1st day of FSE 2000 in San Diego)   ---
          November 15

          Anita Sarma

          Shreyas Doshi

          Jon Hartowicz

          TAOS: Testing with Analysis and Oracle Support

           
          November 22

          Jeff von Ronne

          Rob Menke

          Safe Typed Single Static Assignment Form

           

          (Day before Thanksgiving)
          November 29

          Cleidson De Souza

          Ibrahim El-sayd

          ---  
          December 6 No Class: Finals week    

          taylor@ics.uci.edu
          Updated November 1, 2000
          http://www.ics.uci.edu/~taylor/softalks/fq96_schedule.html FQ96 ICS 229 Softalks Schedule

          FQ96 ICS 229 Softalks Schedule

          Softalks meets Wednesdays, from 3:00 to 4:50 in CS 253.

          1, 2, or even 3 speakers may be scheduled for each date, depending on the nature of the talks to be given. Note that this quarter, however, Softalks is only scheduled for an hour and fifty minutes each Wednesday.

          This quarters' speakers and topics (subject, as usual, to change):

          Date Speakers Topics Notes
          October 2 Neno Medvidovic FSE talk ---
          October 9 --- --- ---
          October 16 No Softalks? --- RNT, DJR, (others?) at FSE in SFO
          October 23 Arthur Reyes
          Ken Anderson
          Domain Theory Evolution
          Update on dissertation progress
          ---
          October 30 --- --- RNT at EDCS in Va.
          November 6 Jim Whitehead
          Shilpa Shukla
          My summer at W3C
          Bug tracking processes
          ---
          November 13 David Hilbert
          Greg Bolcer
          Expectation agents
          ICSP talk
          ---
          November 20 --- --- RNT, DSR at ICSE PC
          November 27 Michele Rousseau London Ambulance System Failure(Day before Thanksgiving)
          December 4 --- --- RNT at ICSP4
          December 11 Neno Medvidovic Architecture Description Languages (Finals Week)

          taylor@ics.uci.edu
          Updated Wed Oct 2 1996
          http://www.ics.uci.edu/~taylor/classes/211/syllabusFQ07.html IN4MATX 211 Fall 2007

          Informatics 211: Software Engineering

          (formerly ICS 221)

          Fall Quarter 2007

          Last update: December 6, 2007

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 221" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 3:30 - 4:50 p.m., CS243

          Web site: http://www.ics.uci.edu/~taylor/classes/211/syllabusFQ07.html

          What's New?

          • [December 6, 2007] Please remember to bring an "Examination Blue Book" to the Final Exam. They are available in the campus bookstore. Buy the large size. They cost $0.27.
          • [November 8, 2007] Due date for CTTC 7 changed. Lecture schedule changed w.r.t. the software adaptation lecture.
          • [November 8, 2007] Due date for the final project installment has been pushed back to December 13th.
          • [November 5, 2007] Andre's lecture is now rescheduled for December 4th.
          • [November 1, 2007] Today's class is CANCELLED; the talk will be rescheduled for later in the quarter.
          • [October 31, 2007] Andre's slides are now available, for the 11/1 lecture.
          • [October 25, 2007] Don's slides are now posted. Note that they are in flash.
          • [October 23, 2007] Due date for the design assignment delayed to November 8th. Project modified to only have a total of 3 deliverables. What I'm looking for in the CTTC's has been clarified in the "Grading and Evaluation" section below.
          • [October 18, 2007] Due date for the design assignment delayed to November 6th. I'll discuss the assignment by Tuesday, October 23.
          • [October 16, 2007] Today's lecture slides are up. See also the note in the "Assignments due" column w.r.t. next week's CTTC.
          • [October 11, 2007] Link to PW92 fixed. Today's lecture slides now up.
          • [October 9, 2007] Links to all the faculty lecturers added.
          • [October 4, 2007] Today's slides uploaded and linked.
          • [October 2, 2007] Professor Alspaugh's slides uploaded and linked.
          • [September 27, 2007] Some of the readings have been changed -- note esp. that for the first lecture.
          • [September 26, 2007] Website goes live on EEE.

          Description - Schedule - Grading - Readings - Policies


          Description

          Catalog description:

          Study of the concepts, methods, and tools for the analysis, design, construction, and measurement of complex software-intensive systems. Underlying principles emphasized. State-of-the-art software engineering and promising research areas covered, including project management. Formerly ICS 221.

          Detailed Description :
          This class has two objectives: (1) provide a useful overview of the state of the art (2) introduce some of the research frontiers of the field. It accomplishes this through a mix of lectures and assignments focused on the state of the art, readings from the research literature, and guest lectures from specialists in sub-areas of software engineering.


          Schedule

          The schedule is subject to change.  Note also that initially the hyperlinks are set to last year's lecture notes. They are being updated incrementally (i.e. just after the lectures are given) to correspond to the lecture slides used this year.
          Week Date Topic Presented by Readings Assignment due
          0
          Sept
          27 Th Course introduction
          Overview of software engineering
          Taylor [Ost07]
          [Bro87]
           
          1

          O

          C

          T

          O

          B

          E

          R

          2 Tu

          Requirements Engineering

          Alspaugh

          [NE00]
          [ATB06]

           
          4 Th

          Overview of SE, continued

          Taylor

          Book excerpt, Chapter 2  
          2 9 TuProcess Scacchi [Sca02a]
          [Sca07]

          CTTC for Wks 0 & 1
          by email
          in .pdf
          by midnight

          11 Th

          Design and Architecture

          FoSE slides
          ArchitectureIntro

          Taylor [PW92]
          [TvdH07]

           
          3 16 Tu Design and Architecture, continued Taylor

          Book excerpt, Chapter 4

          [TMA+96]

          CTTC for Wk 2

          Reqts. spec for lunar lander due. See email of 10/9/07 for detailed instructions.

          18 Th Programming Languages in Software Engineering Lopes
          [Baj06]
          [KLM+97]

          [McIlroy68]
           
          4 23 Tu

          Implementation
          Issues, Application, Project

          Taylor

          Book excerpt, Chapter 9

          CTTC for Wk 3. Note please omit McIlroy's paper from your CTTC, and you may choose to omit from your summary (but not your reading!) either [TMA+96] or [Baj06]. You do need to include Chapter 4. Thus your CTTC will cover 3 papers.
          25 ThSoftware Engineering aspects of Ubicomp
          Patterson
          [HeerNBH03]
          [SDA99]
           
          5
          30 TuIssues, Application, Project Taylor   CTTC for Wk 4

          N

          O

          V

          E

          M

          B

          E

          R

          1 Th

          Configuration Management & Coordination

          POSTPONED until December 4th

           

          [ELC+05]
          [SNvdH03]

           
          6 6 Tu

          Hypertext and e-Commerce

          Taylor

          [B-L94]
          [FT02] (see note below for shorter version)

          CTTC for Wk 5

          8 Th

          The World-Wide-Web

          Issues, Application, Project

          Taylor


          Design assignment for lunar lander due.
          7 13 Tu Program Comprehension Sim

          [KMCA06]

          [Sto06]

          CTTC for Wk 6
          15 Th Software Adaptation Taylor    
          8 20 Tu

          Issues, Application, Project

          Taylor

          [Oreizy97]

           
          22  ThThanksgiving Holiday  

           

           

          9 27  Tu Analysis & Testing Ziv [Wey82] [CPR+89]

          CTTC for Wk 8 & Wk 7

          29 Th Software Engineering and HCI Redmiles

          [Red02]
          [GL85]

           
          10 December 4 Tu

          Configuration Management & Coordination

          van der Hoek See above for November 1st. CTTC for week 9
          6 Th Issues, Application, Project Taylor    
          Exam 11 Tu Exam from 4:00-6:00 Taylor Final project installment due 12/13, by midnight


          Grading and Evaluations

          Grading.
          There are four elements to your grade: a final exam, a little development project that will evolve throughout the quarter, short paper summaries, and class attendance and participation.

          The final exam will entail your writing an evaluation of some sub-area of software engineering and identifying what you believe to be promising, or at least necessary, research directions. You'll be able to choose, in advance, the sub-area you'll write about. The purpose here is to demonstrate that you've thought substantively about an area of software engineering, to the point where you can identify some important needs, trends, opportunities, insights, ...

          The purpose of the development project is simply to provide a concrete point of reference for discussion of the various techniques and ideas that will be covered during the quarter. Most likely we'll do a requirements specification, several designs, probably an implementation, and some analyses. To ensure that the focus of your time is about thinking about the issues and not grinding through loads of detail, we'll focus on <tbd> Details of this project will be forthcoming.

          The short paper analyses "allow you" to demonstrate that you've read and thought about the assigned readings. Readings are assigned, as shown in the schedule, each week of the class. You are obliged to write a short analysis of each week's papers. The analysis should be about 500 words long (total, per week, not per reading). I am not interested in reading a paraphrase of each paper's abstract. I am interested in reading your assessment of each paper: what points do you believe to be the important ones? Do you believe those points? Why or why not? What points did the author(s) not address that they should have? Since multiple papers are assigned you'll have to learn how to present incisive, cut-to-the-chase (CTTC) analyses in few words.

          Your CTTC's on week i's topic are due as shown on the schedule. 

          They must be submitted as follows:

          • by email
          • with the subject line "Informatics 211 CTTC i" where i is what you think it is.
          • with the summary attached as a PDF (Adobe Acrobat) document
          • by midnight Pacific time on the due date

          Summary of Assessment:

          Cut-to-the-chase summaries 30%
          Development project 30%
          Final exam 20%
          Class attendance and participation 20%

          No grades of incomplete (I) will be given for this course. 


          Readings

          The majority of the readings in the course will be papers available through the IEEE or ACM Digital Libraries. Occasionally, there will be chapters taken from books.

          If you did not study software engineering as an undergraduate, the following books are recommended for background and reference.

          • Software Engineering: Principles and Practice, by Hans van Vliet, John Wiley & Sons, Ltd, 2000., or the new 2nd edition. ISBN-10: 0471975087. $63 at Amazon.com
          • The Mythical Man-Month: Essays on Software Engineering (20th Anniversary Edition), by Frederick P. Brooks, Jr., 1995, Addison-Wesley. $26.20 at Amazon.com. Note the Amazon sales rank for this book is 1600 out of ALL books! Interestingly, it is ranked #1 is books on Microprocessor design, but #4 in Software Development. Go figure.

          For additional coverage of software engineering research, consult the reading list for the Phase II exam in software.

          List of Papers and Book Chapters.

          [ATB06] Thomas A. Alspaugh, Bill Tomlinson, and Eric Baumer. Using Social Agents to Visualize Software Scenarios. ACM Symposium on Software Visualization (SoftVis'06), pages 87-94, September 2006.
          http://dx.doi.org/10.1145/1148493.1148507

          [Baj06] Sushil Bajracharya, Trung Ngo, Erik Linstead, Paul Rigor, Yimeng Dou, Pierre Baldi, Cristina Lopes. "Sourcerer: A Search Engine for Open Source Code." (in submission, 2006).

          [B-L94] Berners-Lee, T., Cailliau, R., Luotonen, A., Nielsen, H. F., and Secret, A. 1994. The World-Wide Web. Commun. ACM 37, 8 (Aug. 1994), 76-82.

          [Bro87] F.P. Brooks. No Silver Bullet: Essence and Accident in Software Engineering. IEEE Computer 20(4):10-19, April 1987.
          (Also appears as Chapter 16 in F.P. Brooks. The Mythical Man-Month, 25th Anniversary Edition. Addison-Wesley, Reading, MA, 1995.)

          [CPR+89] L.A. Clarke, A. Podgurski, D. J. Richardson, and Steven J. Zeil. "A Formal Evaluation of Data Flow Path Selection Criteria". IEEE Transactions on Software Engineering, 15(11), November 1989, pp. 1318-1332.

          [ELC+] J. Estublier, D. Leblang, G. Clemm, R. Conradi, A. van der Hoek, W. Tichy, D. Wiborg-Weber, Impact of the Research Community on the Field of Software Configuration Management, ACM Transactions on Software Engineering and Methodology, 14(4):2005, pages 1-48.

          [FT02] Fielding, R. T. and Taylor, R. N. 2002. Principled design of the modern Web architecture. ACM Trans. Inter. Tech. 2, 2 (May. 2002), 115-150. Shorter version appeared as: [FT00] Fielding, R. T. and Taylor, R. N. 2000. Principled design of the modern Web architecture. In Proceedings of the 22nd international Conference on Software Engineering (Limerick, Ireland, June 04 - 11, 2000). ICSE '00. ACM Press, New York, NY, 407-416. DOI= http://doi.acm.org/10.1145/337180.337228

          [GL85] Gould, J., Lewis, C. Designing for usability: key principles and what designers think, Communications of the ACM, Volume 28 Issue 3, March 1985, pp. 300-311.

          [HeerNBH03] Jeffrey Heer, Alan Newberger, Chris Beckmann, and Jason I. Hong. liquid: Context-Aware Distributed Queries. Proceedings of UbiComp 2003: Ubiquitous Computing, 5th International Conference, Seattle, WA, USA, October 12-15, 2003.

          [KLM+97] G. Kiczales, J. Lamping, A. Mendhekar, C. Maeda, C.V. Lopes, Jean-Marc Loingtier, John Irwin. Aspect-Oriented Programming, proceedings of the European Conference on Object-Oriented Programming (ECOOP, Finland), Springer-Verlag, June 1997.

          [KMCA06] Ko, A.J. Myers, B.A. Coblenz, M.J. Aung, H.H. An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks. IEEE Transactions on Software Engineering, Vol. 32, No. 12 (Dec. 2006)

          [McIlroy68] M.D. McIlroy. "Mass Produced Software Components", in P. Naur and B. Randell, "Software Engineering, Report on
          a conference sponsored by the NATO Science Committee, Garmisch, Germany, 7th to 11th October 1968", Scientific Affairs Division, NATO, Brussels, 1969, 138-155.

          [NE00] Bashar Nuseibeh and Steve Easterbrook. Requirements engineering: a roadmap. In 22nd International Conference on Software Engineering (ICSE '00), pp. 35-46. June 2000.
          http://dx.doi.org/10.1145/336512.336523

          [Ost07] Osterweil, L. A Future for Software Engineering? Future of Software Engineering (FOSE '07) pp. 1-11. http://doi.ieeecomputersociety.org/10.1109/FOSE.2007.1

          [Oreizy97] Oreizy, P.; Gorlick, M.M.; Taylor, R.N.; Heimhigner, D.; Johnson, G.; Medvidovic, N.; Quilici, A.; Rosenblum, D.S.; Wolf, A.L.; "An architecture-based approach to self-adaptive software" Intelligent Systems and Their Applications, IEEE [see also IEEE Intelligent Systems] Volume 14, Issue 3, May-June 1999 Page(s):54 - 62 Digital Object Identifier 10.1109/5254.769885

          [PW92] D.E. Perry and A.L. Wolf. "Foundations for the Study of Software Architecture". ACM Software Engineering Notes, 17(4):40-52, October 1992.

          [Red02] Redmiles, D. Supporting the End Users' Views, Working Conference on Advanced Visual Interfaces (AVI 2002, Trento, Italy), May 2002, pp. 34-42.

          [SDA99] Daniel Salber, Anind K. Dey, and Gregory D. Abowd. The context toolkit: aiding the development of context-enabled applications. Proceedings of the 1999 Conference on Human Factors in Computing Systems, CHI 1999, Pittsburgh, PA, USA, May 15-20, 1999.

          [SNvdH03] A. Sarma, Z. Noroozi, and A. van der Hoek, Palantír: Raising Awareness among Configuration Management Workspaces <>, Twenty-fifth International Conference on Software Engineering, May 2003, pages 444–453.

          [Sca02a] W. Scacchi, Process Models in Software Engineering, in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, Wiley, 993-1005, 2002. (provides an overall introduction and survey of software process topics through 2001).

          [Sca07] W. Scacchi, Free/Open Source Software Development: Recent Research Results and Methods, in M.V. Zelkowitz (ed.), Advances in Computers, 69, 243-295, 2007. http://www.ics.uci.edu/%7Ewscacchi/Papers/New/Draft_Chapter_Scacchi.pdf

          [Sto06] Margaret-Anne Storey. "Theories, tools and research methods in program comprehension: past, present and future".
          Software Quality Journal, Volume 14, Number 3, Pages 187-208, September, 2006.

          [TMA+96] R. Taylor, N. Medvidovic, K. Anderson, E.J. Whitehead, J. Robbins. "A Component- and Message-Based Architectural Style for GUI Software," IEEE Transactions on Software Engineering, June 1996.

          [TvdH07] Richard N. Taylor and Andre van der Hoek. “Software Design and Architecture: The once and future focus of software engineering.” In Future of Software Engineering 2007. Edited by Lionel C. Briand and Alexander L. Wolf. pp. 226-243. IEEE Computer Society (2007). http://doi.ieeecomputersociety.org/10.1109/FOSE.2007.21

          [TN92] I. Thomas and B.A. Nejmeh. Definitions of tool integration for environments. IEEE Software, 9(2):29-35, March 1992.

          [Wey82] E.J. Weyuker. On testing non-testable programs, Computer Journal, 25(4):465-- 470, November 1982.


          Policies

          Course Evalutions. The online evaluation window for fall quarter will run from 7pm Friday, November 30 through 11:45pm Sunday, December 9.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          (C) University of California, 2005, 2006, 2007. Portions adapted from T.A. Alspaugh; S. Sim; and a cast of thousands.
          http://www.ics.uci.edu/~taylor/ics227/syllabus.html ICS 227 Syllabus

          Information and Computer Science 227
          User Interfaces and Software Engineering

          Winter Quarter, 1998
          Location: CS 253
          Time: WF 11:00 - 12:20
          Course code: 36607


          Instructor | Overview and FAQ | Textbooks | Assignments | Schedule | Academic Dishonesty |
          (Last modified Mon Jan 05 1998)

          What's New

          1. [Jan 05] Home page posted. The textbook may be obtained from the Engineering Copy Center, EGR 203. The extra readings will be available starting Jan 06.

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (714) 824-6429
            • Hours: Tuesday and Thursday, 3:30 - 4:30 Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (714) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Exploration of current developments in systems and tools for creation and run-time management of graphical user interfaces. Object specification, constraint specification and maintenance, control paradigms, separation of concerns, support infrastructures, and multi-media issues are also discussed.

          Themes: UI implementation, software architectures, and composition technologies.


          Textbook and Readings

          • Textbook: "Introduction to User Interface Software", by Dan R. Olsen, Jr. (Publisher uncertain, but probably Morgan Kaufmann, 1998.) Copies available in the Engineering Copy Center (203 EGR).
          • (Very) Optional textbook: "User Interface Management Systems", by Dan R. Olsen, Jr. Morgan Kaufmann, 1992.
          • Readings:
            • "Introduction to the workshop on software engineering and human-computer interaction: Joint research issues" Joelle Coutaz and R.N. Taylor, in "Software Engineering and Human-Computer Interaction", Lecture Notes in Computer Science 896, May 1994. Pages 1-3.
            • "User interface technology and software engineering environments" R.N. Taylor, in "Software Engineering and Human-Computer Interaction", Lecture Notes in Computer Science 896, May 1994. Pages 113-126.
            • "Emergence of the separable user interface", Ernest Edmonds. ICL Technical Journal, 7(1):54-63 (1990).
            • (Optional) "The X Window System." Robert W. Scheifler and Jim Gettys. ACM Transactions on Graphics, 5(2):79-109 (April 1986).
            • "A cookbook for using the model-view-controller paradigm in Smalltalk-80." Glenn E. Krasner and Stephen T. Pope. Journal of Object-Oriented Programming, 1(3):26-49, August/September 1988.
            • "Declarative interface models for user interface construction tools: the Mastermind approach", P. Szekely, P. Sukaviriya, et.al.
            • "Chiron-1: A software architecture for user interface development, maintenance, and run-time support." Richard N. Taylor, Kari A. Nies, Gregory Alan Bolcer, Craig A. MacFarlane, Kenneth M. Anderson, and Gregory F. Johnson. ACM Transactions on Human-Computer Interaction, 2(2):105-144 (June 1995).
            • "A Metamodel for the runtime architecture of an interactive system." The UIMS Tool Developers Workshop. SIGCHI Bulletin, 24(1):32-37 (January 1992).
            • "A component and message-based architectural style for GUI software." Richard N. Taylor, Nenad Medvidovic, Kenneth M. Anderson, E. James Whitehead, Jr., Jason E. Robbins, Kari A. Nies, Peyman Oreizy, and Deborah L. Dubrow. IEEE Transactions on Software Engineering, 22(6):390-406 (June, 1996.)
            • "From single-user architectural design to PAC: a generic software architecture model for CSCW", G. Calvary, J. Coutaz, and L. Nigay. CHI'97, pp. 242-249.
            • "Development Tools," Brad Myers?
            • There will also be about 4 additional papers from the latest UIST conference...

          Assignments and Assessment

          Name Assignment Weight
          Comparative toolkits Update the provided comparison matrix with 3 or 4 columns based on more recent material: e.g. the latest MS toolkits, subArctic, Java AWT 1.1, Sun's Swing, JavaBeans, Netscape's Internet Foundation Classes, VB Controls, Amulet, Mastermind 15%
          Paper summaries/analyses Write a 1-page (or less) summary of 5 of the extra-to-textbook readings and a 1-page (or more) critical analysis of same. 15%
          Project/paper Develop a project and/or write a term paper on an approved topic 60%
          Participation Participation in class discussions 10%

          Suggested (pre-approved?) Project/Paper Topics

          • Tools for identifying, managing, and tracking event flow in apps
          • Supporting cut-and-paste in a dynamic C2 architecture (related to OLE and OpenDoc models for embedded editing)
          • Supporting fast semantic feedback in C2-style architectures
          • Supporting selective undo in a disributed groupware application using C2-style architectures. May include general mechanisms for utilizing the command object notion of Olsen:Chapters 14-15 as the basis for message structures. Reformulate the C2 frameworks based on an improved notion of message?
          • A full domain-specific (?) software architecture based for editing applications, starting from the ideas of Olsen:Chapter 12.
          • Re-engineer the Endeavors UI to support undo and editing of all three kinds of fundamental Endeavors objects, using ideas from Olsen:Chapter 12.
          • JavaBeans and Swing: an evaluation and methodology for use within the C2 context (connectors and C2 framework).
          • A new approach to support compositional or distributed (or both) user interfaces (may start out as a survey/issues/objectives paper/project)

          Schedule

          Subject to change

          Week Date Class Session Readings
          1 January 7 Introduction UI toolkit matrix
          [Coutaz&Taylor; Taylor]
          -- January 9 UI development process Olsen, Ch. 1
          2 January 14
          Class held earlier in the day?
          Task analysis and basic graphics Olsen, Ch. 2 and 3
          -- January 16 Event handling Olsen, Ch. 4
          3 January 21 X windows and Xtk (Ackerman) ---
          -- January 23 Basic interaction: MVC Olsen, Ch. 5; [Krasner&Pope]
          4 January 28
          Class held on 1/26
          [NIST]: class held on 1/26
          Interfaces from widgets
          Olsen, Ch. 7
          -- January 30 Model-based systems: Chiron-1 [Taylor et.al.]
          5 February 4 Model-based systems: Mastermind [Szekeley et.al.]
          -- February 6 Software architectures and UIs [Arch/Slinky] [C2]
          6 February 11
          Class held on 2/12
          [EDCS Melbourne]: class held on 2/12
          C2 frameworks; Java AWT events
          (Hilbert and Medvidovic)
          ---
          -- February 13 [BART]: no class ---
          7 February 18 Undo, groupware, and macros Olsen, Ch. 14, [Calvary, et.al.]
          -- February 20 Cut, copy, and paste Olsen, Ch. 13
          8 February 25 [GSAW]: no class ---
          -- February 27 UI elements as components [ Java Beans spec]
          9 March 4
          Class held on 3/5
          [ISSTA]: class held on 3/5
          Issues discussion; Input models
          Olsen, Ch. 8
          -- March 6 Architectures for graphical editing Olsen, Ch. 12
          10 March 11 Student presentations ---
          -- March 13
          Class held on 3/12
          [BART]: class held on 3/12
          Student presentations
          ---
          Exams March 18 Final Exam? 10:30-12:30

          Academic Dishonesty

          Cheating in ICS 227 will be dealt with in accordance with ICS policy and UCI policy. Please familiarize yourself with those documents.
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS125_FQ04/syllabus.html ICS 125 Syllabus

          Information and Computer Science 125:
          Project in Software System Design

          Fall Quarter, 2004
          Lecture: TTh 2:00 - 3:20
          Location: CS 174
          Course code: 36340

          Discussion Section (REQUIRED): MWF: 4:00 - 4:50
          Location: ET 202
          Course code: 36341



          Instructor | Overview and FAQ | Textbooks | Teams | Assignments | Costs and Benefits | TA | Keeping in Touch | Computing | Disabilities | Academic Dishonesty
          (Last modified September 30, 2004 )

          What's New

          Watch this spot for new information regarding ICS 125. It may link to other web pages or to updates to this page.
          • [September 28] The performance appraisal form is now available and linked from the syllabus below.
          • [September 27] The list of candidate projects is now available. Please review it before class on Tuesday.
          • [September 21] The course survey form is available. Please print the form, fill it out, staple the two pages together, and bring it to the first meeting of the class. If you do not fill it out ahead of time you will have to fill it out before leaving class on September 28th.
          • [September 21] There will not be any meeting of the discussion section on either Friday, September 24th or Monday, September 29th.
          • [September 21] Website opened.

          Course Staff

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor insert-an-at-sign-here ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday, 3:30 - 4:30. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Donald Bren School of Information and Computer Sciences,
            University of California, Irvine
            Irvine, California 92697-3425

          Teaching Assistants

          • Hazel Asuncion (meeting times by appointment)
          • Ben Pillet (meeting times by appointment)

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Specification, design, construction, testing, and documentation of a complete software system using concepts learned in ICS 52, 121, and 141. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems.

          This course will emphasize techniques and notations essential to creating software systems based on the principles discussed in ICS 121: well-understood requirements, usability and user interface design, architectural design and module specification, well-planned testing, effective oral and written communication of concepts, proper programming style, group coordination, product documentation and software process.

          Attendance at all lectures is mandatory. In general, there will not be much lecturing in the class. Instead, class time will be highly interactive, and all students are expected to participate.

          Prerequisites:

          ICS 51 with a grade of C or better; ICS 121 and 141; Mathematics 2A-B and 67.

          Editorial note: What 2A-B and 67 have to do with this class is beyond me!

          Is the Discussion Section required?

          Yes! The discussion section is essential for two reasons:

          1. You will need to meet with your teammates REGULARLY. The best correlation with failure that I've seen in this class over the past decade has been teams which were unable or unwilling to establish a regular meeting schedule and to keep to it. In other words, EVERYONE needs to be in attendance at team meetings. The discussion section time period is the guaranteed time period for your team to meet.
          2. Since this is a large class and each team will be making presentations during the quarter, we'll have to use some of the discussion sections for those presentations.

          What will the projects be?

          Candidate projects can come from many places: in prior years some ICS 125 teams worked on projects that sponsoring local companies suggested. Other teams worked on projects related to on-going research programs in the ICS department. Other projects may come from the students. Choice of projects is related to many goals. One key goal is to pick a project of appropriate size. It must be big enough to challenge a team of four students, but not so big as to commandeer everyone's life! As a result we will spend some time at the beginning of the fall attempting to size various projects. These planning estimates will be revisited as the course progresses. Another goal is to select a variety of projects for the class. Since each team will be making regular project presentations to the rest of the class, diversity of projects will enable students to learn from experiences across a range of project topics. Still another goal is to work on something fun and interesting! I've had students working on flight simulators, generating HTML pages, linking MPEG movie frames via hypertext to other artifacts, and building graphical program editors. Here are this quarter's opportunities.

          How will teams be composed?

          Each team will have 4 or 5 people. I will attempt to balance a team's aggregate expertise with the needs of a particular project. I will also attempt to accommodate some personal preferences for teammates. The course survey form, which you will turn in on the first day of class, is a key instrument in assigning the teams. In the end however, I make all the assignments, both of team composition and of project.

          What's the Drop/Add policy?

          Since ICS 125 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds of ICS 125 will be permitted after the end of the FIRSTweek of class.


          Lecture Topics

          ICS 125 is on a tight time schedule, thus there is not much time for review. You are expected to recall the material covered in ICS 121 and the other prerequisite courses. Short supplementary lectures may be given on:

          • Teamwork and team organizations
          • Project management
          • Project cost and schedule estimation
          • Usability evaluation and user interface prototyping
          • Software architectural choice
          • Configuration management and version control
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects chosen by the teams. If several of the projects, for example, are concerned with Internet applications, then lectures on protocols and Web technologies will be included.

          Textbooks

          Remember reading The Mythical Man-Month? If you do, you can expect to profit from that experience in this class. If you don't, you need to read it, cover to cover BEFORE the class begins. Don't worry, it is a quick and fun read.. Depending on the projects chosen additional readings from various sources may be required.

          • REQUIRED (from earlier classes):
            • Author: Fred Brooks
            • Title: The Mythical Man-Month, Anniversary Edition

              Note that this book has a lot of white space and blank pages, so it really will not take you long to read these chapters.

          • BACKGROUND/GENERAL REFERENCES:
            • Stephen Schach. Classical and Object-Oriented Software Engineering (Third Edition). Irwin, Chicago IL, 1996.

            • or Classical and Object-Oriented Software Engineering with UML and C++
              or Classical and Object-Oriented Software Engineering with UML and Java
            • Ghezzi, Jazayeri, & Mandrioli. Fundamentals of Software Engineering. Prentice Hall, Englewood Cliffs NJ, 1991.
            • Rumbaugh, et.al. Object-Oriented Modeling and Design.
            • Mary Shaw and David Garlan. Software Architecture, Perspectives on an Emerging Discipline. Prentice Hall, Englewood Cliffs NJ, 1996.

            All are available from amazon.com.


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for approximately 80% of your grade; this is broken down between deliverables, a team Web page, and presentations. The approximately remaining 20% will be divided among individual course logs, teamwork, individual leadership demonstrated, and the final. These are guidelines intended to help students plan their work in this course. However, the instructor does reserve the
          right to make changes in these evaluation criteria.A critcal aspect of success, however, and thus of assessment, is an effectively functioning team. Just because a team's code "works" at the end of the quarter does not mean that they have earned an A. If the team did a poor job on the requirements and design, for instance, their grade would be lower, despite "working" code. Put another way, if your team has to pull an all-nighter to get a working system, in all likelihood you will not receive the grade you want.

          Deliverables

          The ICS 125 project nominally consists of five major assignments. The relative weighting of each deliverable, intended to provide you with some guidance as to how much effort should be devoted to these tasks, and how much importance I ascribe to them, is indicated in the table below along with the due date, or approximate due date. The on-line versions of the assignments may still be under construction (watch the "what's new" section to see when they are available).
           
           
          Deliverable/Schedule Item Weight  Description NOTE: the URLs below are currently to the assignments from PREVIOUS quarters and are hence subject to revision. Due Date (subject to change)
          Individual Web Page
          . For an example see http://www.ics.uci.edu/~mdiallo/ or Scott Hendrickson October 1st
            Teams Designated
          . . September 30th
            Projects Selected/Assigned
          . . September 30th (tentative)
          Team Web Page
          . For an example October 7th
          Prospectus and Plan 
          10 
          Prospectus  October 8th
            Prospectus Reviews
          . week 3  
          Requirements Specification
          15 
          Requirements October 19
            Requirements Reviews
          . week 4  
          Architecture/Module Specifications 
          20 
          Architecture/Design November 2
            Design Reviews
          . Starting on 11/3  
          Implementation 
          20 
          Implementation/1st demonstration November 22
            Code Reviews/Demos
          . week 9  
          Testing/Test Documentation
          15 
          Implementation/2nd demonstration/Quality Assurance Report December 2
            Demonstrations
          . week 10 + finals week (TBD)  

          Variations on this schedule may be made to accommodate the particular needs of a given project or a given team. Also, note that a team's grade for a phase is a function not only of the document/specification developed but also of any associated test plan and any reviews conducted in class, with the instructor, or with the customer.

          Have questions about your intellectual property rights (IPR)? Take a look at the UC's view of the subject.

          Deliverable Due Dates

          Specific due dates/times will be indicated for each assignment. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          Deliverable Reviews

          Each deliverable will be reviewed, some reviews will be conducted before the whole class.

          Your customer should be invited to your team's Prospectus and Requirements review as well as your demonstration (and, possibly even your design and code reviews depending on the nature of your customer).  The review is your team's chance to inform as well as obtain feedback and ideas from all relevant parties; your document will be reviewed at this time by course staff and clients as well as the rest of the class.  This review is formal, however, and each team should have presented and negotiated both relevant documents to the customer prior to the review (if you haven't, it may be unpleasantly obvious by the interactions at this time).

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in. They must reside permanently on your team's website, or alternatively, in a subversion repository (details to be announced).

          All deliverable documents, with the exception of performance appraisals as discussed below, must be prepared on-line and be available as part of your project home page either as either HTML or .pdf files. NO MS Word files. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving:
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Phase clerical person
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, design, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this application shall be marked ``N/A''.
          Agendas and Minutes.
          Every deliverable shall be accompanied by agendas for and minutes of team meetings held during the associated period of time. In contrast to previous years much more "grade weight" will be assigned to this part of your deliverables. Details on this will be given in class.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page.
          Project WebPage.
          The project deliverables, except for the performance appraisals, shall be maintained in a project homepage.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last and except for the "Agendas and Minutes" section, you will also have the opportunity to ``fix it'' based on its evaluation. You may hand in an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. You should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Course Log

          During your career you will need to keep track of how you spend your time either for you employer or to improve your own productivity. Throughout this course, you will practice doing this by keeping a course log recording the time you spend on all activities related to this course. At the beginning of each week you must submit the previous week's log to the TA. A sheet showing what should be on the log is available.

          Keep a copy of your logs: you will need them at the end of the quarter for the final review.

          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time entry applies for descriptions of activities and records the amount of time spent in hours, to the nearest quarter hour.

          You will be marked down only for failing to submit logs each week, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.

          Summary of what you turn in, and when:

          1. Weekly, by individual, using a dropbox: course log for preceding week.
          2. Per deliverable, by team, on website: deliverable documents.
          3. Per deliverable, by team, on paper, performance appraisals. Exception: not required for the Prospectus.
          4. End of quarter, by individual, on paper: collection of the course logs for the quarter.
          5. End of quarter, by individual, on paper, "peer apportionment of credit"

          Team Composition, Activities, and Peer Apportionment of Credit

          The danger most students perceive in working on projects with other students is being saddled with (what they think is) a "non-producer". This is particularly true when you don't get to choose all your teammates (the situation here). Many factors dictate the use of a multi-person project for this course. You will not, after all, be able to choose your workmates in the future. One thing we'll discuss in the class is how to fix dysfunctional teams. Nonetheless, to alleviate your concerns and to grade you appropriately, at the end of the term project you will be asked to divide 100 points among the members of your project team, excluding yourself, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          Team Organization

          There are several obvious dangers to group work that can be circumvented. Ensure that there is adequate coordination among the team members. Know each other's login names for electronic mail. Know each other's phone numbers. Meet at least twice per week (outside of class lecture) at the same, pre-determined time each week (so as to avoid confusion). The Discussion Section is designed to guarantee that such meetings are possible for everyone. You are strongly urged to use that time slot.

          Have a contingency plan for submitting a document on time even if the responsible manager becomes unavailable.

          You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.

          Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. Date, time began, time ended
          2. agenda for the meeting
          3. team members present and reason for any member's absence
          4. major design decisions discussed
          5. task assignments made (i.e. action items)
          6. open issues
          7. questions to be asked and plan for getting them answered
          8. future meetings scheduled
          9. topics for next meeting.

          Cost and Benefits

          This course will demand a lot, but I think that you may well find this to be the most rewarding courses that you will take in your undergraduate career. ICS Alumni have said repeatedly that ICS 125 was the most important class that they took at UCI. The techniques presented in class actually work and will help you in future software development.

          At the end of the class I encourge you to make copies of your project website/notebook for each team member. Take them with you when you go to a job interview. Students from past ICS 125 classes have frequently said that it was their project notebook that clinched a job for them. Some interviewers have commented that the quality of the process followed by the ICS 125 teams and the quality of the product exceeds those of the production engineers in their companies.


          Keeping in Touch

          Check the course syllabus page regularly. Announcements concerning assignments, changes to the lecture schedule, and so on will be made there.

          An important note about email:

          Because of, at least, spam, I will not respond to email that originates from any other domain than uci.edu. Thus if you send me email you must send it from your UCI account. If you send me email from any other domain, especially AOL, hotmail, or yahoo, it will automatically get routed to my spam folder, where it will be duly ignored.


          Teams and Meetings

          As noted earlier, teams will use (at least) the discussion sections for team meetings.
          ICS 125 FQ 2004 Teams and TA Assignments
          Hazel Asuncion Ben Pillet
          Working Spheres Creating and Updating Palantir Views
          Integration of xlinkit with ArchStudio 3 Robustifying Palantir
          unexceptional.net: PHASE II GXL Validator Plug-in
          File-sharing - moving towards WYSIWYG (2 teams) Blah Blah Blah (3 teams)
          New Computer Based Choral Musical Score Display (2 teams) Java-based Genome Browser (2 teams)
          New True 3-D Digital Display System  
             


          Computing

          To facilitate sharing of files among team members, each team will have an account where the team web site and project documents must be maintained.

          The primary computing facilities will be the ICS Labs. Also available is the ICS 125 team project room, CS 193.  The hardware environment and software environment is posted on the lab's web site as well as the lab hours.

          Policies governing the use of the ICS 125 lab in CS 193 are available at http://www.ics.uci.edu/~lab/policies/index.html You will also find there a form you can fill out to obtain an access code that will give you admission to the room 24 hours a day. We'll talk about the potential use of this room in class.

          Choice of computing platform for implementation will depend on the projects chosen. Where possible and reasonable, Java will be the implementation language used.


          Disabilities

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion


          Policies (Academic Honesty and Computing Use)

          Cheating in ICS 125 will be dealt with in accordance with ICS cheating policy, which is in keeping with the UCI academic [dis]honesty policy.  Please familiarize yourself with these documents, as you are held accountable to them.

          You are also bound by all policies posted at ICS's Computing Support Policies, including ICS's Ethical Use of Computing Policy, as well as UCI's Computer Use Policy.


          Donald Bren School of Information and Computer Sciences,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/classes/121/syllabusFQ08.html IN4MATX 121 Fall 2008

          Informatics 121: Software Design I

          Fall Quarter 2008

          Last update: December 1, 2008

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 121" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 11:00-12:20, ICS 243

          TA Yongjie Zheng (zhengy at ics.uci.edu). Office hours: TBD
          Discussion M 11:00-11:50, ICS 209
          Web site: http://www.ics.uci.edu/~taylor/classes/121/syllabusFQ08.html

          What's New?

          • [November 5, 2008] Schedule and assignments updated, yet again. Note the date change on Assignment #6.

          Description - Schedule - Grading - Readings - Policies


          Description

          Catalog description:

          Introduction to software design principles, paradigms, tools, and techniques. Topics include alternative architectural styles, iterative refinement, design patterns, mapping design onto code, design tools, and design notations. Includes extensive practice in creating designs and study of existing designs. Prerequisite: Informatics 102 with a grade of C or better.


          Textbook and Assigned Readings

          There will be a lot of assigned reading from numerous sources during the quarter... especially the first half.

          Books

          1. Alexander, C. The Timeless Way of Building. Oxford University Press: New York, 1979.
          2. Brand, S. How Buildings Learn: What Happens After They're Built. Penguin Books, 1994.
          3. Brooks, F.P. The Mythical Man-Month: Essays on Software Engineering.  2 ed., Addison-Wesley, 1995.
          4. Eberhart, M. Why Things Break: Understanding the World by the Way It Comes Apart. Harmony Books: New York, 2003.
          5. Gamma, E., Helm, R., Johnson, R., and Vlissides, J. Design Patterns: Elements of Reusable Object-Oriented Software.  Addison-Wesley Professional Computing Series.  Addison-Wesley: Reading, MA, 1995.
          6. Jackson, M. System Development. Prentice Hall: Englewood Cliffs, N.J., 1983.
          7. Jackson, M. Problem Frames. Addison-Wesley Professional: Reading, MA, 2001.
          8. Jackson, M.A. Principles of Program Design. Academic Press, 1975.
          9. Jones, J.C. Design Methods: Seeds of Human Futures.   John Wiley & Sons, Ltd.: New York, 1970.
          10. Kelley, T., Littman, J., and Peters, T. The Art of Innovation: Lessons in Creativity from IDEO, America's Leading Design Firm. Currency/Doubleday: New York, 2001.
          11. Kruchten, P. The Rational Unified Process: An Introduction.   Addison-Wesley: Reading, MA, 2000.
          12. Norman, D.A. The Design of Everyday Things.  1st Basic paperback ed., Basic Books: New York, 2002.
          13. Petroski, H. To Engineer is Human.  St. Martin's Press 1985.
          14. Petroski, H. The Evolution of Useful Things.  Alfred A. Knopf, Inc., 1992.
          15. Petroski, H. Invention by Design: How engineers get from thought to thing.  Harvard University Press, 1996.
          16. Schön, D. The Reflective Practitioner: How Professionals Think in Action., Basic Books, Inc. Publishers: New York, 1983.
          17. Simon, H.A. The Sciences of the Artificial.  2nd ed.  The MIT Press, 1981.
          18. Van Duyne, D.K., Landay, J.A., and Hong, J.I. The Design of Sites : Patterns, Principles, and Processes for Crafting a Customer-centered Web Experience. Addison-Wesley: Boston, 2003.
          19. Yourdon, E. Techniques of Program Structure and Design. Prentice-Hall: Englewood Cliffs, N.J., 1975.
          20. Yourdon, E. and Constantine, L.L. Structured Design: Fundamentals of a Discipline of Computer Program and Systems Design. Prentice-Hall, Inc., 1979.

          Articles

          1. Abbott, R.J. Program Design by Informal English Descriptions. Communications of the ACM. 26(11), p. 882-894, 1983. http://doi.acm.org/10.1145/182.358441
          2. Booch, G. Object-Oriented Development. IEEE TSE. 12(2), p. 211-221, 1986.
          3. Fischer, G. Communities of Interest: Learning through the Interaction of Multiple Knowledge Systems. User Modeling. 2001.
          4. Freeman, P. The Central Role of Design in Software Engineering. Software Engineering Education Freeman, P. and Wasserman, A. eds. Springer-Verlag: New York, 1976.
          5. Freeman, P. The Central Role of Design in Software Engineering: Implications for Research. In Software Engineering: Research Directions. p. 121-132, Academic Press, 1980.
          6. Ommering, R.v., Linden, F.v.d., Kramer, J., and Magee, J. The Koala Component Model for Consumer Electronics Software. IEEE Computer. 33(3), p. 78-85, March, 2000.
          7. Parnas, D.L. On the Criteria to be Used in Decomposing Systems into Modules. Communications of the ACM. 15(12), p. 1053-1058, 1972. http://doi.acm.org/10.1145/361598.361623
          8. Parnas, D.L. On the Design and Development of Program Families. IEEE TSE. 2(1), p. 1-9, 1976.
          9. Parnas, D.L. Designing Software for Ease of Extension and Contraction. IEEE TSE. 5(2), p. 128-137, 1979.
          10. Parnas, D.L., Clements, P.C., and Weiss, D.M. The Modular Structure of Complex Systems. IEEE TSE. 11(3), p. 259-266, March, 1985.
          11. Parnas, D.L. and Clements, P.C. A Rational Design Process: How and Why to Fake It. IEEE TSE. 12(2), p. 251-257, February, 1986.
          12. Perry, D.E. and Wolf, A.L. Foundations for the Study of Software Architecture. ACM SIGSOFT Software Engineering Notes. 17(4), p. 40-52, October, 1992. http://doi.acm.org/10.1145/141874.141884
          13. Spector, A. and Gifford, D. A Computer Science Perspective of Bridge Design. Communications of the ACM. 29(4), p. 267-283, April, 1986. http://doi.acm.org/10.1145/5684.6327
          14. Tracz, W. DSSA (Domain-Specific Software Architecture): Pedagogical Example. ACM SIGSOFT Software Engineering Notes. 20(3), July, 1995. http://doi.acm.org/10.1145/219308.219318

           

           


          Schedule

          The schedule is subject to change. 
          Week Date Topic Discussion/Assignments
          0
          September
          25 Th Introduction to the topic of design [Spector & Gifford 86]
          1
          30 Tu[Spector & Gifford 86] "Pool papers" assigned; due 10/7 (Assignment #1)

          O

          C

          T

          O

          B

          E

          R

          2 Th Essence and Accidents (Brooks)
          2
          7 Tu Pool papers Brand (chapters 1 & 2) and Schon (chapter 3) ; due 10/14 (Assignment #2)
          9 Th Pool papers  
          3
          14 Tu Brand and Schon Jones, Part I; due 10/21 (Assignment #3)
          16 Th No class  
          4
          21 Tu Brand and Schon Parnas (#7 & #11); due 10/30 (Assignment #4)
          23 Th

          Jones & Design Exercise

          (pdf) (Design Exercise #1)

           
          5
          28 Tu Design Exercise  
          30 Th Parnas on Design Abbott and Booch; due 11/11 (Assignment #5)
          6

          N

          O

          V

          E

          M

          B

          E

          R

          4 Tu Parnas on Design  
          6 Th Abbott and Booch; uses hierarchy Design exercise (project)
          7
          11 Tu Veteran's Day! (no class)  
          13 Th Design exercise (project) discussion  
          8
          18 Tu Architectural patterns and styles Tracz and Ommering; due 11/25 (Assignment #6)
          20 Th Patterns, styles, and the Web  
          9
          25 Tu Product Families  
          27 Th Thanksgiving Day! (no class)  
          10

          December

          2 Tu Student presentations (design exercise) DUE (before class)
          4 Th

          Student presentations (design exercise)

          Review for Final Exam

          Course evaluations (bring your laptop)

           
          Exam 9 Tu Final Exam, 10:30-12:30 p.m.  


          Grading and Evaluations

          Grading (Tentative).

          • Final: 25%
          • Assignments, Design Exercises, and Presentations: 75%

          Policies

          Course Evalutions. The window for fall quarter online evaluations will open at 05:00 PM 11/28/2008 through 11:45 PM 12/07/2008.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          © University of California, 2008.
          http://www.ics.uci.edu/~taylor/ICS280_fq01.html ICS 280 Syllabus Fall 2001

          Information and Computer Science 280:
          Peer-to-Peer Architectures

          Fall Quarter, 2001
          Location: SST 220B (Need a map?)
          Monday and Wednesday, 2:00 — 3:20
          Course code: 36607
          (Last modified Wednesday, November 28, 2001)

          WHAT'S NEW?

          [November 19, 2001] Schedule updated. Due date for final paper posted.

          [October 29, 2001] Some new links added to the list of commercial vendors.

          [October 25, 2001] A few updates to the schedule.

          [October 15, 2001] A variety of small changes to the schedule, plus addition of the Citations section.

          [October 8, 2001] Art Hitomi's slides are now available.

          [October 4, 2001] .pdf versions of intro slides and Greg's slides now available.

          [October 2, 2001] Greg Bolcer's slides from Monday, October 1, are now available.

          [September 27, 2001] Posted my lecture slides from week one, and updated a few more of the references.

          [September 25, 2001] Added links to some Gnutella items

          [September 20, 2001] Public unveiling of the website.



          Overview | Textbook | Schedule | Academic Dishonesty | Citations

          Instructors

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Monday and Wednesday: 11-12:20. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview

          We will examine the peer-to-peer phenomenon, looking at the underlying technical issues and notional applications. We will attempt to identify the key open topics, assess their importance in the long term, and develop a research agenda for tackling those issues.

          The class will operate as a research seminar: I will lecture occasionally, but there will be numerous presentations by students. The focus of class time will be discussion of what's been learned outside of class.

          Grading will be based on participation in the class, and a term paper +/- a project. (That means, you'll almost assuredly have to write a term paper, and that paper in many cases will reflect work you've done in a project.) Projects can certainly be worked on in teams. We might even be using p2p technologies to support the teams! It is highly unlikely that there will be a final exam.

          Prerequisite Knowledge

          Students should have some background in software engineering including exposure to the basic ideas of software architecture. This is a research seminar and students will be expected to participate actively in class discussions and presentations. Some form of project will take place and to be successful with that students will need to be able to independently acquire, install, adapt, and experiment with externally developed p2p systems. Students will also need to independently discover, obtain, and analyze publications germane to the topic, and present the results of those investigations to the class.

          Research Issues

          There's a lot of research issues in this domain. The following list gives you some idea.

          • The relationship between requirements and architecture-based development, with a focus on mapping between them, or transformation, or development: all this as applied particularly to the P2P domain
          • Supporting groups of people with peer-to-peer (p2p) infrastructure. "WebDAV grows up." Supporting little processes; social issues; quick setup and tear-down.
          • Search technologies in the p2p world.
          • Designing, modeling, and implementing p2p applications
            • formal models
            • an architecture based approach; xADL + richer groups, process, and security
            • responsibility, ownership, and intellectual property models
          • Analyzing p2p architectures
          • Pervasive p2p applications. Think ubiquitous computing as implemented by p2p applications and micro-networking.
          • P2P infrastructures and network issues/security issues.
          • Instant messaging, p2p, and process. Protocols for this, infrastructure, and interoperability.
          • Events: XML rep and interchange in the p2p space and beyond
          • The Ultimate Peer-to-Peer Protocol (TuP3)

          Comparison Factors

          The following list is incomplete, but illustrates the kinds of factors we'll be looking at in evaluating peer-to-peer architectures

          • Overview
            • Contact Information
            • Overview
            • Best Feature
            • Most Telling Criticisms
            • Business Model
            • Target Companies
            • Investors
            • Terms and Conditions
            • History
            • Product Trajectory
            • Source of Info
          • Peer Applications
            • Searching
            • Synchronization
            • Collaboration
            • Decentralized Work
            • Remote Control
            • Dispatching
            • Backup
            • E/M-commerce
            • Home Networking
          • Clients
            • Main GUI
            • Coupled/Decoupled
            • Others
            • Ease of Use
            • Installation
            • Learning Curve
            • Drag & Drop
            • Auditing
            • Extensions
            • Electronic Wallet
          • Networking
            • Disconnected
            • Mobile
            • Security
            • Firewall
            • Writeable
            • Two-way Routing
            • Replication
            • Discovery
            • Wireless Access
          • Scalability
            • Broadcast/Anycast
            • Namespaces
            • Event Notification
            • Web Integration
            • Publish/Subscribe
            • Embedded
            • SyncML/XML
            • WML/XHTML
          • Robustness
          • Extensibility
          • Open Source

          Example P2P systems and infrastructure

          We'll categorize and examine several of these...

          • Magi
          • Groove.Net (and since their site is such a mess, you might zip to http://devzone.groove.net/library/ )
          • eMikolo
          • Lightshare
          • Flycode (Apple Soup)
          • Kalepa
          • MojoNation
          • Napster
          • Gnutella
          • Freenet
          • Aimster 1.0
          • Scour
          • Static (is now Blue Falcon)
          • GameSpy a bit off-beat, I'll admit, but you might try looking at this link
          • iMesh
          • Pointera
          • GoneSilent (Infrasearch)
          • Popular Power (now out of business, but the original site is still there)
          • United Devices
          • Radio Userland
          • KnowNow
          • Infraworks
          • Agranat
          • US Software
          • Enfish
          • Engenia (and they have now acquired the assets of Roku)
          • Kazaa
          • Morpheus/MusicCity
          • Omniprise
          • Think Desk
          • Avaki
          • HotComm

          Textbooks

          Required:
          • Peer-to-Peer: Harnessing the Power of Disruptive Technologies, Edited by Andy Oram. O'Reilly.2001
          • Other readings from journals and conferences, as assigned.

          You might be interested in looking at:

          • A p2p "research" report from O'Reilly (actually, just a little ad for it) And you thought textbooks were expensive!

           


          Schedule (Subject to change)

          Week Week Date Date Lecture topic Lecture Topic Schedule items Discussion Topic Assignments Assignments Readings
          1 September 24

          Introduction to the P2P concept

          Intro slides in pdf

              Chapters 1-4
          -- September 26 Napster, Gnutella, and other early uses    

          The Gnutella protocol

          Steven Bellovin's slides

          The Wired article

          2 October 1

          Greg Bolcer, Endeavors Technology, Inc.

          Greg's slides in .pdf

              Chapter 8
          -- October 3        
          3 October 8

          Art Hitomi, and the Magi SDK.

          In ICS2-136!

               
          -- October 10 Discussion of the projects, papers and assignment of some journal and conference papers to be read and reported back to the class.      
          4 October 15 Project proposals presented   Detailed project proposals due.  
          -- October 17 Continuation of project proposals; discussion of what's expected on the survey      
          5 October 22 Collaborative workflow discussion   Proposal for survey portion of paper due (with list of papers to be included)  
          -- October 24 (Class was cancelled)      
          6 October 29

          Ad hoc, collaborative workflow discussion, continued, with Peter Kammer, ETI, guest.

          Survey presentations begin (if there are volunteers --- hint!)

               
          -- October 31

          Rohit Khare, CTO

          KnowNow, Inc.

               
          7 November 5        
          -- November 7        
          8 November 12 NO CLASS Veteran's Day    
          -- November 14 Project presentations begin      
          9 November 19

          Jini

          Maulik Oza & Mukesh Rajan

               
          -- November 21 No class      
          10 November 26

          Adrita Bhor, et.al.

          Rob and Girish

          James Yeung

          Roberto, et.al.

               
          -- November 28

          Emily Oh

          Veronica, John, and Salvador

          Eugen Nistor

               
          Exams   Final paper/project report due 9:00a.m., December 6th.  

           


          Academic Dishonesty

          I've never had an instance of cheating in a graduate seminar in my life. Nonetheless I suppose someone might plagiarize sooner or later. Thus: "Cheating in ICS 280 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. Don't do it."


          Citations

          Here's some background documents that may be relevant to your projects.

          1. "Peer-to-peer: A security nightmare or a secure opportunity" The Magi folks.
          2. "An efficient and open implementation of the Minstrel broadcasting infrastructure" Stefan Haberl, TU Wien. (I have this document... let me know if you want to borrow it.)
          3. "An implementation of the Millicent micro-payment protocol and its application in a pay-per-view business model." Michael Puhrerfellner. TU Wien. (Ditto)
          4. "The JEDI event-based infrastructure and its application to the development of the OPSS WFMS" G. Cugola, E. DiNitto, and A. Fuggetta. IEEE TSE. (If you want the real article, you'll have to do to the IEEE TSE site.)
          5. The February issue of CACM. It has a set of articles on securing network software applications (like p2p) and on intellectual property (yes, less relevant to the class).
          6. "P2P Networking: An information-sharing alternative" IEEE Computer, July 2001, vo. 34. no. 7.
          7. Two KnowNow whitepapers on Event-based Notification. ISENS and EBI

           


          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/classes/221/syllabusWQ09.html IN4MATX 221 Winter 2009

          Informatics 221: Software Architecture

          Winter Quarter 2009

          Course Code 37215

          Last update: March 12, 2009

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 221" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 3:30 - 4:50 p.m., PSCB 220 (Building #413)

          Web site: http://www.ics.uci.edu/~taylor/classes/221/syllabusWQ09.html

          What's New?

          • [January 5, 2009] Website goes live on EEE.

          Description - Textbook and Readings - Schedule - Grading - Policies


          Description

          Catalog description:

          Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures. Formerly ICS 223.


          Textbook (REQUIRED)

          Software Architecture: Foundations, Theory, and Practice. Richard N. Taylor, Nenad Medvidovic, and Eric M. Dashofy. Copyright © 2010 John Wiley & Sons, Inc. (ISBN-13: 978-0470-16774-8)

          • Software Architecture: Foundations, Theory, and Practice at Wiley.com, the publisher's site
          • Software Architecture: Foundations, Theory, and Practice at Amazon.com
          • Software Architecture: Foundations, Theory, and Practice at Barnes and Noble

           


          Schedule

          The schedule is subject to change. 
          Week Date Topic Individual Class Topics Readings Homework
          1

          J

          A

          N

          U

          A

          R

          Y

          6 Tu Introduction The Big Idea Chapter 1  
          8 Th Architectures in Context Chapter 2  
          2 13 Tu Basic Concepts and Introduction to Design Basic Concepts Chapter 3  
          15 Th Designing Architectures Chapter 4 Homework #1 assigned
          3 20 Tu Designing Architectures Architectural Styles  
          22 Th Styles and Greenfield Design  
          4 27 Tu Connectors Software Connectors Chapter 5  
          29 Th Choosing Connectors  
          5

          F

          E

          B

          R

          U

          A

          R

          Y

          3 Tu Modeling Introduction to Modeling Chapter 6  
          5 Th Modeling and Notations  
          6 10 Tu Visualization Visualizing Software Architectures Chapter 7  
          12 Th Homework #1 preview session Student-developed models    
          7 17 Tu Analysis Analysis of Software Architectures Chapter 8 (selections) Homework #1 due
          19 Th Implementation Implementing Architectures Chapter 9  
          8 24 Tu Implementation Techniques (Asuncion) 
          26  Th Architectures in the Real-World Applied Architectures: P2P systems Chapter 11

          More help with the existing lunar lander example

          Today's slides

          9

          M

          A

          R

          C

          H

          3 Tu No class.      
          5 Th Architectures in the Real-World Applied Architectures Chapter 11  
          10 10 Tu Domain-Specific Software Engineering Introduction to DSSE Chapter 15  
          12 Th DSSE and Product Lines Homework #2 due
          Exam 17 Tu   Exam from 4:00 -- 6:00  


          Grading and Evaluations

          Grading.
          There are 3 elements to your grade: a final exam, homework, and class attendance and participation.

          Summary of Assessment:

          Homework 70%
          Final exam 20%
          Class attendance and participation 10%

          No grades of incomplete (I) will be given for this course. 


          Policies

          Course Evalutions. The online evaluation window for winter quarter will run from TBA through TBA.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          (C) University of California, 2009.
          http://www.ics.uci.edu/~taylor/ics280e/syllabus.html ICS 280 E Syllabus

          Information and Computer Science 280E
          Software Architectures and Internet Protocols

          Winter Quarter, 2000
          Location: PSCB 240
          Time: TTh 11:00 - 12:20
          Course code: 36545

          Instructor | Overview and FAQ | Textbooks | Assignments | Schedule | Academic Dishonesty |
          (Last modified Wed 2 February, 2000 10:40)

          What's New

          1. [January 25] Reading list and assignments updated.
          2. [January 12] Almost all of the publications are now available on line. See the reading list below.
          3. [January 4, 2000] Home page posted.

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday, 3:30 - 4:30 Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
          • Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview

          We'll examine the relationships between software architectures and application-level Internet protocols. The goal of the class will be to develop a clearly articulated research strategy for getting us to the next major successor of today's WWW. The working hypothesis is that event-based architectures will form the backbone of that endeavor. In terms of topics we'll be looking at:

          • the existing app-level protocols
          • some work in software architecture
          • Roy Fielding's work on architectures as it underlies the design of the modern WWW
          • work in Internet-scale event notification

          This will be a read-papers-and-present ideas type of seminar.


          Textbook and Readings

          • Textbook: None.
          • This is just a beginning, temporary list of readings.

            1. A. Carzaniga, D. Rosenblum, A. Wolf. Design of a Scalable Event Notification Service: Interface and Architecture, Technical Report CU-CS-863-98, Department of Computer Science, University of Colorado, August 1998.
            2. R. Fielding. Shared Leadership in the Apache Project. Communications of the ACM, 42(4), April 1999, pp. 42-43.
            3. R. Fielding. Architectural Styles and the Design of Network-Based Software Architectures. Ph.D Dissertation, Information and Computer Science, University of California, Irvine. June 2000 (to appear). In the meantime, read Roy's survey paper, Software Architectural Styles for Network-Based Applications, July 1999.
            4. R. Fielding, J. Gettys, J. Mogul, H. Frystyk, T. Berners-Lee, L. Masinter, P. Leach. Hypertext Transfer Protocol -- HTTP/1.1. Internet Draft Standard RFC 2616, June 1999. Obsoletes RFC 2068.
            5. R. Fielding and R. Taylor. Principled Design of the Modern Web Architecture. Submitted for publication. (Handed out in class.)
            6. R. Fielding, E. Whitehead, Jr., K. Anderson, P. Oreizy, G. Bolcer, and R. Taylor. Web-based Development of Complex Information Products. Communications of the ACM, 41,8, pp. 84-92. (August 1998.)
            7. R. Khare. Seventh Heaven. A regular column in IEEE Internet Computing, throughout 1998 and 1999.
            8. N. Medvidovic, P. Oreizy, R. Taylor, R. Khare, M. Guntersdorfer. An Architecture-Centered Approach to Software Environment Integration. Submitted for publication.
            9. N. Medvidovic, D. Rosenblum, and R. Taylor. A Language and Environment for Architecture-Based Software Development and Evolution. Proceedings of the 1999 International Conference on Software Engineering (ICSE 99). Los Angeles, pp. 44-53, May 1999.
            10. D. Rosenblum, A. Wolf. A Design Framework for Internet-Scale Event Observation and Notification. In Proceedings of the Sixth European Software Engineering Conference, number 1301 in Lecture Notes in Computer Science, pp. 344-360. Springer-Verlag, 1997.
            11. R. Taylor, N. Medvidovic, K. Anderson, E.J. Whitehead, J. Robbins, K. Nies, P. Oreizy and D. Dubrow. A Component- and Message-based Architectural Style for GUI Software. IEEE Transactions on Software Engineering, 22(6):390-406, June 1996.
            12. http://www.ics.uci.edu/IRUS/twist/wisen98/
            13. TWIST 99: Internet-Scale Namespaces Workshop
            14. http://www.tibco.com
            15. http://www.opengrid.com
            16. Antonio Carzaniga. Architectures for an Event Notificaiton Service Scalable to Wide-Area Networks. Ph.D. dissertation, Dipartimento di Elettronica e Informazione, Politecnico di Milan, December 1998.
            17. Manfred Hauswirth. Internet-Scale Push Systems for Information Distribution - Architecture, Components, and Communication. Dissertation, Distributed Systems Group, Technische Universitat Wien, August 1999.
            18. E.J. Whitehead, Jr. Control Choices and Network Effects in Hypertext Systems. Proceedings of Hypertext 99, pp. 75-82.
            19. Wireless Access Protocols
            20. Broadband communication
            21. Active networks and ad hoc networks (from Rosenblum's 280)
            22. Protcols that address QoS.
            23. The Internet Standards Process
            24. Clark's paper on the Internet

             


          Assignments and Assessment

          Name Assignment  Weight 
          Presentations  These are oral reports to the class covering the papers. 40% 
          Summary paper    60% 

          Schedule (Subject to Change)

          Date
          Topic
          Reading
          Assigned
          Summary Documents Due
          Week 1
          January 11/13

          Topic introduction.

          ITR Proposal review.

          ITR Proposal.  
          Week 2
          January 18/20

          Eric Dashofy: Carzaniga's dissertation.

          Jie Ren: ESEC 97

          Rajat Mathur: Carzaniga, etl.al TR

          Jeff Ronne: HTTP/1.1 (Thursday)

          Rohit Khare: summary of the Seventh Heaven series

          1. The Siena papers.
          2. The HTTP/1.1 protocol specification
          3. The 7th Heaven articles
          1. The Siena TR and the ESEC Siena paper
          2. The 7th Heaven articles (you choose whether to review the summary article or the various individual articles).
          Week 3
          January 25/27

          Rohit: Continuation of the Seventh Heaven series (Tuesday)

          Taylor: hand-waving about the C2 architectural style

           

          1. The Fielding/Taylor paper
          2. Fielding survey paper (Software Architectural Styles for Network-Based Applications)
          1. Fielding/Taylor paper
          Week 4
          February 1/3

          Rajat: The Fielding/Taylor paper

          Jeff: The Fielding survey paper

          Michael: The "Cobbler's Children" paper (Web-based Development ....)

          Rohit: Hauswirth dissertation (Push Systems)

          1. Web-based Development of Complex Information Product
          2. Hauswirth dissertation
          1. The Fielding survey paper (in lieu of the dissertation)
          2. Web-based development....
          Week 5
          February 8/10
          No Class (Vienna ICSE PC meeting)
          Week 6
          February 15/17

          Eric: Control Choices

          Adoption Issues

          Killer Apps, intro apps, feasible apps, local-scale apps

          1. Control Choices and Network Effects in Hypertext Systems
          2. Shared Leadership in the Apache Project
          3. The Internet Standards Process
          1. Extension of Roy's summary of style classification to include push systems/architectures.
          2. "What's an Event" 1 or 2 page discussion of definition of an event
          3. Control Choices and Network Effects in Hypertext Systems
          4. Shared Leadership in the Apache Project
          Week 7
          February 22/24
               
          Week 8
          February 29/March 2
          (Class on March 2 is at risk of rescheduling or cancellation)    
          Week 9
          March 7/9
               
          Week 10
          March 14/16
               

           


          Academic Dishonesty

          Cheating in ICS 280E will be dealt with in accordance with ICS policy and UCI policy. Please familiarize yourself with those documents. 
          Department of Information and Computer Science,

          University of California, Irvine CA 92697-3425 http://www.ics.uci.edu/~taylor/ics125_fq99/syllabus.html ICS 125 Syllabus

          Information and Computer Science 125:
          Project in Software System Design

          Fall Quarter, 1999
          Lecture: TTh 9:30 - 10:50
          Location: CS 180
          (Need directions to campus? See the maps directory.
          Course code: 36165

          Discussion Section (REQUIRED): MWF: 12:00 - 12:50
          Location: CS 180
          Course code: 36168



          Instructor | Overview and FAQ | Textbooks | Teams | Assignments | Costs and Benefits | TA | Keeping in Touch | Computing | Academic Dishonesty | Schedule
          (Last modified Thu Nov 18 1999)

          What's New

          Watch this spot for new information regarding ICS 125. It may link to other web pages or to updates to this page.
          • [November 29] The final appraisal form is now available. Please note that this is *confidential* and is *not* done on a team basis. This is the form earlier described in class and at below. Instructions on submittal are included in the form.
          • [November 18] The presentation on giving demonstrations is available on the course website.
          • [November 18] I've changed the implementation assignment slightly. Look under the presentation instructions and under the description of the size of the implementation.
          • [November 18] The article on boothmanship is available on the course website.
          • [November 10] The implementation assignment is now available. So is the final assignment. Note that I have provided a skimpy description of it. I'm trusting you to fill in the necessary and obvious details.
          • [November 10] The schedule page has been slightly revised.
          • [October 13] Pointers to all the teams' websites, their customers, and their office locations have been added. Check out the Teams table. (And note that a couple of the teams have names and eye candy already!)
          • [October 13] The document that Tamara Wolfe provided regarding the PSYO website project is available in both Word and .pdf formats.
          • [October 7] The slides on project management that I showed today are available. The PDF file also has some excellent material on project plans that I didn't talk about today, but probably will on Tuesday. In any event you should find this material useful as you create your project plans.
          • [September 24] The class TA has been assigned: Jie Ren. Contact info below.
          • [September 22] On the first day of class (September 28th) you will need to turn in a copy of the initial class survey . You can print out the survey from the website, fill in the form, initial at the bottom, and bring it to the class. Otherwise you'll have to take time at the end of the class to fill it in then. In any event you must turn the survey in on the 28th.
          • [September 22] The web site now has the first three assignments available: the prospectus, the requirements, and the design. The remaining two will be available later.
          • [September 17] Web site opened.

          Course Staff

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday, 11:00 - 12:20 Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Teaching Assistant(s)

          • Jie Ren Send mail to: jie@ics.uci.edu

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Specification, design, construction, testing, and documentation of a complete software system using concepts learned in ICS 52, 121, and 141. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems.

          This course will emphasize techniques and notations essential to creating software systems based on the principles discussed in ICS 121: well-understood requirements, usability and user interface design, architectural design and module specification, well-planned testing, effective oral and written communication of concepts, proper programming style, group coordination, product documentation and software process.

          All students are expected to attend all lecture and discussion sections. In general, there will not be much lecturing in the class. Instead, class time will be highly interactive, and all students are expected to participate. About half of the time will be spent performing reviews of the artifacts developed. These reviews will take up all lecture and discussion periods the week following the due date for each deliverable.

          Prerequisites:

          ICS 51 with a grade of C or better; ICS 121 and 141; Mathematics 2A-B-C.

          Is the Discussion Section required?

          Yes! The discussion section is essential for two reasons:

          1. You will need to meet with your teammates REGULARLY. The best correlation with failure that I've seen in this class over the past decade has been teams which were unable or unwilling to establish a regular meeting schedule and to keep to it. In other words, EVERYONE needs to be in attendance at team meetings. The discussion section time period is the guaranteed time period for you team to meet.
          2. Since this is a large class, and since each team will be making at least 4 presentations during the quarter, we'll have to use some of the discussion sections for those presentations.

          What will the projects be?

          Candidate projects can come from many places: in AY 98-99 some ICS 125 teams worked on projects that sponsoring local companies suggested. Other teams worked on projects related to on-going research programs in the ICS department. Other projects may come from the students.

          Choice of projects is related to many goals.

          One key goal is to pick a project of appropriate size. It must be big enough to challenge a team of four students, but not so big as to commandeer everyone's life! As a result we will spend some time at the beginning of the fall attempting to size various projects. These planning estimates will be revisited as the course progresses.

          Another goal is to select a different project for each team. Since each team will be making regular project presentations to the rest of the class, diversity of projects will enable students to learn from experiences across a range of project topics.

          Still another goal is to work on something fun and interesting! I've had students working on flight simulators, generating HTML pages, linking MPEG movie frames via hypertext to other artifacts, and building graphical program editors. There are many opportunities....

          How will teams be composed?

          Each team will have 4 or 5 people. I will attempt to balance a team's aggregate expertise with the project they've decided to work on. I will also attempt to accommodate personal preferences for teammates. The course survey form, which you will complete the first day of class, is a key instrument in assigning the teams.

          What's the Drop/Add policy?

          Since ICS 125 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds of ICS 125 will be permitted after the end of the SECOND week of class.


          Lecture Topics

          ICS 125 is on a tight time schedule, thus there is not much time for review. You are expected to recall the material covered in ICS 121 and the other prerequisite courses. Short supplementary lectures are anticipated on:

          • Teamwork and team organizations
          • Project management
          • Project cost and schedule estimation
          • Usability evaluation and user interface prototyping
          • Software architectural choice
          • Configuration management and version control
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects chosen by the teams. If several of the projects, for example, are concerned with Internet applications, then lectures on protocols and Web technologies will be included. Similarly if most projects will be using some particular kind of infrastructure, such as CORBA or ActiveX, then there will be lectures on that.

          Textbooks

          Regardless of the project chosen, all students will be required to read The Mythical Man-Month. Depending on the projects chosen additional readings from various sources may be required.

          • REQUIRED:
            • Author: Fred Brooks
            • Title: The Mythical Man-Month, Anniversary Edition
            • New Price:$ 24.50
            • Used Price:$ 18.40
            • also available from amazon.com for $24.95.
            • Note that this book has a lot of white space and blank pages, so it really will not take you long to read these chapters.

          • BACKGROUND/GENERAL REFERENCES:
            • Stephen Schach. Classical and Object-Oriented Software Engineering (Third Edition). Irwin, Chicago IL, 1996.

            • or Classical and Object-Oriented Software Engineering with UML and C++
              or Classical and Object-Oriented Software Engineering with UML and Java
            • Ghezzi, Jazayeri, & Mandrioli. Fundamentals of Software Engineering. Prentice Hall, Englewood Cliffs NJ, 1991.
            • Rumbaugh, et.al. Object-Oriented Modeling and Design.
            • Mary Shaw and David Garlan. Software Architecture, Perspectives on an Emerging Discipline. Prentice Hall, Englewood Cliffs NJ, 1996.

            All are available from amazon.com.


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for 80% of your grade; this is broken down between deliverables, a team Web page, and presentations. The remaining 20% will be divided among individual course logs, teamwork, individual leadership demonstrated, and the final.

          Deliverables

          The ICS 125 project nominally consists of five major assignments. Their relative weighting (as a percentage of your final grade) of each deliverable is indicated in the table below along with the due date. The on-line versions of the assignments may still be under construction (watch the what's new section to see when they are available).
           
           
          Deliverable Weight  Description Due Date
          Individual Web Page . . 04 October
            Teams Designated
          . . 05 October
            Projects Selected/Assigned
          . . 07 October
          Team Web Page . . 11 October
          Prospectus and Plan 
          10 
          Prospectus  15 October
            Prospectus Reviews
          . week 4 18-22 October
          Requirements Specification
          15 
          Requirements  22 October
            Requirements Reviews
          . week 5 25-29 October
          Architecture/Module Specifications 
          20 
          Architecture/Design  05 November
            Design Reviews
          . week 7 8-12 November
          Implementation 
          20 
          Implementation 19 November
            Code Reviews
          . week 9 22-24 November
          Testing/Test Documentation
          15 
          Integration/Testing 03 December
            Demonstrations
          . week 10 29 November - 03 December

          The deliverables are weighted according to the relative amount of time and effort we expect you will spend on each (and not necessarily on their importance with respect to software development). Variations to this may be made to accommodate the particular needs of a given project or a given team. Also, note that the grade for a deliverable will consist not only of the document/specification, developed during that phase but also the test plan developed along side it as well as the review conducted in class the following week.

          Deliverable Due Dates

          Deliverables are due at 12:50 PM on the date indicated in the table above. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          Unless directed otherwise, deliverables must be turned in directly to the TA or placed in the TA's mailbox before that time.

          Deliverable Reviews

          Each deliverable will be reviewed in class. Each team will be given 15-20 minutes to present their project. You will be given guidance in class on how to conduct these presentations.

          Your customer should be invited to your team's Prospectus and Requirements review as well as your demonstration (and, possibly even your design and code reviews depending on the nature of your customer).  The review is your team's chance to inform as well as obtain feedback and ideas from all relevant parties; your document will be reviewed at this time by course staff and clients as well as the rest of the class.  This review is a formality, however, and each team should have presented and negotiated both relevant documents to the customer prior to the review (if you haven't, it may be unpleasantly obvious by the interactions at this time).

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in. They must reside permanently on your team's website.

          All deliverable documents must be prepared on-line and be available as part of your project home page either as either HTML or .pdf files. NO MS Word files. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Phase clerical person
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, design, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this application shall be marked ``N/A''.
          Minutes.
          Every deliverable shall be accompanied by minutes of team meetings held during the associated period of time.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page. A form is available at http://www.ics.uci.edu/~taylor/ics125_fq99/performance_appraisal.pdf . A .ps file is also available.
          Project WebPage.
          The project deliverables, except for the performance appraisals, shall be maintained in a project homepage.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last, you will also have the opportunity to ``fix it'' based on its evaluation. You may hand in an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. You should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Course Log

          During your career you will need to keep track of how you spend your time either for you employer or to improve your own productivity. Throughout this course, you will practice doing this by keeping a course log recording the time you spend on all activities related to this course. At the beginning of each week you must email the previous week's log to the TA. A sheet showing what should be on the log is available at: http://www.ics.uci.edu/~taylor/ics125_fq99/logform.html .

          Keep a copy of your logs: you will need them at the end of the quarter for the final review.

          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time entry applies for descriptions of activities and records the amount of time spent in hours, to the nearest quarter hour.

          You will be marked down only for failing to email logs each week, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.


          Team Composition, Activities, and Peer Apportionment of Credit

          As discussed in class, teams will be assigned on as fair a basis as possible for the project. The danger most students perceive in working on projects with other students is in being saddled with (what they think is) a "non-producer". This is particularly true when you don't get to choose all your teammates (the situation here). Many factors dictate the use of a multi-person project for this course. You will not, after all, be able to choose your workmates in the future. Therefore, to alleviate your concerns and to grade you appropriately, at the end of the term project you will be asked to divide 100 points among the members of your project team, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          Team Organization

          There are several obvious dangers to group work that can be circumvented. Ensure that there is adequate coordination among the team members. Know each other's login names for electronic mail. Know each other's phone numbers. Meet at least twice per week (outside of class lecture) at the same, pre-determined time each week (so as to avoid confusion). The Discussion Section is designed to guarantee that such meetings are possible for everyone. You are strongly urged to use that time slot.

          Have a contingency plan for submitting a document on time even if the responsible manager becomes unavailable.

          You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.

          Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. agenda for the meeting
          2. team members present and reason for any member's absence
          3. major design decisions discussed
          4. task assignments made
          5. future meetings scheduled

          Cost and Benefits

          This course will demand a lot, but I think that you may well find this to be the most rewarding courses that you will take in your undergraduate career. ICS Alumni have said repeatedly that ICS 125 was the most important class that they took at UCI. The techniques presented in class actually work and will help you in future software development.

          At the end of the class I encourge you to make copies of your project website/notebook for each team member. Take them with you when you go to a job interview. Students from past ICS 125 classes have frequently said that it was their project notebook that clinched a job for them. Some interviewers have commented that the quality of the process followed by the ICS 125 teams and the quality of the product exceeds those of the production engineers in their companies.


          Keeping in Touch

          Check the course syllabus page regularly . Announcements concerning assignments will be made there, changes to the lecture schedule will be announced there, and so on.

          Teams and Meetings

          As noted earlier, teams will use (at least) the discussion sections for team meetings.
          Team Designator Home page Customer Contact Office
          Compaq Team Compaq Roger Olds CS Trailer Room 1
          Posties-1 Posties-1 Joe Feise CS Trailer Room 2
          Posties-2 DAV Posties Joe Feise CS Trailer Room 2
          PSYO-1 Wild, Wild Web Dick Taylor and Tamara Wolfe and Chris Russell CS Trailer Room 3
          PSYO-2 One In a Million Industries Dick Taylor and Tamara Wolfe and Chris Russell CS Trailer Room 3
          PSYO-3 Team Mugen Dick Taylor and Tamara Wolfe and Chris Russell CS Trailer Room 5
          IMPP-1 The Zot Project Rohit Khare CS Trailer Room 8
          IMPP-2 HKPlayers Rohit Khare CS Trailer Room 8
          Workflow-1 Reservation Pro Peter Kammer CS Trailer Room 9
          Workflow-2 Workflo2 Peter Kammer CS Trailer Room 9


          Computing

          To facilitate sharing of files among team members, each team will have an account on a Unix (Sun Solaris 2) machine, where the team web site and project documents must be maintained.

          The primary computing facilities will be the ICS Labs, which provide Sun Solaris and Windows/NT machines.  The hardware environment and software environment is posted on the lab's web site as well as the lab hours and availability for Fall 1999.

          The system specification for each deliverable may be done on Suns, Macintoshes, PCs, or any other available platform. Choice of computing platform for implementation will depend on the projects chosen. Where possible and reasonable, Java will be the implementation language used. (Scads of Java information is available on-line, including a tutorial, reference material and many, many packages, such as those available from Gamelan.)

          Choice of computing platforms will depend on the projects chosen. Where possible and reasonable Java will be the implementation language used.


          Policies (Academic Honesty and Computing Use)

          Cheating in ICS 125 will be dealt with in accordance with ICS cheating policy, which is in keeping with the UCI academic [dis]honesty policy.  Please familiarize yourself with these documents, as you are held accountable to them.

          You are also bound by all policies posted at I CS's Computing Support Policies, including ICS's Ethical Use of Computing Policy, as well as UCI's Computer Use Policy.


          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ics221/syllabus.html ICS 221 Syllabus

          Information and Computer Science 221A: Software Engineering

          Fall Quarter, 1996
          Location: CS 253
          Tuesday and Thursday, 9:30 -- 10:50
          Course code: 36620
          (Last modified Wed Sept 18 1996)

          Instructors

          • Professor Richard N. Taylor and the Software Faculty
          • Electronic Mail: taylor@ics.uci.edu
          • Office: ICS2-203
          • Office Phone: (714) 824-6429
          • Fax: (714) 824-4056
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425


          Overview | Readings | Schedule | Assignments | Enrolled Students

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          Study of the concepts, methods, and tools for the analysis, design, construction, and measurement of complex software-intensive systems. Underlying principles emphasized. State-of-the-art software engineering and promising research areas covered, including principles of software engineering, requirements analysis, design, implementation, testing, and project management.

          This class is a graduate survey of software engineering topics. The instructors assume background in software engineering, as well as some experience in developing software systems. For example, the course assumes knowledge of the basic ideas of: software development phases (e.g., requirements engineering, design, implementation), software development processes (such as the spiral model and the waterfall model), software design methodologies (such as design based on information hiding), software testing and analysis strategies, and project management issues (e.g. chief programmer teams). Students new to this area are advised to take ICS 121, as this course will not repeat basic material, but rather will be focused on issues at the frontier of software engineering. At the end of the class students should be knowledgeable about what key problems are currently being pursued by software researchers, what the key approaches are, and who are many of the leading researchers.

          The vehicles for learning about the topics are readings from the textbook, readings from the technical literature, lectures, and classroom discussion. The class is co-taught by the software faculty in order for specialty topics to be presented in depth.


          Readings

          Required textbook:
          • Fundamentals of Software Engineering, by Carlo Ghezzi, Mehdi Jazayeri, and Dino Mandrioli, 1991. Prentice-Hall.
          Additional readings will be assigned from the "Software Phase II Exam Reading List" to supplement the text.

          Schedule

          Subject to change

          Date Topic Instructor Papers Text Readings
          October 1 Introduction Taylor --- Ch. 1, 2
          October 3 Overview Taylor [Bro87] Ch. 3
          October 8 Software Processes Rosenblum [Boe88] [RW92] Ch. 7
          October 10 Software Process Tech. Taylor [Ost87] Ch. 8
          October 15 Extensible Systems Franz (SFO ISAW-2) --- ---
          October 17 Extensible Systems Franz (SFO FSE) --- ---
          October 22 User Interface Software Taylor [TNB+94] ---
          October 24 Software Architectures Taylor --- ---
          October 29 tbd (Manassas) --- ---
          October 31 Environments Taylor [TM81] Ch. 9
          November 5 Environments/Interoperability Taylor [Kad92] ---
          November 7 HCI and Cognitive Design Redmiles --- ---
          November 12 Design Environments Redmiles [FGNR92] ---
          November 14 Formal Methods Richardson --- Ch. 6
          November 19 Testing Rosenblum (ICSEPC) --- ---
          November 21 Analysis and Testing Richardson (ICSEPC) --- ---
          November 26 Hyperware and SW Eng. Taylor --- ---
          November 28 Thanksgiving/No class --- --- ---
          December 3 Metrics and Measurement Selby (Brighton ICSP) [BSH86] ---
          December 5 Software Practice Selby (Brighton ICSP) --- ---

          Assignments and Assessment

          • Paper/topic summaries. For each paper assigned during the class (expectation: about 10-12) you are required to submit a two-page (appx. 800 word) summary and evaluation. One page should summarize the key points or contributions of the paper. The second page should assess the applicability of the ideas, problems in applying the ideas, or provide other critical evaluation. In aggregate these summaries will account for 40% of your grade.
          • Term paper. Accounting for 30% of your grade, the term paper is an in-depth exploration of some current topic in software engineering. (The paper must be new for this class, not a warmed-over paper written for some other ICS class.) This paper may be largely survey in character. Substantial latitude will be allowed in choosing the topic, but all topics must be approved in advance by the instructor.
          • Class attendance and participation is critical. This will account for 10% of your grade.
          • Final exam. A final exam, worth 20%, will allow you to demonstrate your understanding of the papers and topics covered in class.

          Enrolled Students

          • your name goes here....


          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS_52_FQ02/syllabus.html ICS 52 Syllabus Fall 2002

          Information and Computer Science 52:
          Introduction to Software Engineering

          Fall Quarter, 2002
          Location: Social Science Plaza A 1100 (Need a map?)
          Monday and Wednesday, 2:00-3:20
          Course code: 36130
          Discussion location: SSLH 100
          Tuesday, Thursday, 5:00 -- 5:50
          Course code for discussion section: 36131
          (Last modified Wednesday, February 19, 2003)

          WHAT'S NEW?

          [December 1, 2002] Today's lecture notes are now available. Please also check your recorded scores... I've sent email with the details.

          [November 25, 2002] I've granted an extension to the due date on the implemenation assignment, but at the penalty of reducing the maximum possible score for that assignment from 7 to 5. Details are in the email sent to the course mailing list. There will NOT be an extension granted for the testing assignment.

          [October 3, 2002] You can find some very helpful guidelines for the composition of good email messages here, courtesy of Ellen Strenski in the UCI writing program.

          [September 29, 2002] Would you like to add this class? So would about 110 other people (literally). The room capacity is 190. Currently there are 175 people enrolled. I will therefore sign in a total of 15 people. Priority will be given as follows: (A) ICS majors who are seniors; (B) non-ICS majors who are both seniors and ICS minors; (C) ICS majors who are juniors. Before I sign an add card you must complete, sign, and return to me this form no later than Wednesday, October 2nd. Give me the form, a filled in add card, and I will process the submissions. Signed add cards will be available Friday, October 4th, from my office (in the bin on the outside of my door).



          Overview | Textbooks | Schedule | Assignments | TAs | Keeping in Touch | Computing | Disabilities | Academic Dishonesty |

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Monday and Wednesday: 3:30-4:30pm. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          Introduction to the concepts, methods, and current practice of software engineering. The study of large-scale software production; software lifecycle models as an organizing structure; principles and techniques appropriate for each stage of production. Laboratory work involves a project illustrating these elements.

          Prerequisite: ICS 23 with a grade of C or better.

          In addition to the skills and concepts introduced in previous classes, students should have these computing skills when they enter the class (or learn them independently in the first week of the quarter):

          • The use of a text editor (Word, etc.) to create documents.
          • The use of a drawing package (Power Point, Visio, etc.) to create graphics for these documents.

          The instructional objectives for the course are as follows:

          • introduce you to the discipline of software engineering;
          • experientially acquaint you with one version of the software lifecycle;
          • provide working knowledge of at least one reasonable technique to be applied in each phase of the lifecycle;
          • provide particular insight into software architecture, design by information hiding, and the problems of software analysis and testing.

          Minimum Knowledge and Skills Expected of Students Who Receive Passing Grades

          Software Life Cycle

          • Mastery:
            • Knowing different life cycles and their appropriateness in different situations
            • Knowing basic principles of software engineering (such as separation of concerns, modularity, and abstraction) and knowing how they apply throughout the software life cycle
          • Proficiency:
            • Understanding tradeoffs and relationships among the various activities in the software life cycle
            • Understand the meaning and use of a set of basic software qualities

          Requirements

          • Mastery:
            • Interviewing a customer to elicit requirements
            • Writing a textual (non-formal) requirements document
          • Proficiency:
            • Understand the structure of a requirements document and know the appropriate kinds of information in such a document

          Architectural Design

          • Mastery:
            • Know the differences among interaction patterns of a set of basic architectural styles
            • Understand the difference between architecture and module design
          • Proficiency:
            • Choosing an appropriate architectural style for a particular problem

          Module Design

          • Mastery:
            • Using provided/exported and required/imported interfaces to define module boundaries
            • Identifying and defining modules in a design
            • Identifying and defining abstract data types in a design
          • Proficiency:
            • Applying coupling, cohesion, fan-in, and fan-out
            • Creating USES and COMPRISES diagrams
          • Exposure:
            • Creating a design for a nontrivial, sizable problem

          Programming

          • Mastery:
            • General rules of programming style and clarity (short rehash from earlier classes)
          • Proficiency:
            • Mapping a module design onto an implementation in source code
          • Exposure:
            • Using existing modules and libraries in an implementation
            • Coding under a heavy deadline (requiring tradeoffs between code quality and code functionality)

          Testing

          • Proficiency:
            • Testing a program for failures
            • Applying white-box testing on short pieces of code
            • Applying black-box testing on short pieces of code
          • Exposure:
            • Understanding the many dimensions of software quality assurance
            • Understanding the inspection and code walk-through process

           


          Textbooks

          Required:
          • Software Engineering, 6th edition, by Ian Sommerville. 2001. Addison-Wesley. $85.15 new at the bookstore.
          • The class notes will be available on the WWW after the class meeting in which they are used. Occasionally the notes will be available ahead of time, but they might be from the previous year's offerings, and thus minor differences may exist as compared to what's used in class this year.

          Schedule (Subject to change)

          Week Date Lecture topic Schedule items Assignments Readings (all from Sommerville, unless noted otherwise)
          1 September 30 Introduction (the slides as of 10/2/02 are now here... a revision of the previous posting)     Chapter 1. Skim 2. All of 3, but with more attention to the first 2 subsections. Skim 4
          -- October 2

          Processes

          (continuation of the lecture notes associated with the first lecture, above)

               
          2 October 7

          Principles & Requirements Engineering

          and here's the slides I used for my keynote talk at STRAW '01

            Requirements assignment issued Chapters 5, 6 (lighter), and 7.
          -- October 9

          Requirements Engineering (URL here is to Sommerville's slides from his Chapter 7)

               
          3 October 14 Architectures     Chapters 10 (key), 11 (a little lighter), and 14.
          -- October 16 Architectures      
          4 October 21 Design  

          Requirements assignment due

          Design assignment issued

          Chapter 15.
          -- October 23 Design, continued from Monday.     examples of module descriptions
          5 October 28 Design & Review      
          -- October 30 Mid-term Exam     Here's last year's midterm exam and solution notes.
          6 November 4 UI Design (these are Sommerville's slides, which I'll be using)      
          -- November 6 Continuation of UI Design      
          7 November 11 No class Veteran's Day   Pages 452-457 provide good material on integration testing. This is part of chapter 20, which is on the schedule below.
          -- November 13 Integration testing and Implementation issues  

          Design (Arch and module interfaces) due .

          Implementation assignment issued.

           
          8 November 18 Testing     Chapters 19 and 20.
          -- November 20 Testing, continued      
          9 November 25 Quality assurance  

          Implementation due

          Testing assignment issued.

          See especially section 20.1.2 (since that's the technique you'll be using on the testing assignment)
          -- November 27       .
          10 December 2 Maintenance and Configuration Mgmt.     Chapter 27 and 29
          -- December 4 Course Review   Testing assignment due on December 4th  
          Exams December 13 FINAL EXAM 1:30p.m. — 3:30p.m.    

          Assignments and Assessment

          Aggregate grade for the project: 45%.
          Midterm: 20%.
          Final: 35%

          The project consists of four assignments. Their relative weighting (as a percentage of your final grade) is as follows:

          Assignment Weight
          Requirements 10

          Architecture and module interfaces (assignment)

          Official Requirements Document

          18

          Implementation assignment

          Official Design Document

          7
          Testing assignment 10

          Specific assignments will be placed on the web, giving the assignment, the required format, and specific grading criteria.

          We do not grade on a curve, meaning that we will not assert in advance that half of the class will receive grades at or below a C+, and half above that. Grading is done based on mastery of the material as exhibited in the exams and the project. If everyone masters the material very well, then everyone will get an A. On the other hand, if no one were to master the material at all adequately, then everyone would receive an F.

          NO LATE ASSIGNMENTS WILL BE ACCEPTED, unless you have a legible excuse from a physician, an extreme family emergency, or unless you are willing to accept an extreme penalty with respect to your assignment's grade.


          Teaching Assistant and Readers

          • TA: Girish Suryanarayana
          • Readers:
            • TBD

          Keeping in Touch

          Check this web site regularly. This is the definitive location for course information. Announcements concerning assignments will be made here. The course mailing list will also be used to make announcements, provide instructions, and so on. The course mailing list will be "read only" from the student's perspective. I.e. the instructor, TA, and readers can post messages to the list,but not students. If you ask one of us a question whose answer is relevant to the rest of the class, we'll post the appropriate material to the list.

          An important note about email

          Any email that you send in conjunction with this class must be sent from a UCI account. That is, if you want any response or action taken, then you must use your UCI account to send the email. Email from yahoo, hotmail, juno, cox, or any other non-UCI site will be ignored.


          Computing

          All computing will be done on the department's NT machines.

          You may use another computer to produce the documents you turn in. (No handwritten assignments allowed).

          All implementation work will be done in Java.

          Please use the computing equipment or instructional purposes only. Also watch out on the social subtleties of electronic mail.


          Disabilities

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion.


          Academic Dishonesty

          Cheating in ICS 52 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. It is lying. Don't do it.
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS_52_FQ04/syllabus.html ICS 52 Syllabus Fall 2004

          Information and Computer Science 52:
          Introduction to Software Engineering

          Fall Quarter, 2004
          Location: Engineering Lecture Hall 100 (Need a map?)
          Monday, Wednesday, Friday, 10:00-10:50
          Course code: 36290
          Discussion location: SSH 100
          Monday, Wednesday 5:00 -- 5:50
          Course code for discussion section: 36291
          (Last modified Monday, November 29, 2004)

          WHAT'S NEW?

          [November 29, 2004] This week's lecture notes are now available.



          Overview | Textbooks | Schedule | Assignments | TAs | Keeping in Touch | Computing | Disabilities | Academic Dishonesty |

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor <at-sign> ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Monday and Wednesday: 11:00-12:00 pm. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Donald Bren School of Information and Computer Sciences,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          Introduction to the concepts, methods, and current practice of software engineering. The study of large-scale software production; software lifecycle models as an organizing structure; principles and techniques appropriate for each stage of production. Laboratory work involves a project illustrating these elements.

          Prerequisite: ICS 23 with a grade of C or better.

          In addition to the skills and concepts introduced in previous classes, students should have these computing skills when they enter the class (or learn them independently in the first week of the quarter):

          • The use of a text editor (Word, etc.) to create documents.
          • The use of a drawing package (Power Point, Visio, etc.) to create graphics for these documents.

          The instructional objectives for the course are as follows:

          • introduce you to the discipline of software engineering;
          • experientially acquaint you with one version of the software lifecycle;
          • provide working knowledge of at least one reasonable technique to be applied in each phase of the lifecycle;
          • provide particular insight into software architecture, design by information hiding, and the problems of software analysis and testing.

          Minimum Knowledge and Skills Expected of Students Who Receive Passing Grades

          Software Life Cycle

          • Mastery:
            • Knowing different life cycles and their appropriateness in different situations
            • Knowing basic principles of software engineering (such as separation of concerns, modularity, and abstraction) and knowing how they apply throughout the software life cycle
          • Proficiency:
            • Understanding tradeoffs and relationships among the various activities in the software life cycle
            • Understand the meaning and use of a set of basic software qualities

          Requirements

          • Mastery:
            • Interviewing a customer to elicit requirements
            • Writing a textual (non-formal) requirements document
          • Proficiency:
            • Understand the structure of a requirements document and know the appropriate kinds of information in such a document

          Architectural Design

          • Mastery:
            • Know the differences among interaction patterns of a set of basic architectural styles
            • Understand the difference between architecture and module design
          • Proficiency:
            • Choosing an appropriate architectural style for a particular problem

          Module Design

          • Mastery:
            • Using provided/exported and required/imported interfaces to define module boundaries
            • Identifying and defining modules in a design
            • Identifying and defining abstract data types in a design
          • Proficiency:
            • Applying coupling, cohesion, fan-in, and fan-out
            • Creating USES and COMPRISES diagrams
          • Exposure:
            • Creating a design for a nontrivial, sizable problem

          Programming

          • Mastery:
            • General rules of programming style and clarity (short rehash from earlier classes)
          • Proficiency:
            • Mapping a module design onto an implementation in source code
          • Exposure:
            • Using existing modules and libraries in an implementation
            • Coding under a heavy deadline (requiring tradeoffs between code quality and code functionality)

          Testing

          • Proficiency:
            • Testing a program for failures
            • Applying white-box testing on short pieces of code
            • Applying black-box testing on short pieces of code
          • Exposure:
            • Understanding the many dimensions of software quality assurance
            • Understanding the inspection and code walk-through process

           


          Textbooks

          Required:
          • Software Engineering, Principles and Practice , by Hans van Vliet. Second Edition. $75.00 new at the bookstore; $56.25 used. ISBN 0-471-97508-7
          • The class notes will (usually) be available on the WWW after the class meeting in which they are used. Occasionally the notes will be available ahead of time, but they might be from the previous year's offerings, and thus minor differences may exist as compared to what's used in class this year.

          Schedule (Subject to change)

          Week Dates Lecture topic Schedule Notes Assignments Readings (all from van Vliet, unless noted otherwise)
          1September 24, 27, 29 Introduction Chapters 1, 2, and 3

          Processes

          Slides from 1st Discussion section
          2October 1, 4, 6

          Principles & Requirements Engineering

          and here's the slides I used for my keynote talk at STRAW '01

          Requirements assignment issued

          Section 11.1

          Chapter 9

          Requirements Engineering (URL here is to Sommerville's slides from his Chapter 7)

          Slides from discussion section (10/6) Example requirements document (from FQ 02)
          3October 8, 11, 13 Architectures Slides from discussion (10/11) Chapter 10
          Architectures Slides from 10/13 discussion Example requirements document (Unical)
          4 October 15, 18, 20 Design

          Requirements assignment due 10/18, in discussion.

          Design assignment issued

          Chapter 11

          Design, continued

          QuiltsRUs example

          Flight control example

          Eric Dashofy's slides on the C2-style

          examples of module descriptions

          ICS 52 standard design notation (though not for C2 designs)

          5 October 22, 25, 27 Design
          UI Design (these are Sommerville's slides, which I'll be using) Chapter 16
          6 October 29, November 1, 3

          Review on the 29th and 1st.

          Mid-term Exam, on November 3

          Here's the midterm exam and solution notes from a couple of years ago.

          Here's this quarter's solution key.

          7
          November 5, 8, 10

          No class on 11/5.

          Integration testing and Implementation issues

          Design (Arch and module interfaces) due Nov. 8th.

          Implementation assignment issued.

          Testing assignment issued.

          8 November 12, 15, 17 Testing Chapter 13
          9November 19, 22, 24 Quality assurance

          Testing assignment (yes, that 's right, testing) due 11/24

           

          10 November 29, December 1, 3 Maintenance and Configuration Mgmt. Chapter 4
          Course Review

          Implementation assignment due.

          Exams Monday, December 6th FINAL EXAM 10:30 - 12:30 The exam is comprehensive. Here's a sample from the past.

          Assignments and Assessment

          Aggregate grade for the project: 45%.
          Midterm: 20%.
          Final: 35%

          The project consists of four assignments. Their relative weighting (as a percentage of your final grade) is as follows:

          Assignment Weight
          Requirements 10

          Architecture and module interfaces

          Design Addendum

          Required Notation

          18

          Implementation

          7
          Testing 10

          Specific assignments will be placed on the web, giving the assignment, the required format, and specific grading criteria.

          Strictly speaking, we do not grade on a curve, meaning that we will not assert in advance that half of the class will receive grades at or below a C+, and half above that. Grading is done based on mastery of the material as exhibited in the exams and the project. If everyone masters the material very well, then everyone will get an A. On the other hand, if no one were to master the material at all adequately, then everyone would receive an F. But grades are adjusted to suit the difficulty of the exams and the assignments. If an exam turns out to be a real bear, then the threshold for an A will be much lower than for an exam that was piece of cake.

          NO LATE ASSIGNMENTS WILL BE ACCEPTED, unless you have a legible excuse from a physician, an extreme family emergency, or unless you are willing to accept an extreme penalty with respect to your assignment's grade.


          Teaching Assistant and Readers

          • TA: Justin Erenkrantz
            • Office: ICS2-246. Note: this building is locked, so to gain entry you need to either make an appointment ahead of time (by email) or phone (824-2776)
            • Hours: MW 3:30 - 5:00
          • Reader:
            • TBD

          Keeping in Touch

          Check this web site regularly. This is the definitive location for course information. Announcements concerning assignments will be made here. The course mailing list will also be used to make announcements, provide instructions, and so on. The course mailing list will be "read only" from the student's perspective. I.e. the instructor, TA, and readers can post messages to the list, but not students. If you ask one of us a question whose answer is relevant to the rest of the class, we'll post the appropriate material to the list.

          An important note about email

          Any email that you send in conjunction with this class must be sent from a UCI account. That is, if you want any response or action taken, then you must use your UCI account to send the email. Email from yahoo, hotmail, juno, cox, or any other non-UCI site will be ignored.


          Computing

          All computing will be done on the department's lab machines.

          You may use another computer to produce the documents you turn in. (No handwritten assignments allowed).

          All implementation work will be done in Java.

          Please use the computing equipment for instructional purposes only. Also watch out on the social subtleties of electronic mail.


          Disabilities

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion.


          Academic Dishonesty

          Cheating in ICS 52 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. It is lying. Don't do it.
          Donald Bren School of Information and Computer Sciences,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS125_fq03/syllabus.html ICS 125 Syllabus

          Information and Computer Science 125:
          Project in Software System Design (Section "B")

          Fall Quarter, 2003
          Lecture: TTh 3:30 - 4:50
          Location: ET 204
          (Need directions to campus? See the maps directory. )
          Course code: 36400

          Discussion Section (REQUIRED): MWF: 8:00 - 8:50
          Location: ET 202
          Course code: 36401



          Instructor | Overview and FAQ | Textbooks | Teams | Assignments | Costs and Benefits | TA | Keeping in Touch | Computing | Disabilities | Academic Dishonesty
          (Last modified November 6, 2003 )

          What's New

          Watch this spot for new information regarding ICS 125. It may link to other web pages or to updates to this page.
          • [October 30] Slides and a paper on so-called "deathmarch projects" are available.
          • [October 30] A slightly condensed version of the slides I used on 10/28 regarding giving demonstrations is now available.
          • [October 29] Dates associated with the design assignment have been updated in keeping with what I said in class on 10/28.
          • [October 9] Have questions about IPR? Take a look at the UC's view of the subject.
            You should also now be keeping track of your time: course logs should be submitted for each week commencing with week 3 (i.e. you'll submit the report on 10/20 for the week of 10/13-10/17. Bonus points if you kept a log for week 2 (in which case it should be submitted on 10/13).
          • [October 7] The slides I used today on management and teams are now available on the website.
          • [October 3] Information on subversion is now available, along with pointers to the various project websites.
          • [September 30] Policies governing the use of the ICS 125 lab in CS 193 are available at http://www.ics.uci.edu/~lab/policies/index.html You will also find there a form you can fill out to obtain an access code that will give you admission to the room 24 hours a day. We'll talk about the potential use of this room in class.
          • [September 29] The list of potential projects for this quarter is now available. We will not be doing all the projects on this list, but all the projects we'll do are on this list.
          • [September 29] The course survey form is available. Please print the form, fill it out, staple the two pages together, and bring it to the first meeting of the class. If you do not fill it out ahead of time you will have to fill it out before leaving class on September 30th.
          • [September 29] There will not be any meeting of the discussion section on Monday, September 29th.
          • [September 29] Website opened.

          Course Staff

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor insert-an-at-sign-here ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday, 11:00 - 12:20 Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            School of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Teaching Assistant

          Scott Hendrickson

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Specification, design, construction, testing, and documentation of a complete software system using concepts learned in ICS 52, 121, and 141. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems.

          This course will emphasize techniques and notations essential to creating software systems based on the principles discussed in ICS 121: well-understood requirements, usability and user interface design, architectural design and module specification, well-planned testing, effective oral and written communication of concepts, proper programming style, group coordination, product documentation and software process.

          Attendance at all lectures is mandatory; attendance at some discussion sections will also be mandatory. In general, there will not be much lecturing in the class. Instead, class time will be highly interactive, and all students are expected to participate.

          Prerequisites:

          ICS 51 with a grade of C or better; ICS 121 and 141; Mathematics 2A-B-C.

          Editorial note: What 2A-B-C have to do with this class is beyond me!

          Is the Discussion Section required?

          Yes! The discussion section is essential for two reasons:

          1. You will need to meet with your teammates REGULARLY. The best correlation with failure that I've seen in this class over the past decade has been teams which were unable or unwilling to establish a regular meeting schedule and to keep to it. In other words, EVERYONE needs to be in attendance at team meetings. The discussion section time period is the guaranteed time period for your team to meet.
          2. Since this is a large class and each team will be making presentations during the quarter, we'll have to use some of the discussion sections for those presentations.

          What will the projects be?

          Candidate projects can come from many places: in prior years some ICS 125 teams worked on projects that sponsoring local companies suggested. Other teams worked on projects related to on-going research programs in the ICS department. Other projects may come from the students. Choice of projects is related to many goals. One key goal is to pick a project of appropriate size. It must be big enough to challenge a team of four students, but not so big as to commandeer everyone's life! As a result we will spend some time at the beginning of the fall attempting to size various projects. These planning estimates will be revisited as the course progresses. Another goal is to select a variety of projects for the class. Since each team will be making regular project presentations to the rest of the class, diversity of projects will enable students to learn from experiences across a range of project topics. Still another goal is to work on something fun and interesting! I've had students working on flight simulators, generating HTML pages, linking MPEG movie frames via hypertext to other artifacts, and building graphical program editors.

          The choices for this quarter reflect these goals. We will be doing ONE of the Raytheon projects, the Unisys project, the Wallace project, and a subset of the others. More than one team can work on the same project, but most likely no more than three teams will work on any one project.

          How will teams be composed?

          Each team will have 4 or 5 people. I will attempt to balance a team's aggregate expertise with the needs of a particular project. I will also attempt to accommodate some personal preferences for teammates. The course survey form, which you will turn in on the first day of class, is a key instrument in assigning the teams. In the end however, I make all the assignments, both of team composition and of project.

          What's the Drop/Add policy?

          Since ICS 125 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds of ICS 125 will be permitted after the end of the FIRSTweek of class.


          Lecture Topics

          ICS 125 is on a tight time schedule, thus there is not much time for review. You are expected to recall the material covered in ICS 121 and the other prerequisite courses. Short supplementary lectures may be given on:

          • Teamwork and team organizations
          • Project management
          • Project cost and schedule estimation
          • Usability evaluation and user interface prototyping
          • Software architectural choice
          • Configuration management and version control
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects chosen by the teams. If several of the projects, for example, are concerned with Internet applications, then lectures on protocols and Web technologies will be included. Similarly if most projects will be using some particular kind of infrastructure, such as CORBA or ActiveX, then there will be lectures on that.

          Textbooks

          Remember reading The Mythical Man-Month? If you do, you can expect to profit from that experience in this class. If you don't, you need to read it, cover to cover BEFORE the class begins. Don't worry, it is a quick and fun read.. Depending on the projects chosen additional readings from various sources may be required.

          • REQUIRED (from earlier classes):
            • Author: Fred Brooks
            • Title: The Mythical Man-Month, Anniversary Edition

              Note that this book has a lot of white space and blank pages, so it really will not take you long to read these chapters.

          • BACKGROUND/GENERAL REFERENCES:
            • Stephen Schach. Classical and Object-Oriented Software Engineering (Third Edition). Irwin, Chicago IL, 1996.

            • or Classical and Object-Oriented Software Engineering with UML and C++
              or Classical and Object-Oriented Software Engineering with UML and Java
            • Ghezzi, Jazayeri, & Mandrioli. Fundamentals of Software Engineering. Prentice Hall, Englewood Cliffs NJ, 1991.
            • Rumbaugh, et.al. Object-Oriented Modeling and Design.
            • Mary Shaw and David Garlan. Software Architecture, Perspectives on an Emerging Discipline. Prentice Hall, Englewood Cliffs NJ, 1996.

            All are available from amazon.com.


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for approximately 80% of your grade; this is broken down between deliverables, a team Web page, and presentations. The approximately remaining 20% will be divided among individual course logs, teamwork, individual leadership demonstrated, and the final. These are guidelines intended to help students plan their work in this course. However, the instructor does reserve the
          right to make changes in these evaluation criteria.A critcal aspect of success, however, and thus of assessment, is an effectively functioning team. Just because a team's code "works" at the end of the quarter does not mean that they have earned an A.

          Deliverables

          The ICS 125 project nominally consists of five major assignments. The relative weighting of each deliverable, intended to provide you with some guidance as to how much effort should be devoted to these tasks, and how much importance I ascribe to them, is indicated in the table below along with the due date, or approximate due date. The on-line versions of the assignments may still be under construction (watch the "what's new" section to see when they are available).
           
           
          Deliverable/Schedule Item Weight  Description Due Date
          Individual Web Page . . October 3rd
            Teams Designated
          . . October 2nd
            Projects Selected/Assigned
          . . October 2nd
          Team Web Page . . October 10th
          Prospectus and Plan 
          10 
          Prospectus (draft) October 10th, 23:59:59 PDT.
            Prospectus Reviews
          . week 3  
          Requirements Specification
          15 
          Requirements (draft) October 20
            Requirements Reviews
          . week 4  
          Architecture/Module Specifications 
          20 
          Architecture/Design (draft) November 5
            Design Reviews
          . Starting on 11/6  
          Implementation 
          20 
          Implementation(draft) November 21?
            Code Reviews
          . week 9 November
          Testing/Test Documentation
          15 
          Integration/Testing(draft) December 4
            Demonstrations
          . week 10 + ? November - December

          Variations on this schedule may be made to accommodate the particular needs of a given project or a given team. Also, note that a team's grade for a phase is a function not only of the document/specification developed but also of any associated test plan and any reviews conducted in class, with the instructor, or with the customer.

          Deliverable Due Dates

          Specific due dates/times will be indicated for each assignment. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          Deliverable Reviews

          Each deliverable will be reviewed, some reviews will be conducted before the whole class. Each team will be given 15-20 minutes to present their project. You will be given guidance in class on how to conduct these presentations.

          Your customer should be invited to your team's Prospectus and Requirements review as well as your demonstration (and, possibly even your design and code reviews depending on the nature of your customer).  The review is your team's chance to inform as well as obtain feedback and ideas from all relevant parties; your document will be reviewed at this time by course staff and clients as well as the rest of the class.  This review is formal, however, and each team should have presented and negotiated both relevant documents to the customer prior to the review (if you haven't, it may be unpleasantly obvious by the interactions at this time).

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in. They must reside permanently on your team's website.

          All deliverable documents, with the exception of performance appraisals as discussed below, must be prepared on-line and be available as part of your project home page either as either HTML or .pdf files. NO MS Word files. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving:
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Phase clerical person
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, design, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this application shall be marked ``N/A''.
          Minutes.
          Every deliverable shall be accompanied by minutes of team meetings held during the associated period of time.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page. A form that I've used in the past is available at http://www.ics.uci.edu/~taylor/ics125_fq99/performance_appraisal.pdf .
          Project WebPage.
          The project deliverables, except for the performance appraisals, shall be maintained in a project homepage.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last, you will also have the opportunity to ``fix it'' based on its evaluation. You may hand in an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. You should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Course Log

          During your career you will need to keep track of how you spend your time either for you employer or to improve your own productivity. Throughout this course, you will practice doing this by keeping a course log recording the time you spend on all activities related to this course. At the beginning of each week you must email the previous week's log to the TA. A sheet showing what should be on the log is available at: http://www.ics.uci.edu/~taylor/ics125_fq99/logform.html .

          Keep a copy of your logs: you will need them at the end of the quarter for the final review.

          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time entry applies for descriptions of activities and records the amount of time spent in hours, to the nearest quarter hour.

          You will be marked down only for failing to email logs each week, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.

          Summary of what you turn in, and when:

          1. Weekly, by individual, by email: course log for preceding week.
          2. Per deliverable, by team, on website: deliverable documents.
          3. Per deliverable, by team, on paper, performance appraisals. Exception: not required for the Prospectus.
          4. End of quarter, by individual, on paper: collection of the course logs for the quarter.
          5. End of quarter, by individual, on paper, "peer apportionment of credit"

          Team Composition, Activities, and Peer Apportionment of Credit

          The danger most students perceive in working on projects with other students is being saddled with (what they think is) a "non-producer". This is particularly true when you don't get to choose all your teammates (the situation here). Many factors dictate the use of a multi-person project for this course. You will not, after all, be able to choose your workmates in the future. One thing we'll discuss in the class is how to fix dysfunctional teams. Nonetheless, to alleviate your concerns and to grade you appropriately, at the end of the term project you will be asked to divide 100 points among the members of your project team, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          Team Organization

          There are several obvious dangers to group work that can be circumvented. Ensure that there is adequate coordination among the team members. Know each other's login names for electronic mail. Know each other's phone numbers. Meet at least twice per week (outside of class lecture) at the same, pre-determined time each week (so as to avoid confusion). The Discussion Section is designed to guarantee that such meetings are possible for everyone. You are strongly urged to use that time slot.

          Have a contingency plan for submitting a document on time even if the responsible manager becomes unavailable.

          You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.

          Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. agenda for the meeting
          2. team members present and reason for any member's absence
          3. major design decisions discussed
          4. task assignments made
          5. future meetings scheduled

          Cost and Benefits

          This course will demand a lot, but I think that you may well find this to be the most rewarding courses that you will take in your undergraduate career. ICS Alumni have said repeatedly that ICS 125 was the most important class that they took at UCI. The techniques presented in class actually work and will help you in future software development.

          At the end of the class I encourge you to make copies of your project website/notebook for each team member. Take them with you when you go to a job interview. Students from past ICS 125 classes have frequently said that it was their project notebook that clinched a job for them. Some interviewers have commented that the quality of the process followed by the ICS 125 teams and the quality of the product exceeds those of the production engineers in their companies.


          Keeping in Touch

          Check the course syllabus page regularly. Announcements concerning assignments, changes to the lecture schedule, and so on will be made there.

          An important note about email:

          Because of, at least, spam, I will not respond to email that originates from any other domain than uci.edu. Thus if you send me email you must send it from your UCI account. If you send me email from any other domain, especially AOL, hotmail, or yahoo, it will automatically get routed to my spam folder, where it will be duly ignored.


          Teams and Meetings

          As noted earlier, teams will use (at least) the discussion sections for team meetings.

          Project home pages and team membership.


          Computing

          To facilitate sharing of files among team members, each team will have an account where the team web site and project documents must be maintained.

          The primary computing facilities will be the ICS Labs. Also available is the ICS 125 team project room, CS 193.  The hardware environment and software environment is posted on the lab's web site as well as the lab hours.

          Choice of computing platform for implementation will depend on the projects chosen. Where possible and reasonable, Java will be the implementation language used.


          Disabilities

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion


          Policies (Academic Honesty and Computing Use)

          Cheating in ICS 125 will be dealt with in accordance with ICS cheating policy, which is in keeping with the UCI academic [dis]honesty policy.  Please familiarize yourself with these documents, as you are held accountable to them.

          You are also bound by all policies posted at ICS's Computing Support Policies, including ICS's Ethical Use of Computing Policy, as well as UCI's Computer Use Policy.


          School of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ics126/wq99_syllabus.html ICS 126 Syllabus

          Information and Computer Science 126 A & B:
          Comprehensive Project in Software System Evolution

          Winter and Spring Quarters, 1999
          Location: CS 253
          Time: TTh 2:00 - 3:20
          Course code: 36155


          Instructor | Overview and FAQ | Textbooks | Teams | Assignments | Costs and Benefits | TA | Keeping in Touch | Computing | Academic Dishonesty | Schedule
          (Last modified Tue Jan 19 1999)

          What's New

          • [Jan 19] The prospectus assignment sheet has been updated slightly.
          • [Jan 14] The source code for the Cargo Routing system has been added to the archive that Peyman posted on the 12th. See: ArchStudio compressed archive and instructions again.
          • [Jan 14] Team offices have been assigned. team126a has Room 6; team126b has Room 7. Contact Candy Mamer for keys, *after* you have read the email I sent out on this topic.
          • [Jan 13] The team accounts are available now. Details were sent out by email to a) the email you gave me on your information form and b) your ics login.
          • [Jan 12] The ArchStudio compressed archive and instructions for installation are available.
          • [Dec 31] Web site opened.

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday, 3:30 - 4:50 Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          The goal of this course is to provide students with an industrial-like experience in software development. Students will undergo the vicissitudes of developing a large-scale software system from the point of view of all involved personnel - customer, developer, and manager. This course is a two-quarter project course in which the students specify, design, construct, test, document, and evolve a complete software system using concepts learned in ICS 52, 121, and 141. Special emphasis is placed on the need for and use of teamwork, management, planning, and other tools for developing and maintaining large-scale software systems. Using techniques for formal specification and analysis at multiple levels of abstraction are also emphasized, as well as those for usability evaluation and evolution.

          A central focus of this course is the application of the software engineering concepts studied in ICS 121 in a significantly sized software development project worked on in teams. This course will will emphasize well-understood requirements, logical object-oriented design, effective oral and written communication of concepts, and group cooperation.

          How does this class differ from ICS 125?

          ICS 125 attempts to address all the issues associated with team-based software development in a ten week period. As a result the class is very intense and very rushed during the last several weeks. Depending on the project chosen there is little time for thoughful analysis of requirements, examination of architectural alternatives, or rigorous testing. And certainly there is no time to "do another lap", evolving the application to meet revised needs or to address shortcomings.

          ICS 126 will be similar to ICS 125 in that a project will be developed by teams of students, but there will be opportunity to spend more time on requirements, design, and analysis. Moreover there will be time to go around the track again. This second lap is a critical learning experience: developers will confront the consequences of decisions they made during initial design and implementation.

          What kind of grade will I get at the end of the first quarter?

          A grade of IP ("in progress") will be assigned at the end of ICS 126A. Final grades will be assigned only upon completion of ICS 126A-B. Naturally, though, intermediate marks will be assigned so that students will know how they are doing as the class progresses.

          Can I get credit for both ICS 125 and ICS 126A&B?

          No. Students may not get credit for both 125 and 126A-B.

          Will this class give me 2 project course credits towards the ICS degree?

          Yes! But you need to understand the ICS degree requirements.

          Students entering the program in the Fall of 1997 have a three-project course requirement, at least two of which must be in different ICS "areas". Students entering the program before FQ97 have a two-project course requirement; a close reading of upper division requirement "C" in the 1996-97 catalog will reveal that these two project classes must be in different areas.

          So, what does this mean for 126A & B?

          If you have entered before FQ97 then you cannot use 126A and 126B to satisfy the entire project class requirement. You will still need to take one other project class from outside the software area.

          What will the projects be?

          Candidate projects can come from many places: in AY 97-98 some ICS 126 teams worked on projects that sponsoring local companies suggested. Other teams worked on projects related to on-going research programs in the ICS department. Other projects may come from the students.

          Choice of projects is related to many goals.

          One key goal is to pick a project of appropriate size. It must be big enough to challenge a team of four students, but not so big as to commandeer everyone's life! As a result we will spend some time at the beginning of the fall attempting to size various projects. These planning estimates will be revisited as the course progresses.

          Another goal is to select a different project for each team. Since each team will be making regular project presentations to the rest of the class, diversity of projects will enable students to learn from experiences across a range of project topics.

          Still another goal is to work on something fun and interesting! I've had students working on flight simulators, generating HTML pages, linking MPEG movie frames via hypertext to other artifacts, and building graphical program editors. There are many opportunities....

          How will teams be composed?

          Each team will have 4 or 5 people. I will attempt to balance a team's aggregate expertise with the project they've decided to work on. I will also attempt to accommodate personal preferences for teammates.

          What's the Drop/Add policy?

          Since ICS 126 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds of ICS 126A will be permitted after the end of the second week of class, unless there are truly extenuating circumstances.

          Composition of the teams will be re-examined at the beginning of ICS 126B. Similar to 126A, drops from 126B will not be permitted after the end of the second week of Winter Quarter.


          Lecture Topics

          Since 126 will not be on the tight time schedule that 125 is there will be time for lectures on material supplementary to that in 121 and the other prerequisite courses. Lectures are anticipated on:

          • Teamwork and team organizations
          • Project management
          • Project cost and schedule estimation
          • Usability evaluation and user interface prototyping
          • Software architectural choice
          • Computer-aided software engineering tools and environments
          • Configuration management and version control
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects chosen by the teams. If several of the projects, for example, are concerned with Internet applications, then lectures on protocols and Web technologies will be included. Similarly if most projects will be using some particular kind of infrastructure, such as CORBA or ActiveX, then there will be lectures on that.

          Textbooks

          Regardless of the project chosen, all students will be required to read The Mythical Man-Month. Depending on the projects chosen additional readings from various sources may be required.

          • REQUIRED:
            • Author: Fred Brooks
            • Title: The Mythical Man-Month, Anniversary Edition
            • New Price:$ 24.50
            • Used Price:$ 18.40

              Note that this book has a lot of white space and blank pages, so it really will not take you long to read these chapters.


          Teams and Meeting Rooms

          Each team will have their own office in the ICS trailers in which to conduct meetings and (at their option) do their work.
          Team Designator Meeting Room Home page Customer Contact
          A ("Argo/C2") CS Trailer Room 6 TeamName? Peyman Oreizy
          B "Cargo Router" CS Trailer Room 7 TeamName? Kari Nies


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for 80% of your grade; this is broken down between deliverables, team process descriptions and updates, a team Web page, and presentations. The remaining 20% will be divided among individual course logs, teamwork, individual leadership, and the final (if any).

          Their relative weighting (as a percentage of your final grade) of each part of the project is as follows for ICS 126A (in the case where there is only a single implementation phase --- if there are two then these will have to be adjusted):

          Assignment Weight On-line versions
          Prospectus and Plan 15 Prospectus
          Requirements 15 Requirements
          Architecture/Module Specifications 25 Architecture/Design
          Implementation 25 Implementation

          Deliverables, and weighting for grading purposes, for ICS 126B will be determined later.

          The final examination may consist of a maintenance activity in which a minor change will be made to your team project or a small essay.

          Assignments and Due Dates

          Deliverables are due at 2:00 PM on the date indicated in the team's plan. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          The ICS 126A part of the project nominally consists of four major assignments. They are equally weighted. Variations to this may be made to accommodate the particular needs of a given project.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last in ICS 126B you will also have the opportunity to ``fix it'' based on its evaluation. You may hand in an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. As discussed above, you should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in.

          All deliverable documents must be prepared on-line and be available as part of your project home page.. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Phase clerical person
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, design, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this application shall be marked ``N/A''.
          Minutes.
          Every deliverable shall be accompanied by minutes of team meetings held during the associated period of time.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page. A form is available at http://www.ics.uci.edu/~taylor/ics126/appraisal.html .
          Project WebPage.
          The project deliverables, except as noted above, shall be maintained in a project homepage.

          Course Log

          During your career you will need to keep track of how you spend your time either for you employer or to improve your own productivity. Throughout this course, you will practice doing this by keeping a course log recording the time you spend on all activities related to this course. At the beginning of each week you must hand in a copy of your log. These logs are confidential and should be handed in independent of your team's deliverable. A log sheet is available at: http://www.ics.uci.edu/~taylor/ics126/logform.html . Keep your log as part of your individual home page, but password protect it.


          OPTION: A better way than to submit the course logs (than each week in class) is be to have the form online in each of your home pages. Create a new one each week, and password protect the web pages in the following way:
          --save the forms in a separate directory in your public_html directory
          -- You place a file called .htaccess in the directory you want to protect. It has the following sequence of commands in it:

          AuthUserFile
          AuthName ByPassword
          AuthType Basic

          <Limit GET>
          require user ics126
          </Limit>

          This means the user has to log in as ics126 and give the correct password (which you will set) to be able to access the course log form. You set the password in the .htpasswd file, be careful not to store the .htpasswd file in your public_html directory.

          To create the .htpasswd file:
          run the program htpasswd, to create it say,
          htpasswd -c ~your_name/.htpasswd username
          and to add a password it is
          htpasswd ~your_name/.htpasswd anotherusername
          assuming of course that the file you pointed to from .htaccess was ~your_name/.htpasswd

          Remember to have both .htpasswd and .htaccess as world readable. This should do the trick, and you will be able to restrict access to your course log file. send in to taylor@ics and jaya@ics the password to give access to your course log form. Keep the user name as "ics126". If you have any problems with this, send mail to jaya@ics.uci.edu


          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time column applies for descriptions of activities and record the amount of time spent in hours, to the quarter hour.

          Keep a copy of your log. You will be marked down only for failing to hand in logs with each phase, handing in illegible logs, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.


          Team Composition, Activities, and Peer Apportionment of Credit

          As discussed in class, teams will be assigned on as fair a basis as possible for the project. The danger most students perceive in working on projects with other students is in being saddled with (what they think is) a "non-producer". This is particularly true when you don't get to choose all your teammates (the situation here). Many factors dictate the use of a multi-person project for this course. You will not, after all, be able to choose your workmates in the future. Therefore, to alleviate your concerns and to grade you appropriately, at the end of the term project you will be asked to divide 100 points among the members of your project team, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          Team Organization

          There are several obvious dangers to group work that can be circumvented. Ensure that there is adequate coordination among the team members. Know each other's login names for electronic mail. Know each other's phone numbers. Meet at least once per week (outside of class lecture) and preferably at the same, pre-determined time each week (so as to avoid confusion). The Discussion Section is designed to guarantee that such meetings are possible for everyone. Have an agenda for each meeting, which is determined by the manager.

          Have a contingency plan for submitting a document on time even if the responsible manager becomes unavailable. You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.

          Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. agenda for the meeting
          2. team members present and reason for any member's absence
          3. major design decisions discussed
          4. task assignments made
          5. future meetings scheduled

          Cost and Benefits

          This course, like ICS 125, will demand a lot, but I think that you may well find this to be the most rewarding courses that you will take in your undergraduate career. ICS Alumni have said repeatedly that ICS 125 was the most important class that they took at UCI. The techniques presented in class actually work and will help you in future software development. Since 126 "does 125" with a "double helping" it should be even more popular.

          At the end of the class I encourge you to make copies of your project website/notebook for each team member. Take them with you when you go to a job interview. Students from past ICS 125 classes have frequently said that it was their project notebook that clinched a job for them. Some interviewers have commented that the quality of the process followed by the ICS 125 teams and the quality of the product exceeds those of the production engineers in their companies. Since ICS 126 should be even more realistic and in-depth than 125 I suspect employers will be even more impressed!


          Teaching Assistant

          • Jaya Vaidyanathan --> Send mail to: --> jaya@ics.uci.edu -->
          -->

          Keeping in Touch

          Read the ics.126 bboard regularly. Announcements concerning assignments will be made there, you can ask questions, changes to the lecture schedule will be announced there, and so on.

          Computing

          Choice of computing platforms will depend on the projects chosen. Where possible and reasonable Java will be the implementation language used. At a minimum, to facilitate sharing of files among team members, each team will have an account on Octavian, a Unix (Solaris 2 on a Sun) machine.

          Scads of Java information is available on-line, including a tutorial, reference material and many, many packages, such as those available from Gamelan.


          Academic Dishonesty

          Cheating in ICS 126 will be dealt with in accordance with ICS policy and UCI policy. Please familiarize yourself with those documents. 
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS_52_WQ04/syllabus.html ICS 52 Syllabus Winter 2004

          Information and Computer Science 52:
          Introduction to Software Engineering

          Winter Quarter, 2004
          Location: Social Science Hall 100 (Need a map?)
          Tuesday and Thursday, 2:00-3:20
          Course code: 36290
          Discussion location: SSLH 100
          Monday, Wednesday, Friday 4:00 -- 4:50
          Course code for discussion section: 36291
          (Last modified Thursday, March 18, 2004)

          WHAT'S NEW?

          [March 18, 2004] Due to popular demand a sample final exam, including some answers, is now available.

          [March 16, 2004] Lecture notes for week 10 are now available.

          [March 12, 2004] The final assignment (test case creation) is now available.

          [March 2, 2004] A solution to the midterm is now available.

          [January 13, 2004] Want to add the class? You can, just use TELE. I will not be signing add cards; you must add on-line through TELE.

          [January 8, 2004] You can find some very helpful guidelines for the composition of good email messages here, courtesy of Ellen Strenski in the UCI writing program.



          Overview | Textbooks | Schedule | Assignments | TAs | Keeping in Touch | Computing | Disabilities | Academic Dishonesty |

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor <at-sign> ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday: 3:30-4:30pm. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          Introduction to the concepts, methods, and current practice of software engineering. The study of large-scale software production; software lifecycle models as an organizing structure; principles and techniques appropriate for each stage of production. Laboratory work involves a project illustrating these elements.

          Prerequisite: ICS 23 with a grade of C or better.

          In addition to the skills and concepts introduced in previous classes, students should have these computing skills when they enter the class (or learn them independently in the first week of the quarter):

          • The use of a text editor (Word, etc.) to create documents.
          • The use of a drawing package (Power Point, Visio, etc.) to create graphics for these documents.

          The instructional objectives for the course are as follows:

          • introduce you to the discipline of software engineering;
          • experientially acquaint you with one version of the software lifecycle;
          • provide working knowledge of at least one reasonable technique to be applied in each phase of the lifecycle;
          • provide particular insight into software architecture, design by information hiding, and the problems of software analysis and testing.

          Minimum Knowledge and Skills Expected of Students Who Receive Passing Grades

          Software Life Cycle

          • Mastery:
            • Knowing different life cycles and their appropriateness in different situations
            • Knowing basic principles of software engineering (such as separation of concerns, modularity, and abstraction) and knowing how they apply throughout the software life cycle
          • Proficiency:
            • Understanding tradeoffs and relationships among the various activities in the software life cycle
            • Understand the meaning and use of a set of basic software qualities

          Requirements

          • Mastery:
            • Interviewing a customer to elicit requirements
            • Writing a textual (non-formal) requirements document
          • Proficiency:
            • Understand the structure of a requirements document and know the appropriate kinds of information in such a document

          Architectural Design

          • Mastery:
            • Know the differences among interaction patterns of a set of basic architectural styles
            • Understand the difference between architecture and module design
          • Proficiency:
            • Choosing an appropriate architectural style for a particular problem

          Module Design

          • Mastery:
            • Using provided/exported and required/imported interfaces to define module boundaries
            • Identifying and defining modules in a design
            • Identifying and defining abstract data types in a design
          • Proficiency:
            • Applying coupling, cohesion, fan-in, and fan-out
            • Creating USES and COMPRISES diagrams
          • Exposure:
            • Creating a design for a nontrivial, sizable problem

          Programming

          • Mastery:
            • General rules of programming style and clarity (short rehash from earlier classes)
          • Proficiency:
            • Mapping a module design onto an implementation in source code
          • Exposure:
            • Using existing modules and libraries in an implementation
            • Coding under a heavy deadline (requiring tradeoffs between code quality and code functionality)

          Testing

          • Proficiency:
            • Testing a program for failures
            • Applying white-box testing on short pieces of code
            • Applying black-box testing on short pieces of code
          • Exposure:
            • Understanding the many dimensions of software quality assurance
            • Understanding the inspection and code walk-through process

           


          Textbooks

          Required:
          • Software Engineering, 6th edition, by Ian Sommerville. 2001. Addison-Wesley. $85.15 new at the bookstore.
          • Note: the latest printing of this textbook includes, in the shrinkwrap, an additional paperback book on testing. It is probably a useful book to have, but it is *not* required for the class. In other words, you can go ahead and buy used copies of the text that do not include the book on testing. Make sure you get the 6th edition though.
          • The class notes will (usually) be available on the WWW after the class meeting in which they are used. Occasionally the notes will be available ahead of time, but they might be from the previous year's offerings, and thus minor differences may exist as compared to what's used in class this year.

          Schedule (Subject to change)

          Week Date Lecture topic Schedule items Assignments Readings (all from Sommerville, unless noted otherwise)
          1 January 13 Introduction     Chapter 1. Skim 2. All of 3, but with more attention to the first 2 subsections. Skim 4
          -- January 15

          Processes

          (continuation of the lecture notes associated with the first lecture, above)

               
          2 January 20

          Principles & Requirements Engineering

          and here's the slides I used for my keynote talk at STRAW '01

            Requirements assignment issued Chapters 5, 6 (lighter), and 7.
          -- January 22

          Requirements Engineering (URL here is to Sommerville's slides from his Chapter 7)

            Example requirements document (from FQ 02)  
          3 January 27 Architectures     Chapters 10 (key), 11 (a little lighter), and 14.
          -- January 29 Architectures      
          4 February 3 Design (now live)  

          Requirements assignment due

          Design assignment issued

          Chapter 12
          -- February 5 Design, continued from Monday.     examples of module descriptions
          5 February 10 Design & Review      
          --

          February 12

          *Date of exam subject to change*

          Mid-term Exam     Here's the midterm exam and solution notes from a couple of years ago.
          6 February 17

          UI Design (these are Sommerville's slides, which I'll be using)

          (URL now fixed)

              Chapter 15. (UI Design)
          -- February 19 Continuation of UI Design      
          7 February 24       Pages 452-457 provide good material on integration testing. This is part of chapter 20, which is on the schedule below.
          -- February 26 Integration testing and Implementation issues  

          Design (Arch and module interfaces) due .

          Implementation assignment issued.(Friday the 27th)

           
          8 March 2 Testing     Chapters 19 and 20.
          -- March 4 Testing, continued      
          9 March 9 Quality assurance  

          Implementation due

          Testing assignment issued.

          See especially section 20.1.2 (since that's the technique you'll be using on the testing assignment)
          -- March 11       .
          10 March 16 Maintenance and Configuration Mgmt.     Chapter 27 and 29
          -- March 18 Course Review   Testing assignment due on ...<tbd>  
          Exams March 25 FINAL EXAM 1:30p.m. — 3:30p.m.    

          Assignments and Assessment

          Aggregate grade for the project: 45%.
          Midterm: 20%.
          Final: 35%

          The project consists of four assignments. Their relative weighting (as a percentage of your final grade) is as follows:

          Assignment Weight
          Requirements 10

          Architecture and module interfaces

          Official Requirements Document

          MS Word version of the requirements doc

          18

          Implementation (Word version)

          Official Design Document (Word version)

          7
          Testing (Word version) 10

          Specific assignments will be placed on the web, giving the assignment, the required format, and specific grading criteria.

          We do not grade on a curve, meaning that we will not assert in advance that half of the class will receive grades at or below a C+, and half above that. Grading is done based on mastery of the material as exhibited in the exams and the project. If everyone masters the material very well, then everyone will get an A. On the other hand, if no one were to master the material at all adequately, then everyone would receive an F.

          NO LATE ASSIGNMENTS WILL BE ACCEPTED, unless you have a legible excuse from a physician, an extreme family emergency, or unless you are willing to accept an extreme penalty with respect to your assignment's grade.


          Teaching Assistant and Readers

          • TA:
          • Readers:
            • TBD

          Keeping in Touch

          Check this web site regularly. This is the definitive location for course information. Announcements concerning assignments will be made here. The course mailing list will also be used to make announcements, provide instructions, and so on. The course mailing list will be "read only" from the student's perspective. I.e. the instructor, TA, and readers can post messages to the list,but not students. If you ask one of us a question whose answer is relevant to the rest of the class, we'll post the appropriate material to the list.

          An important note about email

          Any email that you send in conjunction with this class must be sent from a UCI account. That is, if you want any response or action taken, then you must use your UCI account to send the email. Email from yahoo, hotmail, juno, cox, or any other non-UCI site will be ignored.


          Computing

          All computing will be done on the department's NT machines.

          You may use another computer to produce the documents you turn in. (No handwritten assignments allowed).

          All implementation work will be done in Java.

          Please use the computing equipment or instructional purposes only. Also watch out on the social subtleties of electronic mail.


          Disabilities

          Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implementationed in a timely fashion.


          Academic Dishonesty

          Cheating in ICS 52 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. It is lying. Don't do it.
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/classes/221/syllabusWQ08.html IN4MATX 221 Winter 2008

          Informatics 221: Software Architecture

          Winter Quarter 2008

          Course Code 37220

          Last update: February 7, 2008

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 221" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 2:00 - 3:20 p.m., DBH 1423

          Web site: http://www.ics.uci.edu/~taylor/classes/221/syllabusWQ08.html

          What's New?

          • [January 8, 2008] Website goes live on EEE.

          Description - Textbook and Readings - Schedule - Grading - Policies


          Description

          Catalog description:

          Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures. Formerly ICS 223.


          Textbook (REQUIRED)

          Software Architecture: Foundations, Theory, and Practice. Richard N. Taylor, Nenad Medvidovic, and Eric M. Dashofy. Copyright © 2009 John Wiley & Sons, Inc. (ISBN: 9780470167748)

          A preprint of this textbook will be available for purchase in class; details in class.


          Schedule

          The schedule is subject to change. 
          Week Date Topic Individual Lecture Topics Slide design and/or presentation Readings Homework
          1

          J

          A

          N

          U

          A

          R

          Y

          8 Tu Introduction The Big Idea Taylor Chapter 1  
          10 Th Architectures in Context Taylor Chapter 2  
          2 15 Tu Basic Concepts and Introduction to Design Basic Concepts (Medvidovic) Chapter 3  
          17 Th Designing Architectures Taylor Chapter 4  
          3 22 Tu Designing Architectures Architectural Styles Taylor Homework #1 due
          24 Th Styles and Greenfield Design Taylor*  
          4 29 Tu Connectors Software Connectors (Medvidovic)* Chapter 5  
          31 Th Choosing Connectors (Medvidovic)  
          5

          F

          E

          B

          R

          U

          A

          R

          Y

          5 Tu Modeling Introduction to Modeling (Dashofy) Chapter 6  
          7 Th Modeling and Notations (Dashofy)  
          6 12 Tu Visualization Visualizing Software Architectures (Dashofy) Chapter 7  
          14 Th Visualization, continued (Dashofy)  
          7 19 Tu Analysis Analysis of Software Architectures (Medvidovic)* Chapter 8 (selections) Homework #2 due
          21 Th Implementation Implementing Architectures (Dashofy)* Chapter 9  
          8 26 Tu Implementation Techniques (Dashofy)*  
          28  Th Non-functional Properties Designing for NFPs (Medvidovic)* Chapter 12 (selections)  
          9

          M

          A

          R

          C

          H

          4  Tu Architectures in the Real-World Applied Architectures Taylor Chapter 11  
          6 Th Domain-Specific Software Engineering Introduction to DSSE (D/M) Chapter 15  
          10 11 Tu DSSE and Product Lines (D/M)  
          13 Th Standards Standards (Dashofy) Chapter 16 (selected)  
          Exam 20 Th   Exam from 1:30 - 3:30    


          Grading and Evaluations

          Grading.
          There are 3 elements to your grade: a final exam, homework, and class attendance and participation. More details TBA.

          Summary of Assessment:

          Homework 70%
          Final exam 20%
          Class attendance and participation 10%

          No grades of incomplete (I) will be given for this course. 


          Policies

          Course Evalutions. The online evaluation window for winter quarter will run from TBA through TBA.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          (C) University of California, 2008.
          http://www.ics.uci.edu/~taylor/ics228/syllabus.html ICS 228 Syllabus

          Information and Computer Science 228
          Software Environments

          Winter Quarter, 2002
          Location: CS 253
          Time: MW 11:00 — 12:20
          Course code: 36615

          Instructor | Overview and FAQ | Textbooks | Assignments | Schedule | Academic Dishonesty |
          (Last modified February 11, 2002)

          What's New

          • February 11, 2002. Andre's slides from today are now available on the site. See below in the schedule for the link.
          • January 21, 2002. The schedule has been altered a bit. And from now on I'll own give pop quizzes on material (papers) from the preceding week. E.g. during week 3 I might ask you questions on papers from week 2. All papers through week 4 are now available.
          • January 11, 2002. Eric has a website for the discussion up. I've put the link down below, in the Discussion Section section, as well as here.
          • January 7, 2002. I've put scans of a couple of the papers on-line. They are really big files, so exercise caution!
          • The documents contained in these pages are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis. They are included here for the sole use of students enrolled in ICS 228. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
          • January 7, 2002. The discussion section will meet Fridays from 11 to 12:20. Location is still to be announced.
          • December 31, 2001 Home page posted (note that not all links are yet functional). Please read the syllabus before the first class meeting and BE SURE to bring a copy of your weekly schedule with you to the first meeting (see the note under "Discussion Section" below.

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Monday and Wednesday: 10-11. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Study of the requirements, concepts, and architectures of comprehensive, integrated, software development and maintenance environments. Major topics include process support, object management, communication, interoperability, measurement, analysis, and user interfaces in the environment context.
          Themes: Software architectures and composition technologies.

          This offering of ICS 228, as with its predecessor, adopts something of a design studio approach to the topic. During the quarter the class will jointly create a software development environment focused on architecture-centric development. In particular, the environment will aim to provide lifecycle support for applications developed in the C2 architectural style. Since this was the focus of the previous class's project, you can expect to build upon their results. Moreover the architeture itself will be an "application" built in the style, so it will be amenable to further evolution using itself.


          Textbook and Readings

          • Textbook: None.
          • Readings: See the schedule for primary required readings. A couple of the papers are available on-line. Others will be made available for reproduction.

          Discussion Section (surprise!)

          Successfully pursuing the project will require some focused instruction in the cornerstone technologies: the xADL architecture representation mechanism, the c2.fw architectural framework, and so on. To provide this instruction we'll be holding discussion sections during the first half of the quarter. Unfortunately this section was not scheduled through the Registrar, so we'll have to quickly identify a time and place where everyone can meet. To faciliate this please bring your schedule to the first meeting of class, and we'll decide the time then.

          Eric Dashofy will be running the Discussion Section and maintaining its website.


          Assignments and Assessment

          Name Assignment  Weight 
          Pop quizzes (no kidding!)  These will be simple, short, in-class quizzes designed to assess whether or not you've read the assigned readings. If you have read the papers these quizzes will be a snap. If you haven't read them, I'll be able to tell. Be sure to bring pen/cil and paper to class everyday, just in case!  15% 
          Progress reports and presentations  These are oral reports to the class covering the progress you've made in the previous week on your part of the course project.  15% 
          Project  You will be developing a part of a software development environment, cooperatively with the rest of the class. This portion of your grade will reflect what you've built as part of the project.  35% 
          Summary paper  By the time you're done with your part of the development project, you'll necessarily know the related work, what you've done, what happened when you tried to integrate with the other students' projects, and how well the C2 framework, et.al., supported you in your task. All you have to do here is capture that understanding in a paper not to exceed 5000 words (unless you are REALLY interesting). With luck, this paper will be publishable.  35% 

          Schedule (Subject to Change)

          Date
          Topic
          Reading
          Assigned
          Project
          Week 1
          January 7/9
          Overview
           

          Themes 

          AST-based environments 

          Event-based communication

          [TM81] W. Teitelman and L. Manister. "The Interlisp Programming Environment", IEEE Computer, 14(4):25-33, April 1981.
           

          [RT84] T. Reps and T. Teitelbaum. "The Synthesizer Generator", in Proceedings of the ACM SIGSOFT/SIGPLAN Software Engineering Symposium on Practical Software Development Environments, pp. 42-48, Pittsburgh, PA, April 1984.

          [Rei90] S.P. Reiss. "Connecting Tools Using Message Passing in the Field Environment", IEEE Software, 7(4):57-66, July 1990.

          The idea
          Week 2
          January 14/16

          Architecture-based development

          The Web of Design

          Taylor, R. N., N. Medvidovic, et al. (1996). "A Component- and Message-Based Architectural Style for GUI Software." IEEE Transactions on Software Engineering 22(6): 390-406.

          Anderson, K. M., R. N. Taylor, et al. (2000). "Chimera: Hypermedia for Heterogeneous Software Development Environments." ACM Transactions on Office Information Systems (TOIS) 18(3): 211-245.

           

          See also Chimera-2

          [FWABOT98] R. Fielding, et.al. "Web-Based Development of Complex Information Products" CACM, August 1998, pp. 84-92. Acrobat PDF (200K).

           
          Week 3

          January 21/23

          No class on the 21st!

          Process-centered environments.

          Heterogeneous environments

          No class on 1/21 (MLK Day holiday)

          [KFP88] G.E. Kaiser, P.H. Feiler and S.S. Popovich. "Intelligent Assistance for Software Development and Maintenance", IEEE Software, 5(3):40-49, May 1988.

          Bandinelli, S., A. Fuggetta, et al. (1994). SPADE: An Environment for Software Process Analysis, Design and Enactment. Software Process Modeling and Technology. A. Finkelstein, J. Kramer and B. A. Nuseibeh, Wiley: 223-248.

          [Kad92] R. Kadia. "Issues Encountered in Building a Flexible Software Development Environment: Lessons Learned from the Arcadia Project", in Proceedings of ACM SIGSOFT '92: Fifth Symposium on Software Development Environments, pp. 169-180, Washington, DC, December 1992. Postscript version.

          Week 4
          January 28/30
          Analysis and environments

          Bandinelli,S., E. Di Nitto, andA. Fuggetta. Supporting Cooperation in the SPADE-1 Environment. IEEE TSE, 22, 12 December 1996. pp. 841-865.

          [TN92] I. Thomas and B.A. Nejmeh. "Definitions of Tool Integration for Environments", IEEE Software, 9(2):29-35, March 1992.

          Robbins, J. E., D. M. Hilbert, et al. (1998). "Extending Design Environments to Software Architecture Design." Automated Software Engineering 5(3): 261-90.

          Vieira, M. E. R., M. S. Dias, et al. (2000). Analyzing Software Architectures with Argus-I. 2000 International Conference on Software Engineering (ICSE 2000), Limerick, Ireland.

          [Med98] N. Medvidovic. "Architecture-based Specification-Time Software Evolution" (Ph.D. dissertation, ICS, UCI) Acrobat PDF (981K) (Parts of this document)

          Armani and Rapide's poset analyzer.

           
          Week 5
          February 4/6
          Architecture-based environments, round 2

          Garlan, D., R. Allen, et al. (1994). Exploiting Style in Architectural Design Environments. ACM SIGSOFT '94 Second Symposium on the Foundations of Software Engineering, New Orleans, LA, ACM Press.

          Medvidovic, N., D. S. Rosenblum, et al. (1999). A Language and Environment for Architecture-Based Software Development and Evolution. 21st International Conference on Software Engineering, Los Angeles, CA, IEEE Computer Society.

          Khare, R., Guntersdorfer, M., P. Oreizy, Medvidovic, N.,, et al. (2000). xADL: Enabling Architecture-Centric Tool Integration with XML. 2001. (You've seen this paper before,listed below under the project))

          Nenad Medvidovic� Peyman Oreizy�� Richard N. Taylor�� Rohit Khare�� Michael Guntersdorfer. An Architecture-Centered Approach to Software Environment Integration

          1st progress report due Wednesday

          (Powerpoint, 2 or 3 slides)

          Week 6
          February 11/13

          Expect the topic above to slide into this week, on to 2/13

          Object management, configuration management, deployment, and environments

           

          Andre van der Hoek: 2/11/02

          L�er, C. and D. S. Rosenblum (2001). Wren - An Environment for Component-Based Development. Joint 8th European Software Engineering Conference (ESEC) and 9th ACM Sigsoft International Symposium on the Foundations of Software Engineering (FSE-9), Vienna, Austria.

          Magee, J., N. Dulay, et al. (1994). "Regis: A Constructive Development Environment for Distributed Programs." IEE/IOP/BCS Distributed Systems Engineering 1(5): 304�312.

           

          Menage and The Software Dock (and ??)

          2nd progress report
           
          Week 7

          February 18/20

          No class on the 18th!

          No class on 2/18 (President's Day holiday)

          Process and architectures, redux.

          Consider the use of Magi? Event-based project awareness tools? Knowledge Depot? "Source Forge"
          3rd progress report
           
          Week 8
          February 25/27

          Integrating with commercial tools and environments

          Visiting lecturer:  Sriram Sankar ??

          COM based integration; J2EE, Rational. Sullivan's papers
          4th progress report
          Week 9
          March 4/6

          Analysis tools, redux.

          Frameworks: NIST/ECMA, ESF, Corporation model

          Harrison, W., H. Ossher, et al. (2000). Software Engineering Tools and Environments: A Roadmap. The Future of Software Engineering. A. Finkelstein. New York, ACM: 261-277.
           
          Week 10
          March 11/13
          Demos of project components
          Review and future directions
           
          Final papers due on 3/13

          Getting Underway with ArchStudio

          The course environment will be built "in the ArchStudio" way, which means drawing from the previous versions of ArchStudio, and using xADL and its associated tools as the technical heart of the environment. You'll need to consult the following websites and read the listed papers.

          xADL

          Paper 1: ICSE 2002

          Paper 2: WICSA 2001

          ArchStudio

          Paper 1: HICSS 2001

          Additional Readings (to be moved into the schedule above)

            Tracz, W. (1998). Avionics Domain Application Generation Environment.

          Potential Project Emphases

          • A generic COM bridge
          • The requirements/design co-evolution. UML-based tool for requirements? Bashar's paper.
          • Web-based environments (the web as the front end for all project interaction: task list, tool access, object access).
          • Integrating with a commercial SDE.
          • Integrating CM: Menage as an archstudio component.
          • Sanity-checking critics that feed into a to-do list (an update of Argo-C2: Argo-xADL). A generic framework in ArchStudio for "analyts". A plug-in architcture for analysis tools, some of which are very small and some of which are heavyweight.
          • Documentation. Framemaker..

          Academic Dishonesty

          Cheating in ICS 228 will be dealt with in accordance with University policy and ICS policy. Please familiarize yourself with those documents. 
          Department of Information and Computer Science,

          University of California, Irvine CA 92697-3425 http://www.ics.uci.edu/~taylor/classes/119/syllabusSQ07.html IN4MATX 119 Spring 2007

          Informatics 119: Advanced Project in Software Engineering

          (formerly ICS 127)

          Spring Quarter 2007

          Last update: May 8, 2007

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 119 " in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 2:00-3:20 p.m, ICS180

          Labs MWF 2:00-3:50 or MWF 10:00-11:50, both in ICS 193
          Web site: http://www.ics.uci.edu/~taylor/classes/119/syllabusSQ07.html

          What's New?

          • [April 9, 2007] The online evaluation window for spring quarter will run from 7pm Friday, June 1 through 11:45pm Sunday, June 10.
          • [April 2, 2007] Website open

          Description - Schedule - Grading - Readings - Policies


          Description

          Catalog description:

          Students work in teams to specify, design, construct, test, and document a complete software system in a specialized application domain using application/domain-specific techniques. Each offering's topic is announced the preceding spring. Prerequisites: Informatics 117 or 118A with a grade of C or better; Mathematics 2C or 2J or Statistics 67/Mathematics 67.

          Project Matters

          • Development Environments:
            • Eclipse
            • ArchStudio4
          • Configuration Management System: SVN
          • Project Host: tps.ics.uci.edu
          • Documents: Web-based (HTML, ...)
            • Diagrams: .png (produced from ArchStudio or OmniGraffle)
            • Dreamweaver, for website development/management, is recommended but not required
            • You may not use Frontpage or Word for producing HTML, or any other web document -- they can produce trashy html.

           


          Schedule

          The schedule is subject to change. 
          Week Date Topic Presented by Readings
          1

          A

          P

          R

          I

          L

          3 Tu Software architectures and architectural styles Taylor Chapter 5
          5 Th ArchStudio 4 and Lunar Lander Asuncion
          2
          10 Tu Software Product Lines Taylor Chapter 15
          12 Th Project status reports The Teams  
          3
          17 Tu Graphics discussion  
          19 Th Project status reports    
          4
          24 Tu Archipelago demonstration Dashofy  
          26 Th Project status reports    
          5

          M

          A

          Y

          1 Tu Demonstrations and Boothmanship    
          3 Th Project status reports    
          6
          8 Tu Myx and see also the Myx white paper on the ArchStudio website    
          10 Th Status reports    
          7
          15 Tu Code/architecture walk-through: Eterative Tale    
          17 Th Code/architecture walk-through: Lunar Lander    
          8
          22  Tu No class    
          24  Th No class    
          9
          29 Tu      
          31 Th      
          10
          June 5      
          7      
          Exam June   No final exam!    


          Grading and Evaluations

          Grading.

          The final exam will entail

          yes, it is the lunar lander "video" game! Details of this project will be forthcoming.


          Readings

          There will be chapters taken from books.

           


          Policies

          Course Evalutions. The window for fall quarter online evaluations will open at 7pm on Friday, November 24 and close at 11:59 p.m. on Sunday, December 3.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          © University of California, 2007.
          http://www.ics.uci.edu/~taylor/ics127/ICS_127_Syllabus.html ICS 125 Syllabus

          Information and Computer Science 127:
          Advanced Project in Software Engineering

          Winter Quarter, 2000
          Lecture: TTh 2:00 - 3:20
          Location: PSCB 120
          (Need directions to campus? See the maps directory.
          Course code: 36158

          Lab Session: MW 3:30 - 4:50
          Location: ET202 (not really!)



          Instructor | Overview and FAQ | Textbooks | Teams | Assignments | Keeping in Touch | Computing | Academic Dishonesty | Schedule
          (Last modified Tuesday, January 4, 2000 14:14)

          What's New

          Watch this spot for new information regarding ICS 127. It may link to other web pages or to updates to this page.
          • [January 4, 2000] Web site opened.

          Course Staff

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday,3:30 - 5:00. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview, Prerequisites, and Frequently Asked Questions

          UCI Catalog Description:

          Students work in teams to specify, design, construct, test, and document a complete software system in a specialized application domain using application/domain-specific techniques. Each offering's topic is announced the preceding spring.

          This course will emphasize techniques and notations essential to creating software systems for which there was not adequate time or opportunity in ICS 125 or 126A. A variety of advanced techniques are discussed, depending on the particular applications targeted.

          All students are expected to attend all lecture sections. In general, there will not be much lecturing in the class. Instead, class time will be highly interactive, and all students are expected to participate. About half of the time will be spent performing reviews of the artifacts developed. These reviews will take up all lecture and discussion periods the week following the due date for each deliverable.

          Prerequisites:

          ICS 125 or 126A; Mathematics 2A-B-C.

          Is the Lab Section required?

          Yes! The discussion section is essential: You will need to meet with your teammates REGULARLY. EVERYONE needs to be in attendance at team meetings. The discussion section time period is the guaranteed time period for you team to meet.

          What will the projects be?

          We'll decide this the first week of classes. Some candidates:

          • A replacement for the Jargo editor
          • An enhancement to the ArchStudio 2.0 environment (see
          • A general workflow editor
          • A hand-held email device
          • A follow on to one of the ICS125 FQ99 projects (e.g. IMPP, DAV posties, ...)

          What's the Drop/Add policy?

          Same as ICS 125, and for the same reasons: Since ICS 127 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds will be permitted after the end of the SECOND week of class.


          Lecture Topics

          Topics will depend upon the interests of the students and the projects chosen. The following list is representative of the topics that might be discussed in class.

          • Software architectures
          • Event-based systems
          • Application-layer Internet protocols
          • Inspections
          • Configuration management and version control
          • Web site usage for project management
          • Testing tools
          • Development environments
          • Usability evaluation and user interface prototyping
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects chosen by the teams. If several of the projects, for example, are concerned with Internet applications, then lectures on protocols and Web technologies will be included. Similarly if most projects will be using some particular kind of infrastructure, such as CORBA or ActiveX, then there will be lectures on that.

          Textbooks

          None required.


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for 80% of your grade; this is broken down between deliverables, a team Web page, and presentations. The remaining 20% will be divided among individual course logs, teamwork, individual leadership demonstrated, and the final.

          Deliverables

          The project nominally consists of five major assignments. Their relative weighting (as a percentage of your final grade) of each deliverable is indicated in the table below along with the due date. The on-line versions of the assignments may still be under construction (watch the what's new section to see when they are available).
           
          Deliverable Weight  Description Due Date
          Individual Web Page . . 14 January
            Teams Designated
          . . 18 January
            Projects Selected/Assigned
          . . 20 January
          Team Web Page . . 21 January
          Prospectus and Plan 
          10 
          Prospectus  25 January
            Prospectus Reviews
          . week 3 25 January
          Requirements Specification
          15 
          Requirements  1 February
            Requirements Reviews
          . week 4 1 February
          Architecture/Module Specifications 
          20 
          Architecture/Design  15 February
            Design Reviews
          . week 6 15 February
          Implementation 
          20 
          Implementation 29 February
            Code Reviews
          . week 8 29 February
          Testing/Test Documentation
          15 
          Integration/Testing 14 March
            Demonstrations
          . week 10 16 March

          The deliverables are weighted according to the relative amount of time and effort we expect you will spend on each (and not necessarily on their importance with respect to software development). Variations to this may be made to accommodate the particular needs of a given project or a given team. Also, note that the grade for a deliverable will consist not only of the document/specification, developed during that phase but also the test plan developed along side it as well as the review conducted in class.

          Deliverable Due Dates

          Deliverables are due at 12:50 PM on the date indicated in the table above. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          Unless directed otherwise, deliverables must be turned in directly to the instructor or placed in the instructors's mailbox before that time.

          Deliverable Reviews

          Each deliverable will be reviewed in class. Each team will be given 15-20 minutes to present their project. You will be given guidance in class on how to conduct these presentations.

          Your customer should be invited to your team's Prospectus and Requirements review as well as your demonstration (and, possibly even your design and code reviews depending on the nature of your customer).  The review is your team's chance to inform as well as obtain feedback and ideas from all relevant parties; your document will be reviewed at this time by course staff and clients as well as the rest of the class.  This review is a formality, however, and each team should have presented and negotiated both relevant documents to the customer prior to the review (if you haven't, it may be unpleasantly obvious by the interactions at this time).

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in. They must reside permanently on your team's website.

          All deliverable documents must be prepared on-line and be available as part of your project home page either as either HTML or .pdf files. NO MS Word files. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Phase clerical person
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, design, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this application shall be marked ``N/A''.
          Minutes.
          Every deliverable shall be accompanied by minutes of team meetings held during the associated period of time.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page. A form is available at http://www.ics.uci.edu/~taylor/ics125_fq99/performance_appraisal.pdf . A .ps file is also available. I know that form is for ICS 125, but it will work here too.
          Project WebPage.
          The project deliverables, except for the performance appraisals, shall be maintained in a project homepage.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last, you will also have the opportunity to ``fix it'' based on its evaluation. You may hand in an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. You should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Course Log

          Throughout this course, just as in ICS 125, you will keep a course log recording the time you spend on all activities related to this course. At the beginning of each week you must email the previous week's log to the instructor. A sheet showing what should be on the log is available at: http://www.ics.uci.edu/~taylor/ics125_fq99/logform.html .

          Keep a copy of your logs: you will need them at the end of the quarter for the final review.

          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time entry applies for descriptions of activities and records the amount of time spent in hours, to the nearest quarter hour.

          You will be marked down only for failing to email logs each week, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.


          Team Composition, Activities, and Peer Apportionment of Credit

          At the end of the term project you will be asked to divide 100 points among the members of your project team, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.


          Keeping in Touch

          Check the course syllabus page regularly http://www.ics.uci.edu/~taylor/ics127/syllabus.html. Announcements concerning assignments will be made there, changes to the lecture schedule will be announced there, and so on.

          Teams and Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. agenda for the meeting
          2. team members present and reason for any member's absence
          3. major design decisions discussed
          4. task assignments made
          5. future meetings scheduled

          As noted earlier, teams will use (at least) the discussion sections for team meetings.
          Team Designator Home page Customer Contact Office
          TBD TBD TBD TBD
                 


          Computing

          To facilitate sharing of files among team members, each team will have an account on a Unix (Sun Solaris 2) machine, where the team web site and project documents must be maintained.

          The primary computing facilities will be the ICS Labs, which provide Sun Solaris and Windows/NT machines.  The hardware environment and software environment is posted on the lab's web site as well as the lab hours and availability for Fall 1999.

          The system specification for each deliverable may be done on Suns, Macintoshes, PCs, or any other available platform. Choice of computing platform for implementation will depend on the projects chosen. Where possible and reasonable, Java will be the implementation language used. (Scads of Java information is available on-line, including a tutorial, reference material and many, many packages, such as those available from Gamelan.)

          Choice of computing platforms will depend on the projects chosen. Where possible and reasonable Java will be the implementation language used.


          Policies (Academic Honesty and Computing Use)

          Cheating in ICS 125 will be dealt with in accordance with ICS cheating policy, which is in keeping with the UCI academic [dis]honesty policy.  Please familiarize yourself with these documents, as you are held accountable to them.

          You are also bound by all policies posted at I CS's Computing Support Policies, including ICS's Ethical Use of Computing Policy, as well as UCI's Computer Use Policy.


          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~taylor/ICS229.html ICS 229


          Information and Computer Science 229
          Seminar in Informatics

          Fall Quarter 2004

          File last updated: November 5, 2004 15:04

          Professor
          Richard N. Taylor
          ICS2 203
          (949) 824 - 6429

          Logistics

          Meeting, part 1 : CS 259, Friday 2:00 (note time change!)

          Meeting, part 2: ICS2-136, Friday at 3:00


          Web Site
          http://www.ics.uci.edu/~taylor/ics229.html/

          Overview
          From the Catalog: "Current research and research trends in Informatics. Forum for presentation and criticism by students of research work in progress. May be repeated for credit. "
          Structure
          All students are expected to attend all sessions. During the first hour of our weekly meeting we will typically have two student presentations. During the second hour we will join the rest of the Informatics Department in the Friday Research Hour. That hour will be devoted to faculty research talks, grad student talks (possibly including talks by students enrolled in 229), and other activities still to be determined.

          Prerequisites
          None.

          Add/Drop Policy
          Determined by the School of ICS policy.

          Grades
          All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) presentation(s) in class,You will receive one overall grade at the end of the class; no partial grades will be given.
          To receive credit for the class you MUST make at least one presentation during the quarter. The presentation can be a dry-run for a conference talk you are scheduled to give, a practice talk for your candidacy exam, or, if you are not that far along in the program, a presentation of some papers you found to be interesting, or perhaps even a tutorial/overview of some interesting tools. Talks are limited to 30 minutes, including questions.

          Schedule
          • September 24
            • Organization and sign-ups for talks
          • October 1
            • Giving talks.
            • Patents, continued.
            • Chasing money and why you should know. Large pots and small pots. Writing proposals.
            • Reviewing papers.
          • October 8
            • Hazel Asuncion
            • Olga Rivero
          • October 15
            • Jigar Kotak
            • Nghi Nguyen
          • October 22
            • Ben Pillet
            • Sushil Bajracharya
          • October 29 NOTE: class will start at 1:30 p.m.!
            • Stacy Tang
            • Mamadou Diallo
            • Sharon Ding
          • November 5 NOTE: class will start at 1:30 p.m.
            • Lihua Xu
            • Sukanya Ratanotayanon
            • Tosin A.
          • November 12 NOTE: class will start at 1:30 p.m.... even though we only two speakers.
            • Alex Baker
            • Michael Gorlick
          • November 19 NOTE: class will start at 1:30 p.m.
            • Norman Su
            • Chris van der Westhuizen
            • Ping Chen
          • November 26 (No class; Thanksgiving break)
          • December 3 NOTE: class will start at 1:30 p.m.
            • Fei Hoffman
            • Justin Harris
            • Joanna Zhou

          http://www.ics.uci.edu/~taylor/classes/211/syllabusFQ06.html IN4MATX 211 Fall 2006

          Informatics 211: Software Engineering

          (formerly ICS 221)

          Fall Quarter 2006

          Last update: November 30, 2006

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 221" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 8:00-9:20 a.m, CS243

          Web site: http://www.ics.uci.edu/~taylor/classes/211/syllabusFQ06.html

          What's New?

          • [November 30, 2006] Eric's slides from today's lecture have been linked to the schedule.
          • [November 28, 2006] The schedule has been updated once more, with the due dates for the last CTTC and for the project have been added.
          • [November 27, 2006] The readings for the lecture on SE+HCI have been added.
          • [November 15, 2006] The course schedule has been revised a bit.
          • [October 31, 2006] (1) Andre's lecture slides are posted. (2) The readings for Susan Sim's lecture are posted.
          • [October 26, 2006] The lectures previously scheduled for November 2 and 21 have been swapped (Analysis/Testing & Hypermedia)
          • [October 18, 2006] The Design for the Lunar Lander is due next week: on October 24th.
          • [October 5, 2006] Walt Scacchi's lecture slides have been linked in.
          • [October 3, 2006] I've posted today's lecture and last week's opening lecture. I also indicated the due date for the first part of the project assignment.
          • [September 29, 2006] Don Patterson's talk has now been scheduled for 10/24, and the readings are available.
          • [September 28, 2006] Thomas Alspaugh's lecture slides are now available. (Linked from the Schedule.) There's also now a link to a soft copy of [Bro87].

          Description - Schedule - Grading - Readings - Policies


          Description

          Catalog description:

          Study of the concepts, methods, and tools for the analysis, design, construction, and measurement of complex software-intensive systems. Underlying principles emphasized. State-of-the-art software engineering and promising research areas covered, including project management. Formerly ICS 221.

          Detailed Description :
          This class has two objectives: (1) provide a useful overview of the state of the art (2) introduce some of the research frontiers of the field. It accomplishes this through a mix of lectures and assignments focused on the state of the art, readings from the research literature, and guest lectures from specialists in sub-areas of software engineering.


          Schedule

          The schedule is subject to change. 
          Week Date Topic Presented by Readings Assignment due
          1
          Sept
          26 Tu Course introduction
          Overview of software engineering
          Taylor[FK00]
          [Bro87]
           
          28 Th

          Requirements Engineering

          Alspaugh [NE00]
          [ATB06]
           
          2

          O

          C

          T

          O

          B

          E

          R

          3 Tu Overview of SE, continued
          Issues, Application, Projec
          t

          Taylor

           

          Book excerpt, Chapter 2  
          5 Th Process Scacchi [Sca02a]
          [Sca02b]
          [Sca04]

          CTTC for Wk 1
          by email
          in .pdf
          by midnight

          3 10 Tu Design and Architecture Taylor [PW92]
          [TMA+96]
          Book excerpt, Chapter 5
           
          12 Th Issues, Application, Project Taylor

          CTTC for Wk 2

          Reqts. spec for lunar lander

          4 17 Tu Programming Languages in Software Engineering Lopes
          (Taylor @ U Minn)
          [Baj06]
          [KLM+97]

          [McIlroy68]
           
          19 Th

          Implementation
          Issues, Application, Project

          Taylor Book excerpt CTTC for Wk 3
          5 24 Tu Software Engineering aspects of Ubicomp
          Patterson
          (Taylor @ Iowa State)
          [HeerNBH03]
          [SDA99]
          Design for LL due.
          26 Th Issues, Application, Project Taylor   CTTC for Wk 4
          6
          31 Tu

          Configuration Management & Coordination

           

          van der Hoek

           

          [ELC+05]
          [SNvdH03]

           

          N

          O

          V

          E

          M

          B

          E

          R

          2 Th

          Hypertext and e-Commerce

           

          Taylor

           

          [B-L94]
          [FT02] (see note below for shorter version)
          CTTC for Wk 5
          7 7 Tu Reverse Engineering Sim
          (Taylor @ FSE)

          [CC90]

          [Sto06]

           
          9 Th No Class.
          (Taylor @ FSE)
            CTTC for Wk 6
          8 14 Tu Issues, Application, Project Taylor    
          16 Th

          Software Evolution

          Taylor

          [Oreizy97]

           
          9 21  Tu Analysis & Testing Richardson

          [Wey82] [CPR+89]

          CTTC for Wk 7&8

          Revised designs due 24:00 on 11/22.

          23  Th Thanksgiving Holiday    
          10 28 Tu Software Engineering and HCI Redmiles

          [Red02]
          [GL85]

           
          30 Th

          Issues, Application, Project

          Eric's slides on myx

          Taylor CTTC for weeks 9&10 due 12/1, by midnight.
          Exam Dec 5 Tu Exam from 8:00-10:00 Taylor Final project installment due 12/8, by midnight


          Grading and Evaluations

          Grading.
          There are four elements to your grade: a final exam, a little development project that will evolve throughout the quarter, short paper summaries, and class attendance and participation.

          The final exam will entail your writing an evaluation of some sub-area of software engineering and identifying what you believe to be promising, or at least necessary, research directions. You'll be able to choose, in advance, the sub-area you'll write about. The purpose here is to demonstrate that you've thought substantively about an area of software engineering, to the point where you can identify some important needs, trends, opportunities, insights, ...

          The purpose of the development project is simply to provide a concrete point of reference for discussion of the various techniques and ideas that will be covered during the quarter. Most likely we'll do a requirements specification, several designs, probably an implementation, and some analyses. To ensure that the focus of your time is about thinking about the issues and not grinding through loads of detail, we'll focus on a simple problem, one version of which was "solved" in the 1960's in less than 80 lines of commented BASIC code: yes, it is the lunar lander "video" game! Details of this project will be forthcoming.

          The short paper summaries "allow you" to demonstrate that you've read and thought about the assigned readings. Readings are assigned, as shown in the schedule, each week of the class. You are obliged to write a short summary and analysis of each week's papers. The summary/analysis should be about 500 words long (total, per week, not per reading). I am not interested in reading a paraphrase of each paper's abstract. I am interested in reading your assessment of each paper: what points do you believe to be the important ones? Do you believe those points? Why or why not? What points did the author(s) not address that they should have? Since multiple papers are assigned you'll have to learn how to present incisive, cut-to-the-chase (CTTC) analyses in few words.

          Your CTTC's on week i's topic are due as shown on the schedule. 

          They must be submitted as follows:

          • by email
          • with the subject line "Informatics 211 CTTC i" where i is what you think it is.
          • with the summary attached as a PDF (Adobe Acrobat) document
          • by midnight Pacific time on the due date

          Summary of Assessment:

          Cut-to-the-chase summaries 30%
          Development project 30%
          Final exam 20%
          Class attendance and participation 20%

          No grades of incomplete (I) will be given for this course. 


          Readings

          The majority of the readings in the course will be papers available through the IEEE or ACM Digital Libraries. Occasionally, there will be chapters taken from books.

          If you did not study software engineering as an undergraduate, the following books are recommended for background and reference.

          • Software Engineering: Theory and Practice, by Hans van Vliet, John Wiley & Sons, Ltd, 2000.
          • The Mythical Man-Month: Essays on Software Engineering (Anniversary Edition), by Frederick P. Brooks, Jr., 1995, Addison-Wesley.

          For additional coverage of software engineering research, consult the reading list for the Phase II exam in software.

          List of Papers and Book Chapters.

          [ATB06] Thomas A. Alspaugh, Bill Tomlinson, and Eric Baumer. Using Social Agents to Visualize Software Scenarios. ACM Symposium on Software Visualization (SoftVis'06), pages 87-94, September 2006.
          http://dx.doi.org/10.1145/1148493.1148507

          [Baj06] Sushil Bajracharya, Trung Ngo, Erik Linstead, Paul Rigor, Yimeng Dou, Pierre Baldi, Cristina Lopes. "Sourcerer: A Search Engine for Open Source Code." (in submission, 2006).

          [B-L94] Berners-Lee, T., Cailliau, R., Luotonen, A., Nielsen, H. F., and Secret, A. 1994. The World-Wide Web. Commun. ACM 37, 8 (Aug. 1994), 76-82.

          [Bro87] F.P. Brooks. No Silver Bullet: Essence and Accident in Software Engineering. IEEE Computer 20(4):10-19, April 1987.
          (Also appears as Chapter 16 in F.P. Brooks. The Mythical Man-Month, 25th Anniversary Edition. Addison-Wesley, Reading, MA, 1995.)

          [CC90] Chikofsky, E.J. Cross, J.H., II. "Reverse engineering and design recovery: a taxonomy"
          IEEE Software, Volume: 7, Issue: 1, pp. 13-17, Jan 1990.

          [CPR+89] L.A. Clarke, A. Podgurski, D. J. Richardson, and Steven J. Zeil. "A Formal Evaluation of Data Flow Path Selection Criteria". IEEE Transactions on Software Engineering, 15(11), November 1989, pp. 1318-1332.

          [ELC+] J. Estublier, D. Leblang, G. Clemm, R. Conradi, A. van der Hoek, W. Tichy, D. Wiborg-Weber, Impact of the Research Community on the Field of Software Configuration Management, ACM Transactions on Software Engineering and Methodology, 14(4):2005, pages 1-48.

          [FT02] Fielding, R. T. and Taylor, R. N. 2002. Principled design of the modern Web architecture. ACM Trans. Inter. Tech. 2, 2 (May. 2002), 115-150. Shorter version appeared as: [FT00] Fielding, R. T. and Taylor, R. N. 2000. Principled design of the modern Web architecture. In Proceedings of the 22nd international Conference on Software Engineering (Limerick, Ireland, June 04 - 11, 2000). ICSE '00. ACM Press, New York, NY, 407-416. DOI= http://doi.acm.org/10.1145/337180.337228

          [FK00] A Finkelstein and J. Kramer, "Software Engineering: A Roadmap" in The Future of Software Engineering, edited by A. Finkelstein, ACM Press, 2000.

          [GL85] Gould, J., Lewis, C. Designing for usability: key principles and what designers think, Communications of the ACM, Volume 28 Issue 3, March 1985, pp. 300-311.

          [HeerNBH03] Jeffrey Heer, Alan Newberger, Chris Beckmann, and Jason I. Hong. liquid: Context-Aware Distributed Queries. Proceedings of UbiComp 2003: Ubiquitous Computing, 5th International Conference, Seattle, WA, USA, October 12-15, 2003.

          [KLM+97] G. Kiczales, J. Lamping, A. Mendhekar, C. Maeda, C.V. Lopes, Jean-Marc Loingtier, John Irwin. Aspect-Oriented Programming, proceedings of the European Conference on Object-Oriented Programming (ECOOP, Finland), Springer-Verlag, June 1997.

          [McIlroy68] M.D. McIlroy. "Mass Produced Software Components", in P. Naur and B. Randell, "Software Engineering, Report on
          a conference sponsored by the NATO Science Committee, Garmisch, Germany, 7th to 11th October 1968", Scientific Affairs Division, NATO, Brussels, 1969, 138-155.

          [NE00] Bashar Nuseibeh and Steve Easterbrook. Requirements engineering: a roadmap. In 22nd International Conference on Software Engineering (ICSE '00), pp. 35-46. June 2000.
          http://dx.doi.org/10.1145/336512.336523

          [Oreizy97] Oreizy, P.; Gorlick, M.M.; Taylor, R.N.; Heimhigner, D.; Johnson, G.; Medvidovic, N.; Quilici, A.; Rosenblum, D.S.; Wolf, A.L.; "An architecture-based approach to self-adaptive software" Intelligent Systems and Their Applications, IEEE [see also IEEE Intelligent Systems] Volume 14, Issue 3, May-June 1999 Page(s):54 - 62 Digital Object Identifier 10.1109/5254.769885

          [PW92] D.E. Perry and A.L. Wolf. "Foundations for the Study of Software Architecture". ACM Software Engineering Notes, 17(4):40-52, October 1992.

          [Red02] Redmiles, D. Supporting the End Users' Views, Working Conference on Advanced Visual Interfaces (AVI 2002, Trento, Italy), May 2002, pp. 34-42.

          [SDA99] Daniel Salber, Anind K. Dey, and Gregory D. Abowd. The context toolkit: aiding the development of context-enabled applications. Proceedings of the 1999 Conference on Human Factors in Computing Systems, CHI 1999, Pittsburgh, PA, USA, May 15-20, 1999.

          [SNvdH03] A. Sarma, Z. Noroozi, and A. van der Hoek, Palantír: Raising Awareness among Configuration Management Workspaces <>, Twenty-fifth International Conference on Software Engineering, May 2003, pages 444–453.

          [Sca02a] W. Scacchi, Process Models in Software Engineering, in J. Marciniak (ed.), Encyclopedia of Software Engineering, 2nd. Edition, Wiley, 993-1005, 2002. (provides an overall introduction and survey of software process topics through 2001).

          [Sca02b] W. Scacchi, Understanding the Requirements for Developing Open Source Software Systems, IEE Proceedings--Software, 149(1), 24-39, February 2002. (provides a comparative study of SE versus OSS requirements processes).

          [Sca04] W. Scacchi, Free/Open Source Software Development Practices in the Computer Game Community, IEEE Software, 21(1), 59-67, January/February 2004. (focuses on OSS development processes applied to computer games).

          [Sto06] Margaret-Anne Storey. "Theories, tools and research methods in program comprehension: past, present and future".
          Software Quality Journal, Volume 14, Number 3, Pages 187-208, September, 2006.

          [TMA+96] R. Taylor, N. Medvidovic, K. Anderson, E.J. Whitehead, J. Robbins. "A Component- and Message-Based Architectural Style for GUI Software," IEEE Transactions on Software Engineering, June 1996.

          [TN92] I. Thomas and B.A. Nejmeh. Definitions of tool integration for environments. IEEE Software, 9(2):29-35, March 1992.

          [Wey82] E.J. Weyuker. On testing non-testable programs, Computer Journal, 25(4):465-- 470, November 1982.


          Policies

          Course Evalutions. The window for fall quarter online evaluations will open at 7pm on Friday, November 24 and close at 11:59 p.m. on Sunday, December 3.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          (C) University of California, 2005, 2006. Portions adapted from T.A. Alspaugh; S. Sim; and a cast of thousands.
          http://www.ics.uci.edu/~taylor/classes/117/syllabusWQ09.html IN4MATX 117 Winter 2009

          Informatics 117: Project in Software System Design

          Winter Quarter 2009

          Course Code 37060

          (Discussion: 37061; Lab: 37062)

          Last update: February 17, 2009

           

          Instructor:

          Richard N. Taylor

          Email:

          (taylor [at] ics [dot] uci [dot] edu)
          To ensure a response to your email, please include "Informatics 117" in the subject line and send your email from a UCI account.

          Office hours:

          After class, or by email appointment

          Lectures:

          Tuesday and Thursday 11:00-12:20, ICS 253

          Web site: http://www.ics.uci.edu/~taylor/classes/117/syllabusWQ09.html

          What's New?

          • [January 5, 2009] Website goes live on EEE.

          Description - Textbook and Readings - Schedule - Grading - Policies


          Description

          Catalog description:

          Specification, design, construction, testing, and documentation of a complete software system using concepts learned in ICS 52, Informatics 101, and Informatics 111. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems. Prerequisites: ICS 51 with a grade of C or better; Informatics 101/CS 141/CSE141 and Informatics 111/CSE121; Mathematics 2A-B and Statistics 67/Mathematics 67. Formerly ICS 125.


          Textbooks

          Remember reading The Mythical Man-Month? If you do, you can expect to profit from that experience in this class. If you don't, you need to read it, cover to cover BEFORE the class begins. Don't worry, it is a quick and fun read.. Depending on the projects chosen additional readings from various sources may be required.

          • REQUIRED (from earlier classes):
            • Author: Fred Brooks
            • Title: The Mythical Man-Month, Anniversary Edition

              Note that this book has a lot of white space and blank pages, so it really will not take you long to read these chapters.

          • BACKGROUND/GENERAL REFERENCES:
            • Software Architecture: Foundations, Theory, and Practice Richard N. Taylor, Nenad Medvidovic, and Eric M. Dashofy. Copyright © 2010 John Wiley & Sons, Inc. (ISBN-13: 978-0470-16774-8)
            • Stephen Schach. Classical and Object-Oriented Software Engineering (Third Edition). Irwin, Chicago IL, 1996.
              or Classical and Object-Oriented Software Engineering with UML and C++
              or Classical and Object-Oriented Software Engineering with UML and Java
            • Ghezzi, Jazayeri, & Mandrioli. Fundamentals of Software Engineering. Prentice Hall, Englewood Cliffs NJ, 1991.

          Lecture Topics

          Informatics 117 is on a tight time schedule, thus there is not much time for review. You are expected to recall the material covered in the prerequisite courses. Short supplementary lectures may be given on:

          • Teamwork and team organizations
          • Project management
          • Project cost and schedule estimation
          • Usability evaluation and user interface prototyping
          • Software architectural choice
          • Configuration management and version control
          • Distributed software engineering
          • Engineering of distributed applications, including distributed object technology
          Specific choice of lecture topics will depend somewhat on the projects.

          Schedule

          The schedule is subject to change. 
          Week Date Topic Project Items
          1

          J

          A

          N

          U

          A

          R

          Y

          6 Tu Introduction Individual website assigned
          8 Th Teams assigned
          2 13 Tu Getting Organized; Managing Time Prospectus assigned
          15 Th  
          3 20 Tu Prospectus Reviews Requirements assigned
          22 Th  
          4 27 Tu Requirements Reviews  
          29 Th  
          5

          F

          E

          B

          R

          U

          A

          R

          Y

          3 Tu Architecture/design  
          5 Th Design assignment available
          6 10 Tu Design reviews  
          12 Th Design due
          7 17 Tu

          Giving demonstrations

          Boothmanship

          Implementation assigned
          19 Th No class  
          8 24 Tu Progress review & presentations  
          26  Th    
          9

          M

          A

          R

          C

          H

          3 Tu No class  
          5 Th Implementation review Implementation due
          10 10 Tu    
          12 Th   Final implementation due
          Exam 17 Th 10:30-12:30 Scheduled Final Exam time.  


          Assignments and Assessment

          The project is the focus of this course and will be assessed accordingly. It will account for approximately 80% of your grade; this is broken down between deliverables, a team Web page, and presentations. The approximately remaining 20% will be divided among individual course logs, teamwork, individual leadership demonstrated, and the final. These are guidelines intended to help students plan their work in this course. However, the instructor does reserve the
          right to make changes in these evaluation criteria.A critcal aspect of success, however, and thus of assessment, is an effectively functioning team. Just because a team's code "works" at the end of the quarter does not mean that they have earned an A. If the team did a poor job on the requirements and design, for instance, their grade would be much lower, despite "working" code. Put another way, if your team has to pull an all-nighter to get a working system, in all likelihood you will not receive the grade you want.

          Deliverables

          The project nominally consists of five major assignments. The relative weighting of each deliverable, intended to provide you with some guidance as to how much effort should be devoted to these tasks, and how much importance I ascribe to them, is indicated in the table below along with the due date, or approximate due date. The on-line versions of the assignments may still be under construction (watch the "what's new" section to see when they are available).
           
           
          Deliverable/Schedule Item Weight  Description Due Date (subject to change)
          Individual Web Page
          .   January 13th
            Teams Designated
          . January 9th  
            Projects Selected/Assigned
          . January 9th  
          Team Web Page
          .   January 15th
          Prospectus and Plan 
          10 
          Prospectus  January 20th
            Prospectus Reviews
          . week 3  
          Requirements Specification
          15 
          Requirements January 29th
            Requirements Reviews
          . week 4 January 27th and/or 29th
          Architecture/Module Specifications 
          20 
          Architecture/Design February 12th
            Design Reviews
          . Week 6 February 12th
          Implementation 
          20 
          Implementation/1st demonstration  
            Code Reviews/Demos
          . week 9 March 5th
          Testing/Test Documentation
          15 
          Implementation/2nd demonstration/Quality Assurance Report March 12th
            Demonstrations
          . week 10 + finals week (TBD)  

          Variations on this schedule may be made to accommodate the particular needs of a given project or a given team. Also, note that a team's grade for a phase is a function not only of the document/specification developed but also of any associated test plan and any reviews conducted in class, with the instructor, with the TA, or with the customer.

          Have questions about your intellectual property rights (IPR)? Take a look at the UC's view of the subject also see http://www.ucop.edu/ott/genresources/pat-pol_97.html

          Deliverable Due Dates

          Specific due dates/times will be indicated for each assignment. NO LATE ASSIGNMENTS WILL BE ACCEPTED. This applies to your final system and all intermediate projects. Since you are working in this class as part of team, it is the team's responsibility to ensure that assignments are turned in on time. Normal excuses for late assignments, such as illness, do not apply in a team setting (unless of course everyone on the team is ill :-)

          Deliverable Reviews

          Each deliverable will be reviewed, some reviews will be conducted before the whole class.

          Your customer should be invited to your team's Prospectus and Requirements review as well as your demonstration (and, possibly even your design and code reviews depending on the nature of your customer).  The review is your team's chance to inform as well as obtain feedback and ideas from all relevant parties; your document will be reviewed at this time by course staff and clients as well as the rest of the class.  This review is formal, however, and each team should have presented and negotiated both relevant documents to the customer prior to the review (if you haven't, it may be unpleasantly obvious by the interactions at this time).

          Document Requirements

          All the documents associated with the above listed phases are integral parts of systematic software development. Their continued, up-to-date existence is necessary for successful system development. Do not delete documents after they have been turned in. They must reside permanently on your team's website.

          All deliverable documents, with the exception of performance appraisals as discussed below, must be prepared on-line and be available as part of your project home page either as either HTML or .pdf files. NO MS Word files. In general, the following should be observed.

          Cover "page".
          Every deliverable shall have identifying information giving:
          Project title
          Development phase and deliverable
          Date
          Team name/number
          Team members
          Phase manager
          Files and locations (href's)

          Table of Contents.
          Every deliverable shall include a table of contents
          Specification.
          The system specification (requirements, architecture, module specs, code) for each deliverable shall correspond in form and content to the outline provided for that phase. Sections that are not necessary for this project shall be marked ``N/A''.
          Agendas and Minutes.
          Every deliverable shall be accompanied by agendas for and minutes of team meetings held during the associated period of time.
          Performance Appraisals.
          Every deliverable shall be accompanied by performance appraisals. Performance appraisals shall NOT be maintained as part of the project's web page.
          Project WebPage.
          The project deliverables, except for the performance appraisals, shall be maintained in a project homepage.

          ``Fixed up'' Deliverables

          For all deliverables, except for the last and except for the "Agendas and Minutes" section, you will also have the opportunity to ``fix it'' based on its evaluation. You may submit an improved version of a deliverable one week after that deliverable has been graded and receive up to 50% of the points deducted on the initial version. The purpose of this exercise is for you to both learn how to use the techniques and so that you do not implement something from a bad design or specification. You should keep the same responsibilities for the improvement phase but assign new responsibilities for the next phase.

          Course Log

          During your career you will need to keep track of how you spend your time either for you employer or to improve your own productivity. Throughout this course, you will practice doing this by keeping a course log recording the time you spend on all activities related to this course. At the beginning of each week you must submit the previous week's log to the TA. A sheet showing what should be on the log is available. The first log you submit will cover Week Two; it will be submitted at the beginning of Week Three.

          Keep a copy of your logs: you will need them at the end of the quarter for the final review.

          Each entry records the date and amount of time spent, type of entry, and text describing the entry. An entry is one of three types:

          • A description records an activity;
          • An explanation says why something happened;
          • A prescription is a plan for something to do later.

          Most entries will be of the first type, but occasionally you should reflect and think about what is going on. The time entry applies for descriptions of activities and records the amount of time spent in hours, to the nearest quarter hour.

          You will be marked down only for failing to submit logs each week, giving too little detail, or failing to keep track of time spent.

          You are especially encouraged to keep track of the kinds of errors you make and the amount of time they consume. The purpose of recording these errors is so that you develop a better understanding of the kinds of mistakes you typically make. With that understanding you can improve your performance in the future, by paying extra attention to those areas in which you've had problems in the past.

          Summary of what you turn in, and when:

          1. Weekly, by individual, using a dropbox: course log for preceding week.
          2. Per deliverable, by team, on website: deliverable documents.
          3. Per deliverable, by team, on paper, performance appraisals. Exception: not required for the Prospectus.
          4. End of quarter, by individual, on paper: collection of the course logs for the quarter.
          5. End of quarter, by individual, on paper, "peer apportionment of credit"

          Team Composition, Activities, and Peer Apportionment of Credit

          How will teams be composed?

          Each team will have 3 to 5 people. I will attempt to balance a team's aggregate expertise with the needs of a particular project. I will also attempt to accommodate some personal preferences for teammates. The course survey form, which you will turn in on the first week of class, is a key instrument in assigning the teams. In the end however, I make all the assignments, both of team composition and of project.

           

          The danger most students perceive in working on projects with other students is being saddled with (what they think is) a "non-producer". This is particularly true when you don't get to choose all your teammates (the situation here). Many factors dictate the use of a multi-person project for this course. You will not, after all, be able to choose your workmates in the future. One thing we'll discuss in the class is how to fix dysfunctional teams. Nonetheless, to alleviate your concerns and to grade you appropriately, at the end of the term project you will be asked to divide 100 points among the members of your project team, excluding yourself, corresponding to how you believe they contributed to the project as a whole (or on a phase-by-phase basis if you wish). In addition, each team member will be appraised for each phase. This ``peer apportionment of credit'' will be used to help determine appropriate individual grades for the project component.

          Team Organization

          There are several obvious dangers to group work that can be circumvented. Ensure that there is adequate coordination among the team members. Know each other's login names for electronic mail. Know each other's phone numbers. Meet at least twice per week (outside of class lecture) at the same, pre-determined time each week (so as to avoid confusion). The Discussion Section is designed to guarantee that such meetings are possible for everyone. You are strongly urged to use that time slot.

          Have a contingency plan for submitting a document on time even if the responsible manager becomes unavailable.

          You are strongly advised to consult weekly with the instructor/TA about your progress, problems, questions, etc.

          Meetings

          Meetings are an important part of a team project. A successful meeting requires that the meeting have a definite purpose and associated agenda (these are the responsibility of the phase manager) and that all decisions be recorded in minutes (the responsibility of the phase clerical person).

          The purpose of minutes is to record decisions made and to be available for updating any team member who misses a meeting. Each deliverable must be accompanied by agendas and minutes for the team meetings held during the associated period of time. I.e., keep the agenda, and the minutes, on-line as part of your project web page. The minutes should outline

          1. Date, time began, time ended
          2. agenda for the meeting
          3. team members present and reason for any member's absence
          4. major design decisions discussed
          5. task assignments made (i.e. action items)
          6. open issues
          7. questions to be asked and plan for getting them answered
          8. future meetings scheduled
          9. topics for next meeting.

          Policies

          What's the Drop/Add policy? Since Informatics 117 has a strong team project orientation, it is essential that the drop/add process be terminated early. Therefore NO drops or adds after the end of the second week of class.

          Course Evalutions. The online evaluation window for winter quarter will run from TBA through TBA.

          Cheating. The UCI academic honesty policy applies. Consequences of cheating in this class: a letter in your UCI file, and the course grade is lowered, most likely to F. Material that is copied from books or Web pages needs to be quoted and the source must be given. If you plagarize, you run the severe risk of failing the class, in a most disgraceful manner.

          Disabilities. If you need an accommodation because of a disability, please contact the instructor and the Disability Services Center as soon as possible.



          (C) University of California, 2009.
          http://www.ics.uci.edu/~taylor/ICS221/221_FQ_02.html ICS 221 Fall 2002 Home Page

           


          Administrative Information

          Classroom: ICS 243

          Course code: 36564

          Lecture Period: Tuesday and Thursday, 3:30 PM � 4:50PM

          Course Instructor: Professor Richard N. Taylor

          Electronic Mail: taylor@ics.uci.edu
          Office: ICS2 203
          Office Phone: (949) 824-6429
          Fax: (949) 824-1715
          Office Hours: MW 3:30-4:30pm. But please send email to establish an appointment.


          Course Overview

          From the UCI Catalog:

          221 Software Engineering (4). Study of the concepts, methods, and tools for the analysis, design, construction, and measurement of complex software-intensive systems. Underlying principles emphasized. State-of-the-art software engineering and promising research areas covered, including project management. Formerly ICS 221A.

          This class is a graduate survey of software engineering topics. Each lecture will focus on a single topic, beginning with some background information about the topic and followed by an examination of one or more important issues with respect to that topic at the frontier of software engineering. Students requiring a more thorough exposition of background material should consult the course textbook, using the suggested readings in the course schedule as guidelines. At the end of the class, students should be knowledgeable about the fundamentals of software engineering, the key problems that are currently being pursued by researchers in the field, and the key approaches that are being proposed and investigated, as well as be familiar with the work of many of the leading researchers.

          The vehicles for learning these topics are readings from the textbook, readings from the technical literature, lectures, and classroom discussion. The class is co-taught by the software faculty so that specialty topics may be presented in depth.

          NOTE: In this course you are bound by the ICS policy on cheating, as well as UC Irvine's policy on academic honesty (which is also printed in the Schedule of Classes). All suspected incidents of academic dishonesty will be reported to the ICS Vice Chair for Graduate Affairs. In addition, if the instructor believes there is evidence confirming the occurrence of academic dishonesty, the student or students involved will be given an automatic F in the course, and a letter documenting the incident will be sent to the student and placed in the student's official academic record.

          Unfortunately plagarism has been a problem in this class during the last 2 years. 10 students were "busted" 2 years ago; last year another 4 or 5 were identified. Don't become a statistic for next year's syllabus. Note that these were not small cases, they were cases of whole pages of copied material, sometimes page by page, sometimes paragraph by paragraph, or even sentence by sentence (e.g., weaving sentences from different papers together). Anything you literally copy at any time must be quoted and cited.


          Schedule (Subject to Change)

          Week

          Date

          Topic

          Instructor

          Assignment Due

          Paper Readings

          Suggested Textbook
          Readings

          1

          October 1

          Course Introduction
          Overview of Software Engineering
          and NSB

          Taylor

           

          [Bro87]

          Ch. 1-3

          October 3

          Software Architecture

          Taylor

           

          [PW92] [TMA+96]

           

          2

          October 8

          Component-Based Software

          Taylor

           

          [McI68]

          October 10

          Architectures, Hypermedia, and Protocols

          Taylor

          Week 1 Summaries

          [FWA+98]

          Ch. 4

          3

          October 15

          *Software Process

          Scacchi

           

          [S02] [OST87]

          Ch. 7

          October 17

          *Workflow

          Scacchi

          Week 2 Summaries

          [BT98] (not required) [Nut96]

          �

          4

          October 22

          *Computer-Supported Cooperative Work(1)

          Dourish

           

          [Gru94] [PMP98]

           

          October 24

          *CSCW (2) and Remote Collaboration Caution! 10.3 MB file!

          Mark

          Week 3 summaries

           

          Ch. 8

          5

          October 29

          *Configuration Management

          van der Hoek

           

          [CW98]

           

          October 31

          *Product-Line Architectures

          van der Hoek

          Week 4 Summaries

          [HMR+01] [Ommering, ICSE 2002]

           

          6

          November 5

          *Software Requirements

          Alspaugh

           

          [Hen80] [AA01]

          Ch. 5

          November 7

          *Formal Specification Methods

          Alspaugh

          Week 5 Summaries

          [vLW98] [HLK95]

           

          7

          November 12

          *Interoperability and Middleware

          Dashofy

           

          [GAO95] [CRW00]

          Ch. 6

          November 14

          *Human-Computer Interaction

          Kobsa

          Week 6 Summaries

          [HR98]

          �

          8

          November 19

          *Aspect-Oreinted Programming

          Lopes

           

          [KLM97] and optionally [TDBL00]

           

          November 21

          (no lecture - FSE conference)

           

          Week 7 Summaries (turn in to the TA)

           

          Ch. 6

          9

          November 26

          *Software Testing Theory

          Richardson

           

          [Wey82] [Wey86] [YT89]**

           

          November 28

          (no lecture � Thanksgiving holiday)

           

           

           

           

          10

          December 3

          Student Presentations (5 minutes each)

          Taylor

           

           

           

          December 5

          Student Presentations (5 minutes each)

          Taylor

          Week 8 & 9 Summaries

           

           

          Finals

          December 10th

          Final Exam (4:00p.m - 6:00p.m.)

          Taylor

          Term Papers due

           

           

           


          Readings

          Readings are assigned from the required textbook:

          • Fundamentals of Software Engineering, second edition, by Carlo Ghezzi, Mehdi Jazayeri, and Dino Mandrioli, Prentice-Hall, 2002. NOTE: this text will be available as of Monday, September 30th. The UCI bookstore placed the order on 9/26. My guess is that they will show up around 10/7/02.

          The following books are highly recommended for background on requirements specification and project management:

          • Software Requirements and Specifications: A Lexicon of Practice, Principles and Prejudices, by Michael Jackson, ACM Press/Addison-Wesley, 1995.
          • The Mythical Man-Month: Essays on Software Engineering (Anniversary Edition), by Frederick P. Brooks, Jr., 1995, Addison-Wesley.

          The following technical papers from the research literature (most of which are from the Software Phase II Reading List) were assigned last year to supplement the text as indicated in the course schedule. We will do a similar thing this year, the first version of the updated list will be posted "real soon now" but additional different papers will appear throughout the course. Copies of these papers and copies of all presentations will be made available in a folder in the file cabinet outside the 4th-floor Copy Center in the main ICS building. Do not take materials away from that area, and please try to keep the folder neat and tidy.

          [AA01] Thomas A. Alspaugh and Annie I. Anton. Scenario networks for software specification and scenario management. NCSU Computer Science Technical Report TR-2001-15, http://www4.ncsu.edu:8030/~taalspau/bin/alspaugh+anton2001-snss.pdf, 2001.

          [AG93] Joanne M. Atlee and John Gannon. "State-Based Model Checking of Event-Driven System Requirements". IEEE Transactions on Software Engineering 19(1):24-40, January 1993.

          [BT98] G. Bolcer and R. Taylor, "Advanced Workflow Management Technologies", Software Process--Improvement and Practice, 4, 125-171, 1998.

          [Bro87] Frederick P. Brooks. "No Silver Bullet: Essence and Accidents of Software Engineering". IEEE Computer 20(4):10-19, April 1987.

          [CKI88] B. Curtis, H. Krasner and N. Iscoe. "A Field Study of the Software Design Process for Large Systems". Communications of the ACM 31(11):1268-1287, November 1988.

          [CRW00] Antonio Carzaniga, David S. Rosenblum, and Alexander L. Wolf. "Achieving Scalability and Expressiveness in an Internet-Scale Event Notification Service". In Proceedings of the Nineteenth ACM Symposium on Principles of Distributed Computing, pp. 219-227, Portland, OR, July 2000.

          [CW98] Reidar Conradi and Bernhard Westfechtel. "Version Models for Software Configuration Management". ACM Computing Surveys 30(2):232-282, June 1998.

          [DR99] Elisabetta Di Nitto and David S. Rosenblum. "Exploiting ADLs to Specify Architectural Styles Induced by Middleware Infrastructures". In Proceedings of the 21st International Conference on Software Engineering, pp. 13-22, Los Angeles, CA, May 1999.

          [FWA+98] R. Fielding, E.J. Whitehead, K. Anderson, G. Bolcer, P. Oreizy and R. Taylor. "Web-based Development of Complex Information Products". Communications of the ACM 41(8):84-92, August 1998.

          [GAO95] David Garlan, Robert Allen, and John Ockerbloom. "Architectural Mismatch: Why Reuse Is So Hard". IEEE Software 12(6): 17-26, November 1995.

          [Gru94] Jonathan Grudin. "CSCW: History and Focus". IEEE Computer 27(5):19-27, May 1994.

          [HLK95] C. Heitmeyer, B. Labaw, and D. Kiskis. Consistency checking of SCR-style requirements specifications. In RE’95: Second IEEE International Symposium on Requirements Engineering, pages 56–63, March 1995.

          [Hen80] Kathryn L. Heninger. "Specifying Software Requirements for Complex Systems: New Techniques and Their Application". IEEE Transactions on Software Engineering SE-6(1):2-13, January 1980.

          [HR98] David Hilbert and David Redmiles. "An Approach to Large-Scale Collection of Application Usage Data Over the Internet". In Proc. 20th International Conference on Software Engineering, pp. 136-145, Kyoto, Japan, April 1998.

          [KLM+97] Gregor Kiczales, John Lamping, Anurag Mendhekar, Chris Maeda, Cristina Videira Lopes,
          Jean-Marc Loingtier, John Irwin. "Aspect-Oriented Programming." Proceedings of the European Conference on Object-Oriented Programming (ECOOP), Finland. Springer-Verlag LNCS 1241. June 1997.

          [McI68] M.D. McIlroy, "'Mass Produced' Software Components". In Software Engineering: A Report on a Conference Sponsored by the NATO Science Committee, P. Naur and B. Randell (eds.), pp. 138-155, Garmisch, Germany, October 1968.

          [Nut96] G. Nutt. "The Evolution Towards Flexible Workflow Systems". Distributed Systems Engineering 3(4):276-294, December 1996.

          [OHR00] Alessandro Orso, Mary Jean Harrold, and David S. Rosenblum."Component Metadata for Software Engineering Tasks". In Proc. 2nd International Workshop on Engineering Distributed Objects, Davis, CA, November 2000.

          [OLK+00] R. van Ommering, F. van der Linden, J. Kramer and J. Magee. "The Koala Component Model for Consumer Electronics Software". IEEE Computer 33(3):78-85, March 2000.

          [Ost87] Leon J. Osterweil. "Software Processes Are Software Too". In Proceedings of the 9th International Conference on Software Engineering, pp. 2-13, Monterey, CA, March 1987.

          [Par93] D. L. Parnas. "Predicate Logic for Software Engineering". IEEE Transactions on Software Engineering 19(9):856-862, September 1993.

          [PC86] D. L. Parnas and P. C. Clements. "A Rational Design Process: How and Why to Fake It". IEEE Transactions on Software Engineering SE-12(2):251-257, February 1986.

          [PW92] Dewayne E. Perry and Alexander L. Wolf. "Foundations for the Study of Software Architecture". ACM Software Engineering Notes 17(4):40-52, October 1992.

          [RHR98] J. Robbins, D. Hilbert and D. Redmiles. "Extending Design Environments to Software Architecture Design". Automated Software Engineering 5(3):261-290, July 1998.

          [S02] W. Scacchi, "Process Models in Software Engineering", in J. Marciniak (ed.), Encyclopedia of Software Engineering, (2nd. Edition), 993-1005, Wiley-Interscience, 2002.

          [TDBL00] Peri Tarr, Maja D’Hondt, Lodewijk Bergmans, and Cristina Videira Lopes. "Workshop on Aspects and Dimensions of Concern: Requirements on, and Challenge Problems For Advanced Separation Of Concerns" Published in ECOOP’2000 Workshop Reader. Springer-Verlag LNCS 1964. 2000.

          [TGC95] E. Tryggeseth, B. Gulla and R. Conradi. "Modelling Systems with Variability using the PROTEUS Configuration Language". In Proc. 5th International Workshop on Software Configuration Management (Springer-Verlag Lecture Notes in Computer Science 1005), pp. 216-240, Seattle, WA, April 1995.

          [TMA+96] Richard N. Taylor, Nenad Medvidovic, Kenneth M. Anderson, E. James Whitehead, Jr., Jason E. Robbins, Kari A. Nies, Peyman Oreizy, and Deborah L. Dubrow. "A Component- and Message-Based Architectural Style for GUI Software". IEEE Transactions on Software Engineering 22(6):390-406, June 1996.

          [vLW98] Axel van Lamsweerde and Laurent Willemet. Inferring declarative requirements
          specifications from operational scenarios. IEEE Transactions on Software
          Engineering, 24(12):1089–1114, December 1998.

          [Wey82] Elaine J. Weyuker. "On Testing Non-Testable Programs". The Computer Journal 25(4): 465-470, November 1982.

          [Wey86] Elaine J. Weyuker. "Axiomatizing Software Test Data Adequacy". IEEE Transactions on Software Engineering SE-12(12):1128-1138, December 1986.

          [Win90] Jeannette M. Wing. "A Specifier's Introduction to Formal Methods". IEEE Computer 23(9):8-24, September 1990.

          [YT89] Michal Young and Richard N. Taylor. "Rethinking the Taxonomy of Fault Detection Techniques". In Proceedings of the 11th International Conference on Software Engineering, pages 53-62, Pittsburgh, May 1989.


          Assignments and Assessment

          • Paper/Topic Summaries (40%)
            • For eight (8) topics discussed throughout the quarter and one or more papers assigned for each of those topics, you are required to submit a two-page (approximately 800-word) summary and evaluation brief. You will choose one topic per week; summaries for each week are due on the Thursday of the following week. This arrangement ensures that you keep up with the readings, that you write about a breadth of topics, and that you do not wait until the end of the quarter to write your summaries. The first page should list your name, the title and authors of the papers, and a summary of the key points or contributions of the papers. (Note: the earlier comments about plagarism apply to these summaries just as much as they apply to all other work. YOU must write the summaries based upon YOUR reading of the materials.) The second page should assess the applicability of the papers' ideas to the topic, problems in applying the ideas, remaining issues related to the topic, and other critical evaluation (such as scalability, applicability and soundness of the ideas). For topics in which more than one paper is assigned, you may choose to discuss all papers assigned or focus on a single paper for that topic (but you may not write two separate briefs on one topic). If there was no paper assigned for a topic you chose, discuss the lecture in detail.
          • Term Paper and Presentation (30%)
            • The term paper is an original, in-depth exploration of some current topic in software engineering. The paper must be new for this class, not a revised paper written for some other class. This paper may be largely survey in nature, and it must adhere to normal standards of scientific presentation, including full citation to the literature where appropriate (especially any directly-quoted material, which should constitute at most a very small fraction of your paper). Substantial latitude will be allowed in choosing the topic, but all topics must be software-related and approved in advance by the instructor. Each student will present in class a 5-to-10 minute overview of their paper towards the end of the quarter.
          • Class Attendance and Participation (10%)
            • Class participation is critical and will share both your experiences and understanding of the class material.
          • Final Exam (20%)
            • The final exam will ask you to demonstrate your understanding of the papers and topics covered in class as well as software issues in general.

          NOTE: There will be NO incompletes ("I" grades) given in this class. You must finish all work for the class by the end of the final exam.


          Instructors

          Faculty

          Thomas Alspaugh

           

          Paul Dourish

          jpd@ics.uci.edu

          Andr� van der Hoek

          andre@ics.uci.edu

          Alfred Kobsa

          kobsa@ics.uci.edu

          David F. Redmiles

          redmiles@ics.uci.edu

          Debra Richardson

          djr@ics.uci.edu

          Walt Scacchi

          wscacchi@ics.uci.edu

          Richard N. Taylor

          taylor@ics.uci.edu

           

           

          Department of Information and Computer Science
          University of California, Irvine
          Last updated November 26, 2002 16:45
          http://www.ics.uci.edu/~taylor/ics127.html ICS 127 SQ 2002

          ICS 127, Spring Quarter 2002

           

          The class will be meeting regularly on TUESDAYS, from 9 to 11.

           

          Room is yet to be decided. Watch this space.

           

          If you are interested in taking this class but not yet officially enrolled, please send me email.

          http://www.ics.uci.edu/~taylor/ICS223/syllabus.html ICS 223 Syllabus Winter 2003

          Information and Computer Science 223:
          Software Architectures

          (offered as ICS 280G in WQ 2003)

          Winter Quarter, 2003
          Location: CS 253 (Need a map?)
          Tuesday and Thursday, 2:00-3:20
          Course code: 36695
          (Last modified January 28, 2003 )

          WHAT'S NEW?

          [January 28, 2003] Bob Monroe's tech report on Armani is now available. I recommend your reading it.

          [January 23, 2003] Debi's slides on Endnote usage are now available.

          [January 20, 2003] Eric's slides from last Thursday are now available. See the schedule below for the URL.

          [January 16, 2003] Assignment 2 made it out by email, so I've deleted it from this page.

          [January 9, 2003] I've posted Haig Krikorian's slides. Note also the correct URL for the Software Architecture website: http://www.isr.uci.edu/projects/swarch/.

          My keynote talk from the STRAW '01 workshop is available.

          Here's a talk on architectures from 1999 that I'll be using parts of in class today.

          That talk was refined and focused a bit for a talk I gave at JPL. I'll be using some of these slides as well

          I now have a URL for the Perry and Wolf paper, for your convenience. See the paper list below.



          Overview | Textbooks | Schedule | Assignments | Academic Dishonesty |

          Instructor

          • Professor Richard N. Taylor
            • Electronic Mail: taylor@ics.uci.edu
            • Office: ICS2-203
            • Office Phone: (949) 824-6429
            • Hours: Tuesday and Thursday: 3:30-4:30pm. Please make appointments by email, however, in order to guarantee the meeting.
          • Fax: (949) 824-1715
          • Mailing Address:
            Department of Informatics
            School of Information and Computer Science,
            University of California, Irvine
            Irvine, California 92697-3425

          Overview and Prerequisite Knowledge

          From the UCI Catalog:

          223 Software Architecture (4). Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures.

          The following is a quote from Neno Medvidovic. It works for this class.

          Software architecture has become an area of intense research in the software engineering community. A number of architecture modeling notations and support tools, as well as new architectural styles, have emerged. The focus of architecture-based software development is shifted from lines-of-code to coarser-grained building blocks and their overall interconnection structure. Explicit focus on architecture has shown tremendous potential to improve the current state-of-the-art in software development and alleviate many of its problems.

          This course will expose you to the concepts, principles, and state-of- the-art methods in software architectures, including domain-specific software architectures (DSSA), architectural styles, architecture description languages (ADL), software connectors, dynamism in architectures, and architecture-based testing and analysis. In the process of studying these concepts, we will make explicit the boundaries of the field and discuss its relationship to other areas of software engineering, specifically requirements, design (including object-oriented design and related notations, such as UML), and implementation. The course will also examine the practical applicability of architecture research, specifically its relationship to the work in software reuse and component interoperability platforms (such as CORBA, JavaBeans, and COM/DCOM).


          Textbooks

          A variety of textbooks are available, including

          • Mary Shaw and David Garlan, Software Architecture: Perspectives on an Emerging Discipline, Prentice-Hall, 1996.
          • Paul Clements, et al., Documenting Software Architectures: Views and Beyond, Addison-Wesley, 2002.
          • Software Architecture: System Design, Development and Maintenance. Edited by Jan Bosch, Morven Gentelman, Christine Hofmeister, Juha Kuusela.. Kluwer Academic Publishers. www.wkap.nl


          ...but these are NOT required (I haven't even ordered "optional" copies) as the primary reading material will be from conference and journal articles.

           

          Here's a first cut at the reading list. This will *definitely* be modified.

          1. D. E. Perry and A. L. Wolf. Foundations for the Study of Software Architectures. ACM SIGSOFT Software Engineering Notes, October 1992.
          2. R. Kazman. Distributed Flight Simulation: A Challenge for Software Architecture. Technical Report, University of Waterloo.
          3. P. Kruchten. The Software Architect – and the Software Architecture Team. 1st Working IFIP Conference on Software Architectures, San Antonio, TX, February 1999.
          4. D. L. Parnas. On the Criteria To Be Used in Decomposing Systems into Modules. Communications of the ACM, December 1972.
          5. T. Korson and J.D. McGregor. Understanding Object-Oriented: A Unifying Paradigm. Communications of the ACM, September 1990.
          6. P. Kruchten. Mommy, Where Do Software Architectures Come from? 1st International Workshop on Architectures for Software Systems, Seattle, WA, April 1995.
          7. N. Medvidovic and R. N. Taylor. A Classification and Comparison Framework for Software Architecture Description Languages. IEEE Transactions on Software Engineering, January 2000.
          8. D. C. Luckham and J. Vera. An Event-Based Architecture Definition Language. IEEE Transactions on Software Engineering, September 1995.
          9. N. Medvidovic et al. A Language and Environment for Architecture-Based Software Development and Evolution. 21st International Conference on Software Engineering, Los Angeles, CA, May 1999.
          10. W. Tracz. DSSA (Domain-Specific Software Architecture) Pedagogical Example. ACM SIGSOFT Software Engineering Notes, July 1995.
          11. D. E. Perry. Generic Descriptions for Product Line Architectures. 2nd International Workshop on Development and Evolution of Software Architectures for Product Families (ARES II), Las Palmas de Gran Canaria, Spain, February 1998. 
          12. R. T. Fielding. Software Architectural Styles for Network-based Applications. Unpublished manuscript, June 1999.
          13. M. Hauswirth and M. Jazayeri. A Component and Communication Model for Push Systems. 7th European Software Engineering Conference with 7th ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE’99), Toulouse, France, September 1999.
          14. D. Batory and S. O'Malley. The Design and Implementation of Hierarchical Software Systems with Reusable Components. ACM Transactions on Software Engineering and Methodology, October 1992.
          15. R. N. Taylor et al. A Component- and Message-Based Architectural Style for GUI Software. IEEE Transactions on Software Engineering, June 1996.
          16. R. T. Fielding and R. N. Taylor. Principled Design of the Modern Web Architecture. 22nd International Conference on Software Engineering (ICSE 2000), Limerick, Ireland, June 2000.
          17. N. R. Mehta et al. Towards a Taxonomy of Software Connectors. 22nd International Conference on Software Engineering, Limerick, Ireland, June 2000. 
          18. E. M. Dashofy et al. Using Off-the-Shelf Middleware to Implement Connectors in Distributed Software Architectures. 21st International Conference on Software Engineering, Los Angeles, CA, May 1999.
          19. J. Magee and J. Kramer. Dynamic Structure in Software Architectures. 4th ACM SIGSOFT Symposium on the Foundations of Software Engineering, San Francisco, CA, October 1996. 
          20. P. Oreizy et al. Architecture-Based Runtime Software Evolution. 20th International Conference on Software Engineering, Kyoto, Japan, April 1998.
          21. M. Moriconi et al. Correct Architecture Refinement. IEEE Transactions on Software Engineering, April 1995.
          22. D. Garlan et al. Architectural Mismatch: Why Reuse Is so Hard. IEEE Software, November 1995.
          23. C. Gacek and B. W. Boehm. Composing Components: How Does One Detect Potential Architectural Mismatches? Workshop on Compositional Software Architectures, Monterey, CA, January 1998.
          24. UML Documentation. (on-line reference)
          25. M. Abi-Antoun and N. Medvidovic. Enabling the Refinement of a Software Architecture into a Design. 2nd International Conference on The Unified Modeling Language (UML’99), Fort Collins, CO, October 1999.
          26. D. Garlan and A. J. Kompanek – Carnegie Melon University. Reconciling the needs of Architectural Description with Object-Modeling Notations. 3rd International Conference on The Unified Modeling Language (UML 2000),
          27. N. Medvidovic et al. Modeling Software Architectures in the Unified Modeling Language. ACM Transactions on Software Engineering and Methodology, 2002.
          28. D. Krieger and R.M. Adler. The Emergence of Distributed Component Platforms. IEEE Computer, March 1998.
          29. E. Di Nitto and D. S. Rosenblum. Exploiting ADLs to Specify Architectural Styles Induced by Middleware Infrastructures. 21st International Conference on Software Engineering, Los Angeles, CA, May 1999.
          30. S. Vinoski. CORBA: Integrating Diverse Applications Within Distributed Heterogeneous Environments. IEEE Communications Magazine, February 1997.
          31. Microsoft Corporation.  The Component Object Model: Technical Overview. (on-line reference) 
          32. Microsoft Corporation.  DCOM Technical Overview. (on-line reference)
          33. S. P. Reiss. Connecting Tools Using Message Passing in the Field Environment. IEEE Software, July 1990
          34. M. J. Maybee et al. Multilanguage Interoperability in Distributed Systems: Experience Report. 18th International Conference on Software Engineering, Berlin, Germany, March 1996.
          35. R. Natarajan and D. S. Rosenblum. Supporting Architectural Concerns in Component Interoperability Standards. IEE Proceedings – Software, December 2000. 
          36. Sun Microsystems, Inc.  Java Beans Specification. (on-line reference)
          37. Sun Microsystems, Inc. Enterprise Java Beans Specification. (on-line reference)

           


          Schedule (Subject to change)

          Week Date Lecture topic Readings
          1 January 7

          Software Architectures within the context of Software Engineering

          • Requirements engineering and software architecture
          • Software architecture and design
          • Software architectures impact on testing and analysis
          • Where the money is: software architectures and product evolution
           
          -- January 9

          Basic Definitions and Formalisms

          • Something old, something new: software architectures through the decades.
          • Difference from design.
          • Differences from patterns.
          • Components, Connectors, Constraints, Styles, and Rationale
          • Models, levels of abstractions
           
          2 January 14

          Describing Architectures

          • Views
          • A field guide to architecture description languages
          • xADL 2.0: an extensible architecture description mechanism
           
          -- January 16  
          3 January 21

          Developing architectures

          • Product families and domain-specific software architectures
           
          -- January 23  
          4 January 28

          Architectural styles

          • Why we care about styles: focused reasoning; guarantees through conformance; design methodology
            selection guidance
          • Primary styles
            “the classics”
            C2
            REST
            DECENT
           
          -- January 30  
          5 February 4

          Connectors

          • USC’s taxonomy
          • Event-based
          • Traditional middleware and architectures
           
          -- February 6    
          6 February 11

          Moving from architectures to implementations

          • Programming language issues
          • Frameworks
           
          -- February 13    
          7 February 18

          Formal description and analysis of architectures

          • Analyses (almost) uniquely enabled by architectures: architectural tradeoff analysis
          • Performance analysis
          • Analysis for architectural design faults
           
          -- February 20    
          8 February 25

          Architecture-driven dynamism and evolution

          • Types of change.
          • An encompassing methodology/framework
          • Change and configuration management
           
          -- February 27    
          9 March 4 Architecture-focused environments  
          -- March 6 NO CLASS (GSAW)  
          10 March 11 Architectures and Business Practices
          Introducing software architectures into an organization
           
          -- March 13    
          Exams March    

          Assignments and Assessment

          This class is going to be a bit different from the usual 280. At this point my plan is that there will not be either a term paper or a term project. Rather I'm just going to ask you to submit weekly progress reports. The "progress" will be with respect to a whole set of items that will be discussed in class. Stay tuned.


          Academic Dishonesty

          Cheating in ICS 223 will be punished in accordance with University policy and ICS policy. Please familiarize yourself with those documents. Note that University policy states that faculty have the responsibility of "assigning an appropriate grade to a student who engages in academic dishonesty." That appropriate grade, for this class, is an F. Cheating is wrong. It is lying. Don't do it.
          Department of Information and Computer Science,
          University of California, Irvine CA 92697-3425
          http://www.ics.uci.edu/~rkwang/courses.html Courses

          Current Courses - Winter 2016

          ICS 61 - Game Systems and Design (co-teaching with Jessica Kernan)
          ICS 167 - Multiplayer Game Systems (co-teaching with Magda El Zarki)
          ICS 169B - Capstone Game Project II (co-teaching with Dan Frost)

          Previous Courses

          Fall 2015: ICS 45J - Programming in Java as a Second Language
          Fall 2015: CS 132 - Computer Networks
          Fall 2015: ICS 139W - Critical Writing on Information Technology
          Spring 2015: ICS 31 - Introduction to Programming (in Python)
          Spring 2015: ICS 45C - Programming in C/C++ as a Second Language
          Winter 2015: ICS 45C - Programming in C/C++ as a Second Language
          Winter 2015: ICS 169B - Capstone Game Project II (co-taught with Dan Frost)
          Fall 2014: CS 132 - Computer Networks
          Fall 2014: ICS 45J - Programming in Java as a Second Language
          Fall 2014: ICS 139W - Critical Writing on Information Technology
          Fall 2011: CS 141 - Concepts in Programming Languages I
          Fall 2011: ICS 22 - Introduction to Computer Science II (in Java)


          http://www.ics.uci.edu/~dstrash/ Darren Strash, PhD

          Darren Strash

          Postdoctoral Researcher
          Karlsruhe Institute of Technology
          Institute of Theoretical Informatics

          • About Me
          • Research
          • Software and Data Sets
          • Teaching
          • Theses Supervised
          • Contact Me

          About Me

          I am a hard-working technophile who is committed to open science and equality in education. My goal is to produce innovative high-quality research, and use my extensive research and practical experience to effectively teach and mentor the next generation of computer scientists.

          In 2011, I received a PhD in Computer Science from University of California, Irvine under the advisement of David Eppstein and Mike Goodrich. From there, I worked in research and development in Intel's Computational Lithography Group until 2014. Now, I am a postdoctoral researcher with Peter Sanders at Karlsruhe Institute of Technology.

          If you are looking for more information, here is my CV, my research statement, and my teaching statement.

          Research

          My passion is to reveal and resolve the mismatch between the theory and practice of algorithms, with applications in large scale network analysis and computational geometry. My work often involves first understanding real-world properties of data sets, then designing algorithms that exploit these properties to gain efficiency that is not possible otherwise. This includes both theoretical efficiency and efficiency of algorithms in practice (algorithm engineering). Some specific areas that interest me are combinatorial optimization, subgraph counting/listing, network visualization, shortest paths, range searching, and dynamic data structures.

          Publications

          Papers in Refereed Journals

          [4] Listing All Maximal Cliques in Large Sparse Real-World Graphs in Near-Optimal Time
          D. Eppstein, M. Löffler, and D. Strash
          ACM J. Exp. Algorithmics 18(3): 3.1, 2013. Special issue for SEA 2011.
          doi:10.1145/2543629

          [3] Category-Based Routing in Social Networks: Membership Dimension and the Small-World Phenomenon
          D. Eppstein, M.T. Goodrich, M. Löffler, D. Strash, and L. Trott
          Theoretical Computer Science 514, 2013, pp. 96-104. Special issue for GA 2011.
          doi:10.1016/j.tcs.2013.04.027
          arXiv:1110.4499

          [2] Extended Dynamic Subgraph Statistics using h-index Parametrized Data Structures
          D. Eppstein, M.T. Goodrich, D. Strash, and L. Trott
          Theoretical Computer Science 447, 2012, pp. 44-52. Special issue for COCOA 2010.
          doi:10.1016/j.tcs.2011.11.034

          [1] Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Edge Crossings
          D. Eppstein, M.T. Goodrich, and D. Strash
          SIAM J. Computing 39 (8), 2010, pp. 3814-3829.
          doi:10.1137/090759112

          Journal Papers in Preparation

          [2] On the Complexity of Barrier Resilience for Fat Regions
          M. Korman, M. Löffler, R.I. Silveira, and D. Strash
          submitted to ACM Trans. on Algorithms.
          arXiv:1302.4707

          [1] Succinct Greedy Geometric Routing in the Euclidean Plane
          M.T. Goodrich and D. Strash

          Papers in Peer-Reviewed Conference Proceedings

          [11] Finding Near-Optimal Independent Sets at Scale
          S. Lamm, P. Sanders, C. Schulz, D. Strash, and R.F. Werneck
          Proceedings of the 18th Workshop on Algorithm Engineering and Experiments (ALENEX 2016), pp. 138-150.
          doi:10.1137/1.9781611974317.12
          arXiv:1509.00764
          The code is freely available under GPL v2.0.

          [10] On Minimizing Crossings in Storyline Visualizations
          I. Kostitsyna, M. Nöllenburg, V. Polishchuk, A. Schulz, and D. Strash
          Proceedings of the 23rd International Symposium on Graph Drawing and Network Visualization (GD 2015), LNCS vol. 9411, pp. 192-198.
          doi:10.1007/978-3-319-27261-0_16
          arXiv:1509.00442

          [9] On the Complexity of Barrier Resilience for Fat Regions
          M. Korman, M. Löffler, R.I. Silveira, and D. Strash
          Proceedings of the 9th International Symposium on Algorithms and Experiments for Sensor Systems, Wireless Networks and Distributed Robotics (ALGOSENSORS 2013), LNCS vol. 8243, pp. 201-216.
          doi:10.1007/978-3-642-45346-5_15
          arXiv:1302.4707

          [8] Dynamic Planar Point Location with Sub-logarithmic Local Updates
          M. Loffler, J.A. Simons, and D. Strash
          Proceedings of the 13th International Symposium on Algorithms and Data Structures (WADS 2013), LNCS vol. 8037, pp. 499-511.
          doi:10.1007/978-3-642-40104-6_43
          arXiv:1204.4714

          [7] Category-Based Routing in Social Networks: Membership Dimension and the Small-World Phenomenon
          D. Eppstein, M.T. Goodrich, M. Löffler, D. Strash, and L. Trott
          Proceedings of the 3rd International Conference on Computational Aspects of Social Networks (CASoN 2011), pp. 102--107.
          doi:10.1109/CASON.2011.6085926
          arXiv:1108.4675

          [6] Listing All Maximal Cliques in Large Sparse Real-World Graphs
          D. Eppstein and D. Strash
          Proceedings of the 10th International Conference on Experimental Algorithms (SEA 2011), LNCS vol. 6630, pp. 403-414.
          doi:10.1007/978-3-642-20662-7_31
          arXiv:1103.0318
          The code and data sets are freely available under GPL v3.0.

          [5] Extended Dynamic Subgraph Statistics using h-index Parametrized Data Structures
          D. Eppstein, M.T. Goodrich, D. Strash, and L. Trott
          Proceedings of the 4th International Conference on Combinatorial Optimization and Applications (COCOA 2010), LNCS vol. 6508, pp. 128-141.
          doi:10.1007/978-3-642-17458-2_12
          arXiv:1009.0783

          [4] Priority Range Trees
          M.T. Goodrich and D. Strash
          Proceedings of the 21st International Symposium on Algorithms and Computation (ISAAC 2010), LNCS vol. 6506, pp. 97-108.
          doi:10.1007/978-3-642-17517-6_11
          arXiv:1009.3527

          [3] Listing All Maximal Cliques in Sparse Graphs in Near-Optimal Time
          D. Eppstein, M. Löffler, and D. Strash
          Proceedings of the 21st International Symposium on Algorithms and Computation (ISAAC 2010), LNCS vol. 6506, pp. 403-413.
          doi:10.1007/978-3-642-17517-6_36
          arXiv:1006.5440

          [2] Succinct Greedy Geometric Routing in the Euclidean Plane
          M.T. Goodrich and D. Strash
          Proceedings of the 20th International Symposium on Algorithms and Computation (ISAAC 2009), LNCS vol. 5878, pp. 781-791.
          doi:10.1007/978-3-642-10631-6_79
          arXiv:0812.3893

          [1] Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Crossings
          D. Eppstein, M.T. Goodrich, and D. Strash
          Proceedings of the 20th ACM-SIAM Symposium on Discrete Algorithms (SODA 2009), pp 150-159.
          doi:10.1137/1.9781611973068.18
          arXiv:0812.0893

          Other Publications

          [3] Category-Based Routing in Social Networks: Membership Dimension and the Small-World Phenomenon
          D. Eppstein, M.T. Goodrich, M. Löffler, D. Strash, and L. Trott
          Workshop on Graph Algorithms and Applications (GA 2011), July 2011.

          [2] Extending Garbage Collection to Complex Data Structures
          L. Effinger-Dean, C. Erickson, M. O'Neill, and D. Strash
          Proceedings of the 3rd Workshop on Semantics, Program Analysis and Computing Environments for Memory Management (SPACE 2006), pp 91-97.

          [1] Garbage Collection for Trailer Arrays
          L. Effinger-Dean, C. Erickson, M. O'Neill, and D. Strash
          Proceedings of the 3rd Workshop on Semantics, Program Analysis and Computing Environments for Memory Management (SPACE 2006), pp 83-90.

          Software and Data Sets

          Quick Cliques

          Software to quickly list all maximal cliques in sparse graphs

          Code released under GPLv3.0 and data sets

          Karlsruhe Maximum Independent Sets

          Software to find near-optimal independent sets in huge complex networks

          Code released under GPLv2.0

          Teaching

          Winter 2015/16

          Computational Geometry

          Summer 2015

          Seminar: Algorithms for Large Social Networks in Theory and Practice

          Theses Supervised

          Bachelor's Theses

          How to Partition a Graph when You Think Like a Vertex, Dec. 2015
          Jan Ebbing
          Supervised with Peter Sanders and Christian Schulz

          Boosting Local Search for the Maximum Independent Set Problem, Nov. 2015
          Jakob Dahlum
          Supervised with Peter Sanders and Christian Schulz

          Contact Me

          Karlsruhe Institute of Technology
          Institute of Theoretical Informatics
          Am Fasanengarten 5, Room 206
          76131 Karlsruhe, Germany

          Phone: +49 721 608-46602
          Fax: +49 721 608-43088
          E-mail: last name AT kit DOT edu

          • © Darren Strash. All rights reserved. Last updated January 18, 2016.
          • Design: HTML5 UP
          http://www.ics.uci.edu/~goodrich/pubs/io.html ACM Computing Surveys: SDCR Working Group on Storage I/O, Cormen/Goodrich

          ACM Computing Surveys 28A(4), December 1996, http://www.cs.jhu.edu/~goodrich/pubs/io.html. Copyright © 1996 by the Association for Computing Machinery, Inc. See the permissions statement below.


          Strategic Directions in Computing Research

          Working Group on Storage I/O Issues in Large-Scale Computing

          Position statement


          Thomas H. Cormen

          Dartmouth College, Department of Computer Science
          6211 Sudikoff Laboratory, Hanover, NH 03755-3510, USA
          thc@cs.dartmouth.edu, http://www.cs.dartmouth.edu/~thc

          Michael T. Goodrich
          The Johns Hopkins University, Department of Computer Science
          Whiting School of Engineering, Baltimore, MD 21218
          http://www.ics.uci.edu/~goodrich/



          Abstract: We present the challenge of synthesizing a coherent model that combines the best aspects of the Parallel Disk Model and Bulk Synchronous Parallel models to develop and analyze algorithms that use parallel I/O, computation, and communication.

          Categories and Subject Descriptors: B.3.2 [Memory Structures]: Design Styles - Mass storage (e.g., magnetic, optical), Primary memory; B.4.4 [Input/Output and Data Communications]: Performance Analysis and Design Aids - Formal models, Worst-case analysis; D.1.3 [Programming Techniques]: Concurrent Programming - Parallel programming; D.4.2 [Operating Systems]: Storage Management - Secondary Storage; D.4.4 [Operating Systems]: Communications Management - Input/Output; Message sending; Network communication; E.2 [Data Storage Representations]: Contiguous representations; E.5 [Files]: Sorting/searching; F.1.2 [Computation by Abstract Devices]: Modes of Computation - Parallelism and concurrency; F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems - Sorting and searching;

          General Terms: Algorithms, Design, Languages, Performance, Theory.

          Additional Key Words and Phrases: I/O, external memory, secondary memory, communication, disk drive, parallel disks, sorting, Parallel Disk Model, Bulk Synchronous Parallel Model.



          A Bridging Model for Parallel Computation, Communication, and I/O

          The past decade has seen the introduction of new and useful models for analyzing the computational and communication complexities of parallel algorithms, as well as useful models to measure I/O complexity. Yet no useful model measures all of computational, communication, and I/O complexity simultaneously.

          Usefulness of a model implies two characteristics. First, the model should be realistic in the sense that its prediction for any algorithm should correspond to observed behavior of real systems. Second, the model should be simple enough to use and understand that one can design, analyze, and implement algorithms without having had extensive experience with the model.

          We maintain that the Bulk Synchronous Parallel, or BSP, model [Valiant 1990] and LogP [Culler et al. 1993] models are useful for computational and communication complexities of parallel algorithms. The Parallel Disk Model, or PDM, [Vitter Shriver 1994] is useful for I/O complexity. The BSP and LogP models, however, ignore I/O, and the PDM does not account for computation or communication. Because we think of I/O as so much slower than computation or communication, the PDM apparently captures the most salient factor in the wall-clock time for algorithms that use parallel I/O.

          What is apparent may not be the case, however.

          Early experiences with algorithms implemented in the PDM indicate that although wall-clock time for a given algorithm follows the prediction of the model, the algorithms themselves are not I/O bound. Even with synchronous (i.e., blocking) I/O, the time spent waiting for I/O is typically less than 50% of the total wall-clock time. This behavior suggests that each parallel disk access gives rise to a given amount of computation and communication for a particular algorithm.

          A sorting algorithm, for example, might repeatedly process "memoryloads" of data by performing a large parallel read, an in-memory sort across all processors, and a large parallel write. The time to perform the in-memory sorts might exceed the combined times of the parallel reads and writes, although it is roughly the same among the memoryloads. Typical algorithms developed for the PDM are similar to this hypothetical sorting algorithm in that they make repeated passes over the data and each pass repeatedly reads in a large amount of data, processes it, and writes out a large amount of data. Processing time (including communication) tends to be about the same each time for a given algorithm. Of course it varies from algorithm to algorithm.

          If these early observations continue to hold as we gain more experience in implementing algorithms for the PDM, we will draw the conclusion that the PDM's predictive power is helpful (for analyzing I/O time) but limited (by omitting computation and communication).

          Note, however, a striking similarity between the BSP model and typical PDM algorithms: bulk processing. In the BSP model, for example, communcation in a network of processors is considered to be the prime computational bottleneck; hence, a computation is organized as a sequence of rounds, where each round consists of each processor performing computations on its internal memory, followed by the sending and receiving of a limited number of messages. Rounds and communication in BSP algorithms are like memoryload processing and I/O, respectively, in PDM algorithms.

          The challenge, therefore, is to synthesize a coherent model that combines the best aspects of the PDM and BSP models to develop and analyze algorithms that use parallel I/O, computation, and communication. Along with such a model, we need programming primitives to enable algorithm implementation under the model. These primitives must be portable and have performance matching the model's requirements.

          We view developing such a model as a challenge because we believe that it will be difficult to simultaneously satisfy the two requirements that it be realistic yet easy to use. Our concern is that a realistic model may have so many parameters as to make it unusable. The PDM, without considering processing, has four parameters: problem size, memory size, disk block size, and disk count. The BSP model also has four parameters: problem size, processor count, latency of the network, and the "gap" time between consecutive messages in pipelined computations. Thus, some natural questions to ask include the following:

          • Can all these parameters be merged in some synthesis?
          • Is there a block size notion in BSP that might be consistent with the PDM'S block size?
          • What is the right set of at most four or five important parameters?

          In summary, we think that it would be valuable to propose a bridging parallel computational model that incorporates computation, communication and I/O in an accurate and easy to use manner. We hope that discussions at the workshop will lead to such a model.

          References

          [Alpern et al. 1994]
          Alpern, B., Carter, L., Feig, E., and Selker, T., 1994. The Uniform Memory Hierarchy Model of Computation, Algorithmica, 12:2/3, August and September 1994, pages 72-109.

          [Cormen Goodrich 1996]
          Cormen, T. H., and Goodrich, M. T., 1996. Position Statement, Strategic Directions in Computing Research: Working Group on Storage I/O Issues in Large-Scale Computing, Computing Surveys, 28A(4), December 1996, http://www.cs.jhu.edu/~goodrich/pubs/io.html.

          [Cormen Hirschl 1996]
          Cormen, T. H., and Hirschl, M., 1996. Early Experiences in Evaluating the Parallel Disk Model with the ViC* Implementation, Parallel Computing, to appear. Also available as Dartmouth College Computer Science Technical Report TR96-293 at ftp://ftp.cs.dartmouth.edu/TR/TR96-293.ps.Z

          [Culler et al. 1993]
          Culler, D., Karp, R., Patterson, D., Sahay, A., Schauser, K. E., Santos, E., Subramonian, R., and von Eicken, T. 1993. LogP: Towards a Realistic Model of Parallel Computation, Proceedings of the Fourth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, May 1993, pages 1-12.

          [Valiant 1990]
          Valiant, L. G., 1990. A Bridging Model for Parallel Computation, Communications of the ACM, 33:8, August 1990, pages 103-111.

          [Vitter Shriver 1994a]
          Vitter, J. S., and Shriver, E. A. M., 1994. Algorithms for Parallel Memory I: Two-level Memories, Algorithmica, 12:2/3, August and September 1994, pages 110-147.

          [Vitter Shriver 1994b]
          Vitter, J. S., and Shriver, E. A. M., 1994. Algorithms for Parallel Memory II: Hierarchical Multilevel Memories, Algorithmica, 12:2/3, August and September 1994, pages 148-169.


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          Last modified: Mon Oct 21 19:18:04 EDT
          Michael T. Goodrich
          http://www.ics.uci.edu/~goodrich/projects/graphs/ Algorithms for Graphs on Surfaces

          Algorithms for Graphs on Surfaces

          Investigators:
          David Eppstein, University of California, Irvine
          Michael Goodrich, University of California, Irvine
          Roberto Tamassia, Brown University

          [image]

          There are number of interesting questions concerned with algorithms for graphs on surfaces. Indeed, examples of such geometric graphs include planar convex hulls, Voronoi diagrams, Delaunay triangulations, line arrangements, visibility graphs, polygon triangulations, and Euclidean minimum spanning trees, which collectively form the backbone of the topics in computational geometry. This work studies algorithms for graphs such that the vertices are points in a geometric space and the edges are curves that are embedded in a surface that is itself embedded in that space. In particular, this project is focused on algorithms for graphs on surfaces, directed at the following three thrust areas:

          • Algorithms for Embedding Graphs on Surfaces: This is the study of methods for producing geometric configurations from a combinatorial graph. Topics of interest in this thrust area include methods for approximate minimum-genus embedding of graphs, algorithms for manifold triangulation for a set of points sampled from an embedded surface, and techniques for drawing trees in the plane, which are of interest in the visualization of hierarchies, such as organization charts and object-oriented class hierarchies.
          • Algorithms for Graphs Embedded on Surfaces: This is the study of graph algorithms that take advantage of additional structure available for graphs embedded on surfaces. Topics of interest in this thrust area include the study of connections between partial cubes and integer lattices, the development of algorithms for graphs embedded in Non-Euclidean spaces, such as hyperbolic spaces, and the design of methods for solving graph problems on quadrilateral meshes, which arise in surface modeling and animation applications.
          • Applications of Algorithms for Graphs on Surfaces: This is the study of applications of algorithms that are specialized for graphs on surfaces. Topics of interest include applications of geometric graphs to computer graphics, computer security, greedy geometric routing in sensor networks, and algorithms for road networks.

          Papers

          • D. Eppstein*, M.T. Goodrich*, E. Kim, and R. Tamstorf, "Approximate Topological Matching of Quadrilateral Meshes," Proc. IEEE Int. Conf. on Shape Modeling and Applications (SMI), 2008, 83--92.
          • G. Bareqet, D. Eppstein, M.T. Goodrich, and A. Waxman, "Straight Skeletons of Three-Dimensional Polyhedra," Proc. 16th European Symposium on Algorithms (ESA), 2008, LNCS 5193, 148-160.
          • D. Eppstein*, M.T. Goodrich*, E. Kim, and R. Tamstorf, "Motorcycle Graphs: Canonical Quad Mesh Partitioning," Proc. 6th European Symposium on Geometry Processing (SGP), 2008, to appear.
          • D. Eppstein and M.T. Goodrich, "Succinct Greedy Graph Drawing in the Hyperbolic Plane," Graph Drawing 2008, to appear.
          • D. Eppstein, M.T. Goodrich, and D. Strash, Linear-Time Algorithms for Geometric Graphs with Sublinearly Many Crossings, SODA 2009, to appear.
          • D. Eppstein. The Topology of Bendless Three-Dimensional Orthogonal Graph Drawing. arxiv:0709.4087. Proc. 16th Int. Symp. Graph Drawing, Heraklion, Crete, 2008.
          • D. Eppstein. Isometric diamond subgraphs. arxiv:0807.2218. Proc. 16th Int. Symp. Graph Drawing, Heraklion, Crete, 2008.
          • D. Eppstein and E. Mumford. Self-Overlapping Curves Revisited. arxiv:0806.1724. 20th ACM-SIAM Symp. Discrete Algorithms, New York, 2009.
          • Charalampos Papamanthou, Franco P. Preparata, and Roberto Tamassia. Algorithms for Location Estimation based on RSSI Sampling. to appear In Proc. of the ICALP Int. Workshop on Algorithms for Sensor Networks (ALGOSENSORS), 2008
          • Roberto Tamassia, Bernardo Palazzi, and Charalampos Papamanthou. Graph Drawing for Security Visualization. to appear In Proc. of the Int. Conference on Graph Drawing (GD), 2008

          Support

          This project is supported in part by the National Science Foundation under Grant 080403.

          *This research was performed while Drs. Eppstein and Goodrich were serving as consultants to Walt Disney Animation Studios.

          http://www.ics.uci.edu/~goodrich/projects/privacy/ Privacy Management, Measurement, and Visualization in Distributed Environments

          Privacy Management, Measurement, and Visualization in Distributed Environments

          Investigators:
          Michael Goodrich, University of California, Irvine
          Roberto Tamassia, Brown University

          [image]

          The aim of this proposal is to develop efficient algorithms for cyber-security and privacy based on paradigms that enable efficient trusted computing in distributed environments. Specifically, this proposal is directed at developing and applying efficient algorithmic techniques to the problems of privacy management, measurement, and visualization. Deliverables of this project include distributed algorithms supporting cyber-security and privacy goals that have low latency and storage requirements, small communication costs, and fast, real-time interaction.

          Papers

          • M. T. Goodrich, R. Tamassia and N. Triandopoulos, "Super-Efficient Verification of Dynamic Outsourced Databases", LNCS: Proc. RSA Conference, Cryptographers' Track (CT-RSA), p. 407, vol. 4964, (2008).
          • M. T. Goodrich, C. Papamanthou, R. Tamassia, and N. Triandopoulos, "Athos: Efficient Authentication of Outsourced File Systems", LNCS: Proc. Information Security Conference, p. , vol. , (2008). Accepted.
          • D. Eppstein and M. T. Goodrich, "Space-efficient straggler identification in round-trip data streams via Newton's identities and invertible Bloom filters", LNCS: Workshop on Algorithms and Data Structures (WADS), p. 638, vol. 4619, (2007).
          • M. T. Goodrich and J. Z. Sun, "Checking value-sensitive data structures in sublinear space", LNCS: 18th Int. Symp. on Algorithms and Computation (ISAAC), p. 353, vol. 4835, (2007).

          Support

          This project is supported by the National Science Foundation under Grant 0713046.

          http://www.ics.uci.edu/~goodrich/projects/pki/ Efficient and Scalable Infrastructure Support

          Efficient and Scalable Infrastructure Support for Dynamic Coalitions

          The project is for certificate revocation schemes and public key infrastructure components to enable secure collaboration within dynamically established coalitions. Novel infrastructure security services are needed for the successful operation of a coalition across multiple domains. Certificate management is especially challenging in a dynamic real-time environment. Also, scalability and integration across domains should be efficiently supported.

          Traditional certificate management schemes use certificate revocation lists maintained by certificate authorities. This approach requires the entire signed list to be transmitted to any user who requests certificate validation. While this approach is secure and is being deployed in practice, it requires substantial (linear size) communication overhead and is not a scalable solution for dynamic coalitions.

          Extending preliminary work on the subject, this project developed a novel certificate revocation scheme in Java that supports fast language and platform independent certificate verification and small communication overhead.

          Papers

          • M. T. Goodrich, M. Shin, R. Tamassia, W. H. Winsborough, Authenticated dictionaries for fresh attribute credentials, Proc. Trust Management Conference, pages 332--347, Springer, LNCS 2692, 2003.
          • M. T. Goodrich, R. Tamassia, N. Triandopoulos and R. Cohen, Authenticated Data Structures for Graph and Geometric Searching, Proc. RSA Conference -- Cryptographers' Track, pages 295--313, Springer, LNCS 2612, 2003.
          • D. J. Polivy and R. Tamassia, Authenticating Distributed Data using Web Services and XML Signatures, Proc. ACM Workshop on XML Security, ACM Press, 2002.
          • M. T. Goodrich, and R. Tamassia and J. Hasic, An Efficient Dynamic and Distributed Cryptographic Accumulator, Proc. Information Security Conference (ISC 2002) Lecture Notes in Computer Science, vol. 2433, Springer-Verlag, pp. 372-388, 2002.
          • R. Tamassia, Efficient Low-Cost Authentication of Distributed Data and Transactions, Conduit, vol. 10, no. 2, Department of Computer Science, Brown University, 2001.
          • A. Anagnostopoulos, M. T. Goodrich, and R. Tamassia, Persistent Authenticated Dictionaries and Their Applications, Proc. Information Security Conference (ISC 2001), Lecture Notes in Computer Science, vol. 2200, Springer-Verlag, pp. 379-393, 2001.
          • M. T. Goodrich, R. Tamassia, and A. Schwerin, Implementation of an Authenticated Dictionary with Skip Lists and Commutative Hashing, Proc. DARPA Information Survivability Conference and Exposition (DISCEX '01), IEEE Press, vol. 2, pp. 68-82, 2001.

          Support

          This project was supported by DARPA under Grant F30602-00-2-0509.


          Michael Goodrich, Project Leader. http://www.ics.uci.edu/~goodrich/teach/cs266/ CS 266 - Computational Geometry

          CS 266 - Computational Geometry

          Professor: Michael T. Goodrich, DBH 4091
          Lectures: TTh 11:00-12:30, SSL 270
          Office hours: MW 1:30-2:30pm, DBH 4091
          (physical/electronic meetings can also be scheduled)

          • Course Description:

            • Algorithms and data structures for geometric computation and graphics programming. Fundamental problems of computational geometry such as convex hulls, Voronoi diagrams, Delaunay triangulations, polygon partitioning, arrangements, geometric searching, hidden surface elimination, motion planning.
          • Announcements:

            • Dr. Goodrich will not be holding office hours on Wednesday, January 27, due to a prior commitment.
            • Midterm 1, Thursday, January 28, 2016, in class. Reading: de Berg et al., Chapters 1-3. Old midterm exams are available:
              • midterm1
              • midterm2
              • midterm3
              • midterm4
            • Midterm 2, Thursday, February 18, 2016, in class. Reading: Chapters 4-6.
            • The lowest score among all the homeworks will be dropped in determining the final grade.
            • Final Exam, Tuesday, March 15, 10:30am-12:30pm. In class. Reading: de Berg et al., Chapters 1-11. Old final exams are available:
              • final1
              • final2
              • final3
              • final4

          • Course Syllabus.

          • Homework Assignments.

          • Extra Course Notes.



          Michael T. Goodrich
          Department of Computer Science
          University of California, Irvine
          Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/ics163/ ICS 163 - Graph Algorithms

          ICS 163 - Graph Algorithms

          Professor: Michael T. Goodrich, CS 458C
          Electronic office hours: Email me anytime
          (physical meetings can also be scheduled at your convenience)

          TA: "Jeremy" Yu Meng, 109, ICS Trailer 2
          Office hours: Monday, Tuesday, and Thursday, 2:00pm-3:00pm.

          • Announcement(s):

            • No class, Monday, January 13. Please see SODA 2003.
            • No class, Monday, January 20 (Martin Luther King, Jr. Day)
            • Goodrich will be away Wednesday and Friday, January 29 and 31. Wednesday is a review session with the TA, and Friday is the 1st Midterm.
            • No class, Friday, February 7. Goodrich will be presenting research findings at UCSD.
            • No class, Monday, February 17 (Presidents Day)
            • 2nd Midterm Exam, Friday, February 21, 9:00am-10:00am (8:30am start for people needing more time).
            • Graded homeworks 5 and 6 are available in the ICS distribution center.
            • Final Exam, Wednesday, March 19, 8:00am-10:00am.

          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.


          Other Course-Related Information

          • Eppstein's Course Web Site from Spring 2002



          Michael T. Goodrich
          Department of Information and Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3425 USA
          http://www.ics.uci.edu/~goodrich/teach/ics8/ ICS 8 - Practical Computer Security

          ICS 8 - Practical Computer Security

          Fall 2010

          Professor: Michael T. Goodrich, DBH 4216

          Lecture: 9:30am to 10:50am TTh, Steinhaus Hall 174
          Office hours: 11am to noon, MTh, DBH 4216, and upon request

          Reader: Patrick Flynn, pflynn@ics.uci.edu
          Office hour: 2pm to 3pm, W, ICS1 464E

          • Course Description:

            • Principles of practical computer security to enable students to defend themselves against malicious threats. Firewalls, anti-virus, secure setup of a wireless access point. Cryptography basics and its application. Embedded devices and related security issues. Network technologies and their vulnerabilities.
          • Announcement(s):

            • Guest lecture, September 23: Ian Harris
            • Guest lectures, October 5 and 7: Darren Strash
            • Quiz 1: October 14, 9:30am to 10:00am, on Chapters 1 and 2, Steinhaus Hall 174. A Grading rubric and Solution set is now available.
            • Quiz 2: October 26, 9:30am to 10:00am, on Chapters 3 and 4, Steinhaus Hall 174.
            • Midterm Exam: November 4, 9:30am to 10:50am, Steinhaus Hall 174
              • Reading: Goodrich-Tamassia, Chapters 1-5
            • No class, November 11, Veteran's Day
            • Quiz 3: Thursday, November 18, 9:30am to 10:00am, on Chapters 5 and 6, Steinhaus Hall 174.
            • No class, November 25, Thanksgiving
            • NOTE: The lowest homework score for each student will be dropped in computing their total grade for homework assignments.
            • Each student completing the online course evaluation at The UCI EEE Web site before December 5, 2010 at 11:45pm will recieve 1% of extra credit for their final score in the entire course.
            • Final Exam: Thursday, December 9, 8:00am to 10:00am, Steinhaus Hall 174
              • Reading: Goodrich-Tamassia, Chapters 1-8,10
          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.



          Michael T. Goodrich
          Department Computer Science
          Donald Bren Hall 4216
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/cs263/ CS 263 - Analysis of Algorithms

          CS 263 - Analysis of Algorithms
          Fall 2009

          Professor: Michael T. Goodrich
          Donald Bren Hall 4216

          Lectures: MWF 9:00am - 9:50am, Sept. 25, 2009 to Dec. 4, 2009
          Office hours: DBH 4216, by appointment

          Announcements

          • Sept. 25, Friday (first day), no class, Graph Drawing 2009
          • Oct. 21, Wednesday, no class, Invited Lecture at Brown University
          • Nov. 4, Wednesday, no class, ACM GIS 2009
          • Nov. 6, Friday, in class, Midterm exam
          • Nov. 9, Monday, no class, ACM CCS 2009
          • Nov. 11, Wednesday, no class, Veterans Day
          • Nov. 27, Friday, no class, Thanksgiving vacation
          • December 9, Wednesday, 8:00am to 10:00am, Final exam

          Course Links

          • Course Syllabus.
          • Homework Assignments.
          • Course Notes and Supplements.


          • Web Site for ICS 263 in 2002
          • Web Site for CS 263 in 2006
          • Web Site for CS 263 in 2007



          Michael T. Goodrich
          Department Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/ics23/ ICS 23 / CSE 23 - Fundamental Data Structures

          ICS 23 / CSE 23 - Fundamental Data Structures

          Fall, 2008

          Professor: Michael T. Goodrich, DBH 4216
          Office Hours: W F, 2:00pm-3:00pm (or appt. by request)

          TA for ICS 23: Darren Strash, http://www.ics.uci.edu/~dstrash/.
          Office hours: M Th, 10:00am-11:00am, DBH 4219

          Reader (Grader): Niel Infante, ainfante@uci.edu
          Office hours: TBA

          Announcement(s)

          • The first day of lectures is Friday, September 26, 2008.
          • Midterm 1: Wednesday, October 15, 2008. .
          • Dr. Goodrich will be away October 27-30, so there will be a substitute instructor during this time.
          • Midterm 2: Wednesday, November 12, 2008. .
          • No class, Friday, November 28, Thanksgiving Day Holiday
          • Final Exam: Wednesday, December 10, 8:00am-10:00am..

          Organization

          • Course Syllabus

          • Lab Manual and Work Schedule



          Michael T. Goodrich
          Department of Computer Science
          Donald Bren Hall 4216
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/uni3/ Seminar: Cyber-Puzzlers

          Univ Studies 3: Cyber-Puzzlers -- Fall 2013

          • Class meetings
            • Lectures: Friday 11-11:50am in Donald Bren Hall 1431
            • Note: there is no class on November 8, 2013, because of the instructor needing to attend the 2013 ACM GIS conference.
            • Note: there is no class on November 29, 2013, because of Thanksgiving

          • Instructor
            • Professor Michael Goodrich -- goodrich (at) uci.edu

          • Class Requirements
            • no homeworks or examinations
            • grades are given based on class participation, i.e., attendence and discussion

          • Course Goals
              This seminar explores problem solving and critical thinking through the study of puzzlers and brain teasers, focusing on problems related to computer science, which are typically used as interview questions for such companies as Google and Microsoft.
              Problem solutions need only high school mathematics and logic.

          • Sample problems
            • It is said that potatoes are 99% water and 1% potato. So, say you take a bunch of potatoes, like 100 pounds of potatoes, and you set them out on your back porch to dry out. As they begin to dry out, the water starts to evaporate. And after a while enough water has evaporated so that the potatoes are now 98% water. If you were to weigh those potatoes at that moment when they are 98% water, how much would they weigh?
            • Three different numbers are chosen at random, and one is written on each of three slips of paper. The slips are then placed face down on the table. The objective is to choose the slip upon which is written the largest number. Here are the rules: You can turn over any slip of paper and look at the amount written on it. If for any reason you think this is the largest, you're done; you keep it. Otherwise you discard it and turn over a second slip. Again, if you think this is the one with the biggest number, you keep that one and the game is over. If you don't, you discard that one too, in which case you're stuck with the third one.
              The chance of getting the highest number is one in three. Or is it? Is there a strategy by which you can improve the odds?
            • How can you identify the one heavy coin out of fifty in just four weighings using a balance scale?
            • In how many ways can you change one dollar (allowing pennies, nickels, dimes, quarters, half dollars)?
            • At one point, a remote island's population of chameleons was divided as follows: 13 red chameleons, 15 green chameleons, 17 blue chameleons. Each time two different colored chameleons would meet, they would change their color to the third color. Is it ever possible for all chameleons to become the same color? Why or why not?


          Michael Goodrich
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          goodrich at ics.uci.edu
          http://www.ics.uci.edu/~goodrich/teach/cs164/ CS 164 - Computational Geometry

          CS 164 - Computational Geometry

          Professor: Michael T. Goodrich, DBH 4091
          Lectures: TTh 11:00-12:30pm, SSL 270
          Office hours: MW 1:30-2:30pm, DBH 4091
          (physical/electronic meetings can also be scheduled)

          TA: Tim Johnson
          Office hours: TTh 1:00-4:00pm, DBH 4061

          Reader: Qiuxi Zhu

          • Course Description:

            • Algorithms and data structures for geometric computation and graphics programming. Fundamental problems of computational geometry such as convex hulls, Voronoi diagrams, Delaunay triangulations, polygon partitioning, arrangements, geometric searching, hidden surface elimination, motion planning.
          • Announcements:

            • The TA, Tim Johnson, will be holding a review session for the second midterm exam preparation in DBH 3011 on Wednesday, February 17, from 3:00-5:00pm.
            • Midterm 1, Thursday, January 28, 2016, in class. Reading: de Berg et al., Chapters 1-3. Old midterm exams are available:
              • midterm1
              • midterm2
              • midterm3
              • midterm4
            • Midterm 2, Thursday, February 18, 2016, in class. Reading: Chapters 4-6.
            • The lowest score among all the homeworks will be dropped in determining the final grade.
            • Final Exam, Tuesday, March 15, 2016, 10:30am-12:30pm. In class. Reading: de Berg et al., Chapters 1-11. Old final exams are available:
              • final1
              • final2
              • final3
              • final4

          • Course Syllabus.

          • Homework Assignments.

          • Extra Course Notes.



          Michael T. Goodrich
          Department of Computer Science
          University of California, Irvine
          Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/cs167/ CS 167 - Introduction to Applied Cryptography

          CS 167 - Introduction to Applied Cryptography

          Winter 2008

          Professor: Michael T. Goodrich, DBH 4216

          Lecture: MWF 9:00am-9:50am, ICS 259
          Office hours: T Th 11:00am-noon, DBH 4216

          Reader: Anton Malykh

          • Announcement(s):

            • Monday, January 21, No class, Martin Luther King Jr. Day (no office hours on the following Tuesday either).
            • Midterm Exam 1: Friday, February 8, 2008, in class.
              • Reading: Trappe-Washington, Chapters 1-6.
            • Monday, February 18, No class, Presidents Day
            • Midterm Exam 2: Friday, February 29, 2008, in class.
              • Reading: Trappe-Washington, Chapters 7-9.
            • Final Exam: 8:00am - 10:00am, Wednesday, March 19, 2008.
              • Reading: previous readings plus Trappe-Washington, Chapters 10,12-14.

          • Course Syllabus.

          • Homework Assignments.



          Michael T. Goodrich
          Department Computer Science
          Donald Bren Hall 4216
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/cs162/ CS 162 - Formal Languages and Automata Theory

          CS 162 - Formal Languages and Automata Theory

          Professor: Michael T. Goodrich, DBH 4091
          Office hours: TTh, 2:00-3:00pm, DBH 4091

          TA: Timothy Johnson
          Office hours: WF, 11:00am-noon, DBH 4061

          Grader: Aniket Shivam
          Office hours: WF, 10:00am-10:30, DBH 3072

          • Announcement(s):

            • Midterm I: Thursday, October 15, in class. Reading: Chapters 1-4 in Hopcroft et al.
            • Midterm II: Thursday, November 12, in class. Reading: Chapters 5-8 in Hopcroft et al.
            • Final (comprehensive): Fri, Dec 11, 10:30-12:30pm, in class. Reading: previous readings and Sections 9.1-9.3, Chapter 10, and Sections 11.1-11.3 in Hopcroft et al.
            • For the overall total homework score, the lowest homework score will be dropped.

          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.


          Other Course-Related Information

          • Eppstein's website from Fall 2003



          Michael T. Goodrich
          Department of Computer Science
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/ics280/ ICS 280 - Computer Security Algorithms

          ICS 280 - Computer Security Algorithms

          Professor: Michael T. Goodrich, CS 458C

          • Announcement(s):

            • None at this time.

          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.


          The interested reader may also wish to visit materials from the related course
          • ICS 247 - Computer Security Algorithms



          Michael T. Goodrich
          Department of Information and Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3425 USA
          http://www.ics.uci.edu/~goodrich/teach/ics160e/ ICS 160E / EECS 114 - Engineering Data Structures and Algorithms

          ICS 160E / EECS 114 - Engineering Data Structures and Algorithms

          Spring, 2005

          Professor: Michael T. Goodrich, CS 458C
          Office Hours: M W, 10:00am-11:00am (or appt. by request)

          TAs:
          Xiaohong Bao (ICS)
          Office hours: W F, 3pm-4pm (or appt. by request)

          Vid Petrovic (Engineering)
          Office hours: W, 4pm-5pm, F, noon-1pm (or appt. by request)

          • Announcement(s):

            • Midterm 1 will be Wednesday, April 27, 2005, in class. This will be a closed-book, closed-notes test.
            • Midterm 2 will be Friday, May 20, 2005, in class. This will be a closed-book, closed-notes test.
            • No class, Monday, May 30, Memorial Day.
            • The last day of lecture is June 10, 2005.
            • The Final Exam will be Wednesday, June 15, 2005, 8:00am-10:00am. This will be a closed-book, closed-notes test.

          • Course Syllabus.

          • Homework Assignments.


          Other Course-Related Information

          • A Supplement Describing New Features of Java 5.0
          • Sun's Java Web Site
          • Apple's Supplement for Java 5.0
          • Klefstad's Course Web Site (as ECE 144) from Fall 2002
          • Klefstad's Course Web Site (as ECE 198) from Spring 2002
          • Spring 2003 ICS160E/ECE144 Course
          • Eppstein's Python implementations of various algorithms



          Michael T. Goodrich
          Department of Information and Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3425 USA
          http://www.ics.uci.edu/~goodrich/teach/cs161/ CS 161 - Design and Analysis of Algorithms

          CS 161 - Design and Analysis of Algorithms

          Professor: Michael T. Goodrich, DBH 4216
          Office hours: MW, 2:00-3:00pm, DBH 4216

          TA: Michael Bannister
          TA: http://www.ics.uci.edu/~mbannist/

          Grader: Silu Yang

          • Announcement(s):

            • Midterm I: January 28, in class. Reading: Sections 1.1-1.5, 2.1-2.5, 4.1-4.7, 5.1, and 5.2.
              • Midterm 1 from 2002
              • Midterm from 1996
            • Midterm II: February 20, in class. Reading: Notes on dynamic programming, and Sections 6.1-6.4, 7.1.
              • Midterm 2 from 2002
            • Final (comprehensive): Tuesday, March 18, 2014, 10:30am-12:30pm, in class. Reading: Previous readings, plus Sections 7.2-7.3, 8.1-8.2, 10.4-10.5, 13.1-13.4.
              • Final from 2002
            • For the overall total homework score, the lowest homework score will be dropped.

          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.


          Other Course-Related Information

          • Eppstein's Lecture notes from Winter 1996
          • Sample exams from Eppstein's Winter 1998 offering
          • Eppstein's Java implementation of a randomized quickselect algorithm
          • Eppstein's Python implementations of various algorithms



          Michael T. Goodrich
          Department of Computer Science
          University of California, Irvine, CA 92697-3435 USA
          http://www.ics.uci.edu/~goodrich/teach/ics247/ ICS 247 - Computer Security Algorithms

          ICS 247 - Computer Security Algorithms

          Professor: Michael T. Goodrich, CS 458C

          • Announcement(s):

            • No class, Monday, January 12.
            • No class, Monday, January 19 (Martin Luther King, Jr. Day)
            • Midterm Exam, TBA.
            • No class, Monday, February 16 (Presidents Day)
            • Final Exam, TBA.

          • Course Syllabus.

          • Homework Assignments.

          • Course Notes.


          The interested reader may also wish to visit materials from when this course was taught as
          • ICS 247 - Computer Security Algorithms, Winter 2003
          • ICS 280 - Computer Security Algorithms, Winter 2002



          Michael T. Goodrich
          Department of Information and Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3425 USA
          http://www.ics.uci.edu/~goodrich/teach/cs295/ CS 295 - Seminar on Algorithms for Cyber-Fraud Prevention and Detection

          CS 295 - Seminar on Algorithms for Cyber-Fraud Prevention and Detection
          Spring 2009

          Professor: Michael T. Goodrich
          Donald Bren Hall 4216

          Meeting times: M, 12:45-2:00pm (AI seminar), DBH 4011, MWF (class presentations), 9:00am-9:50, DBH 1423
          Office hours: DBH 4216, MWF 10:00am-10:50am

          Announcements

          • No class April 1, April Fools Day (too soon to schedule talks)
          • All add/drops for this course must be processed by Friday, April 17, 2009. Note: there will be no office hours this day, so please have add/drop cards signed by the end of class on this day. Also, so as to accomodate additional talks, Dr. Irani has gratiously offered to let us to use 2-3 slots in her Friday afternoon CS269S Theory Seminar for any talks that we cannot schedule during our normal time.
          • No class May 18 and 20, IEEE Symposium on Security and Privacy
          • No class May 25, Memorial Day (University holiday)
          • Some Tips for Giving Good Research Talks

          • Course Syllabus.

          Course Presentation Schedule

          • April 8, Rohit Nahar, Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)
          • April 10, Eric Hennigan, A Model for Delimited Information Release, with slides.
          • April 13, Anh Le, Drive-By Pharming, with slides.
          • April 15, Minh Doan, Click Fraud Resistant Methods for Learning Click-Through Rates
          • April 17, Tara Choudhury, Phoolproof Phishing Prevention
          • April 20, Di Ma, Preserving Privacy in Social Networks Against Neighborhood Attacks
          • April 22, Srinivas Seshu Chippada, Protecting Users against Phishing Attacks
          • April 24, Kadambari Agarwal, The Role of Reputation Systems in Reducing On-Line Auction Fraud
          • April 27, Bhavin Madhani, Learning to detect phishing emails with slides
          • April 29, Sushant Dewan, Web wallet: preventing phishing attacks by revealing user intentions
          • May 1, Rishab Nithyanand, Undesirable and fraudulent behaviour in online auctions
          • May 4, Chandan Purushothama, New Algorithms for Mining the Reputation of Participants of Online Auctions
          • May 6, Guoqin Zheng, Addressing Click Fraud in Content Delivery Systems
          • May 8, Noopur Vyas, Adaptive Fraud Detection
          • May 11, Fabio Soldo, Large Scale Spamming Botnet Detection
          • May 13, Emiliano De Cristofaro, Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography
          • May 15, Qingyun Li, Netprobe: a fast and scalable system for fraud detection in online auction networks
          • May 18, no class
          • May 20, no class
          • May 22, Ameya Lokare Pay-per-percentage of impressions: an advertising method that is highly robust to fraud
          • May 25, no class
          • May 27, Lowell Trott, Challenges in mining social network data: processes, privacy, and paradoxes
          • May 29, Nazia Chorwadwala, Federated Identity Management
          • June 1, Akanksha Gupta, Fraud detection in electronic auction
          • June 3, Rahim Sonawalla, SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
          • June 5, open



          The development of this course was made possible, in part, through a gift from Experian.

          Michael T. Goodrich
          Department Computer Science
          Computer Science Building
          University of California, Irvine, CA 92697-3435 USA
          http://ccsw.ics.uci.edu/15/info.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Submission guidelines

          CCSW is soliciting full papers of up to 12 pages which will be judged based on the quality per page. Thus, shorter, high-quality papers are encouraged, and papers may be perceived as too long if they are repetitive or verbose. Submissions must use the ACM SIG Proceedings Templates (available at the ACM website) in double-column format with a font no smaller than 9 point. Only PDF files will be accepted. Submissions not meeting these guidelines risk rejection without consideration of their merits. Accepted papers will be published by the ACM Press and/or the ACM Digital Library.

          Submissions must be anonymous, and authors should refer to their previous work in the third-person. Submissions must not substantially overlap with papers that have been published or that are simultaneously submitted to a journal or a conference with proceedings. Each accepted paper must be presented by one registered author. Submissions not meeting these guidelines risk immediate rejection. For questions about these policies, please contact the chairs.

          Please submit your paper via EasyChair.

          Important Dates

          • Paper Submission Due (extended):
          • June 10, 2015
            June 17, 2015
          • Acceptance Notification:
          • July 19, 2015
          • Camera Ready Due:
          • July 29, 2015
        • http://ccsw.ics.uci.edu/15/organizers.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Chairs

          Program Chairs

          • Cristina Nita-Rotaru, Purdue University
          • Florian Kerschbaum, SAP

          Steering Chair

          • Gene Tsudik, University of California, Irvine

          General Chair

          • Indrajit Ray, Colorado State University

          Web and Publicity Chair

          • Luca Ferretti, University of Modena and Reggio Emilia

          Steering Committee

          • Srdjan Capkun, ETH Zurich
          • Kristen Lauter, Microsoft
          • Ahmad Sadeghi, Technical University Darmstadt
          • Rei Safavi-Naini, University of Calgary
          • Moti Yung, Google and Columbia University

          Program Committee

          • Giuseppe Ateniese, Sapienza University Rome
          • Elli Androulaki, IBM Zurich
          • Erik-Oliver Blass, Airbus Research
          • Sherman Chow, The Chinese University of Hong Kong
          • Sara Foresti, Università degli studi di Milano
          • Michael Goodrich, University of California Irvine
          • Hannes Hartenstein, Karlsruhe Institute of Technology
          • Limin Jia, Carnegie Mellon University
          • Rob Johnson, Stony Brook University
          • Chris Kanich, University of Illinois at Chicago
          • Aniket Kate, Saarland University
          • Aggelos Kiayias, University of Athens
          • Adam Lee, University of Pittsburgh
          • Michael Locasto, University of Calgary
          • Sharad Mehrotra, University of California Irvine
          • Prateek Mittal, Princeton University
          • Refik Molva, Eurecom
          • Alina Oprea, RSA Labs
          • Charalampos Papamanthou, University of Maryland
          • Mariana Raykova, SRI
          • Christian Rossow, Saarland University
          • Thomas Schneider, Technical University Darmstadt
          • Joerg Schwenk, Ruhr-University Bochum
          • Haya Shulman, Technical University Darmstadt
          • Gianluca Stringhini, University College London
          • Mahesh Tripunitara, University of Waterloo
          • Marten vanDijk, University of Connecticut
          • David Zage, Sandia National Labs
          • Yinqian Zhang, Ohio State University
          http://ccsw.ics.uci.edu/15/past.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Past editions

          • CCSW 2014, November 7, Scottsdale, Arizona
          • CCSW 2013, November 8, Berlin
          • CCSW 2012, October 19, Raleigh, NC
          • CCSW 2011, October 21, Chicago, IL
          • CCSW 2010, October 8, Chicago, IL
          • CCSW 2009, November 13, Chicago, IL
          http://ccsw.ics.uci.edu/15/registration.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Please register in the main CCS 2015 website.

          http://ccsw.ics.uci.edu/15/speakers.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Mike Reiter

          Lawrence M. Slifkin Distinguished Professor, University of North Carolina

          Side-Channels in Multi-Tenant Environments

          Due to the massive adoption of computing platforms that consolidate potentially distrustful tenants' applications on common hardware - both large (public clouds) and small (smartphones) - the security provided by these platforms to their tenants is increasingly being scrutinized. In this talk we review highlights from the last several years of research on a long-suspected but, until recently, largely hypothetical attack vector on such platforms, namely "side-channel attacks". In these attacks, one tenant learns sensitive information about another tenant simply by running on the same hardware with it, but without violating the logical access control enforced by the platform's isolation software (virtual machine monitor or operating system). We will then summarize various strategies we have explored to defend against side-channel attacks in their various forms, both inexpensive defenses against specific attacks and more holistic but expensive protections.

          Bio: Michael Reiter is the Lawrence M. Slifkin Distinguished Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research interests include all areas of computer and communications security and distributed computing. His professional responsibilities during his career so far have included Director of Secure Systems Research at Bell Labs; founding Technical Director of CyLab at Carnegie Mellon University; program chair for the the flagship computer security conferences of the IEEE, the ACM, and the Internet Society; and Editor-in-Chief of ACM Transactions on Information and System Security, among others. Dr. Reiter was named an ACM Fellow in 2008 and an IEEE Fellow in 2014.

          Chenxi Wang

          Chief Strategy Officer, Twistlock

          Cloud Security: The Industry Landscape and the Lure of Zero-Knowledge Protection

          Cloud computing is a change agent to how information technologies are consumed by businesses and consumers. The agility, scale, and resiliency brought by the cloud fundamentally changed the IT economy for many organizations. However, security assurance for cloud continues to be a barrier for adoption. This talk surveys the current cloud security technology landscape and more specifically the subject of "zero-knowledge protection" (ZKP). Borrowed from zero-knowledge proof, ZKP is a concept that allows cloud users to leverage cloud application functions without revealing critical data to the cloud infrastructure. ZKP has far-reaching impact on privacy, government surveillance, and data residency. There is also much misconception on what ZKP is and is not capable of doing. This talk looks at the specifics of ZKP technologies, the use cases for which ZKP provides the most value, and the ensuing societal impact. We will examine how ZKP can work across various layers of the cloud, from IaaS to SaaS, and briefly touch on how ZKP can function with some of the newer cloud technologies like Linux Containers and Docker.

          Bio: Dr. Chenxi Wang is Chief Strategy Officer of Twistlock. She is responsible for corporate strategy and marketing. Dr. Wang joins Twistlock from Ciphercloud, another successful Silicon Valley startup. Prior to that, Chenxi built an illustrious career at Forrester Research and Intel Security. At Forrester, Chenxi covered mobile, cloud, and enterprise security, and wrote many hard hitting research papers. At Intel Security, she led the ubiquity strategy that spans both hardware and software platforms. Chenxi started her career as a faculty member of Computer Engineering at Carnegie Mellon University. Chenxi is a sought-after public speaker and a trusted advisor for IT executives. She has been quoted/featured by New York Times, Wall Street Journal, Forbes.com, Fox Business News, Bloomberg, Dark Reading, and many trade media outlets. Chenxi holds a Ph.D. in Computer Science from University of Virginia. Her Ph.D. thesis received an ACM Samuel Alexander award for excellence in research.

          Bruce Grenfel

          VP Security and Compliance, Concur/SAP

          Being Successful in the Cloud - Special secret or just plain old logic

          Moving to the cloud, has become a financially attractive proposition. Cloud Computing provides many benefits and services to its customers who just pay for the utilization of hardware and software resources. Benefits are manifold and include: low cost of entry, the promise of elasticity, outsource of core competence through SaaS models. On demand services which they can access via internet without the need of upfront investment in expensive computers or a large storage system capacity and without paying any equipment maintenance fees. Furthermore, this has generated innovative opportunities in the business world to create new business echo systems or community clouds, open platforms, creating connected networks that improve our ability leverage information from many sources accelerating our understanding of how to continue to reinvent our businesses and deliver business benefits to our customers However, while this is highly attractive to CFO's and business management, the "online world" has become increasingly attractive to criminal elements in the world. Its almost a daily event to hear about another breach and the cost to businesses continues to escalate causing direct financial impact and reputational damage to breached organizations and businesses. Industry sectors have created standards and mitigating activities required of businesses to reduce the financial impact of breaches. Data jurisdiction has influenced governments to create new laws and regulations to protect the privacy of their citizens and the subsequent penalties for not abiding by these laws and regulations continues to increase. As a result of the above mentioned changes, while Cloud computing continues to be functionally and financially attractive, maintenance of the security of information has significantly changed client and prospective client behavior. Over the last 2-3 years it has been reported that 35% of CIO's hold security as the number one reason why they will not move their computing requirements into the cloud. Furthermore, while larger organizations have invested in security, reducing the risk of criminal access to their information, criminals have changed their targets to the smaller organizations and it has been reported that 64% of breaches have resulted from breaches of suppliers to the larger organizations causing a massive 900% increase in annual client due diligence over the last 2 years to ensure the continued security of client information in the cloud. The risks to privacy, potential loss of competitive advantage, reputational damage and outright fraud continue to be a barrier to entry into the Cloud computing space. Mr. Grenfell’s presentation is aimed at challenging technologists to step up efforts to develop technological methods to reduce barriers to adoption of Cloud Computing and using his 11 year experience of building a Security Program at Concur Technologies and the feedback of many clients what is necessary to reduce barriers to adoption and moreover what in necessary to be part of a Cloud based echo system of connected networks.

          Bio: Bruce Grenfell is VP, Security and Compliance for Concur Technologies Inc. He received his degree in Electronics Engineering at Sussex University in the UK. He Joined Concur in the UK in 2004 to develop and implement a Security and Compliance strategy for Concur Technologies. Since joining Concur he has driven a continuously improving Security program in a financially relevant Cloud business. An ITIL accredited, IT leader 30 years of international IT experience. Has held responsibility for the Security and Compliance, Data Centre Management, Network Operations, Applications, Telecommunications, Desktop and Technical services within the European and Asia Pacific regions. A Security Compliance leader with a proven track record of success within SaaS, Security and Service Management in B2B, B2C, consultancy services, professional services and corporate markets. Strong operational leadership with data center facility roll-out / management experience. Has managed and directed the delivery of hosted services, Web Technologies, networks/ technical services, IT strategies, IT policies, data center operations, boardroom negotiations & global infrastructure technologies within leading-edge markets.

          http://ccsw.ics.uci.edu/15/program.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Accepted papers and Schedule

          8.45 - 8.55 Welcome
          8.55 - 9.55 Keynote: Mike Reiter (University of North Carolina)

          Side-Channels in Multi-Tenant Environments

          9.55 - 10.45 Session I: System Security (Session Chair: Rei Safavi-Naini)
          • How Private is Your Private Cloud? - Security Analysis of Cloud Control Interfaces
          • Dennis Felsch (Ruhr-University Bochum); Mario Heiderich (Ruhr-University Bochum); Frederic Schulz (Ruhr-University Bochum); Jörg Schwenk (Ruhr-University Bochum)
          • Return Of The Covert Channel, Data Center Style
          • Ken Block (Northeastern University); Guevara Noubir (Northeastern University)
          11.10 - 12.10 Keynote: Chenxi Wang (Twistlock)

          Cloud Security: The Industry Landscape and the Lure of Zero-Knowledge Protection

          2.00 - 2.50 Keynote: Bruce Grenfell (Concur/SAP)

          Being Successful in the Cloud - Special secret or just plain old logic

          2.50 - 3.40 Session II: Applied Cryptography I (Session Chair: Aniket Kate)
          • Performance Analysis of Linux RNG in Virtualized Environments
          • Rashmi Kumari (University of Calgary); Mohsen Alimomeni (University of Calgary); Reihaneh Safavi Naini (University of Calgary)
          • Fast Order-Preserving Encryption from Uniform Distribution Sampling
          • Yong Ho Hwang (Samsung); Sungwook Kim (Samsung); Jae Woo Seo (Samsung)
          4.00 - 4.50 Session III: Applied Cryptography II (Session Chair: Marten van Dijk)
          • Exploring Privacy Preservation in Outsourced K-Nearest Neighbors with Multiple Data Owners
          • Frank Li (University of California Berkeley); Eui Chul Richard Shin (University of California Berkeley); Vern Paxson (UC Berkeley / ICSI)
          • ORAM based forward privacy preserving Dynamic Searchable Symmetric Encryption Schemes
          • Panagiotis Rizomiliotis (University of the Aegean); Stefanos Gritzalis (University of the Aegean)
          http://ccsw.ics.uci.edu/15/index.html CCSW 2015
          CCSW
          2015

          The 7th ACM Cloud Computing
          Security Workshop

          October 16, 2015, The Denver Marriot City Center, Denver, Colorado, USA

          in conjunction with the 22nd ACM Conference on Computer Communications Security (CCS)

          • Home
          • Submission Info
          • Registration
          • Organizers
          • Program
          • Speakers
          • Past editions

          Overview

          The CCSW workshop brings together researchers and practitioners in all security and privacy aspects of cloud-centric and outsourced computing, including:

          • practical cryptographic protocols for cloud security
          • outsourced privacy-preserving computation
          • secure cloud resource virtualization mechanisms
          • secure data management outsourcing (e.g., database as a service)
          • practical privacy and integrity mechanisms for outsourcing
          • privacy-enhancing technologies for the cloud
          • foundations of cloud-centric threat models
          • secure computation outsourcing
          • remote attestation mechanisms in clouds
          • sandboxing and VM-based enforcements
          • trust and policy management in clouds
          • secure identity management mechanisms
          • new cloud-aware web service security paradigms and mechanisms
          • cloud-centric regulatory compliance issues and mechanisms
          • business and security risk models and clouds
          • cost and usability models and their interaction with security in clouds
          • scalability of security in global-size clouds
          • trusted computing technology and clouds
          • binary analysis of software for remote attestation and cloud protection
          • network security (DOS, IDS etc.) mechanisms for cloud contexts
          • security for emerging cloud programming models
          • energy/cost/efficiency of security in clouds
          • security for software defined networking

          We would like to especially encourage novel paradigms and controversial ideas that are not on the above list. The workshop is to act as a fertile ground for creative debate and interaction in security-sensitive areas of computing impacted by clouds.

          Impact

          CCSW has had a significant impact in our research community. As of April 2015, in the Google Scholar Metrics entry for CCS (which encompasses CCSW), 4 of the top 20 cited papers of the past five years come from CCSW. One way to look at it is that you're as likely or perhaps more likely to have a top-20 paper publishing in CCSW than in CCS! (thanks to Ari Juels for noticing this)

          Organizers

          Program Chairs:

          • Cristina Nita-Rotaru, Purdue University
          • Florian Kerschbaum, SAP

          General Chair:

          • Indrajit Ray, Colorado State University

          Steering Chair:

          • Gene Tsudik, University of California, Irvine

          Sponsors

          http://www.ics.uci.edu/~wscacchi/Presentations/ Index of /~wscacchi/Presentations

          Index of /~wscacchi/Presentations

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          [   ]AeA-June06.ppt26-Jun-2006 23:42 9.6M 
          [DIR]COSST/30-Apr-2007 14:25 -  
          [DIR]CRITO-Consortium/30-Apr-2007 14:25 -  
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          [   ]Chicago-MLS.ppt29-Nov-2005 15:08 2.1M 
          [DIR]Debbie-TBS/22-Nov-2005 11:22 -  
          [DIR]ESEC-FSE07/19-Sep-2007 10:50 -  
          [DIR]FOSS-Community-Dev/30-Apr-2007 14:25 -  
          [DIR]GSAW-2009/05-May-2009 12:29 -  
          [DIR]GameGrid/10-Apr-2007 15:58 -  
          [DIR]GameLab/29-May-2013 11:30 -  
          [DIR]HRI/18-Jan-2006 18:30 -  
          [   ]ICGSE2011.pdf24-Aug-2011 17:06 3.3M 
          [   ]ISR-Forum-Panel-June2004.ppt07-Jun-2004 18:29 140K 
          [   ]ISR-Forum-Panel.ppt07-Jun-2004 18:29 140K 
          [DIR]ISR-Seminar/02-Jun-2006 10:00 -  
          [   ]InnovationInComputing.pdf07-May-2008 11:44 867K 
          [   ]InnovationInComputing.ppt07-May-2008 11:44 874K 
          [DIR]JPL-Understanding-Process/18-Oct-2001 14:09 -  
          [DIR]MKIDS/08-Sep-2002 18:40 -  
          [DIR]NPS/02-Dec-2011 01:01 -  
          [DIR]Nasa-ARC-talk/01-Nov-1999 11:36 -  
          [DIR]OSS-Requirements/19-May-2005 12:57 -  
          [DIR]OSS-Strategies/11-Oct-2004 23:11 -  
          [   ]OSS-Summit-Dec04.ppt13-Dec-2004 23:57 52K 
          [DIR]OSS/01-Feb-2012 17:04 -  
          [DIR]OSSE04/14-Jun-2004 16:38 -  
          [DIR]OpenGovt/07-Apr-2003 17:43 -  
          [DIR]OrgSystems/02-May-2001 09:51 -  
          [DIR]ProSim03/08-May-2003 14:56 -  
          [DIR]ProSim04/13-Feb-2005 21:55 -  
          [DIR]ProSim99/10-May-2004 15:00 -  
          [DIR]Process/16-Oct-2009 03:08 -  
          [DIR]SSDVE/20-Oct-1999 17:10 -  
          [   ]Scacchi-Alspaugh-Presentation-14Dec2012.pdf14-Dec-2012 08:35 2.0M 
          [   ]Scacchi-Alspaugh-Presentation-References-14Dec2012.pdf14-Dec-2012 10:19 84K 
          [   ]Scacchi-OSS-Ecosystems-7July15.pdf07-Jul-2015 17:27 1.9M 
          [   ]SciTech-R+D-VW-Scacchi.pdf28-Jan-2010 10:49 5.0M 
          [DIR]Software-Acquisition-Web/23-Feb-2000 18:24 -  
          [   ]Strategies-Developing-Deploying-FOSS-Securities-Conf.ppt19-Apr-2004 13:46 161K 
          [   ]Thumbs.db07-May-2013 14:24 8.0K 
          [   ]VRC-DSD.ppt09-Nov-2008 00:38 2.9M 
          [DIR]WESAS/18-Mar-2002 13:36 -  
          [DIR]Workshop2003/19-May-2005 13:04 -  
          [TXT]sp-tutor.html12-Feb-2005 18:42 68K 
          [DIR]untitled folder 2/07-Sep-2007 02:49 -  

          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~wscacchi/Papers/KnowledgeWeb/ Developing a Knowledge Web for Process Redesign
          Developing a Knowledge Web for Business Process Redesign

          Walt Scacchi* and Andre Valente**
          *Information and Computer Science Dept.
          University of California at Irvine
          **Intelligent Systems Division, USC Information Sciences Institute
          University of Southern California
          E-mail: wscacchi@ics.uci.edu, valente@isi.edu

          *** Draft for Review ***
          (c) Copyright, Walt Scacchi and Andre Valente 1999

          Overview

          In this paper, we describe our effort at developing and demonstrating a prototype knowledge-based Web environment for modeling, diagnosing and redesigning complex business processes. Our goal was to investigate how a modern knowledge representation system, Loom [MB95], can favorably leverage the development and evolution of a knowledge web that links narrative, informal and formal descriptions of on-line cases on business process redesign. In so doing, we demonstrate concepts, techniques and tools that facilitate the development of a knowledge web management system (KWMS) in an application domain of interest to enterprises throughout the world.

          We start with a brief background discussion of commercial approaches to knowledge management, as well as to introduce our conception of knowledge webs and knowledge web management systems. We next describe three problems that drive our investigation. The problems are:

          • how to acquire global knowledge for how to redesign complex business processes,
          • how to develop knowledge representation and operationalization methods that utilize Web resources and technologies, and
          • how to facilitate the continuing evolution and improvement of a knowledge web for business process reengineering.
          In response, we present a strategy for how to address these problems in a way that combines the capabilities of a large knowledge acquisition team with intelligent systems technologies and the Web. This includes how Web resources and knowledge-based systems were used, the kinds of classification taxonomies that were developed, and how relevant domain and instance-level knowledge was formally represented. In particular, we show how we used Loom and its corresponding Web browser interface, Ontosaurus, in developing a KWMS. Our results demonstrate that an evolving knowledge web can be developed and supported by automated reasoning, analysis and classification mechanisms to incrementally integrate new domain facts and to derive implied relations. Our conclusions then follow.

          Background

          Knowledge management (KM) within modern or virtual enterprises is an emerging area for research investigations, as well as for the development and deployment of commercially available systems [O98]. Commercial systems like Lotus Notes focus on capturing and managing unstructured information that is created and shared by people in enterprises. This information serves as a narrative knowledge base that is to be organized and managed. Other systems from KM tool vendors such as Autonomy Inc. and InXight Inc. provide computational mechanisms that process unstructured information in a Lotus Notes text base or corporate Intranet repository. These mechanisms add value by automatically constructing category (word phrases or co-occurrences) hierarchies and hypertext indices/links that characterize a set of related documents. This is possible in that these mechanisms employ term clustering or classification techniques based on statistical, information theoretic and linguistic analyses of a text database. By their very nature, text analysis techniques such as these provide their value through a shallow syntactic or surface-level processing of a document or text base. However, this value can be enhanced through use of synonym tables, thesauri, domain keyword vocabularies or other weak models to augment the analyses. Nonetheless, these significant capabilities are now commercially available to enterprises that want to begin to manage their text-based knowledge assets.

          On the research front, much work in KM remains to be done that builds on results from knowledge engineering, reasoning and problem-solving mechanisms, and other aspects of intelligent systems. Here classic problems in knowledge acquisition, representation and operationalization must be addressed, as they must by commercial KM systems. As noted above, commercial KM system can employ weak models to great success in acquiring and representing (via hypertext links) interrelated text documents that can be operationalized (distributed and navigated) through Web browsers and repository servers. But there is little use of knowledge engineering, reasoning or intelligent systems techniques in commercial KM tools. In contrast, our effort builds from these techniques.

          One of the keys to understanding the current emphasis on unstructured information is to realize there is a wide range of levels of formalization in the ways knowledge is represented in KM systems. At the same time, the level of formalization or structure of the knowledge source restricts the mechanisms available to manipulate it, provide services and solve problems. This is presented in Figure 1.


          Figure 1. The Knowledge dimension: forms and mechanisms

          Along the top is a sort of the notational forms in which knowledge can be captured and represented. From left to right we increase the formalization and precision of knowledge, while from right to left, we accommodate more informality and ambiguity. Knowledge forms towards the left end are relatively easy for people to create and update, while knowledge forms to the right increasingly demand knowledge engineering, incremental analysis and truth maintenance capability. Along the bottom is a sort of representative knowledge processing tools or mechanisms. From left to right we note an increase in the ability to derive some semantic interpretation from an associated knowledge form: from co-occurrence of specified keywords to logical expressions and deductive assertions. Conversely, from right to left we increase the amount and diversity of information we can search or browse for relevant knowledge. Clearly, most knowledge found on the Web is towards the left end. Further, most of the KM tools concentrate on the left half. In this study, we seek to broaden this scope by organizing, interrelating and managing business process redesign knowledge that spans this dimension.

          The research in this article is focused on the domain of "business process redesign", or BPR. BPR involves the transformation of enterprise processes, information infrastructure, work situations and surrounding resources into more optimal configurations. BPR is in many ways a precursor of KM in enterprises from historical, organizational and technological perspectives [DP98]. Though much has been written about BPR and hundreds of BPR case studies have appeared (in print and on the Web), there has been little effort at developing an underlying formalization as to what constitutes BPR. For example, an initial effort classify and retrieve relevant cases has been demonstrated using case-based reasoning techniques on a set of eight cases represented as attribute-value tuples [KS96]. Other notable efforts employ knowledge-based capabilities to help redesign specific business processes [N97, ST96]. Nonetheless, there has been progress in developing concepts, techniques and tools for formalizing and engineering enterprise processes for business and software development from a knowledge-based perspective [MS96, N97, SM97]. In addition, this progress with enterprise process technology has also led to the integration and use of Web technologies [SN97]. Finally, we have an existing process modeling ontology available to us [MS96]. We thus use our existing enterprise process ontology and domain expertise as the starting point for representing knowledge about BPR. Subsequently, we choose to acquire and represent knowledge of BPR from case studies found on the Web.

          By combining formalized knowledge representations for enterprise processes with Web technologies, we make a move towards the development of intelligent support tools for managing BPR knowledge over the Web. Our goal in this study is to acquire and represent a knowledge web for BPR that is operationalized and managed by a prototype knowledge web management system (KWMS). The knowledge web is the target knowledge base we wish to acquire and represent; the environment for operationalizing and managing this knowledge works as a KWMS.

          Problems

          In order to realize our research goal, we must address three problems. The first problem is how to acquire global knowledge for how to redesign complex business processes. Our solution entails a number of activities. We start by using popular search engines and index servers available on the Web. Using keywords such as "business process redesign", "process reengineering" and "case studies", we quickly locate hundreds of links to possible sources of BPR knowledge cases. Of course, given the variability in the precision and recall of these information retrieval services, a substantial effort is needed to browse, filter and select BPR narratives that can subsequently be coded and classified as useful cases. As a result, we employed a group of 30 graduate students participating in a graduate seminar on BPR to browse, filter, select, code and classify the 200+ BPR cases we found on the Web. The commercial KM tool capabilities noted earlier were not available to us at the time. Thus we could not avoid this problem. Furthermore, even if these tools were available to us, browsing a rough sample of retrieved BPR links reveals that some BPR narratives are short (1-2 printed pages), while others bring up long reports or online books (100-200+ pages). Similarly, some narratives describe experience reports, while others tell of lessons learned or prescribe best practices [O98]. Subsequently, there is a great deal of variability and uncertainty with respect to the kind of results one gets through a surface level analysis of BPR case texts found on the Web, depending on what cases are used for analysis and categorization, and what order you choose to analyze them. As such, our team-based knowledge acquisition effort could produce models of BPR knowledge that might be inconsistent, logically incomplete, or inadequately traceable when integrated. We therefore needed ways and means to enable the construction of a consistent, logically complete and traceable knowledge web for BPR.

          The second problem we faced was how to develop knowledge representation and operationalization methods that utilize Web resources and technologies. This problem is related to the preceding one. In particular, our graduate student collaborators were organized into six teams who could openly discuss and share their emerging knowledge as to what knowledge they were finding in the Web-based BPR cases. Cases came from all over the world, though for practical reasons, we limited our efforts to those available in English and accessible without fee. All students were trained for what to examine in each case, and each team was assigned to develop a specific BPR case taxonomy. Cases were examined for information that could minimally conform to the ontology of objects, attributes, relations and constraints specified in the process meta-model [MS96]. In simple terms, students had to identify the organizational roles of the participants identified in the case, the information processing tasks these participants performed within the business process, the resources used or consumed in the tasks, any off-line or on-line tools or applications, any Internet/Web elements, and what changes occurred to any of these as a result of the process redesign described in the case. Beyond this, the students then had to identify one or more process redesign heuristics that could characterize each selected case. Open discussion and collaboration was again encouraged in identifying the process redesign heuristics, including a lecture discussion of one case study that identifies ten process redesign heuristics [SN97]. Overall, more than 30 process redesign heuristics were identified and classified.

          Next, six taxonomies were identified for grouping and organizing access to the BPR cases discovered on the Web. These taxonomies classified and indexed the cases according to:

          • Generic type of industry process: financial, manufacturing, research and development, marketing, etc.
          • As-is "problems" with existing process: off-line information processing, workflow delays, lack of information sharing, etc.
          • To-be "solutions" sought for redesigned process: automate off-line information processing tasks, streamline workflow, use email and databases to share information, etc.
          • Use of intranet, extranet or Web-based process redesign solutions: build intranet portal for Human Resources information, use HTML form-EDI translators for procurement from Internet-based suppliers, etc.
          • BPR how-to guidelines or lessons learned: explicit methods or steps for how to understand and model the as-is process, identify process redesign alternatives, involve process users in selecting redesign alternatives, etc.
          • BPR heuristics: parallelize sequence of mutually exclusive tasks, unfold multi-stage review/approval loops, disintermediate or flatten internal management structures, require and verify all data before processing, etc.
          In turn, each of these taxonomies could then be represented as hierarchically nested indices of Web links to the corresponding cases in ways familiar to Web users. Typically, each taxonomy indexes 60-120 cases out of the total set of more than 200. This means that some cases could appear in one taxonomy but not another, while other cases might appear in more than one, and still others might not appear in any of these taxonomies if they were judged to not possess the minimal information needed for characterization and modeling.

          The last problem we addressed was how to facilitate the continuing evolution and improvement of a knowledge web for BPR. Proposing technically elegant solutions to the preceding two problems that would be costly, take a long time, or produce inconsistent results would defeat our purpose. Similarly, we cannot expect that a no-cost effort requiring little time will produce dramatic results, at least not in a research study. Nonetheless, we wanted to find a means that could incorporate and integrate knowledge acquired from new BPR cases that might later be posted on the Web in a streamlined, evolutionary and incremental manner.

          With these problems at hand, we now describe our approach to solving them using a formal knowledge system.

          Approach

          A key factor in achieving the goals describe in the previous section is the ability to handle knowledge in the more formal end of the spectrum shown in Figure 1. Representing knowledge about a business process using an informal process model, graphic diagram, or in textual form is not always adequate. If we want to automate reasoning with the model to perform some task, we need to use a richer and more structured representation. There are a variety of languages and formalisms where we can represent a business process model. For example, we find business process models expressed using a markup language like XML or even HTML. This provides enough structure to build simple applications that can browse, summarize or search the model contents. However, for complex tasks like evaluating, diagnosing or simulating a process model, however, we may need to perform truly automated reasoning, and for that we need a formal knowledge representation language [SM97].

          One of the reasons why formal knowledge representations are not used more frequently is that developing a formalized knowledge base is generally not a casual undertaking. Instead, it is one where circumstances motivate the use or reliance on formal models of domain knowledge and problem solving expertise. In a domain such as Air Campaign Planning [VRMS99], the inherently complexity of the task at hand together with the multiplicity of system applications users employ underscores the need to invest in a developing a formalized knowledge based. Similarly, in a domain like BPR, there is widespread opportunity to practice and reuse known concepts, techniques and tools to realize economic gain, organizational transformation or reinvention. This justifies the investment in developing and engineering a formal knowledge base for BPR interlinked to supporting cases and navigational indices on the Web.

          In particular, this points to the need to develop knowledge bases that can be reused easily. Thus, our approach consisted of developing a generic, reusable knowledge base or ontology [VRMS99] of business processes and then using it for creating specific knowledge bases of processes in a given industry or company. In turn, the reusable part of these some of these knowledge bases can be transformed into reusable ontologies, in a layered approach.

          Once we have the knowledge of business processes formalized in a knowledge representation system we can use the reasoning mechanisms provided by that system to develop applications that analyze a specific process model. In order to test these ideas, we used the ontology of business processes to build a tool to diagnose business process models. The tool reasons with a given process model using a classification mechanisms and lists the problems found. Its knowledge base contains a description of a taxonomy of types of problems in business process models. We then tested this tool by applying it to model cost accounting business processes in a large utility company in California.
           
           

          Tools for handling formal knowledge

          In developing knowledge bases and ontologies for the domain of BPR we used two tools. First, in order to represent BPR knowledge formally and reason with it, we selected the Loom knowledge representation system [MB95]. Loom is a language and environment for constructing ontologies and intelligent applications [VRMS99]. It has been distributed to more than 80 universities and corporations, and is being used in numerous DARPA-sponsored projects in planning, software engineering and intelligent integration of information. Declarative knowledge in Loom consists of definitions of concept, relations and instances. A deductive engine called a classifier utilizes forward-chaining, semantic unification and object-oriented truth maintenance technologies in order to compile the declarative knowledge into a network designed to efficiently support on-line deductive query processing. By using Loom to re-implement the process meta-model ontology, we can construct formal models of business processes, classification taxonomies and process redesign heuristics. In turn, we can then analyze, query, browse and identify relevant redesign alternatives for processes that have been modeled in Loom and linked over the Web. Finally, we can take advantage of Loom's deductive classifier so that as new BPR cases are acquired, taxonomically classified and formally modeled, our knowledge web can evolve with automated support.

          Second, in order to support the visualization of the knowledge bases constructed, we used Ontosaurus [ONTO], a Web browser interface to the Loom system. Ontosaurus is a client-server tool in which a server (written in CommonLisp) with Loom and one or more knowledge bases loaded replies to queries and produces Web pages describing several aspects of the knowledge base. It is also able to provide simple facilities for producing general queries and editing the contents of knowledge bases. Figure 2 shows a typical browser window accessing Ontosaurus. The display consists of three window panels; Toolbar (top), Reference (left side) and Content (right side). The Toolbar panel consists of buttons to perform various operations such as select domain theory, display theory, save updates, etc. The Reference and Content panels are designed to display contents of a selected ontology. Links in both panels display their contents in the Content window. This facilitates exploring various links associated with a word or concept in the Reference window without the need to continuously go back and forth. The bookmark window holds user-selected links for Web pages to Ontosaurus pages, and is managed by the buttons in the bottom of the bookmark window.

          Figure 2. An Ontosaurus browser interface display.

          Formally Representing Business Process Models and Process Redesign

          We now describe how we built a knowledge-based system to represent and diagnose models of BPR. The system is based on an ontology of business processes expressed in Loom. Loom provides a semantic network knowledge representation framework based on description logics. In Loom it is possible to define concepts that have roles or slots to specify its attributes. A key feature of description logic representations is that the semantics of the representation language are very precisely specified. This precise specification makes it possible for the classifier to determine whether one concept subsumes another based solely on the formal definitions of the two concepts. The classifier is an important tool for evolving ontologies because it can be used to automatically organize a set of Loom concepts into a classification hierarchy or taxonomy based solely on their definitions. This capability is particularly important as the ontology becomes large, since the classifier will find subsumption relations that people might overlook, as well as modeling errors that could make the knowledge base inconsistent.

          Modeling Business Process Knowledge in Loom

          The specific process ontology (or meta-model) we employed is centered on the notion of resource [MS96]. There are three basic types of resource: process, agent and simple resource (which is a placeholder for all non-agent resources that are not processes). In order to facilitate communication with a user community that is accustomed to object-oriented modeling, we chose a modeling style in Loom that is closest to object-orientation. For example, information about concepts is stored into roles, similarly to classes and properties in an object-oriented language. The definition of the concept process appears in the left frame of Figure 3.

          We also made use of Loom?s superior expressiveness. For instance, in Loom, roles, slots or properties are first-class objects called relations. Relations are defined separately, and may contain not only type restrictions but also cardinality restrictions. It also allows the definition of inverse relations, such as in the example below, where we define the relation intelligence-collective-role-own-resource. Note also how we define the domain of the relation as a logical composition over some existing concepts (types).

          (defrelation intelligence-collective-role-own-resource
          :domain (:or intelligent-agent process-role collective-agent)
          :range resource
          :inverse resource-belong-to-intelligence-collective-role)

          To create a business process model entails creating a number of instances of the available concepts. This is done in a manner similar to how objects that are instances of classes are created in object-oriented languages. Instances are created in Loom with the command tellm. For example, the following is the definition of a business process in a company where we applied the system to support process modeling, analysis and redesign. The definition specifies, for example, that the instance Produce-work-order has the attribute process-require-resource filled by the value work-order-preparation-info (which is itself an instance defined elsewhere).

          (tellm (:about Produce-work-order
          process
          (process-require-resource work-order-preparation-info)
          (process-require-resource work-order-data)
          (process-provide-resource work-order)
          (process-assigned-to-agent-role business-unit)
          (process-require-tool-resource accounting-computer-system)))

          Modeling Types of Problems in a Business Process Model

          We used the taxonomy of problems in business processes models described above and elsewhere [MS97]. There are three types of problems that arise when modeling business processes. First, consistency problems refer to conflicts in the specification of several properties of a given process. For example, a typical consistency problem is to have a process with the same name as one of its outputs (something that occurs surprisingly often in practice, perhaps because the output is often the most visible characteristic of a process). Second, completeness problems cover incomplete specifications of the process. For instance, a typical completeness problem occurs when we specify a process with no inputs (a "miracle", which can produce outputs with no inputs) or no outputs (a "black hole", where inputs disappear without generating any output). Third, traceability problems are caused by incorrect specification of the origin of the model itself: its author and responsible. Subsequently, a process model that is consistent, complete and traceable can be said to be internally correct.

          One of the main reasons that we selected Loom as a representation language was its capability to represent easily and naturally abstract patterns of data that are the very definition of the problems we discussed above. This capability is very handy to produce simple and readable representations of the types of problems with process models. For example, we can define a black-hole in plain English as "a process with no outputs". This can be easily described in Loom as a process that provides exactly zero resources:

          (defconcept black-hole
          :is (:and process
          (:exactly 0 process-provide-resource)))

          Figure 3. Ontosaurus display with Process concept definition loaded in the Reference window and a process redesign instance in the Contents window

          Many of the simple types of problems can be specified similarly. A more interesting example is the definition of a process with the same name as one of its inputs. Here we use logical expressions to describe conditions that an instance must satisfy in order for a concept to apply. For instance, below we show the specification of process-and-input-with-same-name in the ontology. The definition reads (roughly) that a process has input with the same name if and only if it is a process and it satisfies the condition that it requires itself.

          (defconcept process-and-input-with-same-name
           :is (:and process
           (:satisfies ?x (process-require-resource ?x ?x))))
           

          The diagnosis tool

          Using the representations discussed above, we built a system that diagnoses business processes. The system operation is very straightforward. The user describes a process model through Ontosaurus for processing by Loom as discussed above. Then the system diagnoses the model provided. One of the advantages of using Loom is that once we define an instance, Loom immediately (and automatically) applies its reasoning engine (the classifier) to find out what concepts apply to that instance. This offers a big advantage, since there is no need to specify an algorithm for the diagnosis process: the diagnosis occurs automatically as we define the model. In addition, the classifier performs truth-maintenance: if we redefine a process to correct a problem found by the system, the classifier will immediately retract the assertion that the problem applies to that process. Thus, we do not need to keep track of the state of the diagnosis as the model changes; instead, the classifier handles the processing activities.

          In order to provide a more direct interface to the diagnosis system, we extended the Ontosaurus browser to display two new types of pages. The first displays a description of process in a less Loom-specific way (e.g., for reporting purposes). The second displays a list of all problems found in the current process model we input.. The extension was rather simple: all we had to do was to design two additional output page templates and code the appropriate responses in the Loom server. Figure 4 shows a screenshot of the Web page constructed by the server to describe the problems found in a model of a cost accounting process (see below).

          Testing the system

          We tested the system with a team assigned the task of redesigning the cost accounting processes of a utility company in California (the name is omitted for confidentiality reasons). The team had to acquire knowledge of the processes, formally represent them, and then eventually propose a to-be solution that redesigned the processes. The team interviewed process experts, met with several employees, examined documentation, organizational charts, etc. The first value-added provided by the KWMS was caused even before using the process diagnosis module. By modeling the process in more or less formal terms (i.e. using the process ontology embedded in the system), the team was able to construct a more coherent model. The KWMS helped the BPR team to be more precise and detailed than when they just used graphic diagrams and narrative text. The second value-added was provided by the diagnosis system, since it helped catch errors early in the modeling process. An example appears in Figure 4. In fact, in the beginning the diagnosis module helped the team to learn how to construct the formal representation of the process model by providing feedback in what they should not do. The number of errors generated by entering new processes steadily diminished with time, showing the value of the system.

          Discussion and Conclusions

          Our investigation demonstrated and prototyped approach to integrate knowledge management and business process reengineering with global resources accessible over the Web. A knowledge web for BPR was acquired, represented and operationalized by a team of collaborating graduate students. We prototyped a Loom-based knowledge web management system that supported the development, use and incremental evolution of a knowledge web grounded in informal BPR case studies found on the Web. This KWMS provides the capability to browse, query, model, and diagnose a knowledge base of formal models of business processes, multiple BPR classification taxonomies, and process redesign heuristics. Subsequently, our team learned and practiced Web-based knowledge management and BPR through participation and contribution. However, the KWMS at this stage lacks the capability to automatically redesign formalized business processes.

          Automated redesign is a problem-solving task that represents the next stage of development of our prototype. However, there are BPR situations where automated redesign may not be a suitable goal or outcome. This is in organizational settings where people seek empowerment and involvement in designing and controlling their process structures and workflow. In settings such as these, the ability to access, search/query, select and evaluate possible process redesign alternatives through a KWMS may be more desirable [cf. SN97]. Thus the ultimate purpose of the KWMS we describe may be in supporting and empowering BPR participants rather than in automating BPR.
           
           


          Figure 4. Generated report from Loom diagnosis of a process redesign case

          Overall, the problems identified and addressed by this research are generic and independent of specific business process types. Nonetheless, we believe the approach we pursued is highly reusable and can for the most part be replicated in other settings. Subsequently, we find there are classes of research and practical problems whose solution may span a knowledge dimension that covers informal, semi-structured, and formal representations of knowledge that must then be interlinked across the Web and formal knowledge systems. This work thus represents one such solution.
           
           

          References

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          [KS96] S. Ku, Y.-H. Suh, and G. Tecuci. Building an Intelligent Business Process Reengineering System: A Case-Based Approach. Intelligent Systems in Accounting, Finance, and Management, 5(1):25-39, 1996.

          [L96] D.B. Leake (ed.). Case-Based Reasoning: Experiences, Lesson and Future Directions, AAAI/MIT Press, Menlo Park, CA, 1996.

          [MB95] MacGregor, R., and Bates, R. Inside the Loom description classifier. SIGART Bulletin 2(3):88-92

          [MS96] P. Mi and W. Scacchi. A Meta-Model for Formulating Knowledge-Based Models of Software Development. Decision Support Systems, 17(3):313-330, 1996.

          [N97] M.E. Nissen. Reengineering the RFP Process through Knowledge-Based Systems. Acquisition Review Quarterly, 4(1):87-100, 1997.

          [O98] D.E. O'Leary. Enterprise Knowledge Management. Computer, 31(3):54-61, 1998.

          [ONTO] Ontosaurus Web Browser home page. http://www.isi.edu/isd/ontosaurus.html

          [SM97] W. Scacchi and P. Mi. Process Life Cycle Engineering: A Knowledge-Based Approach and Environment. Intelligent Systems in Accounting, Finance and Management, 6:83-107, 1997.

          [SN97] W. Scacchi and J. Noll. Process-Driven Intranets: Life Cycle Support for Process Reengineering. IEEE Internet Computing, 1(4):42-49, 1997.

          [ST96] P.G. Selfridge and L.G. Terveen. Knowledge Management Tools for Business Process Support and Reengineering. Intelligent Systems in Accounting, Finance and Management, 5:15-24, 1996.

          [VRMS99] A. Valente, T. Russ, R. MacGregor, and W. Swartout. Building and (Re)Using an Ontology for Air Campaign Planning. IEEE Intelligent Systems, 14(1):27-36, 1999. http://www.ics.uci.edu/~wscacchi/Pubs-OrgStudies.html Pubs-OrgStudies Research Publications on Organizational Studies of Computing/Software Development [OS]
           

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          2. "The DoD Common High Order Programming Language Effort (ADA): What Will the Impacts Be?" (with R. Kling) SIGPLAN Notices, Vol. 14(2), pp. 29-41, (1979)
          3. "Recurrent Dilemmas of Computer Use in Complex Organizations," (with R. Kling) Proceedings 1979 National Computer Conference, New York, AFIPS Press, Vol. 48, pp. 107-116, (1979)
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          5. The Process of Innovation in Computing: A Study of the Social Dynamics of Computing, Ph.D. Dissertation, Information and Computer Science Dept., University of California, Irvine, CA 1981.
          6. "Evaluation of Software Development Life Cycle Methodology," (with van den Bosch, F., J.R. Ellis, P. Freeman, L. Johnson, D. Robinson, W. B. Scheft, A. von Staa, L. Tripp) ACM Software Engineering Notes, Vol. 7(1), pp. 45-60, (1982)
          7. "The Web of Computing: Computing Technology as Social Organization," (with R. Kling), in M. Yovits (ed.), Advances in Computers, Vol. 21, pp. 3-85, Academic Press, New York, (1982).
          8. "Problems and Strategies for Computer-Aided Design Work," (with L. Gasser and E. Gerson) Proc. IEEE Intern. Conf. on Computer-Aided Design, San Jose, CA, pp. 166-167, (1983).
          9. "Managing Software Engineering Projects: A Social Analysis," IEEE Trans. Software Engineering, Vol. SE-10(1), pp. 49-59, (Jan. 1984)
          10. "Social and Organizational Consequences of New Generation Technology, Proceedings of the 1984 ACM Annual Conference, San Francisco, CA. pp. 267-270, (October, 1984)
          11. "Software Evolution: A Comparative Case Study of Articulation Work," (with S. Bendifallah) Proceedings Aarhus Conference on the Development and Use of Systems and Tools, Aarhus, Denmark, pp. 59-82, (August, 1985)
          12. "Applying Social Analysis of Computing to System Development," Proceedings Aarhus Conference on the Development and Use of Systems and Tools, Aarhus, Denmark, pp. 477-500, (August, 1985)
          13. "Difficulties in Realizing Large-Scale Educational Computing Projects," Topics in Computer Education: National Educational Computer Policy Alternatives, ACM, New York, pp.163-178, (1986)
          14. "Understanding Software Maintenance Work," (with S. Bendifallah) IEEE Trans. Software Engineering, Vol. SE-13(3), pp. 311-323, (March, 1987). Reprinted in Tutorial on Software Maintenance and Computers, D. Longstreet (ed.), IEEE Computer Society (1990)
          15. "The Life Cycle Costs of Personal Computers in an Educational Institution," (with S. Nayle), Education and Computing, Vol. 3, pp. 75-87, (1987)
          16. "Difficulties in Realizing Large-Scale Educational Computing Projects," Education and Computing, Vol. 3, pp. 89-99, (1987)
          17. "Understanding Software Technology Transfer: Barriers to Innovation Engineering," (Invited Paper), Transfering Software Engineering Tool Technology, pp. 130-135, IEEE Computer Society, (November 1987)
          18. "Understanding Software Productivity: A Comparative Review of Empirical Studies," Proc. 22nd. Hawaii Intern. Conf. Systems Science, Volume II, pp. 969-977, (January 1989).
          19. "Work Shifts and Structures: An Empirical Study of Software Specification Work," (with S. Bendifallah), 11th. Intern. Conf. Software Engineering, Pittsburgh, PA pp. 260-270, (May 1989)
          20. "On the Power of Domain-Specific Hypertext Environments", Journal American Society Information Science Vol. 40(3), pp. 183-191, (May 1989)
          21. "Designing Software Systems to Facilitate Social Organization", in M.J. Smith and G. Salvendy (eds.), Work with Computers, Vol. 12A, Advances in Humans Factors and Ergonomics, Elsevier, New York, pp. 64-72, (1989)
          22. "Qualitative Techniques and Tools for Measuring, Analyzing, and Simulating Software Production Processes," in V. Basili, D. Rombach, and R. Selby (eds.), Empirical and Experimental Issues in Software, Springer-Verlag, Munich, Germany (1993).
          23. "Understanding Software Productivity: Towards a Knowledge-Based Approach," Intern. J. Software Engineering and Knowledge Engineering, Vol. 1(3), pp. 293-321, (1991). Revised and reprinted in Advances in Software Engineering and Knowledge Engineering, D. Hurley (ed.), Volume 4, (1995).
          24. "Software Technology Transfer," in J. Marciniak (ed.), Encyclopedia of Software Engineering, John Wiley and Sons, New York, pp. 1323-1327, (1994)
          25. "Understanding the Requirements for Information System Documentation," (with A. Jazzar), Proc. 1995 ACM Conf. Organizational Computing Systems, San Jose, CA, 268-279, (August 1995).
          26. "Reengineering Procurement for Internet-Based Electronic Commerce: A Case Study," J. Information Technology and Management, 2(3), 313-334, 2001.
          27. "When is `Software Development' Research?", (in preparation).
          http://www.ics.uci.edu/~wscacchi/Papers/Software_Process_Redesign/ Index of /~wscacchi/Papers/Software_Process_Redesign

          Index of /~wscacchi/Papers/Software_Process_Redesign

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          [TXT]Paper-Draft.html24-Sep-1999 12:52 52K 
          [IMG]Process_Model_Diagnosis.gif24-Sep-1999 12:52 58K 
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          [   ]SPIP-ProSim99.pdf28-Feb-2001 11:50 460K 
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          [IMG]process-figure.gif24-Sep-1999 12:52 4.4K 

          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~wscacchi/Papers/VISTA/VISTA.html VISTA'97 Paper

          Virtual System Acquisition:
          Approach and Transitions

          Walt Scacchi and Barry Boehm

          University of Southern California

          Los Angeles, CA 90089-1421 USA

          213-740-4782, 213-740-8494 (fax)

          wscacchi@rcf.usc.edu, boehm@sunset.usc.edu

          July 1997

           Revised version appears in Acquisition Review Quarterlty, 5(2):185-216, Spring 1998

          Abstract

          In this paper, we describe a radically new approach for the acquisition of software-intensive systems. We start by reviewing problems and opportunities for improving the acquisition of these systems. We put forward a statement of objective on the need to make the software system acquisition more agile and adaptive, through the evolutionary modeling, simulation, and development of the system being acquired. We describe a new vision for the re-tooling and re-engineering software system acquisition into a form we call, VISTA , denoting an approach to the virtual acquisition of these systems. We then outline the VISTA approach to software acquisition. This is followed by a discussion of the technical and organizational transitions that must be investigated and managed to ensure the eventual success of such a radical change to software system acquisition.
           


          About the authors

          Walt Scacchi is a research professor and Director of the ATRIUM Laboratory, Marshall School of Business, at USC. He has been on the faculty at USC since 1981, and is a faculty principal at the USC Center for Software Engineering.

          Barry Boehm is the TRW Professor of Software Engineering and Computer Science at USC, and Director of the USC Center for Software Engineering. Between 1989 and 1992, he served within DoD as Director of the DARPA Information Science and Technology Office and the Software and Intelligent Systems Technology Office. He also served as Director of the DDR&E Software and Computer Technology Office, and as Director of two major DoD software initiatives: the DoD Software Technology Plan and the DDR&E Software Action Plan.


          Acknowledgments

          Preparation of this article was supported by grants from the Air Force Rome Laboratory and the Deputy Assistant Secretary of the Air Force for Computers, Communications, and Support Systems, under contract F30602-94-C-0195, and the Office of Naval Research, under contract N00014-94-1-0889. None of the material in this report should be construed as a statement of policy, procedure, or endorsement by ONR, US Air Force, US Navy, or any other US government agency.


          Problems and Opportunities for Improving Acquisition

          The acquisition of major software-intensive systems is often problematic. Recent reports from the US General Accounting Office (E.g., GAO 1995, GAO 1997) describe a number of problems with the way complex systems are acquired. The current problems in their acquisition include:
          • difficulty in establishing viable and cost-effective system requirements
          • overly optimistic cost, schedule, and performance estimates
          • concurrent development and production of systems
          • commitment to system production before adequate demonstration or testing that determines system viability is completed.
          To no surprise, modern and future weapon systems increasingly represent software-intensive systems. In addition, DoD and other government agencies rely on the acquisition and use of computer-based information systems to manage their recurring organizational and operational activities. Many of these management information systems are often running on out-dated computing platforms that must be replaced or modernized.

          DoD has established acquisition strategies that move it toward commercial acquisition practices. One strategy embodies the idea that the feasibility and ability to produce advanced technologies can often be demonstrated before they are incorporated into acquisition programs. For example, the use of advanced concept technology demonstrations can more directly involve war fighters/users in demonstrating the operational feasibility of new technologies and concepts before commitments are made to full-scale acquisition. Another strategy rooted in the Defense Acquisition Workforce Improvement Act (DAWIA) establishes benchmarks for a more professional acquisition workforce with defined training and education requirements, and acquisition career paths. The goal of this act is to provide an acquisition workforce that is more responsible for improving program costs and schedule estimates. Finally, in 1994 OSD began pursuing a strategy to re-engineer the systems acquisition review process. This includes an effort to reduce acquisition costs (including overhead costs) through the adoption of business processes characteristic of world-class commercial buyers and suppliers.

          The overall way in which the federal government conducts its acquisition practices has been reviewed and redesigned in response to the Federal Acquisition Streamlining Act (FASA) of 1994. Among other things, the FASA requires incentives and a performance-based approach to managing acquisition programs. This emphasizes streamlining the acquisition process and proposes greater reliance on commercial products and processes. Also, concepts for applying commercial practices to DoD software system acquisition have been addressed in Defense Science Board reports.

          Thus, we are at a time when there is substantial opportunity to rethink how the acquisition of software-intensive systems should occur to address the recurring problems. At the same time, we should pursue new opportunities to re-engineer the systems acquisition process  that can realize savings, efficiencies, increased satisfaction, and continuous improvement. Similarly, we should provide a strategy for managing the transition to these re-engineered system acquisition processes, as they can represent a radical departure from current practices. Subsequently, we seek to explore how these opportunities can be pursued through use of advanced information processing tools, techniques and concepts. Our objective is to make the acquisition of software-intensive systems more agile and adaptive. Relevant information technologies include those for: (1) re-tooling system acquisition processes to better assess the feasibility of system acquisitions; (2) digital libraries for organizing and sharing information gathered during system acquisitions and program management; and (3) Internet-based electronic commerce services and capabilities for streamlining procurement actions, lead times, and supply chain logistics (cf. Nissen 1997, Scacchi 1997). However, in this paper and in related materials (Boehm and Scacchi 1996), we focus our discussion to the first of these areas.

          Steps toward more agile acquisition of future systems

          In general terms, our overall goal is to address the recurring problems that plague system acquisition efforts. Our approach suggests  ways in which new modeling and simulation techniques can help in re-engineering how software-intensive systems are acquired by the DoD and other government agencies. This means that we seek to identify new concepts, tools, and techniques for acquiring software-intensive systems that fulfill four goals: First, to establish viable and cost-effective system requirements. Second, to establish realistic cost, schedule, and performance estimates. Third, to mitigate against concurrent development and production of systems. Fourth, to enable adequate demonstration and testing of system viability before a commitment to system production must be made. Based on the results from a series of workshops and Blue Ribbon Panels of leading military, industry, and academic experts that addressed the problems of  large-scale software system acquisition (Boehm and Scacchi, 1996), we can identify five issues involved in achieving and transitioning to the overall goal:

          We need to first baseline our current understanding of strengths and weaknesses of current "as-is" process capabilities for acquiring software-intensive systems. Guidelines, best practices, and lessons learned are being collected and disseminated. The Software Technology Support Center (STSC 1995) and the Software Program Managers Network (SPMN 1997) have assembled recent collections. Nonetheless, we also need to understand how they are employed; as well as identify the operational problems which may inhibit their application and success.

          We need to develop scenarios for new "to-be" acquisition process capabilities that exploit an evolutionary "virtual" approach to the acquisition of software-intensive systems. Such an approach emphasizes the incremental acquisition of virtual prototypes for a new software-intensive system. These prototypes start as models of the intended system. These system models can be analyzed and simulated to determine which system requirements and risks have been addressed. As familiarity and confidence with the prototypes' increases, their realism and functionality increases with the incremental integration of system components. In this way, virtual prototypes of systems can be incrementally modeled and iteratively reconfigured with simulated or actual subsystem components. The development and production of a growing number of complex Electro-mechanical assemblies are now designed, tested, and refined through the use of computational models and simulations as virtual prototypes (Garcia, Gocke, and Johnson 1994). Similarly, the availability of Battle Labs suggests the use of virtual battlefields and command centers for trying out or exercising complex defense systems in alternative scenarios, through computer-based modeling and simulation test-beds operating within networked laboratories (Cothran 1996, Wilson 1996). Accordingly, approaches such as these may also prove to be effective in supporting the acquisition of software systems. In this way, viability and cost-effectiveness of system requirements can be demonstrated, validated, and refined in an incremental manner. Similarly, estimates for the cost, schedule, and performance of an ever-more complete actual system can also be developed and refined incrementally. Subsequently, we should also consider developing methods and scenarios for how to transition from the as-is to the to-be acquisition process we envision.

          We need to articulate the design and operational concept for a wide-area modeling and simulation infrastructure whose primary purpose is to serve as a test-bed/delivery platform for agile acquisition of software-intensive systems. Such an infrastructure may need to support collaboration and resource sharing between software system researchers and developers at geographically distributed sites. It may operate as a modeling and simulation collaboratory (Kouzes, Meyers, and Wulf 1996) for software system acquisition. Similarly, such an infrastructure may need to support a hypermedia repository or digital library of technical data and information that can be accessed and shared over the Internet or World Wide Web (WWW). Such a digital library should store and organize access to software acquisitions assets. These may include publications, model and simulation libraries, reusable software subsystem components, system demonstration scenarios, multi-media presentations and annotations. In addition, the digital library may provide paths to super computing environments that support massively parallel simulations, etc.

          We also want to understand how future acquisition processes or capabilities might exploit the full range of technology strategies/options at hand. The goal is to minimize cost, maximize customer satisfaction (via system performance and quality attributes) and minimize acquisition/development cycle time. Relevant technologies that can support this goal include the use of knowledge-based systems, multi-media, the Internet, electronic commerce for selling and buying software components, architecture-based software system development, high performance computing and communications, etc. Will new modes of academic research or industrial activity be required to most effectively support agile acquisition? If so, what are they? Similarly, what institutional or marketplace incentives are needed to help make them happen?

          We need to prioritize and estimate the relative costs and benefits of candidate investments in modeling and simulation capabilities that support software system acquisition. We need to identify areas in which needs can be met largely through available technology. We also need to identify areas in which acquisition research and the development of automated acquisition support environments promise an attractive return-on-investment.

          Background and Foreground

          We may now be at the threshold of a new era in the acquisition and development of software-intensive systems. From this point, we can look back to where we have been and what we have experienced. Then we can look forward toward the horizon to see what lies ahead.

          Looking back: why use models and simulations to support program acquisitions

          In looking back, we see that the acquisition and development of software-intensive systems was guided by the classic "waterfall" system life cycle. In such an approach, DoD customers were expected to be able to articulate their needs and requirements for new system capabilities prior to system development. Developers or contractors could then take these requirements as their starting point. Then they would systematically develop, test, and deliver results to the customer according to a sequence of development milestones and documentation standards. While this approach has much rational appeal, its practice and outcome was often less than satisfactory. The overall experience was that it was difficult for customers to fully articulate their system requirements prior to the beginning of system development. Furthermore, when system development took years, the customer (and the developer) recognized their requirements were changing, sometimes very rapidly. Consequently, far too many systems developed under contract were delivered that did not meet critical system requirements. In the worst cases, the software systems were effectively non-operational. Subsequently, more customers and developers began to recognize that perhaps these shortfalls in software acquisition and development were systemic, rather than simply characteristic of particular programs or development organizations.

          In response to the seemingly inevitable shortfalls with the classic approach, effort to find an alternative began. This led to an incremental "spiral" development approach. In the classic approach, there is little visibility regarding operational software system capabilities until late in the development cycle. In contrast, the spiral approach embraces a more evolutionary and iterative development model. Accordingly, operational software capabilities become visible in evolutionary increments, rather than all at once. Subsequent development iterations then add and integrate more increments until the final system is ready. Thus the spiral approach seeks to build and deliver software-intensive systems through evolutionary development. Consequently, guidelines now put forth in military or public standards such as  MIL-STD-498, ANSI J-STD-016, and US 12207 encourage use of an incremental spiral approach when acquiring and developing software intensive systems.

          Why should we use models and simulations to support the incremental acquisition of complex software systems? In simplest terms, we can identify three reasons: First, to facilitate early identification and reduction the risks associated with complex system acquisition programs. Second, to better understand what kinds of system requirements and architectures are feasible and affordable given various programmatic and technological constraints. Third, to gain insight into how to better manage system engineering effort so as to improve the overall likelihood of a successful, acquisition effort. However, the creation, use, and reliance of models and simulations to support incremental acquisition efforts cannot guarantee such outcomes. Clearly, models and simulations of complex systems will never be more than assumption-laden approximations of the systems being acquired. This is the fate of all models and simulations (cf. Smith 1996). Nonetheless, the process of building, using, and evolving such models and simulations in support of decision-making activities in large system acquisition efforts can be characterized as one of  consensus validation (Dutton and Kraemer 1985). Thus, the value of supporting system acquisition through modeling and simulation will be found in the process of working with them, rather than in the calculations performed along the way. Modeling and simulation can be employed to help identify where consensus can be established and validated, as well as to identify where disagreements can be found, and their  consequences examined.

          Program managers, contractors, customers, and acquisition directorate staff can employ models and simulations coordinated by a negotiation support system. Such a system can support the elicitation, capture, and validation of points of agreement among system acquisition participants. In addition, such a system can help these people surface assumptions, debate their merits or implications, and negotiate alternative system configurations and functional features (cf. Boehm et al. 1995). In this manner, computer-based models and simulations, together with an information sharing and negotiation support environment, provide a  more articulate medium to express opinions and stimulate alternative conceptions of system acquisition problems and challenges. Without such articulate models and simulations, system acquisition participants are left to their private intuitions and conceptions of system design, program cost drivers, and the like. This in turn can easily obscure problems in system design or performance, increase the likelihood of miscommunication and systemic conflict, and increase the likelihood of problematic system acquisition and costly post-deployment support of the resulting systems. Thus, we believe that models, simulations, and associated environments can play a significant role in supporting the incremental acquisition of complex software systems.

          Looking ahead: An emerging case study

          We see many opportunities for improving the effectiveness and responsiveness of the acquisition of software-intensive systems across their life cycle. Many of these opportunities result from the availability of new technologies and development capabilities that make the acquisition of software-intensive system more agile. Agility can lead to more cost-effective, more timely, and higher quality results in software system acquisition. Modeling and simulation technologies that support virtual prototyping (Garcia, Gocke, Johnson 1994) and simulation-based design of complex hardware systems are being used to support major program acquisitions, such as that for the SC-21 class of battleships (SC21, 1997). We believe a similar effort is appropriate for acquisition of the large software systems associated with such hardware systems. Accordingly, by examining the currently proposed software systems intended to support  SC-21 class ships, we can better motivate and articulate a vision for how new modeling and simulation technologies can be used to help support the incremental acquisition of complex software systems.
           
           

          There is no single architecture or final design envisioned for SC-21 ships. Instead, the SC-21 ships could be built following the commercial practice of developing a product line with common subsystems or reusable designs. Figure 1 helps show what this means. Here we see four alternative views of the overall architecture of SC-21 ships. The intent to enable the choice of the final architecture of each ship to be determined by emerging need or threat. Nonetheless, any such SC-21 ship will still have some configuration of common sub-systems for weapons, command deck, flight operations, etc. As such, all of the alternative versions of ship architecture displayed in Figure 1 would be members of the SC-21 product line.

          Figure 1. Alternative overall architectures for SC-21 ships (SC21, 1997)

          Building these ships according to different architectural configurations represents a fundamental change in how such ships will be acquired, developed, and operated. The system life cycle for these ships will be iterative, incremental, and ongoing. Figure 2 conveys a vision for how various computer-based modeling and simulation technologies, such as virtual weapon system modeling and simulation-based design, may be employed to support the acquisition, development, and operation of SC-21 ships.
           
           

          Figure 2. Vision for how modeling and simulation can support new system acquisition and development (SC21, 1997)



          SC-21 ships will be software-intensive systems. All major sub-systems and overall system capabilities supporting each ship's operations depend on software. Figure 3 proposes a suggested allocation of shipboard sub-system capabilities that will be implemented in software systems. Total number of software instructions or source lines of code (SLOC) to realize the proposed capabilities is estimated at greater than 8.4 million SLOC. However, much of this software can potentially be reused across the SC-21 line of ships. Nonetheless, development costs for software of this size and complexity is often estimated in the range of  $100,000,000 to $1,000,000,000. Thus, what can be done to help (a) understand the feasibility of alternative software sub-system architectures associated with the SC-21 ship family, and (b) manage the progress, costs, and risks associated with the acquisition and development of this software.
           
           


           
           

          Figure 3. Software systems proposed for SC-21 ships (SC21, 1997)

          At present, there is an emerging consensus for what  technological capabilities are needed to support the acquisition and development of software-intensive systems such as the family of SC-21 ships (cf. Boehm and Scacchi, 1996). Much like the SC-21 family of ship hardware and major sub-systems employs recent advances in modeling and simulation technologies, similar technologies could be brought together to support the acquisition and development of the software systems for these ships. Accordingly, we can now outline a strategy for how this would work. We then follow with a discussion of the technological and organizational transitions likely to be encountered in the course of adopting this strategy.  Along the way, we describe an approach  for how to assess the feasibility of complex software systems through its incremental development spiral. In addition, we describe a road map that lays out the research, technology, and usage's needed to support the acquisition of software systems, such as those for the SC-21 line of ships.

          The VIrtual SysTem Acquisition (VISTA) Vision

          The virtual acquisition of software systems refers to a strategic process by which an evolving series of ever more complete and operational system versions are acquired through a series of short duration acquisition life cycles. In this way, emphasis is on re-framing and reducing acquisition cycle times from years to months (or weeks!) so as to focus attention on the incremental and iterative acquisition of the evolving capability associated with the target software system.

          Reductions in acquisition cycle time enable an increase in the number of  incremental acquisition cycles over time. The VISTA approach seeks to help more rapidly identify, address, and resolve the risks associated with the acquisition and development of complex software-intensive systems (Boehm and Scacchi 1996; GAO 1997; Haimes, Schooff, and Chittister 1997). Thus, we need tools that enable customers and developers to rapidly model, incrementally evolve, and satisfy (sub) sets of system capability requirements in each iterative system version. Early acquisition cycles only need to focus on acquiring systems that only represent computational models and simulations of the operational capability of the target software system. Later acquisition cycles then focus on incrementally evolving or replacing the models and simulations with fully operational system modules. In this manner, there will always be an operational version of the system to evaluate and demonstrate throughout the system's acquisition and development cycle.

          Models and simulations represent descriptive, formalized, and sharable understandings of a system. They can represent a system's concept of operation, architecture, and its ability to support its intended mission. However, by focusing effort to enable such preliminary system capabilities to move through a fast acquisition life cycle, the goal is to establish and validate consensus on whether current models and simulations of the software system's components/architecture address specific system requirements. In addition, the goal is to determine whether other underdeveloped or unrecognized system requirements have emerged that need to be addressed in subsequent acquisition/development cycles. As such, the goal here is more closely aligned with the idea of incrementally growing and evolving the target system in a more organic and adaptive manner.

          Our first take on the requirements for how this might work can be outlined as follows:

          • Acquisition participants should be able to architect, construct, assemble, execute, and analyze automated models of the overall software system capability for acquisition.
          • Component models should represent elements of target environment (including people), information system infrastructure, informational products, and development, operation, and post deployment processes.
          • The initial modeling and simulation of these elements should represent the first pass through the system's requirements generation and development cycle.
          • Participants should be able to iteratively refine and incrementally evolve the system model test-bed from previous steps. Then they should be able to selectively replace component models with simulated, prototype, or actual component elements.
          • Participants should be able to iteratively refine and evolve intermediate hybrid system test-beds and progressively replace remaining component models with simulations, prototypes, or actual component elements. This helps to insure that a full-scale test-bed is developed, operational, and ready for post R&D deployment or transition into commercial use.
          Subsequently, we can take this outline of requirements for what we envision and reformulate it into a first-cut prescriptive process, which we call the VISTA Approach.

          The VISTA Approach

          At this point, we outline a series of steps that articulate how software system acquisition and development become intertwined during virtual system acquisition processes. As modeling and simulation drive most of these steps, we first describe what types of models are necessary. We will also characterize what these models may look like, and how they could be represented. Then we will briefly describe how these models and simulations would be incrementally replaced when evolving the system.

          Modeling and simulation in VISTA

          For this discussion, we assume the envisioned system is within the scope of available software system product families at hand.  If not, then a domain analysis leading to the construction and refinement of an appropriate meta-model will be needed. Product families and their associated "smart" product models (SC21 1997), documents, development processes, tools, and organizational agents are defined and represented using meta-models. Detailed examples of their use can be found elsewhere (Mi and Scacchi 1990, Mi and Scacchi 1996, Scacchi and Mi 1997). We then begin with the elicitation and modeling of a virtual system model (VSM) for the system to be acquired.

          The VSM is a composite model: a model composed from other models. At least three types of models are needed to characterize a complex software system. One class of models is needed to represent the functional operation and data required for information processing by the system. We will call models of this type; information element models (IEMs). Once an IEM is replaced with an operational system component, it becomes an information element (IE). IEMs are used to model the structure, behavior, and performance (estimated, measured, or required) of the computing hardware and software that inputs, processes, and outputs system data. A second class of models is needed to depict the functional behavior of the IEs embedded within a man-machine system (E.g., Command and Control System, Theater Air Dominance System, Mission Support System, etc. in Figure 3) to be acquired and built. We call these system element models (SEMs), and when replaced, system elements (SEs). The third class is needed to represent the "system of systems", sensors, and environmental context in which the embedded man-machine systems operate. These are called environment element models (EEMs), and when replaced, environment elements (EEs).

          Each type of model requires a computational mechanism that can support model entry/definition, interpretation, simulation, and animated visualization. Commercially available discrete-event simulation packages represent one such mechanism. These packages are well suited for simulating models that are represented as queuing networks whose arrival queues and service rates are specified according to statistical or algebraic models.

          Different types of models may require different kinds of simulation; thus different tools may be needed. For example, modeling and simulating the "look and feel" and event-based operation of a graphic user interface for a Military Support Training System may employ multimedia authoring or navigation tools. Commercially available tools such as Macromedia Director, Microsoft Powerpoint, or even Web browsers accessing virtual reality content across an Intranet can be used for this purpose. Rapid application development (RAD) tools (Visual Basic, PowerBuilder, Visual Cafe for Java, etc.) and expert system shells (E.g., M.4 from Teknowledge) that support software prototyping or visual programming with persistent databases can enable the modeling and simulation of complex, rule-based, state-transition software applications. These are tools for developing virtual prototypes of IEs (cf. Garcia, Gocke, Johnson 1994). With these tools, it is possible to model, simulate, or approximate the behavior of software applications using stubbed, canned, or pre-calculated input/output data values as place holders for complex calculations required of an eventual software system implementation. As such, modeling and simulating a VSM may benefit from use of a computing environment where multiple types of models and simulations can be defined, composed, simulated, and displayed. Furthermore, it may be desirable for such an environment to be accessible over the Internet to facilitate the sharing, discussion, and review of modeling and simulation efforts among the different organizational representatives participating in a program acquisition.

          IEMs can be modeled in a variety of ways. A common tactic may be to depict IEMs as hierarchically decomposed black boxes (closed systems), white boxes (open systems), or gray boxes (closed systems with limited internal visibility). These boxes are placeholders for hardware or software system modules that are to be acquired and developed. Each box can represent a computation unit that can receive inputs or event signals, perform some calculation, then produce some outputs, state transition, or some new event. They can be modeled and simulated using any of the tools noted above. However, depending on the kind of acquisition concern we wish to address, particular tool choices may be most appropriate. For example, in SC-21 class ships, it may initially be an open question as to what level of computer performance is required to satisfactorily operate Mission Support software systems. A desktop PC is probably inadequate, while a large mainframe may be too much, too large, or too expensive. Thus, it seems appropriate to consider modeling the required computing hardware as a computational module with mid-range performance or processing throughput (E.g., 10-100 transactions per second) as a starting point. Further, since determining system performance throughput under different Mission Support workloads or traffic volume is necessary, then a discrete-event simulation package may be best to use.

          However, the software system modules to operate on this anticipated hardware may or may not be so readily understood. If we initially have little knowledge of what calculations or information is required in processing Mission Support data, then the software's model may simply equate to that of a module that produces a stream of input/output data transactions, say in the range of 0-8 transactions per second. Alternatively, as knowledge increases, software modules may be identified which perform different functions.

          It should be possible to evaluate alternative architectural configurations or compositions of software modules as a way to understand whether system performance parameters are sensitive to the alternatives. For example, in a Mission Support Combat Training System, one could separate user input capture and verification, calculation and database update, and output to user display as three distinct software modules. Should these modules be configured in as a linear sequence, a fully interconnected concurrent network, or bundled together as a single large module? Which alternative configuration would be easiest to build and test? Which would have the best performance? Which would be the least cost? Perhaps we could guess the best answer(s). However, if we can model, simulate, and collaboratively discuss the three architectural alternatives, then we can begin to articulate a basis that can lead to a consensus answer that can be backed up with evaluated alternatives and simulation results.

          Would the consensus results from such a modeling and simulation exercise be more believable than someone's best guess? In lieu of some controlled experiment, the answer is to that is subjective. However, the modeling and simulation results would be explicit, repeatable, and subject to trade-off analysis and consensus validation. In addition, these results can be open to challenge and reformation in a manner that may be more tractable than someone's best guess. Nonetheless, if someone such as a software architect experienced in the design of Mission Support Combat Training Systems can argue persuasively about her best guess, then this alternative could be represented in an IEM, simulated, compared and validated.

          SEMs provide the ability to embed software systems within man-machine systems setting. SEMs embed IEMs or IEs in a user-driven input and output environment. Users create inputs in response to their work assignments, and to information output from the system and displayed to them. For example, when using a Training System, users may select among "menu items" or enter system commands. This may cause the Training System to process their input, provide an updated user interface display, then wait for the user's next input action. As such, SEMs must model user behavior in driving and responding to system actions or events, as well as model system behavior in response to user actions. While user behavior is open-ended, only a range of possible user-system interactions will be modeled. For example, users can only provide either acceptable input, acceptable but erroneous input that is detected, or unacceptable input. SEM simulation may include the use of software "drivers" that cause the arrival of user input or input events, together with system responses or service time intervals that follow statistical formulas or some other characterization function. SEM simulation can then be supported using common discrete-event simulation tools if user behavior is being simulated. Alternatively, if the system's behavior is being simulated for real users, then multimedia or RAD tools may be employed to provide simulated user interfaces for real users to evaluate. As with the IEM simulations, the plausibility and consensus validation process noted above will also apply here.

          EEMs provide the ability to embed the man-machine systems in its overall environmental context. For example, weapons control systems may be designed to utilize various sensors (radar, sonar, satellites, etc.) to zero in on their targets. These sensors may themselves be complex systems. Similarly, weapons control systems will interact with many other shipboard systems, including those for Mission Support, Command and Control, etc. These systems must act in concert to realize the overall effectiveness of a complex system of systems that a ship of the SC-21 class represents. Therefore, EEMs must model the interoperation and integration of multiple systems. This may entail modeling the overall patterns of data or messaging traffic between systems, as well as between systems and users as a group. Alternatively, in response to different scenarios for total system engagement, the EEMs may be used to model the ebb and flow of information across the system of systems. With this, we expect that the patterns of information flow on a SC-21 ship in response to a hostile attack scenario will be different than the flows associated with routine ship operations and maintenance scenario. Subsequently, these information traffic or flow patterns can be modeled and simulated using discrete-event simulation capabilities, and the validation process described earlier again applies here.

          Overall, the remaining challenge is to integrate and interoperate the different models, simulations, and elements. This is the purpose of a collaborative test-bed such as a Battleship Lab for SC-21 class ships (cf. Cothran 1996, Kouzes, Meyers, and Wulf 1996, Wilson 1996). It may serve to support the integration and interoperation of multiple, mixed mode models and simulation tools, as well as of multiple system elements with many models and simulations. At this time, developing such a test-bed may be an expensive but nonetheless necessary proposition. However, even if the cost of such test-beds approaches 5-10% of system development costs, such an investment may be reasonable given that the total overall effectiveness of the system platform is long-lived, software-intensive, and thus software-dependent.

          Again, our objective is to find ways to facilitate the articulation and elaboration of requirements, risks, and cost-drivers for complex, software-intensive systems. It is also to assist those involved in system acquisition to understand how modeling and simulation tools and techniques can be used. As such, we now turn to provide a brief description of how incremental system acquisition and development would proceed in replacing the system models with operational elements and system components.

          Incremental replacement of system models with operational system components

          Given that we have outlined the overall VISTA approach for modeling and simulation, we can describe how this approach could work in the context of acquiring a software system. We examine software systems for SC-21 class ships, although we limit our discussion to a representative subset of software systems for these ships. We use Mission Support systems in our discussion. Accordingly, we describe how the information, system, and environment elements for Mission Support are incrementally acquired and developed in a series of spiraling iterations following the approach. We show how these elements can change in progressing from models to actual software system architectures. Similarly, we identify what difference it makes to improve the acquisition of software.

          The VISTA approach begins with the acquisition of a VSM for Mission Support. A team of participants from the program office, acquisition directorate, user representatives, and prospective contractors may specify the VSM. The team might employ a wide-area collaboratory environment to share and record information giving rise to the VSM. However, perhaps only the contractors would be tasked with the modeling development activity.

          The VSM can be subjected to analysis, simulation, redesign, visualization, and walk-through. Figure 4 provides a concept diagram for how this might appear if we focus on an architectural configuration of IEMs (the computer or software elements), SEMs (the physical or human elements), and the EEMs (the external stimuli outside the system boundary). As shown in Figures 4 through 6, multiple IEMs, SEMs, and EEMs are used. This reflects the notion that the scope and depth of different models may be limited, compartmentalized, or may be divided among different organization contractors, sub-contractors, program office, etc.).

          Figure 4. Initial VSM Development Cycle

          In acquiring an initial VSM for Mission Support systems, many kinds of models are used. For example, IEMs designate computer hardware and software subsystems. SEMs denote Operational Readiness Test System, Combat Training System, and Display System. Also, EEMs are needed for other shipboard systems (E.g., Command and Control System), sensors, and environment factors (weather, combat vs. routine operations, etc.). Emphasis in developing the initial VSM is on deciding what kinds of modeling and simulation tools to use for the different types of model elements. Also, emphasis is directed at how to integrate the modeled elements into an architectural configuration so that the simulated elements can interoperate. This is shown in Figure 4. Subsequently, if all VSM element models can be satisfactorily simulated at this point using a discrete-event simulation package, then the integration and interoperation challenges are reduced or eliminated.

          Given that the VSM can be developed, we need to exercise and test it to explore the proposed system's ability to satisfy the requirements of its customers, users, development contractors, program managers, etc. Similarly, we need to explore the trade-off among desired system functional capabilities, performance objectives and costs. A wide-area software requirement negotiation and collaboration environment, such as the Win-Win environment developed at USC (Boehm et al. 1995), could be used for this purpose. Collaboration environments like Win-Win enable various system acquisition and development participants to discuss the relative merits of the VSM, its ability to identify or demonstrate system requirements, and to determine and validate where there is consensus in these areas. For example, user representatives may believe that response time to user input commands should not be more than one second. The contractors may note that while such system performance may be essential for the Combat Training System, it may not be needed by the Operational Readiness Test System. Thus, it would be unnecessarily costly to the program to make it so. To help clarify their position, the contractors input the two alternative system performance requirements into the computer hardware IEM simulation. Executing the simulation using the two performance measures may produce interesting comparative results. For instance, if users of the Operational Readiness Test System can accept, a four second response time, the required computer hardware performance can be realized at an appreciably lower cost, perhaps saving millions of dollars (cf. Boehm and Scacchi 1996). With this result at hand, the team agrees to revise the requirements for this information element. As such, the VSM is revised and calibrated to use this information.  This helps to illustrate the how iterative analysis, simulation,  performance monitoring, and benchmarking can improve understanding system requirements, and how to identify areas where virtual system acquisition efforts can reduce costs.

          In a later acquisition and development cycle, the team decides to assemble particular element components using fully operational and architecturally configured sub-assemblies. Here, the contractors must replace the corresponding model or simulation elements with operational prototypes or actual operating elements. Figure 5 provides a diagram for how this hybrid system and hybrid test-bed might appear. For example, an EEM for sonar and radar sensors may be replaced with a test-bed instrument that can generate realistic sensor input data. The Display System for Mission Support may now be fully operational, and the computer hardware that supports the Display System may be operational. Accordingly, the Display System SEM can be replaced with the operational Display System SE, and the computer hardware IEM can be replace with its corresponding IE. Nonetheless, even with these virtual system elements replaced with operational components, the overall VSM test-bed can still be accessed and evaluated using a collaborative wide-area environment for requirements negotiation and validation (cf. Boehm et al. 1995, Kouzes, Meyers, Wulf, 1996).

          Once operational components are integrated into the VSM, it becomes possible to more systematically walk-through, exercise, monitor, record, and replay the revised VSM hybrid tested. This can help to validate choices, explore further tradeoffs, and articulate systemic bottlenecks or processing failures in the system's architecture (Scacchi and Mi 1997). For example, while evaluating the operational performance of the Display System that interacts with the Combat Training System, it appears to users that important information of the user display is being updated too fast for users to act appropriately. Instead, the rate of information display needs to be slowed, or the information content needs to be aggregated and summarized. Thus, from the user standpoint, the current system operation in the VSM is risky or infeasible. As before, system element parameters need to be adjusted, otherwise alternative system architectures need to be considered and evaluated.

          Figure 5. Intermediate VSM Development Cycle

          With an intermediate VSM, further elaboration is needed to field a deployable system (see Figure 2). If this is the case, then the acquisition and development team must revisit the selection of software/system components to develop. Otherwise, they can perform partly-simulated operational test and evaluation, then experimentally field the system either across a wide-area Intranet test-bed (Scacchi and Noll 1997), or in a Battleship Lab test-bed, in order to continue to calibrate and refine the VSM for further post deployment studies. Thus, here we seek to illustrate how virtual system acquisition can help identify potential risks and attendant cost drivers that may not be manifest until field operation stages of the system's overall life cycle.
           
           

          When further system capabilities are needed, the participants can exercise the VSM. This means they may adjust simulation parameters, have users test-drive and evaluate system prototypes, etc. to determine tradeoffs and validate priorities through consensus. Consequently, they may choose to revisit the selection of components to acquire and develop. Jumping ahead, the acquisition and development participants can continue to evolve and continuously improve the emerging system architecture. This entails iterating through the preceding steps until all remaining system component simulations or prototypes are replaced by their operational counterparts. Figure 6 provides a diagram for how this late stage system architecture might now appear.

          Figure 6. Final VSM Development Cycle




          Here we see that all of the system and information element models have been replaced with their operational elements. Some EEMs however remain, since they may designate other major shipboard system undergoing concurrent development. Thus, while the sensor test-bed may be operational and integrated to interoperate with the Mission Support Systems, the Command and Control System as well as other major systems may not yet be operational and available for integration. However, these other systems must still conform to their EEMs placeholders for use with the Mission Support System. Subsequently, an additional capability is required for characterizing or extracting an updated EEM from this VSM. This updated information needs to be used in other VSMs corresponding  to environment elements that constitute the system of systems. From a technical standpoint, this requires addressing problems in system component interface definition, and in managing concurrent access to different versions of these components or model placeholders. From an organizational standpoint, failing to coordinate access and propagation of component interface definitions or changes is a common problem that precipitates difficulty in systems integration and interoperability. Knowing where problems lie, and being able to prevent or circumvent them through virtual system acquisition, provides another capability for reducing risks and costs associated with the development of software-intensive systems.

          Finally, throughout the overall VISTA process we have just outlined, current best practices in software program management (SPMN 1997), and a consensus recommendation from the Blue Ribbon Panels (Boehm and Scacchi 1996), point to the opportunity to track and manage software feasibility/risk using new program management support tools. Figure 7 provides a view of the user interface "dashboard" to such a tool, as well as suggesting how program management information may be conveyed.

          Figure 7. A program management dashboard for assessing software development progress (SPMN 1997)




          Participants in a virtual system acquisition also need to track, organize, record, and store records of the steps they took. Furthermore, they may need to document what transpired, how, by whom, why, and with what outcomes. These records and documents represent important knowledge assets emerging from the acquisition effort. Capturing and organizing this information is often cumbersome and haphazard. However, we find that these knowledge assets can be easily captured and linked to the virtual system models and elements using hypertext mechanisms commonly available in information sharing and requirement negotiation support environments (Noll and Scacchi 1991, Boehm et al. 1995), rather than being cast as a mountain of paper.

          With this basis for VISTA approach, we can now put forward a matrix of the transitional steps for how to realize the technical basis for supporting VISTA. This is then followed by a description of the organizational transitions for VISTA.

          Mapping the technological transitions to VISTA

          Although the VISTA-based approach may be a radical departure from traditional system acquisition practice, getting there may be best achieved in an evolutionary manner. To be clear, the VISTA approach is new, but the tools, techniques, and concepts it involves--incremental acquisition and development, virtual prototyping, wide-area collaboratories, software requirements negotiation and validation environments, etc.--are beginning to be used in system acquisition efforts. Thus, as VISTA implies the need to use an automated support environment for modeling, simulation, and program management, the required tools and techniques for such an environment can be investigated, refined, and deployed in a multi-staged manner. An integrated information management environment to support the acquisition and development of complex software systems, such as those for the SC-21 program, is not yet available. However, such an environment can be constructed and put into use following the roadmap outlined below and elsewhere (Boehm and Scacchi 1996). The resulting environment can then be positioned to support large system acquisition programs.

          We can explain the technological basis to support the transition to VISTA in terms that cover its anticipated (i) usage in acquisition, (ii) its technology, and (iii) the research needed to realize its technology and usage. At the same time, we can characterize how each of these three aspects correspond to the software system development life cycle stages that include (a) system concept definition, (b) architecture definition, and (c) on-going spiral development. Together, we can associate each of these into a matrix that organizes the VISTA research, technology, and acquisition usage as shown in Table 1.
           
           


             
          -- Technology Maturity -- 
               

          Research

          Technology

          Acquisition
          Usage

                     
          Software/

          System

          Life

          Cycle

          Stages

          Concept
          Definition

           
          Software Feasibility 

          Heuristics 

          VISTA-1:

          Top-Level 

          Feasibility Advisor, 

          Parametric Models 

          Concept 

          Feasibility 

          Determination

          Architecture 
          Definition

           
          Arch. representation 

          and analysis M&S, 

          Advanced cost/ 

          schedule/quality M&S. 

          VISTA-2:

          Models and Simulations 

          of Subsystems 

          and Elements 

          Architecture 

          Feasibility 

          Determination

          Spiral
          Development

           
          Integration 

          into commercial 

          SDEs 

          VISTA-3:

          Hybrid Measurement, 

          Modeling and Simulation 

          Environment 

          Virtual 

          System 

          Acquisition

          Table 1: VISTA Research, Technology, and Usage Context

          Moving from top to bottom, right to left, we can outline the associated operational concepts for VISTA, thereby characterizing the technological transitions "from ends to means."
           

          Concept Feasibility Determination: Given a new mission or strategic objective, determine whether appropriate technology, architectures, and resources can be feasibly brought together into a new software-intensive system in an affordable and timely manner.

          Architecture Feasibility Determination: Given a proposed software system architecture, determine whether it can satisfy mission or strategic objectives in an affordable and timely manner.

          Virtual System Acquisition: Given a feasible system concept and architecture, acquire the proposed architecture as a series of modeled, simulated, or implemented subsystems. These subsystems can be evolutionarily developed by progressively replacing or transforming the modeled or simulated subsystems with prototyped or real implementations.

          VISTA-1, Top-Level Feasibility Advisor, Parametric Models: A top-level feasibility analysis-modeling environment is needed for checking established acquisition heuristics and parameters. Such an environment could be used to determine whether the candidate technologies, architectures, and resources can be brought together to address a new mission or strategic objectives. This environment would represent the first version of the VISTA support environment (VISTA-1). The environment proposed by the Software Program Managers Network (cf. Figure 7), together with software cost estimation tools, software requirements negotiation capabilities, and access to a collection of software feasibility heuristics are available today for experimentation and initial usage (Boehm et al. 1995, STSC 1995, SPMN 1997).

          VISTA-2, Software-Intensive Models and Simulations: VISTA-2is an enhanced VISTA-1 environment for software-intensive modeling and simulation. It could be used to prototype, analyze, and execute system architectural capabilities and functionality, then reconcile these performance characteristics against the cost, schedule, and quality trade-off among proposed architectural design alternatives. VISTA-2 is used order to determine whether proposed application system architectures are viable.

          VISTA-3, Hybrid Measurement, Modeling and Simulation Environment: The VISTA-3 environment is built to expand the capabilities of VISTA-2. In order to acquire incrementally developed software application systems, VISTA-3 can be used to support the cooperative modeling, simulation, and measurement of the performance capabilities of an evolving application system, its subsystems, and their collective architectural design.

          Software Feasibility Heuristics: We need to collect, validate, and refine a knowledge base of best practice heuristics for software system acquisition, architecture, and overall development. This knowledge could help provide plausible advice for how to assess the top-level feasibility of an emerging software application system. These heuristics can help determine what matters, and which technology, architecture, or resource characteristics affect the overall feasibility of the system (Rechtin 1991, STSC 1995, SPMN 1997).

          Architecture representation and analysis M&S and Advanced cost/schedule/quality M&S: We need to research and develop new architectural representations that support incremental building and evolving large application systems using models or simulations. These representations also must be able to incorporate the architectures of its subsystems, whether as already implemented or newly development components. We further need to be able to represent the cost, schedule, and quality associated with the development of different software components or architectural configurations.

          Integration into commercial software development environments (SDEs): In order for VISTA tools to be broadly applied across the spectrum of DoD or other large-scale system acquisitions, they need to become available as extensions (E.g., "plug-ins" or "helper applications") to commercially available software engineering environments.

          With this context for VISTA research, technology, and acquisition usage in mind, we can now more simply characterize the overall concept for how the VISTA might be employed. This can be outlined in four steps:

          • Pre-proposal Requirements Analysis: Use the VISTA environment to analyze feasibility of the system's concept and mission requirements (a sanity check on the technical perspective for a new mission program to determine rough order of magnitude for cost, architecture, other risk items, etc.) prior to the Request For Proposal.
          • Proposal Analysis: Upon receipt of development contractors' proposals, use VISTA to analyze each proposal for feasibility, determine which proposals are in competitive range, and what assistance is needed to evaluate the technical perspective (E.g., architecture) of those proposals within competitive range.
          • Project Start-up: Use VISTA to evaluate the feasibility of resources (cost, people, etc.) and schedule of proposed system design. This could also be used for "fly-off" scenarios as well, when competing designs are being evaluated.
          • Ongoing Program Review: Use VISTA to re-analyze feasibility at progress milestones during development life cycle, as well as when significant program or system requirements changes occur.
          VISTA should be applicable to product-line software system architectures, as well as to unique non-product-line software systems. It appears that the VISTA may be more readily suited to product-line software system architectures, since their recurring development can accommodate the collection, refinement, and calibration of the VISTA for the product-line's application domain. However, it may also be useful for (portions of) non-product-line software, especially where a well-conceived reference model standard, such as the Air Force's Horizon Architecture defines the software. Nonetheless, within the domains of C4I, air traffic control, MIS, and other applications, we may expect future systems to be more likely to conform to product-line architectures. Industry trends and corporate strategies may then lead system development contractors to focus their expertise and core competencies around the mastery of product-lines, rather than individual products or contracts.

          Managing the organizational transitions to VISTA

          The move to adopt, implement, routinize, and replicate the VISTA approach seems to be a radical departure from current system acquisition practices and processes. While we believe that a compelling technical argument can be made for the VISTA approach, we must also address the kinds of organizational situations or changes that must be part of the transition to VISTA.

          Personnel will be unfamiliar with VISTA and what is required to re-engineer the processes they enact during system acquisition. Mutually respected, collaborative education, elicitation, and information sharing among the participating user, development contractor, and program management organizations will be required. WWW-based collaborative work environments or acquisition collaboratories (cf. Kouzes, Meyers, and Wulf 1996) can help provide the information infrastructure needed to support this. However, participation and engagement in re-engineering system acquisition, development, and program management must span all levels of the organization chart, and must achieve commitment, resources, and strategic attention from executive and senior management in order to increase the likelihood of success (Bashein, Markus, and Riley 1994).

          Our characterization of as-is system acquisition processes and practices, as well as to-be VISTA based approaches are understated. Clearly, there is far more detail to system acquisition or virtual system acquisition processes and practices than can be described here. Furthermore, we recognize that both as-is and to-be approaches to system acquisition are put into practice in different ways, in different organizational settings, for different system acquisitions. Capturing, understanding, and describing these variations require systematic research, empirical investigation, and wide-area dissemination. However, experience has shown that this attention to detail can lead to distinguishing what's common from what's circumstantial. Such detail will help surface specific actions to take to successfully engage personnel to collaborative identify and perform the organizational transformations needed to transition from the as-is to the to-be.

          Next, as the world moves towards a globally networked information infrastructure based on the Internet and WWW, we recognize that the information systems and computer-based tools supporting the acquisition, development, and program management will increasingly become heterogeneous relative to one another (cf. Noll and Scacchi 1991, Scacchi and Noll 1997). Interoperability will not be easily achieved without the experience and expertise needed to make it happen. However, new information technologies are rapidly emerging that will give rise to new ways to more rapidly configure, interconnect, and integrate software systems in order to enable them to interoperate. Furthermore, what's likely to be critical during early VISTA-based acquisition and development cycles is realizing interoperability at the organizational process level , rather than only at the traditional system function level. Experience shows that addressing and resolving interoperability between distinct organizations, such as those participating in a system acquisition, can often lead to ways to obviate, minimize, or avoid system function interoperability dependencies (STSC 1995). This helps to refine, streamline, and focus both system architecture and system development processes.

          Last, as indicated earlier, attention in this article is directed at emphasizing the re-tooling and re-engineering system acquisition processes and system feasibility assessment. However, a greater payoff can potentially result from complementary incorporation of process reengineering concepts, techniques, and tools into VISTA approaches (cf. Nissen 1997, Scacchi and Mi 1997, Scacchi and Noll 1997, Scacchi et al. 1997). For example, recent efforts at redesigning acquisition and procurement processes for the Navy have identified a number of ways these processes can be transformed and streamlined to realize substantial reduction in cycle times and administrative costs (Nissen 1997, Scacchi et al. 1997). However, these capabilities have not been used to support the acquisition of large software systems and thus require further investigation. Nonetheless, the vision of a 21st. Century "digital government" raises such matters to be the subject of systematic acquisition research and empirical investigation befitting a grand challenge to the academic, industrial, and government research community (Schorr and Stolfo 1997). Subsequently, the acquisition community needs to stimulate research that can find new ways to radically streamline program operations, reduce system costs, and improve service quality through re-engineering, reinvention, and systematic utilization of emerging information technologies and infrastructures.

          Conclusions

          In this article, we identified opportunities for research and application of modeling, simulation, and evolutionary development technologies to re-tooling and re-engineering system acquisition processes. These tools and techniques can help to analyze overall feasibility and risks at various points in the system acquisition life cycle. Such a capability offers the potential to reduce software system acquisition risks and avoidable costs, as well as explore alternative system options in order to develop more affordable, capable, and flexible systems. Subsequently, we use the new SC-21 battleship program as a case study to help illustrate and explain how virtual system acquisition can work.

          We put forward a vision and approach for how to rethink the manner in which software-intensive systems can be acquired across the acquisition life cycle. Central to this vision is a new approach to virtual system acquisition we call VISTA. We believe that VISTA offers a new strategy for how to address, resolve, or mitigate the recurring problems that accompanies complex system acquisition. Major program acquisitions such as the SC-21 class of ships, the Joint Strike Fighter and others are positioned to take advantage of timely investment and adoption of VISTA strategies and support environments.

          VISTA is a new approach to the acquisition of software-intensive systems. It seeks to build on knowledge of best practices in as-is acquisition and development processes, as well as moving toward a re-tooled and reengineered to-be software systems acquisition and development process. The acquisition of complex systems such as the SC-21 class of ships will use virtual prototyping and manufacturing tools to acquire and build virtual ships using collaborative wide-area computer-based environments. However, modeling and simulation tools and techniques have not yet been proposed to support the acquisition and development of the software systems needed to make the overall ship system operational and effective. Thus, we propose to fill this gap with the VISTA approach.

          We believe that tools, techniques, and concepts embodied in the VISTA approach merit consideration and application in forthcoming large-scale system acquisitions. These include incremental acquisition interleaved with development, virtual prototyping, wide-area collaboratories, and software requirement negotiation and validation environments. However, it would be misleading to indicate that they are being used together in the manner we suggest. The VISTA approach needs to be experimentally applied and refined. Accordingly, an R&D technology roadmap was presented that lays out a path for the iterative, incremental evolution and integration of the technologies needed to support the VISTA vision. The technologies needed to support the VISTA approach need to be brought together and made accessible to different acquisition participants.

          The VISTA approach we presented is a vision of how the acquisition of software-intensive systems can be designed and streamlined for use in the years ahead. Major system acquisition programs such as the SC-21 battleships or Joint Strike Fighter aircraft are representative candidates for the VISTA approach. The success of programs such as these will depend in part on the successful acquisition and development of the software systems that enable these platforms to do their job. VISTA represents a substantial department from and alternative to present software system acquisition practices (STSC 1995, SPMN 1997). Nonetheless, we have cast it in a manner that shows how to incrementally transition from the technology and organizational practices that today support software system acquisition to the VISTA approach we envision.

          Finally, moving to adopt and practice VISTA-based system acquisitions is not without its risks. Accordingly, we have sought to identify the technological and organizational transitions that must be researched, modeled and simulated to help reduce the risks and improve our understanding of how to evolve system acquisition practices and support environments to help see the way to the VISTA. In this sense, the VISTA approach could be demonstrated by applying it to the acquisition and development of a software system that incorporates the concepts in this paper and related reports (Boehm and Scacchi 1996).

          References

          B.J. Bashein, M.L. Markus, P. Riley. Preconditions for BPR success: and how to prevent failures, Information Systems Management, 7-13, 1994.

          B. Boehm, P. Bose, E. Horowitz, and M. J. Lee, Software Requirements Negotiation and Renegotiation Aids: A Theory-W Based Spiral Approach, Proc. 17th International Conference on Software Engineering, Seattle, WA, April 1995.

          B. Boehm and W. Scacchi. Simulation and Modeling for Software Acquisition (SAMSA), Final Report, Center for Software Engineering, University of Southern California, Los Angeles, CA, http://sunset.usc.edu/SAMSA/samcover.html , March 1996.

          J. Cothran. Battle Labs: Tools and Scope,  Acquisition Review Quarterly , Winter 1996.

          W.H. Dutton and K.L. Kraemer. Modeling as Negotiating: The Political Dynamics of Computer Models in the Policy Process, Ablex, Norwood, NJ, 1985.

          Lt. Col. A.B. Garcia, Col. R.P. Gocke Jr., Col. N.P. Johnson Jr. Virtual Prototyping: Concept to Production, Defense Systems Management College Press, Fort Belvoir, March 1994.

          General Accounting Office.  Defense Weapons Systems Acquisition, Report GAO/HR-95-4, 1995.

          General Accounting Office. Air Traffic Control--Immature Software Acquisition Processes Increase FAA System Acquisition Risks, Report GAO/AIMD-97-47, 1997.

          R.T. Kouzes, J.D. Meyers, and W.A. Wulf. Collaboratories -- Doing Science on the Internet, Computer, 29(8):40-48,  August, 1996.

          P. Mi and W. Scacchi. A Knowledge-Based Environment for Modeling and Simulating Software Engineering Processes. IEEE Trans. Knowledge and Data Engineering , 2(3):283-294, 1990.

          P. Mi and W. Scacchi. A Meta-Model for Formulating Knowledge-Based Models of Software Development. Decision Support Systems, 17(3):313-330. 1996.  http://www.usc.edu/dept/ATRIUM/Papers/Process_Meta_Model.ps

          M.E. Nissen. Reengineering the RFP Process Through Knowledge-Based Systems. Acquisition Review Quarterly, 4(1):87-100, Winter 1997 .

          J. Noll and W. Scacchi. Integrated Diverse Information Repositories: A Distributed Hypertext Approach, Computer, 24(12):38-45, December 1991.

          W. Rechtin. System Architecting: Creating and Building Complex Systems . Prentice-Hall, Englewood Cliffs, NJ. 1991.

          W. Scacchi and P. Mi. Process Life Cycle Engineering: A Knowledge-Based Approach and Environment. Intern. J. Intelligent Systems in Accounting, Finance, and Management, 6(1):83-107, 1997. http://www.usc.edu/dept/ATRIUM/Papers/Process_Life_Cycle.html

          W. Scacchi and J. Noll. Process-Driven Intranets: Life-Cycle Support for Process Reengineering. IEEE Internet Computing, 1(5):42-49, September-October 1997.

          W. Scacchi, J. Noll, C. Knight, and Capt. F.J. Miller. Re)Engineering Research Grants Management: From Acquisition Reform to Knowledge Brokering at ONR, Paper presented at the NSF Workshop on Research and Development Opportunities for Federal Information Services, Arlington, VA,  May 1997.  http://www.usc.edu/dept/ATRIUM/NSF-FIS-Workshop.html

          R.M. Schooff, Y.Y. Haimes, and C.G. Chittister. A Holistic Management Framework for Software Acquisition, Acquisition Review Quarterly, Winter 1997.

          H. Schorr and S. Stolfo. Towards the Digital Government of the 21st. Century, Final Report, NSF Workshop on Research and Development Opportunities for Federal Information Services, http://www.isi.edu/nsf/final.html , June 1997.

          B.C. Smith, Limits of Correctness in Computers, in R. Kling (ed.), Computerization and Controversy, Academic Press, New York, NY, 810-825, 1996.

          (SC21) SC-21 Information System, http://sc21.crane.navy.mil, 1997.

          (SPMN) Software Program Managers Network. The Condensed Guide to Software Acquisition Best Practices, October 1997. Available from SPMN at http://www.spmn.com/products.html .

          (STSC) Software Technology Support Center. Guidelines for Successful Acquisition and Management of Software-Intensive Systems: Weapon Systems, Command and Control Systems, Management Information Systems. Volumes 1 & 2. Dept. of the Air Force, February 1995.

          J. Wilson. Battle Labs: What Are They, Where Are They Going? Acquisition Review Quarterly, Fall 1996.
           
           

          http://www.ics.uci.edu/~wscacchi/Presentations/OSS-Requirements/Understanding-OSSD-Requirements-Abstract.html Understanding-OSSD-Requirements-Abstract



          Understanding the Requirements for Developing Open Source Software Systems


          Walt Scacchi
          Institute for Software Research
          University of California, Irvine
          Irvine, CA 92697-3425 USA
          http://www.ics.uci.edu/~wscacchi
          wscacchi@ics.uci.edu

          Abstract
          This study presents an initial set of findings from an empirical study of socio-technical processes, system configurations, organizational contexts, and interrelationships that give rise to open source software. In this presentation, the focus is directed at understanding the requirements for open software development efforts, and how the development of these requirements differs from those traditional to software engineering and requirements engineering. Four open software development communities are described, examined, and compared to help discover what these differences may be. Eight kinds of "software informalisms" are found to play a critical role in the elicitation, analysis, specification, validation, and management of requirements for developing open software systems. Subsequently, understanding the roles these software informalisms take in a new formulation of the requirements development process for open source software is the focus of this study. This focus enables considering a reformulation of the requirements engineering process and its associated artifacts or (in)formalisms to better account for the requirements for developing open source software systems.


          http://www.ics.uci.edu/~dan/class/165/projeval.html Project Submissions and Evaluation

          CompSci 165 Project Submissions and Evaluation

          Project Policy

          • Each project is to be done by teams of size two (see Team Policy, below)
          • You may make use of ideas found in any book, journal, or website, or obtained from talking with any person.
            HOWEVER, you must be careful to give proper attribution to any such sources
            by indicating in your code and in your project write-up the full reference for each such source
            • book/journal title, author, and page number, or
            • full URL of the webpage, or
            • name of person from whom you received a "private communication"
          • Code must be original, and may not be copied or shared from any other source, except as provided by the class instructor

          Team Policy and Registration

          • Teams for each of the CompSci 165 projects consist of two CompSci 165 students
          • For each project, students choose their teammates by mutual consent and agree who will be the team head member
          • For each project, the head member of each team shall send a "registration email"
            • to be sent from the head member's UCI email address
            • to be sent to the class Grading Assistant, with cc to the class instructor and to the other team member's UCI email address
            • contains (1) full names and (2) UCI ID names of the project team members
              but do not submit ID numbers
            • to be sent before 9:00am on the registration due date listed on the Class Schedule
            A return email will acknowledge receipt of the team registration
          • Students who have not registered on time will be assigned to teams by the instructor
            • the instructor will also designate who is to be the head member for assigned teams
            • students on assigned teams will receive email notification of their team membership roster

          Project Submissions

          • Projects are to be submitted to the class dropbox before 9:00am on the submission deadline date listed on the Class Schedule.
            Instructions for using EEE dropbox can be found at https://eee.uci.edu/help/dropbox/students/
          • No late submissions will be accepted
          • For each project, the team head member should submit a zip file containing the following:
            • makefile, enabling automatic compilation
            • All source files
            • README.txt, containing documentation, analysis and other comments
            • If you have any, sample run outputs should be in a file named OUTPUT.txt

          Project Evaluation

          Projects will be evaluated based on the following broad criteria
          • Correctness -- the program system works according to project requirements
            • C/C++ source compiled (using the make utility) using gcc/g++ on Linux
            • a makefile must be provided
            • no syntax errors, no run-time errors, and the correct output must be produced
            • improper input gracefully handled, allocated space returned after it no longer is needed,
              and error-checking performed after calling any system or library function
          • Program structure -- well modularized, no gross inefficiencies (space or time)
          • Internal documentation -- concise comments, indentation, descriptive variable names, explain "magic" numbers
          • README.txt -- keep this very short please
            • how to use the program
            • description of data structure implementation (some of this may be internal)
            • theoretical complexity analysis of the underlying algorithms
            • production of a function (based on empirical observations and analysis)
              to predict the time required by your system for larger instances of the problem
          • Bonus points -- each project may specify special features or competitive characteristics (versus other teams)


          Last modified: Feb 1, 2016 http://www.ics.uci.edu/~dan/class/165/TIME/index.html Timing

          Timing on your computer

          In order to determine the relative execution efficiency of various algorithms, you will need to use a timer package.  I have available (see below) for your use such a package written in C.  However, with advance approval from me, you may use other packages. Note that CPU time is used by your program (user time) as well as the system (system time). We will be interested in capturing the values of the user time usage only.

          A problem:  On some systems, the timing function gives times in multiples of .01 seconds.  On others, the timing coarseness may be significantly smaller or larger.  (You should determine the timing coarseness for your machine.)  Thus, for example, on a system with .01 second timing coarseness, timing anything which takes less than one fiftieth of a second could result in more than a 50% error.  Your programs, for small values of n, should be easily this fast.

          A solution:  When the algorithm to be timed is too fast to obtain an accurate measurement, the algorithm can be run many times in succession, with the cumulative time being measured.  This time can then be divided by the number of runs to obtain a more accurate measurement of the time required by an individual run.  As little as possible, other then the successive calls, should occur while the timing is taking place.  In fact, for particularly speedy code, the time spent updating the loop variable may be of the same order of magnitude as the time you are attempting to measure.  To account for this, you can subsequently time the loop with a (nearly) empty body and subtract this measure from your first measure, the net result reflecting more accurately the time spent within the code of interest. One should note that some optimizing compilers will optimize loops that contain an empty body by discarding them.

          Timer package

          Program file timer.c should be compiled and linked with your program system.  It defines the following routines:
          • user_time()
            returns the total amount of user cpu time used by the current process
          • start_timer()
            starts the timer [actually, it only records the current value of user_time()]
          • elapsed_time()
            returns the amount of cpu time that has been used since the last call to start_timer()

          Header file timer.h declares the routines that are defined in timer.c and it should be included in all program files that invoke any of those routines.

          Note that the MinGW system does not have the proper version of include timing files. If you were to try to compile the timer.c program file in MinGW, you would get compilation errors. Accordingly, for your own personal use (not to be used for course submissions), you may make use of file timerMinGW.c instead, which I believe should work on the MinGW system.


          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan (at) ics.uci.edu
          Last modified: Mar 13, 2014 http://www.ics.uci.edu/~dan/class/165/compilers.html Compiler Considerations for CompSci 165

          Compiler Considerations for CompSci 165

          • Be aware that using an ANSI compliant compiler that has the standard libraries is a necessity
          • Many such compilers are available, including gcc which is available on all ICS machines
          • I enjoyed using the djgpp command line environment for the PC (XP and 32-bit Windows 7)
          • After getting 64-bit Windows 7, I enjoy using the MinGW command line environment
            • However, note that MinGW does not have all of the timing include files (although there is a way to fix that)
          • Some people enjoy using the Code::Blocks IDE
            • But others have said that they had compilation problems using it
            • It seems to be missing some library functions and/or include files needed for timing
          • The Microsoft compilers that I have seen do not use the standard libraries
            • This page has Microsoft's suggestions on how to write code that works in the largest number of C compilers (i.e., portable code)
            • Here is a tip from a CompSci 165 student on using Visual Studio

          Last modified: Mar 13, 2014 http://www.ics.uci.edu/~dan/midi/stones/index.html Stones

          Stones

          All Over Now Angie As Tears Go By
          Brown Sugar Can't Be Seen Fool To Cry
          Get Off My Cloud Jumpin Jack Flash Miss You
          Paint it Black Ruby Tuesday Satisfaction
          She's So Cold Start Me Up You Can't Always Get
          What You Want

          Dan Hirschberg
          Computer Science
          University of California, Irvine, CA 92697-3425
          dan at ics.uci.edu
          Last modified: March 15, 2000 http://www.ics.uci.edu/~dan/midi/beatles/ Beatles

          Beatles

          A Day In The Life
          A Hard Day's Night
          A Taste Of Honey
          Abbey Road medley
          Across The Universe
          Act Naturally
          All My Loving
          All Together Now
          And I Love Her
          And Your Bird Can Sing
          Any Time At All
          Baby It's You
          Baby You're A Rich Man
          Back In The USSR
          Bad To Me
          Because
          Being For The Benefit Of Mr. Kite
          Birthday
          Black Bird
          Boys
          Can't Buy Me Love
          Carry That Weight
          Chains
          Come Together
          Day Tripper
          Dear Prudence
          Do You Wanna Know A Secret
          Drive My Car
          Eight Days A Week
          Eleanor Rigby ( 2 )
          Everybodys Got Something_to_hide
          Fixing A Hole
          Flying
          Fool On The Hill
          For No One
          Free As A Bird ( 2 )
          From Me To You
          Get Back
          Getting Better
          Girl
          Golden Slumbers
          Got To Get You Into My Life
          Hello Goodbye
          Help
          Here Comes The Sun
          Here There And Everywhere
          Hey Bulldog
          Hey Jude
          Honey Don't
          Honey Pie
          I Am The Walrus
          I Call Your Name
          I Don't Want To Spoil The Party
          I Follow The Sun
          I Me Mine
          I Saw Her Standing There
          I should have Known Better
          I Want to Hold Your Hand
          I Will
          I'm Lookin Through You
          I'm So Tired
          I've Just Seen A Face
          If I Fall
          Imagine
          In My Life
          It Won't Be Long
          It's Only Love
          Julia
          KC
          Lady Madonna
          Let It Be ( 2 )
          Liverpool
          Love
          Love Me Do
          Love You To
          Lovely Rita
          Lucy In The Sky With Diamonds
          Magical Mystery Tour
          Maxwell's Silver Hammer
          Maybe I'm amazed
          Me And My Monkey
          Mean Mr. Mustard
          Michelle
          Misery
          Mother Natures Son
          Mustard
          No Reply
          Norwegian Wood
          Nowhere Man ( 2 )
          Ob-La-Di, Ob-La-Da ( 2 )
          Oh! Darling
          Old Brown Shoes
          Only A Northern Song
          Paperback Writer
          Pennylane
          Piggies
          Please Mr. Postman
          Please Please Me
          Real Love
          Revolution
          Rocky Racoon
          Roll Over Beethoven
          Run For Your Life
          Savory Truffle
          Sexy Sadie
          She Loves You
          She's A Woman
          She's Leaving Home
          Something
          Strawberry Fields Forever
          Sun King
          Taxman
          The End
          The Fool On The Hill
          The Inner Light
          The Long And Winding Road
          The Night Before
          The Word
          There's A Place
          This Boy
          Ticket To Ride
          Til There Was You
          Twist And Shout ( 2 )
          Two Of Us
          Wait
          We Can Work It Out
          What Goes On
          When I'm Sixty-Four
          While My Guitar Gently Weeps
          Why
          Wild Honey Pie
          With A Little Help From My Friends
          Yellow Submarine ( 2 )
          Yer Blues
          Yesterday
          You Never Give Me Your Money
          You Won't See Me
          You're Gonna Lose That Girl
          You've Got To Hide Your Love Away
          You've Really Got a Hold on Me
          Your Mother Should Know


          Dan Hirschberg
          Computer Science
          University of California, Irvine, CA 92697-3425
          Last modified: Oct 15, 1999 http://www.ics.uci.edu/~theory/269/160212.html Theory Seminar, February 12, 2016

          ICS Theory Group

          Winter 2016: Theory Seminar
          ICS, Room 243, 1:00pm


          February 12, 2016:

          Algorithms for Two-Pass Connected Component Labeling

          Siddharth Gupta

          Connected Component Labeling (CCL) is an algorithmic application of Graph Theory. It's is an important step in pattern recognition and image processing. It assigns labels to the pixels such that adjacent pixels sharing the same features are assigned the same label. Typically, CCL requires several passes over the data.

          In this talk, I will focus on two-pass technique where each pixel is given a provisional label in the first pass whereas an actual label is assigned in the second pass. I will discuss about two algorithms for CCL along with Union-Find Technique used in those algorithms. I will also present a parallel implementation for the faster algorithm.

          (Joint work with Mostofa Ali Patwary and Ankit Agrawal in IPDPSW 2014.)

          http://www.ics.uci.edu/~theory/269/160219.html Theory Seminar, February 19, 2016

          ICS Theory Group

          Winter 2016: Theory Seminar
          ICS, Room 243, 1:00pm


          February 19, 2016:

          Higher Lower Bounds from the 3SUM Conjecture

          Tsvi Kopelowitz

          Abstract:

          The 3SUM Conjecture has proven to be a valuable tool for proving conditional lower bounds on dynamic data structures and graph problems. This line of work was initiated by Pătraşcu (STOC 2010) who reduced 3SUM to an offline SetDisjointness problem. However, the reduction introduced by Pătraşcu suffers from several inefficiencies, making it difficult to obtain tight conditional lower bounds from the 3SUM conjecture.

          In this talk I'll discuss the deficiencies of Pătraşcu's framework, then give new and efficient reductions from 3SUM to offline SetDisjointness and offline SetIntersection (the reporting version of SetDisjointness) which leads to polynomially higher lower bounds on several problems.

          (Joint work with Seth Pettie and Ely Porat from SODA 2016.)

          About the speaker:

          Tsvi Kopelowitz is a postdoctoral fellow in the Department of Electrical Engineering and Computer Science at the University of Michigan. He earned his Ph.D. from Bar-Ilan University in 2011, and has also been a postdoctoral fellow at the Weizmann Institute of Science.

          http://www.ics.uci.edu/~theory/269/quarters.html Theory Seminar ICS Theory Group

          ICS 269: Theory Seminar


          The Theory Group meets Fridays at 1:00.
          Programs are available for the following quarters:

            Winter 1996  
          Fall 1996 Winter 1997 Spring 1997
          Fall 1997 Winter 1998 Spring 1998
          Fall 1998 Winter 1999 Spring 1999
          Fall 1999 Winter 2000 Spring 2000
          Fall 2000 Winter 2001 Spring 2001
          Fall 2001 Winter 2002 Spring 2002
          Fall 2002 Winter 2003 Spring 2003
          Fall 2003 Winter 2004 Spring 2004
          Fall 2004 Winter 2005 Spring 2005
          Fall 2005 Winter 2006 Spring 2006
          Fall 2006 Winter 2007 Spring 2007
          Fall 2007 Winter 2008 Spring 2008
          Fall 2008 Winter 2009 Spring 2009
          Fall 2009 Winter 2010 Spring 2010
          Fall 2010 Winter 2011 Spring 2011
          Fall 2011 Winter 2012 Spring 2012
          Fall 2012 Winter 2013 Spring 2013
          Fall 2013 Winter 2014 Spring 2014
          Fall 2014 Winter 2015 Spring 2015
          Fall 2015    


          ICS, UCI,
          http://www.ics.uci.edu/~theory/269/160129.html Theory Seminar, January 29, 2016

          ICS Theory Group

          Winter 2016: Theory Seminar
          ICS, Room 243, 1:00pm


          January 29, 2016:

          Algorithmic Complexity of Power Law Networks

          Timothy Johnson

          It was experimentally observed that the majority of real-world networks are scale-free and follow power law degree distribution. The aim of this paper is to study the algorithmic complexity of such “typical” networks. The contribution of this work is twofold.

          First, we define a deterministic condition for checking whether a graph has a power law degree distribution and experimentally validate it on real-world networks. This definition allows us to derive interesting properties of power law networks.

          Secondly, we give a novel theoretical explanation why many algorithms run faster on real-world data than what is predicted by algorithmic worst-case analysis. We show how to exploit the power law degree distribution to design faster algorithms for a number of classic $\mathsf{P}$-time problems including transitive closure, maximum matching, determinant, PageRank and matrix inverse. Moreover, we deal with the problems of counting triangles and finding maximum clique.

          In contrast to previously done average-case analyses, we believe that this is the first “waterproof” argument that explains why many real-world networks are easier.

          (Based on a paper by Paweł Brach, Marek Cygan, Jakub Łącki, and Piotr Sankowski at SODA 2016.)

          http://www.ics.uci.edu/~theory/269/160122.html Theory Seminar, January 22, 2016

          ICS Theory Group

          Winter 2016: Theory Seminar
          ICS, Room 243, 1:00pm


          January 22, 2016:

          More Analysis of Double Hashing with Balanced Allocations

          Michael Mitzenmacher

          Abstract:

          With double hashing, for a key $x$, one generates two hash values $f(x)$ and $g(x)$, and then uses combinations $(f(x) +i g(x)) \bmod n$ for $i=0,1,2,\ldots$ to generate multiple hash values in the range $[0,n-1]$ from the initial two. For balanced allocations, keys are hashed into a hash table where each bucket can hold multiple keys, and each key is placed in the least loaded of $d$ choices. Here we extend a coupling argument used by Lueker and Molodowitch to show that double hashing and ideal uniform hashing are asymptotically equivalent in the setting of open address hash tables to the balanced allocation setting. We also discuss the potential for and bottlenecks limiting the use this approach for other multiple choice hashing schemes.

          A preliminary version of this paper was presented at ANALCO 2016.

          About the speaker:

          Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University. He has authored or co-authored over 150 conference and journal publications on a topics including algorithms for the Internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. His work on low-density parity-check codes shared the 2002 IEEE Information Theory Society Best Paper Award and won the 2009 ACM SIGCOMM Test of Time Award. His textbook on randomized algorithms and probabilistic techniques in computer science was published in 2005 by Cambridge University Press. He currently serves as SIGACT Chair.

          Mitzenmacher graduated summa cum laude with a B.A. in mathematics and computer science from Harvard in 1991. After studying mathematics for a year in Cambridge, England, on the Churchill Scholarship, he obtained his Ph. D. in computer science at U.C. Berkeley in 1996. He then worked at Digital Systems Research Center until joining the Harvard faculty in 1999.

          http://www.ics.uci.edu/~theory/269/160205.html Theory Seminar, February 5, 2016

          ICS Theory Group

          Winter 2016: Theory Seminar
          ICS, Room 243, 1:00pm


          February 5, 2016:

          On the Complexity of an Unregulated Traffic Crossing

          Nil Mamano

          The steady development of motor vehicle technology will enable cars of the near future to assume an ever increasing role in the decision making and control of the vehicle itself. In the foreseeable future, cars will have the ability to communicate with one another in order to better coordinate their motion. This motivates a number of interesting algorithmic problems. One of the most challenging aspects of traffic coordination involves traffic intersections. In this paper we consider a simple and fundamental geometric optimization problem involving coordinating the motion of vehicles through an intersection. We are given a set of $n$ vehicles in the plane, each modeled as a unit length line segment that moves monotonically, either horizontally or vertically, subject to a maximum speed limit. Each vehicle is described by a start and goal position and a start time and deadline. The question is whether, subject to the speed limit, there exists a collision-free motion plan so that each vehicle travels from its start position to its goal position prior to its deadline. We present two results. We begin by showing that this problem is $\mathsf{NP}$-complete with a reduction from 3-SAT. Second, we consider a constrained version in which cars traveling horizontally can alter their speeds while cars traveling vertically cannot. We present a simple algorithm that solves this problem in $O(n\log n)$ time.

          (Based on a paper by Philip Dasler and David M. Mount at WADS 2015.)

          http://www.ics.uci.edu/~dan/pubs/DataCompression.html Data Compression

          Data Compression

          Debra A. Lelewer and Daniel S. Hirschberg

          Table of Contents

          Abstract
          INTRODUCTION
          1. FUNDAMENTAL CONCEPTS
          1.1 Definitions
          1.2 Classification of Methods
          1.3 A Data Compression Model
          1.4 Motivation
          2. SEMANTIC DEPENDENT METHODS
          3. STATIC DEFINED-WORD SCHEMES
          3.1 Shannon-Fano Coding
          3.2 Static Huffman Coding
          3.3 Universal Codes and Representations of the Integers
          3.4 Arithmetic Coding
          4. ADAPTIVE HUFFMAN CODING
          4.1 Algorithm FGK
          4.2 Algorithm V
          5. OTHER ADAPTIVE METHODS
          5.1 Lempel-Ziv Codes
          5.2 Algorithm BSTW
          6. EMPIRICAL RESULTS
          7. SUSCEPTIBILITY TO ERROR
          7.1 Static Codes
          7.2 Adaptive Codes
          8. NEW DIRECTIONS
          9. SUMMARY
          REFERENCES

          Abstract

          This paper surveys a variety of data compression methods spanning almost forty years of research, from the work of Shannon, Fano and Huffman in the late 40's to a technique developed in 1986. The aim of data compression is to reduce redundancy in stored or communicated data, thus increasing effective data density. Data compression has important application in the areas of file storage and distributed systems.

          Concepts from information theory, as they relate to the goals and evaluation of data compression methods, are discussed briefly. A framework for evaluation and comparison of methods is constructed and applied to the algorithms presented. Comparisons of both theoretical and empirical natures are reported and possibilities for future research are suggested.

          INTRODUCTION

          Data compression is often referred to as coding, where coding is a very general term encompassing any special representation of data which satisfies a given need. Information theory is defined to be the study of efficient coding and its consequences, in the form of speed of transmission and probability of error [Ingels 1971]. Data compression may be viewed as a branch of information theory in which the primary objective is to minimize the amount of data to be transmitted. The purpose of this paper is to present and analyze a variety of data compression algorithms.

          A simple characterization of data compression is that it involves transforming a string of characters in some representation (such as ASCII) into a new string (of bits, for example) which contains the same information but whose length is as small as possible. Data compression has important application in the areas of data transmission and data storage. Many data processing applications require storage of large volumes of data, and the number of such applications is constantly increasing as the use of computers extends to new disciplines. At the same time, the proliferation of computer communication networks is resulting in massive transfer of data over communication links. Compressing data to be stored or transmitted reduces storage and/or communication costs. When the amount of data to be transmitted is reduced, the effect is that of increasing the capacity of the communication channel. Similarly, compressing a file to half of its original size is equivalent to doubling the capacity of the storage medium. It may then become feasible to store the data at a higher, thus faster, level of the storage hierarchy and reduce the load on the input/output channels of the computer system.

          Many of the methods to be discussed in this paper are implemented in production systems. The UNIX utilities compact and compress are based on methods to be discussed in Sections 4 and 5 respectively [UNIX 1984]. Popular file archival systems such as ARC and PKARC employ techniques presented in Sections 3 and 5 [ARC 1986; PKARC 1987]. The savings achieved by data compression can be dramatic; reduction as high as 80% is not uncommon [Reghbati 1981]. Typical values of compression provided by compact are: text (38%), Pascal source (43%), C source (36%) and binary (19%). Compress generally achieves better compression (50-60% for text such as source code and English), and takes less time to compute [UNIX 1984]. Arithmetic coding (Section 3.4) has been reported to reduce a file to anywhere from 12.1 to 73.5% of its original size [Witten et al. 1987]. Cormack reports that data compression programs based on Huffman coding (Section 3.2) reduced the size of a large student-record database by 42.1% when only some of the information was compressed. As a consequence of this size reduction, the number of disk operations required to load the database was reduced by 32.7% [Cormack 1985]. Data compression routines developed with specific applications in mind have achieved compression factors as high as 98% [Severance 1983].

          While coding for purposes of data security (cryptography) and codes which guarantee a certain level of data integrity (error detection/correction) are topics worthy of attention, these do not fall under the umbrella of data compression. With the exception of a brief discussion of the susceptibility to error of the methods surveyed (Section 7), a discrete noiseless channel is assumed. That is, we assume a system in which a sequence of symbols chosen from a finite alphabet can be transmitted from one point to another without the possibility of error. Of course, the coding schemes described here may be combined with data security or error correcting codes.

          Much of the available literature on data compression approaches the topic from the point of view of data transmission. As noted earlier, data compression is of value in data storage as well. Although this discussion will be framed in the terminology of data transmission, compression and decompression of data files for storage is essentially the same task as sending and receiving compressed data over a communication channel. The focus of this paper is on algorithms for data compression; it does not deal with hardware aspects of data transmission. The reader is referred to Cappellini for a discussion of techniques with natural hardware implementation [Cappellini 1985].

          Background concepts in the form of terminology and a model for the study of data compression are provided in Section 1. Applications of data compression are also discussed in Section 1, to provide motivation for the material which follows.

          While the primary focus of this survey is data compression methods of general utility, Section 2 includes examples from the literature in which ingenuity applied to domain-specific problems has yielded interesting coding techniques. These techniques are referred to as semantic dependent since they are designed to exploit the context and semantics of the data to achieve redundancy reduction. Semantic dependent techniques include the use of quadtrees, run length encoding, or difference mapping for storage and transmission of image data [Gonzalez and Wintz 1977; Samet 1984].

          General-purpose techniques, which assume no knowledge of the information content of the data, are described in Sections 3-5. These descriptions are sufficiently detailed to provide an understanding of the techniques. The reader will need to consult the references for implementation details. In most cases, only worst-case analyses of the methods are feasible. To provide a more realistic picture of their effectiveness, empirical data is presented in Section 6. The susceptibility to error of the algorithms surveyed is discussed in Section 7 and possible directions for future research are considered in Section 8.

          NEXT section

          http://www.ics.uci.edu/~dan/class/260/homework.html CompSci 260 Homework Assignments

          CompSci 260 Homework Assignments

          These homework problems extend and complement the material presented in the lectures and in the text

          PROTOCOLS

          • Homework is to be turned in to the class dropbox before 9:30am on the due date
            instructions for using EEE dropbox can be found at https://eee.uci.edu/help/dropbox/students/
          • No late submissions will be accepted
          • Homework sets will not be graded; they will only be checked to determine that you at least made a serious effort to do the problems
          • Some solutions will be discussed in class
          • I may call on one of the students who has solved a problem to present the solution to the class

          ASSIGNMENTS


          Last modified: Jan 3, 2016 http://www.ics.uci.edu/~dan/class/161/syllabus161.html Course Outline for CompSci 161

          Course Outline for CompSci 161

          ( * indicates topic to be discussed only if time allows,     week indications are approximate)

          Introduction [GT 1 and Appendix A;   CLRS 1-3, 6.1-6.3 and Append.A,B,C;   B 1, 3]
          1 Course requirements, induction proofs [B 3.4]
          Complexity and asymptotics [GT 1.1;   CLRS 3;   B 1.4]
          Models of computation
          Examples of algorithm analysis
           
          Searching, sorting, lower bounds [GT 5;   CLRS 6, 8;   B 4-5]
          2 Searching - sequential, binary, interpolation [GT 3.1;   B 1.6]
          Insertion sorts - straight, binary, Shellsort [B 4.2, 4.10]
          Lower bounds on sorting - inversions, travel, decision [GT 8.3;   CLRS 8.1;   B 4.7]
          3 Heapsort [GT 5.3-5.4;   CLRS 6;   B 4.8]
          Distribution sorts - bucket, lexicographic [GT 9.1;   CLRS 8.3;   B 4.11]
          Lower bounds on selection [B 5.1-5.3, 5.5]
          4 *Review
          .........first exam
           
          Divide-and-conquer [GT 8, 9, 11;   CLRS 4, 7, 9;   B 3.6-3.7]
          Selection of median [GT 9.2;   CLRS 9;   B 5.4]
          5 DC paradigm - Weighted median [GT 9.3], MaxMin
          Integer multiplication [GT 11.2]
          DC Theorem [GT 11.1;   CLRS 4.3;   B 3.6-3.7]
          Time analysis of Mergesort, Quicksort [GT 8.1-8.2;   CLRS 7;   B 4.4-4.6]
          Strassen matrix multiplication [GT 11.3;   CLRS 4.2;   B 12.3.4], *Skyline problem
           
          Dynamic programming [GT 12;   CLRS 15;   B 10]
          6 Product of matrices [GT 12.1;   CLRS 15.2;   B 10.3]
          Common subsequences [GT 12.5;   CLRS 15.4]
          Optimal binary search trees [CLR 15.5;   B 10.4]
          7 Nim, *Fibonacci numbers
          *Review
          8 .........second exam
           
          Graph algorithms [GT 13-15;   CLRS 22-25;   B 7-9]
          Minimum spanning trees - Prim, Kruskal, Boruvka, hybrid [GT 15;   CLRS 23;   B 8]
          9 Depth-first search - components, biconnectivity [GT 13.2, 13.5;   CLRS 22.3;   B 7.6-7.7]
          Transitive closure (Warshall), all-pairs shortest paths [GT 13.4.2, 14.5;   CLRS 25.1-.2;   B 9]
          Single-source shortest path (Dijkstra) [GT 14.2;   CLRS 24.2-24.3;   B 4.3]
           
          Other topics
          10 Pseudo-polynomial algorithms - 0/1 knapsack dynamic pgm'ng [GT 12.6]
          String matching - KMP algorithm [GT 23.3;   CLRS 32.1, 32.4;   B 11.3]
          *Probabilistic algorithms [CLR 5.3, 31.8]
          *Review
          11 .........final exam (cumulative)

          You are responsible for having read GT 1-3, 5, 8-9, 11-15

          References

          GT
          Goodrich and Tamassia, Algorithm Design and Applications, John Wiley, 2014.
          CLRS
          Cormen, Leiserson, Rivest, and Stein Introduction to Algorithms (3rd ed.), MIT Press, 2009.
          B
          Baase, Van Gelder Computer Algorithms (3rd ed.), Addison-Wesley, 2000.

          Last modified: Feb 10, 2016 http://www.ics.uci.edu/~dan/class/161/schedule.html Homework and Exam Schedule — CompSci 161 Winter 2016

          Homework and Exam Schedule — CompSci 161 Winter 2016

          week Monday   Wednesday   Friday
          1 Jan 4 homework #1   Jan 6     Jan 8 homework #2
          2 Jan 11 #1 due   Jan 13 homework #3   Jan 15  
          3 Jan 18     NO CLASS       Jan 20     Jan 22 #2 due
          4 Jan 25 #3 due   Jan 27 First Midterm   Jan 29 homework #4
          5 Feb 1     Feb 3 homework #5   Feb 5 #4 due
          6 Feb 8 homework #6   Feb 10     Feb 12 #5 due
          7 Feb 15     NO CLASS       Feb 17     Feb 19 #6 due
          8 Feb 22 Second Midterm   Feb 24 homework #7   Feb 26  
          9 Feb 29 homework #8   Mar 2 #7 due   Mar 4 homework #9
          10 Mar 7 #8 due   Mar 9     Mar 11 #9 due
          11             Mar 18 Final Exam at 8am

          Last modified: Jan 11, 2016 http://www.ics.uci.edu/~dan/class/161/policies.html Homework and Examination Policies

          Homework and Examination Policies

          Homework Policies

          • Each set of homework exercises will contain some required problems —
            these are to be turned in to the class dropbox before 9:00am on the due date.
            Instructions for using EEE dropbox can be found at https://eee.uci.edu/help/dropbox/students/
          • No late homework submissions will be accepted
          • Additionally, there will be suggested problems — these are not to be turned in
          • Some problems are marked with an *, indicating that they may be more challenging
          • Homework sets will not be graded for correctness; they will only be checked to determine that you at least made a serious effort to do the problems
          • Each student will have their worst homework assignment dropped, thus taking care of one instance of illness or forgetfulness
          • Solutions to homework exercises will be discussed in discussion sessions

          Quiz Policies

          • All quizzes are in-class, closed-book, closed-notes
          • Quizzes are to be strictly individual efforts — absolutely no collaboration is permitted
          • Quizzes will be handed out at the beginning of class, or in the middle of class, and will be collected 7-10 minutes later
            ( late-comers must be seated before being given a quiz, and will not be given any extra time )

          Examination Policies

          • All examinations are in-class, closed-book, closed-notes
          • Examinations are to be strictly individual efforts — absolutely no collaboration is permitted
          • Graded midterms may be picked up from the TA in discussion period or during TA office hours


          Last modified: Dec 21, 2014 http://www.ics.uci.edu/~dan/class/161/gradePolicies.html Grading Policies

          Grading Policies

          • Grades are awarded (also with + or -) as follows:
              A  = excellent
              B  = good
              C  = fair, a solid pass
              D  = poor, just barely passing
              F  = failing, does not meet minimum standards
          • There are no predefined percentage cut-offs for the various grades.
            The translation of percentages to letter grades will be tailored depending on the difficulty of the exam,
            but not a function of the class performance on the exam.

          • There is not a predefined fraction of the class that will get any particular grade on any exam or overall.
            The law of large numbers often applies, and the class average tends to be between C and B, but cohorts do vary in performance.


          Last modified: Nov 3, 2010 http://www.ics.uci.edu/~dan/class/161/references.html Reference Books for CompSci 161

          Reference Books for CompSci 161

          Author Title call number publisher ISBN number
          Aho, Hopcroft, Ullman Design & Analysis of Computer Algorithms QA 76.6 A36 1974 Addison-Wesley 0-201-00029-6
          Baase, Van Gelder Computer Algorithms, 3rd ed. QA 76.9.A43 B33 2000
          Addison-Wesley 978-020-161244-8
          Brassard, Bratley Algorithmics: Theory & Practice QA 9.6 B73 1988 Prentice Hall 0-13-023243-2
          Brassard, Bratley Fundamentals of Algorithmics QA 9.58 B73 1996 Prentice Hall 978-013-335068-5
          Cormen, Leiserson, Rivest, and Stein Introduction to Algorithms, 3rd ed. QA 76.6 I5858 2009 MIT Press 978-0-262-03384-8
          Dasgupta, Papadimitriou, Vazirani Algorithms QA 9.58 .D37 2008 McGraw-Hill 978-007-352340-8
          Goodrich, Tamassia Algorithm Design and Applications QA 76.9 A43 G668 2014 John Wiley 978-1-118-33591-8
          978-1-119-17216-1 (p)
          Kleinberg, Tardos Algorithm Design QA 76.9.A43 K54 2006 Addison-Wesley 978-032-129535-4
          Knuth The Art of Computer Programming, Vol. 1 (3rd ed.) QA 76.5 K57 v.1 1997 Addison-Wesley 0-201-89683-4
          Knuth The Art of Computer Programming, Vol. 3 (2nd ed.) QA 76.5 K57 v.3 1998 Addison-Wesley 0-201-89685-0
          Reingold Combinatorial Algorithms QA 164.R43 1977 Prentice-Hall 0-13-152447-X
          Smith Design and Analysis of Algorithms QA 9.58 .S57 1989 PWS-Kent 0-534-91572-8

          Last modified: Dec 9, 2015 http://www.ics.uci.edu/~dan/genealogy/Krakow/Bimage.html Krakow Birth Record Images 1837-89

          Krakow Birth Record Images 1837-89

                      SELECT year and Akt                
          Year 1837 1838 1839 1840   1841   1842   1843  
          num of Akts     640     547     607     542     551     560     632
          missing          avail 1-144      
          Year 1844 1845 1846 1847 1848 1849 1850  
          num of Akts     620     636     568     500     471     481     540
          Year 1874 1875 1876 1877 1878 1879 1880 1881
          num of Akts     792       832       917       897       876       955       805       861  
          missing       no akt 780       208-10    202-04   
          Year 1882 1883 1884 1885 1886 1887 1888 1889
          num of Akts     866       854       803       823       817       821       852       858  
          missing       616-18             
          HUNDREDs: 0  1  2  3  4  5  6  7  8  9 
          TENs: 0  1  2  3  4  5  6  7  8  9 
          ONEs: 0  1  2  3  4  5  6  7  8  9 
          specials 
            1837: a-b b-f f-h h-k k-n o-s s s-z, extras 
            1838: a-b c-g g-k k-m m-s s-w w-z 
            1839: a-b b-f f-h h-k k-l l-o o-r r-s s-w w-z
            1844: a-b b-f f-g g-k k-l l-p p-s s-t t-z 
            1845: a-b b-f f-h h-k k-n o-s s-w w-z
            1846: a-b b b-e e-f f-g g-h h-j
                          k k-l l-m m-r r-s s s-t NOTE u-z
            1847: NOTE a-b b b-e e-f f-g g-h h-k
                          k-l l-m m-p p-r r-s s-t t-w w-z
            1848: a-b b b-e e-f NOTE f-h h-j
                          i-k k-l l-m m-p p-s s s-u w w-z
            1849: a-b  b  b-e  e-f  f-g  g-j 
                          j-k  k-l  l-m  m-o  p-r  r-s  s  s-w  w-z 
            1850: a-b  b  b-e  e-f  f-g  g-h  h-k 
                          k  k-l  l-m  m-p  p-r  r-s  s  s-w  w  w-z 
            1848: about JudaLeib b.1846#62
            1871-75: A 12 23 3 4 5   
                         
          B 1 1 12 2 2 3 3 3 4 4 4 45 5 5 5
                         
          C 123 45    D 12 23 34 45 5    E 3 4 5 5
                         
          F 1 12 2 23 3 34 4 4 45 5 5
                         
          G 1 1 12 2 23 3 3 4 4 45 5 5 5
                         
          H 1 12 2 23 3 34 4 45 5 5    IJ 12 23 45 5
                         
          K 1 1 12 2 23 3 34 4 4 45 5 5
                         
          L 1 12 2 2 23 3 34 4 4 5 5 5
                         
          M 1 12 23 3 4 45 5    N 12 234 45 5
                         
          O 12 345 5    P 1 12 23 34 45 5   
                         
          R 1 12 2 23 3 3 34 4 45 5 5
                         
          S 1 12 23 3 34 4 5 5
                                  1 12 2 23 3 34 4 5 5
                                  12 23 34 45 5
                         
          T 12 23 34 45 5    U 1234 45    V 1234 5 (W3)
                         
          W 1 12 2 23 (V5) 3 34 4 45 5 5
                         
          Z 12 234 45 5
            1874: a-b b-d d-f f-g g-h h-k k-l
                          l-o o-r r-s s-v w-z
            1875: a-b b-f f-h h-l l-p p-s s-w w-z
            1874: 38 65 67 143 182 183 188
                          190 193 194 202 204 205 208 209
            1876:
            1877:
            1878:
            1881:
            1883: 205 430 580 583 652 745
            1884:
            1886:
            1888:
          http://www.ics.uci.edu/~dan/genealogy/Krakow/Dimage.html Krakow Death Record Images 1869-89

          Krakow Death Record Images 1869-89

           
              SELECT
              year & Akt

           
          Year 1869 1870 1871 1872 1873 1874 1875 1876
          num of Akts     553     730     404     526     1053     649     367     395
          missing 411-420(A)         441-450(A) 361-367(B)  
            HUNDREDS:   0    1    2    3    4    5    6    7    8    9    10
            TENS+ONES:   01+  11+  21+  31+  41+  51+  61+  71+  81+  91+
           
            SELECT
            year & Akt

           
          Year 1877 1878 1879 1880 1881 1882 1883
          num of Akts     563     534     443     551     628     473     510
          Note   10/page thru 270            
          Year 1884 1885 1886 1887 1888 1889
          num of Akts   524 +1 647     486     543     511     507
          Note also 445a 626-635, 636-645   paged       6-10 missing
            HUNDREDS:     0      1      2      3      4      5      6
            TENS+ONES: 01     11     21     31     41     51     61     71     81     91
                                        06     16     26     36     46     56     66     76     86     96
          1869-76 Akts pages:   (A):   Akt num,   record date,   death district,da,mo,yr,hs (5 cols),   burial da,mo,yr,city (4 cols),   person given name, parent/spouse, surname, occupation
                                           (B):   sex M/F (2 cols),   age yrs+months (2 cols),   status sngl/marr/wdw (3 cols),   cause of death

          1869-75 Death Indexes              
          1869   a-c   d-f   g-h   h-l   l-o   p-s   s-w   w-z
          1870   a-b   b-f   f-g   h-k   k-l   l-o   o-r   r-s   s-v   v-z
          1871   a-d   d-g   g-l   l-p   p-s   s-z   z
          1872   a-c   d-g   g-h   h-l   l-p   p-s   s-t   u-z
          1873   a-b   b-b   c-f   f-f   g-g   g-h   h-k   k-l   l-m   m-p   p-r   r-s   s-t   t-z
          1874   a-b   * b-f   * f-g   g-j   j-l   l-m   m-r   r-s   s-v   v-z
          1875   a-g   g-m   m-t   t-z
          http://www.ics.uci.edu/~dan/genealogy/Krakow/search.html Search Krakow Records

          Search Krakow Records

          Enter PATTERN
            to be searched:
          Norm Power
          DM soundex

          Surnames are usually in ALL CAPS
          smalls & CAPS
                  are different
          case insensitive
           
                   
          SELECT DATABASE(s)       At most 60 records displayed per database.       (Availability)
          all pre-1830 records all 1831-55 all post-1855    Family pages
            1798-1830 1831-1855 1856-1889 1890-1942
          census 1790, 1795
                hs owners 1797, 1807
          census 1870-1900
          voters 1911,1929,1935
                emigres Antwerp,
                Ellis Island, 1940 lists
          Births B B B  
          Marriages
          banns
          Deaths
          M,D M,D M,D M,D


           click for KRAKOW WEBSITE        Dan Hirschberg             Last modified: May 14, 2015             Viewed times since 12-15-'02. http://www.ics.uci.edu/~dan/genealogy/Krakow/1798image.html Krakow Record Images 1798-1809

          Krakow Record Images 1798-1809

          1798-1809  BIRTHS       ( * indicates page 1 has last births of previous year )
                      SELECT year and Page                
          Year 1798 1799 1800 1801 1802 1803
          num of Pages     12       11       8       9       8*       2  
          Year 1804 1805 1806 1807 1808 1809
          num of Pages     3*       6*       8       4*       3*       3*  
          Page: 1  2  3  4  5  6  7  8  9  10  11  12 
          1798-1808  MARRIAGES     ( * indicates page 1 has last marriages of previous year )  
                      SELECT year and Page                
          Year 1798 1799 1800 1801 1802 1803
          num of Pages     5       4       2*       5       6       3*  
          Year 1804 1805     1806 1807 1808
          num of Pages     4*       9       0       1       1  
          Page: 1  2  3  4  5  6  7  8  9 
          http://www.ics.uci.edu/~dan/genealogy/Krakow/help.html Free access to records

          Free access to records

          You can access many records for free by going to your local Mormon (LDS) History Center or other genealogical research center.
          There are many LDS locations in the U.S., Canada, and England, and quite a few locations in several other countries.
          You can find locations for your country at a Family Search web page.

          If they do not have a particular microfilm immediately available, you can make arrangements for them to obtain a loaner for a small fee that covers their postal costs.
          (A loaner film may require a few weeks to arrive.)

          Israel does not have any LDS centers.

          • The Central Archives in the Sprinzak building on the Givat Ram campus of the Hebrew University has some early LDS Krakow microfilms, to about 1845
          • The Goldmann Jewish Genealogy Center at Beth Hatefutsoth has all LDS Krakow microfilms to 1855 plus English translations of births 1851-55

           click for KRAKOW WEBSITE        Dan Hirschberg       dan at ics.uci.edu       Last modified: Sep 25, 2013 http://www.ics.uci.edu/~dan/genealogy/Krakow/LDSstats.html Krakow Vital Records Statistics

          Krakow Vital Records Statistics

          • number indicates at least this many records recorded that year
          • the associated names are available on-line for ALL records except
                    births (1856-70), marriages (1853-68), deaths (1855-68)
                    post-1900 records are not displayed on-line to the general public
          • bold blue numbers indicate additional record details available via search engine
          • bold red numbers indicate record images available on-line

          Year Births Marr's Deaths
          1798-99 336 66 -
          1800-04 712 183 -
          1805-09 745 112 -
          1810 67 - 58
          1811 288 23 126
          1812 201 36 154
          1813 143 36 153
          1814 117 41 119
          1815 71 18 102
          1816 17 17 74
          1817 219 23 207
          1818 390 28 210
          1819 437 44 248
          1820 313 +1 39 172 -1
          1821 386 49 277
          1822 415 48 285
          1823 465 43 272
          1824 477 48 268
          1825 421 62 280
          1826 424 66 279
          1827 350 73 278
          1828 307 58 339
          1829 436 63 396
          1830 467 52 388
             
          Year Births Marr's Deaths
          1831 383 60 444
          1832 466 95 420
          1833 530 117325
          1834 611 91 276
          1835 660 70 406
          1836 592 73 435
          1837 640 82 393
          1838 547 66 298
          1839 607 57 283
          1840 542 82 323
          1841 555 87 351
          1842 560 62 330
          1843 632 64 371
          1844 620 102 290
          1845 636 40 398
          1846 568 37 538
          1847 500 52 777
          1848 471 27 401
          1849 481 30 736
          1850 540 30 238
          1851 618 38 284
          1852 588 14 450
          1853 581 - 398
          1854 518 - 381
          1855 355 - -
             
          Year Births Marr's Deaths
          1852*-61 - 399 (107) -
          1856-60 2709 - -
          1861 222 - -
          1862 377 56 (17) -
          1863 359 65 (21) -
          1864 1017 35 (12) -
          1865 580 25 (8) -
          1866 790 42 (15) -
          1867 499 59 (20) -
          1868 748 42 (18) -
          1869 851 34 553
          1870 741 25 730
          1871 802 (490) 48 404
          1872 783 (523) 43 526
          1873 783 (555) 53 1053
          1874 792 60 +1 649
          1875 832 68 367
          1876 917 -1 68 395
          1877 897 +1 62 563
          1878 876 58 534
          1879 955 (3 missing) 81 443
          1880 805 (3 missing) 84 551
          1881 861 81 628
          1882 866 105 473
          1883 854 (3 missing) 123 510
          1884 803 110 524 +1
          1885 823 115 647 +1
          1886 817 119 486
          1887 821 160 543
          1888 852 144 511
          1889 858 137 (38) 507
          1890 863 117 (8) 295+
          1891 901 147 (16)  
          1892   165 (12)  
             
          Year Births
          1900 1035
          1901 925
          1902 921
          1903 913
          1904 922 -5
          1905 956 -9
          1906 956 -3
          1907 975 -8
          1908 960
          1909 973 -7
          1910 971 -3
          1911 937 -6
          1912 968 -3
          1917 530
          1918 546
          1939 1074 -6
          1940 599 -2
          1941 130 -2
          1942 57


          Last modified: Mar 9, 2015 http://www.ics.uci.edu/~dan/genealogy/Krakow/LDSfilm.html LDS Film Contents

          LDS Film Contents

          Notation:
          • B = Birth
          • M = Marriage
          • D = death
          • Akts = actual records
          • Index = list of names in alphabetical order with pointers to record numbers

          Krakow

          LDS film Contents Notes
          719252 B Akts 1810 no index, contains both Jewish/non-Jewish records
          718912 B Akts 1798-1809, 1811-19; D Akts 1816-19 Akts not numbered 1798-1811, no index 1815-19
          718913 B Akts 1820-29 no index 1820-21, index only first names 1825-27
          718914 B Akts 1830-36  
          718915 B Akts 1837-43  
          718916 B Akts 1844-50  
          718917 B Akts 1851-55 no index 1855
          718918 M Akts 1798-1808, 1811-16 Akts not numbered 1798-1808
          718919 M Akts 1817, M ReligIndex 1817-38, M Akts 1818-29 no index 1817-21
          718920 M Akts 1830-39  
          718921 M Akts 1840-52  
          719257 D Akts 1810 Akts not numbered, contains both Jewish/non-Jewish records
          718922 D Akts 1811-16, 1820-28 no index 1825-27
          718923 D Akts 1829-38  
          718924 D Akts 1839-47  
          741914 D Akts 1848-54  
          1201162 B Akts 1874, B Index 1874, B Akts 1875 #1-#180  
          1201163 B Akts 1875 [#181+], B Akts 1876 [#766+],
          B Akts 1877, M Akts 1876-77,
          D Akts 1876-77, B Index 1871-75 [A-S]
           
          1201164 B Index 1871-75 [T-Z], BMD Index 1877  
          1895664 B Akts 1876, M Akts+Index 1869-75,
          BMD Akts 1878-79, B Akts 1880 #1-#480
           
          1895665 BMD Akts 1880*-82, B Akts 1883 #1-#687  
          1895666 BMD Akts 1883*-85, BM Akts 1886  
          1895667 D Akts 1886, BMD Akts 1887-88, BD Akts 1889,
          BM Index 1878
           
          1895668 M Index 1878, BMD Index 1879-80, B Index 1881-85,
          MD Index 1881, BMD Index 1882-85, BM Index 1886
           
          1895669 D Index 1886, BMD Index 1887-88, BD Index 1889,
          Death Akts 1869-75
           
          2332843 1870 census - GMINA 7: Numer domu 1-42  
          2332844 1870 census - GMINA 8: Numer domu 1-39  
          2332845 1870 census - GMINA 8: Numer domu 40-71  
          2332846 1870 census - GMINA 8: Numer domu 72-141  
          2332848 1870 census - GMINA 8: Numer domu 226-273  
          2332849 1870 census - GMINA 8: Numer domu 274-351  

          Tarnow

          LDS film Contents Notes
          948420 D Index 1863-76, B Akts 1849-63  
          948421 B Akts 1863-70  
          948422 M Akts 1848-70, D Akts 1855-70  
          742702 B Akts 1808-49, D Akts 1808-53  


           click for KRAKOW WEBSITE        Dan Hirschberg       dan@ics.uci.edu       Last modified: Feb 25, 2012 http://www.ics.uci.edu/~dan/genealogy/Krakow/family.html Early Family Trees

          Early Family Trees

          ( explanation of tree format )

          A-K:

          L-Z: 

          As of Apr 2012, these family trees contained over 70,000 distinct individuals including   93% of all Krakow births 1830-55, and   79% of all Krakow births 1874-80.

           click for KRAKOW WEBSITE        Dan Hirschberg       dan at ics.uci.edu       Last modified: Sep 20, 2014       Viewed times since May 4, 2006 http://www.ics.uci.edu/~dan/genealogy/Krakow/CENimage.html Krakow Census Images 1870

          Krakow Census Images 1870

                      SELECT District and Image number        
          District 7 8
          number of Images             309     2984
          THOUSANDS: 0  1  2                 
          HUNDREDs: 0  1  2  3  4  5  6  7  8  9 
          TENs: 0  1  2  3  4  5  6  7  8  9 
          ONEs: 0  1  2  3  4  5  6  7  8  9 
          http://www.ics.uci.edu/~dan/genealogy/Krakow/other/index.html Other Jewish Krakow Documents

          Other Jewish Krakow Documents

          • Historical Atlas Polsky: no. 1st Map of the regional capital Krakow czteroletnego days the Diet (1788-1792)
          • Krakow students who successfully completed school year (1868,69,71)
          • Military Draft Krakow (1849-50)
          • tombstone inscriptions in Miodowa cemetery (text last updated Feb 22, 2004), Excel last updated Sep 3, 2003
          • Leipziger messgaste: die judischen besucher der Leipziger messen in den jahren 1675 bis 1764. by Max Freudenthal. J. Kauffmann Verlag, 1928.
            contains names of Jews from Krakow who traveled to Leipzig for fairs in the 1700s
            Cover   Intro   pp150-1   pp152-3   original Excel file   working file
          • Jews in Cracow at the time of the Confederacy of Bar (1768-72) by Majer Balaban
            (The zipped rtf version includes footnotes)
          • Articles by George J. Alexander
            • Krakow Jews around 1800 - A web of relationships
            • Krakow buildings: Aleksandrowicz residences 1802 - 1939
            • Searching for roots in Krakow, Poland
          • contents of LDS microfilms
          • equivalent names
          • endings of surnames
          • Polish occupations
          • Jewish population in Krakow
          • pp.497-498 in Balaban's book
          • 1890 NY arrivals from Poland
          • 1891 Galicia Business Directory
          • history and pictures of Kazimierz
          • names appearing on the memorial plaques of Kovea Itim Latorah synagogue
          • Krakow ghetto register
          • a page from Leszek Hondo's 1999 book
          • Krakow Shtetl link -- contains these items and many more
            • charity funds and donors (1888-1931)
            • school records (1866, 1870)
            • Lyceum students 1874-1982
            • synagogue records (1887, 1905, 1939)
              including the Remuh Shul Yahrzeits c1939 (zipped Excel)
            • cemetery records
            • maps of Poland and Krakow
            • tax records 1818-1821


           click for KRAKOW WEBSITE        Dan Hirschberg       dan at ics.uci.edu       Last modified: Jun 4, 2010 http://www.ics.uci.edu/~dan/genealogy/Krakow/Mimage.html Krakow Marriage Record Images 1869-88

          Krakow Marriage Record Images 1869-88

                              SELECT   year & Akt                
          Year 1869 1870 1871 1872 1873 1874 1875
          num of Akts     34     25     48     43     53     60     68
          Year 1876 1877 1878 1879 1880 1881 1882
          num of Akts     68     62     58     81     84     81     105
          Year 1883 1884 1885 1886 1887 1888  
          num of Akts     123     110     115     119     160     144
          HUNDREDs: 0  1 
          TENs: 0  1  2  3  4  5  6  7  8  9 
          ONEs: 0  1  2  3  4  5  6  7  8  9 
          Marriage Indexes        
          1869   1870   1871   1872   1873   1874 a-r   s-z   1875  
          http://www.ics.uci.edu/~dan/genealogy/Krakow/analysis.html Marriage Analysis

          Marriage Analysis

          There have been 4208 marriages during 1798-1888 for which both partner's ages have been extracted from the marriage record.
          The following is an analysis of the partners' ages based on the years (not months) listed on the marriage record.

          Ages

          Grooms ranged in age from 13 to 78 years.
          Brides ranged in age from 13 to 67 years.

          Relative ages

          The groom was older in 2944 (70%), the bride was older in 843 (20%), and equal ages in 421 (10%) of the records.

          Groom older by years                  Equal          Bride older by years
           10+ 9   8   7   6   5   4   3   2   1   0   1   2   3  4  5  6  7  8 9 10+
          408 72 110 130 199 265 327 413 524 496 421 297 205 107 90 40 31 21 19 5 28
          

          Greatest age difference with older groom and younger bride: 73-22 in m.1884#23
          Next greatest difference: 73-27 in m.1886#85
          Greatest age difference with younger groom and older bride: 18-40 in m.1804(Jan 22)
          Next greatest difference: 33-51 in m.1884#58

          Children

          Among the records that I have reviewed, the greatest number of children born to one couple was 19. In this case, there were 16 sons and 3 daughters.

          Multiple marriages with the same pair of surnames

          Analyzing marriages 1795-1855, there were 7 marriages where the couple's surnames were:

             HOROWICZ/HOROWITZ - LANDAU   (or vice versa)
          

          There were 3 marriages involving each of the following name pairs:

             BINENFELD - SILBERFELD
             BORNSTEIN - KUNSTLER
             BUCHBERG - FLEISIG
             EINTRACHT - FIGATNER
             GOLDES - HOROWITZ
             HOROWICZ - SOBEL
             LANDAU - SILBERFELD
          

          Marriage partners with same surname

          The couple may have been from unrelated families, or they may have been cousins.
          The list of such surnames from marriages 1798-1855:

             CYPRES      1804 Feb 17
             DANCYK      1826 #17
             DEMBITZER   1844 #81
             DEUTSCHER   1849 #18
             EIBUSZYTZ   1855 #154
             GRUNWALD    1846 #34
             HOROWITZ    1820 #32
             INFELD      1840 #58
             KORNGOLD    1843 #62
             MANNE       1841 #77
             MIESES      1843 #37
             PAMM        1832 #30
             PITZELE     1833 #107 and  1838 #6
             RITTERMAN   1822 #9   and  1836 #17
             THORN       1841 #85
             WAXNER      1843 #61 (maybe not, as the groom's surname might be WEXER)
          
          The list of such surnames from marriages 1856-1888:
             BERISCH     1880 #52
             BLODER      1887 #146
             BRUMMER     1880 #67
             CUKER       1878 #24
             DEMBICER    1871 #39
             HOFFMANN    1883 #123
             HOROWITZ    1872 #19  and  1880 #45
             INFELD      1888 #105
             KAMPEL      1888 #110
             KORNGOLD    1883 #79
             LIEBESKIND  1883 #22
             LORIA       1878 #18  and  1881 #64
             ROSENZWEIG  1872 #16
             SCHERMANT   1879 #25
             SPANLANG    1885 #20
             SPITZEL     1888 #67
             WETZSTEIN   1859 #316
          


           click for KRAKOW WEBSITE        Dan Hirschberg       dan at ics.uci.edu       Last modified: Jan 17, 2009 http://www.ics.uci.edu/~dan/genealogy/Krakow/early.html Early Records (before 1810)

          Early Records (before 1810)

          Before mid-1805, surnames were not commonly used in Krakow.
          Patronymics were the norm, the father's given name being used as a second name.
          (In earlier times, a person's occupation was often used as a second name.)
          The databases indicate surnames in all caps.   Inferences are bracketed (e.g., [PISEK]).

          The progress of inferring surnames has been enhanced by the contributions of many genealogists.
          There are 1971 individuals in the 1790 census, 1916 individuals in the 1795 census, and 1793 births listed 1798-1809.
          As of November 2003, 66% of the 1795 census families, 47% of the 1795 census entries, and 83% of pre-1810 births, have an identified surname.
          As of November 2003, 40% of the 1795 census entries have been found in the 1790 census.

          It appears that houses 112-136 in 1790 correspond to houses 113-137 in 1795. Other house numbers appear to be consistent.

          Images of the Kazimierz 1790 census can be found in image numbers 273 thru 295 at Polish archives of the Krakow area for 1790-92.

          Databases of early records

          database last updated
          1790 Census         Feb- 1-'15
          1795 Census  hs 1-115   Sep-15-'15
          Births 1798-1809 Feb-21-'13
          Marriages 1798-1808 Sep- 8-'15
          Houseowners 1807  (some from 1797) Nov- 4-'03


           click for KRAKOW WEBSITE        Dan Hirschberg       dan at ics.uci.edu       Last modified: Oct 16, 2015 http://www.ics.uci.edu/~dan/genealogy/Krakow/explain_tree.html

          Format of family trees

          • Each direct descendent of the Head of the tree is described in one line, with
            • generation number
            • gender — 'x' for male, 'o' for female, '?' for unknown
            • given name
            • surname in ALL CAPS
            • birth and death dates (if known) and city (if known)
            • if known, a '#' precedes a reference number to a record in the archival book of that year
              for the city of Krakow, unless another city is cited
              • in some instances, an event was recorded in a later year,
                in which case that fact will be shown in parentheses.
                For example:             Mar 15, 1834 (1836 #75)
                indicates that the birth on Mar 15, 1834 was recorded in birth record 75 of the year 1836 in Krakow

          • A person's spouse is shown in the following line
            • '+' before the name indicates a wife
            • '*' before the name indicates a husband

          • The children of a (married) couple are shown, in chronological order when birthdates are known,
            under the lines of the (married) couple with a common indentation
            and a common generation number that is one more than their parent's

          • A person's second spouse (presumably after death or divorce of the first spouse)
            is shown after the lines of the descendents of the first spouse

          • '??' to the left of a person's entry indicates significant uncertainty of the association of that person to the family.
            Such uncertainty typically arises as to whether
            • that person is a child of the listed parents, or
            • one of the listed parents is the true parent for that subfamily (as opposed to some other person with the same name)
          http://www.ics.uci.edu/~dan/class/6b/hw.html ICS 6B - Boolean Algebra & Logic - Winter 2010

          ICS 6B - Boolean Algebra & Logic - Winter 2010

          Homework assignments

          Unless otherwise indicated, all problems are from [Rosen].

          A few problems from each problem set will be graded for correctness. The remainder of the homework will not be graded for correctness, only for effort.

          Homework is to be submitted on the table in the front of the lecture room before the start of lecture.
          Late homework submitted before the end of lecture on the due date will lose half its score.
          Homework will not be accepted after lecture of the due date.
          • Homework 1: Due Wednesday of week 2, Jan 13.
            • 1.1: 11(b)(d)(f), 14, 19(b)(d), 24(b)(c), 28(b)(d)(f)
            • 1.2: 5, 8(b)(d), 10(c)(d), 11(c)(d), 20
            • 1.3: 2,4, 6(b)(d)(f), 7(b)(d), 17
          • Homework 2: Due Wednesday of week 3, Jan 20.
            • 1.4: 5(b)(d)(f), 10(b)(d)(f)(h)(j), 13(b)(d)(f)(h)(j)(l)(n), 14(b)(d)(f)
            • 1.5: 19,23, 34(b)(d), 35
            • 1.6: 1,6,24
            • 1.7: 4
          • Homework 3: Due Wednesday of week 4, Jan 27.
            • 2.1: 5,8,13,16,22
            • 2.2: 1,4,19,24
            • 2.3: 2,6,10,17
          • Homework 4: Due Wednesday of week 5, Feb 3.
            • 8.1: 1,4,7,8,29,30
            • 8.2: 5,7,10,13,16,18
            • 3.8: 4,10,17
          • Homework 5: Due Wednesday of week 6, Feb 10.
            • 8.3: 1,3,6,8, 14,29,32 [note that the terms asymmetric and irreflexive are defined on page 528].
            • 8.4: 2,3,6,8,10, 18(b)(d)(f)(h), 21,24, 25(a),(c), 28(b)(d), 33
          • Homework 6: Due Wednesday of week 7, Feb 17.
            • 8.5: 2,22,24, 26,30,32,39, 41(b)(d). (Note: to do exercise 26, you first need to solve exercise 1)
            • 8.6: 1,4,6,7,10,15,16, 18(b), 20, 23(b)(d), 25
          • Homework 7: Due Wednesday of week 8, Feb 24.
            • 11.1: 1,3,5,7,15,17,23,24,28
            • 11.2: 2(b)(d), 4,6, 12(b)(d)
          • Homework 8: Due Wednesday of week 9, Mar 3.
            • 11.3: 2,4,6,10
            • 12.1: 1, 5(a)(c), 14(a)(b), 16(a)(b), 20
            • 12.2: 1,3,5,10,16
          • Homework 9: Due Wednesday of week 10, Mar 10.
            • 12.3: 1,9,11,20,21,25,37
            • 12.5: 7,10
          Last modified: Mar 12, 2010 http://www.ics.uci.edu/~dan/class/267/studentproj/proj12.html CompSci 267: student projects

          CompSci 267: Approved Student Projects -- Spring 2012

            Week 8     (starting on Wednesday)

          • Survey of Compression via Substring Enumeration

            Week 9     (note that Monday of week 9 is Memorial Day)

          • Data Compression on Compiler Intermediate Representations
          • Coding of Sets of Words

            Week 10

          • Compression Techniques in Instant Search Systems (Top-K Framework)
          • Enumerative Coding for Image Prediction Residues
          • Hierarchical Data Aggregation of Probable Events in Seismic Networks
          • Application of Bayesian Image Analysis on Lossy Image Compression

          Last modified: Jun 4, 2012 http://www.ics.uci.edu/~dan/class/267/refs.html CompSci 267: Data Compression

          References

          General References

          • T. Bell, J.G. Cleary, and I.H. Witten, Text Compression. Prentice-Hall, Englewood Cliffs, NJ, 1990.
          • J.D. Gibson, T. Berger, T. Lookabaugh, D. Lindberg, and R.L. Baker, Digital Compression for Multimedia. Morgan Kaufmann, San Francisco, 1998.
          • M. Nelson and J. Gailly, The Data Compression Book, 2nd edition. M & T Books, New York, 1996.
          • K. Sayood, Introduction to Data Compression, 2nd ed. Morgan Kaufmann, San Francisco, 2000.
          • D. Salomon, Data Compression: The Complete Reference. Springer-Verlag, New York, 1998.
          • I.H. Witten, A. Moffat, and T.C. Bell, Managing Gigabytes: Compressing and Indexing Documents and Images, 2nd ed., Morgan Kaufmann, San Francisco, 1999.

          Focused References

          1. Introduction
            • [EnglishEntropy] T.M. Cover and R.C. King, "A convergent gambling estimate of the entropy of English," IEEE Trans. on Info. Theory 24 (1978) 413-421.
          2. Coding Techniques
            • [Huffman] D.A. Huffman, "A method for construction of minimum-redundancy codes," Proc. of the IRE 40 (1952) 1098-1101.
            • [Adaptive Huffman] J.S. Vitter, "Design and analysis of dynamic Huffman codes," J.ACM 34 (1987) 825-845.
            • [LenLim Huff] L.L. Larmore and D.S. Hirschberg, "A fast algorithm for optimal length-limited codes," Journal ACM 37 (1990) 464-473.
            • [AlphaPartition] D. Chen, Y.-J. Chiang, etal., "Optimal alphabet partitioning for semi-adaptive coding of sources of unknown sparse distributions," Proc. Data Compression Conf. (2003) 372-381.
            • [Tunstall] S. Savari, "Variable-to-fixed length codes and plurally parsable dictionaries," Proc. Data Compression Conf. (1999) 453-462.
            • [ArithCoding] I.H. Witten, R.M. Neal, and J.G. Cleary, "Arithmetic coding for data compression," Comm. ACM 30 (1987) 520-540.
            • [Q-coder] W.B. Pennebaker, J.L. Mitchell, G.G. Langdon, and R.B. Arps, "An overview of the basic principles of the Q-coder adaptive binary arithmetic coder", IBM Journal of Research and Development 32 (1988) 771-726.
            • [Z-coder] L. Bottou, P.G. Howard, and Y. Bengio, "The Z-coder adaptive binary coder," Proc. Data Compression Conf. (1998) 13-22.
            • [unequal prefix codes] P. Bradford, M.J. Golin, L.L. Larmore, and W. Rytter, "Optimal prefix-free codes for unequal letter costs: Dynamic programming with the Monge property," LNCS 1461 (1998) 43-54.
          3. Modeling
            • [LZ77] J. Ziv and A. Lempel, "A universal algorithm for sequential data compression," IEEE Trans. on Infor. Theory IT-23 (1977) 337-343.
            • [LZ77 optimality] A.D. Wyner and J. Ziv, "The sliding-window Lempel-Ziv algorithm is asymptotically optimal," Proc. of the IEEE 82 (1994) 872-877.
            • [LZ78] J. Ziv and A. Lempel, "Compression of individual sequences via variable-rate coding" IEEE Trans. on Infor. Theory IT-24 (1978) 530-536.
            • [LZFG] E.R. Fiala and D.H. Greene, "Data compression with finite windows," Comm. ACM 32 (1989) 490-505.
            • [LZSS] J.A. Storer and T.G. Szymanski, "Data compression via textual substitution," Jour. ACM 29 (1982) 928-951.
            • [LZW] T.A. Welch, "A technique for high-performance data compression," IEEE Computer 17 (1984) 8-19.
            • [PPM] J.G. Cleary and W.J. Teahan, "Unbounded length contexts for PPM," The Computer Journal 40 (1997) 67-75.
            • [PPM] A. Moffat, "Implementing the PPM data compression scheme," IEEE Trans. on Comm. COM-38 (1990) 1917-1921.
            • [Symbol Ranking] P. Fenwick, "Symbol ranking text compression with Shannon recodings," J. of Universal Computer Science 3 (1997) 70-85.
            • [BWT] M. Burrows and D.J. Wheeler, "A block-sorting lossless data compression algorithm," Digital Systems Research Center rpt. 124 (1994).
            • [BWT] G. Manzini, "An analysis of the Burrows-Wheeler Transform," Jour. ACM 48 (2001) 407-430.
          4. Text Compression Systems
            • [CRC] D.V. Sarwate, "Computations of cyclic redundancy checks via table look-up," CACM 31 (1988) 1008-1013.
          5. Image Compression
            • [CondlRLE] H. Gharavi, "Conditional run-length and variable-length coding of digital pictures," IEEE Trans. Commun. 35 (1987) 671-677.
            • [Progressive FELICS] P.G. Howard and J.S. Vitter, "Fast progressive lossless image compression," Proc. 1994 IST/SPIE Int'l Symp. on Electronic Imaging Science and Technology (1994).
            • [JPEG-LS] M. Weinberger, G. Seroussi, and G. Sapiro, "LOCO-I: A low complexity, context-based, lossless image compression algorithm," Proc. Data Compression Conf. (1996) 140-149.
            • [JBIG2] JBIG committee, "14492 FCD -- lossy/lossless coding of bi-level images," July 16, 1999.
            • [JPEG2000] ISO/ITU "FCD 15444-1 -- JPEG 2000 image coding system," March 16, 2000.
          6. Lossy Compression
            • [Quantization] A. Gersho, "Quantization," IEEE Communications Magazine 15 (Sep 1977).
            • [ViterbiAlg] G.D. Fiorney, Jr. "The Viterbi algorithm," Proc. IEEE 61 (1973) 268-278.
            • [TrellisCodes] M. Marcellin and T. Fischer, "Trellis coded quantization of memoryless and Gauss-Markov sources," IEEE Trans. Commun. 38 (1990) 82-93.
            • [TrellisCodes] M. Marcellin, "On entropy-constrained trellis coded quantization," IEEE Trans. Commun. 42 (1994) 14-16.
            • [LappedTransform] H. Malvar and D. Staelin, "The LOT: Transform coding without blocking effects," IEEE Trans. Acoustics, Speech, & Signal Proc. 37 (1989) 553-559.
          7. Lossy Image Compression
            • V. Bhaskaran and K. Konstantinides, Image and Video Compression Standards: Algorithms and Architectures, Kluwer Academic Publishers, 1995.
            • A. Said and W.A. Pearlman, "A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees," IEEE Transactions on Circuits and Systems for Video Technology 6 (1996) 243-250.
          8. Audio Compression


          Last modified: Jan 15, 2009 http://www.ics.uci.edu/~dan/class/267/studentproj/proj02.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Winter 2002

          • Z-coder
          • Quasi-arithmetic coding
          • Encoding/decoding with multiple processors
          • An implementation of JPEG compression
          • Anti-dictionaries for compression
          • Unbounded length contexts for PPM
          • Compression systems for embedded systems
          • Compressing web graphs
          • Multiple description coding for images
          • On-line Linear Build Time Suffix Trees for BWT
          • Fastmesh compression
          • HDTV
          • Wavelets
          • Modifications of uniform quantization apppled in wavelet coder
          • Embedded trellis coded quantization
          http://www.ics.uci.edu/~dan/class/267/studentproj/proj08.html CompSci 267: student projects

          CompSci 267: Approved Student Projects -- Fall 2008

          • Fast Decoding of Prefix Encoded Texts
          • Secure LZW Compression
          • Dual Needs of Compression and Encryption
          • An Efficient PPM
          • Chip Test Vector Compression
          • Code Compression for Embedded Systems
          • Secure Power-Reduced Code Compression
          • Floating Point DC in Msg Passing Systems

          Last modified: Jan 15, 2009 http://www.ics.uci.edu/~dan/class/267/homework.html CompSci 267 Homework

          CompSci 267 Homework

          Rules

          • There will be homework problem sets roughly once a week
          • Submit solutions to the problems of each problem set
          • These submissions are due at the beginning of class on the indicated due date
          • A selected subset of problems will be graded for correctness, and the remainder will be graded for reasonable effort
          • Most problems are expected to be doable by all students,
            but a few problems are quite hard and most students will not be able to do them
          • Solutions will be discussed in class at which point you grade yourself for each problem
            • Each problem is worth 10 points
              ( if there are n parts, each part is worth 10/n points )
              get half credit for wrong answer if there was good effort in right direction
            • Add the points for all parts of a problem,
              if the sum is y then write ⌈y⌉ inside a circle near that problem
            • Example: A problem has 3 parts, of which 2 parts correct, 1 part half credit → 9 points
          • Add up points obtained for all problems in the homework assignment
            and write that sum inside a circle at the top right of the front page

          Assignments

          • Homework set #1 due W Apr 11 (wk 2)
          • Homework set #2 due W Apr 18 (wk 3)
          • Homework set #3 due W Apr 25 (wk 4)
          • Homework set #4 due W May 2 (wk 5)
          • Homework set #5 due W May 9 (wk 6)
          • Homework set #6 due M May 21 (wk 8)
          • Homework set #7 due M Jun 4 (wk 10)

          Last modified: May 20, 2012 http://www.ics.uci.edu/~dan/class/267/studentproj/proj03.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Winter 2003

          • Implicit multi-byte-input adaptive Huffman coding
          • Comparison of data compression algorithms allowing random access decoding
          • Implementation and analysis of quasi-arithmetic coding
          • Implementation and analysis of Z-coder adaptive binary coder
          • A study of web graph compression techniques
          • An analysis of selected BWT implementations
          • Dictionary-based compressed pattern matching
          • A study of searches on compressed data
          • Code compression
          • Image compression using zerotrees of wavelet coefficients
          • Reduction of artifacts in image compression
          • A survey on JPEG image compression techniques

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Jul 6, 2005 http://www.ics.uci.edu/~dan/class/267/studentproj/proj09.html CompSci 267: student projects

          CompSci 267: Approved Student Projects -- Fall 2009

          • Semantic Compression for General Purpose
          • Compressed Sensing for Image and Audio Signal Compression

          Last modified: Nov 5, 2009 http://www.ics.uci.edu/~dan/class/267/studentproj/proj05.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Fall 2005

          • Data compression in database systems
          • Fast decoding of prefix encoded texts
          • Dictionary and text compression
          • Compression techniques for fast entropy coders
          • Lossless video compression
          • MPEG-4 ALS coding
          • Survey of lossless audio compression
          • Voice compression techniques

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Dec 5, 2005 http://www.ics.uci.edu/~dan/class/267/datasets/index.html Datasets

          Datasets

          These files (other than the Calgary corpus) are from Sayood's book.
          • Calgary Corpus - zipped or individual files
          • Special text files
          • Images
          • Audio
          • Speech
          A raw image viewer.
          http://www.ics.uci.edu/~dan/class/267/studentproj/proj03b.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Fall 2003

          • Hardware Implementation of Lossless Data Compression
          • Data Compression in Designing High-Efficiency Hardware Configurations
          • Improving Dynamic Branch Predictor Performance with Data Compression
          • JPEG2000
          • A Study of Relational Database Compression Techniques

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Jul 6, 2005 http://www.ics.uci.edu/~dan/class/267/studentproj/proj04.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Fall 2004

          • Algorithms for MPEG-4 ALS
          • Improvements on the IEEE Float Audio Transform for Lossless Compression
          • JPEG 2000
          • Techniques for Code Compression
          • Code Compression Techniques for Embedded Systems
          • Code Compression using Variable-to-Fixed Coding based on Arithmetic Coding
          • Hybrid Prefix Code: Efficiency and Algorithm
          • Compressed Pattern Matching
          • Neural and Fractal Image Compression

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Jul 6, 2005 http://www.ics.uci.edu/~dan/class/267/links.html Data Compression Links

          Data Compression Links

          General

          • Data Compression Conference
          • comp.compression FAQ
          • Archive Comparison Test
          • Calgary corpus compression challenge,
          • Data Compression Info

          Lossless compression

          • comprehensive treatment in 1986
          • Context tree weighting project (see DCC'96, pp. 132-139)
          • zlib home page
          • gzip file format specs
          • szip
          • M software
          • range coding (AC variant) [JBIG2]

          Image (lossy) compression

          • JPEG:
            • Independent JPEG Group (IJG)
            • JPEG.org
            • JPEG-LS
          • MPEG:
            • starting pts
          • Wavelets:
            • Amara's Wavelet Page
            • Introduction to Wavelets
          • set partitioning in hierarchical trees (SPIHT)

          Other useful links

          • File format for internet FAX (TIFF)
          • GIF89a specification
          http://www.ics.uci.edu/~dan/class/267/studentproj/proj00.html ICS 267: student projects

          ICS 280 (267): Approved Student Projects -- Winter 2000

          • Dynamic data structure for reverse lexicographically sorted prefixes
          • BWT and proposed enhancements
          • DjVu image compression
          • Z-coder adaptive binary coder
          • Generation and compression of inverted index files
          • PPM
          • Predictive compression of image data
          • Wavelet image compression
          • Off-line dictionary-based compression
          • Code compression for embedded systems
          • Syntax-directed compression using attribute grammars
          http://www.ics.uci.edu/~dan/class/267/syllabus267.html CompSci 267: Data Compression

          CompSci 267: Data Compression -- Spring 2012

          1. Introduction
            • terminology and applications
            • performance measures
            • information theory
            • types of codes -- time variance, I/O code rates, decodability
            • fixed finite codes -- enumerative coding, phasing in, multiple-length, start-step-stop
            • fixed infinite codes -- Elias, Even-Rodeh, Fibonacci, Golomb, Rice, variable-byte
          2. Coding Techniques
            • variable-sized codes -- Kraft-MacMillan, universal compression
            • compact codes
            • Shannon theorem, Shannon coding
            • Shannon-Fano coding
            • Huffman coding
            • adaptive Huffman coding
            • length-limited Huffman coding
            • alternating runlength Huffman
            • modified Huffman coding
            • fast decoding of Huffman encodings
            • Tunstall codes
            • arithmetic coding
            • variable-cost alphabet
            • coding increasing integer sequences
          3. Modeling
            • introduction and categorization
            • dictionary methods (defined-word schemes) -- MTF
            • dictionary methods (free-parse schemes) -- LZ, LZ-77/-78 families
            • context modeling -- PPMC, SCM, Symbol Ranking, BWT
          4. Text Compression Systems
            • PC systems and performance
            • error control
            • CRC
          5. Lossless Image Compression
            • run-length encoding -- BinHex, PDQ
            • bi-level images -- facsimile, quadtrees, Hilbert curve, JBIG, JBIG2
            • lossless grayscale -- cond'l RLE, MLP, progressive FELICS, JPEG-LS
            • lossless color -- PNG
          6. Lossy Compression
            • distortion and perception measures
            • scalar quantization -- uniform, Lloyd-Max, Jayant
            • block truncation coding
            • vector quantization -- LBG, tree-structured, lattice, trellis
            • differential encoding -- DPCM, ADPCM, delta modulation
            • transform coding
            • wavelets
          7. Lossy Image Compression
            • expressing color
            • JPEG -- DCT quantization and encoding, JPEG modes
            • MPEG video -- MPEG-1, MPEG-2
          8. Audio Compression
            • audio quality, sampling
            • waveform codecs
            • companding -- mu-law, A-law
            • subband coding
            • subband masking
            • MPEG audio -- layers 1,2,3 and MDCT
            • speech

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Nov 9, 2011 http://www.ics.uci.edu/~dan/class/267/proj.html CompSci 267: Data Compression

          CompSci 267 Project

          Write a short (5-8 page) paper that summarizes the results of your implementation/analysis of an advanced compression technique.
          Your paper will give background (describe the problem, its motivation, and approaches to its solution) and give some solution analysis (absolute or comparative).
          Suggestions of how one might obtain some improvement would be great, and achieving actual improvement would be wonderful.

          Your project might be one or more of: a survey, a theoretical discussion of aspects not covered in the literature, an analysis of your or someone else's implementation, etc.

          You should browse appropriate journals and conference proceedings to consider candidate topics for this project.
          I do maintain a small list of papers that I am particularly interested in being considered by class students.

          As a rule, I will not approve a proposed topic if it has already been approved for someone else in the class.
          Thus, there is some incentive for submitting your proposal well ahead of the deadline.

          Note that, besides the technical content, clarity, structure, grammar, and spelling will also have an effect on your grade.
          View the evaluation form that I will use in reviewing your project.

          Schedule

          • Topic Approval
            • submit a proposal (Title, Abstract of at most 60 words, References) by Thursday of week 3
              [ The abstract describes the essence of what it is you will be doing.
              The references are papers that provide background information for the project
              and which will be cited in the paper that you write as part of this project. ]
            • submission may be in writing or via email
            • receive approval (or be told "no") usually within one day
          • Presentation Scheduled
            • I will schedule your oral presentation to be during weeks 8-10
            • I will post the schedule by the end of week 5
            • it is unlikely, but subsequent course scheduling conflicts might require me to delay your oral presentation
          • Oral Presentation
            • 13-17 minutes per person, you will receive a 1-minute warning if necessary
            • you are encouraged to use the computer projector
            • a copy of the slides is to be emailed to me as an attachment before 9am of the day of your oral presentation
          • Submit Written Report
            • 5-7 double-spaced pages, not including references
            • at most 28 lines per page, 11 or 12 point font
            • citations should be in the format used in a journal
            • do not plagiarize -- give proper attribution for all copied ideas, sentences, and diagrams
            • submit report (on paper) to me by Tuesday of week 10
            • LATE SUBMISSIONS WILL NOT BE ACCEPTED
          • Receive Feedback
            • see me during week 11 to receive the evaluation of your written report and class presentation
            • sample evaluation form


          Last modified: Mar 13, 2012 http://www.ics.uci.edu/~dan/class/267/studentproj/proj07.html CompSci 267: student projects

          CompSci 267: Approved Student Projects -- Fall 2007

          • Analysis of the error-resilient LZW
          • XML compressions and queries
          • Code compression techniques
          • Lossless image compression
          • Cryptographic issues with compression
          • Augmenting AC3 to use 8-band frequency decomposition

          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: Dec 7, 2007 http://www.ics.uci.edu/~dan/class/267/studentproj/proj06.html CompSci 267: student projects

          CompSci 267: Approved Student Projects -- Fall 2006

          • Error Resilience and Error Recovery in Data Compression
          • Compression of Encrypted Data
          • Data Hiding via Lossy Compression Techniques
          • Design and Implementation of the JPEG2000 Encoder
          • Medical Image Compression Techniques
          • Distributed Video Coding
          • Adaptive DFCM
          • Network Coding
          • XML Compression
          • Compression of Laser Range Scan data
          • Compression of Floating-Point Data
          • Compression of Numerical Simulation Data


          Dan Hirschberg
          Computer Science Department
          University of California, Irvine, CA 92697-3435
          dan at ics.uci.edu
          Last modified: May 7, 2007 http://www.ics.uci.edu/~dan/class/267/studentproj/proj01.html ICS 267: student projects

          ICS 267: Approved Student Projects -- Winter 2001

          • A new implementation of unbounded length context for PPM
          • A fast algorithm for making suffix arrays (and for BWT)
          • Z-coder adaptive binary coder
          • Compression of arbitrarily-sortable string lists
          • Image compression using pyramid scheme with horizontal 2D methods
          • Hybrid fractal and transform-based image compression
          • Compression of sequences in bioinformatics applications
          • Studying a modification of the MPEG-2 video codec
          http://www.ics.uci.edu/~jpd/talks.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          recent talks

          • From Privacy and Security to Collective Information Practices. Department of Computing Science, University of Glasgow. July 20, 2005.
          • The Culture of Information: Ubiquitous Computing and Representations of Reality. Anderson School of Management, UCLA. June 9, 2005.
          • Play, Flex, and Slop, or, My inner child was last seen on the side of a milk carton. Art Center College of Design, Pasadena. April 28, 2005.
          • Privacy, Security... and Risk and Danger and Secrecy and More: Understanding Collective Information Practices. Department of Computer Science, University of Calgary. March 16, 2005.
          • Privacy, Security... and Risk and Danger and Secrecy and More: Understanding Collective Information Practices. Intel Research Privacy Forum, Portland, OR. March 2, 2005.
          • From Privacy and Security to Collective Information Practices. Department of Computer Engineering/Center for Science, Technology and Society, Santa Clara University. February 7, 2005.
          • Context and Practice. Conference on Context-Aware Computing, Copenhagen, Denmark. November 30, 2004.
          • The Social and the Technical in Ubiquitous Computing. OCCHI meeting, November 3, 2004.
          • Software as an Embodied Phenomenon. Keynote presentation at IEEE Symposium on Human-Centric Computing, Rome, Italy. September 27, 2004.
          • Meaning and Practice in Encounters with Urban Space. Street Talk Urban Computing Workshop, Intel Research Berkeley. July 16, 2004.
          • Security as Experience and Practice: Supporting Everyday Security. Workshop on Usable Privacy and Security Software, Rutgers, NJ. July 8, 2004.
          • The Social and the Technical in Ubiquitous Computing. Information Science Seminar, Cornell. July 1, 2004.
          • Interactive and Collaborative Technologies. Cal-(IT)2 Day, UC San Diego. April 12, 2004.
          • Embodied Interaction and the Experience of Computation. CNRS seminar, Paris. March 23, 2004.
          • Ambiguity, Openness, and Technological Design. Art Center College of Design, Pasadena. February 2, 2004.
          http://www.ics.uci.edu/~jpd/schedule.html

          Schedule for Paul Dourish (Winter 2007)

          [Detailed schedule]

          Weekly Schedule

          No one who spells their name "Jakob Nielsen" can be a "usability" expert. -- Ken Anderson

          More people are killed every year by pigs than by sharks, which shows you how good we are at evaluating risk. -- Bruce Schneier

          If man had not been his own classifier, he would never have thought of founding a separate order for his own reception. -- Charles Darwin

          It is remarkable how Darwin rediscovers, among the beasts and plants, the society of England with its division of labour, competition, opening up of new markets, 'inventions' and Malthusian 'struggle for existance.' It is Hobbes' bellum omnium contra omnes and is reminiscent of Hegel's Phenomenology, in which civil society figures as an 'intellectual animal kingdom,' whereas, in Darwin, the animal kingdom figures as civil society. -- Karl Marx

          The playful nip denotes the bite, but it does not denote what would be denoted by the bite. -- Gregory Bateson

          There's no better way to feel like a moron that to read anything by Dourish. -- anonymous student

          http://www.ics.uci.edu/~jpd/misc/directions.html Directions to Paul's Office Directions to Paul's Office (Bren Hall 5086)

          First, follow the campus directions to reach UCI, and check the campus map. You're headed for Donald Bren Hall (building 314, grid F6 on the map) and the closest lot is 12B. (Note: Be sure to look for Donald Bren Hall, not the Bren Event Center, which is on the other side of campus.)

          If you're coming from the north, your best bet is to take the 405 to the 73, then take 73 south; get off at Bison, which is the last exit before it becomes a toll road. At the top of the off-ramp, take a left onto Bison. Follow Bison onto the campus, and take the second right onto East Peltason. The entrance to lot 12B the first left turn after Bison, just after you crest the hill (there's a left-turn lane, but no light or stop sign.)

          If you're coming from the south on the 73, get off at the Bison exit, and take a right onto Bison and onto the campus, then follow the directions above.

          If you're coming from the south on the 405, then get off at Culver. Turn left onto Culver and follow it until you reach Campus (after University and Harvard). Take a right onto Campus, and, at the second (I think) light, a left onto East Peltason. Follow East Peltason around to the south side of the campus; the entrance to lot 12B will be on your right after a stop sign at Los Trancos.

          On your right as you enter lot 12B, there are around ten "pay and display" spots for which you can purchase a ticket at the blue machine. The majority of lot 12B is a permit-only lot, though, so you'll proabbly need to get a parking permit. You can pick one up from the any of the parking structures. If I've arranged a permit for you, then you should just be able to give your name and they'll give you one. The closest parking structure is the "Engineering Parking Structure" on East Peltason between Gabrielino and Anteater (marked ICS/ENG on the campus map, E7). The map shows the entrance at Gabrielino, although I think it's actually at Anteater. So, if you're coming from Bison, you pass lot 12B, pick up your permit from the structure, and then double back.

          Be sure to get a "reserved" parking permit, which will allow you to park in spaces marked "AR"; lot 12B requires AR permits.

          Park in lot 12B, and walk towards the bottom right corner (reckoning from the entrance.) Turn right to walk along the footpath (the "ring mall").

          The corner of the parking lot you're looking for looks like this:

          And then the route to follow looks like this:

          That's Bren Hall that you can see through the trees; as you approach, the shade on the sixth-floor patio is pretty distinctive. It's the first large building directly on the path you're following:

          The part of the building you can see here is one of two arms that form an "L" shape, pointing away from you as you come from this direction. The entrance to the building is at the inside of the L. As you come past the first wing of the building, you should turn right. You'll see a glass-covered stairway, like this:

          The entrance to the building is a door at the base of this stairway. Go through the doorway and into the lobby on the right. Turn left to find the elevators (they will be on the left again) and take one to the fifth floor.

          Turn right on exiting the elevator (it's the only way to go) and then immediate left down the corridor. Take the next left and follow the corridor; my office is 5086, which will be on your right, a couple of doors after the corridor makes another left turn.

          http://www.ics.uci.edu/~jpd/publications Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          All publications have been moved to my new website. http://www.ics.uci.edu/~jpd/misc/scotland.html

          Scotland (and England)

          "It is to Scotland that we look for our idea of cilivisation." -- Voltaire.

          Yes, Scotland and England are different countries. Both are member nations of the United Kingdom of Great Britain and Northern Ireland (the other two being Northern Ireland and Wales). Referring to Great Britain or to the United Kingdom as "England" is a good way to piss off a Scot.

          You see, it's not just that we're different countries. You have to appreciate that we've only been part of the same country for a few hundred years, and that we'd spent most of the preceeding thousand years at war with each other.

          When the Romans invaded Britain, they soon gave up trying to take Scotland, having found the Scottish land inhospitable and the Scottish people less than entirely pleased to see them. The Roman solution was to build walls to keep the Scots out (portions of both the Antonine Wall and the better-known Hadrian's Wall are still visible).

          At that point, Scotland was still a collection of smaller fiefdoms, generally at war with each other. (There wasn't much else to do.) Through a series of conquests, though, gradually these various independent groups came togehter to form a unified nation, with various fits and starts, between about 1030 and 1130.

          And so Scotland and England continued as separate countries, with regular border skirmishes, for hundreds of years. Edward I of England (known as the "Hammer of the Scots") was particularly obsessed with the idea of claiming the Scottish lands as his own. He did pretty well, too, but, after a number of Scottish uprisings, Edward was finally trounced at the Battle of Bannockburn in 1314 by Robert the Bruce, subsequently Robert I of Scotland. Scotland, a free nation again, asserted its independence in the Declaration of Arbroath, a letter from the Scottish aristocracy to the Pope.

          Things got heated again around about the time of the Reformation. Elizabeth I of England was rather worried about the popularity of her cousin, the new Scottish Queen, Mary (Queen of Scots); but in particular, Elizabeth was worried about the religious angle. Mary had been brought up in Catholic France, where she had married the Dauphin (the French crown prince), who subsequently died. When Mary began her personal reign in 1561, rumours were rife that she might marry the crown prince of Spain, another major Catholic power; and that would have left Elizabeth in a very dangerous position. European Catholics were pretty unhappy about the behaviour of Elizabeth's father, Henry VIII, and wanted to reclaim England for Rome.

          So Elizabeth schemed and arranged for Mary to marry one of Elizabeth's courtiers, Henry, Lord Darnley, in 1565. It was never a particular happy marriage, but Mary bore Darnley a son, James. Darnley, however, was jealous of Mary's close relationship to her (Catholic) secretary, David Riccio; and, on March 9, 1566, Mary, pregnant with James, witnessed as a gang of Darnley and his drunken friends grabbed Riccio at Holyrood Palace and brutally murdered him. Pointedly, it was Darnley's dagger that was left in the body.

          Things went downhill fast after that; religious tension was running high, since Scotland was in the midst of a religious reformation, headed by John Knox. Darnley himself was murdered (strangled, and the house he was in partly blown up) in February 1567. Mary was abducted by the Earl of Bothwell, and then married him in May that year. But she couldn't last long, and abdicated later in the year, in favour of her son, James.

          Eventually, Mary fled south to try to get help from her cousin, Elizabeth. Elizabeth seized the opportunity and had Mary arrested. After a personal reign of just six years, Mary was to spend the next twenty years under house arrest in England, before finally being beheaded in 1587.

          When Elizabeth died, childless and without having named a successor, this left the English parliament in a rather unfortunate position, since the closest person in line for her throne was James -- the son of Elizabeth's cousin Mary, and the King of Scotland, whose personal reign had begun in 1585. The English invited James to take the throne of England, and so 1603 saw the Union of the Crowns; a single monarch over both England and Scotland, still two separate countries. James became James VI of Scotland and James I of England, known (now) as James the VI and I. (Notice, by the way, that this is after Elizabeth I. Britain's current monarch is claimed to be Elizabeth II; but in fact she's only Elizabeth II of England. As far as Scotland, or Britain, is concerned, she's only Elizabeth I. This caused a certain amount of upset in Scotland when she came to the throne.)

          Things get a little confusing after that, since James' son, Charles I, had a little trouble with a guy called Oliver Cromwell. Confusion and dispute during the Restoration--and in particular the English parliament's offer of the throne to William of Orange in order to secure a Protestant succession--led to a succession of Jacobite claimants to the throne, living in France, from where two Scottish rebellions were subsequently to be organised (in 1715 and 1745). However, in 1707, during the reign of William's daughter, Anne, legislation was finally enacted to unify the kingdoms.

          And so, a little over 100 years after the Union of the Crowns, the two countries saw the Union of the Parliaments in 1707, and became member nations of a United Kingdom of Great Britain. (Ireland wasn't added until 1801, at which point it became the United Kingdom of Great Britain and Ireland; after 1921, the United Kingdom of Great Britain and Northern Ireland.)

          Of course, after less than 300 years, some of the details are still a little rough. For instance, the third verse of the National Anthem for the United Kingdom commemerates General Wade, the military overseer of Scotland installed by the English between the 1715 and 1745 uprisings:

          God grant that Marshall Wade
          May by thy mighty aid
          Victory bring
          May He sedition hush
          And like a torrent rush
          Rebellious Scots to crush
          God Save the King.

          Since I first wrote this web page, in 1996, things have changed. In particular, the current Labour administration in the UK has created a Scottish parliament, with jurisdiction over local matters such as edication and agriculture, and with limited tax-raising powers. This is the first elected Scottish parliament since 1707, and on May 12, 1999, Winnie Ewing MSP opened it with the words, "The Scottish Parliament, adjourned on 25th day of March 1707, is hereby reconvened."

          Scotland looks like this.


          Paul Dourish http://www.ics.uci.edu/~jpd/misc/apecity.html Ape City? Ape City?

          Yep, Ape City. When they made "Conquest of the Planet of the Apes", the fourth of the Planet of the Apes series, the filmmakers wanted to find a location for the futuristic city conquered by the apes. They found the perfect location on the UC Irvine campus.

          Now, many years ago, when I first learned this fabulous fact, I came across a web page positively replete with photos of the campus infested with apes. When I went looking again, though, the best I could find was this one:

          That's the Social Sciences tower you see in the background. I'm sure you can spot the architectural similarity to other buildings on campus, such as the main ICS building (left) or administration building (right):

          However, just as I was resigning myself to the sad loss of these images of UCI's finest hour, I was saved by Jack Muramatsu, who sent me this fine set of pictures, and Robert Nideffer, who Photoshop'd them to perfection. Click on the thumbnail for a larger version.

          The Social Science plaza features extensively in the movie. In fact, in the end, the apes defeat Man by setting fire to Social Science. I'm sure there's some symbolism hidden there...

          http://www.ics.uci.edu/~jpd/software.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          Available Softare Systems

          Periodically, we make some of the software systems that we are studying available for wider use, either as part of our research strategy, or just out of the goodness of our hearts. Currently available systems are listed below.

          • Augur, a visual tool for distributed software developers.
          • Jung, an open-source Java library for graph manipulation.
          • Vavoom, a visual virtual machine for Java (not currently available for download)
          • Soylent, a tool for analysing temporal and social structures in email.

          http://www.ics.uci.edu/~jpd/research.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          Research Themes

          My research revolves around three primary themes:

          Ubiquitous Computing (Ubicomp)
          Computation is migrating out of the desktop PC and into the everyday world, in the form of information appliances, everyday digital devices, wireless networks, smart environments, and mobile, handheld and wearable devices. Increasingly, the world itself is an interface to computation. How can we understand these phenomena, and design for them effectively?
          Computer-Supported Cooperative Work (CSCW)
          Much of what we do, even when we're working "alone", is in fact collaborative. We work as members of teams, groups, organizations, and societies, and we coordinate our activities with others with and through computation. What is the impact of technology on our interactions and collaborations with each other?
          Social Studies of Science and Technology
          The design, use, and impact of technologies is determined not solely by technical factors but also by how those technologies are shaped by social pressures and demands. These social considerations also shape what questions science asks and how we evaluate the answers.

          Bringing these together, much of my research considers the social analysis of encounters between people, technology, and the everyday world. Empirically, it draws primarily on ethnographic investigation of work practice (but also on lab studies, survey data, and other sources of information). Analytically, it draws on a range of largely phenomenological positions, especially ethnomethodology. Technically, it combines interests in databases, networks, software engineering, and user interfaces to create novel ways for people to encounter computation. A central concern is understanding how the experience of interactive technology allows people to create and share new forms of practice.

          When I say the experience of technology, I mean not just its usability, or other engineering-oriented metrics of effectiveness; instead, I mean the ways in which people experience the technology as useful or meaningful. Similarly, when I talk about practice, I mean ways of acting with and through technology; not just how people use computers, but how they adapt and adopt them, incorporating them into their lives and their work.

          Right now, I have two main projects.

          The first is concerned with privacy and security. In this work, we think of privacy not as something that people have but more as something that people do. What kinds of information practice do people engage in, and what do they achieve through them? We are interested in people's collective ways of orienting towards information as private, public, sensitive, or secret, or ways of thinking of activities as being risky, secure, dangerous, or appropriate, and the factors that both shape and are shaped by those forms of social meaning. This work has empirical, technical, and conceptual elements; it is being carried out in collaboration with a range of people, including David Redmiles in Informatics, Simon Cole in Criminology, and Jenny Terry in Women's Studies.

          The second is concerned with spatiality. How do advanced information technologies -- cell phones, wireless and wired networks, augmented environments, etc. -- cause us to re-encounter the spaces through which we move? How do we understand space in the first place? Where do our notions of "place" and "space" come from, and what kinds of relationship do they have to our patterns of movement and action in everday space? Although we take spatiality very much for granted, the goal of this work is to think about it as a social and cultural product. We have turned, for example, to studies of the aboriginal Australian encounter with the mythic landscape, the Native American experience of the moral landscape, or the many different forms of urban spatiality as ways of provoking new imaginaries of space and technology.

          For more detail, see my list of publications or check my students' web pages, all of which are probably updated more often that this page.

          http://www.ics.uci.edu/~jpd/publications.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          All publications have been moved to my new website. http://www.ics.uci.edu/~jpd/personal.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          personal

          I'm Scottish (hence the accent). I grew up in Glasgow, and then committed an act of cultural heresy by moving to Glasgow's evil arch-nemesis, Edinburgh. On the other hand, that wasn't as bad as when I later moved to England...

          I studied Computer Science and Artificial Intelligence at the University of Edinburgh. I also worked there to pay my way through school; I worked first in the Centre for Speech Technology Research) (part of the Department of Artificial Intelligence), and later at the University Computing Service, writing an implementation of Linda (cool) in occam (yuk) on a huge parallel transputer machine that eventually became the basis of the Edinburgh Parallel Computing Center.

          After graduating, I moved to Cambridge (the original one) to join what was then called Rank Xerox EuroPARC, and is now the Cambridge Lab of the Xerox Research Center Europe. I worked on a variety of projects around the general topic of computer support for workgroup collaboration, including the "RAVE" media space, the Portholes distributed awareness system, the Freeflow workflow architecture and the Prospero toolkit for collaborative applications. This last was also the topic of my PhD, which I took concurrently during my last few years there.

          I came to the US in 1996 to work in the Discourse Architecture Lab at Apple Computer. When I arrived, the immigration official made some snide remarks about the fact that I wouldn't need a three-year visa, and he was right; Apple disbanded its research lab 10 months later. I moved up the road to Xerox PARC, to join the Computer Science Lab, home to the bean-bag chairs and various other holdouts from the seventies. The main project I worked on was a large interactive document system called Placeless Documents, although some side projects kept us amused.

          We just bought a house. It's lovely, but needs some work. It's going to be quite a project...

          http://www.ics.uci.edu/~jpd/students.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          Students

          It is my privilege to work with the rockin'-est, ass-kickin'-est students around. They can often be found squaring circles or proving P=NP. Working from their secret underground lunar base, they are the scourge of super-villains the world over, and regularly save the planet from alien invasion. (You haven't seen any alien invaders around, have you? See? They're that good.) Don't mess with my students.

          Researchers

          • Charlotte Lee
          • Kari Nies
          • Irina Shklovski

          Ph.D Students

          • Johanna Brewer
          • Judy Chen
          • Lilly Irani
          • Eric Kabisch
          • Amanda Williams

          M.S. Students

          • Shadi Shariat

          Visitors

          • Susanna Heyman

          My former students are equally inspiring:

          PhD students, as chair:

          • Madhu Reddy. "Time to Work Together: Temporality, Collaboration and Information Seeking." UCI, June 2003. (Now at Penn State University)
          • Danyel Fisher. "Social and Temporal Structures in Everyday Collaboration." UCI, August 2004. (Now at Microsoft Research)
          • Jack Muramatsu. "Social Regulation of Online Multiplayer Games." UCI, November 2004.
          • Jennifer Rode. "An Ethnographic Examination of the Relationship of Gender & End-User Programming." UCI, April 2008.

          PhD students, as committee member:

          • David Durand. "Palimpsest: Change-Oriented Concurrency Control for the Support of Collaborative Applications." Boston University, April 1999.
          • Wayne Lutters. "Supporting Reuse: IT and the Role of Archival Boundary Objects in Collaborative Problem Solving." UCI, August 2001. (Now at University of Maryland Baltimore County)
          • Cathy Blake. "Information Synthesis: A Mixed-Initiative Meta-Analytic Approach to Facilitate Knowledge Discovery from Scientific Text". UCI, November 2003. (Now at University of North Carolina.)
          • Charlotte Lee. "The Role of Boundary Negotiating Documentary Artifacts in the Collaborative Design of a Museum Exhibition." UCLA, December 2003. (Now at UC Irvine.)
          • Louise Barkhuus. "The Context Gap: A Challenge for Context-Aware Computing." IT University, Copenhagen, December 2004. (Now at UC San Diego.)
          • Cleidson de Souza. "On the Relationship between Software Dependencies and Coordination: Field Studies and Tool Support." UCI, June 2005. (Now at University of Belem).
          • Giovanni Iachello. "Design by Proportionality: Predicting and Increasing the Acceptance of Ubiquitous Computing Applications." Georgia Institute of Technology, March 2006.
          • Anita Sarma. "Palantir: Enhancing Configuration Management Systems with Workspace Awareness to Detect and Resolve Emerging Conflicts." UCI, December 2007.

          MS students, as primary advisor:

          • Johan Byttner, "The Visual Virtual Machine: A study of runtime visualization of Java programs." Royal Institute of Technology (Stockholm, Sweden), February 2002.
          • Jon Froehlich, "Unifying Artifacts and Activities in a Visual Tool for Distributed Software Development Teams." UCI, June 2004. (Now at University of Washington.)
          • Carolina Johansson, "Incorporating Social Navigation into a Usable Security System." Uppsala University (Sweden), June 2006.
          • Paul DiDioiga, "Security CoPilot: Visualizations for usable security" UCI, June 2007.
          • Shadi Shariat. Project Grow: An Attempt to Address the Problem of Food Insecurity within Urban Communities by Employing an Artistic, Activist, and Technical Framework. UC Irvine (Infomration and Computer Science/Arts Computation Engineering), October 2007.

          MS students, as committee member:

          • Eric Conrad (2005).
          • Margaret Watson (2005).
          • Cina Hazegh (2006).
          • Eric Kabisch (2006).
          • Greg Elliott (2007).
          • Shan Jiang (2007).

          Otherwise as examiner or advisor:

          • Martin Svensson, "Designing, Defining, and Evaluating Social Navigation". Stockholm University, February 2002.
          • Giulio Jaccuci
          • Minna Rasenen, "Islands of Connectivity", Royal Institute of Technology, Stockholm, January 2007.
          • Ylva Ferneaus, "Let's Create a Digital Patchwork." Stockholm University, March 2007.

          Previous visitors and researchers:

          • Louise Barkhuus (visitor, 2003-04)
          • Arianna Bassoli (visitor, 2006-07)
          • Johan Byttner (visitor, 2002-03)
          • Rogerio DePaula (postdoc, 2004-05)
          • Mads Ingstrup (visitor, 2005-06)
          • Carolina Johansson (visitor, 2005-06)
          • Akira Karasudani (visitor, 2004-05)
          • Scott Lederer (visitor, 2004-05)
          • Sophia Liu (research assistant, 2003-04)
          • Mads Soegaard (visitor, 2006)
          http://www.ics.uci.edu/~jpd/misc/erdos.html Paul's Erdos Number

          The publication path linking me to Paul Erdos is:

          • Paul Erdos ->
          • Ernst Specker ->
          • Karl Lieberherr ->
          • Paul Dourish

          (The former shortest path was Erdos -> Allen R. Freedman -> Richard Ladner -> Anthony LaMarca -> Paul Dourish.)

          In turn, this gives me an "Einstein number" of 5 (unless there's some shortcut, but that seems unlikely). However, my Bacon number remains infinite. http://www.ics.uci.edu/~jpd/classes.shtml Paul Dourish

          Paul Dourish

          Dept of Informatics / Donald Bren School of Information and Computer Sciences / UC Irvine

          home bio classes students research publications talks software personal schedule

          What's New?

          New papers: Interact 2007, OzCHI 2007, DUX 2007

          I'm on sabbatical 2007-08.

          current and upcoming classes

          I am on sabbatical for AY 07-08, and not teaching again until next year.                                                                                                                                                                                                      

          logo

          iPubMed

          iPubMed
          Mobile iPubMed
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          Goal

          The goal of the iPubMed Search is to make it easier to search for publications on MEDLINE database. iPubMed features:

          • Instant search on title, author, journal, MeSH heading, affiliation, and abstract
          • Error-tolerant search engine
          • Updates data from NIH daily
          • Includes references supplied by publishers

          UCI Contributors

          Current

          • Inci Cetindil (Ph.D. Student)
          • Jamshid Esmaelnezhad (Ph.D. Student)
          • Taewoo Kim (MS Student)
          • Chen Li (Faculty)
          • Xiaohui Xie (Faculty)

          Alumni

          • Shengyue Ji (Ph.D. Student)
          • Andrea Zilio (MS Student)

          Tsinghua contributors are available at this mirror site.

          Acknowledgements

          The project is supported by the US NSF award No. IIS-0742960, the NIH grant 1R21LM010143-01A1, a Google Research award, the National Natural Science Foundation of China under Grant No. 60873065, and the National High Technology Development 863 Program of China under Grant No. 2007AA01Z152.

          About iPubMed on Mobile Phones

          To make it easy to search on MEDLINE publications using mobile phones, we developed applications on both iPhone and Android phones, using the iPubMed server as the backend. Our mobile applications have the following features:

          • Supports instant search on title, author, journal, MeSH heading, affiliation, and abstract;
          • Allows minor errors in the keywords;
          • Allows users to bookmark search queries and articles;
          • Allows users to email articles.

          Contributors

          • Jim Bui, jimb@uci.edu;
          • Du The Du, dtdu@uci.edu;
          • Anthony Wang, anthonyw@uci.edu;
          • Jerry Zhang, jerryzhang93@gmail.com;
          • Chen Li (Faculty)

          We thank Inci Cetindil for her help in the project.

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          http://flamingo.ics.uci.edu/releases/1.0/ FLAMINGO Package (Approximate String Matching) Release 1.0

          FLAMINGO Package
          (Approximate String Matching)

          Release 1.0 (April 17, 2007)

          Department of Computer Science, UC Irvine

          This version is outdated. Our most recent release is here.

          Contributors

          • Chen Li (Faculty)
          • Yiming Lu (Ph.D. Student)
          • Rares Vernica (Ph.D. Student)
          • Liang Jin, graduated from UC Irvine in 2005.
          « Back to Flamingo Main Page

          This release (in C++) includes the source code of several algorithms for approximate string matching. They include algorithms of our recently published papers, an algorithm of our ongoing work, and an algorithm invented by Microsoft Researchers in a paper published in VLDB 2006.

          The motivation of this research is to efficiently answer the following two types of approximate string queries:

          • Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
          • Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?

          There are various string similarity functions, such as edit distance, jaccard, and cosine. The following is the list of algorithms corresponding to the source directory structure:

          • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
          • SEPIA: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
          • StringMap: This algorithm maps strings from the edit-distance metric place to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
          • FilterTree: This is a new algorithm we are developing to support approximate string queries using a tree structure.
          • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate selection queries.
          In addition, we have provided some commonly used functions in the "util" directory.

          [ICO]Name

          [DIR]Parent Directory
          [DIR]docs/
          [DIR]download/
          [DIR]src/
          [DIR]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award, No. IIS-0238586, the NSF-funded RESCUE project, a Google Research Award, and a fund from CalIt2.

          License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, and FilterTree is permitted under the terms of the GNU Public License (GPL). The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://flamingo.ics.uci.edu/releases/2.0/ FLAMINGO Package (Approximate String Matching) Release 2.0

          FLAMINGO Package
          (Approximate String Matching)

          Release 2.0 (October 14, 2008)

          Department of Computer Science, UC Irvine

          This version is outdated. Our most recent release is here.

          Contributors

          • Alexander Behm (Ph.D. Student)
          • Shengyue Ji (Ph.D. Student)
          • Liang Jin, graduated from UC Irvine in 2005.
          • Chen Li (Faculty)
          • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
          • Yiming Lu, graduated from UC Irvine in 2008.
          • Rares Vernica (Ph.D. Student)
          « Back to Flamingo Main Page

          Getting Started

          Please refer to the Flamingo Getting Started Guide.

          Introduction

          This release (in C++) includes the source code of several algorithms for approximate string matching developed at UC Irvine. It includes algorithms for approximate selection queries, selectivity estimation for approximate selection queries, approximate queries on mixed types, and others. Although an implementation for approximate joins is included, the focus of this release is on approximate selection queries.

          Here is a brief explanation of the terms used above:

          • Approximate String Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
            This functionality is implemented by the modules Common, FilterTree, Listmerger, StringMap, and PartEnum. We recommend getting started with the FilterTree module for this purpose.
          • Selectivity Estimation for Approximate String Search: Given a collection of strings and a single string, how can we estimate the number of strings that are "similar to" the given string? This functionality is implemented in the SEPIA module.
          • Approximate String Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?

          There are various string similarity functions, such as Levenshtein Distance (aka the Edit Distance), Jaccard Similarity, Cosine Similarity, and Dice Similarity. The following is a description of the modules corresponding to the source directory structure:

          • Common: This module contains classes for supporting the following similarity functions / distance measures: Levenshtein Distance (aka Edit Distance), Jaccard Similarity, Cosine Similarity, Dice Similarity. It also provides functionality for decomposing strings into grams.
          • FilterTree: This module provides functionality for approximate string search using an inverted-list index. Furthermore, query performance can be improved by adding filters, i.e. partitioning the string collection into disjoint subsets according to some property (e.g. the length of the strings). The use of filters is facilitated by a hierarchical structure (the FilterTree), in which each level in the tree corresponds to one filter. We have implemented the length and checksum filter.
          • ListMerger: Answering approximate string queries based on an inverted-list index requires finding elements that occur at least T times on the inverted lists belonging to the grams in the query string (T depends on the similarity metric and the similarity threshold). This problem is commonly referred to as the T-occurrence problem. This module implements several algorithms for solving the T-occurrence problem as described in "Efficient Merging and Filtering Algorithms for Approximate String Searches", Chen Li, Jiaheng Lu and Yiming Lu, ICDE 2008.
          • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
          • Sepia: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published in the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
          • StringMap: This algorithm maps strings from the edit-distance metric space to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
          • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate string matching queries, excluding approximate joins.
          In addition, we have provided some commonly used functions in the util directory.

          Changes in Version 2.0

          • The code design of filtertree has been completely re-worked.
          • Added separate module listmerger, which implements various list-merging algorithms.
          • Added support for Jaccard, Cosine and Dice similarity functions.
          • Moved gram generators and similarity functions to "common" module.
          • Due to the new design, we removed the positional filter but added the checksum filter.
          • Several bugfixes in all modules.

          [ICO]Name

          [DIR]Parent Directory
          [DIR]docs/
          [DIR]src/
          [DIR]flamingo-2.0.tgz2.2M
          [DIR]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft and a fund from CalIt2.
          Many thanks to Sattam Alsubaiee, Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

          License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, and FilterTree is permitted under the terms of the BSD license. The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://www.ics.uci.edu/~chenli/sigmod07ugrad/ ACM SIGMOD 2007 Undergraduate Scholarship Program
          ACM SIGMOD 2007
          UNDERGRADUATE SCHOLARSHIP PROGRAM
          CALL FOR APPLICATIONS (DEADLINE: MAY 1, 2007)
          ACM SIGMOD/PODS 2007 CONFERENCE
          Beijing, China
          June 11-14, 2007

          The ACM Special Interest Group on Management of Data (SIGMOD) wants undergraduate students who have an interest in database systems to experience the cutting edge research presented at the ACM SIGMOD / PODS Joint Confs. (Intl. Conf. on Management of Data and Symposium on Principles of Database Systems). In order to help those undergraduate students attend the conference, hence encouraging them to pursue a career related to database research, a scholarship program was introduced by SIGMOD in 2000 and successfully continued in the following years. It is worthwhile noting that the program has been of a truly international nature.

          Each scholarship provides up to US$1,000 to defray conference attendance costs (e.g., travel and lodging) plus Conference registration. Awardees will be responsible for their own travel arrangements, including health insurance if necessary. Selected students will be recognized at the ACM SIGMOD 2007 awards sessions.

          All undergraduate students who have completed or are enrolled in a database course by the application deadline are eligible for the scholarship. Note that, for the purpose of this award, a student is considered an undergraduate student if he/she has not yet obtained an BS (or equivalent) degree or has obtained that degree on or after December 2006, and he/she is not enrolled in a graduate program at the time of the application. If the applicant's school system is "non-traditional", and the applicant consider him/herself eligible then the committee should be contacted before the application is submitted. All application materials must be received no later than May 1, 2007. Decisions will be sent to the applicants by May 9, 2007.

          In order to apply for the scholarship, students must send an email to sigmod07ugrad AT ics DOT uci DOT edu.   The subject of the email should be "<candidate's name> ACM SIGMOD Scholarship APPLICATION".  The following information should be included (not attached) in the email in plain text. No HTML, PDF, Postscript or any other type of data will be accepted.

          1. List all courses (and respective final grades) taken so far in the undergraduate program as well as current GPA.
          2. List any scholarships, awards or honors earned.
          3. Current academic status, including number of years until graduation.
          4. Professional Organization Affiliations (e.g., ACM, SIGMOD, IEEE, Local Chapters, etc.)
          5. List of publications (if any) and or non-trivial (e.g., non-course) projects in which the applicant participated.
          6. In 100 words or less, write about "why am I interested in database research and how would I benefit from attending SIGMOD/PODS 2007."
          Additionally, the student must have a qualified person send an email to sigmod07ugrad AT ics DOT uci DOT edu.  The subject of the email should be "<candidate's name> ACM SIGMOD Scholarship NOMINATION". The following information should be included (not attached) in the email in plain text as well (again, no HTML, PDF, Postscript or any other type of data will be accepted). 
          1. Nominator's Name
          2. University and Position
          3. Professional Organization Affiliations (e.g., ACM, SIGMOD, IEEE, etc.)
          4. A statement about the relative standing (performance-wise) of the nominee at his/her University, in particular in database courses, and how far from graduation the nominee is (i.e., what percentage of the program has been completed at application time).
          5. In 100 words or less, justify "why the nominee deserves to receive this scholarship".
          By sending a nomination, all nominators explicitly agree to be contacted by the ACM SIGMOD Undergraduate Scholarship Program Committee to clarify any of the points of his/her nomination. All applications should be plain text with the proper subject lines as explained above. Any application that does not satisfy these conditions may be flagged as junk mail and automatically discarded without further notification.

          The decisions made by the ACM SIGMOD Undergraduate Scholarship Program Committee will be final. The Committee also reserves the right to not award the scholarships to any applicant. We look forward to receiving a large number of applications and wish all applicants the best of luck.


          Important information regarding visas:

          Please check this page about information related to getting visa to China. A general suggestion is to start the process to get a visa to China as earliest as possible.


          Important Dates:

          Submission Deadline: May 1, 2007, 5 PM, PST
          Notification of results: May 9, 2007


          Program Committee

          Magdalena Balazinska, University of Washington, USA
          Alin Dobra, University of Florida, USA
          Claudio Gutierrez, Universidad de Chile, Chile
          Chen Li, UC Irvine, USA (Chair)
          Qiong (Joan) Luo, Hong Kong University of Science and Technology, China
          Felix Naumann, Hasso-Plattner-Institut, Potsdam, Germany
          Prasan Roy, IBM Research, India
          Tengjiao Wang, Beijing University, China


          http://flamingo.ics.uci.edu/releases/2.0.1/ FLAMINGO Package (Approximate String Matching) Release 2.0.1

          FLAMINGO Package
          (Approximate String Matching)

          Release 2.0.1 (November 7, 2008)

          Department of Computer Science, UC Irvine

          This version is outdated. Our most recent release is here.

          Contributors

          • Alexander Behm (Ph.D. Student)
          • Shengyue Ji (Ph.D. Student)
          • Liang Jin, graduated from UC Irvine in 2005.
          • Chen Li (Faculty)
          • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
          • Yiming Lu, graduated from UC Irvine in 2008.
          • Rares Vernica (Ph.D. Student)
          « Back to Flamingo Main Page

          Getting Started

          Please refer to the Flamingo Getting Started Guide.

          Introduction

          This release (in C++) includes the source code of several algorithms for approximate string matching developed at UC Irvine. It includes algorithms for approximate selection queries, selectivity estimation for approximate selection queries, approximate queries on mixed types, and others. Although an implementation for approximate joins is included, the focus of this release is on approximate selection queries.

          Here is a brief explanation of the terms used above:

          • Approximate String Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
            This functionality is implemented by the modules Common, FilterTree, Listmerger, StringMap, and PartEnum. We recommend getting started with the FilterTree module for this purpose.
          • Selectivity Estimation for Approximate String Search: Given a collection of strings and a single string, how can we estimate the number of strings that are "similar to" the given string? This functionality is implemented in the SEPIA module.
          • Approximate String Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?

          There are various string similarity functions, such as Levenshtein Distance (aka the Edit Distance), Jaccard Similarity, Cosine Similarity, and Dice Similarity. The following is a description of the modules corresponding to the source directory structure:

          • Common: This module contains classes for supporting the following similarity functions / distance measures: Levenshtein Distance (aka Edit Distance), Jaccard Similarity, Cosine Similarity, Dice Similarity. It also provides functionality for decomposing strings into grams.
          • FilterTree: This module provides functionality for approximate string search using an inverted-list index. Furthermore, query performance can be improved by adding filters, i.e. partitioning the string collection into disjoint subsets according to some property (e.g. the length of the strings). The use of filters is facilitated by a hierarchical structure (the FilterTree), in which each level in the tree corresponds to one filter. We have implemented the length and checksum filter.
          • ListMerger: Answering approximate string queries based on an inverted-list index requires finding elements that occur at least T times on the inverted lists belonging to the grams in the query string (T depends on the similarity metric and the similarity threshold). This problem is commonly referred to as the T-occurrence problem. This module implements several algorithms for solving the T-occurrence problem as described in "Efficient Merging and Filtering Algorithms for Approximate String Searches", Chen Li, Jiaheng Lu and Yiming Lu, ICDE 2008.
          • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
          • Sepia: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published in the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
          • StringMap: This algorithm maps strings from the edit-distance metric space to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
          • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate string matching queries, excluding approximate joins.
          In addition, we have provided some commonly used functions in the util directory.

          Changes in Version 2.0.1

          • Fixed compatibility issues for GCC 4.3.2

          [ICO]Name

          [DIR]Parent Directory
          [DIR]docs/
          [DIR]src/
          [DIR]flamingo-2.0.1.tgz2.2M
          [DIR]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft and a fund from CalIt2.
          Many thanks to Sattam Alsubaiee, Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

          License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, and FilterTree is permitted under the terms of the BSD license. The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://flamingo.ics.uci.edu/releases/3.0/ FLAMINGO Package (Approximate String Matching) Release 3.0

          FLAMINGO Package
          (Approximate String Matching)

          Release 3.0 (March 29, 2010)

          Department of Computer Science, UC Irvine

          Contributors

          • Alexander Behm (Ph.D. Student)
          • Shengyue Ji (Ph.D. Student)
          • Liang Jin, graduated from UC Irvine in 2005.
          • Chen Li (Faculty)
          • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
          • Yiming Lu, graduated from UC Irvine in 2008.
          • Rares Vernica (Ph.D. Student)
          « Back to Flamingo Main Page

          Getting Started

          Please refer to the Flamingo Getting Started Guide.

          Introduction

          This release (in C++) includes the source code of several algorithms for approximate string matching developed at UC Irvine. It includes algorithms for approximate selection queries, selectivity estimation for approximate selection queries, approximate queries on mixed types, and others. Although an implementation for approximate joins is included, the focus of this release is on approximate selection queries.

          Here is a brief explanation of the terms used above:

          • Approximate String Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
            This functionality is implemented by the modules Common, FilterTree, Listmerger, StringMap, and PartEnum. We recommend getting started with the FilterTree module for this purpose.
          • Selectivity Estimation for Approximate String Search: Given a collection of strings and a single string, how can we estimate the number of strings that are "similar to" the given string? This functionality is implemented in the SEPIA module.
          • Approximate String Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?

          There are various string similarity functions, such as Levenshtein Distance (aka the Edit Distance), Jaccard Similarity, Cosine Similarity, and Dice Similarity. The following is a description of the modules corresponding to the source directory structure:

          • Common: This module contains classes for supporting the following similarity functions / distance measures: Levenshtein Distance (aka Edit Distance), Jaccard Similarity, Cosine Similarity, Dice Similarity. It also provides functionality for decomposing strings into grams.
          • FilterTree: This module provides functionality for approximate string search using an inverted-list index. Furthermore, query performance can be improved by adding filters, i.e. partitioning the string collection into disjoint subsets according to some property (e.g. the length of the strings). The use of filters is facilitated by a hierarchical structure (the FilterTree), in which each level in the tree corresponds to one filter. We have implemented the length and charsum filter. This package contains three flavors of indexes: in-memory indexes compressed & uncompressed and a disk-based index.
          • ListMerger: Answering approximate string queries based on an inverted-list index requires finding elements that occur at least T times on the inverted lists belonging to the grams in the query string (T depends on the similarity metric and the similarity threshold). This problem is commonly referred to as the T-occurrence problem. This module implements several algorithms for solving the T-occurrence problem as described in "Efficient Merging and Filtering Algorithms for Approximate String Searches", Chen Li, Jiaheng Lu and Yiming Lu, ICDE 2008. In addition, we have implemented efficient algorithms for disk-based indexes.
          • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
          • SEPIA: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published in the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
          • StringMap: This algorithm maps strings from the edit-distance metric space to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
          • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate string matching queries, excluding approximate joins.
          • TopK: This package contains algorithms for efficient Top-K approximate string search.
          In addition, we have provided some commonly used functions in the util directory.

          Changes in Version 3.0 (compared to Version 2.0.1)

          • Added Compressed Indexers based on the Techniques from:
            "Space-Constrained Gram-Based Indexing for Efficient Approximate String Search", by Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu, in ICDE 2009
          • Added Module for Top-K Approximate String Search from:
            "Efficient top-k algorithms for fuzzy search in string collections", by Rares Vernica, Chen Li, in KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)
          • Added Disk-Based Inverted Index, Disk-Based StringContainer and Efficient Search Algorithms using the Disk-Based Components from:
            "Answering Set-Similarity Selection Queries on Large Disk-Resident Data Sets", by Alexander Behm, Chen Li, Michael J. Carey, UCI Technical Report 2010
          • Added Some Auto-Tuning Features, e.g. Automatic Choice of Partitioning Filter

          Bibtex

          @misc{misc/flamingo3.0-2010,
                author = {Alexander Behm and Rares Vernica and Shengyue Ji and Jiaheng Lu and Liang Jin and Yiming Lu and Chen Li},
                year = {2010},
                title = {{UCI} {Flamingo} {Package} 3.0},
                url = {http://flamingo.ics.uci.edu/releases/3.0/},
                institution = {University of California, Irvine, School of Information and Computer Sciences}
          } 
          
          [ICO]Name

          [DIR]Parent Directory
          [DIR]docs/
          [DIR]src/
          [DIR]flamingo-3.0.tgz2.4M
          [DIR]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft, a fund from CalIt2, the NSF CluE Project and the ASTERIX Project funded by the NSF.
          Many thanks to Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

          License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, and FilterTree is permitted under the terms of the BSD license. Permission to use, copy, modify, and distribute the implementations of the compression techniques DiscardLists and CombineLists is permitted under the terms of the following Academic BSD License. The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.

          Academic BSD License:
          The (compression techniques) DiscardLists and CombineLists are the proprietary property of The Regents of the University of California (“The Regents.”)
          Copyright © 2009 The Regents of the University of California, Irvine. All Rights Reserved.
          Redistribution and use in source and binary forms, with or without modification, are permitted by nonprofit, research institutions for research use only, provided that the following conditions are met:

          • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
          • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
          • Neither the name of The Regents nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

          The end-user understands that the program was developed for research purposes and is advised not to rely exclusively on the program for any reason.

          THE SOFTWARE PROVIDED IS ON AN "AS IS" BASIS, AND THE REGENTS AND CONTRIBUTORS HAVE NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. THE REGENTS AND CONTRIBUTORS SPECIFICALLY DISCLAIM ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES, INCLUDING BUT NOT LIMITED TO PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, LOSE OF USE, DATA OR PROFITS, OR BUSINESS INTERRUPTION, HOWEVER CAUSED AND UNDER ANY THEORY OF LIABILITY WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

          If you do not agree to these terms, do not download or use the software. This license may be modified only in a writing signed by authorized signatory of both parties.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://www.ics.uci.edu/~cs222/ http://www.ics.uci.edu/~cs223/ ICS 223 Redirection

          Redirecting to latest newsletter. If you're not redirected within a couple of seconds, click here:
          CS 223 Web Site

          http://asterixdb.ics.uci.edu/documentation/index.html AsterixDB -
          AsterixDB

          • Last Published: 2014-07-14
          • Version: 0.8.6
          • |
          • Documentation Home
          • Documentation
          • Installing and Managing AsterixDB using Managix
          • AsterixDB 101: An ADM and AQL Primer
          • AsterixDB Javascript SDK
          • Asterix Data Model (ADM)
          • Asterix Query Language (AQL)
          • AQL Functions
          • AQL Allen's Relations Functions
          • AQL Support of Similarity Queries
          • Accessing External Data
          • REST API to AsterixDB

          Hyracks

          AsterixDB: A Big Data Management System

          Table of Contents

          • What Is AsterixDB?
          • Getting and Using AsterixDB

          What Is AsterixDB? [Back to TOC]

          In a nutshell, AsterixDB is a full-function BDMS (Big Data Management System) with a rich feature set that distinguishes it from pretty much any other Big Data platform that’s out and available today. We believe that its feature set makes it well-suited to modern needs such as web data warehousing and social data storage and analysis. AsterixDB has:

          • A semistructured NoSQL style data model (ADM) resulting from extending JSON with object database ideas
          • An expressive and declarative query language (AQL) that supports a broad range of queries and analysis over semistructured data
          • A parallel runtime query execution engine, Hyracks, that has been scale-tested on up to 1000+ cores and 500+ disks
          • Partitioned LSM-based data storage and indexing to support efficient ingestion and management of semistructured data
          • Support for query access to externally stored data (e.g., data in HDFS) as well as to data stored natively by AsterixDB
          • A rich set of primitive data types, including spatial and temporal data in addition to integer, floating point, and textual data
          • Secondary indexing options that include B+ trees, R trees, and inverted keyword (exact and fuzzy) index types
          • Support for fuzzy and spatial queries as well as for more traditional parametric queries
          • Basic transactional (concurrency and recovery) capabilities akin to those of a NoSQL store

          Getting and Using AsterixDB [Back to TOC]

          You are most likely here because you are interested in getting your hands on AsterixDB—so you would like to know how to get it, how to set it up, and how to use it. The following is a list of the supporting documents that we have available today:

          1. Installing AsterixDB using Managix : This is our installation guide, and it is where you should start. This document will tell you how to obtain, install, and manage instances of AsterixDB, including both single-machine setup (for developers) as well as cluster installations (for deployment in its intended form).

          2. AsterixDB 101: An ADM and AQL Primer : This is a first-timers introduction to the user model of the AsterixDB BDMS, by which we mean the view of AsterixDB as seen from the perspective of an “average user” or Big Data application developer. The AsterixDB user model consists of its data modeling features (ADM) and its query capabilities (AQL). This document presents a tiny “social data warehousing” example and uses it as a backdrop for describing, by example, the key features of AsterixDB. By working through this document, you will learn how to define the artifacts needed to manage data in AsterixDB, how to load data into the system, how to use most of the basic features of its query language, and how to insert and delete data dynamically.

          3. Asterix Data Model (ADM), Asterix Functions, Asterix functions for Allen’s Relations, and Asterix Query Language (AQL) : These are reference documents that catalog the primitive data types and built-in functions available in AQL and the reference manual for AQL itself.

          4. REST API to AsterixDB : Access to data in an AsterixDB instance is provided via a REST-based API. This is a short document that describes the REST API entry points and their URL syntax.

          To all who have now come this far: Thanks for your interest in AsterixDB, and for kicking its tires in its Beta form. In addition to getting the system and trying it out, please sign up as a member of the AsterixDB user mailing list (asterixdb-users (at) googlegroups.com) so that you can contact us easily with your questions, issues, and other feedback. We want AsterixDB to be a “big hit” some day, and we are anxious to see what users do with it and to learn from that feedback what we should be working on most urgently in the next phase of the project.


          Copyright © 2014. All Rights Reserved.
          http://fr.ics.uci.edu/widgets/chilepersonfinder/ Family Reunification for Chile Earthquake - Person Finder Search Widget

          FAMILY REUNIFICATION
          FOR CHILE EARTHQUAKE

          Live Demo

          People Finder Search Widget

          We are providing an interactive search widget that can be embedded into any website. The widget enables interactive and error tolerant searches over the People Finder: Chile Earthquake data.

          To integrate our widget into your site just copy and paste the following HTML code into your web pages:

          <link rel="stylesheet" type="text/css"
              href="http://fr.ics.uci.edu/widgets/chilepersonfinder/isearch.css">
          <div id="isearch"></div>
          <script type="text/javascript"
              src="http://fr.ics.uci.edu/widgets/chilepersonfinder/isearch.min.js">
          </script>

          Above is a demo of how the widget will look on your site.


          « Back to Family Reunification for Chile Earthquake

          http://www.ics.uci.edu/~raccoon/content.htm Raccoon content
          News
          People
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          About

          http://www.ics.uci.edu/~raccoon/news.html The Raccoon Project: Peer-Based Data Integration and Sharing

                    The UCI Database Group

           

           News:

          ·        March, 2006: We wrote a technical report titled “Data Exchange with Arithmetic Comparisons” by Foto Afrati, Chen Li, and Vassia Pavlaki.

          ·        March, 2006: We wrote a technical report titled “Protecting Individual Information Against Inference Attacks in Data Publishing” by Chen Li, Houtan Shirani-Mehr, and Xiaochun Yang.

          ·        November 29, 2004: The source code (version 2.0) of the system demonstration is released.

          ·        August 27, 2004: The source code (version 1.0) of the system demonstration is released.

          ·        July 2004: The following paper is accepted by the International Conference of Very Large Database (VLDB), 2004: “Secure XML Publishing without Information Leakage in the Presence of Data Inference.” It was a joint work of Chen and Prof. Xiaochun Yang from the Northeastern University, China during her visit at UCI in the summer of 2003.

          ·        March 30 - April 2, 2004: Qi and Chen gave a demonstration of the system in the 20th International Conference on Data Engineering, Omni Parker House Hotel, Boston, USA.

          ·        September 13-16, 2003: Chen attended the NSF IDM PI annual workshop, Seattle, WA, even though our project just got funded J

          ·        September 1st, 2003: The project became officially funded by the NSF CAREER Proposal, No. IIS-0238586, titled "CAREER: Peer-Based Data Integration and Sharing of Heterogeneous Sources."

           

           

           

          http://dblp.ics.uci.edu/authors/ Search Author
          dblp.uni-trier.de
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          Search Author


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          Home | Conferences | Journals | Series | FAQ — Search: Faceted | Complete | Author
          Copyright © Tue Feb 3 16:53:46 2009 by Michael Ley (ley@uni-trier.de)

          http://dblp.ics.uci.edu/
          http://hobbes.ics.uci.edu/examples.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Examples


          Data sets

          1. Human Genome (HG18 - hg18.fa)

          hg76_500k.fastq: 500k single-end reads of length 76bp.
          - We picked the fist 500k reads from DRR000617_1.fastq.

          hg100_1m.fastq: 1 million single-end reads of length 100bp.
          - We picked the first 1 million reads from SRR062634_1.filt.fastq.

          hg100_1m_pe1.fq & hg100_1m_pe2.fq: 1 million paired-end reads of length 100bp.
          - We picked the first 1 million pairs of reads from SRR062634_1.filt.fastq & SRR062634_2.filt.fastq

          2. Caenorhabdities Elegans (WormBase WS201 - ce.fa)

          ce100_1m.fastq: 1 million single-end reads of length 100bp.
          - We picked the first 1 million reads from SRR065390_1.fastq

          3. Drosophila Melanogaster (FlyBase release 5.42 - dm.fa)

          dm100_1m.fastq: 1 million single-end reads of length 100bp for D. melanogaster
          - We picked the first 1 million reads from SRR497711_1.fastq


          Constructing Hobbes Indexes

          HG18
          ./hobbes-index --sref hg18.fa -i hg18.hix -g 11 -p 4

          C. elegans
          ./hobbes-index --sref ce.fa -i ce.hix -g 11 -p 4

          D. melanogaster
          ./hobbes-index --sref dm.fa -i dm.hix -g 11 -p 4


          Running Hobbes for Single-End Reads

          1. Edit Distance

          genome = HG18, read length = 100bp, # of reads = 1 million, threshold = 5
          ./hobbes -q hg100_1m.fastq --sref hg18.fa -i hg18.hix -a --indel -v 5 -n 1000000 -p 1 > out.sam
          ./hobbes -q hg100_1m.fastq --sref hg18.fa -i hg18.hix -a --indel -v 5 -n 1000000 -p 16 > out.sam

          genome = C. elegans, read length = 100bp, # of reads = 1 million, threshold = 5
          ./hobbes -q ce100_1m.fastq --sref ce.fa -i ce.hix -a --indel -v 5 -n 1000000 -p 1 > out.sam
          ./hobbes -q ce100_1m.fastq --sref ce.fa -i ce.hix -a --indel -v 5 -n 1000000 -p 16 > out.sam

          genome = D. melanogaster, read length = 100bp, # of reads = 1 million, threshold = 5
          ./hobbes -q dm100_1m.fastq --sref dm.fa -i dm.hix -a --indel -v 5 -n 1000000 -p 1 > out.sam
          ./hobbes -q dm100_1m.fastq --sref dm.fa -i dm.hix -a --indel -v 5 -n 1000000 -p 16 > out.sam

          2. Hamming Distance

          genome = HG18, read length = 76bp, # of reads = 500k, threshold = 3
          ./hobbes -q hg76_500k.fastq --sref hg18.fa -i hg18.hix -a --hamming -v 3 -n 500000 -p 1 > out.sam
          ./hobbes -q hg76_500k.fastq --sref hg18.fa -i hg18.hix -a --hamming -v 3 -n 500000 -p 16 > out.sam

          genome = HG18, read length = 100bp, # of reads = 500k, threshold = 5
          ./hobbes -q hg100_1m.fastq --sref hg18.fa -i hg18.hix -a --hamming -v 5 -n 500000 -p 1 > out.sam
          ./hobbes -q hg100_1m.fastq --sref hg18.fa -i hg18.hix -a --hamming -v 5 -n 500000 -p 16 > out.sam


          Running Hobbes for Paired-End Reads

          1. Edit Distance

          genome = HG18, read length = 100bp, # of reads = 1 million, threshold = 5
          ./hobbes --pe --seqfq1 hg100_1m_pe1.fq --seqfq2 hg100_1m_pe2.fq --sref hg18.fa -i hg18.hix -a --indel -v 5 --min 110 --max 290 -n 1000000 -p 1 > out.sam
          ./hobbes --pe --seqfq1 hg100_1m_pe1.fq --seqfq2 hg100_1m_pe2.fq --sref hg18.fa -i hg18.hix -a --indel -v 5 --min 110 --max 290 -n 1000000 -p 16 > out.sam

          2. Hamming Distance

          genome = HG18, read length = 100bp, # of reads = 500k, threshold = 5
          ./hobbes --pe --seqfq1 hg100_1m_pe1.fq --seqfq2 hg100_1m_pe2.fq --sref hg18.fa -i hg18.hix -a --hamming -v 5 --min 110 --max 290 -n 500000 -p 1 > out.sam
          ./hobbes --pe --seqfq1 hg100_1m_pe1.fq --seqfq2 hg100_1m_pe2.fq --sref hg18.fa -i hg18.hix -a --hamming -v 5 --min 110 --max 290 -n 500000 -p 16 > out.sam


          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/people.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          People

        • Athena Ahmadi, PhD Student
        • Alex Behm, PhD Student (now at Cloudera)
        • Nagesh Honnalli, MS Student (now at Amazon AWS)
        • Jongik Kim, Faculty member at Chonbuk National University in Korea
        • Chen Li, Faculty
        • Thanh Nguyen, PhD Student
        • Lingjie Weng, PhD Student
        • Xiaohui Xie, Faculty
        • � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/index.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          About Hobbes

          Hobbes is a sequence mapping software in development at School of Information and Computer Sciences at UC Irvine.

          News

          07/13/2015

          We released Hobbes3 (Hobbes3 source code). Hobbes3 significantly improves accuracy and mapping speed for edit distance in all mapping mode (and m mapping mode as well).

          12/03/2013

          A new version of Hobbes2 has been released (Hobbes 2.1 source code). This version supports m mapping mode.

          06/17/2013

          We released Hobbes2 (Hobbes 2.0 source code) under a BSD license.

          01/25/2013

          We released a new version of Hobbes source code under a BSD license. The new version is faster and return more mappings

          01/23/2012

          We released the Hobbes source code under a BSD license.

          11/01/2011

          We released Hobbes 1.3 which includes mostly bugfixes and usability improvements.

          09/01/2011

          A new version of Hobbes has been released. This version supports indels and paired end reads.

          03/31/2011

          Hobbes has been released for the first time. The current version supports hamming distance. The Hobbes team is trying to add support for indels and paired end reads in the next version.

          Let Us Know What You Think

          The Hobbes team would appreciate your feedback.

          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/faq.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Hobbes FAQ

          This FAQ provides answers to basic questions about Hobbes. You may also refer to the following papers for the technical details of Hobbes.

          • "Hobbes: optimized gram-based methods for efficient read alignment", Nucleic Acid Research 2011.
          • "Improving read mapping using additional prefix grams", BMC Bioinformatics 2014.

          Questions

          1. Why doesn't Hobbes align any of the reads?
          2. What part of the code requires/uses popcnt instruction?
          3. How do I fix compilation errors?
          4. Why does Hobbes crash when building an index using hobbes-index?
          5. How do I find unalignable reads from Hobbes output.
          6. How were benchmark scores in the Hobbes2 paper generated and what do they mean?

          Answers

          1. Why doesn't Hobbes align any of the reads?

            Given a distance threshold t, a read needs to contain at least t + 2 non-overlapping q-grams. That is, the length of a read needs to be greater than q * (t + 2). Otherwise, Hobbes doesnot align any of the reads.

            We recommend to set a gram length to "read length/(threshold + 3)" when building an index.

          2. What part of the code requires/uses popcnt instruction?

            Hobbes3 does not use popcnt instruction any more. Previous versions of Hobbes use popcnt to count the number of 1's in a BitVector. For the details of BitVectors, please refer to the following Hobbes paper.

            "Hobbes: optimized gram-based methods for efficient read alignment", Nucleic Acids Research 2011.

          3. How do I fix compilation errors?

            To complie Hobbes3, you need a proper version gcc (4.2 or later).

            If you choose to support compressed reads by running "build.sh compress", there might be link errors. In case of linker errors, you need to check names of shared libraries used in Hobbes3.

            The names of shared libraries of zip, bzip2, and boost iostream should be libz.so, libbz2.so, and libboost_iostream.so, respectively. However, the names of these libraries often contain version number in their suffixes. In those cases, you need to create a symbolic link. If gcc fails to find libboost_iostream in your system, for example, you need to make a symbolic link named "libboost_iostream.so" pointing to libboost_iostream.so.x.x.x, where x.x.x is a version number.

          4. Why does Hobbes crash when building an index using hobbes-index?

            Please check the genome file format. The problem is often caused by an additional blank line at the end of the file. Please delete additional blank lines at the end of a genome file.

          5. How do I find unalignable reads from Hobbes output.

            Hobbes3 outputs unaligned reads by default (Hobbes2 does not output unaligned reads as other mappers such as razerS3 and masai do not).

          6. How were benchmark scores in the Hobbes2 paper generated and what do they mean?

            They were generated from the Rabema benchmark. If you refer to the following Rabema paper, you can figure it out how to generate the values and what they mean.

            "A novel and well-defined benchmarking method for second generation read mapping", BMC Bioinformatics 2011.

          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/manual.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Manual

          Hobbes3 is a software package for efficiently mapping DNA snippets (reads) against a reference DNA sequence. It can map short and long reads, and supports Hamming distance (only substitutions) and edit distance (substitutions/insertions/deletions). Hobbes3 accepts both single-end and paired-end reads for alignment, and can run on multiple CPU cores using multithreading. It supports three input formats (Fastq, Fasta, and text files) and the SAM output format. Ambiguous bases such as the 'N' character are treated as mismatches.

          Manual page for the previous versions (Hobbes 1.x) is here

          System Requirements

        • We developed and tested Hobbes3 under Ubuntu 14.04.2 LTS 64-bit.
        • GCC. Hobbes3 uses GCC builtin functions. We used GCC 4.8.2
        • CMake. Required for compiling Hobbes3 (sudo apt-get install cmake).
        • The following libraries are required if you want to map compressed reads
             - libbz2 and libz (sudo apt-get install libbz2-dev libz-dev).
             - boost-iostreams (sudo apt-get install libboost-iostreams-dev).
        • Compiling Hobbes3

        • Download the Hobbes3 source from here.
        • Extract the contents of the archive (tar -zxvf hobbes-3.0.tar.gz).
        • cd into the Hobbes3 source root directory (cd hobbes3.0).
        • Run either "build.sh nocompress" or "build.sh compress".
        •    - build.sh nocompress: compressed reads are not supported.
             - budil.sh compress: compressed reads are supported.
               (the "compress" option requires libboost, libbz2, libz libraries).
        • The Hobbes3 binaries are placed in the "build" directory.
        • Constructing a Hobbes3 Index

          Usage

          ./hobbes-index --sref <input fasta file> \
                         -i <output index file> -g <gram length> -p <number of threads>
          

          Example

          ./hobbes-index --sref hg18.fa -i hg18.hix -g 11 -p 4

          Options

          --sref <file> Reference sequence file to index in fasta format.
          --dref <dir> Uses all fasta files in given directory as reference sequence. File names become chromosome names.
          -i <file> Create Hobbes3 index into given file.
          -g <int> Use given gram length to build a Hobbes3 index. We recommend a gram length of 11. We support gram lengths up to 16, but the index size will increase dramatically after gram length 13.
          -p <int> Use given number of parallel pthreads to construct the index.
          --noprogress Disable progress indicator.

          Mapping Reads with Hobbes3

          Single-End Reads

          1) Hamming distance (substitutions only):
          ./hobbes -q <input fastq file> --sref <fasta reference file>       \
                   -i <hobbes index file> -a --hamming -v <hamming distance> \
                   -n <number of reads> -p <number of threads>
          
          2) Edit distance (substitutions/insertions/deletions):
          ./hobbes -q <input fastq file> --sref <fasta reference file>  \
                   -i <hobbes index file> -a --indel -v <edit distance> \
                   -n <number of reads> -p <number of threads>
          
          Examples:

          hobbes -q reads.fq --sref hg18.fa -i hg18.hix -a --hamming -v 2 -n 10000 -p 16
          hobbes -q reads.fq --sref hg18.fa -i hg18.hix -a --indel -v 2 -n 10000 -p 16

          Paired-End Reads

          1) Hamming distance (substitutions only):
          ./hobbes --pe                                                               \
                   --seqfq1 <first read fastq file> --seqfq2 <second read fastq file> \
                   --min <minimum insert size> --max <maximum insert size>            \
                   --sref <fasta reference file> -i <hobbes index file>               \
                   -a --hamming -v <hamming distance> -n <number of reads>            \
                   -p <number of threads>
          
          2) Edit distance (substitutions/insertions/deletions):
          ./hobbes --pe                                                               \
                   --seqfq1 <first read fastq file> --seqfq2 <second read fastq file> \
                   --min <minimum insert size> --max <maximum insert size>            \
                   --sref <fasta reference file> -i <hobbes index file>               \
                   -a --indel -v <hamming distance> -n <number of reads>              \
                   -p <number of threads>
          
          Examples:
          ./hobbes --pe --seqfq1 reads1.fq --seqfq2 reads1.fq  --min 50 --max 150 \
                   --sref hg18.fa -i hg18.hix -a --hamming -v 2 -n 10000 -p 16
          ./hobbes --pe --seqfq1 reads1.fq --seqfq2 reads1.fq  --min 50 --max 150 \
                   --sref hg18.fa -i hg18.hix -a --indel   -v 2 -n 10000 -p 16
          

          Read Input Options

          -q <file> Map single-end reads in given fastq file.
          -r <file> Map single-end reads in given line-by-line text file.
          -f <file> Map single-end reads in given fasta file.
          -c <string> Map given single-end read (only maps a single read).
          --seqfq1 <file> First fastq file for paired-end reads. Requires --pe.
          --seqfq2 <file> Second fastq file for paired-end reads. Requires --pe.
          --gzip Reads file is compressed with gzip.
          --bzip2 Reads file is compressed with bzip2.
          top

          Reference Sequence Options

          --sref <file> Reference sequence file in fasta format.
          --dref <dir> Uses all fasta files in given directory as reference sequence. File names become chromosome names.
          top

          Index Options

          -i <file> Use given Hobbes3 index to perform mapping.
          top

          Mapping Options

          Hobbes3 can find all or at most k mappings per read. Note that the running time varies accordingly. If a read has exact mappings, Hobbes3 guarantees to find them. Otherwise, it finds mapping(s) within the specified distance. By default, Hobbes3 maps against the forward and reverse reference, (see --norc and --nofw).

          -a Find all mapping locations.
          -m <int> Find those reads such that the maximum number of distinct mapping locations is less than or equal to a given threshold (single-end mapping only).
          -k <int> Find upto 'k' mappings per read (single-end mapping only).
          --hamming Map reads using using Hamming distance.
          --indel Map reads using edit distance. Uses heuristics to speed up the search, and is not guaranteed to find the best possible mapping locations (but very often it does).
          -v <int> Distance threshold. Finds reads within given distance threshold (use --hamming for Hamming distance and --indel for edit distance).
          --pe Enable paired-end mapping mode. See --seqfq1 and --seqfq2.
          --min <int> Minimum insert size for paired-end mappings.
          --min <int> Maximum insert size for paired-end mappings.
          -n <int> Aligns given number of reads (first ones). By default, all the reads are aligned.
          --norc Maps against forward reference only.
          --nofw Maps against reverse reference only.
          top

          Output Options

          Hobbes3 produces results in the SAM output format with CIGAR strings.
          By default, mappings are printed to stdout.

          --sam-nohead Suppresses the header lines (starting with '@').
          --sam-nosq Suppresses the @SQ header lines.
          --mapout <file> Prints the mappings to a specified file.
          top

          Other options

          -p <int> Runs given number of parallel pthreads to perform the mapping.
          --noprogress Disable progress indicator.
          --version Prints version information.
          --help Prints usage information.
          top

          The SAM Output Format

          Hobbes3 produces results in the SAM format. It outputs one mapping per line. A single read may appear at multiple lines, where the primary mapping is placed first. Reads that are unmapped are not printed. Each line has the following tab separated fields:

          1. Name of the read mapped.

          2. SAM bitwise FLAG.

          3. RNAME: Reference sequence name of the mapping. If @SQ header lines are present, RNAME must be present in one of the SQ-SN tag.

          4. POS: 1-based leftmost mapping POSition of the first matching base. The first base in a reference sequence has coordinate 1.

          5. MAPQ: Mapping Quality. A value 255 indicates that the mapping quality is not available. Since we don't support this yet, it's set to 255.

          6. CIGAR: CIGAR string.

          7. RNEXT: Reference sequence name of the NEXT fragment in the template. Currently unavailable and hence set to`*'.

          8. PNEXT: Position of the NEXT fragment in the template. In single-end alignment, it's set to 0; in paired-end alignment, it's the positon of it's mate pair.

          9. TLEN: Signed insert size, it is set as 0 for single-end reads or when the information is unavailable.

          10. SEQ: Read sequence. If the current mapping is not a primary mapping, it is set to `*'.

          11. QUAL: ASCII of base QUALity plus 33 (same as the quality string in the Sanger FASTQ format). If either the input file is not in the FASTQ or the current mapping is not a primary mapping, it is set to `*'.

          12. Optional fields: For descriptions of all possible optional fields, see the SAM format specification. The fields relevant to Hobbes3 is,

            1. NM:i:<N> - Mapped read has hamming/edit distance of <N>.

          top    

          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://www.ics.uci.edu/~yunh/ Yun Huang@UCI

          Yun Huang

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • Home
          • Research
          • Publications
          • Professional Activities
          • Teaching and Mentoring

          Short Bio

          Yun Huang earned a bachelor's degree in computer science from Tsinghua University , Beijing, China.
          She received both her master's degree and doctorate from the Donald Bren School of Information and Computer Sciences (ICS), at UC Irvine . Her advisor is Prof. Nalini Venkatasubramanian.

          Contact

          2084 Donald Bren Hall (DBH),
          Department of Computer Science,
          University of California, Irvine,
          Irvine, CA 92697-3435
          Office Phone: (949) 824-3011
          Email: yunh AT ics.uci.edu

          Last Updated on April 20, 2009 | website designed by Yun Huang and Andreas Viklund

          http://hobbes.ics.uci.edu/contact.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Contact Info

          Email us at: hobbes AT ics.uci.edu


          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/download.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Downloads

          Current Release

        • Hobbes3 (Hobbes 3.0) Source Code
        • Hobbes3 License

        • Please consult the manual for system requirements and compilation instructions.

          Previous Releases

        • Hobbes 2.1 Source Code

        • Hobbes 2.0 Source Code

        • Hobbes 1.5 Source Code

        • Hobbes 1.4 Source Code

        • Hobbes 1.3 Source Code


        • Release History

          Hobbes3 (Hobbes 3.0) Source (July 13, 2015)

          Hobbes2 (Hobbes 2.1) Source and Binaries (December 3, 2013)

          Hobbes2 (Hobbes 2.0) Source (June 17, 2013)

          Version 1.5 Source (January 25, 2013), Improved speed and accuracies

          Version 1.4 Source (June 25, 2012), Improved speed and accuracies

          Version 1.3 Source (February 03, 2012), Fixed bugs and improved usability

          Version 1.3 Source (January 23, 2012)

          Version 1.3 (November 01, 2011)

          Version 1.2 (September 01, 2011)

          Version 1.1 (July 15, 2011)

          Version 1.0 (March 31, 2011)

          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://hobbes.ics.uci.edu/quickstart.shtml Hobbes Genome Sequence Mapping, UC Irvine

          Hobbes

          Genome Sequence Mapping

          Information Systems Group

          Institute for Genomics and Bioinformatics

          Bren School of ICSUC Irvine

          • About
          • Downloads
          • Quick Start
          • Examples
          • Manual
          • People
          • FAQ
          • Contact

          Quick Start

          Compiling Hobbes3

          Download Hobbes3 source and compile it as follows.
          $ tar -zxvf hobbes-3.0.tar.gz
          $ cd hobbes3.0
          $ ./build.sh nocompress
          $ cd build
          $ ./hobbes --help
          

          Building an index

          Given a genome sequence file "genome.fasta", you can build an index by issuing the following command:
          
          $ ./hobbes-index --sref genome.fasta -i genome.hix -g 11 -p 4
          
          
          The command makes an index file named "genome.hix" using gram length 11. hobbes-index uses --sref to specify an input genome sequence file, -i to specify output index file, and -g to specify the gram length. -p enables multithreading and the command builds an index using 4 threads.
          * Hobbes2 builds an index very fast. For the whole human genome of HG18, for example,
            the command above built an index in 9 minutes on a machine with 94 GB of RAM, and
            dual quad-core Intel Xeons X5670 at 2.93 GHz, running a 64-bit Ubuntu OS.
          

          Aligning single-end reads

          Given a read file "read.fastq" and a genome sequence file "genome.fasta" with its index file "genome.hix", the following command finds all mappings within edit distance 5.
          
          $ ./hobbes -q read.fastq --sref genome.fasta \
                     -i genome.hix -a --indel -v 5 -p 16 --mapout output.sam
          
          
          hobbes uses -q to specify an input read file, --sref to specify a genome file, and -i to specify an index file for the genome file. By using -a, you can make hobbes produce all mapping locations.--indel indicates gapped alignment with an edit distance threshold of 5, which is specified by -v. -p enables multithreading and the command aligns reads using 16 threads. hobbes produces results in the SAM format. The command output results to a file named "output.sam", which is specified by --mapout. If --mapout is not specified, hobbes outputs results to stdout. In this case, you can redirect results to an output file as follows.
            
          $ ./hobbes -q read.fastq --sref genome.fasta \
                     -i genome.hix -a --indel -v 5 -p 16 > output.sam
          
          
          If you want to align reads with a Hamming distance threshold instead of an edit disance, you can replace --indel with --hamming as follows.
          
          $ ./hobbes -q read.fastq --sref genome.fasta \
                     -i genome.hix -a --hamming -v 5 -p 16 --mapout output.sam
          
          
          If you specify the number of reads, N, using -n, hobbes maps only the first N reads from the input read file. By using -n, you can see the progress with an estimated time to complete alignment. It can be useful for testing your pipeline.
          
          $ ./hobbes -q read.fastq --sref genome.fasta \
                     -i genome.hix -a --indel -v 5 -n 10000 -p 16 --mapout output.sam
          
          76% MAPPING READS: 7616/10000; 0'26"/0'35"
          
          

          Aligning paired-end reads

          To align paired-end reads, you should use --pe and specify two input read files using --seqfq1 and --seqfq2, respectively. You also need to specify minimum insert size and maximum insert size using --min and --max, respectively. Other options are exactly the same as those of single-end alignment. Given two read files "read1.fastq" and "read2.fastq", for example, the following command produces paired-end mappings.
          
          $ ./hobbes --pe --seqfq1 read1.fastq --seqfq2 read2.fastq --min 110 --max 290 \
                     --sref genome.fasta -i genome.hix -a --indel -v 5  -n 10000 -p 16  \
                     --mapout output.sam
          
          

          � 2015 ISG | Website maintained by Jongik Kim | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on Jul 13, 2015

          http://flamingo.ics.uci.edu/toolkit/ FLAMINGO Toolkit

          FLAMINGO Toolkit

          Last updated: October 14, 2008

          Department of Computer Science, UC Irvine

          Contributors

          • Chen Li (Faculty)
          • Rares Vernica (Ph.D. Student)

          This toolkit includes the implementation of edit distance (aka Levenshtein distance) function as a User Defined Function (UDF) for the MySQL database software.

          For more details, please see UdfDoc.

          Periodic update of the package will be reflected on this page.

          [ICO]Name

          [DIR]Parent Directory
          [DIR]src/
          [DIR]docs/
          [   ]toolkit_2008-10-14.tgz9.4K
          [TXT]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft and a fund from CalIt2.
          Many thanks to Sattam Alsubaiee, Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

          License Agreement: Permission to use, copy, modify, and distribute is permitted under the terms of the BSD license.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://jujube.ics.uci.edu/www/animals.html Interactive Fuzzy Search to Help Autistic Kids

          Search For Animals [Back]


          http://jujube.ics.uci.edu/www/fruits.html Interactive Fuzzy Search to Help Autistic Kids
          Search For Fruits [Back]


          http://jujube.ics.uci.edu/www/cartoon.html Interactive Fuzzy Search to Help Autistic Kids
          Search For Cartoon Characters [Back]


          http://www.ics.uci.edu/~chenli/personal.html Personal

          Chen Li

          • Many people are confused by my name, since both "Chen" and "Li" can be a first name and a last name in Chinese. In fact, "Chen" is my FIRST name, meaning "Morning," since I was born in a morning. "Li," often written as "Lee," is my LAST name, which is very popular in Chinese. Most people call me "Chen." The following is my name in Chinese:  

          • My son, Dennis Danhao Li, was born on Dec. 12, 12:45 PM, 2002, at the UCI Medical Center. He was 8 lbs. and 7 ozs (3820g) and 20 in (51cm). Here are some pictures!
          • Our second son, Kenny Kangxiong Li, was born on Feb. 11, 11:12 AM, 2005, at the UCI Medical Center. He was 8 lbs. and 10 ozs (3920g) and 19.5 in (49.5cm). Here are some pictures.
          Our two sons looked almost identical when they were born! Here's Dandan's picture after he was born:

          Here's Kangkang's picture after he was born:

          http://www.ics.uci.edu/~chenli/pdf-font-types/index.html PDF Fonts

          Use Type-1 Fonts in PDF Papers
          Chen Li
          Department of Computer Science, UC Irvine
          May 11, 2005

          Recently many publishers have strict requirements about the fonts used in camera-ready paper submissions. Specifically, they require authors to substitute Adobe Postscript Type 1 fonts for any bitmap fonts (e.g., Postscript Type 3 bitmapped fonts, PCL fonts, MacOS bitmap fonts, Windows vector (outline) fonts). Being the Proceedings Chair of ACM PODS 2005, I set up the instructions for camera-ready submissions. In the process of editing the proceedings, I saw many common problems for authors to meet such a requirement. I wrote this page so that it may benefit other authors with similar problems. As always, comments are very welcome.

          • Font Requirements
          • Why Such Requirements?
          • How do you tell what fonts your pdf file is using?
          • LaTeX dvips command to produce PDF files using Type 1 fonts only
          • Common Problems
            • Use the right dvips option
            • Missing fonts
            • Type 3 fonts from figures (xfig)
          • Related Links

          Font Requirements

          The following are some general font requirements by ACM. Authors should substitute Adobe Postscript Type 1 fonts for any bitmap fonts (e.g., Postscript Type 3 bitmapped fonts, PCL fonts, MacOS bitmap fonts, Windows vector (outline) fonts). Bitmapped fonts display poorly on screen in PDF files and sometimes cause printing problems. Whenever possible, please substitute Type 1 Postscript fonts for Type 3 fonts in LaTEX files, then generate PS or PDF. LaTEX FILES: PDFs generated from LaTEX files generally do not display well on screen because the fonts that are generated from LaTEX for the PDFs are bitmap images.

          Why Such Requirements?

          The simple reason for having these requirements is to make sure your PDF papers can be displayed nicely when they are browsed by people. Type 1 fonts are displayed much better than Type 3 fonts, even though the paper in Type 1 fonts may print the same as the one in Type 3 fonts. Click this page to see good examples to illustrate the quality difference of different fonts. I copied their examples here. Here are examples from pdf files viewed in Acrobat Reader at 400% zoom.

           Default LaTeX/dvips 
          behavior
          Example with bitmap fonts
          Package times.sty
          (part of PSNFSS)
          forces use of
          Postscript fonts
          Example using PSNFSS
           Outline fonts from AMS 
          embedded in PDF file
          Example using AMS type-1
fonts


          How do you tell what fonts your pdf file is using?

          When you open your PDF file using Adobe Acrobat Reader, go to "File --> Document Properties --> Fonts" to see the fonts used in your PDF file. Make sure to click "List All Fonts" to see all the fonts. You may also bring up this box by typing "Ctrl+Alt+F". The following is an example.


          LaTeX dvips command to produce PDF files using Type 1 fonts only

          I used the following dvips command (in MiKTeX) to produce PDF files using Type 1 fonts only:

          dvips -t letter -Ppdf -G0 -j0 mypaper.dvi -o mypaper.ps

          Check this link for more information about the options.


          Common Problems

          If your PDF file includes type 3 fonts, you may check the following to get rid of such fonts.

          1. Use the right dvips option

          Make sure to use the right options in the dvips command. Try the following:

          dvips -t letter -Ppdf -G0 -j0 mypaper.dvi -o mypaper.ps

          2. Missing fonts

          If your LaTeX environment doesn't have all the necessary fonts used by your file, when you use dvips, it will produce a file called:

          missfont.log

          Check if this file exists. If it does, it means your environment misses some fonts, and needs to install them. Check this link for more information.

          3. Type 3 fonts from figures (xfig)

          Often type-3 fonts are introduced from figures. In particular, I found that eps files produced by xfig can introduce type-3 fonts. In particular, according to http://leon.bottou.com/nips/,
              * xfig "patterned" shapes are implemented with bitmap fonts.
                Use "solid" shapes instead.
          
          As an example, the following two xfig files both draw the same circle. The file "pattern.fig" uses a "patterned" shape to draw the circle, and its corresponding "pattern.pdf" is using a type-3 font. The file "solid.fig" uses a "slide" shape to draw the circle, and its corresponding "slide.pdf" is not introducing any type-3 font.

          A circle using a patterned shape (type-3 font introduced)

          • pattern.fig
          • pattern.eps
          • pattern.pdf

          A circle using a solid shape (type-3 font not introduced)

          • solid.fig
          • solid.eps
          • solid.pdf

          Related Links

          • Computer Modern and AMSFonts in Type 1 (PostScript) Form, http://www.ams.org/tex/type1-fonts.html
          • Getting Good PDF from LaTeX, http://web.gat.com/diag/pci/ltx2pdf.html
          • Type-3 fonts in xfig, http://leon.bottou.com/nips, Search for "xfig"
          http://flamingo.ics.uci.edu/releases/4.1/ FLAMINGO Package (Approximate String Matching) Release 4.1

          FLAMINGO Package
          (Approximate String Matching)

          Release 4.1 (February 22, 2012)

          Department of Computer Science, UC Irvine

          Contributors

          • Sattam Alsubaiee (Ph.D. Student)
          • Alexander Behm (Ph.D. Student)
          • Shengyue Ji (Ph.D. Student)
          • Liang Jin, graduated from UC Irvine in 2005.
          • Chen Li (Faculty)
          • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
          • Yiming Lu, graduated from UC Irvine in 2008.
          • Rares Vernica (Ph.D. Student)
          « Back to Flamingo Main Page

          Getting Started

          Please refer to the Flamingo Getting Started Guide.

          Introduction

          This release (in C++) includes the source code of several algorithms for approximate string matching developed at UC Irvine. It includes algorithms for approximate selection queries, location-based approximate keyword search, selectivity estimation for approximate selection queries, approximate queries on mixed types, and others. Although an implementation for approximate joins is included, the focus of this release is on approximate selection queries.

          Here is a brief explanation of the terms used above:

          • Approximate String Search: Given a collection of strings and a single string, how to find those strings in the collection that are "similar to" the given string?
            This functionality is implemented by the modules Common, FilterTree, Listmerger, StringMap, and PartEnum. We recommend getting started with the FilterTree module for this purpose.
          • Selectivity Estimation for Approximate String Search: Given a collection of strings and a single string, how can we estimate the number of strings that are "similar to" the given string? This functionality is implemented in the SEPIA module.
          • Approximate String Join: Given two collections of strings (possibly the same collection), how to find those pairs of strings that are "similar to" each other?
          • Location-Based Approximate Keyword Search: Given a collection of spatial objects with descriptive keywords, find those objects within a given spatial region that have a given set of keywords. In addition, the keywords don't need to match exactly, but approximately.
            This functionality is implemented by the module: LBAK-Tree

          There are various string similarity functions, such as Levenshtein Distance (aka the Edit Distance), Jaccard Similarity, Cosine Similarity, and Dice Similarity. The following is a description of the modules corresponding to the source directory structure:

          • Common: This module contains classes for supporting the following similarity functions / distance measures: Levenshtein Distance (aka Edit Distance), Jaccard Similarity, Cosine Similarity, Dice Similarity. It also provides functionality for decomposing strings into grams.
          • FilterTree: This module provides functionality for approximate string search using an inverted-list index. Furthermore, query performance can be improved by adding filters, i.e. partitioning the string collection into disjoint subsets according to some property (e.g. the length of the strings). The use of filters is facilitated by a hierarchical structure (the FilterTree), in which each level in the tree corresponds to one filter. We have implemented the length and charsum filter. This package contains three flavors of indexes: in-memory indexes compressed & uncompressed and a disk-based index.
          • ListMerger: Answering approximate string queries based on an inverted-list index requires finding elements that occur at least T times on the inverted lists belonging to the grams in the query string (T depends on the similarity metric and the similarity threshold). This problem is commonly referred to as the T-occurrence problem. This module implements several algorithms for solving the T-occurrence problem as described in "Efficient Merging and Filtering Algorithms for Approximate String Searches", Chen Li, Jiaheng Lu and Yiming Lu, ICDE 2008. In addition, we have implemented efficient algorithms for disk-based indexes.
          • MAT-Tree: MAT-tree is an indexing structure to support queries on data with an approximate string predicate and a numeric predicate. A typical query is: "Find employee records whose name is similar to Speilberg and whose age is close to 45." The indexing structure is proposed in the following paper: "Indexing Mixed Types for Approximate Retrieval," Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung, VLDB 2005, Trondheim, Norway.
          • SEPIA: This technique solves the problem of estimating the selectivity of an approximate string predicate. It can answer questions such as: "From a collection of strings, how many of them have an edit distance within 3 to a given string?". Such information can be used in optimizing queries of approximate string matching. The technique was published in the paper: "Selectivity Estimation for Fuzzy String Predicates in Large Data Sets," Liang Jin and Chen Li, VLDB 2005, Trondheim, Norway.
          • StringMap: This algorithm maps strings from the edit-distance metric space to a high-dimensional Euclidean space, and uses a multi-dimensional indexing structure to answer approximate queries. The algorithm is published in the paper: "Efficient Record Linkage in Large Data Sets," by Liang Jin, Chen Li, and Sharad Mehrotra, in 8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
          • PartEnum: This algorithm is published in the paper: "Efficient Exact Set-Similarity Joins," Arvind Arasu, Venkatesh Ganti, Raghav Kaushik, VLDB 2006. We implemented the algorithm to support approximate string matching queries, excluding approximate joins.
          • TopK: This package contains algorithms for efficient Top-K approximate string search.
          • LBAK-Tree: This module implements location-based approximate keyword search as described in "Supporting Location-Based Approximate Keyword Search", by Sattam Alsubaiee, Alexander Behm and Chen Li. It enhances an R*-Tree with inverted indexes for approximate selection queries. It implements various algorithms for choosing R*-Tree nodes to place inverted indexes in. The FilterTree module is used to provide the inverted indexes for approximate selection queries.
          In addition, we have provided some commonly used functions in the util directory.

          Changes in Version 4.1 (compared to Version 4.0)

          • Added support for Damerau-Levenshtein distance (edit distance also allowing swapping of two adjacent characters).
          • Compilation/warning fixes for OSX submitted by Jim Apple from On Time Systems. Thanks a lot, Jim!
          • Fixed 64-bit issues in LBAK-Tree.

          Bibtex

          @misc{misc/flamingo4.1-2010,
                author = {Alexander Behm and Rares Vernica and Sattam Alsubaiee and Shengyue Ji and Jiaheng Lu and Liang Jin and Yiming Lu and Chen Li},
                year = {2010},
                title = {{UCI} {Flamingo} {Package} 4.1},
                url = {http://flamingo.ics.uci.edu/releases/4.1/},
                institution = {University of California, Irvine, School of Information and Computer Sciences}
          } 
          
          [ICO]Name

          [DIR]Parent Directory
          [DIR]docs/
          [DIR]src/
          [DIR]flamingo-4.1.tgz2.8M
          [DIR]README.txt

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF-funded RESCUE project, a Google Research Award, a gift fund from Microsoft, a fund from CalIt2, the NSF CluE Project and the ASTERIX Project funded by the NSF.
          Many thanks to Minh Doan, and Kensuke Ohta for their valuable testing and feedback on the code and documentation.

          License Agreement: Permission to use, copy, modify, and distribute the implementations of MAT-Tree, SEPIA, StringMap, FilterTree, and LBAK-Tree is permitted under the terms of the BSD license. Permission to use, copy, modify, and distribute the implementations of the compression techniques DiscardLists and CombineLists is permitted under the terms of the following Academic BSD License. The implementation of the PartEnum algorithm invented by Microsoft researchers is limited to non commercial use, which would be covered under the royalty free covenant that Microsoft made public.

          Academic BSD License:
          The (compression techniques) DiscardLists and CombineLists are the proprietary property of The Regents of the University of California (“The Regents.”)
          Copyright © 2009 The Regents of the University of California, Irvine. All Rights Reserved.
          Redistribution and use in source and binary forms, with or without modification, are permitted by nonprofit, research institutions for research use only, provided that the following conditions are met:

          • Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
          • Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
          • Neither the name of The Regents nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

          The end-user understands that the program was developed for research purposes and is advised not to rely exclusively on the program for any reason.

          THE SOFTWARE PROVIDED IS ON AN "AS IS" BASIS, AND THE REGENTS AND CONTRIBUTORS HAVE NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS. THE REGENTS AND CONTRIBUTORS SPECIFICALLY DISCLAIM ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY OR CONSEQUENTIAL DAMAGES, INCLUDING BUT NOT LIMITED TO PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, LOSE OF USE, DATA OR PROFITS, OR BUSINESS INTERRUPTION, HOWEVER CAUSED AND UNDER ANY THEORY OF LIABILITY WHETHER IN CONTRACT, STRICT LIABILITY OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

          If you do not agree to these terms, do not download or use the software. This license may be modified only in a writing signed by authorized signatory of both parties.


          For any questions regarding this release, please send email to flamingo AT ics.uci.edu

          http://fr.ics.uci.edu/chile/ Family Reunification for Chile Earthquake

          FAMILY REUNIFICATION
          FOR CHILE EARTHQUAKE

           

          Search Interfaces

          • Search interfaces for the Person Finder: Chile Earthquake website:
            • Search Page
              Interactive and error-tolerant search page;
            • Search Widget
              Interactive and error-tolerant search widget that can be embedded into any website.


          People

          • Chen Li (Faculty)

          • Sattam Alsubaiee (Student)
          • Alexander Behm (Student)
          • Inci Cetindil (icetindil ..AT.. gmail.com) (Student)
          • Shengyue Ji (Student)
          • Dustin Lakin (dustin.lakin ..AT.. gmail.com) (Student)
          • Rares Vernica (Student)

          « Back to homepage

          http://jujube.ics.uci.edu/localsearch/fuzzysearch/geonamesdemo/ Fuzzy Keyword Search on Spatial Data
          Powered by Flamingo Project

          Fuzzy Keyword Search on Spatial Data


          The purpose of this demo is to show our solution on how to find relevant answers to mistyped spatial-keyword queries. It is built using the GeoNames geographical database which contains geographical objects (such as lakes and hills). We used the objects residing in the U.S., and the final dataset had about 1.8 million objects. The total data size was 100MB.

          Fuzzy Keyword Search on Spatial Data Using the CoPhIR Dataset
          Keywords
          Location


           

          Try these:
          Search for "univesity irbine" near Irvine
          Search for "hosbitul" near Los Angeles
          Search for "church catholyk" near San Francisco
          Search for "adope kreek" near 1600 Amphitheatre Parkway Mountain View, CA 94043


           
          For questions about this demo, please contact Sattam Alsubaiee.
          http://jujube.ics.uci.edu/localsearch/fuzzysearch/flickrdemo/ Fuzzy Keyword Search on Spatial Data
          Powered by Flamingo Project

          Fuzzy Keyword Search on Spatial Data


          The purpose of this demo is to show our solution on how to find relevant answers to mistyped spatial-keyword queries. It is built using a multimedia metadata collection extracted from Flickr pages, called CoPhIR Test Collection We processed the dataset to extract the photos taken in the U.S. based on their latitude and longitude values. Moreover, we used the keywords in the title, description, and tags of a photo as its textual attribute. The final dataset had about two million objects. Each record had a URL corresponding to the photo or a page including the photo. The total data size was about 400MB.

          Fuzzy Keyword Search on Spatial Data Using the GeoNames Dataset
          Keywords
          Location


           

          Try these:
          Search for "girardilli choclates" near San Francisco
          Search for "colesium" near Los Angeles
          Search for "desnyland hotels" near Anaheim
          Search for "google selicon valley" near 1600 Amphitheatre Parkway Mountain View, CA 94043


           
          For questions about this demo, please contact Sattam Alsubaiee.
          http://fr.ics.uci.edu/widgets/redcross/info.html Family Reunification for Haiti Earthquake - Red Cross Search Widget

          FAMILY REUNIFICATION

          Red Cross Search Widget

          We are providing an interactive search widget that can be embedded into the International Committee of the Red Cross FamilyLinks website. The widget allows for interactive and error tolerant searches over the Red Cross FamilyLinks data.

          The widget is designed for the Haiti - Earthquake 2010 - List page. Integration can be very easily done with by replacing the current Input search box with the following HTML code:

          <link rel="stylesheet" type="text/css"
              href="http://fr.ics.uci.edu/widgets/redcross/isearch.css">
          <input type="text" id="input" autocomplete="off" size=80>
          <script type="text/javascript"
              src="http://fr.ics.uci.edu/widgets/redcross/isearch.js">
          </script>

          Bellow is a screen-shot of how the page will look after the integration:


          (click to enlarge)

          Live Demo



          « Back to Family Reunification for Haiti Earthquake

          http://fr.ics.uci.edu/haiticrisis Family Reunification for Haiti Earthquake - Person Finder Search Page

          Family Reunification for Haiti Earthquake


          Search over 55,000 entries from Person Finder: Haiti Earthquake.
          Enter keywords related to name, location, or description of the person you are looking for.

          Search Results for:

          To view or add information, select a name below.

          « Back to Family Reunification for Haiti Earthquake
          http://asterix.ics.uci.edu/fuzzyjoin/CHANGELOG.html CHANGELOG
          UP | HOME

          CHANGELOG

          Author: Rares Vernica <rares (at) ics.uci.edu>

          Table of Contents

          • 1 fuzzyjoin-0.0.2.tgz (April 11th, 2011)
          • 2 fuzzyjoin-mapreduce-RWE-2010-04-23.tgz (April 23rd, 2010)
          • 3 fuzzyjoin-mapreduce-1.0.tgz (March 24th, 2010)

          1 fuzzyjoin-0.0.2.tgz (April 11th, 2011)

          • change package name
          • reorganize code
          • fix bugs

          2 fuzzyjoin-mapreduce-RWE-2010-04-23.tgz (April 23rd, 2010)

          • code release for ACM SIGMOD 2010 Repeatability & Workability Evaluation http://event.cwi.nl/SIGMOD-RWE/2010/
          • added scripts to run experiments and generate the figures in the SIGMOD 2010 "Efficient Parallel Set-Similarity Joins Using MapReduce" paper

          3 fuzzyjoin-mapreduce-1.0.tgz (March 24th, 2010)

          • initial code release

          Date: 2011-04-12 09:44:30 PDT

          HTML generated by org-mode 7.4 in emacs 24

          http://asterix.ics.uci.edu/fuzzyjoin/README.html README
          UP | HOME

          README

          Author: Rares Vernica <rares (at) ics.uci.edu>

          Table of Contents

          • 1 Copyright
          • 2 Overview
          • 3 Quick Start
            • 3.1 Build
            • 3.2 Self-join
              • 3.2.1 Upload raw data
              • 3.2.2 Generate records
              • 3.2.3 Balance records across nodes
              • 3.2.4 Run set-similarity self-join
            • 3.3 R-S join
              • 3.3.1 Upload raw data
              • 3.3.2 Generate records
              • 3.3.3 Balance records across nodes
              • 3.3.4 Run set-similarity join
          • 4 Configuration
          • 5 Directory Structure and Tasks
          • 6 Dataset
          • 7 Source Code Overview

          1 Copyright

          Copyright 2010-2011 The Regents of the University of California

          Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

          http://www.apache.org/licenses/LICENSE-2.0

          Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS"; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

          2 Overview

          This guide describes how to use the source code developed for the study in:

          Efficient Parallel Set-Similarity Joins Using MapReduce.
          Rares Vernica, Michael J. Carey, Chen Li
          SIGMOD 2010 
          

          3 Quick Start

          The only requirement for running the code is a Hadoop cluster. It does not have to be a full-fledged cluster, a single-node pseudo-distributed installation of Hadoop is enough. For more details about starting a Hadoop cluster please see http://hadoop.apache.org/common/docs/current/quickstart.html The code works with Hadoop version 0.17 or higher.

          3.1 Build

          $ cd fuzzyjoin-hadoop
          fuzzyjoin-hadoop$ ant
          

          3.2 Self-join

          Here are the steps to perform a self-join on a small sample of the DBLP dataset. We use 100 DBLP entries, title and authors as the join attributes, Jaccard similarity and a 0.5 similarity threshold.

          3.2.1 Upload raw data

          fuzzyjoin-hadoop$ hadoop fs -put \
            ../data/dblp-small/raw-000 dblp-small/raw-000
          

          The file dblp-small.raw.txt contains one record per line. On each line the fields are separated by ":" and contain DBLP id, publication title, authors (concatenated with " ") and other information available about the publication (concatenated with " ").

          3.2.2 Generate records

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbuild -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml
          

          This job assigns unique record-IDs to each record. The RIDs are integers and are appended in front of each record. After this job, each record contains five fields: RID, DBLP id, title, authors, other information.

          3.2.3 Balance records across nodes

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbalance -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml
          

          To skip this step, run:

          fuzzyjoin-hadoop$ hadoop fs -mv \
            dblp-small/recordsbulk-000 dblp-small/records-000
          

          3.2.4 Run set-similarity self-join

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            fuzzyjoin -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml
          

          This will run the three stages required to do fuzzy joins: token ordering (Tokens), kernel (RIDPairs), and record join (RecordPairs). It will use the basic alternative for each stage. In total it will run five Hadoop jobs (TokensBasic.phase1, TokenBasic.phase2, RIDPairsImproved, RecordPairsBasic.phase1, RecordPairsBasic.phase2).

          Each stage can be run separately using different alternatives by replacing fuzzyjoin in the above command with the name of the stage and the alternative. For example, to run the one-phase token ordering (TokensImproved), type:

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            tokensimproved -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml
          

          To get the list with all the available stages and alternatives, type:

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar
          

          To see the results, type:

          fuzzyjoin-hadoop$ hadoop fs -cat "dblp-small/recordpairs-000/part-*"
          

          Each line contains a pair of records that fuzzy join and their similarity. The format of the line is record 1;threshold;record2, where record1 and record2 have the same format as described in step 3.

          3.3 R-S join

          Here are the steps to perform a join between a small sample of the DBLP dataset and a small sample of the CITESEERX dataset. We use 100 DBLP entries and 100 CITESEERX entries, title and authors as the join attributes, Jaccard similarity and a 0.5 similarity threshold.

          3.3.1 Upload raw data

          fuzzyjoin-hadoop$ hadoop fs -put \
            ../data/pub-small/raw.dblp-000 pub-small/raw.dblp-000
          fuzzyjoin-hadoop$ hadoop fs -put \
            ../data/pub-small/raw.csx-000 pub-small/raw.csx-000
          

          The raw directory contains two files, one for each dataset.

          3.3.2 Generate records

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbuild -conf src/main/resources/fuzzyjoin/pub.quickstart.xml \
            -Dfuzzyjoin.data.suffix.input=dblp
          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbuild -conf src/main/resources/fuzzyjoin/pub.quickstart.xml \
            -Dfuzzyjoin.data.suffix.input=csx
          

          Each job generates records for one of the datasets.

          3.3.3 Balance records across nodes

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbalance -conf src/main/resources/fuzzyjoin/pub.quickstart.xml \
            -Dfuzzyjoin.data.suffix.input=dblp
          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbalance -conf src/main/resources/fuzzyjoin/pub.quickstart.xml \
            -Dfuzzyjoin.data.suffix.input=csx
          

          To skip this step, run:

          fuzzyjoin-hadoop$ hadoop fs -mv \
            pub-small/recordsbulk.dblp-000 pub-small/records.dblp-000
          fuzzyjoin-hadoop$ hadoop fs -mv \
            pub-small/recordsbulk.csx-000 pub-small/records.csx-000
          

          3.3.4 Run set-similarity join

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            fuzzyjoin -conf src/main/resources/fuzzyjoin/pub.quickstart.xml
          

          To see the results, type:

          fuzzyjoin-hadoop$ hadoop fs -cat "pub-small/recordpairs-000/part-*"
          

          Each line contains a pair of records that fuzzy join and their similarity. The format of the line is record-DBLP;threshold;record-CITESEERX, where record-DBLP and record-CITESEERX have the same format as described in the self-join case.

          4 Configuration

          The XML files provided with the -conf argument above contain various configuration parameters. Using the configuration parameters, a user can specify the location of the data, the similarity function and threshold, the join attributes and other settings. Moreover the user can specify additional parameters in the command line using the -D option.

          The default parameters and more details about each parameter are in:

          fuzzyjoin-hadoop/src/main/resources/fuzzyjoin/default.xml 
          

          All these parameters and other constants are defined in:

          fuzzyjoin-core/src/main/java/edu/uci/ics/fuzzyjoin/FuzzyJoinConfig.java
          fuzzyjoin-hadoop/src/main/java/edu/uci/ics/fuzzyjoin/hadoop/FuzzyJoinDriver.java
          

          5 Directory Structure and Tasks

          The following directory structure is used for self-joins:

          |
          |- raw-000
          |- recordsbulk-000
          |- recordsbulk-001
          |- ...
          |- records-000
          |- records-001
          |- ...
          |- tokens-000
          |- ...
          |- tokens.phase1-000
          |- ...
          |- ridpairs-000
          |- ...
          |- recordpairs-000
          |- ...
          |- recordpairs.phase1-000
          |- ...
          

          The raw-000 directory contains the original files, one record per line. The recordsbulk directory contains the original data where each record starts with an integer RID. The number after the directory name represents the copy number (000 is the original data, 001 is the first copy, etc.). The records directory contains the same data as the recordsbulk directory except that multiple copies are aggregated and the data is balanced across nodes. The number after the directory name represents how many copies are aggregated (000 is of only one copy: recordsbulk-000, 001 is for two copies: recordsbulk-000 and recordsbulk-001, etc.). So records-n represents an increased dataset, where n denotes how many times the dataset was increased. For the rest of the directories the number after the directory name has the same meaning. The tokens directory contains the list of tokens. The ridpairs directory contains the RID pairs that fuzzy-join. The recordpairs directory contains the record pairs that fuzzy-join. The phase1 prefix that appears for some directories represents the output of the first MapReduce job for the tasks with two MapReduce jobs (i.e., tokensbasic and recordpairsbasic).

          Bellow is a table with each task input and output directories:

          TaskInputOutput
          recordbuildrawrecordsbulk
          recordbalancerecordsbulkrecords
          tokens basic/improvedrecordstokens
          ridpairs improved/ppjoinrecords, tokensridpairs
          recordpairs basic/improvedrecords, ridpairsrecordpairs
          recordgeneraterecordsbulk-000, tokens-000recordsbulk

          For R-S joins, the first few directories also carry the name of the dataset (name of the R dataset or of the S dataset) in order to differentiate between them:

          |
          |- raw.DATASET_R-000
          |- raw.DATASET_S-000
          |- recordsbulk.DATASET_R-000
          |- recordsbulk.DATASET_R-001
          |- ...
          |- recordsbulk.DATASET_S-000
          |- recordsbulk.DATASET_S-001
          |- ...
          |- records.DATASET_R-000
          |- records.DATASET_R-001
          |- ...
          |- records.DATASET_S-000
          |- records.DATASET_S-001
          |- ...
          

          where DATASET_R and DATASET_S are the names of the two datasets. In our R-S join example we used dblp for DATASET_R and csx for DATASET_S.

          6 Dataset

          By default the dataset is assumed to have one record per line. The fields of each record are delimited by ":". The first filed of each record is an integer RID. This settings can be changed in:

          fuzzyjoin-core/src/main/java/edu/uci/ics/fuzzyjoin/FuzzyJoinConfig.java
          

          The dataset can be increased using the recordgenerate task:

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordgenerate -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml \
            -Dfuzzyjoin.data.copy=10 \
            -Dfuzzyjoin.data.norecords=100
          

          This stats 9 MapReduce jobs, each of them generating a new copy of the dataset. The fuzzyjoin.data.copy parameter specifies the number of times the dataset should be increased, while the fuzzyjoin.data.norecords parameter specifies the number of records in the original dataset (it is used to generate unique and increasing RIDs). All the following tasks also need to have the same value for the fuzzyjoin.data.copy parameter in order to use the increased dataset. This task can only be ran after running recordbuild and tokensbasic or tokensimproved on the original dataset. After this task, the recordbuild task needs to be ran (it cannot be skipped on the increased dataset):

          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            recordbalance -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml \
            -Dfuzzyjoin.data.copy=10
          fuzzyjoin-hadoop$ hadoop jar target/fuzzyjoin-hadoop-0.0.2.jar \
            fuzzyjoin -conf src/main/resources/fuzzyjoin/dblp.quickstart.xml \
            -Dfuzzyjoin.data.copy=10
          

          7 Source Code Overview

          The source code is divided into two modules:

          • fuzzyjoin-core: general fuzzy-join code in fuzzyjoin-core/src/main/java
            • edu.uci.ics.fuzzyjoin: main memory fuzzy-join
            • edu.uci.ics.fuzzyjoin.similarity: similarity functions and filters
            • edu.uci.ics.fuzzyjoin.invertedlist: inverted lists index
            • edu.uci.ics.fuzzyjoin.recordgroup: alternatives for grouping records
            • edu.uci.ics.fuzzyjoin.tokenizer: tokenizes
            • edu.uci.ics.fuzzyjoin.tokenorder: alternatives for ordering tokens
          • fuzzyjoin-hadoop: Hadoop specific fuzzy-join code in fuzzyjoin-hadoop/src/main/java
            • edu.uci.ics.fuzzyjoin.hadoop: main program
            • edu.uci.ics.fuzzyjoin.hadoop.datagen: classes for building records and increasing dataset size
            • edu.uci.ics.fuzzyjoin.hadoop.recordpairs: Stage 3
            • edu.uci.ics.fuzzyjoin.hadoop.ridpairs: Stage 2
            • edu.uci.ics.fuzzyjoin.hadoop.ridrecordpairs: alternative to Stage 2 and 3 where records are not projected
            • edu.uci.ics.fuzzyjoin.hadoop.tokens: Stage 1

          Date: 2011-04-12 09:58:14 PDT

          HTML generated by org-mode 7.4 in emacs 24

          http://asterix.ics.uci.edu/fuzzyjoin/FAQ.html FAQ
          UP | HOME

          FAQ

          Author: Rares Vernica <rares (at) ics.uci.edu>

          Table of Contents

          • 1 Copyright
          • 2 What should I do if I get java.lang.OutOfMemoryError: Java heap space in the Map phase of Stage 2, Kernel (ridpairsimproved or ridpairsppjoin)?
          • 3 Where can I get more help?

          1 Copyright

          Copyright 2010-2011 The Regents of the University of California

          Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

          http://www.apache.org/licenses/LICENSE-2.0

          Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS"; BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

          2 What should I do if I get java.lang.OutOfMemoryError: Java heap space in the Map phase of Stage 2, Kernel (ridpairsimproved or ridpairsppjoin)?

          Stage 1, Token Ordering (tokesbasic or tokensimproved) produces a list of unique tokens that are loaded into memory by Stage 2. The list is output in the tokens.n directory in HDFS. The reason for the OutOfMemoryError might be the fact that the list of tokens does not fit into memory.

          The first thing you should check is whether you are using the right tokenizer for your data. For example, if each join field value is a list of words, then the word tokenizer would be appropriate. Otherwise, if each join field value is a contiguous string of characters, then a n-gram tokenizer might be appropriate. The tokenizer can be specified in the command line with the -Dfuzzyjoin.tokenizer= option or in the XML file specified with the -conf option. For more details please see:

          hadoop/fuzzyjoin/resources/conf/fuzzyjoin/default.xml
          

          3 Where can I get more help?

          Please email Rares Vernica <rares (at) ics.uci.edu> with any questions you might have.

          Date: 2011-04-12 09:58:19 PDT

          HTML generated by org-mode 7.4 in emacs 24

          http://isg.ics.uci.edu/visitors.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

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          VISITING ISG

          The UCI Information Systems Group is located on the 2nd and 3rd floors of Donald Bren Hall (DBH). If you are coming by car, the simplest place to park is the nearby Anteater Parking Structure (APS). APS is an indoor/outdoor parking structure with a manned guard booth that sells visitor permits, and it is just a short, 3-4 minute southwesterly walk from APS to our offices in Donald Bren Hall. Landmark-wise, DBH is right next door to the University Club, which you will likely see signs for. (If you are walking to DBH from APS and reach the University Club, you've gone one building too far.) If you are traveling to UCI by cab, the University Club is the drop-off/pick-up point to shoot for. Excellent directional info and local/campus maps can be found at here. More UCI visitor information can be found at http://www.ics.uci.edu/about/visit/index.php as well as http://uci.edu/campusmap/.

          Last Updated on January 07, 2011

          http://isg.ics.uci.edu/index.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

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          About ISG

          Photo by Heri Ramampiaro. © 2010 ISG

          The Information Systems Group (ISG) at UC Irvine is part of the Department of Computer Science within the Donald Bren School of Information and Computer Sciences.

          ISG is a broad and vital group consisting of five UCI CS faculty members (Profs. Carey, Jain, Li, Mehrotra, and Venkatasubramanian), adjunct faculty member (Prof. Kalashnikov), students, visitors, project staff, and other affiliates. The mission of ISG is broad-ranging, as the combination of the next-generation Web, diverse forms of multimodal data, and new devices have created a world rich with heterogeneous forms of information that need to be located, accessed, queried, managed, archived, and integrated much like the more traditional (yet ever important) enterprise data of yesteryear. ISG's mission is to address this rapidly evolving information infrastructure by conducting research on all aspects of modern data and information systems.

          ISG faculty interests range from theory to systems, from principles to practice, and from design and experimentation to deployment and applications. Application focus areas include emergency response, situational awareness, and cyber-physical systems. Research topics with ISG cover architectures, algorithms, and performance evaluation of a variety of next-generation information systems and techologies. Current topics include database systems, data analysis and data cleansing, data warehousing systems, information integration tools, multimedia information systems and search, semantic Web, adaptive middleware, experiential computing, peer-to-peer systems, mobile and pervasive computing, service-oriented architectures, search techniques, scalable data-intensive computing, and data sharing and dissemination. Current projects within ISG are exploring challenges in the realization of such systems stemming from information diversity and heterogeneity (e.g., multidimensional data, XML, text, multimedia data, and sensor streams) and due to emerging application needs (including privacy and security, mobility, quality of data, quality of service, and reliability and robustness in extreme situations).

          M.S./Ph.D. Studies in Information Systems at UC Irvine

          The faculty of the Information Systems Group (ISG) at UC Irvine is looking for a handful of excellent prospective students who are seeking an exciting, active place to study and do research on databases and information systems starting each Fall. Several large projects are underway and several new ones are just beginning and will provide excellent opportunities for incoming students to "get in on the ground floor" of interesting new research initiatives.

          ISG is a part of the CS Department within the Bren School of Information and Computer Sciences at UC Irvine, an environment that offers a number of unique advantages and opportunities. Being one of just a handful of such schools across the country, ICS offers a broad, stimulating intellectual environment for graduate studies. ICS is comprised of three departments - Computer Science, Informatics, and Statistics - covering all traditional computer science areas as well as related areas such as software engineering, human-computer interaction and usability, collaboration technologies, and statistical machine learning and data mining and analysis.

          UC Irvine itself, in the words of our current chancellor, "combines the strengths of a major research university with the bounty of an incomparable Southern California location." Thus, in addition to a stimulating research and educational environment, graduate study in ISG at UC Irvine offers attractive off-hours opportunities. Our location in Orange County offers easy access to a wide variety of outdoor and indoor entertainment and sporting activities.

          More information about graduate study in CS at UC Irvine can be found on the CS Department's graduate studies page. Applications for admission for a given Fall are due by January 15th of that calendar year. More specifics about ISG, its faculty, and its projects can be found on our new ISG web site. Check us out!

          © 2009 ISG | Website maintained by Yingyi Bu | Created by Yun Huang | Original design Andreas Viklund

          Last Updated on January 07, 2011

          http://isg.ics.uci.edu/partnerships.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

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          Sponsors

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          Last Updated on January 07, 2011

          http://www.ics.uci.edu/~yingyib/ Yingyi Bu's Homepage
          Lydia
          Yingyi Bu

          Email: yingyib@ics.uci.edu






          I received my Ph.D. in Computer Science at University of California, Irvine, advised by Prof. Michael J. Carey. Before that, I got a B.Sc. from Nanjing University, China, an M.Phil. from The Chinese University of Hong Kong, and also fulltimely worked in Microsoft SQL Server group .

          News: 

          • 2015/08/20    I defended my thesis and joined Couchbase. (a related news:-) )
          • 2015/06/22    After 5 years's R&D of Algebricks, our paper about the framework has been accepted to SOCC'15!
          • 2015/03/01    AsterixDB is accepted to be an Apache incubator project!
          • 2014/11/11    Our paper about the Facade compiler and runtime for Big Data applciations has been accepted to ASPLOS'15!
          • 2014/08/05    After 3 years's R&D of Pregelix, our paper about the system has been accepted to VLDB'15!
          • 2014/08/04    After 5 years's R&D of AsterixDB, our paper about the system has been accepted to VLDB'15!
          • 2014/07/06    Our paper about graph connectivity analysis on Pregel has been accepted to VLDB'15!
          • 2014/06/23    Start a returned internship in Google Research, working with Chris Olston and Peter Hawkins!
          • 2014/05/09    New Pregelix website is online now! Checkout our juicy perf. numbers!
          • 2013/08/13    AsterixDB team are visiting Couchbase for the Couchbase/AsterixDB workshop!
          • 2013/06/11    I'm awarded the 2013-2015 Google Fellowship in Structured Data!
          • Archived news

          Research Interests

          My primary area of research interest is in building and evaluating Big Data management systems.

          Current projects:

          • Pregelix. Pregelix is an open-source implementation of Google's Pregel programming model. We architect the Pregel programming model on top of a general-purpose data-parallel execution engine, which leads to better scaling properties, out-of-core support, physical flexibility, and software simplicity.
          • AsterixDB. We are working towards an open source data-intensive computing platform, with new technologies for ingesting, storing, managing, indexing, querying, analyzing, and subscribing intensive semi-structured data.

          Past projects:

          • HaLoop. In HaLoop, we designed and implemented a modified version of the Hadoop MapReduce framework for efficiently support data-intensive iterative data analysis.

          Publications (dlbp entry) (google scholar)

          Algebricks: A Data Model-Agnostic Compiler Backend for Big Data Languages [PDF][PPT]
          (In the News)
          Vinayak Borkar, Yingyi Bu, Preston Carman, Nicola Onose, Till Westmann, Pouria Pirzadeh, Michael J. Carey, Vassilis J. Tsotras
          In Proceedings of the 2015 ACM SIGMOD/SIGOPS Symposium on Cloud Computing (SOCC 2015)
          Kohala, Hawaii, August 27 - August 29, 2015.
          Facade: A Compiler and Runtime for (Almost) Object-Bounded Big Data Applications [PDF][PPT]
          Khanh Nguyen, Kai Wang, Yingyi Bu, Lu Fang, Jianfei Hu, and Guoqing Xu
          In Proceedings of the 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2015)
          Istanbul, Turkey, March 2015.
          Pregelix: Big(ger) Graph Analytics on A Dataflow Engine [PDF][PPT][Open Source System][Tech Report]
          (In the News)
          Yingyi Bu, Vinayak Borkar, Jianfeng Jia, Michael J. Carey, Tyson Condie
          In Proceedings of the Very Large Database Endowment, Volume 8 (VLDB 2015)
          Kohala, Hawaii, August 31 - September 5, 2015.
          AsterixDB: A Scalable, Open Source BDMS [PDF][PPT][Open Source System][Tech Report]
          (Start Apache incubation in March 2015) (In the News) (Press Release)
          Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak Borkar, Yingyi Bu, Michael J. Carey, Inci Cetindil, Madhusudan Cheelangi, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann
          (Alphabetical Ordered)
          In Proceedings of the Very Large Database Endowment, Volume 7 (VLDB 2015)
          Kohala, Hawaii, August 31 - September 5, 2015.
          Pregel Algorithms for Graph Connectivity Problems with Performance Guarantees [PDF][PPT][Implementation]
          Da Yan, James Cheng, Kai Xing, Yi Lu, Wilfred Ng, Yingyi Bu
          In Proceedings of the Very Large Database Endowment, Volume 7 (VLDB 2015)
          Kohala, Hawaii, August 31 - September 5, 2015.
          A Bloat-Aware Design for Big Data Applications [PDF][PPT][An independent Chinese translation]
          (Open-source systems using our design paradigm: AsterixDB, Hyracks, Pregelix )
          Yingyi Bu, Vinayak Borkar, Guoqing Xu, and Michael J. Carey
          In Proceedings of the 2013 ACM SIGPLAN International Symposium on Memory Management (ISMM 2013)
          Seattle, WA, June 20-21, 2013.
          The HaLoop Approach to Large-Scale Iterative Data Analysis [PDF][Implementation]
          Yingyi Bu, Bill Howe, Magdalenda Balazinska, Michael D. Ernst
          The VLDB Journal (VLDBJ), Volume 21, Number 2, April 2012.
          HaLoop: Efficient Iterative Data Processing on Large Clusters [PDF][PPT][Talk in Berkeley][Implementation]
          (Best of VLDB 2010 )
          Yingyi Bu, Bill Howe, Magdalenda Balazinska, Michael D. Ernst
          In Proceedings of the Very Large Database Endowment, Volume 3 (VLDB 2010)
          Singapore, 11-17 September, 2010. (Acceptance Rate: 33/204 = 16.1%)
          Efficient Anomaly Monitoring Over Moving Object Trajectory Streams [PDF][PPT][Source Code][Dataset]
          Yingyi Bu, Lei Chen, Ada Wai-Chee Fu, Dawei Liu
          In Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009)
          Paris, France, June 28-July 1, 2009. (Acceptance Rate: 105/537 = 19.6%)
          Privacy Preserving Serial Data Publishing By Role Composition [PDF][PPT][Source Code][Dataset Link]
          Yingyi Bu, Ada Wai-Chee Fu, Raymond Chi-Wing Wong, Lei Chen, Jiuyong Li
          In Proceedings of the Very Large Database Endowment, Volume 1 (VLDB 2008)
          Auckland, New Zealand on 24-30 Aug, 2008. (Acceptance Rate: 46/273 = 16.8%)
          WAT: Finding Top-K Discords in Time Series Database [PDF][Source Code]
          Yingyi Bu, Tat-Wing Leung, Ada Wai-Chee Fu, Eamonn Keogh, Jian Pei, Sam Meshkin
          In Proceedings of the 2007 SIAM International Conference on Data Mining (SDM 2007)
          Minneapolis, MN, USA, April 26-28, 2007. (Acceptance Rate: 25%)

          System Demos and Posters

          Pregelix: Dataflow-Based Big Graph Analytics [PDF][Open Source System]
          Yingyi Bu
          In Proceedings of the 2013 ACM SIGMOD/SIGOPS Symposium on Cloud Computing (SOCC 2013)
          Santa Clara, CA, October 1-3, 2013.
          Comparing SSD-placement strategies to scale a Database-in-the-Cloud [PDF]
          Yingyi Bu, Hongrae Lee, Jayant Madhavan
          In Proceedings of the 2013 ACM SIGMOD/SIGOPS Symposium on Cloud Computing (SOCC 2013)
          Santa Clara, CA, October 1-3, 2013.
          ASTERIX: An Open Source System for "Big Data" Management and Analysis [PDF]
          Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak R. Borkar, Yingyi Bu, Michael J. Carey, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Nicola Onose, Pouria Pirzadeh, Rares Vernica, Jian Wen
          In Proceedings of Very Large Data Bases Endowment, Volume 5 (VLDB 2012)
          Istanbul, Turkey, August 27-31, 2012.

          Honors and Awards

          • 2013-2015 Google Fellowship in Structured Data
          • 2013-2014 Facebook Fellowship Finalist
          • 2010 Yahoo! Key Scientific Challenage Award

          Last modified: August 5, 2014.
          http://isg.ics.uci.edu/courses.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • About
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          • Research
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          • Events
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          • Undergraduate
            • CS122A
            • CS122B
            • CS143
            • CS190
          • Graduate
            • CS212
            • CS222
            • CS223
            • CS224
            • CS230
            • CS237
            • CS295
            • CS295
            • CS295
            • CS295

          Undergraduate Courses

          CS122A: Introduction to Data Management

          Winter 2012 / Spring 2012 - Prof. Carey(Spring 2012) / Prof. Mehrotra(Winter 2012)

          This course provides students with an introduction to the design of databases and the use of database management systems for applications. We will cover the entity relationship (E/R) approach to database design. We will cover the relational data model, mapping E/R designs to relations, relational design theory, abstract query language such as relational algebra, and programming in SQL. Students will get exposure to how relational database management systems are used to implement a database. Time permitting, we will also consider advanced database management features such as object-oriented and object-relational database systems. This course is aimed at database design and use of database management systems in implementing database applications.

          CS122B: Projects in Database Management and Web Applications

          Fall 2011 / Spring 2012 - Prof. Li

          This course exposes students to advanced programming concepts and provides students with a greater focus on using DBMS techniques to build Web-based applications. It is intended for two purposes: (1) It introduces students to the modern data management techniques including database connectivity, Web application development, extending database functions, database administration, and XML. (2) It teaches students how to use these technologies to build real-world applications. The course builds on CS122A, which introduces students to the classical relational databases and SQL programming.

          CS143: Principles of Operating Systems

          Spring 2011 - Nalini Venkatasubramanian

          This course covers principles and concepts of process and resource management, especially as seen in operating systems. Processes, memory management, protection, scheduling, file systems, and I/O systems.

          CS190: ENTREPRENEURSHIP

          Winter 2011 - Prof. Jain

          TBD

          ^ top

          Graduate Courses

          CS212: Multimedia Systems and Applications

          Winter 2011 - Prof. Jain

          This course will discuss elements of multimedia systems. Multimedia is becoming the common mode of synchronous, asynchronous, local, and remote communication. Most of the current assisted communication technologies use single medium. Adoption of multimedia has significant social implications. More relevant to this course, it has significant opportunity for technology development. Multimedia computing and communication systems started receiving attention more than a decade ago. Naturally, early systems dealt with very limited aspect of multimedia. With progress in technology, we will have to address several computing and communication issues in dealing with creation, communication, storage, access, and presentation of multimedia. In this course, we will discuss fundamentals of multimedia systems and address emerging issues that are being addressed as well as should be addressed. In discussing topics, our emphasis will be on concepts and fundamentals.

          CS222: Principles of Database Management

          Fall 2011 - Prof. Carey

          This course provides an implementor's view of database management systems. It covers fundamental principles and implementation techniques, issues, and trade-offs related to database management systems. Topics covered include storage management, buffer management, record-oriented file systems, access methods, query optimization, and query processing,. This course is a must for those students wishing to explore database management as an area of research and/or who plan on taking CS223 or CS224. A significant portion of database systems research as well as industrial database and information system development deals with adapting the basic database techniques covered in this course to new advances in hardware and software technologies or to the requirements of different applications and data types.

          CS223: Transaction Processing and Distributed Data Management

          Winter 2011 - Prof. Mehrotra

          The course covers fundamental principles underlying transaction processing including database consistency, concurrency control, database recovery and fault-tolerance. The course includes transaction processing in centralized, distributed, parallel, and client-server environments. It also covers distributed database systems. The course is a prerequisite for further advanced data management courses and research principles of data management.

          CS224: Advances in Database Management System Technology

          Spring 2011 - Prof. Mehrotra

          This course covers selected topics in advanced database research. Its content differs in each offering. This quarter we will focus on Web search. We will present recent papers in the literature. Students will do a substantial project.

          CS230: Distributed Computer System

          Winter 2011 - Prof. Venkatasubramanian

          Principles of distributed computing systems. Topics covered include message-passing, remote procedure calls, distributed shared memory synchronization, resource and process/thread management, distributed file systems, naming and security. Prerequisite: consent of instructor.

          CS237: Distributed Systems Middleware

          Spring 2011 - Prof. Venkatasubramanian

          This course discusses concepts, techniques and issues in developing distributed systems middleware that provides high performance in large scale distributed and networked environments. Issues in ensuring the satisfaction of QoS requirements for multimedia applications via middleware environments will also be discussed. The course will cover existing middleware standards and solutions such as DCE, CORBA, DCOM,.NET,EJB,J2EE, XML and discuss their relative advantages and shortcomings.

          CS295: Data-Intensive Computing Projects

          Fall 2011 - Prof. Carey

          n this course, students will review the current state of the art in data-intensive computing and then focus on doing a project of their choosing related to the field. The course will begin with some initial all-hands reading, lectures, and perhaps exercises involving MapReduce and Hadoop as well as UCI's own ASTERIX software. Students will then spend the bulk of the course taking a much deeper dive into some particular aspect of data-intensive computing that interests them. Students from fields like machine learning or social science may wish to explore the application of data-intensive computing techniques to problems in their area; students interested in programming languages may choose to compare (hands-on!) several alternative data-intensive languages and/or platforms; students interested in data management techniques may wish to benchmark new storage technologies (e.g., some of the emerging "NoSQL stores") or to spend the quarter working as an extension of the ASTERIX development team by adding new types and functions, operators, and/or storage features to the ASTERIX/Hyracks platform under development at UCI.

          CS295: Information Quality and Entity resolution

          Fall 2010 - Prof. Kalashnikov

          The effectiveness of data-driven technologies as decision support tools, data exploration and scientific discovery tools is closely tied to the quality of data on which such techniques are applied. It is well recognized that the outcome of the analysis is only as good as the quality of data on which the analysis is performed. That is why today organizations spend a tangible percent of their budgets on cleaning tasks such as removing duplicates, correcting errors, filling missing values, to improve data quality prior to pushing data through the analysis pipeline. The objective of this course is to deepen our understanding of recent trends in data quality research. The course focuses specifically, but not exclusively, on data management techniques for solving Entity Resolution (ER) problem. The ER challenge arises because objects in the real world are referred to using references or descriptions that are not always unique identifiers of the objects, leading to ambiguity. This ambiguity must be resolved, or taken into account, when analyzing the data to produce meaningful results. The course is based on student presentations of prominent recent publications in the area of information quality.

          CS295: Middleware for Mobile and Pervasive Computing

          TBD

          CS295: ENTREPRENEURSHIP

          Winter 2011 - Prof. Jain

          TBD

          ^ top

          Last Updated on January 07, 2011

          http://isg.ics.uci.edu/people.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • About
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          • People
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          • Faculty
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          • Alumni

          Faculty

          Thumbnail example
          Michael Carey

          Prof. Carey's interests include database system architecture, data-intensive computing, information integration, middleware, distributed systems, and computer system performance evaluation.

          Thumbnail example
          Ramesh Jain

          Prof. Jain's interests combine multimedia information systems, visual computing, and intelligent systems, and include multimedia search and experiential computing for live as well as archived data.

          Thumbnail example
          Chen Li

          Prof. Li's interests straddle databases and information systems, including data integration, data cleaning, data warehousing, Web search, and large-scale information processing using parallel computing.

          Thumbnail example
          Sharad Mehrotra

          Prof. Mehrotra's interests span various aspects of next-generation data and information management systems, including issues related to multimodality, uncertainty, data quality, heterogeneity, mobility, privacy, and security.

          Thumbnail example
          Nalini Venkatasubramanian

          Prof. Venkatasubramanian's interests relate to the emerging global information infrastructure, and include distributed systems, middleware, multimedia systems and applications, and mobile applications.

          Thumbnail example
          Phillip Sheu (EECS)

          Prof. Sheu's interests include semantic computing, object-relational data management, knowledge engineering, and their applications to areas including complex biomedical systems.

          Adjunct Faculty

          Thumbnail example
          Dmitri Kalashnikov

          Prof. Kalashnikov's interests include data management, database systems, data mining, information quality, entity resolution, web entity search, disambiguation, spatial, spatio-temporal, moving-object databases, similarity retrieval, uncertainty in databases, streaming data and sensor databases.

          Research Faculty

          Thumbnail example
          Naveen Ashish

          Dr. Ashish's interests are primarily in data management and artificial intelligence, particularly the application of technqiues from the latter to the former.

          Staff

          Thumbnail example
          Vinayak Borkar

          ASTERIX Project Lead Software Engineer. Vinayak's interests include database systems, information integration, XML query processing, and data-intensive parallel computing.

          Thumbnail example
          Christopher Davison

          Technology Manager: Dr. Davison's research interests are primarily within the domain of business continuity/disaster recovery planning, but also span such diverse topics as leadership, privacy, ethics, and information technology strategic planning.

          Thumbnail example
          Jay Lickfett

          RESCUE / SAFIRE Lead Software Engineer: Jay's interests include application of IT research to the emergency management domain.

          ^ top

          Postdoctoral and Associated Researchers

          Thumbnail example
          Bijit Hore

          Dr. Hore current research focuses on privacy-preservation and disclosure-control in data management, data mining and pervasive-space applications. His broader research interest spans database technology, data mining, graph theory and combinatorial optimization.

          Students

          Ph.D. Students

          Sattam Mubark Alsubaiee
          Yasser Altowim
          Hotham Altwaijry
          Yingyi Bu
          Inci Cetindil
          Mamadou Hassimiou Diallo
          Stylianos Doudalis
          Raman Grover
          Zachary Heilbron
          Laleh Jalali
          Jianfeng Jia
          YeSun Joung
          Young-Seok Kim
          Ngoc Do Minh
          Pouria Pirzadeh
          Zhijing Qin
          Reza Rahimi
          Ish Rishabh
          Arjun Satish
          Jie Xu
          Jikai Yin
          Liyan Zhang
          Ye Zhao
          Qiuxi Zhu
          ^ top

          Visitors

          Hongzhi WangFall 2012 - Summer 2013from Harbin Instituite of Technology
          Jinhui TangWinter 2010 - Winter 2010from National University of Singapore
          Alessandro BonFall 2009 - presentfrom Italy
          Stefano BonettiFall 2008 - presentfrom Italy
          Chiara ChiappiniFall 2008 - presentfrom Italy
          Amedeo D'ascanioFall 2009 - presentfrom Italy
          Guoliang LiWinter and Spring 2008from Tsinghua University, China
          Vassia PavlakiSummer 2005 and April 2006from NTUA, Greece
          Bin WangSummer 2006, Summer 2007from Northeastern University, China
          Xiaochun YangSummer 2006, Summer 2007from Northeastern University, China
          ^ top

          Alumni

          Ph.D. Alumni

          Alex Behm2013first appointment Cloudera
          Leila Jalali2013first appointment IBM DB2
          Mingyan Gao2012first appointment Google
          Pinaki Sinha2012first appointment Apple
          Setareh Rafatirad2012first appointment George Mason University
          Vivek Singh2012postdoc at MIT
          Ronen Vaisenberg2012first appointment Google
          Rabia Nuray-Turan2011first appointment Metavana
          Rares Vernica2011first appointment HP Labs
          Shengyue Ji2011first appointment Google
          Vidhya Balasubramanian2008
          Kaushik ChakrabartiMicrosoft Research
          Stella Zhaoqi Chen2008first appointment Microsoft
          Mayur Deshpande2007first appointment Google
          Bo Gong2008first appointment Oracle
          Sebastian Gutierrez-Nolasco2007first appointment NASA Ames Research Center
          Hakan HacigumusIBM Almaden Research
          Ramaswamy Hariharan2008first appointment Yellowpage Research
          Qi Han2006now at Colorado School of Mines
          Yun Huang2008Now Postdoctoral Fellow at Carnegie Mellon University
          Hojjat Jafarpour2010first appointment NEC Lab
          Ravi Chandra Jammalamadaka2008first appointment Ebay
          Minyoung Kim2008first appointment SRI
          Iosif Lazaridis2006
          Kyoungwoo Lee2008
          Daniel Massaguer2009
          Shivajit Mohapatra2006first appointment Motorola Research Lab
          Yiming Ma2007first appointment Nokia Research Center
          Dawit Yimam Seid2007
          Michal Shmueli-Scheuer2009first appointmetn IBM Research
          Michael Ortega-Binderbergerfirst appointment IBM Santa Teresa Labs
          Kringkrai PorkaewAsst. Professor, King Mongkut University of Technology
          Xingbo Yu2007first appointment Oracle
          Bo Xing2009first appointment Ericsson Research

          M.S. Alumni

          Vijay Rajakumar2010first appointment Bimple
          Minh Doan2010first appointment TheFind
          Guangqiang Li2010first appointment MarkLogic
          Ling Ling2010first appointment IBM Hawthorne
          Lin Shao2010IBM Sillicon Valley Lab
          Humeyra Altintas2006
          Bhaskar Chatterjee2006
          Jonathan Cristoforetti2007first appointment Google
          Zhenghua FuPelco Lab
          Liang Jin2005first appointment Microsoft
          Yiming Lu2008first appointment Microsoft
          Jia Li2005now working in the bay area
          Houtan Shirani-Mehr2006now Ph.D. student at USC
          Xiaoping Weifirst appointment CNET Networks
          Xin Zheng2005first appointment Samsung
          Qi Zhong 2005first appointment Microsoft

          Postdoctoral Alumni

          Nicola Onose2009-2011now at Google
          Jiaheng Lu2006-2008now a faculty at Renmin University, China
          Bohyung Han
          Ansgar Scherp
          Utz Westermann
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          Last Updated on January 07, 2011

          http://isg.ics.uci.edu/publications.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

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          By Faculty

          Prof. Michael Carey [DBLP List][Personal List]
          Prof. Ramesh Jain [DBLP List][Personal List]
          Prof. Chen Li[DBLP List][Personal List]
          Prof. Sharad Mehrotra[DBLP List][Personal List]
          Prof. Nalini Venkatasubramanian[DBLP List][Personal List]
          Prof. Phillip Sheu[DBLP List][Personal List]

          Recent Publications

          • (August. 2014) VLDB’15: Yingyi Bu, Vinayak Borkar, Jianfeng Jia, Michael J. Carey, Tyson Condie. Pregelix: Big(ger) Graph Analytics on A Dataflow Engine. Proceedings of International Conference on Very Large Data Bases (PVLDB 2015).
          • (August. 2014) VLDB’15: Sattam Alsubaiee, Yasser Altowim, Hotham Altwaijry, Alexander Behm, Vinayak Borkar, Yingyi Bu, Michael J. Carey, Inci Cetindil, Madhusudan Cheelangi, Khurram Faraaz, Eugenia Gabrielova, Raman Grover, Zachary Heilbron, Young-Seok Kim, Chen Li, Guangqiang Li, Ji Mahn Ok, Nicola Onose, Pouria Pirzadeh, Vassilis Tsotras, Rares Vernica, Jian Wen, Till Westmann. AsterixDB: A Scalable, Open Source BDMS. Proceedings of International Conference on Very Large Data Bases (PVLDB 2014).
          • (May. 2014) Secure Cloud Computing’14: Kerim Yasin Oktay, Mahadevan Gomathisankaran, Murat Kantarcioglu, Sharad Mehrotra, Anoop Singhal: Towards Data Confidentiality and a Vulnerability Analysis Framework for Cloud Computing. Secure Cloud Computing 2014: 213-238
          • (May. 2014) VLDB’14: Progressive Approach to Relational Entity Resolution. Yasser Altowim, Dmitri V. Kalashnikov, and Sharad Mehrotra. In PVLDB, 7(11) Sep 1-5, 2014.
          • (May. 2014) VLDB’14: Sattam Alsubaiee, Alexander Behm, Vinayak Borkar, Zachary Heilbron, Young-Seok Kim, Michael J. Carey, Markus Dreseler, Chen Li. Storage Management in AsterixDB. Proceedings of International Conference on Very Large Data Bases (PVLDB 2014).
          • (March. 2014) MMIR: Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra. Context Assisted Face Clustering Framework with Human-in-the-Loop. In International Journal of Multimedia Information Retrieval (MMIR), 2014.
          • (Feb. 2014) SIGMOD'14: Jie Xu, Dmitri V.Kalashnikov, and SharadMehrotra. Efficient Summarization Framework for Multi-Attribute Uncertain Data. ACM SIGMOD 2014 (to appear).
          • (Feb. 2014) DASSFA'14: Hakan Hacigumus, Bala Iyer, Sharad Mehrotra. Secure Computation over Outsourced Data: A 10-Year Retrospective. DASFAA 2014. Invited Paper accompanying the 10 Year Best Paper Award.
          • (Feb. 2014) ICISS'14: Kerim Yasin Oktay, Vaibhav Khadilkar, Murat Kantarcioglu, Sharad Mehrotra. Risk Aware Approach to Data Confidentiality in Cloud Computing. ICISS 2014.
          • (Feb. 2014) Commun. ACM: Nabil Adam, Randy Stiles, Andrew Zimdars, Ryan Timmons, Jackie Leung, Greg Stachnick, Jeff Merrick, Robert Coop, Vadim A. Slavin,Tanya Kruglikov, John Galmiche, Sharad Mehrotra. Consequence analysis of complex events on critical U.S. infrastructure. Commun. ACM 56(6): 83-91 (2013).
          • (Feb. 2014) Computers and Security: Ravi Chandra Jammalamadaka, Roberto Gamboni, Sharad Mehrotra, Kent E. Seamons, Nalini Venkatasubramanian. A middleware approach for outsourcing data securely. Computers and Security 32: 252-266 (2013).
          • (Feb. 2014) IEEE Data Eng. Bull.: Sharad Mehrotra. Letter from the Special Issue Editor. Social Media Analytics. IEEE Data Eng. Bull. 36(3): 2-3 (2013).
          • (Feb. 2014) IEEE Intelligent Systems: Sharad Mehrotra. Technological Challenges in Emergency Response. IEEE Intelligent Systems 28(4): 5-8 (2013).
          • (Feb. 2014) DASSFA'13: Chen Li, Sharad Mehrotra, Liang Jin. Record Linkage: A 10-Year Retrospective. DASFAA (1) 2013: 3-12. Invited Paper Accompanying the 10 Year Best Paper Award.
          • (Oct. 2013) TKDE: Jie Xu, Dmitri V.Kalashnikov, and SharadMehrotra. Query Aware Determinization of Uncertain Objects. IEEE Transactions on Knowledge and Data Engineering.
          • (Oct. 2013) TPDS: Kyunbaek Kim, Nalini Venkatasubramanian, Sharad Mehrotra. Efficient and Reliable Application Layer Multicast for Flash Dissemination. IEEE Transactions on Parallel and Distributed Systems.
          • (Oct. 2013) Pervasive and Mobile Computing Journal: Ronen Vaisenberg; Alessio Della Motta; Sharad Mehrotra. Scheduling Sensors for Monitoring Sentient Spaces Using an Approximate POMDP Policy. Pervasive and Mobile Computing Journal.
          • (July. 2013) PVLDB'13: Hotham Altwaijry, Dmitri V. Kalashnikov, Sharad Mehrotra. Query-Driven Approach to Entity Resolution. Proceedings of International Conference on Very Large Data Bases (PVLDB 2013).
          • (July. 2013) IEEE Cloud 2013: Erman Pattuk, Murat Kantarcioglu, Vaibhav Khadilkar, Huseyin Ulusoy, and Sharad Mehrotra. BigSecret: A Secure Data Management Framework for Key-Value Stores. IEEE Cloud 2013.
          • (July. 2013) VLDBJ: Dmitri V. Kalashnikov. Super-EGO: Fast Multi-Dimensional Similarity Join. The International Journal on Very Large Data Bases (VLDB Journal).
          • (July. 2013) Machine Vision and Applications: Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra, and Ronen Vaisenberg. Context-based person identification framework for smart video surveillance. Machine Vision and Applications, 2013.
          • (Feb. 2013) ICMR'2013 (Best Paper Award): Liyan Zhang, Dmitri Kalashnikov, Sharad Mehrotra. A Unified Framework for Context Assisted Face Clustering. 2013 ACM International Conference on Multimedia Retrieval.
          • (Feb. 2013) IEEE PERCOM 2013: Ronen Vaisenberg, Alessio Della Motta, Sharad Mehrotra, Deva Ramanan. Scheduling Sensors for Monitoring Sentient Spaces using an Approximate POMDP Policy. IEEE PERCOM 2013.
          • (Feb. 2013) IEEE Data Eng. Bull.: Sharad Mehrotra. Letter from the Special Issue Editor. IEEE Data Eng. Bull. 35(4): 3 (2012)
          • (Feb. 2013) IEEE Data Eng. Bull.: Vaibhav Khadilkar, Kerim Yasin Oktay, Murat Kantarcioglu, Sharad Mehrotra. Secure Data Processing over Hybrid Clouds. IEEE Data Eng. Bull. 35(4): 46-54 (2012).
          • (Feb. 2013) CollaborateCom 2012: Nabil Adam, Jayan Eledath, Sharad Mehrotra, Nalini Venkatasubramanian. Social media alert and response to threats to citizens (SMART-C). CollaborateCom 2012: 181-189.
          • (July 2012) MTAP: Mukesh Saini, Pradeep K. Atrey, Sharad Mehrotra, and Mohan Kankanhalli. W3-Privacy: Understanding What, When, and Where Inference Channels in Multi-Camera Surveillance Video. Multimedia Tools and Applications (MTAP) Journal (Accepted July 2012).
          • (July 2012) SDM 2012: Chunwang Zhang, Junjie Jin, Ee-Chien Chang, Sharad Mehrotra. Secure Collaborative Editing over Low-Cost Cloud Storage Services. International Workshop on Secure Data Management 2012.
          • (July 2012) TMCC 2012: M. Saini, P. K. Atrey, S. Mehrotra, and M S. Kankanhalli. Privacy aware publication of surveillance video. Inderscience Int. J. of Trust Management in Computing and Communications. (Accepted: June 8, 2012).
          • (July 2012) TMCC 2012: M. Saini, P. K. Atrey, S. Mehrotra, and M S. Kankanhalli. Privacy aware publication of surveillance video. Inderscience Int. J. of Trust Management in Computing and Communications. (Accepted: June 8, 2012).
          • (July 2012) SDM 2012: Indexing Encrypted Documents for Supporting Efficient Keyword Search, B. Hore, M. Diallo, E. Chang, S, Mehrotra. The 9th International Workshop on Secure Data Management 2012.
          • (April 2012) IEEE Cloud 2012: Secure Collaborative Editing over Low-Cost Cloud Storage Services Chunwang Zhang, Junjie Jin, Ee-Chien Chang,Sharad Mehrotra, IEEE Cloud 2012.
          • (April 2012) IEEE Cloud 2012: Risk-Aware Workload Distribution in Hybrid Clouds, Kerim Oktay, Vaibhav Khadilkar, Bijit Hore, Murat Kantarcioglu, Sharad Mehrotra, Bhavania Thuraisingham. IEEE Cloud 2012.
          • (April 2012) IEEE Cloud 2012: CloudProtect: Managing Data Privacy in Cloud Applications, Mamadou Diallo, Bijit Hore, Ee-Chien Chang, Sharad Mehrorta, Nalini Venkatasubramanian. IEEE Cloud 2012.
          • (March 2012) VLDB Journal: Yingyi Bu, Bill Howe, Magdalena Balazinska, Michael D Ernst. The HaLoop approach to large-scale iterative data analysis. The VLDB Journal, Vol 21, Issue 2, 2012 (Special Issue of VLDB2010 Best Papers).
          • (March 2012) TMS/DEVS: Leila Jalali, Carolyn Talcott, Sharad Mehrotra, Nalini Venkatasubramanian. Formal Specification of Multisimulation using Maude. TMS/DEVS: Theory of Modeling and Simulation (formerly known as Modeling and Simulation), March 2012.
          • (March 2012) Advances in Multimedia: Mukesh Kumar Saini, Pradeep Atrey, S. Mehrotra, M. Kankanhalli. Adaptive Transformation for Robust Privacy Protection in Video Surveillance. Advances in Multimedia, 2012.
          • (March 2012) MULTIMEDIA SYSTEMS: Pradeep K. Atrey, Sabu Emmanuel, Sharad Mehrotra and Mohan S. Kankanhalli. Guest editorial: Privacy-aware multimedia surveillance systems. From the issue entitled "Special Issue on Privacy-aware multimedia surveillance systems". MULTIMEDIA SYSTEMS Volume 18, Number 2, 95-97, DOI: 10.1007/s00530-011-0251-z
          • (March 2012) IEEE Transactions on Computers: Mayur Deshpande, Kyungbaek Kim, Bijit Hore, Sharad Mehrotra, Nalini Venkatasubramanian. ReCREW: A Reliable Flash Dissemination System. IEEE Transactions on Computers, 2012.
          • (November 2011) Formal Modeling: Leila Jalali, Sharad Mehrotra, Nalini Venkatasubramanian. Multisimulations: Towards Next Generation Integrated Simulation Environments. Formal Modeling: Actors, Open Systems, Biological Systems 2011: 352-367.
          • (November 2011) Multimedia Communications Technical Committee: Mukesh Saini, Pradeep K. Atrey, Sharad Mehrotraf and Mohan S. Kankanhalli. Considering Implicit Channels in Privacy Analysis of Video Data. Multimedia Communications Technical Committee, IEEE Computer Society, E-letter,Vol. 6, No. 11, November 2011.
          • (November 2011) ACM JDIQ: Rabia Nuray-Turan, Dmitri V. Kalashnikov, and Sharad Mehrotra. Adaptive connection strength models for relationship-based entity resolution. In ACM Journal of Data and Information Quality (ACM JDIQ), 2012.
          • (October 2011) TODS: R. Nuray, D.Kalashnikov, and S. Mehrotra. Exploiting Web Querying for Web People Search. TODS (To appear).
          • (September 2011) ICDE'12: Raman Grover and Michael Carey. Extending Map-Reduce for Efficient Predicate-Based Sampling. ICDE-2012 (To appear).
          • (August 2011) TODS: Rabia Nuray-Turan, Dmitri Kalashnikov, Sharad Mehrotra, Yaming Yu. Attribute and object selection queries over objects with probabilistic attributes. ACM Transactions on Database Systems (TODS), to appear.
          • (July 2011) AAMS-PS'2011: M. Saini, P. K. Atrey, S. Mehrota and M. S. Kankanhalli. Anonymous surveillance. IEEE ICME Workshop on Advances in Automated Multimedia Surveillance of Public Safety (AAMS-PS'2011), July 2011, Barcelona, Spain
          • (July 2011) SoCC'2011: Sai Wu, Feng Li, Sharad Mehrotra, Beng Chin Ooi. Query Optimization for Massively Parallel Data Processing. ACM Symposium on Cloud Computing (SOCC), 2011
          • (June 2011) VLDB Journal: Bijit Hore, Mustafa Canim, Sharad Mehrotra, Murat Kantarcioglu. Secure Multidimensional Range Queries over Outsourced Data. Accepted for publication in the VLDB Journal.
          • (June 2011) TPDS'2011: Hojjat Jafarpour, Bijit Hore, Sharad Mehrotra, and Nalini Venkatasubramanian. CCD: A Distributed Publish/Subscribe Framework for Rich Content Formats. In IEEE Transactions on Distributed and Parallel Systems (TPDS) 2011.
          • (June 2011) Securing Cyber-Physical Infrastructures: Foundations and Challenges: Sharad Mehrotra, Nalini Venkatasubramanian, Mark-Oliver Stehr, Carolyn Talcott. Pervasive Sensing, Event Detection and Situational Awareness. In "Securing Cyber-Physical Infrastructures: Foundations and Challenges", eds. Sajal Das, Krishna Kant, Nan Zhang, to appear 2011.
          • (Mar 2011) Distributed and Parallel Databases: Alexander Behm, Vinayak R.Borkar, Michael J. Carey, Raman Grover, Chen Li, Nicola Onose, Rares Vernica, Alin Deutsch, Yannis Papakonstantinou, Vassilis J. Tsotras. ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models. In Distributed and Parallel Databases.
          • (Mar 2011) IJCAI 2011: Yabin Zheng, Chen Li and Maosong Sun. An Efficient Error-Tolerant Chinese Pinyin Input Method. In IJCAI 2011.
          • (Mar 2011) IQ2S'11: Liyan Zhang, Ronen Vaisenberg, Sharad Mehrotra, and Dmitri V. Kalashnikov. Video Entity Resolution: Applying ER Techniques for Smart Video Surveillance. In Workshop on Information Quality and Quality of Service for Pervasive Computing (IQ2S 2011) in Conjunction with IEEE PERCOM 2011, invited paper, Mar 21-25, 2011.
          • (Mar 2011) ICMR'11: Pinaki Sinha, Sharad Mehrotra, Ramesh Jain. Summarization of Personal Photologs Using Multidimensional Content and Context. ACM International Conference on Multimedia Retrieval (ICMR) 2011, Trento, Italy.
          • (Mar 2011) ICME'11: Pinaki Sinha, Ramesh Jain. Extractive Summarization of Personal Photos From Life Events. IEEE International Conference Multimedia Expo (ICME) 2011, Barcelona.
          • (Mar 2011) WWW'11: Pinaki Sinha, Sharad Mehrotra, Ramesh Jain. Effective Sharing of Large Collections of Personal Photos Through Summarization. International World Wide Web Conference (WWW) 2011, Hyderabad. (Short Paper)
          • (Jan 2011) Spring SIW'11: Interoperability of Multiple Autonomous Simulators in Integrated Simulation Environments, Leila Jalali, Sharad Mehrotra, Nalini Venkatasubramanian, Proceedings of the Simulation Interoperability Standards Organization (SISO) Spring SIW'11, 2011.
          • (Dec 2010) MDS'10: Middleware Solutions for Integrated Simulation Environments, Leila Jalali, Sharad Mehrotra, Nalini Venkatasubramanian, The proceedings of 7th Middleware Doctoral Symposium, 2010
          • (Oct 2010) ACM SIGSPATIAL GIS 2010: Supporting Location-Based Approximate-Keyword Queries, Sattam Alsubaiee, Alexander Behm, and Chen Li, ACM SIGSPATIAL GIS 2010
          • (July 2010) ACM Middleware 2010: FaReCast: Fast, Reliable Application Layer Multicast for Flash Dissemination, Kyungbaek Kim, Sharad Mehrotra, Nalini Venkatasubramanian
          • (July 2010) IEEE Globecom 2010: Accessing the impact of geographically correlated failures on overlay-based data dissemination, Kyungbaek Kim, Nalini Venkatasubramanian
          • (June 2010) ICME 2010: Privacy Modeling for Video Data Publication, Mukesh SAINI, Pradeep ATREY, Sharad MEHROTRA, Sabu EMMANUEL, Mohan S. KANKANHALLI
          • (June 2010) VLDB 2010: Building Disclosure Risk Aware Query Optimizers for Relational Databases, Mustafa Canim, Murat Kantarcioglu, Bijit Hore, Sharad Mehrotra.
          • (Apr 2010) Transactions on Knowledge and Data Engineering: A Semantics-Based Approach for Speech Annotation of Images, D. Kalashnikov, S. Mehrotra, J. Xu, N. Venkatasubramanian.
          • (Mar 2010) Source-code/demo Releases: We are glad to announce three releases on fuzzy string matching.
          • (Mar 2010) SECON 2010 paper: Disruption-Tolerant Spatial Dissemination. Bo Xing, Sharad Mehrotra and Nalini Venkatasubramanian.
          • (Mar 2010) Journal of Real-Time Image Processing, Vol. 5, Issue 1: SEMARTCam scheduler: semantics driven real-time data collection from indoor camera networks to maximize event detection. Ronen Vaisenberg, Sharad Mehrotra and Deva Ramanan
          • (Mar 2010) ISCRAM 2010: Community Driven Data Integration. Naveen Ashish and Sharad Mehrotra.
          • (Feb 2010) SIGMOD 2010 industrial paper: Graphical XQuery in the AquaLogic Data Services Platform. Vinayak Borkar, Michael Carey, Sebu Koleth, Alex Kotopoulis, Kautul Mehta, Joshua Spiegel, Sachin Thatte, Till Westmann.
          • (Feb 2010) IEEE PerCom on Pervasive Wireless Networks 2010: Gateway Designation for Timely Communications in Instant Mesh Networks. Bo Xing, Mayur Deshpande, Sharad Mehrotra and Nalini Venkatasubramanian.
          • (Feb 2010) PerCom 2010: Exploring Quality in MultiSensor Pervasive Systems - A Localization Case Study. Stefano Bonetti, Sharad Mehrotra, Nalini Venkatasubramanian.
          • (Feb 2010) PerNem 2010: Practical Experiences in Enabling and Ensuring Quality Sensing In Emergency Response Applications. Chris Davison, Daniel Massaguer, Lilia Paradi, M. Reza Rahimi, Bo Xing, Qi Han, Sharad Mehrotra, Nalini Venkatasubramania.
          • (Feb 2010) SIGMOD 2010 paper: Efficient Parallel Set-Similarity Joins Using MapReduce. Rares Vernica, Michael J. Carey, Chen Li
          • (Dec 2009) EDBT 2010 paper: Efficient and scalable multi-geography route planning. Vidhya Balasubramanian, Dmitri V. Kalashnikov, Sharad Mehrotra, and Nalini Venkatasubramanian.
          • (Oct 2009) Best student paper of SIMA 2009 at MILCOM 2009: Situation Based Control for Cyber-Physical Environments. Vivek K. Singh, and Ramesh Jain
          • (Oct 2009) Best paper of ACM SIGMM WSM 2009: Motivating Contributors in Social Media Networks. Vivek K. Singh, Ramesh Jain, Mohan Kankanhalli
          • (Oct 2009) ARM'09 Paper: A Reflective Middleware Architecture for Simulation Integration, Leila Jalali, Nalini Venkatasubramanian and Sharad Mehrotra.
          • (Jul 2009) ACM/IFIP/USENIX Middleware 2009:
            Paper: CCD: Efficient Customized Dissemination in Distributed Publish/Subscribe. Hojjat Jafarpour, Bijit Hore, Sharad Mehrotra, Nalini Venkatasubramanian.
            Paper: Middleware for Pervasive Spaces: Balancing Privacy and Utility. Daniel Massaguer, Bijit Hore, Mamadou H. Diallo, Sharad Mehrotra, Nalini Venkatasubramanian.
            Doctoral Symposium: A Semantic Approach for Building Pervasive Spaces. Daniel Massaguer, Sharad Mehrotra, Nalini Venkatasubramanian
          • (Apr 2009) DEBS 2009 paper: MICS: An Efficient Content Space Representation Model for Publish/Subscribe Systems. Hojjat Jafarpour, Sharad Mehrotra, Nalini Venkatasubramanian and Mirko Montanari.
          • (Apr 2009) IEEE SAINT paper: Dynamic Load Balancing for Cluster-based Publish/Subscribe System. Hojjat Jafarpour, Sharad Mehrotra, and Nalini Venkatasubramanian.
          • (Apr 2009) ISI paper: The Software EBox: Information Integration for Situational Awareness. Naveen Ashish, Jay Lickfett, Sharad Mehrotra, and Nalini Venkatasubramanian.
          • (Apr 2009) CSIE 2009 paper: XAR: An Integrated Framework for Free Text Information Extraction. Naveen Ashish, Sharad Mehrotra, and Pouria Pirzadeh.
          • (Mar 2009) AFRL Workshop: Reliable Dissemination of Mission-Critical Content over Heterogeneous Wireless Networks. Bo Xing, Sharad Mehrotra and Nalini Venkatasubramanian. AFRL Workshop on Proactive Computing in Wireless Adhoc Networks.
          • (Mar 2009) CRA-W Workshop: Panel on Making Pervasive Computing Truly Pervasive: The need for bridging the networking and Data Management communities. Panel Moderators: Sriram Chellappan and Nalini Venkatasubramanian. CRA-W Workshop on Pervasive Computing.
          • (Mar 2009) IEEE INFOCOM 2009 Workshop: An Experimental Study on Wi-Fi Ad-Hoc Mode for Mobile Device-to-Device Video Delivery. Bo Xing, Karim Seada, Nalini Venkatasubramanian. IEEE INFOCOM 2009 Workshop on Mobile Video Delivery (MoViD 2009).
          • (Mar 2009) IGI Global book chapter: XAR: An Integrated Framework for Semantic Extraction and Annotation Cases on Semantic Interoperability for Information Systems Integration: Practices and Applications. Naveen Ashish and Sharad Mehrotra. (Editor: Yannis Kalfoglou)
          • (Feb 2009) SIGMOD 2009:
            Paper: Access Control in the AquaLogic Data Services Platform. Vinayak Borkar, Michael Carey, Daniel Engovatov, Panagiotis Reveliotis, Dmitry Lychagin, Josh Spiegel, Sachin Thatte, and Till Westmann
            Paper: Exploiting Context Analysis for Combining Multiple Entity Resolution Systems. Stella Chen, Dmitri Kalashnikov, and Sharad Mehrotra
            Paper: Efficient Type-Ahead Search on Relational Data: a TASTIER Approach. Guoliang Li, Shengyue Ji, Chen Li, and Jianhua Feng
            Demo: MEDIALIFE: From Images to a Life Chrnicle. Amarnath Gupta, Setareh Rafatirad, Mingyan Gao, and Ramesh Jain.
          • (Feb 2009) NSF workshop on Database and Application Security: A Middleware Approach for Managing Privacy of Outsourced Personal Data. Sharad Mehrotra and Bijit Hore
          • (Jan 2009) WWW 2009 paper: Efficient Interactive Fuzzy Keyword Search. Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng
          • (Jan 2009) Journal of Bioinformatics paper: Human Genomes as Email Attachments. Scott Christley, Yiming Lu, Chen Li, and Xiaohui Xie. It was ranked the top 1 most-frequently read article in January 2009.
          • (Dec 2008) INFOCOM 2009 paper: RADcast: Enabling Reliability Guarantees for Content Dissemination in Ad Hoc Networks. Bo Xing, Sharad Mehrotra, and Nalini Venkatasubramanian
          • (Dec 2008) PerCom 2009:
            Paper: Privacy-Preserving Event Detection in Pervasive Spaces. Bijit Hore, Jehan Wickramasuriya (Motorola, Inc.), Sharad Mehrotra, Nalini Venkatasubramanian, and Daniel Massaguer
            Demo: Proximiter: Enabling Mobile Proximity-Based Content Sharing on Portable Devices. Bo Xing, Karim Seada, and Nalini Venkatasubramanian
          • (Oct 2008) MMCN 2009: Exploiting Semantics For Scheduling Data Collection From Sensors On Real-Time To Maximize Event Detection. Ronen Vaisenberg, Sharad Mehrotra, and Deva Ramanan
          • (Oct 2008) CCNC 2009 demo: PassItOn: An Opportunistic Messaging Prototype on Mobile Devices. Bo Xing, Karim Seada, Peter Boda, and Nalini Venkatasubramanian.
          • (Sep 2008) ICDE 2009:
            Paper: Best-Effort Top-k Query Processing Under Budgetary Constraints. Michal Shmueli-Scheuer, Chen Li, Yosi Mass, Haggai Roitman, Ralf Schenkel, and Gerhard Weikum
            Paper: Space-Constrained Gram-Based Indexing for Efficient Approximate String Search. Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu
            Paper: Using Semantics for Speech Annotation of Images. Chaitanya Desai, Dmitri Kalashnikov, and Sharad Mehrotra
            Paper: Updates in the AquaLogic Data Services Platform. Michael Blow, Vinayak Borkar, Michael Carey, Christopher Hillery, Alexander Kotopoulis, Dmitry Lychagin, Radu Preotiuc-Pietro, Panagiotis Reveliotis, Joshua Spiegel, and Till Westmann
            Demo: WEST: Modern Technologies for Web People Search. Dmitri Kalashnikov, Zhaoqi Chen, Rabia Nuray-Turan, Sharad Mehrotra, and Zheng Zhang
            Tutorial: Efficient Approximate Search on String Collections. Chen Li and Marios Hadjieleftheriou (AT&T Labs--Research)

          Last Updated on January 07, 2011

          http://www.ics.uci.edu/~rares/isg-logo/ UCI ISG Graphical Identity and Technical Reports

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          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • About
          • News
          • People
          • Research
          • Publications
          • Events
          • Courses
          • Partnerships
          • Visitors
          • Current Projects
            • ASTERIX
            • CONTESSA
            • CYPRESS
            • DYNAMO
            • Dissemination
            • E2E
            • EMME
            • FLAMINGO
            • MAPGrid
            • Privacy
            • RESCUE
            • Responsphere
            • SAFIRE
            • SATWARE
            • SHERLOCK
            • TASTIER
            • xTUNE
          • Selected Past Projects
            • Compose
            • DAS
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            • MARS
            • QUASAR
            • RACCOON
          • Past ISG Faculty Projects
            • ALDSP
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            • TSIMMIS

          Current Projects

          ASTERIX

          (Profs. Carey and Li) Active, Scalable, Transactional Enterprise Repository for Information in XML. A new effort to develop a scalable semistructured information management system, based on XML and XQuery technologies, targeting very large shared-nothing compute clusters.

          CONTESSA

          (Prof. Venkatasubramanian with colleagues at UIUC, WUSTL and SRI.) This project adresses various aspects of context sensitive system adaptation to support interoperability with a specific focus on the use of formal methods to develop and reason about adaptive systems.

          CYPRESS

          (Prof. Venkatasubramanian and Mehrotra with colleagues at SRI.) The CYPRESS is a CYber Physical RESilliance and Sustainability project derived its name from the Cypress tree which represents vitality, durability and sustainability.

          DYNAMO

          (Prof. Venkatasubramanian with UCI colleagues) A middleware framework (Linux based) for cross-layer adaptation of power-saving mechanisms at different system layers for improved QoS and battery lifetime on low-power mobile devices.

          Dissemination: Customized Content Dissemination in the Large

          (Profs. Mehrotra and Venkatasubramanian) A project studying principles and techniques for timely, accurate information dissemination to the public to encourage self-protective actions (such as evacuation or sheltering-in-place) to reduce exposure to those at risk and provide reassurance to those not at risk under natural and human-induced threats.

          E2E

          (Prof. Jain) Environment to Environment Connectivity. A project to develop concepts and technology to make sensory interactions a primary modus operandi in cyber-physical spaces.

          EMME

          (Prof. Jain) Experiential Media Management Environment. A research project on the organization of personal multimedia data for individuals for search, sharing, and storytelling.

          FLAMINGO

          (Prof. Li) A research project to create an open-source library for data cleaning and fuzzy query processing, aimed at making query answering and information retrieval efficient in the presence of inconsistencies and errors.

          MAPGrid

          (Prof. Venkatasubramanian) A project on devising efficient resource discovery algorithms and data placement strategies for providing mobile users with multimedia services by leveraging heterogeneous and intermittently available grid resources.

          Privacy Middleware for the Internet

          (Prof. Mehrotra) An effort to design privacy middleware to sit between applications and Web storage services to allow users to benefit from the services without having to compromise on their privacy.

          RESCUE (Responding to Crises and Unexpected Events)

          (Profs. Mehrotra, Venkatasubramanian, and Kalashnikov) An NSF ITR project that is exploring how scalable and robust information technology solutions can empower first responders and response organizations to mitigate a crisis and dynamically improve crisis response.

          Responsphere (An IT Infrastructure for Responding to the Unexpected)

          (Prof. Mehrotra) Responsphere is an IT infrastructure test-bed that incorporates a multidisciplinary approach to emergency response drawing from academia, government, and private enterprise. The IT infrastructure can facilitate rapid and seamless access to and dissemination of information.

          SAFIRE

          (Profs. Mehrotra and Venkatasubramanian) A DHS-funded project exploring how situational awareness created through usage of multiple sensor technologies (e.g., radio communication, video, environmental sensors, physiological sensors) can improve the safety of fire fighters in both large structural and wild fires.

          SATWARE

          (Profs. Mehrotra and Venkatasubramian) A project that aims at realizing sentient spaces by exploring a multimodal sensor data stream through the use of query, analysis, and transformation middleware.

          SHERLOCK

          (Profs. Mehrotra and Kalashnikov) A project on graph-based exploitation of semantics to improve data quality, including semantics implicit in the data and/or explicit constraints/rules, to address ambiguity in entity resolution, record linkage, Web people search, speech-based tagging, and multimedia event detection.

          TASTIER

          (Prof. Li) TASTIER is a joint research project between Tsinghua University and UC Irvine. It focuses on efficient autocompletion, type-ahead search on large data sets of various types, such as relational data, documents, semi-structured data.

          xTUNE

          (Prof. Venkatasubramanian with UCI/SRI colleagues) This research effort to integrate formal analytical methods for understanding cross-layer end-to-end timing/reliability issues into the design and adaptation processes for mobile distributed systems, leading to a Linux-based middleware framework for cross-layer adaptation of power-saving mechanisms for improved QoS and battery lifetime on low-power devices.

          ^ top

          Selected Past Projects

          Compose|Q

          (Prof. Venkatasubramanian) A reflective architecture for safe composability of middleware services to achieve performance/QoS tradeoffs based on core services that can be reused under constraints to ensure safety composition of "ilities" (security, reliability, and quality of service).

          Database as a Service

          (Prof. Mehrotra) One of the first projects to explore the data privacy and search challenges in hosting a large-scale data management service over the internet.

          FORGE

          (Prof. Venkatasubramanian with other UCI/UCSD colleagues) Techniques for cross-Layer adaptation that meets QoS/energy tradeoffs for mobile multimedia applications, including: content transcoding, network traffic shaping, backlight optimizations, memory adaptations.

          Family Reunification

          (Prof. Li) Techniques to help people find their loved ones during or after a disaster.

          MARS (Multimedia Analysis and Retrieval System)

          (Prof. Mehrotra) An integrated multimedia information retrieval and data management system that provided support for multimedia information as first-class objects capable of being stored and retrieved based on their rich internal content.

          QUASAR (Quality Aware Data Management)

          (Prof. Mehrotra) An early project on progressive database query evaluation techniques that exploit the tradeoff between cost and result quality.

          RACCOON

          (Prof. Li) An early project on distributed data integration and sharing.

          ^ top

          Other Selected Past IGS Faculty Projects (and Products!)

          ALDSP

          (Prof. Carey and BEA colleagues) The AquaLogic Data Services Platform, a commercial service-oriented information integration platform that Prof. Carey contributed to, as an architect and engineering director, while an employee of BEA. (Recently renamed as the Oracle Data Service Integrator.)

          IBM DB2

          (Prof. Carey and IBM colleagues) A commercial relational database management system that Prof. Carey contributed to, as leader of a team that added various object-relational SQL extensions, while an employee of IBM.

          Shore

          (Prof. Carey and Wisconsin colleagues) A University of Wisconsin project that Prof. Carey co-led to create a persistent object system intended to serve a wide variety of target applications, including hardware and software CAD systems, persistent programming languages, geographic information systems, satellite data repositories, and multimedia applications.

          TSIMMIS

          (Prof. Li and Stanford colleagues) A pioneering Stanford University project on semistructured data integration that Prof. Li participated in while he was a Ph.D. student there.

          ^ top

          Last Updated on January 07, 2011

          http://isg.ics.uci.edu/isg_photo.html

          Photo by Heri Ramampiaro. © 2010 ISG

          Names listed by the order faces appear, from left to right

          People who stand: Reza Rahimi, Arjun Satish, Guangqiang Li, Alex Behm, Hotham Altwaijry, Shengyue Ji, Sattam Mubark Alsubaiee, Kyungbaek Kim, Lin Shao, Vinayak Borkar, " "," ", Mamadou Hassimiou Diallo, Dmitri Kalashnikov," ",Pouria Pirzadeh, Ronen Vaisenberg, Ish Rishabh, Pinaki Sinha, Michael Carey, Ramesh Jain, Sharad Mehrotra, Chen Li, Rares Vernica, Hamed Pirsiavash, Leila Jalali,

          People who do not stand: Nicola Onose, Mingyan Gao, Inci Cetindil, Ling Ling, Liyan Zhang, " ", Caitlin Lustig,Setareh Rafatirad, Vivek Singh, Nalini Venkatasubramanian, Bijit Hore http://isg.ics.uci.edu/news.html ISG

          ISG

          Information Systems Group

          Bren School of ICS

          UC Irvine

          • About
          • News
          • People
          • Research
          • Publications
          • Events
          • Courses
          • Partnerships
          • Visitors

          News

          • (August. 2014) AsterixDB papers: Three AsterixDB papers about the LSM storage, the overall system, and the Pregelix system respectively are accepted to PVLDB!
          • (January 2014) DASFAA 10-year Best Paper Award: The paper "Efficient Execution of Aggregation Queries over Encrypted Relational Databases” by Hakan Hacıgümüş, Bala Iyer, and Sharad Mehrotra, which appeared at the DASFAA 2004 conference, has been selected as the DASFAA 10-year Best Paper Award winner for the year 2014.
          • (Dec. 2013) Keynote at ICISS: Sharad Mehrotra gave a keynote talk entitled "Risk-based Data Processing in Clouds" at the Ninth international Conference on Information Systems Security, Kolkata, India in Dec. 2013.
          • (Sept. 2013) Keynote in BIRTE: Mike Carey gave a keynote talk entitled "AsterixDB: A New Platform for Real-Time Big Data BI" at the BIRTE Workshop affiliated with the VLDB 2013 Conference in Riva del Garda, Italy.
          • (Sept. 2013) 2013 Google Graduate Student Award in ICS: Liyan Zhang received the 2013 Google Graduate Student Award in ICS.
          • (July 2013) Paper in VLDBJ: Dmitri V. Kalashnikov. Super-EGO: Fast Multi-Dimensional Similarity Join. The International Journal on Very Large Data Bases (VLDB Journal).
          • (July 2013) Paper in Machine Vision and Applications: Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra, and Ronen Vaisenberg. Context-based person identification framework for smart video surveillance. Machine Vision and Applications, 2013. Accepted.
          • (Sept. 2013) 2013 Google Fellowship in Structured Data: Yingyi Bu received the 2013 Google Fellowship in Structured Data.
          • (April 2013) Best Paper Award in ICMR'13: Our paper "A Unified Framework for Context Assisted Face Clustering" by Liyan Zhang, Dmitri Kalashnikov, and Sharad Mehrotra won 2013 ACM International Conference on Multimedia Retrieval.
          • (April 2013) Prof. Jain is featured in the UCI news: Prof. Jain's work on mobile health aid is featured at http://news.uci.edu/features/focused-on-the-big-picture/
          • (February 2013) Alex's Graduation Party: Photos from Alex's graduation party.
          • (January 2013) DASFAA 10-year Best Paper Award: The paper "Efficient Record Linkage in Large Data Sets” by Liang Jin, Chen Li, and Sharad Mehtrotra, which appeared at the DASFAA 2003 conference, has been selected as the DASFAA 10-year Best Paper Award winner for the year 2013.
          • (August 2012) Keynote: Mike Carey gave a keynote talk entitled "BDMS Benchmarking: Practices, Pitfalls, and Possibilities" at the TPC-TC Workshop affiliated with the VLDB 2012 Conference in Istanbul, Turkey.
          • (August 2012) Keynote: Chen Li gave a keynote talk titled "Search as You Type: From Research to Commercialization" at the DBRank workshop in conjunction with VLDB 2012 in Istanbul, Turkey.
          • (Apr 2012) SIGMOD Test-of-Time Award: The 2012 edition of the annual ACM SIGMOD Test-of-Time Award, given to the most impactful paper from the SIGMOD Conference a decade ago, is being given to UCI alumnus Hakan Hacigumus, IBM collaborator Bala Iyer, and ISG faculty members Chen Li and Sharad Mehotra for their paper "Executing SQL over Encrypted Data in the Database-Service-Provider Model". This paper anticipated the world of “Database as Service” (DaaS) much before it came about (and which continues to grow in importance).
          • (Apr 2012) Yahoo! Key Scientific Challenges Program winner: Raman Grover is one of the winners of the 2012 Yahoo! Key Scientific Challenges Program, sponsored by Yahoo! Inc.
          • (October 2011) Paper in TODS: Our paper titled "Exploiting Web Querying for Web People Search" by R. Nuray, D. Kalashnikov, and S. Mehrotra has been accepted for publication in TODS.
          • (September 2011) Paper in ICDE'12: Our paper titled "Extending Map-Reduce for Efficient Predicate-Based Sampling" by Raman Grover and Michael Carey has been accepted for publication in ICDE-2012.
          • (August 2011) NSF grant: Prof. Nalini Venkatasubramanian and Dr. Kyungbaek Kim recently received a $205,000 grant from the National Science Foundation to lead a research project on “GeoSocial Alerting Systems”.
          • (August 2011) NSF grant: Prof. Mehrotra and his postdoctoral scholar Bijit Hore received an NSF award from the CNS division to explore query processing in mixed security environments wherein data migrates across different components each of which may offer different levels of security guarantees and may be susceptible to different attacks.
          • (August 2011) NSF grant: Prof. Mehrotra and Kalashnikov received an NSF award from the IIS division to build a Query and Goal Driven Entity Resolution Framework.
          • (August 2011) Paper in TODS: Our paper titled "Attribute and object selection queries over objects with probabilistic attributes" by Rabia Nuray-Turan, Dmitri Kalashnikov, Sharad Mehrotra, and Yaming Yu has been accepted for publication in ACM TODS.
          • (June 2011) Microsoft Speller Challenge winner: Prof. Chen Li and a group of students participated in the 2011 Microsoft Speller Challenge and took third place. Congratulations to the team!
          • (June 2011) Yahoo! Best Dissertation Fellowship Award: Ronen receives Yahoo! Best Dissertation Fellowship Award for 2011-2012 for his work on "Scalability in Event Detection Systems", nominated by Professor Sharad Mehrotra.
          • (June 2011) Best Student Poster Award in CCGrid2011 : Reza receives best Student Poster Award from IEEE/ACM CCGrid2011: “Cloud Based Framework for Rich Content Mobile Applications”, Reza Rahimi , Nalini Venkatasubramanian.
          • (April 2011) DARPA grant: The UCI team consisting of Profs. Sharad Mehrotra, Nalini Venkatasubramanian, and Dmitri V. Kalashnikov in collaboration with ICSI, Berkeley and SRI have been awarded a DARPA grant to explore Speech-Based Situational Awareness for Event Response.
          • (April 2011) NSF grant: Profs. Mehrotra, Carey, Jain, Venkatasubramanian, and Li have been funded by NSF to develop I-sensorium, to serve as a "living laboratory" to support research in several related areas of cyber-physical systems: including theoretical foundations and underlying principles of building sentient systems; engineering, software, and systems level challenges; and novel application contexts where such sentient systems can be used. Further information about this project can be found here.
          • (March 2011) Paper in IJCAI 2011: Our paper titled "An Efficient Error-Tolerant Chinese Pinyin Input Method" by Chen Li and his Tsinghua collaborators (Yabin Zheng and Maosong Sun) has been accepted for publication in IJCAI 2011.
          • (March 2011) Paper in Distributed and Parallel Databases: Our paper titled "ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models" by the ASTERIX project has been accepted for publication in Distributed and Parallel Databases.
          • (December 2010) Chancellor’s Club Fund for Excellence Fellowship: Pinaki Sinha is selected by the UCI Graduate Division as one of six recipients (campus-wide) of a 2010-2011 Chancellor’s Club Fund for Excellence Fellowship.
          • (November 2010) Yahoo! Best Student Dissertation Award: Shengyue Ji received Yahoo! Best Student Dissertation Award for his work on "Efficient Instant Search", nominated by Professor Chen Li.
          • (Octobor 2010) NSF grant: Prof. Venkatasubramanian, Dutt, and Mehrotra have received an NSF award to to develop a semantic foundation, cross-layer system architecture and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS) based on the principles of "computation reflection".
          • (September 2010) SIGMM Technical Achievement Award: Professor Ramesh Jain received the prestigious SIGMM Technical Achievement award, which will be presented at the ACM International Conference on Multimedia 2010 that will be held October 25-29, 2010 in Florence, Italy.
          • (July 2010) Lecture Series: The ISG group has published videos and slides from its 2009-10 Scalable Data Management lecture series for access by members of the data-intensive computing community around the world
          • (May 2010) Intel grant: Prof. Li received an Intel grant to study compression of personal human genome data. See the ICS news for details.
          • (Apr 2010) Excellent Demo Award at DASFAA 2010: Fuzzy Keyword Search on Spatial Data. Sattam Alsubaiee, Chen Li.
          • (Apr 2010) Facebook Fellow: Vinayak Borkar is one of five recipients of the inaugural Facebook Fellowships in support of his work in the Cloud Computing area.
          • (Apr 2010) Yahoo! Key Scientific Challenges Program winner: Rares Vernica is one of the winners of the 2010 Yahoo! Key Scientific Challenges Program, sponsored by Yahoo! Inc. for his work on "Efficient Similarity-Based Operators for Social Data", nominated by Prof. Michael J. Carey and Prof. Chen Li
          • (Apr 2010) NEC Research Award: Prof. Mehrotra and Bijit Hore (postdoctoral student, ISG group) have been awarded an NEC Research Award for $60,000 to study Risk Containment in Cloud Computing Services.
          • (Mar 2010) NSF grant: Prof. Chen Li received an NSF award to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake.
          • (Feb 2010) IBM fellowship award: Ronen Vaisenberg has received an IBM Ph.D. Fellowship award, nominated by Professor Sharad Mehrotra.
          • (Jan 2010) Google research award: Prof. Kalashnikov and Mehrotra have been awarded a Google research award for $50,000 for their work on "Graph based Disambiguation Framework for Web People Search".
          • (Oct 2009) Best student paper of SIMA 2009 at MILCOM 2009: Situation Based Control for Cyber-Physical Environments. Vivek K. Singh, and Ramesh Jain
          • (Oct 2009) Best paper of ACM SIGMM WSM 2009: Motivating Contributors in Social Media Networks. Vivek K. Singh, Ramesh Jain, Mohan Kankanhalli
          • (Aug 2009) NSF grant: ASTERIX gets NSF funding: Prof. Michael Carey and Chen Li's multi-UC-campus project "ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis" has been funded at $2.7M for three years from the NSF Data Intensive Computing program. The project, based at UCI, also includes UCSD and UCR participants. UCI's share is $1.8M.
          • (May 2009) SIGMOD 2009 programming contest: Shengyue Ji, Wen Pu(UIUC) and Mingyan Gao have been selected as one of the five finalist teams for the SIGMOD 2009 programming contest (Main Memory Transactional Index.
          • (Mar 2009) New Research Project: Prof. Chen Li officially launches TASTIER: a joint research project with Tsinghua University on efficient auto-complete and type-ahead search on large data sets.
          • (Mar 2009) CHORUS talk on MMSE: At the final conference of the famous European CHORUS project on Multimedia Search Engines (MMSE), Professor Ramesh Jain will be the first (and the only one from USA) speaker presenting state of art and the future of multimedia search technology. More information is available here.
          • (Mar 2009) ASTERIX gets seed funding: Michael Carey and Chen Li's project "ASTERIX: A Scalable Platform for XML Information Analysis" has received a UC Discovery Grant for $132K, including $52K from UC and $80K from eBay.
          • (Mar 2009) PerCom 2009 PhD forum competition: Ronen Vaisenberg won the first prize in the PerCom 2009 PhD forum competition.
          • (Feb 2009) NSF grant: Chen Li received an NSF CluE grant (about $221,000) on large-scale data cleaning using Hadoop.
          • (Jan 2009) Google research award: Michael Carey received a Google Research Award ($70,000) to work on OpenII (Open Information Integration).
          • (2009) NSF grant: Sharad Mehrotra and Nalini Venkatasubramanian received a $135,000 NSF grant on Dispatcher selection (joint with SRI).
          • (2009) DHS grant: Sharad Mehrotra and Nalini Venkatasubramanian received a $1,000,000 SAFIRE grant from DHS.
          • (July 2013) Paper in Machine Vision and Applications: Liyan Zhang, Dmitri V. Kalashnikov, Sharad Mehrotra, and Ronen Vaisenberg. Context-based person identification framework for smart video surveillance. Machine Vision and Applications, 2013. Accepted.
          • (Sept. 2013) 2013 Google Fellowship in Structured Data: Yingyi Bu received the 2013 Google Fellowship in Structured Data.
          • (April 2013) Best Paper Award in ICMR'13: Our paper "A Unified Framework for Context Assisted Face Clustering" by Liyan Zhang, Dmitri Kalashnikov, and Sharad Mehrotra won 2013 ACM International Conference on Multimedia Retrieval.
          • (April 2013) Prof. Jain is featured in the UCI news: Prof. Jain's work on mobile health aid is featured at http://news.uci.edu/features/focused-on-the-big-picture/
          • (February 2013) Alex's Graduation Party: Photos from Alex's graduation party.
          • (January 2013) DASFAA 10-year Best Paper Award: The paper "Efficient Record Linkage in Large Data Sets” by Liang Jin, Chen Li, and Sharad Mehtrotra, which appeared at the DASFAA 2003 conference, has been selected as the DASFAA 10-year Best Paper Award winner for the year 2013.
          • (August 2012) Keynote: Mike Carey gave a keynote talk entitled "BDMS Benchmarking: Practices, Pitfalls, and Possibilities" at the TPC-TC Workshop affiliated with the VLDB 2012 Conference in Istanbul, Turkey.
          • (August 2012) Keynote: Chen Li gave a keynote talk titled "Search as You Type: From Research to Commercialization" at the DBRank workshop in conjunction with VLDB 2012 in Istanbul, Turkey.
          • (Apr 2012) SIGMOD Test-of-Time Award: The 2012 edition of the annual ACM SIGMOD Test-of-Time Award, given to the most impactful paper from the SIGMOD Conference a decade ago, is being given to UCI alumnus Hakan Hacigumus, IBM collaborator Bala Iyer, and ISG faculty members Chen Li and Sharad Mehotra for their paper "Executing SQL over Encrypted Data in the Database-Service-Provider Model". This paper anticipated the world of “Database as Service” (DaaS) much before it came about (and which continues to grow in importance).
          • (Apr 2012) Yahoo! Key Scientific Challenges Program winner: Raman Grover is one of the winners of the 2012 Yahoo! Key Scientific Challenges Program, sponsored by Yahoo! Inc.
          • (October 2011) Paper in TODS: Our paper titled "Exploiting Web Querying for Web People Search" by R. Nuray, D. Kalashnikov, and S. Mehrotra has been accepted for publication in TODS.
          • (September 2011) Paper in ICDE'12: Our paper titled "Extending Map-Reduce for Efficient Predicate-Based Sampling" by Raman Grover and Michael Carey has been accepted for publication in ICDE-2012.
          • (August 2011) NSF grant: Prof. Nalini Venkatasubramanian and Dr. Kyungbaek Kim recently received a $205,000 grant from the National Science Foundation to lead a research project on “GeoSocial Alerting Systems”.
          • (August 2011) NSF grant: Prof. Mehrotra and his postdoctoral scholar Bijit Hore received an NSF award from the CNS division to explore query processing in mixed security environments wherein data migrates across different components each of which may offer different levels of security guarantees and may be susceptible to different attacks.
          • (August 2011) NSF grant: Prof. Mehrotra and Kalashnikov received an NSF award from the IIS division to build a Query and Goal Driven Entity Resolution Framework.
          • (August 2011) Paper in TODS: Our paper titled "Attribute and object selection queries over objects with probabilistic attributes" by Rabia Nuray-Turan, Dmitri Kalashnikov, Sharad Mehrotra, and Yaming Yu has been accepted for publication in ACM TODS.
          • (June 2011) Microsoft Speller Challenge winner: Prof. Chen Li and a group of students participated in the 2011 Microsoft Speller Challenge and took third place. Congratulations to the team!
          • (June 2011) Yahoo! Best Dissertation Fellowship Award: Ronen receives Yahoo! Best Dissertation Fellowship Award for 2011-2012 for his work on "Scalability in Event Detection Systems", nominated by Professor Sharad Mehrotra.
          • (June 2011) Best Student Poster Award in CCGrid2011 : Reza receives best Student Poster Award from IEEE/ACM CCGrid2011: “Cloud Based Framework for Rich Content Mobile Applications”, Reza Rahimi , Nalini Venkatasubramanian.
          • (April 2011) DARPA grant: The UCI team consisting of Profs. Sharad Mehrotra, Nalini Venkatasubramanian, and Dmitri V. Kalashnikov in collaboration with ICSI, Berkeley and SRI have been awarded a DARPA grant to explore Speech-Based Situational Awareness for Event Response.
          • (April 2011) NSF grant: Profs. Mehrotra, Carey, Jain, Venkatasubramanian, and Li have been funded by NSF to develop I-sensorium, to serve as a "living laboratory" to support research in several related areas of cyber-physical systems: including theoretical foundations and underlying principles of building sentient systems; engineering, software, and systems level challenges; and novel application contexts where such sentient systems can be used. Further information about this project can be found here.
          • (March 2011) Paper in IJCAI 2011: Our paper titled "An Efficient Error-Tolerant Chinese Pinyin Input Method" by Chen Li and his Tsinghua collaborators (Yabin Zheng and Maosong Sun) has been accepted for publication in IJCAI 2011.
          • (March 2011) Paper in Distributed and Parallel Databases: Our paper titled "ASTERIX: Towards a Scalable, Semistructured Data Platform for Evolving-World Models" by the ASTERIX project has been accepted for publication in Distributed and Parallel Databases.
          • (December 2010) Chancellor’s Club Fund for Excellence Fellowship: Pinaki Sinha is selected by the UCI Graduate Division as one of six recipients (campus-wide) of a 2010-2011 Chancellor’s Club Fund for Excellence Fellowship.
          • (November 2010) Yahoo! Best Student Dissertation Award: Shengyue Ji received Yahoo! Best Student Dissertation Award for his work on "Efficient Instant Search", nominated by Professor Chen Li.
          • (Octobor 2010) NSF grant: Prof. Venkatasubramanian, Dutt, and Mehrotra have received an NSF award to to develop a semantic foundation, cross-layer system architecture and adaptation services to improve dependability in instrumented cyberphysical spaces (ICPS) based on the principles of "computation reflection".
          • (September 2010) SIGMM Technical Achievement Award: Professor Ramesh Jain received the prestigious SIGMM Technical Achievement award, which will be presented at the ACM International Conference on Multimedia 2010 that will be held October 25-29, 2010 in Florence, Italy.
          • (July 2010) Lecture Series: The ISG group has published videos and slides from its 2009-10 Scalable Data Management lecture series for access by members of the data-intensive computing community around the world
          • (May 2010) Intel grant: Prof. Li received an Intel grant to study compression of personal human genome data. See the ICS news for details.
          • (Apr 2010) Excellent Demo Award at DASFAA 2010: Fuzzy Keyword Search on Spatial Data. Sattam Alsubaiee, Chen Li.
          • (Apr 2010) Facebook Fellow: Vinayak Borkar is one of five recipients of the inaugural Facebook Fellowships in support of his work in the Cloud Computing area.
          • (Apr 2010) Yahoo! Key Scientific Challenges Program winner: Rares Vernica is one of the winners of the 2010 Yahoo! Key Scientific Challenges Program, sponsored by Yahoo! Inc. for his work on "Efficient Similarity-Based Operators for Social Data", nominated by Prof. Michael J. Carey and Prof. Chen Li
          • (Apr 2010) NEC Research Award: Prof. Mehrotra and Bijit Hore (postdoctoral student, ISG group) have been awarded an NEC Research Award for $60,000 to study Risk Containment in Cloud Computing Services.
          • (Mar 2010) NSF grant: Prof. Chen Li received an NSF award to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake.
          • (Feb 2010) IBM fellowship award: Ronen Vaisenberg has received an IBM Ph.D. Fellowship award, nominated by Professor Sharad Mehrotra.
          • (Jan 2010) Google research award: Prof. Kalashnikov and Mehrotra have been awarded a Google research award for $50,000 for their work on "Graph based Disambiguation Framework for Web People Search".
          • (Oct 2009) Best student paper of SIMA 2009 at MILCOM 2009: Situation Based Control for Cyber-Physical Environments. Vivek K. Singh, and Ramesh Jain
          • (Oct 2009) Best paper of ACM SIGMM WSM 2009: Motivating Contributors in Social Media Networks. Vivek K. Singh, Ramesh Jain, Mohan Kankanhalli
          • (Aug 2009) NSF grant: ASTERIX gets NSF funding: Prof. Michael Carey and Chen Li's multi-UC-campus project "ASTERIX: A Highly Scalable Parallel Platform for Semistructured Data Management and Analysis" has been funded at $2.7M for three years from the NSF Data Intensive Computing program. The project, based at UCI, also includes UCSD and UCR participants. UCI's share is $1.8M.
          • (May 2009) SIGMOD 2009 programming contest: Shengyue Ji, Wen Pu(UIUC) and Mingyan Gao have been selected as one of the five finalist teams for the SIGMOD 2009 programming contest (Main Memory Transactional Index.
          • (Mar 2009) New Research Project: Prof. Chen Li officially launches TASTIER: a joint research project with Tsinghua University on efficient auto-complete and type-ahead search on large data sets.
          • (Mar 2009) CHORUS talk on MMSE: At the final conference of the famous European CHORUS project on Multimedia Search Engines (MMSE), Professor Ramesh Jain will be the first (and the only one from USA) speaker presenting state of art and the future of multimedia search technology. More information is available here.
          • (Mar 2009) ASTERIX gets seed funding: Michael Carey and Chen Li's project "ASTERIX: A Scalable Platform for XML Information Analysis" has received a UC Discovery Grant for $132K, including $52K from UC and $80K from eBay.
          • (Mar 2009) PerCom 2009 PhD forum competition: Ronen Vaisenberg won the first prize in the PerCom 2009 PhD forum competition.
          • (Feb 2009) NSF grant: Chen Li received an NSF CluE grant (about $221,000) on large-scale data cleaning using Hadoop.
          • (Jan 2009) Google research award: Michael Carey received a Google Research Award ($70,000) to work on OpenII (Open Information Integration).
          • (2009) NSF grant: Sharad Mehrotra and Nalini Venkatasubramanian received a $135,000 NSF grant on Dispatcher selection (joint with SRI).
          • (2009) DHS grant: Sharad Mehrotra and Nalini Venkatasubramanian received a $1,000,000 SAFIRE grant from DHS.

          Last Updated on January 07, 2011

          http://flamingo.ics.uci.edu/releases/4.0/docs/TopkDoc.html TopkDoc – Group

          Back to Index


          AppString > AppStringDoc

          Top-k

          Overview

          The module contains an implementation of the technique presented in [1].

          Usage

          For compiling instructions, please see CompileDoc.

          The module uses C++ STL TR1 library provided by GNU GCC.

          An example of how to use the module is available in src/topk/example.cc.

          Interface

          The module is divided in three components:

          • Top-k Index
          • Top-k Query Index
          • Top-k Search Algorithms

          The Top-k Index (Topk::Index) class is defined in src/topk/topkindex.h. Its main methods are:

              Index();
              Index(const std::string &filename);
          
              template<class InputIterator> 
              void build(InputIterator begin, InputIterator end, const GramGen &gramGen);
          
              void load(const std::string &filename);
          
              void save(const std::string &filename)
                const;
          

          The Top-k Query Index (Topk::IndexQuery) class contains a part of the index that is relevant for a particular query. This class is used by the search algorithms. It is defined in src/topk/topkindex.h and its main method is:

              IndexQuery(const Index &idx, const Query &query);
          

          The Top-k Search Algorithms are defined in multiple files. All the algorithms define the getTopk method. Its prototype is:

              template<
                class RandomAccessIterator1, 
                class RandomAccessIterator2, 
                class OutputIterator>  
              void getTopk(
                const RandomAccessIterator1 data, 
                const RandomAccessIterator2 weights, 
                const Index &idx, 
                const Query &que, 
                IndexQuery &idxQue, 
                OutputIterator topk);
          

          The most popular algorithm is the heap-based algorithm. It is defined in the src/topk/topkheap.h.

          The main idea is that a Topk::Index object can be created to hold the index. The index can be build by specifying an iterator over a sequence of strings and a gram generator. Additionally, the index can be saved to disk and then loaded from disk. When a query comes, a Topk::IndexQuery object is created using the Topk::Index instance and the query. Finally, the search algorithm of choice can applied.

          Contributors

          • Rares Vernica

          [1] Rares Vernica, Chen Li: Efficient top-k algorithms for fuzzy search in string collections. KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)


          Back to Index

          http://flamingo.ics.uci.edu/releases/4.0/docs/FilterTreeDoc.html FilterTreeDoc – Group

          Back to Index


          AppString > AppStringDoc

          1. Filtertree
            1. Introduction
            2. Approximate String Search
            3. Overview
              1. Indexing Options
              2. Other Functionality
            4. Answering Approximate String Queries
            5. The Filtertree Structure
              1. Recommendations
            6. High-Level Overview of Important Components
              1. General Components
              2. Indexers, Searchers, and ListMergers
            7. StatsGen Output
            8. Contributors

          Filtertree

          Introduction

          This module supports efficient approximate string search on a collection of strings. An approximate query asks for all strings in the collection that are "similar" to the query string for a given similarity function and similarity threshold.

          Approximate String Search

          Many applications need to answer approximate string queries. The following are a few examples:

          • Spellchecking: suggest good words for a possibly mistyped word.
          • Record linkage: identify records that could represent the same real-world entity.
          • The "Did you mean" feature by many search engines partially relies on finding words similar to a given keyword.

          Overview

          Indexing Options

          We provide three different flavors of indexing+searching, each of which provide specific optimizations for improving query performance:

          1. All structures are in main memory, including:
            • String collection
            • Filtertree
            • Inverted index
          1. All structures are in main memory. We use lossy compression on the inverted index to save space.
            • String collection in memory
            • Filtertree in memory
            • Inverted index in memory (compressed)
          1. The data and inverted index is on disk.
            • String collection on disk
            • Filtertree in memory
            • Inverted index on disk

          Other Functionality

          We support the following similarity functions / distance measures:

          • Levenshtein Distance (aka the Edit Distance) http://en.wikipedia.org/wiki/Levenshtein_distance
          • Jaccard Similarity http://en.wikipedia.org/wiki/Jaccard_Similarity_Coefficient
          • Cosine Similarity http://en.wikipedia.org/wiki/Cosine_similarity
          • Dice Similarity http://en.wikipedia.org/wiki/Dice%27s_coefficient

          We support the following single-signature filters (e.g. for partitioning the string collection):

          • Length Filter
          • Charsum Filter

          The index structure (filtertree + inverted index) can be saved/loaded to/from a file.

          Answering Approximate String Queries

          To answer queries efficiently this module uses an inverted-list index on the q-grams of the strings in the collection. That is, each string is decomposed into substrings (grams) of size q using a sliding window, and then for each gram we build a list of string ids containing that gram (the inverted list of that gram). The process of answering queries is based on the observation that if two strings are similar, then they must share a certain number of common grams (depending on the similarity function and similarity threshold). False-positives must be removed in a post-processing step, i.e. the true similarities are computed.

          In addition to the above we can further increase the performance of queries by using filters. A single-signature filter partitions the string collection into disjoint subsets based on some criteria. For answering a query we only need to consider some of the subsets. For example, if we were looking for all strings in the collection within an edit-distance of 1 to the string "abcde", then we know that any answer string must have a length in [4,6]. So, if we partition the string collection using the length of the strings we can avoid processing irrelevant string ids during query answering. The charsum filter is similar to the length filter. We partition the data strings based on their charsums (sum of characters in the string). For query answering we can determine a range of charsums that answers must lie in.

          The Filtertree Structure

          The filtertree structure facilitates the use of filters. Each level in the tree partitions the string collection based on one filter. Each leaf node contains a gram inverted-index on the subset of strings belonging to that leaf. For answering a query we traverse the tree to identify leaf nodes that "survive" the filtering criteria, and probe the inverted indexes attached to those leaves to get a list of candidate answers. The following is an example of a filtertree with a fanout of 3 and both the length and charsum filter applied:

          Filtertree structure with two partitioning filters

          Recommendations

          In most cases, using exactly one partitioning filter yields the best performance. An intuitive (but simplified) explanation is as follows:

          • For the in-memory indexes, there is trade-off between processing several inverted indexes with short lists or processing fewer inverted indexes with longer lists
          • For the disk-based indexes the layout of the inverted lists in the file can be optimized if exactly one partitioning filter is used

          High-Level Overview of Important Components

          • The purpose of this overview is to give the reader a feeling for the code design
          • Many details, such as methods, method parameters, and attributes have been left out for simplicity
          • Blue components are meant for in-memory indexes
          • Yellow components are meant for disk-based indexes
          • Red components are meant for in-memory indexes compressed with CombineLists
          • Green components are meant for in-memory indexes compressed with DiscardLists

          General Components

          Code overview of general components

          Indexers, Searchers, and ListMergers

          Code overview of indexers, searchers and mergers

          StatsGen Output

          The StatsGenerator allows collecting of performance data on the approximate string search library. For example, different filters, merging algorithms, datasets, query workloads can be tested. A good start is perftest.cc included in the filtertree folder. The performance numbers are written to an output file (e.g. perftest.cc writes to "perftest_search_stats.txt"). The StatsGenerator is intended for advanced users who are familiar with the algorithmic details of approximate string search. The numbers generated depict different steps in the process of query answering and will only be understood by people familiar with the subject. For getting an idea of the query performance using certain parameters it is sufficient to focus on the field "total time" which measures the average query performance of the given workload.

          Contributors

          • Alexander Behm (design, main author)
          • Chen Li (design, project leader)
          • Rares Vernica (design)
          • Shengyue Ji (design, implementation)
          • Yiming Lu (design, implementation)

          Back to Index

          http://flamingo.ics.uci.edu/releases/4.0/docs/PartEnumDoc.html PartEnumDoc – Group

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          AppString > AppStringDoc

          PartEnum

          Overview

          The module contains an implementation of the technique presented in [1]. The technique was invented in the Data Cleaning Project at Microsoft, Research.

          Usage

          For compiling instructions, please see CompileDoc.

          The module uses C++ STL TR1 library provided by GNU GCC and Boost 1.34.1 library.

          On systems with the aptitude package manager (e.g. Ubuntu, Debian) you can install all required packages by typing the following as root user (or using sudo):

          $ sudo apt-get install libboost-dev
          

          An example of how to use the module is available in src/partenum/example.cc.

          Interface

          The main class of the module is ParEnum which is declared in src/partenum/partenum.h.

          The main methods of PartEnum are:

            PartEnum(const vector<string> &data, 
                     unsigned q, 
                     unsigned editdist, 
                     unsigned n1, 
                     unsigned n2);
          
            PartEnum(const vector<string> &data, 
                     const string &filename);
          
            void build();
            void saveIndex(const string &filename) const;  
          
            void search(const string &query, vector<unsigned> &results);
            void search(const string &query, const unsigned editdist,
                        vector<unsigned> &results);
          

          The main idea is that the user can create a PartEnum object by specifying a vector of strings (dataset) and a few extra parameters (see [1] for details) or load an existing object from a file. If the object was not loaded, then it needs to be built. Next, the user has the option of saving the object to a file. In order to search approximately in the dataset for a given string, the user calls the function search.

          Performance

          Pentium D 3.4GHz Dual Core, 2GB memory, Linux (Ubuntu), g++. A data set of 54,000 person names.

          Technique Dataset Size Ed Threshold Q Time (ms) Index size (MB) Comments
          Scan 54k 1 - 11.86 1.3
          Scan 54k 2 - 21.30 1.6
          Scan 54k 3 - 35.49 4.2
          -
          PartEnum 54k 1 2 1.21 57.3 n1=2,n2=8
          PartEnum 54k 2 2 12.04 60.2 n1=3,n2=8
          PartEnum 54k 3 1 35.24 34.8 n1=2,n2=7

          Contributors

          • Rares Vernica

          [1] Arvind Arasu, Venkatesh Ganti, Raghav Kaushik: Efficient Exact Set-Similarity Joins. VLDB 2006: 918-929


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          http://flamingo.ics.uci.edu/releases/4.0/docs/SepiaDoc.html SepiaDoc – Group

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          AppString > AppStringDoc

          SEPIA

          Overview

          The module contains the implementation of the technique presented in [1] and [2].

          Usage

          For compiling instructions, please see CompileDoc.

          An example of how to use the module is available in src/sepia/example.cc.

          Interface

          The main class of the module is Sepia which is declared in src/sepia/sepia.h.

          The main methods of Sepia are:

            Sepia(const vector<string> &dataset, 
                  unsigned thresholdMin,
                  unsigned thresholdMax);
            Sepia(const vector<string> &dataset,
                  const string &filename);
          
            void build();
            void saveData(const string &filename) const;
            
            float getEstimateSelectivity(const string &query, unsigned editdist) const;
          

          The main idea is that the user can create a Sepia object by specifying a vector of strings (dataset) and a few extra parameters (for details see [1]) or load an existing one from a file. If the object was not loaded, then it needs to be built. Next, the user has the option of saving the object to a file. In order to estimate the selectivity of a given string, the user calls select.

          Performance

          The performance results are available in [1] and [2].

          Contributors

          • Rares Vernica

          [1] Liang Jin and Chen Li: Selectivity Estimation for Fuzzy String Predicates in Large Data Sets. VLDB 2005: 397-408

          [2] Liang Jin, Chen Li, Rares Vernica: SEPIA: estimating selectivities of approximate string predicates in large Databases. VLDB J. 17(5): 1213-1229 (2008)


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          http://flamingo.ics.uci.edu/releases/4.0/docs/MatTreeDoc.html MatTreeDoc – Group

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          AppString > AppStringDoc

          MAT-Tree

          Overview

          MAT-tree: A tree-based structure for indexing string and numeric attributes. Using MAT-tree, we can perform range queries on both string and numeric attributes. [1]

          Usage

          The program can be compiled using Visual C or gnu C++.

          Compile the project in Visual C, and run accordingly. You can also write a makefile and compile it using a GNU C compiler.

          Interface

          Main files:

          • RETree.cpp - includes main() and the MAT-tree functions
          • index.h, index.cpp - defines data structures, such as Node, Record, Query, etc.
          • node.cpp, Rect.cpp - defines basic functions for Node and Rect
          • Trie.h, Trie.cpp - defines the Trie class and all Trie functions
          • distance.h, distance.cpp - defines Distance1 class to calculate the edit distance between two strings
          • NFA.h, NFA.cpp - defines the automaton class
          • NFANode.h, NFANode.cpp - defines the Node of NFA
          • NFATransition.h, NFATransition.cpp - defines the Edge of NFA
          • NFAMatch.h, NFAMatch.cpp - Modifies Meyer's algorithm, calculates the edit distance between a string and an automaton
          • data.zip - data (text file zipped)
          • query.zip - queries (text file zipped)

          Useful parameters:

          const int MAXLEN = 100; //maximum length of a string attribute
          const int PGSIZE = 256; //page size
          const int TRIELEN = 1000; //maximum size of a Trie (in string representation)
          const int K = 400;  //# of centers in MAT-tree
          const int STRDELTA = 3; //threshold for string attribute
          const int NUMDELTA = 4; //threshold for numeric attribute
          const int SIZES = 80000; //size of the dataset
          const int ALPH_SIZE = 29; //size of the alphabet
          #define DATAFILE "data.txt" //input file for dataset
          #define QUERYFILE "query.txt" //query file
          const int NUMQUERY = 10; //# of queries to run
          

          Prepare DATAFILE and QUERYFILE. Each record is in one line, with a string followed by by aq numeric value. In the case there are white spaces in the string, you need to replace them with special characters first.

          Performance

          The performance results are available in [1].

          Contributors

          • Chen Li
          • Liang Jin

          [1] Liang Jin, Nick Koudas, Chen Li, Anthony K. H. Tung: Indexing Mixed Types for Approximate Retrieval. VLDB 2005: 793-804


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          http://flamingo.ics.uci.edu/releases/ Index of /releases

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          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  
          [DIR]1.0/16-Apr-2013 18:54 -  
          [DIR]2.0.1/16-Apr-2013 18:54 -  
          [DIR]2.0/16-Apr-2013 18:53 -  
          [DIR]3.0/16-Apr-2013 18:53 -  
          [DIR]4.0/16-Apr-2013 18:53 -  
          [DIR]4.1/16-Apr-2013 18:54 -  
          [TXT]apache.footer.html16-Apr-2013 18:54 1.1K 
          [TXT]apache.header.html16-Apr-2013 18:54 1.0K 

          Apache/2.2.15 (CentOS) Server at flamingo.ics.uci.edu Port 80
          http://flamingo.ics.uci.edu/releases/4.0/docs/CommonDoc.html CommonDoc – Group

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          AppString > AppStringDoc

          1. Common
            1. Introduction
            2. Overview
            3. Contributors

          Common

          Introduction

          This module provides the core functionality for approximate string search. It includes similarity metrics, gram generators and the query class.

          Overview

          1. We support the following similarity functions / distance measures:
            • Levenshtein Distance (aka the Edit Distance) http://en.wikipedia.org/wiki/Levenshtein_distance
            • Jaccard Similarity http://en.wikipedia.org/wiki/Jaccard_Similarity_Coefficient
            • Cosine Similarity http://en.wikipedia.org/wiki/Cosine_similarity
            • Dice Similarity http://en.wikipedia.org/wiki/Dice%27s_coefficient
          1. We support generation of fixed length grams into various C++ containers, with or without positional information. Grams can be strings or hashed strings (hashed to an unsigned int).
          1. The query class contains the query string, similarity measure, similarity threshold, etc.

          Contributors

          • Alexander Behm (design, implementation)
          • Chen Li (design, project leader)
          • Jiaheng Lu (implementation)
          • Rares Vernica (design, main author)
          • Shengyue Ji (design, implementation)

          Back to Index

          http://flamingo.ics.uci.edu/releases/4.0/docs/StringMapDoc.html StringMapDoc – Group

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          AppString > AppStringDoc

          StringMap

          Overview

          This module supports fuzzy string searching, i.e., finding strings similar to a given a string or a collection of strings, where similarity is defined using edit distance. It supports both join queries and string-string-search queries. The approach first maps strings into a high-dimensional Euclidean space. An R-tree is constructed from the mapped objects. The program will sample from the strings to get a new threshold to be used to do search in the Euclidean space. A similarity string search is converted to a spatial search in the new Euclidean space using R-trees. [1,2]

          Usage

          • make clean
          • make
          • date; time ./stringmap_unittest; date
          • After running it once, you can rerun it by using ./stringmap_unittest load to save some time to do the mapping and construct the R-tree for the single-string-search case.
          • stringmap_unittest.cpp shows how to use StringMap. Some parameters can be tuned, such as the sampling percentages, which should depend on the data size.
          • Some R-tree parameters are defined in rtreeparams.h. e.g., the dimensionality of the Euclidean space is set to 20.

          Interface

          This package includes three data files of names:

          • source1.txt (zipped to source1.zip) and source2.txt (zipped to source2.zip) are used for testing join queries
          • source.txt (zipped to source.zip) is used for testing single-string-search queries, which is a combination of source1.txt and source2.txt.
          • source-big.txt (zipped to source-big.zip) is used for testing single-string-search queries, which has more than 111K full names.

          Performance

          We ran this package on a Linux platform with a 2.8GHZ Pentium 4 with 1GB RAM. The join on source1.txt and source2.txt (2000 x 2000 strings) took about 105 seconds (including the time to compute the recall, which was using nested loop and expensive), and with a recall of 98.9%. We ran 100 single-string-search queries on 100K name strings, and the average search time is 0.079 seconds, with the average recall 98.8%.

          Contributors

          • Chen Li
          • Liang Jin

          [1] Liang Jin, Chen Li, Sharad Mehrotra: Efficient Record Linkage in Large Data Sets. DASFAA 2003: 137-

          [2] Chen Li, Liang Jin, Sharad Mehrotra: Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques. World Wide Web 9(4): 557-584


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          http://flamingo.ics.uci.edu/releases/4.0/docs/LbakTreeDoc.html LbakTreeDoc – Group

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          AppString > AppStringDoc

          1. LBAK-Tree
            1. Introduction
            2. Overview
              1. Fixed-Level (FL)
              2. Variable-Level (VL)
              3. Variable-Level Exploiting Keyword Frequencies (VLF)
            3. Contributors
            4. References

          LBAK-Tree

          Introduction

          This module provides location-based approximate keyword search.

          For example, it can answer queries such as find Alkatras near San Fransisco. Notice that Alkatras is misspelled but the LBAK-Tree can still find useful answers. In short, the LBAK-Tree answers queries with a spatial component and a keyword component, where the keywords don't need to match exactly but approximately.

          Overview

          The LBAK-Tree is based on a hierarchical spatial index that is enhanced with inverted indexes for approximate string lookups. In our implementation we use an R*-Tree as spatial index and use the FilterTreeDoc module (part of Flamingo) to implement the inverted indexes for approximate string lookups.

          Our paper Supporting Location-Based Approximate-Keyword Queries describes three variants of the LBAK-Tree which differ in where they place inverted indexes in the spatial index. This module contains all their implementations:

          Fixed-Level (FL)

          • Inverted indexes are placed at one level in the R*-Tree

          Illustration of FL algorithm

          Variable-Level (VL)

          • Inverted indexes can be placed at different levels in the R*-Tree

          Illustration of VL algorithm

          Variable-Level Exploiting Keyword Frequencies (VLF)

          • Inverted indexes are placed at various levels,
          • Keyword-frequencies are exploited to improve performance

          Illustration of VLF algorithm

          Please have a look at example.cc in the lbaktree/src folder to get started!

          Contributors

          • Sattam Alsubaiee (design, main author)
          • Shengyue Ji (R*-Tree implementation)
          • Alexander Behm (author)
          • Chen Li (project leader)

          References

          • Sattam Alsubaiee, Alexander Behm, Chen Li: Supporting Location-Based Approximate-Keyword Queries, ACM SIGSPATIAL GIS 2010

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          http://flamingo.ics.uci.edu/releases/4.0/src/ Index of /releases/4.0/src

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          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  
          [DIR]common/16-Apr-2013 18:53 -  
          [DIR]filtertree/16-Apr-2013 18:53 -  
          [DIR]lbaktree/16-Apr-2013 18:53 -  
          [DIR]listmerger/16-Apr-2013 18:53 -  
          [DIR]mattree/16-Apr-2013 18:53 -  
          [DIR]partenum/16-Apr-2013 18:53 -  
          [DIR]sepia/16-Apr-2013 18:53 -  
          [DIR]stringmap/16-Apr-2013 18:53 -  
          [DIR]topk/16-Apr-2013 18:53 -  
          [DIR]util/16-Apr-2013 18:53 -  

          Apache/2.2.15 (CentOS) Server at flamingo.ics.uci.edu Port 80
          http://flamingo.ics.uci.edu/index.html The FLAMINGO Project on Data Cleaning

          The FLAMINGO Project on Data Cleaning

          Department of Computer Science, UC Irvine

          Objective

          The Flamingo Project focuses on data cleaning, i.e., how to deal with errors and inconsistencies in information systems. As an example, in many applications such as data integration, commercial organizations need to collect data from various sources to conduct analysis and make decisions. Often, the data from these different sources can have inconsistencies. For instance, we use first name, last name, SSN, and birthday to identify a person. However, the same name, e.g., "Schwarzenegger", may be misspelled as "Swarzzengaer" or other forms. Such errors make it more challenging to link records from different places and answer queries approximately. We are developing algorithms in order to make query answering and information retrieval efficient in the presence of such inconsistencies and errors.

          With the NSF award IIS-0844574, we plan to study the following problems. Supporting fuzzy queries is becoming increasingly more important in applications that need to deal with a variety of data inconsistencies in structures, representations, or semantics. Many existing algorithms require an offline analysis of data sets to construct an efficient index structure to support online query processing. Fuzzy join queries of data sets are more time consuming due to the computational complexity. The PI is studying three research problems: (1) constructing high-quality inverted lists for fuzzy search queries using Hadoop; (2) supporting fuzzy joins of large data sets using Hadoop; and (3) using the developed techniques to improve data quality of large collections of documents.

          With the NSF award 1030002, we will study how to support powerful keyword search with efficient indexing structures and algorithms in a clouding-computing infrastructure. A main application is supporting family reunification in disasters such as the Haiti Earthquake. Check our portals for the Haiti Earthquake and Chile Earthquake. The main challenge is how to use limited programming primitives in the cloud to implement index structures and search algorithms.

          Our qSpeller project page for the Microsoft Speller Challenge.

          News

          • (1/13/2013) Our DASFAA 2003 paper titled "Efficient Record Linkage in Large Data Sets" received the 10-year Best Paper Award for DASFAA 2013. It was my first paper in the area of data cleaning and approximiate string search in the context of the Flamingo project.
          • (2/2012) We are glad to release version of our Flamingo Package on approximate string matching.
          • (7/2011) Our team won the third prize at the Microsoft Speller Challenge. Here is our project page.
          • (4/22/2011) Chen Li gave an invited talk titled "The Flamingo Software Package on Approximate String Queries" at the DQIS 2011 workshop in Hong Kong. Here is the Powerpoint file.
          • (10/2010) Out paper titled "Answering Approximate String Queries on Large Data Sets Using External Memory" has been accepted for publication in ICDE 2011.
          • (9/2010) Our paper titled "Supporting Location-Based Approximate-Keyword Queries" has been accepted for publication in ACM SIGSPATIAL GIS 2010.
          • (3/2010) We are glad to release the third version of our Flamingo Package on approximate string matching.
          • (3/2010) We are glad to release the source code of our SIGMOD 2010 paper titled "Efficient Parallel Set-Similarity Joins Using MapReduce"
          • (3/2010) We are glad to release two Fuzzy Keyword Search on Spatial Data demos.
          • (3/2010) We are glad to receive an NSF award 1030002 to support research on powerful keyword search with efficient indexing structures and algorithms in a cloud-computing environment, especially in the domain of family reunification in disasters such as the Haiti Earthquake.
          • (2/2010) Our paper titled "Efficient Parallel Set-Similarity Joins Using MapReduce" has been accepted by the SIGMOD 2010 conference.
          • (2/2009) We are glad to receive an NSF award IIS-0844574 from the NSF CluE program to support our research on large-scale data cleaning using MapReduce/Hadoop environments. In addition to receiving the NSF support, we will also use software and services on a Google-IBM cluster to explore innovative research ideas in data-intensive computing.
          • (11/07/2008) We updated our Flamingo Package (2.0.1) for compatibility with the latest GCC version (4.3.2).
          • (10/14/2008) We are glad to release the second version of our Flamingo Package on approximate string matching.
          • (10/14/2008) We are glad to release the Flamingo Toolkit that contains UDF functions for MySQL.
          • (4/1/2008) We are glad to release the PSearch Prototype to support interactive, fuzzy search for UCI Directory.
          • (4/17/2007) We are glad to release the first version of our Flamingo Package on approximate string matching.

          Fuzzy Keyword Search on Spatial Data

          We present a solution to support Fuzzy Keyword Search on Spatial Data.

          Releases

          • Latest

          • 4.1 (February 22nd, 2012)
          • 4.0 (October 23rd, 2010)
          • 3.0 (March 29th, 2010)
          • 2.0.1 (November 7th, 2008)
          • 2.0 (October 14th, 2008)
          • 1.0 (April 17th, 2007)

          • Toolkit (October 14th, 2008), UDF functions for MySQL

          People

          • Sattam Alsubaiee (Ph.D. Student)
          • Alexander Behm (Ph.D. Student)
          • Shengyue Ji (Ph.D. Student)
          • Chen Li (Faculty)
          • Rares Vernica (Ph.D. Student)

          Alumni and Visitors

          • Guoliang Li, spring of 2008, visitor from Tsinghua University, China.
          • Jiaheng Lu, postdoc, 2006-2008. Now a faculty at Renmin University, China.
          • Yiming Lu, graduated from UC Irvine in 2008
          • Bin Wang and Xiaochun Yang, summers of 2006, 2007, and 2008, visitors from Northeastern University, China
          • Liang Jin, graduated from UC Irvine in 2005

          Publications

          • Answering Approximate String Queries on Large Data Sets Using External Memory
            Alexander Behm, Chen Li, Michael J. Carey.
            ICDE 2011 (accepted for publication).
          • Supporting Location-Based Approximate-Keyword Queries
            Sattam Alsubaiee, Alexander Behm, Chen Li. PDF PPTX Source Code
            ACM SIGSPATIAL GIS 2010.
          • Efficient Parallel Set-Similarity Joins Using MapReduce
            Rares Vernica, Michael J. Carey, Chen Li. PDF Full Version Source Code
            SIGMOD 2010.
          • Fuzzy Keyword Search on Spatial Data (Demo)
            Sattam Alsubaiee and Chen Li PDF Demo
            DASFAA 2010.
          • Efficient top-k algorithms for fuzzy search in string collections.
            Rares Vernica, Chen Li. PDF PDF slides Source Code
            KEYS 2009: 9-14. (Workshop on Keyword Search on Structured Data, collocated with SIGMOD 2009)
          • Efficient Interactive Fuzzy Keyword Search
            Shengyue Ji, Guoliang Li, Chen Li, and Jianhua Feng PDF PPTX ConferenceLink
            WWW 2009.
          • Space-Constrained Gram-Based Indexing for Efficient Approximate String Search
            Alexander Behm, Shengyue Ji, Chen Li, and Jiaheng Lu PDF Full Version PPTX Source Code
            ICDE 2009.
          • Efficient Approximate Search on String Collections (Tutorial)
            Marios Hadjieleftheriou, Chen Li PPT Part1, PPT Part2
            ICDE 2009.
          • Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently PDF PPT
            Xiaochun Yang, Bin Wang, Chen Li.
            SIGMOD 2008.
          • Efficient Merging and Filtering Algorithms for Approximate String Searches PDF PPT Source Code
            Chen Li, Jiaheng Lu, and Yiming Lu.
            ICDE 2008.
          • SEPIA: Estimating Selectivities of Approximate String Predicates in Large Databases Source Code
            Liang Jin, Chen Li, and Rares Vernica.
            VLDB Journal 2007. It's an extended version of the SEPIA paper in VLDB05.
          • VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams. PDF PPT
            Chen Li, Bin Wang, and Xiaochun Yang.
            VLDB 2007, Vienna, Austria
          • Relaxing Join and Selection Queries. PDF PPT Source Code
            Nick Koudas, Chen Li, Anthony Tung, and Rares Vernica.
            VLDB 2006, Seoul, Korea.
          • Selectivity Estimation for Fuzzy String Predicates in Large Data Sets. PDF PPT Source Code
            Liang Jin and Chen Li.
            VLDB 2005, Trondheim, Norway.
          • Indexing Mixed Types for Approximate Retrieval. PDF PPT Source Code
            Liang Jin, Nick Koudas, Chen Li, Anthony K.H. Tung.
            VLDB 2005, Trondheim, Norway.
          • NNH: Improving Performance of Nearest-Neighbor Searches Using Histograms. PDF Full Version PPT
            Liang Jin, Nick Koudas, Chen Li.
            EDBT 2004, Heraklion - Crete, Greece.
          • Efficient Record Linkage in Large Data Sets. PDF, PPT Source Code
            Liang Jin, Chen Li, and Sharad Mehrotra.
            8th International Conference on Database Systems for Advanced Applications (DASFAA) 2003, Kyoto, Japan.
            Received 10-year Best Paper Award for DASFAA 2013.
          • Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques
            Chen Li, Liang Jin, and Sharad Mehrotra
            World Wide Web Journal, Volume 9, Number 4, pages 557-584, December 2006.
            This journal article is an extended version of the DASFAA03 paper.

          Acknowledgements: This release is partially supported by the NSF CAREER Award No. IIS-0238586, the NSF award No. IIS-0742960, the NSF award IIS-0844574, the NSF award 1030002, the NSF-funded RESCUE project, the NIH grant 1R21LM010143-01A1, a Google Research Award, a gift fund from Microsoft, a research grant from Amazon.com to allow us to use their MapReduce cluster, and a fund from CalIt2.
          Many thanks to Minh Doan and Kensuke Ohta for their valuable testing and feedback on the code and documentation.


          For any questions regarding this project, please send email to flamingo AT ics.uci.edu

          http://flamingo.ics.uci.edu/releases/4.0/docs/ Wiki Index

          Index of Wiki Pages


          AppString Documentation
          • GettingStartedDoc
          • CompileDoc

          Modules
          • CommonDoc
          • FilterTreeDoc
          • ListMergerDoc
          • MatTreeDoc
          • PartEnumDoc
          • SepiaDoc
          • StringMapDoc
          • UtilDoc
          • TopkDoc
          • LbakTreeDoc
          http://flamingo.ics.uci.edu/releases/4.0/docs/GettingStartedDoc.html GettingStartedDoc – Group

          Back to Index


          AppString > AppStringDoc

          1. Getting Started
            1. Introduction
            2. Downloading the Package
            3. Requirements
            4. Wrappers (Simplest Way To Use The Library)
            5. Step-By-Step Guide
              1. Step1: Compiling The Library
              2. Step2: Using The Library In An Application
              3. Step3: Compiling And Running The Application
            6. Basic Usage
            7. Example Files

          Getting Started

          Introduction

          This tutorial will guide through the basics steps needed to perform approximate string search on a collection of strings using this library. This guide focuses on how to use the FilterTree (FilterTreeDoc) module.

          Downloading the Package

          • The entire package may be downloaded from http://flamingo.ics.uci.edu/releases/4.0/flamingo-4.0.tgz

          Requirements

          Most modules in this release were developed and tested on Ubuntu Linux using the GNU GCC/G++ compiler.

          In order to compile and run most modules you will need the following:

          • Linux environment (preferably a Debian based Linux, e.g. Ubuntu)
          • C++ compiler (GCC/G++ version 4.0 or later)
          • CMake
          • C++ Boost library, http://www.boost.org (only required for PartEnumDoc)

          On systems with the aptitude package manager (e.g. Ubuntu, Debian) you can install all required packages by typing the following as root user (or using sudo):

          $ apt-get install gcc g++ cmake libboost-dev
          

          The module MatTreeDoc was developed in Visual C++. No makefile is provided for that module. We recommend using Windows and Visual C++ for that module.

          Wrappers (Simplest Way To Use The Library)

          For your convenience, we have added wrappers that contain all necessary objects as described in section "Basic Usage". All you need to do to build an index and execute queries, is to create an instance of a wrapper. These wrappers initialize components with default values and are the simplest and fastest way to use our library - at the expense of being able to control tuning parameters (which filters are used, fanout, etc.).

          We recommend browsing through the code in filtertree/wrappers/src/example.cc.

          Step-By-Step Guide

          In this guide we will use a wrapper to show you how to perform approximate string search using the edit distance.

          Step1: Compiling The Library

          Let us first test whether we can successfully compile the Flamingo libraries.

          Let us say you have extracted the Flamingo archive to the following directory: /home/joe/flamingo-4.0

          The wrappers are part of the filtertree module which we compile as follows:

          cd /home/joe/flamingo-4.0/src/filtertree/
          cmake .
          make
          

          This should compile filtertree and the modules that it depends on, namely, util, common and listmerger.

          The libraries and executables are placed in the build folder of their corresponding modules, e.g., the filtertree binaries are in /home/joe/flamingo-4.0/src/filtertree/build/, the listmerger binaries are in /home/joe/flamingo-4.0/src/listmerger/build/, and so on.

          Your /home/joe/flamingo-4.0/src/filtertree/build/ should look something like this after compilation:

          joe@joe-machine:~/flamingo-4.0/src/filtertree/build$ ls -l
          -rwxr-xr-x 1 joe joe     18 2010-10-21 13:06 cleanup.sh
          -rwxr-xr-x 1 joe joe 482093 2010-10-22 12:11 example_ft
          -rwxr-xr-x 1 joe joe 307624 2010-10-22 12:11 example_wrappers
          -rwxr-xr-x 1 joe joe 106208 2010-10-22 12:11 libfiltertree-lib.so
          -rwxr-xr-x 1 joe joe   7043 2010-10-22 12:11 libwrappers-lib.so
          -rwxr-xr-x 1 joe joe 376594 2010-10-22 12:12 perftest_ft
          -rwxr-xr-x 1 joe joe 453066 2010-10-22 12:12 unittest_ft
          

          (note that the exact file sizes may differ from yours)

          Step2: Using The Library In An Application

          Now that we have compiled the library, we are ready to include it into an application.

          Let us assume you wish to use the library in an application located in /home/joe/searchapp that consists of one source file /home/joe/searchapp/src/main.cc

          We assume a directory structure similar to the ones used in Flamingo, i.e., we expect /home/joe/searchapp to have a folder src and a folder build.



          We will discuss how to use a simple in-memory index. More examples can be found in /home/joe/flamingo-4.0/src/filtertree/wrappers/example.cc.



          You can copy and paste the following lines of source code into /home/joe/searchapp/src/main.cc for us to compile:

          #include "filtertree/src/wrappers/wrappers.h"
          
          int main() {
            GramGenFixedLen gramGen(2); // using 2-grams
            
            StringContainerVector strContainer(true);
            strContainer.initStatsCollector(&gramGen);
            strContainer.fillContainer("/home/joe/flamingo-4.0/src/filtertree/data/female_names.txt", 4000);
            
            // create wrapper using edit distance (ed) and build index
            // params: stringcontainer, gramgenerator, use partitioning filter?
            WrapperSimpleEd wrapper(&strContainer, &gramGen, true);
            wrapper.buildIndex();
            
            // perform search
            float editDistance = 1.0f;
            string queryString = "kathrin";
            vector<unsigned> resultStringIDs;
            wrapper.search(queryString, editDistance, resultStringIDs);
            cout << "SIMILAR STRINGS: " << endl;
            for(unsigned i = 0; i < resultStringIDs.size(); i++) {
              string tmp;
              strContainer.retrieveString(tmp, resultStringIDs[i]);
              cout << tmp << endl;
            }
          }
          

          In this example all data structures are stored in main memory.

          This application will use the first 4000 lines of /home/joe/flamingo-4.0/src/filtertree/data/female_names.txt as the data strings.

          It will build an index to support approximate string search and answer a query that asks for all data strings that are within an edit-distance of 1 to "kathrin".

          Finally, the results will be displayed.

          Please refer to /home/joe/flamingo-4.0/src/filtertree/wrappers/example.cc and /home/joe/flamingo-4.0/src/filtertree/example.cc for more examples.

          Step3: Compiling And Running The Application

          We recommend you use CMake to generate a makefile to build your application. Hand-crafting a makefile is also possible but requires more effort and understanding. We will discuss how to use CMake to build your application.

          We assume the following (as in the previous sections):

          • Your application is in /home/joe/searchapp
          • Your application contains one source file /home/joe/searchapp/src/main.cc
          • Flamingo is in /home/joe/flamingo-4.0

          Let us create the CMakeLists.txt used by CMake to generate a makefile. Following the convention in Flamingo we will put it in /home/joe/searchapp/CMakeLists.txt.

          You can copy and paste the following lines into /home/joe/searchapp/CMakeLists.txt:

          cmake_minimum_required(VERSION 2.6)
          
          # files to compile
          set(APPLICATION_EXEC_SRC 
            src/main.cc
          )
          
          # where to look for header files
          include_directories (
            .
            ../flamingo-4.0/src/
            ../flamingo-4.0/src/filtertree/
            ../flamingo-4.0/src/filtertree/src/
            ${CMAKE_SOURCE_DIR}/../
            include
            lib  
          )
          
          # where to look for dependent libraries
          link_directories(
            ${CMAKE_SOURCE_DIR}/../flamingo-4.0/src/common/build/
            ${CMAKE_SOURCE_DIR}/../flamingo-4.0/src/util/build/
            ${CMAKE_SOURCE_DIR}/../flamingo-4.0/src/listmerger/build/
            ${CMAKE_SOURCE_DIR}/../flamingo-4.0/src/filtertree/build/
          ) 
          
          # have cmake also build the filtertree module (if not built already)
          # the filtertree module will build util, common and listmerger
          add_subdirectory(../flamingo-4.0/src/filtertree/ ../flamingo-4.0/src/filtertree/)
          
          # GCC command line args
          add_definitions(-Wall -O3 -DDEBUG_TIMER_FANCY -DDEBUG_STAT -DED_MATRIX_DIM=2000)
          
          # create executable
          add_executable(searchapp ${APPLICATION_EXEC_SRC})
          add_dependencies(searchapp wrappers-lib filtertree-lib common-lib util-lib listmerger-lib)
          target_link_libraries(searchapp wrappers-lib filtertree-lib common-lib util-lib listmerger-lib rt)
          
          set(EXECUTABLE_OUTPUT_PATH "${CMAKE_CURRENT_SOURCE_DIR}/build/")
          

          The above is a very simple CMakeLists.txt. To compile your application, cd into /home/joe/searchapp/ and type:

          cmake .
          make
          

          This should compile your application and all necessary libraries and link them together properly.

          Let us test the application. The compilation should have placed your searchapp executable in /home/joe/searchapp/build. To run the application:

          cd '''/home/joe/searchapp/build'''
          ./searchapp
          

          You should see the following output (or similar):

          INPUTFILE: "/home/joe/flamingo-4.0/src/filtertree/data/female_names.txt"
          100% FILLING CONTAINER: 4000/4000; 0'0"/0'0"   
          100% INSERTING INTO INDEX: 4000/4000; 0'0"/0'0"   
          SIMILAR STRINGS: 
          kathryn
          kathrin
          kathrine
          katherin
          

          Congratulations, you have successfully created your first application using The Flamingo Package!

          Basic Usage

          Approximate string search can be performed in two basic steps: (1) building the index, and (2) answering queries using the index. We will now discuss the basic components for each of the steps at a high-level.

          1. Building The Index, Needed Components
            • String Container (stores the data strings on which you want to perform queries)
            • Gram Generator (decomposes strings into grams)
            • Indexer (builds the filter tree and the inverted lists, needs a String Container and a Gram Generator)
          1. Answering Queries Using The Index, Needed Components:
            • List Merger (solves the T-occurrence problem given a set of inverted lists and a merging-threshold)
            • Indexer (builds the filtertree and the inverted-lists, needs a String Container and a Gram Generator)
            • Searcher (answers queries, needs a List Merger and an Indexer)
            • Similarity Metric (represents the similarity metric to be used)
            • Query (contains the query string, the similarity metric and the similarity threshold)

          Refer to filtertree/src/example.cc for some advanced examples.

          Example Files

          Apart from reading this guide, we recommend you browse through the code of some example files. We have provided these files to help you understand how to use the library as quickly as possible.

          • filtertree/src/wrappers/example.cc
          • filtertree/src/example.cc
          • filtertree/src/perftest.cc

          Back to Index

          http://flamingo.ics.uci.edu/releases/4.0/docs/ListMergerDoc.html ListMergerDoc – Group

          Back to Index


          AppString > AppStringDoc

          1. ListMerger
            1. Introduction
            2. Overview
            3. Contributors
            4. References

          ListMerger

          Introduction

          This module provides list-merging algorithms for solving the T-occurrence problem in approximate string search.

          Overview

          The following list-merging algorithms are included:

          • HeapMerger - pop list-heads to a heap, push from heap and count occurrences of each element
          • ScanCount - uses one counter for every possible stringid (scan count array), traverses inverted lists and increments counts in the array
          • MergeOpt - separate long lists from short lists, for short lists use heap merge, for long lists do binary search on candidates from short lists
          • MergeSkip - like heapmerger, but uses the merging-threshold to skip elements on the lists
          • DivideSkip - combines MergeOpt and MergeSkip, skipping is used for the short lists, binary search is done on the long lists
          • OnDiskMergerSimple - reads inverted lists and runs DivideSkipMerger
          • OnDiskMergerAdapt - balances costs of reading inverted lists (from disk) and post-processing candidates (from disk)

          Contributors

          • Alexander Behm (design, main author)
          • Chen Li (design, implementation, project leader)
          • Jiaheng Lu (design, main author)

          References

          • Chen Li, Jiaheng Lu, Yiming Lu: Efficient Merging and Filtering Algorithms for Approximate String Searches, ICDE'08, April 2008

          Back to Index

          http://psearch.ics.uci.edu/about.html About PSearch
          psearch
          University of California, Irvine
          Powered by the iPubmed Project

          About PSearch Project

          Goal

          The goal of the PSearch Project is to make it easier to search for UCI People. It has a single input search box, which allows keyword queries on people name, UCInetID, telephone number, department, and title. It has the following features:

          • Supports interactive search: search as you type;
          • Allows minor errors in the keywords;
          • Supports synonyms, e.g., "William = Bill", and "OIT = Office of Information Technology";
          • Allows multiple keywords.

          We use the UCI Directory data provided by OIT.

          Contributors

          • Chen Li (Faculty)
          • Guoliang Li (Research Associate)
          • Rares Vernica (Student)
          • Shengyue Ji (Student)
          • Inci Cetindil (Student)

          Acknowledgements

          • We thank OIT for providing us the UCI Directory data.
          • PSearch is powered by the iPubmed Project, which is supported by the NSF CAREER Award, No. IIS-0238586, the NSF-funded RESCUE project, a Google Research Award, and a fund from CalIt2.
          • NIH grant 1R21LM010143-01A1

          Contact

          Send questions and comments to helpdesk AT ics DOT uci DOT edu. If you want to use a simlar interface for your applications, please feel free to contact us.

          http://www.ics.uci.edu/~kibler/javacourse/java.html Java

          Why Java?



          Java: a language for team programming

          Java Texts

          • The Java Programming Language by Ken Arnold and James Gosling.
            Covers fundamentals of language, but not important packages.
          • Computing Concepts using Java Essentials by Cay Horstmann.
            Covers Java for the beginning programming student. Used in 1A.
          • Core Java 2nd Ed by Cay Horstmann and Gary Cornell
            language + applets, user-interface, delegation event-model (JDK 1.1), and new graphical widgets (e.g. scrollpane).
          • Java in a Nutshell (2nd Edition) (JDK 1.1)
            A complete brief description of the language plus a list of the methods and classes in the JDK 1.1 packages. JDK 1.0 had 8 packages and JDK 1.1 has 23. Moreover the old packages have been extended. These new packages cover important extensions such as JavaBeans, reflections, serialization, JAR, and a new delegation event-model which replaces the old model.
          • Thinking in Java by Bruce Eckel.
            This text covers JDK 1.1 with some information about JDK 1.2. It is meant for the serious programmer who has already programmed in some language, preferably C++. I like this text a lot. He provides practical advice. The text covers the topics in Java in a Nutshell in greater depth.
          • Graphic Java 1.1: Mastering the AWT 2nd Ed.
            thorough discussion of components, lightweight components, custom components. More than you want to know. Good reference.
          • Symantic Visual Café Sourcebook by Cary Jardin and Pam Dixon. A complete guide to Creating Java Applets and applications with Visual Café . Note: only similar to Visual Cafe Pro.
          • For an up-to-date discussion of the Java and its use, see http://www.javasoft.com/.
          • Any book that covers JDK1.1 that you can learn from. Everyone learns differently.

          Java History

          • Sun (Gosling) creates Oak, a language for embedded systems.
          • Specification of virtual machine yields platform independence.
          • Internet access promotes multi-platform languages.
          • Oak becomes Java.
          • World goes crazy.
          • Warning: Java is changing.

          Object-Oriented Language History

          • 1967 Simula - perceived as simulation language
          • 1970's study: limited programmer memory/comprehension
          • 1980's Smalltalk - slow, graphical, totally object-oriented
          • 1980's Object Pascal (basis of Delphi) dynamic types, database and window event access
          • 1986 Eiffel: correctness
          • 1985 (first release) C++ : a better, object-oriented C
          • 1990 CLOS Common Lisp Object System
          • 1995 Sun released Java : multi-platform, maintenance

          Special Purpose Languages

          Matlab, Mathematica, SPSS, Latex, html, vrml, ...

          Evolution of Programming Languages

          • More than 1000 programming languages: Why?
          • User Problems --> Software Solutions --> Hardware Implementations
          • Machine Languages --> Assembly Language --> General Purpose Languages
          • Early Concerns
            • Efficiency
            • Code size
            • Compiler correctness
            • Compiler uniformity
          • Trends
            • addresses --> variable --> local variable --> records
            • instructions --> macros --> functions and procedures
            • run-time errors --> compile-time errors --> assertions+exceptions
            • code gets shorter
            • languages and compilers more complicated
            • increased functionality
            • increased information hiding via encapsulation
            • run-time increases
          • What have we learned?
          • Multiple Goals: Sometimes goals conflict, other times they support one another.
          • Language design decisions relate to a preference of one goal over another, e.g. automatic array bounds checking, pointer arithmetic, memory release, etc.
          • Where do people (cognitive abilities and foibles) fit in?
          • What's wrong/right with Java.

          General Purpose Programming Language Goals

          For a good discussion of programming language design issues see: Design and Evolution of C++ by Bjorne Stroustrup.
          For principles of Object-oriented design see

          Java summary

          • A Java program is a collection of classes definitions plus one special class, called the "driver".
          • Every class is a descendant of the class Object.
          • Java is Object-oriented, simpler than C++, safer than C++ (not always), multi-programmer, multi-platform, graphical, efficient (but slower than C++), comprehensible.

          What Java doesn't have

          Are these bad or good features?
          • Assertions (pre, post, invariant)
          • Multiple Inheritance
          • Genericity:
            • can't have types as arguments
            • e.g. can't have Vector of reals where compiler checks type.
          • Classes can't be created dynamically

          Warning: Java is not extension of C

          Java Differences with C

          • Most C expressions are legal in Java, but not all.
          • goto is a reserved word in Java, but it does not have a goto statement. Instead Java has a labelled break statement.
          • Java has the class String, which has over 30 methods and several constructors. The method length() returns the length of the string.X
          • You cannot do pointer arithmetic in Java.
          • You cannot free memory in Java. Instead Java uses garbage collection, which runs asynchronously in a separate thread.
          • You cannot pass functions as parameters in Java. Instead you define a function-object class and pass these objects around.
          • There are no global functions or variables in Java.
          • You cannot pass function pointers in Java.
          • Arrays are unusually objects in Java.
            • int[] myarray = new int[10]; creates an array of integers of size 10.
            • Arrays are created dynamically at run-time.
            • As is C, array indexing starts at 0.
            • myarray.length returns the size of the array.
            • Array bounds are checked at runtime.
            • Unlike most objects, you cannot inherit from arrays.

          Classes

          Inheritance

          Interfaces

          Applets

          User-Interface

          Event Handling

          Glossary

          • An Abstract method is a method prototype, i.e. one without a body.
          • Abstract class has at least one abstract method.
          • An interface specifies only abstract methods and static data members.
          • The signature of a method is the types of the arguments together with the method name. It does not include the return type.
          • The scope of a variable is the region in the program where the variable can be accessed.
          • The lifetime of a variable is the range of instructions where storage is bound to the variable.
          • Declaring a variable/object allocates no storage and notifies the compiler that the variable is defined elsewhere.
          • Defining a variable/object causes storage to be allocated and defines the meaning or interpretation of the storage.
          • Short-circuit evaluation means that boolean expressions are evaluate only as far as they need to be. Once the value is known, further evaluation stops.
          • Static members are globals to the class.
          • Max Gobble rule means the lexical analyzer defines a lexeme to be the longest string that matches a keyword. For example i+++j means i++ + j and not i + ++j.
          • A package is a related collection of classes. If no accessibility priviledges are specified, each member of the collection is visible to all within the package.

          Style Guidelines



          • Braces should line-up one under another.
          • Spaces should support parsing.
          • Comments should explain what a method does, not how.
          • For documentation use javadoc.
          • No magic constants.

          Design Guidelines



          • If code isn't correct, it is worthless.
          • Code should be understandable.
          • Code should be as efficient as it needs to be.
          • Code should be modifiable.
          • Use object decomposition, problem decomposition, and function decomposition.
          • Encapsulate.
          • Copy and edit suggests making a class.
          http://www.ics.uci.edu/~kibler/ics171/homeworks/Logic.htm untitled

          Logic Assignment:


          Translate the following into first order logic, then into clausal form

          1. All birds have feathers.
          a)
          b)
          2. Squigs have feathers
          a)
          b)
          3. What do you get from resolving 1b with 2b.


          4. Put BOTH these well-formed formulas (wffs) into clausal form: (Both means treat them as a pair of axioms about the world).

          a) for all x, y [ On(x,y) implies Above(x,y)] (1)
          b) for all x, y, z [ Above(x,y) & Above(y,z) implies Above(x,z)] (2)






          5. Using the clause in 4 plus the statements:
          On(B,A) (3)
          On(A,Table) (4)
          Prove (by refutation resolution) that Above(B,Table).
          Remember a refutation proof starts with the negation of the predicated to be proven, i.e. not Above(B,Table) (5)

          I have numbered each of the clauses so that you explain each step in the proof by saying: resolving clause #n with clause #m. Number new clauses in your own proof.

          To start you off:

          Not Above(B,y) or not Above(y,Table) #6 by resolving #2 and #5.

          There are several steps in the proof and you may need to search a while.
          http://www.ics.uci.edu/~kibler/ics171/ Index of /~kibler/ics171

          Index of /~kibler/ics171

          [ICO]NameLast modifiedSizeDescription

          [DIR]Parent Directory  -  
          [DIR]Lectures/01-Sep-2004 08:29 -  
          [DIR]RNLectures/28-Oct-2001 10:19 -  
          [DIR]homeworks/21-Aug-2004 05:52 -  
          [DIR]syllabus/07-Aug-2004 22:19 -  

          Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
          http://www.ics.uci.edu/~kibler/ics171/homeworks/LearningHwk.htm Learning with Weka

          Note: Start this homework early. You may have to use the computers at school but you can download the free open-source Weka software

          Also read over all the questions before you start, otherwise you may have to repeat some experiments.

          In this homework you will use the Weka software to analyze the iris data set, a standard data set used to evaluated statistical algorithms. This data set is provided with Weka.

          For each of the algorithms below, report the accuracies using 2-fold and 10-fold cross-validation.

            1. ZeroR which is the dumbest algorithm of all. It�s the baseline.

            2. k-Nearest-Neighbor (Ibk) with k = 1, k=3, and k = 5. In the IBL folder.

            3. j48: the decision tree algorithm. In the Trees folder.

            4. Part: the algorithm for generating rules. In the Rules folder.

            5. Naïve Bayes: a statistical approach using Bayes Rule. In the Bayes folder

            1. Do you expect that 2-fold or 10-fold CV will yield a higher estimate of the accuracy of the algorithm?
            2. Why?
            3. Does your data support this conclusion? Be specific.
          1. Which learning methods produced interpretable results?
          2. From the 10-fold CV data, order the algorithms by accuracies.
          3. For the remaining questions, only consider the decision tree algorithm with 10 fold CV. Report the confusion matrix for the decision tree algorithm
          4. List, in order, the classes predicted with highest precision, i.e. the probability that the example was of class "a" given that the algorithm predicted it was of class "a". Show how the probabilities were computed.
          5. List, in order, the classes predicted with highest recall, i.e. the probability that the example was predicted to be of class "a" given that it was of class "a". Show how the probabilities were computed.
          http://www.ics.uci.edu/~kibler/ics171/homeworks/Probability.htm untitled


          Probability Questions

          1. Suppose A is the event of getting at exactly one ace when dealt 5 cards from an ordinary deck of cards. Suppose B is the event of getting all hearts when dealt 5 cards. Compute the following probabilities. For questions a-d do not simplify the arithmetic computations. For e you need to do the calculation.
            a) P(A)
            b) P(B)
            c) P(A|B)
            d) P(A&B) (you may use Bayes Rule)
            e) Are A and B independent?

          2. From the text (new edition) pg 489, do problem 13.6. Be careful about the difference between BOLD P (a vector) and non-bold P. Here is the problem. Using the joint probability shown in Figure 13.3 (handed out in class) compute:
            a) P(toothache)
            b) P(Cavity)
            c) P(Toothache | cavity)
            d) P(Cavity | toothache or cavity)
          3. From the text (new edition) pg489, do problem 13.8. For convenience, I've rewritten the problem. Be careful that you use all the information.
            After your yearly checkup, the doctor has bad news and good news. The bad news is that you tested positive for a serious disease and that the test is 99% accurate (i.e., the probability of testing positive when you do have the disease is 0.99, as is the probability of testing negative when you don't have the disease). The good news is that this is a rare disease, striking only 1 in 10,000 people. Why is it good news that the disease is rare? What are the chances that you actually have the disease?
            Hint: Besides Bayes Rule, recall that P(A)+P(not A) = 1. This extends to P(X|Y)+P(not X|Y) =1.
          http://www.ics.uci.edu/~kibler/ics171/homeworks/SearchCoding.html Local Improvement and Exhaustive Search for the Traveling Salesman Problem (TSP)

          In this assignment you will code two different ways of solving the traveling salesman problem. First you read in a file of 100 cities that will be posted in a masterhit file for the course. Each line of the file will contain two integers, separated by a blank, that represent the position of a city. This list of cities represents 100 problems where problem n is to find a route through all the cities from 1 to n.

          1. Write an exhaustive search algorithm to solve TSP. Your program probably will not be able to solve problems much beyond 10. For each problem that your program can solve in less than 1 minute (approximately) of cpu time, report the cost of path that your problem solver finds. So the output of your program is a table where each row is number of cities followed by cost of tour.
          2. Write an two iterative improvement problem solvers for TSP. Report the results of these algorithm for each problem that the exhaustive problem solver could solve. Also report how well iterative improvement problem solvers do do on problems of size 10, 20, up to 100 cities. So in this case the output of the program is two tables.
          Algorithm Notes:

          For the exhaustive search I recommend using depth first search or simply writing a program to compute all permutations. Breadth first search algorithms will likely use too much memory although a use of A* may work. You can find Java programs for permutations on the web. If you use such a program, you should credit the original authors. In any case you will need to modify the algorithm to suit this problem.

          The iterative improvement algorithms differ by the heuristics they use. One algorithm uses the "move" heuristic while the other algorithm uses the "uncross" heuristic. The "move" heuristic moves a city into a new position in the tour if it improves the tour length. The "uncross" reverses a subpath of the tour if that operation improves the tour length.

          Extra Credit: No extra credit is allowed on late homework. Provide a graphical user interface that allows you to see the tours. This is non-trivial. The graphical interface should display the route, the cost of the route, allow the user to choose the search method, and show some of the improved paths (under user control). http://www.ics.uci.edu/~kibler/ICS174/Assignment8.html Assignment 8. Disease Diagnosis
          In this assignment you will apply standard Machine Learning algorithms as implemented in the Weka suite to proteomic data. Some classifiers make additional unstated assumptions about the data. If that is not satisfied, the algorithm bombs. You may need to try several classifiers.

          1. Weka should be installed on the ICS machines. If you want to run on your own machine, use any search engine on Weka and download the free, open source software. Every time I download the software, it is a bit different. They change the interface and update the algorithms.
          2. Get the file mouse.arff which is on masterhit. This is a file of proteomic data generated by a Ciphergen machine at UCI in Steve Lipkin's laboratory.
          3. Run the classifiers Naive Bayes, IB1, and J48 and one more classifier(of your choice) and report the results on predicting the disease.
          4. Turn in a *.doc file with the following information. The algorithms should be evaluated via 10-fold cross-validation, unless this is too expensive. Do not simply paste the results from Weka into a *.doc file. Weka produces a lot of output. Only include information that you interpret. At a minimum you should report the generalization accuracy, precision, and recall for each algorithm, which algorithm performs best, and, if possible, why. These results can all be computed from the confusion matrix. Also for each algorithm give a short description (a few sentences) of what the algorithm computes. You may need to do a little background reading for this, but much of this was covered in ics171. You can also find information on the web and in the documentation for Weka.
          http://www.ics.uci.edu/~kibler/ICS174/Assignment5.html Assignment 5. Hierarchical Clustering This assignment requires that you use the similarity matrix computed assignment 3. We will provide the matrix for consistency. Implement (in Java or Python) and turn in the following hierarchical clustering program.

          Part 1. Pseudo-Code for Hierarchical Clustering.
          1. Initialize each cluster to be a singleton. I suggest that you use the java utility BitSet to do this. So initially you will have 10 BitSets, each with a single bit turned on. You might keep these BitSets is a List or ArrayList(that will be more convenient than an array).
          2. While more than one cluster exists do:
          3. Find the two closest clusters. The similarity between two clusters is the average similarity between all pairs, one member from each of the clusters. The similarity between any two elements is given by the similarity matrix.
          4. Now output the elements of the two closest clusters and their union. (No change to any data structures).
          5. Remove one of the clusters and replace the other by the union of the two.
          6. back to the beginning of the while loop
          Part 2.
          1. Put the output of your program in a *.doc file.
          2. What is the time complexity for finding the distance between two clusters, in the worse case?
          3. What is the time complexity for finding the two closest clusters?
          4. What is the time complexity for the entire algorithm?
          5. In your clustering there is one time that a 5-cluster (a cluster with 5 elements) is merged with a 3 cluster. Looking back at the original virus file, and only at the year property of each virus, what property is true for elements of the 5-cluster? What property is true for all elements in the 3-cluster?
          http://www.ics.uci.edu/~kibler/ICS174/Assignment6.html Assignment 6. Almost Markov Modeling In this coding assignment you will use 3rd order statistic to model the putative genes "ManyYeastGenes.txt" deposited in Masterhit. Using the model you will then predict whether some sequences are genes are not. The sequences are only pieces of DNA and do not contain the usual stop and stop sequences. You should turn in the code (remember to concatenating all *.java files into one *.java file or zip them) and the output of the program in a separate *.doc file.
          1. First output: the model
            To build the model you will read in sequences in fasta format. From this data you should compute the third-order statistics, i.e. frequency of each of the 64 codons, or triple of nucleotides. In building this model you are given the correct reading frame. Your program should output these frequencies in a readable way, for example: aaa 16 aac 21 etc.
          2. Second output: the predictions of the Model on the strings used to build the model.
            To score a sequence versus the model compute the correlation of the codon distributions. More specifically let x be the codon frequencies of the model and y codon frequencies of the string. Each of these is a length 64 vector. The correlation of x and y is x dot y/ squareRoot((x dot x) * (y dot y)). Dot is the inner product of two vectors. Did you notice anything unusually in the output?
          3. Third output: On masterhit there will five sequences, Unknowns.txt in fasta format. Give the scores relative to your model for each sequence. Since the reading frame is not known for the sequence, you need to compute the score for each reading frame. You need not compute the scores for the reverse complement, so only three reading frames need to be consider. From these scores, identify which sequences are likely to be genes and which are not.
          http://www.ics.uci.edu/~kibler/ICS174/Assignment2.html Assignment 2 There are many version of blast searches. The simplest are:
          • blastX : computes all 6 ORFs and matches them against a chosen protein database, such as NR.
          • blastN: searches the given ORF against a chosen nucleotide database, such as EMBL.
          • blastp: search a sequence of amino-acids against a chosen database.
          1. Read and know Chapter 2. Your next assignment will involve coding a similarity search.
          2. I will provide everyone with the three ORFs in the file ThreeMysteryOrfs.txt in Masterhit for the course.. Go to the Yeast database (http://mips.gsf.de/genre/proj/yeast/index.jsp) and try to identity each ORFs. To do this look at the first 20,000 bps of chromosome I. Hand in (I mean deposit) a description of each gene, as indicated below.
          3. Using basic blast (http://www.ncbi.nlm.nih.gov/BLAST), run blastx on each of the orfs. Specify that your sequence is dna and that you want to search the NR data base. NR stands for non-redundant, however it is redundant. Do not change any of the defaults. Depending on the load, this may take 1/2 hour. Do not wait till the last minute or the load can be very bad. Use the three top hits from blastx (if there are 3 hits) and describe the genes found as before. To get a description follow the first hot-link provided.
          4. Again use basic blast, but this time run blastn on the three mystery orfs. Again describe the genes that are found, as below. Only use the top most hit and the first hot-link. In this case you need to run blastn on each sequence separately. Select the database EMBL.
          Example gene description: Fields are only filled in if appropriate information is found.
          • location:
          • gene name(s):
          • gene product(s): These may be hypothetical, but note that.
          • gene function(s): These may be hypothetical, but note that.
          Note: Each of questions 2, 3, 4 require that you describe the genes. You may get different descriptions from the different tools. http://www.ics.uci.edu/~kibler/ICS174/Assignment7.html Assignment 7. Discoverying Regulatory Elements
          In this assignment you will use some of the programs at Jacques Van Helden's website (http://rsat.ulb.ac.be/rsat/) to discover regulatory elements.
          1. For the genes, download the 800 upstream region (select -800 to -1) for the genes in the MET family. Gene names are entered 1 per line. The organism is Saccharomyces cerevisiae (yeast). Here are the names:
            MET3 MET2 MET14 MET6 SAM1 SAM2 MET1 MET30 MUP3
          2. Use ogligo-analysis with size 6 and report the significant elements that you found. Do not present all the results of the analysis.
          3. Repeat the last question, but this time select Markov-Model of order 2 as the background model. If you get no answers, can you explain why. If it finds anything, does it correspond to anything found previously.
          4. Repeat the last question but this time select Markov-Model of order 3 as the background model.
          5. On the same file, run Gibbs sampling and report the top pwm. Compute and report the consensus sequence corresponding to this matrix. Does it match with any regulatory elements that you found by other search methods?
          http://www.ics.uci.edu/~kibler/ICS174/Assignment1.html Assignment 1
          1. Part 1. Write a Java or Python program to find candidate genes in a DNA sequence of about 20,000 base pairs. The file is in the Files directory of masterhit\instructional\ics-174\files and called MysteryDNA.txt. The definition of a candidate gene is a subsequence beginning with atg, ending with taa, tag, or tga, having no other stop codons interior to the sequence in the same open reading frame, and having a length of at least 60 codons. Note and warning: the beginning atg establishes the reading frame. You only check codons that are a multiple of 3 away from the start codon, i.e. codons in the proper reading frame. There may be other atg's in the sequence. The output of the program should be a list of four numbers, one per line. This quadruple consists of
            1. the start codon position where you count, like most humans, from 1. You count the number of nucleotides. It would be senseless to count codons since no reading frame is established. That's the way biologists count.
            2. the length in codons, including the start and end codon. Also we continue to count from 1.
            3. The probability of seeing the candidate gene. This is the product of the probability of a start codon times the probability of the number of non-stop codons times the probability of a stop codon. Here we assume (and it is not true) that each codon is equally likely.
            4. The expected number of such candidate genes in the entire given dna sequence. This is the product of the number of possible places the candidate gene could occur times the probability of the candidate gene.
            You need only deal with candidate strings in the given dna sequence and need not worry about candidate on the other strand (the reverse complement).
          2. Part 2. In a separate document (text or word) submit the answers to the following questions.
            1. A pseudo-code description of the algorithm.
            2. The output of your program.
            3. A worse case time-complexity analysis, i.e. given the O notation for the time complexity.
            4. A description of a string of length n where the time complexity is O(n^2).
            5. A description of a string of length n where the time complexity is O(n).
            6. The reverse complement of the start and stop codons.
            7. A description, similar to the one in part 1, of what you would search for if you wanted to find candidate genes on the complementary DNA strand.
          http://www.ics.uci.edu/~kibler/ICS174/Assignment3.html Assignment 3. Similarity a la Needleman-Wunsch
          As in assignment 1, in this assignment your deliverables consists of two two parts. You need to hand in a) the code for computing the global similarity of a family of dna strings and in a separate document, the output of your program together with the answers to a number of questions given below.
          1. Write a Java or Python program to a) read in a fasta file of strings, b) compute the global similarity between any two strings and c) print out the similarity matrix for all pairs of strings. Deposit this program in your folder. I thought writing the program to read in the strings was harder than the program for global similarity. In the worse case and with a loss of credit, you can simply create an array of strings where you hard code the strings.
          2. From the masterhit course site get the file of 10 viruses. These are in fasta format. Use the settings gap = -1, mismatch = -1, and match = +1. With these settings compute the similarity matrix (10 by 10) and put the matrix in a *.doc file. Add to the word file the answers to the following questions.
          3. Which two strings have the greatest similarity and what is their similarity?
          4. Which two strings have the least similarity and what is their similarity.
          5. Describe strings s1 and s2 of lengths n and m which would have maximum similarity? Use the scoring scheme where the gap cost is -1. What would their similarity score be?
          6. Describe strings s1 and s2 of lengths n and m which have minimum similarity? Again use the scoring scheme where the gap cost is -1. What would their similarity be?
          http://www.ics.uci.edu/~kibler/ICS174/Assignment4.html Assignment 4. Multiple Sequence Alignment
          You can search for ClustalW or use the program at http://www.ebi.ac.uk/clustalw/. You should not change any defaults. It is not an acceptable excuse to say that ClustalW was unavailable when you wanted to use it. Therefore do this assignment early.
          1. On the same viral sequences as the last assignment, run the ClustalW program to produce a multiple alignment. In your *.doc file, put the alignment of the 10 viruses that end with position 300. This is just part of entire output of ClustalW.
          2. Columns in the alignment are marked with a star, colon, period and a blank. Select the columns corresponding to the first occurrence of each of these symbols and form the Probability Weight Matrix. The full probability weight matrix for these 4 columns would have 20 rows, but you can simplify your answer to only include those amino acids that have varying probabilities plus an extra row for all the rest.
          3. What is the entropy of each column. Entropy was defined in ics171 and is sum of -pi*log(pi) where the log is taken base 2 and pi is the probability of entry i, in this case of amino acid i. Recall that we define 0*log(0) as 0 since the limit e*log(e) as e goes to 0 is 0.
          4. What is the match score of the sequence NNNN with this PWM. Recall that the match score is the sum of the corresponding probabilities.
          5. What sequence of four amino acids would yield the highest score and what is that score?
          6. Does the ClustalW program guarantee to find the optimal multiple alignment?
          http://www.ics.uci.edu/~pattis/ICS-33/frameindex.html ICS-33

          ICS-33
          Winter 2016


          * Fact Sheet
          * Announcements
          * Email Archive
          * MessageBoard Forums
          * Syllabus

          * Lecture Schedule/Notes
          * Weekly Schedule
          * Programming Assignments

          * Handouts (General)
          * Course Software
          * Sample Programs

          * Python 3.4 Language Reference
          * Python 3.4 Library Reference
          * Course Library Reference

          * Solutions (in EEE Drop Box)
          * Grades(zipped .xlsm file)
          * Find ID Hashed (grade key)

          * Send Anonymous Email
              to INSTRUCTOR

          * Checkmate Homework Dropoff

          Local Site Search

          If any links are inoperative, please inform your instructor ASAP.


          ICS-33 Home Page

          pattis@ics.uci.edu
          http://www.ics.uci.edu/~pattis/ICS-33/fact.html Fact Sheet

          Fact Sheet

          ICS-33: Intermediate Programming


          Staff Information

          InstructorRichard Pattis
          Email
          pattis@ics.uci.edu
          AIM Handlerichardepattis
          Office Location    DBH 4062 (Bren Hall)
          Office Phone (949) 824-2704
          Office Hours Mon 12:00pm - 1:00pm and 3:00 pm - 4:00pm
          Tues 12:00pm - 2:00pm
          Wed 12:00pm - 1:00pm (except when announced) and 3:00pm - 4:00pm
          Thr None
          Fri 12:00pm - 1:00pm
          Course Motto   ABC: Always Be Coding


          Rich Pattis
          pattis@ics.uci.edu
          Instructor






          Abhisaar Sharma
          abhisaas@uci.edu
          TA Labs: 1 and 4
          Hossein Tajik
          tajikh@uci.edu
          TA Labs: 2 and 6
          Gregor Urban
          gurban@uci.edu
          TA Labs: 3 and 5






          Raam Nachiappan
          Lab 1 Tutor
          Anthony Luong
          Lab 3 Tutor
          Jenny Zeng
          Lab 3 Tutor






          John Li
          Lab 4 Tutor
          Juston Lin
          Lab 5 Tutor
          Brandon Cabrera
          Lab 6 Tutor

          Course Help

          Instructor Office Hours: Please feel free to drop by any time during my office hours, without an appointment (these are open office hours). If you cannot make any of these times and want to schedule an appointment at some other time, e-mail me to arrange one (or, if the question is small, just send me e-mail asking it; also see MessageBoard forums below). I cannot provide much debugging help if there are many students waiting to see me.

          Instructor Email: I answer e-mail periodically throughout the day (from about 6:30am to 10:00pm). If you have a question that would be of interest to the entire class, please post it on the appropriate MessageBoard Forum (see below).

          Instructor Online Hours: I will hold online evening office hours every weekday evening before our class meets (Monday-Thursday, 9:00pm to 10:00pm) where I am reachable by my AIM handle, richardepattis.

          MessageBoard Forums: I have created a Message Board for this class and started five MessageBoard Forums (see below). I prefer students ask questions on these Forums so that everyone (staff and other students) can see the questions and everyone can participate in answering the questions (and exploring the answers). I expect students to read these Message Boards regularly to keep current on their discussions. Avoid duplicate posts: before posting, first check whether another student has already posted on that topic. When you do post a question, choose a clear and descriptive title.

          1. Python and Eclipse: Ask/answer/discuss questions about the Python Programming Language, the Eclipse Integrated Development Environment, or how to use them together.

          2. Lecture Material: Ask/answer/discuss questions about the readings and the materials discussed in class.

          3. Quizzes and Exams: Ask/answer/discuss questions about the quizzes and exams (written and programming). DO NOT post solutions or any code, but feel free to ask about/discuss all other aspects of the quizzes and exams (including asking for clarification, input/output examples, etc: be specific).

          4. Programming Assignments: Ask/answer/discuss questions about the programming assignments: specification, relevant Python, etc. DO NOT post solutions or any code, but feel free to ask about/discuss all other aspects of the programming assignments (including asking for clarification, input/output examples, etc: be specific).

          5. Find Programming Partners: Find a programming partner. Relevant information is your Lab (partners should at least have labs meeting at the same time), approximate skill level (best to match with someone your own level), work habits (e.g., prefer to work days, evenings, weekends; do/don't want to work early to get extra for early submissions), preferred location to work (if your dorm/home, say where that is), ...
          Feel free to ask questions and answer other students' questions on these forums (subject to the prohibition of posting code for Quizzes and Exams and Programming Assignments). I will read all Forums early every morning and answer all unresolved questions.

          In past quarters, the message board/forum mechanism have seen a good amount of traffic: course material has been clarified, questions have been asked and answered, and many interesting discussions have ensued. But the efficacy of these discussions depends on your participation. If the need for other forums arise, I will create them: feel free to suggest other forums to me.

          Course Email: The instructor, staff, and all students registered for the course can send email to everyone else by emailing the address ics33-W16@classes.uci.edu. All messages sent via this email address will be archived on the Email Archive As the instructor, I will frequently use this mechanism to broadcast timely information to all the students. Students should use this email address more selectively: mostly you should be using the forums mentioned above to ask questions, so all students can see your questions/comments and participate in the discussion. One reasonable student use of this email list is to send email if Checkmate (the program submission system) appears to be down; by sending class email on this topic, I will learn about the problem and everyone will know I know about the problem. In such a case (when I bring Checkmate back up), I'll send a follow-up email message to everyone.

          Finally, remember to read the Announcements link daily, on the course web; I tend to post long-lived information here, rather than via the forums or email.


          Lecture/Lab Meeting Places and Times

          Lecture/Lab(s) Staff Classroom Days Start Stop
          Lecture A Rich Pattis DBH 100 MWF 2:00 pm 2:50 pm
          Lab 1 Abhisaar Sharma and Hossein Tajik ICS 189 TuTh 8:00 am 9:50 am
          Lab 2 Cancelled
          Lab 3 Gregor Urban ICS 189 TuTh 10:00 am 11:50 am
          Lab 4 Hossein Tajik ICS 192 TuTh 10:00 am 11:50 am
          Lab 5 Gregor Urban ICS 189 TuTh 12:00 pm 1:50 pm
          Lab 6 Abhisaar Sharma ICS 192 TuTh 12:00 pm 1:50 pm

          It is well known (but often underappreciated) fact that consistent attendence of lectures/lab is strongly correlated with good course performance; therefore, I expect all my students to attend lectures and labs regularly. Plan on arriving punctually -I may make important announcements at the start of a lecture- paying attention, and treating your classmates with respect.

          If you do miss a lecture, your first point of contact should be other students who attended the lecture to determine what you missed and how you can catch up (see the course web too). One of the most "bewildering" questions faculty hear is, "I missed lecture yesterday; did you say or do anything important?"


          Bottom Line

          As a lecturer at UCI, my primary responsiblity is teaching; my primary activities are preparing course materials, teaching in class, interacting with students (in class, during [online] office hours, on MessageBoard Forums) and grading.
          http://www.ics.uci.edu/~pattis/ICS-46/fact.html Fact Sheet

          Fact Sheet

          ICS-46: Data Structure Implementation and Analysis


          Staff Information

          InstructorRichard Pattis
          Email
          pattis@ics.uci.edu
          AIM Handlerichardepattis
          Office Location    DBH 4062 (Bren Hall)
          Office Phone (949) 824-2704
          Office Hours Mon 12:00pm - 1:00pm and 3:00 pm - 4:00pm
          Tues 12:00pm - 2:00pm
          Wed 12:00pm - 1:00pm (except when announced) and 3:00pm - 4:00pm
          Thr None
          Fri 12:00pm - 1:00pm


          Rich Pattis
          pattis@ics.uci.edu
          Instructor










          Filjor Broka
          fbroka@uci.edu
          TA
          Igor Burago
          iburago@uci.edu
          TA
          Myoungseo (Matthew) Kim
          myoungsk@uci.edu
          Reader
          Saeed Mirzamohammadi
          saeed@uci.edu
          Reader
          Karthik Prasad
          prasadkr@uci.edu
          Reader












          Madeline Chan
          Tutor
          Shannon Hu
          Tutor
          Binh Le
          Tutor
          Tim Nguyen
          Tutor
          Neilson Wong
          Tutor
          Han Zhao
          Tutor

          Course Help

          Instructor Office Hours: Please feel free to drop by any time during my office hours, without an appointment (these are open office hours). If you cannot make any of these times and want to schedule an appointment at some other time, e-mail me to arrange one (or, if the question is small, just send me e-mail asking it). I answer e-mail periodically throughout the day (from about 6:30am to 10:00pm). If you have a question that would be of interest to the entire class, please post it on the appropriate MessageBoard Forum (see below).

          Instructor Online Hours: I will hold online evening office hours every weekday evening before our class meets (Monday-Thursday, 9:00pm to 10:00pm) where I am reachable by by AIM handle richardepattis.

          MessageBoard Forums: I have created a Message Board for this class and started five MessageBoard Forums. I prefer students ask questions on these Forums so that everyone (staff and students) can see the questions and everyone can participate in answering the qusetions (and exploring the answers). Before posting on a topic, check whether another student has already posted on that topic.

          1. C++ and Eclipse: Ask/answer/discuss questions about the C++ Programming Language, the Eclipse Integrated Development Environment, or how to use them together.

          2. Lecture Material: Ask/answer/discuss questions about the readings and the materials discussed in class.

          3. Quizzes and Exams: Ask/answer/discuss questions about the quizzes and exams (written and programming). DO NOT post solutions or any code, but feel free to ask about/discuss all other aspects of the quizzes and exams (including asking for clarification, input/output examples, etc: be specific).

          4. Programming Assignments: Ask/answer/discuss questions about the programming assignments: specification, relevant Python, etc. DO NOT post solutions or any code, but feel free to ask about/discuss all other aspects of the programming assignments (including asking for clarification, input/output examples, etc: be specific).

          5. Find Programming Partners: Find a programming partner. Relevant information is your Lab (partners should at least have labs meeting at the same time), approximate skill level (best to match with someone your own level), work habits (e.g., prefer to work days, evenings, weekends; do/don't want to work early to get extra for early submissions), preferred location to work (if your dorm/home, say where that is), ...
          Feel free to ask questions and answer other students' questions on these forums (subject to the prohibition of posting code for Quizzes and Exams and Programming Assignments) I will also read all Forums early in the morning and answer unresolved questions. When you post a question, choose the title line carefully; first check whether that question has already been asked.

          In past quarters, the message board/forum mechanism have seen a good amount of traffic: course material has been clarified, questions have been asked and answered, and many interesting discussions have ensued. But the efficacy of these discussions depends on your participation. If the need for other forums arise, I will create them: feel free to suggest forums to me.

          Course Email: The instructor, staff, and all students registered for the course can send email to everyone else by emailing the address ics46-W15@classes.uci.edu. All messages sent via this email address will be archived on the Email Archive As the instructor, I will frequently use this mechanism to broadcast timely information to all the students. Students should use this email address more selectively: mostly you should be using the forums mentioned above to ask questions, so all students can see your questions/comments and participate in the discussion. One reasonable student use of this email list is to send email if Checkmate (the program submission system) appears to be down; by sending email I will learn about the problem and everyone will know I know. In such a case (when I bring Checkmate back up), I'll send a follow-up email message to everyone.

          Finally, remember to read the Announcements link daily, on the course web; I tend to post long-lived information here, rather than via the forums or email.


          Lecture/Lab Meeting Places and Times

          Lecture/Lab(s) Classroom Staff Days Start Stop
          Lecture SSLH 100 Rich Pattis MWF 11:00 am 11:50 pm
          Common Lab 0 ICS2 162 Binh Le MF 8:00 am 9:50 am
          Common Lab 1 ICS2 162 Karthik Prasad and Han Zhao MW 12:00 noon 1:50 pm
          Common Lab 2 ICS2 162 Shannon Hu and Saeed Mirzamohammadi (M only) MW 2:00 pm 3:50 pm
          Common Lab 3 ICS2 162 Tim Nguyen MW 4:00 pm 5:50 pm
          Common Lab 4 ICS2 162 Igor Burago and Neilson Wong TuTh 12:00 noon 1:50 pm
          Common Lab 5 ICS2 162 Myounseo(Matthew) Kim and Madeline Chan TuTh 2:00 pm 3:50 pm
          Common Lab 6 ICS2 162 Filjor Broka and Saeed Mirzamohammadi (Tue only) TuTh 4:00 pm 5:50 pm
          It is well known (but often underappreciated) fact that consistent attendence of lectures/lab is strongly correlated with good course performance; therefore, I expect all my students to attend lectures and labs regularly. Plan on arriving punctually -I may make important announcements at the start of a class- paying attention, and treating your classmates with respect.

          If you do miss a class, your first point of contact should be other students who attended the class to determine what you missed and how you can catch up (see the course web too). One of the most "bewildering" questions faculty hear is, "I missed class yesterday; did you say or do anything important?"


          Bottom Line

          As a lecturer at UCI, my primary responsiblity is teaching; my primary activities are preparing course materials, teaching in class, interacting with students (in class, during [online] office hours, on MessageBoard Forums) and grading.
           
          http://www.ics.uci.edu/~pattis/ICS-46/frameindex.html ICS-46

          ICS-46
          Winter 2016


          * Fact Sheet
          * Announcements
          * Email Archive
          * Forums on MessageBoard
          * Syllabus

          * Lecture Schedule/Notes
          * Weekly Schedule
          * Programming Assignments

          * Handouts (General)
          * Course Software
          * Sample Programs

          * C++ Language Reference
          * C++ STL@cplusplus
          * GoogleTest

          * Solutions (in EEE Drop Box)
          * Grades(zipped .xlsm file)
          * Find ID Hashed (grade key)

          * Anonymous Email to INSTRUCTOR

          * Checkmate Homework Dropoff

          If any links are inoperative, please inform your instructor ASAP.


          ICS-46 Home Page

          pattis@ics.uci.edu
          http://www.ics.uci.edu/~kobsa/courses/courses.html Alfred Kobsa -- Course Information

           

          Course information

          Alfred Kobsa, Room 5092 Bren Hall
          <lastname> @ uci.edu

          Hours in Fall 2015: after class, or Wednesdays 2-3:00pm (send email beforehand)


           
          Quarter Course Times and Location Discussion




          Fall 2014 INF 241: Introduction to Ubiquitous Computing
          INF 231: User Interface Design and Evaluation


          MoWe  3:30-4:50pm   DBH 1300
          MoWe  5:00-6:20pm   DBH 130
          none
          Group 3: Mo 11-12:50pm, ICS 225
          Group 1: Tu 4-5:50pm, ICS 253
          Group 2: Th 4-5:50pm, ICS 253
          Spring 2015
          INF 132: Project in HCI and User Interfaces
          INF 235: Advanced User Interaction Architectures
          MoWe 11am-12:20pm, SH 128
          MoWe 2:00-3:20pm, SSL 168
          Fr 1-1:50pm, ET 202
          Fall 2014
          INF 231: User Interface Design and Evaluation MoWe 11am-12:20pm, ICS 180
          Tu 4-5:50pm, SSL 117
          Th 4-5:50pm, SSL 117
          We 4-5:50pm, ICS 209
          Spring 2014
          INF 132: Project in HCI and User Interfaces MoWe  9:30-10:50pm   PCB 1200 Tue  6-6:50pm, ET 204
          Winter 2014
          INF 295: Privacy in Electronic Environments
          MoWe 11am-12:20pm, SSTR 102

          Fall 2013
          INF 231: User Interface Design and Evaluation TuTh 11am-12:30pm, DBH 1300 Mo 12:00-1:30pm, RH 192
          Tu 2pm-3:30pm, RH 192
          Spring 2012
          INF 132: Project in HCI and User Interfaces MoFr 5:00-6:20pm, ICS 174 11-11:50am, DHB 1200
          Winter 2012
          INF 131: Human-Computer Interaction TuTh  9:30-10:50am, ICS 174 M 4-4:50pm, ICF 103
          Fall 2011
          INF 231: User Interface Design and Evaluation TuTh  11am-12:20pm, DBH 1300 Th 2:00- 3:50p  ICS 225
          F 12:00- 1:50p  ICS 209
          Spring 2011
          INF 132: Project in HCI and User Interfaces MW 3:30-4:50pm, PCB 1300
          Fr 11-11:50am, PCB 1300
          Winter 2011
          INF 131: Human-Computer Interaction
          TuTh  3:30- 4:50pm, PSCB 140
          Mo 4-4:50pm, DBH 1500
          Fall 2010
          INF 231: User Interface Design and Evaluation TuTh  3:30- 4:50pm, DBH 1200

          Spring 2010
          INF 233: Knowledge-Based User Interfaces
          TuThu 12:30-1:50pm ICS 225

          Spring 2010
          INF 132: Project in HCI and User Interfaces TuTh 2-3:20pm DBH 1300
          Fr 11-11:50am, DBH 1300
          Winter 2010 INF 131: Human-Computer Interaction TuTh 2:00-3:20  DBH 1300 Mo 3:00-3:50 ICF 103
          Spring 2009 INF 132: Project in HCI and User Interfaces TuTh  8-9:20  DBH 1300 Mo 9-9:50  DBH 1300
          Winter 2009 INF 131: Human-Computer Interaction TuTh  11-12:20  ICS 180

          Thu 8-8:50, ICS 180

          Fall 2008 INF 231: Human-Computer Interaction (Edition A) TuTh  11-12:20  ICS 253

          Varies, see syllabus

          Spring 2008 INF 132: Project in HCI and User Interfaces TuTh 9:30-10:50  ICS180 Mo 9-9:50  ICS 180
          Winter 2008 INF 131: Human-Computer Interaction TuTh  11-12:20 DBH 1300 Wed 10-10:50am ICS 180
          Fall 2007 INF 231: Human-Computer Interaction TuTh  11-12:20 DBH 1423 Varies, see syllabus
          Spring 2007 INF 295: Privacy in Electronic Environments TuTh 9:30-10:50 ICS 243  
          Winter 2007 132/105: Project in HCI and User Interfaces

          TuTh  11-12:20 ICF 101

          Fr 2-2:50 SE2 1306

          Fall 2006 131/104: Human-Computer Interaction
          231/205: Human-Computer Interaction

          TuTh   8:00- 9:20 CS180
          TuTh 12:30-1:50 CS 243

          Fr 12-1:50 CS 192
          F  8-8:50 CS 180

          Spring 2005 Uni Stu 3: Computers in our Everyday Lives Fr  2:00-2:50pm  CS 432  
          Fall 2004 ICS 104: Human-Computer Interaction
          ICS 205: Human-Computer Interaction

          TuTh   8:00- 9:20 ELH 110
          MW  4:00-5:20pm PSCB 120

          MWF  4-4:50pm SST 220b
          F  4-4:50pm SST 220b

          Spring 2004 Freshmen Seminar: Theory of Science for Students of Information and Computer Science Fr 9:00-9:50  
          Winter 2004 ICS 280: Advanced Topics in Information Visualization WF 8:30-9:50 ELH 110  
          Fall 2003 ICS 104: Human-Computer Interaction
          ICS 205: Human-Computer Interaction

          TuTh   8:00- 9:20 ELH 110
          TuW  9:30-10:50 SST120

          MWF   9:00- 9:50 ET 204

          Spring 2003 ICS 206: Knowledge-Based User Interfaces Tu   4:30- 7:20p CS 253  
          Winter 2003 ICS 105: Project in Human-Computer Interaction and Interfaces
          TuTh 9:30-10:50 ICF 101 MWF 3:00-3:50  EIC 128
          Fall 2002

          ICS 104: Human-Computer Interaction
          ICS 205: Human-Computer Interaction

          TuTh  2:00- 3:20p SH 174
          MW    9:00-10:20 ELH110
          MWF 4:00- 4:50p PSCB 140
          Spring 2002

          ICS 205: Human-Computer Interaction
          ICS 280: Advanced Topics in Information Visualization

          MW 12:30-1:50 PSCB 230
          TTh 12:30-1:50 CS 213

          MW 2-3 p.m.
          Winter 2002 ICS 105: Project in Human-Computer Interaction and Interfaces

          MWF 2:00-2:50, ELH 110

          MW 3:00-3:30
          Fall 2001 ICS 104: Human-Computer Interaction

          MW 2-3:20, SE2 1304
          MWF 4-4:50, ET 202

          MW 3:30-4:30 p.m.
          Spring 2001 ICS 280: Advanced Topics in Information Visualization T Th 9:30-10:50, PSCB 12 T Th 11:00-12:00
          Winter 2001 ICS 205: Human-Computer Interaction MW 9:30-10:50, IERF B011 M W 11:00-12:00
          Fall 2000 ICS 105: Project in Human-Computer Interaction and Interfaces   T Th 1:50-2:30 
          Spring 2000 ICS 205: Human-Computer Interaction   MW 1:50-2:30 
































































































          http://www.ics.uci.edu/~kobsa/kobsa-publi.htm Publications Alfred Kobsa

          Open Access Repository persuant to the UC Open Access Policy
          Est. 1995.  Last Update: Aug. 3, 2015
           


           

          Publications Alfred Kobsa

          Orcid: 0000-0002-7691-4955
          SCOPUS: 6701707333

           

          • Books authored
          • Books and Journals edited
          • Proceedings Volumes edited
          • Refereed Journal Papers
          • Refereed Conference Papers
          • Refereed Book Contributions
          • Refereed Workshop Contributions
          • Major Contributions to Newsletters
          • Other Publications
           
           

          A. Books authored

          A2. Kobsa, A. (1985): Benutzermodellierung in Dialogsystemen (User Modeling in Dialog Systems). Berlin - New York: Springer. [Print] [DOI 10.1007/978-3-642-70867-1]

          A1. Retti, J., W. Bibel, B. Buchberger, E. Buchberger, W. Horn, A. Kobsa, I. Steinacker, R. Trappl, H. Trost (1984, 2nd revised edition 1986, reprinted 2014): Artificial Intelligence - Eine Einführung. Stuttgart: Teubner. [Print]
           
           

          E. Books and Journals edited

          E10. Kobsa, A., Editor-in-Chief: User Modeling and User-Adapted Interaction: The Journal of Personalization Research. Heidelberg, Germany: Springer Verlag (since 1991).

          E9. Brusilovsky, P., A. Kobsa, W. Nejdl, eds. (2007): The Adaptive Web: Methods and Strategies of Web Personalization. Heidelberg, Germany: Springer Verlag. [Print] [DOI 10.1007/978-3-540-72079-9]

          E8. Ardissono, L., A. Kobsa and M. Maybury, eds. (2004, reprinted 2013): Personalized Digital Television: Targeting Programs to Individual Viewers. Dordrecht, Netherlands: Kluwer Academic Publishers. [Print] [DOI 10.1007/1-4020-2164-X]

          E7. Kobsa, A., ed. (2001): User Modeling and User-Adapted Interaction, Ten Year Anniversary Issue. Dordrecht, Netherlands: Kluwer Academic Publishers. [OpenURL]

          E6. Haller, S., A. Kobsa and S. McRoy, eds. (1999, reprinted 2010): Computational Models of Mixed-Initiative Interaction. Dordrecht, Netherlands: Kluwer Academic Publishers.

          E5. Brusilovsky, P., A. Kobsa, J. Vassileva, eds. (1998, reprinted 2013): Adaptive Hypertext and Hypermedia. Dordrecht, Netherlands: Kluwer Academic Publishers. [DOI 10.1007/978-94-017-0617-9]

          E4. A. Kobsa, ed (1994): Special Issue on User Modeling Shell Systems. User Modeling and User-Adapted Interaction 4(2). [OpenURL]

          E3. Endres-Niggemeyer, B., T. Herrmann, A. Kobsa and D. Rösner, eds. (1990): Interaktion und Kommunikation mit dem Computer. Berlin - Heidelberg: Springer. [DOI 10.1007/978-3-642-75591-0]

          E2. Kobsa, A. and W. Wahlster, eds. (1989, reprinted 2011): User Models in Dialog Systems. New York etc: Springer Symbolic Computation. [Print] [DOI 10.1007/978-3-642-83230-7] [Review: DOI 10.1145/122319.1062596]

          E1. Kobsa, A. and W. Wahlster, eds. (1988): Computational Linguistics 14(3): Special Issue on User Modeling.
           
            

          P. Proceedings Volumes edited 

          P16. Kobsa, A., M. Zhou, M. Ester and Y. Koren (2014): RecSys '14 Eighth ACM Conference on Recommender Systems, Foster City, CA. ACM Press. [URL]

          P15. Jannach, D., S. Anand, B. Mobasher and A. Kobsa (2012): AAAI Workshop on Intelligent Techniques For Web Personalization and Recommender Systems (ITWP 2012), Toronto, Canada. [URL]

          P14. Anand, S., D. Jannach, B. Mobasher and A. Kobsa (2011): 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2011), in conjunction with IJCAI-2011, Barcelona, Catalonia, Spain, CEUR-WS.org/Vol-756 [URL] [author copy]

          P13. P. De Bra, A. Kobsa, and D. Chin (2010): User Modeling, Adaptation, and Personalization: 18th International Conference, UMAP 2010, Big Island, Hawaii. Lecture Notes in Computer Science Vol. 6075, Berlin, Heidelberg, New York: Springer Verlag. [Print] [Online]

          P12. D. Jannach, W. Geyer, J. Freyne, S. S. Anand, C. Dugan, B. Mobasher, and A. Kobsa (2009): Proceedings of the ACM RecSys'09 Workshop on Recommender Systems and the Social Web, New York, NY, CEUR-WS.org/Vol-532. [URL]

          P11. Anand, S., B. Mobasher, A. Kobsa, and D. Jannach, eds. (2009): Proceedings of the 7th International Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2009), in conjunction with IJCAI-2009, Pasadena, CA, CEUR-WS.org/Vol-528. [URL]

          P10. D. Cheng, Kinshuk, A. Kobsa, K. Partridge and Z. Yu, eds. (2009): Proceedings of the UMAP-09 Workshop on Personalization in Mobile and Pervasive Computing, Trento, Italy, CEUR-WS.org/Vol-478. [URL]

          P9. Anand, S., B. Mobasher, A. Kobsa, and D. Jannach, eds. (2008): Proceedings of the 2008 AAAI Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, Chicago, IL. AAAI Press, Menlo Park, CA. [URL]

          P8. Mobasher, B., S. Anand, A. Kobsa, and D. Jannach, eds. (2007): Proceedings of the AAAI Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, Vancouver, CA. Technical Report WS-07-08, AAAI Press, Menlo Park, CA. [URL]

          P7. Kobsa, A., R. K. Chellappa and S. Spiekermann, eds. (2006): Proceedings of PEP06, CHI 2006 Workshop on Privacy-Enhanced Personalization, Montréal, Canada. [URL]

          P6. Garello, P., S. Iacono, A. Kobsa, G. Lacoste and E. Goderniaux, eds. (2005): Proceedings of the Workshop on the Collaboration between  FP6/IST and NSF/ITR Projects, Ljubljana, Slovenia.

          P5. Kobsa, A. and L. Cranor, eds. (2005): Proceedings of the UM05 Workshop on Privacy-Enhanced Personalization (PEP05), Edinburgh, Scotland. [URL]

          P4. Kobsa, A. and C. Stephanidis, eds. (1999): Proceedings of the 5th ERCIM Meeting 'User Interfaces for All', Dagstuhl Castle, Germany. [URL]

          P3. Goodman, B., Kobsa, A., Litman, D. (1994): Proceedings of the Fourth International Conference on User Modeling, Hyannis, MA.

          P2. Kobsa, A. and W. Pohl, eds. (1993): Proceedings of the First German Workshop 'Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen'. Berlin, Germany.

          P1. Kobsa, A. and W. Wahlster, eds. (1986): Proceedings of the First International Workshop on User Modeling, Maria Laach, Germany
           
           

          J. Refereed Journal Papers

          J32. Kobsa, A., H. Cho and B. Knijnenburg (forthcoming): The Effect of Personalization Provider Characteristics on Privacy Attitudes and Behaviors: An Elaboration Likelihood Model Approach. To appear in the Journal of the Association for Information Science and Technology (JASIST). [DOI 10.1002/asi.23629] [author copy]

          J31. Knijnenburg, B., A. Kobsa and H. Jin (2013): Dimensionality of Information Disclosure Behavior. International Journal of Human-Computer Studies, Special Section on Privacy Methodologies in HCI, Vol. 71(12), 1144–1162. [DOI 10.1016/j.ijhcs.2013.06.003] [author copy]

          J30. Knijnenburg, B. and A. Kobsa (2013): Making Decisions about Privacy: Information Disclosure in Context-Aware Recommender Systems. ACM Transactions on Intelligent Interactive Systems 3(3), Article 20. [DOI 10.1145/2499670] [ACM Authorizer]

          J29. Kobsa, A., R. Nithyanand, G. Tsudik, E. Uzun (2013): Can Jannie Verify? Usability of Display-Equipped RFID Tags for Security Purposes. Journal of Computer Security 21(3), 347-370.  [DOI 10.3233/JCS-130470] [author copy].

          J28. Y. Wang and A. Kobsa (2013): A PLA-Based Privacy-Enhancing User Modeling Framework and its Evaluation. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 23(1), 41-82. [DOI 10.1007/s11257-011-9114-8] [author copy]

          J27. A. Kobsa, Patil, S. and B. Meyer (2012): Privacy in Instant Messaging: An Impression Management Model. Behavior and Information Technology 31(4), 355-370. [DOI 10.1080/01449291003611326] [author copy]

          J26. Patil, S., A. Kobsa, A. John and D. Seligmann (2011): Methodological Reflections on a Field Study of a Globally Distributed Software Project. Information and Software Technology 53(9), Special Issue on Studying Work Practices in Global Software Engineering, 969-980. [DOI 10.1016/j.infsof.2011.01.013] [author copy]

          J25. Patil, S. and A. Kobsa (2010): Enhancing Privacy Management Support in Instant Messaging. Interacting with Computers 22(3), 206-217. [DOI 10.1016/j.intcom.2009.10.002] [author copy]

          J24. Cámara, J. and A. Kobsa (2009): Facilitating Controlled Tests of Website Design Changes Using Aspect-Oriented Software Development and Software Product Lines. LNCS Transactions on Large Scale Data and Knowledge Centered Systems 1(1), 116-135. [DOI 10.1007/978-3-642-03722-1_5] [author copy]

          J23. Kobsa, A. (2007): Privacy-Enhanced Personalization (cover article). Communications of the ACM 50(8), 24-33. [DOI 10.1145/1278201.1278202] [Japanese Translation] [ACM Authorizer]

          J22. Kobsa, A. and J. Fink (2006): An LDAP-Based User Modeling Server and its Evaluation. User Modeling and User-Adapted Interaction: The Journal of Personalization Research 16(2), 129-169. [DOI 10.1007/s11257-006-9006-5] [author copy]

          J21. Mark, G. and A. Kobsa (2005): The Effects of Collaboration and System Transparency on CIVE Usage: An Empirical Study and Model. Presence 14(1), 60-80, MIT Press. [DOI 10.1162/1054746053890279] [author copy]

          J20. Kobsa, A. and J. Schreck (2003): Privacy through Pseudonymity in User-Adaptive Systems. ACM Transactions on Internet Technology 3(2), 149-183. [DOI 10.1145/767193.767196] [ACM Authorizer]

          J19. Yimam-Seid, D. and A. Kobsa (2003): Expert Finding Systems for Organizations: Problem and Domain Analysis and the DEMOIR Approach. Journal of Organizational Computing and Electronic Commerce 13(1), 1-24. [DOI: 10.1207/S15327744JOCE1301_1] [author copy]

          J18. Schwab, I. and Kobsa, A. (2002): Adaptivity through Unobstrusive Learning. KI 3(2002), Special Issue on Adaptivity and User Modeling, 5-9. [author copy]

          J17. Kobsa, A. (2002): Personalized Hypermedia and International Privacy. Communications of the ACM 45(5), 64-67. [DOI 10.1145/506218.506249] [ACM Authorizer]

          J16. Fink, J. and A. Kobsa (2002): User Modeling in Personalized City Tours. Artificial Intelligence Review 18(1), 33-74. [DOI 10.1023/A:1016383418977] [author copy]

          J15. A. Kobsa (2001): Generic User Modeling Systems. User Modeling and User-Adapted Interaction 11(1-2), 49-63. [DOI 10.1023/A:1011187500863] [author copy]

          J14. Kobsa, A., J. Koenemann and W. Pohl (2001): Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16(2), 111-155. [DOI 10.1017/S0269888901000108]  [author copy]

          J13. Fink, J. and A. Kobsa (2000): A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web. User Modeling and User-Adapted Interaction 10(2-3), Special Issue on Deployed User Modeling, 209-249. [DOI 10.1023/A:1026597308943] [author copy]

          J12. J. Fink, A. Kobsa and A. Nill (1998): Adaptable and Adaptive Information Provision for All Users, Including Disabled and Elderly People. New Review of Hypermedia and Multimedia 4, 163-188. [DOI 10.1080/13614569808914700] [author copy]

          J11. Kobsa, A., D. Müller and A. Nill (1996): KN-AHS: An Adaptive Hypertext Client of the User Modeling System BGP-MS. Review of Information Science 1(1). Reprint of C13.

          J10. Kobsa, A. and W. Pohl (1995): The User Modeling Shell System BGP-MS. User Modeling and User-Adapted Interaction 4(2), 59-106. [DOI 10.1007/BF01099428] [autor copy] [Software download]

          J9. Kobsa, A. (1990): Modeling the User's Conceptual Knowledge in BGP-MS, a User Modeling Shell System. Computational Intelligence 6(4), 193-208. [DOI 10.1111/j.1467-8640.1990.tb00295.x]

          J8. Kobsa, A. (1990): User Modeling in Dialog Systems: Potentials and Hazards. AI & Society 4(3), 214-240. [DOI 10.1007/BF01889941] [author copy]

          J7. Allgayer, J., K. Harbusch, A. Kobsa, C. Reddig, N. Reithinger, D. Schmauks (1989): XTRA: A Natural-Language Access System to Expert Systems. Intl. J. of Man-Machine Studies 31, 161-195. [DOI 10.1016/0020-7373(89)90026-6]

          J6. Kobsa, A. (1988): User Models and Dialog Models: United They Stand... In: Computational Linguistics 14(3), 91-95. [ACL]

          J5. Wahlster, W. and A. Kobsa (1986): Dialog-Based User Models. Proceedings of the IEEE 74(7), 948-960. [DOI 10.1109/PROC.1986.13574] [author copy]

          J4. Kobsa, A. (1984): Knowledge Representation: a Survey of its Mechanisms, a Sketch of its Semantics. Cybernetics and Systems 14, 41-89. [DOI 10.1080/01969728408927736]

          J3. Kobsa, A. (1984): What is Explained by AI-Models? Communication and Cognition 17(2-3), 49-65. Reprinted in R. Born, ed. (1987): AI - The Case Against. Beckenham: Croom Helm.

          J2. Kobsa, A., H. Trost und R. Trappl (1983): Ist benutzerangepasstes Dialogverhalten auch ohne Dialogpartnermodell möglich? Angewandte Informatik 9/83, 383-387.

          J1. Kobsa, A. (1983): Präsuppositionsanalyse zum Aufbau von Dialogpartnermodellen. Conceptus 17(40/41), 165-179.
           

          C. Refereed Conference Papers

          C65. Knijnenburg, B., A. Kobsa (2014): Increasing Sharing Tendency Without Reducing Satisfaction: Finding the Best Privacy-Settings User Interface for Social Networks. 35th International Conference on Information Systems, Auckland, New Zealand. [URL] [author version]

          C64. Kobsa, A. (2014): User Acceptance of Footfall Analytics with Aggregated and Anonymized Mobile Phone Data. In: C. Eckert, S. K. Katsikas and G. Pernul, eds.: Trust, Privacy and Security in Digital Business: 11th International Conference. Heidelberg etc.: Springer Verlag, 168–179. [DOI 10.1007/978-3-319-09770-1_15] [author copy]

          C63. Kobsa, A., B. Knijnenburg, B. Livshits (2014): Let’s Do It at My Place Instead? Attitudinal and Behavioral Study of Privacy in Client-Side Personalization. ACM CHI Conference on Human Factors in Computing Systems, Toronto, Ontario, Canada, 81-90. [DOI 10.1145/2556288.2557102] [ACM Authorizer] [30 sec. video summary]

          C62. Knijnenburg, B., A. Kobsa and H. Jin (2013): Counteracting the Negative Effect of Form Auto-Completion on the Privacy Calculus. Proceedings of the 34th International Conference on Information Systems, Milan, Italy, Paper 18-2 [URL] [author copy]

          C61. Page, X., B. Knijnenburg and A. Kobsa (2013): FYI: Communication Style Preferences Underlie Differences in Location-Sharing Adoption and Usage. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Zurich, Switzerland, 153-162. Honorable mention. [DOI 10.1145/2493432.2493487] [ACM Authorizer]

          C60. Knijnenburg, B., A. Kobsa and H. Jin (2013): Preference-based Location Sharing: Are More Privacy Options Really Better? 2013 ACM SIGCHI Conference on Human Factors in Computing Systems, Paris, France, 2667-2676. [DOI 10.1145/2470654.2481369] [ACM Authorizer] [30 sec. video summary]

          C59. Knijnenburg, B. and A. Kobsa (2013): Helping Users with Information Disclosure Decisions: Potential for Adaptation. IUI 2013: ACM International Conference on Intelligent User Interfaces, Santa Monica, CA, 407-416. [DOI 10.1145/2449396.2449448] [ACM Authorizer]

          C58. Page, X., B. Knijnenburg and A. Kobsa (2013): What a Tangled Web We Weave: Lying Backfires in Location-Sharing Social Media. 16th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW 2013), San Antonio, Texas, 273-284. [DOI 10.1145/2441776.2441808] [ACM authorizer]

          C57. Knijnenburg, B., S. Bostandjiev, J. O'Donovan, A. Kobsa (2012): Inspectability and Control in Social Recommenders. Proceedings of the 6th ACM Conference on Recommender Systems (RecSys 2012), Dublin, Ireland, 43-50. [DOI 10.1145/2365952.2365966] [author copy]

          C56. Page, X., A. Kobsa and B. Knijnenburg (2012): Don’t Disturb My Circles! Boundary Preservation Is at the Center of Location-Sharing Concerns. Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublin, Ireland. [URL] [author copy]

          C55. Knijnenburg, B., M. Willemsen and A. Kobsa (2011): A Pragmatic Procedure to Support the User-Centric Evaluation of Recommender Systems. Proceedings of the 5th ACM Conference on Recommender Systems, Chicago, IL [author copy] [ACM Authorizer]

          C54. Kobsa, A., R. Nithyanand, G. Tsudik and E. Uzun (2011): Usability of Display-Equipped RFID Tags for Security Purposes. In V. Atluri and C. Diaz, eds.: Computer Security – ESORICS 2011, Leuven, Belgium. Springer Verlag, Berlin, Heidelberg, 434-451. [DOI 10.1007/978-3-642-23822-2_24] [author copy]

          C53. Patil, S., X. Page and A. Kobsa (2011): With A Little Help From My Friends: Can Social Navigation Inform Interpersonal Privacy Preferences? Proceedings of the 2011 ACM Conference on Computer Supported Cooperative Work, Hangzhou, China, 391-394 [DOI 10.1145/1958824.1958885] [ACM authorizer]

          C52. Patil, S., A. Kobsa, A. John and D. Seligmann (2010): Comparing Privacy Attitudes of Knowledge Workers in the U.S. and India. 3rd ACM International Conference on Intercultural Collaboration, Copenhagen, Denmark, 141-150. [DOI 10.1145/1841853.1841875] [ACM Authorizer]

          C51. Wang, Y. and A. Kobsa (2010): Privacy in Cross-System Personalization. Intelligent Information Privacy Symposium: Papers from the AAAI Spring Symposium, Stanford, CA. Technical Report SS-10-05, AAAI Press, 184-186. [author copy]

          C50. Page, X. and A. Kobsa (2010): Navigating the Social Terrain with Google Latitude. iConference 2010, Urbana-Champaign, IL, p.174-178. [URL] [author copy]

          C49. Wang, Y. and A. Kobsa (2009): Privacy in Online Social Networking at Workplace. IEEE International Conference on Computational Science and Engineering, Vancouver, Canada, 975-978.[DOI 10.1109/CSE.2009.438] [author copy]

          C48. Page, X. and A. Kobsa (2009): The Circles of Latitude: Adoption and Usage of Location Tracking in Online Social Networking. IEEE International Conference on Computational Science and Engineering, Vancouver, Canada, 1027-1030. [DOI 10.1109/CSE.2009.195] [author copy]

          C47. Hendrickson, S., Y. Wang, A. van der Hoek, R. Taylor, A. Kobsa (2009): Modeling PLA Variation of Privacy-Enhancing Personalized Systems. Proceedings of the 13th International Software Product Line Conference, San Francisco, CA, 2009, 71-80. [ACM DL] [author copy]

          C46. Patil, S., A. Kobsa, A. John, L. S. Brotman, and D. Seligmann (2009): Interpersonal Privacy Management in Distributed Collaboration: Situational Characteristics and Interpretive Influences. In: T. Gross, J. Gulliksen, P. Kotzé, L. Oestreicher, P. Palanque, M. Winckler, R. O. Prates., eds: INTERACT 2009, Uppsala, Sweden, Part II, LNCS 5727, Hetidelberg etc.: Springer, 143-­156. [DOI 10.1007/978-3-642-03658-3_19 [author copy]

          C45. Kobsa, A., R. Sonawalla, G. Tsudik, E. Uzun and Y. Wang (2009): Serial Hook-Ups: A Comparative Usability Study of Secure Device Pairing Methods. Fifth Symposium On Usable Privacy and Security (SOUPS-09), Mountain View, CA, Article 10, 12 pages. [DOI 10.1145/1572532.1572546] [ACM Authorizer]

          C44. Cámara, J. and A. Kobsa (2009): Facilitating Controlled Tests of Website Design Changes: a Systematic Approach. In: M. Gaedke, M. Grossniklaus and O. Díaz, eds.: Web Engineering: 9th International Conference, ICWE 2009, San Sebastián, Spain. Heidelberg etc.: Springer, 370-378. [DOI 10.1007/978-3-642-02818-2_30] [author copy].

          C43. Wang, Y. and A. Kobsa (2009). Performance Evaluation of a Privacy-Enhancing Framework for Personalized Websites. In: G.-J. Houben, G. McCalla, F. Pianesi and M. Zancanaro, eds.: User Modeling, Adaptation, and Personalization, 17th International Conference, UMAP 2009. Trento, Italy, 78-89.[DOI 10.1007/978-3-642-02247-0_10] [author copy]

          C42. Patil, S. and A. Kobsa (2009): Why is Evaluating Usability of Privacy Designs So Hard? Lessons Learned from a User Study of PRISM. iConference 2009, Chappel Hill, NC, 4 pages. [URL] [author copy]

          C41. Nguyen, D. H., A. Kobsa and G. Hayes (2008): An Empirical Investigation of Concerns of Everyday Tracking and Recording Technologies. Tenth International Conference on Ubiquitous Computing, Seoul, South Korea, 182-191. [DOI 10.1145/1409635.1409661] [ACM Authorizer]

          C40. M. Vigo, A. Kobsa, M. Arrue, J. Abascal (2007): User-Tailored Web Accessibility Evaluations. 18th ACM Conference on Hypertext and Hypermedia, Manchester, UK, 95-104. [DOI 10.1145/1286240.1286267]  [ACM Autorize]

          C39. Wang, Y. and A. Kobsa (2007): Respecting Users' Individual Privacy Constraints in Web Personalization. In C. Conati, K. McCoy and G. Paliouras, eds.: UM07, 11th International Conference on User Modeling, Corfu, Greece, 157–166. Berlin - Heidelberg - New York: Springer-Verlag [DOI 10.1007/978-3-540-73078-1_19] [author copy]

          C38. Wang, Y., A. Kobsa, A. van der Hoek and J. White (2006): PLA-based Runtime Dynamism in Support of Privacy-Enhanced Web Personalization. Proceedings of the 10th International Software Product Line Conference, Baltimore, MD, IEEE Press, 151-162 [DOI 0.1109/SPLINE.2006.1691587] [author copy]

          C37. Patil, S. and A. Kobsa (2005): Uncovering Privacy Attitudes in Instant Messaging. Proceedings of Group'05, 5th ACM Conference on Supporting Group Work, Sanibel Island, FL, 101-104.[DOI 10.1145/1099203.1099220] [ACM Authorizer]

          C36. Patil, S. and A. Kobsa (2005): Privacy in Collaboration: Managing Impression. Proceedings of the First International Conference on Online Communities and Social Computing, Las Vegas, NV. [author copy]

          C35. Kobsa, A. (2004): User Experiments with Tree Visualization Systems. Proceedings of InfoVis 2004, IEEE Symposium on Information Visualization, Austin, TX, 9-16. [DOI 10.1109/INFVIS.2004.70] [author copy]

          C34. Patil, S. and A. Kobsa (2004): Instant Messaging and Privacy. In: A. Dearden and L. Watts, eds: Proceedings of HCI 2004, Leeds, England, 85-88. [author copy]

          C33. Küpper, D. and A. Kobsa (2003), Tailoring the Presentation of Plans to Users’ Knowledge and Capabilites. In: A. Günter, R. Kruse, B. Neumann, eds.: KI 2003: Advances in Artificial Intelligence, 26th Annual German Conference on Artificial Intelligence. Springer Verlag (LNCS 2821), Heidelberg, Germany, 606-617 [DOI 10.1007/978-3-540-39451-8_44] [author copy].

          C32. V. Gonzáles, A. Kobsa (2003): A Workplace Study of the Adoption of Information Visualization Systems. Proceedings of I-KNOW'03: 3rd International Conference on Knowledge Management, Graz, Austria, 92-102. Journal of Universal Computer Science. [author copy]

          C31. Mark, G., K. Carpenter, A. Kobsa (2003): A Model of Synchronous Collaborative Information Visualization. Proceedings of the Sixth International Conference on Information Visualisation (IV03), London, U.K. IEEE Press, Los Alamitos, CA, 373-381. [DOI 10.1109/IV.2003.1218013] [author copy]

          C30. V. González, A. Kobsa (2003): Benefits of Information Visualization Systems for Administrative Data Analysts. Proceedings of the Sixth International Conference on Information Visualisation (IV03), London, U.K. IEEE Press, Los Alamitos, CA, 331-336. [DOI 10.1109/IV.2003.1217999] [author copy]

          C29. Mark, G., K. Carpenter, A. Kobsa (2003): Are There Benefits in Seeing Double? A Study of Collaborative Information Visualization. CHI 2003 Human Factors in Computing Systems, Fort Lauderdale, CA, 273-274. [DOI 10.1145/765891.766023]  [ACM Authorizer]

          C28. Kobsa, A. and J. Fink (2003): Performance Evaluation of User Modeling Servers Under Real-World Workload Conditions. In: P. Brusilovsky, A. T. Corbett and F. de Rosis, eds.: User Modeling 2003: 9th International Conference, UM 2003, Johnstown, PA, Springer Verlag (LNCS), 143-153. [DOI 10.1007/3-540-44963-9_20] [author copy] Received Best Evaluation Paper Award.

          C27. Mark, G., A. Kobsa, V. González (2002): Do Four Eyes See Better than Two? Collaborative versus Individual Discovery in Data Visualization Systems. Proceedings of the Fifth International Conference on Information Visualisation (IV02), London, U.K. Los Alamitos, CA, IEEE Press, 249-255. [DOI 10.1109/IV.2002.1028784] [author copy]

          C26. Küpper, D. and A. Kobsa (2002): Generating and Presenting User-Tailored Plans. 2002 International Conference on Intelligent User Interfaces, San Francisco, CA, 198-199. [DOI 10.1145/502716.502755] [ACM Autorizer]

          C25. Kobsa, A. (2001): Tailoring Privacy to Users' Needs (Invited Keynote). In M. Bauer, P. J. Gmytrasiewicz and J. Vassileva, eds.: User Modeling 2001: 8thInternational Conference. Berlin - Heidelberg: Springer Verlag, 303-313. [DOI 10.1007/3-540-44566-8_52] [author copy]

          C24. Kobsa, A. (2001): An Empirical Comparison of Three Commercial Information Visualization Systems. Proceedings of InfoVis 2001, IEEE Symposium on Information Visualization, San Diego, CA, 123-130. [OpenURL] [author copy] "© IEEE".

          C23. Küpper, D. and A. Kobsa (2001): User-Tailored Plan Presentation. In M. Bauer, P. J. Gmytrasiewicz and J. Vassileva, eds.: User Modeling 2001: 8th International Conference. Berlin - Heidelberg: Springer Verlag, 243-246. [OpenURL] [author copy].

          C22. Kobsa, A. (1999): Adapting Web Information to Disabled and Elderly Users (Invited Paper). WebNet-99, Honolulu, HI, pp. 32-37. [author copy]

          C21. Küpper, D. and A. Kobsa (1999): User-Tailored Plan Generation. In: J. Kay, ed. UM99 User Modeling: Proceedings of the Seventh International Conference. Wien New York: Springer Verlag, 45-54. [author copy]

          C20. Pieper, M. and A. Kobsa (1999): Talking to the Ceiling: An Interface for Bed-Ridden Manually Impaired Users. In: M. W. Altom and M. G. Williams, eds.: CHI99 Extended Abstracts, Video Demonstrations. Pittsburgh, PA, 9-10. [DOI 10.1145/632716.632723] [ACM Authorizer]; [Real Video] (4.6 MB):

          C19. Fink, J., A. Kobsa, A. Nill (1998): Towards a user-adapted information environment on the Web. Multimedia and Standardization 98, Paris, France. [author copy]

          C18. Fink, J., A. Kobsa and A. Nill (1997): Benutzerorientierte Adaptivität und Adaptierbarkeit im Projekt AVANTI (User-oriented adaptivity and adaptability in the AVANTI project). In: R. Liskowski, B. M. Velichkovsky, and W. Wünschmann, eds.: Software-Ergonomie 97. Usability Engineering: Integration von Mensch-Computer-Interaktion und Software-Entwicklung, Dresden, 135-143. Stuttgart: Teubner. [DOI 10.1007/978-3-322-86782-7_10] [author copy] 

          C17. Fink, J., A. Kobsa and A. Nill (1997): Adaptable and Adaptive Information Access for All Users, Including the Disabled and the Elderly. In: A. Jameson, C. Paris and C. Tasso, eds.: User Modeling: Proceedings of the Sixth International Conference UM97. Wien New York: Springer, 171-173. [author copy]

          C16. Fink, J., A. Kobsa and J. Schreck (1997): Personalized Hypermedia Information through Adaptive and Adaptable System Features: User Modeling, Privacy and Security Issues. In: A. Mullery, M. Besson, M. Campolargo, R. Gobbi and R. Reed, eds.: Intelligence in Services and Networks: Technology for Cooperative Competition. Berlin Heidelberg: Springer (LNCS 1238), 459-467. [DOI: 10.1007/3-540-63135-6_44] [author copy]

          C15. Fink, J., A. Kobsa and A. Nill (1996): User-Oriented Adaptivity and Adaptability in the AVANTI Project. Proceedings of the Conference 'Designing for the Web: Empirical Studies', Redmond, WA, Oct. 30, 1996. [author copy]

          C14. Kobsa, A. (1995): Supporting User Interfaces for All Through User Modeling. Proceedings HCI International '95, Yokohama, Japan, 155-157. [author copy]

          C13. Kobsa, A., D. Müller and A. Nill (1994): KN-AHS: An Adaptive Hypertext Client of the User Modeling System BGP-MS. Proceedings of the Fourth International Conference on User Modeling, Hyannis, MA, 99-105. [author copy].

          C12. D. Müller, A. Nill und A. Kobsa (1994): KN-AHS: Ein adaptiver Hypertext-Client der Benutzermodellierungs-Shell BGP-MS (An adaptive hypertext client in the user modeling shell BGP-MS). In: W. Rauch, F. Strohmeier, H. Hiller and C. Schlögl, eds.: Mehrwert von Information - Professionalisierung von Informationsarbeit. Proceedings des 4. Internationalen Symposiums für Informationswissenschaft (ISI 94), 311-322. Konstanz, Universitätsverlag.

          C11. Kobsa, A. (1993): Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen (invited paper). In: O. Herzog, Th. Christaller und D. Schütt, Hrsg.: 17. Fachtagung KI. Berlin: Springer. [author copy]

          C10. Allgayer, J., A. Kobsa, C. Reddig, N. Reithinger (1990): PRACMA: Processing Arguments between Controversially-Minded Agents. Proceedings of the 5th Rocky Mountain Conference on Artificial Intelligence, Las Cruces, NM, 63-68.

          C9. Kobsa, A. (1989): User Modeling in Dialog Systems: Potentials and Hazards. In Klaus Brunnstein, Simone Fischer-Hübner and Rolf Engelbrecht, eds.: Proceedings of the IFIP/GI Conference on Opportunities and Risks of Artificial Intelligence Systems, Hamburg, Germany, 147-165. Revised version appeared as J8.

          C8. Ripplinger, B. and A. Kobsa (1988): PLUG: Benutzerführung auf Basis einer dynamisch veränderlichen Zielhierarchie (User-guidance based on a dynamic goal hierarchy). In: W. Hoeppner, ed.: GWAI-88, 12th German Workshop on Artificial Intelligence. Berlin: Springer. [DOI 10.1007/978-3-642-74064-0_26]

          C7. Kobsa, A., J. Allgayer, C. Reddig, N. Reithinger, D. Schmauks, K. Harbusch and W. Wahlster (1986): Combining Deictic Gestures and Natural Language for Referent Identification. Proceedings of the 11th International Conference on Computational Linguistics, Bonn, West Germany, 356-361. [DOI 10.3115/991365.991471] [author copy]

          C6. Kobsa, A. (1985): Using Situation Descriptions and Russellian Attitudes for Representing Beliefs and Wants. Proceedings of the 9th International Joint Conference on Artificial Intelligence, Los Angeles, CA, pp. 513-515. [URL]

          C5. Kobsa, A. (1985): VIE-DPM: a User Model in a Natural-Language Dialogue System. In: J. Laubsch, ed.: GWAI-84, 8th German Workshop on Artificial Intelligence. Berlin: Springer. [DOI 10.1007/978-3-642-46546-8_11]

          C4. Kobsa, A. (1984): Three Steps in Constructing Mutual Belief Models from User Assertions. Proceedings of the 6th European Conference on Artificial Intelligence, Pisa, pp. 423-426.

          C3. Kobsa, A. (1984): Benutzermodellierung auf Basis eines Semantischen Netzwerks. In: W. Barth und W. Purgathofer, Hrsg.: 7. Tagung ``Berichte aus den Informatikinstituten", TU Wien.

          C2. Kobsa, A. (1982): On Regarding AI Programs as Theories. In: R. Trappl, ed.: Cybernetics and Systems Research. Amsterdam: North-Holland.

          C1. Kobsa, A. (1982): What is Explained by AI-Models? Proceedings of the 1982 European Conference on Artificial Intelligence (Discussion Papers), Orsay, France, pp. 11-12.
           
           

          B. Refereed Book Contributions

          B24. Patil, S. and A. Kobsa (2009): Privacy Considerations in Awareness Systems: Designing with Privacy in Mind. In P. Markopoulos, B. de Ruyter and W. Mackay, eds.: Awareness Systems: Advances in Theory, Methodology and Design. Berlin, Heidelberg, New York: Springer Verlag, pp. 187-206. [DOI 10.1007/978-1-84882-477-5_8] [author copy]

          B23. Wang, Y. and A. Kobsa (2008): Privacy Enhancing Technologies. In: M. Gupta and R. Sharman, eds.: Handbook of Research on Social and Organizational Liabilities in Information Security. Hershey, PA: IGI Global, 352-375. [DOI 10.4018/978-1-60566-132-2.ch013] [author copy]

          B22. Yang, W. and A. Kobsa (2008): Technical Solutions for Privacy-Enhanced Personalization. In: C. Mourlas and P. Germanakos, eds.: Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies. Hershey, PA: IGI Global, 353-376. [DOI 10.4018/978-1-60566-032-5.ch017] [author copy]

          B21. Kobsa, A. (2007): Privacy-Enhanced Web Personalization. In P. Brusilovsky, A. Kobsa, W. Nejdl, eds.: The Adaptive Web: Methods and Strategies of Web Personalization. Berlin, Heidelberg, New York: Springer Verlag, 628-670. [DOI 10.1007/978-3-540-72079-9_21] [author copy]

          B20. Kobsa, A. (2007): Generic User Modeling Systems. In P. Brusilovsky, A. Kobsa, W. Nejdl, eds.: The Adaptive Web: Methods and Strategies of Web Personalization. Berlin, Heidelberg, New York: Springer Verlag, 136-154. [DOI 10.1007/978-3-540-72079-9_4] [author copy]

          B19. A. Kobsa (2004): Adaptive Interfaces. In: W. S. Bainbridge, ed.: Encyclopedia of Human-Computer Interaction. Great Barrington, MA: Berkshire Publishing. [URL] [author copy]

          B18. Patil, S. and A. Kobsa (2004): Preserving Privacy in Awareness Systems. In: R. Hammwöhner, M. Rittberger and W. Semar, eds.: Wissen in Aktion. Festschrift for Rainer Kuhlen. University of Konstanz Press, Konstanz, Germany, 119-129. [URL] [author copy]

          B17. Teltzrow, M. and A. Kobsa (2004): Impacts of User Privacy Preferences on Personalized Systems: a Comparative Study. In: C.-M. Karat, J. Blom and J. Karat, eds: Designing Personalized User Experiences for eCommerce. Dordrecht, Netherlands: Kluwer Academic Publishers, 315-332.[DOI 10.1007/1-4020-2148-8_17] [author copy]

          B16. A. Kobsa (2004): Adaptive Verfahren – Benutzermodellierung (Adaptive Methods – User Modeling). In: R. Kuhlen, T. Seeger and D. Strauch, eds.: Grundlagen der Information und Dokumentation (5th completely revised edition). Munich, Germany: K. G. Saur, 299-302. [author version]

          B15. Yimam-Seid, D. and A. Kobsa (2003): Expert Finding Systems for Organizations: Problem and Domain Analysis and the DEMOIR Approach. In M. Ackerman, V. Pipek and V. Wulf, eds.: Beyond Knowledge Management: Sharing Expertise. Cambridge, MA: MIT Press (revised version of J19).

          B14. Kobsa, A., D. Müller and A. Nill (1998): KN-AHS: An Adaptive Hypertext Client of the User Modeling System BGP-MS (with Postscript). In M. Maybury and W. Wahlster, eds.: Readings in Intelligent User Interfaces. San Mateo, CA: Morgan Kaufmann. Reprinted from Proc. UM94.

          B13. Kobsa, A., A. Nill and J. Fink (1997): Hypertext and Hypermedia Clients of the User Modeling System BGP-MS. In M. Maybury, ed.: Intelligent Multimedia Information Retrieval. Boston, MA: MIT Press. [author copy]

          B12. Endres-Niggemeyer, B., A. Kobsa and P. Schmidt (1994): TAST — A System that Supports Group Authoring of Computer Manuals. In K. Ehlich, C. Noack and S. Scheiter, eds.: Instruktion durch Text und Diskurs, VS Verlag für Sozialwissenschaften, 221–242. [DOI 10.1007/978-3-322-90830-8_14]

          B11. Kobsa, A. (1994): Conceptual Hierarchies: Approaches from Artificial Intelligence and Connectionism. In: H. Best, B. Endres-Niggemeyer, M. Herfurth und P. P. Ohly, Hrsg.: Informations- und Wissensverarbeitung in den Sozialwissenschaften: Beiträge zur Umsetzung neuer Informationstechnologien. Opladen, Westdt. Verlag.

          B10. Kobsa, A. (1993): User Modeling: Recent Work, Prospects and Hazards. In: M. Schneider-Hufschmidt, T. Kühme and U. Malinowski, eds. (1993): Adaptive User Interfaces: Principles and Practise. Amsterdam: North Holland Elsevier. [author copy]

          B9. Kobsa, A. (1991): Utilizing Knowledge: The Components of the SB-ONE Knowledge Representation Workbench. In: J. Sowa, ed.: Principles of Semantic Networks: Explorations in the Representation of Knowledge. San Mateo, CA: Morgan Kaufmann.

          B8. Kobsa, A. (1989): A Taxonomy of Beliefs and Goals for User Models in Dialog Systems. In: A. Kobsa and W. Wahlster, eds.: User Models in Dialog Systems. New York etc: Springer Symbolic Computation.

          B7. Kobsa, A. (1987): What is Explained by AI-Models? In: R. Born, ed.: AI - The Case Against. Beckenham: Croom Helm. (Reprinted from Communication and Cognition 17(2-3), pp. 49-65).

          B6. Kobsa, A. (1986): Benutzermodellierung in einem natürlichsprachigen Dialogsystem (User Modeling in a natural-language dialog system). In: G. Dirlich, C. Freksa, U. Schwatlo und K. Wimmer (1986): Kognitive Aspekte der Mensch-Computer-Interaktion. Berlin etc.: Springer. [DOI 10.1007/978-3-642-46577-2_14]

          B5. Kobsa, A. (1986): Generating a User Model from Wh-Questions in the VIE-LANG System. In: P. Hellwig und H. Lehmann, Hrsg.: Trends in der Linguistischen Datenverarbeitung. Hildesheim etc.: Olms.

          B4. Kobsa, A. and H. Trost (1984): Representing Belief Models in Semantic Networks. In: R. Trappl, ed.: Cybernetics and Systems Research II. Amsterdam: North-Holland.

          B3. Kobsa, A. (1982): Wissensrepräsentation in der Künstlichen-Intelligenz-Forschung (Knowledge Representation in Artificial Intelligence Research). In: R. Born, Hrsg.: Sprache - Information - Wirklichkeit. Wien: VWGÖ-Verlag.

          B2. Kobsa, A. (1982): Künstliche Intelligenz und Kognitive Psychologie (Artificial Intelligence and Cognitive Psychology). In: H. Schauer und M. J. Tauber, Hrsg.: Informatik und Psychologie. Wien - München: Oldenbourg. Reprinted in: ÖGAI-Journal 1 (1982), pp. 21-39. Revised version in: Retti, J., B. Buchberger, E. Buchberger, W. Horn, A. Kobsa, I. Steinacker, R. Trappl, H. Trost (1984): Artificial Intelligence - Eine Einführung. Stuttgart: Teubner.

          B1. Kobsa, A. (1985): AI-Dictionary 7. Teil: Cognitive Science. In: E. Buchberger, Hrsg. (1987): AI-Dictionary: Die Begriffswelt der Wissensverarbeitung und der Künstlichen Intelligenz. Wien: ÖGAI. (Revised version of ÖGAI-Journal 4(2-3), pp. 35-38.).
           
           

          W. Refereed Workshop Papers

          W34. Li, Y., B. Knijnenburg, A. Kobsa and C. Nguyen (2015): Cross-Cultural Privacy Prediction. Workshop "Privacy Personas and Segmentation", 2015 Symposium On Usable Privacy and Security (SOUPS), Ottawa, Canada.

          W33. Lee, H., M. Bellato, S. Jain, F. Spanghero, R. Singer-heinze, Y.-W. Lin, S. Gupta, G. Ward, A. Kobsa (2015): Racial Violence Archive: Public Information System on Incidents of Violence during the Civil Rights Period. iConference 2015 Social Media Expo [URL] Received Best Workshop Paper Award

          W32. T. Kaczmarek, A. Kobsa, R. Sy and G. Tsudik (2015): An Unattended Study of Users Performing Security Critical Tasks under Adversarial Noise. Workshop on Usable Security, Network and Distributed System Security Symposium, San Diego, CA. [URL]

          W31. H. Wu, B. Knijnenburg and A. Kobsa (2014): Improving the prediction of users’ disclosure behavior… by making them disclose more predictably? Workshop on Privacy Personas and Segmentation at the 2014 Symposium On Usable Privacy and Security, Menlo Park, CA [URL] [author version]

          W30. Knijnenburg, B., A. Kobsa, G. Saldamli (2012): Privacy in Mobile Personalized Systems: The Effect of Disclosure Justifications. Workshop on Usable Privacy and Security for Mobile Devices (U-PriSM), 2012 Symposium On Usable Privacy and Security, Washington, D.C. [URL] [author viersion]

          W29. Kobsa, A., Y. Chen, T. Wang (2012): Discovering Personal Behavioral Rules in a Health Management System. Workshop on Wellness Interventions and HCI at the 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), San Diego, CA, 324-327. [URL] [author copy]

          W28. Knijnenburg, B., A. Kobsa, S. Moritz, M. A. Svensson (2011): Exploring the Effects of Feed-forward and Feedback on Information Disclosure and User Experience in a Context-Aware Recommender System. UMAP 2011 Workshop on Decision Making and Recommendation Acceptance Issues in Recommender Systems, Girona, Spain, 35-42. [author copy]

          W27. Patil, S., A. Kobsa, A. John, D. Seligmann (2009): Comparing Privacy Attitudes of Knowledge Workers in India and the U.S. Interact 2009 Workshop on Culture and Technologies for Social Interaction, Upsala, Sweden.[URL] [author copy]

          W26. Tang, J., N. Gertsch, H. J. Choi, A. Kobsa, S. Habibi (2009):Ad As You Go: A Study of Ad Placement on Personal Navigation Devices. Proceedings of the UMAP-09 Workshop on Personalization in Mobile and Pervasive Computing, Trento, Italy. [URL] [author copy]

          W25. Wang, Y. and A. Kobsa (2006): Impacts of Privacy Laws and Regulations on Personalized Systems. In A. Kobsa, R. Chellappa and S. Spiekermann, eds.: Proceedings of PEP06, CHI 2006 Workshop on Privacy-Enhanced Personalization, Montréal, Canada, 44-46. [author copy]

          W24. Kobsa, A. and M. Teltzrow (2006): Convincing Users to Disclose Personal Data. In A. Kobsa, R. Chellappa and S. Spiekermann, eds.: Proceedings of PEP06, CHI 2006 Workshop on Privacy-Enhanced Personalization, Montréal, Canada, 39-41. [author copy]

          W23. Nguyen, D. and A. Kobsa (2006): Better RFID Privacy Is Good for Consumers, and Manufacturers, and Distributors, and Retailers. In A. Kobsa, R. Chellappa and S. Spiekermann, eds.: Proceedings of PEP06, CHI 2006 Workshop on Privacy-Enhanced Personalization, Montréal, Canada, 63-65. [author copy]

          W22. Wang, Y. and A. Kobsa (2005): A Software Product Line Approach for Handling Privacy Constraints in Web Personalization. In A. Kobsa and L. Cranor, eds.: Proceedings of PEP05, UM05 Workshop on Privacy-Enhanced Personalization, Edinburgh, Scotland, 35-45. [author copy]

          W21. Patil, S. and A. Kobsa (2005): Designing with Privacy in Mind. In: Proceedings of the CHI-2005 Workshop on Awareness Systems: Known Results, Theory, Concepts and Future Challenges, Portland, OR. [author copy]

          W20. Kobsa, A. and Teltzrow, M. (2005): Impacts of Contextualized Communication of Privacy Practices and Personalization Benefits on Purchase Behavior and Perceived Quality of Recommendation. In: M. van Setten, S. McNean and J. Konstan, eds.: "Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research" (IUI 2005), San Diego, CA, 48-53. [URL] [author copy]

          W19. Kobsa, A. and Teltzrow, M. (2005): Contextualized Communication of Privacy Practices and Personalization Benefits: Impacts on Users’ Data Sharing and Purchase Behavior. In: D. Martin and A. Serjantov, eds: Privacy Enhancing Technologies: Fourth International Workshop, PET 2004, Toronto, Canada. Springer LNCS 3424, 329-343. [DOI 10.1007/11423409_21] [author copy]

          W18. Teltzrow, M. and A. Kobsa (2004): Communication of Privacy and Personalization in E-Business. Proceedings of the Workshop “WHOLES: A Multiple View of Individual Privacy in a Networked World”, Stockholm, Sweden. [author copy]

          W17. Kobsa, A. (2003): A Component Architecture for Dynamically Managing Privacy Constraints in Personalized Web-Based Systems. In R. Dingledine, ed.: Privacy Enhancing Technologies: Third International Workshop, PET 2003, Dresden, Germany, Springer-Verlag, LNCS 2760, 177-188. [DOI: 10.1007/978-3-540-40956-4_12] [author copy].

          W16. Teltzrow, M.and A. Kobsa (2003): Impacts of User Privacy Preferences on Personalized Systems - a Comparative Study. CHI-2003 Workshop "Designing Personalized User Experiences for eCommerce: Theory, Methods, and Research", Fort Lauderdale, FL. [author copy]

          W15. Kobsa, A. (2003): Pseudonymous yet Personalized Interaction with Websites that Utilize Network-wide User Modeling Services. 2003 HCIC Winter Workshop, Winter Park, CO. [author copy]

          W14. Yimam, D. and A. Kobsa (2000): DEMOIR: A Hybrid Architecture for Expertise Modeling and Recommender Systems. Proceedings of the Ninth IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises. Gaithersburg, MD, 67-74. [DOI 10.1109/ENABL.2000.883706]  [author copy]

          W13. Yimam, D. and A. Kobsa (2000): Centralization vs. Decentralization Issues in Internet-based Knowledge Management Systems: Experiences from Expert Recommender Systems. Workshop on Organizational and Technical Issues in the Tension Between Centralized and Decentralized Applications on the Internet (TWIST2000). University of California Software Institute, Irvine, CA. [author copy]

          W12. Schwab, I., A. Kobsa and I. Koychev (2000): Learning about Users from Observation. In: Adaptive User Interfaces: Papers from the 2000 AAAI Spring Symposium. Menlo Park, CA: AAAI Press. [author copy]

          W11. Specht, M. and A. Kobsa (1999): Interaction of Domain Expertise and Interface Design in Adaptive Educational Hypermedia. Proceedings of the Second Workshop on Adaptive Systems and User Modeling on the World Wide Web at WWW-8, Toronto, Canada, and UM99, Banff, Canada, 89-93. [author copy]

          W10. Pohl, W., A. Kobsa and O. Kutter (1995): User Model Acquisition Heuristics Based on Dialogue Acts. International Workshop on the Design of Cooperative Systems, Antibes-Juan-les-Pins, France, 471-486. [author copy]

          W9. Kobsa, A. and W. Pohl (1994): Acquiring Primary and Secondary Assumptions in BGP-MS. Proceedings des ABIS-94 GI Workshops `Adaptivität und Benutzermodellierung in interaktiven Software-Systemen', GMD, St. Augustin, 77-86. [author copy]

          W8. Pohl, W., A. Kobsa und O. Kutter (1993): Benutzermodellaufbau durch Präsuppositionsanalyse in interaktiven Softwaresystemen (Acquiring user models through presupposition analysis in interactive software systems). Proceedings des Workshops 'Adaptivität und Benutzermodellierung in interaktiven Softwaresystemen', Berlin, Sept. 1993.

          W7. Kobsa, A. (1992): User Modeling: Recent Work, Prospects and Hazards. Proceedings of the Workshop on User Adapted Interaction, Bari, Italy (invited paper).

          W6. Kobsa, A. (1992): Towards Inferences in BGP-MS: Combining Modal Logic and Partition Hierarchies for User Modeling. Proceedings of the 3rd International Workshop on User Modeling, Dagstuhl, Germany.

          W5. Kobsa, A. (1991): First Experiences with the SB-ONE Knowledge Representation Workbench in Natural-Language Applications. In: Workshop Notes of the AAAI Spring Symposium on Implemented Knowledge Representation and Reasoning Systems, Stanford, CA. Revised version in SIGART Newsletter, Summer 1991. [DOI 10.1145/122296.122306] [ACM Authorizer]

          W4. Kobsa, A. (1991): Reification in SB-ONE. International Workshop on Terminological Logics, Dagstuhl, Germany. Report DFKI-D-91-13, German AI Center, Saarbrücken, Germany, 72-74.

          W3. Kobsa, A. (1990): Modeling the User's Conceptual Knowledge in BGP-MS, a User Modeling Shell System. Proceedings of the 2nd International Workshop on User Modeling, Honolulu, HI. Extended and revised version in Computational Intelligence 5(4), 1990.

          W2. Kobsa, A. (1989): The SB-ONE Knowledge Representation Workbench (Extended Abstract). Preprints of the Workshop on Formal Aspects of Semantic Networks, Santa Catalina Island, CA, Feb. 1989.

          W1. Kobsa, A. (1986): Natürlichsprachlicher Zugang zu Expertensystemen (Natural-language access to expert systems). Berichte des 2. Saarbrücker Computer Forums, pp. 154-157.
           
           

          N. Major Contributions to Newsletters

          N12. Mobasher, B., S. Anand, A. Kobsa, D. Jannach (2008): Intelligent Techniques for Web Personalization and Recommender Systems in e-Commerce. Workshop report, AAAI Magazine 30(1), p.112. [URL]

          N11. Kobsa, A. (1998): Benutzergerechte Informationsumgebungen (User-Tailored Information Environments). Der GMD-Spiegel 3/4, Dez. 1998.

          N10. Fink, J., Kobsa, A., Jaceniak, I.: (1997): Individualisierung von Benutzerschnittstellen mit Hilfe von Datenchips für Personalisierungsinformation (Individualing user interfaces based on data chips with personalization information). GMD-Spiegel 1/1997, pp. 16-17. [author version]

          N9. Kobsa, A. (1991): First Experiences with the SB-ONE Knowledge Representation Workbench. SIGART Newsletter, Summer 1991. Revised version of the article in the Workshop Notes of the AAAI Spring Symposium on Implemented Knowledge Representation and Reasoning Systems, Stanford, CA., 1991. [DOI 10.1145/122296.122306]

          N8. Patel-Schneider, P., B. Owsnicki-Klewe, A. Kobsa, N. Guarino, R. MacGregor, W. Mark, D. McGuiness, B. Nebel, A. Schmiedel, J. Yen (1990): Term Subsumption Languages in Knowledge Representation. AAAI Magazine 11(2), 16-23. [URL]

          N7. Kobsa, A. (1989): Wie sag ich's dem Werkzeug? Chancen und Grenzen natürlichsprachlicher und anderer Interaktionsformen. LDV-Forum 6(2), 29-33. URL

          N6. Kobsa, A. (1988): A Selected Bibliography of the Field of User Modeling in Artificial Intellgence Dialog Systems. SIGART Newsletter, Fall 1988.

          N5. Kobsa, A. (1988): KI in der DDR. KI 2/88, 28-31. English translation in: AI Communications 1(3), 20-25. [DOI 10.3233/AIC-1988-1303]

          N4. Kobsa, A. (1985): AI-Dictionary 7. Teil: Cognitive Science. ÖGAI-Journal 4(2-3), pp. 35-38. Revised version in: E. Buchberger, Hrsg. (1987): AI-Dictionary: Die Begriffswelt der Wissensverarbeitung und der Künstlichen Intelligenz. Wien: ÖGAI.

          N3. Kobsa, A. (1984): VIE-DPM: a User Model in a Natural-Language Dialogue System. ECCAI-Newsletter, 4/84, pp. 9-11.

          N2. Kobsa, A., E. Buchberger und I. Steinacker (1983): Funktion, Inhalt und Aufbau von Partnermodellen in natürlichsprachigen Dialogsystemen. ÖGAI-Journal 2(2), pp. 27-40.

          N1. Kobsa, A. (1982): Künstliche Intelligenz und Kognitive Psychologie. In: ÖGAI-Journal 1 (1982), pp. 21-39. (Reprinted in H. Schauer und M. J. Tauber, Hrsg.: Informatik und Psychologie. Wien - München: Oldenburg. Revised version in: Retti, J., B. Buchberger, E. Buchberger, W. Horn, A. Kobsa, I. Steinacker, R. Trappl, H. Trost (1984): Artificial Intelligence - Eine Einführung. Stuttgart: Teubner.
           

          O. Other Publications

          O35. Knijnenburg, B. P., N. Rao and A. Kobsa (2012): Experimental Materials Used in the Study on Inspectability and Control in Social Recommender Systems. Technical Report # UCI-ISR-12-4, Institute for Software Research, University of California, Irvine, U.S.A. [URL] [author copy]

          O34. Page, X. and A. Kobsa (2011): Personality-based Privacy Management for Location-sharing in Diverse Subpopulations (Poster). iConference, Seattle, WA. [DOI 10.1145/1940761.1940890] [ACM authorizer]

          O33. Wang, Y. and A. Kobsa (2010): A Generic Privacy-Enhancing Personalization Infrastructure (System Demonstration). Adjunct Proceedings of the 18th International Conference on User Modeling, Adaptation and Personalization, Big Island, HI (eds. F. Bohnert and L. M. Quiroga), p.61-63. [author copy]

          O32. Jannach, D., W. Geyer, C. Dugan, J. Freyne, S. S. Anand, B. Mobasher, A. Kobsa: Workshop on Recommender Systems and the Social Web. Proceedings RecSys 2009:421-422 [DOI 10.1145/1639714.1639805] [ACM Authorizer]

          O31. Cheng, D., A. Kobsa, Kinshuk, K. Partridge, and Z. Yu (2009): Personalization in Mobile and Pervasive Computing: Workshop Summary. In: D. Cheng, Kinshuk, A. Kobsa, K. Partridge and Z. Yu, eds. (2009): Proceedings of the UMAP-09 Workshop on Personalization in Mobile and Pervasive Computing, Trento, Italy, CEUR-WS.org/Vol-478. [URL] [author copy]. Also in: Adjunct Proceedings of the 17th International Conference on User Modeling, Adaptation and Personalization (eds. G.-J. Houben, G. McCalla, F. Pianesi and M. Zancanaro), p. 107-108.

          O30. A. Kobsa (2008): Keynote Talk: Privacy-Enhanced Personalization. In: G. A. Tsihrintzis, M. Virvou, R. J. Howlett, and L. C. Jain, eds.: New Directions in Intelligent Interactive Multimedia of Studies in Computational Intelligence. Berlin - Heidelberg: Springer-Verlag. [DOI 10.1007/978-3-540-68127-4_3]

          O29. G. Tsudik, M. Burmester, A. Juels, A. Kobsa, D. Molnar, R. Di Pietro, M. Rieback (2008): RFID Security and Privacy: Long-term Research or Short-term Tinkering? Proceedings of the First ACM Conference on Wireless Network Security, Alexandria, VA, p. 160. [DOI: 10.1145/1352533.1352560] [ACM Authorizer]

          O28. A. Kobsa (2007): Trustbus'07 Keynote Talk: Privacy Enhanced Personalization. In: C. Lambrinoudakis, G. Pernul, A. Min Tjoa (Eds.): Trust, Privacy and Security in Digital Business, 4th International Conference, TrustBus 2007, Regensburg, Germany, September 3-7, 2007, p.1 [DOI: 10.1007/978-3-540-74409-2_1]

          O27. P. Brusilovksy, A. Kobsa and W. Nejdl (2007): Preface to "The Adaptive Web: Methods and Strategies of Web Personalization". Heidelberg, Germany: Springer Verlag, 2007, V-VI.

          O26. A. Kobsa, R. K. Chellappa and S. Spiekermann (2006): Privacy-Enhanced Personalization. Proceedings of CHI-2006 (Extended Abstracts), Montréal, Canada, 1631-1634. [DOI: 10.1145/1125451.1125749] [ACM Authorizer]

          O25. A. Kobsa (2004): Foreword to Josef Fink: User Modeling Servers – Requirements, Design, and Evaluation. Amsterdam, Netherlands: IOS Press (Infix). [author copy]

          O24. Grinstein, G.; Kobsa, A.; Plaisant, C.; Stasko, J.T.(2003): Which Comes First, Usability or Utility? IEEE Visualization 2003, Seattle, WA, 605-606. [IEEE]

          O23. A. Kobsa (2003): Foreword to Detlef Küpper: User Modeling for User-Specific Plan Generation and Presentation. Amsterdam, Netherlands: IOS Press (Infix). [author copy]

          O22. Patil, S. and A. Kobsa (2003): The Challenges in Preserving Privacy in Awareness Systems. Boaster, 2003 HCIC Winter Workshop, Winter Park, CO. Technical Report UCI-ISR-03-3, April 2003. [author copy]

          O21. Kobsa, A. (2003): Foreword to Jörg Schreck: Security and Privacy in User Modeling. Dordrecht, Netherlands: Kluwer Academic Publishers. [author copy]

          O20. Schwab, I., A. Kobsa, I. Koychev (2003): Learning User Interests through Positive Examples Using Content Analysis and Collaborative Filtering. Memo, GMD -- German National Research Center for Information Technology, Sankt Augustin, Germany [author copy]

          O19. Kobsa, A. (2001). Preface. User Modeling and User-Adapted Interaction 11(1-2), Ten Year Anniversary Issue, 1-4. [DOI 10.1023/A:1011191716506] [author copy]

          O18. Kobsa, A. (1999): Recent User Modeling Research in Germany. In: J. Kay, ed. UM99 User Modeling: Proceedings of the Seventh International Conference. Wien New York: Springer Verlag, 380.

          O17. Kobsa, A. (1998): Foreword to Wolfgang Pohl: Logic-Based Representation and Reasoning for User Modeling Shell Systems. Amsterdam, Netherlands: IOS Press (Infix). [author copy]

          O16. Kobsa, A. and Stephanidis, C. (1998): Adaptable and Adaptive Information Access for All Users, Including Disabled and Elderly People. Proceedings of the 2nd Workshop on Adaptive Hypertext and Hypermedia HYPERTEXT'98, Pittsburgh, MA. [author copy]

          O15. Kobsa, A., Pohl, W. and Fink, J. (1996): A Standard for the Performatives in the Communication between Applications and User Modeling Systems (Draft). [author copy]

          O14. Kobsa, A. (1996): User Modeling. In G. Strube, B. Becker,C. Freksa, U. Hahn, K. Opwis.and G. Palm, eds.: Wörterbuch der Kognitionswissenschaft (Encyclopedia of Cognitive Science), Klett-Cotta, Stuttgart, Germany.

          O13. Kobsa, A. (1995): Special Issue on User Modeling Shell Systems: Editorial. User Modeling and User-Adapted Interaction 4(2), ii-v. [DOI: 10.1007/BF01099427]

          O12. Kobsa, A. (1994): User Modeling and User-Adapted Interaction. CHI'94 Conference Companion (Tutorials), Boston, MA, 415-416. [DOI: 10.1145/259963.260532] [ACM Authorizer]

          O11. Kobsa, A. (1991): Preface [to first journal issue]. User Modeling and User-Adapted Interaction 1(1), v-viii. [DOI: 10.1007/BF00158949]

          O10. Kobsa, A. (1990): The SB-ONE Knowledge Representation Workbench (Extended Version). Memo No. 50, SFB 314: AI - Knowledge-Based Systems, Dept. of Computer Science, University of Saarbrücken, Germany.

          O9. Kobsa, A. (1989): Conceptual Hierarchies in Classical and Connectionist Architecture. Technical Report 89-010, International Computer Science Institute, Berkeley, CA; Report No. A246, Linguistic Agency Duisburg, West Germany; and Memo No. 42, SFB 314: AI - Knowledge-Based Systems, University of Saarbrücken, Germany.

          O8. Allgayer, J., R. Jansen-Winkeln, A. Kobsa, N. Reithinger, C. Reddig und D. Schmauks (1989): XTRA: Ein natürlichsprachliches Zugangssystem zu Expertensystemen. Memo Nr. 39, SFB 314: KI - Wissensbasierte Systeme, Fachbereich Informatik, University of Saarbrücken, Germany.

          O7. Kobsa, A. and W. Wahlster (1988): Special Issue on User Modeling: Preface. Computational Linguistics 14(3), 1-4. [ACL]

          O6. Kobsa, A.(1988): Discussion Section on the Relationship between User Models and Discourse Models: Introduction to the Discussion Section. Computational Linguistics 14(3), 79 - 81. [ACL]

          O5. Kobsa, A. and C. Freksa, eds. (1985): Erhebung zur Kognitionsforschung im deutschsprachigen Raum. Gemeinsamer Bericht des Arbeitskreises Kognition im FA 1.2. der GI, des Arbeitskreises Cognitive Science der Österr. Gesellschaft für Artificial Intelligence, und des Münchner Arbeitskreises für Künstliche Intelligenz und Cognitive Science.

          O4. Kobsa, A. (1982): Wissensrepräsentation: Die Darstellung von Wissen im Computer. Schriftenreihe der Österreichischen Gesellschaft für Kybernetik, Wien.

          O3. Kobsa, A. (1982): Zur Repräsentation von Dialogpartnermodellen in Semantischen Netzwerken. Bericht 82-07, Institut für Medizinische Kybernetik und Artificial Intelligence, Universität Wien, Austria.

          O2. Kobsa, A. (1982): Präsuppositionsanalyse zum Aufbau von Dialogpartnermodellen: Offene Frageformen und Aufforderungen syntaktisch identer Struktur. Bericht 82-09, Institut für Medizinische Kybernetik und Artificial Intelligence, Universität Wien, Austria.

          O1. Kobsa, A. und E. R. Reichl (1981): Die durchschnittliche Anzahl von Nachbargebieten in kartographischen Gliederungen. Institutsbericht, Institut für Informatik, Universität Linz, Austria.



          http://www.ics.uci.edu/~kobsa/kobsa-researchframe.htm Research Alfred Kobsa <body bgcolor="#33FFFF"> </body> http://www.ics.uci.edu/~kobsa/talks/talks.htm Recent keynote/invited talks

           

          Selected Keynotes & Invited Talks



          • Privacy and the Limits of A/B Testing, Carnegie Mellon University, 2015 PDF Icon

          • Towards Personalized Privacy Defaults. ISR Forum, UC Irvine, 2014  Terminal icon (22 min)

          • Personalizing Privacy. IBM Almaden, Google and Samsung Research, 2013.

          • Dimensionality in the Disclosure of Personal Information. Ben Gurion University, Israel, 2013  Terminal icon (27 min)

          • Personalization with User-Tailored Privacy, ISMIS'12/WIC'12, Macau, China PDF icon

          • Tailored Privacy for Internationally Operating Personalized Websites. Microsoft Research, Redmond, WA, 2011. Terminal icon (79 min).

          • Privacy-Enhanced Personalization. IHM 2010, Luxembourg. Terminal icon (62 min)

          • Privacy-Enhanced Personalization, CSAIL, MIT, 2007

          • Contextualized Communication of Privacy Practices and Personalization Benefits. Yonsei University, Seoul, Korea, 2006 PDF icon

          • Privacy-Enhanced Personalization. Microsoft Research, Redmond, WA, 2006  Terminal icon (49 min)

          • Personalization and Privacy in Web-Based Systems, IBM Watson, 2003

          • Universal Design and Privacy: Making AVANTI Legal. 7th ERCIM Workshop "User Interfaces for All", Paris, France, October 2002. PDF icon

          • Tailoring Privacy to Users' Needs. 8th International Conference on User Modeling, Sonthofen, Germany, July 2001 PDF icon

          • User Modeling, Privacy and Security. 1st Int'l Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems, Trento, Italy, Aug. 30, 2000.

          • Adapting Web Information to Disabled and Elderly Users. WebNet-99, Honolulu, HI. PDF icon

          http://www.ics.uci.edu/~kobsa/kobsa-vita.html Alfred Kobsa: Curriculum Vitae

          Last Update: Jan. 7, 2015 10:02 PM

           

          Alfred Kobsa: Curriculum Vitae


          Current Research Areas: personalization, privacy, human-computer interaction, personal healthcare support, information visualization.
           

          Academic Education
           
          1982-85  University of Vienna, Austria Ph.D. in Computer Science (with distinction)
          1980-81  University of Salzburg, Austria Non-degree studies in psychology, linguistics, philosophy and formal logic
          1975-80 University of Linz, Austria Master of Computer Science (with distinction)
          Master of Business Administration (with distinction)


          Scientific Positions
           
          Since 2003 Professor, Donald Bren School of Information and Computer Sciences, Univ. of California, Irvine, U.S.A.
          2012, 2013
          Visiting Researcher, Microsoft Research, Redmond, WA
          2013
          Visiting Research Professor, National University of Singapore
          2013
          Visiting Researcher, Telekom Innovation Labs at Ben Gurion University and University of Haifa, Israel
          2005-2006 Visiting Scientist, Institute for Information Systems, Humboldt University, Berlin, Germany
          2000-2003 Associate Professor, School of Information and Computer Sciences, Univ. of California, Irvine, U.S.A.
          1995-2005

          Professor, Dept. of Computer Science, University of Essen, Germany

          1995-2000 Director, GMD FIT, German Nat'l Research Center for Information Technology, St. Augustin
          1991-95 Associate Professor, Dept. of Information Science, Univ. of Konstanz, Germany
          1993 Visiting Researcher, Dept. of Computer Science, Columbia University, New York
          1985-91 Senior Researcher and Project Director, Dept. of Computer Science, Univ. of Saarbrücken
          1989 Substitute Full Professor in Computational Linguistics / Linguistic Informatics, Univ. of Duisburg
          1988 Visiting Scientist, International Computer Science Institute, Berkeley, CA
          1985 Research Associate, Austrian Research Institute for Artificial Intelligence, Vienna, Austria
          1982-84 Research Associate, Dept. of Medical Cybernetics and Artificial Intelligence, Univ. of Vienna
          1980-81 Researcher, Dept. of Computer Science, University of Linz, Austria


          Other positions
           
          1997-1999 Co-Director, humanIT Human Information Technologies GmbH, St. Augustin, Germany


          Professional Offices
           
          Societies User Modeling, Inc. (Founding President, 1994-1999; Director-at-large, 1999-2003; Advisory Board Member, 2003-12)
          Orange County ACM SIGCHI chapter (co-founder, Program Chair; 2002-04)
          European Coordinating Committee on Artificial Intelligence (Delegate, 1984-85) 
          ÖGAI Working Group on Cognitive Science (Chairman, 1982-85) 
          ACM, SIGCHI, AAAI, German Informatics Society, FIFF, AACE APC (member)

          Editorial

          User Modeling and User-Adapted Interaction (Editor-in-Chief, since 1991)
          ACM Transaction on Intelligent Interactive Systems (Associate Editor, 2009-2014)
          Universal Access in the Information Society (Editorial Board Member, 2000-2015)
          World-Wide Web (Editorial Board Member, since 1996)
          Dissertations in Artificial Intelligence Series, German Informatics Society (Editorial Board Member, since 1996)
          Springer Lecture Notes in Computer Science (Editorial Board Member,  2007-2012)
          Information Technology and Decision Making (Editorial Board Member, 2001-07)
          Review of Information Science (Editorial Board Member, 1996-98)

          Conference/Workshop
          Chair or Co-Chair
          2014 ACM Recommender Systems Conference (RecSys'14), Foster City, Silicon Valley, CA
          AAAI-2012 Workshop on Intelligent Techniques for Web Persoanlization and Recommender Systems, Toronto, Canada
          IJCAI-2011 Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, Barcelona, Spain
          18th International Conference on User Modeling, Adaptation and Personalizaton (UMAP 2010), Big Island, HI.
          2009 ACM RecSys Workshop on Recommender Systems and the Social Web (New York)
          IJCAI-09 Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, Pasadena, CA
          UMAP-09 Workshop on Personalization in Mobile and Pervasive Computing, Trento, Italy 
          AAAI-08 Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, Chicago, IL
          AAAI-07 Workshop on Intelligent Techniques for Web Personalization, Vancouver, Canada
          PEP06, CHI-2006 Workshop on Privacy-Enhanced Personalization, Montreal, Canada
          EU-IST / NSF ITR Collaboration Workshop, Ljubljana, Slovenia, 2005
          PEP05, UM05 Workshop on Privacy-Enhanced Personalization, Edinburgh, Scotland, 2005
          e-Accessibility, Dagstuhl Castle, Germany, 2003
          Third Workshop on Adaptive Hypertext and Hypermedia, at ACM Hypertext'01, Århus, Denmark, and UM-01, Sonthofen, Germany
          Fifth ERCIM Workshop on User Interfaces for All, Dagstuhl, Germany, 1999
          Second Workshop on Adaptive Systems and User Modeling on the World Wide Web at WWW-8, Toronto, Canada, and UM99, Banff, Canada, 1999
          UM94 and UM96 Workshops on the Standardardization of User Modeling Shells, Hyannis, MA, 1994 and Kailua-Kona, HI, 1996
          Fourth International Conference on User Modeling, Hyannis, MA, Aug.1994 
          First German Workshop on Adaptivity and User Modeling in Interactive Software Systems, Berlin, Germany, 1993 
          Workshops on Knowledge Representation and Inference in User Modeling, Saarbrücken, 1990 and Konstanz, 1992
          German KL-ONE Workshop, Saarbrücken, Germany, 1987
          First International Workshop on User Modeling, Maria Laach, Germany, 1986

          Conference Program 
          Committees

          UMAP 2016 (Halifax, Canada), RecSys 2015 (Vienna, Austria; Senior Member), UMAP-15 (Dublin, Ireland), AAAI ICWSM-14 (Ann Arbor, MI; Senior Member), UMAP-14 (Aalborg, Denmark; Area Chair), AAAI ICWSM-13 (Cambridge, MA; Senior Member), UMAP-13 (Rome, Italy), 2012 ACM RecSys (Dublin, Ireland; Senior Member); EC-Web 2012 (Vienna, Austria), ISMIS'12 (Macau, China), UMAP 2012 (Montréal, Canada), EC-Web 2011 (Toulouse, France), UMAP 2011 (Girona, Spain), EC-Web 2010 (Bilbao, Spain), FLAIRS-22 (Sanibel Island, FL), UMAP-2009 (Trento, Italy), AAAI-2008 (Chicago, IL), AH-2008 (Hanover, Germany), ARES-08 (Barcelona, Spain), ECIS-08 (Galway, Ireland), AAAI 2007 (Vancouver, BC), IEEE InfoVis 2007 (Sacramento, CA), IFIP IDMAN’07 (Rotterdam, Netherlands), IUI 2007 (Oahu, HI), Recommender 07 (Minneapolis, MN), UM07 (Corfu, Greece), AAAI-06 (Boston, MA), AH 2006 (Dublin, Ireland), IEEE InfoVis 2006 (Baltimore, MD), WWW-2006 (Edinburgh, Scotland), IEEE InfoVis 2005 (Minneapolis, MN), I-KNOW 05 (Graz, Austria), ISWC-05 (Galway, Ireland), IUI-2005 (San Diego, CA), UAHCI-2005 (Las Vegas, NE), UM05 (Edinburgh, Scotland), WWW-05 (Chiba, Japan), AH-2004 (Eindhoven, Netherlands), e-Society 2004 (Madrid, Spain), PRICAI-04 (Auckland, New Zealand), WWW-2004 (New York, NY), IEEE InfoVis 2003 (Seattle, WA), ACM Hypertext'03 (Nottingham, UK), INTERACT-2003 (Zürich, Switzerland), UAHCI-03 (Heraklion, Greece), UM03 (Johnstown, PA), WWW2003 (Budapest, Hungary), AAAI-2002 (Edmonton, Calgary; Senior PC member), IEEE ICALT-2002 (Kazan, Tatarstan), IEEE InfoVis-2002 (Boston, MA), PRICAI-2002 (Tokyo, Japan), AH-2002 (Malaga, Spain), UAHCI-01 (New Orleans, Louisiana), UM01 (Sonthofen, Germany), IEEE ICALT-2001 (Madison, WI), WebNet 2001(Orlando, Florida), PRICAI2000 (Melbourne, Australia), ACM CUU 2000 (Washington, D.C.), AIMSA-2000 (St. Constantine, Bulgaria), IWIPS-2000 (Baltimore, MD), COOP-2000 (Sophia Antipolis, France), ECCE-00 (Linköping, Sweden), ISI-00 (Graz, Austria), WebNet 2000 (San Antonio, TX), WebNet 99 (Honolulu, HI), UM99 (Banff, Canada), ECCE-98 (Limerick, Ireland), CoopIS'98 (New York, NY), COOP-98 (Antibes-Juan-les-Pins, France), IBERAMIA'98 (Lisbon, Portugal),  ISI-98 (Prague, Czechia), UM97 (Chia Laguna, Italy), ECCE-96 (Madrid, Spain), EuroAIED-96 (Lisbon, Portugal), UM-96 (Kailua-Kona, HI), COOP-96 (Antibes-Juan-les-Pins, France), ICC-95 (Cluj-Napoca, Romania), HIM-95 (Konstanz, Germany), ISI-94 (Graz, Austria), ECAI-94 (Amsterdam, Netherlands), ECCE-94 (St. Augustin, Germany), ISI-90 (Konstanz, Germany), UM-90 (Honolulu, HI), KONNAI-90 (Salzburg, Austria), GLDV-89 (Darmstadt, Germany), ÖGAI-87 (Vienna, Austria), GWAI/ÖGAI-86 (Ottenstein, Austria)

          Workshop Program
          Committees
          ACM ASIACCS 2016 Workshop on Internet-of-Things Privacy, Trust, and Security (Xi'an, China), ACM ASIACCS 2015 Workshop on Internet-of-Things Privacy, Trust, and Security (Singapore), UMAP-13 Workshop on User Modeling for Enabling Technology for All (Rome, Italy), WWW 2013 Workshop on Social Recommender Systems (Rio de Janeiro, Brazil), ACM WSDM 2013 Workshop on Context-aware Retrieval and Recommendation (Rome, Italy), ACM CSCW 2012 Workshop on Measuring Networked Social Privacy (San Antonio, TX), ACM RecSys 2012 Workshop on Human Decision Making in Recommender Systems, UMAP 2012 Workshop on Augmented User Modeling (Montreal, Canada), UMAP 2012 Workshop on Personalized Knowledge Modeling with Big (Usage & Context) Data (Montreal, Canada), ACM KDD 2012 Workshop on Context Discovery and Data Mining (Beijing, China), IUI 2012 Workshop on Ubiquitous Personalization (Lisbon, Portugal), IUI 2012 Workshop on Context-awareness in Retrieval and Recommendation (Lisbon, Portugal), ACM RecSys 2011 Workshop on Human Decision Making in Recommender Systems (Chicago, IL), IJCAI 2011 Workshop on Intelligent Techniques for Web Personalization & Recommendation (Barcelona, Spain), UMAP 2011 Workshop on Personalization Approaches in Learning Environments, UMAP 2011 Workshop on Augmenting User Models with Real World Experiences to Enhance Personalization and Adaptation (Girona, Spain), RecSys 2010 Workshop on User-Centric Evaluation of Recommender Systems and Their Interfaces (Barcelona, Spain), RecSys 2010 International Workshop on Information Heterogeneity and Fusion in Recommender Systems (Barcelona, Spain), HCI India 2010 Workshop on Modelling for Accessibility Personalization in HCI (Bombay, India), IUI 2010 Workshop on Social Recommender Systems  (Hong Kong, China), IJCAI 2009 Workshop on Intelligence and Interaction (Pasadena, CA), IIMSS-09 Workshop on Human-Computer Interaction in Knowledge-based Environments (Mogliano Veneto, Italy), UM07 Workshop on Ubiquitous and Decentralized User Modeling (Corfu, Greece), UM07 Workshop Towards User Modelling and Adaptive Systems for All (Corfu, Greece), PERVASIVE-06 Workshop on Real-World Experiences with RFID and Sensor Networks (Dublin, Ireland), UM05 Workshop on Decentralized, Agent-Based and Social Approaches to User Modeling (Edinburgh, Scotland), AAAI-04 Workshop on Semantic Web Personalization (San Jose, CA), AH'04 Workshop on Personalization in Future TV (Eindhoven, Netherlands), UM03 Workshop on User Modeling for Ubiquitous Computing (Johnstown, PA), i3 2000 Workshop on Interactive Learning Environments for Children (Athens, Greece), UI4ALL-00 (Florence, Italy), HT98 Workshop on Adaptive Hypertext and Hypermedia (Pittsburgh, PA), ABIS-98 (Saarbrücken, Germany), UI4ALL-98 (Stockholm, Sweden), AAAI-99 Symposium on Modal and Temporal Logic based Planning for Open Networked Multimedia Systems (North Falmouth, MA), ABIS-97 (Dortmund, Germany), UI4ALL-97 (Obernai, France), UI4ALL-96 (Prague, Czech Republic), UI4ALL-95 (Heraklion, Greece), Italian Workshop on User-Adapted Interaction 1994 (Bari, Italy), ABIS-94 (St. Augustin, Germany), IJCAI-91 Workshop on User Models and Natural-Language Dialog (Sydney, Australia), AAAI-91 Workshop on Intelligent Multimedia Interfaces (Stanford, CA).

          Journal Reviewer

          ACM Computing Surveys, ACM Transactions on [Office] Information Systems, ACM Transactions on Computer-Human Interaction, ACM/Springer Journal on Multimedia Systems, AI & Society, Angewandte Informatik, Applied Artificial Intelligence, Artificial Intelligence Review, The Computer Journal, Communications of the ACM, Cybernetics & Systems Journal, Data and Knowledge Engineering, Human Computer Interaction, IEEE Privacy & Security, IEEE Transactions on Visualization and Computer Graphics, Information Technology & Decision Making, Interacting with Computers, International Journal of Electronic Commerce, International Journal of Information Technology and Decision Making, International Journal of Man-Machine Studies, Journal for Digital Information, KI, Knowledge Engineering Review, New Review of Hypermedia and Multimedia, Swiss Journal of Psychology, Universal Access in the Information Society, World Wide Web

          Conference Reviewer

          CHI-16 (San Jose. CA), CHI-13 (Paris, France), UbiComp-12 (Pittsburgh, PA), InfoViz-09 (Atlantic City, NJ), ECIS09 (Verona, Italy), UbiComp07 (Innsbruck, Austria), VAST-06 (Baltimore, MD), CHI-2004 (Vienna, Austria), CHI-2002 (Minneapolis, MN), ECAI-2002 (Lyon, France), UIST-2002 (Paris, France), UIST-2001 (Orlando, FL),COOP-95 (Antibes-Juan-Les-Pins, France), CHI-94 (Boston, MA), INTERCHI'93 (Amsterdam, Netherlands), ISI-92 (Saarbrücken, Germany), CHI-92 (Monterey, CA), ECAI-92 (Vienna, Austria), CAIA-91 (Miami, FL), GWAI-91 (Bonn, Germany), ECAI-90 (Stockholm, Sweden), GWAI-90 (Eringerfeld, Germany), IJCAI-89 (Detroit, MI), ÖGAI-89 (Innsbruck, Austria), ECAI-88 (Munich, Germany), IJCAI-87 (Milan, Italy), GWAI-87 (Geseke, Germany, EMCSR-84&86 (Vienna, Austria)

          Reviewing for Publishers Addison-Wesley, Springer Verlag, Kluwer Academic Publishers, German Information Science Association Press, Wiley

          Reviewing for 
          Funding Agencies
          Australian Research Council, Austrian Ministry of Science and Research, Austrian Science Fund, Commission of the European Union (ESPRIT, Information Society Technologies, Disabled & Elderly, Telematics, Language Engineering, Design for All), Danish National Research Foundation, German Science Foundation, Natural Sciences and Engineering Research Council of Canada, U.S. National Science Foundation (Cyber-Enabled Discovery and Innovation, Cybertrust, Digital Libraries, Digital Society and Technology, Human-Centered Computing, Information Technology Research, Research Infrastructure, Secure and Trustworthy Cyberspace, Social-Computational Systems), Netherlands Organization for Scientific Research, Science Foundation Ireland, German Volkswagen Foundation

          Advisory Boards Scientific Advisory Board of the German National Social Science Information Center (IZ Sozialwissenschaften, 1997-2002), User Modeling Association (2003-2012), Adaptive Hypermedia and WWW Conference series (2002-2009).

          Executive Boards Institute for Software Research, University of California, Irvine (since 2000)

          Steering Committees
          Steering Committeee of the ACM Conference Series on Recommender Systems (since 2013)


          Research Grants
           
          2015-19
          Privacy Cognizant IoT Environment
          DARPA Brandeis (w/ PI Mehrotra, co-PI Venkasubramanian)
          $4,972,000
          2015-17
          Effects of Auditory Stimuli on Security Tasks
          National Science Foundation (w/ co-PIs Tsudik and Berg)
          $240,000
          2015
          Online Job Clubs: A Smartphone Pilot Project
          UCI Multi-Investigator Research Grant (w/ PI Sugie, co-PI Ward)
          $18,621
          2015
          Justice Propulsion Lab & Racial Violence Archive
          UC Center for New Racial Studies (w/ PI Ward, co-PI Sugie)
          $27,500
          2014-17
          User-Tailored Approach to Privacy Decision Support
          National Science Foundation (single PI)
          $666,000
          2014-16 Self-Discovery Tool for Latina Breast Cancer Survivors National Cancer Institute (7 PIs from UCI and CSUF) $100,000
          2008-14
          Industry gifts (mostly related to research on privacy)
          Samsung, Intel, Google, Disney, Magellan, Ericsson, TCL, Qualcomm (all single PI)
          $375,000
          2012-13
          User Errors in Security-Related Behavior
          National Science Foundation, SaTC (w/ G. Tsudik)
          $50,000
          2011
          Various Seed Grants
          UCI, Bren School of ICS (5 PIs)
          $43,000
          2009-2012 Usable Location Privacy in Geo-Social Networks National Science Foundation (3 PIs) $300,000
          2008-2012 Decentralized Virtual Activities & Technologies National Science Foundation (6 PIs) $3,000,000
          2008-2013

          User-Aided Secure Association of Wireless Devices

          National Science Foundation (3 PIs) $460,000
          2005-2006 Instant Messaging and Privacy CRITO (single PI) $15,000
          2004-2006 European IST collaboration (privacy area) National Science Foundation (single PI) $91,000
          2004-2006 Privacy Protection in E-Commerce Alexander v. Humboldt Foundation (w/ O. Günther) $45,000
          2003-2007 Privacy in Personalized Systems National Science Foundation (Single PI) $360,000
          2002-2006 Distributed Software Development National Science Foundation (8 PIs)
          $1,800,000
          2002-2003 Information Visualization CRITO (w/ J. Kim)
          $ 15,000
          2001-2003 International Privacy CRITO (Single PI)
          $ 40,000
          1999-00 Industrial Projects German Industries
          $ 45,000
          1997-00 LaboUR (Machine Learning) German Science Foundation (w/ W. Pohl)
          $ 400,000
          1998 BGP-MS (User Modeling Shell) SERI, Korea
          $ 25,000
          1996-98 COBRA (Business Intelligence) European Commission (ACTS, 6 institutions)
          $ 1,600,000
          1995-98 AVANTI (Universal Access) European Commission (ACTS, 8 institutions)
          $ 3,500,000
          1992-96 BGP-MS (User Modeling Shell) German Science Foundation
          $ 600,000
          1992-95 Research and Instrumentation University of Konstanz
          $ 60,000
          1985-95 Conference travel German Science Foundation
          $ 15,000


          Awards and Honors

          2015
          Mercator Fellow of the German Science Foundation
          2007, 2014
          Google Research Award
          2006 Humboldt Research Award
          2003 Best Evaluation Paper Award at the Ninth International Conference on User Modeling
          2001 UCI Chancellor's Award for Excellence in Fostering Undergraduate Research
          1998-2007
          Listings in Marquis Who is Who in the World; America; Science and Engineering
          1985 Heinz Zemanek Award of the Austrian Computer Society
          1985 Austrian Presidential Theodor Körner Award
          1984 Scientific Award of the Cultural Department of the City of Vienna, Austria
          1982 Merit Scholarship of the Faculty of Technical and Natural Science, Technical University of Vienna
          1981 Merit Scholarship of the Faculty of Natural Science, University of Salzburg, Austria
          1978-83 PRO SCIENTIA fellowship
          1977-80 Merit Scholarships of the Schools of Natural Science and Engineering, and Social and Economic Sciences, University of Linz, Austria


          Teaching and Student Supervision
          • 20 different classes in the fields of Information Science, Human-Computer Interaction, User Modeling, Information Visualization, Privacy, Knowledge Representation, Natural-Language Interfaces, Algorithms, and Cognitive Science, taught at the Universities of Linz and Vienna, Austria; Saarbrücken, Kaiserslautern, Duisburg, Konstanz and Essen, Germany; Rome, Italy; and Irvine, California.
          • Courses and tutorials on dialog systems and user modeling held at German and Spanish summer schools and at CAIA-91, SIGIR-92, AAAI-93, IJCAI-93, ECAI-94, and CHI-94
          • About 25 master theses supervised in several areas of informatics and information science (6 months full-time work) and computer science (12 months full-time work)
          • Supervised 10 and co-supervised 9 PhD theses.
             

          Scientific publications

          List of all publications (including many online copies) available at http://www.ics.uci.edu/~kobsa/kobsa-publi.htm


          Rankings

          Google Scholar: 8,500+ citations (3,000 in the last five years), h=45, i10=100
          Microsoft Academic Search: #79 in Human-Computer Interaction (field rating, worldwide)
          Erdös-Number 4 (via G. Tsudik, M. Joye and P. L. Montgomery)

           
          Scientific presentations

          Conference keynotes ISMIS'12/WIC'12 (Macau, China), IHM-2010 (Luxembourg), INREDIS-2009 (Madrid, Spain), OCG-2009 (Vienna, Austria), FLAIRS-2008 (Miami, FL), KES-IIMSS-2008 (Piraeus, Greece), DEXA-2007 (Regensburg, Germany), UM2001 (Sonthofen, Germany), AH 2000 (Trento, Italy), WebNet'99 (Honolulu, HI), KI-93 (Berlin, Germany), LAUD-93 (Duisburg, Germany)
          Conference Panels NPRS-2015 Panel on Privacy Perspective: Individual/Consumer, CPDP-2014 Panel on User-Centered Data Ecosystems (Brussels, Belgium), UMAP-2012 UMUAI panel (Montreal, Canada), UMAP-2010 University-Industry Panel (Big Island, HI), ACM WiSEC08 Panel on RFID Security and Privacy (Alexandria, VA), TrustBus-07 Panel on Identity Management (Regensburg, Germany), E-Challenges 2005 Panel on "Collaboration between NSF/ITR and FP6/IST" (Ljubljana, Slovenia), AAAS-05 Panel "Privacy and Security" (Washington, D.C.), IUI-05 Workshop Panel "Beyond Personalization" (San Diego, CA), IEEE VIS'03 Panel on Usability vs. Utility (Seattle, WA), I-KNOW-2003 Panel on Information and Knowledge Visualization (Graz, Austria), EXPO-2000 Panel on Access to Knowledge (Hanover, Germany), AH-2000 Panel on Adaptive Hypermedia Evaluation (Trento, Italy), AAAI-2000 Panel on Adaptive User Interfaces for the Real World (Stanford, CA), WebNet-99 Panel on Adaptive Hypertext and Hypermedia (Honolulu, Hawaii), UM-99 National Projects Panel (Banff, Canada), HT99 Panel on Adaptive Hypertext and Hypermedia (Darmstadt, Germany), CAIA-91 Panel on Multimedia in AI (Miami, FL)

          About 300 scientific presentations given in the North America, Europe, Asia and Australia

           

          http://www.ics.uci.edu/~kobsa/phds/phds.htm Ph.D. Students Alfred Kobsa

           

          Graduated Ph.D. Students

           

           

          Primary Ph.D. Advisor

          Bart Knijnenburg (currently Assistant Professor, Human-Centered Computing Division, Clemson University, NC):
          A User-Tailored Approach to Privacy Decision Support
          Department of Informatics, University of California, 2015
          Excerpts of dissertation published in IJMMS, TIIS, IUI13, CHI13, ICIS13, ICIS14
          Received a Google PhD Fellowship in Privacy
          Xinru Page (currently Assistant Professor in the Department of Computer Information Systems, Bentley University, MA):
          Factors that Influence Adoption and Use of Location-Sharing Social Media
          Department of Informatics, University of California, 2014
          (received 2014 Yahoo! Best Dissertation Award and 2015 iSchools Doctoral Dissertation Award)
          Excerpts of dissertation published in ICCSE09, iConference10, ICSWM12, CSCW13, UbiComp13 (received honorable mention)
          Yang Wang (currently Assistant Professor in the School of Information Studies, Syracuse University, NY):
          Privacy-Enhanced Personalization: a Dynamic Software Product Line Approach.
          Department of Informatics, University of California, 2010
          Excerpts of dissertation published in UM05, SPLC05, UM07, IUI book 08, PEPch 08, UMAP09
          , SPLC09, UMUAI
           
          Sameer Patil (currently Research Scientist, Computer Science & Engineering, Polytechnic School of Engineering, New York University):
          Reconciling Awareness and Privacy Needs in Loosely Coupled Collaboration.
          Department of Informatics, University of California, Irvine, 2009.
          Excerpts of dissertation published in HCI04, GROUP05, ICOCSC05, iConference09, INTERACT09, AS book 09, IwC
          , B&IT, IST
           
          Josef Fink (currently Professor at the University of Applied Science, Frankfurt/M., Germany):
          User Modeling Servers – Requirements, Design, and Evaluation.
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 2003.
          Revised version of dissertation published with IOS Press, Netherlands (Infix), 2004. What makes this work imporant?
          Excerpts of dissertation published in UMUAI 10(3-4), AIR 18(1), and UM 1999 and UM 2003 (received Best Evaluation Paper Award).
           
          Detlef Küpper (currently Professor at the University of Applied Science, Aalen, Germany):
          User-Adaptive Plan Generation and Presentation (in German).
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 2002.
          Revised version of dissertation published with IOS Press, Netherlands (Infix), 2004. What makes this work imporant?.
           
          Jörg Schreck (currently Security Analyst at Telefónica Europe, Germany):
          Security and Privacy in User Modeling.
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 2001.
          Revised version of dissertation appeared in 2003 with Kluwer Academic Publishers. What makes this work imporant?
           
          Ludwin Fuchs (currently with The Boeing Company, Seattle, WA):
          Situation-Oriented Support of Group Awareness in CSCW Systems (in German).
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 1998.
           
          Wolfgang Pohl (currently with the German Informatics Society, Bonn, Germany):
          Logic-Based Representation and Reasoning for User Modeling Shell Systems.
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 1997.
          Revised version of disseration published with IOS Press, Netherlands (Infix), 1998.   What makes this work imporant?
          Summary of thesis published in User Modeling and User-Adapted Interaction 9(3), 217-282.
           
          Harold Paredes-Frigolett (currently Associate Professor, Diego Portales University, Chile):
          Integrating World Knowledge with Cognitive Parsing: A Fine-Grained, Weakly Interactive Computational Approach.
          Dept. of Mathematics and Computer Science, University of Essen, Germany, 1996.
           
          Ian Beaumont (currently with SAP AG, Walldorf, Germany):
          User Modelling in the Hypertext-Based Medical Tutoring System ANATOM-TUTOR.
          Dept. of Information Science, University of Konstanz, Germany, 1996.
          Summary of Ph.D. thesis published in User Modeling and User-Adapted Interaction 4(1), 21-45.

           

          Secondary Ph.D. Advisor

          Jan Kolter (currently with zeb Consuting, Germany).
          User-Centric Privacy – A Usable and Provider-Independent Privacy Infrastructure
          School of Business, Economics and Management Information Systems, University of Regensburg, Germany, 2009.
          Excerpts of thesis published at SEC07, DBSec07
          , ARES07, SEC09, ARES09
          Max Teltzrow (currently with Daimler AG, Berlin, Germany).
          A Quantitative Analysis of E-Commerce: Channel Conflicts, Data Mining, and Consumer Privacy
          School of Business Administration and Economics, Humboldt University, Berlin, Germany, 2005.
          Excerpts of thesis published at ACM E-Commerce 2003, EC-Web 2003, Karat & Blom & Karat (eds.), PET 2004
           
          Uwe Behrens (currently Senior Software Architect at RTT Realtime Technology, Munich, Germany).
          Rendering with Poxels: A Software Architecture for Programmable Shading Systems.
          Dept. of Computer Science, University of Bremen, Germany, 1999.
           
          Marcus Sohlenkamp (currently with Deutsche Telekom T-Systems, Bonn, Germany):
          Supporting Group Awareness in Multi-User Environments through Perceptualization.
          Dept. of Computer Science, University of Paderborn, Germany, 1998.
           
          Wolfgang Broll (currently Professor at the Technical University of Ilmenau, Germany):
          An Object-Oriented Interaction Model for the Support of Distributed Virtual Environments (in German).
          Dept. of Computer Science, University of Göttingen, Germany, 1998.
           
          Fahri Yetim (currently Lecturer and Adjunct Professor at the University of Oulu, Finland):
          Explanations in Human-Computer Interaction: A Framework for the Integration of Hypertext and Artificial Intelligence Methods (in German).
          Dept. of Information Science, University of Konstanz, Germany.

           

          External/Foreign Reviewer/Examiner


          Kirstie Hawkey (currently Assistant Professor at Dalhousie University, Canada)
          Managing the Visual Privacy of Incidental Information in Web Browsers
          Dept. of Computer Science, Dalhousie University, Halifax, Canada, 2006.
          Excerpts of thesis published at CHI 2006, WWW 2006, Graphics Interface 2007

          Mathias Bauer (currently CEO and Chief Scientist of mineway, Saarbrücken, Germany)
          An Evidence-Theoretic Approach to Plan Recognition (in German)
          Dept. of Computer Science, University of Saarbrücken, Germany, 1996.
          Bruno Errico
          Intelligent Agents and User Modelling.
          Dipartimento di Informatica e Sistemistica, Università di Roma "La Sapienza", 1997.

           

           

           

           
          http://www.ics.uci.edu/~kobsa/kobsa-affiliations.htm Affiliations Alfred Kobsa

           

           

          Affiliations Alfred Kobsa

           

           

          • Department of Informatics, Bren School of Information and Computer Sciences, University of California, Irvine (Faculty)
          • Department of Computer Science, Bren School of Information and Computer Sciences, University of California, Irvine (Faculty)
          • Institute of Software Research (ISR), University of California, Irvine (Executive Board Member)
          • Center for Computer Games and Virtual Worlds, Bren School of ICS, University of California, Irvine (Faculty Associate)
          • Center for Cyber-Security and Privacy, Bren School of ICS, University of California, Irvine (Area Leader)
          • Center for Digital Transformation, Paul Merage School of Business, University of California, Irvine (Faculty Affiliate)
          • Center for Machine Learning and Intelligent Systems, Bren School of ICS, University of California, Irvine (Faculty Affiliate)
          • California Institute for Telecommunication and Information Technology, University of California, Irvine (Faculty Affiliate)
          http://www.ics.uci.edu/community/icsjobs/ ics.jobs @ the bren school of information and computer sciences
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          Copyright Inquiries | UCI Directory | Intranet | icswebmaster
          http://www.ics.uci.edu/~dechter/publications/r68.html Dr. Rina Dechter @ UCI
          |
          R68
          Constraint Satisfaction
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          A constraint satisfaction problem (csp) defined over a constraint network consists of a finite set of variables, each associated with a domain of values, and a set of constraints. A solution is an assignment of a value to each variable from its domain such that all the constraints are satisfied. Typical constraint satisfaction problems are to determine whether a solution exists, to find one or all solutions and to find an optimal solution relative to a given cost function. An example of a constraint satisfaction problem is the well known k-colorability...

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r227.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports


          R226
          Weighted heuristic anytime search: new schemes for optimization over graphical models
          Natalia Flerova, Radu Marinescu, and Rina Dechter

          Abstract
          Weighted heuristic search (best-first or depth-first) refers to search with a heuris- tic function multiplied by a constant w [Pohl (1970)]. The paper shows, for the first time, that for optimization queries in graphical models the weighted heuristic best-first and weighted heuristic depth-first branch and bound search schemes are competitive energy-minimization anytime optimization algorithms. Weighted heuristic best-first schemes were investigated for path-finding tasks. However, their potential for graphical models was ignored, possibly because of their memory costs and because the alternative depth-first branch and bound seemed very appropriate for bounded depth. The weighted heuristic depth-first search has not been studied for graphical models. We report on a significant empirical evaluation, demonstrating the potential of both weighted heuristic best-first search and weighted heuristic depth-first branch and bound algorithms as approximation anytime schemes (that have suboptimality bounds) and compare against one of the best depth-first branch and bound solvers to date.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r220.html Dr. Rina Dechter @ UCI
          |

          R220
          Empowering Mini-Bucket in Anytime Heuristic Search with Look-Ahead: Preliminary Evaluation
          William Lam, Kalev Kask, and Rina Dechter

          Abstract
          The paper explores the potential of look-ahead methods within the context of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., weighted CSPS or MAP inference). We study how these methods can be used to compensate for the approximation error of the initially generated Mini-Bucket heuristics, within the context of anytime Branch-And-Bound search.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r152.html Dr. Rina Dechter @ UCI
          |

          R152
          Studies in Solution Sampling

          Vibhav Gogate and Rina Dechter

          Abstract
          We introduce novel algorithms for generating random solutions from a uniform distribution over the solutions of a boolean satisfiability problem. Our algorithms operate in two phases. In the first phase, we use a recently introduced SampleSearch scheme to generate biased samples while in the second phase we correct the bias by using either Sampling/Importance Resampling or the Metropolis- Hastings method. Unlike state-of-the-art algorithms, our algorithms guarantee convergence in the limit. Our empirical results demonstrate the superior performance of our new algorithms over several competing schemes.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r34.html Dr. Rina Dechter @ UCI
          |
          R34
          GSAT and Local Consistency
          Kalev Kask (kkask@ics.uci.edu)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          It has been shown that hill-climbing constraint satisfaction methods like min-conflicts [Minton et al., 1990] and GSAT [Selman et al., 1992] can outperform complete systematic search methods like backtracking and backjumping on many large classes of problems. In this paper we investigate how preprocessing improves GSAT. In particular, we will focus on the effect of enforcing local consistency on the performance of GSAT. We will show that enforcing local consistency on uniform random problems has very little effect on the performance of GSAT. However, when the problem has hierarchical structure, local consistency can significantly improve GSAT. It has been shown [Konolige, 1994] that there are certain structured problems that are very hard for GSAT while being very easy for the Davis-Putnam procedure. We will show that they become very easy for GSAT once a certain level of local consistency is enforced.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r173.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
          |

          R173
          New Mini-Bucket Partitioning Heuristics for Bounding the Probability of Evidence

          Emma Rollon and Rina Dechter

          Abstract
          Mini-Bucket Elimination (MBE) is a well-known approximation algorithm deriving lower and upper bounds on quantities of interest over graphical models. It relies on a procedure that partitions a set of functions, called bucket, into smaller subsets, called mini-buckets. The method has been used with a single partitioning heuristic throughout, so the impact of the partitioning algorithm on the quality of the generated bound has never been investigated. This paper addresses this issue by presenting a framework within which partitioning strategies can be described, analyzed and compared. We derive a new class of partitioning heuristics from first-principles geared for likelihood queries, demonstrate their impact on a number of benchmarks for probabilistic reasoning and show that the results are competitive (often superior) to state-ofthe- art bounding schemes.

          [pdf] [Color pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r94.html Dr. Rina Dechter @ UCI
          |
          R94
          A General Scheme for Automatic Generation of Search Heuristics from Specification Dependencies
          Kalev Kask, Rina Dechter
          Abstract
          The paper presents and evaluates the power of a new scheme that generates search heuristics mechanically for problems expressed using a set of functions or relations over a finite set of variables. The heuristics are extracted from a parameterized approximation scheme called Mini-Bucket elimination that allows controlled trade-off between computation and accuracy. The heuristics are used to guide Branch-and- Bound and Best-First search. Their performance is compared on two optimization tasks: the Max-CSP task defined on deterministic databases and the Most Probable Explanation task defined on probabilistic databases. Benchmarks were random data sets as well as applications to coding and medical diagnosis problems. Our results demonstrate that the heuristics generated are effective for both search schemes, permitting controlled trade-off between preprocessing (for heuristic generation) and search.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r62a.html Dr. Rina Dechter @ UCI
          |
          R62a
          Mini-Buckets: A General Scheme for Generating Approximations in Automated Reasoning
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The class of algorithms for approximating reasoning tasks presented in this paper is based on approximating the general bucket elimination framework. The algorithms have adjustable levels of accuracy and efficiency, and they can be applied uniformly across many areas and problem tasks. We introduce these algorithms in the context of combinatorial optimization and probabilistic inference.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r148.html Dr. Rina Dechter @ UCI
          |

          R148
          AND/OR Multi-Valued Decision Diagrams for Constraint Optimization

          Robert Mateescu, Radu Marinescu and Rina Dechter

          Abstract
          We propose a new top down search-based algorithm for compiling AND/ORMulti-Valued Decision Diagrams (AOMDDs), as representations of the optimal set of solutions for constraint optimization problems. The approach is based on AND/OR search spaces for graphical models, state-of-the-art AND/OR Branch-and-Bound search, and on decision diagrams reduction techniques. We extend earlier work on AOMDDs by considering general weighted graphs based on cost functions rather than constraints. An extensive experimental evaluation proves the efficiency of the weighted AOMDD data structure.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r84.html Dr. Rina Dechter @ UCI
          |
          R84
          An Anytime Approximation for Optimizing Policies Under Uncertainty
          Rina Dechter (dechter@ics.uci.edu)
          Abstract
          The paper presents a scheme for approximation for the task of maximizing the expected utility over a set of policies, that is, a set of possible ways of reacting to observations about an un�certain state of the world. The scheme which is based on the mini�bucket idea for approximating variable elimination algorithms, is parameterized, allowing a flexible control between efficiency and accuracy. Furthermore, since the scheme outputs a bound on its accuracy, it allows an anytime scheme that can terminate once a desired level of accuracy is achieved. The presented scheme should be viewed as a guiding framework for approximation that can be improved in a variety of ways.

          PostScript | PDF


          http://www.ics.uci.edu/~dechter/publications/r109.html Dr. Rina Dechter @ UCI
          |
          R109
          Unifying Cluster-Tree Decompositions for Automated Reasoning
          Kalev Kask, Rina Dechter, Javier Larrosa and Avi Dechter
          Abstract
          The paper provides a unifying perspective of tree-decomposition algorithms appearing in various automated reasoning areas such as join-tree clustering for constraint-satisfaction and the clique-tree al- gorithm for probabilistic reasoning. Within this framework, we in- troduce a new algorithm, called bucket-tree elimination (BTE), that extends Bucket Elimination (BE) to trees, and show that it can pro- vide a speed-up of n over BE for various reasoning tasks. Time-space tradeo�s of tree-decomposition processing are analyzed.

          PDF
          PS



          http://www.ics.uci.edu/~dechter/publications/r108.html Dr. Rina Dechter @ UCI
          |
          R108
          Approximate Decomposition: A Method for Bounding and Estimating Probabilistic and Deterministic Queries
          David Larkin
          Abstract
          In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intractable for exact algorithms because of the large number of dependency relationships in their structure. Our method e.ectively maps such a dense problem to a sparser one which is in some sense �closest�. Exact methods can be run on the sparser problem to derive bounds on the original answer, which can be quite sharp. On one large CPCS network, for example, we were able to calculate upper and lower bounds on the conditional probability of a variable, given evidence, that were almost identical in the average case.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r160a.html Dr. Rina Dechter @ UCI
          |

          R160a
          Refined Bounds for Instance-Based Search Complexity of Counting and Other #P Problems

          Lars Otten and Rina Dechter

          Abstract
          This paper presents measures for upper and lower bounding the instance- based complexity of AND/OR search algorithms for solution counting and related #P problems. This can be of utmost importance in selecting the right set of pa- rameters for fitting an algorithm to a problem instance and in devising heuristics during execution. To this end we estimate the size of the search space, with spe- cial consideration given to the impact of determinism in a problem. The resulting schemes are evaluated empirically on a variety of problem instances; in many cases relatively tight bounds are obtained, far better than those implied by the tree width or hypertree width. Specific results are provided detailing how these measures can be useful for discriminating between variable orderings.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r85.html Dr. Rina Dechter @ UCI
          |
          R85
          Constraint Satisfaction
          Rina Dechter (dechter@ics.uci.edu) & Francesca Rossi (frossi@math.unipd.it)
          Abstract
          Constraints are a declarative knowledge representation formalism that allows for a compact and expressive modeling of many real-life problems. Constraint satisfaction and propagation tools, as well as constraint programming languages, are successfully used to model, solve, and reason about many classes of problems, such as design, diagnosis, scheduling, spatio-temporal reasoning, resource allocation, configuration, network optimization, and graphical interfaces.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r19.html Dr. Rina Dechter @ UCI
          |
          R19
          Combining Qualitative and Quantitative Constraints in Temporal Reasoning
          Itay Meiri (itay@cs.ucla.edu)

          Abstract
          This paper presents a general model for temporal reasoning that is capable of handling both qualitative and quantitative information. This model allows the representation and processing of many types of constraints discussed in the literature to date, including metric constraints (restricting the distance between time points) and qualitative, disjunctive constraints (specifying the relative position of temporal objects). Reasoning tasks in this unified framework are formulated as constraint satisfaction problems and are solved by traditional constraint satisfaction techniques, such as backtracking and path consistency. New classes of tractable problems are characterized, involving qualitative networks augmented by quantitative domain constraints, some of which can be solved in polynomial time using arc and path consistency.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r208a.html Dr. Rina Dechter @ UCI
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          R208a
          Compiling Probabilistic Conformant Planning into Mixed Dynamic Bayesian Network
          Junkyu Lee

          Abstract
          Probabilistic conformant planning is a task of finding a plan that achieves the goal without sensing, where the outcome of an action is probabilistic and the initial state is uncertain. In this thesis, we formulate the probabilistic conformant planning as marginal Maximum A Posteriori (MAP) probabilistic inference based on the finite horizon state transition model. In practice, most of the planning problems are expressed in Probabilistic Planning Domain Definition Languag. Therefore, we developed a translation that reads a PPDDL instance and compiles the instance into a graphical model to provide a planning problem to existing marginal MAP solvers. The compilation is based on SAT encoding of planning problems, and the encoding is extended from the linear encodings used to solve classical planning problems by SAT solvers. The graphical model is obtained by converting CNF clauses into a mixed network, where the probabilistic state transitions are compiled as Bayesian network and deterministic constraints are compiled as an auxiliary network. We performed empirical evaluation to compare marginal MAP algorithms and Probabilistic-FF planner. The experiment results show that marginal MAP algorithms were able to solve selected problem domains.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r127.html Dr. Rina Dechter @ UCI
          |
          R127
          AND/OR Branch-and-Bound Search for Pure 0/1 Integer Linear Programming Problems
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. In this paper we extend the recently introduced AND/OR Branch-and-Bound algorithm (AOBB) [1] for solving pure 0/1 Integer Linear Programs [2]. Since the variable selection can have a dramatic impact on search performance, we introduce a new dynamic AND/OR Branch-and-Bound algorithm able to accommodate variable ordering heuristics. The effectiveness of the dynamic AND/OR approach is demonstrated on a variety of benchmarks for pure 0/1 integer programming, including instances from the MIPLIB library, real-world combinatorial auctions and random uncapacitated warehouse location problems.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r15a.html Dr. Rina Dechter @ UCI
          |
          R15a
          On the Feasibility of Distributed Constraint Satisfaction
          Zeev Collin (zeev@cs.technion.ac.il), Rina Dechter (dechter@ics.uci.edu) & Shmuel Katz (katz@cs.technion.ac.il)

          Abstract
          This paper characterizes connectionist-type architectures that allow a distributed solution for classes of constraint-satisfaction problems. The main issue addressed is whether there exists a uniform model of computation (where all nodes are indistinguishable) that guarantees convergence to a solution from every initial state of the system, whenever such a solution exists. We show that even for relatively simple constraint networks, such as rings, there is no general solution using a completely uniform, asynchronous, model. However, some restricted topologies like trees can accommodate the uniform, asynchronous, model and a protocol demonstrating this fact is presented. An almost-uniform, asynchronous, networkconsistency protocol is also presented. We show that the algorithms are guaranteed to be selfstabilizing, which makes them suitable for dynamic or error-prone environments.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r200.html Dr. Rina Dechter @ UCI
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          R200
          Semiring-Based Mini-Bucket Partitioning Scheme
          Emma Rollon, Javier Larrosa and Rina Dechter

          Abstract
          Graphical models are one of the most prominent frameworks to model complex systems and efficiently query them. Their underlying algebraic properties are captured by a valuation structure that, most usually, is a semiring. Depending on the semiring of choice, we can capture probabilistic models, constraint networks, cost networks, etc. In this paper we address the partitioning problem which occurs in many approximation techniques such as mini-bucket elimination and join-graph propagation algorithms. Roghly speaking, subject to complexity bounds, the algorithm needs to find a partition of a set of factors such that best approximates the whole set. While this problem has been addressed in the past in a particular case, we present here a general description. Furthermore, we also propose a general partitioning scheme. Our proposal is general in the sense that it is presented in terms of a generic semiring with the only additional requirements of a division operation and a refinement of its order. The proposed algorithm instantiates to the particular task of computing the probability of evidence, but also applies directly to other important reasoning tasks. We demonstrate its good empirical behaviour on the problem of computing the most probable explanation.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r26.html Dr. Rina Dechter @ UCI
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          R26
          Default reasoning using classical logic
          Rachel Ben-Eliyahu(rachel@cs.ucla.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          In this paper we show how propositional default theories can be characterized by classical propositional theories: for each finite default theory, we show a classical propositional theory such that there is a one-to-one correspondence between models for the latter and extensions of the former. This means that computing extensions and answering queries about coherence, set-membership and set-entailment are reducible to propositional satisfiability. The general transformation is exponential but tractable for a subset which we call 2-DT - a superset of network default theories and disjunction-free default theories. Consequently, coherence and set-membership for the class 2-DT is NP-complete and set-entailment is co-NP-complete. This work paves the way for the application of decades of research on efficient algorithms for the satisfiability problem to default reasoning. For example, since propositional satisfiability can be regarded as a constraint satisfaction problem (CSP), this work enables us to use CSP techniques for default reasoning. To illustrate this point we use the taxonomy of tractable CSPs to identify new tractable subsets for Reiter's default logic. Our procedures allow also for computing stable models of extended logic programs.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r190.html Dr. Rina Dechter @ UCI
          |

          R190
          Search Algorithms for m Best Solutions for Graphical Models
          Rina Dechter, Natalia Flerova and Radu Marinescu

          Abstract
          The paper focuses on finding the m best solutions to combinatorial optimization problems using Best-First or Branch and Bound search. Specifically, we present m-A*, extending the well-known A* to the m-best task, and prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since Best-First algorithms have memory problems, we also extend the memory efficient Depth-First Branch-and-Bound to the m-best task. We extend both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of Best-First and Branch-and-Bound confirm that Best-First is largely superior when memory is available, but Branch-and-Bound is more robust, while both styles of search benefit greatly when the heuristic evaluation function has increased accuracy.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r210.html Dr. Rina Dechter @ UCI
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          R210
          Weighted anytime search: new schemes for optimization over graphical models
          Natalia Flerova, Radu Marinescu and Rina Dechter

          Abstract
          Weighted search (best-first or depth-first) refers to search with a heuristic function multiplied by a constant w [Pohl (1970)]. The paper shows for the first time that for graphical models optimization queries weighted best-first and weighted depth-first Branch and Bound search schemes are competitive energy-minimization anytime optimization algorithms. Weighted best-first schemes were investigated for path-finding tasks, however, their potential for graphical models was ignored, possibly because of their memory costs and because the alternative depth-first Branch and Bound seemed very appropriate for bounded depth. The weighted depth-first search has not been studied for graphical models. We report on a significant empirical evaluation, demonstrating the potential of both weighted best-first search and weighted depth-first Branch and Bound algorithms as approximation anytime schemes (that have suboptimality bounds) and compare against one of the best depth-first Branch and Bound solvers to date.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r158.html Dr. Rina Dechter @ UCI
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          R158
          AND/OR Search Strategies for Combinatorial Optimization in Graphical Models.

          Radu Marinesu

          Abstract
          This thesis presents a new generation of search algorithms for solving combinatorial optimization problems over graphical models. The new algorithms exploit the principles of problem decomposition using the AND/OR search spaces, avoid redundant solution of subproblems using memory, focus on relevant promising portions of the solution space using the power of the mini-bucket heuristics and prune irrelevant spaces using constraint propagation. As we show throughout the chapters of this thesis, putting all these principles together yields powerful algorithms whose performance improves upon earlier schemes significantly, sometimes by several orders of magnitude. We demonstrate the applicability and the generality of our algorithms on optimization tasks over both probabilistic and deterministic graphical models, often showing superior performance on real application such as linkage analysis and circuit design and diagnosis. The following paragraphs elaborate.

          Our algorithms explore the AND/OR search spaces of the underlying graphical model. The AND/OR search space is a unifying paradigm for advanced search schemes for graphical models exploiting problem decomposability, which can translate into exponential time savings for search algorithms. In conjunction with the AND/OR search space we also investigate a class of partition-based heuristic functions, based on the Mini-Bucket approximation.

          We start by introducing depth-first Branch-and-Bound search algorithms that explore the AND/OR tree, use a variety of sources for heuristic guidance and incorporate some dynamic variable ordering heuristics. We then extend the depth-first AND/OR Branch-and-Bound and best-first search algorithms with the ability to recognize identical subproblems and avoid redundant solutions by caching (similar to good and no-good recording), thus traversing the AND/OR search graph. We also extend all the principles acquired within the general framework of depth-first and best-first schemes to the well known 0-1 Integer Linear Programs.

          Our empirical evaluation shows conclusively that the new AND/OR search algorithms improve dramatically over current state-of-the-art approaches exploring the traditional OR search space, in many cases by several orders of magnitude. We illustrate one by one the gain obtained by exploiting problem's decomposition (using AND modes), equivalence (by caching), branching strategy (via dynamic variable ordering heuristics), control strategy (depth-first or best-first) as well as the impact of the lower bound heuristic strength. As well, we investigate the impact of exploiting hard constraint (i.e., determinism) in the problem, the initial upper bound provided to the algorithm, and the quality of the guiding variable orderings.

          In the last part of the thesis we also show how our AND/OR search algorithms can be used as compilation algorithms for AND/OR decision diagrams. We present a new algorithm for compiling AND/OR Multi-Valued Decision Diagrams (AOMDDs) that represent the set of optimal solutions. We extend earlier work on AND/OR decision diagrams by considering general weighted graphical models based on cost functions rather than constraints. On various domains we show that we sometimes get a substantial reduction beyond the initial trace of state-of-the-art search algorithms.

          Finally, the starting chapter of this thesis (Chapter \ref{ch2}) sets the stage for this whole work by comparing the power of static and dynamic mini-bucket heuristics over regular search spaces and compares against a number of popular stochastic local search algorithms, as well as against the class of iterative belief propagation algorithms.


          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r217.html Dr. Rina Dechter @ UCI
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          R217
          BPLS: Cutset-Driven Local Search For MPE and Improved Bounds for Minimal Cutsets in Grids
          Alon Milchgrub

          Abstract
          The problem of finding an optimum of a multivariate function described as a sum of potentials over (small) subsets of variables is one of fundamental interest both in probabilistic inference and other fields. In this thesis we present a cycle-cutset driven stochastic local search algorithm which approximates the optimum of sums of unary and binary potentials, called Belief Propagation Local Search or BPLS. We evaluate empirically the effects of different components of BPLS on its performance and present the results of extensive and comprehensive experiments conducted on several problem sets. We further suggest several novel heuristics designed to improve the exploration of the search space based on previous results and more detailed analysis of the energy function. Some of the heuristics and observations made are general, and may be applied in other local search algorithms and in other contexts. We present experimental results supporting the contribution of these heuristics. Finally, we compare the performance of the leading variants of BPLS to the state-of-the-art GLS+ and against a hybrid. We show that in general the performance of BPLS is on-par with GLS+ and that it significantly outperforms GLS+ on the CSP problem set. Our results are significant because for the past decade, GLS+ is well established as the best stochastic local search for MPE. Moreover, BPLS reaches strong local optima in the limit (conditionally optimal on every tree), and as such provides an effective and convergent algorithm alternative to min-sum BP schemes. In the second part of this thesis we explore the notion of tree inducing cycle-cutset and present novel theoretical results. We prove that in grids of any size there exists a minimal cycle-cutset whose complement induces a single connected tree. More generally, any cycle-cutset in a grid can be transformed to a tree-inducing cycle-cutset, no bigger than the original one in a series of steps from a given cutset to another. We use this result to improve the known lower bounds on the size of a minimal cycle-cutset in certain cases of grids, thus equating the lower bound to the known upper bound.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r154.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R154
          Evaluating the Impact of AND/OR Search on 0-1 Integer Linear Programming

          Radu Marinescu and Rina Dechter

          Abstract
          AND/OR search spaces accommodate advanced algorithmic schemes for graphical models which can exploit the structure of the model. We extend and evaluate the depth-first and best-first AND/OR search algorithms to solving 0-1 Integer Linear Programs (0-1 ILP) within this framework. We also include a class of dynamic variable ordering heuristics while exploring an AND/OR search tree for 0-1 ILPs. We demonstrate the effectiveness of these search algorithms on a variety of benchmarks, including real-world combinatorial auctions, random uncapacitated warehouse location problems and MAX-SAT instances.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r70.html Dr. Rina Dechter @ UCI
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          R70
          Optimizing With Constraints: A Case Study in Scheduling Maintenance of Electric Power Units
          Daniel Frost(frost@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          A well-studied problem in the electric power industry is that of optimally scheduling preventative maintenance of power generating units within a power plant. We show how these problems can be cast as constraint satisfaction problems and provide an "iterative learning" algorithm which solves the problem in the following manner. In order to find an optimal schedule, the algorithm solves a series of CSPs with successively tighter cost-bound constraints. For the solution of each problem in the series we use constraint learning, which involves recording additional constraints that are uncovered during search. However, instead of solving each problem independently, after a problem is solved successfully with a certain cost-bound, the new constraints recorded by learning are used in subsequent attempts to find a schedule with a lower cost-bound. We show empirically that on a class of randomly generated maintenance scheduling problems iterative learning reduces the time to find a good schedule. We also provide a comparative study of the most competitive CSP algorithms on the maintenance scheduling benchmark.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r75.html Dr. Rina Dechter @ UCI
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          R75
          Temporal Reasoning With Constraints
          Dissertation submitted in partial satisfaction for the requirements for the degree of Doctor of Philosophy in Information and Computer Science
          Edward Moshe Schwalb (eschwalb@ics.uci.edu)

          Abstract
          This dissertation is focused on representing and reasoning about temporal information. We design general temporal languages supported by specialized efficient inference procedures. The contribution is in combining the existing logic-based temporal reasoning languages with the existing temporal constraint models, and in designing new efficient inference algorithms for the combined languages. We explore a speciffic combination of Datalog, a polynomial fragment of logic programming, with Temporal Constraint Satisfaction Problems (TCSP). To render this combination meaningful, attention is given to the formal syntax, semantics and the inference algorithms employed. We address some historical challenges relevant to the introduction of time and constraints into logic programming.

          The dissertation surveys and develops new and improved temporal constraint processing algorithms. When processing traditional Constraint Satisfaction Problems (CSP), path-consistency (PC) algorithms are polynomial. We demonstrate that when processing temporal constraints, PC is exponential, and thus does not scale up. To remedy this problem, two new polynomial algorithms are introduced: Upper Lower Tightening (ULT) and Loose Path Consistency (LPC). These algorithms are complete for a class of problems, called the STAR class. The empirical evaluation of these algorithms demonstrates a substantial performance improvement (up to six orders of magnitude) relative to other algorithms. We also demonstrate the existance of a phase transition for TCSPs.


            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r160.html Dr. Rina Dechter @ UCI
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          R160
          Refined Bounds for Instance-Based Search Complexity of Counting and Other #P Problems

          Lars Otten and Rina Dechter

          Abstract
          We present measures for bounding the instance-based complexity of AND/OR search algorithms for solution counting and related #P problems. To this end we estimate the size of the search space, with special consideration given to the impact of determinism in a problem. The resulting schemes are evaluated empirically on a variety of problem instances and shown to be quite powerful.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r56.html Dr. Rina Dechter @ UCI
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          R56
          Backjump-based Backtracking for Constraint Satisfaction Problems
          Rina Dechter, Daniel Frost
          Abstract
          The performance of backtracking algorithms for solving finite-domain constraint satisfaction problems can be improved substantially by look-back and look-ahead methods. Look-back techniques extract information by analyzing failing search paths that are terminated by dead-ends. Look-ahead techniques use constraint propagation algorithms to avoid such dead-ends altogether. This survey describes a number of look-back variants including backjumping and constraint recording which recognize and avoid some unnecessary explorations of the search space. The last portion of the paper gives an overview of look-ahead methods such as forward checking and dynamic variable ordering, and discusses their combination with backjumping.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r203a.html Dr. Rina Dechter @ UCI
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          R203a
          On Minimal Tree-Inducing Cycle-Cutsets and Their Use in a Cutset-Driven Local Search
          Alon Milchgrub and Rina Dechter

          Abstract
          We prove that in grids of any size there exists a minimal cycle-cutset that its com- plement induces a single connected tree. More generally, any cycle-cutset in a grid can be transformed to a tree-inducing cycle-cutset, no bigger than the origi- nal one. We use this result to improve the known lower bounds on the size of a minimal cycle-cutset in some cases of grids, thus equating the lower bound to the known upper bound. In addition, we present a cycle-cutset driven stochastic local search algorithm in order to approximate the minimal energy of a sum of unary and binary potentials. We show that this method is on-par and even surpasses the state-of-the-art on some grid problems, when both are initialized by elementary means.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r77.html Dr. Rina Dechter @ UCI
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          R77
          Branch and Bound with Mini-Bucket Heuristics
          Kalev Kask(kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The paper describes a branch and bound scheme that uses heuristics generated mechanically by the mini-bucket approximation. This scheme is presented and evaluated for optimization tasks such as finding the Most Probable Explanation (MPE ) in Bayesian networks. The mini-bucket scheme yields monotonic heuristics of varying strengths which cause different amounts of pruning, allowing a controlled tradeoff between preprocessing and search. The resulting Branch and Bound with Mini-Bucket heuristic (BBMB), is evaluated using random networks, probabilistic decoding and medical diagnosis networks. Results show that the BBMB scheme overcomes the memory explosion of bucket-elimination allowing a gradual tradeoff of space for time, and of time for accuracy.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r112.html Dr. Rina Dechter @ UCI
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          R112
          A Complete Anytime Algorithm for Treewidth
          Vibhav Gogate and Rina Dechter
          Abstract
          In this paper, we present a Branch and Bound algorithm called QuickBB for computing the treewidth of an undirected graph. This algorithm performs a search in the space of perfect elimination ordering of vertices of the graph. The algorithm uses novel pruning and propagation techniques which are derived from the theory of graph minors and graph isomorphism. We present a new algorithm called minor-min-width for computing a lower bound on treewidth that is used within the branch and bound algorithm and which improves over earlier available lower bounds. Empirical evaluation of QuickBB on randomly generated graphs and benchmarks in Graph Coloring and Bayesian Networks shows that it is consistently better than complete algorithms like QuickTree(Shoikhet and Geiger) in terms of cpu time. QuickBB also has good anytime performance, being able to generate a better upper bound on treewidth of some graphs whose optimal treewidth could not be computed up to now.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r195.html Dr. Rina Dechter @ UCI
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          R195
          Anytime AND/OR Depth-first Search for Combinatorial Optimization
          Lars Otten and Rina Dechter

          Abstract
          One popular and efficient scheme for solving combinatorial optimization problems over graphical models exactly is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This article 1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR search spaces) and the anytime behavior of depth- first search (DFS), 2) presents a new search scheme to address this issue while maintaining desirable DFS memory properties, and 3) analyzes and demonstrates its effectiveness through comprehensive empirical evaluation. Our work is applicable to any problem that can be cast as search over an AND/OR search space.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r100.html Dr. Rina Dechter @ UCI
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          R100
          Bayesian Inference in the Presence of Determinism
          David Larkin and Rina Dechter
          Abstract
          In this paper, we consider the problem of performing inference on Bayesian networks which exhibit a substantial degree of determinism. We improve upon the determinismexploiting inference algorithm presented in [4], showing that the information brought to light by constraint propagation may be exploited to a much greater extent than has been previously possible. This is con.rmed with theoretical and empirical studies.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r45.html Dr. Rina Dechter @ UCI
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          R45
          Systematic Versus Stochastic Constraint Satisfaction
          Eugene C. Freuder (ecf@cs.unh.edu), Rina Dechter (dechter@ics.uci.edu), Matthew L. Ginsberg (ginsberg@cs.uoregon.edu), Bart Selman (selman@research.att.com) & Edward Tsang (edward@essex.ac.uk)

          Abstract
          Constraint satisfaction problems (CSPs) involve finding values for problem variables that satisfy restrictions on which combinations of values are allowed [Freuder and Mackworth, 1994; Tsang, 1993]. They have many applications, including planning and scheduling, design and configuration, vision and language, temporal and spatial reasoning. The map coloring problem is a simple example, where the problem variables correspond to countries, the values to colors, and the constraints specify that neighboring countries cannot have the same color.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r46.html Dr. Rina Dechter @ UCI
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          R46
          A Graph-Based Method for Improving GSAT
          Kalev Kask (kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          GSAT is a randomized greedy local repair procedure that was introduced for solving propositional satisfiability and constraint satisfaction problems. We present an improvement to GSAT that is sensitive to the problem's structure. When the problem has a tree structure the algorithm is guaranteed to find a solution in linear time. For non-tree networks, the algorithm designates a subset of nodes, called cutset, and executes a regular GSAT algorithm on this set of variables. On all the rest of the variables it executes a specialized local search algorithm for trees. This algorithm finds an assignment that, like GSAT, locally minimizes the sum of unsatisfied constraints and also globally minimizes the number of conflicts in every tree-like sub-network. We will present results of experiments showing that this new algorithm outperforms regular GSAT on sparse networks whose cycle-cutset size is bounded by 30% of the nodes.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r82.html Dr. Rina Dechter @ UCI
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          R82
          A New Perspective on Algorithms for Optimizing Policies Under Uncertainty
          Rina Dechter (dechter@ics.uci.edu)
          Abstract
          The paper takes a fresh look at algorithms for maximizing expected utility over a set of policies, that is, a set of possible ways of reacting to observations about an uncertain state of the world. Using the bucket- elimination framework, we characterize the complexity of this optimization task by graph-based parameters, and devise an improved variant of existing algorithms. The improvement is shown to yield a dramatic gain in complexity when the probabilistic subgraph (of the influence diagram) is sparse, regardless of the complexity introduced by its utility subgraph.

          PostScript | PDF


          http://www.ics.uci.edu/~dechter/publications/r145.html Dr. Rina Dechter @ UCI
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          R145
          Studies in Lower Bounding Probability of Evidence using the Markov Inequality

          Vibhav Gogate, Bozhena Bidyuk and Rina Dechter.

          Abstract
          Computing the probability of evidence even with known error bounds is NP-hard. In this paper we address this hard problem by settling on an easier problem. We propose an approximation which provides high confidence lower bounds on probability of evidence but does not have any guarantees in terms of relative or absolute error. Our proposed approximation is a randomized importance sampling scheme that uses the Markov inequality. However, a straight-forward application of the Markov inequality may lead to poor lower bounds. We therefore propose several heuristic measures to improve its performance in practice. Empirical evaluation of our scheme with stateof- the-art lower bounding schemes reveals the promise of our approach.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r96.html Dr. Rina Dechter @ UCI
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          R96
          Hybrid Processing of Beliefs and Constraints
          Rina Dechter, David Larkin
          Abstract
          This paper explores algorithms for processing probabilistic and deterministic information when the former is represented as a belief network and the latter as a set of boolean clauses. The motivating tasks are 1. evaluating belief networks having a large number of deterministic relationships and 2. evaluating probabilities of complex boolean queries or complex evidence information over a belief network. We present and analyze a variable elimination algorithm that exploits both types of information, and provide empirical evaluation demonstrating its computational benefits.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r87a.html Dr. Rina Dechter @ UCI
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          R87a
          Using Mini-Bucket Heuristics for Max-CSP
          Kalev Kask (kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)
          Abstract
          This paper evaluates the power of a new scheme that generates search heuristics mechanically. This approach was presented and evaluated first in the context of optimization in belief networks. In this paper we extend this work to Max-CSP. The approach involves extracting heuristics from a parameterized approximation scheme called Mini-Bucket elimination that allows controlled trade-of between computa- tion and accuracy. The heuristics are used to guide Branch-and-Bound and Best First search, whose performance are compared on a number of constraint problems. Our results demonstrate that both search schemes exploit the heuristics effectively, permitting controlled trade-off between preprocessing (for heuristic generation) and search.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r97.html Dr. Rina Dechter @ UCI
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          R97
          Epsilon-cutset effect in Bayesian networks of arbitrary topology
          Bozhena Bidyuk, Rina Dechter
          Abstract
          The paper investigates the behavior of iterative belief propagation algorithm (IBP) in Bayesian networks with loops. In multiply connected network, IBP is only guaranteed to converge in linear time to the correct posterior marginals when evidence nodes form a loop-cutset. We propose an e-cutset criteria that IBP will converge and compute posterior marginals close to correct when a single value in the domain of each loop-cutset node receives very strong support compared to other values thus producing an effect similar to the observed loop-cutset. We investigate the support for this criteria analytically and empirically and show thatit is consistent with previous observations of IBP performance in multiply connected networks.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r213.html Dr. Rina Dechter @ UCI
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          R213
          Evaluating Weighted DFS Branch and Bound over Graphical Models
          Natalia Flerova, Radu Marinescu and Rina Dechter

          Abstract
          Weighted search was explored significantly in recent years for path-finding problems, but until now was barely considered for optimization tasks such as MPE/MAP and Weighted CSPs. An important virtue of weighted search schemes, especially in the context of anytime search, is that they are w-optimal, i.e. when terminated, they return a weight w, and a solution cost C, such that C ≤ w·C* , where C* is the optimal cost. In this paper we introduce Weighted Branch and Bound (WBB) for graphical models and provide a broad empirical evaluation of its performance compared with one of the best unweighted anytime search scheme, BRAOBB (won Pascal 2011 competition). We also compare against weighted best-first (WBF). Our results show that W BB can be superior to both un-weighted BB and to weighted BF on a significant number of instances. We also illustrate the benefit of weighted search in providing suboptimality relative error bounds.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r136.html Dr. Rina Dechter @ UCI
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          R136
          A New Algorithm for Sampling CSP Solutions Uniformly at Random
          Vibhav Gogate and Rina Dechter
          Abstract
          The paper presents a method for generating solutions of a constraint satisfaction problem (CSP) uniformly at random. Our method relies on expressing the constraint network as a uniform probability distribution over its solutions and then sampling from the distribution using state-of-the-art probabilistic sampling schemes. To speed up the rate at which random solutions are generated, we augment our sampling schemes with pruning techniques used successfully in the CSP literature such as conflict-directed back-jumping and no-good learning.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r143.html Dr. Rina Dechter @ UCI
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          R143
          Best-First AND/OR Search for Graphical Models

          Radu Marinescu and Rina Dechter

          Abstract
          The paper presents and evaluates the power of best-first search over AND/OR search spaces in graphical models. The main virtue of the AND/OR representation is its sensitivity to the structure of the graphical model, which can translate into significant time savings. Indeed, in recent years depth-first AND/OR Branch-and-Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for. In this paper we introduce two classes of best-first AND/OR search algorithms: those that explore a context-minimal AND/OR search graph and use static variable orderings, and those that use dynamic variable orderings but explore an AND/OR search tree. The superiority of the best-first search approach is demonstrated empirically on various real-world benchmarks.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r162a.html Dr. Rina Dechter @ UCI
          | publications

          R162a
          Advancing AND/OR Search for Optimization Using Diverse Principles.

          Radu Marinescu and Rina Dechter

          Abstract
          In recent years, several Branch-and-Bound and best-first search algorithms were developed to explore the AND/OR search graph for solving general constraint optimization problems. Previous work showed the tremendous gain obtained by exploiting problem's decomposition (using AND nodes), equivalence (by caching) and irrelevance (via the power of lower bound heuristics). In this paper, we show the additional improvements that can be gained by bringing together all the above, as well as diverse refinements and optimizing principles such as exploiting determinism via constraint propagation, using good initial upper bounds generated via stochastic local search and improving the quality of the guiding pseudo tree. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r153.html Dr. Rina Dechter @ UCI
          |

          R153
          Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models

          Radu Marinescu and Rina Dechter

          Abstract
          In this paper we explore the impact of caching during search in the context of the recent framework of AND/OR search in graphical models. Specifically, we extend the depth-first AND/OR Branch-and-Bound tree search algorithm to explore an AND/OR search graph by equipping it with an adaptive caching scheme similar to good and no-good recording. Furthermore, we present best-first search algorithms for traversing the same underlying AND/OR search graph and compare both algorithms empirically. We focus on two common optimization problems in graphical models: finding the Most Probable Explanation (MPE) in belief networks and solving Weighted CSPs (WCSP). In an extensive empirical evaluation we demonstrate conclusively the superiority of the memory intensive AND/OR search algorithms on a variety of benchmarks.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r211.html Dr. Rina Dechter @ UCI
          |

          R211
          Memory-Efficient Tree Size Prediction for Depth-First Search in Graphical Models
          Levi H. S. Lelis, Lars Otten, and Rina Dechter

          Abstract
          We address the problem of predicting the size of the search tree explored by Depth-First Branch and Bound (DFBnB) while solving optimization problems over graphical models. Building upon methodology introduced by Knuth and his student Chen, this paper presents a memory-efficient scheme called Retentive Stratified Sampling (RSS). Through empirical evaluation on probabilistic graphical models from various problem domains we show impressive prediction power that is far superior to recent competing schemes.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r95.html Dr. Rina Dechter @ UCI
          |
          R95
          A General Scheme for Multiple Lower Bound Computation in Constraint Optimization
          Rina Dechter, Kalev Kask, Javier Larrosa
          Abstract
          Computing lower bounds to the best-cost extension of a tuple is an ubiquous task in constraint optimization. A particular case of special interest is the computation of lower bounds to all singleton tuples, since it permits domain pruning in Branch and Bound algorithms. In this paper we introduce MCTE(z ), a general algorithm which allows the computation of lower bounds to arbitrary sets of tasks. Its time and accuracy grows as a function of z allowing a controlled tradeoff between lower bound accuracy and time and space to fit available resources. Subsequently, a specialization of MCTE(z ) called MBTE(z) is tailored to computing lower bounds to singleton tuples. Preliminary experiments on Max-CSP show that using MBTE(z) to guide dynamic variable and value orderings in branch and bound yields a dramatic reduction in the search space and, for some classes of problems, this reduction is highly costeffective producing signifiant time savings and is competative against specialized algorithms for Max-CSP.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r185.html Dr. Rina Dechter @ UCI
          |

          R185
          Inference Schemes for M Best Solutions for Soft CSPs

          Emma Rollon, Natalia Flerova and Rina Dechter

          Abstract
          The paper present a formalization of the m-best task within the unifying framework of semirings. As a consequence, known inference algorithms are defined and their correctness and completeness for the m-best task are immediately implied. We also describe and analyze a Bucket Elimination algorithm for solving the m-best task, elim-m-opt, presented in an earlier workshop1 and introduce an extension to the mini-bucket framework, yielding a collection of bounds for each of the m-best solutions. Some empirical demonstration of the algorithms and their potential for approximations are provided.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r64.html Dr. Rina Dechter @ UCI
          |
          R64
          Bucket Elimination: a Unifying Framework for Processing Hard and Soft Constraints
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The Constraint Satisfaction framework is quite restricted. Nevertheless, it is this restrictiveness that allowed the developments of very useful concepts such as constraint propagation (also know as "consistency enforcement"), through which various tractable subclasses had emerged and by which general purpose algorithms such as backtracking were improved [8,6,7,2]. However, real life problems frequently call for extending the basic model to allow nondeterminism as the representation of preferences among solutions. Such extensions relate the CSP model to known models for combinatorial optimization developed in the Operation Research community as well as to more recent frameworks such as Probabilistic Networks [9]. In this note I argue that extending the CSP model to a richer set of tasks can be done elegantly using a unifying framework which I call "bucket elimination". I believe that this framework will allow hybrids of two fundamental problem solving paradigms: elimination and conditioning, will address computational issues such as time-space tradeoffs, and will allow developing approximation algorithms, all within this general, and therefore, widely applicable framework. In the rest of this note I outline the basic framework.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r89.html Dr. Rina Dechter @ UCI
          |
          R89
          Boosting Search With Variable Elimination
          Javier Larrosa (javier@ics.uci.edu)
          Abstract
          Variable elimination is the basic step of Adaptive Consistency[4]. It transforms the problem into an equivalent one, having one less variable. Unfortunately, there are many classes of problems for which it is infeasible, due to its exponential space and time complexity. However, by restricting variable elimination so that only low arity constraints are processed and recorded, it can be effectively combined with search, because the elimination of variables, reduces the search tree size. In this paper we introduce VarElimSearch(S;k), a hybrid meta-algorithm that combines search and variable elimination. The parameter S names the particular search procedure and k controls the tradeoff between the two strategies. The algorithm is space exponential in k. Regarding time, we show that its complexity is bounded by k and a structural parameter from the constraint graph. We also provide experimental evidence that the hybrid algorithm can outperform state�of�the�art algorithms in binary sparse problems. Experiments cover the tasks of finding one solution and the best solution (Max�CSP). Specially in the Max�CSP case, the advantage of our approach can be overwhelming.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r203.html Dr. Rina Dechter @ UCI
          |

          R203
          On Minimal Tree-Inducing Cycle-Cutsets and Their Use in a Cutset-Driven Local Search
          Alon Milchgrub and Rina Dechter

          Abstract
          We prove that in grids of any size there exists a minimal cycle-cutset that its com- plement induces a single connected tree. More generally, any cycle-cutset in a grid can be transformed to a tree-inducing cycle-cutset, no bigger than the origi- nal one. We use this result to improve the known lower bounds on the size of a minimal cycle-cutset in some cases of grids, thus equating the lower bound to the known upper bound. In addition, we present a cycle-cutset driven stochastic local search algorithm in order to approximate the minimal energy of a sum of unary and binary potentials. We show that this method is on-par and even surpasses the state-of-the-art on some grid problems, when both are initialized by elementary means.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r17.html Dr. Rina Dechter @ UCI
          |
          R17
          Constraint Networks
          Rina Dechter(dechter@ics.uci.edu)

          Abstract
          Constraint-based reasoning is a paradigm for formulating knowledge as a set of constraints without specifying the method by which these constraints are to be satis ed. A variety of techniques have been developed for finding partial or complete solutions for different kinds of constraint expressions. These have been successfully applied to diverse tasks such as design, diagnosis, truth maintenance, scheduling, spatiotemporal reasoning, logic programming and user interface. Constraint networks are graphical representations used to guide strategies for solving constraint satisfaction problems (CSPs).

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r212.html Dr. Rina Dechter @ UCI
          |

          R211
          Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics
          William Lam, Kalev Kask, Rina Dechter, and Alexander Ihler

          Abstract
          We explore the use of iterative cost-shifting as a dynamic heuristic generator for solving MPE in graphical models via Branch and Bound. When mini-bucket elimination is limited by its memory budget, it may not provide good heuristics. This can happen often when the graphical model has a very high induced width with large variable domain sizes. In addition, we explore a hybrid setup where both MBE and the iterative cost-shifting bound are used in a combined heuristic. We compare these approaches with the most advanced statically generated heuristics.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r92.html Dr. Rina Dechter @ UCI
          |
          R92
          Bucket-Tree Elimination for Automated Reasoning
          Kalev Kask, Rina Dechter, Javier Larrosa & Fabio Cozman
          Abstract
          The paper extends several variable elimination schemes into a two-phase message passing algorithm along a bucket-tree. Our analysis shows that the new algorithm, called Bucket-Tree Elimination (BTE), may provide a substantial speed-up over standard variable-elimination for important probabilistic reasoning tasks. The algorithm is developed and analyzed within a unifying view of tree-clustering methods, making crisp the relationship between the two approaches, and allowing enhancement schemes to be transferred. In particular we show how time-space tradeoffs of BTE are cast within the tree-decomposition framework.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r125.html Dr. Rina Dechter @ UCI
          |
          R125
          Advances in AND/OR Branch-and-Bound Search for Constraint Optimization
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. In [1] we introduced a linear space AND/OR Branch-and-Bound (AOBB) search scheme that explores the AND/OR search tree for solving optimization tasks. In this paper we extend the algorithm by equipping it with a context-based adaptive caching scheme similar to good and nogood recording, thus it explores an AND/OR graph rather than the AND/OR tree. We also improve the algorithm by using a new heuristic for generating close to optimal height pseudo-trees, based on a well known recursive decomposition of the hypergraph representation. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r51.html Dr. Rina Dechter @ UCI
          |
          R51
          Identifying Independencies in Causal Graphs with Feedback
          Judea Pearl (judea@cs.ucla.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          We show that the d-separation criterion constitutes a valid test for conditional independence relationships that are induced by feedback systems involving discrete variables.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r131.html Dr. Rina Dechter @ UCI
          |
          R131
          Dynamic Orderings for AND/OR Branch-and-Bound Search in Graphical Models
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. Since the variable selection can have a dramatic impact on search performance when solving optimization tasks, we introduce in this paper a new dynamic AND/OR Branchand- Bound algorithmic framework which accommodates variable ordering heuristics. The efficiency of the dynamic AND/OR approach is demonstrated empirically in a variety of domains.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r202.html Dr. Rina Dechter @ UCI
          |

          R202
          Benchmark on DAOOPT and GUROBI with the PASCAL2 Inference Challenge Problems
          Junkyu Lee, William Lam, and Rina Dechter

          Abstract
          We report the PASCAL2 benchmark for DAOOPT and GUROBI on MPE task with 330 optimally solved instances from 8 benchmark domains. DAOOPT outperformed GUROBI in 3 domains, while GUROBI was faster than DAOOPT in the rest of the 5 domains. We show that DAOOPT performed well in domains where it could have high quality initial solutions for pruning the AND/OR search space, or skip search when the heuristic upper bounds were converged to the optimal due to MPLP/JGLP algorithms. GUROBI presented excellent performance if cutting planes were applied progressively and its heuristic algorithms could find the optimal solution at the root of branch-and-cut tree.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r219.html Dr. Rina Dechter @ UCI
          |

          R219
          Pushing Forward Marginal MAP with Best-First Search
          Radu Marinescu, Rina Dechter, and Alexander Ihler

          Abstract
          Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best-first search algorithms and demonstrate their effectiveness against recent branch and bound schemes through extensive empirical evaluations. Our results show that best-first search improves significantly over existing depth-first approaches, in many cases by several orders of magnitude, especially when guided by relatively weak heuristics.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/otherPubs.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Other Publications
          publications | recent talks

            CP 2000 proceedings
            REES(pdf)



          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r47.html Dr. Rina Dechter @ UCI
          |
          R47
          An Evaluation Of Structural Parameters For Probabilistic Reasoning: Results On Benchmark Circuits
          Yousri El Fattah (yousri@rsc.rockwell.com) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Many algorithms for processing probabilistic networks are dependent on the topological properties of the problem's structure. Such algorithms (e.g., clustering, conditioning) are effective only if the problem has a sparse graph captured by parameters such as tree width and cycle-cutset size. In this paper we initiate a study to determine the potential of structure-based algorithms in real-life applications. We analyze empirically the structural properties of problems coming from the circuit diagnosis domain. Speciffically, we locate those properties that capture the effectiveness of clustering and conditioning as well as of a family of conditioning+clustering algorithms designed to gradually trade space for time. We perform our analysis on 11 benchmark circuits widely used in the testing community. We also report on the effect of ordering heuristics on tree-clustering and show that, on our benchmarks, the well known max-cardinality ordering is substantially inferior to an ordering called min-degree.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r138.html Dr. Rina Dechter @ UCI
          |
          R138
          A Comparison of Time-Space Schemes for Graphical Models
          Robert Mateescu and Rina Dechter
          Abstract
          We investigate three parameterized algorithmic schemes for graphical models that can accommodate trade-offs between time and space: 1) AND/OR Adaptive Caching (AOC(i)); 2) Variable Elimination and Conditioning (VEC(i)); and 3) Tree Decomposition with Conditioning (TDC(i)). We show that AOC(i) is better than the vanilla versions of both VEC(i) and TDC(i), and use the guiding principles of AOC(i) to improve the other two schemes. Finally, we show that the improved versions of VEC(i) and TDC(i) can be simulated by AOC(i), which emphasizes the unifying power of the AND/OR framework.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r41.html Dr. Rina Dechter @ UCI
          |
          R41
          Local And Global Relational Consistency
          Rina Dechter (dechter@ics.uci.edu) & Peter van Beek (vanbeek@cs.ualberta.ca)

          Abstract
          Local consistency has proven to be an important concept in the theory and practice of constraint networks. In this paper, we present a new definition of local consistency, called relational consistency. The new definition is relation-based, in contrast with the previous definition of local consistency, which we characterize as variable-based. We show the conceptual power of the new definition by showing how it unifies known elimination operators such as resolution in theorem proving, joins in relational databases, and variable elimination for solving linear inequalities. Algorithms for enforcing various levels of relational consistency are introduced and analyzed. We also show the usefulness of the new definition in characterizing relationships between properties of constraint networks and the level of local consistency needed to ensure global consistency.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r162.html Dr. Rina Dechter @ UCI
          |

          R162
          Advancing AND/OR Search for Optimization Using Diverse Principles.

          Radu Marinescu and Rina Dechter

          Abstract
          In recent years, several Branch-and-Bound and best-first search algorithms were developed to explore the AND/OR search graph for solving general constraint optimization problems. Previous work showed the tremendous gain obtained by exploiting problem's decomposition (using AND nodes), equivalence (by caching) and irrelevance (via the power of lower bound heuristics). In this paper, we show the additional improvements that can be gained by bringing together all the above, as well as diverse refinements and optimizing principles such as exploiting determinism via constraint propagation, using good initial upper bounds generated via stochastic local search and improving the quality of the guiding pseudo tree. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r90.html Dr. Rina Dechter @ UCI
          |
          R90
          On The Duel Representation Of Non-Binary Semiring-Based CSP's
          Javier Larrosa (javier@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)
          Abstract
          It is well known that any non-binary CSP can be reformulated as a binary CSP. In this paper we show that the same translation methods can be applied in the soft constraints framework. We observe that any non-binary soft constraint CSP can be reformulated as a problem with only binary and unary constraints. Interestingly, the translation leads to binary constraints that are hard (define conditions of mandatory satisfaction) and unary constraints that are soft (define a preference criterion among the set of solutions). We elaborate our observation in the semiring based framework.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r38.html Dr. Rina Dechter @ UCI
          |
          R38
          Temporal Reasoning with Constraints on Fluents and Events
          Eddie Schwalb (eschwalb@ics.uci.edu), Kalev Kask (kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          We propose a propositional language for temporal reasoning that is computationally effective yet expressive enough to describe information about fluents, events and temporal constraints. Although the complete inference algorithm is exponential, we characterize a tractable core with limited expressibility and inferential power. Our results render a variety of constraint propagation techniques applicable for reasoning with constraints on fluents.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r218a.html Dr. Rina Dechter @ UCI
          |

          R218a
          Caching in Context-Minimal OR Spaces
          Rina Dechter, Levi H. S. Lelis, and Lars Otten

          Abstract
          In empirical studies we observed that caching can have very little impact in reducing the search effort in Branch and Bound search over context-minimal OR spaces. For example, in one of the problem domains used in our experiments we reduce only by 1% the number of nodes expanded when using caching in context-minimal OR spaces. By contrast, we reduce by 74% the number of nodes expanded when using caching in context-minimal AND/OR spaces on the same instances. In this work we document this unexpected empirical finding and provide explanations for the phenomenon.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r71.html Dr. Rina Dechter @ UCI
          |
          R71
          Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding
          Irina Rish (irinar@ics.uci.edu), Kalev Kask (kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [14]. Moreover, it was observed that iterative application of the (linear time) belief propagation algorithm designed for polytrees [15] outperformed state of the art decoding algorithms, even though the corresponding networks may have many cycles. This paper demonstrates empirically that an approximation algorithm approx-mpe for solving the most probable explanation (MPE) problem, developed within the recently proposed mini-bucket elimination framework [4], outperforms iterative belief propagation on classes of coding networks that have bounded induced width. Our experiments suggest that approximate MPE decoders can be good competitors to the approximate belief updating decoders.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r147.html Dr. Rina Dechter @ UCI
          |
          R147
          AND/OR Search Spaces for Graphical Models
          Submitted in partial satisfaction of the requirements for the degree of Doctor Of Philosophy in Information and Computer Science Robert Mateescu
          Abstract
          Graphical models are a widely used knowledge representation framework that captures independencies in the data and allows for a concise representation. Well known examples of graphical models include Bayesian networks, constraint networks, Markov random fields and influence diagrams. Graphical models are applicable to diverse domains such as planning, scheduling, design, diagnosis and decision making.

          This dissertation is concerned with graphical model algorithms that leverage the structure of the problem. We investigate techniques that capitalize on the independencies expressed by the model�s graph by decomposing the problem into independent components, resulting in often exponentially reduced computational costs.
          The algorithms that we develop can be characterized along three main dimensions: (1) search vs. dynamic programming methods; (2) deterministic vs. probabilistic information; (3) approximate vs. exact algorithms.

          We introduce the AND/OR search space perspective for graphical models. In contrast to the traditional OR search, the new AND/OR search is sensitive to problem decomposition. The AND/OR search tree search is in most cases exponentially smaller (and never larger) than the OR search tree. The AND/OR search graph is exponential in the treewidth of the graph, while the OR search graph is exponential in the pathwidth.

          We introduce mixed networks, a new graphical model framework that combines belief and constraint networks. By keeping the two types of information separate we are able to more efficiently exploit them by specific methods. We describe the primary algorithms for processing such networks, based on inference and on AND/OR search.

          In terms of approximate algorithms, we investigate message-passing schemes based on join tree clustering and belief propagation. We introduce Mini-Clustering (MC), which performs bounded inference on a tree decomposition. We then combine MC with the iterative version of Pearl�s belief propagation (IBP), creating Iterative Join-Graph Propagation (IJGP). We show empirically that IJGP is one of the most powerful approximate schemes for belief networks. Through analogy with arc consistency algorithms from constraint networks, we show that IBP and IJGP infer zero-beliefs correctly, and empirically show that this also extends to extreme beliefs.

          We apply the AND/OR paradigm to cutset conditioning and show that the new method is a strict improvement, often yielding exponential savings. The AND/OR cutset is the inspiration of a new caching scheme for AND/OR search, which led to the design of our most powerful and flexible algorithm AND/OR Adaptive Caching.

          Furthermore we make a comparison of AND/OR search and inference methods. We analyze them side by side by describing the context minimal graph that they traverse. We also investigate three hybrid schemes, based on search and inference and show that Adaptive Caching is never worse than the other two.

          Finally, we apply the AND/OR perspective to decision diagrams. We extend them with AND nodes capturing function structure decomposition, resulting in AND/OR Multi- Valued Decision Diagrams (AOMDDs). The AOMDD is a canonical form that compiles a graphical model and has size bounded exponentially by the treewidth, rather than pathwidth (as is the case for OR decision diagrams). We present two compilation algorithms, one based on AND/OR search, the other based on a Variable Elimination schedule.

          PDF


          http://www.ics.uci.edu/~dechter/publications/r225.html Dr. Rina Dechter @ UCI


          R225
          Look-ahead with Mini-Bucket Heuristics for MPE
          Rina Dechter, Kalev Kask, William Lam, and Javier Larrosa

          Abstract
          The paper investigates the potential of look-ahead in the con-text of AND/OR search in graphical models using the Mini-Bucket heuristic for combinatorial optimization tasks (e.g., MAP/MPE or weighted CSPs). We present and analyze the complexity of computing the residual (a.k.a Bellman update) of the Mini-Bucket heuristic and show how this can be used to identify which parts of the search space are more likely to benefit from look-ahead and how to bound its overhead. We also rephrase the look-ahead computation as a graphical model, to facilitate structure exploiting inference schemes. We demonstrate empirically that augmenting Mini-Bucket heuristics by look-ahead is a cost-effective way of increasing the power of Branch-And-Bound search.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r130.html Dr. Rina Dechter @ UCI
          |
          R130
          An Anytime Scheme for Bounding Posterior Beliefs
          Bozhena Bidyuk and Rina Dechter
          Abstract
          This paper presents an any-time scheme for computing lower and upper bounds on posterior marginals in Bayesian networks. The scheme draws from two previously proposed methods, bounded conditioning (Horvitz, Suermondt, & Cooper 1989) and bound propagation (Leisink & Kappen 2003). Following the principles of cutset conditioning (Pearl 1988), our method enumerates a subset of cutset tuples and applies exact reasoning in the network instances conditioned on those tuples. The probability mass of the remaining tuples is bounded using a variant of bound propagation. We show that our new scheme improves on the earlier schemes.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r80.html Dr. Rina Dechter @ UCI
          |
          R80
          Resolution versus Search: Two Strategies for SAT
          Irina Rish(irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)
          Abstract
          The paper compares two popular strategies for solving propositional satisfiability, backtracking search and resolution, and analyzes the complexity of a directional resolution algorithm (DR) as a function of the "width" (w*) of the problem's graph. Our empirical evaluation confirms theoretical prediction, showing that on low-w* problems DR is very eficient, greatly outperforming the backtracking-based Davis- Putnam-Logemann-Loveland procedure (DP). We also emphasize the knowledgecompilation properties of DR and extend it to a tree-clustering algorithm that facilitates query answering. Finally, we propose two hybrid algorithms that combine the advantages of both DR and DP. These algorithms use control parameters that bound the complexity of resolution and allow time/space trade-offs that can be adjusted to the problem structure and to the user's computational resources. Empirical studies demonstrate the advantages of such hybrid schemes.

          PostScript | PDF


          http://www.ics.uci.edu/~dechter/publications/r104.html Dr. Rina Dechter @ UCI
          |
          R104
          Systematic vs. Non-systematic Algorithms for Solving the MPE Task
          Radu Marinescu, Kalev Kask, and Rina Dechter
          Abstract
          The paper explores the power of two systematic Branch and Bound search algorithms that exploit partition-based heuristics, BBBT (a recent algorithm for which the heuristic information is constructed during search and allows dynamic variable/value ordering) and its predecessor BBMB (for which the heuristic information is pre-compiled) and compares them against a number of popular local search algorithms for the MPE problem. We show empirically that the new Branch and Bound algorithm, BBBT is powerful and is sometimes superior to BBMB in that it can exploit bounded-space information more effectively. Second, when viewed as approximation schemes, BBBT/BBMB together are highly competitive with the best known SLS algorithms and are superior, especially when the domain sizes increase beyond 2. This is in contrast to the performance of SLS vs. systematic search on CSP/SAT problems, where SLS often significantly outperforms systematic algorithms. As far as we know, BBBT/BBMB are currently among the best performing algorithms for solving the MPE task.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r201.html Dr. Rina Dechter @ UCI
          |

          R201
          Anytime AND/OR Best-First Search for Optimization in Graphical Models
          Natalia Flerova, Radu Marinescu and Rina Dechter

          Abstract
          Depth-first search schemes are known to be more cost-effective for solving graphical models tasks than Best-First Search schemes. In this paper we show however that anytime Best-First algorithms recently developed for path-finding problems, can fare well when applied to graphical models. Specifically, we augment best-first schemes designed for graphical models with such anytime capabilities and demonstrate their potential when compared against one of the most competitive depth-first branch and bound scheme. Though Best-First search using weighted heuristics is successfully used in many domains, the crucial question of weight parameter choice has not been systematically studied and presents an interesting machine learning problem.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r156.html Dr. Rina Dechter @ UCI
          |

          R156
          AND/OR Importance Sampling

          Vibhav Gogate and Rina Dechter

          Abstract
          The paper introduces AND/OR importance sampling for probabilistic graphical models. In contrast to importance sampling, AND/OR importance sampling caches samples in the AND/OR space and then extracts a new sample mean from the stored samples. We prove that AND/OR importance sampling may have lower variance than importance sampling; thereby providing a theoretical justification for preferring it over importance sampling. Our empirical evaluation demonstrates that AND/OR importance sampling is far more accurate than importance sampling in many cases.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r174.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
          |

          R174
          Active Tuples-based Scheme for Bounding Posterior Beliefs

          Bozhena Bidyuk, Rina Dechter and Emma Rollon

          Abstract
          The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime manner. The scheme uses the cutset conditioning principle to tighten existing bounding schemes and to facilitate anytime behavior, utilizing a fixed number of cutset tuples. The accuracy of the bounds improves as the number of used cutset tuples increases and so does the computation time. We demonstrate empirically the value of our scheme for bounding posterior marginals and probability of evidence using a variant of the bound propagation algorithm as a plug-in scheme.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r182.html Dr. Rina Dechter @ UCI
          |

          R182
          Bucket and mini-bucket Schemes for M Best Solutions over Graphical Models

          Natalia Flerova, Emma Rollon and Rina Dechter

          Abstract
          The paper focuses on finding thembest solutions of a combinatorial optimization problem defined over a graphical model (e.g., themmost probable expla- nations for a Bayesian network). We describe elim- m-opt, a new bucket elimination algorithm for solv- ing the m-best task, provide efficient implementa- tion of its defining combination and marginaliza- tion operators, analyze its worst-case performance, and compare it with that of recent related algo- rithms. An extension to the mini-bucket frame- work, yielding a collection of bounds for each of the m-best solutions is discussed and empirically evaluated. We also formulate the m-best task as a regular reasoning task over general graphical mod- els defined axiomatically, which makes all other in- ference algorithms applicable.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r149.html Dr. Rina Dechter @ UCI
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          R149
          AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Graphical Models

          Robert Mateescu, Rina Dechter and Radu Marinescu

          Abstract
          Inspired by the recently introduced framework of AND/OR search spaces for graphical models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in order to capture function decomposition structure and to extend these compiled data structures to general weighted graphical models (e.g., probabilistic models). We present the AND/OR Multi-Valued Decision Diagram (AOMDD) which compiles a graphical model into a canonical form that supports polynomial (e.g., solution counting, belief updating) or constant time (e.g. equivalence of graphical models) queries. We provide two algorithms for compiling the AOMDD of a graphical model. The first is search-based, and works by applying reduction rules to the trace of the memory intensive AND/OR search algorithm. The second is inference-based and uses a Bucket Elimination schedule to combine the AOMDDs of the input functions via the the APPLY operator. For both algorithms, the compilation time and the size of the AOMDD are, in the worst case, exponential in the treewidth of the graphical model, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs). We introduce the concept of semantic treewidth, which helps explain why the size of a decision diagram is often much smaller than the worst case bound. We provide an experimental evaluation that demonstrates the potential of AOMDDs.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r72.html Dr. Rina Dechter @ UCI
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          R72
          Stochastic Local Search for Bayesian Networks
          Kalev Kask (kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The paper evaluates empirically the suitability of Stochastic Local Search algorithms (SLS) for finding most probable explanations in Bayesian networks. SLS algorithms (e.g., GSAT, WSAT [16]) have recently proven to be highly effective in solving complex constraint-satisfaction and satisfiability problems which cannot be solved by traditional search schemes. Our experiments investigate the applicability of this scheme to probabilistic optimization problems. Speciffically, we show that algorithms combining hill-climbing steps with stochastic steps (guided by the network's probability distribution) called G+StS, outperform pure hill-climbing search, pure stochastic simulation search, as well as simulated annealing. In addition, variants of G+StS that are augmented on top of alternative approximation methods are shown to be particularly effective.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r181.html Dr. Rina Dechter @ UCI
          |

          R181
          Pushing the Power of Stochastic Greedy Ordering Schemes for Inference in Graphical Models

          Kalev Kask, Andrew E. Gelfand, Lars Otten, and Rina Dechter

          Abstract
          We study iterative randomized greedy algorithms for generating (elimination) orderings with small induced width and state space size - two parameters known to bound the complexity of inference in graphical models. We propose and implement the Iterative Greedy Variable Ordering (IGVO) algorithm, a new variant within this algorithm class. An empirical evaluation using different ranking functions and conditions of randomness, demonstrates that IGVO finds significantly better orderings than standard greedy ordering implementations when evaluated within an anytime framework. Additional order of magnitude improvements are demonstrated on a multicore system, thus further expanding the set of solvable graphical models. The experiments also confirm the superiority of the MinFill heuristic within the iterative scheme.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r29a.html Dr. Rina Dechter @ UCI
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          R29a
          Directional Resolution: The Davis-Putnam Procedure, Revisited
          Rina Dechter (dechter@ics.uci.edu) & Irina Rish (irinar@ics.uci.edu)

          Abstract
          The paper presents an algorithm called directional resolution, avariation on the original Davis-Putnam algorithm, and analyzes its worst-case behavior as a function of the topological structure of propositional theories. The concepts of induced width and diversity are shown to play a key role in bounding the complexity of the procedure. The importance of our analysis lies in highlighting structure-based tractable classes of satisfiability and in providing theoretical guarantees on the time and space complexity of the algorithm. Contrary to previous assessments, we show that for many theories directional resolution could be an effective procedure. Our empirical tests confirm theoretical prediction, showing that on problems with a special structure, namely k-tree embeddings (e.g. chains, (k,m)-trees), directional resolution greatly outperforms one of the most effective satisfiability algorithms known to date, the popular Davis-Putnam procedure. Furthermore, combining a bounded version of directional resolution with the Davis-Putnam procedure results in an algorithm superior to either components.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r0.html Dr. Rina Dechter @ UCI
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          R0
          Generalized Best-First Search Strategies and the Optimality of A
          Rina Dechter and Judea Pearl

          Abstract
          This paper reports several properties of heuristic best-first search strategies whose scoring functionsfdepend on all the information available from each candidate path, not merely on the current cost g and the estimated completion cost h. It is shown that several known properties of A* retain their form (with the minmax offplaying the role of the optimal cost), which helps establish general tests of admissibility and general conditions for node expansion for these strategies. On the basis of this framework the computational optimality of A*, in the sense of never expanding a node that can be skipped by some other algorithm having access to the same heuristic information that A* uses, is examined. A hierarchy of four optimality types is defined and three classes of algorithms and four domains of problem instances are considered. Computational performances relative to these algorithms and domains are appraised. For each class-domain combination, we then identify the strongest type of optimality that exists and the algorithm for achieving it. The main results of this paper relate to the class of algorithms that, like A*, return optimal solutions (i.e., admissible) when all cost estimates are optimistic (i.e., h<=h*). On this class, A* is shown to be not optimal and it is also shown that no optimal algorithm exists, but if the performance tests are confirmed to casesin which the estimates are also consistent, then A* is indeed optimal. Additionally, A* is also shown to be optimal over a subset of the latter class containing all best-first algorithms that are guided by path-dependent evaluation functions.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r157.html Dr. Rina Dechter @ UCI
          |

          R157
          On the Practical Significance of Hypertree vs. Tree Width.

          Rina Dechter, Lars Otten, and Radu Marinesu

          Abstract
          The recently introduced notion of hypertree width has been shown to provide a broader characterization of tractable constraint and probabilistic networks than the tree width. This paper demonstrates empirically that in practice the bounding power of the tree width is still superior to the hypertree width for many benchmark instances of both probabilistic and deterministic networks.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r187.html Dr. Rina Dechter @ UCI
          |

          R187
          Mini-bucket Elimination with Moment Matching

          Natalia Flerova, Alexander Ihler, Rina Dechter and Lars Otten

          Abstract
          We investigate a hybrid of two styles of algorithms for deriving bounds for optimization tasks over graphical models: non-iterative message-passing schemes exploiting variable duplication to reduce cluster sizes (e.g. MBE) and iterative methods that re-parameterize the problem’s functions aiming to produce good bounds even if functions are processed independently (e.g. MPLP). In this work we combine both ideas, augmentingMBE with re-parameterization,which we call MBE with Moment Matching (MBE-MM). The results of preliminary empirical evaluations show the clear promise of the hybrid scheme over its individual components (e.g., pureMBE and pure MPLP).Most significantly, we demonstrate the potential of the new bounds in improving the power of mechanically generated heuristics for branch and bound search.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r32.html Dr. Rina Dechter @ UCI
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          R32
          Compiling Relational Data into Disjunctive Structure: Empirical Evaluation
          Rina Dechter (dechter@ics.uci.edu) & Eddie Shwalb (eschwalb@ics.uci.edu)

          Abstract
          Recent work in knowledge compilation suggests that relations which can be described precisely by either Horn theories or tree constraint networks are identifiable in output polynomial time. Algorithms for computing approximations using these languages were also proposed. Upon testing such approximations on artificially generated and real life data, it was immediately observed that they yield numerous superfluous models. As a result, although certain entailment queries can be answered reliably, these methods may be ineffective for a large class of membership queries. To improve the approximation quality, we investigate here the k-decomposition problem, that is, determining whether a relation can be described by a disjunction of k tractable theories. The paper discusses the complexity of this task, outlines several algorithms for computing both exact and approximate k-decompositions, and evaluates the potential of this approach empirically. We focus on the class of tree constraint networks and Horn theories and report results on artificially generated relations and on three real life cases. Our experiments show that for uniform random relations, the quality of upper bound approximations improves as k increases. However, when we require very high accuracy, decomposition is not effective since k grows linearly with the size of the data. When the data comes from a near-tractable source, the approach is useful. Experiments show that for the King Rook King problem the generalizing power of such methods is comparable to that of recently developed learning algorithms.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r222.html Dr. Rina Dechter @ UCI
          |

          R222
          Paralleizing AND/OR Branch-and-Bound
          Lars Otten and Rina Dechter

          Abstract
          We present parallel AND/OR Branch-and-Bound which uses the power of a computational grid to push the boundaries of feasibility for combinatorial optimization. Two variants of the scheme are described, one of which aims to use machine learning techniques for parallel load balancing. Indepth analysis identifies two inherent sources of parallel search space redundancies that, together with general parallel execution overhead, can impede parallelization and render the problem far from embarrassingly parallel. We conduct extensive empirical evaluation on hundreds of CPUs, the first of its kind, with overall positive results. In a significant number of cases parallel speedup is close to the theoretical maximum and we are able to solve many very complex problem instances orders of magnitude faster than before; yet analysis of certain results also serves to demonstrate the inherent limitations of the approach due to the aforementioned redundancies.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r197.html Dr. Rina Dechter @ UCI
          |

          R197
          Preliminary Empirical Evaluation of Anytime Weighted AND/OR Best-First Search for MAP
          Natalia Flerova, Radu Marinescu, and Rina Dechter

          Abstract
          We explore the potential of anytime best-first search schemes for combinatorial optimization tasks over graphical models (e.g., MAP/MPE). We show that recent advances in extending best-first search into an anytime scheme have a potential for optimization for graphical models. Importantly, these schemes come with upper bound guarantees and are sometime competitive with known effective anytime branch-and-bound schemes.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r54.html Dr. Rina Dechter @ UCI
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          R56
          Backtracking Algorithms for Constraint Satisfaction Problems
          Rina Dechter (dechter@ics.uci.edu) & Daniel Frost (frost@ics.uci.edu)

          Abstract
          Over the past twenty five years many backtracking algorithms have been developed for constraint satisfaction problems. This survey describes the basic backtrack search within the search space framework and then presents a number of improvements developed in the past two decades, including look-back methods such as backjumping, constraint recording, backmarking, and look-ahead methods such as forward checking and dynamic variable ordering.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r27.html Dr. Rina Dechter @ UCI
          |
          R27
          Diagnosing Tree-Decomposable Curcuits
          Yousri El Fattah (fattah@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          This paper describes a diagnosis algorithm called structure-based abduction (SAB) which was developed in the framework of costraint networks [12]. The algorithm exploits the structure of the constraint network and is most efficient for near-tree problem domains. By analyzing the structure of the problem domain, the performance of such algorithms can be bounded in advance. We present empirical results comparing SAB with two model-based algorithms, MBD1 and MBD2, for the task of finding one or all minimal-cardinality diagnosis. MBD1 uses the same computing strategy as algorithm GDE [9]. MBD2 adopts a breadth-first search strategy similar to the algorithm DIAGNOSE [24]. The main conclusion is that for nearly acyclic circuits, such as the N-bit adder, the performance of SAB being linear provides definite advantages as the size of the circuit increases.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r92a.html Dr. Rina Dechter @ UCI
          |
          R92a
          Unifying Tree-Decomposition Schemes for Automated Reasoning
          Kalev Kask, Rina Dechter, Javier Larrosa & Fabio Cozman
          Abstract
          The paper provides a unifying perspective of tree-decomposition algorithms appearing in various automated reasoning areas, such as join-tree clustering for constraint-satisfaction and the clique-tree algorithm for probabilistic reasoning. Subsequently, the paper extends the variable-elimination scheme called bucket-elimination (BE) into a two-phase message passing along a bucket-tree (BTE), making it another instance of tree-decomposition. Our analysis shows that the new algorithm BTE may provide a substantial speed-up over BE for important reasoning tasks. Time-space tradeoffs are cast within the tree-decomposition framework.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r115.html Dr. Rina Dechter @ UCI
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          R115
          AND/OR Search Spaces for Graphical Models
          Rina Dechter
          Abstract
          The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometime reduce the search space exponentially. Indeed, most algorithmic advances in searchbased constraint processing and probabilistic inference can be viewed as searching an AND/OR search tree or graph. Familiar parameters such as the depth of a spanning tree, tree-width and path-width are shown to play a key role in characterizing the effect of AND/OR search graphs vs the traditional OR search graphs.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r41a.html Dr. Rina Dechter @ UCI
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          R41a
          Local And Global Relational Consistency
          Rina Dechter (dechter@ics.uci.edu) & Peter van Beek (vanbeek@cs.ualberta.ca)

          Abstract
          Local consistency has proven to be an important concept in the theory and practice of constraint networks. In this paper, we present a new definition of local consistency, called relational consistency. The new definition is relation-based, in contrast with the previous definition of local consistency, which we characterize as variable-based. We show the conceptual power of the new definition by showing how it unifies known elimination operators such as resolution in theorem proving, joins in relational databases, and variable elimination for solving linear inequalities. Algorithms for enforcing various levels of relational consistency are introduced and analyzed. We also show the usefulness of the new definition in characterizing relationships between properties of constraint networks and the level of local consistency needed to ensure global consistency.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r176a.html Dr. Rina Dechter @ UCI
          |

          R176a
          Load Balancing for Parallel Branch and Bound

          Lars Otten and Rina Dechter

          Abstract
          A strategy for parallelization of a state-of-the-art Branch and Bound algorithm for weighted CSPs and other graphical model optimization tasks is introduced: independent worker nodes concurrently solve subproblems, managed by a Branch and Bound master node; the problem cost functions are used to predict subproblem complexity, enabling efficient load balancing, which is crucial for the performance of the parallelization process. Experimental evaluation on up to 20 nodes yields very promising results and suggests the effectiveness of the scheme. The system runs on loosely coupled commodity hardware, simplifying deployment on a larger scale in the future.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r118.html Dr. Rina Dechter @ UCI
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          R118
          Counting-Based Look-Ahead Schemes for Constraint Satisfaction
          Kalev Kask, Rina Dechter, and Vibhav Gogate
          Abstract
          The paper presents a new look-ahead scheme for backtrack- ing search for solving constraint satisfaction problems. This look-ahead scheme computes a heuristic for value ordering and domain pruning. The heuristic is based on approximating the number of solutions extending each partial solution. In particular, we investigate a recent partition- based approximation of tree-clustering algorithms, Iterative Join-Graph Propagation (IJGP), which belongs to the class of belief propagation algorithms that attracted substantial interest due to their success for probabilistic inference. Our empirical evaluation demonstrates that the counting-based heuristic approximated by IJGP yields a scalable, focused search.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r44a.html Dr. Rina Dechter @ UCI
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          R44a
          Topological Parameters For Time-Space Tradeoff
          Rina Dechter (dechter@ics.uci.edu), Yousri El Fattah

          Abstract
          In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for optimization tsks. By analyzing the problem structure the user can select from a spectrum of hybrid algorithms, the on that best meets a giben time-space specification. To determine the potential of this approach, we analyze the structural prperties of problems coming from the circuit diagnosis domain. The analysis demonstrate ho the tradeoffs associated with various hybrids can beexplicated and be used for each problem instance.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r206.html Dr. Rina Dechter @ UCI
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          R204
          Weighted Best First Search for MAP
          Natalia Flerova, Radu Marinescu and Rina Dechter

          Abstract
          The paper considers Weighted Best First (WBF) search schemes, popular for path-finding domain, as approximations and as anytime schemes for the MAP task. We demonstrate empirically the ability of these schemes to effectively provide approximations with guaranteed suboptimality and also show that as anytime schemes they can be competitive on some benchmarks with one of the best state-of-the-art scheme, Depth-First Branch-and-Bound.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r129.html Dr. Rina Dechter @ UCI
          |
          R129
          Memory Intensive Branch-and-Bound Search for Graphical Models
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. AND/OR Branch-and-Bound (AOBB) is a new algorithm that explores the AND/OR search tree for solving optimization tasks in graphical models. In this paper we extend the algorithm to explore an AND/OR search graph by equipping it with a context-based adaptive caching scheme similar to good and no-good recording. The efficiency of the new graph search algorithm is demonstrated empirically on various benchmarks, including the very challenging ones that arise in genetic linkage analysis.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r157a.html Dr. Rina Dechter @ UCI
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          R157a
          On the Practical Significance of Hypertree vs. Tree Width.

          Rina Dechter, Lars Otten, and Radu Marinesu

          Abstract
          In 2000, [4] presented a new graph parameter, the hypertree width, and showed that it provides a broader characterization of tractable constraint networks than the treewidth. In 2005, [5] extended this result to general probabilistic graphical models, showing that the hypertree width yields bounds on inference algorithms when functions are expressed relationally. The main contribution of this paper is in demonstrating empirically that in practice the bounding power of the treewidth is still superior to the hypertree width for many benchmark instances of both probabilistic and deterministic networks. Specifically, we show that the treewidth yields a far tighter bound on the algorithm's performance when the graphical model has a low level of determinism. A secondary theoretical contribution of the paper is in showing that the hypertree width bound is also relevant to search algorithms and to functions which are specified via decision trees.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r224.html Dr. Rina Dechter @ UCI


          R224
          From Exact to Anytime Solutions for Marginal MAP
          Junkyu Lee, Radu Marinescu, Rina Dechter and Alexander Ihler

          Abstract
          This paper explores the anytime performance of search-based algorithms for solving the Marginal MAP task over graphical models. The current state-of-the-art for solving this challenging task is based on best-first search exploring the AND/OR graph with the guidance of heuristics based on mini-bucket and variational cost-shifting principles. Yet, those schemes are uncompromising in that they solve the problem exactly, or not at all, and often suffer from memory problems. In this work, we explore the well known principle of weighted search for converting best-first search solvers into anytime schemes. The weighted best-first search schemes report a solution early in the process by using inadmissible heuristics, and subsequently improve the solution. While it was demonstrated recently that weighted schemes can yield effective anytime behavior for pure MAP tasks, Marginal MAP is far more challenging (e.g., a conditional sum must be evaluated for every solution). Yet, in an extensive empirical analysis we show that weighted schemes are indeed highly effective anytime solvers for Marginal MAP yielding the most competitive schemes to date for this task.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r161.html Dr. Rina Dechter @ UCI
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          R161
          Approximate Solution Sampling (and Counting) on AND/OR spaces

          Vibhav Gogate and Rina Dechter

          Abstract
          In this paper, we describe a new algorithm for sampling solutions from a uniform distribution over the solutions of a constraint network. Our new algorithm improves upon the Sampling/Importance Resampling (SIR) component of our previous scheme of SampleSearch-SIR by taking advantage of the decomposition implied by the network�s AND/OR search space.We also describe how our new scheme can approximately count and lower bound the number of solutions of a constraint network. We demonstrate both theoretically and empirically that our new algorithm yields far better performance than competing approaches.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r78.html Dr. Rina Dechter @ UCI
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          R78
          275B Class Project: Empirical Evaluations of Some Benchmark Bayesian Networks
          Authors: Igor Cadez, Hong Zhao, Stephen Bay, Scott Gafney, Dmitry Pavlov

          Abstract
          This paper reports students' projects performed during the 275a class: "Network-based reasoning: belief networks", in the department of Information and Computer Science at UC-Irvine, taught by Rina Dechter. Students were required to select one problem from the Bayesian Repository Benchmarks and to run a comparative study of several known algorithms and report the results. This paper include five of the students' reports. The problems used are: Pigs, Diabetes, Insurance, HailFinder and ALARM.
            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r215.html Dr. Rina Dechter @ UCI
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          R215
          STLS: Cutset-Driven Local Search For MPE
          Alon Milchgrub and Rina Dechter

          Abstract
          In this paper we present a cycle-cutset driven stochastic local search algorithm which approximates the optimum of sums of unary and binary potentials, called Stochastic Tree Local Search or ST LS. We study empirically two pure variants of ST LS against the state-of-the art GLS + scheme and against a hybrid.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r144.html Dr. Rina Dechter @ UCI
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          R144
          Best-First AND/OR Search for Most Probable Explanations

          Radu Marinescu and Rina Dechter

          Abstract
          The paper evaluates the power of best-first search over AND/OR search spaces for solving theMost Probable Explanation (MPE) task in Bayesian networks. The main virtue of the AND/OR representation of the search space is its sensitivity to the structure of the problem, which can translate into significant time savings. In recent years depth-first AND/OR Branch-and- Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for. The main contribution of this paper is in showing that a recent extension of AND/OR search algorithms from depth-first Branch-and-Bound to best-first is indeed very effective for computing the MPE in Bayesian networks. We demonstrate empirically the superiority of the best-first search approach on various probabilistic networks.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r151a.html Dr. Rina Dechter @ UCI
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          R151a
          AND/OR Branch-and-Bound Search for Combinatorial Optimization in Graphical Models

          Radu Marinescu and Rina Dechter

          Abstract
          We introduce a new generation of depth-first Branch-and-Bound algorithms that explore the AND/OR search tree using static and dynamic variable orderings for solving general constraint optimization problems. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation, which can translate into significant time savings for search algorithms. The focus of this paper is on linear space search which explores the AND/OR search tree rather than the search graph and therefore make no attempt to cache information. We investigate the power of the mini-bucket heuristics within the AND/OR search space, in both static and dynamic setups. We focus on two most common optimization problems in graphical models: finding the Most Probable Explanation (MPE) in Bayesian networks and solving Weighted CSPs (WCSP). In extensive empirical evaluations we demonstrate that the new AND/OR Branch-and-Bound approach improves considerably over the traditional OR search strategy and show how various variable ordering schemes impact the performance of the AND/OR search scheme.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r191.html Dr. Rina Dechter @ UCI
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          R191
          Advances in Distributed Branch and Bound
          Lars Otten and Rina Dechter

          Abstract
          We describe a distributed version of an advanced branch and bound algorithm over graphical models. The crucial issue of load balancing is addressed by estimating subproblem complexity through learning, yielding impressive speedups on various hard problems using hundreds of parallel CPUs.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r142.html Dr. Rina Dechter @ UCI
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          R142
          Approximate Counting by Sampling the Backtrack-free Search Space

          Vibhav Gogate and Rina Dechter

          Abstract
          We present a new estimator for counting the number of solutions of a Boolean satisfiability problem as a part of an importance sampling framework. The estimator uses the recently introduced SampleSearch scheme that is designed to overcome the rejection problem associated with distributions having a substantial amount of determinism. We show here that the sampling distribution of SampleSearch can be characterized as the backtrack-free distribution and propose several schemes for its computation. This allows integrating SampleSearch into the importance sampling framework for approximating the number of solutions and also allows using SampleSearch for computing a lower bound measure on the number of solutions. Our empirical evaluation demonstrates the superiority of our new approximate counting schemes against recent competing approaches.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r120.html Dr. Rina Dechter @ UCI
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          R120
          AND/OR Branch-and-Bound for Graphical Models
          Radu Marinescu and Rina Dechter
          Abstract
          The paper presents and evaluates the power of a new framework for optimization in graphical models, based on AND/OR search spaces. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation. We develop our work on Constraint Optimization Problems (COP) and introduce a new generation of depth-first Branch-and-Bound algorithms that explore an AND/OR search space and use static and dynamic mini-bucket heuristics to guide the search. We focus on two optimization problems, solvingWeighted CSPs (WCSP) and finding theMost Probable Explanation (MPE) in belief networks. We show that the new AND/OR approach improves considerably over the classic OR space, on a variety of benchmarks including random and real-world problems. We also demonstrate the impact of different lower bounding heuristics on Branch-and-Bound exploring AND/OR spaces.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r24.html Dr. Rina Dechter @ UCI
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          R24
          On Improving Connectionist Energy Minimization
          Gadi Pinkas & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Symmetric networks designed for energy minimization such as Boltzman machines and Hopfield nets are used frequently for optimization, constraint satisfaction and approximation of NP-hard problems. Nevertheless, finding a global solution (i.e., a global minimum for the energy function) is not guaranteed and even a local solution may take an exponential number of steps. We propose an improvement to the standard local activation function used for such networks. The improved algorithm guarantees that a global minimum is found in linear time for tree-like subnetworks. The algorithm is uniform and does not assume that the network is tree-like. It can identify tree-like subnetworks even in cyclic topologies (arbitrary networks) and avoid local minima along these trees. For acyclic networks, the algorithm is guaranteed to converge to a global minimum from any initial state of the system (selfstabilization) and remains correct under various types of schedulers. For general (cyclic) topologies, we show how our tree-like algorithm can be extended using the cycle-cutset idea. The general algorithm optimizes tree-like subnetworks and has some performance guarantees that are related to the size of the network's cycle-cutset. In any case, the algorithm performs no worse than the standard algorithms. On the negative side, we show that in the presence of cycles, no uniform algorithm exists that guarantees optimality even under a sequential synchronous scheduler. In addition, no uniform algorithm exists to optimize even acyclic networks when the scheduler is asynchronous.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r151.html Dr. Rina Dechter @ UCI
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          R151
          AND/OR Branch-and-Bound Search for Combinatorial Optimization in Graphical Models

          Radu Marinescu and Rina Dechter

          Abstract
          This is the first of two papers presenting and evaluating the power of a new framework for combinatorial optimization in graphical models, based on AND/OR search spaces. We introduce a new generation of depth-first Branch-and-Bound algorithms that explore the AND/OR search tree using static and dynamic variable orderings. The virtue of the AND/OR representation of the search space is that its size may be far smaller than that of a traditional OR representation, which can translate into significant time savings for search algorithms. The focus of this paper is on linear space search which explores the AND/OR search tree. In the second paper we explore memory intensive AND/OR search algorithms. In conjunction with the AND/OR search space we investigate the power of the mini-bucket heuristics in both static and dynamic setups. We focus on two most common optimization problems in graphical models: finding the Most Probable Explanation in Bayesian networks and solving Weighted CSPs. In extensive empirical evaluations we demonstrate that the new AND/OR Branch-and-Bound approach improves considerably over the traditional OR search strategy and show how various variable ordering schemes impact the performance of the AND/OR search scheme.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r124.html Dr. Rina Dechter @ UCI
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          R124
          Modeling Transportation Routines using Hybrid Dynamic Mixed Networks
          Vibhav Gogate, Rina Dechter, Bozhena Bidyuk, Craig Rindt and James Marca
          Abstract
          This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filtering and Constraint Propagation to address the complexity of modeling and reasoning in HDMNs. We use this framework to model a person�s travel activity over time and to predict destination and routes given the current location. We present a preliminary empirical evaluation demonstrating the effectiveness of our modeling framework and algorithms using several variants of the activity model.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r49.html Dr. Rina Dechter @ UCI
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          R49
          To Guess Or To Think? Hybrid Algorithms For SAT
          Irina Rish (irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Complete algorithms for solving propositional satisfiability fall into two main classes: backtracking search (e.g., the Davis-Putnam Procedure [1]) and resolution (e.g., the original Davis-Putnam Algorithm [2] and Directional Resolution [4]). Backtracking may be viewed as a systematic "guessing" of variable assignments, while resolution is inferring, or "thinking". Experimental results show that "pure guessing" or "pure thinking" might be inefficient. We propose an approach that combines both techniques and yields a family of hybrid algorithms, parameterized by a bound on the "effective" amount of resolution allowed. The idea is to divide the set of propositional variables into two classes: conditioning variables, which are assigned truth values, and resolution variables, which are resolved upon. We report on preliminary experimental results demonstrating that on certain classes of problems hybrid algorithms are more efficient than either of their components in isolation.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r130a.html Dr. Rina Dechter @ UCI
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          R130a
          An Anytime Scheme for Bounding Posterior Belief
          Bozhena Bidyuk and Rina Dechter
          Abstract
          This report presents an any-time scheme for computing lower and upper bounds on posterior marginals in Bayesian networks. The scheme draws from two previously proposed methods, bounded conditioning [9] and bounds propagation algorithm [16]. Following the principles of cutset conditioning [18], our method enumerates a subset of cutset tuples and applies exact reasoning in the network instances conditioned on those tuples. The probability mass of the remaining tuples is bounded using a variant of bound propagation. We show that our new scheme improves on the earlier schemes.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r166.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R166
          SampleSearch:Importance sampling in presence of Determinism

          Vibhav Gogate, and Rina Dechter

          Abstract
          The paper focuses on developing effective importance sampling algorithms for mixed probabilistic and deterministic graphical models. The use of importance sampling in such graphical models is problematic because it generates many useless zero weight samples which are rejected yielding an inefficient sampling process. To address this rejection problem, we propose the SampleSearch scheme that augments sampling with systematic constraint-based backtracking search. We characterize the bias introduced by the combination of search with sampling, and derive a weighting scheme which yields an unbiased estimate of the desired statistics (e.g. probability of evidence). When computing the weights exactly is too complex, we propose an approximation which has a weaker guarantee of asymptotic unbiasedness. We present results of an extensive empirical evaluation demonstrating that SampleSearch outperforms other schemes in presence of significant amount of determinism.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r86.html Dr. Rina Dechter @ UCI
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          R86
          Topological Parameters For Time-Space Tradeoff
          Rina Dechter (dechter@ics.uci.edu) & Yousri El Fattah (yousri@rsc.rockwell.com)
          Abstract
          In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure, the user can select from a spectrum of algorithms, the one that best meets a given time-space specification. To determine the potential of this approach we analyze the structural properties of problems coming from the circuit diagnosis domain. The analysis demonstrates how the tradeoffs associated with various hybrids can be used for each problem instance.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r159.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R159
          Mixed deterministic and probabilistic networks

          Robert Mateescu and Rina Dechter

          Abstract
          The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to develop mixed networks stems from the desire to fully exploit the deterministic information (constraints) that is often present in graphical models. Several concepts and algorithms specific to belief networks and constraint networks are combined, achieving computational efficiency, semantic coherence and user- interface convenience. We define the semantics and graphical representation of mixed networks, and discuss the two main types of algorithms for processing them: inference-based and search-based. A preliminary experimental evaluation shows the benefits of the new model.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r199.html Dr. Rina Dechter @ UCI
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          R199
          Predicting the Size of Depth-First Branch and Bound Search Trees
          Levi H. S. Lelis, Lars Otten, and Rina Dechter

          Abstract
          This paper provides algorithms for predicting the size of the Expanded Search Tree (EST) of Depth-first Branch and Bound algorithms (DFBnB) for optimization tasks. The prediction algorithm is implemented and evaluated in the context of solving combinatorial optimization problems over graphical models such as Bayesian and Markov networks. Our methods extend to DFBnB the approaches provided by Knuth-Chen schemes that were designed and applied for predicting the EST size of backtracking search algorithms. Our empirical results demonstrate good predictions which are superior to competing schemes.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r208.html Dr. Rina Dechter @ UCI
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          R208
          Applying Marginal MAP Search to Probabilistic Conformant Planning: Initial Results
          Junkyu Lee, Radu Marinescu, and Rina Dechter

          Abstract
          In this position paper, we present our current progress in applying marginal MAP algorithms for solving the conformant planning problems. Conformant planning problem is formulated as probabilistic inference in graphical models compiled from relational PPDDL domains.The translation from PPDDL into Dynamic Bayesian Network is developed by mapping the SAT encoding of the ground PPDDL into factored representation. We experimented with recently developed AND/OR branch and bound search algorithms for marginal MAP over instances from the international planning competition domains, and we show that several domains were solved efficiently.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r165.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R165
          Sampling Algorithms for Probabilistic Graphical Models with Determinism.

          Vibhav Gogate

          Abstract
          Mixed constraint and probabilistic graphical models occur quite frequently in many real world applications. Examples include: genetic linkage analysis, functional/software verification, target tracking and activity modeling. Query answering and in particular probabilistic inference on such graphical models is computationally hard often requiring exponential time in the worst case. Therefore in practice sampling algorithms are widely used for providing an approximate answer. In presence of deterministic dependencies or hard constraints, however, sampling has to overcome some principal challenges. In particular, importance sampling type schemes suffer from what is known as the rejection problem in that samples having zero weight may be generated with probability arbitrarily close to one yielding useless results. On the other hand, Markov Chain Monte Carlo techniques do not converge at all often yielding highly inaccurate estimates.

          In this thesis, we address these problems in a two fold manner. First, we utilize research done in constraint satisfaction and satisfiability communities for processing constraints to reduce or eliminate rejection. Second, mindful of the time overhead in sample generation due to determinism, we both make and utilize advances in statistical estimation theory to make the "most" out of the generated samples.

          Utilizing constraint satisfaction and satisfiability research, we propose two classes of sampling algorithms - one based on consistency enforcement and the other based on systematic search. The consistency enforcement class of algorithms work by shrinking the domains of random variables, by strengthening constraints, or by creating new ones, so that some or all zeros in the problem space can be removed. This improves convergence because of dimensionality reduction and also reduces rejection because many zero weight samples will not be generated. Our systematic search based techniques called SampleSearch manage the rejection problem by interleaving sampling with backtracking search. In this scheme, when a sample is supposed to be rejected, the algorithm continues instead with systematic backtracking search until a strictly positive-weight sample is generated. The strength of this scheme is that any state-of-the-art constraint satisfaction or propositional satisfiability search algorithm can be used with minor modifications. Through large scale experimental evaluation, we show that SampleSearch outperforms all state-of-the-art schemes when a significant amount of determinism is present in the graphical model. Subsequently, we combine SampleSearch with known statistical techniques such as Sampling Importance Resampling and Metropolis Hastings yielding efficient algorithms for sampling solutions from a uniform distribution over the solutions of a Boolean satisfiability formula. Unlike state-of-the-art algorithms, our SampleSearch-based algorithms guarantee convergence in the limit.

          As to statistical estimation, we make two distinct contributions. First, we propose several new statistical inequalities extending the one-sample Markov inequality to multiple samples which can be used in conjunction with SampleSearch to probabilistically lower bound likelihood tasks over mixed networks. Second, we present a novel framework called AND/OR importance sampling which generalizes the process of computing sample mean by exploiting AND/OR search spaces for graphical models. Specifically we provide a spectrum of AND/OR sample means which are defined on the same set of samples but derive different estimates trading variance with time. At one end is the AND/OR sample tree mean which has smaller variance than the conventional OR sample tree mean and has the same time complexity. At the other end is the AND/OR graph sample mean which has even lower variance but has higher time and space complexity. We demonstrate empirically that AND/OR sample means are far closer to the exact answer than the conventional OR sample mean.


          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r29.html Dr. Rina Dechter @ UCI
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          R29
          Directional Resolution: The Davis-Putnam Procedure, Revisited
          Rina Dechter (dechter@ics.uci.edu) & Irina Rish (irinar@ics.uci.edu)

          Abstract
          The paper presents algorithm directional resolution, a variation on the original Davis-Putnam algorithm, and analyzes its worst-case behavior as a function of the topological structure on the theories. Th notions of induced width and diversity are shown to play a key role in bounding the complexity of the procedure. The inportance of our analysis lies in highlighting structure-based tractable classes of satisfiability and in providing theoretical guarantees on the time and space complexity of the algorithm. Contrary to previous assesments, we show that for many theories directional resolution could be an effective procedure. Our empirical test confirm theoretical prediction, showing that on problems with special structures, like chains, directional resolution greatly outperforms one of the most effective satisfiability algorithms known to date, namely the popular Davis-Puntnam procedure.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r194.html Dr. Rina Dechter @ UCI
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          R194
          Empirical Evaluation of AND/OR Multivalued Decision Diagrams for Inference

          William Lam and Rina Dechter

          Abstract
          AND/OR Multi-valued Decision Diagrams (AOMDD) were shown to provide a more compact representation of discrete-domain real-valued functions compared to other decision diagram variants. We show the performance of AOMDDs on inference tasks in graphical models. We introduce the elimination operator to AOMDDs, which in conjunction with the combination operator introduced in previous work, yields a full bucket elimination (BE) scheme using AOMDDs as an alternative function representation to tables. We show that we are able to solve instances that do not fit in main memory when using tables.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r110.html Dr. Rina Dechter @ UCI
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          R110
          Iterative Algorithms for Graphical Models
          Robert Mateescu
          Abstract
          Probabilistic inference in Bayesian networks, and even reasoning within error bounds are known to be NP-hard problems. Our research focuses on investigating approximate message-passing algorithms inspired by Pearl�s belief propagation algorithm and by variable elimination. We study the advantages of bounded inference provided by anytime schemes such as Mini-Clustering (MC), and combine them with the virtues of iterative algorithms such as Iterative Belief Propagation (IBP). Our resulting hybrid algorithm Iterative Join-Graph Propagation (IJGP) is shown empirically to surpass the performance of both MC and IBP on several classes of networks. IJGP can also be viewed as a Generalized Belief Propagation algorithm, a framework which recently allowed connections with approximate algorithms from statistical physics, showing that convergence points are in fact stationary points of the Bethe (or the more general Kikuchi) free energy. Although there is still little understanding why or when IBP works well, it exhibits tremendous performance on different classes of problems, most notably coding and satisfiability problems. We investigate the iterative algorithms for Bayesian networks by making connections with well known constraint processing algorithms, which help explain when IBP infers correctly extreme beliefs. This study gives an account of why iterating helps, and identifies classes of easy and hard problems for IBP (and IJGP). Finally, we plan to investigate iterative message-passing algorithms in other graph-based frameworks such as influence diagrams and planning.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r89a.html Dr. Rina Dechter @ UCI
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          R89a
          Boosting Search with Variable Elimination in Constraint Optimization and Constraint Satisfaction Problems
          Javier Larrosa: javier@ics.uci.edu & Rina Dechter: dechter@ics.uci.edu
          Abstract
          There are two main solving schemas for constraint satisfaction and optimization problems i) search, whose basic step is branching over the values of a variables, and ii) dynamic programming, whose basic step is variable elimination. Variable elimination is time and space exponential in a graph parameter called induced width, which renders the approach infeasible for many problem classes. However, by restricting variable elimination so that only low arity constraints are processed and recorded, it can be effectively combined with search, because the elimination of variables may reduce drastically the search tree size. In this paper we introduce BE-BB(k), a hybrid general algorithm that combines search and variable elimination. The parameter k controls the tradeoff between the two strategies. The algorithm is space exponential in k. Regarding time, we show that its complexity is bounded by k and a structural parameter from the constraint graph. We provide experimental evidence that the hybrid algorithm can outperform state-of-the-art algorithms in constraint satisfaction. Max-CSP and Weighted CSP. Especially in optimization tasks, the advantage of our approach over plain search can be overwhelming.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r23.html Dr. Rina Dechter @ UCI
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          R23
          Finding All Solutions If You Can Find One
          Rina Dechter (dechter@ics.uci.edu) & Alon Itai (dechter@ics.uci.edu)

          Abstract
          We address the problem of enumerating (producing) all models of a given theory. We show that the enumeration task can be performed in time proportional to the product of the number of models and the effort needed to generate each model in isolation. In other words, the requirement of generating a new solution in each iteration does not in itself introduce substantial complexity. Consequently, it is possible to decide whether any tractably satisfiable formula has more than K solutions in time polynomial in the size of the formula and in K. In the special cases of Horn formulas and 2-CNFs, although counting is #P-complete, to decide whether the count exceeds K, is polynomial in K.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r39.html Dr. Rina Dechter @ UCI
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          R39
          Look-Ahead Value Ordering For Constraint Satisfaction Problems
          Daniel Frost (frost@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Looking ahead during search is often useful when solving constraint satisfaction problems. Previous studies have shown that looking ahead helps by causing dead-ends to occur earlier in the search, and by providing information that is useful for dynamic variable ordering. In this paper, we show that another benefit of looking ahead is a useful domain value ordering heuristic, which we call look-ahead value ordering or LVO. LVO counts the number of times each value of the current variable con icts with some value of a future variable, and the value with the lowest number of con icts is chosen first. Our experiments show that look-ahead value ordering can be of substantial benefit, especially on hard constraint satisfaction problems.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r135.html Dr. Rina Dechter @ UCI
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          R135
          Compiling Constraint Networks into AND/OR Multi-Valued Decision Diagrams (AOMDDs)
          Robert Mateescu and Rina Dechter
          Abstract
          Inspired by AND/OR search spaces for graphical models recently introduced, we propose to augment Ordered Decision Diagrams with AND nodes, in order to capture function decomposition structure. This yields AND/OR multivalued decision diagram (AOMDD) which compiles a constraint network into a canonical form that supports polynomial time queries such as solution counting, solution enumeration or equivalence of constraint networks.We provide a compilation algorithm based on Variable Elimination for assembling an AOMDD for a constraint network starting from the AOMDDs for its constraints. The algorithm uses the APPLY operator which combines two AOMDDs by a given operation. This guarantees the complexity upper bound for the compilation time and the size of the AOMDD to be exponential in the treewidth of the constraint graph, rather than pathwidth as is known for ordered binary decision diagrams (OBDDs).

          PDF



          http://www.ics.uci.edu/~dechter/publications/r189.html Dr. Rina Dechter @ UCI
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          R189
          A System for Exact and Approximate Genetic Linkage Analysis of SNP Data in Large Pedigrees
          Mark Silberstein, Omer Weissbrod, Lars Otten, Anna Tzemach, Andrei Anisenia, Oren Shtark, Dvir Tuberg, Eddie Galfrin, Irena Gannon, Adel Shalata, Zvi U. Borochowitz, Rina Dechter, Elizabeth Thompson, Dan Geiger

          Abstract
          Motivation: The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system which streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations, and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes.
          Results: Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman- Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities via a study of a large complex pedigree affected with metabolic syndrome.
          Availability: Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp . The system source code can also be downloaded from the system website.

          [PDF] - [Suppl]

          http://www.ics.uci.edu/~dechter/publications/r216.html Dr. Rina Dechter @ UCI
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          R216
          Weighted Best-First Search for W-Optimal Solutions over Graphical Models
          Natalia Flerova, Radu Marinescu, Pratyaksh Sharma and Rina Dechter

          Abstract
          The paper explores the potential of weighted best-first search schemes as anytime optimization algorithms for solving graphical models tasks such as MPE (Most Probable Explanation) or MAP (Maximum a Posteriori) and WCSP (Weighted Constraint Satisfaction Problem). While such schemes were widely investigated for path-finding tasks, their application for graphical models was largely ignored, possibly due to their memory requirements. Compared to the depth-first branch and bound, which has long been the algorithm of choice for optimization in graphical models, a valuable virtue of weighted best-first search is that they are w-optimal, i.e. when terminated, they return a solution cost C and a weight w, such that C≤w·C*, where C* is the optimal cost. We report on a significant empirical evaluation, demonstrating the usefulness of weighted best-first search as approximation anytime schemes (that have suboptimality bounds) and compare against one of the best depth-first branch and bound solver to date. We also investigate the impact of different heuristic functions on the behaviour of the algorithms.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r83.html Dr. Rina Dechter @ UCI
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          R83
          Processing Boolean Queries Over Belief Networks
          Rina Dechter (dechter@ics.uci.edu)
          Abstract
          The paper presents a variable elimination method for computing the probability of a cnf query over a belief network. We present a bucket-elimination algorithm whose complexity is controlled by the induced-width of the moral graph combined with the interaction graph of the cnf. We show that the algorithm can be easily extended to answer a host of additional cnf-related queries such as finding the most probable model of the cnf theory, or finding the most probable tuple satisfying the cnf theory, as well as belief assessment conditioned on disjunctive type observations.

          PostScript | PDF


          http://www.ics.uci.edu/~dechter/publications/r186.html Dr. Rina Dechter @ UCI
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          R186
          Sampling-based Lower Bounds for Counting Queries

          Vibhav Gogate and Rina Dechter

          Abstract
          It is well known that computing relative approximations of weighted counting queries such as the probability of evidence in a Bayesian network, the partition function of a Markov network, and the number of solutions of a constraint satisfaction problem is NP-hard. In this paper, we settle therefore on an easier problem of computing high-confidence lower bounds and propose an algorithm based on importance sampling and Markov inequality for it. However, a straight-forward application of Markov inequality often yields poor lower bounds because it uses only one sample. We therefore propose several new schemes that extend it to multiple samples. Empirically, we show that our new schemes are quite powerful, often yielding substantially higher (better) lower bounds than state-of-the-art schemes.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r67.html Dr. Rina Dechter @ UCI
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          R67
          On the impact of causal independence
          Irina Rish (irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Reasoning in Bayesian networks is exponential in a graph parameter w* known as induced width (also known as tree-width and max-clique size). In this paper, we investigate the potential of causal independence (CI) for improving this performance. We consider several tasks, such as belief updating, finding a most probable explanation (MPE), finding a maximum aposteriori hypothesis (MAP), and finding the maximum expected utility (MEU). We show that exploiting CI in belief updating can significantly reduce the effective w*, sometimes down to the induced width of the unmoralized network's graph. For example, for poly-trees, CI reduces complexity from exponential to linear in the family size. Similar results hold for the MAP and MEU tasks, while the MPE task is less sensitive to CI. These enhancements are incorporated into bucket-elimination algorithms based on known approaches of network transformations [10, 13] and elimination [18]. We provide an ordering heuristic which guarantees that exploiting CI will never hurt the performance. Finally, we discuss an efficient way of propagating evidence in CI-networks using arc-consistency, and apply this idea to noisy-OR networks. The resulting algorithm generalizes the Quickscore algorithm [9] for BN2O networks.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r180.html Dr. Rina Dechter @ UCI
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          R180
          Stopping Rules for Randomized Greedy Triangulation Schemes

          Andrew E. Gelfand, Kalev Kask, and Rina Dechter

          Abstract
          Many algorithms for performing inference in graphical models have complexity that is exponential in the treewidth - a parameter of the underlying graph structure. Computing the (minimal) treewidth is NP- complete, so stochastic algorithms are sometimes used to find low width tree decompositions. A common approach for finding good decompositions is iteratively executing a greedy triangulation algorithm (e.g. min-fill) with randomized tie-breaking. However, utilizing a stochastic algorithm as part of the inference task introduces a new problem - namely, deciding how long the stochastic algorithm should be allowed to execute before performing inference on the best tree decomposition found so far. We refer to this dilemma as the Stopping Problem and formalize it in terms of the total time needed to answer a probabilistic query. We propose a rule for discontinuing the search for improved decompositions and demonstrate the benefit (in terms of time saved) of applying this rule to Bayes and Markov network instances.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r36.html Dr. Rina Dechter @ UCI
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          R36
          Constraint Restrictiveness versus Local and Global Consistency
          Peter van Beek (vanbeek@cs.ualberta.ca)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Abstract Constraint networks are a simple representation and reasoning framework with diverse applications. In this paper, we identify two new complementary properties on the restrictiveness of the constraints in a network-constraint tightness and constraint looseness-and we show their usefulness for estimating the level of local consistency needed to ensure global consistency, and for estimating the level of local consistency present in a network. In particular, we present a sufficient condition, based on constraint tightness and the level of local consistency, that guarantees that a solution can be found in a backtrack-free manner. The condition can be useful in applications where a knowledge base will be queried over and over and the preprocessing costs can be amortized over many queries. We also present a sufficient condition for local consistency, based on constraint looseness, that is straightforward and inexpensive to determine. The condition can be used to estimate the level of local consistency of a network. This in turn can be used in deciding whether it would be useful to preprocess the network before a backtracking search, and in deciding which local consistency conditions, if any, still need to be enforced if we want to ensure that a solution can be found in a backtrack-free manner. Two deffinitions of local consistency are employed in characterizing the conditions: the traditional variable-based notion and a new deffinition of local consistency called relational consistency. New algorithms for enforcing relational consistency are introduced and analyzed.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r155.html Dr. Rina Dechter @ UCI
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          R155
          Bounding Search Space Size via (Hyper)tree Decompositions.

          Lars Otten and Rina Dechter

          Abstract
          This paper develops a measure for bounding the performance of AND/OR search algorithms for solving a variety of queries over graphical models. We show how drawing a connection to the recent notion of hypertree decompositions allows to exploit determinism in the problem specification and produce tighter bounds. We demonstrate on a variety of practical problem instances that we are often able to improve upon existing bounds by several orders of magnitude.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r52.html Dr. Rina Dechter @ UCI
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          R52
          Structure-Driven Algorithms for Truth Maintenance
          Rina Dechter (dechter@ics.uci.edu) & Avi Dechter (avi@cs.ucla.edu)

          Abstract
          This paper studies truth-maintenance and belief revision tasks on singly-connected structures for the purpose of understanding how structural features could be exploited in such tasks. We present distributed algorithms and show that, in the JTMS framework, both belief revision and consistency maintenance are linear in the size of the knowledgebase on singly connected structures. However, the ATMS task is exponential in the branching degree of the network. The singly-connected model, while restrictive, is useful for three reasons. First, efficient algorithms on singly-connected models can be utilized in more general structures by employing well-known clustering techniques. Second, these algorithms can serve as approximations or as heuristics in algorithms that perform truth-maintenance on general problems. Finally, the analysis provides insights for understanding the sources of the computational difficulties associated with JTMS and ATMS.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r194a.html Dr. Rina Dechter @ UCI
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          R194a
          Empirical Evaluation of AND/OR Multivalued Decision Diagrams for Compilation and Inference

          William Lam and Rina Dechter

          Abstract
          AND/OR Multi-valued Decision Diagrams (AOMDD) were shown provide a more compact representation of discrete-domain real- valued functions compared to other decision diagram variants. We show the performance of AOMDDs on compilation and inference tasks in graphical models. We introduce the elimination operator to AOMDDs, which in conjunction with the combination operator introduced in previous work, yields a full bucket elimination (BE) scheme using AOMDDs as an alternative function representation to tables. For compilation, we show that we can achieve a more compact AOMDD compared to previous work. For inference, we show that we are able to solve instances that do not fit in main memory when using tables.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r99.html Dr. Rina Dechter @ UCI
          R99 tutorials |
          Tree Approximation for Belief Updating
          Robert Mateescu, Rina Dechter and Kalev Kask
          Abstract
          The paper presents a parameterized approximation scheme for probabilistic inference. The scheme, called Mini-Clustering (MC), extends the partition-based approximation offered by mini-bucket elimination, to tree decompositions. The benefit of this extension is that all single-variable beliefs are computed (approximately) at once, using a two-phase message-passing process along the cluster tree. The resulting approximation scheme allows adjustable levels of accuracy and efficiency, in anytime style. Empirical evaluation against competing algorithms such as iterative belief propagation and Gibbs sampling demonstrates the potential of the MC approximation scheme for several classes of problems.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r111.html Dr. Rina Dechter @ UCI
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          R111
          New Look-Ahead Schemes for Constraint Satisfaction
          Kalev Kask, Rina Dechter and Vibhav Gogate
          Abstract
          This paper presents new look-ahead schemes for backtracking search when solving constraint satisfaction problems. The look-ahead schemes compute a heuristic for value ordering and domain pruning, which influences variable orderings at each node in the search space. As a basis for a heuristic, we investigate two tasks, both harder than the CSP task. The first is finding the solution with min-number of conflicts. The second is counting solutions. Clearly each of these tasks also finds a solution to the CSP problem, if one exists, or decides that the problem is inconsistent. Our plan is to use approximations of these more complex tasks as heuristics for guiding search for a solution of a CSP task. In particular, we investigate two recent partitionbased strategies that approximate variable elimination algorithms, Mini-Bucket-Tree Elimination and Iterative Join-Graph Propagation (ijgp). The latter belong to the class of belief propagation algorithm that attracted substantial interest due to their surprising success for probabilistic inference. Our preliminary empirical evaluation is very encouraging, demonstrating that the countingbased heuristic approximated by by IJGP yields a very focused search even for hard problems.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r98.html Dr. Rina Dechter @ UCI
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          R98
          Generating Random Solutions for Constraint Satisfaction Problems
          Rina Dechter Kalev Kask, Eyal Bin, and Roy Emek
          Abstract
          The paper presents a method for generating solutions of a constraint satisfaction problem (CSP) uniformly at random. The main idea is to transform the constraint network into a belief network that expresses a uniform random distribution over its set of solutions and then use known sampling algorithms over belief networks. The motivation for this tasks comes from hardware verification. Random test program generation for hardware verification can be modeled and performed through CSP techniques, and is an application in which uniform random solution sampling is required.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r126.html Dr. Rina Dechter @ UCI
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          R126
          AND/OR Search Spaces for Graphical Models
          Rina Dechter and Rina Dechter
          Abstract
          The paper introduces an AND/OR search space perspective for graphical models that include probabilistic networks (directed or undirected) and constraint networks. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometimes reduce the search space exponentially. Indeed, most algorithmic advances in search-based constraint processing and probabilistic inference can be viewed as searching an AND/OR search tree or graph. Familiar parameters such as the depth of a spanning tree, treewidth and pathwidth are shown to play a key role in characterizing the effect of AND/OR search graphs vs. the traditional OR search graphs. We compare memory intensive AND/OR graph search with inference methods, and place various existing algorithms within the AND/OR search space.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r171.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R171
          On Combining Graph-based Variance Reduction schemes

          Vibhav Gogate and Rina Dechter

          Abstract
          In this paper, we consider two variance reduction schemes that exploit the structure of the primal graph of the graphical model: Rao-Blackwellised w-cutset sampling and AND/OR sampling. We show that the two schemes are orthogonal and can be combined to further reduce the variance. Our combination yields a new family of estimators which trade time and space with variance. We demonstrate experimentally that the new estimators are superior, often yielding an order of magnitude improvement over previous schemes on several benchmarks.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r209.html Dr. Rina Dechter @ UCI
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          R209
          AND/OR Search for Marginal MAP
          Radu Marinescu, Rina Dechter, and Alexander Ihler

          Abstract
          Marginal MAP problems are known to be very difficult tasks for graphical models and are so far solved exactly by systematic search guided by a join-tree upper bound. In this paper, we develop new AND/OR branch and bound algorithms for marginal MAP that use heuristics extracted from weighted mini-buckets enhanced with message-passing updates. We demonstrate the effectiveness of the resulting search algorithms against previous join-tree based approaches, which we also extend to accommodate high induced width models, through extensive empirical evaluations. Our results show not only orders-of-magnitude improvements over the state-of-the-art, but also the ability to solve problem instances well beyond the reach of previous approaches.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r132.html Dr. Rina Dechter @ UCI
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          R132
          Improving Bound Propagation
          Bozhena Bidyuk and Rina Dechter
          Abstract
          This paper extends previously proposed bound propagation algorithm [11] for computing lower and upper bounds on posterior marginals in Bayesian networks. We improve the bound propagation scheme by taking advantage of the directionality in Bayesian networks and applying the notion of relevant subnetwork. We also propose an approximation scheme for the linear optimization subproblems. We demonstrate empirically that while the resulting bounds loose some precision, we achieve 10-100 times speedup compared to original bound propagation using a simplex solver.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r76.html Dr. Rina Dechter @ UCI
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          R76
          On the Complexity of Interval-Based Constraint Networks
          Rony Shapiro, Yishai A. Feldman, and Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Acyclic constraint satisfaction problems with arithmetic constraints and domains consisting of sets of disjoint intervals have exponential complexity, since disjunctions of intervals may be introduced while propagating through the constraints. This has prompted many researchers to use approximations on the bounds of sets of intervals, resulting in sound, but incomplete, algorithms. We delineate the complexity of propagation of sets of intervals through arithmetic constraints. For many types of constraint networks, our analysis shows linear, rather than exponential, complexity bounds. Furthermore, exponential complexity is a worst-case scenario that is surprisingly hard to achieve. In some cases, the number of disjoint intervals in the output of an acyclic constraint satisfaction problem is independent of the number of disjoint intervals in the input. Some empirical results are presented, showing that the worst-case bound is not achieved for random intervals.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r12a.html Dr. Rina Dechter @ UCI
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          R12a
          Uncovering Trees In Constraint Networks
          Itay Meiri (itay@cs.ucla.edu) & Rina Dechter (dechter@ics.uci.edu), Judea Pearl (judea@cs.ucla.edu)

          Abstract
          This paper examines the possibility of removing redundant information from a given knowledge base and restructuring it in the form of a tree to enable efficient problem-solving routines. We offer a novel approach that guarantees removal of all redundancies that hide a tree structure. We develop a polynomial-time algorithm that, given an arbitrary binary constraint network, either extracts (by edge removal) a precise tree representation from the path-consistent version of the network or acknowledges that no such tree can be extracted. In the latter case, a tree is generated that may serve as an approximation to the original network.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r141.html Dr. Rina Dechter @ UCI
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          R141
          SampleSearch: A Scheme that Searches for Consistent Samples
          Vibhav Gogate and Rina Dechter
          Abstract
          Sampling from belief networks which have a substantial number of zero probabilities is problematic. MCMC algorithms like Gibbs sampling do not converge and importance sampling schemes generate many zero weight samples that are rejected, yielding an inefficient sampling process (the rejection problem). In this paper, we propose to augment importance sampling with systematic constraint-satisfaction search in order to overcome the rejection problem. The resulting SampleSearch scheme can be made unbiased by using a computationally expensive weighting scheme. To overcome this an approximation is proposed such that the resulting estimator is asymptotically unbiased. Our empirical results demonstrate the potential of our new scheme.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r26a.html Dr. Rina Dechter @ UCI
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          R26a
          Influence in Inheritance Networks Using Propositional Logic and Constraint Networks Techniques
          Rachel Ben-Eliyahu(rachel@cs.ucla.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          This paper focuses on network default theories. Etherington [Etherington, 1987] has established a correspondence between inheritance networks with expections and a subset of Reiter's default logic called network default theories, thus providing a formal semantics and a notion of correct inference for such networks. We show that any such propositional network default theory can be compiled in polynomial time into a classical propositona theory such that the set of models of the latter coincides with the set of extensions of the former. We then show how constraint satisfaction teachniques can be used to compute extensions an to identify tractable network default theories. For any porpositional network theory, our algorithms compute all its extensions and verifies if a given conclusion is in one or all extensions.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r101.html Dr. Rina Dechter @ UCI
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          R101
          Iterative Join-Graph Propagation
          Rina Dechter, Kalev Kask and Robert Mateescu
          Abstract
          The paper presents an iterative version of join-tree clustering that applies the message passing of join-tree clustering algorithm to join-graphs rather than to join-trees, iteratively. It is inspired by the success of Pearl's belief propagation algorithm (BP) as an iterative approximation scheme on one hand, and by a recently introduced mini-clustering (MC(i)) success as an anytime approximation method, on the other. The proposed Iterative Join-graph Propagation (IJGP) is both anytime and iterative. It belongs to the class of generalized belief propagation methods, recently proposed using analogy with algorithms in statistical physics. Empirical evaluation of this approach on a number of problem classes demonstrates that it is almost always superior to IBP and MC(i), and is sometimes more accurate by as much as several orders of magnitude.

          PostScript



          http://www.ics.uci.edu/~dechter/publications/r134.html Dr. Rina Dechter @ UCI
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          R134
          Exploiting Graph Cutsets for Sampling-Based Approximations in Bayesian Networks
          Submitted in partial satisfaction of the requirements for the degree of Doctor Of Philosophy in Information and Computer Science Bozhena Bidyuk
          Abstract
          Automated reasoning with graphical models has found many practical applications in domains such as planning, vision, speech recognition, genetic linkage analysis, diagnostics, and many others. Graphical models, combining graph theory and probability theory, facilitate a compact and structured representation for problems with uncertainty and provide a mechanism for answering queries such as computing the probability of an event given observations.
          Several exact algorithms for reasoning with graphical models exist. However, exact computation is not always possible due to prohibitive time and memory demands. In general, computing exact posterior marginals and even approximating posterior marginals within a desired degree of precision is NP-hard. In practice, we often choose methods that can quickly compute approximate answers to Bayesian queries, trading accuracy for speed. Approximation methods include algorithms for approximate inference, stochastic sampling, network simplifications (simplifying the structure of the underlying graph), and variational approximations. We often obtain a more flexible computation scheme, balancing complexity and accuracy, by combining exact and approximate computation. This dissertation focuses on combining search with existing sampling and bounding methods yielding two new schemes for approximating and bounding posterior marginals in Bayesian networks. Those two new schemes, cutset sampling and any-time bounds, exploit the network structure to bound the complexity of exact computation.
          Cutset sampling for computing approximate posterior marginals samples only a subset of variables, a cutset of the underlying graph. Since reducing the size of the sampling set results in lower sampling variance, cutset-based sampling converges faster than sampling on a full set of variables. Two variants of cutset sampling algorithm were developed. One, based on Gibbs sampling, is a general approach to collapsed Gibbs sampling in Bayesian networks. The second algorithm implements the likelihood weighting on a cutset. The proposed any-time bounds framework is an any-time scheme for computing bounds on posterior marginals. It enumerates a subset of cutset tuples and performs exact inference over these tuples and then bounds the remaining probability mass.
          Both methods exploit the problem�s underlying network structure to control the time and space complexity of the computations. They focus on finding a cutset of the graph such that the complexity of exact reasoning is bounded when the cutset variables are assigned. The dissertation proposes a new algorithm for finding a minimum cost cutset that yields the specified complexity bound on exact inference.

          PDF


          http://www.ics.uci.edu/~dechter/publications/r113.html Dr. Rina Dechter @ UCI
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          R113
          On finding minimal w-cutset problem
          Bozhena Bidyuk and Rina Dechter
          Abstract
          The complexity of a reasoning task over a graphical model is tied to the induced width of the underlying graph. It is well-known that conditioning (assigning values) on a subset of variables yields a subproblem of the reduced complexity where instantiated variables are removed. If the assigned variables constitute a cycle-cutset, the rest of the network is singly-connected and therefore can be solved by linear propagation algorithms. A w-cutset is a generalization of a cycle-cutset defined as a subset of nodes such that the subgraph with cutset nodes removed has induced-width of w or less. In this paper we address the problem of finding a minimal w- cutset in a graph. We relate the problem to that of finding the minimal w-cutset of a treedecomposition. The latter can be mapped to the well-known set multi-cover problem. This relationship yields a proof of NP-completeness on one hand and a greedy algorithm for finding a w- cutset of a tree decomposition on the other. Empirical evaluation of the algorithms is presented.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r102.html Dr. Rina Dechter @ UCI
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          R102
          Cycle-Cutset sampling for Bayesian Networks
          Bozhena Bidyuk, and Rina Dechter
          Abstract
          The paper presents a new sampling methodology for Bayesian networks called cutset sampling that samples only a subset of the variables and applies exact inference for the others. We show that this approach can be implemented efficiently when the sampled variables constitute a cycle-cutset for the Bayesian network and otherwise it is exponential in the induced-width of the network's graph, whose sampled variables are removed. Cutset sampling is an instance of the well known Rao-Blakwellisation technique for variance reduction investigated in [5, 2, 13]. Moreover, the proposed scheme extends standard sampling methods to non-ergodic networks with ergodic subspaces. Our empirical results confirm those expectations and show that cutset sampling is superior to Gibbs sampling for a variety of benchmarks, yielding a simple, yet powerful sampling scheme.

          PostScript PDF


          http://www.ics.uci.edu/~dechter/publications/r207.html Dr. Rina Dechter @ UCI
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          R207
          Bottom-Up Approaches to Approximate Inference and Learning in Discrete Graphical Models.
          Andrew Gelfand

          Abstract

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r192.html Dr. Rina Dechter @ UCI
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          R192
          A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound over Graphical Models
          Lars Otten and Rina Dechter

          Abstract
          We study the problem of complexity estimation in the context of parallelizing an advanced Branch and Bound-type algorithm over graphical models. The algorithm's pruning power makes load balancing, one crucial element of every distributed system, very challenging. We propose using a statistical regression model to identify and tackle disproportionally complex parallel subproblems, the cause of load imbalance, ahead of time. The proposed model is evaluated and analyzed on various levels and shown to yield robust predictions. We then demonstrate its effectiveness for load balancing in practice.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r44.html Dr. Rina Dechter @ UCI
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          R44
          Topological Parameters For Time-Space Tradeoff
          Rina Dechter (dechter@ics.uci.edu) & Yousri El Fattah (yousri@rsc.rockwell.com)

          Abstract
          In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specification.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r33.html Dr. Rina Dechter @ UCI
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          R33
          On The Minimality And Global Consistency Of Row-Convex Constraint Networks
          Peter van Beek (vanbeek@cs.ualberta.ca)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Constraint networks have been shown to be useful in formulating such diverse problems as scene labeling, natural language parsing, and temporal reasoning. Given a constraint network, we often wish to (i) find a solution that satisfies the constraints and (ii) find the corresponding minimal network where the constraints are as explicit as possible. Both tasks are known to be NP-complete in the general case. Task (i) is usually solved using a backtracking algorithm, and task (ii) is often solved only approximately by enforcing various levels of local consistency. In this paper, we identify a property of binary constraints called row convexity and show its usefulness in deciding when a form of local consistency called path consistency is sufficient to guarantee that a network is both minimal and globally consistent. Globally consistent networks have the property that a solution can be found without backtracking. We show that one can test for the row convexity property effciently and we show, by examining applications of constraint networks discussed in the literature, that our results are useful in practice. Thus, we identify a class of binary constraint networks for which we can solve both tasks (i) and (ii) efficiently. Finally, we generalize the results for binary constraint networks to networks with non-binary constraints.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r15.html Dr. Rina Dechter @ UCI
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          R15
          Self-Stabilizing Distributed Constraint Satisfaction
          Zeev Collin (zeev@cs.technion.ac.il), Rina Dechter (dechter@ics.uci.edu) & Shmuel Katz (katz@cs.technion.ac.il)

          Abstract
          Distributed architectures and solutions are described for classes of constraint satisfaction problems, called network consistency problems. An inherent assumption of these architectures is that the communication network mimics the structure of the constraint problem. The solutions are required to be self-stabilizing and to treat arbitrary networks, which makes them suitable for dynamic or error-prone environments. We first show that even for relatively simple constraint networks, such as rings, there is no self-stabilizing solution that guarantees convergence from every initial state of the system using a completely uniform, asynchronous model (where all processors are identical). An almost-uniform , asynchronous, network consistency protocol with one specially designated node is shown and proven correct. We also show that some restricted topologies such as trees can accommodate the uniform, asynchronous model when neighboring nodes cannot take simultaneous steps.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r177.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R177
          M best solutions over Graphical Models

          Natalia Flerova and Rina Dechter

          Abstract
          Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problem-solving and reasoning tasks. In particular, it can be used for any combinatorial optimization task such as finding most probable configurations in a Bayesian network. In this paper we present a new algorithm elim-m-opt, extending bucket elimination for the task of finding m best solutions for an optimization task for any value of m. We formulate our algorithm using general notion of combination and marginalization operators and show that our approach is sound. We provide complexity analysis and compare it with related work. Potential extension to the mini-bucket framework and its impact on heuristic-search for m-best are discussed.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r76A.html Dr. Rina Dechter @ UCI
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          R76A
          Bucket elimination: A unifying framework for reasoning
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Bucket elimination is an algorithmic framework that generalizes dynamic programming to accommodate many problem-solving and reasoning tasks. Algorithms such as directional-resolution for propositional satisfiability, adaptive-consistency for constraint satisfaction, Fourier and Gaussian elimination for solving linear equalities and inequalities, and dynamic programming for combinatorial optimization, can all be accommodated within the bucket elimination framework. Many probabilistic inference tasks can likewise be expressed as bucket-elimination algorithms. These include: belief updating, finding the most probable explanation, and expected utility maximization. These algorithms share the same performance guarantees; all are time and space exponential in the inducedwidth of the problem's interaction graph.
          While elimination strategies have extensive demands on memory, a contrasting class of algorithms called "conditioning search" require only linear space. Algorithms in this class split a problem into subproblems by instantiating a subset of variables, called a conditioning set, or a cutset. Typical examples of conditioning search algorithms are: backtracking (in constraint satisfaction), and branch and bound (for combinatorial optimization).
          The paper presents the bucket-elimination framework as a unifying theme across probabilistic and deterministic reasoning tasks and show how conditioning search can be augmented to systematically trade space for time.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r79.html Dr. Rina Dechter @ UCI
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          R79
          Mini-Bucket Heuristics for Improved Search
          Kalev Kask(kkask@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)
          Abstract
          The paper is a second in a series of two papers evaluating the power of a new scheme that generates search heuristics mechanically. The heuristics are extracted from an approximation scheme called mini-bucket elimination that was recently introduced. The first paper introduced the idea and evaluated it within Branch-and-Bound search. In the current paper the idea is further extended and evaluated within Best-First search. The resulting algorithms are compared on coding and medical diagnosis problems, using varying strength of the mini-bucket heuristics.

          Our results demonstrate an effective search scheme that permits controlled tradeoff between preprocessing (for heuristic generation) and search. Best-first search is shown to outperform Branch-and-Bound, when supplied with good heuristics, and sufficient memory space.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r123.html Dr. Rina Dechter @ UCI
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          R123
          Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints
          Vibhav Gogate and Rina Dechter
          Abstract
          In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r42.html Dr. Rina Dechter @ UCI
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          R42
          Variable Independence in Markov Decision Problems
          Irina Rish (irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          In decision-theoretic planning, the problem of planning under uncertainty is formulated as a multidimensional, or factored MDP. Traditional dynamic programming techniques are inecient for solving factored MDPs whose state and action spaces are exponential in the number of the state and action variables, correspondingly. We focus on exploiting problems' structure imposed by variable independence that implies decomposability of transitional probabilities, rewards, and policies, and is captured by theinteraction graph of an MDP, obtained from its in uence diagram. Using the framework of bucket elimination[9], we formulate a variable elimination algorithm elim-meu-id for computing maximum expected utility, given an inuence diagram, and apply it to MDPs. Traditional dynamic programming techniques for solving finite and infinite-horizon MDPs, such asbackward induction, value iteration, andpolicy iteration, can be also viewed as bucket elimination algorithms applied to a particular ordering of the state and decision variables. The time and space complexity of elimination algorithms is O(exp(wo*)), where wo* the induced width of the interaction graph along the ordering o of its nodes. Unifying framework of bucket elimination makes complexity analysis and variable ordering heuristics developed in constraint-based and probabilistic reasoning applicable to decision-theoretic planning. As we show, selecting "good" orderings improves the efficiency of traditional MDP algorithms.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r122.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R122
          The Relationship Between AND/OR Search and Variable Elimination
          Robert Mateescu and Rina Dechter
          Abstract
          In this paper we compare search and inference in graphical models through the new framework of AND/OR search. Specifically, we compare Variable Elimination (VE) and memoryintensive AND/OR Search (AO) and place algorithms such as graph-based backjumping and no-good and good learning, as well as Recursive Conditioning [7] and Value Elimination [2] within the AND/OR search framework.

          PDF



          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r35a.html Dr. Rina Dechter @ UCI
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          R35a
          In Search Of The Best Constraint Satisfaction Search
          Daniel Frost (frost@ics.uci.edu)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          We present the results of an empirical study of several constraint satisfaction search algorithms and heuristics. Using a random problem generator that allows us to create instances with given characteristics, we show how the relative performance of various search methods varies with the number of variables, the tightness of the constraints, and the sparseness of the constraint graph. A version of backjumping using a dynamic variable ordering heuristic is shown to be extremely effective on a wide range of problems. We conducted our experiments with problem instances drawn from the 50% satisfiable range.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r121.html Dr. Rina Dechter @ UCI
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          R121
          AND/OR Cutset Conditioning
          Robert Mateescu and Rina Dechter
          Abstract
          Cutset conditioning is one of the methods of solving reasoning tasks for graphical models, especially when space restrictions make inference (e.g., jointree-clustering) algorithms infeasible. The wcutset is a natural extention of the method to a hybrid algoritm that performs search on the conditioning variables and inference on the remaining problems of induced width bounded by w. This paper takes a fresh look at these methods through the spectrum of AND/OR search spaces for graphical models. The resulting AND/OR cutset method is a strict improvement over the traditional one, often by exponential amounts.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r188.html Dr. Rina Dechter @ UCI
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          R188
          Learning Subproblem Complexities in Distributed Branch and Bound

          Lars Otten and Rina Dechter

          Abstract
          In the context of distributed Branch and Bound Search for Graphical Models, effective load balancing is crucial yet hard to achieve due to early pruning of search branches. This paper proposes learning a regression model over structural as well as cost function-based features to more accurately predict subproblem complexity ahead of time, thereby enabling more balanced parallel workloads. Early results show the promise of this approach.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r140.html Dr. Rina Dechter @ UCI
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          R140
          Best-first AND/OR Search for 0/1 Integer Programming
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces are a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. In this paper we introduce an AND/OR search algorithm that explores a context-minimal AND/OR search graph in a best-first manner for solving 0/1 Integer Linear Programs (0/1 ILP). We also extend to the 0/1 ILP domain the depth-first AND/OR Branch-and-Bound search with caching algorithm which was recently proposed by [1] for solving optimization tasks in graphical models. The effectiveness of the best-first AND/OR search approach compared to the depth-first AND/OR Branch-and-Bound search is demonstrated on a variety of benchmarks for 0/1 ILPs, including instances from the MIPLIB library, real-world combinatorial auctions, random uncapacitated warehouse location problems and MAX-SAT instances.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r163.html Dr. Rina Dechter @ UCI
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          R163
          Maximum Likelihood Haplotyping through Parallelized Search on a Grid of Computers

          Lars Otten, Rina Dechter, Mark Silberstein, and Dan Geiger

          Abstract
          Graphical models such as Bayesian networks have many applications in computational biology, numerous algo- rithmic improvements have been made over the years. Yet many practical problem instances remain infeasible as technology advances and more data becomes available, for instance through SNP genotyping and DNA se- quencing. We therefore suggest a scheme to parallelize a graphical model search algorithm on a computational grid, with applications to finding the most likely haplotype configuration in general pedigrees. Through this we can obtain faster solution times than sequential algorithms and solve previously infeasible problem instances.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r117a.html Dr. Rina Dechter @ UCI
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          R117a
          The Impact of AND/OR Search Spaces on Constraint Satisfaction and Counting
          Rina Dechter and Robert Mateescu
          Abstract
          The contribution of this paper is in vieweing search for constraint processing in the context of AND/OR search spaces and in demonstrating the impact of this view on solutions counting. In a companion paper we introduce the AND/OR search space idea for probabilistic reasoning. In contrast to the traditional (OR) search space view, the AND/OR search tree displays some of the independencies present in the graphical model explicitly and may sometimes reduce the search space exponentially. Familiar parameters such as the depth of a spanning tree, tree-width and path-width are shown to play a key role in characterizing the effect of AND/OR search graphs vs the traditional OR search graphs. Empirical evaluation focusing on counting demonstrates the spectrum of search and inference within the AND/OR search spaces.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r114.html Dr. Rina Dechter @ UCI
          |
          R114
          Mixtures of Deterministic-Probabilistic Networks
          Rina Dechter and Robert Mateescu
          Abstract
          The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with constraint networks, defining the semantics and graphical representation. We also introduce a new linear space search algorithm based on an AND/OR search space. This provides the basis for understanding the benefits of processing the constraint information separately, resulting in the pruning of the search space. When the constraint part is tractable or has a small number of solutions, using the mixed representation can be exponentially more effective than using pure belief networks in which constraints are modeled as conditional probability tables. The experimental results we provide confirm that even weak forms of constraint propagation such as forward checking are more effective than using the auxiliary network.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r168.html Dr. Rina Dechter @ UCI
          |

          R168
          Towards Parallel Search for Optimization in Graphical Models

          Lars Otten and Rina Dechter

          Abstract
          We introduce a strategy for parallelizing a state-of-the-art sequential search algorithm for optimization on a grid of computers. Based on the AND/OR graph search framework, the procedure exploits the structure of the underlying problem graph. Worker nodes concurrently solve subproblems that are generated by a single master process. Subproblem generation is itself embedded into an AND/OR Branch and Bound algorithm and dynamically takes previous subproblem solutions into account. Drawing upon the underlying graph structure, we provide some theoretical analysis of the parallelization parameters. A prototype has been implemented and we present promising initial experimental results on genetic haplotyping and Mastermind problem instances, at the same time outlining several open questions.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r25.html Dr. Rina Dechter @ UCI
          |
          R25
          Propositional Semantics for Disjunctive Logic Programs
          Rachel Ben-Eliyahu(rachel@cs.ucla.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          In this paper we study the properties of the class of head-cycle-free extended disjunctive logic programs (HEDLPs), which includes, as a special case, all nondisjunctive extended logic programs. We show that any propositional HEDLP can be mapped in polynomial time into a propositional theory such that each model of the latter corresponds to an answer set, as defined by stable model semantics, of the former. Using this mapping, we show that many queries over HEDLPs can be determined by solving propositional satisfiability problems. Our mapping has several important implications: It establishes the NP-completeness of this class of disjunctive logic programs; it allows existing algorithms and tractable subsets for the satisfiability problem to be used in logic programming; it facilitates evaluation of the expressive power of disjunctive logic programs; and it leads to the discovery of useful similarities between stable model semantics and Clark's predicate completion.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r74.html Dr. Rina Dechter @ UCI
          |
          R74
          Evaluating Constraint Processing Algorithms
          Rina Dechter (dechter@ics.uci.edu) & Daniel Frost (frost@ics.uci.edu)

          Abstract
          Since for most artificial intelligence problems worstcase analysis does not necessarily reflect actual performance and since informative performance guarantees are not always available, empirical evaluation of algorithms is necessary. To do that we need to address the question of distributions, and benchmarks. Based on our study of CSP algorithms we propose the use of multiple types of benchmarks and multiple forms of presenting the results. The benchmarks should include: 1. Individual problem instances representing domains of interest, 2. Parameterized random problems, 3. Application-based parameterized random problems. Results should be presented using 1. Average and variances of the data, 2. frequency and distribution graphs, 3. scatter diagrams.

          The target is to identify a small number of algorithms (not one) that are dominating, namely proved superior on some class of problems. For dominating algorithms we wish to identify problem characteristics on which they are likely to be good.


            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r167.html Dr. Rina Dechter @ UCI
          |

          R167
          Robust Solutions in Unstable Optimization Problems

          Maria Silvia Pini, Francesca Rossi, Kristen Brent Venable, and Rina Dechter

          Abstract
          We consider constraint optimization problems where costs (or preferences) are all given, but some are tagged as possibly unstable, and provided with a range of alternative values. We also allow for some uncontrollable variables, whose value cannot be decided by the agent in charge of taking the decisions, but will be decided by Nature or by some other agent. These two forms of uncertainty are often found in many scheduling and planning scenarios. For such problems, we define several notions of desirable solutions. Such notions take into account not only the optimality of the solutions, but also their degree of robustness (of the optimality status, or of the cost) w.r.t. the uncertainty present in the problem. We provide an algorithm to find solutions accordingly to the considered notions of optimality, and we study the properties of these algorithms. For the uncontrollable variables, we propose to adopt a variant of classical variable elimination, where we act pessimistically rather than optimistically.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r226.html Dr. Rina Dechter @ UCI


          R226
          Applying Search Based Probabilistic Inference Algorithms to Probabilistic Conformant Planning: Preliminary Results
          Junkyu Lee, Radu Marinescu, and Rina Dechter

          Abstract
          Probabilistic conformant planning problems can be solved by probabilistic inference algorithms after translating their PPDDL specifications into graphical models. We present two translation schemes that convert probabilistic conformant planning problems as graphical models. The first encoding is based on the probabilistic extension of the serial encoding of PDDL in SatPlan, and the second encoding compiles a graphical model from the finite-domain representation of the SAS+ formalism. We show that a probabilistic conformant plan can be found by answering a marginal MAP inference, and the plan is optimal with respect to the length of the plan as well as the probability of achieving the goal. Since a common task of the conformant planning is to find a plan achieving the goal with a probability that exceeds a threshold, we can consider relaxing of the marginal MAP query to the pure MAP which is far easier to compute. The success probability of the suboptimal plan derived by a pure MAP solver can be re-evaluated by solving a summation problem, also a hard task. The probabilistic inference algorithms for marginal MAP that we evaluated are based on anytime AND/OR branch and bound search guided by weighted mini-bucket heuristics. Our preliminary evaluation highlights the potential and the challenges in this methodology of applying search based probabilistic inference algorithms to probabilistic conformant planning.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r48.html Dr. Rina Dechter @ UCI
          |
          R48
          Bucket Elimination: A Unifying Framework for Probabilistic Inference
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination-type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's structure.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r49a.html Dr. Rina Dechter @ UCI
          |
          R49a
          To Guess Or To Think? Hybrid Algorithms For SAT
          Irina Rish (irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Complete algorithms for solving propositional satisfiability fall into two main classes: backtracking (e.g., the Davis-Putnam Procedure [4]) and resolution (e.g., Directional Resolution [9]). Roughly speaking, backtracking amounts to "guessing" (making assumption), while resolution invokes "thinking" (inference). Experimental results show that both "pure guessing" and "pure thinking" might be inefficient. We propose an approach that combines resolution and backtracking and yields a family of hybrid algorithms, parameterized by a bound on the "effective" amount of resolution allowed. The idea is to divide the set of propositional variables into two classes: conditioning variables, which are assigned truth values, and resolution variables, which are resolved upon. We report on preliminary experimental results demonstrating that on certain classes of problems hybrid algorithms are more effective than either the Davis-Putnam Procedure or Directional Resolution in isolation.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r175.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
          |

          R174
          BEEM : Bucket Elimination with External Memory

          Kalev Kask, Rina Dechter and Andrew E. Gelfand

          Abstract
          A major limitation of exact inference algorithms for probabilistic graphical models is their extensive memory usage, which often puts real-world problems out of their reach. In this paper we show how we can extend inference algorithms, particularly Bucket Elim- ination, a special case of cluster (join) tree decomposition, to utilize disk memory. We pro- vide the underlying ideas and show promising empirical results of exactly solving large problems not solvable before.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r128.html Dr. Rina Dechter @ UCI
          |
          R128
          AND/OR Graph Search for Genetic Linkage Analysis
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying framework for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. AND/OR Branch-and-Bound (AOBB) is a new algorithm that explores the AND/OR search tree for solving optimization tasks in graphical models. In this paper we extend the algorithm to explore an AND/OR search graph by equipping it with a context-based adaptive caching scheme similar to good and no-good recording. The efficiency of the new graph search algorithm is demonstrated empirically on the very challenging benchmarks that arise in genetic linkage analysis.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r28.html Dr. Rina Dechter @ UCI
          |
          R28
          On Computing Minimal Models
          Rachel Ben-Eliyahu(rachelb@cs.technion.ac.il) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          This paper addresses the problem of computing the minimal models of a given CNF propositional theory. We present two groups of algorithms. Algorithms in the first group are efficient when the theory is almost Horn, that is, when there are few non-Horn clauses and/or when the set of all literals that appear positive in any non-Horn clause is small. Algorithms in the other group are efficient when the theory can be represented as an acyclic network of low-arity relations. Our algorithms suggest several characterizations of tractable subsets for the problem of finding minimal models.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r48a.html Dr. Rina Dechter @ UCI
          |
          R48
          Bucket Elimination: A Unifying Framework for Probabilistic Inference
          Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Probabilistic inference algorithms for finding the most probable explanation, the maximum aposteriori hypothesis, and the maximum expected utility and for updating belief are reformulated as an elimination-type algorithm called bucket elimination. This emphasizes the principle common to many of the algorithms appearing in that literature and clarifies their relationship to nonserial dynamic programming algorithms. We also present a general way of combining conditioning and elimination within this framework. Bounds on complexity are given for all the algorithms as a function of the problem's structure.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r218.html Dr. Rina Dechter @ UCI
          |

          R218
          Caching in Context-Minimal OR Spaces
          Rina Dechter, Levi H. S. Lelis, and Lars Otten

          Abstract
          In empirical studies we observed that caching can have very little impact in reducing the search effort in Branch and Bound search over context-minimal OR spaces. For example, in one of the problem domains used in our experiments we reduce only by 1% the number of nodes expanded when using caching in context-minimal OR spaces. By contrast, we reduce by 74% the number of nodes expanded when using caching in context-minimal AND/OR spaces on the same instances. In this work we document this unexpected empirical finding and provide explanations for the phenomenon.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r137.html Dr. Rina Dechter @ UCI
          |
          R137
          Cutset Sampling for Bayesian Networks
          Bozhena Bidyuk and Rina Dechter
          Abstract
          The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network structure-exploiting application of the Rao-Blackwellisation principle to sampling in Bayesian networks. It improves convergence by exploiting memory-based inference al- gorithms. It can also be viewed as an anytime approximation of exact cutset-conditioning algorithm (Pearl, 1988). Cutset sampling can be implemented efficiently when the sampled variables constitute a loop-cutset of the Bayesian network and, more generally, when the induced width of the network�s graph conditioned on the observed sampled variables is bounded by a constant w. We demonstrate empirically the benefit of this scheme on a range of benchmarks.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r161a.html Dr. Rina Dechter @ UCI
          | publications

          R161a
          Approximate Solution Sampling (and Counting) on AND/OR spaces

          Vibhav Gogate and Rina Dechter

          Abstract
          In this paper, we describe a new algorithm for sampling solutions from a uniform distribution over the solutions of a constraint network. Our new algorithm improves upon the Sampling/Importance Resampling (SIR) component of our previous scheme of SampleSearch-SIR by taking advantage of the decomposition implied by the network�s AND/OR search space.We also describe how our new scheme can approximately count and lower bound the number of solutions of a constraint network. We demonstrate both theoretically and empirically that our new algorithm yields far better performance than competing approaches.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r172.html Dr. Rina Dechter @ UCI
          |

          R172
          Importance Sampling based Estimation over AND/OR Search Spaces for Graphical Mode

          Vibhav Gogate and Rina Dechter

          Abstract
          It is well known that the accuracy of importance sampling can be improved by reducing the variance of its sample mean and therefore variance reduction schemes have been the subject of much research. In this paper, we introduce a family of variance reduction schemes that generalize the sample mean from the conventional OR search space to the AND/OR search space for graphical models. The new sample means allow trading time and space with variance. At one end is the AND/OR sample tree mean which has the same time and space complexity as the conventional OR sample tree mean but has smaller variance. At the other end is the AND/OR sample graph mean which requires more time and space to compute but has the smallest variance. Theoretically, we show that the variance is smaller in the AND/OR space because the AND/OR sample mean is defined over a larger virtual sample size compared with the OR sample mean. Empirically, we demonstrate that the AND/OR sample mean is far closer to the true mean than the OR sample mean.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r146.html Dr. Rina Dechter @ UCI
          |

          R146
          AND/OR Multi-Valued Decision Diagrams (AOMDDs) forWeighted Graphical Models

          Robert Mateescu and Rina Dechter

          Abstract
          Compiling graphical models has recently been under intense investigation, especially for probabilistic modeling and processing. We present here a novel data structure for compiling weighted graphical models (in particular, probabilistic models), called AND/OR Multi-Valued Decision Diagram (AOMDD). This is a generalization of our previous work on constraint networks, to weighted models. The AOMDD is based on the frameworks of AND/OR search spaces for graphical models, and Ordered Binary Decision Diagrams (OBDD). The AOMDD is a canonical representation of a graphical model, and its size and compilation time are bounded exponentially by the treewidth of the graph, rather than pathwidth as is known for OBDDs. We discuss a Variable Elimination schedule for compilation, and present the general APPLY algorithm that combines two weighted AOMDDs, and also present a search based method for compilation method. The preliminary experimental evaluation is quite encouraging, showing the potential of the AOMDD data structure.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r128a.html Dr. Rina Dechter @ UCI
          |
          R128a
          AND/OR Branch-and-Bound for Linkage Analysis
          Radu Marinescu and Rina Dechter
          Abstract
          AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sensitivity to the structure of the model, which can translate into exponential time savings for search algorithms. In [MD05a] we introduced a linear space AND/OR Branch-and-Bound (AOBB) search scheme that explores the AND/OR search tree for solving optimization tasks. In this paper we extend the algorithm by equipping it with a context-based adaptive caching scheme similar to good and nogood recording, thus it explores an AND/OR graph rather than the AND/OR tree. We also improve the algorithm by using a new heuristic for generating close to optimal height pseudo-trees, based on a well known recursive decomposition of the hypergraph representation. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r88.html Dr. Rina Dechter @ UCI
          |
          R88
          An Implementation of the Combinatorial Auction Problem in ECLiPSe
          Robert Menke (rmenke@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)
          Abstract
          In a traditional auction, items are placed "up for bids" in an arbitrary sequence. For many bidders, this model is inadequate because the individual items increase in value when held in conjunction with other items. Combinatorial auctions allow bidders to bid upon multiple items simultaneously. While this resolves the problems for the bidders, it increases the problem of the auctioneer: determining the optimal selection of bids to maximize revenue in NP-complete.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r40.html Dr. Rina Dechter @ UCI
          |
          R40
          Processing Disjunctions in Temporal Constraint Networks
          Eddie Schwalb (eschwalb@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The framework of Temporal constraint Satisfaction Problems (TCSP) has been proposed for representing and processing temporal knowledge. Deciding consistency of TCSPs is known to be intractable. As demonstrates in this paper, even local consistency algorithms like path-consistency can be exponential due to the fragmentation problem. We present two new polynomial approximation algorithms, Upper-Lower-Tightening (ULT) and Loose-Path-Consistency (LPC), which are efficient yet effective in detecting inconsistencies and reducing fragmentation. The experiments we performed on hard problems in the transition region show that LPC is the superior algorithm. When incorporated within backtrack search LPC is capable of improving performance by orders of magnitude.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r103.html Dr. Rina Dechter @ UCI
          |
          R103
          Generating Random Solutions from a Constraint Satisfaction Problem with Controlled Probability
          David Larkin
          Abstract
          In this paper we address the question of drawing elements from the set of solutions of a constraint satisfaction problem with well defined probability. A distribution over solutions can be specified with the level of precision appropriate for the information on hand. This has application in functional verification, where CSPs are often used to specify the set of legal test programs that a verifier might try to run. With this technique, the probability that a randomly generated test program has any particular properties can be precisely controlled.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r105.html Dr. Rina Dechter @ UCI
          |
          R105
          An Empirical Study of w-Cutset Sampling for Bayesian Networks
          Bozhena Bidyuk, and Rina Dechter
          Abstract
          The paper studies empirically the time-space trade-off between sampling and inference in the cutset sampling algorithm. The algorithm samples over a subset of nodes in a Bayesian network and applies exact inference over the rest. As the size of the sampling space decreases, requiring less samples for convergence, the time for generating each single sample increases. Algorithm wcutset sampling selects a sampling set such that the induced-width of the network when the sampling set is observed is bounded by w, thus requiring inference whose complexity is exponentially bounded by w. In this paper, we investigate the performance of w-cutset sampling as a function of w. Our experiments over a range of randomly generated and real benchmarks, demonstrate the power of the cutset sampling idea and in particular show that an optimal balance between inference and sampling benefits substantially from restricting the cutset size, even at the cost of more complex inference.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r133.html Dr. Rina Dechter @ UCI
          |
          R133
          Cutset Sampling with Likelihood Weighting
          Bozhena Bidyuk and Rina Dechter
          Abstract
          The paper extends the principle of cutset sampling over Bayesian networks, presented previously for Gibbs sampling, to likelihood weighting (LW). Cutset sampling is motivated by the Rao-Blackwell theorem which implies that sampling over a subset of variables requires fewer samples for convergence due to the reduction in sampling variance. The scheme exploits the network structure in selecting cutsets that allow efficient computation of the sampling distributions. In particular, as we show empirically, likelihood weighting over a loop-cutset (abbreviated LWLC), is time-wise cost-effective. We also provide an effective way for caching the probabilities of the generated samples which improves the performance of the overall scheme. We compare LWLC against regular liklihood-weighting and against Gibbsbased cutset sampling.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r117.html Dr. Rina Dechter @ UCI
          |
          R117
          The Impact of AND/OR Search Spaces on Constraint Satisfaction and Counting
          Rina Dechter and Robert Mateescu
          Abstract
          The contribution of this paper is in demonstrating the impact of AND/OR search spaces view on solutions counting. In contrast to the traditional (OR) search space view, the AND/OR search space displays independencies present in the graphical model explicitly and may sometimes reduce the search space exponentially. Empirical evaluation focusing on counting demonstrates the spectrum of search and inference within the AND/OR search spaces.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r214.html Dr. Rina Dechter @ UCI
          |

          R214
          Empirical Evaluation of weighted Heuristic Search with advanced Mini-Bucket Heuristics for Graphical Models
          Pratyaksh Sharma, Natalia Flerova and Rina Dechter

          Abstract
          Weighted search (best-first or depth-first) refers to search with a heuristic function multiplied by a constant w. The current work extends the previous investigation of weighted search algorithms with the mini-bucket heuristic. We perform empirical analysis of various such algorithms with more advanced heuristics such as Join-Graph Linear Programming and Mini-Bucket Elimination with Moment Matching.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r196.html Dr. Rina Dechter @ UCI
          |

          R196
          Winning the PASCAL 2011 MAP Challenge with Enhanced AND/OR Branch-and-Bound
          Lars Otten, Alexander Ihler, Kalev Kask, and Rina Dechter

          Abstract
          This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Challenge, which placed first in all three time limit categories, 20 seconds, 20 minutes, and 1 hour. Our baseline is a branch-and-bound algorithm that explores the context-minimal AND/OR search graph of a graphical model guided by a mini-bucket heuristic. Augmented with recent advances that convert the algorithm into an anytime scheme, that improve the heuristic power via cost-shifting schemes, and using enhanced variable ordering schemes, it constitutes one of the most powerful MAP/MPE inference methods to date.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r87.html Dr. Rina Dechter @ UCI
          |
          R87
          New Search Heuristics for Max-CSP
          Kalev Kask (kkask@ics.uci.edu)
          Abstract
          This paper evaluates the power of a new scheme that generates search heuristics mechanically. This approach was presented and evaluated first in the context of optimization in belief networks. In this paper we extend this work to Max-CSP. The approach involves extracting heuristics from a parameterized approximation scheme called Mini-Bucket elimination that allows controlled trade-off between computation and accuracy. The heuristics are used to guide Branch-and-Bound and Best-First search, whose performance are compared on a number of constraint problems. Our results demonstrate that both search schemes exploit the heuristics effectively, permitting controlled trade-off between preprocessing (for heuristic generation) and search. These algorithms are compared with a state of the art complete algorithm as well as with the stochastic local search anytime approach, demonstrating superiority in some problem cases.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r70b.html Dr. Rina Dechter @ UCI
          |
          R70b
          Maintenance Scheduling Problems As Benchmarks For Constraint Algorithms
          Daniel Frost(frost@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the electric power industry: optimally scheduling preventive maintenance of power generating units within a power plant. We show how these scheduling problems can be cast as constraint satisfaction problems and used to define the structure of randomly generated non-binary CSPs. The random problem instances are then used to evaluate several previously studied algorithms. The paper also demonstrates how constraint satisfaction can be used for optimization tasks. To find an optimal maintenance schedule, a series of CSPs are solved with successively tighter cost-bound constraints. We introduce and experiment with an "iterative learning" algorithm which records additional constraints uncovered during search. The constraints recorded during the solution of one instance with a certain cost-bound are used again on subsequent instances with tighter cost-bounds. Our results show that on a class of randomly generated maintenance scheduling problems, iterative learning reduces the time required to find a good schedule.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r221a.html Dr. Rina Dechter @ UCI
          |

          R221a
          Probabilistic Inference Modulo Theories
          Rodrigo de Salvo Bras, Claran O'Reilly, Vibhav Gogate, and Rina Dechter

          Abstract
          We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, equalities and inequalities on discrete sorts). While many solutions to probabilistic inference over logic representations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean that our proposed algorithm can leverage first-order representations to solve some inference problems in constant or polynomial time in the domain size (the number of values that variables can take), as opposed to exponential time offered by propositional algorithms.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r106.html Dr. Rina Dechter @ UCI
          |
          R106
          A Simple Insight Into Properties Of Iterative Belief Propagation
          Rina Dechter, and Robert Mateescu
          Abstract
          In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitrary networks (the so called, iterative or loopy belief propagation (IBP)) it is identical to an arc-consistency algorithm relative to zero-belief queries (namely assessing zero posterior probabilities). This implies that zero-belief conclusions derived by belief propagation converge and are sound. More importantly, it suggests that the inference power of IBP is as strong and as weak as that of arcconsistency. This allows the synthesis of belief networks for which belief propagation is useless on one hand, and focuses the investigation on classes of belief networks for which belief propagation may be zero-complete. Finally, all the above conclusions apply also to Generalized belief propagation algorithms that extend iterative belief propagation and allow a crisper understanding of their power.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r170.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
          |

          R170
          Some New Empirical Analysis of Evaluating of Iterative Join-Graph Propagation

          Emma Rollon and Rina Dechter

          Abstract
          In previous works authors showed that IBP (or equivalently, the more general class of algorithm called IJGP) is sound with respect to the inference of zero beliefs. In this report, we empirically investigate the behaviour of IBP/IJGP for near zero inferred beliefs. Specifically, we explore the hypothesis that if IBP infers that the belief of a variable is close to zero, then this inference is relatively accurate. The study includes some previously published empirical results and signifcant new analysis of empirical evaluation carried on in UAI 2006 and UAI 2008 benchmarks. We will see that while our empirical results support the hypothesis on benchmarks having no determinism, the results are quite mixed for networks with determinism.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r164.html Dr. Rina Dechter @ UCI [an error occurred while processing this directive]

          Publications & Technical Reports
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          R164
          Join-Graph Propagation Algorithms

          Robert Mateescu, Kalev Kask, Vibhav Gogate, and Rina Dechter

          Abstract
          The paper investigates parameterized approximate message-passing schemes that are based on bounded inference and are inspired by Pearl’s belief propagation algorithm (BP). We start with the bounded inference mini-clustering algorithm and then move to the iterative scheme called Iterative Join-Graph Propagation (IJGP), that combines both iteration and bounded inference. The algorithm IJGP belongs to the class of Generalized Belief Propagation algorithms, a framework that allowed connections with approximate algorithms from statistical physics and is shown empirically to surpass the performance of mini-clustering and belief propagation, as well as a number of other state-of-the-art algorithms on several classes of networks. We also provide insight into the accuracy of IBP and IJGP by relating these algorithms to well known classes of constraint propagation schemes.

          [pdf]

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/publications/r184.html Dr. Rina Dechter @ UCI
          |

          R184
          Heuristic Search for m Best Solutions with Applications to Graphical Models

          Rina Dechter and Natalia Flerova

          Abstract
          The paper focuses on finding the m best solutions to a combinatorial optimization problems using Best-First or Branch-and-Bound search. We are interested in graphical model optimization tasks (e.g., Weighted CSP), which can be formulated as finding the m-best solution-paths in a weighted search graph. Specifically, we present m-A*, extending the well-known A* to the m-best problem, and prove that all A*’s properties are maintained, including soundness and completeness of m- A*, dominance with respect to improved heuristics and most significantly optimal efficiency compared with any other search algorithm that use the same heuristic function. We also present and analyse m-B&B, an extension of a Depth First Branch and Bound algorithm to the task of finding the m best solutions. Finally, for graphical models, a hybrid of A* and a variable-elimination scheme yields an algorithm which has the best complexity bound compared with earlier known m-best algorithms.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r136a.html Dr. Rina Dechter @ UCI
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          R136a
          A New Algorithm for Sampling CSP Solutions Uniformly at Random
          Vibhav Gogate and Rina Dechter
          Abstract
          The paper presents a method for generating solutions of a constraint satisfaction problem (CSP) uniformly at random. The main idea is to express the CSP as a factored probability distribution over its solutions and then generate samples from this distribution using probabilistic sampling schemes. We suggest parameterized sampling schemes that sample solutions from the output of a generalized belief propagation algorithm. To speed up the rate at which random solutions are generated, we augment our sampling algorithms with techniques used successfully in the CSP literature to improve search such as conflict-directed back-jumping and no-good learning. The motivation for this tasks comes from hardware verification. Random test program generation for hardware verification can be modeled and performed through CSP techniques, and is an application in which uniform random solution sampling is required.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r198.html Dr. Rina Dechter @ UCI
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          R198
          Extending the Reach of AND/OR Search for Optimization over Graphical Models
          Lars Otten

          Abstract
          This thesis presents substantial enhancements to the state of the art in combinatorial optimization over graphical models. Our contributions are relevant in the context of both exact and approximate reasoning over Bayesian and Markov networks, weighted constraint satisfaction problems, and other related queries. While the focus of this work is on probabilistic and constraint inference, we also draw from the areas of distributed computing and statistical learning. Relevant practical applications we consider include genetic linkage analysis, protein side-chain prediction, medical diagnosis, resource scheduling, and signal processing.

          We extend AND/OR Branch-and-Bound (AOBB), a leading algorithm for optimization queries over graphical models. AOBB applies the principle of depth-first branch-and-bound to AND/OR search spaces, which exploit conditional independencies via problem decomposition and merge unifiable subproblems through caching of partial solutions. This thesis presents fundamental extensions to AOBB in three regards.

          First, we significantly improve the applicability of AOBB as an approximation scheme. We analyze and demonstrate the inherent conflict between problem decomposition (through AND/OR search spaces) and the anytime behavior of AOBB and depth-first search in general. We introduce a new algorithm, Breadth-Rotating AND/OR Branch-and-Bound (BRAOBB), which drastically improves upon AOBB with respect to its anytime performance while maintaining desirable depth-first complexity guarantees. Comprehensive analysis and experimental evaluation demonstrate the scheme’s effectiveness. Furthermore, our entry based on BRAOBB placed first in all three optimization tracks of the PASCAL 2012 Probabilistic Inference Challenge.

          Second, we investigate the instance-based run-time complexity of AOBB. The asymptotic worst-case bounds are both time and space exponential in the problem’s induced width, but often prove to be very loose due to the algorithm’s powerful pruning, as we show empirically. We identify a range of (sub)problem features and develop learning schemes to estimate runtime complexity based on statistical regression analysis. We conduct extensive experimental evaluation within and across various problem classes and demonstrate convincing predictive performance.

          Third, we describe a parallel AND/OR Branch-and-Bound scheme that pushes the boundaries of feasibility for exact reasoning by orders of magnitude. We adapt the paradigm of parallel tree search to AND/OR search spaces; our implementation distributes conditioned subproblems on a grid of independent computers. In this context, we show how the pruning power of AOBB can cause large variance in subproblem complexity, which makes load balancing extremely elusive and impairs parallel performance. We thus propose load balancing based on the run-time estimation scheme presented earlier in the thesis, learning a complexity model offline from previously solved subproblems. Through experimental results using hundreds of computers on problem instances from a variety of classes we show convincing parallel performance with several orders of magnitude speedup over sequential AOBB, but we also highlight and analyze some inherent limitations.

          Our implementations of AOBB, BRAOBB and parallel AOBB are available online under an open-source license.


          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r91.html Dr. Rina Dechter @ UCI
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          R91
          Up and Down Mini-Buckets: A Scheme for Approximating Combinatorial Optimization Tasks
          Kalev Kask: kkask@ics.uci.edu, Javier Larrosa: javier@ics.uci.edu & Rina Dechter: dechter@ics.uci.edu
          Abstract
          The paper addresses the problem of computing lower bounds on the optimal costs associated with each unary assignment of a value to a variable in combinatorial optimization problems. This task is instrumental in probabilistic reasoning and is also important for the development of admissible heuristic functions that can guide search algorithms for optimal solutions. The paper presents UD-MB, a new algorithm that applies the mini-bucket elimination idea [Dechter and Rish, 1997] to accomplish this task. We show empirically that UD-MB may achieve a substantial speed up over a brute-force approximation method via mini-buckets.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r204.html Dr. Rina Dechter @ UCI
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          R204
          Does Better Inference Mean Better Learning?
          Andrew E. Gelfand, Rina Dechter, and Alexander Ihler

          Abstract
          Maximum Likelihood learning of graphical models is not possible in problems where inference is intractable. In such settings it is common to use approximate inference (e.g. Loopy BP) and maximize the so-called "surrogate" likelihood objective. We examine the effect of using different approximate inference methods and, therefore, different surrogate likelihoods, on the accuracy of parameter estimation. In particular, we consider methods that utilize a control parameter to trade computation for accuracy. We demonstrate empirically that cheaper, but worse quality approximate inference methods should be used in the small data setting as they exhibit smaller variance and are more robust to model mis-specification. 

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r35.html Dr. Rina Dechter @ UCI
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          R35
          Dead-End Driven Learning
          Daniel Frost (frost@ics.uci.edu)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          The paper evaluates the effectiveness of learning for speeding up the solution of constraint satisfaction problems. It extends previous work (Dechter 1990) by introducing a new and powerful variant of learning and by presenting an extensive empirical study on much larger and more difficult problem instances. Our results show that learning can speed up backjumping when using either a fixed or dynamic variable ordering. However, the improvement with a dynamic variable ordering is not as great, and for some classes of problems learning is helpful only when a limit is placed on the size of new constraints learned.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r62.html Dr. Rina Dechter @ UCI
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          R62
          Mini-buckets: A General Scheme For Bounded Inference
          Rina Dechter (dechter@ics.uci.edu) & Irina Rish (rish@us.ibm.com)

          Abstract
          This article presents a class of approximation algorithms that extend the idea of bounded-complexity inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algorithms. This yields a parameterized scheme, called mini-buckets, that offers adjustable trade-off between accuracy and efficiency. The mini-bucket approach to optimization problems, such as finding the most probable explanation (MPE) in Bayesian networks, generates both an approximate solution and bounds on the solution quality.We present empirical results demonstrating successful performance of the proposed approximation scheme for the MPE task, both on randomly generated problems and on realistic domains such as medical diagnosis and probabilistic decoding.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r66.html Dr. Rina Dechter @ UCI
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          R66
          Temporal Constraints: A Survey
          Schwalb, E. and L. Vila

          Abstract
          Abstract. Temporal Constraint Satisfaction is an information technology useful for representing and answering queries about the times of events and the temporal relations between them. Information is represented as a Constraint Satisfaction Problem (CSP) where variables denote event times and constraints represent the possible temporal relations between them. The main tasks are two: (i) deciding consistency, and (ii) answering queries about scenarios that satisfy all constraints. This paper overviews results on several classes of Temporal CSPs: qualitative interval, qualitative point, metric point, and some of their combinations. Research has progressed along three lines: (i) identifying tractable subclasses, (ii) developing exact search algorithms, and (iii) developing polynomial-time approximation algorithms. Most available techniques are based on two principles: (i) enforcing local consistency (e.g. path-consistency), and (ii) enhancing naive backtracking search.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r81.html Dr. Rina Dechter @ UCI
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          R81
          Efficient Reasoning In Graphical Models Dissertation
          Submitted in partial satisfaction of the requirements for the degree of Doctor Of Philosophy in Information and Computer Science Irina Rish(irinar@ics.uci.edu)
          Abstract
          Automated reasoning is a field of artificial intelligence concerned with answering queries and drawing new conclusions from previously stored knowledge. It includes many areas such as theorem-proving, game playing, propositional satis ability, constraint satisfaction, planning, scheduling, probabilistic inference and decision-making. This dissertation is focused on reasoning in graphical frameworks such as constraint and belief networks, where domain knowledge is represented by a graph depicting variables as nodes and dependencies (e.g., propositional clauses, constraints, probabilities, and utilities) as edges. Some reasoning tasks can be formulated as combinatorial optimization or constraint satisfaction problems, while others can be viewed as knowledge compilation, or inference. We approach those tasks using a general graph-based algorithmic framework that combines a dynamic-programming technique called variable elimination with backtracking search, and investigate the effect of problem structure on the performance of such algorithms.

          PostScript | PDF


          http://www.ics.uci.edu/~dechter/publications/r223.html Dr. Rina Dechter @ UCI


          R223
          Searching For M Best Solutions In Graphical Models
          Natalia Flerova, Radu Marinescu and Rina Dechter

          Abstract
          The paper focuses on finding the m best solutions to combinatorial optimization problems using best-first or depth-first branch and bound search. Specifically, we present a new algorithm m-A*, extending the well-known A* to the m-best task, and for the first time prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since best- first algorithms require extensive memory, we also extend the memory-efficient depth-first branch and bound to the m-best task.

          We adapt both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments confirm theory that the best-first approach is largely superior when memory is avail- able, but depth-first branch and bound is more robust. We also show that our m-best algorithms are competitive with recent schemes, e.g. (Fromer & Globerson, 2009) and (Batra, 2012).


          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r221.html Dr. Rina Dechter @ UCI
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          R221
          Probabilistic Inference Modulo Theories
          Rodrigo de Salvo Bras, Claran O'Reilly, Vibhav Gogate, and Rina Dechter

          Abstract
          We present SGDPLL(T), an algorithm that solves (among many other problems) probabilistic inference modulo theories, that is, inference problems over probabilistic models defined via a logic theory provided as a parameter (currently, equalities and inequalities on discrete sorts). While many solutions to probabilistic inference over logic represen- tations have been proposed, SGDPLL(T) is simultaneously (1) lifted, (2) exact and (3) modulo theories, that is, parameterized by a background logic theory. This offers a foundation for extending it to rich logic languages such as data structures and relational data. By lifted, we mean that our proposed algorithm can leverage first-order representations to solve some inference problems in constant or polynomial time in the domain size (the number of values that variables can take), as opposed to exponential time offered by propositional algorithms.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r193.html Dr. Rina Dechter @ UCI
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          R193
          Join-graph based cost-shifting schemes
          Alexander Ihler, Natalia Flerova, Rina Dechter, and Lars Otten

          Abstract
          We develop several algorithms taking advantage of two common approaches for bounding MPE queries in graphical models: mini-bucket elimination and message-passing updates for linear programming relaxations. Both methods are quite similar, and offer useful perspectives for the other; our hybrid approaches attempt to balance the advantages of each. We demonstrate the power of our hybrid algorithms through extensive empirical evaluation. Most notably, a Branch and Bound search guided by the heuristic function calculated by one of our new algorithms has recently won first place in the PASCAL2 inference challenge.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r93.html Dr. Rina Dechter @ UCI
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          R93
          On The Time Complexity Of Bucket Elimination Algorithms
          Javier Larrosa
          Abstract
          In this short note, we prove the time complexity of full-bucket and mini-bucket elimination [1, 2]. In previous papers, when discussing the complexity of these algorithms, only the importance of the exponential contribution was emphasized and the rest of the contributions to the cost were not carefully considered. In this note, we address this fact and provide a non-trivial bound for the non-exponential contribution to the complexity of both algorithms. We demonstrate the result for the Additive Combinatorial Optimization Problem case.

          PostScript | PDF



          http://www.ics.uci.edu/~dechter/publications/r119.html Dr. Rina Dechter @ UCI
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          R119
          AND/OR Tree Search for Constraint Optimization
          Radu Marinescu and Rina Dechter
          Abstract
          The paper presents and evaluates the power of a new framework for constraint optimization, based on the concept of AND/OR search trees. The virtue of the AND/OR search tree representation is that its size may be smaller than that of a traditional OR search tree. We introduce a new generation of depth first Branch-and-Bound algorithms that traverse an AND/OR search space and use the Mini-Bucket approximation scheme to generate heuristics to guide the search. Our preliminary experimental work shows that the new approach is competitive and in many cases superior to state of the art systematic search algorithms that explore the regular OR space.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r107.html Dr. Rina Dechter @ UCI
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          R107
          Semi-Independent Partitioning: A Method for Approximating the Solution to Constraint Optimization Problems
          David Larkin
          Abstract
          In this paper we introduce a new method for approximating the solution to constraint optimization problems called semi-independent partitioning. We show that our method is a strict generalization of the mini buckets algorithm [3] which allows a richer and more e.ective set of approximation strategies. We demonstrate with theoretical analysis and empirical results that it generally produces a better answer than mini buckets in much less time.

          PDF
          PS



          http://www.ics.uci.edu/~dechter/publications/r63.html Dr. Rina Dechter @ UCI
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          R63
          A Scheme For Approximating Probabilistic Inference
          Rina Dechter (dechter@ics.uci.edu) & Irina Rish (irinar@ics.uci.edu)

          Abstract
          This paper describes a class of probabilistic approximation algorithms based on bucket elimination which offer adjustable levels of accuracy and effciency. We analyze the approximation for several tasks: finding the most probable explanation, belief updating and finding the maximum a posteriori hypothesis. We identify regions of completeness and provide preliminary empirical evaluation on randomly generated networks.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r30.html Dr. Rina Dechter @ UCI
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          R30
          Coping With Disjunctions in Temporal Constraint Satisfaction Problems
          Eddie Shwalb (eschwalb@ics.uci.edu)& Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Path-consistency algorithms, which are polynomial for discrete problems, are exponential when applied to problems involving quantitative temporal information. The source of complexity stems from specifying relationships between pairs of time points as disjunction of intervals. We propose a polynomial algorithm, called ULT, that approximates path-consistency in Temporal Constraint Satisfaction Problems (TCSPs). We compare ULT empirically to path-consistency and directional path-consistency algorithms. When used as a preprocessing to backtracking, ULT is shown to be 10 times more effective then either DPC or PC-2.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r42a.html Dr. Rina Dechter @ UCI
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          R42a
          Value Iteration And Policy Iteration Algorithms For Markov Decision Problem
          Elena Pashenkova, Irina Rish (irinar@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          In this paper we consider computational aspects of decision-theoretic planning modeled by Markov decision processes (MDPs). Commonly used algorithms, such as value iteration (VI) [Bellman, 1957] and several versions of modified policy iteration (MPI) [Puterman, 1994] (a modification of the original Howard's policy iteration (PI) [Howard, 1960]), are compared on a class of problems from the motion planning domain. Policy iteration and its modifications are usually recommended as algorithms demonstrating a better performance than value iteration [Russel & Norvig, 1995], [Puterman, 1994]. However, our results show that their performance is not always superior and depends on the parameters of a problem and the parameters of the algorithms, such as number of iterations in the value determination procedure in MPI. Moreover, policy iteration applied to non-discounted models without special restrictions might not even converge to an optimal policy, as in case of the policy iteration algorithm introduced in [Russel & Norvig, 1995]. We also introduce a new stopping criterion into value iteration based on policy changes. The combined value-policy iteration (CVPI) algorithm proposed in the paper implements this criterion and generates an optimal policy faster then both policy and value iteration algorithms.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r12.html Dr. Rina Dechter @ UCI
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          R12
          Uncovering Trees In Constraint Networks
          Rina Dechter (dechter@ics.uci.edu), Itay Meiri (itay@cs.ucla.edu) & Judea Pearl (judea@cs.ucla.edu)

          Abstract
          This paper examines the possibility of removing redundant information from a given knowledge base and restructuring it in the form of a tree to enable efficient problemsolving routines. We offer a novel approach that guarantees removal of all redundancies that hide a tree structure. We develop a polynomial-time algorithm that, given an arbitrary binary constraint network, either extracts (by edge removal) a precise tree representation from the path-consistent version of the network or acknowledges that no such tree can be extracted. In the the latter case, a tree is generated that may serve as an approximation to the original network.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r153a.html Dr. Rina Dechter @ UCI
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          R153a
          Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models

          Radu Marinescu and Rina Dechter

          Abstract
          In this paper we explore the impact of caching on search in the context of the recent framework of AND/OR search in graphical models. Specifically, we extend the depth-first AND/OR Branch-and-Bound tree search algorithm to explore an AND/OR search graph by equipping it with an adaptive caching scheme similar to good and no-good recording. Furthermore, we present best-first search algorithms for traversing the same underlying AND/OR search graph and compare both depth-first and best-first approaches empirically. We focus on two common optimization problems in graphical models: finding the Most Probable Explanation (MPE) in belief networks and solving Weighted CSPs (WCSP). In an extensive empirical evaluation we demonstrate conclusively the superiority of the memory intensive AND/OR search algorithms on a variety of benchmarks including random and real-world problem instances.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r70a.html Dr. Rina Dechter @ UCI
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          R70a
          Constraint Processing for Optimal Maintenance Scheduling
          Daniel Frost(frost@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          A well-studied problem in the electric power industry is that of optimally scheduling preventative maintenance of power generating units within a power plant. We show how these problems can be cast as constraint satisfaction problems and provide an "iterative learning" algorithm which solves the problem in the following manner. In order to find an optimal schedule, the algorithm solves a series of CSPs with successively tighter cost-bound constraints. For the solution of each problem in the series we use constraint learning, which involves recording additional constraints that are uncovered during search. However, instead of solving each problem independently, after a problem is solved successfully with a certain cost-bound, the new constraints recorded by learning are used in subsequent attempts to find a schedule with a lower cost-bound. We show empirically that on a class of randomly generated maintenance scheduling problems iterative learning reduces the time to find a good schedule. We also provide a comparative study of the most competitive CSP algorithms on the maintenance scheduling benchmark.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r157b.html Dr. Rina Dechter @ UCI
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          R157b
          Bounding Graphical Models Processing by Hypertree Width.

          Lars Otten and Rina Dechter

          Abstract
          In 2000, Gottlob et al. [3] introduced a new graph parameter, the hypertree width, and showed that it provides a broader characterization of tractable constraint networks than the treewidth. In 2005 this observation was extended to general graphical models [5], showing that the hypertree width yields bounds on inference algorithms. This paper explores further the practical properties of the hypertree width parameter for bounding the complexity of constraint satisfaction and optimization tasks. To that end we study empirically the effectiveness of the treewidth vs. hypertree width over common network benchmarks.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r53.html Dr. Rina Dechter @ UCI
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          R53
          Looking at Full Looking Ahead
          Daniel Frost (frost@ics.uci.edu) & Rina Dechter (dechter@ics.uci.edu)

          Abstract
          Haralick and Elliott's full looking ahead algorithm [4] was presented in the same article as forward checking, but is not as commonly used. We give experimental results which indicate that on some types of constraint satisfaction problems, full looking ahead outperforms forward checking. We also present three new looking ahead algorithms, all variations on full looking ahead, which were designed with the goal of achieving performance equal to the better of forward checking and full looking ahead on a variety of constraint satisfaction problems. One of these new algorithms, called smart looking ahead, comes close to achieving our goal.

            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/publications/r176.html Dr. Rina Dechter @ UCI
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          R176
          Finding Most Likely Haplotypes in General Pedigrees through Parallel Search with Dynamic Load Balancing

          Lars Otten and Rina Dechter

          Abstract
          General pedigrees can be encoded as Bayesian networks, where the common MPE query corresponds to finding the most likely haplotype configuration. Based on this, a strategy for grid parallelization of a state-of-the-art Branch and Bound algorithm for MPE is introduced: independent worker nodes concurrently solve subproblems, managed by a Branch and Bound master node. The likelihood functions are used to predict subproblem complexity, enabling efficient automation of the parallelization process. Experimental evaluation on up to 20 parallel nodes yields very promising results and suggest the effectiveness of the scheme, solving several very hard problem instances. The system runs on loosely coupled commodity hardware, simplifying deployment on a larger scale in the future.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r183.html Dr. Rina Dechter @ UCI
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          R183
          Anytime AND/OR Depth-First Search for Combinatorial Optimization

          Lars Otten and Rina Dechter

          Abstract
          One popular and efficient scheme for solving exactly combinatorial optimization problems over graphical models is depth-first Branch and Bound. However, when the algorithm exploits problem decomposition using AND/OR search spaces, its anytime behavior breaks down. This paper 1) analyzes and demonstrates this inherent conflict between effective exploitation of problem decomposition (through AND/OR search spaces) and the anytime behavior of depth-first search (DFS), 2) presents a first scheme to address this issue while maintaining desirable DFS memory properties, 3) analyzes and demonstrates its effectiveness. Our work is applicable to any problem that can be cast as search over an AND/OR search space.

          [pdf]

          http://www.ics.uci.edu/~dechter/publications/r116.html Dr. Rina Dechter @ UCI
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          R116
          Approximation Algorithms for Probabilistic Reasoning: Sampling and Iterative Inference
          Bozhena Bidyuk
          Abstract
          The complexity of the exact inference increases exponentially with size and complexity of the network. As a result, the exact inference methods become impractical for large networks and we seek to approximate the results. A variety of approximation methods exist. This research focuses on two approximation methods for finding posterior marginals P(xije) in Bayesian networks: iterative belief updating (defined by Pearl [Pearl 1988]) and sampling.
          The belief updating is an exact inference method for singly-connected networks. It can be applied to loopy networks to obtain approximate answers. The algorithm is based on message passing: in some order, each node computes and sends messages to its neighbors incorporating the latest messages it recieved. In a singly-connected network, we can order nodes so that it will be sufficient for each node to pass one messages in each direction. In a loopy network, the nodes compute several iterations of messages to achieve convergence (or to demonstrate the lack of convergence). Thus, belief updating in loopy networks is often referred to as Iterative Belief Propagation or IBP. Although IBP generally computes only approximate answers, it is known to perform extremely well in several special classes of networks such as coding networks and noisy-or networks. At the same time, we know that in some instances IBP does not converge or generates approximate answers far from correct. Currently, we do not have any methodology that would allow us in general case to predict the convergence of IBP or provide some practical error bounds on the approximate marginals it computes. In this research work, we examine the influence of the -cutset criteria on the convergence and quality of approximate marginals computed by IBP. We conjecture the -cutset (defined as a cycle-cutset with extreme posterior marginals) has effect similar to an observed cycle-cutset which breaks the loops and leaves the network singly-connected. We prove that the conjecture is true for Bayesian networks without evidence and show that the error in the approximate marginals computed by IBP converges to 0 as  tends to 0. We provide empirical support for instances of Bayesian networks with evidence.
          The idea behind the sampling methods for Bayesian networks is to generate a set of samples (where a sample in a vector space X = fX1; :::;XNg is just an assignment of values to the elements of vector X) and then estimate the posterior marginals of interest from samples. In general, the quality of the approximate answers depends primarily on the number of samples generated and the approximate values converge to the exact values as number of samples increases. However, the sampling variance increases with the size of the sampling space. In this research work, we focus on the the variance reduction techniques on the example of the Gibbs sampler for Bayesian networks. It is obvious that we can achieve the reduction in variance by sampling only a subset of variables. However, the implication is that we have to carry out a lot more analytical computations which may render the whole approach impractical. We demonstrate that we can reduce sampling space efficiently if we take into consideration the underlying network structure. The time/space complexity of the exact inference in Bayesian networks is exponential in the induced width of the graph. In our sampling scheme, called w-cutset sampling, we sample a subset of variables (called a cutset) that is carefully chosen to reduce the complexity of the graph bounded by the induced width w. We analyze the problem of finding an optimal w-cutset of a graph (NP-hard in general case) and provide a heuristic algorithm for finding w-cutset in practice. We show empirically that w-cutset sampling typically finds better approximate answers than standard Gibbs sampler for a range of w values although its performance eventually deteriorates as w increases.

          PDF



          http://www.ics.uci.edu/~dechter/publications/r69.html Dr. Rina Dechter @ UCI
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          R69
          Algorithms and Heuristics for Constraint Satisfaction Problems
          Daniel Frost(frost@ics.uci.edu)

          Abstract
          This dissertation presents several new algorithms and heuristics for constraint satisfaction problems, as well as an extensive and systematic empirical evaluation of these new techniques. The goal of the research is to develop algorithms which are effective on large and hard constraint satisfaction problems.

          The dissertation presents several new combination algorithms. The BJ+DVO algorithm combines backjumping with a dynamic variable ordering heuristic that utilizes a forward checking style look-ahead. A new heuristic for selecting a value called Look-ahead Value Ordering (LVO) can be combined with BJ+DVO to yield BJ+DVO+LVO. A new learning, or constraint recording, technique called jumpback learning is described. Jump-back learning is particularly e ective because it takes advantage of e ort that has already been expended by BJ+DVO. This type of learning can be combined with either BJ+DVO or BJ+DVO+LVO. Learning is shown to be helpful for solving optimization problems that are cast as a series of constraint problems with successively tighter cost-bound constraints. The constraints recorded by learning are used in subsequent attempts to nd a solution with a lower cost-bound.


            [ps] [pdf]

          http://www.ics.uci.edu/~dechter/acp_award.html Dr. Rina Dechter @ UCI :: ACP Award
          Dr. Rina Dechter
          Prof. Rina Dechter, Ph.D
          Artificial Intelligence
          Office: DBH 4232
          Phone: 1.949.824.6556
          Email: dechter_at_ics.uci.edu
          Highlights and News
          BOOK (2013)
          Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
          AWARD
          2013 ACM Fellow (link 1 / link 2)
          PASCAL CHALLENGE (2012)
          Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
          UAI COMPETITION (2010)
          Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
          CS 275
          Constraint Networks Course Page
          MINI-SCHOOL
          UCI Lifted Algorithms Mini-School (November 3-6)
          BOOK (2010)
          'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
          IJCAI 2013 TUTORIAL
          Constraint Processing and Probabilistic Reasoning
          More
          Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

          2007 ACP Research Excellence Award to Rina Dechter

          ACP Award Ceremony ACP is the Association of Constraint Programming. Its announcement of the ACP 2007 research excellence award reads as follows:

          For the third time we are giving the "ACP award for Research Excellence in constraint programming", whose purpose is to celebrate those CP researchers who have made exceptional and very influential research contributions to CP with their work.

          This year the award committee, consisting of Francesca Rossi, Pedro Meseguer, Christian Bessiere (program chair of CP 2007), Eugene Freuder and Pascal Van Hentenryck (the receipients of the last two research awards), decided to give the 2007 ACP award on research excellence on constraint programming to Rina Dechter, for a program of sustained high quality research in constraint processing, with numerous influential results and great impact on Artificial Intelligence.

          Rina's nomination was presented by Javier Larrosa, who put together an excellent nomination package consisting of very enthusiastic support from seven leading researchers in CP, AI, and DB. Rina Dechter obtained her Ph.D. in Computer Science from UCLA in 1985 with Judea Pearl. She the remained there until 1987 as a post-doc and in 1988 as a research associate. She then joined the Technion in Israel as senior lecturer from 1988 to 1990, and finally she joined UC Irvine in 1990, where she is now full professor of computer science.

          Rina's research activity is very extensive (she has published 90 conference papers and 35 journal papers) and includes several pioneering and very influential structure-based techniques, such as directional consistency, adaptive consistency, tree clustering, cycle-cutset schemes, backjumping, and search-based non-good learning. Moreover, she introduced the temporal constraint framework, which is central in any scheme for planning and scheduling, including applications such as the NASA Deep Space project. She also defined the bucket elimination framework, which unifies dynamic programming for combinatorial optimization, probabilistic reasoning, and planning under uncertainty, obtaining very inspiring and influential results in all these fields.

          Her book "Constraint Processing", published in 2003, covers most of the main notions and techniques in constraint processing, and has been very instrumental for the dissemination and visibility of CP, as well as the creation of a uniform body of knowledge, the birth of new university courses on CP, and the introduction of new young people to the field.

          Rina's numerous and very influential results have inspired a whole generation of CPers, as well as researchers in planning, scheduling, knowledge representation and reasoning, uncertainty, probabilistic inference, and combinatorial optimization.

          Award talk Slides

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/books/materials.html Dr. Rina Dechter @ UCI :: Constraint Processing, the book :: Commentary
          Dr. Rina Dechter
          Prof. Rina Dechter, Ph.D
          Artificial Intelligence
          Office: DBH 4232
          Phone: 1.949.824.6556
          Email: dechter_at_ics.uci.edu
          Highlights and News
          BOOK (2013)
          Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
          AWARD
          2013 ACM Fellow (link 1 / link 2)
          PASCAL CHALLENGE (2012)
          Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
          UAI COMPETITION (2010)
          Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
          CS 275
          Constraint Networks Course Page
          MINI-SCHOOL
          UCI Lifted Algorithms Mini-School (November 3-6)
          BOOK (2010)
          'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
          IJCAI 2013 TUTORIAL
          Constraint Processing and Probabilistic Reasoning
          More
          Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

          Constraint Processing

          by Rina Dechter
          Published by Morgan Kaufmann

          Chapter Slides

          • Chapter 1
          • Chapter 2
          • Chapter 3
          • Chapter 4
          • Chapter 5
          • Chapter 6
          • Chapter 7
          • Chapter 8
          • Chapter 9
          • Chapter 10
          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/books/about.html Dr. Rina Dechter @ UCI :: Constraint Processing, the book :: Commentary
          Dr. Rina Dechter
          Prof. Rina Dechter, Ph.D
          Artificial Intelligence
          Office: DBH 4232
          Phone: 1.949.824.6556
          Email: dechter_at_ics.uci.edu
          Highlights and News
          BOOK (2013)
          Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
          AWARD
          2013 ACM Fellow (link 1 / link 2)
          PASCAL CHALLENGE (2012)
          Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
          UAI COMPETITION (2010)
          Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
          CS 275
          Constraint Networks Course Page
          MINI-SCHOOL
          UCI Lifted Algorithms Mini-School (November 3-6)
          BOOK (2010)
          'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
          IJCAI 2013 TUTORIAL
          Constraint Processing and Probabilistic Reasoning
          More
          Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

          Constraint Processing

          by Rina Dechter
          Published by Morgan Kaufmann


          About The Book

          Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics. Today, constraint problems are used to model cognitive tasks in vision, language comprehension, default reasoning, diagnosis, scheduling, temporal and spatial reasoning.

          In Constraint Processing, Rina Dechter, synthesizes these contributions, along with her own significant work, to provide the first comprehensive examination of the theory that underlies constraint processing algorithms. Throughout, she focuses on fundamental tools and principles, emphasizing the representation and analysis of algorithms.

          Readership: Researchers and practitioners in artificial intelligence and operations research; graduate students and senior undergraduate students in artificial intelligence and operations research

          ISBN: 1-55860-890-7

          Pages: 450

          Imprint: Morgan Kaufmann

          Publication Date: 5 May 2003

          Price: $65.95

          Table of Contents

          • Preface
          • Introduction
          • Constraint Networks
          • Consistency-Enforcing Algorithms
          • Directonal Consistency
          • General Search Statagies: Look Ahead
          • General Search Statagies: Look-Back
          • Local Search Algorithms
          • Advanced Consistency Methods
          • Tree-Decomposition Methods
          • Hybrid of Search and Inference: Time-Space Trade-offs
          • Tractable Constraint Languages
          • Temporal Constraint Netwoks
          • Constraint Optimization
          • Probabilistic Networks
          • Constraint Logic Programming
          • Bibliography

          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/books/commentary.html Dr. Rina Dechter @ UCI :: Constraint Processing, the book :: Commentary
          Dr. Rina Dechter
          Prof. Rina Dechter, Ph.D
          Artificial Intelligence
          Office: DBH 4232
          Phone: 1.949.824.6556
          Email: dechter_at_ics.uci.edu
          Highlights and News
          BOOK (2013)
          Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
          AWARD
          2013 ACM Fellow (link 1 / link 2)
          PASCAL CHALLENGE (2012)
          Solvers by current and former students won first places in all nine categories of the 2011 PASCAL Probabilistic Inference Challenge. (link 1 / link 2)
          UAI COMPETITION (2010)
          Our solvers IJGP and FormulaSampleSearch won first places in two categories each, Daoopt won third place in three categories. (link 1 / link 2)
          CS 275
          Constraint Networks Course Page
          MINI-SCHOOL
          UCI Lifted Algorithms Mini-School (November 3-6)
          BOOK (2010)
          'Heuristic, probability and Causality: a tribute to Judea Pearl' (link)
          IJCAI 2013 TUTORIAL
          Constraint Processing and Probabilistic Reasoning
          More
          Home Biographical Research Overview My Group Publications Selected Talks Awards Software Courses My Books

          Constraint Processing

          by Rina Dechter
          Published by Morgan Kaufmann

          Commentary

          The text:

          • Examines the basic practical aspects of each topic and then tackles more advanced issues, including current research challenges
          • Builds the reader's understanding with definitions, examples, theory, algorithms and complexity analysis.
          • Synthesizes three decades of researchers work on constraint processing in AI, databases and programming languages, operations research, management science, and applied mathematics

          This book provides a comprehensive and much needed introduction to the field by one of its foremost experts. It is beautifully written and presents a unifying framework capturing a wide range of techniques for processing symbolic, numerical, and probabilistic information.
          --Bart Selman, Cornell University

          I've been waiting a long time for a good theoretical introduction to constraint programming. Rina Dechter's book is just this. If you want to understand why this technology works, and how to make it work for you, then I recommend you read this book.
          --Toby Walsh, Cork Constraint Computation Centre

          The book is rigorous but it is not difficult to read. An abundance of examples illustrate concepts and algorithms. The reader is well guided through technical issues, so intuition is never hidden by technicalities.
          --Pedro Meseguer, Institut d'Investigaci� en Intel.ling�ncia Artificial (IIIA-CSIC)

          An indispensable resource for researchers and practitioners in AI and optimization.
          --Henry Kautz, University of Washington


          School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
          http://www.ics.uci.edu/~dechter/courses/ics-275a/winter-2016/index.shtml Dr. Rina Dechter @ UCI
          Dr. Rina Dechter - University of California at Irvine ZOT!
          home | publications | book | courses | research Revised on Feb. 18, 2016



          CompSci 275 Winter 2016, Constraint Networks
          [ main | project |

          • Instructor: Rina Dechter
          • Section: 34985
          • Classoom: DBH 1423
          • Days: Tuesday & Thursday
          • Time: 12:30 pm - 1:50 pm
          • Office hours: Tuesday 3:00 pm - 4:00 pm
          • Exam: Mar 3rd, in class


          Course Goals
          Constraint satisfaction is a simple but powerful tool. Constraints identify the impossible and reduce the realm of possibilities to effectively focus on the possible, allowing for a natural declarative formulation of what must be satisfied, without expressing how. The field of constraint reasoning has matured over the last three decades with contributions from a diverse community of researchers in artificial intelligence, databases and programming languages, operations research, management science, and applied mathematics.

          The purpose of this course is to familiarize students with the theory and techniques of constraint processing, using the constraint graphical model. This model offers a natural language for encoding world knowledge in areas such as scheduling, vision, diagnosis, prediction, design, hardware and software verification, and bio-informatics, and it facilitates many computational tasks relevant to these domains such as constraint satisfaction, constraint optimization, counting and sampling . The course will focus on techniques for constraint processing. It will cover search and inference algorithms, consistency algorithms and structure based techniques and will focus on properties that facilitate efficient solutions. Extensions to general graphical models such as probabilistic networks, cost networks, and influence diagrams will be discussed as well as example applications such as temporal reasoning, diagnosis, scheduling, and prediction.


          Textbook

          Required textbook: Rina Dechter, Constraint Processing, Morgan Kaufmann


          Grading Policy
          Homeworks and projects (80%), midterm (20%).


          Assignments:
          There will be weekly homework-assignments, a project, and an exam.


          Syllabus:

          Project Information

          Week Topic Slides
          Lecture
          Homework
          Additional Reading
          Date  
          Week 1
          • Chapters 1,2: Introductions to constraint network model. Graph representations, binary constraint networks.
          Set 1

          Numberjack Tutorial
          Homework 1
          (due 01-14)
          Numberjack
          MiniZinc

          Code Examples
          01-05

          01-07
          Week 2
          • Chapter 3: Constraint propagation and consistency enforcing algorithms, arc, path and i-consistency
          Set 2

          Lecture 3

          Lecture 4
          Homework 2
          (due 01-21)
          Constraint Propragation by Christian Bessiere 01-12

          01-14

          Week 3
          • Chapter 4: Graph concepts (induced-width), Directional consistency, Adaptive-consistency, bucket-elimination.
          Set 3 Lecture 5

          Lecture 6
          Homework 3
          (due 01-28)
          The Sat Solving Revolution: Solving, Sampling and Counting by Moshe Vardi 01-19

          01-21

          Week 4
          • Chapter 5: Backtracking search: Look-ahead schemes: forward-checking, variable and value orderings. DPLL.
          Set 4 Lecture 7

          Lecture 8
          Homework 4
          (due 02-04)
          Complete Algorithms by Darwiche and Pipatsrisawat 01-26

          01-28
          Week 5
          • Chapter 6: Backtracking search; Look-back schemes: backjumping, constraint learning. SAT solving and solvers (e.g., MAC, Minisat).
          Set 5 Lecture 9

          Lecture 10
          Homework 5
          (due 02-11)
          Minisat
          WALKSAT
          RSAT
          02-02

          02-04
          Week 6
          • Chapter 7: Stochastic local search, SLS, GSAT, WSAT
          • Satisfiability solving
          Satisfiability

          Set 6
          Lecture 11

          Lecture 12
          SATHandbook-CDCL 02-09

          02-11
          Week 7
          • Chapter 8: Advanced consistency methods; relational consistency and bucket-elimination, row-convexity, tightness, looseness.
          Set 7 Lecture 13

          Lecture 14
          Homework 6
          (due 02-23)
          02-16

          02-18
          Week 8
          • Chapter 13: Constraint Optimization, soft constraints
          02-23

          02-25
          Week 9
          • Chapter 13: Constraint Optimization, soft constraints (continued)


          Exam, in class
          03-01

          03-03
          Week 10


            Week 11



              Resources on the Internet
              • Books
                • Francesca Rossi, Peter van Beek, Toby Walsh. Handbook of Constraint Programming. Elsevier Science, 2006
                • Kimball Marriott, Peter Stuckey. Programming with Constraints: An Introduction. The MIT Press, 1998
              • Software links
                • Numberjack modeling language
                  • Numberjack main page
                  • Tutorial
                • MiniZinc modeling language
                  • MiniZinc page
              • Links for Satisfiability
                • Satisfiability tutorials by Joao Marques-Silva
                • SAT Solving : A Mini Course
                • A satisfiability tutorial by Youssef Hamadi



                School of Information and Computer Science University of California, Irvine, CA 92697-3435 Dr. Rina Dechter dechter at ics.uci.edu
                http://www.ics.uci.edu/~dechter/courses/ics-275a/winter-2016/code/ Index of /~dechter/courses/ics-275a/winter-2016/code

                Index of /~dechter/courses/ics-275a/winter-2016/code

                [ICO]NameLast modifiedSizeDescription

                [DIR]Parent Directory  -  
                [   ]aus.mzn12-Jan-2016 16:22 550  
                [   ]coloringsolved.py07-Jan-2016 22:08 940  
                [   ]msq.mzn12-Jan-2016 16:22 607  
                [   ]msqsolved.py07-Jan-2016 22:08 565  
                [   ]nq.mzn12-Jan-2016 16:22 389  
                [   ]nqsolved.py07-Jan-2016 22:08 899  

                Apache/2.2.15 (CentOS) Server at www.ics.uci.edu Port 80
                http://www.ics.uci.edu/~dechter/past_courses.html Dr. Rina Dechter @ UCI :: Courses
                Past Courses
                  • Current Courses
                  2014
                  • CompSci 276 Belief Networks
                  • CompSci 275 Constraint Networks
                  2013
                  • CompSci 295 Planning as Inference
                  • CompSci 276 Belief Networks
                  2012
                  • CompSci 271 Introduction to Artificial Intelligence
                  2011
                  • CompSci 276 Belief Networks, Spring 2011
                  • CompSci 175 Project in AI, Spring 2011
                  • ICS-295 Reasoning and Modeling with Graphical Models, Winter 2011
                  2010
                  • ICS-275 Constraint networks, Fall 2010
                  • ICS-295 Advance topic in Graphical Models, Spring 2010
                  • ICS-179Algorithms for Probabilistic and Deterministic Graphical Models, Spring 2010
                  • ICS-175 Project in Artificial Intelligence, Winter 2010
                  2009
                  • ICS-276 - Belief Networks, Fall 2009
                  • ICS-275 - Constraint Networks, Spring 2009
                  2008
                  • ICS-271 - Introduction to Artificial Intelligence, Fall 2008
                  • ICS-295 - CompSci 295
                  2007
                  • ICS-275a - Constraint Networks, Spring 2007
                  • ICS-295 -Reasoning and Modeling with Graphical Models, Spring 2007
                  • ICS-275B -Network-Based Reasoning - Bayesian Networks, Fall 2007
                  2006
                  • ICS-171 - Introduction to Artificial Intelligence, Fall 2006
                  • ICS-271 - Intorduction to Artificial Intelligence, Fall 2006
                  2005
                  • ICS-275b - Network-Based Reasoning - Bayesian Networks, Spring 2005
                  • ICS-280 - Current Topics in Graphical Models with Focus on Genetic Linkage Analysis, Spring 2005
                  2004
                  • ICS-270a - Introduction to Artificial Intelligence, Fall 2004
                  • ICS 171 - Introduction to Artificial Intelligence, Fall 2004
                  • ICS 175A - Project In Artificial Intelligence, Spring 2004
                  • ICS 280 - Current Topics in Graphical models with focus on Text-mining and Transportation areas, Spring 2004
                  • ICS 6A - Discrete Mathematics for Computer Science, Winter 2004
                  2003
                  • ICS 175A - Project In Artificial Intelligence, Spring 2003
                  • ICS 280 - Advanced Automatd Reason, Spring 2003
                  • ICS-270a - Intorduction to Artificial Intelligence, Winter 2003
                  • ICS 275A - Constraint Networks, Fall 2003
                  2002
                  • ICS-275B - Network-Based Reasoning - Belief Networks, Fall 2002
                  • ICS-280 - Causality: models, Reasoning and Inference Seminar, Spring 2002
                  • ICS 6A - Discrete Mathematics for Computer Science, Winter 2002
                  2001
                  • ICS 175A - Bayesian Networks and Constraint Networks, Fall 2001
                  • ICS 275A - Constraint Networks, Fall 2001
                  • ICS 270A - Intorduction to Artificial Intelligence, Spring 2001
                  2000
                  • ICS 275B - Network-Based Reasoning - Belief Networks, Fall 2000
                  • ICS 175A - Project in AI, Spring 2000
                  • ICS 6A - Discrete Mathematics for Computer Science, Fall 2000
                  • ICS 280 - Seminar, Spring 2000
                  1999
                  • ICS 275A - Constraint Networks, Fall 1999
                  • ICS 280 - Selected Topics in Automated Reasoning, Spring 1999
                  • ICS 171 - Introduction to Artificial Intelligence, Spring 1999
                  • ICS 6A - Discrete Mathematics for Computer Science, Winter 1999
                  1998
                  • ICS 171 - Introduction to Artificial Intelligence, Spring 1998
                  • ICS 280 - Selected Topics in Automated Reasoning, Spring 1998
                  • ICS 275B - Network-Based Reasoning - Belief Networks, Fall 1998
                http://www.ics.uci.edu/~zhengliz/ Zhengli Zhao

                Zhengli Zhao

                • Position: Graduate Student
                • Area: Artificial Intelligence (AI)
                • Advisor: Dr. Rina Dechter
                • Office: DBH 4099
                • Office Fax: +1(949)824-4056
                • E-mail: zhengliz@uci.edu

                Projects

                Other Interests

                Here are some pointers on how to learn HTML.

                Information and Computer Science
                University of California, Irvine
                Irvine, CA 92697-3425
                Last modified: 25 Nov 2015 http://www.ics.uci.edu/~kkask/ Kalev Kask @ UCI :: Home
                Kalev Kask
                Artificial Intelligence
                Email: kkask@uci.edu
                Home Publications Courses
                Research

                My research is in the field of Automated Reasoning in Artificial Intelligence and focused on Graphical Models.
                School of Information and Computer Science University of California, Irvine, CA 92697-3435 Kalev Kask kkask@uci.edu
                http://www.ics.uci.edu/~nflerova/ Natalia Flerova, UC Irvine

                Natalia Flerova, UC Irvine

                my photo

                About me

                I am currently a PhD candidate in Prof. Rina Dechter's Automated Reasoning group.

                In Jun 2012 I received an MS in Computer Science from University of California Irvine. Prior to that in 2005 I earned a specialist degree with honours from Baltic State Techical University. 

                Teaching

                I am currently an instructor for CS 175 class.

                Research

                My research focuses on reasoning in graphical models. More specifically, I work on combinatorial optimization in probabilistic (Bayesian) and weighted constraint networks.


                Publications

                Below is the list of peer-reviewed publications and technical reports.

                • Abstract | PDF Natalia Flerova, Radu Marinescu, Pratyaksh Sharma and Rina Dechter. "Weighted Best-First Search for W-Optimal Solutions over Graphical Models." in Proceedings of Planning, optimization and search (PlanSOpt) 2015 (a workshop of AAAI'15).

                • Abstract | PDF Pratyaksh Sharma, Natalia Flerova and Rina Dechter. "Empirical Evaluation of weighted Heuristic Search with advanced Mini-Bucket Heuristics for Graphical Models." ICS Technical Report, 2014.

                • Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Evaluating Weighted DFS Branch and Bound over Graphical Models " in Proceedings of SoCS 2014.

                • Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Weighted anytime search: new schemes for optimization over graphical models" Under review in Special Issue on Annals of Mathematics and Artificial Intelligence.

                • Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter, "Weighted Best First Search for MAP." in proceedings of International Symposium on Artificial Intelligence and Mathematics (ISAIM-2014), January, 2014.

                • Abstract | PDF Natalia Flerova, Radu Marinescu and Rina Dechter. "Anytime AND/OR Best-First Search for Optimization in Graphical Models." to appear in Inferning'13 (a workshop of ICML'2013).

                • Abstract | PDF Natalia Flerova, Radu Marinescu, and Rina Dechter. "Preliminary Empirical Evaluation of Anytime Weighted AND/OR Best-First Search for MAP" in Proceedings of DISCML 2012 (a workshop of NIPS 2012).

                • Abstract | PDF | Poster Alexander Ihler, Natalia Flerova, Rina Dechter, and Lars Otten. "Join-graph based cost-shifting schemes" in Proceedings of UAI 2012.

                • Abstract | PDF | Slides Rina Dechter, Natalia Flerova, and Radu Marinescu. "Search Algorithms for m Best Solutions for Graphical Models" in Proceedings of AAAI 2012.

                • Abstract | PDF Natalia Flerova, Alexander Ihler, Rina Dechter, and Lars Otten. "Mini-bucket Elimination with Moment Matching" in Proceedings of DISCML 2011 (a workshop of NIPS 2011).

                • Abstract | PDF | Slides Emma Rollon, Natalia Flerova, and Rina Dechter. "Inference Schemes for M Best Solutions for Soft CSPs" in Proceedings of Soft 2011 (a workshop of CP 2011).

                • Abstract | PDF | Slides Rina Dechter and Natalia Flerova. "Heuristic Search for m Best Solutions with Applications to Graphical Models" in Proceedings of Soft 2011 (a workshop of CP 2011).

                • Abstract | PDF | Long version Natalia Flerova, Emma Rollon, and Rina Dechter. "Bucket and mini-bucket Schemes for M Best Solutions over Graphical Models" in in GKR 2011 (a workshop of IJCAI 2011).

                • PDF Natalia Flerova. "Calculating LOD score: experimental comparison" in ICS Internal report, September, 2010.

                • Abstract | PDF Natalia Flerova and Rina Dechter. "M best solutions over Graphical Models" in in CRAGS 2010 (a workshop of CP 2010)


                Back to top

                Information

                Office: DBH 4099

                Office hours: TBA

                nflerova_at_uci.edu


                Navigation

                CS 175 website

                Publications


                http://www.ics.uci.edu/~magda/netsys270.html netsys270.html

                Principle of Data Transmission

                 

                Prof. Magda El Zarki

                Dept. of CS

                Donald Bren School of ICS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Course Outline  | Homework Problems | Quiz Solutions

                http://www.ics.uci.edu/~magda/CS620.html CS620.html

                Online Game Systems

                 

                Prof. Magda El Zarki

                Dept. of CS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Introduction | Course Outline  | Announcements | Presentation Schedule

                http://www.ics.uci.edu/~magda/ics167.html ics_167.html

                ICS 167 - MultiPlayer Game Systems 

                Prof. Magda El Zarki

                Dept. of CS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Introduction | Course Description | Projects/Class Schedule | Projects | Quiz Solutions

                http://www.ics.uci.edu/~magda/netlabG.html ics_x33.html

                Mastering Networks

                An Internet Lab Course
                 

                Prof. Magda El Zarki

                Dept. of CS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Course Outline | Lab/Class Schedule | Announcements | Quiz Solutions

                http://www.ics.uci.edu/~magda/netlabU.html ics_x33.html

                Mastering Networks

                An Internet Lab Course
                 

                Prof. Magda El Zarki

                Dept. of CS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Course Outline | Lab/Class Schedule | Announcements | Quiz Solutions

                http://www.ics.uci.edu/~magda/netsys230.html netsys230

                Wireless and Mobile Networking

                 

                Prof. Magda El Zarki

                Dept. of ICS

                University of California, Irvine

                elzarki@uci.edu

                http://www.ics.uci.edu/~magda


                Course Outline | Announcements | Projects

                http://www.ics.uci.edu/~ziv/ooad/intro_to_se/sld023.htm Software Engineering Principles

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                http://www.ics.uci.edu/~ziv/ooad/intro_to_se/sld019.htm The Software Process

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                http://www.ics.uci.edu/~ziv/diss/conclusionpaper/node4.html Comprehensive Case Study next up previous
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                Comprehensive Case Study

                As discussed in chapter gif, the case study at Beckman was confined to a single group of developers, a single Bayesian model of confidence-level uncertainty, and a single scenario of change with associated questions. We are very interested in expanding the scope of our field studies, both within Beckman as well as with other software development organizations.

                A more comprehensive study than the one reported in this dissertation would include one or more of the following enhancements:

                • Measure the effects of using the Bayesian models over time, especially its impacts on the quality of human judgment and decision making.
                • Construct additional Bayesian models for additional artifact networks or to model other situations or properties. We are particularly interested in constructing Bayesian models at different levels of granularity, comparing the efficacy of resulting models, and, eventually, combining Bayesian models with different granularities.
                • Afford automated support for definition and revision of software belief networks. Of special interest are UI design issues in constructing belief-network software as well as integration of Bayesian capabilities with existing tools, e.g., REBUS, DOORS, and RequisitePro.
                • Finally, this dissertation focused on incorporation of subjective information into software models. We are interested in combining subjective and objective data in future models. Specifically, we wish to include project data such as code quality and developer productivity, failure rate and severity, etc., in our models as factors that influence confidence and, ultimately, human judgment and decision making.


                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Other software qualities

                In this dissertation, we focused on implementability and later validity as interesting software qualities for which causal relationships can be established and belief values can be determined. We believe, however, that uncertainty should also be modeled for additional software qualities, including, among others, correctness, reliability, efficiency, safety, and maintainability. Of particular relevance is the work carried out at CSR, discussed in chapter gif, where uncertainty is modeled for software testability and dependability assessment tasks. As mentioned earlier, qualities associated with entities must be consistent with causal relationships such that the resulting network is meaningful.



                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Final Thoughts

                This dissertation offers a path that, if followed, helps address software development problems for which uncertainty plays an important role, specifically with respect to human judgment and decision making. We strongly believe that software uncertainty modeling - using Bayesian belief networks - will help managers to better plan, schedule, estimate, and evaluate software projects. It will also help developers to better trace and navigate interrelated artifacts in large software spaces, understand and discover system qualities and properties, and, ultimately, achieve better judgment and make better decisions.



                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Monitoring the testing process

                We intend to study the relationship between the Bayesian approach and software testing. This relationship is important not only since testing should have significant implications for belief and confidence levels, but also since a Bayesian model should have ramifications for human judgment and decision making during testing. For example, one important question in software testing is ``How much testing is enough?''. This question may be addressed by explicit modeling of uncertainty, if sufficient testing is defined in terms of levels of confidence in select system entities, for example, its code modules. As testing progresses, confidence levels increase as long as test execution is successful. Testing is guided and monitored by continuous update and comparison of confidence levels against predefined thresholds. Testers are notified and may take appropriate action whenever thresholds are exceeded. This approach may be especially useful in safety-critical systems, where confidence requirements and constraints are often specified numerically. With regard to CEquencer, we intend to enhance our Bayesian model to include testing information - as it becomes available at Beckman - and monitor the effects of the Bayesian approach on testing decisions.



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                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Other uncertainty modeling techniques

                In this dissertation, we have used Bayesian networks to model uncertainty in software development. Alternative approaches to uncertainty modeling and logical inference exist, however, that must be weighed and compared against Bayesian methods. Those include Certainty-Factor approaches, Dempster-Shafer approaches, fuzzy logic, and default, monotonic, and nonmonotonic logic [Ste95]. Specifically, relative merits and pitfalls of these techniques should be evaluated against the Bayesian approach in the context of software engineering situations.



                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Summary of Contributions

                This dissertation offers the following contributions:

                • Defining the Maxim of Uncertainty in Software Engineering. This maxim recognizes the abundance of software uncertainties and their relations and impacts on process decisions and risk management. Also, as corollary, it is suggested that software development include the search, identification, modeling and management of software uncertainties.
                • Defining an approach to explicitly model uncertainty using software belief networks. This approach is anchored in a Bayesian interpretation of relationships and dependencies among software artifacts. It includes notions of human judgment, confidence, belief, and evidence, often ignored by software modeling techniques.
                • Implementing the SoftBayes Java applet. This applet allows software systems to be defined as graphs of related artifacts and to be annotated with uncertainty information. Once defined, software belief networks are subject to Bayesian updating, using a core engine such as provided by JavaBayes.
                • Conducting a case study to substantiate the maxim above and to evaluate the applicability of Bayesian belief networks to a real software project. Results of the CEquencer study indicate that explicit modeling of software uncertainties improves developers' ability to identify and track changes to levels of confidence in software artifacts and relations.
                • Finally, several confidence factors were identified as influencing expert's beliefs in software requirements. Recording those confidence factors is deemed beneficial to future similar projects where requirements uncertainty is captured explicitly.

                Several impediments and limitations of our approach were observed, including the initial cost of obtaining prior belief values, the need to ensure that software belief networks retain causality and variable independence, and the assumption that software developers, domain experts, and related project information are available and accessible.

                Our experience in using Bayesian networks for software uncertainty modeling, specifically for the CEquencer system, indicates that:

                • The conceptual view of both software systems and Bayesian networks as interrelated ``webs'' of nodes and links seems to offer a convenient metaphor that also maps well into subsequent design and implementation. Specifically, early depictions of CEquencer artifact webs were developed as Java applets and placed on the World Wide Web (see [Ziv97]); these applets were then viewed and reviewed by Beckman developers for accuracy and relevancy.
                • The CEquencer system, like most other software, proved to be fraught with uncertainties, thereby confirming our suspicions for at least one real-life system. Specifically, CEquencer software embodies many, often subtle, problem domain uncertainties, including uncertainties stemming from laws of physics and chemistry in the software's operational environment as well as from vaguely defined boundaries between software versus hardware components.
                • Our notion of software uncertainties was well received by Beckman developers, offering convenient means in which to describe their confidences and beliefs regarding CEquencer software. The CEquencer requirements analyst has recently incorporated confidence levels into her requirements capture process.
                • The questionnaire session at Beckman revealed that, for the given set of questions, a significant difference between the means of the subjects' scores was identified. This suggests that subjects' ability to answer the questions increased with the addition of confidence level information. This confirms a statistically significant relationship between the availability of confidence level information and developers' ability to trace and track software artifacts and related confidences.

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                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                References

                Ste95
                Mark Stefik. Introduction to Knowledge Systems. Morgan Kaufmann, San Francisco, CA, 1995.

                Ziv97
                Hadar Ziv. Java applets for Beckman CEquencer software, March 1997. http://www.ics.uci.edu/ ziv/java/bayesian_example.html.



                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Conclusions and Future Work

                 



                • Summary of Contributions
                • Future Work
                  • Comprehensive Case Study
                  • Monitoring the testing process
                  • Other software qualities
                  • Other uncertainty modeling techniques
                  • Modeling uncertainty in software processes
                • Final Thoughts


                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Modeling uncertainty in software processes

                In this dissertation, we focused on software product uncertainties, such as modeled for CEquencer software, though process uncertainties were treated briefly (e.g., path selection criteria in Table gif). Despite the emphasis on artifact uncertainties, we firmly believe that process uncertainties should also be modeled. We contend that software-process modeling formalisms must be augmented to include uncertainty values; that an environment for supporting definition and execution of process models should include capabilities for representation and interpretation of belief values and should allow for Bayesian updating of those values; and that Bayesian updating procedures must be carried out during process execution, such that belief values and confidence levels are continuously updated as new evidence arrives.

                The provision and update of belief values may be greatly enhanced in software process frameworks that include process measurement capabilities. Such capabilities constitute a rich source of information regarding the current state of various elements and support the collection of statistical and empirical data that may significantly improve the accuracy of prior belief value estimation.

                We expect that by modeling software process uncertainties, one may achieve a more realistic representation of the process, enable automated belief revision by means of Bayesian updating, and support prediction and guidance of future development activities.



                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
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                Future Work



                • Comprehensive Case Study
                • Monitoring the testing process
                • Other software qualities
                • Other uncertainty modeling techniques
                • Modeling uncertainty in software processes


                Hadar Ziv
                Fri Jun 20 16:25:19 PDT 1997
                http://www.ics.uci.edu/~ziv/diss/intropaper/node5.html Dissertation Organization next up previous
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                Dissertation Organization

                The rest of this dissertation is organized as follows: In Chapter Two we present background information in the form of a brief historical perspective of thirty years of related software engineering research. Specifically, Chapter Two covers areas of software visualization, hypertext and hypermedia, object-oriented methods, and AI inference diagrams. Chapter Three discusses software uncertainty in detail, including sources of uncertainty and uncertainty in development phases. In Chapter Four we present an uncertainty modeling technique called Bayesian Belief Networks and justify its applicability to software engineering situations. Several examples of Bayesian networks are included. Chapter Five describes the architecture and components of the CEquencer system, selected as case study for this dissertation. In Chapter Six we present results and impressions from our study of uncertainty modeling at Beckman. Chapter Seven describes three specific applications of Bayesian networks in practice as well as related work in uncertainty modeling. Finally, Chapter Eight presents conclusions and future work.

                In conclusion of the introduction, we wish to point out that uncertainty abounds not only in software development but also in most engineering and scientific pursuits as well as many everyday situations (For uncertainty in everyday situations see, for example, Stefik [Ste95], pp. 460.). Detailed exploration of uncertainty in general is, therefore, beyond the scope of this dissertation. Nevertheless, we hope that the maxims, techniques, tools, and results described here will help identify research opportunities as well as provide a solid foundation for future work in software uncertainty modeling. We encourage readers to consider occurrences and consequences of uncertainty in their own domains of interest and expertise. Our future plans include additional modeling of software uncertainties at Beckman as well as exploring other domains where uncertainty modeling may be useful. We are particularly interested in ``Bayesian Internets,'' i.e., the modeling of information retrieval uncertainties on the Internet and WWW.


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                Hadar Ziv
                Fri Jun 20 16:22:31 PDT 1997
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                Premises and Hypothesis

                 

                To arrive at the approach taken in this dissertation, one must follow the statements in this section. Most statements, such as ``software complexity hinders its understanding'' or ``users get disoriented in large hypertext spaces,'' are either widely accepted or substantiated later. These statements therefore constitute the premises for this dissertation. One statement, however, is less accepted and requires further investigation: ``Explicit modeling of software uncertainties improves human judgment and decision making during software development.'' This statement therefore constitutes the hypothesis for this dissertation and will be further elaborated and validated.

                Our initial observation is that software is extremely complex, i.e., that ``software systems are perhaps the most intricate of man's handiworks'' [Bro95]. Software complexity is not only ubiquitous, it is also intrinsic: ``The complexity of software is an essential property, not an accidental one'' [Bro87]. ``By essential we mean that we may master this complexity, but we can never make it go away'' [Boo94]. Complexity hinders software understanding, yet understanding must be afforded if development is to be successful.

                To manage complexity, software development concerns are often separated in time and space. Thus, software is typically developed in distinct phases by different teams of developers. Large-scale software is often long-lived and delivered in multiple version, thereby comprising a family of related programs [Par79].

                The product of software development is typically a large and complex collection of software elements of diverse types. Software artifact collections often contain not only millions of source code lines, but also versioned releases, volumes of user manuals and documentation, requirements and design specifications, test cases and test results, and so forth. Moreover, these software objects are typically integrated and correlated within themselves and with each other in surprisingly intricate and subtle ways [SZ92]. This poses substantial impediments to software understanding, particularly with respect to traceability, visualization, and navigation issues, thereby leading to the following research question.

                Research Question:

                Given the intricacy and complexity of software artifact relationships, what can be done to improve traceability, visualization, and navigation of large artifact collections?

                As a first step, it is proposed that software systems be viewed as large hypertexts, since hypertext offers a coherent and consistent metaphor for viewing both inter- as well as intra-artifact relationships (cf. [ZO95, SZ92]). Here, it suffices to define hypertext informally as providing node-and-link views and navigation of software artifact collections. Hypertext is defined more formally and completely in Chapter gif. We therefore contend that a hypertext metaphor of software artifacts and relations should improve developer's ability to trace or track related elements in large and complex software spaces.

                Despite the above, hypertext is no ``silver bullet'' for relieving software artifact complexities and uncertainties. Specifically, hypertext introduces several concerns regarding its efficacy for software engineering [ZO95]. Here, we are particularly concerned with the well-known navigation problem of user confusion and disorientation in large hypertext spaces as well as the fact that hypertext systems generally are not designed with software engineering in mind. This means that hypertext does not necessarily address the unique needs of software developers and users nor does it model or make use of the unique characteristics of software systems. Additional means should, therefore, be provided to further facilitate understanding of software artifact collections. The following statements concern this facilitation.

                We claim that software engineering is fraught with uncertainties. Software uncertainties contribute significantly to the overall complexity and unpredictability of software development. Like complexity, uncertainty is inherent to the engineering of software systems. This observation is summarized succinctly in [ZRK96] as the Maxim of Uncertainty in Software Engineering:

                Uncertainty is inherent and inevitable in software processes and products.

                Examples of software uncertainties abound - many are provided in this dissertation. Starting in Chapter Four, we focus on uncertainties associated with developers' confidence levels in software artifacts, including, among others, requirements specifications, design elements, code modules, and testing information. These confidence levels fluctuate frequently during development. Their modeling should, therefore, include a scheme for confidence revision and updating.

                We now arrive at the high-level statement of the hypothesis for this dissertation.

                High-level Research Hypothesis:

                Explicitly modeling uncertainty improves human judgment and decision making during software development.

                This hypothesis is too vague to be investigated and validated directly. Instead, we require a more specific formulation, including a specific technique for software uncertainty modeling. To model software uncertainties, we turn to artificial intelligence research in modeling and management of uncertainty. We select a specific uncertainty modeling technique called Bayesian belief networks [Pea88]. Detailed reasons for this choice are provided in Chapter Four.

                Here, it suffices to say that Bayesian networks offer a clear conceptual model of causality among related elements and include algorithms for belief revision and updating. This leads to our specific hypothesis below. The hypothesis speaks in terms of software artifacts, which include, among others, requirements elements, design nodes, code modules, and test information, relationships among those artifacts, established as part of the development process, and confidence levels, which reflect developer confidences in certain qualities and properties of software artifacts.

                Specific Research Hypothesis:

                Bayesian-network models of software artifact uncertainties improves understanding of associated confidence levels, compared to simply following linked information, ultimately leading to better human decision making.

                In the long run, we believe that proper adoption and use of uncertainty modeling techniques will improve human judgment and decision making during software development. For the purposes of this dissertation, we focused on the specific hypothesis above and took steps toward its validation, as follows:

                1. An existing software system was selected as case study. The system of choice, called CEquencer, is currently under development at Beckman Instruments in Fullerton, CA. CEquencer is designed to control and communicate with various hardware devices that in turn are used by biologists, chemists, and other scientists to separate laboratory specimens into molecular constituents in order to determine their DNA sequences. CEquencer software artifacts are described in Chapter Five.
                2. CEquencer requirements were investigated throughly, and a hypertext model as well as a Bayesian-network model of those requirements were constructed.
                3. A key subsystem of CEquencer, called FSM Run, was investigated thoroughly, resulting in a hypertext model of software artifacts, including all FSM Run code modules as well as related requirements.
                4. Software uncertainty information was collected for FSM Run artifacts. The information was elicited from Beckman experts and developers and organized in a Bayesian network.
                5. Our main hypothesis was evaluated against FSM Run code modules and related requirements, as identified in (2) above. In a questionnaire session with the Beckman team, developers were asked to trace artifacts and associated confidence levels with and without the Bayesian-network representation of confidence values.
                6. Results were gathered for the case study and analyzed for statistical significance. Results of the questionnaire session are summarized in Chapter Six.

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                About this document ...

                This document was generated using the LaTeX2HTML translator Version 96.1 (Feb 5, 1996) Copyright © 1993, 1994, 1995, 1996, Nikos Drakos, Computer Based Learning Unit, University of Leeds.

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                latex2html intropaper.tex.

                The translation was initiated by Hadar Ziv on Fri Jun 20 16:22:31 PDT 1997


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                References

                Boe89
                Barry W. Boehm, editor. Software risk management. IEEE Computer Society Press, Washington, D.C., 1989.

                Boe91
                Barry W. Boehm. Software risk management: principles and practices. IEEE Software, 8(1):32-41, January 1991.

                Boo94
                Grady Booch. Object Oriented Analysis and Design with Applications. The Benjamin/Cummings series in object-oriented software engineering. The Benjamin/Cummings Publishing Company, Redwood City, California, second edition, 1994.

                Bro87
                Frederick P. Brooks. No silver bullet: Essence and accidents of software engineering. IEEE Computer, 20(4):10-19, April 1987.

                Bro95
                Frederick P. Brooks. The Mythical Man-Month: Anniversary Edition. Addison-Wesley, Reading, MA, 1995.

                Gem97
                Art Gemmer. Risk management: Moving beyond process. IEEE Computer, 30(5):33-43, May 1997.

                HMW95
                David E. Heckerman, Abe Mamdani, and Michael P. Wellman. Real-world applications of bayesian networks. Communications of the ACM, 38(3), March 1995. Special Issue on Uncertainty in AI.

                IEE94
                Computer Society IEEE. IEEE guide for the use of IEEE standard dictionary of measures to produce reliable software. In IEEE Standards Collection Software Engineering. IEEE Computer Society Press, New York, New York, 1994.

                Lit79
                Bev Littlewood. How to measure software reliability and how not to. IEEE Transactions on Reliability, R-28(2):103-110, June 1979.

                LS93
                Bev Littlewood and Lorenzo Strigini. Validation of ultrahigh dependability for software-based systems. Communications of the ACM, 36(11):69-80, November 1993.

                Nea90
                Richard E. Neapolitan. Probabilistic reasoning in expert systems: theory and algorithms. Wiley, New York, New York, 1990.

                Par79
                David Lorge Parnas. Designing software for ease of extension and contraction. IEEE Transactions on Software Engineering, SE-5(2):128-138, March 1979.

                Pea88
                Judea Pearl. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann Publishers, San Mateo, CA, 1988.

                Ste95
                Mark Stefik. Introduction to Knowledge Systems. Morgan Kaufmann, San Francisco, CA, 1995.

                SZ92
                Dani Steinberg and Hadar Ziv. Software Visualization and Yosemite National Park. In Proceedings of the Twenty-Fifth Annual Hawaii International Conference on System Sciences, January 1992.

                ZO95
                Hadar Ziv and Leon J. Osterweil. Research issues in the intersection of hypertext and software development environments. In Richard N. Taylor and Joëlle Coutaz, editors, Software Engineering and Human-Computer Interaction, volume 896 of Lecture Notes in Computer Science, pages 268-279. Springer-Verlag, Berlin Heidelberg, 1995.

                ZRK96
                Hadar Ziv, Debra J. Richardson, and René Klösch. The uncertainty principle in software engineering. Technical Report UCI-TR-96-33, University of California, Irvine, August 1996.



                Hadar Ziv
                Fri Jun 20 16:22:31 PDT 1997
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                Software Uncertainty Modeling

                 

                Software is a large and complex artifact, produced primarily by humans for humans. Software development and operational use are therefore dominated by human action, human judgment and decision making, and inevitably human error. The outcome is, therefore, often uncertain and unpredictable and, along with uncertainty in natural phenomena, leads to unavoidable software uncertainties. Most if not all software failures are experienced by or otherwise affect human users. Similarly, most software defects can be traced to human origins, be it an error introduced directly by a developer or a defect that is only indirectly human-related. An IEEE software engineering standard, for example, identifies two key sources of software uncertainty as ``poor knowledge or understanding of the problem domain'' and ``insufficient, untimely orientation to user needs and to overall quality'' [IEE94].

                The observation that ``software engineering is fraught with uncertainty'' is often used to explain the known symptoms of the software crisis, including high costs and unexpected slips in development schedule as well as unpredictable failures of operational software. The abundance of uncertainty also justifies extensive measures taken by researchers and practitioners attempting to identify and alleviate software risks and uncertainties.

                While software risk management has long been an active field of research producing significant contributions (cf. [Boe89, Boe91]), the subject of software uncertainties has largely been overlooked, with the notable exception of software reliability models [Lit79] and models for validation of software dependability [LS93]. This omission is particularly surprising in light of extensive research in modeling and management of uncertainty carried out in Artificial Intelligence (AI) domains (cf. [Pea88, Nea90, HMW95]).

                Thus, despite the pervasiveness of human involvement and related software uncertainties, surprisingly few attempts have been made to model those uncertainties explicitly. This dissertation aims to remedy this situation, initially by presenting the Maxim of Uncertainty in Software Engineering (MUSE) and as a corollary, urging that software uncertainties be modeled directly, using an appropriate uncertainty modeling technique.

                Ideally, an uncertainty modeling technique should include probabilistic notions of uncertainty and confidence, provide for multiple sources of evidence and influence, and support dynamic updating of uncertainty values during software development. In this dissertation, we claim that Bayesian Belief Networks meet these desiderata and are therefore suitable for software uncertainty modeling.

                There are many perceived benefits of modeling software uncertainty, including, among others:

                1. Uncertainty permeates software development, yet is generally absent in most software models. A model providing explicit representation of software uncertainties would therefore be more accurate (and possibly more useful) than one where uncertainties are disregarded.
                2. The search for uncertainty plays a pivotal role in an overall software risk management strategy. Following Gemmer [Gem97], ``It is impossible to manage risk without ferreting out elements of uncertainty. Uncertainty is present in all decisions we face.'' A key risk minimization activity is to ``systematically search for uncertainty wherever it may be.''
                3. Finally, software uncertainty modeling may help guide and ultimately improve software process decisions. Based on changes and fluctuations in software uncertainty levels, for example, a project manager may decide to ``conclude testing,'' ``instigate regression testing,'' or else ``shuffle testing priorities.''

                We generally believe that software uncertainty modeling holds promise for improved human understanding and decision making in a wide range of development activities. In this dissertation, however, we focus on specific benefits of uncertainty modeling for specific development situations. To this end, we describe several premises and propose a specific research hypothesis to be validated against a real software project. The premises and hypothesis for this dissertation are described next.


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                Introduction

                 



                • Software Uncertainty Modeling
                • Premises and Hypothesis
                • Dissertation Contributions
                • Dissertation Organization


                Hadar Ziv
                Fri Jun 20 16:22:31 PDT 1997
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                Dissertation Contributions

                We consider the key contributions of this dissertation to be:

                1. Defining succinctly the Maxim of Uncertainty in Software Engineering, identifying the abundance of uncertainties and their relevance to software process decisions and project risk management. Also, as corollary, we suggest strongly that the software life cycle include search, identification, modeling and management of software uncertainties.
                2. Defining an approach to explicitly model uncertainty in relevant software situations. This approach is anchored in a Bayesian interpretation of relationships and dependencies among software artifacts. It is suggested and demonstrated that Bayesian belief networks, originally described by Pearl [Pea88], may be used for this purpose.
                3. Implementing a prototype Java applet that allows software systems to be defined as graphs of related artifacts as well as be annotated with uncertainty information. These software belief networks can then be subject to Bayesian updating.
                4. Conducting a case study to substantiate the maxim above and to evaluate the applicability of Bayesian belief networks to a real software project. To this end, we selected as case study the CEquencer system described earlier. Though confined, our case study indicates that explicit modeling of software uncertainties improves developers' ability to identify and track changes to levels of confidence in software artifacts and relations.


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                Fri Jun 20 16:22:31 PDT 1997
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                http://www.ics.uci.edu/~ziv/ooad/classes/sld017.htm Reminder: Binary Tree Client Code

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld006.htm Behavior

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld015.htm Abstraction and Encapsulation

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld008.htm Classes and Objects

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld001.htm Classes and Objects Lecture 3

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                Notes:

                1

                http://www.ics.uci.edu/~ziv/ooad/classes/sld019.htm Chapter Summary

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld003.htm What is an Object (2)

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld013.htm Example UML Classes

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld009.htm Examples of Classes

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld002.htm What is an Object (1)

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld012.htm The Class Diagram

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld014.htm Example UML Classes

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld005.htm Definition of State

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld018.htm Class Implementation

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                http://www.ics.uci.edu/~ziv/ooad/classes/sld010.htm Attributes

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                http://www.ics.uci.edu/~gbowker/pubframe.htm

                Infrastructural Musings


                 

                 

                 

                 

                 

                 

                 

                 

                 

                 

                 

                 

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                http://www.ics.uci.edu/~gbowker/content.htm

                Geof Bowker's Papers


                This is an occasional collection of papers (viz updated occasionally).


                How things actor-network

                Lest we remember

                Of lungs and lungers

                Multiple bodies of the record

                Things Come Together - Information Convergence

                Some recent literature of cyberspace

                Building the next generation Biological Information Infrastructure

                Sorting Things Out: Classification and Its Consequences

                Biodiversity datadiversity

                Mapping Biodiversity

                Theoretical Issues in the Design of Collaboratories

                On new infrastructures for distributed knowledge

                Pure, Real and Rational Numbers: The American Imaginary of Countability

                The Knowledge Economy

                Instrumentalizing the Truth of Practice

                Keeping Knowledge Local

                Time, Money and Biodiversity

                An International Framework to Promote Access to Data

                The Past and the Internet

                Promoting Access to Data

                A Learning Trajectory for Ontology Building

                Information Ecology: Open System Environment for Data, Memories and Knowing

                That Elusive Object of Desire: a science studies history of science studies

                Metadata , trajectoires et « énaction » (avec Florence Millerand)

                Metadata: Trajectories and Enactment in the Life of an Ontology (with Florence Millerand)

                Future Directions in ICT Research

                Living in the Human World

                Fog of Data (webcast)

                Information Infrastructures for Distributed Collective Practices (with Bill Turner, Les Gasser and Manuel Zacklad) (note - requires institutional access to CSCW - email me if this is a problem)

                A Plea for Pleats

                Infrastructure and its Orphans (video of talk with Susan Leigh Star)

                Toward Cyberinfrstructure Studies (NSF Report)

                Interview with Geof Bowker and Susan Leigh Star (Italian)

                Extending African Knowledge Infrastructures (with Steve Jackson, Paul Edwards, Archer Bacheller, Steve Cisler and Leigh Star)

                All knowledge is local (talk)

                Feminist science and technology studies: A patchwork of moving subjectivities (interview with Geoffrey Bowker, Sandra Harding, Anne Marie Mol, Susan Leigh Star and Banu Subramaniam)

                Between meaning and machine: Learning to represent
                the knowledge of communities

                All Knowledge Is Local

                My home page

                http://www.ics.uci.edu/~gbowker/record_fn.html 1 . Many studies of the doctor-patient contact, for example, focus on the way the patient's symptoms and medical problem are constituted in the doctor-pa tient interaction . In his recent work, Paul Atkinson has broadened this approach to include, for example, discussions between specialists (1995). Here also, however, the emphasis is on the constitution of a patient's medical situation through talk .

                2. Many have taken similar departures before us: see e.g. Deleuze and Guattari (1988), Haraway (1991) and Mol (1993).

                3. Hodgkin's Disease is a form of cancer of the lymphoid tissues. We start out with a thought experiment since we want to convey the sheer impossibility of clinical work without the record. Our fieldnotes have several instances where the record was (temporarily) not available, and then the unwillingness to undertake any potential consequential action is always prominent. For stylistic reasons, however, we preferred introducing Mr. Wood immediately, so as not to introduce confusion by presenting other patients.

                4. Although they do not focus specifically on the medical record, the following classical studies provide valuabl e insights on the role of time in medical work: Roth (1979/1963); Richman and Goldthorp (1977); Zerubavel (1979); Atkinson (1981); Horobin and McIntosh (1983); Frankenberg (1992).

                5. For the notion of different, co-existing zones or orders of time see e.g. Kavanagh (1995), Frankenberg (1992), and Zerubavel (1979).

                6. This refers to the bone marrow treatment: the bone marrow cells are reinfused after high doses of chemotherapeutics have done their toxic yet hopefully curative work.

                7 . In a study performed in Sweden it was shown that in 1975, coders disagreed with doctors' attributions of rheumatoid arthritis 38% of the time (Fagot-Largeault, p. 228).

                8 . In Rewriting the Soul, Ian Hacking (1995, pp. 234-257) discusses the problem of usi ng current categories - such as child abuse - to describe past actions - such as Lewis Carrol's collections of pictures of children. When the category did not exist in social discourse, he argues, there is a real sense in which does not apply. Action, he argues, is always "action under a description". By this he means that whenever we describe a past action, we only have access to it through a set of constructed categories (a description). Thus when I seek to describe the behavior of someone I consider a "bully", the latter category only fits the action within the context of my middle class latter twentieth century definition of bullying - a nineteenth century English patriarch might have seen someone with a "bit of backbone" (a description not available today...). Hacking's concern is to demonstrate that where there was in the past no category of child abuse there was no abuse (even though, he is quick to point out, there might have been bad action...) (ibid, pp. 55-68). However his argument becomes much stronger when it is applied directly to social organization mediated by record keeping. Where there is no logging of the action of child abuse in a record within an organizational discourse, there is no child abuse. Recording something as having been so makes it so - particularly when the record is itself the only trace of the past action.

                9. These three dimensions are not to be taken as exhaustive or even as necessarily central: they are dimensions in which the record's role is spelled out particular ly clearly.

                10. We wish to thank Bruno Latour for pointing out the the analytical significance of the centrality of this legal aspect of record-keeping (personal communication).

                11. One could say that a specific nursing record does exist - but that record is hardly ever used for research, administrative and/or financial purposes.

                12 . See also Goody (1977) and Latour (1987) on the power of writing and written traces.

                13. As opposed to for example an ordering by means of a list of the patient's problems, as in Weed's problem oriented record (1971).

                14. With regards to the electronic medical record, it is tantalizing to assert a connection between the databases drawn upon and the work organization. There have been three major epochs in the development of databases: the hierarchical, the relational and the object-oriented (Khoshafien, 1993). All three are still in use today - in medical and other databases. The hierarchical database echoes the hierarchical organizational structure most favoured in the 1960s; the relational database echoes the more team model of the 1970s; and object orientation is the nec plus altra of radical outsourcing. Organizational theorists have certainly drawn these connections: arguing a one-to-one relationship between database structure and organization; with mismatches leading to problems. Thus it has been argued that relational databases are sometimes problematic since `they have a flat, two dimensional view of the world' (Stead et al. 1992). Although the co mplex mediations we describe here are unlikely to end up in any rigid repetition, the way a particular type of story gets told, enforced and mediated through different nodes in the network is an important issue for further research.

                15 . See Abbott (19 90) for domain knowledge as a professionalizing tool; see Bowker (forthcoming) on nursing records. http://www.ics.uci.edu/~gbowker/actnet_fn.html [1 ]As Holmes explained to Watson when he uncovered the chain of deductions (each link so simple) that allowed him to produce a thrust of `magical' insight

                2 Proust, (1989: 1301, fn.1 to p.580).

                3 We are referring to Latour, 1993 and 1996 in seeing knowledge production and political work as twin outcomes of a single set of processes.

                4 We are borrowing this phrase from Hacking, 1995. http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm Fielding Dissertation: CHAPTER 5: Representational State Transfer (REST)

                [Top] [Prev] [Next]


                CHAPTER 5

                Representational State Transfer (REST)

                This chapter introduces and elaborates the Representational State Transfer (REST) architectural style for distributed hypermedia systems, describing the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, while contrasting them to the constraints of other architectural styles. REST is a hybrid style derived from several of the network-based architectural styles described in Chapter 3 and combined with additional constraints that define a uniform connector interface. The software architecture framework of Chapter 1 is used to define the architectural elements of REST and examine sample process, connector, and data views of prototypical architectures.

                5.1 Deriving REST

                The design rationale behind the Web architecture can be described by an architectural style consisting of the set of constraints applied to elements within the architecture. By examining the impact of each constraint as it is added to the evolving style, we can identify the properties induced by the Web's constraints. Additional constraints can then be applied to form a new architectural style that better reflects the desired properties of a modern Web architecture. This section provides a general overview of REST by walking through the process of deriving it as an architectural style. Later sections will describe in more detail the specific constraints that compose the REST style.

                5.1.1 Starting with the Null Style

                There are two common perspectives on the process of architectural design, whether it be for buildings or for software. The first is that a designer starts with nothing--a blank slate, whiteboard, or drawing board--and builds-up an architecture from familiar components until it satisfies the needs of the intended system. The second is that a designer starts with the system needs as a whole, without constraints, and then incrementally identifies and applies constraints to elements of the system in order to differentiate the design space and allow the forces that influence system behavior to flow naturally, in harmony with the system. Where the first emphasizes creativity and unbounded vision, the second emphasizes restraint and understanding of the system context. REST has been developed using the latter process. Figures 5-1 through 5-8 depict this graphically in terms of how the applied constraints would differentiate the process view of an architecture as the incremental set of constraints is applied.

                The Null style (Figure 5-1) is simply an empty set of constraints. From an architectural perspective, the null style describes a system in which there are no distinguished boundaries between components. It is the starting point for our description of REST.

                Figure 5-1: The null style

                5.1.2 Client-Server

                The first constraints added to our hybrid style are those of the client-server architectural style (Figure 5-2), described in Section 3.4.1. Separation of concerns is the principle behind the client-server constraints. By separating the user interface concerns from the data storage concerns, we improve the portability of the user interface across multiple platforms and improve scalability by simplifying the server components. Perhaps most significant to the Web, however, is that the separation allows the components to evolve independently, thus supporting the Internet-scale requirement of multiple organizational domains.

                Figure 5-2: The client-server style

                5.1.3 Stateless

                We next add a constraint to the client-server interaction: communication must be stateless in nature, as in the client-stateless-server (CSS) style of Section 3.4.3 (Figure 5-3), such that each request from client to server must contain all of the information necessary to understand the request, and cannot take advantage of any stored context on the server. Session state is therefore kept entirely on the client.

                Figure 5-3: The client-stateless-server style

                This constraint induces the properties of visibility, reliability, and scalability. Visibility is improved because a monitoring system does not have to look beyond a single request datum in order to determine the full nature of the request. Reliability is improved because it eases the task of recovering from partial failures [133]. Scalability is improved because not having to store state between requests allows the server component to quickly free resources, and further simplifies implementation because the server doesn't have to manage resource usage across requests.

                Like most architectural choices, the stateless constraint reflects a design trade-off. The disadvantage is that it may decrease network performance by increasing the repetitive data (per-interaction overhead) sent in a series of requests, since that data cannot be left on the server in a shared context. In addition, placing the application state on the client-side reduces the server's control over consistent application behavior, since the application becomes dependent on the correct implementation of semantics across multiple client versions.

                5.1.4 Cache

                In order to improve network efficiency, we add cache constraints to form the client-cache-stateless-server style of Section 3.4.4 (Figure 5-4). Cache constraints require that the data within a response to a request be implicitly or explicitly labeled as cacheable or non-cacheable. If a response is cacheable, then a client cache is given the right to reuse that response data for later, equivalent requests.

                Figure 5-4: The client-cache-stateless-server style

                The advantage of adding cache constraints is that they have the potential to partially or completely eliminate some interactions, improving efficiency, scalability, and user-perceived performance by reducing the average latency of a series of interactions. The trade-off, however, is that a cache can decrease reliability if stale data within the cache differs significantly from the data that would have been obtained had the request been sent directly to the server.

                The early Web architecture, as portrayed by the diagram in Figure 5-5 [11], was defined by the client-cache-stateless-server set of constraints. That is, the design rationale presented for the Web architecture prior to 1994 focused on stateless client-server interaction for the exchange of static documents over the Internet. The protocols for communicating interactions had rudimentary support for non-shared caches, but did not constrain the interface to a consistent set of semantics for all resources. Instead, the Web relied on the use of a common client-server implementation library (CERN libwww) to maintain consistency across Web applications.

                Figure 5-5: Early WWW architecture diagram

                Developers of Web implementations had already exceeded the early design. In addition to static documents, requests could identify services that dynamically generated responses, such as image-maps [Kevin Hughes] and server-side scripts [Rob McCool]. Work had also begun on intermediary components, in the form of proxies [79] and shared caches [59], but extensions to the protocols were needed in order for them to communicate reliably. The following sections describe the constraints added to the Web's architectural style in order to guide the extensions that form the modern Web architecture.

                5.1.5 Uniform Interface

                The central feature that distinguishes the REST architectural style from other network-based styles is its emphasis on a uniform interface between components (Figure 5-6). By applying the software engineering principle of generality to the component interface, the overall system architecture is simplified and the visibility of interactions is improved. Implementations are decoupled from the services they provide, which encourages independent evolvability. The trade-off, though, is that a uniform interface degrades efficiency, since information is transferred in a standardized form rather than one which is specific to an application's needs. The REST interface is designed to be efficient for large-grain hypermedia data transfer, optimizing for the common case of the Web, but resulting in an interface that is not optimal for other forms of architectural interaction.

                Figure 5-6: The uniform-client-cache-stateless-server style

                In order to obtain a uniform interface, multiple architectural constraints are needed to guide the behavior of components. REST is defined by four interface constraints: identification of resources; manipulation of resources through representations; self-descriptive messages; and, hypermedia as the engine of application state. These constraints will be discussed in Section 5.2.

                5.1.6 Layered System

                In order to further improve behavior for Internet-scale requirements, we add layered system constraints (Figure 5-7). As described in Section 3.4.2, the layered system style allows an architecture to be composed of hierarchical layers by constraining component behavior such that each component cannot "see" beyond the immediate layer with which they are interacting. By restricting knowledge of the system to a single layer, we place a bound on the overall system complexity and promote substrate independence. Layers can be used to encapsulate legacy services and to protect new services from legacy clients, simplifying components by moving infrequently used functionality to a shared intermediary. Intermediaries can also be used to improve system scalability by enabling load balancing of services across multiple networks and processors.

                Figure 5-6: The uniform-layered-client-cache-stateless-server style

                The primary disadvantage of layered systems is that they add overhead and latency to the processing of data, reducing user-perceived performance [32]. For a network-based system that supports cache constraints, this can be offset by the benefits of shared caching at intermediaries. Placing shared caches at the boundaries of an organizational domain can result in significant performance benefits [136]. Such layers also allow security policies to be enforced on data crossing the organizational boundary, as is required by firewalls [79].

                The combination of layered system and uniform interface constraints induces architectural properties similar to those of the uniform pipe-and-filter style (Section 3.2.2). Although REST interaction is two-way, the large-grain data flows of hypermedia interaction can each be processed like a data-flow network, with filter components selectively applied to the data stream in order to transform the content as it passes [26]. Within REST, intermediary components can actively transform the content of messages because the messages are self-descriptive and their semantics are visible to intermediaries.

                5.1.7 Code-On-Demand

                The final addition to our constraint set for REST comes from the code-on-demand style of Section 3.5.3 (Figure 5-8). REST allows client functionality to be extended by downloading and executing code in the form of applets or scripts. This simplifies clients by reducing the number of features required to be pre-implemented. Allowing features to be downloaded after deployment improves system extensibility. However, it also reduces visibility, and thus is only an optional constraint within REST.

                Figure 5-8: The REST style

                The notion of an optional constraint may seem like an oxymoron. However, it does have a purpose in the architectural design of a system that encompasses multiple organizational boundaries. It means that the architecture only gains the benefit (and suffers the disadvantages) of the optional constraints when they are known to be in effect for some realm of the overall system. For example, if all of the client software within an organization is known to support Java applets [45], then services within that organization can be constructed such that they gain the benefit of enhanced functionality via downloadable Java classes. At the same time, however, the organization's firewall may prevent the transfer of Java applets from external sources, and thus to the rest of the Web it will appear as if those clients do not support code-on-demand. An optional constraint allows us to design an architecture that supports the desired behavior in the general case, but with the understanding that it may be disabled within some contexts.

                5.1.8 Style Derivation Summary

                REST consists of a set of architectural constraints chosen for the properties they induce on candidate architectures. Although each of these constraints can be considered in isolation, describing them in terms of their derivation from common architectural styles makes it easier to understand the rationale behind their selection. Figure 5-9 depicts the derivation of REST's constraints graphically in terms of the network-based architectural styles examined in Chapter 3.

                Figure 5-9: REST derivation by style constraints

                5.2 REST Architectural Elements

                The Representational State Transfer (REST) style is an abstraction of the architectural elements within a distributed hypermedia system. REST ignores the details of component implementation and protocol syntax in order to focus on the roles of components, the constraints upon their interaction with other components, and their interpretation of significant data elements. It encompasses the fundamental constraints upon components, connectors, and data that define the basis of the Web architecture, and thus the essence of its behavior as a network-based application.

                5.2.1 Data Elements

                Unlike the distributed object style [31], where all data is encapsulated within and hidden by the processing components, the nature and state of an architecture's data elements is a key aspect of REST. The rationale for this design can be seen in the nature of distributed hypermedia. When a link is selected, information needs to be moved from the location where it is stored to the location where it will be used by, in most cases, a human reader. This is unlike many other distributed processing paradigms [6, 50], where it is possible, and usually more efficient, to move the "processing agent" (e.g., mobile code, stored procedure, search expression, etc.) to the data rather than move the data to the processor.

                A distributed hypermedia architect has only three fundamental options: 1) render the data where it is located and send a fixed-format image to the recipient; 2) encapsulate the data with a rendering engine and send both to the recipient; or, 3) send the raw data to the recipient along with metadata that describes the data type, so that the recipient can choose their own rendering engine.

                Each option has its advantages and disadvantages. Option 1, the traditional client-server style [31], allows all information about the true nature of the data to remain hidden within the sender, preventing assumptions from being made about the data structure and making client implementation easier. However, it also severely restricts the functionality of the recipient and places most of the processing load on the sender, leading to scalability problems. Option 2, the mobile object style [50], provides information hiding while enabling specialized processing of the data via its unique rendering engine, but limits the functionality of the recipient to what is anticipated within that engine and may vastly increase the amount of data transferred. Option 3 allows the sender to remain simple and scalable while minimizing the bytes transferred, but loses the advantages of information hiding and requires that both sender and recipient understand the same data types.

                REST provides a hybrid of all three options by focusing on a shared understanding of data types with metadata, but limiting the scope of what is revealed to a standardized interface. REST components communicate by transferring a representation of a resource in a format matching one of an evolving set of standard data types, selected dynamically based on the capabilities or desires of the recipient and the nature of the resource. Whether the representation is in the same format as the raw source, or is derived from the source, remains hidden behind the interface. The benefits of the mobile object style are approximated by sending a representation that consists of instructions in the standard data format of an encapsulated rendering engine (e.g., Java [45]). REST therefore gains the separation of concerns of the client-server style without the server scalability problem, allows information hiding through a generic interface to enable encapsulation and evolution of services, and provides for a diverse set of functionality through downloadable feature-engines.

                REST's data elements are summarized in Table 5-1.

                Table 5-1: REST Data Elements
                Data Element Modern Web Examples
                resource the intended conceptual target of a hypertext reference
                resource identifier URL, URN
                representation HTML document, JPEG image
                representation metadata media type, last-modified time
                resource metadata source link, alternates, vary
                control data if-modified-since, cache-control

                5.2.1.1 Resources and Resource Identifiers

                The key abstraction of information in REST is a resource. Any information that can be named can be a resource: a document or image, a temporal service (e.g. "today's weather in Los Angeles"), a collection of other resources, a non-virtual object (e.g. a person), and so on. In other words, any concept that might be the target of an author's hypertext reference must fit within the definition of a resource. A resource is a conceptual mapping to a set of entities, not the entity that corresponds to the mapping at any particular point in time.

                More precisely, a resource R is a temporally varying membership function MR(t), which for time t maps to a set of entities, or values, which are equivalent. The values in the set may be resource representations and/or resource identifiers. A resource can map to the empty set, which allows references to be made to a concept before any realization of that concept exists -- a notion that was foreign to most hypertext systems prior to the Web [61]. Some resources are static in the sense that, when examined at any time after their creation, they always correspond to the same value set. Others have a high degree of variance in their value over time. The only thing that is required to be static for a resource is the semantics of the mapping, since the semantics is what distinguishes one resource from another.

                For example, the "authors' preferred version" of an academic paper is a mapping whose value changes over time, whereas a mapping to "the paper published in the proceedings of conference X" is static. These are two distinct resources, even if they both map to the same value at some point in time. The distinction is necessary so that both resources can be identified and referenced independently. A similar example from software engineering is the separate identification of a version-controlled source code file when referring to the "latest revision", "revision number 1.2.7", or "revision included with the Orange release."

                This abstract definition of a resource enables key features of the Web architecture. First, it provides generality by encompassing many sources of information without artificially distinguishing them by type or implementation. Second, it allows late binding of the reference to a representation, enabling content negotiation to take place based on characteristics of the request. Finally, it allows an author to reference the concept rather than some singular representation of that concept, thus removing the need to change all existing links whenever the representation changes (assuming the author used the right identifier).

                REST uses a resource identifier to identify the particular resource involved in an interaction between components. REST connectors provide a generic interface for accessing and manipulating the value set of a resource, regardless of how the membership function is defined or the type of software that is handling the request. The naming authority that assigned the resource identifier, making it possible to reference the resource, is responsible for maintaining the semantic validity of the mapping over time (i.e., ensuring that the membership function does not change).

                Traditional hypertext systems [61], which typically operate in a closed or local environment, use unique node or document identifiers that change every time the information changes, relying on link servers to maintain references separately from the content [135]. Since centralized link servers are an anathema to the immense scale and multi-organizational domain requirements of the Web, REST relies instead on the author choosing a resource identifier that best fits the nature of the concept being identified. Naturally, the quality of an identifier is often proportional to the amount of money spent to retain its validity, which leads to broken links as ephemeral (or poorly supported) information moves or disappears over time.

                5.2.1.2 Representations

                REST components perform actions on a resource by using a representation to capture the current or intended state of that resource and transferring that representation between components. A representation is a sequence of bytes, plus representation metadata to describe those bytes. Other commonly used but less precise names for a representation include: document, file, and HTTP message entity, instance, or variant.

                A representation consists of data, metadata describing the data, and, on occasion, metadata to describe the metadata (usually for the purpose of verifying message integrity). Metadata is in the form of name-value pairs, where the name corresponds to a standard that defines the value's structure and semantics. Response messages may include both representation metadata and resource metadata: information about the resource that is not specific to the supplied representation.

                Control data defines the purpose of a message between components, such as the action being requested or the meaning of a response. It is also used to parameterize requests and override the default behavior of some connecting elements. For example, cache behavior can be modified by control data included in the request or response message.

                Depending on the message control data, a given representation may indicate the current state of the requested resource, the desired state for the requested resource, or the value of some other resource, such as a representation of the input data within a client's query form, or a representation of some error condition for a response. For example, remote authoring of a resource requires that the author send a representation to the server, thus establishing a value for that resource that can be retrieved by later requests. If the value set of a resource at a given time consists of multiple representations, content negotiation may be used to select the best representation for inclusion in a given message.

                The data format of a representation is known as a media type [48]. A representation can be included in a message and processed by the recipient according to the control data of the message and the nature of the media type. Some media types are intended for automated processing, some are intended to be rendered for viewing by a user, and a few are capable of both. Composite media types can be used to enclose multiple representations in a single message.

                The design of a media type can directly impact the user-perceived performance of a distributed hypermedia system. Any data that must be received before the recipient can begin rendering the representation adds to the latency of an interaction. A data format that places the most important rendering information up front, such that the initial information can be incrementally rendered while the rest of the information is being received, results in much better user-perceived performance than a data format that must be entirely received before rendering can begin.

                For example, a Web browser that can incrementally render a large HTML document while it is being received provides significantly better user-perceived performance than one that waits until the entire document is completely received prior to rendering, even though the network performance is the same. Note that the rendering ability of a representation can also be impacted by the choice of content. If the dimensions of dynamically-sized tables and embedded objects must be determined before they can be rendered, their occurrence within the viewing area of a hypermedia page will increase its latency.

                5.2.2 Connectors

                REST uses various connector types, summarized in Table 5-2, to encapsulate the activities of accessing resources and transferring resource representations. The connectors present an abstract interface for component communication, enhancing simplicity by providing a clean separation of concerns and hiding the underlying implementation of resources and communication mechanisms. The generality of the interface also enables substitutability: if the users' only access to the system is via an abstract interface, the implementation can be replaced without impacting the users. Since a connector manages network communication for a component, information can be shared across multiple interactions in order to improve efficiency and responsiveness.

                Table 5-2: REST Connectors
                Connector Modern Web Examples
                client libwww, libwww-perl
                server libwww, Apache API, NSAPI
                cache browser cache, Akamai cache network
                resolver bind (DNS lookup library)
                tunnel SOCKS, SSL after HTTP CONNECT

                All REST interactions are stateless. That is, each request contains all of the information necessary for a connector to understand the request, independent of any requests that may have preceded it. This restriction accomplishes four functions: 1) it removes any need for the connectors to retain application state between requests, thus reducing consumption of physical resources and improving scalability; 2) it allows interactions to be processed in parallel without requiring that the processing mechanism understand the interaction semantics; 3) it allows an intermediary to view and understand a request in isolation, which may be necessary when services are dynamically rearranged; and, 4) it forces all of the information that might factor into the reusability of a cached response to be present in each request.

                The connector interface is similar to procedural invocation, but with important differences in the passing of parameters and results. The in-parameters consist of request control data, a resource identifier indicating the target of the request, and an optional representation. The out-parameters consist of response control data, optional resource metadata, and an optional representation. From an abstract viewpoint the invocation is synchronous, but both in and out-parameters can be passed as data streams. In other words, processing can be invoked before the value of the parameters is completely known, thus avoiding the latency of batch processing large data transfers.

                The primary connector types are client and server. The essential difference between the two is that a client initiates communication by making a request, whereas a server listens for connections and responds to requests in order to supply access to its services. A component may include both client and server connectors.

                A third connector type, the cache connector, can be located on the interface to a client or server connector in order to save cacheable responses to current interactions so that they can be reused for later requested interactions. A cache may be used by a client to avoid repetition of network communication, or by a server to avoid repeating the process of generating a response, with both cases serving to reduce interaction latency. A cache is typically implemented within the address space of the connector that uses it.

                Some cache connectors are shared, meaning that its cached responses may be used in answer to a client other than the one for which the response was originally obtained. Shared caching can be effective at reducing the impact of "flash crowds" on the load of a popular server, particularly when the caching is arranged hierarchically to cover large groups of users, such as those within a company's intranet, the customers of an Internet service provider, or Universities sharing a national network backbone. However, shared caching can also lead to errors if the cached response does not match what would have been obtained by a new request. REST attempts to balance the desire for transparency in cache behavior with the desire for efficient use of the network, rather than assuming that absolute transparency is always required.

                A cache is able to determine the cacheability of a response because the interface is generic rather than specific to each resource. By default, the response to a retrieval request is cacheable and the responses to other requests are non-cacheable. If some form of user authentication is part of the request, or if the response indicates that it should not be shared, then the response is only cacheable by a non-shared cache. A component can override these defaults by including control data that marks the interaction as cacheable, non-cacheable or cacheable for only a limited time.

                A resolver translates partial or complete resource identifiers into the network address information needed to establish an inter-component connection. For example, most URI include a DNS hostname as the mechanism for identifying the naming authority for the resource. In order to initiate a request, a Web browser will extract the hostname from the URI and make use of a DNS resolver to obtain the Internet Protocol address for that authority. Another example is that some identification schemes (e.g., URN [124]) require an intermediary to translate a permanent identifier to a more transient address in order to access the identified resource. Use of one or more intermediate resolvers can improve the longevity of resource references through indirection, though doing so adds to the request latency.

                The final form of connector type is a tunnel, which simply relays communication across a connection boundary, such as a firewall or lower-level network gateway. The only reason it is modeled as part of REST and not abstracted away as part of the network infrastructure is that some REST components may dynamically switch from active component behavior to that of a tunnel. The primary example is an HTTP proxy that switches to a tunnel in response to a CONNECT method request [71], thus allowing its client to directly communicate with a remote server using a different protocol, such as TLS, that doesn't allow proxies. The tunnel disappears when both ends terminate their communication.

                5.2.3 Components

                REST components, summarized in Table 5-3, are typed by their roles in an overall application action.

                Table 5-3: REST Components
                Component Modern Web Examples
                origin server Apache httpd, Microsoft IIS
                gateway Squid, CGI, Reverse Proxy
                proxy CERN Proxy, Netscape Proxy, Gauntlet
                user agent Netscape Navigator, Lynx, MOMspider

                A user agent uses a client connector to initiate a request and becomes the ultimate recipient of the response. The most common example is a Web browser, which provides access to information services and renders service responses according to the application needs.

                An origin server uses a server connector to govern the namespace for a requested resource. It is the definitive source for representations of its resources and must be the ultimate recipient of any request that intends to modify the value of its resources. Each origin server provides a generic interface to its services as a resource hierarchy. The resource implementation details are hidden behind the interface.

                Intermediary components act as both a client and a server in order to forward, with possible translation, requests and responses. A proxy component is an intermediary selected by a client to provide interface encapsulation of other services, data translation, performance enhancement, or security protection. A gateway (a.k.a., reverse proxy) component is an intermediary imposed by the network or origin server to provide an interface encapsulation of other services, for data translation, performance enhancement, or security enforcement. Note that the difference between a proxy and a gateway is that a client determines when it will use a proxy.

                5.3 REST Architectural Views

                Now that we have an understanding of the REST architectural elements in isolation, we can use architectural views [105] to describe how the elements work together to form an architecture. Three types of view--process, connector, and data--are useful for illuminating the design principles of REST.

                5.3.1 Process View

                A process view of an architecture is primarily effective at eliciting the interaction relationships among components by revealing the path of data as it flows through the system. Unfortunately, the interaction of a real system usually involves an extensive number of components, resulting in an overall view that is obscured by the details. Figure 5-10 provides a sample of the process view from a REST-based architecture at a particular instance during the processing of three parallel requests.

                Figure 5-10: Process view of a REST-based Architecture

                REST's client-server separation of concerns simplifies component implementation, reduces the complexity of connector semantics, improves the effectiveness of performance tuning, and increases the scalability of pure server components. Layered system constraints allow intermediaries--proxies, gateways, and firewalls--to be introduced at various points in the communication without changing the interfaces between components, thus allowing them to assist in communication translation or improve performance via large-scale, shared caching. REST enables intermediate processing by constraining messages to be self-descriptive: interaction is stateless between requests, standard methods and media types are used to indicate semantics and exchange information, and responses explicitly indicate cacheability.

                Since the components are connected dynamically, their arrangement and function for a particular application action has characteristics similar to a pipe-and-filter style. Although REST components communicate via bidirectional streams, the processing of each direction is independent and therefore susceptible to stream transducers (filters). The generic connector interface allows components to be placed on the stream based on the properties of each request or response.

                Services may be implemented using a complex hierarchy of intermediaries and multiple distributed origin servers. The stateless nature of REST allows each interaction to be independent of the others, removing the need for an awareness of the overall component topology, an impossible task for an Internet-scale architecture, and allowing components to act as either destinations or intermediaries, determined dynamically by the target of each request. Connectors need only be aware of each other's existence during the scope of their communication, though they may cache the existence and capabilities of other components for performance reasons.

                5.3.2 Connector View

                A connector view of an architecture concentrates on the mechanics of the communication between components. For a REST-based architecture, we are particularly interested in the constraints that define the generic resource interface.

                Client connectors examine the resource identifier in order to select an appropriate communication mechanism for each request. For example, a client may be configured to connect to a specific proxy component, perhaps one acting as an annotation filter, when the identifier indicates that it is a local resource. Likewise, a client can be configured to reject requests for some subset of identifiers.

                REST does not restrict communication to a particular protocol, but it does constrain the interface between components, and hence the scope of interaction and implementation assumptions that might otherwise be made between components. For example, the Web's primary transfer protocol is HTTP, but the architecture also includes seamless access to resources that originate on pre-existing network servers, including FTP [107], Gopher [7], and WAIS [36]. Interaction with those services is restricted to the semantics of a REST connector. This constraint sacrifices some of the advantages of other architectures, such as the stateful interaction of a relevance feedback protocol like WAIS, in order to retain the advantages of a single, generic interface for connector semantics. In return, the generic interface makes it possible to access a multitude of services through a single proxy. If an application needs the additional capabilities of another architecture, it can implement and invoke those capabilities as a separate system running in parallel, similar to how the Web architecture interfaces with "telnet" and "mailto" resources.

                5.3.3 Data View

                A data view of an architecture reveals the application state as information flows through the components. Since REST is specifically targeted at distributed information systems, it views an application as a cohesive structure of information and control alternatives through which a user can perform a desired task. For example, looking-up a word in an on-line dictionary is one application, as is touring through a virtual museum, or reviewing a set of class notes to study for an exam. Each application defines goals for the underlying system, against which the system's performance can be measured.

                Component interactions occur in the form of dynamically sized messages. Small or medium-grain messages are used for control semantics, but the bulk of application work is accomplished via large-grain messages containing a complete resource representation. The most frequent form of request semantics is that of retrieving a representation of a resource (e.g., the "GET" method in HTTP), which can often be cached for later reuse.

                REST concentrates all of the control state into the representations received in response to interactions. The goal is to improve server scalability by eliminating any need for the server to maintain an awareness of the client state beyond the current request. An application's state is therefore defined by its pending requests, the topology of connected components (some of which may be filtering buffered data), the active requests on those connectors, the data flow of representations in response to those requests, and the processing of those representations as they are received by the user agent.

                An application reaches a steady-state whenever it has no outstanding requests; i.e., it has no pending requests and all of the responses to its current set of requests have been completely received or received to the point where they can be treated as a representation data stream. For a browser application, this state corresponds to a "web page," including the primary representation and ancillary representations, such as in-line images, embedded applets, and style sheets. The significance of application steady-states is seen in their impact on both user-perceived performance and the burstiness of network request traffic.

                The user-perceived performance of a browser application is determined by the latency between steady-states: the period of time between the selection of a hypermedia link on one web page and the point when usable information has been rendered for the next web page. The optimization of browser performance is therefore centered around reducing this communication latency.

                Since REST-based architectures communicate primarily through the transfer of representations of resources, latency can be impacted by both the design of the communication protocols and the design of the representation data formats. The ability to incrementally render the response data as it is received is determined by the design of the media type and the availability of layout information (visual dimensions of in-line objects) within each representation.

                An interesting observation is that the most efficient network request is one that doesn't use the network. In other words, the ability to reuse a cached response results in a considerable improvement in application performance. Although use of a cache adds some latency to each individual request due to lookup overhead, the average request latency is significantly reduced when even a small percentage of requests result in usable cache hits.

                The next control state of an application resides in the representation of the first requested resource, so obtaining that first representation is a priority. REST interaction is therefore improved by protocols that "respond first and think later." In other words, a protocol that requires multiple interactions per user action, in order to do things like negotiate feature capabilities prior to sending a content response, will be perceptively slower than a protocol that sends whatever is most likely to be optimal first and then provides a list of alternatives for the client to retrieve if the first response is unsatisfactory.

                The application state is controlled and stored by the user agent and can be composed of representations from multiple servers. In addition to freeing the server from the scalability problems of storing state, this allows the user to directly manipulate the state (e.g., a Web browser's history), anticipate changes to that state (e.g., link maps and prefetching of representations), and jump from one application to another (e.g., bookmarks and URI-entry dialogs).

                The model application is therefore an engine that moves from one state to the next by examining and choosing from among the alternative state transitions in the current set of representations. Not surprisingly, this exactly matches the user interface of a hypermedia browser. However, the style does not assume that all applications are browsers. In fact, the application details are hidden from the server by the generic connector interface, and thus a user agent could equally be an automated robot performing information retrieval for an indexing service, a personal agent looking for data that matches certain criteria, or a maintenance spider busy patrolling the information for broken references or modified content [39].

                5.4 Related Work

                Bass, et al. [9] devote a chapter on architecture for the World Wide Web, but their description only encompasses the implementation architecture within the CERN/W3C developed libwww (client and server libraries) and Jigsaw software. Although those implementations reflect many of the design constraints of REST, having been developed by people familiar with the Web's architectural design and rationale, the real WWW architecture is independent of any single implementation. The modern Web is defined by its standard interfaces and protocols, not how those interfaces and protocols are implemented in a given piece of software.

                The REST style draws from many preexisting distributed process paradigms [6, 50], communication protocols, and software fields. REST component interactions are structured in a layered client-server style, but the added constraints of the generic resource interface create the opportunity for substitutability and inspection by intermediaries. Requests and responses have the appearance of a remote invocation style, but REST messages are targeted at a conceptual resource rather than an implementation identifier.

                Several attempts have been made to model the Web architecture as a form of distributed file system (e.g., WebNFS) or as a distributed object system [83]. However, they exclude various Web resource types or implementation strategies as being "not interesting," when in fact their presence invalidates the assumptions that underlie such models. REST works well because it does not limit the implementation of resources to certain predefined models, allowing each application to choose an implementation that best matches its own needs and enabling the replacement of implementations without impacting the user.

                The interaction method of sending representations of resources to consuming components has some parallels with event-based integration (EBI) styles. The key difference is that EBI styles are push-based. The component containing the state (equivalent to an origin server in REST) issues an event whenever the state changes, whether or not any component is actually interested in or listening for such an event. In the REST style, consuming components usually pull representations. Although this is less efficient when viewed as a single client wishing to monitor a single resource, the scale of the Web makes an unregulated push model infeasible.

                The principled use of the REST style in the Web, with its clear notion of components, connectors, and representations, relates closely to the C2 architectural style [128]. The C2 style supports the development of distributed, dynamic applications by focusing on structured use of connectors to obtain substrate independence. C2 applications rely on asynchronous notification of state changes and request messages. As with other event-based schemes, C2 is nominally push-based, though a C2 architecture could operate in REST's pull style by only emitting a notification upon receipt of a request. However, the C2 style lacks the intermediary-friendly constraints of REST, such as the generic resource interface, guaranteed stateless interactions, and intrinsic support for caching.

                5.5 Summary

                This chapter introduced the Representational State Transfer (REST) architectural style for distributed hypermedia systems. REST provides a set of architectural constraints that, when applied as a whole, emphasizes scalability of component interactions, generality of interfaces, independent deployment of components, and intermediary components to reduce interaction latency, enforce security, and encapsulate legacy systems. I described the software engineering principles guiding REST and the interaction constraints chosen to retain those principles, while contrasting them to the constraints of other architectural styles.

                The next chapter presents an evaluation of the REST architecture through the experience and lessons learned from applying REST to the design, specification, and deployment of the modern Web architecture. This work included authoring the current Internet standards-track specifications of the Hypertext Transfer Protocol (HTTP/1.1) and Uniform Resource Identifiers (URI), and implementing the architecture through the libwww-perl client protocol library and Apache HTTP server.


                [Top] [Prev] [Next] © Roy Thomas Fielding, 2000. All rights reserved. [How to reference this work.]
                http://www.ics.uci.edu/~lopes/datasets/ UCI Source Code Data Sets

                UCI Source Code Data Sets

                Welcome to the UCI Source Code Data Sets

                This page is a repository of various data sets we have curated in our research in large scale analysis of source code. These data sets are available for other researchers and individuals to use. Please refer to the terms of usage that come with each data set for any restrictions in usage.

                Currently available data sets:

                • [04-22-2010] SDS_source-repo-18k is a tarball of Sourcerer Code Repository archived on 04-22-2010. It contains 18,000 java projects (~390GB).
                • [06-04-2010] Koders-log-2007 is a compressed file (in 7z format) containing a Microsoft SQL Server database backup storing a yearlong usage data of Koders.com, an Internet-scale code search engine. (~188MB).
                • [11-14-2013] sourcerer-maven-aug12 is a compressed tarball (.tar.gz) containing 2,232 projects from the Maven Central repository (~80GB).

                Questions, Issues and More Information

                Please use the issue tracker in github.

                Citation Policy

                If you publish material based on data sets obtained from this repository, then, in your acknowledgments, please note the assistance you received by using this repository. This will help others to obtain the same data sets and replicate your experiments. We suggest the following pseudo-APA reference format for referring to this repository:

                C. Lopes, S. Bajracharya, J. Ossher, P. Baldi (2010). UCI Source Code Data Sets [http://www.ics.uci.edu/~lopes/datasets]. Irvine, CA: University of California, Bren School of Information and Computer Sciences.

                Here is a BiBTeX citation as well:

                    @misc{Lopes+Bajracharya+Ossher+Baldi:2010 ,
                    author = "C. Lopes and S. Bajracharya and J. Ossher and P. Baldi",
                    year = "2010",
                    title = "{UCI} Source Code Data Sets",
                    url = "http://www.ics.uci.edu/$\sim$lopes/datasets/",
                    institution = "University of California, Irvine, 
                       Bren School of Information and Computer Sciences" }
                


                This work has been partially supported by the National Science Foundation.

                (c) the mondego group

                http://mondego.ics.uci.edu/projects/clonedetection/ Code Clone Detection

                Scaling Token-Based Code Clone Detection

                The Team
                Hitesh Sajnani, Vaibhav Saini, Cristina Lopes

                Abstract

                We propose a new token-based approach for large scale code clone detection which is based on a filtering heuristic that reduces the number of token comparisons when the two code blocks are compared. We also present a MapReduce based parallel algorithm that uses the filtering heuristic and scales to thousands of projects. The filtering heuristic is generic and can also be used in conjunction with other token-based approaches. In that context, we demonstrate how it can increase the retrieval speed and decrease the memory usage of the index-based approaches.
                In our experiments on 36 open source Java projects, we found that: (i) filtering reduces token comparisons by a factor of 10, and thus increasing the speed of clone detection by a factor of 1.5; (ii) the speed-up and scale-up of the parallel approach using filtering is near-linear on a cluster of 2-32 nodes for 150-2800 projects; and (iii) filtering decreases the memory usage of index-based approach by half and the search time by a factor of 5.​

                Replicating the Experiment

                System Requirements

                You need a machine with at least 12GB ram running ubuntu or mac-osx, with Java-1.6 or higher installed. Please note that you need system with higher ram because the experiment involves very large subject systems. However, if you are running this on smaller systems (see Tools below), there is no such constraint.

                Steps to replicate the experiment:

                1. Click here to download the distribution (dist.zip).
                2. unzip dist.zip. Navigate to dist/ using terminal
                3. execute the command ./runexp.sh 1     Please Note: this will run the experiment 1 time. To run it n times, change the command to ./runexp.sh n
                You can see the generated output in the ./output folder. Files with extension .txt will have computed clones and the files with .csv extension will have the performance analysis result.

                Tools

                In order to run the tool on any arbitrary project, please follow the steps below:

                A. Generating the input file of the project for which you want to detect clones
                1. Click here to download input generator for the code clone detector (ast.zip).
                2. Unzip ast.zip and import the project ast in your eclipse workspace.
                3. Run it as an "Eclipse Application". This should open another eclipse instance where you will import the projects for which you want to generate the input file.
                4. After importing the project in the workspace of the new eclipse instance, click on the "Sample Menu" in the top menu bar and then click on the "Sample command" to run. This should generate the output (desired input file) in the path specified by variable "outputdirPath".
                5. Please note that you will have to change the location of output directory on line 61 of SampleHandler.java.this.outputdirPath = "/Users/vaibhavsaini/Documents/codetime/repo/ast/output/"; to your desired output directory.
                6. The generated input file name will be of the format: <ProjectName>-clone-INPUT.txt. For example, if your project name is jython, then the generated input file name should be jython-clone-INPUT.txt

                B. Running the clone detection tool on the generated input file
                1. Click here to download the CloneDetector (tool.zip).
                2. Unzip tool.zip and navigate to tool/ using terminal
                3. Copy the input file generated above (<ProjectName>-clone-INPUT.txt) into input/dataset directory.
                4. Open cd.sh, and assign <ProjectName> as value to the variable arrayname (line #5). For example, If your generated input file is jython-clone-INPUT.txt, line #5 should be arrayname=(jython)
                5. Execute the command ./cd.sh

                C. Generated output
                1. The generated output will be in the ./output folder.
                2. Files with extension .txt will have the computed clones and the files with .csv extension will have the time taken to detect clones

                Data

                The table below describes all the subject systems, and the corresponding output. This data was used to calculate and report numbers in the paper. Column 1 has links to the source code of each subject system. Column 2 is the input file that is generated by running INPUTGEN tool on the subject systems. This input file is in turn used by NCCF and FCCD to compute clones. Column 4 has links to the computed clones. Since both the tools produced exactly the same output, you will see only one file per subject system. Column 4 has the final analysis results - runtime for tool, and total token comparisons done. These numbers are different for both the tools, so you will see two analysis files per project - one produced by NCCF, and another by FCCD.
                Subject System Generated Input for Tool Clones Detected Analysis
                Ant-1.9.2 ant.zip ant-clones.zip ant-analysis.zip
                Cocoon-2.2.0 cocoon.zip cocoon-clones.zip cocoon-analysis.zip
                Maven-3.0.5 maven.zip maven-clones.zip maven-analysis.zip
                Lucene-4.4.0 lucene.zip lucene-clones.zip lucene-analysis.zip
                Hadoop-Rev:1531458 hadoop.zip hadoop-clones.zip hadoop-analysis.zip
                http://www.ics.uci.edu/grad/degrees/degree_inf-ict.php concentration in informatics - track in interactive and collaborative technology @ the bren school of information and computer sciences
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                Information and Computer Science Degree

                The UCI General Catalogue is the official guide to all degree and graduation requirements; the information below is intended for general planning purposes only. Please see here for previous calendar years


                Concentration in Informatics - Track in Interactive and Collaborative Technology (INF-ICT), (M.S. and Ph.D.)

                The ICT track of the Informatics concentration focuses on studying and enhancing the relationship between the design and use of interactive systems and their applications in real-world settings.

                • CURRENT DEGREE REQUIREMENTS
                UCI enjoys an international reputation for its research on the human and social dimensions of computer system design and use. There are two principal strands to this work. The first concerns the interaction between people and computers, including novel forms of interactive experience beyond traditional desktop computing. The second concerns the role that computer systems can play in collaborative work between individuals, groups, and organizations.

                Examples of topics that ICT researchers investigate include: adoption and diffusion of collaborative technologies; software architectures for user-adaptive systems; universal access to interactive systems; design principles for information visualization; and new patterns of computer-mediated work such as virtual teams.

                The approach used places equal emphasis on three areas of investigation--empirical, theoretical, and technical. The empirical work involves studying technology in use, using both laboratory techniques and fieldwork investigations of real-world settings. The theoretical research employs and develops analytic understanding of the relationships among technology, people, organizations, and social settings. Finally, in the technical work, ICT researchers develop new models and technologies for interaction with and collaboration through technology. Students in the ICT track gain a thorough grounding in all three areas.

                The cornerstone of this approach is to study real use of technology, as it occurs in real-world settings. ICT researchers believe that the success or failure of technology depends on how people can fit that technology into real practice, balancing technical, cognitive, social, and cultural dimensions.

                Using an interdisciplinary approach, ICT research aims at a deeper understanding of interaction with and collaboration through technology, and at exploiting these insights for the design of better systems.

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                http://mondego.ics.uci.edu/datasets/wikipedia-events/ Index of /datasets/wikipedia-events

                Index of /datasets/wikipedia-events

                [ICO]NameLast modifiedSizeDescription

                [DIR]Parent Directory  -  
                [DIR]files/10-Feb-2012 17:39 -  

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                http://sourcerer.ics.uci.edu/ The Sourcerer Project

                The Sourcerer Project

                Exploring Open Source software

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                Java 8

                July 31, 2015

                The Sourcerer tools can now handle Java 8's lambda.

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                2015 sees the redesign of Sourcerer website. Take a look around and let us know what you think.

                Quick Links

                • Sourcerer public repository on GitHub.
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                http://www.ics.uci.edu/~mgiorgio/inf212/homework-instructions.html Programming Assignments & Peer Grading

                Programming Assignments & Peer Grading

                Rubrics

                • HW 1
                • HW 2
                • HW 3
                • HW 7

                Development environment

                Projects will be developed and submitted using Cloud 9 (https://c9.io/). This environment supports all the languages that are allowed in this course.

                Working on your assignment

                Languages

                The programming languages that can be used for the assignments are:
                1. Java
                2. PHP
                3. C#
                4. Ruby
                5. Javascript
                6. C++ (gcc 4.8)
                7. Go
                8. Perl

                Cloud 9 Workspace

                Choosing a username

                If you don't want the students who you're peer-grading to know your identity, please pick a C9 username different enough from your name so they can't guess who you're. If you don't care about them knowing, just ignore this paragraph.

                Creating a new repository

                Since you will share your workspace with other students, you will have to create a new one for each assignment, otherwise, students will be able to see the stuff you’re working on for this week. That’s far from being a smooth way to work so, in order to reduce the overhead, you can just clone https://github.com/mgiorgio/inf212w15.git and you will get the right structure to start working right away on your weekly homework. You can clone the repository directly from Cloud 9 (Create Workspace --> Clone repository). The repository you will create must be public (non private). You can refer to https://docs.c9.io/creating_new_workspace.html for further details.

                Choosing a repository name

                In Cloud 9, your workspace can be accessed in read-only mode by anyone having your workspace's URL. In order to cope with that situation, please just put some random characters at the end of the workspace name. For example, if your username is abc and you are working on the HW3, you can choose something like hw3-18785 so that nobody can see your stuff unless they receive your URL. Your URL will look like http://ide.c9.io/abc/hw3-18785.

                Protecting your workspace

                In order to prevent anyone else from looking at the stuff you’re working on, you must change the default sharing options. In order to do so, go to “Share” at the top-right corner and uncheck the “Public” option.

                File organization

                Your submission files must be organized as follows:

                • Workspace name (+ random string)
                  • Readings
                    • ~200-word summary plain text file.
                  • Project
                    • stop_words.txt
                    • src
                      • Source code file.
                      • pride-and-prejudice.txt
                      • README.txt (optional)

                  Before submitting your assignment

                  Programs must be submitted ready to be executed. If something needs to be changed on the code to run your program or it doesn’t compile, it won’t be possible to evaluate your homework and you will receive zero points. If it’s expected that special parameters needs to be passed to execute the program, a README.txt file must be created in the source code’s folder explaining the program must be run.

                  Submission

                  Sharing your stuff

                  Make your workspace public again

                  Now that your workspace needs to be seen by your peer-graders, you need to make it public again. In order to do so, go to the “Share” option at the top-right corner and check the “Public” checkbox.

                  Submitting your stuff (a.k.a. how does the TA know that you did your assignment?)

                  For each assignment, a text file must be uploaded to EEE containing:

                  1. A link to your workspace. That link will be passed to your peer-graders and the TA.
                  2. The language you used.

                  You have time to upload this file until Saturday night. Keep in mind that if you solved the weekly assignment but did not upload this file, your work will not be considered.

                  Peer Grading

                  At the beginning of the quarter, each student must choose at least three languages to do peer grading on other students’ projects. For example, if one student chooses Java, PHP and Ruby, the student will (very likely) receive programs written in those languages to be graded. This is not guaranteed, but we will do our best to assign programs written in languages that the grader feels comfortable with.

                  For each assignment, students will peer-grade three programs. The score and comments must be uploaded to EEE.

                  Part of your score will come from your job as a peer-grader. Keep in mind that the score you assign to other students is as important as the feedback you give them.

                  Permission to execute stuff in C9

                  By default, you will not have permissions to execute files in a workspace that is not yours. Consequently, in order to execute other students' code, you will request writing permissions. There is going to be an option in C9 to do so when you enter the URL that they shared.

                  Rubric

                  You will receive a rubric each week that you must use for the peer-grading. It is going to be as detailed as possible to minimize the deviation from different graders.

                  Summary

                  To sum this up, each week you will need to:

                  1. Clone the repository listed above.
                  2. Create a file in C9 with the summary for the assigned reading.
                  3. Solve the programming assignment in C9.
                  4. Upload a text file to EEE with the link to your C9’s workspace and the language you used.

                  After receiving the assignments that you will peer-grade:

                  1. Evaluate them.
                  2. Upload a text file to EEE with a score and comments for each assignment you are peer-grading.
                    1. http://www.ics.uci.edu/~mgiorgio/ Matias Giorgio

                      Matías Giorgio

                      • Position: Graduate Student
                      • Area: Software
                      • Advisor: Richard N. Taylor
                      • Office: DBH 5209 (Taylor's Lab)
                      • E-mail: mgiorgio@uci.edu

                      Please, refer to my personal web page for up-to-date information or take a look at my blog.



                      Information and Computer Science
                      University of California, Irvine
                      Irvine, CA 92697-3425
                      Last modified: Apr 8, 2014 http://www.ics.uci.edu/~ardalan/courses/advanced_os/index.html Ardalan Amiri Sani

                      Advanced Operating System

                      Instructor: Ardalan Amiri Sani

                      • Home
                      • Schedule
                      This course will cover advanced topics in operating systems. The course will mostly consist of lectures by the instructor, but it will also have some presentations by the students.

                      Grading

                      • Paper presentation: 20%
                      • Paper summaries: 20%
                      • Course project: 40%
                      • Class participation: 20%

                      Paper presentations

                      • Each presentation is performed by a team of students (2 to 3).
                      • Sign up for a presentation slot by sending me an email.
                      • Your email should contain the name of all the team members and your top three preferred presentation dates (see the Schedule tab for the available dates). Note that you're signing up for the dates and not the topics. The schedule of topics discussed in each week is tentative and might change although that there is a good chance that it will not.
                      • Each presentation will be 30 minutes followed by 10 minutes of Q&A.
                      • All the team members must engage in the presentation (ideally equally).
                      • Presentation will be graded based on quality of presentation and the students' understanding of the paper content.

                      Paper summaries

                      • Submit the summaries before the class starts.
                      • Submissions will be through EEE.

                      Projects

                      • Project is open for you to choose. Suggest a project that you think is related to the course.
                      • The project can be related to your research but cannot be something that you have already done or you are doing as part of your ongoing research.
                      • If you do not have an idea for the project, you can choose one of the default projects (to be announced in the class)
                      • Each project is performed by a team of students (2 to 5).
                      • Project proposal. Deadline: Jan. 22. Pick your team members and submit a one-page project proposal write-up. In the write-up, introduce the project, motivate it, explain what you will exactly implement, and present a timeline.
                      • Project midterm report. Deadline: Feb. 19. Submit a two-page midterm report. In the report, discuss what has been done and what is left to do.
                      • Project final report. Deadline: Mar. 18. Submit a three-page final report. In the report, discuss the motivation, design, implementation, and evaluation of the system you have built.
                      • Submissions will be through EEE.

                      http://www.ics.uci.edu/~saeed/index.html Saeed Mirzamohammadi
                      SAEED MIRZAMOHAMMADI

                      Ph.D. Student
                      Computer Science Department
                      University of California, Irvine
                      Email: saeed at uci dot edu
                      Office: Donald Bren Hall #3086

                      HOME         PUBLICATION         CONTACT

                      Welcome

                      I'm currently a Ph.D. student in the Department of Computer Science at the University of California, Irvine working with Prof. Ardalan Amiri Sani . I received my B.Sc. degree from Sharif University of Technology.

                      Research Interests:

                      • Mobile Computing
                      • Operating System

                      ©2007 Keep it Simple  |  Design by Reality Software
                      http://www.ics.uci.edu/~sjordan/courses/ics11/outline.html Econ 11 / ICS 11 Course Outline
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 Course Outline

                      The Internet and Public Policy (4). How the Internet works. Current public policy issues concerning the Internet. Introductory economics. Communications law. Interactions between information technology, economics, and law. Case studies about Internet and communications policy. (II or III)

                      Lecture

                      Topic

                      Reading (page numbers for Leon-Garcia & Schiller refer to green reader page numbers, page numbers for Kurose refer to textbook page numbers)

                      Assignment

                      1 (1/10) Networks: telephone networks, terminology, Internet topology, packets, delays, layers, applications

                      Tanenbaum 2.5, Leon-Garcia 1.1.2(6-10)

                       

                      2 (1/12) Kurose 1.1(2-9), Kurose 1.7(61-68), Kurose 1.3-1.4.1(25-40), Leon-Garcia 1.1.3(10-23) PS1
                      3 (1/17) Kurose 1.4.2-1.5 (40-54), Kurose 2.1-2.2(85-90,100-102,110-112), Kurose 2.4(120-126), Kurose 2.6(146-147,151-153)  
                      4 (1/19) Economics: utility, demand, production, supply, equilibrium, public goods, externalities, market power Kurose 7.1.1 (598-601), Schiller 19(62-67), Schiller 3(40-44), Schiller 20(95-100) PS2
                      5 (1/24) Schiller 20(100-104,106-107), Schiller 3(48-50), Schiller3(37-40,44-46,50-56)  
                      6 (1/26)

                      Schiller 4(122-129)

                      PS3
                      7 (1/31) Law & Public Policy, Economics: monopoly Schiller 23(145-157), Benjamin 2, Sterling 1.3  
                      8 (2/2) Economics: monopoly, regulations, subsidies, anti-trust Schiller 23(157-165), Schiller 26(170-186)  

                      9 (2/7)

                      Privacy case study see case materials  
                      10 (2/9) PS4
                      11 (2/14) Networks: congestion control, addresses, routing, Ethernet, Wi-Fi Kurose 3.1(197-203), Kurose 3.3 (210-214), Kurose 3.7(281-289), Kurose 4.4.2(348-351), Kurose 4.1.1(315-320)  
                      12 (2/16)

                      Kurose 2.5(132-141), Kurose 4.6.2-4.6.3(398-407), Kurose 5.3(455-468), Kurose 5.4.1-5.4.2(469-474), Kurose 5.5(475-486)

                       

                      13 (2/21)

                      Copyright Infringement case study see case materials  
                      14 (2/23)  
                      15 (2/28) Networks: cellular, cable, QoS Kurose 6.4(558-563), Tanenbaum 2.7-2.8, Kurose 7.1(597-607), Kurose 7.5-7.6(647-672) PS5
                      16 (3/1) Law Leon-Garcia 1.3(25-30), Black 2.5.5  
                      17 (3/6) Net Neutrality case study see case materials  
                      18 (3/8)  
                      19 (3/13) Review    
                      20 (3/15) Review    
                      Final (3/20 4:00-6:00pm)   all readings listed above  
                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/ics11/links.html Econ 11 / ICS 11 Useful Links
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 Useful Links

                      Please send new useful links for this list to Scott Jordan.

                      • Congressional bills
                      • Congressional Research Service reports
                      • Bit & Byte unit conversions
                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/ics11/case_studies/index.html Econ 11 / ICS 11 Case Studies
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 Case Studies

                      There will be three case studies during the course, as indicated on the course outline.

                      Case study assignments

                       

                      Instructions for what to do on a case study if your group is a Lobbyist

                      Instructions for what to do on a case study if your group is a Staffer

                      Instructions for what to do on a case study if you are a Reporter

                       

                      Case Study #1 Privacy (presentations on 2/7 and 2/9)

                      Case Study #2 Copyright Protection (presentations on 2/21 and 2/23)

                      Case Study #3 Net Neutrality (presentations on 3/6 and 3/8)

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/ics11/policies.html Econ 11 / ICS 11 Course Policies
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 Course Policies

                      Problem Sets:

                      • There will be five problem sets during the term. See the course outline for tentative assignment dates. All problem sets will be posted on the course web page.
                      • Problem sets are usually due one week after they are assigned. They are due at the beginning of class, and must be turned in either in lecture or to the instructor's mailbox in 3019 Bren Hall.
                      • No late homework will be accepted.
                      • Some problems on the problem set will be graded based on effort, but not on the correctness of the answer. Other problems will be graded based both on effort and on the knowledge demonstrated. It will not be announced beforehand which problems fall into which category.
                      • Graded problem sets will normally be returned one week after the due date. Solutions will be available online when the problem sets are returned.
                      • Your lowest problem set score will be dropped at the end of the quarter.

                      Case Studies:

                      • There will be three case studies during the term. See the case study webpage for details.
                      • Case studies will be done in groups of 3 or 4 students.
                      • Groups will be formed at the beginning of the third week of the quarter.
                      • Each group will act as a Lobbyist for one case study, and as a Analyst for another case study.
                      • In addition, each student will act as a Reporter for the remaining case study.

                      Grading Policy:

                      Letter grades are based on the instructor's evaluation of your demonstrated performance in the course. An overall score in the course will be calculated using the following weighting:
                      • Problem Sets (20%)
                      • Case study Lobbyist presentation and report (20%)
                      • Case study Analyst presentation (20%)
                      • Case study Reporter (5%)
                      • Participation (5%)
                      • Final (30%)

                      No absolute scale will be used in assigning letter grades to each overall score. Instead, the instructor will use his judgment to decide what letter grade is appropriate for each overall score range. The instructor reserves the right to override this policy in individual cases where the student has demonstrated mastery of the material on the final, but this is rare.

                      All grades (problem sets, case studies, final) will be available through eee.uci.edu.

                      Course Electronic Textbooks:

                      Click here for instructions on how to purchase these two electronic books

                      • Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley. (UCI reserve)
                      • The Economy Today, Bradley Schiller, McGraw-Hill (very old edition UCI reserve) and Communication Networks, Alberto Leon-Garcia, McGraw-Hill (UCI reserve)

                      Reference Texts:

                      • Telecommunications Law and Policy, Stuart Minor Benjamin, Douglas Gary Lichtman, Howard A. Shelanski, and Philip J. Weiser, Carolina Academic Press. (publisher, author page)
                      • Shaping American Telecommunications, Christopher H. Sterling, Phyllis W. Bernt, Martin B.H. Weiss, Lawrence Erlbaum Associates, Publishers. (publisher, UCI reserve)
                      • Telecommunications Law in the Internet Age, Sharon K. Black, Morgan Kaufmann Publishers. (publisher, UCI reserve)
                      • Computer Networks, Andrew Tanenbaum and David Wetherall, Prentice Hall. (publisher, UCI reserve)
                      • Interactive applets from Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley.
                      • A practical guide for policy analysis : the eightfold path to more effective problem solving, Eugene Bardach, CQ Press. (publisher, UCI reserve)

                      Policy on Academic Honesty:

                      • UCI Policy on Academic Honesty. This includes a definition of plagiarism.
                      • On problem sets, you are strongly encouraged to work in groups to discuss your approach to solving each problem, but you must work individually in progressing from that point toward the solution. You must turn in only your own work. Use of any solutions from any source other than a student's own work is considered plagiarism.
                      • On case studies, the work presented is expected to represent the participation of all members of the group. Anything that is other than the group's own work must be properly cited or it is considered plagiarism.
                      • Students agree that by taking this course all required papers are subject to submission for textual similarity review to Turnitin.com for the detection of plagiarism.  All submitted papers will be included as source documents in the Turnitin.com reference database solely for the purpose of detecting plagiarism of such papers. Use of the Turnitin.com service is subject to the Usage Policy agreement posted on the Turnitin.com site.

                      Add Drop Policy:

                      • Adds: Only allowed through the end of week 1, as space permits.
                      • Drops: Only allowed through the end of week 2.

                      Attendance Policy:

                      • Attendance at all case studies is expected. Students are responsible for all material covered in all lectures, and the instructor will not provide notes. Posted lecture slides will be missing some key material. Participation in case studies is a significant part of the course grade.
                      • To be respectful to your classmates, please turn off cell phones when in the classroom, and avoid discussion that is not part of classroom activity.

                       

                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/ics11/problems/index.html Econ 11 / ICS 11 Problem Sets
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 Problem Sets

                      Advice:

                      Many problems will require a good bit of interpretation. You will find that often the problem gives you more information than you need, or not enough. This is intentional. Hence, I expect you to struggle with some problems. There is no one correct answer to many problems. An answer is almost never sufficient without an explanation.  This is particularly true if you want to convince me that your approach, although it may be different than mine, is reasonable. Any assumptions you have made should be stated clearly at the top of the solution. On a small scale, this makes the problems much more representative (than most textbook problems) of problems you will encounter in real life.

                      You are welcome to come ask me questions about how to approach a problem. You may discuss how to approach a problem with as many other students as you like, but you must work out the solution by yourself. Identical solutions presented by different students will be considered cheating if it is clear that collaboration proceeded beyond discussing the approach to the problem.

                      Problem sets should be completed on paper and turned in at the beginning of lecture. Explain what you are doing in English. An answer or some arithmetic, without an explanation in English, will often not receive full credit. This will also help us give you partial credit when you do something wrong early in the problem upon which later results depend. Make sure you answer all questions asked. Often problems include multiple questions. Please mark final answers in boxes to make grading easier.

                      We will make available solution sets online when each problem set is graded and returned. You are encouraged to obtain these solutions and to compare them to your solution.

                       

                      Problem Set #1, assigned 1/12, due 1/19 -- slides from 1/17 discussion section

                      Problem Set #2, assigned 1/19, due 1/26 -- slides from 1/23 discussion section

                      Problem Set #3, assigned 1/16, due 2/2 -- slides from 1/30 discussion section

                      Problem Set #4, assigned 2/9, due 2/16-- slides from 2/13 discussion section

                      Problem Set #5, assigned 2/23, due 3/1 -- slides from 2/27 discussion section

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/ics11/overview.html Econ 11 / ICS 11
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        Econ 11 / ICS 11 The Internet and Public Policy

                      Economics 11 / ICS 11
                      The Internet and Public Policy
                      Winter quarter 2012, TuTh 3:30-4:50pm

                      The course will focus on 4 case studies concerning the federal government’s role in regulating and encouraging the Internet that illustrate how technology, economics, politics and law are combined to form public policy --

                      • Privacy – How should Internet users be able to control the use of information about them online?
                      • Broadband – The 2009 Federal stimulus bill included 11 billion dollars to subsidize building broadband Internet networks. Why? As a taxpayer, should you help subsidize broadband Internet in rural areas?
                      • Copyright Infringement– Should law make it more difficult for peer-to-peer file sharing users to find copyrighted music and video files? Are these measures effective?
                      • Net Neutrality – Should Internet providers be allowed to prioritize some Internet traffic over others? Should they be allowed to slow down your file-sharing traffic? Should they be allowed to favor some websites over others?

                      In order to address these case studies, you’ll also learn some basics about the Internet, economics, and law --

                      • Learn how the Internet works – How does your computer find where a webserver is located? What do those IP numbers mean? How do packets find their way? How does the Internet control how fast you can send? What was Senator Ted Stevens talking about when he called the Internet a “series of tubes”?
                      • Learn basic microeconomics – How do consumers decide how much to buy? How do companies decide how much to sell? How are prices chosen? How can monopolies manipulate prices to maximize profit? Why do you have more choices for wireless providers than for wired phone providers?
                      • Learn how telephone, wireless, cable tv, and the Internet are regulated – Are there limits to what your telephone and cable companies can charge? Why can’t you purchase only the cable channels you want? Is your Internet provider allowed to look inside all your packets to see what you are doing on the Internet?

                      Course overlap: The economics material overlaps with Econ 20A, 100A, and 100B. The Internet architecture material overlaps with CS 132 and EECS 148. Students who have taken any of these courses may still take Econ 11 / ICS 11, since they will find the remaining material to be new and challenging. (Note: This means that this course will be challenging for Econ and BusEcon majors, regardless of what counselors may have told you ...)

                      Degree requirement credit: You can choose to count the course toward General Education requirements under either category II (Science and Technology) or category III (Social and Behavioral Sciences) but not both. (Note: The General Education requirements apply to students who entered UCI Fall 2008 or later. Students who entered earlier may fulfill either the Breadth requirements in the catalogue at the time they entered UCI, or the new General Education requirements. Please consult your student affairs office for details.) This course may or may not count toward major requirements; please consult your student affairs office for details.

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs132/policies.html CS 132 Course Policies
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 132 Course Policies

                      Problem Sets:

                      • There will be six problem sets during the term. See the course outline for tentative assignment and due dates. All problem sets will be posted on the course web page.
                      • Problem sets are due at the beginning of class, and must be turned in either in lecture or to the instructor's mailbox in 3019 Bren Hall.
                      • No late homework will be accepted.
                      • Some problems on the problem set will be graded based on effort, but not on the correctness of the answer. Other problems will be graded based both on effort and on the knowledge demonstrated. It will not be announced beforehand which problems fall into which category.
                      • Graded problem sets will normally be returned one week after the due date. Solutions will be available online when the problem sets are returned.
                      • Your lowest problem set score will be dropped at the end of the quarter.

                      Project:

                      • There will be one project to be completed during the second half of the term. It will be on a topic of your choice, based on something we cover in class. Typically, this will focus on one technology or one type of application.
                      • Projects will be done in groups of 3 students. You must form your group and choose a topic by May 6.
                      • Some projects will be chosen for presentation to the class during the last two weeks of the quarter. Others will turn in a report.
                      • See the project webpage for details.

                      Grading Policy:

                      Letter grades are based on the instructor's evaluation of your demonstrated performance in the course. An overall score in the course will be calculated using the following weighting:
                      • Problem Sets (25%)
                      • Project (35%)
                      • Final (40%)

                      No absolute scale will be used in assigning letter grades to each overall score. Instead, the instructor will use his judgment to decide what letter grade is appropriate for each overall score range. The instructor reserves the right to override this policy in individual cases where the student has demonstrated mastery of the material on the final, but this is rare.

                      All grades (problem sets, project, final, and the final course letter grade) will be available through eee.uci.edu.

                      Course Text:

                      • Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley. (publisher, UCI reserve) [Instructions on purchasing the book]

                      Reference Texts:

                      • Interactive applets, Interactive Exercises, and Powerpoint Slides from Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley.
                      • Computer Networks, Andrew Tanenbaum and David Wetherall, Prentice Hall. (publisher, UCI reserve)
                      • Communication Networks : Fundamental Concepts and Key Architectures, Albert Leon-Garcia & Indra Widjaja, McGraw Hill. (publisher, UCI reserve)
                      • Communication Networks: A First Course, Jean Walrand, McGraw Hill. (google, UCI reserve)
                      • Computer Networks: A Systems Approach, Larry Peterson & Bruce Davie, Morgan Kaufmann. (google, UCI reserve)
                      • High-Performance Communication Networks, Jean Walrand & Pravin Varaiya, Morgan Kaufmann. (google, UCI reserve)
                      • Data and Computer Communications, William Stallings, Prentice-Hall. (publisher, UCI reserve)

                      Policy on Academic Honesty:

                      • UCI Policy on Academic Honesty. This includes a definition of plagiarism. Plagiarism Leaning Module, and reports of incidents.
                      • On problem sets, you are strongly encouraged to work in groups to discuss your approach to solving each problem, but you must work individually in progressing from that point toward the solution. You must turn in only your own work. Use of any solutions from any source other than a student's own work is considered plagiarism.
                      • On the project, the work presented is expected to represent the participation of all members of the group. Anything that is other than the group's own work must be properly cited or it is considered plagiarism.
                      • Students agree that by taking this course all required papers are subject to submission for textual similarity review to Turnitin.com for the detection of plagiarism.  All submitted papers will be included as source documents in the Turnitin.com reference database solely for the purpose of detecting plagiarism of such papers. Use of the Turnitin.com service is subject to the Usage Policy agreement posted on the Turnitin.com site.

                      Add Drop Policy:

                      • UCI add/drop policies
                      • ICS add/drop policies
                      • Course add/drop policy: The instructor normally only allows additions to the course, as space allows, until the end of week 1.

                      Attendance Policy:

                      • Attendance at project presentations is expected. Students are responsible for all material covered in all lectures, and the instructor will not provide notes. Posted lecture slides will be missing some key material.
                      • To be respectful to your classmates, please turn off cell phones when in the classroom, and avoid discussion that is not part of classroom activity.

                       

                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs132/links.html CS 132 Useful Links
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 132 Useful Links

                      Please send new useful network links for this list to Scott Jordan.

                      Internet Protocols

                      • The Internet Engineering Task Force
                      • Internet Protocol Specifications

                      Internet Topology & Measurement

                      • Internet Traffic Report
                      • Internet Topology
                      • Historical Internet Topology
                      • Historical Internet Topology
                      • Internet Tomography
                      • Number of Internet Hosts
                      • Number of Internet Users
                      • Root Nameserver List

                      Networking Bibliographies & Other Lists

                      • Network Bibliography
                      • Standard Papers on the Foundations of Networks
                      • Computing Research Repository (CoRR)
                      • whatis.com
                      • Telecoms Virtual Library
                      • TechFest Library

                      Technical Societies

                      • IEEE
                      • IEEE Communiations Society
                      • ACM

                      Technical Magazines (access from UCI hosts only)

                      • IEEE Spectrum
                      • Communications of the ACM
                      • IEEE Communications Magazine
                      • IEEE Wireless Communications Magazine
                      • IEEE Network Magazine

                      Technical Journals (access from UCI hosts only)

                      • IEEE Publications
                      • IEEE/ACM Transactions on Networking
                      • IEEE Journal on Selected Areas in Communications
                      • ACM Publications
                      • ACM Wireless Networks
                      • ACM Mobile Networks and Applications
                      • ACM Computer Communication Review
                      • ACM International Journal of Network Management
                      • ACM Transactions on Internet Technology
                      • Computer Networks (Elsevier)
                      • Performance Evaluation (Elsevier)
                      • Queueing Systems (Kluwer)
                      • Telecommunication Systems (Kluwer)

                      Technical Conference Proceedings (access from UCI hosts only)

                      • IEEE InfoCom
                      • IEEE ICC
                      • IEEE GlobeCom
                      • IEEE VTC
                      • IEEE WCNC
                      • ACM MobiCom
                      • ACM SigMetrics
                      • ACM SigComm

                      Networking Magazines

                      • Wired

                      White Papers

                      • Web ProForum Tutorials

                      Writing Technical Papers

                      • Writing Technical Articles

                      Networking Policy

                      • Telecommunications Act of 1996
                      • Telecommunications Policy Research Conference
                      • Benton Foundation
                      • Educause

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs132/outline.html CS 132 Course Outline
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 132 Course Outline

                      132 Computer Networks (4). Computer network architectures, protocols, and applications. Internet congestion control, addressing, and routing. Local area networks. Multimedia networking. Prerequisite: Statistics 67 or EECS 55. Same as EECS 148.

                      Lecture

                      Topic

                      Sub-topic

                      Reading (Kurose-Ross)

                      Assignment

                      4/1 Telephone networks course intro, telephone networks Tanenbaum 2.5 (pp. 118-121, 124-125)  
                      4/3 telephone networks Tanenbaum 2.5 (pp. 137-143, 147-151) PS1 assigned
                      4/8 Network Architecture terminology, topology, packet switching 1.1-1.3, 1.7 PS1 due
                      4/10 performance, layers 1.4-1.6 PS2 assigned
                      4/15 Applications http, e-commerce, email 2.1-2.2, 2.4 PS2 due
                      4/17 file sharing, streaming, VoIP 2.6, 7.1 PS3 assigned
                      4/22 TCP sockets, udp, flow/congestion, timeouts/acks/nums, abp 3.1-3.4.2 PS3 due
                      4/24 srp, tcp 3.4.3-3.7 PS4 assigned
                      4/29 IP addressing
                      2.5, 4.1-4.2, 4.4, 5.4.1

                      PS4 due

                      form project group

                      5/1 routing
                      4.3, 4.5
                       
                      5/6 ospf, bgp
                      4.5, 4.6
                      project group & topic due

                      5/8

                      Local Area Networks aloha
                      5.1-5.3
                      PS5 assigned
                      5/13 ethernet
                      5.4-5.5

                      PS5 due

                      initial project report assigned

                      5/15 forwarding, wi-fi
                      5.7, 6.1-6.3
                       
                      5/20 Security, Convergence, Communications Law security 8.1-8.4, 8.6-8.7

                      intial project report due

                      PS6 assigned

                      project reports & presentations assigned

                      5/22 cable, cellular, multimedia, QoS, law 5.3.4, Tanenbaum 2.7-2.8, 7.1-7.5  
                      5/27 Project Presentations     PS6 due
                      5/29      
                      6/3     project reports due
                      6/5      
                      6/12 1:30-3:30pm Final      
                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs132/project.html CS 132 Project
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 132 Project

                      Forming a group and choosing a topic (due May 6):

                      Please choose a topic that focuses on a networking technology and/or on a networking application. You should not focus exclusively on a technology at OSI layer 1. If you choose an application that lies at OSI layer 7, you should relate it to other standardized application layer protocols and/or relate it to requirements at lower layers.

                      You cannot cover material covered in lecture.

                      Your project will consist of learning about the topic and presenting what you have learned. You are not expected to build or design anything.

                      You should work in a group of two, three, or four students. If you are looking for other students to work with, you might want to post to the CS 132 MessageBoard.

                      By May 6, your group should post information about your proposed project on the course wiki. Please include the names of the students in your group, a project title, and a short (one paragraph) description of your topic. The description need not describe everything you will investigate, but it should be enough to give me an idea of what will be included and what is outside the bounds of your interest.

                      Learning about your topic:

                      You should find and read information related to your topic. This information should be composed of a few independent sources. You can use any source (paper or online) that you deem respectable. Here are a few magazines that I believe are generally reliable, to give you a few examples:

                      • IEEE Spectrum
                      • IEEE Communications Magazine
                      • IEEE Network Magazine
                      • IEEE Personal Communications Magazine
                      • Communications of the ACM

                      UCI has online subscriptions to these and many other publications. To take advantage of this, you must either use a computer on the UCI campus or use the UCI VPN.

                      A reasonable amount of information would probably be 3-5 articles from these magazines, or a corresponding amount from other sources.

                      Initial Report (due May 20)

                      Write a 1-2 page report explaining the scope of your project. Include an initial list of your references.

                      Turn in the report by May 20 7pm. Only one member of your group should do the following:

                      1. If your report is in a format other than Word or PDF, then convert it to PDF. There are several free conversion tools on the Internet. Rename the report file to include your group number, e.g. Group3InitialReport.
                      2. Post the report in the CS 132 Dropbox.

                      Presentation or Final Report:

                      You will either turn in a final report or give a presentation to the class. We will select which one you should do.

                      Presentation:

                      If selected to give a presentation, your presentation will take place on one of the following days:

                      • May 27
                      • May 29
                      • June 3
                      • June 5

                      Each time slot will be 15 minutes. You are expected to attend all days regardless of which day you are presenting.

                      Your talk should use a set of computer projected slides. You may use either ppt or pdf formats. You may use your own laptop or you may use the computer in the classroom.

                      You should practice your talk to learn the timing, as I will cut you off when you have used 15 minutes. Although 15 minutes might seem like a long time, you will not have time to present everything you have learned. Therefore, you should decide what you think is the most important information for your classmates.

                      YOU MUST INCLUDE ON YOUR LAST SLIDE A BIBLIOGRAPHY OF YOUR INFORMATION SOURCES. Please include the following information for each:

                      1. Author of the document.
                      2. Title of the document (e.g. article title or web page title).
                      3. Venue where the document was published (e.g. journal, conference, magazine, publisher, name of website).
                      4. Where to find the document within the venue (e.g. volume number and page number, or path within webpage sitemap), if available.
                      5. Date written; for webpages without a date written include the date accessed.
                      6. URL, if the reference is available online. This should be the direct URL provided by the venue, if available. I should be able to put this URL in my webrowser and immediately obtain the reference.

                      Only one member of your group should do the following by the day you present:

                      1. If your slides are in a format other than Powerpoint or PDF, then convert it to PDF. There are several free conversion tools on the Internet. Rename the file to include your group number, e.g. Group3Slides.
                      2. Post the file in the CS 132 Dropbox.

                      Report:

                      If selected to write a report, you must complete your report by June 3.

                      Your report should be approximately 4-5 pages per person in your group, not including the bibliography.

                      YOU MUST INCLUDE IN YOUR WRITTEN REPORT A BIBLIOGRAPHY OF YOUR INFORMATION SOURCES. Please include the following information for each:

                      1. Author of the document.
                      2. Title of the document (e.g. article title or web page title).
                      3. Venue where the document was published (e.g. journal, conference, magazine, publisher, name of website).
                      4. Where to find the document within the venue (e.g. volume number and page number, or path within webpage sitemap), if available.
                      5. Date written; for webpages without a date written include the date accessed.
                      6. URL, if the reference is available online. This should be the direct URL provided by the venue, if available. I should be able to put this URL in my webrowser and immediately obtain the reference.

                      Only one member of your group should do the following by June 3 at 10pm:

                      1. If your report is in a format other than Powerpoint or PDF, then convert it to PDF. There are several free conversion tools on the Internet. Rename the file to include your group number, e.g. Group3Report.
                      2. Post the file in the CS 132 Dropbox.

                      Grading:

                      You will be graded primarily on the effectiveness of your transmission of information to your classmates.

                      For presentations, we will assign scores for preparation (how well did you demonstrate that you know the material), content (which material did you include in your presentation), presentation (how well did you communicate this information to your classmates), and length (how closely did you match the 15 minute target).

                      For reports, we will assign scores for preparation (how well did you demonstrate that you know the material), content (which material did you include in your report), and presentation (how well did you communicate this information).

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs132/problems/index.html CS 132 Problem Sets
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 132 Problem Sets

                      Advice:

                      Many problems will require a good bit of interpretation. You will find that often the problem gives you more information than you need, or not enough. This is intentional. Hence, I expect you to struggle with some problems. There is no one correct answer to many problems. An answer is almost never sufficient without an explanation.  This is particularly true if you want to convince me that your approach, although it may be different than mine, is reasonable. Any assumptions you have made should be stated clearly at the top of the solution. On a small scale, this makes the problems much more representative (than most textbook problems) of problems you will encounter in real life.

                      You are welcome to come ask me questions about how to approach a problem. You may discuss how to approach a problem with as many other students as you like, but you must work out the solution by yourself. Identical solutions presented by different students will be considered cheating if it is clear that collaboration proceeded beyond discussing the approach to the problem.

                      Problem sets should be completed on paper and turned in at the beginning of lecture. Explain what you are doing in English. An answer or some arithmetic, without an explanation in English, will often not receive full credit. This will also help us give you partial credit when you do something wrong early in the problem upon which later results depend. Make sure you answer all questions asked. Often problems include multiple questions. Please mark final answers in boxes to make grading easier.

                      We will make available solution sets online when each problem set is graded and returned. You are encouraged to obtain these solutions and to compare them to your solution.

                      Problem Set #1, assigned 4/3 due 4/8

                      Problem Set #2, assigned 4/10 due 4/15

                      Problem Set #3, assigned 4/17 due 4/22

                      Problem Set #4, assigned 4/24 due 4/29

                      Problem Set #5, assigned 5/8 due 5/13

                      Problem Set #6, assigned 5/20 due 5/27

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs232/problems/index.html CS 232 Problem Sets
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 232 Problem Sets

                      Advice:

                      Many problems will require a good bit of interpretation. You will find that often the problem gives you more information than you need, or not enough. This is intentional. Hence, I expect you to struggle with some problems. There is no one correct answer to many problems. An answer is almost never sufficient without an explanation.  This is particularly true if you want to convince me that your approach, although it may be different than mine, is reasonable. Any assumptions you have made should be stated clearly at the top of the solution. On a small scale, this makes the problems much more representative (than most textbook problems) of problems you will encounter in real life.

                      You are welcome to come ask me questions about how to approach a problem.

                      Problem sets do not count toward your course grade. If you wish your problem set to be reviewed by a course reader, please complete it on paper and turn it in at the beginning of lecture. Explain what you are doing in English.

                      We will make available solution sets online when each problem set is graded and returned. You are encouraged to obtain these solutions and to compare them to your solution.

                      Problem Set #1, assigned 10/9, due 10/16

                      Problem Set #2, assigned 10/18, due 10/25

                      Problem Set #3, assigned 10/30, due 11/6

                      Problem Set #4, assigned 11/27, due 12/4

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs232/policies.html CS 232 Course Policies
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 232 Course Policies

                      Problem Sets:

                      • There will be several four problem sets during the term. Tentative dates that the problem sets will be assigned are on the course outline. All problem sets will be posted on the course web page.
                      • Problem sets are usually due one week after they are assigned. They are due at the beginning of class, and must be turned in either in lecture or to the instructor's mailbox in 3019 Bren Hall.
                      • No late homework will be accepted.
                      • You are strongly encouraged to work in groups to discuss your approach to solving each problem, but you should work individually in progressing from that point toward the solution.
                      • Problem sets will be graded based on effort, but not on the correctness of the answer.
                      • Graded problem sets will normally be returned one week after the due date. Solutions will be available online when the problem sets are returned.
                      • Your lowest problem set score will be dropped at the end of the quarter.

                      Grading Policy:

                      Letter grades are based on the instructor's evaluation of your demonstrated performance in the course. An overall score in the course will be calculated using the following weighting:
                      • Problem Sets (10%)
                      • Midterm (35%)
                      • Final (55%)

                      No absolute scale will be used in assigning letter grades to each overall score. Instead, the instructor will use his judgment to decide what letter grade is appropriate for each overall score range. The instructor reserves the right to override this policy in individual cases where the student has demonstrated mastery of the material on the final, but this is rare.

                      All grades (problem sets, midterm, final, and the final course letter grade) will be available through eee.uci.edu.

                      Course Text:

                      • Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley. (publisher, UCI reserve) [Instructions on purchasing the book]

                      Reference Texts:

                      • Interactive applets, Interactive Exercises, and Powerpoint Slides from Computer Networking: A Top-Down Approach Featuring the Internet, James Kurose & Keith Ross, Addison-Wesley.
                      • Computer Networks, Andrew Tanenbaum and David Wetherall, Prentice Hall. (publisher, UCI reserve)
                      • Communication Networks : Fundamental Concepts and Key Architectures, Albert Leon-Garcia & Indra Widjaja, McGraw Hill. (publisher, UCI reserve)
                      • Communication Networks: A First Course, Jean Walrand, McGraw Hill. (google, UCI reserve)
                      • Computer Networks: A Systems Approach, Larry Peterson & Bruce Davie, Morgan Kaufmann. (google, UCI reserve)
                      • High-Performance Communication Networks, Jean Walrand & Pravin Varaiya, Morgan Kaufmann. (google, UCI reserve)
                      • Data and Computer Communications, William Stallings, Prentice-Hall. (publisher, UCI reserve)

                      Policy on Academic Honesty:

                      • UCI Policy on Academic Honesty

                      Add Drop Policy:

                      • UCI add/drop policies
                      • ICS add/drop policies
                      • Course add/drop policy: The instructor normally only allows additions to the course, as space allows, until the end of week 1.

                      Attendance Policy:

                      • Students are responsible for all material covered in all lectures, and the instructor will not provide notes. Posted lecture slides will be missing some key material.
                      • To be respectful to your classmates, please turn off cell phones when in the classroom, and avoid discussion that is not part of classroom activity.

                      CS MS Comprehensive Exam:

                      • The CS 232 portion of the CS MS Comprehensive Exam will consist of the course final. Students scoring a B or better on the final will pass the CS 232 portion of the CS MS Comprehensive Exam.
                      • You must let me know by the end of the second week of the quarter if you wish to attempt the CS 232 portion of the CS MS Comprehensive exam.

                       

                       

                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs232/outline.html CS 232 Course Outline
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 232 Course Outline

                      232 Computer and Communication Networks (4). Network architecture of the Internet, telephone networks, cable networks, and cell phone networks. Network performance models. Advanced concepts and implementations of flow and congestion control, addressing, internetworking, forwarding, routing, multiple access, streaming, and quality-of-service. Prerequisite: EECS148, CS 132, or consent of instructor. Same as EECS 248A and Networked Systems 201.

                      Lecture

                      Topic

                      Sub-topic

                      Reading (Kurose-Ross)

                      Assignment

                      1 Oct architecture course intro, circuit switching, telephone topology 1.1, 1.3; Tanenbaum 2.5  
                      3 Oct packet switching, terminology, Internet topology, cellular topology, cable topology 1.2-1.3, 1.7; Tanenbaum 2.7-2.8  
                      8 Oct technology convergence; packet switching metrics (delay) 1.4; 3.6.1  
                      10 Oct packet switching models & metrics (M/M/1, M/M/1/n, packet loss, M/G/1 heavy-tailed packet lengths, fluid flow, throughput, networks of queues, bandwidth-delay product) Leon-Garcia Appendix A PS1
                      15 Oct circuit switching models & metrics (M/M/n/n, call blocking, multiple cells, handoff), Internet parts, Internet layers, contracts 1.5  
                      17 Oct applications concepts (end-to-end, identifiers, client-server, peer-to-peer, protocol, ports, state), http, e-commerce, email 2.1-2-2, 2.4  
                      22 Oct file sharing, streaming, VoIP; application layer traffic models 2.6, 7.1-7.3 PS2
                      24 Oct application layer traffic models & practices (QoS, CAC, IntServ, RTSP, playout, rate, RTP, RTCP) 7.4-7.5  
                      29 Oct tcp concepts (reliability, connection-oriented, flow/congestion), sockets, timeouts/nums, windows, abp, srp 3.1-3.4  
                      31 Oct udp/tcp, congestion control, tcp (tahoe, reno, vegas), ecn 3.5, 3.6.2-3.6.3, 3.7 PS3
                      5 Nov transport layer traffic models & practices (rate scheduling, dccp, sctp); addressing: by layer, cidr, dhcp, nat 4.4.2, 5.4.1  
                      7 Nov midterm  
                      12 Nov ip addressing: dns, arp; IP problems 2.5, 5.4.1, 4.1-4.2  
                      14 Nov forwarding (datagram tables, virtual circuit tables, MPLS, switching, queuing), OSPF 4.3-4.5, 4.6.2, 5.5  
                      19 Nov hierarchical routing, BGP, transit & peering, broadcast, multicast; network layer traffic models & practices (packet scheduling & dropping, diffServ) 4.6.3-4.7, 7.5  
                      21 Nov lans lan problems, polling, tokens, aloha 5.1-5.3 PS4
                      26 Nov ethernet, switching 5.4  
                      3 Dec wireless lans, wi-fi; link & physical layer traffic models 6.3  
                      10 Dec 4:00pm-6:00pm Final      
                      �

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~sjordan/courses/cs232/links.html CS 232 Useful Links
                      Scott Jordan
                      Department of Computer Science University of California, Irvine
                        CS 232 Useful Links

                      Please send new useful network links for this list to Scott Jordan.

                      Internet Protocols

                      • The Internet Engineering Task Force
                      • Internet Protocol Specifications

                      Internet Topology & Measurement

                      • Internet Traffic Report
                      • Internet Topology
                      • Historical Internet Topology
                      • Historical Internet Topology
                      • Internet Tomography
                      • Number of Internet Hosts
                      • Number of Internet Users
                      • Root Nameserver List

                      Networking Bibliographies & Other Lists

                      • Network Bibliography
                      • Standard Papers on the Foundations of Networks
                      • Computing Research Repository (CoRR)
                      • whatis.com
                      • Telecoms Virtual Library
                      • TechFest Library

                      Technical Societies

                      • IEEE
                      • IEEE Communiations Society
                      • ACM

                      Technical Magazines (access from UCI hosts only)

                      • IEEE Spectrum
                      • Communications of the ACM
                      • IEEE Communications Magazine
                      • IEEE Wireless Communications Magazine
                      • IEEE Network Magazine

                      Technical Journals (access from UCI hosts only)

                      • IEEE Publications
                      • IEEE/ACM Transactions on Networking
                      • IEEE Journal on Selected Areas in Communications
                      • ACM Publications
                      • ACM Wireless Networks
                      • ACM Mobile Networks and Applications
                      • ACM Computer Communication Review
                      • ACM International Journal of Network Management
                      • ACM Transactions on Internet Technology
                      • Computer Networks (Elsevier)
                      • Performance Evaluation (Elsevier)
                      • Queueing Systems (Kluwer)
                      • Telecommunication Systems (Kluwer)

                      Technical Conference Proceedings (access from UCI hosts only)

                      • IEEE InfoCom
                      • IEEE ICC
                      • IEEE GlobeCom
                      • IEEE VTC
                      • IEEE WCNC
                      • ACM MobiCom
                      • ACM SigMetrics
                      • ACM SigComm

                      Networking Magazines

                      • Wired

                      White Papers

                      • Web ProForum Tutorials

                      Writing Technical Papers

                      • Writing Technical Articles

                      Networking Policy

                      • Telecommunications Act of 1996
                      • Telecommunications Policy Research Conference
                      • Benton Foundation
                      • Educause

                       

                      Scott Jordan last modified June 9, 2015 UCI • CS • Networked Systems
                      http://www.ics.uci.edu/~andre/informatics223s2011.html Informatics 223: Applied Software Design Techniques (Spring 2011)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
                      CodeExchange
                      Crowd Development
                      PorchLight
                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 223
                      Applied Software Design Techniques
                      Spring 2011

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Logistics
                      Location: AIRB 1030
                      Day and time: Monday and Wednesday, 9:30-10:50

                      Catalogue Description
                      223 Applied Software Design Techniques (4). Study of concepts, representations, techniques, and case studies in structuring software systems, with an emphasis on design considerations. Topics include static and dynamic system structure, data models, abstractions, naming, protocols and application programmer interfaces.

                      Structure
                      The class will be discussion-oriented. Papers must have been read beforehand, and discussion will be seeded by questions, observations, and assertions from all students in the class.

                      Case Study
                      Each student will perform a case study by adopting one software system that they will use to illustrate the techniques with concrete examples. Based on the case study, a few slides should be prepared weekly that illustrate the findings in the example system.

                      Poster
                      Each student will create a poster, which is based on a new "invention". Specifically, at the end of the quarter each student will present a new technique, a new modeling notation, a new approach, a new tool, or any other new "thing" that they invent as a result of their experience in the class. The technique will not have to be fully demonstrated, but the concept and novelty should be clear from the poster.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) questions, obaservations, and assertions that feed the discussions in class, (c) case study slides, and (d) poster.

                      Course Mailing List
                      To send mail: 37200-S11@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/s11/37190

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 223. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Papers
                      1 March 28 Welcome
                      March 30 Design & Abstraction Spector & Gifford: Case Study: A Computer Science Perspective on Bridge Design
                      Taylor & van der Hoek: Software Design and Architecture: The Once and Future Focus of Software Engineering
                      Kramer: Is Abstraction the Key to Computing?
                      2 April 4 Case study discussion
                      April 6 Models & UML Seidewitz: What Models Mean
                      Fowler: UML Distilled: A Brief Guide to the Standard Object Modeling Language (third edition)
                      Bell: Death by UML Fever
                      3 April 11 Case study discussion
                      April 13 Architecture Medvidovic & Taylor: A Classification and Comparison Framework for Software Architecture Description Languages
                      Aldrich, Chambers & Notkin: ArchJava: Connecting Software Architecture to Implementation
                      Georgas & Taylor: Policy-Based Self-Adaptive Architectures: A Feasibility Study in the Robotics Domain
                      4 April 18 Case study discussion
                      April 20 Patterns Lea: Christopher Alexander: An Introduction for Object-Oriented Designers
                      Gamma, Helm, Johnson, Vlissides: Design Patterns: Elements of Reusable Object-Oriented Software
                      Garlan, Allen, Ockerbloom: Exploiting Style in Architectural Design Environments
                      5 April 25 Case study discussion
                      April 27 Data Models Conklin: Hypertext: An Introduction and Survey
                      Capriero & Gelernter: Linda in Context
                      van der Lingen & van der Hoek: An Experimental, Pluggable Infrastructure for Modular Configuration Management Policy Composition
                      6 May 2 Case study discussion
                      May 4 Naming Berners-Lee, Fielding & Masinter: Uniform Resource Identifiers (URI): Generic Syntax
                      Carzaniga, Rosenblum & Wolf: Design and Evaluation of a Wide-Area Event Notification Service
                      Musen: Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture
                      7 May 9 Case study discussion
                      May 11 APIs Parnas: On the Criteria To Be Used in Decomposing Systems into Modules
                      Thau: Design considerations for the Apache Server API and Apache API Notes
                      Hartmann, Doorley & Klemmer: Hacking, Mashing, Gluing: Understanding Opportunistic Design
                      8 May 16 (No lecture, André out of town)
                      May 18 (No lecture, André out of town)
                      9 May 23 (No lecture, André out of town)
                      May 25 (No lecture, André out of town)
                      10 May 30 (No lecture, Memorial Day)
                      June 1 Protocols Emmerich: Engineering Distributed Objects, chapters 3, 4 & 8
                      Gudgin, Hadley, Mendelsohn, Moreau, Nielsen, Karmarkar & Lafon: Simple Object Access Protocol (SOAP) 1.2
                      Whitehead & Goland: WebDAV: A Network Protocol for Remote Collaborative Authoring on the Web
                      Finals week June 9, 8:00 - 10:00 Poster Presentations

                      Andre's picture
                      contact
                      email
                      andre@ics.uci.edu

                      skype
                      awvanderhoek

                      aim
                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics223s2007.html Informatics 223: Applied Software Design Techniques (Spring 2007)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
                      CodeExchange
                      Crowd Development
                      PorchLight
                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 223
                      Applied Software Design Techniques
                      Spring 2007

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Logistics
                      Location: BH 1423
                      Day and time: Friday, 09:00-11:50

                      Catalogue Description
                      223 Applied Software Design Techniques (4). Study of concepts, representations, techniques, and case studies in structuring software systems, with an emphasis on design considerations. Topics include static and dynamic system structure, data models, abstractions, naming, protocols and application programmer interfaces.

                      Structure
                      The class will be discussion-oriented. Papers must have been read beforehand, and discussion will be seeded by short presentations summarizing the key points of each paper. Presentations will be rotated among those in the class.

                      Case Study
                      Each student will perform a case study by adopting one software system that they will use to illustrate the techniques with concrete examples. Based on the case study, two slides should be prepared weekly that illustrate the findings in the example system.

                      Poster
                      Each student will create a poster, which is based on a new "invention". Specifically, at the end of the quarter each student will present a new technique, a new modeling notation, a new approach, a new tool, or any other new "thing" that they invent as a result of their experience in the class. The technique will not have to be fully demonstrated, but the concept and novelty should be clear from the poster.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) presentation(s) in class, (c) case study slides, and (d) poster.

                      Course Mailing List
                      To send mail: 37190-s07@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=37190&quarter=S07

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in ICS 223. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Papers
                      1 April 6 Welcome
                      2 April 13 Design & Abstraction Spector & Gifford: Case Study: A Computer Science Perspective on Bridge Design
                      Taylor & van der Hoek: Software Design and Architecture: The Once and Future Focus of Software Engineering
                      Kramer: Is Abstraction the Key to Computing?
                      3 April 20 No lecture - André at ATL
                      4 April 27 Models & UML Seidewitz: What Models Mean
                      Fowler: UML Distilled: A Brief Guide to the Standard Object Modeling Language (third edition)
                      Bell: Death by UML Fever
                      5 May 4 Architecture Hatcliff, Deng, Dwyer, Jung & Ranganath: Cadena: An Integrated Development, Analysis, and Verification Environment for Component-based Systems
                      Allen, Douence & Garlan: Specifying and Analyzing Dynamic Software Architectures
                      Rakic & Medvidovic: Increasing the Confidence in Off-the-Shelf Components: A Software Connector-based Approach
                      6 May 11 Patterns Lea: Christopher Alexander: An Introduction for Object-Oriented Designers
                      Gamma, Helm, Johnson, Vlissides: Design Patterns: Elements of Reusable Object-Oriented Software
                      Garlan, Allen, Ockerbloom: Exploiting Style in Architectural Design Environments
                      7 May 18 Data Models Capriero & Gelernter: Linda in Context
                      Conklin: Hypertext: An Introduction and Survey
                      van der Hoek, Carzaniga, Heimbigner & Wolf: A Testbed for Configuration Management Policy Programming
                      8 May 25 No lecture - André at ICSE
                      9 June 1 Naming Berners-Lee, Fielding & Masinter: Uniform Resource Identifiers (URI): Generic Syntax
                      Carzaniga, Rosenblum & Wolf: Design and Evaluation of a Wide-Area Event Notification Service
                      Musen: Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture
                      10 June 8 Standardization Thau: Design considerations for the Apache Server API and Apache API Notes
                      Whitehead & Goland: WebDAV: A network protocol for remote collaborative authoring on the Web
                      Hanseth, Monteiro & Hatling: Developing Information Infrastructure: The Tension Between Standardisation and Flexibility
                      Finals week June 13, 9-11am Poster Presentations

                      Andre's picture
                      contact
                      email
                      andre@ics.uci.edu

                      skype
                      awvanderhoek

                      aim
                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics121f2009.html Informatics 121: Software Design I (Fall 2009)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2009

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Nick Mangano
                      nmangano@ics.uci.edu

                      Logistics
                      Location: AIRB 1030
                      Day and time: Tuesday and Thursday, 12:30-13:50

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisite: Informatics 102 with a grade of C or better.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) midterm, and (c) assignments and class project.

                      Grade distribution will be as follows:

                      • Readings: 10%
                      • Bus stop: 20%
                      • Design Studio 2: 20%
                      • Video analysis: 10%
                      • Design Studio 3: 20%
                      • Design Studio 4: 20%

                      Course Mailing List
                      To send mail: 37050-F09@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f09/37050/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 24 Design Exercises Lecture 1 Assignment 1 out (see slides; download text here)
                      2 September 29 Design Exercises Lecture 2 Assignment 1 due
                      Assignment 2 out (see slides; download text here)
                      October 1 Defining Design Lecture 3 Assignment 2 due
                      Assign 3, part 1 out (see slides)
                      3 October 6 Design Exercises Lecture 4 Assignment 3, part 1 due
                      Assignment 3, part 2 out (see slides)
                      October 8 Design Theory Lecture 5
                      4 October 13 Design Exercises Assignment 3, part 2 due
                      October 15 Design theory Lecture 6 Assignment 4 out (see slides; download text here and here)
                      5 October 20 Design Studio I Lecture 7 Assignment 4 due
                      Design Studio I out (see slides)
                      October 22 Design Theory Lecture 8 Design Studio I continued (see slides)
                      6 October 27 Design Studio I Lecture 9 Design Studio I continued (see slides)
                      October 29 Design Studio I Lecture 10 Design Studio I continued (see slides)
                      7 November 3 Design Studio I Design Studio I continued
                      November 5 Design Studio I Lecture 11 Design Studio I continued
                      Video analysis out (see slides)
                      8 November 10 Video analysis Lecture 12 Video analysis due
                      November 12 Design Theory Lecture 13
                      9 November 17 Design Studio II Lecture 14 Design Studio II out (see slides and see design documentary format )
                      November 19 Design Studio II Lecture 15 Design Studio II continued
                      10 November 24 Design Studio II Lecture 16
                      November 26 Thanksgiving, no lecture
                      11 December 1 Design Studio III Lecture 17 Design Studio III out (see slides and see the design brief)
                      December 3 Design Studio III
                      Finals week December 11, 10:30 - 12:30 Design Studio III

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics291sf2015.html Informatics 291s: Literature Survey in Software Engineering (Fall 2015)
                      Course Title
                      Informatics 291s
                      Literature Survey in Software Engineering
                      Fall 2015

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5038
                      Phone: +1 949-824-6326
                      Office hours: Tuesday, 9-10

                      Logistics
                      Location: ICS 259
                      Lecture day and time: Thursday, 09:30-10:50

                      Catalogue Description
                      291s Literature Survey in Software Engineering (2). Reading and analysis of relevant literature in Software Engineering under the direction of a faculty member.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class participation, (b) weekly summaries, (c) practice questions.

                      • Class participation: 50%
                      • Weekly summaries: 40%
                      • Practice questions: 10%

                      Course Mailing List
                      To send mail: 37430-F15@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f15/37430/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-6272 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Schedule
                      Week Date Topic Slides
                      1 September 24 Introduction Lecture 1
                      2 October 1 History and Discipline of Software Engineering
                      3 October 8 Analysis and Safety
                      4 October 15 Process
                      5 October 22 Requirements and Architecture
                      6 October 29 Design
                      7 November 5 Student panel
                      8 November 12 Environments and Metrics
                      9 November 19 Faculty panel
                      10 November 26 No class: Thanksgiving
                      11 December 3 Question and answer
                      December 4 Phase II exam

                      http://www.ics.uci.edu/~andre/informatics122f2006.html Informatics 122: Software Design II (Fall 2006)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 122
                      Software Design II
                      Fall 2006

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: ICS2 207
                      Phone: +1 949-824-6326

                      Logistics
                      Location: CS 243
                      Day and time: Monday and Wednesday, 14:00-15:20

                      Catalogue Description
                      122 Software Design II (4). Introduction to advanced software design principles, paradigms, and techniques. Topics include large-scale design, software reuse, product-line architectures, design recovery, refactoring, application frameworks, real-time systems, design-for-context. Case studies of existing designs and extensive practice with real-world designs. Prerequisite: Informatics 121.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) midterm and final, and (c) assignments and class project.

                      Grade distribution will be as follows:

                      • Paper summaries: 5%
                      • Design change project: 10%
                      • Design patterns project: 10%
                      • Design recovery project: 10%
                      • Midterm: 15%
                      • Component review: 10%
                      • Final project: 20%
                      • Final: 20%

                      Course Mailing List
                      To send mail: 36660-F06@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=36660&quarter=F06

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 25 Review and Preview Lecture 1 Assignment 1 out (see slides; download text here and here)
                      September 27 Good Design/Bad Design Lecture 2 Assignment 1 due
                      Assignment 2a out (see slides; download the design here and a sample maze here)
                      2 October 2 Good Design/Bad Design Lecture 3 Assignment 2a due
                      Assignment 2b out (see slides)
                      October 4 Good Design/Bad Design Lecture 4 Assignment 2b due
                      3 October 9 Design Patterns Lecture 5 Assignment 3 out (see slides; download the design here)
                      October 11 Design Patterns No slides Assignment 3 due
                      4 October 16 Design Studio: Rapid Implementation Design No slides
                      October 18 Design Recovery Lecture 6 Assignment 4 out (see slides; download the code here)
                      5 October 23 Design Recovery Lecture 7 Assignment 4 due
                      October 25 No lecture No slides
                      6 October 30 Midterm No slides
                      November 1 Components Lecture 8 Assignment 5 out (see slides)
                      7 November 6 Components Lecture 9 Assignment 5 due
                      November 8 Large-Scale Design Lecture 10 Assignment 6 out (see slides; this is the Design Studio Final Project; download the weekly evaluation form here)
                      8 November 13 Design Studio: Final Project No slides Assignment 6 due (part 1)
                      November 15 Design Studio: Final Project No slides Assignment 6 due (part 2)
                      9 November 20 Cancelled
                      November 22 No lecture No slides
                      10 November 27 No lecture (Sony Imageworks visit) No slides
                      November 29 Design Studio: Final Project No slides Assignment 6 due (part 3)
                      Finals week December 8, 13:30 - 15:30 Design Studio: Final Project Lecture 11 Assignment 6 due (part 4)

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                      http://www.ics.uci.edu/~andre/informatics121s2007.html Informatics 121: Software Design I (Spring 2007)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 121
                      Software Design I
                      Spring 2007

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Co-Teacher
                      Alex Baker
                      http://www.ics.uci.edu/~abaker
                      abaker@ics.uci.edu
                      Office: Donald Bren Hall 5221
                      Phone: +1 949-824-3100

                      Teaching Assistant
                      Nick Noack
                      nnoack@ics.uci.edu

                      Reader
                      Vivian Olivera
                      volivera@ics.uci.edu

                      Logistics
                      Location: BH 1431
                      Day and time: Tuesday and Thursday, 11:00-12:20

                      Catalogue Description
                      121 Software Design I (4). Introduction to software design principles, paradigms, tools, and techniques. Topics include alternative architectural styles, iterative refinement, design patterns, mapping design onto code, design tools, and design notations. Includes extensive practice in creating designs and study of existing designs. Prerequisite: Informatics 102 with a grade of C or better.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) midterm and final, and (c) assignments and class project.

                      Grade distribution will be as follows:

                      • Play-Doh: 10%
                      • Readings: 10%
                      • Design Studio 1: 20%
                      • Design Studio 2: 20%
                      • Midterm: 15%
                      • Final: 25%

                      Course Mailing List
                      To send mail: 37060-s07@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=37060&quarter=S07

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 April 3 Design Exercises Lecture 1 Assignment 1 out (see slides; download text here)
                      April 5 Design Exercises Lecture 2 Assignment 1 due
                      Assignment 2 out (see slides)
                      2 April 10 Design Exercises Lecture 3 Assignment 2 due
                      Assignment 3 out (see slides; download text here)
                      April 12 Defining Design Lecture 4 Assignment 3 due
                      Assignment 4 out (see slides; download text here)
                      3 April 17 Design Theory Lecture 5 Assignment 4 due
                      Assignment 5 out (see slides; download text here)
                      April 19 System Design Lecture 6 Assignment 5 due
                      4 April 24 System Design Lecture 7
                      April 26 Design Studio I Lecture 8
                      5 May 1 Design Studio I Lecture 9
                      May 3 Design Studio I No slides
                      6 May 8 Design Studio I No slides
                      May 10 Design Studio I Lecture 10
                      7 May 15 Midterm
                      May 17 Design Studio II Lecture 11
                      8 May 22 Design Studio II No slides
                      May 24 Design Studio II No slides
                      9 May 29 Design Studio II No slides
                      May 31 Design Studio II No slides
                      10 June 5 Design Studio II No slides
                      June 7 Wrap-up
                      Finals week June 12, 10:30 - 12:30 Final

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics121f2011.html Informatics 121: Software Design I (Fall 2011)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2011

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Fang Deng
                      fdeng@ics.uci.edu

                      Logistics
                      Location: AIRB 1030
                      Lecture day and time: Tuesday and Thursday, 12:30-13:50
                      Discussion day and time: Friday, 14:00-15:30

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisite: Informatics 113 with a grade of C or better.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, and (b) assignments, and (c) course projects.

                      Grade distribution will be as follows:

                      • Readings: 10%
                      • Design studio 1: 20%
                      • Design studio 2: 10%
                      • Video analysis: 10%
                      • Design studio 3: 25%
                      • Design studio 4: 25%

                      Course Mailing List
                      To send mail: 37050-f11@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f11/37050/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 22 Design exercises Lecture 1 Reading 1 out (see slides; download text here)
                      2 September 27 Design exercises Lecture 2 Reading 1 due
                      Reading 2 out (see slides; download text here)
                      September 29 Defining design Lecture 3 Reading 2 due
                      Design studio 1, part 1 out
                      3 October 4 Design studio 1 Lecture 4 Design studio 1, part 1 due
                      Design studio 1, part 2 out (team evaluation form)
                      October 6 Design is difficult Lecture 5
                      4 October 11 Design studio 1 Design studio 1, part 2 due
                      October 13 Design essence and tradeoffs Lecture 6 Video analysis out
                      5 October 18 Design studio 2 Lecture 7 Video analysis due
                      Design studio 2, part 1 out (read Michael Jackson and Mary Shaw)
                      October 20 Design studio 2 Lecture 8 Design studio 2, part 1 due
                      Design studio 2, part 2 out
                      6 October 25 Design notations Lecture 9  
                      October 27 (lecture canceled) Design studio 2, part 2 due
                      7 November 1 Guest lecture: Ping Chen (Google)
                      November 3 Design studio 3 Lecture 10 Design studio 3, part 1 out
                      8 November 8 Design studio 3 Lecture 11 Design studio 3, part 1 due
                      Design studio 3, part 2 out
                      November 10 Guest lecture: Jim Dibble (Cooper)
                      9 November 15 Design studio 3 Lecture 12 Design studio 3, part 2 due (team evaluation form)
                      Design studio 3, part 3 out
                      November 17 Design studio 3 Lecture 13 Design studio 3, part 3 due
                      10 November 22 Design studio 4 Lecture 14
                      November 24 (no lecture, Thanksgiving)
                      11 November 29 Design studio 4
                      December 1 Design studio 4
                      Finals week December 9, 10:30 - 12:30 Design studio 4

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                      http://www.ics.uci.edu/~andre/informatics209sf2006.html Informatics 209S: Seminar in Informatics (Fall 2006)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 209S
                      Seminar in Informatics
                      Fall 2006

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: ICS2 207
                      Phone: +1 949-824-6326

                      Logistics
                      Location: CS 253 (first hour) and ICS2 136 (second hour)
                      Day and time: Friday, 14:00-16:00

                      Catalogue Description
                      209S Seminar in Informatics (2). Current research and research trends in Informatics. Forum for presentation and criticism by students of research work in progress. May be repeated for credit. Formerly ICS 229.

                      Course Structure
                      This particular incarnation of the seminar will focus on ideas. Specifically, each week everyone is expected to bring to the table a new idea that pertains to Informatics research. Each week, we will hear brief synopses of all ideas and choose one or two for extensive discussion. The goal is to hone our abilities to critically examine ideas, to learn to be supportive even if we are skeptical, to gain an understanding of what we consider good and bad research ideas, and to illustrate the role of a group in furthering ideas.

                      Attendance
                      All students are expected to attend all seminars (attendance will be taken). During the first hour of our weekly meeting we will proceed per the above. During the second hour we will join the rest of the Informatics Department in the Friday Research Hour. That hour will be devoted to faculty research talks, open discussions, panels, and other activities still to be determined.

                      Grades
                      All students enrolled in the course will earn a grade based upon: (a) a weekly one-page write-up of their idea, (b) class attendance, and (c) class participation.

                      Course Mailing List
                      To send mail: 36910-F06@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=36910&quarter=F06

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Schedule
                      Week Date Topic
                      1 September 22 No lecture (Andre in Tokyo, ASE 2006).
                      You should attend the Informatics research hour nonetheless.
                      2 September 29 Introduction
                      3 October 6 No lecture (Andre in San Diego, Grace Hopper 2006).
                      You should attend the Informatics research hour nonetheless.
                      4 October 13 No lecture (Andre in Florianopolis, ICGSE 2006).
                      You should attend the Informatics research hour nonetheless.
                      5 October 20 No lecture (Andre in Rio de Janeiro, UFRJ).
                      You should attend the Informatics research hour nonetheless.
                      6 October 27 Idea Discussion
                      7 November 3 Idea Discussion
                      8 November 10 No lecture (Veteran's Day Holiday)
                      9 November 17 Idea Discussion and Wrap-up
                      10 November 24 No lecture (Thanksgiving)

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                      http://www.ics.uci.edu/~andre/informatics117s2009.html Informatics 117: Project in Software System Design (Spring 2009)
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                      Course Title
                      Informatics 117
                      Project in Software System Design
                      Spring 2009

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Reader
                      Kyle Strasser
                      http://www.ics.uci.edu/~kstrasse/
                      kstrasse@ics.uci.edu
                      Office: Donald Bren Hall 5209

                      Logistics
                      Location: ICS1 243
                      Day and time: Tuesday and Thursday, 11:00-12:20

                      Catalogue Description
                      117 Project in Software System Design (4). Specification, design, construction, testing, and documentation of a complete software system using concepts learned in ICS 52, Informatics 101, and Informatics 111. Special emphasis on the need for and use of teamwork, careful planning, and other techniques for working with large systems.

                      Prerequisites
                      ICS 51 with a grade of C or better; Informatics 101/CS 141/CSE141 and Informatics 111/CSE121; Mathematics 2A-B and Statistics 67/Mathematics 67. Formerly ICS 125. (Exceptions may be granted, please talk to the instructor.)

                      Add/Drop Policy
                      Given the interest in the class this quarter, and given the importance of starting as a team as soon as possible, no adds or drops will be allowed after the FIRST week of classes.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) presentation(s) in class, (c) overall project, and (d) performance appraisals by team members. You will receive one overall grade at the end of the class; no partial grades will be given, nor will a particular distribution be enforced among the above four. You will, however, receive weekly feedback concerning your progress.

                      Course Mailing List
                      To send mail: 37040-S09@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/s09/37040/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Schedule
                      Week Date Topic
                      1 March 31 Welcome
                      April 2 ...
                      2 April 7 ...
                      April 9 ...
                      3 April 14 ...
                      April 16 ...
                      4 April 21 ...
                      April 23 ...
                      5 April 28 ...
                      April 30 ...
                      6 May 5 ...
                      May 7 ...
                      7 May 12 ...
                      May 14 ...
                      8 May 19 (No lecture, André at ICSE)
                      May 21 ...
                      9 May 26 ...
                      May 28 ...
                      9 June 2 ...
                      June 4 ...
                      Finals week June 9, 10:30-12:30 Final demos

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                      http://www.ics.uci.edu/~andre/informatics122w2010.html Informatics 122: Software Design II (Winter 2010)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 122
                      Software Design II
                      Winter 2010

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: 949 824 6326

                      Reader
                      Mitch Dempsey
                      http://www.ics.uci.edu/~mdempsey/
                      mdempsey@ics.uci.edu
                      Office: ICS2 110

                      Logistics
                      Location: AIRB 1030 (except when noted)
                      Day and time: Tuesday and Thursday, 09:30-10:50

                      Catalogue Description
                      122 Software Design II (4). Introduction to implementation design: designing the internals of a software application. Topics include design aesthetics, design implementation, design recovery, design patterns, and component reuse. Includes practice in designing and case studies of existing designs. Prerequisite: Informatics 121.

                      Book
                      Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series, Hardcover), Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) assignments, (c) the final class project, and (d) team evaluations.

                      Grade distribution will be as follows (as suitably adjusted with team evaluations):

                      • Design aesthetics: 15%
                      • Design implementation: 15%
                      • Design recovery: 15%
                      • Design patterns: 15%
                      • Component review: 15%
                      • Final project: 25%

                      Course Mailing List
                      To send mail: 37070-w10@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/w10/37070/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Please also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 122. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 January 5 Review and Preview lecture1.ppt
                      January 7 Design Aesthetics lecture2.ppt Assignment 1, part 1 out (individual, moderate)
                      2 January 12 Design Aesthetics lecture3.ppt Assignment 1, part 1 due
                      Assignment 1, part 2 out (individual, moderate)
                      January 14 (in ICS 259) Design Aesthetics Reflection lecture4.ppt Assignment 1, part 2 due
                      Assignment 1, p part 3 out
                      3 January 19 Design Aesthetics Reflection lecture5.ppt Assignment 1, part 3 due
                      Assignment 2 out (individual, heavy)
                      January 21 Design Aesthetics lecture6.ppt
                      4 January 26 (in ICS 259) Design Aesthetics Reflection Assignment 2 due
                      January 28 Design Recovery lecture7.ppt Assignment 3 out (team, heavy, team-evaluation.doc)
                      5 February 2 Design Recovery Reflection
                      February 4 (in ICS 259) Design Patterns lecture8.ppt Assignment 3 due
                      Assignment 4 out (team, moderate, team-evaluation.doc)
                      6 February 9 No lecture
                      February 11 No lecture
                      7 February 16 Design Patterns (same slides as Febrruary 4)
                      February 18 Reuse lecture9.ppt Assignment 4 due
                      Assignment 5 out (team, moderate, team-evaluation.doc)
                      8 February 23 Reuse
                      February 25 Reuse Presentations lecture10.ppt Assignment 5 due
                      Final Design Project out (team, heavy)
                      9 March 2 Final Design Project
                      March 4 Final Design Project
                      10 March 9 Final Design Project
                      March 11 Final Design Project
                      Finals week March 18, 08:00 - 10:00 Final Design Project Final Design Project due

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                      andre@ics.uci.edu

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics121f2013.html Informatics 121: Software Design I (Fall 2013)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
                      CodeExchange
                      Crowd Development
                      PorchLight
                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2013

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Benjamin (Ben) Koehne
                      bkoehne@uci.edu
                      Office hours: 14:30-16:30, Tuesdays, 5231 Bren Hall

                      Logistics
                      Location: ICS 174
                      Lecture day and time: Tuesday and Thursday, 12:30-13:50
                      Discussion day and time: Friday, 14:00-15:20 (note: in HG 1800)

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisites: Informatics 45 or ICS 23/CSE23 or ICS 33/CSE43, with a grade of C or better and upper-division standing.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) readings, and (c) course projects.

                      Grade distribution will be as follows (adjusted by class attendance/participation as need be):

                      • Readings: 10%
                      • Design studio 1: 22.5%
                      • Design studio 2: 22.5%
                      • Design studio 3: 22.5%
                      • Design studio 4: 22.5%

                      Course Mailing List
                      To send mail: 37050-f13@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f13/37050/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 26 Design exercises Lecture 1 Reading 1 out (download text here)
                      2 October 1 No class - André keynote at CONISOFT 2013
                      October 3 No class - André keynote at CONISOFT 2013
                      3 October 8 No class - André SCALE research meeting/IBM visit
                      October 10 Defining design Lecture 2 Reading 1 due
                      Reading 2 out (download text here)
                      4 October 15 Design cycle Lecture 3 Reading 2 due
                      Design studio 1 out
                      October 17 Design studio 1 group work Lecture 4 Design studio 1 due (part 1)
                      Design studio 1 out (part 2)
                      5 October 22 Design studio 1 presentations Design studio 1 due (part 2)
                      October 24 Design is difficult Lecture 5 Design studio 2 out (part 1)
                      6 October 29 Design essence Lecture 6
                      October 31 Design studio 2 group work Lecture 7 Design studio 2 due (part 1)
                      Design studio 2 out (part 2)
                      Papers for this design studio available here and here)
                      7 November 5 Design studio 2 group work Lecture 8
                      November 7 Design notations Lecture 9
                      8 November 12 Design methods Lecture 10 Design studio 2 due (part 2)
                      Design studio 3 out (part 1)
                      November 14 Class canceled
                      9 November 19 Design studio 3 presentations Design studio 3 due (part 1)
                      November 21 Silent sticky notes Lecture 11 Design studio 3 out (part 2)
                      10 November 26 Storyboarding Lecture 12
                      November 28 No class - Thanksgiving
                      11 December 3 Design studio 3 presentations Lecture 13 Design studio 3 due (part 2)
                      Design studio 4 out
                      December 5 Design studio 4
                      Finals week December 13, 10:30 - 12:30 Design studio 4 due

                      Andre's picture
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                      andre@ics.uci.edu

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics121f2012.html Informatics 121: Software Design I (Fall 2012)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
                      CodeExchange
                      Crowd Development
                      PorchLight
                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2012

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Christian Adriano
                      adrianoc@uci.edu

                      Logistics
                      Location: ET 204
                      Lecture day and time: Monday and Wednesday, 11:00-12:20
                      Discussion day and time: Friday, 14:00-15:30 (note: in ET 202)

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisites: Informatics 45 or ICS 23/CSE23 or ICS 33/CSE43, with a grade of C or better and upper-division standing.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, and (b) assignments, and (c) course projects.

                      Grade distribution will be as follows:

                      • Readings: 10%
                      • Design studio 1: 20%
                      • Design studio 2: 10%
                      • Video analysis: 10%
                      • Design studio 3: 25%
                      • Design studio 4: 25%

                      Course Mailing List
                      To send mail: 37050-f12@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f12/37050//

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 October 1 Design exercises Lecture 1 Reading 1 out (download text here)
                      October 3 Design exercises Lecture 2 Reading 1 due
                      Reading 2 out (download text here)
                      2 October 8 Defining design Lecture 3 Reading 2 due
                      October 10 Designing is difficult Lecture 4 Design studio 1, part 1 out
                      3 October 15 Design studio 1 Lecture 5 Design studio 1, part 1 due
                      October 17 Design studio 1 Lecture 6
                      4 October 22 Design studio 1 presentations Design studio 1, part 2 due
                      October 24 Design essence and tradeoffs Lecture 7 Video analysis out
                      5 October 29 Design studio 2 Lecture 8 Video analysis due
                      Papers for design studio available here and here)
                      October 31 Design studio 2 Lecture 9
                      6 November 5 Design notations Lecture 10
                      November 7 Design techniques Lecture 11 Design studio 2 due
                      Design studio 3, part 1 out
                      7 November 12 No lecture, Veterans Day)
                      November 14 No lecture, André traveling)
                      8 November 19 Design studio 3 presentations Design studio 3, part 1 due
                      November 21 Design studio 3 presentations
                      9 November 26 Design studio 3 Lecture 12 Design studio 3, part 2 out
                      November 28 Design studio 3 Lecture 13
                      10 December 3 Design studio 4 Lecture 14 Design studio 3, part 2 due
                      Design studio 4 out
                      December 5 Design studio 4
                      Finals week December 14, 08:00 - 10:00 Design studio 4 presentations Design studio 4 due

                      Andre's picture
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                      andre@ics.uci.edu

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/ics52w2012.html ICS 52: Introduction to Software Engineering (Winter 2012)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
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                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      ICS 52
                      Introduction to Software Engineering
                      Winter 2012

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Ankita Raturi
                      sudokita@gmail.com

                      Reader
                      Fang Deng
                      fdeng@ics.uci.edu

                      Logistics
                      Location: SSH 100
                      Lecture day and time: Monday, Wednesday, and Friday, 10:00-10:50
                      Discussion day and time: Monday, Wednesday, and Friday, 17:00-17:50

                      Catalogue Description
                      52 Introduction to Software Engineering (6). Introduction to the concepts, methods, and current practice of software engineering. The study of large-scale software production; software life cycle models as an organizing structure; principles and techniques appropriate for each stage of production. Laboratory work involves a project illustrating these elements. Prerequisite: ICS 23 with a grade of C or better. Only one course from ICS 52, ICS 105, and Informatics 43 may be taken for credit.

                      Book
                      Hans van Vliet, Software Engineering: Principles and Practice, Third Edition.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon:

                      • Project: 45%
                        • Requirements: 10%
                        • Design: 20%
                        • Testing: 15%
                      • Midterm: 20%
                      • Final: 35%

                      Late Assignments
                      No late assignments will be accepted.

                      Academic Dishonesty
                      Failure to comply with the UC Irvine Academic Honesty will policy result in a final grade of F.

                      Course Mailing List
                      To send mail: 36630-W12@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail//w12/36630/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in ICS 52. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 January 9 Defining software engineering Lecture 1
                      January 11 Defining software engineering Lecture 2 Read chapter 1 van Vliet
                      January 13 Life cycles Lecture 3 Read chapter 3 van Vliet
                      2 January 16 No lecture (Martin Luther King Jr.'s Day)
                      January 18 No lecture (André in Zürich)
                      January 20 No lecture (André in Zürich)
                      3 January 23 Fundamental principles Lecture 4 Read chapters 6.2, 12.1.1, 12.1.2 van Vliet
                      January 25 Requirements engineering Lecture 5 Read chapter 9 van Vliet
                      January 27 Requirements engineering No slides Assignment 1 out
                      Discussion today
                      4 January 30 Architecture design Lecture 6 Read chapter 11 van Vliet
                      Discussion today
                      February 1 Architecture design No slides Discussion today
                      February 3 Architecture design No slides Discussion today
                      5 February 6 Architecture design No slides Assignment 1 due
                      Assignment 2 out
                      February 8 Architecture design Lecture 7
                      February 10 Architecture design No slides
                      6 February 13 Module design No slides Assignment 2 - architecture due
                      February 15 Module design No slides
                      February 17 Midterm  
                      7 February 20 No lecture (President's Day)
                      February 22 Module design Lecture 8
                      February 24 Module design No slides
                      8 February 27 Implementation Lecture 9

                      Assignment 2 - design due
                      Assignment 3 out

                      February 29 Testing Lecture 10
                      March 2 No lecture (André grant meeting)
                      9 March 5 Testing Lecture 11 Read chapter 13 van Vliet
                      Assignment 3 out
                      March 7 Testing No slides  
                      March 9 No lecture  
                      10 March 12 What's next No slides
                      March 14 Review No slides
                      March 16 No lecture (except for handing in assignment)   Assignment 3 due
                      Finals week March 19, 10:30 - 12:30 Final

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/ics223w2006.html ICS 223: Software Architecture (Winter 2006)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Calico
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                      Christian Adriano
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                      Course Title
                      Information and Computer Science 223
                      Software Architecture
                      Winter 2006

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: ICS2 207
                      Phone: +1 949-824-6326

                      Logistics
                      Location: CS 253
                      Day and time: Tuesday and Thursday, 12:30-13:50

                      Catalogue Description
                      223 Software Architecture (4). Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures.

                      Structure
                      The class will be discussion oriented. Papers must have been read beforehand and discussion will be seeded by critical opinions, questions, and challenges that each student will write on the whiteboard each week at the beginning of the discussion. These discussions will primarily take place on Tuesdays.

                      Project
                      Each student will perform a practical research project. The research project must result in a tangible technology, the form of which will be discussed in class. Discussion regarding progress and issues that may arise will take place on Thursdays.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) the critical opinions, questions, and challenges shared with the class to seed the discussions, and (c) the class project.

                      Course Mailing List
                      To send mail: 36720-w06@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=36720&quarter=W06

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in ICS 223. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Papers
                      1 January 10 Welcome
                      January 12 Introduction to Software Architecture Perry & Wolf: Foundations for the Study of Software Architecture
                      Kruchten: Mommy, Where Do Software Architectures Come From?
                      Kruchten: Common Misconceptions about Software Architecture
                      2 January 17 Architecture Description Languages Kruchten: The 4+1 View Model of Architecture
                      Medvidovic & Taylor: A Classification and Comparison Framework for Software Architecture Description Languages
                      Dashofy, van der Hoek & Taylor: An Infrastructure for the Rapid Development of XML-based Architecture Description Languages
                      January 19 A Historical Perspective DeRemer & Kron: Programming-in-the-Large versus Programming in-the-Small
                      Wiederhold, Wegner & Ceri: Toward Megaprogramming
                      Prieto-Diaz & Neighbors: Module Interconnection Languages
                      3 January 24 Requirements and Architecture Nuseibeh: Weaving the Software Development Process Between Requirements and Architecture
                      Chung, Nixon & Yu: Using Non-Functional Requiremets to Systematically Select Among Alternatives in Architectural Design
                      Grünbacher, Medvidovic & Egyed: Reconciling Software Requirements and Architectures with Intermediate Models
                      January 26 Introduction to Projects
                      4 January 31 Product Line Architectures Tracz: DSSA (Domain-Specific Software Architecture) Pedagogical Example
                      Garg, Critchlow, Chen, Van der Westhuizen & van der Hoek: An Environment for Managing Evolving Product Line Architectures
                      Sinnema, Deelstra, Nijhuis & Bosch: COVAMOF: A Framework for Modeling Variability in Software Product Families
                      February 2 Project Discussion
                      5 February 7 Design Rationale Asundi, Kazman & Klein: Using Economic Considerations to Choose Among Architecture Design Alternatives
                      Tang, Ali Babar, Gorton & Han: A Survey of Architecture Design Rationale
                      Jansen & Bosch: Software Architecture as a Set of Architectural Design Decisions
                      February 9 Project Proposal Due
                      6 February 14 Connectors and Implementation Mehta, Medvidovic & Phadke: Towards a Texonomy of Software Connectors
                      Di Nitto & Rosenblum: Exploiting ADLs to Specify Architectural Styles Induced by Middleware Infrastructures
                      Aldrich, Chambers & Notkin: ArchJava: Connecting Software Architecture to Implementation
                      February 16 Project Discussion
                      7 February 21 Refinement Moriconi, Qian & Riemenschneider: Correct Architecture Refinement
                      Garlan, Cheng & Kompanek: Reconciling the Needs of Architectural Description with Object-Modeling Notations
                      Batory, Sarvela & Rauschmayer: Scaling Step-Wise Refinement
                      February 23 Project Discussion
                      8 February 28 Mismatch and Analysis Garlan, Allen & Ockerbloom: Architectural Mismatch: Why Reuse is So Hard
                      Magee, Kramer, Giannakopoulou: Behaviour Analysis of Software Architectures
                      Allen, Garlan & Ivers: Formal Modeling and Analysis of the HLA Component Integration Standard
                      March 2 Project Discussion
                      9 March 7 Dynamism Magee & Kramer: Dynamic Structure in Software Architectures
                      Oreizy, Medvidovic & Taylor: Architecture-Based Runtime Software Evolution
                      Georgas, van der Hoek & Taylor: Architectural Runtime Configuration Management in Support of Dependable Self-Adaptive Software
                      March 9 Preliminary Project Demonstrations
                      10 March 14 Other Developments Kazman & Carrière: Playing Detective: Reconstructing Software Architecture from Available Evidence
                      Garlan, Cheng & Schmerl: Increasing System Dependability through Architecture-based Self-repair
                      Baniassad & Clarke: Theme: An Approach for Aspect-Oriented Analysis and Design
                      March 16 Project Discussion
                      Finals week March 24, 10:30 - 12:30 Final Project Demonstrations

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics43f2012.html Informatics 43: Introduction to Software Engineering (Fall 2012)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Calico
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                      Christian Adriano
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                      Course Title
                      Informatics 43
                      Introduction to Software Engineering
                      Fall 2012

                      Professors
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Hadar Ziv
                      hadarziv97@gmail.com
                      Office: DBH 5062

                      Teaching Assistant
                      Lee Martie
                      lmartie@uci.edu
                      Office hours: Friday 13:00-15:00

                      Reader
                      Ankita Raturi
                      sudokita@gmail.com
                      Office hours: Monday 12:30-13:30 and Thursday 15:30-16:30

                      Logistics
                      Location: SSPA 1100
                      Day and time: Monday and Wednesday, 14:00-15:20

                      Catalogue Description
                      43 Introduction to Software Engineering (4). Concepts, methods, and current practice of software engineering. Large-scale software production, software life cycle models, principles and techniques for each stage of development. Laboratory project applying these concepts. Only one course from Informatics 43, ICS 52, and ICS 105 may be taken for credit.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: assignments, midterm, and final.

                      Course Mailing List
                      To send mail: 37000-f12@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f12/37000/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 43. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 October 1 Introduction Lecture 1
                      October 3 Case study (Facebook) No slides Assignment 1 out
                      2 October 8 Software architecture Lecture 2
                      October 10 Case study (Facebook) No slides Assignment 1 due
                      Assignment 2 out
                      3 October 15 Evolution Lecture 3
                      October 17 Case study (Facebook) No slides Assignment 2 due
                      4 October 22 User orientation Lecture 4
                      October 24 Case study (Facebook) No slides Assignment 3 out
                      5 October 29 Software process Lecture 5
                      October 31 Midterm review Assignment 3 due
                      6 November 5 Midterm
                      November 7 Requirements Lecture 6
                      7 November 12 No class (holiday)
                      November 14 Requirements interview with TA Assignment 4 out
                      8 November 19 Notations Lecture 7
                      November 21 Case study (prison bartering)
                      9 November 26 Testing and analysis Lecture 8 Assignment 4 due
                      November 28 Case study (prison bartering) Assignment 5 out
                      10 December 3 Tools No slides
                      December 5 Review Assignment 5 due
                      Finals week December 14, 13:30 - 15:30

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                      http://www.ics.uci.edu/~andre/ics228s2006.html ICS 228: Software Environments (Spring 2006)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Information and Computer Science 228
                      Software Environments
                      Spring 2006

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: ICS2 207
                      Phone: +1 949-824-6326

                      Logistics
                      Location: CS 253
                      Day and time: Tuesday and Thursday, 11:00-12:20

                      Catalogue Description
                      228 Software Environments (4). Study of the requirements, concepts, and architectures of comprehensive, integrated, software development and maintenance environments. Major topics include process support, object management, communication, interoperability, measurement, analysis, and user interfaces in the environment context.

                      Structure
                      The class will be discussion oriented. Papers must have been read beforehand and discussion will be seeded by critical opinions, questions, and challenges that each student will write on the whiteboard each week at the beginning of the discussion. These discussions will primarily take place on Tuesdays.

                      Project
                      Each student will perform a practical research project. The research project must result in a tangible technology, the form of which will be discussed in class. Discussion regarding progress and issues that may arise will take place on Thursdays.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) their demonstrated understanding of the class topic, (b) class attendance and participation, (c) the critical opinions, questions, and challenges shared with the class to seed the discussions, and (d) the class project.

                      Course Mailing List
                      To send mail: 36718-s06@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=36718&quarter=S06

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in ICS 228. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Papers
                      1 April 4 Welcome
                      April 6 Introduction to Software Environments Rich & Waters: Automatic Programming: Myths and Prospects
                      Dart, Ellison, Feiler & Habermann: Overview of Software Development Environments
                      Kadia: Issues Encountered in Building a Flexible Software Development Environment
                      2 April 11 A Historical Perspective Dolotta & Mashey: An Introduction to the Programmers Workbench
                      Reps & Teitelbaum: The Synthesizer Generator
                      Teitelman & Masinter: The Interlisp Programming Environment
                      April 13 A Historical Perspective Swinehart, Zellweger, Beach & Hagmann: A Structural View of the Cedar Programming Environment
                      Harel, Lachover, Naamad, Pnueli, Politi, Sherman & Shtul-Trauring: STATEMATE: A Working Environment for the Development of Complex Reactive Systems
                      Taylor, Belz, Clarke, Osterweil, Selby, Wileden, Wolf & Young: Foundations for the Arcadia Environment Architecture
                      3 April 18 Under the Hood Tarr & Clarke: Pleiades: An Object Management System for Software Engineering Environments
                      Anderson, Taylor & Whitehead: Chimera: Hypermedia for Heterogenous Software Development Environments
                      Heineman & Kaiser: An architecture for Integrating Concurrency Control into Environment Frameworks
                      April 20 Under the Hood Bandinelli, Di Nitto & Fuggetta: Supporting Cooperation in the SPADE-1 Environment
                      van der Lingen & van der Hoek: An Experimental, Pluggable Infrastructure for Modular Configuration Management Policy Composition
                      Nentwich, Emmerich & Finkelstein: Consistency Management with Repair Actions
                      4 April 25 Building Software Environments Ballance, Graham & Van de Vanter: The Pan Language-Based Editing System for Integrated Development Environments
                      Boudier, Gallo, Minot & Thomas: An Overview of PCTE & PCTE+
                      Ossher & Harrison: Support for Change in RPDE3
                      April 27 Project Introduction
                      5 May 2 Building Software Environments Thomas & Nejmeh: Definitions of Tool Integration for Environments
                      Clemm & Osterweil: A Mechanism for Environment Integration
                      Reiss: Connecting Tools Using Message Passing in the Field Environment
                      May 4 Project Proposal Due
                      6 May 9 Modern Software Environments Medvidovic, Rosenblum & Taylor: A Language and Environment for Architecture-Based Software Development and Evolution
                      Grundy, Mugridge & Hosking: Constructing Component-based Software Engineering Environments: Issues and Experiences
                      des Rivières & Wiegand: Eclipse: A Platform for Integrating Development Tools
                      May 11 Project Discussion
                      7 May 16 Software Environments for Reuse Habermann: Programming Environments for Reuse
                      Braga, Werner & Mattoso: Odyssey: A Reuse Environment based on Domain Models
                      Lüer & Rosenblum: Wren—An Environment for Component-Based Development
                      May 18 Project Discussion
                      8 May 23 Distributed Software Environments Ben-Shaul & Kaiser: A Paradigm for Decentralized Process Modeling and its Realization in the Oz Environment
                      Augustin, Bressler & Smith: Accelerating Software Development Through Collaboration
                      Hall, Heimbigner & Wolf: A Cooperative Approach to Support Deployment Using the Software Dock
                      May 25 Project Discussion
                      9 May 30 New Developments Zimmermann, Weißgerber, Diehl & Zeller: Mining Version Histories to Guide Software Change
                      Mockus & Herbsleb: Expertise Browser: A Quantitative Approach to Identifying Expertise
                      Cubranic, Murphy, Singer & Booth: Hipikat: A Project Memory for Software Development
                      June 1 Preliminary Project Demonstrations
                      10 June 6 New Developments Henkel & Diwan: CatchUp! Capturing and Replaying Refactorings to Support API Evolution
                      Ducasse & Lanza: The Class Blueprint: Visually Supporting the Understanding of Classes
                      Van der Westhuizen, Chen & van der Hoek: Emerging Design: New Roles and Uses for Abstraction
                      June 8 Project Discussion
                      Finals week June 13, 10:30 - 12:30 Final Project Demonstrations

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics122f2007.html Informatics 122: Software Design II (Fall 2007)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Ph.D. students
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                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 122
                      Software Design II
                      Fall 2007

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Co-Teacher
                      Alex Baker
                      http://www.ics.uci.edu/~abaker
                      abaker@ics.uci.edu
                      Office: Donald Bren Hall 5221
                      Phone: +1 949-824-8904

                      Teaching Assistant
                      Kristina Winbladh
                      http://www.ics.uci.edu/~awinblad
                      awinblad@ics.uci.edu
                      Office: Donald Bren Hall 5243

                      Logistics
                      Location: ICS 190
                      Day and time: Tuesday and Thursday, 09:30-10:50

                      Catalogue Description
                      122 Software Design II (4). Introduction to advanced software design principles, paradigms, and techniques. Topics include large-scale design, software reuse, product-line architectures, design recovery, refactoring, application frameworks, real-time systems, design-for-context. Case studies of existing designs and extensive practice with real-world designs. Prerequisite: Informatics 121.

                      Book
                      Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series, Hardcover), Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) assignments, (c) the final class project, and (d) team evaluations.

                      Grade distribution will be as follows (as suitably adjusted with team evaluations):

                      • Design aesthetics: 14%
                      • Design implementation: 14%
                      • Design recovery: 14%
                      • Design patterns: 14%
                      • Component review: 14%
                      • Final project: 30%

                      Course Mailing List
                      To send mail: 37060-f07@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f07/37060/CLASS MAIL LISTS

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 122. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 25 No lecture (quarter not started yet)
                      September 27 Review and Preview lecture1.ppt Assignment 1, part 1 out
                      2 October 2 Design Aesthetics lecture2.ppt Assignment 1, part 1 due
                      Assignment 1, part 2 out
                      October 4 Design Aesthetics lecture3.ppt Assignment 1, part 2 due
                      Assignment 1, part 3 out
                      NOTE: DUE OCTOBER 7, 6pm
                      3 October 9 Design Aesthetics lecture4.ppt Assignment 2 out
                      October 11 Design Aesthetics lecture5.ppt
                      4 October 16 Design Aesthetics No slides
                      October 18 Design Implementation No slides Assignment 2 due
                      5 October 23 Design Recovery lecture6.ppt Assignment 3 out (team evaluation form)
                      October 25 Design Patterns lecture7.ppt
                      6 October 30 Design Recovery lecture8.ppt Assignment 3 due
                      Assignment 4 out (team evaluation form)
                      NOTE: DUE NOVEMBER 12, 6pm
                      November 1 CANCELLED
                      7 November 6 Design Patterns Slides from lecture 8.ppt
                      November 8 Components lecture9.ppt Assignment 5 out (team evaluation form)
                      8 November 13 Design Patterns lecture10.ppt
                      November 15 Large-Scale Design lecture11.ppt
                      9 November 20 Components No slides Assignment 5 due
                      Final Design Project out (team evaluation form)
                      November 22 No lecture: Thanksgiving
                      10 November 27 Final Design Project No slides Final Project due, part 1
                      November 29 Final Design Project No slides Final Project due, part 2
                      11 December 4 Final Design Project No slides Final Project due, part 3
                      December 6 No lecture (end of quarter)
                      Finals week December 13, 08:00 - 10:00 Final Project due, part 4

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                      AW van der Hoek
                      http://www.ics.uci.edu/~andre/informatics122w2009.html Informatics 122: Software Design II (Winter 2009)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
                      CodeExchange
                      Crowd Development
                      PorchLight
                      Ph.D. students
                      Christian Adriano
                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 122
                      Software Design II
                      Winter 2009

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: 949 824 6326

                      Co-Teacher
                      Alex Baker
                      http://www.ics.uci.edu/~abaker
                      abaker@ics.uci.edu
                      Office: Donald Bren Hall 5221
                      Phone: 1 949 824 8904

                      Reader
                      Kyle Strasser
                      http://www.ics.uci.edu/~kstrasse/
                      kstrasse@ics.uci.edu
                      Office: Donald Bren Hall 5209

                      Logistics
                      Location: ICS 180
                      Day and time: Monday and Wednesday, 15:00-16:20

                      Catalogue Description
                      122 Software Design II (4). Introduction to advanced software design principles, paradigms, and techniques. Topics include large-scale design, software reuse, product-line architectures, design recovery, refactoring, application frameworks, real-time systems, design-for-context. Case studies of existing designs and extensive practice with real-world designs. Prerequisite: Informatics 121.

                      Book
                      Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series, Hardcover), Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) assignments, (c) the final class project, and (d) team evaluations.

                      Grade distribution will be as follows (as suitably adjusted with team evaluations):

                      • Design aesthetics: 14%
                      • Design implementation: 14%
                      • Design recovery: 14%
                      • Design patterns: 14%
                      • Component review: 14%
                      • Final project: 30%

                      Course Mailing List
                      To send mail: 37070-W09@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/w09/37070/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Please also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 122. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 January 5 Review and Preview lecture1.ppt
                      January 7 Design Aesthetics lecture2.ppt Assignment 1, part 1 out
                      2 January 12 Design Aesthetics lecture3.ppt Assignment 1, part 1 due
                      Assignment 1, part 2 out
                      January 14 Design Aesthetics lecture4.ppt Assignment 1, part 2 due
                      Assignment 1, part 3 out
                      NOTE: DUE JANUARY 19, 4pm
                      3 January 19 (no lecture)
                      January 21 Design Aesthetics lecture5.ppt Assignment 2 out
                      4 January 26 Design Aesthetics lecture6.ppt
                      January 28 Design Aesthetics & Implementation Reflection No slides Assignment 2 due
                      5 February 2 Design Recovery lecture7.ppt Assignment 3 out (team evaluation form #1)
                      February 4 Design Patterns lecture8.ppt
                      6 February 9 Design Recovery Reflection lecture9.ppt Assignment 3 due
                      Assignment 4 out (team evaluation form #2)
                      NOTE: DUE FEBRUARY 21, 4pm
                      February 11 Design Patterns (same slides as lecture 8)
                      7 February 16 (no lecture)
                      February 18 (lecture canceled)
                      8 February 23 Component Reuse lecture10.ppt Assignment 5 out (team evaluation form #3)
                      February 25 Design Patterns Reflection lecture11.ppt
                      9 March 2 Component Reuse Reflection lecture12.ppt Assignment 5 due
                      Final Design Project out
                      March 4 Final Design Project (no lecture)
                      10 March 9 Final Design Project (no slides)
                      March 11 Final Design Project (no slides)
                      Finals week March 16, 16:00 - 18:00 Final Design Project Reflection & pizza Final Design Project due

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                      http://www.ics.uci.edu/~andre/informatics121s2006.html Informatics 121: Software Design I (Spring 2006)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Calico
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                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 121
                      Software Design I
                      Spring 2006

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: ICS2 207
                      Phone: +1 949-824-6326

                      Logistics
                      Location: CS 180
                      Day and time: Tuesday and Thursday, 14:00-15:20

                      Catalogue Description
                      121 Software Design I (4). Introduction to software design principles, paradigms, tools, and techniques. Topics include alternative architectural styles, iterative refinement, design patterns, mapping design onto code, design tools, and design notations. Includes extensive practice in creating designs and study of existing designs. Prerequisite: Informatics 102 with a grade of C or better.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) midterm and final, and (c) assignments and class project.

                      Grade distribution will be as follows:

                      • Play-Doh: 10%
                      • Readings: 10%
                      • Google Maps: 20%
                      • Educational Game: 20%
                      • Midterm: 15%
                      • Final: 25%

                      Course Mailing List
                      To send mail: 37026-s06@classes.uci.edu
                      To view the archive: http://eee.uci.edu/toolbox/mla/message_list.php?ccode=37026&quarter=S06

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 April 4 Design Exercises Lecture 1
                      April 6 Design Exercises Lecture 2 Assignment 1 out (see slides)
                      2 April 11 Design Exercises Lecture 3 Assignment 1 due
                      Assignment 2 out (see slides; download text here)
                      April 13 Defining Design Lecture 4 Assignment 2 due
                      Assignment 3 out (see slides; download text here)
                      3 April 18 Design Theory Lecture 5 Assignment 3 due
                      Assignment 4 out (see slides; download text here)
                      April 20 Design Theory Lecture 6 Assignment 4 due
                      Assignment 5 out (see slides)
                      4 April 25 Google Maps Studio No slides Assignment 5, part 1 due
                      April 27 Google Maps Studio No slides Assignment 5, part 2 due
                      5 May 2 System Design Lecture 7
                      May 4 System Design No slides
                      6 May 9 Google Maps Studio No slides Assignment 5, part 3 due
                      May 11 Midterm
                      7 May 16 System Design Lecture 8 Assignment 6 out (see slides)
                      May 18 Educational Game Studio No slides Assignment 6, part 1 due
                      8 May 23 Implementation Design Lecture 9
                      May 25 Educational Game Studio No slides Assignment 6, part 2 due
                      9 May 30 Implementation Design No slides Assignment 6, part 3 due
                      June 1 Educational Game Studio No slides
                      10 June 6 Implementation Design No slides
                      June 8 Wrap-up Lecture 10 Assignment 6, part 4 due
                      Finals week June 15, 13:30 - 15:30 Final

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                      http://www.ics.uci.edu/~andre/informatics221f2013.html Informatics 221: Software Design I (Fall 2013)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 221
                      Software Architecture
                      Fall 2013

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5228 (you may also find me in DBH 5038)
                      Phone: +1 949-824-6326

                      Logistics
                      Location: DBH 1300
                      Lecture day and time: Tuesday and Thursday, 14:00-15:20

                      Catalogue Description
                      221 Software Architecture (4). Study of the concepts, representation techniques, development methods, and tools for architecture-centric software engineering. Topics include domain-specific software architectures, architectural styles, architecture description languages, software connectors, and dynamism in architectures.

                      Textbook
                      Software Architecture: Foundations, Theory, and Practice. Richard N. Taylor, Nenad Medvidovic, and Eric M. Dashofy. Copyright © 2010 John Wiley & Sons, Inc. (ISBN-13: 978-0470-16774-8)

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) course projects, and (c) final.

                      Grade distribution will be as follows (adjusted by class attendance/participation as need be):

                      • Class attendance and participation: 10%
                      • Course projects: 60%
                      • Final exam: 30%

                      Course Mailing List
                      To send mail: 37330-f13@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f13/37330/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 26 Big idea Lecture 1 Read chapter 1
                      2 October 1 No class - André keynote at CONISOFT 2013
                      October 3 No class - André keynote at CONISOFT 2013
                      3 October 8 No class - André SCALE research meeting/IBM visit
                      October 10 Architectures in context Lecture 2 Read chapters 2 and 3 (small quiz on Tuesday)
                      4 October 15 Basic concepts &
                      Designing architectures
                      Lecture 3 &
                      Lecture 4
                      Read chapter 4
                      October 17 Designing architectures &
                      Lecture 4 &
                      Lecture 5
                      5 October 22 System discussion!
                      October 24 Designing Software Architectures Lecture 5
                      6 October 29 System discussion!
                      October 31 Connectors
                      Modeling
                      Lecture 6 &
                      Lecture 7
                      Read chapters 5 and 6
                      7 November 5 Modeling
                      DSSE
                      Lecture 7 &
                      Lecture 8
                      November 7 DSSE Lecture 8 Read chapter 15
                      8 November 12 Product lines Lecture 9 &
                      November 14 Class cancelled
                      9 November 19 Deployment and Mobility
                      Adaptation
                      Lecture 10
                      Lecture 11
                      Read chapters 10 and 14
                      November 21 Design practice
                      10 November 26 Concept presentations
                      November 28 No class - Thanksgiving
                      11 December 3 Final presentations
                      December 5 Final presentations
                      Finals week December 12, 13:30 - 15:30 Final

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                      http://www.ics.uci.edu/~andre/informatics121f2010.html Informatics 121: Software Design I (Fall 2010)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
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                      Lee Martie
                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2010

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Teaching Assistant
                      Nadine Amsel
                      namsel@uci.edu

                      Logistics
                      Location: AIRB 1030
                      Day and time: Tuesday and Thursday, 11:00-12:20

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisite: Informatics 113 with a grade of C or better.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, and (b) assignments, and (c) course projects.

                      Grade distribution will be as follows:

                      • Readings: 10%
                      • Design studio 1: 20%
                      • Design Studio 2: 20%
                      • Video analysis: 10%
                      • Design Studio 3: 20%
                      • Design Studio 4: 20%

                      Course Mailing List
                      To send mail: 37050-f10@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f10/37050/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-7494 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 121. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 23 (no lecture, André out of town)
                      2 September 28 Design Exercises Lecture 1 Reading 1 out (see slides; download text here)
                      September 30 Design Exercises Lecture 2 Reading 1 due
                      Reading 2 out (see slides; download text here)
                      3 October 5 Defining Design Lecture 3 Reading 2 due
                      Desgin Studio 1, part 1 out (see slides)
                      October 7 Design Studio I Lecture 4 Design Studio I, part 1 due
                      Design Studio I, part 2 out (see slides)
                      4 October 12 Defining Design Lecture 5
                      October 14 Design Studio I (no slides, team presentations) Design Studio I, part 2 due (teamevaluation-1.doc)
                      5 October 19 Design Techniques Lecture 6 Design Studio II, part 1 out (see slides)
                      October 21 Design Studio II Lecture 7 Design Studio II, part 1 due
                      Design Studio II, part 2 out (see slides and the Michael Jackson paper)
                      6 October 26 Design Studio II Lecture 8 Design Studio II, part 2 due
                      Design Studio II, part 3 out (see slides)
                      October 28 Design Example (no slides, example design on the whiteboard)
                      7 November 2 Design Studio II (no slides, discussion)
                      November 4 Design Studio II Lecture 9 Design Studio II, part 3 due
                      Design Studio III, part 1 out (see slides)
                      8 November 9 (no lecture, André out of town)
                      November 11 (no lecture, André out of town)
                      9 November 16 Design Notations Lecture 10 Design Studio III, part 1 due
                      Design Studio III, part 2 out (see slides)
                      November 18 Design Rationale Lecture 11
                      10 November 23 Design Studio IV Lecture 12 Design Studio III, part 2 due
                      Design Studio IV out
                      November 25 (no lecture, Thanksgiving)
                      11 November 30 Design Studio IV Design Studio IV, posters
                      December 2 Design Studio IV Lecture 13 Design Studio IV, preliminary designs
                      Finals week December 7, 10:30 - 12:30 Design Studio IV Design Studio IV, final posters and designs

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                      http://www.ics.uci.edu/~andre/informatics121f2015.html Informatics 121: Software Design I (Fall 2015)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
                      projects
                      Calico
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                      Ph.D. students
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                      Gerald Bortis
                      Lee Martie
                      Course Title
                      Informatics 121
                      Software Design I
                      Fall 2015

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: DBH 5038
                      Phone: +1 949-824-6326
                      Office hours: Tuesday, 9-10

                      Teaching Assistants
                      Rohan Achar
                      rachar@uci.edu
                      Office hours: Monday, 13:00-14:00, ICS1 408A

                      Fernando Spaghero
                      fspanghe@uci.edu
                      Office hours: Wednesday, 14:00-15:00, ICS1 414

                      Swanand Pethe
                      spethe@uci.edu
                      Office hours: Thursday, 13:00-14:00, DBH 5013

                      Logistics
                      Location: DBH 1100
                      Lecture day and time: Tuesday and Thursday, 14:00-15:20
                      Discussion day and time: Monday, 09:00-09:50 (ICS 180); Monday 10:00-10:50 (ICS 180); Wednesday 09:00-09:50 (ICS 180); or Wednesday 10:00-10:50 (MTSB 118)

                      Catalogue Description
                      121 Software Design I (4). Introduction to application design: designing the overall functionality of a software application. Topics include general design theory, software design theory, and software architecture. Includes practice in designing and case studies of existing designs. Prerequisites: Informatics 45 or ICS 23/CSE23 or ICS 33/CSE43, with a grade of C or better and upper-division standing.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) course projects, (b) midterm, (c) final. Grade distribution will be as follows (adjusted by group attendance and participation as need be):

                      • Projects: 60%
                      • Midterm: 15%
                      • Final: 25%

                      Course Mailing List
                      To send mail: 37050-f15@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/f15/37050/

                      Students with Disabilities
                      Any student who feels he or she may need an accommodation based on the impact of a disability should contact me privately to discuss his or her specific needs. Also contact the Disability Services Center at (949) 824-6272 as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

                      Schedule
                      Week Date Topic Slides Assignments
                      1 September 24 Defining design Lecture 1
                      2 September 29 Defining design Lecture 2 Design studio 1, part 1 out
                      October 1 Design cycle Lecture 3
                      3 October 6 Design cycle Lecture 4 Design studio 1, part 1 due
                      Design studio 1, part 2 out
                      October 8 Design practice Lecture 5
                      4 October 13 Design failure Lecture 6
                      October 15 Design failure Lecture 7 Design studio 1 due
                      5 October 20 Design practice Lecture 8
                      October 22 Application design Lecture 9 Design studio 2, part 1 out
                      6 October 27 Application design Lecture 10
                      October 29 Design practice (no slides)
                      7 November 3 Midterm
                      November 5 Design notations Lecture 11
                      8 November 10 Architecture design Lecture 12 Design studio 2, part 1 due
                      Design studio 2, part 2 out
                      November 12 Design practice (no slides)
                      9 November 17 Design practice (no slides)
                      November 19 Design practice Design studio 2, part 2 due
                      Design studio 3 out
                      10 November 24 Interaction design Lecture 13
                      November 26 No class: Thanksgiving
                      11 December 1 No class: Andre traveling
                      December 3 Wrap up Lecture 14 Design studio 3 due
                      Finals week December 10, 13:30 - 15:30 Final

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                      http://www.ics.uci.edu/~andre/informatics223s2009.html Informatics 223: Applied Software Design Techniques (Spring 2009)
                      home | teaching | research | publications | bio | resume (PDF) | address André van der Hoek
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                      Course Title
                      Informatics 223
                      Applied Software Design Techniques
                      Spring 2009

                      Professor
                      André van der Hoek
                      http://www.ics.uci.edu/~andre
                      andre@ics.uci.edu
                      Office: Donald Bren Hall 5228
                      Phone: +1 949-824-6326

                      Logistics
                      Location: ICS1 243
                      Day and time: Tuesday and Thursday, 12:30-13:50

                      Catalogue Description
                      223 Applied Software Design Techniques (4). Study of concepts, representations, techniques, and case studies in structuring software systems, with an emphasis on design considerations. Topics include static and dynamic system structure, data models, abstractions, naming, protocols and application programmer interfaces.

                      Structure
                      The class will be discussion-oriented. Papers must have been read beforehand, and discussion will be seeded by questions, observations, and assertions from all students in the class.

                      Case Study
                      Each student will perform a case study by adopting one software system that they will use to illustrate the techniques with concrete examples. Based on the case study, two slides should be prepared weekly that illustrate the findings in the example system.

                      Poster
                      Each student will create a poster, which is based on a new "invention". Specifically, at the end of the quarter each student will present a new technique, a new modeling notation, a new approach, a new tool, or any other new "thing" that they invent as a result of their experience in the class. The technique will not have to be fully demonstrated, but the concept and novelty should be clear from the poster.

                      Grades
                      All students enrolled in the course will earn a letter grade based upon: (a) class attendance and participation, (b) questions, obaservations, and assertions that feed the discussions in class, (c) case study slides, and (d) poster.

                      Course Mailing List
                      To send mail: 37190-S09@classes.uci.edu
                      To view the archive: https://eee.uci.edu/classmail/s09/37190/

                      Students with Disabilities
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                      Copyright
                      The documents below are included to ensure timely dissemination of scholarly and technical work on a non-commercial basis and are for the sole use of students enrolled in Informatics 223. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be resposted without the explicit permission of the copyright holder.

                      Schedule
                      Week Date Topic Papers
                      1 March 31 Welcome
                      April 2 Design & Abstraction Spector & Gifford: Case Study: A Computer Science Perspective on Bridge Design
                      Taylor & van der Hoek: Software Design and Architecture: The Once and Future Focus of Software Engineering
                      Kramer: Is Abstraction the Key to Computing?
                      2 April 7 Case study discussion
                      April 9 Models & UML Seidewitz: What Models Mean
                      Fowler: UML Distilled: A Brief Guide to the Standard Object Modeling Language (third edition)
                      Bell: Death by UML Fever
                      3 April 14 (No lecture; baby born)
                      April 16 (No lecture; baby born)
                      4 April 21 Case study discussion
                      April 23 Architecture Medvidovic & Taylor: A Classification and Comparison Framework for Software Architecture Description Languages
                      Aldrich, Chambers & Notkin: ArchJava: Connecting Software Architecture to Implementation
                      Georgas & Taylor: Policy-Based Self-Adaptive Architectures: A Feasibility Study in the Robotics Domain
                      5 April 28 Case study discussion
                      April 30 Patterns Lea: Christopher Alexander: An Introduction for Object-Oriented Designers
                      Gamma, Helm, Johnson, Vlissides: Design Patterns: Elements of Reusable Object-Oriented Software
                      Garlan, Allen, Ockerbloom: Exploiting Style in Architectural Design Environments
                      6 May 5 Case study discussion
                      May 7 Data Models Conklin: Hypertext: An Introduction and Survey
                      Capriero & Gelernter: Linda in Context
                      van der Lingen & van der Hoek: An Experimental, Pluggable Infrastructure for Modular Configuration Management Policy Composition
                      7 May 12 (No lecture; André at Adobe)
                      May 14 Naming Berners-Lee, Fielding & Masinter: Uniform Resource Identifiers (URI): Generic Syntax
                      Carzaniga, Rosenblum & Wolf: Design and Evaluation of a Wide-Area Event Notification Service
                      Musen: Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture
                      8 May 19 (No lecture, Andr´ at ICSE)
                      May 21 (No lecture, André at ICSE)
                      9 May 26 Case study discussion
                      May 28 Protocols Emmerich: Engineering Distributed Objects, chapters 3, 4 & 8
                      Gudgin, Hadley, Mendelsohn, Moreau, Nielsen, Karmarkar & Lafon: Simple Object Access Protocol (SOAP) 1.2
                      Whitehead & Goland: WebDAV: A Network Protocol for Remote Collaborative Authoring on the Web
                      10 June 2 APIs Parnas: On the Criteria To Be Used in Decomposing Systems into Modules
                      Thau: Design considerations for the Apache Server API and Apache API Notes
                      Hartmann, Doorley & Klemmer: Hacking, Mashing, Gluing: Understanding Opportunistic Design
                      June 4 Interchange Fallside & Walmsley: XML Schema Part 0: Primer
                      Garlan, Monroe & Wile: Acme: An Architecture Description Interchange Language
                      Hanseth, Monteiro & Hatling: Developing Information Infrastructure: The Tension Between Standardisation and Flexibility
                      Finals week June 11, 10:30-12:30 Poster Presentations

                      Andre's picture
                      contact
                      email
                      andre@ics.uci.edu

                      skype
                      awvanderhoek

                      aim
                      AW van der Hoek
                      http://www.ics.uci.edu/~theory/research.html Center for Algorithms and Theory of Computation ICS Theory Group

                      Research in Algorithms and Theory of Computation at UC-Irvine

                      The goal of research in theoretical computer science is to produce results, supported by rigorous proof, about problems dealing with computers and their applications. The questions to be investigated are often motivated by practical problems, but the goal of understanding the fundamental structure of the problem is often as important as producing a solution of immediate applicability. Despite this emphasis, it turns out that results that first might appear to be only of theoretical value are sometimes of profound relevance to practical problems.

                      In particular, one of the major subareas of theoretical computer science, and the one pursued by the faculty and graduate students at UCI, is concrete complexity: We look at specific problems and try to determine the complexity (i.e., the amount of resources required) for obtaining a solution. Our work falls into three main areas: design of algorithms and data structures; analysis; problem complexity.

                      Design of Algorithms and Data Structures

                      Given a problem, we try to find efficient solution methods. A data structure is a way of organizing information; sometimes the design of an appropriate data structure can be the foundation for an efficient algorithm, and we have made a number of significant contributions to the field of data structures. In addition, one of our members has written a widely used and respected text on data structures, and is presently completing a second more introductory text.

                      In addition to the design of new data structures, we are also interested in efficient algorithms for problems arising in a variety of fields. Often such problems can be represented in terms of trees, graphs, or strings, and we are interested in the design of efficient solutions for such problems.

                      The field of computational geometry investigates the complexity of problems involving two-dimensional (or higher) spaces. This is an active research area which has not only theoretical depth but also practical applications in areas such as pattern recognition, VLSI layout, statistics, and image processing. One major area of our work is the investigation of certain properties of geometric constructs which can be modeled by graphs. We have also explored how solutions to geometric problems such as linear programming or the minimum spanning tree can be made dynamic, i.e., how we can efficiently maintain the solution when the input data are subject to change.

                      Also of interest is the compression of data. For example, we have reduced the complexity of algorithms for compressing strings, and have also investigated the compression of structures such as quadtrees which are used for storing spatial data.

                      Current work in genetics provides an exciting application area for algorithms. Some work done long ago by our present faculty, on longest common subsequences and on PQ-trees, has turned out to be valuable in solving problems that arise in genetics. More recently, one of our faculty has introduced sophisticated new methods for speeding the solution of problems such as DNA sequence comparison.

                      Much of our work has dealt with the fast solution of problems by a single processor. The combination of declining cost of processors and the desire for fast solutions to problems has led to a great deal of interest in the use of parallelism to speed up computation. One natural question is thus: how long does it take to solve a given problem with a limited number of parallel processors? Some of us have been especially interested in solving problems on graphs very quickly without using an excessive number of processors.

                      Analysis

                      Once a solution method has been proposed, we seek to find a rigorous statement about its efficiency; analysis of algorithms can go hand-in-hand with their design, or can be applied to known algorithms. Some of this work is motivated in part by the theory of NP-completeness, which strongly suggests that certain problems are just too hard to solve exactly and efficiently all of the time. It may be, though, that the difficult cases are relatively rare, so we attempt to investigate the behavior of problems and algorithms under assumptions about the distribution of inputs.

                      Our group at UCI has made major contributions in the area of probabilistic analysis. We have done work in algorithms for problems such as packing, partitioning, marking algorithms, and hashing. In particular, we have obtained a surprising result about the behavior of a well known marking algorithm, and an elegant analysis of double hashing.

                      Probability can provide a powerful tool even when we do not assume a probability distribution of inputs. In an approach called randomization, one can introduce randomness into the algorithm itself so that even on on worst-case input it works well with high probability. For example, for the classical List Update Problem, which involves organizing data so that we can perform searches efficiently, one of our faculty has shown how to use randomization to beat the inherent limit of a deterministic approach.

                      An area of considerable recent interest is on-line algorithms. Here we investigate the performance of algorithms which must provide answers based on part of the input before the remainder is available. A good example is memory paging---when swapping, the system must decide which memory pages to keep in its cache before it sees which ones will actually be used later. Earlier analysis of this problem had not been fully successful in explaining why a common heuristic performs so well. One of our faculty developed a new approach which formally models locality of reference, and thus can better explain the performance of paging algorithms.

                      Problem complexity

                      When efficient solutions appear difficult, negative results can sometimes provide very helpful guidance. Two major types of results are possible here.

                      • In some cases one can actually prove that, under some model, the problem does not admit solution without a certain level of resources.
                      • For many problems, good bounds of the above type are not available, but the problem can be shown to be equivalent in complexity to some well-known class of problems. For example, if a problem is NP-complete it cannot be solved in polynomial time unless P=NP, which is a major open question.

                      Such results can save wasted effort by researchers, and in some cases might also suggest that algorithms from a different model should be considered.


                      Department of Computer Science
                      University of California, Irvine, CA 92697-3425
                      http://www.ics.uci.edu/~theory/doctorates.html Theory Doctorates
                      Doctorates

                      Former doctoral students

                      Year Student Advisor Thesis title
                      1980 Dov Harel G. Lueker Efficient Algorithms with Threaded Balanced Trees
                      1985 Martin Katz D. Volper Geometric Retrieval: Data Structures and Computational Complexity
                      Francis Murgolo G. Lueker Approximation Algorithms for Combinatorial Optimization Problems
                      1986 Lawrence L. Larmore D. Hirschberg Methods of Solving Breakpoint Problems
                      1988 James H. Hester D. Hirschberg Probabilistically Faster Search Structures
                      Kadri Krause G. Lueker Efficient Parallel Algorithms for Recognition and Analysis of TSSP Graphs
                      1990 Mariko Molodowitch G. Lueker Analysis and Design of Algorithms: Double Hashing and Parallel Graph Searching
                      Cheng F. Ng D. Hirschberg Computational Complexity of Stable Matching Problems
                      1991 Debra A. Lelewer Brum D. Hirschberg Data Compression on Machines with Limited Memory
                      1993 Hari Asuri G. Lueker Parallel Algorithms for Sparse Graphs
                      1994 Lynn M. Stauffer D. Hirschberg Parallel and High-Speed Data Compression
                      1997 Vitus Leung S. Irani Scheduling with Conflicts and Applications to Traffic Signal Control
                      Steven S. Seiden D. Hirschberg / S. Irani Randomization in Online Computation
                      1998 Jonathan Kent Martin D. Hirschberg Machine Learning of Classifications via Generalized Linear Models: Theoretical and Practical Considerations
                      2002 David Hart D. Eppstein Algorithms for Geometric Shortest Paths along Routes
                      2003 Joseph Wang D. Eppstein Graph Algorithms for Complex Networks
                      2006 John Augustine S. Irani Near-Optimal Solutions for Powering-Down Problems and Scheduling Jobs in FPGAs
                      Yu (Jeremy) Meng M. Goodrich Confluent Graph Drawing
                      Zheng (Jonathan) Sun M. Goodrich Algorithms for Hierarchical Structures, with Applications to Security and Geometry
                      2008 Josiah Carlson D. Eppstein Solving Some Combinatorial Problems Embedded in Trees
                      2009 Kevin Wortman D. Eppstein Minimum Dilation Stars
                      Nodari Sitchinava M. Goodrich Parallel External Memory Model and Algorithms for Multicore Architectures
                      2011 Darren Strash D. Eppstein / M. Goodrich   Algorithms for Geometric Graphs and Social Networks
                      2013 Lowell Trott M. Goodrich Geometric Algorithms for Social Network Analysis
                      2014 Joe Simons D. Eppstein / M. Goodrich New Dynamics in Geometric Data Structures
                      Paweł Pszona M. Goodrich Practical Algorithms for Sparse Graphs
                      2015 Michael Bannister D. Eppstein Lower Bounds and Fixed-Parameter Tractability of Drawing Graphs
                      Jenny Lam S. Irani Cache Optimization for the Modern Web

                      Department of Computer Science
                      University of California, Irvine, CA 92697-3435
                      http://www.ics.uci.edu/~dillenco/pubs/intpoly.html

                      Conference Paper:

                      M. B. Dillencourt, D. M. Mount, and A. J. Saalfeld, On the Maximum Number of Intersections of Two Polyhedra in 2 and 3 Dimensions, in Proceedings of the Fifth Canadian Conference on Computational Geometry, Waterloo, Ontario, August, 1993, 49-54.

                      This paper must be downloaded in two pieces:

                      • The main text
                      • Figure 3
                      http://www.ics.uci.edu/~dillenco/pubs/triinscrib.html

                      Conference Paper:

                      M. B. Dillencourt and W. D. Smith, A linear-time algorithm for testing the inscribability of trivalent polyhedra, in Proceedings of the Eighth ACM Symposium on Computational Geometry, Berlin, Germany, June 1992, 177-185.

                      Updated Version:

                      M. B. Dillencourt and W. D. Smith,
                      A linear-time algorithm for testing the inscribability of trivalent polyhedra, International Journal of Computational Geometry & Applications, 5(1-2), March-June 1995, 21-36. http://www.ics.uci.edu/~dillenco/pubs/haminscrib.html

                      Conference Paper:

                      M. B. Dillencourt, Finding Hamiltonian Cycles in Delaunay Triangulations Is NP-Complete, in Proceedings of the Fourth Canadian Conference on Computational Geometry, St. Johns, Newfoundland, August, 1992, 223-228.

                      Updated Version:

                      M. B. Dillencourt, Finding Hamiltonian Cycles in Delaunay Triangulations Is NP-Complete, Discrete Applied Mathematics, 64(3), February 1996, 207-217. http://www.ics.uci.edu/~dillenco/pubs/new1ham.html

                      Conference Paper:

                      M. B. Dillencourt and W. D. Smith, Graph-theoretical conditions for inscribability and Delaunay realizability, in Proceedings of the Sixth Canadian Conference on Computational Geometry, Saskatoon, Saskatchewan, August, 1994, 287-292.

                      Updated Version:

                      M. B. Dillencourt and W. D. Smith, Graph-theoretical conditions for inscribability and Delaunay realizability, Discrete Mathematics, 161(1-3), December 1996, 63-77. http://www.ics.uci.edu/~dillenco/pubs/noncomplex.html

                      Conference Paper:

                      T. K. Dey, M. B. Dillencourt, and S. K. Ghosh, Triangulating with High Connectivity, in Proceedings of the Sixth Canadian Conference on Computational Geometry, Saskatoon, Saskatchewan, August, 1994, 339-343.

                      Updated Version:

                      T. K. Dey, M. B. Dillencourt, S. K. Ghosh, and J. M. Cahill, Triangulating with High Connectivity, Computational Geometry: Theory & Applications, 8(1), June 1997, 39-56. http://sli.ics.uci.edu/Projects/Projects SLI | Projects / Projects
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                      Current research projects


                      Cluster Trees for Adaptive Inference -- We use a cluster tree formalism (specifically an RC-Tree data structure) to organize the process of inference in factor graphs. This enables us to rapidly find changes and update results when small changes are made to the model.

                      Statistical models for event detection in count data -- Automatic event detection in data consisting of discrete counts, using variants of Poisson processes to model the underlying (non-event) phenomena. The methods produce a probabilistic model of "normal" behavior (useful for compression, visualization, and planning), and identify periods of anomalous measurement periods. These events can be flagged for further scrutiny, or for "known events" can provide estimates of the total associated counts as (for example) an indicator of popularity. Multiple data sources can be combined to estimate indirectly observed variables, such as total building occupancy.

                      Belief propagation -- Understanding the behavior of belief propagation, one of the most popular methods of approximate, variational inference in graphical models. Our research has developed convergence conditions for the algorithm, bounds on the approximation error in the estimated marginal distributions, and theoretical analysis of message errors due to quantization, message censoring, or other sources.

                      Sensor networks and distributed inference -- As technology improves, we are increasingly able to monitor our environment with "smart" sensors which observe their surroundings, process information locally, and pass that information on via wireless communications. Such sensors form the basis of many types of environmental awareness, from understanding patterns of aggregate behavior such as traffic to real-time monitoring and tracking.

                      Nonparametric representations for probabilistic inference -- Sample based representations for probabilistic uncertainty are popular in both static and dynamic models. Kernel density estimation techniques, for example, can be applied to estimate information content, learn predictors, and represent multi-modal uncertainty without strong modeling assumptions. In dynamic problems, such as nonlinear state space tracking, particle filtering provides similarly flexible representations of uncertainty. Nonparametric belief propagation combines both techniques to provide an extension of particle filtering to more general graphs.


                      Last modified June 24, 2008, at 02:08 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Code/Code SLI | Code / Code
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                      Open-source Code

                      This page contains links to open-source code written by members of the group or our collaborators.


                      Kernel Density Estimation (KDE) Toolbox for Matlab
                      • Kernel density estimation and many related problems
                      • KD-tree data structures for fast spatial computations
                      • Includes products of Gaussian mixtures, for NBP

                      Nonparametric Belief Propagation?
                      • Example NBP Code by Danny Bickson
                        • Uses KDE Toolbox; sample applications from low-density lattice decoding (LDLC), multiuser detection, fault identification and compressive sensing.

                      Adaptive Inference in Factor Trees, for Matlab
                      • Efficient incorporation of model changes into a tree-structured factor graph
                      • Model updates take O(log n) time each
                      • Query marginal distributions in O(log n) time; maintain MAP configurations in O(d log n/d) time, where d is the number of changes in the configuration.

                      Markov-modulated Poisson processes for event detection in count data
                      • Mote count sensors

                      Gaussian Process Regression with uncertain time-shifts, used for gene expression time series.

                      Uses Gaussian process regression to estimate the profile of each gene, while allowing each replicate organism to shift slightly in time relative to the others, to account for biological variation in development speed. (:endif:)


                      • Mutual information-based learning
                      Last modified April 17, 2013, at 02:54 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://www.ics.uci.edu/~qlou/ Qi Lou's Home Page
                      Qi Lou

                      About me

                      I am a Ph.D. student in the Computer Science Department at University of California, Irvine, under the supervision of Prof. Alexander Ihler. I've received my B.S. and M.S. in mathematics from Zhejiang University, and M.S. in computer science from Oregon State University.

                      Email: qlou AT uci.edu
                      CS179 office hours: Monday from 10:30am to 12:00pm at DBH 3013


                      Research      Publications      Internship      Appointments     

                      Research

                      I am generally interested in machine learning, computer vision and artificial intelligence. Currently, I am working on inference and sampling techniques in probabilistic graphical models.

                      Publications

                      Novelty detection under multi-instance multi-label framework
                      Qi Lou, Raviv Raich, Forrest Briggs, Xiaoli Fern
                      IEEE International Workshop on Machine Learning for Signal Processing, 2013. Ranked top 14 among 105 accepted papers.
                      Instance annotation for multi-instance multi-label learning
                      Forrest Briggs, Xiaoli Fern, Raviv Raich, Qi Lou
                      ACM Transactions on Knowledge Discovery from Data, Vol. 7 Iss. 3, 2013.
                      A framework for analyzing bioacoustics audio recordings as multiple instance multiple label data
                      Raviv Raich, Forrest Briggs, Qi Lou, Xiaoli Fern, David Mellinger
                      Proceedings of Meetings on Acoustics Vol. 19 Iss. 1, 2013.
                      Curve intersection using hybrid clipping
                      Qi Lou, Ligang Liu
                      Computers & Graphics 36(5): 309-320 (2012). Oral presentation on Shape Modeling International 2012.

                      Internship

                      • Research Intern at Virginia Tech
                        • Machine Learning & Perception Group, hosted by Prof. Dhruv Batra, 08/2013 ~ 11/2013, 04/2014 ~ 08/2014.

                      Appointments

                      • Graduate Student Researcher, Teaching Assistant at UCI
                      • Research Assistant, Teaching Assistant at OSU
                      Copyright 2015 Qi Lou | Last updated: 09/29/2015
                      http://sli.ics.uci.edu/Pubs/Pubs SLI | Pubs / Publications
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                      Pubs /

                      Publications

                      2010

                      "Understanding Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; IEEE Trans. Knowledge Data Engineering, to appear. (Preliminary version, 2009: TR-09-06, PDF)
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Nonparametric Belief Propagation"; Sudderth, Ihler, Isard, Freeman, Willsky; Communications of the ACM 53(10), Oct. 2010 pp. 95-103.
                      [ Abstract ] |[ BibTeX ] | [ Link ]

                      "Negative Tree-reweighted Belief Propagation"; Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2010
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Covering Trees and Lower Bounds on Quadratic Assignment"; Yarkony, Fowlkes, Ihler; Computer Vision & Pattern Recognition (CVPR), June 2010
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Particle Filtered MCMC-MLE with Connections to Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; Int'l Conf on Machine Learning (ICML), June 2010
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Learning with Blocks: Composite Likelihood and Contrastive Divergence"; Asuncion, Liu, Ihler, Smyth; AI & Statistics (AISTATS), April 2010
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Estimating Replicate Time-Shifts Using Gaussian Process Regression"; Liu, Lin, Anderson, Smyth, Ihler; Bioinformatics 26(6), Mar. 2010, pp. 770-776; doi:10.1093/bioinformatics/btq022
                      [ Abstract ] |[ BibTeX ] | [ Link ]

                      2009

                      "Particle-Based Variational Inference for Continuous Systems"; Ihler, Frank, Smyth; Neural Information Processing Systems, Dec. 2009
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Bayesian detection of non-sinusoidal periodic patterns in circadian expression data"; Chudova, Ihler, Lin, Andersen, Smyth; Bioinformatics 25(23), Dec. 2009, pp. 3114-3120; doi: 10.1093/bioinformatics/btp547.
                      [ Abstract ] |[ BibTeX ] | [ Link ]

                      "Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation"; Ihler, Newman; ICS Technical Report 09-06, Oct. 2009.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Adaptive Updates for MAP Configurations with Applications to Bioinformatics"; Acar, Ihler, Mettu, Sumer; in IEEE Statistical Signal Processing (SSP), Sept. 2009
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "A Low Density Lattice Decoder via Non-parametric Belief Propagation"; Bickson, Ihler, Avissar, Dolev; Allerton Conference on Communication, Control, and Computing, Sept. 2009
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Circadian Clock Genes Contribute to the Regulation of Hair Follicle Cycling"; Lin, Kumar, Geyfman, Chudova, Ihler, Smyth, Paus, Takahashi, Andersen; PLoS Genetics, 5(7):e1000573. July 2009. doi:10.1371/journal.pgen.1000573
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ Link ]

                      "Particle Belief Propagation"; Ihler, McAllester; in Twelfth International Conference on Artificial Intelligence and Statistics (AIStats), April 2009.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      2008

                      "Probabilistic Analysis of a Large Scale Urban Traffic Sensor Data Set"; Hutchins, Ihler, Smyth; in Second International Workshop on Knowledge Discovery from Sensor Data 2008.
                      [ Abstract ] |[ BibTeX ]| [ PDF ] | [ PS ]

                      "Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation"; Porteous, Newman, Ihler, Asuncion, Smyth, Welling; in ACM Knowledge Discovery and Data Mining (KDD) 2008.
                      [ Abstract ] | [ BibTeX ]| [ PDF ]

                      "Adaptive Inference in General Graphical Models"; Acar, Ihler, Mettu, Sumer; in Uncertainty in Artificial Intelligence (UAI) 2008.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      2007

                      "Graphical Models for Statistical Inference and Data Assimilation"; Ihler, Kirshner, Ghil, Robertson, Smyth; Physica D: Nonlinear Phenomena, June 2007. (Survey of graphical model methods)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ Link ]

                      "Learning to detect events with Markov-modulated Poisson processes"; Ihler, Hutchins, Smyth; ACM Transactions on Knowledge Discovery from Data, Vol 1 Issue 3, Dec. 2007.
                      [ Abstract ] |[ BibTeX ] | [ Link ]

                      "Modeling Count Data from Multiple Sensors: A Building Occupancy Model"; Hutchins, Ihler, Smyth; in Computational Advances in Multisensor Adaptive Processing (CAMSAP) 2007.
                      [ Abstract ] | [ BibTeX ] | [ PDF ] | [ PS ]

                      "Adaptive Bayesian Inference"; Acar, Ihler, Mettu, Sumer; in Neural Information Processing Systems (NIPS) 2007.
                      [ Abstract ] | [ BibTeX ] | [ PDF ] | [ PS ]

                      "Accuracy Bounds for Belief Propagation"; Ihler; in Uncertainty in Artificial Intelligence (UAI) 2007.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Graphical Models and Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Kreidl, Moses, Wainwright, Williams, Willsky; in Wireless Sensor Networks: Signal Processing and Communications, Wiley 2007.
                      [ Abstract ] | [ BibTeX ] | [ Link ]

                      2006

                      "Distributed Fusion in Sensor Networks"; Cetin, Chen, Fisher, Ihler, Moses, Wainwright, Willsky; IEEE Signal Processing Magazine, July 2006.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation"; Porteous, Ihler, Smyth, Welling; in Uncertainty in Artificial Intelligence (UAI) 2006.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Adaptive Event Detection with Time-Varying Poisson Processes"; Ihler, Hutchins, Smyth; in Knoweldge Discovery and Data Mining (KDD) 2006.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Learning Time-Intensity Profiles of Human Activity Using Nonparametric Bayesian Models"; Ihler, Smyth; in Neural Information Processing Systems (NIPS) 2006.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      2005

                      "Inference in Sensor Networks: Graphical Models and Particle Methods"; Ihler; Ph.D. Thesis, MIT, 2005
                      [ Abstract ] | [ BibTeX ] | [ PDF ] | [ PS (zipped) ]

                      "Loopy Belief Propagation: Convergence and Effects of Message Errors"; Ihler, Fisher, Willsky; Journal of Machine Learning Research, May 2005. (Full version of NIPS'04 paper)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; Journal of Selected Areas in Communication, Apr. 2005. (Expanded version of IPSN/ICASSP papers)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Particle Filtering Under Communications Constraints"; Ihler, Fisher, Willsky; in Statistical Signal Processing (SSP) 2005.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Estimating Dependency and Significance for High-Dimensional Data"; Siracusa, Tieu, Ihler, Fisher, Willsky; in ICASSP 2005.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      2004

                      "An Overview of Fast Multipole Methods"; Ihler; 2004 (MIT Area Exam)
                      [ Abstract ] | [ BibTeX ] | [ PDF ] | [ PS ]

                      "Nonparametric Hypothesis Tests for Statistical Dependency"; Ihler, Fisher, Willsky; IEEE Transactions on Signal Processing, Aug. 2004.
                      [ Abstract ] |[ BibTeX ] | [ PDF ]

                      "Message Errors in Belief Propagation"; Ihler, Fisher, Willsky; in Neural Information Processing Systems (NIPS) 2004. (Outstanding Student Paper Award)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Nonparametric Belief Propagation for Sensor Network Self-Calibration"; Ihler, Fisher, Moses, Willsky; in ICASSP 2004.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Nonparametric Belief Propagation for Self-Calibration in Sensor Networks"; Ihler, Fisher, Moses, Willsky; in Information Processing in Sensor Networks (IPSN) 2004. (Best Student Paper Award)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Communications-Constrained Inference"; Ihler, Fisher, Willsky; LIDS Tech Report 2601 (Lossless and lossy encoding of sample-based density estimates)
                      [ Abstract ] | [ BibTeX ] | [ PDF ]

                      2003

                      "Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; in Computer Vision and Pattern Recognition (CVPR) 2003. (also AI Memo # AIM-2002-020)
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Efficient Multiscale Sampling from Products of Gaussian Mixtures"; Ihler, Sudderth, Freeman, Willsky; in Neural Information Processing Systems (NIPS) 2003.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      "Hypothesis Testing over Factorizations for Data Association"; Ihler, Fisher, Willsky; in Information Processing in Sensor Networks (IPSN) 2003.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      2002

                      "Nonparametric Belief Propagation"; Sudderth, Ihler, Freeman, Willsky; LIDS Technical Report # 2551, Aug. 2002.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      2001

                      "Nonparametric Estimators for Online Signature Authentication"; Ihler, Fisher, Willsky; in ICASSP 2001.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      2000

                      "Maximally Informative Subspaces: Nonparametric Estimation for Dynamical Systems"; Ihler; Masters' Thesis, MIT, 2000
                      [ Abstract ] | [ BibTeX ] | [ PDF ] | [ PS (zipped) ]

                      1999

                      "Learning Informative Statistics: A Nonparametric Approach"; Fisher, Ihler, Viola; in Neural Information Processing Systems (NIPS) 1999.
                      [ Abstract ] |[ BibTeX ] | [ PDF ] | [ PS ]

                      Last modified December 08, 2010, at 07:15 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Projects/GraphicalModels SLI | Projects / GraphicalModels
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                      Graphical Models

                      Graphical models are used to encode structured relationships among collections of random variables. These relationships may be logical (for example, expressing forbidden combinations of values), but most often are probabilistic, expressing the relative likelihood of co-occurrence. These relationships are expressed as a graph in which directly related variables are connected, which can then be used to simplify and automate reasoning over the full system.

                      Our work has focused on several aspects of graphical models, including

                      • Understanding and extending variants of the popular belief propagation algorithm for inference, or estimating the values of some variables given observations of others
                      • Adaptive or incremental inference methods, which organize calculations so that they can be efficiently reused later to rapidly find changes and update results after small modifications of the model
                      • Graphical models defined over continuous-valued variables, for example 2- or 3-D positions in tracking
                      • Learning the structure and parameters of graphical models from data



                      Belief Propagation

                      Loopy belief propagation (Pearl 1988) is a popular algorithm for approximate inference. Its popularity stems in part from its extremely effective application to channel coding, first to turbo decoding and then to low-density parity check (LDPC) codes (McEliece et al. 1998). While initially not well understood, in recent years a host of theoretical results have been obtained which help to quantify its behavior.

                      Belief propagation comes in two general flavors: sum-product, which attempts to estimate the marginal probabilities of outcomes, and max-product, which attempts to estimate the most likely configuration of the variables.

                      Convergence and accuracy of sum-product


                      Fig 1: Bethe and SAW trees

                      We have developed several results on the behavior of the sum-product algorithm, including convergence conditions and bounds on the accuracy of the resulting marginal probabilities. Our techniques are based on analyzing the Bethe tree (or computation tree) of the BP algorithm, at right. The Bethe tree is formed by "unrolling" the graph G around a root node (say, "1"), so that the root of the Bethe tree corresponds to node "1", the next level corresponds to "1"'s neighbors, the next to their neighbors (not including their parent, "1"), and so on. (Figure 1, both black and red nodes.) If G is a tree, each node will appear only once, but if not each node may have many copies within the Bethe tree. BP at level L from the bottom of the Bethe tree corresponds to the Lth iteration of loopy BP.

                      Intuitively, convergence then corresonds to mixing, or decoupling of the root distribution from the initial conditions at the leaves. If for any set of initial conditions, the belief at the root is the same, then BP must converge to a unique fixed point. Again intuitively, this is likely to happen if the correlations among variables is weak, and sufficiently weak that the dependence from level to level dies off fast enough to offset the increasing number of nodes at each level. We have developed several sufficient conditions that guarantee this, using mixing properties of the factors of G (NIPS 2004, JMLR 2005). Mooij & Kappen (2007) extended our analysis to factor graphs.

                      The accuracy of the the beliefs can be assessed using a subtree, called the self-avoiding walk (SAW) tree (Fig 1, black nodes). In essence, it corresponds to the unrolling of the graph G, up to the point at which each path forms a loop. Self-avoiding is a slight misnomer, since the walks forming this graph do intersect themselves, but only at the terminal point. Marginal probability bounds can be computed by applying our convergence analysis to only this subtree.

                      Reweighted sum-product variants

                      We are also very interested in so-called reweighted variants of sum-product, which enable bounds on the normalization constant (called the partition function) of the distribution. These bounds can be used in learning, since for normalized models they correspond to the data likelihood, and can also be used to produce bounds on marginal probabilities.

                      Our negative tree-reweighted BP work shows how tree-reweighted sum-product can be modified to produce a lower bound on the partition function (as opposed to an upper bound). The resulting algorithm generalizes the structured mean field approach.

                      Reweighed max-product variants

                      Reweighted approaches to optimization are a powerful tool for combinatorial search. These methods are closely related to linear programming relaxations, and provide both upper and lower bounds on the optimal configuration. Our work has developed efficient data structures to optimize and solve the resulting bounds, such as the covering tree (Yarkony et al. 2010).


                      Adaptive Inference


                      Fig. 2: Hierarchical clustering

                      Adaptive inference describes the problem of repeatedly modifying and performing inference on a model. Since the sequence of models to be used are very similar to one another (i.e., only incremental changes are made at each stage), the results of previous inferential calculations can be used to compute the new results much faster than if performed from scratch.

                      We use a tree contraction process to define a hierarchical clustering of the nodes in the factor graph (Fig. 2). This clustering then implies a (partial) elimination ordering on the variables in the graph. The contraction process guarantees that for any change to the model, only O(log n) computations must be recomputed.

                      Our data structure can be used to incorporate arbitrary changes, including model structure or observations, in O(log n) time. New marginal probabilities can be computed (queried) in O(log n) time. Moreover, without knowing their number or position a priori, we can find all changes to the optimizing configuration in O(k log n) time, where k is the number of variables whose optimal configuration has changed.


                      Continuous random variables


                      Fig 3: NBP uses Gaussian mixtures to represent beliefs

                      Graphical models for discrete valued random variables are fairly well-studied, but continuous (and non-Gaussian) random variables are much more complex, despite the fact that many real-world problems consist precisely of such systems. We have developed several algorithms for dealing with continuous-valued distributions, including nonparametric belief propagation (which represents beliefs using Gaussian mixture distributions) and particle belief propagation (which uses importance-weighted samples).


                      Learning graphical models

                      One of the most important tasks in probabilistic models is their construction based on collections of data (statistical learning). We are interested in estimating both the structure of the model, and in its parameters given a fixed structure. Recent emphases include using composite likelihood to improve the accuracy of the model while preserving efficiency, and exploring sequential Monte Carlo approaches to learning.

                      Last modified July 06, 2010, at 10:31 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Projects/DataMining SLI | Projects / DataMining
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                      Data Mining

                      Data mining describes the task of organizing and searching massive quantities of data for patterns of interest, for example summarizing large subsets of the data (clustering), finding unusual or unexpected patterns (anomaly detection), or constructing new, more meaningful representations of the data (latent space representations). Our group uses probabilistic models to perform these tasks -- graphical models are learned from the data to represent and assess expected behavior.


                      Mining sensors and count data

                      We have used graphical models to provide automatic event (anomaly) detection in data consisting of discrete counts, using variants of Poisson processes to model the underlying phenomena. Data examined include (a) counts of entries and exits through one or more doors of a UCI campus building, captured via a wireless optical sensor (image, right; data available from the UCI ML repository); (b) vehicle counts collected via loop sensor data on the I-405 freeway, collected by CalTrans (data available from CalTrans' website); (c) traffic accident data (date and time of accident report) from North Carolina.

                      We used Markov-modulated Poisson processes to describe the superposition of normal and abnormal behavior in the data. "Normal" behavior are represented using a standard, time-varying Poisson process, while "event" behavior consists of sustained periods with increases (or more rarely, decreases) in the number of counts observed. A Markov chain model captures the notion of event persistence, allowing the model to find slight but sustained changes in activity and more accurately estimate the duration of a detected event. An example is shown at right, where the blue curve shows the observed counts, black the estimated normal profile; red bars show the estimated probability of an event at each time, and the bottom-most panel shows a partial ground truth of known events, held out from the model for validation purposes.

                      Matlab code available here.


                      Text mining and LDA

                      We have also worked with the latent Dirichlet allocation (LDA) model for mining unstructured collections of text, such as books, articles, or web pages. LDA describes each document as a collection of semantic "topics", only a few of which appear in any given document. The topics in each document, and words associated with each topic, are learned in an unsupervised manner. Our work has focused on making the estimation process more computationally efficient, developing fast sampling algorithms (Porteous et al. 2008) and improving the efficiency and accuracy of parallel or distributed implementations (Ihler & Newman 2009), which allow ever-larger data sets to be automatically organized.


                      Computational Biology

                      High-throughput experiments in biology have made it critical to identify genes which are expressed in interesting ways, for example cycling through periodic patterns over time. Typically, many thousands of genes are evaluated and must be identified and organized by their expression profiles. We have used graphical models to detect periodic behavior in genes (Chudova et al. 2009), and to improve our estimates of their shapes by separating out effects due to changes in the individual's rate of development (Liu et al. 2010).

                      Another major problem in computational biology is identifying regulatory network structure, i.e., determining which genes or transcription factors play a role in controlling the subsequent expression of other genes. This problem can be formulated as learning a graphical model, where the connectivity of the graph is used to represent which pairs of genes are directly interacting.

                      Last modified July 06, 2010, at 10:23 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Projects/Sensor SLI | Projects / Sensor Networks
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                      Sensor Networks

                      Improvements in sensing technology and wireless communications have greatly enhanced our ability to view and understand the world we live in. Collaborative networks of sensors can be deployed for relatively little cost, gathering and reporting real-time data about their locale. I am interested in the estimation and information processing problems which arise in sensor networks.


                      An example graph

                      Sensor localization and message-passing algorithms

                      An important first step in many sensor deployments is to localize the sensors, i.e., obtain estimates of each sensors' position and/or orientation. Almost equally important is to gauge the amount of uncertainty remaining in our estimates -- are our estimates sufficiently accurate, and if not, where do they need improvement? Self-localization methods, which exploit local measurements of relative position (such as received signal strength or time delay between sensors), are desirable to minimize cost and effort.


                      Beliefs after first iteration

                      We formulate the localization problem as an inference problem on a graphical model, allowing us to apply generic inference algorithms from machine learning to estimate sensor position. In particular, we apply nonparametric belief propagation (NBP), a variant of the popular belief propagation algorithm. This provides a distributed estimation process in which each sensor sends local information to its neighbors, successively refining its position estimate. The resulting, distribution-like belief functions also provide estimates of the uncertainty in position for each sensor.

                      Communications cost for density estimates


                      KD-Tree for density approximation

                      In the localization problem, and more generally in object tracking tasks for sensor networks, sensors maintain a belief or posterior distribution about the objects' state (position, velocity, etc.), and must communicate these functions to exchange information. A typical example is "leader-based" tracking: a particular sensor is nominated as the "leader node" and tracks the object until it leaves that sensors' vicinity. At this point, the sensor "hands off" its leadership position to a closer sensor, sending its current estimate of the state to the new leader.

                      How expensive is this hand-off process? For particle-based filters, it would seem to grow linearly with the number of samples. However, these samples comprise a density estimate, and it is reasonable to think that the asymptotic cost should scale with the complexity of the density, and perhaps with the fidelity to which it is represented. We examine this problem in some detail, and propose algorithms for simultaneously approximating particle-based density estimates and representing those approximations efficiently for communication.

                      Last modified September 16, 2008, at 05:15 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
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                      Bren School of Information and Computer Science
                      University of California, Irvine
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                      Last modified September 23, 2008, at 02:44 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
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                      Last modified July 21, 2011, at 10:50 AM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://www.ics.uci.edu/~ihler/code/event.html

                      Code Packages


                      KDE Toolbox

                      Event Detection

                      Adaptive Inference

                      Gaussian Process Regression with Time-shifts


                      Statistical Models for Event Detection in Count Data

                      Sensor

                      This page contains links to code from several papers on automatic event detection in data consisting of discrete counts, using variants of Poisson processes to model the underlying phenomena.

                      Data examined include (a) counts of entries and exits through one or more doors of a UCI campus building, captured via a wireless optical sensor (image, right; data available from the UCI ML repository); (b) vehicle counts collected via loop sensor data on the I-405 freeway, collected by CalTrans (data available from CalTrans' website); (c) traffic accident data (date and time of accident report) from North Carolina.

                      Markov-Modulated Poisson Process

                      Work with Padhraic Smyth and Jon Hutchins at UCI; published in SIGKDD 2006. Matlab code available here. Events

                      This work describes a discrete-time model, in which the data are modelled as consisting of the superposition of "normal" behavior, represented using a Poisson process, and "event" behavior consisting of increases (or more rarely, decreases) in the number of counts observed. The event behavior is captured using a Markov chain model to capture the idea of event persistence, allowing the model to find slight but sustained changes in activity and more accurately estimate the duration of a detected event. An example is shown at right, where the blue curve shows the observed counts, black the estimated normal profile; red bars show the estimated probability of an event at each time, and the bottom-most panel shows a partial ground truth of known events, held out from the model for validation purposes.

                      Dirichlet Process Mixture Model

                      Published at NIPS 2005; code not yet made available.

                      http://sli.ics.uci.edu/Code/GPRTimeshift/ SLI | Code / Gaussian Process Regression with Time-shifts
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                      Gaussian Process Regression with Time-shifts

                      Examples from the paper

                      GPR, fixed timesGPR with timeshift

                      Overview

                      Time-course gene expression data sets provide important insights into dynamic aspects of biological processes such as circadian rhythms, cell cycle, and organ development. A typical microarray time-course expression data set consists of measurements taken at a relatively small number of time-points (e.g., 5 to 10), where at each time-point microarray measurements are obtained on a few (say, 3) replicate samples. There has been considerable work in recent years in bioinformatics on the development of statistical techniques for accurately inferring expression profiles from such data, in the face of both measurement noise and biological variation across replicates.

                      However, a source of variation that has received little attention to date is uncertainty about the precise biological time at which measurements were taken. Specifically, replicates may be measured at the same chronological time, yet could be at different stages of development due to the replicate individuals having developed at different rates. Although the underlying true expression profiles for each gene may be noisy, we can infer time-shifts for each replicate by analyzing all genes simultaneously. In particular, we simultaneously estimate the profile shapes using a Gaussian process regression (GPR) model and estimate the time shifts by a maximum a-posteriori optimization.

                      Code

                      This code implements a Gaussian process regression (GPR) model with uncertainty in the independent axis (in our case, time).

                      • ZIP file (includes all code and the expression data used in our paper)

                      For more information on the model or its results, please see our publication

                      • Estimating Replicate Time Shifts Using Gaussian Process Regression?, Bioinformatics, to appear.

                      Copyright / license

                      The Gaussian Process with Time-Shifting code was written by Qiang Liu, and are copyrighted under the (lesser) GPL:

                          Copyright (C) 2009 Qiang Liu
                      

                      This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; version 2.1 or later. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.

                      The authors may be contacted via email at: qliu1 (at) uci.edu

                      Changes

                      Last modified December 06, 2009, at 04:42 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://www.ics.uci.edu/~ihler/code/adaptive.html

                      Code Packages


                      KDE Toolbox

                      Event Detection

                      Adaptive Inference

                      Gaussian Process Regression with Time-shifts


                      Adaptive Inference in Graphical Models

                      About Adaptive Inference

                      Adaptive inference refers to the task of recomputing the results of an inference problem which is only slightly different from one which has already been solved. It uses concepts from incremental computing, in which the amount of work we expect to do to solve a problem is (hopefully) proportional to the amount of change in the problem from our already-solved version. In particular, we use rake and compress trees (RC-Trees) to cache sufficient statistics of the inference process in such a way that a large fraction of them are guaranteed to be reusable no matter what (small) changes are made to the original graphical model, including changing factors or potential functions, adding or removing variables, or modifying the graph connectivity. For a system of N variables, the sum-product version of the code can update the marginal distribution of K specified variables in O(K log N/K) time. The max-product version of the code keeps track of an optimal configuration of variables (the MPE or MAP configuration), and after any change which induces K changes in the optimal configuration, finds all those changes in O(K log N/K) time, without the locations needing to be specified.

                      For more information, please see our publications on the subject:

                      • Adaptive Bayesian Inference, NIPS 2007
                      • Adaptive Inference in General Graphical Models, UAI 2008
                      • Adaptive Updates for MAP Configurations with Applications to Bioinformatics, SSP 2009

                      The code can be downloaded here. Please read the short usage and disclaimer statements that follow, however.

                      Using the Code

                      This code is mainly intended for educational purposes, to provide a working implementation for anyone wishing to make use of the algorithms in their own work. It is not optimized, nor does it have the greatest of user interfaces.

                      The Matlab functions given here were used in the NIPS 2007 publication, and assume that the underlying graph is tree-structured. The SSP 2009 paper also used mainly tree-structured graphs, including HMMs, and the code should work for these problems as well, but I haven't tried it.

                      The code includes versions for sum-product (computing marginal distributions) and dynamic programming for optimization (max-product to compute sufficient statistics, then selecting a configuration of the variables in the downward pass). The functions corresponding to each are designated "SP" and "MP", respectively.

                      Using the MEX Functions

                      The algorithms involved in this work rely heavily on pointers and memory re-allocation, neither of which is a strong point of the Matlab programming environment. To make the algorithms scale in the proper way, certain aspects of Matlab's memory management system were bypassed; this is done using MEX files to directly access and change the array and cell array contents. However, these functions are undocumented and are not guaranteed; they worked for me but may not work for you. Here is the disclaimer in the C files:

                      WARNING! WARNING! WARNING! This MEX file is not for the faint of heart. It attempts to circumvent Matlab's memory-handling functions in order to give more efficient code, and uses undocumented functions to do so. It comes with no guarantees whatsoever. It may not work on other versions of Matlab, or other platforms, or even at all. It may result in memory leaks, segmentation faults, destroy data, set your computer on fire, or suck it into a black hole, for all I know. If you do not accept these possible risks, do not use this MEX file.

                      If that doesn't deter you, assuming you have MEX properly set up (see my KDE toolbox page for some pointers on that subject) you should be able to compile the functions using the "buildmex" script.

                      COPYRIGHT / LICENSE

                      The adaptive inference code for Matlab was written by Alexander Ihler, and are copyrighted under the (lesser) GPL:

                      Copyright (C) 2007-2008 Alexander Ihler

                      This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; version 2.1 or later. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.

                      The authors may be contacted via email at: ihler (at) alum (.) mit (.) edu

                      CHANGELOG

                      
                      

                      http://www.ics.uci.edu/~ihler/code/kde.html

                      Code Packages


                      KDE Toolbox

                      Event Detection

                      Adaptive Inference

                      Gaussian Process Regression with Time-shifts


                      Kernel Density Estimation Toolbox for MATLAB (R13)

                      MATLAB KDE Class Description

                      The KDE class is a general matlab class for k-dimensional kernel density estimation. It is written in a mix of matlab ".m" files and MEX/C++ code. Thus, to use it you will need to be able to compile C++ code for Matlab. Note that the default compiler for Windows does not support C++, so you will need GCC under Linux, or GCC or Visual C++ for Windows. Bloodshed supplies a nice development environment along with the MinGW compiler. See this page for help setting up MEX with MinGW.

                      [NOTE: Since several compiled mexglx and dll files are included, you may not need to re-compile the toolbox at all; however, I recommend it if possible for compatibility reasons.]

                      Kernels supported are:

                      • Gaussian

                      • Epanetchnikov (truncated quadratic)

                      • Laplacian (Double-exponential)

                      For multivariate density estimates, the code supports product kernels -- kernels which are products of the kernel function in each dimension. For example, for Gaussian kernels this is equivalent to requiring a diagonal covariance. It can also support non-uniform kernel bandwidths -- i.e. bandwidths which vary over kernel centers.

                      The implementation uses "kd-trees", a heirarchical representation for point sets which caches sufficient statistics about point locations etc. in order to achieve potential speedups in computation. For the Epanetchnikov kernel this can translate into speedups with no loss of precision; but for kernels with infinite support it provides an approximation tolerance level, which allows tradeoffs between evaluation quality and computation speed. In particular, we implement Alex Gray's "Dual Tree" evaluation algorithm; see Gray and Moore, "Very Fast Multivariate Kernel Density Estimation using via Computational Geometry", in Proceedings, Joint Stat. Meeting 2003 for more details. This gives a tolerance parameter which is a percent error (from the exact, N^2 computation) on the value at any evaluated point. In general, "tolerance" parameters in the matlab code / notes refers to this percent tolerance. This percentage error translates to an absolute additive error on the mean log-likelihood, for example. An exception to this is the gradient calcuation functions, which calculate using an absolute tolerance value. This is due to the difficulty of finding a percentage bound when the function calculated is not strictly positive.

                      We have also recently implemented the so-called Improved Fast Gauss Transform, described in [Yang, Duraiswami, and Gumerov, "Improved Fast Gauss Transform", submitted to the Siam Journal of Scientific Computing]. This often performs MUCH faster than the dual tree algorithm mentioned above, but the error bounds which control the computation are often quite loose, and somewhat unwieldy (for example, it is difficult to obtain the fractional error bounds provided & used by the dual tree methods and other functions in the KDE toolbox). Thus for the moment we have left the IFGT separate, with alternate controls for computational complexity (see below, and the file "evalIFGT.m").

                      Getting Started

                      Download and unzip the KDE class to a directory called @kde.

                      (If desired) Compile the MEX functions. This can be done by going to the "@kde/mex" directory in Matlab, and copying and pasting the code from the "makemex.m" file into the Matlab window. If this fails, make sure that MEX / C++ compilation works. The KDE toolbox is tested in Matlab R13 and later; it may work in ealier versions as well. Re-compiling may not be required, depending on your platform and version; "mexglx" (Linux 32-bit), "mexa64" (Linux 64-bit), and "dll" (Windows 32-bit) files are included. If you have trouble, recompile.

                      Thanks to Ankur Datta for compiling Mac versions of the MEX files and making them available. (I do not even own a Mac and cannot vouch for their operation, etc.; use at your own risk.)

                      NOTE: MS Visual C++ has a bug in dealing with "static const" variables; I think there is a patch available, or you can change these to #defines.

                      Operate from the class' parent directory, or add it to your MATLAB path (e.g. if you unzip to "myhome/@kde", cd in matlab to the "myhome" dir, or add it to the path.)

                      Objects of type KDE may be created by e.g.

                      p = kde( rand(2,1000), [.05;.03] ); % Gaussian kernel, 2D % BW = .05 in dim 1, .03 in dim 2.

                      p = kde( rand(2,1000), .05, ones(1,1000) ); % Same as above, but uniform BW and % specifying weights

                      p = kde( rand(2,1000), .05, ones(1,1000), 'Epanetchnikov'); % Quadratic kernel; just 'E' or 'e' also works

                      p = kde( rand(2,1000), 'rot' ); % Gaussian kernel, 2D, BW chosen by "rule of thumb" (below)

                      To see the kernel shape types, you can use:

                      plot(-3:.01:3, evaluate(kde(0,1,1,T),-3:.01:3) ); % where T = 'G', 'E', or 'L'

                      Kernel sizes may be selected automatically using e.g.

                      p = ksize(p, 'lcv'); % 1D Likelihood-based search for BW

                      p = ksize(p, 'rot'); % "Rule of Thumb"; Silverman '86 / Scott '92

                      p = ksize(p, 'hall'); % Plug-in type estimator (estimates each dim. separately)

                      Density estimates may be visualized using e.g.

                      plot(p);

                      or

                      mesh(hist(p));

                      See "help kde/plot" and "help kde/hist" for more information.

                      Also, the demonstration programs @kde/examples/demo_kde_#.m may be helpful.

                      Usage Examples

                      The demonstration programs in @kde/examples/demo_kde_#.m (where # is one of 1,2,3) may be helpful.

                      KDE Matlab class definition

                      The following is a simple list of all accessible functions for the KDE class. Use "help functionname" in Matlab for more information.

                      Constructors:

                      kde( )

                      empty kde

                      kde( kde )

                      re-construct kde from points, weights, bw, etc.

                      kde( points, bw )

                      construct Gauss kde with weights 1/N

                      kde( points, bw, weights)

                      construct Gaussian kde

                      kde( points, bw, weights,type)

                      potentially non-Gaussian

                      marginal( kde, dim)

                      marginalize to the given dimensions

                      condition( kde, dim, A)

                      marginalize to ~dim and weight by K(x_i(dim),a(dim))

                      resample( kde, [kstype] )

                      draw N samples from kde & use to construct a new kde

                      reduce( kde, ...)

                      construct a "reduced" density estimate (fewer points)

                      joinTrees( t1, t2 )

                      make a new tree with t1 and t2 as the children of a new root node

                      Accessors: (data access, extremely limited or no processing req'd)

                      getType(kde)

                      return the kernel type of the KDE ('Gaussian', etc)

                         
                      getDim

                      get the dimension of the data

                      getNpts

                      get the # of kernel locations

                      getNeff

                      "effective" # of kernels (accounts for non-uniform weights)

                         
                      getPoints(kde)

                      Ndim x Npoints array of kernel locations

                      adjustPoints(p,delta)

                      shift points of P by delta (by reference!)

                      rescale(kde,alpha)

                      rescale a KDE by the (vector) alpha

                         
                      getBW(kde,index)

                      return the bandwidth assoc. with x_i (Ndim x length(index))

                      adjustBW(kde,newBW)

                      set the bandwidth(s) of the KDE (by reference!) Note: cannot change from a uniform -> non-uniform bandwidth

                      ksize

                      automatic bandwidth selection via a number of methods

                        LCV

                      1D search using max leave-one-out likelihood criterion

                        HALL, HJSM

                      Plug-in estimator with good asymptotics; MISE criterion

                        ROT, MSP

                      Fast standard-deviaion based methods; AMISE criterion

                        LOCAL

                      Like LCV, but makes BW propto k-th NN distance (k=sqrt(N))

                         
                      getWeights

                      [1 x Npts] array of kernel weights

                      adjustWeights

                      set kernel weights (by reference!)

                         
                      sample(P,Np,KSType)

                      draw Np new samples from P and set BW according to KSType

                         

                      Display: (visualization / description)

                      plot(kde...)

                      plot the specified dimensions of the KDE locations

                      hist(kde...)

                      discretize the kde at uniform bin lengths display : text output describing the KDE

                      double

                      boolean evaluation of the KDE (non-empty)

                      Statistics: (useful stats & operations on a kde)

                      mean

                      find the (weighted) mean of the kernel centers

                      covar

                      find the (weighted) covariance of the kernel centers

                      knn(kde, points, k)

                      find the k nearest neighbors of each of points in kde

                      entropy

                      estimate the entropy of the KDE

                      kld

                      estimate divergence between two KDEs

                      evaluate(kde, x[,tol])

                      evaluate KDE at a set of points x

                      evaluate(p, p2 [,tol])

                      same as above, x = p2.pts (if we've already built a tree)

                      evalIFGT(kde, x, N)

                      evaluate using the N-term IFGT (requires uniform BW Gaussian kernels)

                      evalIFGT(p, p2, N)

                      evalAvgLogL(kde, x)

                      compute Mean( log( evaluate(kde, x) ))

                      evalAvgLogL(kde, kde2)

                      same as above, but use the weights of kde2

                      evalAvgLogL(kde)

                      self-eval; leave-one-out option

                      llGrad(kde,kde2)

                      estimate the gradient of log-likelihood for kde evaluated at the points of kde2

                      entropyGrad(p)

                      estimate gradient of entropy (uses llGrad)

                      miGrad(p,dim)

                      estimate gradient for mutual information between p(dim), p(~dim)

                      klGrad(p1,p2)

                      estimate gradient direction of KL-divergence

                      Mixture products: (NBP stuff) (GAUSSIAN KERNELS ONLY)

                      productExact

                      exact computation (N^d kernel centers)

                      productApprox

                      accessor for other product sampling methods

                        prodSampleExact

                      sample N points exactly (N^d computation)

                        prodSampleEpsilon

                      kd-tree epsilon-exact sampler

                        prodSampleGibbs1

                      seq. index gibbs sampler

                        prodSampleGibbs2

                      product of experts gibbs sampler

                        prodSampleGibbsMS1

                      multiresolution version of GS1

                        prodSampleGibbsMS2

                      multiresolution version of GS2

                        prodSampleImportance

                      "mixture" importance sampling

                        prodSampleImportGauss

                      gaussian importance sampling

                       

                      COPYRIGHT / LICENSE

                      The kde package and all code were written by Alex Ihler and Mike Mandel, and are copyrighted under the (lesser) GPL:

                      Copyright (C) 2003 Alexander Ihler

                      This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; version 2.1 or later. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.

                      The authors may be contacted via email at: ihler (at) alum (.) mit (.) edu

                      CHANGELOG

                      CHANGE LOG FOR KERNEL DENSITY ESTIMATION CLASS
                      ==============================================
                      
                      08/14/07   ATI   Fixed 64-bit support for productApprox functions + bugfix
                      07/16/07   ATI   Fixed 64-bit support for most functions
                      01/08/04   ATI   Added support for LOO estimate in llGrad; fixed computation
                                       of norm. constant for LOO version of evaluate
                      11/22/04   ATI   Added (original) Fast Gauss Transform (Greengard & Strain '91), 
                                       using newer (correct) error bound of Baxter & Roussos '02
                      11/09/04   ATI   Fixed bug in IFGT eval (incorrect scale factor)
                      10/02/04   ATI   Added support for Yang, Duraiswami, and Gumerov's Improved Fast
                                       Gauss Transform.  Extremely fast; loose bounds on absolute error.
                      09/17/04   ATI   Fixed permutation bug in adjustBW, and bug in llGrad for evals
                                       between two distributions.  Added explicit discrete resampling.
                      08/04/04   ATI   Updated productApprox:import, reduceKD; added ISE type to reduceKD
                      07/09/04   ATI   Fixed small bugs: condition.m, jointrees.m, productExact.m
                                       Improved speed of sample.m 
                      02/19/04   ATI   Added "llHess" (Hessian) and "modes" (mode-finding) functions
                      01/29/04   ATI   fixed 0 vs 1-base err in "index" ret'n values of productApprox
                      01/22/04   ATI   Added "ise" method and epsilon-exact MEX implementation
                                       Fixed bug in epsilon and exact products of variable-BW densities
                                       Improved implementation of "condition" for fixed-BW densities
                      12/28/03   ATI   Removed "abs" KL method, replaced with "ise" estimate method
                                       ("abs" was not a good est. of KL but served as an est. of ISE)
                      12/13/03   ATI   Fixed bug in KNN function and some bugs with the "reduce" f'n & "kld"
                                       Added some example demonstration functions
                      12/05/03   ATI   Added "reduce" function
                                       Fixed bug in "productExact" (thanks Chunhua Shen)
                                       Fixed bug in adjustBW (caused crashing or termination)
                      11/18/03   ATI   Added support for additional KL-divergence estimates
                      10/28/03   ATI   Added support for "kde(pts,'kstype')" constructor
                      10/24/03   ATI   Fixed an error in "adjustWeights"; added mex & dll files to tarfile
                      

                      http://www.ics.uci.edu/~qliu1/publication.html Qiang Liu's Publications
                      Qiang Liu
                      home
                      Publications
                      CV (PDF)
                      TA (CS178)

                      Qiang Liu's Publications

                      Qiang Liu

                      Distributed Estimation, Information Loss and Exponential Families
                      Liu, Ihler; To Appear in Advances in Neural Information Processing Systems (NIPS) 2014.

                      Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy
                      Zhou, Liu, Platt, Meek; International Conference on Machine Learning (ICML), June 2014. [Code] 

                      Marginal structured SVM with hidden variables
                      Ping, Liu, Ihler; International Conference on Machine Learning (ICML), June 2014.

                      Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
                      Liu, Ihler, Steyvers; Advances in Neural Information Processing Systems (NIPS) 2013.

                      Variational Planning for Graph-based MDPs;
                      Cheng, Liu, Chen, Ihler; Advances in Neural Information Processing Systems (NIPS) 2013.

                      Variational Algorithms for Marginal MAP;
                      Liu, Ihler; Journal of Machine Learning Research (JMLR) 2013.

                      Variational Inference for Crowdsourcing;
                      Liu, Peng, Ihler; Advances in Neural Information Processing Systems (NIPS) 2012. [Appendix, Code] 

                      Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis;
                      Geyfman M, Kumar V, Liu Q, Ruiz R, Gordon W, Espitia F, Cam E, Millar SE, Smyth P, Ihler A, Takahashi JS, Andersen B; Proc Natl Acad Sci USA doi:10.1073/pnas.120959210 (2012). 

                      Belief Propagation for Structured Decision Making;
                      Liu, Ihler; Uncertainty in Artificial Intelligence (UAI) 2012. [Appendix] 

                      Distributed Parameter Estimation via Pseudo-likelihood;
                      Liu, Ihler; International Conference on Machine Learning (ICML) 2012. [Appendix] 

                      Computational Approaches to Sentence Completion;
                      Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J.C. Burges, Ainur Yessenalina, and Qiang Liu; in ACL 2012, ACL/SIGPARSE, July 2012.

                      Variational algorithms for marginal MAP;
                      Liu, Ihler; Uncertainty in Artificial Intelligence (UAI) 2011. [Full Version] 

                      Bounding the Partition Function using Holder's Inequality;
                      Liu, Ihler; International Conference on Machine Learning (ICML) 2011. 

                      Learning Scale Free Networks by Reweighted l1 Regularization;
                      Liu, Ihler; AI & Statistics 2010. (notable paper award)

                      Negative Tree Reweighted Belief Propagation;
                      Liu, Ihler; Uncertainty in Artificial Intelligence (UAI), July 2010.

                      Particle Filtered MCMC-MLE with Connections to Contrastive Divergence;
                      Asuncion, Liu, Ihler, Smyth; Int'l Conf on Machine Learning (ICML), June 2010.

                      Learning with Blocks: Composite Likelihood and Contrastive Divergence;
                      Asuncion, Liu, Ihler, Smyth; AI & Statistics (AISTATS), April 2010.

                      Estimating Replicate Time-Shifts Using Gaussian Process Regression;
                      Liu, Lin, Anderson, Smyth, Ihler; Bioinformatics 26(6), Mar. 2010, pp. 770-776; doi:10.1093/bioinformatics/btq022.

                      Page generated 2014-10-21 19:25:20 PDT, by jemdoc. (source)
                      http://www.ics.uci.edu/~qliu1/TA_CS178.html TA for CS178, Machine Learning
                      Qiang Liu
                      home
                      Publications
                      CV (PDF)
                      TA (CS178)

                      TA for CS178, Machine Learning

                      • A list of Matlab commands (which I showed the in discussion hour)

                      Page generated 2012-10-08 23:34:46 PDT, by jemdoc. (source)
                      http://www.ics.uci.edu/~qliu1/index.html Qiang Liu
                      Qiang Liu
                      home
                      Publications
                      CV (PDF)
                      TA (CS178)

                      Qiang Liu

                      alt text 

                      Qiang Liu
                      Ph.D. Candidate
                      Advisor: Prof. Alexander Ihler
                      Information & Computer Science
                      University of California at Irvine
                      qliu1(at)uci.edu

                      ("Qiang" sounds like "Chee-ah-ng", and "Liu" as "l-yo")


                      New.

                      I will be an assistant professor in the Department of Computer Science at Dartmouth College starting in Summer 2015.

                      PhD positions are available. Please email me if interested.

                      Workshops co-organized:

                      • Crowdsourcing: Theory, Algorithms and Applications, NIPS’13,

                      • Machine Learning Meets Crowdsourcing, ICML’13.

                                

                      Research

                      My research area is machine learning and statistics, with interests spreading over the pipeline of data collection (mainly crowdsourcing), learning, inference, decision making, and various applications under the framework of probabilistic graphical models.

                      Crowdsourcing. All machine learning processes start from data collection. Crowdsourcing is a modern approach to collect large amounts of labeled data by hiring anonymous workers through online platforms such as Amazon Mechanical Turk. Unfortunately, the crowdsourced workers are often unreliable and uncontrollable, raising many challenging computational questions, such as how to aggregate labels from workers with different expertise, how to combine and balance noisy (but cheap) crowdsourced labels and accurate (but expensive) expert labels, and how to crowdsource complicated objectives such as protein structures.

                      • We reform the problem of aggregating crowdsourced labels into a standard inference problem on a factor graph, which we solve using a class of variational inference algorithms. We show that both the naïve majority voting method and a previous algorithm by Karger et al. 2012 are special cases of one of our belief-propagation-type algorithms with special priors. We demonstrate significant improvement on the performance by using better priors, see NIPS2012, code.

                      • Control items with known answers can be used to evaluate workers’ performance, and hence improve the combined results on the target items with unknown answers. This raises the problem of how many control items to use when the total number of items each workers can answer is limited: more control items evaluates the workers better, but leaves fewer resources for the target items that are of direct interest, and vice versa. We perform theoretical analysis and provide surprisingly simple answers for this problem, see here.

                      • A preliminary thought on combining structured labels such as the protein folding, see here.

                      Learning. Learning refers to constructing probabilistic models from empirical data, either to estimate the model parameters with predefined model structures, or even to estimate the model structures solely from data? I am interested in developing efficient, possibly distributed, learning algorithms, that perform well on real world data.

                      • Here is an efficient distributed learning algorithm based on smartly combining local estimators defined by pseudo-likelihood components: ICML2012.

                      • Here is a structure learning algorithm for recovering scale-free networks, thought to appear commonly in the real world: AISTATS2011 (notable paper award).

                      • Here are some earlier works on contrastive divergence and MCMC-MLE: ICML2010; AISTATS2010.

                      Inference. With given graphical models, either handcrafted or learned from data, inference refers to answering queries, such as marginal probability (or partition function), maximum a posteriori (MAP) estimation, or marginal MAP, the hybrid of marginalization and MAP. I am interested in developing efficient inference algorithms, mostly based on variational methods and in the form of belief-propagation-like message passing algorithms.

                      • Marginal MAP is notoriously difficult even on tree-structured graphs. We developed a general variational dual representation for marginal MAP, and propose a set of variational approximation algorithms, including an interesting “mixed-product” BP that is a hybrid of max-product, sum-product and a special “argmax-product” message updates, and a convergent proximal point algorithm that works by iteratively solving pure marginalization tasks. See JMLR2013; UAI2011 (Slides).

                      • We proposed an efficient approximate inference algorithm for calculating the log-partition function that unifies Rina Dechter's “one-pass” mini-bucket algorithm with iterative variational algorithms, such as tree reweighted BP. Our method inherits the advantages of both, and easily scales to large clique sizes. Our algorithm can provide both upper and lower bounds for the log-partition function. See ICML2011.

                      • Tree reweighted BP provides an upper bound on the log-partition function, while naïve mean field and structured mean field give lower bounds. We show that tree reweighted BP provably gives a lower bound if its weights are set to take negative values in a particular way. We also show that such “negative” tree reweighted BP reduces to structured mean field as the weights approach infinity. For the full story, see UAI2010.

                      Structured decision making. In practice, we often need to take a sequence of actions to achieve a predefined goal, usually under uncertain environments where information is observed sequentially and interactively as we progress. Decision networks (also called influence diagrams) are graphical model style representations of such structured decision making problems under uncertainty. Just like Bayesian networks generalize Markov chains or hidden Markov chains, decision networks generalize Markov decision processes (MDP), or partially observable decision processes (POMDP). Unfortunately, the problem of finding the optimal actions for decision networks is much more challenging than answering queries on Bayesian networks, especially in cases where limited information is observed or where multi-agent cooperation is required (such as in robot soccer games).

                      • We extend the powerful variational inference framework for solving decision networks, based on which we propose an efficient BP-type algorithm and a convergent proximal point algorithm. Our framework enables us to translate basically any variational algorithm to solve influence diagrams. See UAI2012.

                      Applications. I am interested in applying these machine learning methods in many application areas.

                      • Natural language processing:

                        • How well can computers solve the SAT sentence completion question? This is the work I involved when I was interning in Microsoft Research Redmond, ACL2012.

                      • Sensor networks:

                        • Here is a distributed algorithm for learning parameters in sensor networks: ICML2012.

                        • My algorithm for solving influence diagrams provides a powerful way to design optimal decentralized detection networks: UAI2012.

                      • Bioinformatics:

                        • PNAS2012; AISTATS2011; Bioinformatics2010 .











                      Page generated 2014-07-25 19:13:38 PDT, by jemdoc. (source)
                      http://sli.ics.uci.edu/Classes/2009W SLI | Classes / Introduction to Artificial Intelligence, CS271
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                      Introduction to Artificial Intelligence, CS271

                      CLOSED : 2009 OFFERING

                      Assignments and Exams:

                      Homeworks and exams temporarily redacted.

                           
                      Student Comment Page

                      Engineering Tower (ET) 201, Tues/Thurs 2pm-3:20pm


                      Introduction to artificial intelligence.

                      CS271 is an introductory course to the elements and algorithms underlying the field of artificial intelligence (AI). AI includes subproblems relevant to many areas of research, including information theory, control, signal processing, optimization, operations research, natural language processing and computer vision. CS271 will provide an introduction to the techniques of automated problem solving, including search, optimization and inference in deterministic and stochastic systems.

                      As in previous years, the course will focus considerable time on deterministic constraints, logic, and search. However, the inclusion and representation of uncertainty is critical in many modern aspects of AI, and will be introduced and discussed alongside deterministic versions of related problem types.

                      Background.

                      The course is intended to be an introduction to artificial intelligence, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from computer science (such as algorithmic complexity and logic); portions of the class will also include basic probability, and familiarity with programming may be helpful for some homeworks.

                      Course format.

                      Two lectures per week. Homeworks due in class approximately every two weeks. Two exams (midterm and final). Grading: 40% homework, 25% midterm, 35% final. Lowest homework score to be dropped.

                      Office Hours.

                      Office hours for the course are Monday 2-3pm, or by appointment.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Russel & Norvig, "Artificial Intelligence: A Modern Approach". Lectures should follow the book fairly closely, with minor exceptions.

                      (Tentative) Schedule of Topics.

                       TopicsSlidesSuggested reading
                      Week 1Class introduction; history of AI; AgentsL1R&N Ch 1-2
                       Formalities of problem solving; state spacesL2R&N Ch 3
                      Week 2Basics of searchL3R&N Ch 3
                       Informed search, heuristics, A*L4R&N Ch 4
                      Week 3A* search continuedL5R&N Ch 4
                       Constraint satisfaction problemsL6R&N Ch 5
                      Week 4CSP continued; connections to probabilityL7R&N Ch 5
                       GamesL8R&N Ch 6
                      Week 5Catch-up / review
                       MIDTERM EXAM
                      Week 6Propositional logicL9R&N Ch 7
                       Prop. Logic continuedL10R&N Ch 7
                      Week 7First-order LogicL11R&N Ch 8,9
                       continuedL12
                      Week 8PlanningL13R&N Ch 11
                       ProbabilityL14R&N Ch 13
                      Week 9Bayesian NetworksL15R&N Ch 14
                       LearningL16R&N Ch 18
                      Week 10Learning ct'dL17
                       Catch-up and review
                      Final Exam03/20/2009Final exam
                      Last modified January 19, 2015, at 04:38 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2011S-274a SLI | Classes / CS274A: Probabilistic Learning
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                      CS274A: Probabilistic Learning

                      CLOSED : 2011 OFFERING

                      Assignments and Exams:

                      HW104/13/11Soln  
                      HW204/27/11Soln  
                      Midterm Soln  
                      HW305/23/11Soln  
                      HW406/03/11Soln  
                      Final Soln  
                           
                      Final06/06/114:00-6:00  
                      Discussion Page

                      Bren Hall 1200, MWF 3-4pm


                      Introduction to probabilistic models, inference, and learning.

                      CS274A is an introductory course to probabilistic approaches to learning from data. Probabilistic models form an important part of many areas of computer science, and probabilistic learning (in this context, automatically constructing probabilistic models from data) has become an important tool in sub-fields such as artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, signal processing, and many more. CS274A will provide an introduction to the concepts and principles which underly probabilistic models, and apply these principles to the development, analysis, and practical application of machine learning algorithms.

                      The course will focus primarily on parametric probabilistic modeling, including data likelihood, parameter estimation using likelihood and Bayesian approaches, hypothesis testing and classification problems, density estimation, clustering, and regression. Related problems, including model selection, overfitting, and bias/variance trade-offs will also be discussed.

                      Background.

                      The course is intended to be an introduction to probabilistic learning, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from probability, linear algebra, multivariate calculus, etc. Homeworks will use the MATLAB programming environment, but no prior experience with MATLAB is required for the course.

                      Course format.

                      Three lectures per week (MWF). Homeworks due in class approximately every two weeks. Two exams (midterm and final). Grading: 40% homework, 25% midterm, 35% final.

                      Office Hours.

                      Office hours for the course are 3pm Tuesdays, or by appointment.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Bishop's "Pattern Recognition and Machine Learning", but lectures are likely to follow the book only loosly. Other recommended reading include MacKay's "Information Theory, Inference, and Learning Algorithms" (available online at http://www.inference.phy.cam.ac.uk/mackay/itila/), Duda, Hart, and Stork's "Pattern Classification", and Hastie, Tibshirani, and Friedman's "Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      (Tentative) Schedule of Topics.

                      All lectures are recorded, but in some the audio is not so good (due to some failure between my hands-free and the recording software); sorry about any of those.

                      Week 1Introduction, probability distributions; frequentist vs. Bayesian viewpointsSlides, Lecture
                       Bayes' rule, exponential family distributionsSlides, Lecture
                       Conditional independence; graphical models; multivariate GaussiansSlides, Lecture
                      For a review of probability, a few good references are: Prof. Smyth's 274A handout #1 on probability; the textbook by Olofsson, "Probability, Statistics & Stochastic Processes" (Bayes Rule, 43-56; random variables and expectation, 77-108; joint distributions, 159-?) and a UCLA stat wiki that is not verbose, but might serve as a reminder; see e.g. Fundamentals, Rules, RVs, Expectations.
                      Week 2More Gaussians; intro to learning, likelihood, parametersRead Prof. Smyth's handout #2; Slides, Lecture
                       ML learning I: data likelihood, univariate ML; bias & varianceSlides, Lecture
                       ML learning II: multivariate; exponential familySlides, Lecture
                      Week 3
                       Bayesian learning I: priors, posterior distributions; MAP & MPE estimatesSlides, Lecture
                       Bayesian learning II: conjugate priors; beta-binomialSlides, Lecture
                      Week 4Bayesian learning III: Gaussian models; Bayes optimal decisionsSlides, Lecture
                       Classification and regression problems as parameter estimationSlides, Lecture
                       Bias/variance and Bayesian priors for regressionSlides, Lecture
                      Week 5(continued)Slides, Lecture
                       Logistic regression; Reading PRML Ch 4Slides, Lecture
                       Review
                      Week 6MIDTERM
                       Mixture models and EM; Reading Smyth handout, PRML Ch 9Slides, Lecture
                       Complexity and model selection; marginal likelihood, BIC approximation; Reading PRML 3.5, 4.4Slides, Lecture
                      Week 7Latent space representations; PCA & Probabilistic PCASlides, Lecture
                       Hidden Markov modelsSlides, Lecture
                       Hidden Markov modelsSlides, Lecture
                      Week 8Monte Carlo methodsSlides, Lecture
                        Slides, Lecture
                        Slides, Lecture
                      Week 9A brief return to graphical models
                       
                       
                      Week 10Misc topics and review
                      Final Exam06/06/2011Final exam, 4-6pm
                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2009S SLI | Classes / Applications of Probability in Computer Science
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                      Assignments and Exams:

                      HW1 Due 04/08/09  
                      Matlab functions: 1,2,3,Sols
                      HW2,Sols Due 04/15/09  
                      Matlab functions: 1,2,Data,Sols
                      HW3,Sols Due 04/22/09  
                      Matlab functions: 1,2,Features,Labels
                      HW4,Sols Due 04/29/09  
                      Midterm,Sols Fri 5/01/09 in class  
                      HW5,Sols Due 05/15/09  
                      HW6,Sols Due 05/27/09  
                      HW7,Data Due 06/05/09  
                      Final Mon 6/8/09, 10:30-12:30  
                           
                      Student Comment Page

                      Lecture: Donald Bren Hall (DBH) 1423, MWF 10-11am

                      Discussion: ICS 243, Mon 1-2pm

                      Instructor: Prof. Alex Ihler

                      TA: Tuan Nguyen


                      Applications of Probability in Computer Science.

                      In this course students will build on the basic probability concepts learned in Math 67 and learn how these ideas can be applied to a broad range of problems in modern computer science. The methods and models used will be mathematical in nature, but will be illustrated using real-world applications. Among the application areas that we will discuss are modeling of text and Web data, network traffic modeling, probabilistic analysis of algorithms and graphs, reliability modeling, simulation algorithms, data mining, and speech recognition. The mathematical methods that we will use to analyze these applications will include basic principles of probability such as Bayes rule, conditional probability, random variables, expectation, and Markov chains.

                      Background.

                      Students are expected to have taken Math 67 (Introduction to Probability and Statistics for Computer Scientists), as it is a prerequisite for the course.

                      Course format.

                      Three lectures per week. Approximately 8 homeworks, due in class on Wednesdays. Two exams (midterm and final). Grading: 40% homework, 25% midterm, 35% final. Lowest homework score will be dropped.

                      Office Hours and Miscellaneous.

                      Office hours for the course are Tuesday 2-3pm (was 1-2pm), or by appointment.

                      The TA's office hours are Thursday 11-12pm, or by appointment.

                      For questions regarding the homeworks and homework grading, please email the TA (...) with your questions. For more general questions about the class, please email Prof. Ihler directly. Please start your subject line with [cs177] (and I will do the same) in order to help keep track of class emails.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      This is particularly important for code-based problems and solutions. All code must be written by you, personally. You may discuss general code principles, algorithms, and Matlab tricks, but do not share code directly.

                      Textbooks.

                      The required textbook for the course is Probability, Statistics, and Stochastic Processes, by Peter Olofsson. ISBN 0471679690. It should be in stock at the bookstore.

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and several ICS labs), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      (Tentative) Schedule of Topics.

                       TopicsReadingSupplemental
                      Week 1Review of basic concepts in probability, random variables, expectation of a random variable, examples and applications.Ch 1.1-1.4; 2.1-2.2; 2.4; 2.5.1-2.5.2
                      Week 2Multiple random variables, joint distributions (for discrete random variables), factorization, law of total probability, conditional probability, Bayes rule.Ch 1.5-1.6.1; 2.5.3Tutorial on power-law distributions
                      Week 3Independence and conditional independence. Applications to data mining and text classification, including discussion of classifying spam emails.No text; review class notes. For more coverage, see these wikipedia articles: Naive Bayes and Bayesian Spam Filtering.Articles on Bayesian spam filtering 1,2; links to the conference on email and anti-spam, CEAS, and associated spam filtering competition.
                      Week 4Entropy and Basic Coding Theory (Compression and Error Correction).No text; review notes. See also: Info Theory, Hamming (7,4) codes, and Huffman codes.
                      Week 5Markov Chains;Chapman-Kolmogorov equations; stationary probabilities. In-class midterm on Friday. 
                      Week 6Markov Chains (continued), applications to ranking of Web pages using the PageRank algorithm. 
                      Week 7Continuous random variables: uniform, Gaussian/Normal. Central Limit Theorem.Ch. 2.3, 2.7; 4.2, 4.3Plinko
                      Week 8The exponential model, memoryless waiting times, and the Poisson process.Ch. 2.6, 2.10; 3.12
                      Week 9Introduction to Queueing Theory: analysis of a simple M/M/1 queueing model. 
                      Week 10Simulation Techniques: pseudorandom number generators, algorithms and methods for generating non-uniform random variates. Applications of Monte Carlo simulation methods. WSJ article on MC
                      Final Exam06/08/2009Final exam
                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2014S-274b SLI | Classes / CS274b: Learning in Graphical Models
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                      Classes /

                      CS274b: Learning in Graphical Models

                      CLOSED : 2014 OFFERING

                      Assignments and Exams:

                      HW1, DataDue 4/15Soln  
                      HW2, Data & CodeDue 4/29Soln  
                      HW3,Due 5/09Soln  
                      HW4, Data & CodeDue 5/22Soln  
                      ProjectsDue 6/13

                      Lecture: ICS 180, TR 2:00pm-3:30pm

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mondays 2-3pm, Bren Hall 4066, or by appointment

                      Graphical models have assumed a central role in representing and reasoning about complex systems across many scientific domains. Examples of graphical models include Bayesian networks and constraint networks from artificial intelligence, Markov random fields from statistics and statistical physics, and factor graphs from coding and information theory. Graphical models provide a common language to represent, make explicit, and communicate modeling assumptions, as well as providing a useful structure for organizing computation and approximations. Today, graphical models are used in many application areas: signal and image processing, computer vision, game theory, operations research, error-correcting codes, and computational biology.

                      The primary goal of this course is to familiarize students with the concepts underlying graphical models, and in particular with learning these models from data. A student who has successfully completed the course should be able to understand a wide variety of well known models in terms of this unifying framework and feel comfortable using it to design new models. The course will contain: (1) formal mathematical sections necessary for the development of the theory, (2) examples of probabilistic models (re)formulated in the language of graphical models and (3) examples of successful applications to real data.

                      The assumed pre-requisite for the course is CS274a (Probabilistic Learning); I will also assume familiarity with Matlab.


                      Textbook

                      Two excellent references are Koller & Friedman (2009), "Probabilistic Graphical Models"; and Murphy (2012), "Machine Learning: A Probabilistic Perspective". We will roughly follow (selected portions of) those texts.


                      Syllabus and Schedule (subject to change)

                      • PDF Graphical models, problem structure and conditional independence
                        • (Optional) Koller lectures: Intro, Flow, Indep in BNs
                      • PDF Basics of inference; inference in trees
                        • (Optional) Koller lectures: Overview, MAP, VE1, VE2
                      • PDF Markov models and hidden Markov models
                      • PDF Maximum likelihood & exponential family models
                        • (Optional) Koller lectures: MLE in BNs
                      • PDF Learning in Bayes nets: Chow-Liu, TANBayes, DAGs
                      • PDF Maximum entropy connections
                      • PDF Loopy models: iterative scaling, IPF, pseudolikelihood
                      • PDF Some slides on Gaussian models, including IPF
                      • PDF More on exact inference; junction trees
                      • PDF Latent variable models; EM
                      • Learning and approximate inference:
                        • PDF Monte Carlo: MCMC-MLE, contrastive divergence
                        • PDF Variational: loopy belief propagation and variants; entropic learning
                      • PDF Conditional random fields (FnT tutorial)
                      • Max-margin Markov networks & structured SVMs
                      • Copula models
                      • Structure learning: basics; sparse learning; independence tests; etc.

                      For more information, see the Spring 2012 CS274b page.


                      Code

                      For the class, I am providing some of my own Matlab code for graphical models, mostly for discrete or Gaussian distributions. I may need to update the code during the class; if so I will include it with the relevant assignment. The main component is a factor class for representing and manipulating the elemental functions that make up a graphical model. In addition to the help in each function, there is some simple documentation here.

                      There are many other software packages available that also aim to simplify the use or study of graphical models, usually also the personal code of the lead researcher. Some good ones include:

                      • BNT: Bayes Net Toolbox (Matlab)
                      • PMTK3: Probabilistic Modeling Toolkit (Matlab)
                      • UGM: Undirected Graphical Models Toolkit (Matlab)
                      • libDAI (C++)
                      • Grante (C++)
                      • UnBBayes (Java),

                      (Note: if you have other suggestions feel free to share them with me and I may add them; but this is not intended to be a complete list of all GM software.)


                      Last modified January 19, 2015, at 04:35 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2012F-178 SLI | Classes / CS178: Machine Learning and Data Mining
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                      CS178: Machine Learning and Data Mining

                      CLOSED : 2012 OFFERING

                      Assignments and Exams:

                      HW1Code10/12/12 Soln
                      HW2Code10/22/12 Soln
                      HW3Code11/15/12 Soln
                      HW4Code12/04/12  
                      HW5Code12/07/12  
                           
                      MidtermIn-class, Thurs11/01/12 Soln
                      Project12/14/12  
                      FinalFri 10:30-12:3012/14/12 Soln
                      Student Comment Page

                      Lecture: Tues/Thurs 12:30-2pm, PSCB 140

                      Discussion: Monday 4-5pm, Bren Hall 1200

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Wed 2:30-3:30pm, Bren Hall 4066, or by appointment

                      Teaching Assistant: Qiang Liu (qliu1@uci.edu)

                      • Office Hours: Thu 4-5pm, Bren Hall 4051 or by appointment

                      Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions. (Or, this link has another nice way to include Matlab code in LaTeX.)


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Bishop's "Pattern Recognition and Machine Learning", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • tba...

                      Slides (by subject)

                      • PDF Introduction to ML
                      • PDF Nearest neighbor methods
                      • PDF Linear regression
                      • PDF Linear classifiers; perceptrons & logistic regression
                      • PDF Various loss functions for regression and classification
                      • PDF VC dimension, shattering, and error rate bounds
                      • PDF Neural networks (multi-layer perceptrons) and deep belief nets
                      • PDF Support vector machines
                      • PDF Decision trees
                        • Videos: Functional form, Learning
                      • PDF Ensembles: Bagging, Gradient Boosting, AdaBoost
                        • Videos: Basics, Bagging, Gradient Boosting, AdaBoost
                      • PDF Bayes classifiers, naive Bayes
                      • PDF Clustering: hierarchical, k-means, EM
                      • PDF Dimensionality reduction: PCA/SVD; latent space representations
                        • Videos: Multivariate Gaussians, PCA

                      Lectures (by date)

                      • L01 (PDF): Introduction; basics; classification and regression
                      • L02: nearest neighbor methods; linear regression & gradient descent
                      • L03: linear regression ct'd
                      • L04: linear classifiers, logistic regression
                      • L05: loss functions; VC dimension
                      • L06: neural networks
                      • L07: support vector machines
                      • ...
                      • L10: ensembles
                      • ...
                      • L16: PCA applications: images, text, collaborative filtering

                      Previous year's lectures (2012, 2011, 2010) are also available.


                      Course Project

                      We will study and make predictions on the 2009 KDD Cup data, a business analytics data set for predicting customer behavior. See the 2009 KDD Cup page for information, to create an account, view the current leaderboard, and upload predictions for testing. Form teams of 2-4 students, and give yourself a team name ("nickname") starting with "uci178-".

                      See full project description here.


                      Last modified January 19, 2015, at 04:35 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2011S-271 SLI | Classes / Introduction to Artificial Intelligence, CS271
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                      Classes /

                      Introduction to Artificial Intelligence, CS271

                      CLOSED : 2011 OFFERING

                      Assignments and Exams:

                      HW104/13/11Soln  
                      HW204/25/11Soln  
                      Midterm Soln  
                      HW3,ZIP05/04/11   
                      HW405/16/11Soln  
                      HW505/27/11Soln  
                      Final Soln  
                           
                      Final06/06/1110:30-12:30  
                      Discussion Page

                      Bren Hall 1500, MWF 10-11am


                      Introduction to artificial intelligence.

                      CS271 is an introductory course to the elements and algorithms underlying the field of artificial intelligence (AI). AI includes subproblems relevant to many areas of research, including information theory, control, signal processing, optimization, operations research, natural language processing and computer vision. CS271 will provide an introduction to the techniques of automated problem solving, including search, optimization and inference in deterministic and stochastic systems.

                      As in previous years, the course will focus considerable time on deterministic constraints, logic, and search. However, the inclusion and representation of uncertainty is critical in many modern aspects of AI, and will be introduced and discussed alongside deterministic versions of related problem types.

                      Background.

                      The course is intended to be an introduction to artificial intelligence, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from computer science (such as algorithmic complexity and logic); portions of the class will also include basic probability, and familiarity with programming may be helpful for some homeworks.

                      Course format.

                      Three lectures per week. Homeworks due in class approximately every two weeks. Two exams (midterm and final). Grading: 40% homework, 25% midterm, 35% final. Lowest homework score to be reduced to 25% of value.

                      Office Hours.

                      Office hours for the course are Monday 4-5pm, or by appointment.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Russel & Norvig, "Artificial Intelligence: A Modern Approach". Lectures should follow the book fairly closely, with minor exceptions.

                      (Tentative) Schedule of Topics.

                       TopicsSlidesSuggested reading
                      Week 1Class introduction; history of AI; AgentsSlides,LectureR&N Ch 1-2
                       Formalities of problem solving; state spacesSlides,LectureR&N Ch 3
                       Basics of searchSlides,LectureR&N Ch 3
                      Week 2Search, heuristicsSlides,LectureR&N Ch 3
                       Informed search, A*Slides,LectureR&N Ch 4
                       A* continued; variantsSlides,LectureR&N Ch 4
                      Week 3No class R&N Ch 6
                       Constraint satisfaction problemsSlides,LectureR&N Ch 6
                       CSPs continuedSlides,LectureR&N Ch 6
                      Week 4CSPs continuedSlides,LectureR&N Ch 6
                       GamesSlides,LectureR&N Ch 5
                       GamesSlides,LectureR&N Ch 5
                      Week 5Propositional logicSlides,LectureR&N Ch 7
                       Review; prop. logic cont'dSlides,Lecture
                       MIDTERM EXAM
                      Week 6PL continuedSlides,LectureR&N Ch 7
                       First-order logicSlides,LectureR&N Ch 8,9
                       FOL continuedSlides,Lecture
                      Week 7FOL inferenceSlides,Lecture
                       Planning 1Slides,LectureR&N Ch 10,11
                       Planning 2Lecture
                      Week 8Probability & Bayesian NetworksSlides,LectureR&N Ch 13,14
                        Slides,Lecture
                       Bayes Nets 1Slides,Lecture
                      Week 9Bayes Nets 2same slides, Lecture
                       Machine LearningSlides, LectureR&N Ch 18.1-4
                        Slides, Lecture
                      Week 10Misc topics & reviewSlides, recording broken
                      Final Exam06/06/2011Final exam
                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2015W-178 SLI | Classes / CS178: Machine Learning and Data Mining
                      SLI
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                      Classes /

                      CS178: Machine Learning and Data Mining

                      Assignments and Exams:

                      HW1Code01/13/15Soln 
                      HW2Code01/20/15Soln 
                      HW3Code01/27/15Soln 
                      MidtermIn-class2/10/15  
                      HW4Code02/24/15Soln 
                      HW5Code03/10/15Soln 
                      Project 3/20/15 
                      FinalThurs 1:30pm-3:30pm3/19/15  

                      Lecture: Tues/Thurs 2pm-3:30pm, ICS 174

                      Discussion: Friday 11am-12pm, ICS 174

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mon 10:30-12:00pm, Bren Hall 4066, or by appointment

                      Teaching Assistant: Nick Gallo (ngallo1@uci.edu)

                      • Office Hours: Fri 1:30-2:30pm, Bren Hall 4051 or by appointment

                      Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions. (Or, this link has another nice way to include Matlab code in LaTeX.)


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.)

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Piazza

                      I use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/winter2015/cs178.

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and student licenses are fairly inexpensive ($100) and even free for UCI students on personal machines; see here for instructions. If you use Octave, please be careful to use Matlab-compatible syntax (not Octave extensions), since otherwise I or the grader may be unable to interpret your code. For Octave, I suggest the SourceForge Octave binaries, e.g., Windows and Mac.

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • Union College / Cyclismo.Org tutorial, more complete
                      • TutorialsPoint
                      • A basic PDF guide by David Houcque
                      • A far more detailed PDF guide by Ed Overman

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      We will also use Matlab classes, including several classes that I've written for the course, to test and evaluate various classifiers. Here are some Notes on Matlab Object-Oriented Programming, using our k-nearest neighbor class as a working example.


                      Syllabus (subject to change)

                      SlidesVideosTopics
                      PDF1 , 2 , 3 , 4Introduction
                      PDF1 , 2Nearest neighbor methods
                      PDF1 , 2Bayes classifiers, naive Bayes
                      PDF1 , 2 , 3 , 4 , 5 , 6Linear regression
                      PDF1 , 2Linear classifiers; perceptrons & logistic regression
                      PDF1VC dimension, shattering, and complexity
                      PDF1 , 2Neural networks (multi-layer perceptrons) and deep belief nets
                      PDF1 , 2 , 3Support vector machines; kernel methods
                      PDF1 , 2Decision trees for classification & regression
                      PDF1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
                      PDF1 , 2 , 3 , 4Unsupervised learning: clustering methods
                      PDF1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations
                      PDF Recommender Systems and Collaborative Filtering
                      PDF Time series, Markov models
                      PDF Markov Decision Processes (slides from Andrew Moore)

                      Course Project

                      • See Project Description pdf on the upper-right of this page (with homework & exams)

                      Last modified December 14, 2015, at 05:20 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2014W-178 SLI | Classes / CS178: Machine Learning and Data Mining
                      SLI
                      (?)
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                      • Publications
                      • Code

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                      Classes /

                      CS178: Machine Learning and Data Mining

                      CLOSED : 2014 OFFERING

                      Assignments and Exams:

                      HW1Code01/15/14Soln 
                      HW2Code01/24/14Soln 
                      HW3Code02/10/14Soln 
                      HW4Code02/28/14Soln 
                      HW5Code03/14/14Soln 
                           
                      MidtermIn-class02/12/14Soln 
                      Project 3/21/14 
                      FinalFri 8:00-10:00am3/21/14  

                      Lecture: Mon/Wed/Fri 11am-12pm, ICS 174

                      Discussion: Monday 4-5pm, Eng Tower (ET) 204

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Wed 2:30-3:30pm, Bren Hall 4066, or by appointment

                      Teaching Assistant: Moshe Lichman (mlichman@uci.edu)

                      • Office Hours: Thu 3:30-4:30pm, Bren Hall 4059 or by appointment

                      Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions. (Or, this link has another nice way to include Matlab code in LaTeX.)


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.)

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Piazza

                      I use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/winter2014/cs178.

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). If you use Octave, please be careful to use Matlab-compatible syntax (not Octave extensions), since otherwise I or the TA may be unable to interpret your code.

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • tba...

                      Syllabus (subject to change)

                      SlidesVideosTopics
                      PDF1 , 2 , 3 , 4Introduction
                      PDF1 , 2Nearest neighbor methods
                      PDF1 , 2Bayes classifiers, naive Bayes
                      PDF1 , 2 , 3 , 4 , 5 , 6Linear regression
                      PDF1 , 2Linear classifiers; perceptrons & logistic regression
                      PDF1VC dimension, shattering, and complexity
                      PDF Neural networks (multi-layer perceptrons) and deep belief nets
                      PDF Support vector machines; kernel methods
                      PDF1 , 2Decision trees for classification & regression
                      PDF1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
                      PDF Unsupervised learning: clustering methods
                      PDF1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations

                      Previous year's lectures (2012b, 2012a, 2011, 2010) are also available.


                      Course Project

                      • tba

                      Last modified January 19, 2015, at 04:34 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2011F-171 SLI | Classes / Intro to Artificial Intelligence, CS171
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                      Intro to Artificial Intelligence, CS171

                      CLOSED : 2011 OFFERING

                      Assignments and Exams:

                      HW1,Code10/04/11Soln  
                      HW2,Code10/11/11Soln  
                      HW3,Code10/21/11Soln  
                      HW410/30/11Soln  
                      HW511/15/11Soln  
                      HW612/02/11Soln  
                           
                      Midterm11/01/112:00-3:30Soln 
                      Final12/08/111:30-3:30  
                      Discussion Page

                      Class: Steinhaus Hall 134, TR 2:00-3:30

                      Recitations: PCB1200, Mon 2-3, or ICF102, Mon 3-4.


                      Introduction to artificial intelligence.

                      CS171 is an introductory course to the elements and algorithms underlying the field of artificial intelligence (AI) for undergraduates. AI includes subproblems relevant to many areas of research, including information theory, control, signal processing, optimization, operations research, natural language processing and computer vision. CS171 will provide an introduction to the techniques of automated problem solving, including search, optimization and inference in deterministic and stochastic systems.

                      As in previous years, the course will focus considerable time on deterministic constraints, logic, and search. However, the inclusion and representation of uncertainty is critical in many modern aspects of AI, and will be introduced and discussed alongside deterministic versions of related problem types.

                      Background.

                      The course is intended to be an introduction to artificial intelligence, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from computer science (such as algorithmic complexity and logic); portions of the class will also include basic probability, and familiarity with programming may be helpful for some homeworks.

                      Course format.

                      Three lectures per week. Homeworks due at the time stated on each homework (usually end of day), approximately every two weeks. Use EEE for electronic submission, or turn in a hard copy. Two exams (midterm and final). Grading: approximately 40% homework (25% regular, 15% programming), 25% midterm, 35% final. Lowest regular homework score to be dropped.

                      Office Hours.

                      Professor Ihler's office hours are Thursday 4-5pm, or by appointment. The TA (Andrew Gelfand) has office hours on Tuesday from 11am-12pm, or by appointment. His office is 4099 DBH.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Russel & Norvig, "Artificial Intelligence: A Modern Approach". Lectures should follow the book fairly closely, with minor exceptions.

                      (Tentative) Schedule of Topics.

                       TopicsSlidesSuggested reading
                      Week 0Class introduction; history of AI; AgentsSlides, Lecture, Discussion (9/26)R&N Ch 1-2, (optional) Building IBM's Watson
                      Week 1Formalities of problem solving; state spacesSlides, Lecture, Discussion (10/3)R&N Ch 3
                       Basics of searchLectureR&N Ch 3
                      Week 2Informed search, A*Slides, Lecture, Discussion (10/10)R&N Ch 3
                       A* variants; local searchSlides, LectureR&N Ch 3-4
                      Week 3Local SearchSlides, Lecture, Discussion (10/17)R&N Ch 5
                       GamesSlides, LectureR&N Ch 5
                      Week 4Constraint SatisfactionSlides, Lecture, Discussion (10/24)R&N Ch 6
                       CSPs 2Slides, LectureR&N Ch 6
                      Week 5CSPs3 ,Propositional logicSlides, Lecture, Discussion (10/31)R&N Ch 7
                       PL continuedLecture
                      Week 6Midterm exam 
                       No class 
                      Week 7Prop logic cont'dSlides, Lecture, Discussion (11/14)
                       First order LogicSlides, LectureR&N Ch 8,9
                      Week 8FOL cont'dSlides, Lecture, Discussion (11/21)
                       Probability & Bayesian NetworksSlides, LectureR&N Ch 13,14
                      Week 9Machine LearningSlides, Lecture, Discussion (11/28)R&N Ch 18.1-4
                       (Thanksgiving holiday) 
                      Week 10Machine LearningSlides, Lecture
                       Review 
                         
                      Final Exam12/08/2011Final exam
                      Connect-Four tournament results (for entertainment only)

                      Additional resources and links:

                      • Ms Pac-Man AI competition
                      • Mario AI competition
                      • StarCraft AI competition
                      Last modified January 19, 2015, at 04:36 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2016W-178 SLI | Classes / CS178: Machine Learning and Data Mining
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                      CS178: Machine Learning and Data Mining

                      Assignments and Exams:

                      HW1Code01/11/16Soln 
                      HW2Code01/21/16Soln 
                      HW3Code02/05/16Old Soln 
                      HW4Code02/26/16  
                      MidtermThurs 2:00pm-3:30pm2/11/16  
                      Project 3/13/16 
                      FinalThurs 1:30pm-3:30pm3/17/16  

                      Lecture: Tues/Thurs 2pm-3:30pm, DBH 1100

                      Discussion: Thurs 7pm-7:50pm, BS3 1200

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Fri 12:30pm-1:30pm, Bren Hall 4066 or by appointment

                      Teaching Assistant: Qi Lou (qlou@uci.edu)

                      • Office Hours: Mon 11am-12pm, Bren Hall 4013 or by appointment (office Bren Hall 4051)

                      Course Notes in development


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Python, using the libraries "numpy" and "matplotlib", as well as course code.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Piazza

                      I use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/winter2016/cs178.

                      Python

                      This year, we will be using Python for most of the programming in the course. I strongly suggest the "full SciPy stack", which includes NumPy, MatPlotLib, SciPy, and iPython notebook for interactive work and visualization; see http://www.scipy.org/install.html for installation information.

                      Here is a simple introduction to numpy and plotting for the course; and of course you can find complete documentation for these libraries as well as many more tutorial guides online.

                      I usually use Python 2.7 by default, but try to program in a 3.0 compatible way; if you find parts of the code do not work for more recent versions of Python please let me know the issue and I will try to fix it.


                      Syllabus (subject to change)

                      SlidesVideosTopics
                      slides1 , 2 , 3 , 4Introduction
                      slides1 , 2Nearest neighbor methods
                      slides1 , 2Bayes classifiers, naive Bayes (there is also a review of probability here)
                      slides, notes1 , 2 , 3 , 4 , 5 , 6Linear regression
                      slides, notes1 , 2Linear classifiers; perceptrons & logistic regression (Python Demo)
                      slides, notes1VC dimension, shattering, and complexity
                      slides, notes1 , 2 , 3Support vector machines; kernel methods (Python Demo)
                      slides, notes1 , 2Neural networks (multi-layer perceptrons) and deep belief nets (Python Demo)
                      slides, notes1 , 2Decision trees for classification & regression (Python Demo)
                      slides1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
                      slides, notes1 , 2 , 3 , 4Unsupervised learning: clustering methods
                      slides1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations
                        Recommender Systems and Collaborative Filtering
                        Time series, Markov models
                        Markov Decision Processes (slides from Andrew Moore)

                      You may find the slides from last year helpful; they are similar from year to year.


                      Course Project

                      • TBD

                      Outside resources

                      • Online lectures
                        • Coursera (Andrew Ng): https://www.coursera.org/learn/machine-learning
                        • Caltech (Yaser Abu-Mustafa): https://work.caltech.edu/lectures.html
                      • Online notes
                        • Stanford (Andrew Ng): http://cs229.stanford.edu/materials.html
                        • OpenCourseware: (Tommi Jaakkola): http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/
                      Last modified February 18, 2016, at 05:45 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2010S-295 SLI | Classes / CS295, Spring 2010: Advanced Topics in Graphical Models
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                      CS295, Spring 2010: Advanced Topics in Graphical Models

                      Info & updates:

                           
                      Student Comment Page

                      Class: Donald Bren Hall (DBH) 1423, Wednesday 2pm-4:20pm

                      Instructors: Rina Dechter (Bren 4232) and Alex Ihler (Bren 4066)


                      Overview

                      The seminar will focus on recent advances in graphical models reasoning and knowledge representation, as well as on exploring some application areas. Each student will be involved in presenting papers/chapters and will be encouraged to focus on a project of interest. Students will write a final report for their research project.

                      Schedule

                      Week

                      Date

                      Topic

                      Readings

                      Week 1 4/1
                      • Rina, Alex - Background, Discussion
                      • Lars - Distributed AND/OR search
                      Lars: Distributed AND/OR search
                      Week 2 4/8
                      • Rina - Modeling Linkage networks as Mixed probabilistic and deterministic Networks
                      • Bozhena Bidyuk - Overview on sampling in graphical models
                      Rina: Modeling Linkage networks slides

                      Paper by E. Thompson

                      Bozhena: Sampling in presence of determinism slides

                      Week 3 4/15
                      • Qiang - Tree-reweighted & Negative tree-reweighted BP
                      • Natasha - Linkage networks continued

                      Qiang: Negative TRBP Slides Δ

                      Natasha: Slides - Analysis of data through inference of identity by descent

                      Week 4 4/22
                      • Natasha - finishing E. Thompson's paper
                      • Alex - L1 regularized methods for model selection
                      • Rina
                      Week 5 4/29
                      • David - Affinity propagation
                      • Edwin - Structure learning for biological networks

                      David: Affinity propagation slides

                      Week 6 5/6
                      • Andrew - Herding and the perceptron cycling theorem
                      • Julian - Covering trees for assignment problems in vision
                      Week 7 5/13
                      • Priya - Conditional random fields and text segmentation
                      • Saumi - Message-passing algorithms in sensor networks
                      • Tianbing
                      Week 8 5/20
                      • Rina
                      Week 9 5/27
                      • David
                      • Lars - Search space estimation through partial exploration
                      • Andrew
                      • Tianbing - Parallel methods for Gibbs sampling

                      Lars: Search space estimation through partial exploration

                      Week 10 6/3
                      • Natasha
                      • Edwin - Learning time-varying network structures
                      • Qiang



                      Possible topics and readings for presentation (related work as sub-bullets)

                      • Matched learning and inference
                        • Wainwright (2006). Estimating the ”wrong” graphical model: Benefits in the computation-limited setting. Journal of Machine Learning Research, 7:1829–1859. PDF
                        • Domke (2008) Learning convex inference of marginals PDF
                        • Kulesza & Pereira (?). Structured Learning with Approximate Inference
                        • ...
                      • Contrastive Divergence
                        • Tieleman & Hinton (2009) Using fast weights to improve persistent contrastive divergence PDF
                        • Welling (2009) Herding dynamic weights in random field models PDF
                        • ...
                      • Parallel or distributed methods
                        • Junction Tree
                          • Pennock (1998) Logarithmic Time Parallel Inference
                          • Namasivayam & Prasanna (2006) Scalable Parallel Implementation... & related
                        • Belief Propagation
                          • Gonzalez et al.(2009) Residual Splash PDF
                          • Gonzalez et al. (2009) Distributed Parallel Inference PDF
                        • MCMC
                          • Ren & Orkoulas (2007) Parallel Markov chain Monte Carlo simulations
                          • Whiley & Wilson (2004) Parallel algorithms for MCMC in latent spatial Gaussian models
                          • Campillo et al. (2009) Parallel and interacting MCMC algorithm
                      • Variational methods and convex optimization
                        • LP relaxations, cutting planes, etc
                        • Optimization; probabilistic rounding algorithms
                      • Sparse structure learning
                        • L1-Regularized learning
                        • Jaakkola et al. (2010 AIStats) Learning B. network structure using LP relaxations
                        • Schmidt & Murphy (2010 AIStats)
                        • Exact structure enumeration methods
                      • Papers of general interest
                        • Rubinstein (2010) Stochastic Enumeration and Splitting Methods for Counting Self-Avoiding Walks PDF
                          • Other relationships of SAWs to inference:
                          • Weitz (2006) Counting independent sets up to the tree threshold PDF
                          • Ihler (2007) Accuracy bounds for belief propagation PDF
                        • ...
                      • Background papers
                        • Mateescu & Dechter (2008) Mixed deterministic and probabilistic networks PDF
                        • Wainwright & Jordan (2008) Graphical Models, Exponential Families, and Variational Methods PDF
                        • Dechter & Mateescu (2006) AND/OR Search Spaces for Graphical Models PDF
                        • Gogate & Dechter (2009) SampleSearch: Importance Sampling in presence of Determinism PDF
                        • Mateescu, Kask, Gogate & Dechter (2010) Join-Graph Propagation Algorithms PDF
                        • Marinescu & Dechter (2009) Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models PDF
                      • Applications
                        • Genetic linkage analysis
                          • Thompson (August 2008) Analysis of data on related individuals through inference of identity by descent PDF
                      • Recent conferences as sources
                        • NIPS (December 2009) papers
                        • AAAI (July 2010)
                        • AIStats preliminary, official
                      Last modified September 13, 2010, at 09:40 AM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2008CSE SLI | Classes / CS/E Senior Design, 2008-2009
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                      CS/E Senior Design, 2008-2009

                      
                      

                      CS/E 181 ABC is a three-course sequence that comprises the Computer Science and Engineering Senior Design Project. For more information on logistics, et cetera, please see the official course webpage:

                      • Fall, Winter

                      See also the design document specification (for 181A):

                      • design documentation outline

                      Looking for ideas? A few project suggestions.

                      Resources for students, including DIY sites, design software, parts and suppliers, and fabrication


                      
                      

                      My groups


                      Multi-touch display and games

                      Peter Anargirou, Gemma Francisco, Casey Kubo, Corey Kubo
                      A large table display and infrared-based multi-touch interface, along with several games. (You can see the IR leds as purple in the photo.)
                      [ Group blog ]


                      Heli-world

                      Max Nanasy, Chuck Edwall, Sean Kocol, Hieu Le
                      A helicopter robot. The group interfaced a mote sensor to a remote control helicopter and inertial measurement unit. The helicopter could respond to positioning changes (tilting), or be controlled through the mote's radio communications.
                      [ Group blog ]


                      Innovative Spotlight

                      Melvin Asuncion, Nihar Desai, Vamshi Mannam, Brian Solloway
                      A spotlight control system. The project uses infrared transmitters to track the position of objects on stage, used an IR camera to sense their position and track their position with two spotlights.
                      [ Group blog ]


                      
                      

                      Other CS/E groups


                      Turtle (The Unmanned Robotic Terra Land Explorer)

                      David Nguyen, Richard Ali, Robert Woo, Tana Ouitavon
                      Mentor: Prof. Ian Harris


                      Optical Wireless Communication

                      Aaron Peschel, Alonzo Garcia, Nathan Goldstick, Per Sjoman, Akufayerem Nwede
                      Mentor: Prof. Eli Bozorgzadeh


                      Project Kraken

                      Ryan Jenkins, Ilya Sukharnikov, William Chang, Victor Rkha, Adrian Tran
                      Mentor: Prof. Steve Jenks


                      Hydrotainment

                      Jason Au, Jonathan Mood, Victor Tran, Shuai Tseng
                      Mentor: Prof. Eli Bozorgzadeh


                      (Several others not pictured; see this list.)


                      Last modified November 02, 2009, at 08:42 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2012S-274b SLI | Classes / CS274b: Learning in Graphical Models
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                      CS274b: Learning in Graphical Models

                      CLOSED : 2012 OFFERING

                      Assignments and Exams:

                      HW1, CodeDue 4/25/12Soln  
                      HW2, ProbsDue 5/17/12Soln,code  
                           
                      MidtermOut 5/9Due 5/16  
                      FinalOut 6/11Due 6/15  
                      ProjectsDue 6/12

                      Lecture: ICS 180, TR 3:30pm-5pm

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mondays 2-3pm, Bren Hall 4066, or by appointment

                      Graphical models have assumed a central role in representing and reasoning about complex systems across many scientific domains. Examples of graphical models include Bayesian networks and constraint networks from artificial intelligence, Markov random fields from statistics and statistical physics, and factor graphs from coding and information theory. Graphical models provide a common language to represent, make explicit, and communicate modeling assumptions, as well as providing a useful structure for organizing computation and approximations. Today, graphical models are used in many application areas: signal and image processing, computer vision, game theory, operations research, error-correcting codes, and computational biology.

                      The primary goal of this course is to familiarize students with the concepts underlying graphical models, and in particular with learning these models from data. A student who has successfully completed the course should be able to understand a wide variety of well known models in terms of this unifying framework and feel comfortable using it to design new models. The course will contain: (1) formal mathematical sections necessary for the development of the theory, (2) examples of probabilistic models (re)formulated in the language of graphical models and (3) examples of successful applications to real data.

                      The assumed pre-requisite for the course is CS274a (Probabilistic Learning); I will also assume familiarity with Matlab.


                      Textbook

                      An excellent reference is Koller & Friedman (2009), "Probabilistic Graphical Models", and we will roughly follow (selected portions of) that text.


                      Syllabus and Schedule (subject to change)

                      • Maximum likelihood & exponential family models
                      • Inference & learning in trees; EM; Chow-Liu
                      • Loopy models: iterative scaling, IPF, pseudolikelihood
                      • Learning and approximate inference:
                        • Monte Carlo: MCMC-MLE, contrastive divergence
                        • Variational: loopy belief propagation and variants; entropic learning
                      • Structure learning: basics; sparse learning; independence tests; etc.
                      • Conditional random fields (FnT tutorial)
                      • Max-margin Markov networks & structured SVMs

                      (Tentative) Schedule of Topics.

                       TopicsSlidesReading
                      Week 0Class introduction; graphical modelsSlides, LecturePGM Ch1-2, 3.1-3.2, 8.2
                       GMs continued; maximum likelihoodSlides, LecturePGM 3.3, 4.1-4.5
                      Week 1Chow-Liu; Expectation-MaximizationSlides, Lecture 1, 2
                       EM, hidden Markov modelsSlides, LecturePGM 19.1-19.2
                      Week 2Learning & inference in loopy modelsSlides, LecturePGM 9, 10, 20.1-20.2
                       ^^ continuedSlides, Lecture^^; see also Jordan notes
                      Week 3Monte Carlo estimates for learningSlides, Lecture
                       no class
                      Week 4Variational algorithmsSlides, LecturePGM 11.1-2, 13.5
                       Variational algorithms ct'dNo slides
                      Week 5Conditional random fieldsNo slidesFnT tutorial
                       Structured SVMsTutorial slides from CVPR 2011; Lecture
                      Week 6CRFs and SSVMs continued
                       Structure learning in BNs
                       Priors, regularization, and Lp-norms
                      Week 7Sparse regularization for variable selection
                       Structure learning in MRFs (Gaussian, discrete)
                      Week 8Structure learning through independence tests
                       Copulas Useful chapter on copula models

                      Code

                      For the class, I am providing some of my own Matlab code for graphical models, mostly for discrete or Gaussian distributions. I may need to update the code during the class; if so I will include it with the relevant assignment. The main component is a factor class for representing and manipulating the elemental functions that make up a graphical model. In addition to the help in each function, there is some simple documentation here.

                      There are many other software packages available that also aim to simplify the use or study of graphical models, usually also the personal code of the lead researcher. Some good ones include:

                      • BNT: Bayes Net Toolbox (Matlab)
                      • PMTK3: Probabilistic Modeling Toolkit (Matlab)
                      • libDAI (C++)
                      • Grante (C++)
                      • UnBBayes (Java),

                      (Note: if you have other suggestions feel free to share them with me and I may add them; but this is not intended to be a complete list of all GM software.)


                      Last modified January 19, 2015, at 04:36 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2011W-178 SLI | Classes / CS178: Machine Learning and Data Mining
                      SLI
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                      Classes /

                      CS178: Machine Learning and Data Mining

                      CLOSED : 2011 OFFERING

                      Assignments and Exams:

                      HW1,Data1/12/11 Soln
                      HW2,Suppl1/20/11 Soln
                      HW3,Suppl2/02/11 Soln
                      HW4,Suppl2/24/11 Soln
                      HW5,Suppl3/11/11 Soln
                      HW6Not graded Soln
                           
                      Midterm2/03/1111:00-12:30Soln 
                      Final3/15/1110:30-12:30  
                      Student Comment Page

                      Lecture: Parkview Classroom Bldg (PCB) 1300, TR 11am-12:30pm

                      Discussion: Parkview Classroom Bldg (PCB) 1300, W 4-5pm

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Monday 2-3pm

                      TA: Yifei Chen (yifeic@uci.edu), Office Bren Hall 4089

                      • Office Hours: Tuesday 2:00pm~3:00pm

                      Course Notes in development


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Bishop's "Pattern Recognition and Machine Learning", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • Unsupervised Learning Challenge, Jan. 3 through Apr. 15
                      • DEFT text mining challenge, Mar. 1 through May 1 or so
                      • More to come...

                      Syllabus and Schedule (may be updated)

                      • 01 PDF,Lecture : Introduction: what is ML; Problems, data, and tools; Visualization
                        • D01 PDF : Introduction to Matlab
                      • 02 PDF,Lecture : Linear regression; SSE; gradient descent; closed form; normal equations; features
                      • 03 PDF, Lecture : Overfitting and complexity; training, validation, and test data
                        • D02 ZIP PDF Introduction to Matlab II
                      • 04 PDF,Lecture : Classification problems; decision boundaries; nearest neighbor methods
                      • 05 PDF,Lecture : probability and classification, Bayes optimal decisions
                        • D03 PDF : Discussion about HW2
                      • 06 PDF, Lecture : Naive Bayes and Gaussian class-conditional distributions
                      • 07 PDF,Lecture : Linear classifiers
                        • D04 PDF : Bayes' Rule and Naive Bayes Model
                      • 08 PDF,Lecture : Logistic regression, online gradient descent, Neural Networks
                      • 09 : Review; Decision trees
                        • D05 PDF : Discussion about HW3 and feedback on HW1, 2
                      • 10 : Midterm Exam
                      • 11 PDF,Lecture : Ensemble methods: Bagging, random forests, boosting
                        • D06 PDF : Details on Decision Tree and Boosting
                      • 12 : (no class today)
                      • 13 [see next lecture] : Unsupervised learning: clustering, k-means, hierarchical agglomeration
                        • D07 PDF : Details on K-means clustering and HW 2, 3 Feedback
                      • 14 PDF,Lecture : Clustering continued, EM
                      • 15 PDF,Lecture: Latent space methods; PCA; Netflix
                        • D08 PDF : Details on EM (for Gaussian mixture model), and PCA
                      • 16 PDF,Lecture : Text representations; naive Bayes and multinomial models; clustering and latent space models
                      • 17 PDF,Lecture : VC-dimension, structural risk minimization; margin methods and support vector machines
                        • D09 PDF, Suppl: Discussion about HW 4, EM clustering Demo, PCA Demo
                      • 18 PDF (awm),Lecture : Support vector machines and large-margin classifiers
                      • 19 PDF (awm), Lecture : Time series; Markov models; autoregressive models
                        • D10 PDF: Discussion about HW5, HW6, including examples of LDA,VC Dimension and SVM
                      • 20 : Review
                      • --- : Final exam, Tuesday March 15, 10:30am-12:30pm

                      Last year's lectures are also available.

                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2012W-178 SLI | Classes / CS178: Machine Learning and Data Mining
                      SLI
                      (?)
                      • Classes
                      • Group
                      • Research
                      • Publications
                      • Code

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                      Classes /

                      CS178: Machine Learning and Data Mining

                      CLOSED : 2012 OFFERING

                      Assignments and Exams:

                      HW1Code01/30/12soln 
                      HW2Code02/10/12soln 
                      HW3Code02/28/12  
                      HW4Code03/15/12  
                           
                      Midterm2/16/122:00-3:30soln 
                      Final3/22/121:30-3:30soln 
                      Student Comment Page

                      Lecture: ICS 259, TR 2pm-3:30pm

                      Discussion: Bren Hall 1500, W 4-5pm

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: 2:00-3:00pm Mondays, Bren Hall 4066, or by appointment

                      Course Notes in development


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Bishop's "Pattern Recognition and Machine Learning", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • tba...

                      Syllabus and Schedule (may be updated)

                      • L01 (PDF): Introduction; classification & regression; nearest neighbor methods
                      • R01 (PDF): Matlab basics
                      • L02 (PDF): Linear regression
                      • L03 (PDF): Linear regression, overfitting, regularization
                      • L04: no class
                      • L05 (PDF): Classification, probability, decisions
                      • R03 (PDF): Matlab classes, Probability
                      • L06 (PDF): Bayes classifiers, Naive Bayes
                      • L07 (PDF): Perceptrons, Logistic regression
                      • R04: Matlab, homework discussion
                      • L08 (PDF): Multi-layer perceptrons (neural networks); decision trees
                      • L09 (PDF 1, PDF 2): VC dimension, Decision trees, Ensemble methods
                      • Guest lecture: David Newman, latent space models
                      • Decision trees; ensemble methods (bagging, boosting) (see L09-2)
                      • Midterm exam: Past years exams: 2011(soln), 2010(soln)
                      • L12, (PDF) Clustering
                      • L13, (PDF) Clustering, latent space representations, collaborative filtering
                      • L14, (PDF) Probability models for unsupervised learning
                      • L15, (PDF) Probability models, data mining
                      • L16, (PDF) Support vector machines
                      • L17, (PDF) Time series, Markov chains, AR models
                      • L18, (PDF) Graphical models
                      • Final exam: Past years exams: 2011(soln), 2010 (soln)

                      Previous year's lectures (2011, 2010) are also available.


                      Projects

                      Your course project is in the nature of an "undirected" homework assignment. Choose a machine learning problem, on your own or from the list below, and explore the implied prediction task to the best of your ability. You can try different learners, choosing from methods we have used in the homework or implementing new ones; different feature representations (feature selection or augmentation); meta-learning algorithms such as bagging and boosting; and hold-out or cross-validation assessment techniques. You should explore the problem in some detail, describing the different ideas you tried and how (and whether or not) they worked, and how you assessed their performance.

                      Examples:

                      • Face detection: this zip file contains a dataset of 24x24 pixel image patches containing faces and non-faces. Learn to predict the presence of a face. Also included is a function for computing the Haar wavelet features and a simple demo of adaBoost, the building blocks of the Viola-Jones technique.
                      • Collaborative filtering: learn to predict how you will rate something, given how others have rated it. This zip file contains a subset of the "Jokes" database for collaborative filtering, in which viewers have rated a subset of jokes on their amusement value, as well as some simple demo code of and SVD-based estimate. I suggest combining it with nearest-neighbor approaches, and trying different levels of complexity (latent space dimension, neighbors, weighting functions, etc.) in your predictors.
                      • Web ranking: learn to predict the relative relevance of a set of returned webpages; this problem is of great importance in both search and advertising, the mainstays of many internet companies. Unfortunately I cannot redistribute the data myself, but you can download Yahoo's ranking challenge data at http://webscope.sandbox.yahoo.com/catalog.php?datatype=c. Here is a zip file with some code for reading their data, which come in "queries" (a web search) with a variable length list of possible responses and their human-rated quality. (See "main.m" for some example code.)

                      Last modified January 19, 2015, at 04:36 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2008S SLI | Classes / CS274A: Probabilistic Learning
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                      CS274A: Probabilistic Learning

                      Assignments and Exams:

                      HW 1 Due 4/8/08 Solutions Δ
                      HW 2 Due 4/24/08 Solutions Δ
                      Midterm Δ Class 5/1/08 Solutions Δ
                      HW 3, Data Δ Due 5/22/08 Solutions Δ
                      HW 4, Data Δ Due 6/05/08  
                      Final Class 6/12/08  
                           
                      Student Comment Page

                      Introduction to probabilistic models, inference, and learning.

                      CS274A is an introductory course to probabilistic approaches to learning from data. Probabilistic models form an important part of many areas of computer science, and probabilistic learning (in this context, automatically constructing probabilistic models from data) has become an important tool in sub-fields such as artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, signal processing, and many more. CS274A will provide an introduction to the concepts and principles which underly probabilistic models, and apply these principles to the development, analysis, and practical application of machine learning algorithms.

                      The course will focus primarily on parametric probabilistic modeling, including data likelihood, parameter estimation using likelihood and Bayesian approaches, hypothesis testing and classification problems, density estimation, clustering, and regression. Related problems, including model selection, overfitting, and bias/variance trade-offs will also be discussed.

                      Background.

                      The course is intended to be an introduction to probabilistic learning, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from probability, linear algebra, multivariate calculus, etc. Homeworks will use the MATLAB programming environment, but no prior experience with MATLAB is required for the course.

                      Course format.

                      Two lectures per week. Homeworks due in class approximately every two weeks. Two exams (midterm and final). Grading: 40% homework, 30% midterm, 30% final.

                      Office Hours.

                      Office hours for the course are Monday 12-1pm and 2-3pm, or by appointment.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Bishop's "Pattern Recognition and Machine Learning", but lectures are likely to follow the book only loosly. Other recommended reading include MacKay's "Information Theory, Inference, and Learning Algorithms" (available online at http://www.inference.phy.cam.ac.uk/mackay/itila/), Duda, Hart, and Stork's "Pattern Classification", and Hastie, Tibshirani, and Friedman's "Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      (Tentative) Schedule of Topics.

                      Week 104/01/2008Introduction, probability distributions, Bayes' ruleSuggested reading: Notes on probability by Prof. Smyth; Bishop, Sec 1.2 & Ch. 2
                       04/03/2008multivariate distributions, Bayes' netsSmyth notes on multivariate distributions; Bishop Ch. 8
                      Week 204/08/2008Markov random fields; introduction to learning, likelihood, parametersBishop Ch. 8
                       04/10/2008Class cancelled
                      Week 304/15/2008Bias/variance; maximum likelihood learning; exponential family, univariateBishop 3.2, 2.4.1, 1.2.4
                       04/17/2008ML learning I: multivariate models
                      Week 404/22/2008Bayesian learning I: priors, posterior distributions; MAP estimates
                       04/24/2008Bayesian learning II: conjugate prior distributions
                      Week 504/29/2008Summary and review
                       05/01/2008MIDTERM EXAM
                      Week 605/06/2008Regression I: linear regressionBishop Ch 3
                       05/08/2008Regression II: priors, logistic regressionBishop 3, 4.2-3
                      Week 705/13/2008More on regression and classification
                       05/15/2008Classification and density estimation
                      Week 805/20/2008Mixture models and EM: mixtures of Gaussians; k-means; EM
                       05/22/2008Mixture models and EM: more on expectation-maximization
                      Week 905/27/2008Learning in graphical models I: forward-backward, EM
                       05/29/2008Learning in graphical models II: iterative fitting
                      Week 1006/03/2008Additional topics: TBD
                       06/05/2008Additional topics: TBD
                      Final Exam06/12/2008In class final exam, 1:30-3:30pm
                      Last modified September 15, 2008, at 01:36 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2010W-274a SLI | Classes / CS274A: Probabilistic Learning
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                      CS274A: Probabilistic Learning

                      CLOSED : 2010 OFFERING

                      Assignments and Exams:

                      HW11/13/10Soln  
                      HW21/27/10Soln  
                      Midterm2/05/10Soln  
                      HW3,Data Δ2/19/10Soln  
                      HW4,ZIP3/12/10   
                      Final3/19/101:30-3:30  
                        Exam solutions  
                           
                      Student Comment Page

                      Lecture: Roland Hall (RH) 184, MWF 2-3pm

                      Instructor: Prof. Alex Ihler


                      Introduction to probabilistic models, inference, and learning.

                      CS274A is an introductory course to probabilistic approaches to learning from data. Probabilistic models form an important part of many areas of computer science, and probabilistic learning (in this context, automatically constructing probabilistic models from data) has become an important tool in sub-fields such as artificial intelligence, data mining, speech recognition, computer vision, bioinformatics, signal processing, and many more. CS274A will provide an introduction to the concepts and principles which underly probabilistic models, and apply these principles to the development, analysis, and practical application of machine learning algorithms.

                      The course will focus primarily on parametric probabilistic modeling, including data likelihood, parameter estimation using likelihood and Bayesian approaches, hypothesis testing and classification problems, density estimation, clustering, and regression. Related problems, including model selection, overfitting, and bias/variance trade-offs will also be discussed.

                      Background.

                      The course is intended to be an introduction to probabilistic learning, and thus has few explicit requirements. Students are expected to be familiar with basic concepts from probability, linear algebra, multivariate calculus, etc. Homeworks will use the MATLAB programming environment, but no prior experience with MATLAB is required for the course.

                      Course format.

                      Three lectures per week (MWF). Homeworks due in class approximately every two weeks. Two exams (midterm and final). Grading: 40% homework, 25% midterm, 35% final.

                      Office Hours.

                      Office hours for the course are 4pm Fridays, or by appointment.

                      Collaboration.

                      Discussion of the course concepts and methods among the students is encouraged; however, all work handed in should be completely your own. In order to strike a balance, we'll use the "work product" rule: while discussing anything related to the homework, you should retain no work product created during the discussion. In other words, you can meet and discuss the problems, describe the solution, etc., but then all parties must go away from the meeting with no record (written notes, code, etc.) from the meeting and do the homework problem on your own. If you work on a whiteboard, just erase it when you're done discussing. Don't show someone else your homework, or refer to it during the discussion, since by this policy you must then throw it away.

                      Textbooks.

                      The required textbook for the course is Bishop's "Pattern Recognition and Machine Learning", but lectures are likely to follow the book only loosly. Other recommended reading include MacKay's "Information Theory, Inference, and Learning Algorithms" (available online at http://www.inference.phy.cam.ac.uk/mackay/itila/), Duda, Hart, and Stork's "Pattern Classification", and Hastie, Tibshirani, and Friedman's "Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      (Tentative) Schedule of Topics.

                      Week 101/04/2010PDF, : Introduction, probability distributions; frequentist vs. Bayesian viewpoints
                       01/06/2010PDF, Lecture : Bayes' rule, exponential family distributions
                       01/08/2010PDF, Lecture : multivariate distributions; conditional independence; Bayes' nets;
                      For a review of probability, a few good references are: Prof. Smyth's 274A handout #1 on probability; the textbook by Olofsson, "Probability, Statistics & Stochastic Processes" (Bayes Rule, 43-56; random variables and expectation, 77-108; joint distributions, 159-?) and a UCLA stat wiki that is not verbose, but might serve as a reminder; see e.g. Fundamentals, Rules, RVs, Expectations.
                      Week 201/11/2010PDF, Lecture : more graphical models; multivariate GaussiansRead Prof. Smyth's handout #2
                       01/13/2010PDF, Lecture : introduction to learning, (multivariate Gaussians), likelihood, parameters
                       01/15/2010PDF, Lecture : ML learning I: data likelihood, univariate ML; bias & variance
                      Week 301/18/2010MLK Holiday
                       01/20/2010PDF, Lecture : ML learning II: exponential family, multivariate
                       01/22/2010PDF, Lecture : ML learning II: multivariate models
                      Week 401/25/2010PDF, Lecture : Bayesian learning I: priors, posterior distributions; MAP & MPE estimates
                       01/27/2010PDF, Lecture : Bayesian learning II: conjugate priors; beta-binomial
                       01/29/2010PDF, Lecture : Bayesian learning III: Gaussian models; Bayes optimal decisions
                      Week 502/01/2010No class
                       02/03/2010Review
                       02/05/2010Midterm exam
                      Week 602/08/2010PDF, Lecture : Hypothesis testing, class-conditional models; predictive distributions
                       02/10/2010PDF, Lecture : Regression I: linear regression; regression as parameter estimation
                       02/12/2010PDF, Lecture : Regression II: bias & variance; priors
                      Week 702/15/2010Presidents day holiday
                       02/17/2010PDF, Lecture : Regression III: priors, posteriors
                       02/19/2010Notes, Audio, Example : Regression to classification: logistic regression; Reading PRML Ch 4
                      Week 802/22/2010Audio : Classification and density estimation
                       02/24/2010Audio : Mixture models and EM: Reading Jordan handout and Smyth handout, PRML Ch 9
                       02/26/2010Mixture models and EM
                      Week 903/01/2010
                       03/03/2010Notes : Complexity and model selection; marginal likelihood, BIC approximation; Reading PRML 3.5, 4.4
                       03/05/2010Hinton talk
                      Week 1003/08/2010Audio : Time series: autoregressive models, HMMs, filtering and smoothing tasks; Reading PRML Ch 13
                       03/10/2010Review?
                       03/12/2010
                      Final Exam03/19/2010Final exam, 1:30-3:30pm
                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2008W SLI | Classes / CS295, Winter 2008: Advanced Methods in Graphical Models
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                      CS295, Winter 2008: Advanced Methods in Graphical Models

                      This course is a highly participatory exploration of recent research directions in learning and inference algorithms for probabilistic models, particularly graphical models (Bayes' nets, Markov random fields, et cetera). The class is structured to include both a student-led seminar portion, similar to a reading group but with more week-to-week structure, and a lecture part in which we will cover additional background, extensions and other related material. The course provides the opportunity to read and understand recent work relevant to research in graphical models and machine learning, while giving the course more structure and continuity than a typical seminar or reading group.

                      Background.

                      Although the first week or two will provide a brief introduction to graphical models, students are expected to have some basic background, such as one of CS 271, 274-276 or equivalent. If unsure, send me an email or come by my office (BH4066) to discuss your background.

                      Course format.

                      You, the student, will have the ability to influence the exact topics we cover. At the first meeting, we will decide on the set and sequence of topics, and students will divide into small groups, each of which will choose one topic for their own. Each week, one group will be responsible for reviewing the literature associated with their topic (with assistance from myself), providing a short written summary beforehand for the rest of the class, and leading a presentation and discussion of the papers during Tuesday class. The Thursday lecture will then proceed to elaborate in more depth, covering extensions or other closely related topics, or giving more background and details, depending on the subject. It is possible (even likely) that we will not make it through all the topics; priority will be given to "Tuesday" topics, with additional coverage by myself during the lecture portion on Thursday if necessary.

                      Note: although there are one or more papers associated with each day's lecture, they should be considered (non-required) supplemental reading -- primary reading and preparation for the week come from the student-prepared summaries for Tuesday.

                      Topics.

                      A selection of possible topics follows; these are subject to change and re-arrangment in the future. If you have additional ideas or suggestions for topics you'd like to see covered, let me know by email or in person and we will discuss them on the first day.

                      DatePresenterTopicand references
                      Week 101/08/2008AlexInitial meeting and organization; introduction to graphical models
                       01/10/2008Alexintroduction continued; exponential families, discrete and Gaussian distributions; inference in trees
                          Jordan et al. 1999 [PDF],
                      Wainwright and Jordan 2003 [PDF]
                           
                      Week 201/15/2008AlexExamples of graphical models; exact vs MCMC vs variational methods
                          Wainwright and Jordan 2003 [PDF]
                       01/17/2008AlexVariational methods I: functionals, convexity; duality of parameters and marginals; the marginal polytope
                          Wainwright and Jordan 2003 [PDF]
                      Yedidia et al. 2005 [PDF]
                           
                      Week 301/22/2008AlexVariational methods II: mean field, belief propagation
                          Wainwright and Jordan 2003 [PDF]
                      Yedidia et al. 2005 [PDF]
                       01/24/2008AlexVariational methods III: belief propagation and tree-reweighted BP
                          Wainwright et al. 2003 [PDF]
                      Yedidia et al. 2005 [PDF]
                      Wainwright et al. 2005 [PDF]
                           
                      Week 401/29/2008Drew"Efficient" methods for belief propagation
                      [ Summary ]
                          Potetz 2007 [PDF]
                      Felzenszwalb and Huttenlocher 2004 [PDF]
                       01/31/2008AlexBelief propagation and mixing properties
                          Tatikonda and Jordan 2001 [PDF]
                      Ihler et al. 2005 [PDF]
                      Mooij and Kappen 2007 [PDF]
                           
                      Week 502/05/2008AlexIntroduction to Markov chain Monte Carlo methods
                          N/A
                       02/07/2008Dave NewmanIntroduction to graphical models for topic modeling and text data
                      [ Slides ]
                          No reading
                           
                      Week 602/12/2008ToddDirichlet processes and applications to topic modeling
                      [ Summary ]
                          Neal 2000 [PDF]
                      Ishwaran and James 2001 [PDF]
                       02/14/2008AlexMore on Dirichlet Processes; Stick-breaking representations
                          Neal 2000 [PDF]
                      Ishwaran and James 2001 [PDF]
                           
                      Week 702/19/2008IanHierarchical Dirichlet processes and other "infinite state" models
                      [ Slides ]
                          Teh et al. 2005 [PDF]
                      Beal et al. 2002 [PDF]
                       02/21/2008AlexOPEN
                          TBD
                           
                      Week 802/26/2008KenLearning model structure via penalized regression
                      [Slides ]
                          Wainwright et al . 2006 [PDF]
                      Meinshausen and Buhlmann 2006 [PDF]
                       02/28/2008AjayLearning mixtures of trees
                      [ Slides ]
                          Meila and Jordan 2000 [PDF]
                      Kirshner 2008 [PDF]
                           
                      Week 903/04/2008DaveMax product and linear programming connections
                      [ Summary ]
                          Weiss et al. 2007 [PDF]
                      Sanghavi et al. 2008 [PDF]
                       03/06/2008AlexOPEN
                          TBD
                           
                      Week 1003/11/2008Chaitanya C.Variational approaches to LDA and topic modeling
                          Blei and Jordan 2004 [PDF]
                      Kurihara et al. 2007 [PDF]
                      Teh et al. 2008 [PDF]
                       03/13/2008AlexOPEN
                          TBD
                      Last modified September 15, 2008, at 01:33 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2013S-77B SLI | Classes / ICS77B / Math77B: Collaborative Filtering
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                      ICS77B / Math77B: Collaborative Filtering

                      CLOSED : 2013 OFFERING

                      Handouts and Assignments:

                      Lab 1Mean predictons
                      HW1Getting started
                      HW2Similarity-based predictionsSoln
                      HW3Linear predictors
                      HW4Clustering
                      HW5Matrix decomposition
                      HW5Blending & Final Write-up

                      Lecture: Tues/Thurs 11-12:30pm, Roland Hall 421 (PRISM Lab)

                      Lab: Tues 2-3:30pm, Roland Hall 421 (PRISM Lab)

                      .... Additional lab hours: Fridays 2:30-4pm, PRISM Lab

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mondays, 2-3pm, Bren Hall 4066

                      Teaching Assistant: Sholeh Forouzan (sforouza@uci.edu)

                      • Office Hours: Prism Lab, Fri 2:30-4

                      Overview

                      Many companies collect data at an unprecedented scale. Online stores such as Amazon collect click patterns and purchases by people navigating their webpages, credit score companies such as Experian and banks record clients' financial histories, Netflix records peoples' interest in movies, and so on.

                      A new field is starting to emerge known as "collaborative filtering" where this type of data is used to predict quantities of interest: What is the next book a customer would buy? Will this person pay his/her loan?, What are the next movies this customer will be interested in?

                      As evidence for the prominence of this problem in industry, Netflix announced a challenge in 2005, in which anyone who could improve their customer recommendation system by more than 10% would receive $1,000,000.

                      This course will be based around several real-world collaborative filtering data sets. Students will study the theoretical aspects of machine learning, clustering, matrix factorizations, and statistical estimation in order to approach the problem of collaborative filtering and recommendation.

                      Note that this class is highly interactive. You set the pace that is right for you! There is no fixed agenda and no exams. We want you to get an appreciation for research, and research can only be learned by doing it yourself. This class will also give you access to (funded) summer research projects.


                      Kaggle class competitions

                      We will have two data sets for testing:

                      • Jester joke ratings
                        • Join here using your uci.edu email
                        • Training data: -10..10=rating, 99=not rated, 98=test point
                        • Test keys: (user,item) pairs in order
                        • output function to convert prediction matrix to output file
                        • See also this page for more details about Jester
                        • Try the Jester interactive rating system here
                      • MovieLens movie ratings

                      More information to come here.


                      Work and Grading

                      The class will consist of

                      • several lab milestones ("homework"), as individuals with collaboration
                      • a presentation of a research paper to the class, in groups of 2-3 students
                      • a project with presentation and write-up, in groups of 2-3 students

                      (These may be subject to change as the course proceeds.)


                      Useful Links

                      • Some course notes on machine learning, in development
                      • A LaTeX template I use for my homeworks and solutions.
                      • This link has another nice way to include Matlab code in LaTeX.
                      • A Matlab cheat-sheet; and another.
                      • Matlab also has a number of toolboxes that can have many useful commands; see here.

                      Miscellaneous notes


                      Readings

                      For the week of May 21st, we will form small groups (2-3 students) and choose one of the following papers per group to present in class. Please let me know your group and paper selection by Thursday, 5/9. Presentations will be 5-7 minutes each, plus 2-3 minutes for questions, on the key ideas and results/conclusions of the paper. (You do not need to present the details of derivations, etc.) I suggest using powerpoint slides, but whiteboard presentation is also OK. Scores will be given based on correctness, organization, professionalism, and clarity.

                      Please discuss the paper among your group and with the instructor and TA early, no later than Tuesday 5/14, to help get on track and correct any issues.

                      Papers:

                      • Pennock et al. (2000): Collaborative Filtering by Personality Diagnosis
                      • Calandrino et al. (2011): Privacy Risks of Collaborative Filtering -- (M. Schwinger, A. Koster, I Elmaleh)
                      • Herlocker et al. (2002): An Empirical Analysis of Design Choices in Neighborhood-based CF -- (I. Ng, R. Shiroma, J. Jones)
                      • Goldbeck (2006): Generating... Recommendations from Trust in Social Networks -- (N. Carrillo, R. Gallego, Z. Kloock)
                      • Boutilier et al. (2003): Active Collaborative Filtering
                      • Si & Jin (2003): Flexible Mixture Model for CF -- (M. Barnett, J. Tan, J. Perez)
                      • Koren & Bell (2007): Advances in Collaborative Filtering (Ch3) (Ch4-5)
                      • Pan et al. (2008): One-class Collaborative Filtering
                      • Chen et al. (2011): Robust Matrix Completion with Corrupted Columns

                      Other reading (not for presentation):

                      • Linden et al. (2003): Amazon.com recommendations
                      Last modified January 19, 2015, at 04:35 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2010W-178 SLI | Classes / CS178: Machine Learning and Data Mining
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                      Classes /

                      CS178: Machine Learning and Data Mining

                      CLOSED : 2010 OFFERING

                      Assignments and Exams:

                      HW1,Data1/13/10Soln 
                      HW2,Data,Script1/22/10Soln 
                      HW3,ZIP1/27/10Soln 
                      Bonus12/08/10  
                      Midterm2/05/10Soln  
                      HW4,ZIP2/19/10Soln 
                      HW53/01/10Soln 
                      HW6,ZIP3/10/10Soln 
                      Final3/15/1010:30-12:30  
                        Exam solutions  
                           
                      Student Comment Page

                      Lecture: Donald Bren Hall (DBH) 1423, MWF 10-11am

                      Discussion: Donald Bren Hall (DBH) 1423, M 4-5pm

                      Instructor: Prof. Alex Ihler, Office Bren Hall 4066

                      TA: Oleksii Kuchaiev


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      The primary textbook for the course is Bishop's "Pattern Recognition and Machine Learning", but we will supplement regularly with handouts and online readings. Other useful textbooks are Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • On-going Australian Rules Football ("Footy") predictions at http://www.csse.monash.edu.au/~footy/
                      • Yahoo's Learning to Rank challenge; closes May 31st with prizes up to $8k and fame throughout the ages, or at least at the next ICML.
                      • A set of tasks for Plagarism and Vandalism detection
                      • The yearly UCSD Data Mining Contest
                      • MLSP Competition on mind reading
                      • Synthetic Visual Reasoning challenge, part of Pascal's Challenge Series
                      • ECCV Face Detection workshop
                      • UAI probabilistic inference competition

                      (Tentative) Syllabus and Schedule

                      • 01 PDF, Lecture : Introduction: what is ML; what problems; data types; tools
                      • 02 PDF, Lecture : Data visualization; probability; histograms; multinomial distributions
                      • 03 PDF, Lecture : Linear regression; SSE; Gradient descent
                      • 04 PDF, Lecture : Linear regression; features; overfitting and complexity
                      • 05 PDF, Lecture : Linear regression; closed form MSE solution; "robust" cost functions
                      • 06 PDF, Lecture : Classification boundaries;
                      • 07 MLK holiday
                      • 08 PDF, Lecture : nearest neighbors classifiers
                      • 09 PDF, Lecture : class-conditional distributions, Bayes optimal decisions, Bayes error rate
                      • 10 PDF, Lecture : HW discussion; Gaussian class-conditional distributions
                      • 11 PDF, Lecture, Notes : Gaussian class-conditional distributions and linear discriminants
                      • 12 PDF, Lecture : Linear classifiers
                      • 13 Class cancelled
                      • 14 Midterm review
                      • 15 Midterm Exam
                      • 16 (Whiteboard) Putting first half in context; what's coming next
                      • 17 PDF, Lecture : Logistic regression, online gradient descent
                      • 18 PDF, Lecture : Neural Networks
                      • 19 Presidents day holiday
                      • 20 PDF, Lecture : Decision trees, CART; (bagging, random forests) : Supplemental reading,
                      • 21 PDF, Lecture : Ensemble methods: Bagging, random forests, boosting (Reading: PRML 14.1-4) (Supplemental: Viola-Jones face detection via AdaBoost)
                      • 22 PDF, Lecture : Unsupervised learning: clustering, k-means, hierarchical agglomeration (Reading: PRML Ch 9)
                      • 23 PDF, Lecture : Clustering: EM
                      • 24 PDF, Lecture : Latent space methods: PCA
                      • 25 (Whiteboard) : Text representations; naive Bayes and multinomial models; clustering and latent space models
                      • 26 Andrew Moore's slides, Lecture : VC-dimension and structural risk minimization
                      • 27 Andrew Moore's slides; recording failed : Support vector machines and large-margin classifiers
                      • 28 (Whiteboard) : Time series, autoregressive models
                      • 29

                      Old syllabus under revision...

                      • 14 Multinomial distributions; naive Bayes & text
                      • 15 Feature selection; Decision trees; CART
                      • 16 Ensemble methods: random forests & bagging;
                      • 17 Ensemble methods: boosting
                      • 19 Clustering: k-means, heirarch agglom;
                      • 20-21 Clustering: EM
                      • 22 PCA, MDS
                      • 23-24 Latent space models; missing data; stochastic gradient ascent
                      • 25-26 LDA
                      • 27-28 Complexity and model selection
                      • 29-30 SVMs, margin classifiers
                      Last modified January 19, 2015, at 04:37 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2012F-273a SLI | Classes / CS273a: Introduction to Machine Learning
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                      Assignments and Exams:

                      HW1Code10/12/12 Soln
                      HW2Code10/22/12 Soln
                      HW3Code11/15/12 Soln
                      HW4Code12/04/12 Soln
                      HW5Code12/07/12  
                           
                      Midtermin-class, Thurs11/01/12 Soln
                      Project12/14/12  
                      FinalTues 4-6pm12/11/12 Soln
                      Student Comment Page

                      Lecture: Tues/Thurs 3:30-5pm, HH 262

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mondays 4:30-5:30, Bren Hall 4066, or by appointment

                      Some Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions.


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      This is an introductory graduate class, intended for first year graduate students. We will assume familiarity with some concepts from probability, calculus, and linear algebra. Programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Bishop's "Pattern Recognition and Machine Learning", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). Personally, I do not recommend the open-source Octave program as a replacement, as the syntax is not 100% compatible and may cause problems (for me or you).

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.


                      Interesting stuff for students

                      • tba...

                      Slides (by subject)

                      • PDF Introduction to ML
                      • PDF Nearest neighbor methods
                      • PDF Linear regression
                      • PDF Linear classifiers; perceptrons & logistic regression
                      • PDF Various loss functions for regression and classification
                      • PDF VC dimension, shattering, and error rate bounds
                      • PDF Neural networks (multi-layer perceptrons) and deep belief nets
                      • PDF Support vector machines
                      • PDF Decision trees
                        • Videos: Functional form, Learning
                      • PDF Ensembles: Bagging, Gradient Boosting, AdaBoost
                        • Videos: Basics, Bagging, Gradient Boosting, AdaBoost
                      • PDF Bayes classifiers, naive Bayes
                      • PDF Clustering: hierarchical, k-means, EM
                      • PDF Dimensionality reduction: PCA/SVD; latent space representations
                        • Videos: Multivariate Gaussians, PCA

                      Lectures (by date)

                      • L01 (PDF): Introduction; basics; classification and regression
                      • L02: nearest neighbor methods; linear regression & gradient descent
                      • L03: linear regression ct'd
                      • L04: linear classifiers, logistic regression
                      • L05: loss functions, VC dimension
                      • L06: neural networks
                      • L07: support vector machines
                      • ...
                      • L10: ensembles
                      • ...
                      • L16: clustering; HAC, k-means, EM
                      • L17: PCA, SVD, and applications

                      You may find my lectures for the undergraduate version of this class helpful: 2012, 2011, 2010


                      Course Project

                      • TBD

                      Last modified January 19, 2015, at 04:36 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2013-iCamp SLI | Classes / iCAMP Undergraduate Summer Research: Collaborative Filtering
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                      iCAMP Undergraduate Summer Research: Collaborative Filtering

                      CLOSED : 2013 OFFERING

                      See also the main iCamp page


                      Schedule

                      Skills workshops: Tuesdays 1-3pm, Roland Hall 306

                      Group meetings: Thurs 2+pm, Roland Hall 421 (PRISM Lab)

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Lab Hours: Mondays 1-2pm; Wed 4-5pm, PRISM Lab

                      Teaching Assistant: Sholeh Forouzan (sforouza@uci.edu)

                      • Lab Hours: Mondays 4-5pm, Fri 1-3pm, PRISM Lab

                      Overview

                      Many companies collect data at an unprecedented scale. Online stores such as Amazon collect click patterns and purchases by people navigating their webpages, credit score companies such as Experian and banks record clients' financial histories, Netflix records peoples' interest in movies, and so on. A new field is starting to emerge known as "collaborative filtering" where this type of data is used to predict quantities of interest: What is the next book a customer would buy? Will this person pay his/her loan?, What are the next movies this customer will be interested in?

                      For our summer undergraduate research project, we will participate in an international competition in collaborative filtering and recommendation systems, sponsored by the 2013 RecSys Conference. The competition aspect is run by Kaggle, an online data science and prediction hosting system. This year, the RecSys competition data comes from Yelp, and involves predicting business ratings given past ratings by the same and other users, as well as a host of metadata such as business location, check-in information, and rating feedback from other users. For the data and more details, please see the Kaggle RecSys competition site.


                      Handouts and code

                      • New train/validation split for testing: MAT (contains Dtrain and Dval), or Script to construct MAT file
                      • Even newer data (Sholeh) ZIP
                      • NEW DATA FORMAT (Alex) ZIP : run "help yelp_data"
                      • Reprocessed data (UPDATED) ZIP
                        • Old reprocessed data: ZIP

                       % Some basic code for manipulating the data: 
                      load yelp_data.mat % Load formatted data
                      % Splitting the data into training and validation sets:
                      nTotal = length(rev_train(:,4)); pi = randperm(nTotal); nTrain = ceil(.8*nTotal); iTrain = pi(1:nTrain); iVal = pi(nTrain+1:end); % Split into Training / Validation / Test user/business/rating sets
                      uTr = rev_train(iTrain,2); uVal = rev_train(iVal,2); uTe = rev_test(:,1); bTr = rev_train(iTrain,3); bVal = rev_train(iVal,3); bTe = rev_test(:,2); rTr = rev_train(iTrain,4); rVal = rev_train(iVal,4); rTe = -1 + 0*uTe; % Construct sparse matrix formats with training & validation data
                      Dtr = sparse([uTr;uVal;uTe],[bTr;bVal;bTe],[rTr;-1+0*rVal;-1+0*rTe]); Dval = sparse([uTr;uVal;uTe],[bTr;bVal;bTe],[rTr;rVal;-1+0*rTe]);

                      Some plots:

                       % The data exhibit a classic "scale free" property
                       uTrainIDs = find(user_average_stars > 0); uTestIDs = find(user_average_stars < 0);
                       Htrain = hist( user_review_count(uTrainIDs), max(user_review_count) );
                       Htest  = hist( user_review_count(uTestIDs), max(user_review_count) );
                       figure; plot(log10(1:length(Htrain)),log10(Htrain+.5),'b.',log10(1:length(Htest)),log10(Htest+.5),'r.');
                       % offset shift mostly just due to different #s of training & test data 
                      
                       % Here's the geospatial distribution of businesses
                       lat = [business(:).latitude]; lon = [business(:).longitude];
                       figure; plot(lat,lon,'b.');
                      
                       % And here's the same thing with "test businesses" marked in red
                       bTestIDs = [business(:).stars]<0;
                       figure; plot(lat,lon,'b.',lat(bTestIDs),lon(bTestIDs),'r.');
                      
                       % You can also try to highlight e.g. number of reviews at each business through size or color:
                       figure; scatter(lat,lon,log([business(:).review_count]));    % size parameter
                       figure; scatter(lat,lon,[],log([business(:).review_count])); % color parameter
                      
                       % let's look at some histograms of the data
                       figure; hist(rTr,1:5);                                                           % more 4&5 than 1&2 ratings
                       % Look at the errors of the per-item predictions:
                       Dhat = yelpMean(Dtr',Dval', 3)';  figure; hist(Dtr(Dtr>0)-Dhat(Dtr>0),-5:.01:5); 
                       % The biggest errors are on the low side; items averaging ~4 but rated 1-2 (?)
                      

                      A simple "sparse mean" function:

                       function [D] = yelpMean(Dtrain,Dval, reg)
                       %function [D] = yelpMean(Dtrain, Dval, reg)
                       % Take mean over items (so, per-user average) in sparse=missing data matrix
                       % Dtrain : training values; -1 to be predicted; Dval : some -1's replaced with validation #s
                       % Use  D = yelpMean(Dtrain',Dval',reg)'  to do per-item average
                      
                       D = Dtrain;
                       [nUsers,nItems]=size(D);
                       DT = D';
                      
                       mnAll = mean(D(D(:)>0));
                      
                       mn = (sum(DT,1)+reg*mnAll+sum(DT<0,1))./(sum(DT>0,1)+reg);
                       for u=1:nUsers, fill=find(DT(:,u)); D(u,fill) = mn(u); end;
                      
                      Last modified January 19, 2015, at 04:35 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2013F-273a SLI | Classes / CS273a: Introduction to Machine Learning
                      SLI
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                      CS273a: Introduction to Machine Learning

                      CLOSED : 2013 OFFERING

                      Assignments and Exams:

                      HW1Code10/03/13 soln
                      HW2Code10/15/13 soln
                      HW3Code10/31/13 soln
                      HW4 11/20/13 soln
                      HW5Code12/06/13 soln
                           
                      Midtermin-classThurs, 11/7/13 soln
                      Project12/13/12  
                      FinalThurs 1:30-3:30pm12/12/12  

                      Lecture: Tues/Thurs 2-3:30pm, BH1600

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Tues 4-5pm, Bren Hall 4066, or by appointment

                      Some Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions.


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      This is an introductory graduate class, intended for first year graduate students. We will assume familiarity with some concepts from probability, calculus, and linear algebra. Programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed.

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Probabilistic Machine Learning", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Grading

                      The course consists of homeworks, some small in-class quizzes, a project, midterm and final exam. Grading is approximately (possibly subject to modification):

                      • 25% homework (drop lowest of approx 6)
                      • 10% project (structured, e.g. Kaggle)
                      • 5% quizzes on reading
                      • 25% midterm, 35% final exam

                      Homeworks are due at 5pm on the listed day (or on EEE at the dropbox closing time). Late homeworks may not be accepted, and will not be after solutions are posted. Please turn in what you have at the deadline.

                      Collaboration

                      Please do form study groups for discussion of the material, including lectures, homework, past exams, etc. Your fellow students are one of your best resources in this course. Piazza is often useful for this as well. However, you are responsible for the material, and should do the homework yourself. In other words, discussing the concepts in the homework, and solution strategies, etc. is fine -- but please do not look at others' solutions, exchange code, etc.

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and if you want a copy for yourself student licenses are fairly inexpensive ($100). You may also use the free alternative Octave (heavily tested but poor GUI), or another alternative FreeMat (newer, less tested), both of which attempt to provide a free, syntax-compatible alternative to Matlab. However, please try to stick to Matlab syntax so that we can run your code in Matlab, and be aware that the code provided to you is likely tested in Matlab and not Octave or FreeMat, and the responsibility for discovering and fixing/working around any bugs will be yours. If you're not comfortable with that, use Matlab.

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • University of Utah, very short
                      • CMU / UMichigan tutorial, also short
                      • University of Florida's tutorial, more complete
                      • Union College / Cyclismo.Org tutorial, also good
                      • UMaryland guide, lots of pointers to other tutorials and reference manuals

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      For getting started, you can actually run simple Octave scripts and functions online at

                      • http://www.compileonline.com/execute_matlab_online.php

                      Interesting stuff for students

                      • tba...

                      Syllabus

                      SlidesVideosTopics
                      PDF1 , 2 , 3 , 4Introduction
                      PDF1 , 2Nearest neighbor methods
                      PDF1 , 2Bayes classifiers, naive Bayes
                      PDF1 , 2Decision trees for classification & regression
                      PDF1 , 2 , 3 , 4 , 5 , 6Linear regression
                      PDF1 , 2Linear classifiers; perceptrons & logistic regression
                      PDF1VC dimension, shattering, and complexity
                      PDF Neural networks (multi-layer perceptrons) and deep belief nets
                      PDF Support vector machines; kernel methods
                      PDF1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
                      PDF Unsupervised learning: clustering methods
                      PDF1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations

                      Course Project

                      See here (pdf) for the full description.

                      For your course project, you will explore data mining and prediction in the wild, in a real life data set and compared against the performance of teams from around the world. We will use a data set from a past Knowledge Discovery in Data (KDD) Cup, a yearly competition in machine learning and data mining associated with the KDD conference. In particular, we will use the 2004 Competition's Particle Physics data set. The challenge is described in full on the webpage: http://osmot.cs.cornell.edu/kddcup/

                      • Teams: Form teams of 2-3 students with whom you will directly collaborate.
                      • Download the data: See http://osmot.cs.cornell.edu/kddcup/datasets.html
                      • Build your learners: I suggest that you try several different models, such as nearest neighb or methods, decision

                      trees, linear classifiers (logistic regression, support vector machines, etc.), naive Bayes classifiers, and/or boosted classifiers (decision stumps, etc.). Each member of your team may try one or two models, and can explore setting them to the data and assessing their performance using validation or cross-validation.

                      • Write up a report (~6 pages) on your methods. Again, see the full description here for details.

                      Last modified January 19, 2015, at 04:34 PM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://sli.ics.uci.edu/Classes/2015W-273a SLI | Classes / CS273a: Introduction to Machine Learning
                      SLI
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                      CS273a: Introduction to Machine Learning

                      Assignments and Exams:

                      HW1Code01/13/15Soln 
                      HW2Code01/20/15Soln 
                      HW3Code01/27/15Soln 
                      MidtermIn-class2/10/15  
                      HW4Code02/24/15Soln 
                      HW5Code03/10/15Soln 
                      Project 3/20/15 
                      FinalTue 4:00-6:00pm3/17/15  

                      Lecture: Tues/Thurs 3:30pm-5:00pm, ICS 174

                      Instructor: Prof. Alex Ihler (ihler@ics.uci.edu), Office Bren Hall 4066

                      • Office Hours: Mon 10:30-12:00pm, Bren Hall 4066, or by appointment

                      Course Notes in development

                      Also, a possibly helpful LaTeX template I use for homeworks and solutions. (Or, this link has another nice way to include Matlab code in LaTeX.)


                      Introduction to machine learning and data mining

                      How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

                      Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike.

                      This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

                      Background

                      We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.)

                      Textbook and Reading

                      There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".

                      Piazza

                      I use Piazza to manage student discussions and questions. Our class link is: http://piazza.com/uci/winter2015/cs273a.

                      Matlab

                      Often we will write code for the course using the Matlab environment. Matlab is accessible through NACS computers at several campus locations (e.g., MSTB-A, MSTB-B, and the ICS lab), and student licenses are fairly inexpensive ($100) and even free for UCI students on personal machines; see here for instructions. If you use Octave, please be careful to use Matlab-compatible syntax (not Octave extensions), since otherwise I or the grader may be unable to interpret your code. For Octave, I suggest the SourceForge Octave binaries, e.g., Windows and Mac.

                      If you are not familiar with Matlab, there are a number of tutorials on the web:

                      • Union College / Cyclismo.Org tutorial, more complete
                      • TutorialsPoint
                      • A basic PDF guide by David Houcque
                      • A far more detailed PDF guide by Ed Overman

                      You may want to start with one of the very short tutorials, then use the longer ones as a reference during the rest of the term.

                      We will also use Matlab classes, including several classes that I've written for the course, to test and evaluate various classifiers. Here are some Notes on Matlab Object-Oriented Programming, using our k-nearest neighbor class as a working example.


                      Syllabus (subject to change)

                      SlidesVideosTopics
                      PDF1 , 2 , 3 , 4Introduction
                      PDF1 , 2Nearest neighbor methods
                      PDF1 , 2Bayes classifiers, naive Bayes
                      PDF1 , 2 , 3 , 4 , 5 , 6Linear regression
                      PDF1 , 2Linear classifiers; perceptrons & logistic regression
                      PDF1VC dimension, shattering, and complexity
                      PDF1 , 2Neural networks (multi-layer perceptrons) and deep belief nets
                      PDF1 , 2 , 3Support vector machines; kernel methods
                      PDF1 , 2Decision trees for classification & regression
                      PDF1, 2, 3, 4Ensembles; bagging, gradient boosting, adaboost
                      PDF1 , 2 , 3 , 4Unsupervised learning: clustering methods
                      PDF1, 2Dimensionality reduction: (Multivariate Gaussians); PCA/SVD, latent space representations
                      PDF Recommender Systems and Collaborative Filtering
                      PDF Time series, Markov models
                      PDF Markov Decision Processes (slides from Andrew Moore)

                      Course Project

                      • See Project Description pdf on the upper-right of this page (with homework & exams)

                      Last modified March 11, 2015, at 11:50 AM
                      Bren School of Information and Computer Science
                      University of California, Irvine
                      http://www.ics.uci.edu/~johnsong/ Todd Johnson
                      contact
                      Todd Johnson
                      johnsong@ics.uci.edu

                      In February 2012 I completed a PhD in computer science at the University of California, Irvine. My advisor was Eric Mjolsness. The main webpage for Eric's research group is Computable Plant.
                      research
                      My research interests are in machine learning, artificial intelligence, and computational biology.

                      My PhD thesis focused on two novel approaches in probabilistic graphical models. Each of these is described on its own page. Each page also includes links to download software packages for the Mathematica programming langauge, and example notebooks.

                      Dependency Diagrams extend factor graphs (a superset of Bayesian networks and Markov random fields). I used them to implement a package that, among other things, automatically generates runnable source code for the process of performing Metropolis-Hastings sampling on arbitrary distributions.

                      Graph-Constrained Correlation Dynamics formalizes a method of representing probability distributions that evolve continuously in time. The software package leverages Dependency Diagrams to optimize GCCD models.

                      My thesis is also available as a PDF.
                      selected side projects
                      • I wrote this implementation of parallel Lam-Delosme simulated annealing for statistical numerical optimization in Mathematica.
                      • I implemented a Bayesian Logic model that predicts the outcomes of basketball games. This required implementing an approximate Gibbs sampler for BLOG, making me (according to Brian Milch) maybe the first person outside of his and Russell's research groups to implement a custom proposer class for BLOG. If this is something you need to do, I would be happy to talk about the process.
                      • David Orendorff, Drew Frank, and I placed third in the 2008 UCSD data mining competition using bagged combinations of neural networks, SVMs, and naive bayes classifiers.
                      personal
                      I read, when I can; my Goodreads page is here. I can be found on Facebook here. I am lucky enough to be married to Ruth Barrett, an excptionally talented neuroscientist. We have a weblog, where we post pictures and occassional goings-on, for friends and family. I also maintain a weblog with some friends from college.

                      Coincidentally, all of the males in my immediate family-in-law are also computer scientists. Sorted by the degree to which our research is related, these fine fellows are:
                      • Leon Barrett (PhD from UC Berkeley, now at Google)
                      • Sam Barrett (PhD student at UT Austin)
                      • Marty Barrett (a professor at East Tennessee State)
                      http://www.ics.uci.edu/~ajfrank/research.html Andrew Frank - Research

                      Andrew Frank - Research

                      Andrew J. Frank PhD Candidate in Computer Science, University of California, Irvine
                      • Home
                      • Research
                      ajfrank@ics.uci.edu View Andrew Frank's LinkedIn profile

                      Unsupervised Learning in Multi-Target Tracking

                      img/mtt.png Broadly speaking, multi-target tracking involves the use of a sensor (e.g. camera or radar) to detect and follow targets (e.g. people or airplanes) as they move around the world. Targets are often detected at fixed time intervals, resulting in data like that shown in Figure 1a. Due to imperfect or low-information sensors, it is difficult to link observations from the same target together across time steps. As a result it can be hard to distinguish between multiple conflicting explanations of a set of observations, like those shown in Figure 1b and 1c. The resolution of this uncertainty is known as the data association problem.

                      The track-oriented multiple hypothesis tracker is a heuristic approach to finding the best data association for a given sequence of observations. It often performs quite well, but is sensitive to a number of parameters describing the appearance and behavior of targets. Both for convenience and for more robust performance, it would be nice to learn these parameters from the unlabeled observations. In this work we use a graphical model to represent the posterior distribution over possible data associations. Belief propagation in this graphical model produces approximate marginals that can be used in the context of an expectation-maximization (EM) algorithm to perform online parameter estimation.

                      Publication:

                      Frank, A.; Smyth, P.; Ihler, A.; , "A graphical model representation of the track-oriented multiple hypothesis tracker," Statistical Signal Processing Workshop (SSP), 2012 IEEE , vol., no., pp.768-771, 5-8 Aug. 2012. (pdf) (bib) (poster)

                      Sampling-Based Variational Message-Passing

                      The sum-product algorithm, a.k.a. belief propagation (BP), is a general algorithm for approximate marginalization in graphical models. At the time of its introduction in the 1980s it was poorly understood and had unpredictable performance. Since then, a new perspective based on variational optimization has led to variants of BP that offer convergence guarantees, bounds on quantities of interest, and more predictable performance.

                      img/pbp.png Belief propagation was originally developed for discrete and Gaussian graphical models. Recent advances have extended the vanilla BP algorithm to arbitrary (non-Gaussian) continuous models, but the variationally motivated extensions remain confined to discrete models. We show that importance sampling, as used in the recent Particle BP algorithm, provides a mechanism for applying these variational extensions (e.g. tree-reweighted BP, mean field, weighted mini-bucket, etc.) to arbitrary continuous models.

                      Publication:

                      A. Ihler, A. Frank, and P. Smyth. Particle-based variational inference for continuous systems. Neural Information Processing Systems, 2009. (pdf) (bib) (poster)

                      Forest Cover Estimation From Satellite Imagery

                      img/forest.png Industrial logging and farming operations are contributing to rapid deforestation in some of the world's largest tropical regions. Since some of this activity is illegal (and thus not reported to the local government), the precise extent of the deforestation is difficult to quantify. Accurate estimates of forest cover are needed to understand the impact of land use change on atmospheric CO2.

                      Satellite imagery offers a way around the lack of reliable reporting. NASA has two satellites in orbit equipped with Moderate Resolution Imaging Spectroradiometers (MODIS) that view the entire Earth's surface every 1 to 2 days. From this satellite imagery, one can compute the Normalized Difference Vegetation Index (NDVI), which is a useful feature in forest cover estimation. However, NDVI detects all green vegetation – not just forest. To further improve the accuracy of satellite-based forest cover estimation, we consider the use of land surface temperature measurements. Using a logistic regression classifier, we show that the day/night temperature range is a useful predictor of tropical forest cover. In a tropical forest, the moist leaves of the canopy mitigate the temperature rise during the day, and the coldest air pools beneath the canopy at night.

                      Publication:

                      van Leeuwen, T. T., A. J. Frank, Y. Jin, P. Smyth, M. L. Goulden, G. R. van der Werf, and J. T. Randerson (2011), Optimal use of land surface temperature data to detect changes in tropical forest cover, J. Geophys. Res., 116, G02002, <doi:10.1029/2010JG001488>. (pdf) (bib)

                      http://www.ics.uci.edu/~ajfrank/index.html Andrew Frank - Home

                      Andrew Frank - Home

                      Andrew J. Frank PhD Candidate in Computer Science, University of California, Irvine
                      • Home
                      • Research
                      ajfrank@ics.uci.edu View Andrew Frank's LinkedIn profile
                      Notice: I defended my thesis in summer 2013 and now work at Google in London, UK.

                      About

                      I am a graduate student in the Computer Science Department at the University of California, Irvine. I am fortunate to be co-advised by Alex Ihler and Padhraic Smyth, and as such I enjoy "dual citizenship" in the Statistical Learning and Inference and DataLab groups at UCI. I plan to defend in the summer of 2013.

                      My research is in the area of approximate inference for probabilistic graphical models. On the theoretical side, I am interested combining sampling-based approximations with variational message-passing algorithms. On the applied side, I am interested in using graphical models to manage association uncertainty in multi-target tracking. Check out my research page for short blurbs about my major projects, along with publications and code.

                      News

                      07/01/2013
                      Thesis: Frank, A. Variational Message-Passing: Extension to Continuous Variables and Applications in Multi-Target Tracking. PhD Thesis. University of California, Irvine, 2013. (pdf) (bib)
                      04/20/2012
                      Publication: Frank, A.; Smyth, P.; Ihler, A.; , "A graphical model representation of the track-oriented multiple hypothesis tracker," Statistical Signal Processing Workshop (SSP), 2012 IEEE , vol., no., pp.768-771, 5-8 Aug. 2012. (pdf) (bib) (poster)
                      04/02/2012
                      Talk: New applications of graphical models for multitarget tracking. Presented at the UCI AI/ML seminar series. (slides)
                      09/29/2011
                      I am co-organizing a new Machine Learning Reading Group with Andrew Gelfand and Chris Dubois. Join us on Wednesdays from 12-1 to discuss the week's paper and bring suggestions for what to read next. You can subscribe to the mailing list to receive weekly notifications with links to the paper.
                      09/12/2011
                      Internship: LinkedIn Product Analytics team with mentor Monica Rogati.
                      04/15/2011
                      Publication: van Leeuwen, T. T., A. J. Frank, Y. Jin, P. Smyth, M. L. Goulden, G. R. van der Werf, and J. T. Randerson (2011), Optimal use of land surface temperature data to detect changes in tropical forest cover, J. Geophys. Res., 116, G02002, <doi:10.1029/2010JG001488>. (pdf) (bib)
                      06/16/2010
                      I am now the curator for the UCI Machine Learning Repository.
                      11/02/2009
                      Talk: Belief Propagation in a Continuous World. Presented at the UCI AI/ML seminar series. (slides)
                      09/04/2009
                      Publication: A. Ihler, A. Frank, and P. Smyth. Particle-based variational inference for continuous systems. Neural Information Processing Systems, 2009. (pdf) (bib) (poster)
                      06/15/2008
                      Third place: UCSD Data Mining Contest, supervised learning category. Fellow team members: Todd Johnson, David Orendorff, Julien Neel.
                      http://www.ics.uci.edu/~qliu1/MLcrowd_ICML_workshop/keynotes.html ICML ’13 Workshop: Machine Learning Meets Crowdsourcing
                      Main
                      Home
                      Overview
                      Call For Papers
                      Organizers
                      Sponsor
                      Schedule
                      Invited Speakers
                      Keynotes
                      Accepted Papers
                      Related Links

                      ICML ’13 Workshop: Machine Learning Meets Crowdsourcing

                      Abstracts of Invited Talks

                      Jeffrey P. Bigham : Crowd Agents: Interactive Crowd-Powered Systems in the Real World

                      Over the past few years, we have been developing and deploying interactive crowd-powered systems that help people get things done in their everyday lives. For instance, VizWiz answers visual questions for blind people in less than a minute, Legion drives robots in response to natural language commands, Chorus supports consistent dialog between end users and the crowd, and Scribe converts streaming speech to text in less than five seconds. Overall, thousands of people have engaged with these systems, providing an interesting look at how end users interact with crowd work in their everyday lives. These systems have collectively informed a new model for real-time crowd work that I call “crowd agents,” which is proving to be especially useful for building interactive crowd-powered systems. In this model, a diverse and changing crowd – the kind easily recruited on the web – is made to act as a single high-quality actor through interface support and computational mediation of each individual’s work. These systems allow us to deploy truly intelligent interactive systems today, and present challenging problems for machine learning going forward to support and eventually replace the humans in the loop.

                      Yiling Chen: Financial Incentives and Crowd Work

                      Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance-contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increase the effectiveness of financial incentives.

                      Panagiotis G. Ipeirotis : Rewarding Crowdsourced Workers

                      We describe techniques for rewarding workers in a crowdsourcing setting. We describe a real-time monetary payment scheme that rewards workers according to their quality, in the presence of uncertainty in quality estimation, while at the same time guaranteeing stable (or increasing) salaries. We report experimental results indicating that the proposed scheme encourages long-term engagement, avoiding churn, and avoiding the common problem of adverse selection and moral hazard. We also describe a set of non-monetary, psychological schemes that actively discourage low-quality workers from participating in tasks. We finish showing that mice and crowdsourced workers are not that different after all.

                      Edith Law : Mixed-Expertise Crowdsourcing

                      To date, most of the research in human computation focuses on tasks that can be performed by any person with basic perceptual capabilities and common sense knowledge. In this talk, I will discuss new directions towards mixed-expertise crowdsourcing, where the crowd consists of people with drastically different motivations, levels and domains of expertise, as well as availabilities. I will illustrate the new opportunities and challenges in mixed-expertise crowdsourcing, by outlining existing work and describing my two ongoing projects – Curio, a micro-task marketplace for crowdsourcing scientific tasks, and SimplyPut, a crowdsourcing platform for improving health literacy through the collaborative summarization of medical information.

                      Mark Steyvers: Aggregating Human Judgments in Combinatorial Problems

                      We analyze the collective performance of individuals in combinatorial problems involving the rankings of events and items (e.g. “what is the order of US presidents?”) as well as traveling salesperson and minimum spanning tree problems. We compare situations in which a group of individuals independently answer these questions with an iterated learning environment in which individuals pass their solution to the next person in a chain. We introduce Bayesian information aggregation models for both the independent and information-sharing environments and treat the collective group knowledge as a latent variable that can be estimated from the observed judgments across individuals. The models allow for individual differences in expertise and confidence in other individuals’ judgments. Initial results suggest that information-sharing environments lead to better collective performance despite the fact that information-sharing increases correlations between judgments. In addition, the models’ estimates of expertise are more indicative of actual performance than the users’ self-rated expertise. Finally, we study situations where the same individual solves the same problem at different points in time. We show that the consistency in answers across repeated problems provides an additional signal to estimate expertise.

                      Page generated 2013-06-21 04:28:47 PDT, by jemdoc. (source)
                      http://www.ics.uci.edu/~qliu1/MLcrowd_ICML_workshop/schedule.html ICML ’13 Workshop: Machine Learning Meets Crowdsourcing
                      Main
                      Home
                      Overview
                      Call For Papers
                      Organizers
                      Sponsor
                      Schedule
                      Invited Speakers
                      Keynotes
                      Accepted Papers
                      Related Links

                      ICML ’13 Workshop: Machine Learning Meets Crowdsourcing

                      Schedule

                      08:00-08:30             Set up of posters around the room, leave up all day

                      08:30-08:35             Welcome

                      08:35-09:10             Jeffrey P. Bigham ; Crowd Agents: Interactive Crowd-Powered Systems in the Real World

                      09:15-09:50             Yiling Chen ; Financial Incentives and Crowd Work

                      09:55-10:00             1.5 min poster lightning

                                                              Sivan Sabato; Feature Multi-Selection among Subjective Features

                                                              Hongwei Li; Error Rate Analysis of Labeling by Crowdsourcing

                                                              Vaibhav Rajan; CrowdControl: An online learning approach for optimal task scheduling in a dynamic crowd platform

                      10:00-10:30             Coffee, poster presentation (10th floor area D, map)

                      10:30-11:05             Mark Steyvers ; Aggregating Human Judgments in Combinatorial Problems

                      11:10-11:16             1.5 min poster lightning

                                                              Alexey Tarasov; Improving Performance by Re-Rating in the Dynamic Estimation of Rater Reliability

                                                              Joel Lehman; Leveraging Human Computation Markets for Interactive Evolution

                                                              Xi Chen; Optimistic Knowledge Gradient for Optimal Budget Allocation in Crowdsourcing

                                                              Chien-Ju Ho; Adaptive Task Assignment for Crowdsourced Classification

                      11:16-11:26             Discussion (10 min)

                      11:26-12:00             Poster Presentation (10th floor area D, map)

                      12:00-02:00             Lunch

                      02:00-02:35             Panagiotis G. Ipeirotis ; Rewarding Crowdsourced Workers

                      02:40-02:50             Adish Singla; Truthful Incentives for Privacy Tradeoff: Mechanisms for Data Gathering in Community Sensing.

                      02:55-03:05             Jian Peng; Crowdsourcing for structured labeling with applications to protein folding

                      03:10-03:30             Discussion (20 min)

                      03:30-04:00             Coffee, Present posters (10th floor area D, map)

                      04:00-04:35             Edith Law ; Mixed-Expertise Crowdsourcing

                      04:40-04:50             Michael Wick; Probabilistic Reasoning about Human Edits in Information Integration

                      04:55-05:30             Wrap-up discussion (35 min)

                      Page generated 2013-06-20 19:31:49 PDT, by jemdoc. (source)
                      http://www.ics.uci.edu/~qliu1/MLcrowd_ICML_workshop/index.html ICML ’13 Workshop: Machine Learning Meets Crowdsourcing
                      Main
                      Home
                      Overview
                      Call For Papers
                      Organizers
                      Schedule
                      Invited Speakers
                      Keynotes
                      Accepted Papers
                      Related Links

                      ICML ’13 Workshop: Machine Learning Meets Crowdsourcing

                      Important Dates: MSR

                      • ICML Workshop, June 21, 2013

                      • Room: Lobby 506-7 (right behind the registration desk)

                      • Poster session: 10th floor area D (map)

                      • [Schedule]   [Keynotes]   [Papers]

                      Overview

                      Our ability to solve challenging scientific and engineering problems relies on a mix of human and machine intelligence. The machine learning (ML) research in the past two decades has created a set of powerful theoretical and empirical tools for exploiting machine intelligence. On the other side, the recent rise of human computation and crowdsourcing approaches enables us to systematically harvest and organize human intelligence, for solving problems that are easy for human but difficult for computers. The past few years have witnessed widespread use of the crowdsourcing paradigm, including task-solving platforms like Amazon Mechanical Turk and CrowdFlower, crowd-powered scientific projects like GalaxyZoo and Foldit game, as well as various successful crowdsourcing business such as crowdfunding and open Innovation, to name a few.

                      This trend yields both new opportunities and challenges for the machine learning community. On one side, crowdsourcing systems provide machine learning researchers with the ability to gather large amount of valuable data and information, leading advances in challenging problems in areas like computer vision and natural language processing. On the other side, crowdsourcing confronts challenges on increasing its reliability, efficiency and scalability, for which machine learning can provide power computational tools. More importantly, building systems that seamlessly integrate machine learning and crowdsourcing techniques can greatly push the frontier of our ability to solve challenging and large-scale problems.

                      The goal of this workshop is to bring together experts on fields related to crowdsourcing such as economics, game theory, cognitive science and human-computer interaction with the machine learning community to have a workshop focused on areas where crowdsourcing can contribute to machine learning and vice versa. We are interested in a wide variety of topics, including but not limited to:

                      State of the field. What are the emerging crowdsourcing tasks and new opportunities for machine learning? What are the latest and greatest tasks being tackled by crowdsourcing and human intelligence and how do these tasks highlight the need for new machine learning approaches that aren’t being studied already?

                      Integrating machine and human intelligence. How to build practical systems that seamlessly integrate machine and human intelligence? Machine learning algorithms can help the crowdsourcing component to manage work flows and control workers’ qualities, while the crowds can be used to handle the tasks that are difficult for machines to adaptively boost the performance of machine learning algorithms.

                      Machine learning for crowdsourcing. Many machine learning approaches have been applied to crowdsourcing on problems such as output aggregation, quality control, work flow management and incentive mechanism design. We expect to see more machine learning contribution to crowdsourcing, either by novel ML methods, or on new crowdsourcing problems.

                      Crowdsourcing for machine learning. Machine learning largely relies on big and high quality data, which can be provided by crowdsourcing systems, perhaps in an automatic and adaptive way. Also, most machine learning algorithms have many design choices that require human intelligence, including tuning hyper-parameters, selecting score functions, and designing kernel functions. How can we systematically “outsource” these typically expert-level design choices to the crowds in order to achieve results that match expert-level human experience?

                      Crowdsourcing complicated tasks. How to design work flows and aggregate answers in crowdsourcing systems that collect structured labels, such as bounding box annotations in computer vision, protein folding structures in biology, or solve complicated tasks such as proof reading, and machine translation? How can machine learning provide help in these cases?

                      Theoretical analysis. There are many open theoretical questions in crowdsourcing that can be addressed by statistics and learning theory. Examples include analyzing label aggregation algorithms such as EM, or budget allocation strategies.

                      Invited Speakers

                      • Jeffrey P. Bigham. University of Rochester

                      • Yiling Chen. Harvard University

                      • Panagiotis G. Ipeirotis. NYU Stern School of Business

                      • Edith Law. Harvard University

                      • Mark Steyvers. UC Irvine

                      Call for Papers

                      Submissions should follow the ICML format and are encouraged to be up to eight pages. Papers submitted for review do not need to be anonymized. There will be no official proceedings, but the accepted papers will be made available on the workshop website. Accepted papers will be either presented as a talk or poster.

                      We welcome submissions both on novel research work as well as extended abstracts on work recently published or under review in another conference or journal (please state the venue of publication in the later case); we particularly encourage submission of visionary position papers on the emerging trends on crowdsourcing and machine learning.

                      Please submit papers in PDF format here.

                      Organizers

                      • Paul Bennett, Dengyong Zhou, John Platt. Microsoft Research, Redmond

                      • Qiang Liu. UC Irvine

                      • Xi Chen, Qihang Lin. CMU

                      Abstracts of Invited Talks

                      Jeffrey P. Bigham : Crowd Agents: Interactive Crowd-Powered Systems in the Real World

                      Over the past few years, we have been developing and deploying interactive crowd-powered systems that help people get things done in their everyday lives. For instance, VizWiz answers visual questions for blind people in less than a minute, Legion drives robots in response to natural language commands, Chorus supports consistent dialog between end users and the crowd, and Scribe converts streaming speech to text in less than five seconds. Overall, thousands of people have engaged with these systems, providing an interesting look at how end users interact with crowd work in their everyday lives. These systems have collectively informed a new model for real-time crowd work that I call “crowd agents,” which is proving to be especially useful for building interactive crowd-powered systems. In this model, a diverse and changing crowd – the kind easily recruited on the web – is made to act as a single high-quality actor through interface support and computational mediation of each individual’s work. These systems allow us to deploy truly intelligent interactive systems today, and present challenging problems for machine learning going forward to support and eventually replace the humans in the loop.

                      Yiling Chen: Financial Incentives and Crowd Work

                      Online labor markets such as Amazon Mechanical Turk (MTurk) have emerged as platforms that facilitate the allocation of productive effort across global economies. Many of these markets compensate workers with monetary payments. We study the effects of performance-contingent financial rewards on work quality and worker effort in MTurk via two experiments. We find that the magnitude of performance-contingent financial rewards alone affects neither quality nor effort. However, when workers working on two tasks of the same type in a sequence, the change in the magnitude of the reward over the two tasks affects both. In particular, both work quality and worker effort increase (alternatively decrease) as the reward increases (alternatively decreases) for the second task. This suggests the existence of the anchoring effect on workers’ perception of incentives in MTurk and that this effect can be leveraged in workflow design to increase the effectiveness of financial incentives.

                      Panagiotis G. Ipeirotis : Rewarding Crowdsourced Workers

                      We describe techniques for rewarding workers in a crowdsourcing setting. We describe a real-time monetary payment scheme that rewards workers according to their quality, in the presence of uncertainty in quality estimation, while at the same time guaranteeing stable (or increasing) salaries. We report experimental results indicating that the proposed scheme encourages long-term engagement, avoiding churn, and avoiding the common problem of adverse selection and moral hazard. We also describe a set of non-monetary, psychological schemes that actively discourage low-quality workers from participating in tasks. We finish showing that mice and crowdsourced workers are not that different after all.

                      Edith Law : Mixed-Expertise Crowdsourcing

                      To date, most of the research in human computation focuses on tasks that can be performed by any person with basic perceptual capabilities and common sense knowledge. In this talk, I will discuss new directions towards mixed-expertise crowdsourcing, where the crowd consists of people with drastically different motivations, levels and domains of expertise, as well as availabilities. I will illustrate the new opportunities and challenges in mixed-expertise crowdsourcing, by outlining existing work and describing my two ongoing projects – Curio, a micro-task marketplace for crowdsourcing scientific tasks, and SimplyPut, a crowdsourcing platform for improving health literacy through the collaborative summarization of medical information.

                      Mark Steyvers: Aggregating Human Judgments in Combinatorial Problems

                      We analyze the collective performance of individuals in combinatorial problems involving the rankings of events and items (e.g. “what is the order of US presidents?”) as well as traveling salesperson and minimum spanning tree problems. We compare situations in which a group of individuals independently answer these questions with an iterated learning environment in which individuals pass their solution to the next person in a chain. We introduce Bayesian information aggregation models for both the independent and information-sharing environments and treat the collective group knowledge as a latent variable that can be estimated from the observed judgments across individuals. The models allow for individual differences in expertise and confidence in other individuals’ judgments. Initial results suggest that information-sharing environments lead to better collective performance despite the fact that information-sharing increases correlations between judgments. In addition, the models’ estimates of expertise are more indicative of actual performance than the users’ self-rated expertise. Finally, we study situations where the same individual solves the same problem at different points in time. We show that the consistency in answers across repeated problems provides an additional signal to estimate expertise.

                      Accepted Papers

                      • Sivan Sabato, Adam Kalai; Feature Multi-Selection among Subjective Features.

                      • Adish Singla, Andreas Krause; Truthful Incentives for Privacy Tradeoff: Mechanisms for Data Gathering in Community Sensing.

                      • Hongwei Li, Bin Yu, Dengyong Zhou; Error Rate Analysis of Labeling by Crowdsourcing; (Supplementary).

                      • Peng Ye, David Doermann; Combining preference and absolute judgements in a crowd-sourced setting.

                      • Vaibhav Rajan, Sakyajit Bhattacharya, L. Elisa Celis, Deepthi Chander, Koustuv Dasgupta, Saraschandra Karanam; CrowdControl: An online learning approach for optimal task scheduling in a dynamic crowd platform.

                      • Alexey Tarasov, Sarah Jane Delany, Brian Mac Namee; Improving Performance by Re-Rating in the Dynamic Estimation of Rater Reliability.

                      • Michael Wick, Ari Kobren, Andrew McCallum; Probabilistic Reasoning about Human Edits in Information Integration.

                      • Joel Lehman, Risto Miikkulainen; Leveraging Human Computation Markets for Interactive Evolution.

                      • Jian Peng, Qiang Liu, Alexander Ihler, Bonnie Berger; Crowdsourcing for structured labeling with applications to protein folding.

                      Related Workshops, Conferences and Resources

                      • ICML 2013 Workshop on Machine Learning Meets Crowdsourcing.

                      • Conference on Human Computation & Crowdsourcing (HCOMP), 2013.

                      • HCOMP 2013 Workshop on Crowdsourcing at Scale.

                      • ICML 2012 Workshop on Machine Learning in Human Computation & Crowdsourcing.

                      • ICML 2011 Workshop on Combining Learning Strategies to Reduce Label Cost.

                      • NIPS 2012 Workshop on Human Computation for Science and Computational Sustainability.

                      • NIPS 2011 Workshop on Computational Social Science and the Wisdom of Crowds.

                      • NIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds.

                      • CVPR 2010 Workshop on Advancing Computer Vision with Humans in the Loop (ACVHL)

                      • 1st-4th Human Computation Workshop (HCOMP).

                      • CrowdCamp 2012, 2013.

                      • CHI 2011 Workshop on Crowdsourcing and Human Computation

                      • See more information on CrowdResearch.org or Mathew Lease's crowdsourcing site.

                      Page generated 2013-10-31 00:19:29 PDT, by jemdoc. (source)
                      http://www.ics.uci.edu/~ihler/code/

                      Code Packages


                      KDE Toolbox

                      Event Detection

                      Adaptive Inference

                      Gaussian Process Regression with Time-shifts


                       
                       
                      This page contains toolboxes and other code written by myself or our group for our research and made available for others. Although we hope you find it helpful, please do not expect a significant amount of technical support or debugging.
                       
                       

                       
                      Matlab Toolboxes
                       
                      • Kernel Density Estimation (KDE) Toolbox for Matlab
                        • A reasonably efficient implementation of spatial data structures for kernel or Parzen window density estimation and similar functions. Most work is done by a k-d tree data structure; versions of the "fast Gauss transform" and nearest neighbor searches are also included. Written as a Matlab class, with methods for many standard distributional operations (estimating entropy, evaluating, etc.)
                       
                       

                       
                      Other Matlab Code
                       
                      • Anomaly and Event Detection in Count Data
                      • Adaptive Inference in Graphical Models
                      • Gaussian Process Regression with Time-shifts
                       
                       
                      http://www.ics.uci.edu/~ihler/papers/bib.html Alexander Ihler: Publication bibtex
                      @INPROCEEDINGS{fisher99b,
                        author = {{Fisher~III}, J. W. and A. T. Ihler and P. Viola},
                        editor = {S. A. Solla and T. K. Leen and K-R. M{\"u}ller},
                        year = 2000,
                        title = {Learning Informative Statistics: A Nonparametric Approach},
                        booktitle = {Neural Information Processing Systems 12},
                        pages = {900--906}
                      }
                      
                      @INPROCEEDINGS{ihler01,
                        author = {A. T. Ihler and J. W. {Fisher~III} and A. S. Willsky},
                        month = may,
                        year = 2001,
                        volume = 6,
                        pages = {3473--3476},
                        title = {Nonparametric estimators for online signature authentication},
                        booktitle = {International Conference on Acoustics, Speech, and Signal Processing}
                      }
                      
                      @TECHREPORT{sudderth02,
                        author = {Erik B. Sudderth and Alexander T. Ihler and William T. Freeman and
                                 Alan S. Willsky},
                        year = 2002,
                        title = {Nonparametric Belief Propagation},
                        number = {MIT//LIDS P-2551},
                        institution = {MIT, Laboratory for Information and Decision Systems}
                      }
                      
                      @INPROCEEDINGS{ihler03a,
                        author = {A. T. Ihler and J. W. {Fisher~III} and A. S. Willsky},
                        month = apr,
                        year = 2003,
                        title = {Hypothesis testing over factorizations for data association},
                        booktitle = {Information Processing in Sensor Networks},
                        pages = {239--253}
                      }
                      
                      @INPROCEEDINGS{ihler03b,
                        author = {Ihler, A. T. and Sudderth, E. B. and Freeman, W. T. and Willsky, A. S.},
                        year = 2004,
                        title = {Efficient Multiscale Sampling from Products of {G}aussian Mixtures},
                        booktitle = {Neural Information Processing Systems 16},
                        editor = "Sebastian Thrun and Lawrence Saul and Bernhard {Sch\"{o}lkopf}",
                        publisher = "MIT Press",
                        address = "Cambridge, MA",
                      }
                      
                      @INPROCEEDINGS{sudderth03a,
                        author = {E. B. Sudderth and A. T. Ihler and W. T. Freeman and A. S. Willsky},
                        year = 2003,
                        title = {Nonparametric Belief Propagation},
                        booktitle = {Computer Vision and Pattern Recognition},
                        pages = {1:605--612}
                      }
                      
                      @INPROCEEDINGS{ihler04a,
                        author = {A. T. Ihler and J. W. {Fisher~III} and R. L. Moses and A. S.
                                 Willsky},
                        year = 2004,
                        title = {Nonparametric Belief Propagation for Self-Calibration in Sensor
                                Networks},
                        booktitle = {Information Processing in Sensor Networks},
                        pages = {225--233},
                      }
                      
                      @INPROCEEDINGS{ihler04b,
                        author = {A. T. Ihler and J. W. {Fisher~III} and R. L. Moses and A. S.
                                 Willsky},
                        year = 2004,
                        title = {Nonparametric Belief Propagation for Sensor Self-Calibration},
                        booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, 
                        pages = {iii:861--864}
                      }
                      
                      @ARTICLE{ihler05a,
                        author = {A. T. Ihler and J. W. {Fisher~III} and R. L. Moses and A. S.  Willsky},
                        year = 2005,
                        title = {Nonparametric Belief Propagation for Self-Calibration in Sensor
                                Networks},
                        journal = {IEEE Journal of Selected Areas in Communication}
                        volume = 23,
                        number = 4,
                        pages = {809--819}
                      }
                      
                      @TECHREPORT{ihler04d,
                        author = {A. T. Ihler and J. W. {Fisher~III} and A. S. Willsky},
                        year = 2004,
                        title = {Communication-Constrained Inference},
                        number = 2601,
                        institution = {MIT, Laboratory for Information and Decision Systems}
                      }
                      
                      @INPROCEEDINGS{ihler04f,
                        author = {A. T. Ihler and J. W. {Fisher~III} and A. S. Willsky},
                        year = 2005,
                        title = {Message Errors in Belief Propagation},
                        booktitle = {Neural Information Processing Systems 17},
                        editor = {Lawrence K. Saul and Yair Weiss and {L\'{e}on} Bottou},
                        publisher = {MIT Press},
                        address = {Cambridge, MA},
                        pages = {609--616}
                      }
                      
                      @ARTICLE{ihler04g,
                        author = {A. T. Ihler and J. W. Fisher~III and A. S. Willsky},
                        month = aug,
                        year = 2004,
                        title = {Nonparametric Hypothesis Tests for Statistical Dependency},
                        journal = {IEEE Transactions on Signal Processing},
                        volume = 52,
                        number = 8,
                        pages = {2234--2249}
                      }
                      
                      @ARTICLE{ihler05b,
                        author = {A. T. Ihler and J. W. Fisher~III and A. S. Willsky},
                        month = may,
                        year = 2005,
                        title = {Loopy Belief Propagation: Convergence and Effects of Message Errors},
                        journal = {Journal of Machine Learning Research},
                        volume = 6,
                        pages = {905--936}
                      }
                      
                      @MISC{ihler-kde,
                        author = {Alexander Ihler},
                        title = {Kernel Density Estimation Toolbox for Matlab},
                        url = {http://www.ics.uci.edu/~ihler/code/}
                      }
                      
                      @inproceedings{siracusa05,
                       title = {Estimating Dependency and Significance for High-Dimensional Data},
                       booktitle = {Proceedings of {ICASSP} 2005 - International Conference on Acoustics, Speech, and Signal Processing},
                       author = {M. Siracusa and K. Tieu and A. Ihler and J. Fisher and A. Willsky},
                       year = { 2005 },
                       location = { Philadelphia (USA) },
                       pages = {1085--1088},
                       isbn={0-7803-8874-7}
                       }
                      
                      @INPROCEEDINGS{ihler05c,
                        author = {Ihler, A. T. and J. W. Fisher~III and A. S. Willsky},
                        month = jul,
                        year = 2005,
                        title = {Particle filtering under communications constraints},
                        booktitle = {Proceedings, IEEE Statistical Signal Processing (SSP)},
                        pages = {89--94}
                      }
                      
                      @ARTICLE{cetin06,
                        author = {M. Cetin and L. Chen and J. Fisher and A. Ihler and R. Moses and M. Wainwright and A. Willsky},
                        month = jul,
                        year = 2006,
                        title = {Distributed Fusion in Sensor Networks: A graphical models perspective},
                        journal = {IEEE Signal Processing Magazine},
                        volume = 23,
                        number = 4, 
                        pages = {43--55}
                      }
                      
                      @INPROCEEDINGS{porteous06,
                       title = {{G}ibbs sampling for coupled infinite mixture models in the stick-breaking representation},
                       booktitle = {Proceedings of {UAI} 2006},
                       author = {I. Porteous and A. Ihler and P. Smyth and M. Welling},
                       month = jul,
                       year = 2006,
                       pages = {385--392},
                       location = { Boston (USA) }
                       }
                      
                      @INPROCEEDINGS{ihler06a,
                       author = {Alexander Ihler and Jon Hutchins and Padhraic Smyth},
                       title = {Adaptive event detection with time-varying {P}oisson processes},
                       booktitle = {KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining},
                       year = {2006},
                       isbn = {1-59593-339-5},
                       pages = {207--216},
                       location = {Philadelphia, PA, USA},
                       doi = {http://doi.acm.org/10.1145/1150402.1150428},
                       publisher = {ACM Press},
                       address = {New York, NY, USA}
                       }
                      
                      @INPROCEEDINGS{ihler06b,
                        author = {Ihler, A. T. and Smyth, P. J.},
                        year = 2007,
                        pages = {625--632},
                        title = {Learning time-intensity profiles of human activity using non-parametric {B}ayesian models},
                        booktitle = {Neural Information Processing Systems 19},
                        editor = {B. Sch\"{o}lkopf and J. Platt and T. Hoffman},
                        publisher = {MIT Press},
                        address = {Cambridge, MA}
                      }
                      
                      @ARTICLE{ihler07a, 
                       title = {Graphical Models for Statistical Inference and Data Assimilation},
                       author = {A. Ihler and S. Kirshner and M. Ghil and A. Robertson and P. Smyth},
                       month = jun,
                       year = { 2007 },
                       journal = {Physica D: Nonlinear Phenomena},
                       volume = 230,
                       issue = {1--2},
                       pages = {72--87}
                       }
                      
                      @INPROCEEDINGS{ihler07b,
                       title = {Accuracy bounds for belief propagation},
                       booktitle = {Proceedings of {UAI} 2007},
                       author = {Alexander Ihler},
                       month = jul,
                       pages = {183--190},
                       year = 2007
                       }
                      
                      @ARTICLE{ihler07c, 
                       author = {Alexander Ihler and Jon Hutchins and Padhraic Smyth},
                       title = {Learning to detect events with Markov-modulated Poisson processes},
                       journal = {ACM Trans. Knowl. Discov. Data},
                       volume = {1},
                       number = {3},
                       year = {2007},
                       issn = {1556--4681},
                       pages = {13},
                       doi = {http://doi.acm.org/10.1145/1297332.1297337},
                       publisher = {ACM},
                       address = {New York, NY, USA},
                       }
                      
                      @incollection{camsap07,
                       title = {Modeling count data from multiple sensors: a building occupancy model},
                       author = {Jon Hutchins and Alexander Ihler and Padhraic Smyth},
                       booktitle = {Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)},
                       month= dec,
                       pages = {241--245},
                       year = {2007}
                      }
                      
                      @inproceedings{uai08,
                        author    = {Umut A. Acar and Alexander T. Ihler and Ramgopal R. Mettu and {\"O}zg{\"u}r S{\"u}mer},
                        title     = {Adaptive inference on general graphical models},
                        booktitle = {UAI},
                        year      = {2008},
                        pages     = {1--8},
                        ee        = {http://uai2008.cs.helsinki.fi/UAI_camera_ready/acar.pdf},
                        crossref  = {DBLP:conf/uai/2008},
                        bibsource = {DBLP, http://dblp.uni-trier.de}
                      }
                      
                      @incollection{nips07,
                       title = {Adaptive Bayesian Inference},
                       author = {Umut Acar and Alexander T. Ihler and Ramgopal R. Mettu and {\"O}zg{\"u}r S{\"u}mer},
                       booktitle = {Advances in Neural Information Processing Systems 20},
                       editor = {J.C. Platt and D. Koller and Y. Singer and S. Roweis},
                       publisher = {MIT Press},
                       address = {Cambridge, MA},
                       pages = {1441--1448},
                       year = {2008}
                      }
                      
                      @PHDTHESIS{ihler-thesis,
                        author = {Alexander T. Ihler},
                        year = 2005,
                        title = {Inference in Sensor Networks: Graphical Models and Particle Methods},
                        school = {MIT}
                      }
                      
                      @MASTERSTHESIS{ihler00,
                        author = {Alexander T. Ihler},
                        month = dec,
                        year = 2000,
                        title = {Maximally Informative Subspaces: Nonparametric Estimation for Dynamical Systems},
                        school = {MIT}
                      }
                      
                      @inproceedings{aistats09,
                        author    = {Alexander Ihler and David McAllester},
                        title     = {Particle Belief Propagation},
                        booktitle = {Proceedings of the Twelfth International Conference on
                                     Artificial Intelligence and Statistics ({AISTATS}) 2009},
                        year      = {2009},
                        pages     = {256--263},
                        editor    = {D. van Dyk and M. Welling},
                        publisher = {JMLR: W\&CP 5},
                        address   = {Clearwater Beach, Florida}
                      }
                      
                      @inproceedings{porteous08,
                       author = {Porteous, Ian and Newman, David and Ihler, Alexander and Asuncion, Arthur and Smyth, Padhraic and Welling, Max},
                       title = {Fast collapsed {G}ibbs sampling for latent {D}irichlet allocation},
                       booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining},
                       year = {2008},
                       isbn = {978-1-60558-193-4},
                       pages = {569--577},
                       location = {Las Vegas, Nevada, USA},
                       doi = {http://doi.acm.org/10.1145/1401890.1401960},
                       publisher = {ACM},
                       address = {New York, NY, USA},
                       }
                      
                      @inproceedings{hutchins08,
                       author = {Jon Hutchins and Alexander Ihler and Padhraic Smyth},
                       title = {Probabilistic Analysis of a Large Scale Urban Traffic Sensor Data Set},
                       booktitle = {Knowledge Discovery from Sensor Data, LNCS \# 5840}
                       year = {2008},
                       pages = {94--114},
                       publisher = {Springer},
                       doi = {10.1007/978-3-642-12519-5_6}
                       }
                      
                      @article{lin09,
                          author = {Lin, Kevin K. AND Kumar, Vivek AND Geyfman, Mikhail AND Chudova, Darya AND Ihler, Alexander T. AND Smyth, Padhraic AND Paus, Ralf AND Takahashi, Joseph S. AND Andersen, Bogi},
                          journal = {PLoS Genet},
                          publisher = {Public Library of Science},
                          title = {Circadian Clock Genes Contribute to the Regulation of Hair Follicle Cycling},
                          year = {2009},
                          month = {07},
                          volume = {5},
                          url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1000573},
                          pages = {e1000573},
                          number = {7},
                          doi = {10.1371/journal.pgen.1000573}
                      }        
                      
                      @incollection{ssp09,
                          title = {Adaptive Updates for MAP Configurations with Applications to Bioinformatics},
                          author = {Umut Acar and Alexander T. Ihler and Ramgopal R. Mettu and {\"O}zg{\"u}r S{\"u}mer},
                          booktitle = {IEEE Statistical Signal Processing (SSP)},
                          month= aug,
                          pages = {413--416},
                          year = {2009}
                      }
                      
                      @incollection{allerton09,
                         title = {A Low Density Lattice Decoder via Non-parametric Belief Propagation},
                         author = {Danny Bickson and Alexander Ihler and Harel Avissar and Danny Dolev},
                         booktitle = {Proc. Allerton Conf. on Comm. Control and Comp.},
                         Xbooktitle = {Proceedings of the 47th Allerton Conference on Communications, Control and Computing},
                         month= aug,
                         pages = {439--446},
                         year = {2009}
                      }
                      
                      @TECHREPORT{tr09-06,
                         author = {Alexander Ihler and David Newman},
                         year = 2009,
                         title = {Bounding Sample Errors in Approximate Distributed Latent {D}irichlet Allocation},
                         number = 09-06,
                         institution = {Information and Computer Science, University of California, Irvine}
                      }
                      
                      @incollection{nips09,
                         title = {Particle-based Variational Inference for Continuous Systems},
                         author = {Alexander Ihler and Andrew Frank and Padhraic Smyth},
                         booktitle = {Advances in Neural Information Processing Systems 22},
                         editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta},
                         pages = {826--834},
                         year = {2009}
                      }
                      
                      @article{chudova09,
                          author = {Darya Chudova and Alexander Ihler and Kevin K. Lin and Bogi Anderson and Padhraic Smyth},
                          journal = {Bioinformatics},
                          publisher = {Oxford},
                          title = {{B}ayesian detection of non-sinusoidal periodic patterns in circadian expression data},
                          year = {2009},
                          month = dec,
                          volume = {25},
                          number = {23},
                          pages = {3114--3120},
                          doi = {10.1093/bioinformatics/btp547}
                      }        
                      
                      @article{liu10,
                          author = {Qiang Liu and Kevin K. Lin and Bogi Anderson and Padhraic Smyth and Alexander Ihler},
                          journal = {Bioinformatics},
                          publisher = {Oxford},
                          title = {Estimating Replicate Time-Shifts Using {G}aussian Process Regression},
                          year = {2010},
                          month = mar,
                          volume = {26},
                          number = {6},
                          pages = {770--776},
                          doi = {10.1093/bioinformatics/btq022}
                      }
                      
                      @incollection{asuncion10a,
                          title = {Learning with Blocks: Composite Likelihood and Contrastive Divergence},
                          author = {Arthur Asuncion and Qiang Liu and Alexander Ihler and Padhraic Smyth},
                          booktitle = {Proceedings of the Thirteenth International Conference on
                                       Artificial Intelligence and Statistics ({AISTATS}) 2010},
                          year = {2010},
                          pages = {33--40},
                          editor    = {},
                          publisher = {JMLR: W\&CP 5},
                          address   = {}
                      }
                      
                      @incollection{asuncion10b,
                          title = {Particle Filtered MCMC-MLE with Connections to Contrastive Divergence},
                          author = {Arthur Asuncion and Qiang Liu and Alexander Ihler and Padhraic Smyth},
                          booktitle = {International Conference on Machine Learning (ICML)},
                          month= june,
                          year = {2010},
                          pages = {47--54},
                          address   = {Haifa, Israel}
                      }
                      
                      @incollection{yarkony10,
                         title = {Covering Trees and Lower Bounds on Quadratic Assignment},
                         author = {Julian Yarkony and Charless Fowlkes and Alexander Ihler},
                         booktitle = {Computer Vision and Pattern Recognition (CVPR)},
                         month= june,
                         year = 2010,
                         pages = {887--894},
                         address = {San Francisco, CA, USA}
                      }
                      
                      @incollection{liu10b,
                         title = {Negative Tree-reweighted Belief Propagation},
                         author = {Qiang Liu and Alexander Ihler},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         Xbooktitle = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
                         Xpublisher = "AUAI Press",
                         Xaddress = "Corvallis, Oregon",
                         month= july,
                         year = 2010,
                         pages = {332--339},
                         address = {Catalina Island, CA, USA}
                      }
                      
                      @article{sudderth10,
                         author = {Erik Sudderth and Alexander Ihler and Michael Isard and William Freeman and Alan Willsky},
                         journal = {Communications of the ACM},
                         title = {Nonparametric Belief Propagation},
                         year = 2010,
                         month = oct,
                         volume = {53},
                         number = {10},
                         pages = {95--103},
                      }
                      
                      @ARTICLE{tkde12,
                         author={Ihler, A. and Newman, D.},
                         journal={Knowledge and Data Engineering, IEEE Transactions on},
                         title={Understanding Errors in Approximate Distributed Latent Dirichlet Allocation},
                         year={2012},
                         month=may,
                         volume={24},
                         number={5},
                         pages={952--960},
                      }
                      
                      @incollection{liu11c,
                         title = {Learning scale free networks by reweighted {L1} regularization},
                         author = {Qiang Liu and Alexander Ihler},
                         booktitle = {AI \& Statistics},
                         month=apr,
                         year =2011,
                      	 pages = {40--48},
                         publisher = {JMLR: W\&CP 15},
                         address = {Ft. Lauderdale, FL, USA}
                      }
                      
                      @incollection{xu11,
                         title = {Multicore Gibbs Sampling in Dense, Unstructured Graphs},
                         author = {Tianbing Xu and Alexander Ihler},
                         booktitle = {AI \& Statistics},
                         month=apr,
                         year =2011,
                         pages = {798--806},
                         publisher = {JMLR: W\&CP 15},
                         address = {Ft. Lauderdale, FL, USA}
                      }
                      
                      @incollection{foulds11,
                         title = {Revisiting MAP Estimation, Message Passing and Perfect Graphs},
                         author = {James Foulds and Nicholas Navaroli and Padhraic Smyth and Alexander Ihler},
                         booktitle = {AI \& Statistics},
                         month=apr,
                         year =2011,
                         pages = {278--286},
                         publisher = {JMLR: W\&CP 15},
                         address = {Ft. Lauderdale, FL, USA}
                      }
                      
                      @article{bickson11,
                         author = {Danny Bickson and Dror Baron and Alexander Ihler and Harald Avissar and Danny Dolev},
                         title = {Fault Detection via Nonparametric Belief Propagation},
                         journal = {IEEE Trans. Signal Process.},
                         year = {2011},
                         month = jun,
                         volume = {59},
                         number = {6},
                         pages = {2602-2613},
                      }
                      
                      @incollection{sumer11,
                         title = {Fast parallel and adaptive updates for dual-decomposition solvers},
                         author = {\"Ozg\"ur S\"umer and Acar, Umut A. and Alexander Ihler and Mettu, Ramgopal R.}
                         booktitle = {Conf. on Artificial Intelligence (AAAI)},
                         month=aug,
                         year =2011,
                         pages = {1076--1082},
                         address = {San Francisco, CA, USA},
                      }
                      
                      @InProceedings{liu11d,
                        author =    {Qiang Liu and Alexander Ihler},
                        title =     {Bounding the Partition Function using H\"older's Inequality },
                        booktitle = {Proceedings of the 28th International Conference on Machine Learning (ICML-11)},
                        series =    {ICML '11},
                        year =      {2011},
                        editor =    {Lise Getoor and Tobias Scheffer},
                        location =  {Bellevue, Washington, USA},
                        isbn =      {978-1-4503-0619-5},
                        month =     {June},
                        publisher = {ACM},
                        address =   {New York, NY, USA},
                        pages=      {849--856},
                      }
                      
                      @incollection{liu11e,
                         title = {Variational algorithms for marginal {MAP}},
                         author = {Qiang Liu and Alexander Ihler},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)},
                         month= july,
                         year = 2011,
                         location = {Barcelona, Spain}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = "453-462"
                      }
                      
                      @incollection{yarkony11a,
                         title = {Planar cycle covering graphs},
                         author = {Julian Yarkony and Alexander Ihler and Charless Fowlkes},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)},
                         month= july,
                         year = 2011,
                         location = {Barcelona, Spain}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = "761-769"
                      } 
                      
                      @incollection{yarkony11b,
                         title = {Tightening {MRF} relaxations with planar subproblems},
                         author = {Julian Yarkony and Ragib Morshed and Alexander Ihler and Charless Fowlkes},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)},
                         month= july,
                         year = 2011,
                         location = {Barcelona, Spain}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = "770-777"
                      }
                      
                      
                      @article{sumer11b,
                         author = {{\"Ozg\"ur S\"umer and Acar, Umut A. and Alexander Ihler and Mettu, Ramgopal R.},
                         title = {Adaptive Exact Inference in Graphical Models},
                         journal = {Journal of Machine Learning Research},
                         year = {2011},
                         month = nov,
                         volume = {12},
                         pages = {3147-3186},
                      }
                      
                      @incollection{flerova11,
                         title = {Mini-bucket Elimination with Moment Matching},
                         author = {Natasha Flerova and Alexander Ihler and Rina Dechter and Lars Otten},
                         booktitle = {NIPS Workshop on Discrete Optimization (DiscML)},
                         month=dec,
                         year =2011,
                      }
                      
                      @incollection{liu12a,
                         title = {Distributed Parameter Estimation via Pseudo-likelihood},
                         author = {Qiang Liu and Alexander Ihler},
                         booktitle = {International Conference on Machine Learning (ICML)},
                         Xbooktitle = {Proceedings of the 29th International Conference on Machine Learning (ICML-12)},
                         Xseries =    {ICML '12},
                         Xeditor =    {John Langford and Joelle Pineau},
                         Xlocation =  {Edinburgh, Scotland, GB},
                         Xisbn =      {978-1-4503-1285-1},
                         Xpublisher = {Omnipress},
                         Xaddress =   {New York, NY, USA},
                         pages =      {1487--1494},
                         month = jul,
                         year = 2012,
                      }
                      
                      @incollection{cheng12,
                         title = {Approximating the Sum Operation for Marginal-MAP Inference},
                         author = {Qiang Cheng and Feng Chen and Jianwu Dong and Wenli Xu and Alexander Ihler},
                         booktitle = {Conf. on Artificial Intelligence (AAAI)},
                         month=jul,
                         year = 2012,
                         pages = {1882--1887},
                      }
                      
                      @incollection{frank12,
                         title = {A Graphical Model Representation of the Track-Oriented Multiple Hypothesis Tracker},
                         author = {Andrew Frank and Padhraic Smyth and Alexander Ihler},
                         booktitle = {Proceedings, IEEE Statistical Signal Processing (SSP)},
                         month=aug,
                         year =2012,
                         pages = {768-771},
                      }
                      
                      @incollection{liu12b,
                         title = {Belief Propagation for Structured Decision Making},
                         author = {Qiang Liu and Alexander Ihler},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Eight Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)},
                         month= aug,
                         year = 2012,
                         location = {Catalina Island, USA}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = {523-532}
                      }
                      
                      @incollection{welling12a,
                         title = {A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation},
                         author = {Max Welling and Andrew Gelfand and Alexander Ihler},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Eight Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)},
                         month= aug,
                         year = 2012,
                         location = {Catalina Island, USA}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = {883-892}
                      }
                      
                      @incollection{ihler12a,
                         title = {Join-graph based cost-shifting schemes},
                         author = {Alexander Ihler and Natalia Flerova and Rina Dechter and Lars Otten},
                         booktitle = {Uncertainty in Artificial Intelligence (UAI)},
                         fullbooktitle = {Proceedings of the Twenty-Eight Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)},
                         month= aug,
                         year = 2012,
                         location = {Catalina Island, USA}
                         publisher = "AUAI Press",
                         address = "Corvallis, Oregon",
                         pages = {397--406}
                      }
                      
                      @article{geyfman+12,
                         author = { Mikhail Geyfman and Vivek Kumar and Qiang Liu and Rolando Ruiz and William Gordon and
                          Francisco Espitia and Eric Cam and Sarah E. Millar and Padhraic Smyth and Alexander Ihler and
                          Joseph S. Takahashi and Bogi Andersen},
                         journal = {Proc. Nat. Acad. Sci.},
                         journalfull = {Proceedings of the National Academy of Sciences},
                         title = {Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and 
                           susceptibility to UVB-induced DNA damage in the epidermis},
                         year = {2012},
                         month = jul,
                         volume = {109},
                         number = {29},
                         pages = {11758--11763},
                      }
                      
                      @incollection{NIPS2012_0328,
                       title ={Variational Inference for Crowdsourcing},
                       author={Qiang Liu and Jian Peng and Alexander Ihler},
                       booktitle = {Advances in Neural Information Processing Systems 25},
                       editor = {P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger},
                       pages = {701--709},
                       year = {2012}
                      }
                      
                      @inproceedings{iccp13,
                       title ={Image Enhancement in Projectors via Optical Pixel Shift and Overlay},
                       author={Behzad Sajadi, Duy Qoc-Lai, Alexander Ihler, M. Gopi, Aditi Majumder},
                       booktitle = {IEEE International Conference on Computational Photography (ICCP)},
                       editor = {P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger},
                       year = {2013}
                      }
                      
                      @inproceedings{acml13,
                       title ={Linear Approximation to ADMM for MAP inference},
                       author={Sholeh Forouzan and Alexander Ihler}
                       booktitle = {JMLR W&CP}
                       issue = 29,
                       pages = {48--61},
                       editor = {Ong, Cheng Soon and Ho, Tu Bao},
                       month = nov,
                       year = {2013}
                      }
                      
                       TBD
                      
                       TBD
                      
                      @article{JMLR:liu13b,
                        author  = {Qiang Liu and Alexander Ihler},
                        title   = {Variational Algorithms for Marginal MAP},
                        journal = {Journal of Machine Learning Research},
                        year    = {2013},
                        volume  = {14},
                        pages   = {3165-3200},
                        url     = {http://jmlr.org/papers/v14/liu13b.html}
                      }
                      








































                      
                        
                      
                      
                      http://www.ics.uci.edu/~ihler/papers/abs.html
                      
                      
                      
                      Alexander Ihler: Publication abstracts
                      
                      
                      
                      

                      Alexander Ihler

                      Associate Professor

                      Information & Computer Science, UC Irvine


                      Bren Hall 4066
                      ph: 949-824-3645
                      fx: 949-824-4056
                      ihler (at) ics.uci.edu /
                      ihler (at) alum.mit.edu


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                      Abstracts

                      Pushing Forward Marginal MAP with Best-First Search

                      Marinescu, Dechter, Ihler

                      Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best-first search algorithms and demonstrate their effectiveness against recent branch and bound schemes through extensive empirical evaluations. Our results show that best-first search improves significantly over existing depth-first approaches, in many cases by several orders of magnitude, especially when guided by relatively weak heuristics.

                      [ BibTex ] | [ PDF ]



                      Boosting Crowdsourcing with Expert Labels: Local vs. Global Effects

                      Liu, Ihler, Fisher

                      Crowdsourcing provides a cheap but efficient approach for large-scale data and information collection. %large scale collection of human intelligence. However, human judgments are inherently noisy, ambiguous and sometimes biased, and should be calibrated by additional (usually much more expensive) expert or true labels. % expert or In this work, we study the optimal allocation of the true labels to best calibrate the crowdsourced labels. We frame the problem as a submodular optimization, and propose a greedy allocation strategy that exhibits an interesting trade-off between a local effect, which encourages acquiring true labels for the most uncertain items, and a global effect, which favors the true labels of the most ``influential" items, whose information can propagate to help the prediction of other items. We show that exploiting and monitoring the global effect yields a significantly better selection strategy, and also provides potentially valuable information for other tasks such as designing stopping rules.

                      [ BibTex ] | [ PDF ]



                      Incremental Region Selection for Mini-bucket Elimination Bounds

                      Forouzan, Ihler

                      Region choice is a key issue for many approximate inference bounds. Mini-bucket elimination avoids the space and time complexity of exact inference by using a top-down partitioning approach that mimics the construction of a junction tree and aims to minimize the number of regions subject to a bound on their size; however, these methods rarely take into account functions' values. In contrast, message passing algorithms often use "cluster pursuit" methods to select regions, a bottom-up approach in which a pre-defined set of clusters (such as triplets) is scored and incrementally added. In this work, we develop a hybrid approach that balances the advantages of both perspectives, providing larger regions chosen in an intelligent, energy-based way. Our method is applicable to bounds on a variety of inference tasks, and we demonstrate its power empirically on a broad array of problem types.

                      [ BibTex ] | [ PDF ]



                      Estimating the Partition Function by Discriminance Sampling

                      Liu, Peng, Ihler, Fisher

                      Importance sampling (IS) and its variant, annealed IS (AIS) have been widely used for estimating the partition function in graphical models, such as Markov random fields and deep generative models. However, IS tends to underestimate the partition function and is subject to high variance when the proposal distribution is more peaked than the target distribution. On the other hand, "reverse" versions of IS and AIS tend to overestimate the partition function, and degenerate when the target distribution is more peaked than the proposal distribution. In this work, we present a simple, general method that gives much more reliable and robust estimates than either IS (AIS) or reverse IS (AIS). Our method works by converting the estimation problem into a simple classification problem that discriminates between the samples drawn from the target and the proposal. We give extensive theoretical and empirical justification; in particular, we show that an annealed version of our method significantly outperforms both AIS and reverse AIS as proposed by Burda et al. (2015), which has been the state-of-the-art for likelihood evaluation in deep generative models.

                      [ BibTex ] | [ PDF ]



                      Distributed Estimation, Information Loss and Exponential Families

                      Liu, Ihler

                      Distributed learning of probabilistic models from multiple data repositories with minimum communication is increasingly important. We study a simple communication-efficient learning framework that first calculates the local maximum likelihood estimates (MLE) based on the data subsets, and then combines the local MLEs to achieve the best possible approximation to the global MLE given the whole dataset. We study this framework's statistical properties, showing that the efficiency loss compared to the global setting relates to how much the underlying distribution families deviate from full exponential families, drawing connection to the theory of information loss by Fisher, Rao and Efron. We show that the ``full-exponential-family-ness" represents the lower bound of the error rate of arbitrary combinations of local MLEs, and is achieved by a KL-divergence-based combination method but not by a more common linear combination method. We also study the empirical properties of both methods, showing that the KL method significantly outperforms linear combination in practical settings with issues such as model misspecification, non-convexity, and heterogeneous data partitions.

                      [ BibTex ] | [ PDF ]



                      Beyond Static Mini-Bucket: Towards Integrating with Iterative Cost-Shifting Based Dynamic Heuristics

                      Lam, Kask, Dechter, Ihler

                      We explore the use of iterative cost-shifting as a dynamic heuristic generator for solving MPE in graphical models via Branch and Bound. When mini-bucket elimination is limited by its memory budget, it may not provide good heuristics. This can happen often when the graphical model has a very high induced width with large variable domain sizes. In addition, we explore a hybrid setup where both MBE and the iterative cost-shifting bound are used in a combined heuristic. We compare these approaches with the most advanced statically generated heuristics.

                      [ BibTex ] | [ PDF ]



                      AND/OR Search for Marginal MAP

                      Marinescu, Dechter, Ihler

                      Marginal MAP problems are known to be very difficult tasks for graphical models and are so far solved exactly by systematic search guided by a join-tree upper bound. In this paper, we develop new AND/OR branch and bound algorithms for marginal MAP that use heuristics extracted from weighted mini-buckets enhanced with message-passing updates. We demonstrate the effectiveness of the resulting search algorithms against previous join-tree based approaches, which we also extend to apply to high induced width models, through extensive empirical evaluations. Our results show not only orders-of-magnitude improvements over the state-of-the-art, but also the ability to solve problem instances well beyond the reach of previous approaches.

                      [ BibTex ] | [ PDF ]



                      Marginal structured SVM with hidden variables

                      Ping, Liu, Ihler

                      In this work, we propose the marginal structured SVM (MSSVM) for structured prediction with hidden variables. MSSVM properly accounts for the uncertainty of hidden variables, and can significantly outperform the previously proposed latent structured SVM (LSSVM; Yu & Joachims (2009)) and other state-of-art methods, especially when that uncertainty is large. Our method also results in a smoother objective function, making gradient-based optimization of MSSVMs converge significantly faster than for LSSVMs. We also show that our method consistently outperforms hidden conditional random fields (HCRFs; Quattoni et al. (2007)) on both simulated and real-world datasets. Furthermore, we propose a unified framework that includes both our and several other existing methods as special cases, and provides insights into the comparison of different models in practice.

                      [ BibTex ] | [ PDF ]



                      Beyond MAP estimation with the track-oriented multiple-hypothesis tracker

                      Frank, Smyth, Ihler

                      The track-oriented multiple hypothesis tracker (TOMHT) is a popular algorithm for tracking multiple targets in a cluttered environment. In tracking parlance it is known as a multi-scan, maximum a posteriori (MAP) estimator – multi-scan because it enumerates possible data associations jointly over several scans, and MAP because it seeks the most likely data association conditioned on the observations. This paper extends the TOMHT, building on its internal representation to support probabilistic queries other than MAP estimation. Specifically, by summing over the TOMHT's pruned space of data association hypotheses one can compute marginal probabilities of individual tracks. Since this summation is generally intractable, any practical implementation must replace it with an approximation. We introduce a factor graph representation of the TOMHT’s data association posterior and use variational message-passing to approximate track marginals. In an empirical evaluation, we show that marginal estimates computed through message-passing compare favorably to those computed through explicit summation over the k-best hypotheses, especially as the number of possible hypotheses increases. We also show that track marginals enable parameter estimation in the TOMHT via a natural extension of the expectation maximization algorithm used in single-target tracking. In our experiments, online EM updates using approximate marginals significantly increased tracker robustness to poor initial parameter specification.

                      [ BibTex ] | [ Link ]



                      Feed-forward hierarchical model of the ventral visual stream applied to functional brain image classification

                      Keator, Fallon, Lakatos, Fowlkes, Potkin, Ihler

                      Functional brain imaging is a common tool in monitoring the progression of neurodegenerative and neurological disorders. Identifying functional brain imaging derived features that can accurately detect neurological disease is of primary importance to the medical community. Research in computer vision techniques to identify objects in photographs have reported high accuracies in that domain, but their direct applicability to identifying disease in functional imaging is still under investigation in the medical community. In particular, Serre et al. (2005) introduced a biophysically inspired filtering method emulating visual processing in striate cortex which they applied to perform object recognition in photographs. In this work, the model described by Serre et al. (2005) is extended to three-dimensional volumetric images to perform signal detection in functional brain imaging (PET, SPECT). The filter outputs are used to train both neural network and logistic regression classifiers and tested on two distinct datasets: ADNI Alzheimer’s disease 2-deoxy-D-glucose (FDG) PET and National Football League players Tc99m HMPAO SPECT. The filtering pipeline is analyzed to identify which steps are most important for classification accuracy. Our results compare favorably with other published classification results and outperform those of a blinded expert human rater, suggesting the utility of this approach.

                      [ BibTex ] | [ Link ]



                      Variational planning for graph-based MDPs

                      Cheng, Liu, Chen, Ihler

                      Markov Decision Processes (MDPs) are extremely useful for modeling and solving sequential decision making problems. Graph-based MDPs provide a compact representation for MDPs with large numbers of random variables. However, the complexity of exactly solving a graph-based MDP usually grows exponentially in the number of variables, which limits their application. We present a new variational framework to describe and solve the planning problem of MDPs, and derive both exact and approximate planning algorithms. In particular, by exploiting the graph structure of graph-based MDPs, we propose a factored variational value iteration algorithm in which the value function is first approximated by the multiplication of local-scope value functions, then solved by minimizing a Kullback-Leibler (KL) divergence. The KL divergence is optimized using the belief propagation algorithm, with complexity exponential in only the cluster size of the graph. Experimental comparison on different models shows that our algorithm outperforms existing approximation algorithms at finding good policies.

                      [ BibTex ] | [ PDF ]



                      Scoring workers in crowdsourcing: How many control questions are enough?

                      Liu, Steyvers, Ihler

                      We study the problem of estimating continuous quantities, such as prices, probabilities, and point spreads, using a crowdsourcing approach. A challenging aspect of combining the crowd's answers is that workers' reliabilities and biases are usually unknown and highly diverse. Control items with known answers can be used to evaluate workers' performance, and hence improve the combined results on the target items with unknown answers. This raises the problem of how many control items to use when the total number of items each workers can answer is limited: more control items evaluates the workers better, but leaves fewer resources for the target items that are of direct interest, and vice versa. We give theoretical results for this problem under different scenarios, and provide a simple rule of thumb for crowdsourcing practitioners. As a byproduct, we also provide theoretical analysis of the accuracy of different consensus methods.

                      [ BibTex ] | [ PDF ]



                      Does better inference mean better learning?

                      Gelfand, Dechter, Ihler

                      Maximum Likelihood learning of graphical models is not possible in problems where inference is intractable. In such settings it is common to use approximate inference (e.g. Loopy BP) and maximize the so-called ``surrogate'' likelihood objective. We examine the effect of using different approximate inference methods and, therefore, different surrogate likelihoods, on the accuracy of parameter estimation. In particular, we consider methods that utilize a control parameter to trade computation for accuracy. We demonstrate empirically that cheaper, but worse quality approximate inference methods should be used in the small data setting as they exhibit smaller variance and are more robust to model mis-specification.

                      [ BibTex ]



                      Linear Approximation to ADMM for MAP inference

                      Forouzan, Ihler

                      Maximum a posteriori (MAP) inference is one of the fundamental inference tasks in graphical models. MAP inference is in general NP-hard, making approximate methods of interest for many problems. One successful class of approximate inference algorithms is based on linear programming (LP) relaxations. The augmented Lagrangian method can be used to overcome a lack of strict convexity in LP relaxations, and the Alternating Direction Method of Multipliers (ADMM) provides an elegant algorithm for finding the saddle point of the augmented Lagrangian. Here we present an ADMM-based algorithm to solve the primal form of the MAP-LP whose closed form updates are based on a linear approximation technique. Our technique gives efficient, closed form updates that converge to the global optimum of the LP relaxation. We compare our algorithm to two existing ADMM-based MAP-LP methods, showing that our technique is faster on general, non-binary or non-pairwise models.

                      [ BibTex ] | [ PDF ]



                      Variational Algorithms for Marginal MAP

                      Liu, Ihler

                      The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters. Unfortunately, marginal MAP can be NP-hard even on trees, and has attracted less attention in the literature compared to the joint MAP (maximization) and marginalization problems. We derive a general dual representation for marginal MAP that naturally integrates the marginalization and maximization operations into a joint variational optimization problem, making it possible to easily extend most or all variational-based algorithms to marginal MAP. In particular, we derive a set of “mixed-product" message passing algorithms for marginal MAP, whose form is a hybrid of max-product, sum-product and a novel “argmax-product" message updates. We also derive a class of convergent algorithms based on proximal point methods, including one that transforms the marginal MAP problem into a sequence of standard marginalization problems. Theoretically, we provide guarantees under which our algorithms give globally or locally optimal solutions, and provide novel upper bounds on the optimal objectives. Empirically, we demonstrate that our algorithms significantly outperform the existing approaches, including a state-of-the-art algorithm based on local search methods.

                      [ BibTex ] | [ PDF ] | [ Link ]



                      Image enhancement in projectors via optical pixel shift and overlay

                      Sajadi, Qoc-Lai, Ihler, Gopi, Majumder

                      Earlier work has explored enhancing the perceived resolution of a display by shifting multiple different low-resolution images by fractions of a pixel and overlaying them in a temporally multiplexed fashion. This increases the manufacturing cost and also sacrifices the temporal resolution that can compromise other capabilities like 3D active stereo. In this paper we propose a method to achieve the same goal in projectors by performing the pixel shift and superposition optically by introducing a simple and inexpensive optical ensemble of a set of lenses on the projector light path. This does not sacrifice the temporal resolution and is extremely easy to implement in practice. However, instead of overlaying different images, we overlay an image with one or more sub-pixel shifted copies of itself. Therefore, we seek a single n × n image which when shifted and overlaid with itself creates a perceptually closer to a higher resolution 2n × 2n target image. This changes the optimization formulation significantly and requires solving a system of sparse linear equations. We take advantage of this sparsity and design a parallel implementation of this optimization in GPUs for real-time computation of the input image critical for its practical implementation. But, since this system is more constrained that using multiple overlaid images, the enhancement of resolution is compromised. However, since the optical design is very simple and inexpensive, it can be deployed on a variety of low-cost projectors and still offer a significant image quality benefit.

                      [ BibTex ] | [ PDF ]



                      Variational inference for crowdsourcing

                      Liu, Peng, Ihler

                      Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the computational task of aggregating the crowdsourced labels provided by a collection of unreliable annotators. We approach this problem by transforming it into a standard inference problem in graphical models, and applying approximate variational methods, including belief propagation (BP) and mean field (MF). We show that our BP algorithm generalizes both majority voting and a recent algorithm by Karger et al. (2011), while our MF method is closely related to a commonly used EM algorithm. In both case, we find that the performance of the algorithms critically depends on the choice of a prior distribution on the workers' reliability; by choosing the prior properly, both BP and MF (and EM) perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.

                      [ BibTex ] | [ PDF ]



                      Winning the PASCAL 2011 MAP Challenge with Enhanced AND/OR Branch-and-Bound

                      Otten, Ihler, Kask, Dechter

                      This paper describes our entry for the MAP/MPE track of the PASCAL 2011 Probabilistic Inference Challenge, which placed first in all three time limit categories, 20 seconds, 20 minutes, and 1 hour. Our baseline is a branch-and-bound algorithm that explores the AND/OR context-minimal search graph of a graphical model guided by a mini-bucket heuristic. Augmented with recent advances that convert the algorithm into an anytime scheme, that improve the heuristic power via cost-shifting schemes, and using enhanced variable ordering schemes, it constitutes one of the most powerful MAP/MPE inference methods to date.

                      [ BibTex ] | [ PDF ]



                      Fast planar correlation clustering for image segmentation

                      Yarkony, Ihler, Fowlkes

                      We describe a new optimization scheme for finding high-quality correlation clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our algorithm on the problem of image segmentation where this approach outperforms existing global optimization techniques in minimizing the objective and is competitive with the state of the art in producing high-quality segmentations.

                      [ BibTex ] | [ PDF ]



                      A graphical model representation of the track-oriented multiple hypothesis tracker

                      Frank, Smyth, Ihler

                      The track-oriented multiple hypothesis tracker is currently the preferred method for tracking multiple targets in clutter with medium to high computational resources. This method maintains a structured representation of the track posterior distribution, which it repeatedly extends and optimizes over. This representation of the posterior admits probabilistic inference tasks beyond MAP estimation that have yet to be explored. To this end we formulate the posterior as a graphical model and show that belief propagation can be used to approximate the track marginals. These approximate marginals enable an online parameter estimation scheme that improves tracker performance in the presence of parameter misspecification.

                      [ BibTex ] | [ PDF ]



                      Join-graph based cost-shifting schemes

                      Ihler, Flerova, Dechter, Otten

                      We develop several algorithms taking advantage of two common approaches for bounding MPE queries in graphical models: mini-bucket elimination and message-passing updates for linear programming relaxations. Both methods are quite similar, and offer useful perspectives for the other; our hybrid approaches attempt to balance the advantages of each. We demonstrate the power of our hybrid algorithms through extensive empirical evaluation. Most notably, a Branch and Bound search guided by the heuristic function calculated by one of our new algorithms has recently won first place in the PASCAL2 inference challenge.

                      [ BibTex ] | [ PDF ]



                      A cluster-cumulant expansion at the fixed points of belief propagation

                      Welling, Gelfand, Ihler

                      We introduce a new cluster-cumulant expansion (CCE) based on the fixed points of iterative belief propagation (IBP). This expansion is similar in spirit to the loop-series (LS) recently introduced in Chertkov and Chernyak (2006). However, in contrast to the latter, the CCE enjoys the following important qualities: 1) it is defined for arbitrary state spaces 2) it is easily extended to fixed points of generalized belief propagation (GBP), 3) disconnected groups of variables will not contribute to the CCE and 4) the accuracy of the expansion empirically improves upon that of the LS. The CCE is based on the same Mobius transform as the Kikuchi approximation, but unlike GBP does not require storing the beliefs of the GBP-clusters nor does it suffer from convergence issues during belief updating.

                      [ BibTex ] | [ PDF ]



                      Belief propagation for structured decision making

                      Liu, Ihler

                      Variational inference algorithms such as belief propagation have had tremendous impact on our ability to learn and use graphical models, and give many insights for developing or understanding exact and approximate inference. However, variational approaches have not been widely adoped for decision making in graphical models, often formulated through influence diagrams and including both centralized and decentralized (or multi-agent) decisions. In this work, we present a general variational framework for solving structured cooperative decision-making problems, use it to propose several belief propagation-like algorithms, and analyze them both theoretically and empirically.

                      [ BibTex ] | [ PDF ]



                      Approximating the sum operation for marginal-MAP inference

                      Cheng, Chen, Dong, Xu, Ihler

                      We study the marginal-MAP problem on graphical models, and present a novel approximation method based on direct approximation of the sum operation. A primary difficulty of marginal-MAP problems lies in the non-commutativity of the sum and max operations, so that even in highly structured models, marginalization may produce a densely connected graph over the variables to be maximized, resulting in an intractable potential function with exponential size. We propose a chain decomposition approach for summing over the marginalized variables, in which we produce a structured approximation to the MAP component of the problem consisting of only pairwise potentials. We show that this approach is equivalent to the maximization of a specific variational free energy, and it provides an upper bound of the optimal probability. Finally, experimental results demonstrate that our method performs favorably compared to previous methods.

                      [ BibTex ] | [ PDF ]



                      Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis

                      Geyfman et al.

                      The role of the circadian clock in skin and the identity of genes participating in its chronobiology remain largely unknown, leading us to define the circadian transcriptome of mouse skin at two different stages of the hair cycle, telogen and anagen. The circadian transcriptomes of telogen and anagen skin are largely distinct, with the former dominated by genes involved in cell proliferation and metabolism. The expression of many metabolic genes is antiphasic to cell cycle-related genes, the former peaking during the day and the latter at night. Consistently, accumulation of reactive oxygen species, a byproduct of oxidative phosphorylation, and S-phase are antiphasic to each other in telogen skin. Furthermore, the circadian variation in S-phase is controlled by BMAL1 intrinsic to keratinocytes, because keratinocyte-specific deletion of Bmal1 obliterates time-of-day–dependent synchronicity of cell division in the epidermis leading to a constitutively elevated cell proliferation. In agreement with higher cellular susceptibility to UV-induced DNA damage during S-phase, we found that mice are most sensitive to UVB-induced DNA damage in the epidermis at night. Because in the human epidermis maximum numbers of keratinocytes go through S-phase in the late afternoon, we speculate that in humans the circadian clock imposes regulation of epidermal cell proliferation so that skin is at a particularly vulnerable stage during times of maximum UV exposure, thus contributing to the high incidence of human skin cancers.

                      [ BibTex ] | [ Link ]



                      Distributed parameter estimation via pseudo-likelihood

                      Liu, Ihler

                      Estimating statistical models within sensor networks requires distributed algorithms, in which both data and computation are distributed across the nodes of the network. We propose a general approach for distributed learning based on combining local estimators defined by pseudo-likelihood components, encompassing a number of combination methods, and provide both theoretical and experimental analysis. We show that simple linear combination or max-voting methods, when combined with second-order information, are statistically competitive with more advanced and costly joint optimization. Our algorithms have many attractive properties including low communication and computational cost and "any-time" behavior.

                      [ BibTex ] | [ PDF ]



                      Mini-bucket elimination with moment matching

                      Flerova, Ihler, Dechter, Otten

                      We investigate a hybrid of two styles of algorithms for deriving bounds for optimization tasks over graphical models: non-iterative message-passing schemes exploiting variable duplication to reduce cluster sizes (e.g. MBE) and iterative methods that re-parameterize the problem's functions aiming to produce good bounds even if functions are processed independently (e.g. MPLP). In this work we combine both ideas, augmenting MBE with re-parameterization, which we call MBE with Moment Matching (MBE-MM). The results of preliminary empirical evaluations show the clear promise of the hybrid scheme over its individual com- ponents (e.g., pure MBE and pure MPLP). Most significantly, we demonstrate the potential of the new bounds in improving the power of mechanically generated heuristics for branch and bound search.

                      [ BibTex ] | [ PDF ]



                      Adaptive exact inference in graphical models

                      Sumer, Acar, Ihler, Mettu

                      Many algorithms and applications involve repeatedly solving variations of the same inference problem, for example to introduce new evidence to the model or to change conditional dependencies. As the model is updated, the goal of \emph{adaptive inference} is to take advantage of previously computed quantities to perform inference more rapidly than from scratch. In this paper, we present algorithms for adaptive exact inference on general graphs that can be used to efficiently compute marginals and update MAP configurations under arbitrary changes to the input factor graph and its associated elimination tree. After a linear time preprocessing step, our approach enables updates to the model and the computation of any marginal in time that is logarithmic in the size of the input model. Moreover, in contrast to max-product our approach can also be used to update MAP configurations in time that is roughly proportional to the number of updated entries, rather than the size of the input model. To evaluate the practical effectiveness of our algorithms, we implement and test them using synthetic data as well as for two real-world computational biology applications. Our experiments show that adaptive inference can achieve substantial speedups over performing complete inference as the model undergoes small changes over time.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Bounding the Partition Function using Holder's Inequality

                      Liu, Ihler

                      We describe an algorithm for approximate inference in graphical models based on H\"older's inequality that provides upper and lower bounds on common summation problems such as computing the partition function or probability of evidence in a graphical model. Our algorithm unifies and extends several existing approaches, including variable elimination techniques such as mini-bucket elimination and variational methods such tree reweighted belief propagation and conditional entropy decomposition. We show that our method inherits benefits from each approach to provide significantly better bounds on sum-product tasks.

                      [ BibTex ] | [ PDF ]



                      Fast parallel and adaptive updates for dual-decomposition solvers

                      Sumer, Acar, Ihler, Mettu

                      Dual-decomposition (DD) methods are quickly becoming important tools for estimating the minimum energy state of a graphical model. DD methods decompose a complex model into a collection of simpler subproblems that can be solved exactly (such as trees), that in combination provide upper and lower bounds on the exact solution. Subproblem choice can play a major role: larger subproblems tend to improve the bound more per iteration, while smaller subproblems enable highly parallel solvers and can benefit from re-using past solutions when there are few changes between iterations. We propose an algorithm that can balance many of these aspects to speed up convergence. Our method uses a cluster tree data structure that has been proposed for adaptive exact inference tasks, and we apply it in this paper to dual-decomposition approximate inference. This approach allows us to process large subproblems to improve the bounds at each iteration, while allowing a high degree of parallelizability and taking advantage of subproblems with sparse updates. For both synthetic inputs and a real-world stereo matching problem, we demonstrate that our algorithm is able to achieve significant improvement in convergence time.

                      [ BibTex ] | [ PDF ]



                      Tightening MRF relaxations with planar subproblems

                      Yarkony, Morshed, Ihler, Fowlkes

                      We describe a new technique for computing lower-bounds on the minimum energy configuration of a planar Markov Random Field (MRF). Our method successively adds large numbers of constraints and enforces consistency over binary projections of the original problem state space. These constraints are represented in terms of subproblems in a dual-decomposition framework that is optimized using subgradient techniques. The complete set of constraints we consider enforces cycle consistency over the original graph. In practice we find that the method converges quickly on most problems with the addition of a few subproblems and outperforms existing methods for some interesting classes of hard potentials.

                      [ BibTex ] | [ PDF ]



                      Planar cycle covering graphs

                      Yarkony, Ihler, Fowlkes

                      We describe a new variational lower-bound on the minimum energy configuration of a planar binary Markov Random Field (MRF). Our method is based on adding auxiliary nodes to every face of a planar embedding of the graph in order to capture the effect of unary potentials. A ground state of the resulting approximation can be computed efficiently by reduction to minimum-weight perfect matching. We show that optimization of variational parameters achieves the same lower-bound as dual-decomposition into the set of all cycles of the original graph. We demonstrate that our variational optimization converges quickly and provides high-quality solutions to hard combinatorial problems 10-100x faster than competing algorithms that optimize the same bound.

                      [ BibTex ] | [ PDF ]



                      Variational algorithms for marginal MAP

                      Liu, Ihler

                      Marginal MAP problems are notoriously difficult tasks for graphical models. We derive a general variational framework for solving marginal MAP problems, in which we apply analogues of the Bethe, tree-reweighted, and mean field approximations. We then derive a ``mixed" message passing algorithm and a convergent alternative using CCCP to solve the BP-type approximations. Theoretically, we give conditions under which the decoded solution is a global or local optimum, and obtain novel upper bounds on solutions. Experimentally we demonstrate that our algorithms outperform related approaches. We also show that EM and variational EM comprise a special case of our framework.

                      [ BibTex ] | [ PDF ]



                      Fault Detection via Nonparametric Belief Propagation

                      Bickson, Baron, Ihler, Avissar, Dolev

                      We consider the problem of identifying a pattern of faults from a set of noisy linear measurements. Unfortunately, maximum a posteriori probability estimation of the fault pattern is computationally intractable. To solve the fault identification problem, we propose a non-parametric belief propagation approach. We show empirically that our belief propagation solver is more accurate than recent state-of-the-art algorithms including interior point methods and semidefinite programming. Our superior performance is explained by the fact that we take into account both the binary nature of the individual faults and the sparsity of the fault pattern arising from their rarity.

                      [ BibTex ] | [ Link ]



                      Revisiting MAP Estimation, Message Passing and Perfect Graphs

                      Foulds, Navaroli, Smyth, Ihler

                      Given a graphical model, one of the most useful queries is to find the most likely configuration of its variables. This task, known as the maximum a posteriori (MAP) problem, can be solved efficiently via message passing techniques when the graph is a tree, but is NP-hard for general graphs. Jebara (2009) shows that the MAP problem can be converted into the stable set problem, which can be solved in polynomial time for a broad class of graphs known as perfect graphs via a linear programming relaxation technique. This is a result of great theoretical interest. However, the article additionally claims that max-product linear programming (MPLP) message passing techniques of Globerson & Jaakkola (2007) are also guaranteed to solve these problems exactly and efficiently. We investigate this claim, show that it does not hold in general, and attempt to repair it with several alternative message passing algorithms.

                      [ BibTex ] | [ PDF ]



                      Multicore Gibbs Sampling in Dense, Unstructured Graphs

                      Xu, Ihler

                      Multicore computing is on the rise, but algorithms such as Gibbs sampling are fundamentally sequential and may require close consideration to be made parallel. Existing techniques either exploit sparse problem structure or make approximations to the algorithm; in this work, we explore an alternative to these ideas. We develop a parallel Gibbs sampling algorithm for shared-memory systems that does not require any independence structure among the variables yet does not approximate the sampling distributions. Our method uses a look-ahead sampler, which uses bounds to attempt to sample variables before the results of other threads are made available. We demonstrate our algorithm on Gibbs sampling in Boltzmann machines and latent Dirichlet allocation (LDA). We show in experiments that our algorithm achieves near linear speed-up in the number of cores, is faster than existing exact samplers, and is nearly as fast as approximate samplers while maintaining the correct stationary distribution.

                      [ BibTex ] | [ PDF ]



                      Learning Scale Free Networks by Reweighted L1 regularization

                      Liu, Ihler

                      Methods for L1-type regularization have been widely used in Gaussian graphical model selection tasks to encourage sparse structures. However, often we would like to include more structural information than mere sparsity. In this work, we focus on learning so-called ``scale-free'' models, a common feature that appears in many real-work networks. We replace the L1 regularization with a power law regularization and optimize the objective function by a sequence of iteratively reweighted L1 regularization problems, where the regularization coefficients of nodes with high degree are reduced, encouraging the appearance of hubs with high degree. Our method can be easily adapted to improve any existing L1-based methods, such as graphical lasso, neighborhood selection, and JSRM when the underlying networks are believed to be scale free or have dominating hubs. We demonstrate in simulation that our method significantly outperforms the a baseline L1 method at learning scale-free networks and hub networks, and also illustrate its behavior on gene expression data.

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                      Understanding Errors in Approximate Distributed Latent Dirichlet Allocation

                      Ihler, Newman

                      Latent Dirichlet allocation (LDA) is a popular algorithm for discovering semantic structure in large collections of text or other data. Although its complexity is linear in the data size, its use on increasingly massive collections has created considerable interest in parallel implementations. ``Approximate distributed'' LDA, or AD-LDA, approximates the popular collapsed Gibbs sampling algorithm for LDA models while running on a distributed architecture. Although this algorithm often appears to perform well in practice, its quality is not well understood theoretically or easily assessed on new data. In this work, we theoretically justify the approximation, and modify AD-LDA to track an error bound on performance. Specifically, we upper-bound the probability of making a sampling error at each step of the algorithm (compared to an exact, sequential Gibbs sampler), given the samples drawn thus far. We show empirically that our bound is sufficiently tight to give a meaningful and intuitive measure of approximation error in AD-LDA, allowing the user to track the trade-off between accuracy and efficiency while executing in parallel.

                      [ BibTex ] | [ PDF ] | [ Link ]



                      Nonparametric Belief Propagation

                      Sudderth, Ihler, Isard, Freeman, Willsky

                      Continuous quantities are ubiquitous in models of real-world phenomena, but are surprisingly difficult to reason about automatically. Probabilistic graphical models such as Bayesian networks and Markov random fields, and algorithms for approximate inference such as belief propagation, have proven to be powerful tools in a wide range of applications in statistics and artificial intelligence. However, applying these methods to models with continuous variables remains a challenging task. In this work we describe an extension of belief propagation to continuous variable models, generalizing particle filtering and Gaussian mixture filtering techniques for time series to more complex models. We illustrate the power of the resulting nonparametric belief propagation algorithm via two applications: kinematic tracking of visual motion and distributed localization in sensor networks.

                      [ BibTex ] | [ Link ]



                      Negative Tree-reweighted Belief Propagation

                      Liu, Ihler

                      We introduce a new class of lower bounds on the log partition function of a Markov random field which makes use of a reversed Jensen's inequality. In particular, our method approximates the intractable distribution using a linear combination of spanning trees with negative weights. This technique is a lower-bound counterpart to the tree-reweighted belief propagation algorithm, which uses a convex combination of spanning trees with positive weights to provide corresponding upper bounds. We develop algorithms to optimize and tighten the lower bounds over the non-convex set of valid parameter values. Our algorithm generalizes mean field approaches (including na\"ive and structured mean field approximations), which it includes as a limiting case.

                      [ BibTex ] | [ PDF ]



                      Covering Trees and Lower Bounds on Quadratic Assignment

                      Yarkony, Fowlkes, Ihler

                      Many computer vision problems involving feature correspondence among images can be formulated as an assignment problem with a quadratic cost function. Such problems are computationally infeasible in general but recent advances in discrete optimization such as tree-reweighted belief propagation (TRW) often provide high-quality solutions. In this paper, we improve upon these algorithms in two ways. First, we introduce covering trees, a variant of TRW which provide the same bounds on the MAP energy as TRW with far fewer variational parameters. Optimization of these parameters can be carried out efficiently using either fixed--point iterations (as in TRW) or sub-gradient based techniques. Second, we introduce a new technique that utilizes bipartite matching applied to the min-marginals produced with covering trees in order to compute a tighter lower-bound for the quadratic assignment problem. We apply this machinery to the problem of finding correspondences with pairwise energy functions, and demonstrate the resulting hybrid method outperforms TRW alone and a recent related subproblem decomposition algorithm on benchmark image correspondence problems.

                      [ BibTex ] | [ PDF ]



                      Particle Filtered MCMC-MLE with Connections to Contrastive Divergence

                      Asuncion, Liu, Ihler, Smyth

                      Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn these models exactly, various approximate learning techniques have been developed, such as contrastive divergence (CD) and Markov chain Monte Carlo maximum likelihood estimation (MCMC-MLE). In this paper, we introduce particle filtered MCMC-MLE, which is a sampling-importance-resampling version of MCMC-MLE with additional MCMC rejuvenation steps. We also describe a unified view of (1) MCMC-MLE, (2) our particle filtering approach, and (3) a stochastic approximation procedure known as persistent contrastive divergence. We show how these approaches are related to each other and discuss the relative merits of each approach. Empirical results on various undirected models demonstrate that the particle filtering technique we propose in this paper can significantly outperform MCMC-MLE. Furthermore, in certain cases, the proposed technique is faster than persistent CD.

                      [ BibTex ] | [ PDF ]



                      Learning with Blocks: Composite Likelihood and Contrastive Divergence

                      Asuncion, Liu, Ihler, Smyth

                      Composite likelihood methods provide a wide spectrum of computationally efficient techniques for statistical tasks such as parameter estimation and model selection. In this paper, we present a formal connection between the optimization of composite likelihoods and the well-known contrastive divergence algorithm. In particular, we show that composite likelihoods can be stochastically optimized by performing a variant of contrastive divergence with random-scan blocked Gibbs sampling. By using higher-order composite likelihoods, our proposed learning framework makes it possible to trade off computation time for increased accuracy. Furthermore, one can choose composite likelihood blocks that match the model's dependence structure, making the optimization of higher-order composite likelihoods computationally efficient. We empirically analyze the performance of blocked contrastive divergence on various models, including visible Boltzmann machines, conditional random fields, and exponential random graph models, and we demonstrate that using higher-order blocks improves both the accuracy of parameter estimates and the rate of convergence.

                      [ BibTex ] | [ PDF ]



                      Estimating Replicate Time-Shifts Using Gaussian Process Regression

                      Liu, Lin, Anderson, Smyth, Ihler

                      Motivation: Time-course gene expression datasets provide important insights into dynamic aspects of biological processes, such as circadian rhythms, cell cycle and organ development. In a typical microarray time-course experiment, measurements are obtained at each time point from multiple replicate samples. Accurately recovering the gene expression patterns from experimental observations is made challenging by both measurement noise and variation among replicates' rates of development. Prior work on this topic has focused on inference of expression patterns assuming that the replicate times are synchronized. We develop a statistical approach that simultaneously infers both (i) the underlying (hidden) expression profile for each gene, as well as (ii) the biological time for each individual replicate. Our approach is based on Gaussian process regression (GPR) combined with a probabilistic model that accounts for uncertainty about the biological development time of each replicate.
                      Results: We apply GPR with uncertain measurement times to a microarray dataset of mRNA expression for the hair-growth cycle in mouse back skin, predicting both profile shapes and biological times for each replicate. The predicted time shifts show high consistency with independently obtained morphological estimates of relative development. We also show that the method systematically reduces prediction error on out-of-sample data, significantly reducing the mean squared error in a cross-validation study.
                      Availability: Matlab code for GPR with uncertain time shifts is available at http://sli.ics.uci.edu/Code/GPRTimeshift/
                      Contact: ihler@ics.uci.edu

                      [ BibTex ] | [ Link ]



                      Particle-Based Variational Inference for Continuous Systems

                      Ihler, Frank, Smyth

                      Since the development of loopy belief propagation, there has been considerable work on advancing the state of the art for approximate inference over distributions defined on discrete random variables. Improvements include guarantees of convergence, approximations that are provably more accurate, and bounds on the results of exact inference. However, extending these methods to continuous-valued systems has lagged behind. While several methods have been developed to use belief propagation on systems with continuous values, recent advances for discrete variables have not as yet been incorporated. In this context we extend a recently proposed particle-based belief propagation algorithm to provide a general framework for adapting discrete message-passing algorithms to inference in continuous systems. The resulting algorithms behave similarly to their purely discrete counterparts, extending the benefits of these more advanced inference techniques to the continuous domain.

                      [ BibTex ] | [ PDF ]



                      Bayesian detection of non-sinusoidal periodic patterns in circadian expression data

                      Chudova, Ihler, Lin, Andersen, Smyth

                      Motivation: Cyclical biological processes such as cell division and circadian regulation produce coordinated periodic expression of thousands of genes. Identification of such genes and their expression patterns is a crucial step in discovering underlying regulatory mechanisms. Existing computational methods are biased toward discovering genes that follow sine-wave patterns.
                      Results: We present an analysis of variance (ANOVA) periodicity detector and its Bayesian extension that can be used to discover periodic transcripts of arbitrary shapes from replicated gene expression profiles. The models are applicable when the profiles are collected at comparable time points for at least two cycles. We provide an empirical Bayes procedure for estimating parameters of the prior distributions and derive closed-form expressions for the posterior probability of periodicity, enabling efficient computation. The model is applied to two datasets profiling circadian regulation in murine liver and skeletal muscle, revealing a substantial number of previously undetected non-sinusoidal periodic transcripts in each. We also apply quantitative real-time PCR to several highly ranked non-sinusoidal transcripts in liver tissue found by the model, providing independent evidence of circadian regulation of these genes.
                      Availability: MATLAB software for estimating prior distributions and performing inference is available for download from http://www.datalab.uci.edu/resources/periodicity/.
                      Contact: dchudova@gmail.com

                      [ BibTex ] | [ Link ]



                      Bounding Sample Errors in Approximate Distributed Latent Dirichlet Allocation

                      Ihler, Newman

                      Latent Dirichlet allocation (LDA) is a popular algorithm for discovering structure in large collections of text or other data. Although its complexity is linear in the data size, its use on increasingly massive collections has created considerable interest in parallel implementations. ``Approximate distributed'' LDA, or AD-LDA, approximates the popular collapsed Gibbs sampling algorithm for LDA models while running on a distributed architecture. Although this algorithm often appears to perform well in practice, its quality is not well understood or easily assessed. In this work, we provide some theoretical justification of the algorithm, and modify AD-LDA to track an error bound on its performance. Specifically, we upper-bound the probability of making a sampling error at each step of the algorithm (compared to an exact, sequential Gibbs sampler), given the samples drawn thus far. We show empirically that our bound is sufficiently tight to give a meaningful and intuitive measure of approximation error in AD-LDA, allowing the user to understand the trade-off between accuracy and efficiency.

                      [ BibTex ] | [ PDF ]



                      Adaptive Updates for MAP Configurations with Applications to Bioinformatics

                      Acar, Ihler, Mettu, Sumer

                      Many applications involve repeatedly computing the optimal, maximum a posteriori (MAP) configuration of a graphical model as the model changes, often slowly or incrementally over time, e.g., due to input from a user. Small changes to the model often require updating only a small fraction of the MAP configuration, suggesting the possibility of performing updates faster than recomputing from scratch. In this paper we present an algorithm for efficiently performing such updates under arbitrary changes to the model. Our algorithm is within a logarithmic factor of the optimal and is asymptotically never slower than re-computing from-scratch: if a modification to the model requires $m$ updates to the MAP configuration of $n$ random variables, then our algorithm requires $O(m\log{(n/m)})$ time; re-computing from scratch requires $O(n)$ time. We evaluate the practical effectiveness of our algorithm by considering two problems in genomic signal processing, CpG region segmentation and protein sidechain packing, where a MAP configuration must be repeatedly updated. Our results show significant speedups over recomputing from scratch.

                      [ BibTex ] | [ PDF ]



                      A Low Density Lattice Decoder via Non-parametric Belief Propagation

                      Bickson, Ihler, Avissar, Dolev

                      The recent work of Sommer, Feder and Shalvi presented a new family of codes called low density lattice codes (LDLC) that can be decoded efficiently and approach the capacity of the AWGN channel. A linear time iterative decoding scheme which is based on a message-passing formulation on a factor graph is given.
                      In the current work we report our theoretical findings regarding the relation between the LDLC decoder and belief propagation. We show that the LDLC decoder is an instance of non-parametric belief propagation and further connect it to the Gaussian belief propagation algorithm. Our new results enable borrowing knowledge from the non-parametric and Gaussian belief propagation domains into the LDLC domain. Specifically, we give more general convergence conditions for convergence of the LDLC decoder (under the same assumptions of the original LDLC convergence analysis). We discuss how to extend the LDLC decoder from Latin square to full rank, non-square matrices. We propose an efficient construction of sparse generator matrix and its matching decoder. We report preliminary experimental results which show our decoder has comparable symbol to error rate compared to the original LDLC decoder.

                      [ BibTex ] | [ PDF ]



                      Circadian Clock Genes Contribute to the Regulation of Hair Follicle Cycling

                      Lin, Kumar, Geyfman, Chudova, Ihler, Smyth, Paus, Takahashi, Andersen

                      The hair follicle renews itself by repeatedly cycling among growth, regression, and rest phases. One function of hair follicle cycling is to allow seasonal changes in hair growth. Understanding the regulation of hair follicle cycling is also of interest because abnormal regulation of hair cycle control genes is responsible for several types of human hair growth disorders and skin cancers. We report here that Clock and Bmal1 genes, which control circadian rhythms, are also important for the regulation of hair follicle cycling, a biological process of much longer duration than 24 hours. Detailed analysis of skin from mice mutated for central clock genes indicates a significant delay in the progression of the hair growth phase. We show that clock genes affect the expression of key cell cycle control genes and that keratinocytes in a critical compartment of the hair follicles in Bmal1 mutant mice are halted in the G1 phase of the cell cycle. These findings provide novel insight into circadian control mechanisms in modulating the progression of cyclic biological processes on different time scales.

                      [ BibTex ] | [ PDF ] | [ Link ]



                      Particle Belief Propagation

                      Ihler, McAllester

                      The popularity of particle filtering for inference in Markov chain models defined over random variables with very large or continuous domains makes it natural to consider sample--based versions of belief propagation (BP) for more general (tree--structured or loopy) graphs. Already, several such algorithms have been proposed in the literature. However, many questions remain open about the behavior of particle--based BP algorithms, and little theory has been developed to analyze their performance. In this paper, we describe a generic particle belief propagation (PBP) algorithm which is closely related to previously proposed methods. We prove that this algorithm is consistent, approaching the true BP messages as the number of samples grows large. We then use concentration bounds to analyze the finite-sample behavior and give a convergence rate for the algorithm on tree--structured graphs. Our convergence rate is $O(1/\sqrt{n})$ where $n$ is the number of samples, independent of the domain size of the variables.

                      [ BibTex ] | [ PDF ]



                      Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation

                      Porteous, Newman, Ihler, Asuncion, Smyth, Welling

                      In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text corpora. Conventional Gibbs sampling schemes for LDA require O(K) operations per sample where K is the number of topics in the model. Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample. On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA. No approximations are necessary, and we show that our fast sampling scheme produces exactly the same results as the standard (but slower) sampling scheme. Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes. For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA, our speedup of 5.7 can save 5 CPU months of computation.

                      [ BibTex ] | [ PDF ]



                      Probabilistic Analysis of a Large Scale Urban Traffic Sensor Data Set

                      Hutchins, Ihler, Smyth

                      Real-world sensor time series are often significantly noisier and more difficult to work with than the relatively clean data sets that tend to be used as the basis for experiments in many research papers. In this paper we report on a large case-study involving statistical data mining of over 300 million measurements from 1700 freeway traffic sensors over a period of seven months in Southern California. We discuss the challenges posed by the wide variety of different sensor failures and anomalies present in the data. The volume and complexity of the data precludes the use of manual visualization or simple thresholding techniques to identify these anomalies. We describe the application of probabilistic modeling and unsupervised learning techniques to this data set and illustrate how these approaches can successfully detect underlying systematic patterns even in the presence of substantial noise and missing data

                      [ BibTex ] | [ PDF ] | [ Link ]



                      Adaptive Inference in General Graphical Models

                      Acar, Ihler, Mettu, Sumer

                      Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of \emph{adaptive inference} is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional probabilities and dependencies in logarithmic time. We give experimental results for an implementation of our algorithm, and demonstrate its potential performance benefit in the study of protein structure.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Modeling Count Data from Multiple Sensors: A Building Occupancy Model

                      Hutchins, Ihler, Smyth

                      Knowledge of the number of people in a building at a given time is crucial for applications such as emergency response. Sensors can be used to gather noisy measurements which when combined, can be used to make inferences about the location, movement and density of people. In this paper we describe a probabilistic model for predicting the occupancy of a building using networks of people-counting sensors. This model provides robust predictions given typical sensor noise as well as missing and corrupted data from malfunctioning sensors. We experimentally validate the model by comparing it to a baseline method using real data from a network of optical counting sensors in a campus building.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Adaptive Bayesian Inference

                      Acar, Ihler, Mettu, Sumer

                      Motivated by stochastic systems in which observed evidence and conditional dependencies between states of the network change over time, and certain quantities of interest (marginal distributions, likelihood estimates etc.) must be updated, we study the problem of \emph{adaptive inference} in tree-structured Bayesian networks. We describe an algorithm for adaptive inference that handles a broad range of changes to the network and is able to maintain marginal distributions, MAP estimates, and data likelihoods in all expected logarithmic time. We give an implementation of our algorithm and provide experiments that show that the algorithm can yield up to two orders of magnitude speedups on answering queries and responding to dynamic changes over the sum-product algorithm.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Learning to detect events with Markov-modulated Poisson processes

                      Ihler, Hutchins, Smyth

                      Time-series of count data occur in many different contexts, including Internet navigation logs, freeway traffic monitoring, and security logs associated with buildings. In this article we describe a framework for detecting anomalous events in such data using an unsupervised learning approach. Normal periodic behavior is modeled via a time-varying Poisson process model, which in turn is modulated by a hidden Markov process that accounts for bursty events. We outline a Bayesian framework for learning the parameters of this model from count time-series. Two large real-world datasets of time-series counts are used as testbeds to validate the approach, consisting of freeway traffic data and logs of people entering and exiting a building. We show that the proposed model is significantly more accurate at detecting known events than a more traditional threshold-based technique. We also describe how the model can be used to investigate different degrees of periodicity in the data, including systematic day-of-week and time-of-day effects, and to make inferences about different aspects of events such as number of vehicles or people involved. The results indicate that the Markov-modulated Poisson framework provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.

                      [ BibTex ] | [ Link ]



                      Accuracy Bounds for Belief Propagation

                      Ihler

                      The belief propagation algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and performance. However, little is known theoretically about when this algorithm will perform well. Using recent analysis of convergence and stability properties in belief propagation and new results on approximations in binary systems, we derive a bound on the error in BP's estimates for pairwise Markov random fields over discrete--valued random variables. Our bound is relatively simple to compute, and compares favorably with a previous method for bounding the accuracy of belief propagation.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Graphical Models for Statistical Inference and Data Assimilation

                      Ihler, Kirshner, Ghil, Robertson, Smyth

                      In data assimilation for a system which evolves in time, one combines past and current observations with a model of the dynamics of the system, in order to improve the simulation of the system as well as any future predictions about it. From a statistical point of view, this process can be regarded as estimating many random variables which are related both spatially and temporally: given observations of some of these variables, typically corresponding to times past, we require estimates of several others, typically corresponding to future times.

                      Graphical models have emerged as an effective formalism for assisting in these types of inference tasks, particularly for large numbers of random variables. Graphical models provide a means of representing dependency structure among the variables, and can provide both intuition and efficiency in estimation and other inference computations. We provide an overview and introduction to graphical models, and describe how they can be used to represent statistical dependency and how the resulting structure can be used to organize computation. The relation between statistical inference using graphical models and optimal sequential estimation algorithms such as Kalman filtering is discussed. We then give several additional examples of how graphical models can be applied to climate dynamics, specifically estimation using multi-resolution models of large-scale data sets such as satellite imagery, and learning hidden Markov models to capture rainfall patterns in space and time.

                      [ BibTex ] | [ PDF ] | [ Link ]



                      Learning Time-Intensity Profiles of Human Activity Using Nonparametric Bayesian Models

                      Ihler, Smyth

                      Data sets that characterize human activity over time through collections of timestamped events or counts are of increasing interest in application areas as humancomputer interaction, video surveillance, and Web data analysis. We propose a non-parametric Bayesian framework for modeling collections of such data. In particular, we use a Dirichlet process framework for learning a set of intensity functions corresponding to different categories, which form a basis set for representing individual time-periods (e.g., several days) depending on which categories the time-periods are assigned to. This allows the model to learn in a data-driven fashion what “factors” are generating the observations on a particular day, including (for example) weekday versus weekend effects or day-specific effects corresponding to unique (single-day) occurrences of unusual behavior, sharing information where appropriate to obtain improved estimates of the behavior associated with each category. Applications to real–world data sets of count data involving both vehicles and people are used to illustrate the technique.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Adaptive Event Detection with Time-Varying Poisson Processes

                      Ihler, Hutchins, Smyth

                      Time-series of count data are generated in many different contexts, such as web access logging, freeway traffic monitoring, and security logs associated with buildings. Since this data measures the aggregated behavior of individual human beings, it typically exhibits a periodicity in time on a number of scales (daily, weekly,etc.) that reflects the rhythms of the underlying human activity and makes the data appear non-homogeneous. At the same time, the data is often corrupted by a number of bursty periods of unusual behavior such as building events, traffic accidents, and so forth. The data mining problem of finding and extracting these anomalous events is made difficult by both of these elements. In this paper we describe a framework for unsupervised learning in this context, based on a time-varying Poisson process model that can also account for anomalous events. We show how the parameters of this model can be learned from count time series using statistical estimation techniques. We demonstrate the utility of this model on two datasets for which we have partial ground truth in the form of known events, one from freeway traffic data and another from building access data, and show that the model performs significantly better than a non-probabilistic, threshold-based technique. We also describe how the model can be used to investigate different degrees of periodicity in the data, including systematic day-of-week and time-of-day effects, and make inferences about the detected events (e.g., popularity or level of attendance). Our experimental results indicate that the proposed time-varying Poisson model provides a robust and accurate framework for adaptively and autonomously learning how to separate unusual bursty events from traces of normal human activity.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Gibbs Sampling for (Coupled) Infinite Mixture Models in the Stick Breaking Representation

                      Porteous, Ihler, Smyth, Welling

                      Nonparametric Bayesian approaches to clustering, information retrieval, language modeling and object recognition have recently shown great promise as a new paradigm for unsupervised data analysis. Most contributions have focused on the Dirichlet process mixture models or extensions thereof for which efficient Gibbs samplers exist. In this paper we explore Gibbs samplers for infinite complexity mixture models in the stick breaking representation. The advantage of this representation is improved modeling flexibility. For instance, one can design the prior distribution over cluster sizes or couple multiple infi- nite mixture models (e.g., over time) at the level of their parameters (i.e., the dependent Dirichlet process model). However, Gibbs samplers for in- finite mixture models (as recently introduced in the statistics literature) seem to mix poorly over cluster labels. Among others issues, this can have the adverse effect that labels for the same cluster in coupled mixture models are mixed up. We introduce additional moves in these samplers to improve mixing over cluster labels and to bring clusters into correspondence. An application to modeling of storm trajectories is used to illustrate these ideas.

                      [ BibTex ] | [ PDF ]



                      Distributed Fusion in Sensor Networks

                      Cetin, Chen, Fisher, Ihler, Moses, Wainwright, Willsky

                      Distributed inference methods developed for graphical models comprise a principled approach for data fusion in sensor networks. The application of these methods, however, requires some care due to a number of issues that are particular to sensor networks. Chief of among these are the distributed nature of computation and deployment coupled with communications bandwidth and energy constraints typical of many sensor networks. Additionally, information sharing in a sensor network necessarily involves approximation. Traditional measures of distortion are not sufficient to characterize the quality of approximation as they do not address in an explicit manner the resulting impact on inference which is at the core of many data fusion problems. While both graphical models and a distributed sensor network have network structures associated with them, the mapping is not one to one. All of these issues complicate the mapping of a particular inference problem to a given sensor network structure. Indeed, there may be a variety of mappings with very different characteristics with regard to computational complexity and utilization of resources. Nevertheless, it is the case that many of the powerful distributed inference methods have a role in information fusion for sensor networks. In this article we present an overview of research conducted by the authors that has sought to clarify many of the important issues at the intersection of these domains. We discuss both theoretical issues and prototypical applications in addition to suggesting new lines of reasoning.

                      [ BibTex ] | [ PDF ]



                      Particle Filtering Under Communications Constraints

                      Ihler, Fisher, Willsky

                      Particle filtering is often applied to the problem of object tracking under non-Gaussian uncertainty; however, sensor networks frequently require that the implementation be local to the region of interest, eventually forcing the large, sample-based representation to be moved among power-constrained sensors. We consider the problem of successive approximation (i.e., lossy compression) of each sample-based density estimate, in particular exploring the consequences (both theoretical and empirical) of several possible choices of loss function and their interpretation in terms of future errors in inference, justifying their use for measuring approximations in distributed particle filtering.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Estimating Dependency and Significance for High-Dimensional Data

                      Siracusa, Tieu, Ihler, Fisher, Willsky

                      Understanding the dependency structure of a set of variables is a key component in various signal processing applications which involve data association. The simple task of detecting whether any dependency exists is particularly difficult when models of the data are unknown or difficult to characterize because of high-dimensional measurements. We review the use of nonparametric tests for characterizing dependency and how to carry out these tests with highdimensional observations. In addition we present a method to assess the significance of the tests.

                      [ BibTex ] | [ PDF ]



                      Nonparametric Belief Propagation for Sensor Network Self-Calibration

                      Ihler, Fisher, Moses, Willsky

                      Automatic self-localization is a critical need for the effective use of ad-hoc sensor networks in military or civilian applications. In general, self-localization involves the combination of absolute location information (\eg GPS) with relative calibration information (\eg distance measurements between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor localization is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then present and demonstrate the utility of \emph{nonparametric belief propagation} (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multi-modal uncertainty. Using simulations of small- to moderately-sized sensor networks, we show that NBP may be made robust to outlier measurement errors by a simple model augmentation, and that judicious message construction can result in better estimates. Furthermore, we provide an analysis of NBP's communications requirements, showing that typically only a few messages per sensor are required, and that even low bit-rate approximations of these messages can have little or no performance impact.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Loopy Belief Propagation: Convergence and Effects of Message Errors

                      Ihler, Fisher, Willsky

                      Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether due to quantization of the messages or model parameters, from other simplified message or model representations, or from stochastic approximation methods. The introduction of such errors into the BP message computations has the potential to affect the solution obtained adversely. We analyze the effect resulting from message approximation under two particular measures of error, and show bounds on the accumulation of errors in the system. This analysis leads to convergence conditions for traditional BP message passing, and both strict bounds and estimates of the resulting error in systems of approximate BP message passing.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Message Errors in Belief Propagation

                      Ihler, Fisher, Willsky

                      Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models. At times, even further approximations are required, whether from quantization or other simplified message representations or from stochastic approximation methods. Introducing such errors into the BP message computations has the potential to adversely affect the solution obtained. We analyze this effect with respect to a particular measure of message error, and show bounds on the accumulation of errors in the system. This leads both to convergence conditions and error bounds in traditional and approximate BP message passing.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Nonparametric Hypothesis Tests for Statistical Dependency

                      Ihler, Fisher, Willsky

                      Determining the structure of dependencies among a set of variables is a common task in many signal and image processing applications, including multi-target tracking and computer vision. In this paper we present an information-theoretic, machine learning approach to problems of this type. We cast this problem as a hypothesis test between factorizations of variables into mutually independent subsets. We show that the likelihood ratio can be written as sums of two sets of Kullback-Leibler (KL) divergence terms. The first set captures the structure of the statistical dependencies within each hypothesis, while the second set measures the details of model differences between hypotheses. We then consider the case when the signal prior models are unknown, so that the distributions of interest must be estimated directly from data, showing that the second set of terms is (asymptotically) negligible and quantifying the loss in hypothesis separability when the models are completely unknown. We demonstrate the utility of nonparametric estimation methods for such problems, providing a general framework for determining and distinguishing between dependency structures in highly uncertain environments. Additionally, we develop a machine learning approach for estimating lower bounds on KL-divergence and mutual information from samples of high-dimensional random variables for which direct density estimation is infeasible. We present empirical results in the context of three prototypical applications: association of signals generated by sources possessing harmonic behavior, scene correspondence using video imagery, and detection of coherent behavior among sets of moving objects.

                      [ BibTex ] | [ PDF ]



                      Communications-Constrained Inference

                      Ihler, Fisher, Willsky

                      In many applications, particularly power-constrained sensor networks, it is important to conserve the amount of data exchanged while maximizing the utility of that data for some inference task. Broadly, this tradeoff has two major cost components—the representation’s size (in distributed networks, the communications cost) and the error incurred by its use (the inference cost).

                      We analyze this tradeoff for a particular problem: communicating a particle-based representation (and more generally, a Gaussian mixture or kernel density estimate). We begin by characterizing the exact communication cost of these representations, noting that it is less than might be suggested by traditional communications theory due to the invariance of the represen- tation to reordering. We describe the optimal, lossless encoder when the generating distribution is known, and pose a sub-optimal encoder which still benefits from reordering invariance.

                      However, lossless encoding may not be sufficient. We describe one reasonable measure of error for distribution-based messages and its consequences for inference in an acyclic network, and propose a novel density approximation method based on KD-tree multiscale representations which enables the communications cost and a bound on error to be balanced efficiently. We show several empirical examples demonstrating the method’s utility in collaborative, distributed signal processing under bandwidth or power constraints.

                      [ BibTex ] | [ PDF ]



                      Nonparametric Belief Propagation for Sensor Network Self-Calibration

                      Ihler, Fisher, Moses, Willsky

                      Automatic self-calibration of ad-hoc sensor networks is a critical need for their use in military or civilian applications. In general, self-calibration involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. estimated distance between sensors) over regions of the network. We formulate the self-calibration problem as a graphical model, enabling application of nonparametric belief propagation (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, can represent multi-modal uncertainty, and admits a wide variety of statistical models. This last point is particularly appealing in that it can be used to provide robustness against occasional high-variance (outlier) noise. We illustrate the performance of NBP using Monte Carlo analysis on an example network.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Nonparametric Belief Propagation for Self-Calibration in Sensor Networks

                      Ihler, Fisher, Moses, Willsky

                      Automatic self-calibration of ad-hoc sensor networks is a critical need for their use in military or civilian applications. In general, self-calibration involves the combination of absolute location information (e.g. GPS) with relative calibration information (e.g. time delay or received signal strength between sensors) over regions of the network. Furthermore, it is generally desirable to distribute the computational burden across the network and minimize the amount of inter-sensor communication. We demonstrate that the information used for sensor calibration is fundamentally local with regard to the network topology and use this observation to reformulate the problem within a graphical model framework. We then demonstrate the utility of \emph{nonparametric belief propagation} (NBP), a recent generalization of particle filtering, for both estimating sensor locations and representing location uncertainties. NBP has the advantage that it is easily implemented in a distributed fashion, admits a wide variety of statistical models, and can represent multi-modal uncertainty. We illustrate the performance of NBP on several example networks while comparing to a previously published nonlinear least squares method.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Efficient Multiscale Sampling from Products of Gaussian Mixtures

                      Ihler, Sudderth, Freeman, Willsky

                      The problem of approximating the product of several Gaussian mixture distributions arises in a number of contexts, including the nonparametric belief propagation (NBP) inference algorithm and the training of product of experts models. This paper develops two multiscale algorithms for sampling from a product of Gaussian mixtures, and compares their performance to existing methods. The first is a multiscale variant of previously proposed Monte Carlo techniques, with comparable theoretical guarantees but improved empirical convergence rates. The second makes use of approximate kernel density evaluation methods to construct a fast approximate sampler, which is guaranteed to sample points to within a tunable parameter $\epsilon$ of their true probability. We compare both multiscale samplers on a set of computational examples motivated by NBP, demonstrating significant improvements over existing methods.

                      [ BibTex ] | [ PDF ] | [ PS ]



                      Nonparametric Belief Propagation

                      Sudderth, Ihler, Freeman, Willsky

                      In many applications of graphical models arising in computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. There exist inference algorithms for discrete approximations to these continuous distributions, but for the high-dimensional variables typically of interest, discrete inference becomes infeasible. Stochastic methods such as particle filters provide an appealing alternative. However, existing techniques fail to exploit the rich structure of the graphical models describing many vision problems. Drawing on ideas from regularized particle filters and belief propagation (BP), this paper develops a nonparametric belief propagation (NBP) algorithm applicable to general graphs. Each NBP iteration uses an efficient sampling procedure to update kernel-based approximations to the true, continuous likelihoods. The algorithm can accomodate an extremely broad class of potential functions, including nonparametric representations. Thus, NBP extends particle filtering methods to the more general vision problems that graphical models can describe. We apply the NBP algorithm to infer component interrelationships in a parts-based face model, allowing location and reconstruction of occluded features.

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                      Hypothesis Testing over Factorizations for Data Association

                      Ihler, Fisher, Willsky

                      The issue of data association arises frequently in sensor networks; whenever multiple sensors and sources are present, it may be necessary to determine which observations from different sensors correspond to the same target. In highly uncertain environments, one may need to determine this correspondence without the benefit of an \emph{a priori} known joint signal/sensor model. This paper examines the data association problem as the more general hypothesis test between factorizations of a single, learned distribution. The optimal test between known distributions may be decomposed into model-dependent and statistical dependence terms, quantifying the cost incurred by model estimation from measurements compared to a test between known models. We demonstrate how one might evaluate a two-signal association test efficiently using kernel density estimation methods to model a wide class of possible distributions, and show the resulting algorithm's ability to determine correspondence in uncertain conditions through a series of synthetic examples. We then describe an extension of this technique to multi-signal association which can be used to determine correspondence while avoiding the computationally prohibitive task of evaluating all hypotheses. Empirical results of the approximate approach are presented.

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                      Nonparametric Belief Propagation

                      Sudderth, Ihler, Freeman, Willsky

                      In applications of graphical models arising in fields such as computer vision, the hidden variables of interest are most naturally specified by continuous, non-Gaussian distributions. However, due to the limitations of existing inference algorithms, it is often necessary to form coarse, discrete approximations to such models. In this paper, we develop a nonparametric belief propagation (NBP) algorithm, which uses stochastic methods to propagate kernel-based approximations to the true continuous messages. Each NBP message update is based on an efficient sampling procedure which can accomodate an extremely broad class of potential functions, allowing easy adaptation to new application areas. We validate our method using comparisons to continuous BP for Gaussian networks, and an application to the stereo vision problem.

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                      Nonparametric Estimators for Online Signature Authentication

                      Ihler, Fisher, Willsky

                      We present extensions to our previous work in modelling dynamical processes. The approach uses an information theoretic criterion for searching over subspaces of the past observations, combined with a nonparametric density characterizing its relation to one-step-ahead prediction and uncertainty. We use this methodology to model handwriting stroke data, specifically signatures, as a dynamical system and show that it is possible to learn a model capturing their dynamics for use either in synthesizing realistic signatures and in discriminating between signatures and forgeries even though no forgeries have been used in constructing the model. This novel approach yields promising results even for small training sets.

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                      Learning Informative Statistics: A Nonparametric Approach

                      Fisher, Ihler, Viola

                      We discuss an information theoretic approach for categorizing and modeling dynamic processes. The approach can learn a compact and informative statistic which summarizes past states to predict future observations. Furthermore, the uncertainty of the prediction is characterized nonparametrically by a joint density over the learned statistic and present observation. We discuss the application of the technique to both noise driven dynamical systems and random processes sampled from a density which is conditioned on the past. In the first case we show results in which both the dynamics of random walk and the statistics of the driving noise are captured. In the second case we present results in which a summarizing statistic is learned on noisy random telegraph waves with differing dependencies on past states. In both cases the algorithm yields a principled approach for discriminating processes with differing dynamics and/or dependencies. The method is grounded in ideas from information theory and nonparametric statistics.

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